The so-called “conciliatory” norm in epistemology and meta-ethics requires that an agent, upon encountering peer disagreement with her judgment, lower her confidence about that judgment. But whether agents actually abide by this norm is unclear. Although confidence is excessively researched in the empirical sciences, possible effects of disagreement on confidence have been understudied. Here, we target this lacuna, reporting a study that measured confidence about moral beliefs before and after exposure to moral discourse about a controversial issue. Our findings indicate (...) that participants do not abide by the conciliatory norm. Neither do they conform to a rival “steadfast” norm that demands their confidence to remain the same. Instead, moral discourse seems to boost confidence. Interestingly, we also find a confidence boost for factual beliefs, and a correlation between the extremity of moral views and confidence. One possible explanation of our findings is that when engaging in moral discourse participants become more extreme in their opinions, which leads them to become more confident about them, or vice versa: they become more confident and in turn more extreme. Although our work provides initial evidence for the former mechanism, further research is needed for a better understanding of confidence and moral discourse. (shrink)

School boosters are tax-exempt organizations that engage in fundraising efforts to provide public schools with supplementary resources. This paper argues that prevailing forms of school boosting are defeasibly unjust. Section 1 shows that inequalities in public education funding in the United States violate John Rawls’s two principles of domestic justice. Section 2 argues that prevailing forms of school boosting exacerbate and plausibly perpetuate these injustices. Section 3 then contends that boosting thereby defeasibly violates Rawlsian principles of nonideal theory for rectifying (...) injustice. Thus, boosting should be presumptively either made illegal or substantially reformed. Finally, Section 4 responds to potential objections. (shrink)

This study explores effective digital marketing strategies that can enhance sales conversions in the competitive e-commerce landscape. As technology continues to transform business practices, especially in online retail, companies must adopt strategic marketing approaches to remain competitive. This qualitative research identifies successful strategies through the collection of data using methods such as listening and recording key information. The data was analyzed through processes of data reduction, data display, and conclusion drawing. The results highlight the critical role of digital marketing in (...) driving traffic, engagement, and ultimately, sales conversions for e- commerce platforms. The study emphasizes the combination of various techniques including search engine optimization (SEO), content marketing, social media marketing, paid advertising, user experience (UX) optimization, customer reviews, personalized content, video marketing, incentives, and cart optimization. These strategies, when effectively implemented, can significantly improve the customer journey, enhance user engagement, and drive overall sales performance. (shrink)

Rice is a staple food for more than half of the world’s population, yet its production is increasingly under threat due to climate change, soil degradation, and stagnating yields. As global food security becomes more precarious, researchers are actively seeking sustainable solutions to meet the growing demand while protecting the environment. A recent global study by Yao et al. (2025) offers a compelling strategy: integrating legumes into rice cropping systems.

Nitrogen fertilizers have long been credited with enhancing crop yields in South Asia, a region where rice farming supports food security for hundreds of millions. However, new research published in Nature Sustainability reveals a major inefficiency in current practices: over half of rice farmers (55%) in the region apply excessive nitrogen, leading to environmental degradation and wasted resources without improving harvests.

A fundamental challenge in virology research lies in effectively detecting malicious code. N-gram analysis has become a cornerstone technique, but selecting the most informative features, especially for longer n-grams, remains crucial for efficient detection. This paper addresses this challenge by introducing a novel feature extraction method that leverages both adjacent and non-adjacent bi-grams, providing a richer set of information for malicious code identification. Additionally, we propose a computationally efficient feature selection approach that utilizes a genetic algorithm combined with Boosting principles. (...) Our experimental results show that this detection system significantly outperforms existing methods in virus detection accuracy. The system improves detection accuracy by 15% and reduces false positives by 20% compared to traditional n-gram techniques. Additionally, it cuts computational overhead by about 30%, making it suitable for real-time applications. These advancements demonstrate the effectiveness and practicality of our approach. Future research will focus on applying our methods to polymorphic viruses and other malware to further enhance their robustness and applicability. (shrink)

The aim of TVE to equip students with the necessary skills for self-reliance and poverty reduction calls for upgrading the skills imparted to students to keep them abreast of dynamic changes in today's world. This study therefore identified different skills in fabric designing Home-Economics students need to enhance their dexterity prowess for self-reliance and poverty reduction. To carry out this research, four research questions were raised and answered using means and mean differences. The study employed the descriptive survey research design. (...) 100 lecturers and 340 final year students of universities that offer home economics education in the five states of the south-eastern geopolitical zone of Nigeria formed the population of the study. No sampling for the population was manageable. A questionnaire of two-scale components for possessed and needed fabric designing skills constructed and validated by experts was used for data collection. The reliability coefficients of the questionnaires were 0.823 and 0.723 respectively for the two scale components. The data analyses revealed knitting by machine, piping skill and other five novel fabric designing skills that are needed by home-economics students for poverty reduction and boosting of the students' finances. It was recommended that the integration of 21st Century Skills in Thematic learning also consistent workshops and conferences should be made mandatory for students to attend amongst others. (shrink)

How to Naturally Increase Pleasure-Boosting Brain Chemicals & Their Impact on Mental Health -/- To enhance well-being, it’s important to balance the brain’s pleasure chemicals. Below is a guide on how to naturally increase each neurotransmitter and what happens when there’s an imbalance. -/- 1. Dopamine (Motivation & Reward) -/- How to Boost Naturally: -/- ✔ Set and achieve small goals → Triggers a dopamine “reward” response. ✔ Engage in enjoyable activities (e.g., hobbies, learning new skills). ✔ Eat dopamine-boosting foods: (...) Bananas, dark chocolate, almonds, green tea. ✔ Exercise regularly (boosts dopamine and endorphins). ✔ Listen to music (stimulates the reward system). -/- Effects of Dopamine Imbalance: -/- ???? Too Low: Leads to lack of motivation, depression, fatigue, and anhedonia (inability to feel pleasure). Often seen in Parkinson’s disease and ADHD. ???? Too High: Can lead to addictive behaviors, impulsivity, and even psychosis (as seen in schizophrenia). -/- 2. Serotonin (Happiness & Mood Stabilization) -/- How to Boost Naturally: -/- ✔ Get sunlight exposure (stimulates serotonin production). ✔ Eat serotonin-boosting foods: Salmon, eggs, nuts, seeds, and complex carbs. ✔ Engage in social activities (boosts serotonin and oxytocin). ✔ Meditation, deep breathing, and prayer increase serotonin. -/- Effects of Serotonin Imbalance: -/- ???? Too Low: Depression, anxiety, irritability, sleep issues. Linked to seasonal affective disorder (SAD). ???? Too High: Can cause excessive calmness, serotonin syndrome (dangerous condition with confusion, high blood pressure). -/- 3. Oxytocin (Love, Trust & Bonding) -/- How to Boost Naturally: -/- ✔ Physical touch (hugs, cuddling, holding hands). ✔ Social bonding (spending time with loved ones, group prayer, communal worship). ✔ Petting animals (boosts oxytocin and lowers stress). ✔ Acts of kindness (helping others raises oxytocin). -/- Effects of Oxytocin Imbalance: -/- ???? Too Low: Can lead to loneliness, difficulty forming relationships, and higher stress levels. ???? Too High: May cause overattachment or excessive trust in untrustworthy people. -/- 4. Endorphins (Euphoria & Pain Relief) -/- How to Boost Naturally: -/- ✔ Exercise (runner’s high from intense workouts). ✔ Laughter (watching comedy, sharing jokes). ✔ Eating spicy foods and dark chocolate. ✔ Listening to music, dancing, or singing. -/- Effects of Endorphin Imbalance: -/- ???? Too Low: Increased pain sensitivity, depression, chronic stress. ???? Too High: Rare in natural conditions but can create dependence on activities that trigger endorphins (e.g., thrill-seeking behavior). -/- 5. Endocannabinoids (Bliss & Relaxation) -/- How to Boost Naturally: -/- ✔ Meditation, yoga, and deep breathing. ✔ Aerobic exercise (long runs or cycling). ✔ Eating omega-3 rich foods (fish, flaxseeds, walnuts). -/- Effects of Endocannabinoid Imbalance: -/- ???? Too Low: Increased stress, anxiety, poor pain regulation. ???? Too High: Can cause brain fog or lack of motivation. -/- 6. GABA (Calm & Relaxation) -/- How to Boost Naturally: -/- ✔ Meditation and mindfulness. ✔ Herbal teas (chamomile, green tea). ✔ Foods rich in magnesium (almonds, spinach, avocados). ✔ Deep breathing exercises. -/- Effects of GABA Imbalance: -/- ???? Too Low: Anxiety, insomnia, panic attacks. ???? Too High: Excessive drowsiness, lack of motivation. -/- 7. Adrenaline & Norepinephrine (Excitement & Alertness) -/- How to Boost Naturally: -/- ✔ Engaging in thrilling activities (sports, dancing, adventure). ✔ Cold showers (boosts norepinephrine). ✔ Challenging yourself with new experiences. -/- Effects of Imbalance: -/- ???? Too Low: Fatigue, lack of focus, low motivation. ???? Too High: Stress, anxiety, high blood pressure. -/- Summary Table: Boosting Pleasure Chemicals Naturally -/- Final Thoughts -/- A balanced lifestyle that includes physical activity, healthy eating, social bonding, meditation, and achieving small goals can optimize pleasure-related brain chemistry. -/- Imbalances in these chemicals can lead to mental health issues like depression, anxiety, addiction, or excessive stress. -/- Religious and spiritual activities like prayer, meditation, communal worship, and gratitude practices naturally boost serotonin, dopamine, oxytocin, and GABA. -/- . (shrink)

Amid growing sustainability challenges, managed grasslands—permanent fields primarily used for grazing or hay production—are increasingly recognized for their vital contribution to a broad spectrum of ecosystem services—these range from forage production and biodiversity conservation to carbon sequestration and landscape aesthetics. A recent study by Schaub et al. (2025) provides compelling evidence that optimizing land-use intensity and implementing policies at the landscape level can substantially enhance grassland multifunctionality—the capacity of these ecosystems to deliver multiple services simultaneously.

While several reviews on potential applications of electroculture are available, in this survey we discuss these issues from history, starting from earliest experiments by Ross. And in the last section, we discuss possible application of laserculture, another form of potential improvement. It is our hope that what we present here may be found useful for improving agricultural performance in many countries, as well as reducing dependence on fertilizer.

The study aimed to identify the strategic sensitivity and its impact on enhancing the creative behavior of Palestinian NGOs in Gaza Strip, and the study used the descriptive analytical approach and the questionnaire as a main tool for collecting data from employees of associations working in Gaza Strip governorates, and the cluster sample method was used and the sample size reached (343) individuals (298) questionnaires were retrieved, and the following results were reached: The relative weight of strategic sensitivity was 79.22 (...) (%), and the relative weight of creative behavior was 78.99 (%), a statistically significant relationship between all strategic sensitivity and creative behavior, and the presence of a sensitivity effect The strategy’s strategy on creative behavior, there are statistically significant differences in the scale dimensions attributable to the gender variable and the differences were in favor of females, there are no statistically significant differences between the averages of strategic sensitivity due to the age variable, and the educational qualification, and there were no statistically significant differences in creative behavior according to The gender variable, age, educational qualification, specialization, and the study presented a set of recommendations, the most important of which are: the need for civil organizations in Gaza Strip to seek funding from external countries in order to provide self-income for associations to face crises and give them independence Mechanism in order to keep them to carry out their role in society, the need to follow up the strategic plan of civil organizations using e-mails as they pave the way to reach excellence and creativity in the field of work. (shrink)

Restoring native woodlands within agricultural landscapes has become a central strategy for addressing biodiversity loss and climate change. Yet, ensuring such efforts are both ecologically effective and economically viable remains a pressing challenge. In a recent study, Dobson et al. (2025) integrate ecological and economic modeling to evaluate how varying woodland planting strategies influence woodland bird populations in England over a century-long timeframe.

Clean, reliable energy is fundamental to both human development and climate action. Yet over 600 million people in Sub-Saharan Africa remain without electricity—among them, 97% of refugees lack access to grid power.

The simulation hypothesis can reinforce a cynical dismissal of human potential. This attitude can allow online platform designers to rationalize employing manipulative neuromarketing techniques to control user decisions. We point to cognitive boosting techniques at both user and designer levels to build critical reflection and mindfulness.

Behavioral intervention techniques leveraging reactive responses have gained popularity as tools for promoting ethical behavior. Choice architects, for example, design and present default opt-out options to nudge individuals into accepting preselected choices deemed beneficial to both the decision-maker and society. Such interventions can also employ mild financial incentives or affective triggers including joy, fear, empathy, social pressure, and reputational rewards. We argue, however, that ethical competence is achieved via reflection, and that heavy reliance on reactive behavioral interventions can undermine the (...) development of ethical competence over the long term. Specifically, drawbacks may occur through motivational displacement, dependency, moral crowding out, loss of personal autonomy, and reactance. We introduce complementary cognitive boosting techniques designed to stimulate reflective cognition, as a more promising long-term strategy for instilling ethical behavior. One such approach is the Moral Self-Awareness (MSA) motivational construct, which incrementally leads agents to increasing levels of ethical reflection. We explain why ethical boosting approaches present more edifying and durable alternatives to reactive behavioral interventions and offer suggestions for social and organizational policy. (shrink)

Imagine that we are on a train playing with some mechanical systems. Why can’t we detect any differences in their behavior when the train is parked versus when it is moving uniformly? The standard answer is that boosts are symmetries of Newtonian systems. In this paper, I use the case of a spring to argue that this answer is problematic because symmetries are neither sufficient nor necessary for preserving its behavior. I also develop a new answer according to which (...) boosts preserve the relational properties on which the behavior of a system depends, even when they are not symmetries. (shrink)

According to the orthodox view, one can appeal to the symmetries of a theory in order to show that it is impossible to measure the properties that are not invariant under such symmetries. For example, it is widely believed that the fact that boosts are symmetries of Newtonian mechanics entails that it is impossible to measure states of absolute motion in a Newtonian world (these states vary under boosts). This paper offers an overview of the various ways by (...) which philosophers have spelled out the connection between the symmetries of a theory and the alleged impossibility of measuring some properties (the variant ones). The paper will use the case of absolute motion as a case study, and will discuss a recent unorthodox view according to which this kind of motion can actually be measured in Newtonian mechanics. The paper ends by considering some avenues by which the discussion can be further developed. (shrink)

The adequacy of similarity to prototype as an account of categorization in natural concepts was assessed by analyzing the monotonicity of the relation between typicality of an item in a category and the probability of a positive categorization response using data from McCloskey and Glucksberg (1978). The analysis revealed a strong underlying similarity-based threshold curve, with systematic deviations. Further data collection showed that deviations from the curve could be attributed to the effects of unfamiliarity and non-categorial associations on typicality judgments, (...) as well as differences between the perceptual appearance of an item (which tended to boost typicality) and its underlying nature (which tended to boost categorization). The results are discussed in terms of the different presuppositions and task constraints involved in rating typicality as opposed to performing a categorization. (shrink)

Does the capacity to think require the capacity to sense? A lively debate on this topic runs throughout the history of philosophy and now animates discussions of artificial intelligence. Many have argued that AI systems such as large language models cannot think and understand if they lack sensory grounding. I argue that thought does not require sensory grounding: there can be pure thinkers who can think without any sensory capacities. As a result, the absence of sensory grounding does not entail (...) that large language models cannot think or understand. I also consider to what extent quasi-sensory grounding can at least boost the performance of a language model. (shrink)

Artificial intelligence has historically been conceptualized in anthropomorphic terms. Some algorithms deploy biomimetic designs in a deliberate attempt to effect a sort of digital isomorphism of the human brain. Others leverage more general learning strategies that happen to coincide with popular theories of cognitive science and social epistemology. In this paper, I challenge the anthropomorphic credentials of the neural network algorithm, whose similarities to human cognition I argue are vastly overstated and narrowly construed. I submit that three alternative supervised learning (...) methods—namely lasso penalties, bagging, and boosting—offer subtler, more interesting analogies to human reasoning as both an individual and a social phenomenon. Despite the temptation to fall back on anthropomorphic tropes when discussing AI, however, I conclude that such rhetoric is at best misleading and at worst downright dangerous. The impulse to humanize algorithms is an obstacle to properly conceptualizing the ethical challenges posed by emerging technologies. (shrink)

The study aimed to determine if differentiated instruction effectively addresses learning gaps in mathematics. In particular, it explored how it can improve the student’s learning gaps concerning mathematical performance and confidence. The study employed a quasi-experimental design with 30 purposively-selected Grade 10 participants divided into differentiated (n = 15) and control groups (n = 15), ensuring the utmost ethical measures. The mean and standard deviation were used to describe the participants’ performance and confidence. Independent samples t-tests were used to determine (...) the significant differences in the performance and confidence between the two groups. In contrast, dependent samples t-tests were used to determine the significant differences in each group’s pre and posttest performance and confidence. Findings bared that the differentiated instruction successfully addressed students’ performance in mathematics even in a short period. It also increased the participants’ confidence when answering fundamental problems. Continuing differentiated instruction activities are recommended since it benefits students who struggle in mathematics, particularly in answering fundamental operations. Differentiated teaching activities in mathematics can boost academic achievement and engagement and prepare students for future success while fostering a positive and inclusive classroom culture that values individual learning needs and preferences. (shrink)

The present study aimed to examine how to improve the effectiveness of moral exemplar-applied interventions based on the pillars of the Self-Determination Theory (SDT) framework, autonomy, competence, and relatedness. Past research has mainly focused on the relatedness and attainability of moral exemplars for predicting motivation outcomes. The data for this study consisted of synthesized data sets from previous studies examining the motivational impacts of distinct moral exemplars and intervention methods. The main syntheses for these data sets used Multilevel Modeling (MLM) (...) focusing on relatability, attainability, and intervention methods, corresponding to relatedness, competence, and autonomy in the SDT, respectively, as predictors. In general, there was a significant interaction effect between the attainability or relatability, and the intervention method. Autonomous instruction methods, which support autonomy, were demonstrated to boost motivational outcomes. Implications from this study support the employment of SDT to examine the use of moral exemplars in moral education and were consistent with previous exemplar studies. (shrink)

Recent AI progress led to a boost in beneficial applications from multiple research areas including VR. Simultaneously, in this newly unfolding deepfake era, ethically and security-relevant disagreements arose in the scientific community regarding the epistemic capabilities of present-day AI. However, given what is at stake, one can postulate that for a responsible approach, prior to engaging in a rigorous epistemic assessment of AI, humans may profit from a self-questioning strategy, an examination and calibration of the experience of their own epistemic (...) agency – especially to counteract both intentional misdirection by unethical actors and unintentional epistemic self-sabotage. In this paper, we expound on a new avenue of utilizing AIVR tools to advance an AI-related misdirection awareness of humans in the deepfake era. Firstly, we harness scientific knowledge from the psychology and neuroscience of magic where the study of misdirection techniques is center stage. Secondly, we connect the latter to creativity research linking human creative potential to inspiration from the seemingly impossible. Overall, AIVR could become an empowering experiential testbed for human epistemic agency enabling a better rational evaluation of AI capabilities. However, a misuse of the same type of tools could yield AIVR safety risks if not counteracted preemptively. (shrink)

rtificial Intelligence (AI) is swiftly becoming a fundamental element in modern healthcare, bringing unparalleled capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper delves into AI's transformative impact on the healthcare sector, highlighting how it enhances patient outcomes, boosts the efficiency of medical practices, and introduces new ethical and operational challenges. Through an analysis of current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, the paper underscores the significant advancements AI has introduced (...) to the field. However, the integration of AI also presents concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a thorough overview of these developments, offering insights into AI's future potential to revolutionize healthcare while addressing the ethical considerations necessary for its responsible adoption. The findings suggest that while AI has the potential to transform healthcare delivery, careful consideration of its implications is essential for ensuring its equitable and effective implementation. (shrink)

Coherentism maintains that coherent beliefs are more likely to be true than incoherent beliefs, and that coherent evidence provides more confirmation of a hypothesis when the evidence is made coherent by the explanation provided by that hypothesis. Although probabilistic models of credence ought to be well-suited to justifying such claims, negative results from Bayesian epistemology have suggested otherwise. In this essay we argue that the connection between coherence and confirmation should be understood as a relation mediated by the causal relationships (...) among the evidence and a hypothesis, and we offer a framework for doing so by fitting together probabilistic models of coherence, confirmation, and causation. We show that the causal structure among the evidence and hypothesis is sometimes enough to determine whether the coherence of the evidence boosts confirmation of the hypothesis, makes no difference to it, or even reduces it. We also show that, ceteris paribus, it is not the coherence of the evidence that boosts confirmation, but rather the ratio of the coherence of the evidence to the coherence of the evidence conditional on a hypothesis. (shrink)

Conceptual engineering is a philosophical method that aims to design and spread conceptual and linguistic devices to cause meaningful changes in the world. So far, however, conceptual engineers have struggled to successfully spread the conceptual and linguistic entities they have designed to their target communities. This paper argues that conceptual engineering is far more likely to succeed if it incorporates empirical data and empirical methods. Because the causal factors influencing successful propagation of linguistic or conceptual devices are as complicated and (...) interwoven as they are, proper empirical research will greatly boost the likelihood that propagation is successful. In arguing for the superiority of empirical conceptual engineering over armchair-based conceptual engineering, this paper proposes a framework for understanding the causal forces at play in propagation. This is a three-part framework between the label of a lexical item, the psychological states associated with the lexical item, and the worldly things associated with the lexical item. By understanding the way causal forces affecting propagation play out at these three levels, conceptual engineers can better conceptualize, study, and harness the different causal forces affecting the success of their conceptual engineering projects. (shrink)

Drawing on behavioral economics, nudges (policy interventions based on choice architecture) have the capacity to strategically shape policymaking, subtly guiding individual behavior by adjusting their decision environment. Applied to enhance vaccinations, boost retirement savings, or promote healthy habits, these interventions align with consensus-defined well-being. As governments adopt nudges, scholars have explored their role in fostering virtues. This paper argues that nudges have the potential to efficiently contribute to virtuous development by instilling and sustaining habits, respecting individual choice, and ensuring deliberation. (...) It introduces the concept of virtue nudges, emphasizing the alignment of habits with virtues for targeted behavioral change and the formation of morally virtuous habits. (shrink)

Evidence that minds are neural electromagnetic fields comes from research into how separate brain activities bind to form unified percepts and unified minds. Explanations of binding using synchrony, attention, and convergence are all problematic. But the unity of EM fields explains binding without these problems. These unified fields neatly explain correlations and divergences between synchrony, attention, convergence, and unified minds. The simplest explanation for the unity of both minds and fields is that minds are fields. Treating minds as the fields' (...) underlying, intrinsic nature has the further virtue of avoiding mind-body problems. There's also rising evidence that EM fields interact with brains. McFadden argues here that fields help shift the focus of attention by initiating synchrony in different neural networks, which boosts their own fields and thereby binds their activity. This evidence that minds are fields that bind and guide brain activities supports free will over epiphenomenalism and supervenience. (shrink)

This research reviews explanation and interpretation for Explainable Artificial Intelligence (XAI) methods in order to boost complex machine learning model interpretability. The study shows the influence and belief of XAI in users that trust an Artificial Intelligence system and investigates ethical concerns, particularly fairness and biasedness of all the nontransparent models. It discusses the shortfalls related to XAI techniques, putting crucial emphasis on extended scope, enhancement and scalability potential. A number of outstanding issuesespecially in need of further work can involve (...) standardization, user-centered design and interdisciplinary in strategies for improving the practical utility of XAI. (shrink)

Misconceived Consent: Miguel has stage IV lung cancer. He has nearly exhausted his treatment options when his oncologist, Dr. Llewellyn, tells him about an experimental vaccine trial that may boost his immune response to kill cancer cells. Dr. Llewellyn provides Miguel with a consent form that explains why the study is being conducted, what procedures he will undergo, what the various risks and benefits are, alternative sources of treatment, and so forth. She even sits down with him, carefully talks through (...) the most important points, and gives him time to ask questions. Though it is a Phase 1 study and the chance that he will benefit is very low, Miguel happily agrees to take part. A week later, after the first experimental injection, she asks him if he is worried about the risks. “Risks?” he asks. “I’m sure this is safe—you’re a doctor, after all!”. (shrink)

Given the laws of our universe, the initial conditions and cosmological constants had to be "fine-tuned" to result in life. Is this evidence for design? We argue that we should be uncertain whether an ideal agent would take it to be so—but that given such uncertainty, we should react to fine-tuning by boosting our confidence in design. The degree to which we should do so depends on our credences in controversial metaphysical issues.

According to some, the current political fracture is best described as political polarization – where extremism and political separation infest an entire whole population. Political polarization accounts often point to the psychological phenomenon of belief polarization – where being in a like-minded groups tends to boost confidence. The political polarization story is an essentially symmetrical one, where both sides are subject to the same basic dividing forces and cognitive biases, and are approximately as blame-worthy. On a very different account, what's (...) going on is best described propaganda – where a discrete set of bad actors have manipulated some part of the media environ-ment. The propaganda story is usually told as a highly asymmetrical story, where only some media consumers are under the spell of the propagandists. Which is right? I consider two analyses of the 2016 American election: Robert Talisse's polarization-style account in Overdoing Democracy, and Benkler et. al.'s propaganda-style account in Network Propaganda. I suggest that the propaganda account has better empirical support. I also offer a diagnosis of the appeal of the polarization story. Those who accept a polarization account are often political centrists, who accuse those at the political extremes of motivated reasoning – of believing what they find comfortable. Such centrists also tend to treat political extremism as the product of the irrational belief polarization, arising from living in like-minded groups. But, I argue, these arguments are too quick. First, we can’t dismiss a group as irrational merely be-cause they are likeminded. The existence of like-minded group can be explained in terms of irrational belief polarization, but it can also be explained by rational convergence on the truth. Second, belief polarization is not always irrational, such as when its emotional effects are used to repair impaired self-confidence. Third, political centrists are also subject to similar debunking argument. When we accept a polarization account, we get to feel the comfort of being “above it all”. Political centrists are just as plausibly subject to the irrational effects of living in like-minded groups. Belief polarization isn’t just for extremists. (shrink)

It is crucial to actively detect the risks of transactions in a financial company to improve customer experience and minimize financial loss. In this study, we compare different machine learning algorithms to effectively and efficiently predict the legitimacy of financial transactions. The algorithms used in this study were: MLP Repressor, Random Forest Classifier, Complement NB, MLP Classifier, Gaussian NB, Bernoulli NB, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Bagging Classifier, Decision Tree Classifier and Deep Learning. The dataset (...) was collected from Kaggle depository. It consists of 6362620 rows and 10 columns. The best classifier with unbalanced dataset was the Random Forest Classifier. The Accuracy 99.97%, precession 99.96%, Recall 99.97% and the F1-score 99.96%. However, the best classifier with balanced dataset was the Bagging Classifier. The Accuracy 99.96%, precession 99.95%, Recall 99.98% and the F1-score 99.96%. (shrink)

Data normalization is a crucial step in database management systems (DBMS), ensuring consistency, minimizing redundancy, and enhancing query performance. Traditional methods of normalization in supermarket sales databases often demand significant manual effort and domain expertise, making the process time-consuming and prone to errors. This paper introduces an innovative machine learning (ML)-based framework to automate data normalization in supermarket sales databases. The proposed approach utilizes both supervised and unsupervised ML techniques to identify functional dependencies, detect anomalies, and suggest optimal schema transformations. (...) Experiments on supermarket sales datasets show substantial improvements in accuracy, scalability, and processing time compared to traditional approaches. The results emphasize the potential of incorporating ML into database management practices to boost operational efficiency and support better decision-making. (shrink)

As more objections have been raised against grant peer-review for being costly and time-consuming, the legitimate question arises whether machine learning algorithms could help assess the epistemic efficiency of the proposed projects. As a case study, we investigated whether project efficiency in high energy physics can be algorithmically predicted based on the data from the proposal. To analyze the potential of algorithmic prediction in HEP, we conducted a study on data about the structure and outcomes of HEP experiments with the (...) goal of predicting their efficiency. In the first step, we assessed the project efficiency using Data Envelopment Analysis of 67 experiments conducted in the HEP laboratory Fermilab. In the second step, we employed predictive algorithms to detect which team structures maximize the epistemic performance of an expert group. For this purpose, we used the efficiency scores obtained by DEA and applied predictive algorithms – lasso and ridge linear regression, neural network, and gradient boosted trees – on them. The results of the predictive analyses show moderately high accuracy, indicating that they can be beneficial as one of the steps in grant review. Still, their applicability in practice should be approached with caution. Some of the limitations of the algorithmic approach are the unreliability of citation patterns, unobservable variables that influence scientific success, and the potential predictability of the model. (shrink)

Abstract: People all over the world have been drinking tea for thousands of centuries, and for good reason. Many types of teas can help you stay healthy by boosting your immune system, reducing inflammation, and even preventing cancer and heart disease. There is sufficient material to show that regularly consuming tea can improve your health over the long term. A deep learning model that categorizes tea disorders has been completed. When focusing on the tea, we must also focus on and (...) take into account the diseases that harm it. The aim of this paper is to diagnose tea diseases based on a dataset containing 8000 picture and 8 classes .The model gave the final accuracy test with an F1-score of 99.41% in an estimated time 3 second, as with these results it proved its effectiveness and good ability in the classification test. (shrink)

Since the concept of “nudge” was introduced in 2008 by Thaler and Sunstein, proposing that small interventions based on changes in choice architectures can alter people’s behavior and make it easier for them to achieve their desired goals, the application in public policy of behavioral economics has gained significant attention. This has led to the emergence of different types of policies based on behavioral insights, which have been used in a variety of areas, including health or finance, with the goal (...) of promoting well-being and addressing social and economic issues. After providing a basic theoretical background as a frame of reference to understand gender violence, this study explores the use of tools from the field of behavioral economics as it is applied in public policy. Regulatory (paternalistic) and non-regulatory (libertarian) interventions based on behavioral insights will be considered: nudges, boosts, shoves, budges, or sludge audits, are examined as potential interventions to use in matters related to this type of violence. After evaluating these behavioral policy instruments and their potential effectiveness, the limitations of the behavioral perspective are recognized, and the need for a multifaceted approach to combating gender violence is acknowledged. The article concludes by arguing that public policies addressing gender violence should not only utilize behavioral tools but should also incorporate strategies related to education, legislation, and social norms. (shrink)

Could the employment of large language models (LLMs) in place of human advisors improve the problem-solving ability of democratic assemblies? LLMs represent the most significant recent incarnation of artificial intelligence and could change the future of democratic governance. This paper assesses their potential to serve as expert advisors to democratic representatives. While LLMs promise enhanced expertise availability and accessibility, they also present specific challenges. These include hallucinations, misalignment and value imposition. After weighing LLMs’ benefits and drawbacks against human advisors, I (...) argue that time-tested democratic procedures, such as deliberation and aggregation by voting, provide safeguards that are effective against human and machine advisor shortcomings alike. Additional protective measures may include custom training for advisor LLMs or boosting representatives’ competencies in query formulation. Implementation of adversarial proceedings in which LLM advisors would debate each other and provide dissenting opinions is likely to yield further epistemic benefits. Overall, promising interventions that would mitigate the LLM risks appear feasible. Machine advisors could thus empower human decision-makers to make more autonomous, higher-quality decisions. On this basis, I defend the hypothesis that LLMs’ careful integration into policymaking could augment democracy’s ability to address today’s complex social problems. (shrink)

The book titled The Collaborative Economy in Action: European Perspectives is one of the important outcomes of the COST Action CA16121, From Sharing to Caring: Examining the Socio-Technical Aspects of the Collaborative Economy that was active between March 2017 and September 2021. The Action was funded by the European Cooperation in Science and Technology - COST. The main objective of the COST Action Sharing and Caring is the development of a European network of researchers and practitioners interested in investigating the (...) collaborative economy models, platforms, and their socio-technological implications. The network involves scholars, practitioners, communities, and policymakers. The COST Action Sharing and Caring helped to connect research initiatives across Europe and enabled scientists to develop their ideas by collaborating with peers. This collaboration opportunity represented a boost for the participants’ research, careers, and innovation potential. The main aim of this book is to provide a comprehensive overview of the collaborative economy in European countries with a variety of its aspects for a deeper understanding of the phenomenon as a whole. For this reason, in July 2017, an open call for country reports was distributed among the members of the COST Action Sharing and Caring. Representatives of the member countries were invited to produce short country reports covering: definition of the CE; types and models of the CE; key stakeholders involved; as well as legislation and technological tools relevant for the CE. Submitted reports varied in length and regarding the level of detail included, in accordance with how much information was available in each respective country at the time of writing. Editors of the book have compiled these early reports into a summary report, which was intended as a first step in mapping the state of the CE in Europe. The Member Countries Report on the Collaborative Economy, edited by Gaia Mosconi, Agnieszka Lukasiewicz, and Gabriela Avram that was published on the Sharing and Caring website, represented its first synergetic outcome and provided an overview of the CE phenomenon as interpreted and manifested in each of the countries part of the network. Additionally, Sergio Nassare-Aznar, Kosjenka Dumančić, and Giulia Priora compiled a Preliminary Legal Analysis of Country Reports on Cases of Collaborative Economy. In 2018, after undertaking an analysis of the previous reports’ strengths and weaknesses, the book editors issued a call for an updated version of these country reports. Prof. Ann Light advised the editorial team, proposing a new format for country reports and 4000 words limit. The template included: Introduction, Definition, Key Questions, Examples, Illustration, Context, Developments, Issues, Other Major Players, and Relevant Literature. The new template was approved by the Management Committee in October 2018. The task force that had supported the production of the first series of country reports acted as mentors for the team of authors in each country. The final reports arrived at the end of 2018, bringing the total number of submissions to 30. A call for book editors was issued, and a new editorial team was formed by volunteers from the participants of the COST Action: Andrzej Klimczuk, Vida Česnuityte, Cristina Miguel, Santa Mijalche, Gabriela Avram, Bori Simonovits, Bálint Balázs, Kostas Stefanidis, and Rafael Laurenti. The editorial team organized the double-blind reviews of reports and communicated to the authors the requirements for improving their texts. After reviews, the authors submitted updated versions of their country reports providing up-to-date interdisciplinary analysis on the state of the CE in 2019, when the reports were collected. During the final phase, the chapters were again reviewed by the lead editors together with all editorial team members. At the time, the intention was to update these reports again just before the end of the COST Action Sharing and Caring in 2021 and to produce a third edition. However, the COVID-19 pandemic changed these plans. Thus, this final volume was created by 82 scholars-editors and contributors-and consists of reports on 27 countries participating in the COST Action. M4 - Citavi. (shrink)

Understanding quantitative research instrumentation is critical for advancing educational research, both theory and practice since it contributes to the accuracy and credibility of research findings (Creswell & Plano Clark, 2017; DeVellis, 2017; Streiner et al., 2014). Using inappropriate or poorly designed instruments can result in inaccurate or unreliable data, compromising the quality of the research findings and limiting the research's usefulness. Understanding the appropriate use of quantitative research instruments is critical from a theoretical standpoint for ensuring that research questions are (...) answered accurately. Researchers (pre-service teachers, in-service teachers, teacher educators, and educational leaders) can get high-quality data that can be used to create and test theories in a particular field by selecting and designing the relevant instrument. Furthermore, using reliable and valid quantitative research instruments can boost the credibility of research findings and aid in developing new research questions and hypotheses. From a practical perspective, understanding quantitative research instrumentation can improve the quality of research practice. Researchers can ensure that the data acquired is accurate and reliable by selecting and creating appropriate instruments, which can help to guide decision-making in a particular subject. Furthermore, reliable and valid instruments can guide policy decisions, program development, and evaluation, resulting in more effective interventions and better outcomes. Where and How to Begin? The choice of research instrument depends on the research question, the nature of the phenomenon being studied, and the available resources (Bryman, 2016; Creswell, 2014; Dörnyei, 2007; Hair et al., 2014). Researchers can acquire accurate and valid data to answer their research questions and contribute to the knowledge base in their field by carefully selecting and building instruments. In education and the social sciences, quantitative research frequently involves using various instruments or tools to collect and analyze data. (shrink)

Action research serves a pivotal purpose in enhancing educational practices as well as the achievement of students, as teachers can now take a more proactive role in the process. Success in action research initiatives depends on the self-efficacy of teacher researchers, their capability to handle their research roles, and the challenges they experience in initiating action research. This study looked into the research self-efficacy, productivity, and the issues the teachers face in conducting action research in one school district in the (...) Philippines. Data were collected through interviews with eight teacher-researchers. Findings show the tendency of less self-efficacy in the teachers’ research activities caused by inadequate research time due to their demands for teaching and poor self-confidence in engaging in research. Teachers experienced work overload, compounding the problem of observing time for research activities. They also lack sufficient funding for action research and institutional support from schools to help facilitate it. To address such challenges and warrant the participation of the teachers in research, there is a need for improved work schedules and a boost in institutional support for capacitating teachers. (shrink)

Recent research in the field of social robotics has shed light on the considerable role played by biases in the design of social robots. Cues that trigger widespread biased expectations are implemented in the design of social robots to increase their familiarity and boost interaction quality. Ethical discussion has focused on the question concerning the permissibility of leveraging social biases to meet the design goals of social robotics. As a result, integrating ethically problematic social biases in the design of robots-such (...) as, e.g., discriminatory gender stereotypes-has been opposed as morally unacceptable. Building on this debate, the present paper explores a related but different question: would it be permissible to design social robots in ways that intentionally challenge widespread discriminatory social biases, thus fostering moral education? The analysis shows that, while the potential benefits of such a design strategy could be significant, its practical endorsement raises important ethical issues. Hence, caution and further discussion are advised. (shrink)

Abstract: Heart diseases are increasing daily at a rapid rate and it is alarming and vital to predict heart diseases early. The diagnosis of heart diseases is a challenging task i.e. it must be done accurately and proficiently. The aim of this study is to determine which patient is more likely to have heart disease based on a number of medical features. We organized a heart disease prediction model to identify whether the person is likely to be diagnosed with a (...) heart disease or not using the medical features of the person. We used many different algorithms of machine learning such as Gaussian Mixture, Nearest Centroid, MultinomialNB, Logistic RegressionCV, Linear SVC, Linear Discriminant Analysis, SGD Classifier, Extra Tree Classifier, Calibrated ClassifierCV, Quadratic Discriminant Analysis, GaussianNB, Random Forest Classifier, ComplementNB, MLP Classifier, BernoulliNB, Bagging Classifier, LGBM Classifier, Ada Boost Classifier, K Neighbors Classifier, Logistic Regression, Gradient Boosting Classifier, Decision Tree Classifier, and Deep Learning to predict and classify the patient with heart disease. A quite helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of heart diseases in any person. The strength of the proposed model was very satisfying and was able to predict evidence of having a heart disease in a particular person by using Deep Learning and Random Forest Classifier which showed a good accuracy in comparison to the other used classifiers. The proposed heart disease prediction model will enhances medical care and reduces the cost. This study gives us significant knowledge that can help us predict the person with heart disease. The dataset was collected from Kaggle depository and the model is implemented using python. (shrink)

Deep Ecology, the Voluntary Human Extinction Movement (VHEMT), and Anti-Civilization Primitivism have frequently been labeled as radical environmental ideologies, owing to their relationship with activities conducted by environmental extremists. Nonetheless, given the serious concerns faced by climate change and biodiversity loss, it is critical to engage with a broad range of perspectives and techniques. Such participation allows us to have access to a greater range of perspectives and a more diverse pool of knowledge, boosting our capacity for creative problem-solving. The (...) purpose of this essay is to reconsider the underlying concepts and principles that drive these theories, as well as to assess their relevance in tackling modern environmental issues. We think that Deep Ecology, the Voluntary Human Extinction Movement (VHEMT), and Anti-Civilization Primitivism should be called "unconventional" or "distinctive" rather than "radical," because they primarily focus on the ontological perspective but do not urge extreme acts. Meanwhile, their distinctiveness helps increase public awareness about environmental issues and promote reflection on and rethinking of the ontological link between mankind and the natural world. (shrink)

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and (...) AI systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation. (shrink)

The study’s goal was to provide an educational intervention in Algebra through GeoGebra that would boost students’ confidence, improve their learning, and correct their most minor mastered skills, allowing them to improve their Algebra performance. The research design was quasi-experimental, with 40 nonrandomly chosen participants comprising the GeoGebra and control groups. Mean and standard deviation was employed to describe the algebra performance and confidence of the respondents. At the same time, independent and dependent t-tests were used to determine the students’ (...) significant difference in algebra performance and confidence in the pre-and post-test between the control and GeoGebra groups. GeoGebra effectively improved algebra confidence, enhanced learning, and remedied the students’ least mastered skills. GeoGebra is recommended as an instructional material in teaching and learning Algebra and can be extended to other mathematics content areas. (shrink)

One of the most attractive, but nevertheless highly controversial proposals to alleviate the negative effects of today’s international patent regime is the Health Impact Fund (HIF). Although the HIF has been drafted to facilitate access to medicines and boost pharmaceutical research, we have analysed the burdens for the global poor a similar proposal designed to promote the use and development of climate-friendly technologies would have. Drawing parallels from the access to medicines debate, we suspect that an analogous “Climate Impact Fund” (...) will increase the already very high scientific and technological supremacy of the developed world over the Global South. We advocate countering this dominance on the ground that countries with scarce research and development capacities will be in a difficult position to reject technologies and will not have a say on how such technologies should look like. Further, addressing global hazards should be an inclusive endeavour and not only a privilege reserved for the developed world. Incentivizing grassroots innovation would be a major step to promote scientific and technological inclusion. (shrink)

Filipino's love for the elderly is undeniable. However, despite the respect they have for the elderly, an increasing amount of elderly abandonment is rising in the Philippines. The drastic increase in statistics of abandonment will still grow over the years because aging is inevitable. The primary goal of this study is to dig deeper into the experiences, challenges, and coping mechanisms of abandoned senior citizens inside of a home for the aged to spread awareness about this certain topic. By the (...) use of Interpretative Phenomenological Analysis, the study come up with the following findings: (1) The abandoned senior citizens chose to be inside the facility because they don't want to be a burden in their original homes. (2) Faith and religious works affect their psychological well-being. (3) Physical appearance does not affect their self-esteem. (4) Social support boosts their psychological well-being as well. (5) One of the biggest reasons why abandoned senior citizens left their homes is being neglected to cause them to leave and stay in home for the aged (6) Lastly, people inside the facilities accepted the fact that death is drawing closer to them but they don't want to die yet. (shrink)

Statistics at the college level is one of the most technical subjects that needs to have good mental health so that the students can perform well. This study focused on the investigation of the level of anxiety and mental health of college students in learning statistics. A total of 120 engineering students participated in the survey selected as complete enumeration. Data collection was done through a modified students' statistics anxiety questionnaire and level of mental health. Descriptive measures were computed to (...) describe the data, and regression and correlation analysis to explain its relationship. Results depicted that engineering students have moderate anxiety and they have moderate mental health in learning statistics. This suggests that these students are somewhat anxious but still have a positive learning experience. The correlation and regression analysis revealed that the level of anxiety and mental health of students are negatively but weakly associated, however not statistically significant. This implies that students' anxiety level has somehow adversely affected the mental health of students but its likelihood is negligible. The study strongly suggests that statistics teachers must manage the class well and apply teaching strategies that boost student confidence as well as improve academic achievement. Moreover, teachers should be trained to recognize signs of anxiety and mental health issues and equipped with strategies to support student learning and well-being needs. (shrink)