Kerr coined the term ‘HARKing’ to refer to the practice of ‘hypothesizing after the results are known’. This questionable research practice has received increased attention in recent years because it is thought to have contributed to low replication rates in science. The present article discusses the concept of HARKing from a philosophical standpoint and then undertakes a critical review of Kerr’s twelve potential costs of HARKing. It is argued that these potential costs are either misconceived, misattributed to (...) HARKing, lacking evidence, or that they do not take into account pre- and post-publication peer review and public availability to research materials and data. It is concluded that it is premature to conclude that HARKing has led to low replication rates. (shrink)

Purpose: The cost of capital is a critical factor influencing investment decisions and company financing. Sustainability reporting also enhances the strategic and organizational legitimacy of companies. Therefore, it is expected that sustainability reporting can influence company legitimacy with enhancing trust of companies, their capital costs is reduced. Based on this premise, the objective of this research is to investigate the impact of sustainability reporting on the cost of capital of companies listed on the Iraq Stock Exchange. Method: for (...) testing of hypothesis Data from 33 listed companies on the Iraq Stock Exchange during the company's period of 2012-2022 was collected and analyzed using a multiple linear regression approach with a combination of panel data analysis. Result: Our findings indicate that Iraqi firms experience higher costs of capital due to their weak implementation of sustainability reporting. Conclusion: reporting on corporate sustainability activities can enhance reputation of companies and, by gaining legitimacy for the companies, their cost of capital is reduced. Implication: These findings can inform policymakers about the need to strengthen requirements for sustainability reporting. Also help investor to form an optimal portfolio based on the policy of companies in compliance with the sustainability reporting. (shrink)

This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a (...) 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Healthcare professionals decide wisely about personalized medicine, treatment plans, and resource allocation by utilizing big data analytics and machine learning. To guarantee that algorithmic recommendations are impartial and fair, however, ethical issues relating to prejudice and data privacy must be taken into account. Big data analytics and machine learning have a great potential to disrupt healthcare, and as these technologies continue to evolve, new opportunities to reform healthcare and enhance patient outcomes may arise. In order to investigate (...) the patient’s outcomes with empirical evidence, this research was conducted using an online survey to incorporate healthcare professionals, patient’s reviews, and clinical staff. The data were analyzed using SmartPLS 4.0 to predict the structural model. The findings revealed a direct impact as positive influence of using machine learning on healthcare performance and patient outcomes through big data analytics. Moreover, it is evident that this can lead to personalized treatment plans, early interventions, and improved patient outcomes. Additionally, big data analytics can help healthcare providers optimize resource allocation, improve operational efficiency, and reduce costs. The impact of big data analytics on patient outcome and healthcare performance is expected to continue to grow, making it an important area for investment and research. (shrink)

The first few years of the 21st century were characterised by a progressive loss of privacy. Two phenomena converged to give rise to the data economy: the realisation that data trails from users interacting with technology could be used to develop personalised advertising, and a concern for security that led authorities to use such personal data for the purposes of intelligence and policing. In contrast to the early days of the data economy and internet surveillance, the (...) last few years have witnessed a rising concern for privacy. As bad data practices have come to light, citizens are starting to understand the real cost of using online digital technologies. Two events stamped 2018 as a landmark year for privacy: the Cambridge Analytica scandal, and the implementation of the European Union’s General Data Protection Regulation (GDPR). The former showed the extent to which personal data has been shared without data subjects’ knowledge and consent and many times for unacceptable purposes, such as swaying elections. The latter inaugurated the beginning of robust data protection regulation in the digital age. Getting privacy right is one of the biggest challenges of this new decade of the 21st century. The past year has shown that there is still much work to be done on privacy to tame the darkest aspects of the data economy. As data scandals continue to emerge, questions abound as to how to interpret and enforce regulation, how to design new and better laws, how to complement regulation with better ethics, and how to find technical solutions to data problems. The aim of the research project Data, Privacy, and the Individual is to contribute to a better understanding of the ethics of privacy and of differential privacy. The outcomes of the project are seven research papers on privacy, a survey, and this final report, which summarises each research paper, and goes on to offer a set of reflections and recommendations to implement best practices regarding privacy. (shrink)

The pace of technological change continues to outstrip the evolution of democratic institutions, creating an urgent need for innovative approaches to democratic reform. However, the experimentation bottleneck - characterized by slow speed, high costs, limited scalability, and ethical risks - has long hindered progress in democracy research. This paper proposes a novel solution: employing generative artificial intelligence (GenAI) to create synthetic data through the simulation of digital homunculi, GenAI-powered entities designed to mimic human behavior in social contexts. (...) By enabling rapid, low-risk experimentation with alternative institutional designs, this approach could significantly accelerate democratic innovation. I examine the potential of GenAI-assisted research to mitigate current limitations in democratic experimentation, including the ability to simulate large-scale societal interactions and test complex institutional mechanisms. While acknowledging potential risks such as algorithmic bias, reproducibility challenges, and AI alignment issues, I argue that the benefits of synthetic data are likely to outweigh their drawbacks if implemented with proper caution. To address existing challenges, I propose a range of technical, methodological, and institutional adaptations. The paper concludes with a call for interdisciplinary collaboration in the development and implementation of GenAI-assisted methods in democracy research, highlighting their potential to bridge the gap between democratic theory and practice in an era of rapid technological change. (shrink)

Digitisation of research data is widely increasing all around the world because it needs more and development of enormous digital technologies. Data curation services are starting to offer many libraries. Research data curation is the collective invaluable and reusable information of the researchers. Collected data preservation is more important. The majority of the higher education institutes preserved the research data for their students and researchers. It is stored for a long time using (...) various formats. It is called research data preservation. Without proper research data management plan and implementation cannot curate the research data. The aim of the study is to identify the Asian Library and Information Science (LIS) faculties’ experiences in the research data preservation and curation during their research. Data management, curation and preservation all are interlinked. For reuse of the research data; data curation is an essential role. For this research, we adopted a survey method and an online questionnaire was shared with 1400 LIS professionals, belonging to the Asian region but the completed study respondents are 125 university faculties from various Asian countries. The study findings are 76.8 per cent generated statistical data followed by 58.4 per cent textual files. By far, the most preferable data analysis tool is Microsoft Excel 82.4 per cent. Moreover, the result shows that generated data is mostly stored by personal computers and laptop hard disks. This study concludes LIS faculties having adequate skills and knowledge on data curation and preservation even though they are expecting more services from their academic institute …. (shrink)

The main thrust of this study is to examine the impact of social cost on the corporate performance of petroleum marketing firms in Nigeria. Ex post facto research design was adopted, secondary sources of data were collected for analysis of results and interpretation of data. The results indicated that social cost positively influences the corporate performance of petroleum marketing firms in Nigeria. Hence, it was recommended that the federal government should mandate all petroleum marketing companies to capture (...) and disclose all quantitative data relating to corporate social responsibility which would serve as a boost to the company performances vis-a-vis increased employee productivity. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security (...) in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. After a thorough investigation, the article shows how these methods may boost cloud storage system performance, security, and usability. Tests show that improved ABKS speeds up search searches and lowers storage costs, making it a viable cloud storage alternative. Exploring sophisticated machine learning algorithms for predictive search improvements and strengthening ABKS against emerging security vulnerabilities are future research priorities. (shrink)

Training artificial intelligence (AI) systems requires vast quantities of data, and AI developers face a variety of barriers to accessing the information they need. Synthetic data has captured researchers’ and industry’s imagination as a potential solution to this problem. While some of the enthusiasm for synthetic data may be warranted, in this short paper we offer critical counterweight to simplistic narratives that position synthetic data as a cost-free solution to every data-access challenge—provocations highlighting ethical, political, (...) and governance issues the use of synthetic data can create. We question the idea that synthetic data, by its nature, is exempt from privacy and related ethical concerns. We caution that framing synthetic data in binary opposition to “real” measurement data could subtly shift the normative standards to which data collectors and processors are held. And we argue that by promising to divorce data from its constituents—the people it represents and impacts—synthetic data could create new obstacles to democratic data governance. (shrink)

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of (...) nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality. (shrink)

This study addresses the issue of maintaining nutritional quality amidst financial hardship faced by families in Plateau State, Nigeria. Surveys research designed was used for the study. The study covers 3 local government areas in Plateau State, ensuring diverse representation. The sample size of 400 families residing in Plateau State was used. Questionnaire was used to collect data for the study which capture the demographic information, dietary habits, household budget allocation for food, and perceptions of nutritional value. Mean (...) and standard deviation was used to present result for the findings. The findings of the study revealed that that there should be cost-effective meal planning strategies in promoting healthy eating habits. Also, socioeconomic factors impact cost-effective meal planning in promoting healthy eating habits. Based on the findings, stakeholders, policymakers, and community organizations should implement interventions that support cost-effective meal planning, enhance nutritional literacy, and alleviate financial burdens on families in Plateau State. This study may empower families to make informed dietary choices and achieve better health outcomes despite economic constraints. (shrink)

This paper explores the challenges and innovations in optimizing data science workflows within cloud computing environments. It begins by highlighting the critical role of data science in modern industries and the pivotal contribution of cloud computing in enabling scalable and efficient data processing. The primary focus lies in identifying and analyzing the key challenges encountered in current data science workflows deployed in cloud infrastructures. These challenges include scalability issues related to handling large volumes of data, (...) resource management complexities in optimizing computational resources, cost management strategies to balance performance with expenses, and ensuring robust data security and privacy measures. The manuscript then delves into innovative solutions and techniques aimed at addressing these challenges. It discusses advancements such as workflow automation tools and frameworks that streamline repetitive tasks, containerization technologies like Docker and Kubernetes for efficient application deployment and management, and the utilization of serverless architectures to enhance scalability and reduce operational costs. Additionally, it explores the benefits of parallel processing frameworks such as Apache Spark and Hadoop in optimizing data processing tasks. The integration of machine learning algorithms for dynamic workflow optimization and effective data management strategies in cloud environments are also examined. Through detailed case studies and application examples across various domains, the manuscript illustrates the practical implementation and outcomes of these optimization strategies. Furthermore, it discusses emerging trends in cloud technologies, the role of AI-driven automation in enhancing workflow efficiencies, and ethical considerations surrounding data science operations in cloud computing. The manuscript concludes with a summary of findings, practical recommendations for organizations seeking to enhance their data science workflows in the cloud, and insights into future research directions to address evolving challenges. (shrink)

Chi phí dữ liệu, nhất là với những loại điều tra khảo sát khắt khe, ngày càng trở thành quá sức với những nhóm nghiên cứu nhỏ, ở các nước đang phát triển.

This study evaluated the impact of fuel subsidy removal on family income and sustainability in Ondo City. The study adopted a descriptive survey research design. Three (3) research questions guided the study. The study population was comprised of all household members in Ondo Metropolis. The total population of the study is two hundred and fifty-three thousand, four hundred and sixty-two (253,462). The sample size was one hundred and eighty-five (185) and was randomly selected using a simple random technique. (...) A structured questionnaire was the research instrument used to elicit responses from the respondents. Split-half reliability was used to determine the instrument's consistency, and a correlation coefficient of 0.76 was obtained. Data collected were analysed using descriptive statistics. The findings revealed that the fluctuation of oil prices in the international market necessitated subsidy removal, and the higher cost of transportation increases the cost of food items. The study concluded that the factors that necessitate fuel subsidy removal have a significant relationship with the possible challenges encountered by households in family sustenance. It was therefore recommended that the government's actions, such as increasing civil servants' wages and providing incentives for business owners, could enable them to cope with the increasing prices of commodities, offering a hopeful outlook for the future. (shrink)

Despite the changes in human behaviour and interactions occasioned by the COVID-19 pandemic, many institutions are yet to adapt to the new normal fully. While some educational institutions switched entirely to e-learning to promote teaching and learning, others could not offer education due to physical and social restrictions. Previous studies in Africa have identified reasons for the poor ICT adoption for educational purposes. However, the degree to which these factors affect ICT utilisation is barely analysed. Using a quantitative approach, this (...) study assessed ICT deployment for teaching in the COVID-19 era by focusing on their availability and challenges. A sample of 344 respondents from a population of 2,867 academic staff at two Nigerian public universities participated in the study. After receiving face validity from experts, a structured questionnaire was used for data collection. Cronbach alpha reliability indices of the questionnaire ranged from 0.72 to 0.94. Descriptive statistics (simple percentages, mean, and standard deviation) and inferential statistics (one-way ANOVA) were used for data analysis. Findings generally revealed a moderate extent in the availability of ICT resources in public universities. The cost of data, computer literacy, and electricity supply are challenges that significantly affected ICT deployment in the COVID-19 era. Based on this finding, it was concluded that personal and institutional challenges affect how ICT resources are deployed in public universities. It was recommended, among other things, that the management of each tertiary institution should apportion proceeds from internally generated revenue to procure ICT resources specific to the need of the school. The study provides the ground for further research into students’ use of ICT for educational purposes. (shrink)

Cloud storage's scalability, accessibility, and affordability have made it essential in the digital age. Data security and privacy remain a major issue due to the large volume of sensitive data kept on cloud services. Traditional encryption is safe but slows data recovery, especially for keyword searches. Secure, fine-grained access control and quick keyword searches over encrypted data are possible using attribute-based keyword search (ABKS). This study examines how ABKS might optimize search efficiency and data security (...) in cloud storage systems. We examine index compression, query processing improvement, and encryption optimization to decrease computational cost and preserve security. (shrink)

This paper provides a comprehensive workflow for organizations considering a move from legacy database systems to Snowflake, demonstrating how a structured migration process can result in enhanced data accessibility, operational flexibility, and long-term scalability. Future directions for research are also discussed, including integration with advanced analytics and machine learning applications, which could further augment Snowflake’s utility in big data ecosystems.

The research evaluates generative AI’s capabilities through a multi-phase framework, addressing how data synthesis, language models, and predictive algorithms contribute to sector-specific applications. In banking, the model assesses the impact of AI-driven chatbot interactions, credit risk assessment, and personalized financial services on customer experience and bank performance. Healthcare applications are explored through image synthesis for diagnostics, predictive modeling in patient care, and drug discovery simulations. The experimental setup is rigorously tested across metrics such as response accuracy, cost-effectiveness, and (...) data privacy to determine the benefits and potential risks associated with generative AI in these fields. (shrink)

PTSD is a common mental health disorder among students across the globe that manifests after encountering traumatic events. This study explored the nexus between poverty and PTSD among students. This review employed a scoping review lens to examine the nexus between PTSD and poverty among students. Literature search was conducted in online databases such as PubMed, Google Scholar, Scopus, and Semantic Scholar. This study followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) extension for scoping reviews (PRISMA-SCR) for (...) study selection and data extraction. Poverty is a significant factor that predisposes individuals to the development of post-traumatic stress disorder after experiencing a traumatic event, as determined by this review. In addition, this review discloses that psychological treatments such as Trauma Focused Behavioural Therapy, Cognitive Behavioural Therapy for PTSD, narrative exposure therapy among others have been identified as effective for treating PTSD; however, the cost of treatment has been found to be unaffordable among poor individuals. This study also revealed that there is a dearth of empirical studies that focus specifically on students' PTSD and Poverty or studies conducted in educational settings with reference to PTSD and poverty. In light of this, this study recommends that future research should investigate the relationships between PTSD and poverty among students. (shrink)

The science of operations research is one of the modern sciences that have made a great revolution in all areas of life through the methods provided by it, suitable and appropriate to solve most of the problems that were facing researchers, scholars and those interested in the development of societies, and the most beneficiaries of this science were companies and institutions that are looking for scientific methods that help them manage their work so that they achieve the greatest profit (...) and the lowest cost, and one of the important methods that have been used in the management of companies we offer in this research two methods, Dynamic programming method. This method has been used in many practical matters and helped decision-makers in companies to achieve a maximum profit and less cost by formulating the reality of the state of the company and the data provided by decision-makers with a dynamic mathematical model that is solved using methods of solving dynamic models and we will provide in this research an example of this through the issue of choosing the optimal investment for the budget of a company so that it achieves a maximum profit, and the method of programming with integers: the method that provided these companies with solutions with integer values suitable for the nature of its work, through the use of the binary integer in the formulation of the appropriate mathematical model on the one hand, and on the other hand, the use of the binary integer variable helped to convert some nonlinear models that lead to some practical problems into linear models, and it should be noted here that in the previous two methods there is something indeterminable because we must make a decision in choosing or not choosing something, but the optimal solution that we will get remains A specific value because we are building the mathematical model for any realistic issue through the data provided by those responsible for the work and these data are calculated quantities and therefore they are uncertain values because their validity depends on the circumstances surrounding the work environment, they may be exposed to increase or decrease, and therefore the optimal solution on which the company will base its decision is suitable for specific values and any change in them can cause the company an uncalculated loss, so in this research we will use the concepts of neutrosophic science, the branch of science founded by the American scientist Florentin Smarandache in 1995 based on his belief that there is no absolute truth, a science that is interested in the study of ideas and concepts that are neither true nor false, but just in between, and we will take the data (calculated quantities) neutrosophic values that are specified or unspecified values are any set close to the calculated quantities, then the resulting mathematical model is a neutrosophic model and the optimal solution has neutrosophic values and thanks to the indefinite uncertainty that these values have, companies from the development of appropriate plans for all circumstances and thus achieve the greatest profit and the lowest cost, and we will clarify the above through two issues, the issue of optimal designation of a warehouse site, which we will formulate the mathematical model of using the neutrosophic integer programming method - and the issue of capital budget, which we will present in two different forms, we use in the first form the neutrosophic integer programming method and in the second the neutrosophic dynamic programming method. (shrink)

This study determined undergraduates’ satisfaction of online food and beverage services amidst financial distress in Nnewi urban, Anambra State, Nigeria. Descriptive survey research design was used by the study. Population consisted of 3,509 students. Sample size was 35. Questionnaire was the instrument used for data collection. Data was analyzed using mean and standard deviation. Findings revealed that different types of food and beverage products were purchased online. The problems undergraduates face in online food and beverage services included (...) delayed delivery, use of artificial food colouring and fear of food contamination. Strategies for enhancing students’ satisfaction of online purchases included timely delivery of products ordered and appropriate delivery mechanism. Findings also showed that the ways online food and beverage purchases can influence undergraduate’s finances included high cost of online food products, additional cost for delivery among others. Among recommendations made was that online food vendors should ensure that food products delivered are same with products ordered by customers in order to enhance customer satisfaction. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine (...) learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

This study presents a description of an open database on scientific output of Vietnamese researchers in social sciences and humanities, one that corrects for the shortcomings in current research publication databases such as data duplication, slow update, and a substantial cost of doing science. Here, using scientists’ self-reports, open online sources and cross-checking with Scopus database, we introduce a manual system and its semi-automated version of the database on the profiles of 657 Vietnamese researchers in social sciences and (...) humanities who have published in Scopus-indexed journals from 2008 to 2018. The final system also records 973 foreign co-authors, 1,289 papers, and 789 affiliations. The data collection method, highly applicable for other sources, could be replicated in other developing countries while its content be used in cross-section, multivariate, and network data analyses. The open database is expected to help Vietnam revamp its research capacity and meet the public demand for greater transparency in science management. (shrink)

This paper delves into the integration of optimization techniques within ABKS to enhance search efficiency and data security in cloud storage environments. We explore various optimization strategies, such as index compression, query processing enhancement, and encryption optimization, which aim to reduce computational overhead while maintaining robust security measures. Through a comprehensive analysis, the paper illustrates how these techniques can significantly improve the performance of cloud storage systems, ensuring both security and usability. Experimental results demonstrate that optimized ABKS not only (...) accelerates search queries but also reduces storage costs, making it a viable solution for modern cloud storage challenges. Future research directions include exploring advanced machine learning algorithms for predictive search optimizations and further improving the resilience of ABKS against emerging security threats. (shrink)

The exponential growth of data storage requirements has become a pressing challenge in hybrid cloud environments, necessitating efficient data deduplication methods. This research proposes a novel Smart Deduplication Framework (SDF) designed to identify and eliminate redundant data, thus optimizing storage usage and improving data retrieval speeds. The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine (...) learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Background: Smart sensors have been developed as diagnostic tools for rehabilitation to cover an increasing number of geriatric patients. They promise to enable an objective assessment of complex movement patterns. -/- Objective: This research aimed to identify and analyze the conflicting ethical values associated with smart sensors in geriatric rehabilitation and provide ethical guidance on the best use of smart sensors to all stakeholders, including technology developers, health professionals, patients, and health authorities. -/- Methods: On the basis of a (...) systematic literature search of the scientific databases PubMed and ScienceDirect, we conducted a qualitative document analysis to identify evidence-based practical implications of ethical relevance. We included 33 articles in the analysis. The practical implications were extracted inductively. Finally, we carried out an ethical analysis based on the 4 principles of biomedical ethics: autonomy, beneficence, nonmaleficence, and justice. The results are reported in categories based on these 4 principles. -/- Results: We identified 8 conflicting aims for using smart sensors. Gains in autonomy come at the cost of patient privacy. Smart sensors at home increase the independence of patients but may reduce social interactions. Independent measurements performed by patients may result in lower diagnostic accuracy. Although smart sensors could provide cost-effective and high-quality diagnostics for most patients, minorities could end up with suboptimal treatment owing to their underrepresentation in training data and studies. This could lead to algorithmic biases that would not be recognized by medical professionals when treating patients. -/- Conclusions: The application of smart sensors has the potential to improve the rehabilitation of geriatric patients in several ways. It is important that patients do not have to choose between autonomy and privacy and are well informed about the insights that can be gained from the data. Smart sensors should support and not replace interactions with medical professionals. Patients and medical professionals should be educated about the correct application and the limitations of smart sensors. Smart sensors should include an adequate representation of minorities in their training data and should be covered by health insurance to guarantee fair access. (shrink)

Employee is the main resource for organization. Recently, there were many concerns of staff resignation within industries. High staff turnovers cause increase of costs of hiring workforces. Owing this issue, “Assessment of Factor of Employee’s Turnover” was proposed for research with the objective of examination of factors causing staff resignation from MVi. In total 26 staff both women and men who resigned 2016 and 2017 was selected for interview. Quantitative data was used. Three main steps were done (...) including research questionnaires and material development, sampling technique and criteria and data analysis and reporting. There were four main parts: the degree of staff satisfaction of each of the variables (1 to 12), the correlation between each of variable (1 to 12) with overall satisfaction, the causes of staff resign of the variable (13-27) and the correlation between each of the staff resign causes (variable 13 to 27). All those data have been transformed into non-parameter with the Spearmen Rho. MVi staff members were appreciated working with MVi. The satisfaction of each variable was that the staff scored high for most factors like staff leave, communication with colleagues, staff capacity development opportunities, and roles and responsibilities. Former staff who worked in 2016 and 2017 was most appreciated with the factors above. Three factors scored by respondents were very low in comparison with other factors. Those are work overload, job security, and salary. All those factors were located between score 3 (neutral and score 4 (satisfied). Among 12 satisfaction variables, three of them were most significantly positively related to staff motivation. For internal factors, the average score was 3.19 which represents mostly true of the statement above. The variables which were significantly related to staff resignation were communication with superiors, finding a higher position with new agencies better salaries, and benefits from the new agency. The recommendations were produced including awarding for staff, review salary scale, tasks, maintaining staff. Qualitative research to explore what job security, salary, and work-overloads are not significantly related to staff satisfaction should be done. Qualitative research on factors of staff resign should be done. (shrink)

Being a researcher is challenging, especially in the beginning. Early Career Researchers (ECRs) need achievements to secure and expand their careers. In today’s academic landscape, researchers are under many pressures: data collection costs, the expectation of novelty, analytical skill requirements, lengthy publishing process, and the overall competitiveness of the career. Innovative thinking and the ability to turn good ideas into good papers are the keys to success.

Academia is a competitive environment. Early Career Researchers (ECRs) are limited in experience and resources and especially need achievements to secure and expand their careers. To help with these issues, this book offers a new approach for conducting research using the combination of mindsponge innovative thinking and Bayesian analytics. This is not just another analytics book. 1. A new perspective on psychological processes: Mindsponge is a novel approach for examining the human mind’s information processing mechanism. This conceptual framework is (...) used to construct models in studies. The framework is highly flexible and widely applicable for many different types of information processes. The mindsponge approach can help researchers discover interesting ideas or even formulate their very own theories when investigating psychosocial phenomena. This approach brings a fresh wind to the current landscape of social sciences and humanities (SSH). 2. Easy-to-follow analysis protocol: The Bayesian Mindsponge Framework (BMF analytics) is useful in terms of computing and visualizing power but also easy to learn and apply. Contrary to being intimidating, the Bayesian analytics section of this book is presented in a reader-friendly manner with a detailed yet clear step-by-step procedure. Examples are from published BMF articles, allowing readers to immediately practice the method and quickly create their own applications. With educational purposes in mind, the book is very suitable for ECRs who are looking to innovate their research methods. 3. Advocating for low-cost, high-quality research: Doing science can be very costly. Mindsponge innovative thinking and BMF analytics help produce impactful studies using openly available data on online repositories. This is based on the authors’ previous works and experiences. The book presents examples of employing the open R package bayesvl on secondary data from different sources. With less financial constraints, researchers can have more freedom of thought to pursue their curiosity and creativity. ECRs in low- and middle-income countries may find this aspect crucial in their careers. 4. Support and collaboration: The authors share their insights from experiences in the academic publishing system to help readers get through the processes of manuscript writing and peer-reviewing more easily. The authors are also ready to support other researchers with further inquiries and collaboration opportunities at the following website, mindsponge(dot)info. This book is for: a) ECRs whose only abundant resources are their innovation capacity and strength of will; b) Researchers in SSH who want to explore a novel approach to thinking and study conducting; c) Low- and middle-income countries’ researchers looking for a cost-effective research protocol; and, d) Innovative thinkers who want to turn their interesting thoughts into good publications. (shrink)

To date researchers planning experiments have always lived by the mantra that 'using equal sample sizes gives the best results' and although unequal groups are also used in experimentation, it is not the preferred method of many and indeed actively discouraged in literature. However, during live study planning there are other considerations that we must take into account such as availability of study participants, statistical power and, indeed, the cost of the study. These can all make allocating equal sample sizes (...) difficult, and sometimes near impossible. This, some might say, means that the study would not adhere to rigorous statistical standard (Rosenbaum and Rubin, 1985). However, here we present evidence that, not only is this a false assumption, but that we may actually gain more power in the study by actually using unequal groups. Here, data from a Sepsis Biomarker study is used, in which the aim is to predict, by biomarker level and presence, whether the patient would go on to develop sepsis. It was found that larger control groups may give more power to studies looking for an effect in the mid range but not for large or small effects. This study shows merit in the hypothesis that more power can be achieved when a larger control group is used. Published by the Ministry of Defence at dstl - Reference DSTLTR92592 P2PP2R-2016-02-23T13:39:45. (shrink)

Tracing graduates has become an imperative for higher education institutions much more during the pandemic. This tracer determined the employment and employability status of the 2019 BSE graduates and identified the competencies they adequately acquired and deemed vital for work. It used descriptive design, and data were collected from the 103 graduates through a google form with open and closed-ended questions administered between November and December 2020. Results revealed that most of the graduates had been employed in teaching and (...) teaching-related jobs but mostly in contractual arrangements within the first and second six months after graduation. Many had their first jobs with meager salaries from the private sector. Communication, pedagogy, information communication technology, time management, and flexibility were the top competencies they adequately acquired and were beneficial in work. The study concluded that these graduates had acquired 21st-century skills in their respective degree programs. These results have corresponding implications for future research in confirming the most employable skills in secondary teaching. As recommended, classroom instruction might emphasize the development of these skills. Eventually, these become the competitive advantage and employability capitals of future graduates. Administering the licensure examination and the release of its results can be done within the first three months after graduation to lessen the cost of waiting. (shrink)

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and (...) traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Working in academia is challenging, even more so for those with limited resources and opportunities. Researchers around the world do not have equal working conditions. The paper presents the structure, operation method, and conceptual framework of the SM3D Portal's community coaching method, which is built to help Early Career Researchers (ECRs) and researchers in low-resource settings overcome the obstacle of inequality and start their career progress. The community coaching method is envisioned by three science philosophies (cost-effectiveness, transparency spirit, and proactive (...) attitude) and established and operated based on the Serendipity-Mindsponge-3D knowledge (SM3D) management framework (i.e., mindsponge thinking and Bayesian Mindsponge Framework analytics serve as the coaching program's foundational theory and analytical tools). The coaching method also embraces Open Science's values for lowering the cost of doing science and encouraging the trainees to be transparent, which is expected to facilitate the self-correcting mechanism of science through open data, open review, and open dialogue. Throughout the training process, members are central beneficiaries by gaining research knowledge and skills, acquiring publication as the training's product, and shifting their mindsets from “I can't do it” to “I can do it,” and at the same time transforming a mentee to be ready for a future mentor's role. The coaching method is thus one of the members, for the member, by the members. -/- • The paper provides the structure, operation method, and conceptual framework of the SM3D Portal's community coaching method, which is built to help Early Career Researchers (ECRs) and researchers in low-resource settings overcome the obstacle of inequality and start their career progress. • The paper presents three major science philosophies envisioning the establishment and operation of scholarly community coaching. • The paper employs the mindsponge theory and BMF analytics to construct a conceptual framework explaining how an environment is created to help shift members’ mindsets from “I can't do it” to “I can do it.”. (shrink)

Background: Use of patients’ medical data for secondary purposes such as health research, audit, and service planning is well established in the UK. However, the governance environment, as well as public understanding about this work, have lagged behind. We aimed to systematically review the literature on UK and Irish public views of patient data used in research, critically analysing such views though an established biomedical ethics framework, to draw out potential strategies for future good practice guidance (...) and inform ethical and privacy debates. Methods: We searched three databases using terms such as patient, public, opinion, and electronic health records. Empirical studies were eligible for inclusion if they surveyed healthcare users, patients or the public in UK and Ireland and examined attitudes, opinions or beliefs about the use of patient data for medical research. Results were synthesised into broad themes using a framework analysis. Results: Out of 13,492 papers and reports screened, 20 papers or reports were eligible. While there was a widespread willingness to share patient data for research for the common good, this very rarely led to unqualified support. The public expressed two generalised concerns about the potential risks to their privacy. The first of these concerns related to a party’s competence in keeping data secure, while the second was associated with the motivation a party might have to use the data. Conclusions: The public evaluates trustworthiness of research organisations by assessing their competence in data-handling and motivation for accessing the data. Public attitudes around data-sharing exemplified several principles which are also widely accepted in biomedical ethics. This provides a framework for understanding public attitudes, which should be considered in the development in any guidance for regulators and data custodians. We propose four salient questions which decision makers should address when evaluating proposals for the secondary use of data. (shrink)

Since the last ice age, when ice enveloped most of the northern continents, the earth has warmed by about five degrees. Within a century, it is likely to warm by another four or five. This revolution in our climate will have immense and mostly harmful effects on the lives of people not yet born. We are inflicting this harm on our descendants by dumping greenhouse gases into the atmosphere. We can mitigate the harm a little by taking measures to control (...) our emissions of these gases, and to adapt to the changes by, for instance, building sea walls around coastlines threatened by rising sea levels. But these measures will be very expensive, and the costs will be born by us, the present generation, whereas the benefits will come to future generations. How much should we sacrifice for the sake of the future? Economists and philosophers have independently worked on the question of our responsibility to future generations. This book brings their work together and applies it to global warming. It suggests a programme for future research on the economic and ethical issues. The book is intended for economists, and for philosophers and other social scientists who have a little knowledge of economic methods. (shrink)

Cloud computing has transformed data management and storage by providing unmatched scalability, flexibility, and cost-effectiveness. However, rising cloud storage use has raised data security and privacy issues. As sensitive data being outsourced to third-party cloud providers, security is crucial. Traditional encryption methods secure data but make data recovery difficult. Specifically, efficiently searching encrypted data without compromising security is difficult.

Mental and behavioral disorders represent a significant portion of the public health burden in all countries. The human cost of these disorders is immense, yet treatment options for sufferers are currently limited, with many patients failing to respond sufficiently to available interventions and drugs. High quality ontologies facilitate data aggregation and comparison across different disciplines, and may therefore speed up the translation of primary research into novel therapeutics. Realism-based ontologies describe entities in reality and the relationships between them (...) in such a way that – once formulated in a suitable formal language – the ontologies can be used for sophisticated automated reasoning applications. Reference ontologies can be applied across different contexts in which different, and often mutually incompatible, domain-specific vocabularies have traditionally been used. In this contribution we describe the Mental Functioning Ontology (MF) and Mental Disease Ontology (MD), two realism-based ontologies currently under development for the description of humanmental functioning and disease. We describe the structure and upper levels of the ontologies and preliminary application scenarios, and identify some open questions. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short-Term Memory (LSTM) and (...) Convolutional Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi-cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

The article considers the problem of ensuring the economic security of trade enterprises by forming an optimal pricing policy. The methodology of formation the minimum and maximum selling prices of trade enterprise, maintenance of margin of economic security, which is based on research of turnover costs and working capital of trade enterprise is offered. Based on statistical data of trade enterprises, the types of prices by product range are determined, which form a stable economic situation and ensure (...) economic security of trade enterprise. The necessity of forming the optimal price policy and selling prices of trade enterprises with the use of Pareto efficiency tools and construction of single-criteria and multi-criteria tasks, indicators of quality of commodity resources of trade enterprises is substantiated. Using the Excel software processor and the «Regression» function, economic-mathematical models of optimal prices for the product range of trade enterprises are built. The results testified to the effectiveness of the proposed model of the optimal pricing policy of trade enterprises, as the obtained values are within the minimum selling prices, which provides economic security of trade enterprises. The process of forming the pricing policy of trade enterprises to ensure economic security is proposed, which is based on the principles of pricing, appropriate methodological tools and monitoring of market environment factors. Organizational, economic, legal, social and market (marketing) mechanisms for the formation of effective pricing policy of trade enterprises aimed at ensuring economic security are identified. This study is practically interesting for personnel of trade enterprises, regardless of organizationallegal forms of ownership and activities, and theoretically – for researchers dealing with pricing. (shrink)

Cloud computing is the computing technology which provides resources like software, hardware, services over the internet. Cloud computing provides computation, software, data access, and storage services that do not require end- user knowledge of the physical location and configuration of the system that delivers the services. Cloud computing enables the user and organizations to store their data remotely and enjoy good quality applications on the demand without having any burden associated with local hardware resources and software managements but (...) it possesses a new security risk towards correctness of data stored at cloud. The data storage in the cloud has been a promising issue in these days. This is due to the fact that the users are storing their valuable data and information in the cloud. The users should trust the cloud service providers to provide security for their data. Cloud storage services avoid the cost storage services avoids the cost expensive on software, personnel maintains and provides better performance less storage cost and scalability, cloud services through internet which increase their exposure to storage security vulnerabilities however security is one of the major drawbacks that preventing large organizations to enter into cloud computing environment. This work surveyed on several storage techniques and this advantage and its drawbacks. (shrink)

Cloud computing has revolutionized the storage and management of data by offering scalable, cost-effective, and flexible solutions. However, it also introduces significant security concerns, particularly related to data leakage, where sensitive information is exposed to unauthorized entities. Data leakage can result in substantial financial losses, reputational damage, and legal complications. This paper proposes a deep learning-based framework for detecting data leakage in cloud environments. By leveraging advanced neural network architectures, such as Long Short- Term Memory (LSTM) (...) and Convolutional Neural Networks (CNNs), the model detects abnormal data access patterns that may indicate leakage. The system operates in real-time, continuously monitoring data interactions between users and the cloud. A large dataset containing normal and abnormal access logs is used to train and validate the model, ensuring it can effectively differentiate between legitimate and malicious activity. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score, with the system achieving over 96% accuracy in identifying potential data leaks. Furthermore, the proposed solution is designed to be scalable and adaptable, making it suitable for dynamic cloud environments with evolving threats. Future enhancements to the system include integrating multi- cloud support and refining the model’s ability to detect sophisticated insider threats. This research highlights the importance of leveraging deep learning for real-time, proactive cloud security. (shrink)

Objectives -/- Surveys in various countries suggest 17% to 80% of doctors prescribe ‘placebos’ in routine practice, but prevalence of placebo use in UK primary care is unknown. Methods -/- We administered a web-based questionnaire to a representative sample of UK general practitioners. Following surveys conducted in other countries we divided placebos into ‘pure’ and ‘impure’. ‘Impure’ placebos are interventions with clear efficacy for certain conditions but are prescribed for ailments where their efficacy is unknown, such as antibiotics for suspected (...) viral infections. ‘Pure’ placebos are interventions such as sugar pills or saline injections without direct pharmacologically active ingredients for the condition being treated. We initiated the survey in April 2012. Two reminders were sent and electronic data collection closed after 4 weeks. Results -/- We surveyed 1715 general practitioners and 783 (46%) completed our questionnaire. Our respondents were similar to those of all registered UK doctors suggesting our results are generalizable. 12% (95% CI 10 to 15) of respondents used pure placebos while 97% (95% CI 96 to 98) used impure placebos at least once in their career. 1% of respondents used pure placebos, and 77% (95% CI 74 to 79) used impure placebos at least once per week. Most (66% for pure, 84% for impure) respondents stated placebos were ethical in some circumstances. Conclusion and implications -/- Placebo use is common in primary care but questions remain about their benefits, harms, costs, and whether they can be delivered ethically. Further research is required to investigate ethically acceptable and cost-effective placebo interventions. (shrink)

By studying the effect of human resource development on strengthening the firm's total capabilities this literature review investigates the relationship between the development of human capabilities and company performance by studying the effects of human resource development on strengthening a firm’s total capabilities. To provide a thorough grasp of the subject, the review synthesizes and analyzes pertinent research articles, academic papers, and industry reports. SPSS version 22 statistical software was used for social sciences. Descriptive and inferential statistics were used (...) to analyze the questionnaire data. To attain a lasting competitive advantage, the assessment also emphasizes the significance of fostering a learning culture, adopting cutting-edge procedures, and putting in place efficient training and development programs. The main findings show that investing in the growth of human capabilities has a favorable impact on the success of the company, especially when those investments are made in conjunction with the growth of the organization's capabilities. Investing in an employee's skill and capability development boosts productivity, enhances innovation, reduces costs, fosters customer loyalty and satisfaction, and provides a company with a competitive advantage. (shrink)

The aim of the Validation Theory (VT) as a meta-empirical construct is to introduce a new vista in the reorganization of the neuroscience, in its role of a science of the Mind-and-Brain unification. The present study focuses on existing discrepancies and contradictions between the methods of basic neurosciences and those prescribed by the psychological science. Our view is that these discrepancies are based on a high penetration of traditional neuroscience methods into the biological processes, coupled with low extrapolation (experimenting with (...) animal models) and vice versa for the psychological and psychopathological methods. A novel epistemological model for integrating psychological and neuroscientific knowledge is proposed. It is represented as a simultaneous investigation of the brain activity with penetrating high resolution functional Magnetic Resonance Imaging and in extenso application of set of psychological tests for exploring correspondence (cross-validation) between their compounds. The proposed approach leads to a revision of the neuroscientific and psychological terms, methods and data, followed by a revision of their relative interplay. This would make possible a practical exchange of expensive but objective fMRI with the lower costing psychological instruments (effect of "minimization"). The approaches proceeding from VT will infiltrate diagnostics and prevention in psychiatry. On a further stage the pharmaco-psychological monitoring will uncover new opportunities. This proofs' based research and practice represents an integral counterpart of the values-based mental health care. In conclusion VT is an evolutionary corner stone in order to traverse the stage of a Brain-Brain paradigm and to reach the point of development of the Mind-Brain paradigm. (shrink)

We simulate trial data to test speculative claims about research methods, such as the impact of publication bias.

The framework leverages a hybrid cloud architecture, combining the scalability of public clouds with the security of private clouds. By employing a combination of client-side hashing, metadata indexing, and machine learning-based duplicate detection, the framework achieves significant storage savings without compromising data integrity. Real-time testing on a hybrid cloud setup demonstrated a 65% reduction in storage needs and a 40% improvement in data retrieval times. Additionally, the system employs blockchain for immutable logging of deduplication activities, enhancing transparency and (...) traceability. This study concludes with an evaluation of the deduplication framework's impact on cost efficiency, system performance, and potential scalability. Future enhancements aim to integrate multi-cloud interoperability and advanced compression algorithms to further refine storage management. (shrink)

Patients with serious illnesses or injuries may decide to quit their medical treatment if they think paying the fees will put their families into destitution. Without treatment, it is likely that fatal outcomes will soon follow. We call this phenomenon “near-suicide”. This study attempted to explore this phenomenon by examining how the seriousness of the patient’s illness or injury and the subjective evaluation of the patient’s and family’s financial situation after paying treatment fees affect the final decision on the treatment (...) process. Bayesian Mindsponge Framework (BMF) analytics were employed to analyze a dataset of 1042 Vietnamese patients. We found that the more serious the illnesses or injuries of patients were, the more likely they were to choose to quit treatment if they perceived that paying the treatment fees heavily affected their families’ financial status. Particularly, only one in four patients with the most serious health issues who thought that continuing the treatment would push themselves and their families into destitution would decide to continue the treatment. Considering the information-filtering mechanism using subjective cost–benefit judgments, these patients likely chose the financial well-being and future of their family members over their individual suffering and inevitable death. Our study also demonstrates that mindsponge-based reasoning and BMF analytics can be effective in designing and processing health data for studying extreme psychosocial phenomena. Moreover, we suggest that policymakers implement and adjust their policies (e.g., health insurance) following scientific evidence to mitigate patients’ likelihood of making “near-suicide” decisions and improve social equality in the healthcare system. (shrink)

Human rights may feel self-apparent to us, but less than 80 years ago, one of the most advanced countries at the time acted based on an utterly contrary ideology. The view of social Darwinism that abandoned the idea of the intrinsic value of human lives instead argued that oppression of the inferior is not only inevitable but desirable. One of the many catastrophic outcomes is the medical data obtained from inhuman experiments at concentration camps. Ethical uncertainty over whether the (...) resulting insights should be a part of the medical literature provides a chance to consider the seemingly irreplaceable social construct of human dignity. Would any medical benefit justify the utilization of this illicit data? Would utilization even qualify as an insult to the dignity of the exploited subjects, or is this a question about intersubjective meaning? This work discusses the wisdom in blind adherence to human dignity, the possibility of retrospective insults, moral complicity, contrary viewpoints, and possible resolutions. (shrink)

A B S T R A C T This paper examines business level strategies and the impact of strategy formulation, strategy content and strategy implementation on competitive performance, all within the food manufacturing sector in Zimbabwe. As far as the author was able to establish by examining the previous studies, none of the previous studies have looked into strategic formulation, business-level strategy and strategy implementation simultaneously in a single study. The objective of this research study is premised on establishing (...) cause and remedies to gain competitiveness in the food manufacturing sector in Zimbabwe. Survey data affecting manufacturing sector competiveness like corruption, finance, technology, labour force, uncompetitive exports, unreliable and high cost electricity were analysed. Furthermore this study considers the moderating effects of environment on the relationship between strategy formulation and competitive performance and strategy content and implementation. The paper also assesses the moderating effect of business level structure on the relationship between strategy formulation content and competitive implementation performance. This study also addresses some of the methodological shortcomings of the previous studies by clearly defining the food manufacturing industry in Zimbabwe, using a good sample size and by using properly validated constructs. The sample size of this research paper consisted of 150 Chief Executive Officers located in Harare, Bulawayo, Mutare, Gweru and Masvingo in Zimbabwean. The research instrument used was the self – administered questionnaire. Data collected was analyzed using both inferential and descriptive statistical tools. The structural model indicates that some of the bivariate relationships become insignificant when strategy formulation is studied along with business-level strategy and strategy implementation simultaneously. Results obtained from the study revealed that strategy formulation and implementation if fully practiced in food manufacturing organisations enhances efficiency, profitability and competitive advantage in a dynamic environments. The paper recommended a conceptual model for Strategy formulation and implementation for competitive advantage in the food manufacturing industry in Zimbabwe. (shrink)