Harmful Actions Research Articles

Institutional Betrayal in Inpatient Psychiatry: Effects on Trust and Engagement With Care.

Patients' experiences of inpatient psychiatry have received limited empirical scrutiny. The authors examined patients' likelihood of experiencing institutional betrayal (harmful actions or inactions toward patients) at facilities with for-profit, nonprofit, or government ownership; patient-level characteristics associated with experiencing institutional betrayal; associations between betrayal and patients' trust in mental health providers; and associations between betrayal and patients' willingness to engage in care postdischarge. Former psychiatric inpatients (N=814 adults) responded to an online survey. Data were collected on patients' demographic characteristics; experiences of institutional betrayal; and the impact of psychiatric hospitalization on patients' trust in providers, willingness to engage in care, and attendance at 30-day follow-up visits. Participants' responses were linked to secondary data on facility ownership type. Experiencing institutional betrayal was associated with less trust in mental health providers (25-percentage-point increase in reporting less trust, 95% CI=17-32), reduced willingness (by 45 percentage points, 95% CI=39-52) voluntarily undergo hospitalization, reduced willingness (by 30 percentage points, 95% CI=23-37) to report distressing thoughts to mental health providers, and lower probability of reporting attendance at a 30-day follow-up visit (11-percentage-point decrease, 95% CI=5-18). Participants treated at a for-profit facility were significantly more likely (by 14 percentage points) to report experiencing institutional betrayal than were those treated at a nonprofit facility (p=0.01). Institutional betrayal is one mechanism through which inpatient psychiatric facilities may cause iatrogenic harm, and the potential for betrayal was larger at for-profit facilities. Further research is needed to identify the determinants of institutional betrayal and strategies to support improvement in care quality.

Analyzing Resampling Techniques for Addressing the Class Imbalance in NIDS using SVM with Random Forest Feature Selection

The purpose of Network Intrusion Detection Systems (NIDS) is to ensure and protect computer networks from harmful actions. A major concern in NIDS development is the class imbalance problem, i.e., normal traffic dominates the communication data plane more than intrusion attempts. Such a state of affairs can pose certain hazards to the effectiveness of detection algorithms, including those useful for detecting less frequent but still highly dangerous intrusions. This paper aims to utilize resampling techniques to tackle this problem of class imbalance in NIDS using a Support Vector Machine (SVM) classifier alongside utilizing features selected by Random Forest to improve the feature subset selection process. The analysis highlights the combativeness of each sampling method, offering insights into their efficiency and practicality for real-world applications. Four resampling techniques are analyzed. Such techniques include Synthetic Minority Over-sampling Technique (SMOTE), Random Under-sampling (RUS), Random Over-sampling (ROS) and SMOTE with two different combinations i.e., RUS SMOTE and RUS ROS. Feature selection was done using Random Forest, which was improved by Bayesian methods to create subsets of features with feature rankings determined by Cumulative Feature Importance Score (CFIS). The CIDDS-2017 dataset is used for the performance evaluation, and the metrics used include accuracy, precision, recall, F-measure and CPU time. The algorithm that performs best overall in the CFIS feature subsets is SMOTE, and the features that give the best result are selected at the 90% level with 25 features. This subset accomplishes a relative accuracy enhancement of 0.08% than the other approaches. The RUS+ROS technique is also fine but somehow slower than SMOTE. On the other hand, RUS+SMOTE shows relatively poor results although it consumes less time in terms of computational time compared to other methods, giving about 50% of the performance shown by the other methods. This paper's novelty is adapting the RUS method as a standalone test for screening new and potentially contaminated datasets. The standalone RUS method is more efficient in terms of computations; the algorithm returned the best result of 98.13% accuracy at 85% at the CFIS level of 34 features with a computation time of 137.812 s. It is also noted that SMOTE is considered to be proficient among all resampling techniques used for handling the problem of class imbalance in NIDS, vice 90% CFIS feature subset. Future research directions could include using these techniques in different data sets and other machine learning and deep learning methods together with ROC curve analysis to provide useful pointers to NIDS designers on how to select the right data mining tools and strategies for their projects.

International governance of advancing artificial intelligence

AbstractNew technologies with military applications may demand new modes of governance. In this article, we develop a taxonomy of technology governance forms, outline their strengths, and red-team their weaknesses. In particular, we consider the challenges and opportunities posed by advancing artificial intelligence, which is likely to have substantial dual-use properties. We conclude that subnational governance, though prevalent and mitigating some risks, is insufficient when the individual rewards from societally harmful actions outweigh normative sanctions, as is likely to be the case with AI. Nationally enforced standards are promising ways to govern AI deployment, but they are less viable in the “race-to-the-bottom” environments that are becoming common. When it comes to powerful technologies with military implications, there is only one multilateral option with a strong historical precedent: a non-proliferation plus norms-of-use regime, which we call NPT+. We believe that a non-proliferation regime may, therefore, be the necessary foundation for AI governance. However, AI may exhibit characteristics that would make a non-proliferation regime less effective than it has proven for nuclear weapons. As an alternative, verification-backed restrictions on AI development and use would address more risks, but they face challenges in the case of advanced AI, and we show how these challenges may not have technical solutions. Perhaps more importantly, we show that there is no clear example of major powers restricting the development of a powerful military technology when that technology lacks a ready substitute. We, therefore, turn to a final alternative, International Monopoly, which was the preferred solution of many scholars and policymakers in the early nuclear era. It should be considered again for governing AI: a monopoly would require less-invasive monitoring, though at the possible cost of eroding national sovereignty. Ultimately, we conclude that it is too soon to tell whether a non-proliferation regime, a verification-based regime, or an International Monopoly is most feasible for governing AI. Nonetheless, a variety of policies would yield a high return across all three scenarios, and we conclude by identifying some of these steps that could be taken today.

The influence of toxic leadership on emotional exhaust and reactive work behavior

Companies are impacted by toxic leadership phenomena, resulting in many dissatisfied employees, low morale, and reduced progress. The fundamental mismatch between good leadership and harmful actions of toxic leaders is the primary cause of the problem. Toxic leadership can also be developed from narcissistic behavior of considering personal interests or using humiliation to maintain power. In this context, employees are negatively affected, resulting in higher stress levels, poorer job satisfaction, and a significant decrease in trust. Therefore, this research aims to explore the impact of toxic leadership and other factors on companies. The sample consists of 187 senior employees in the accounting department who worked in manufacturing companies. The results showed that toxic leadership influences role stress, while role stress affects emotional exhaustion and reactive work behavior. Moreover, future research should be conducted using other samples such as hospital employees or pay attention to other aspects related to role stress.

Comparative Analysis of Bribery as, Criminal Wrongdoing

There is a consensus that bribery, which is explicitly outlawed in the Magna Carta, should be considered a criminal act. Elucidate the criminalisation of bribery may be unexpectedly for those who see harmful actions as the primary criterion for determining what should be made a criminal offence. The predicament faced by these intellectuals is as follows. Bribing sometimes encompasses innocuous misconduct, but the rationale for criminalising it does not stem from any undue damage inherent in bribing itself. The rationale is on the potential damage that may occur if bribery is not penalised, considering the limited efficiency of civil law in reducing its prevalence. According to some theorists, the basis for criminalising activity based on the concept of "remote harm" is considered a secondary and possibly problematic kind of criminalisation. This paper presents a challenge for these theories in elucidating not only the reasons behind bribery but also the reasons behind other significant offences, such as rape, which are often seen as fundamental instances of criminal misconduct. Indeed, criminal law has the lawful authority to include a broad array of harm-related justifications for including action within its boundaries and central principles. One cause for the activity involving inflicting unnecessary injury is the reality itself.

PIoT‐fortifying IoT device identity and data access: A security framework empowered by blockchain

AbstractThe IoT, which refers to the process in which billions of devices are connected to the Internet, has emerged as an essential component of today's society. Yet, the safety of data exchange and the identification of devices in IoT networks have emerged as significant concerns in recent years. Also, there will be a significant drop in the effectiveness of the conventional consensus process. This study presents a novel consensus technique, Proof of IoT (PIoT), to address the security and authentication‐related issues in the IoT Infrastructure. It uses blockchain‐based smart contracts to securely transfer the data and uniquely identify the device on the IoT‐based network. PIoT uses a reputation assessment technique that involves complex cryptographic NuCypher‐based security computations to authenticate and update the data across IoT devices. This contract assures that only authorized parties can access the information recorded in the blockchain. A reward‐based filtering technique is developed for PIoT to identify malicious nodes with harmful actions, preventing them from contributing to consensus. Compared to specific conventional methods, PIoT performs better in simulations. Compared with LPBFT, DAC4SH Consensus, the Point of Interaction in blockchain‐based PIoT improved by 74.7% and 96.8%, respectively. When compared to DAC4SH, PIoT shows an overall 15% improvement in the security of the blockchain under the IoT. In addition to providing a secure method of communication between IoT devices, the given study also provides security against various IoT attacks.

Reproductive health rights: a bibliometric analysis of childbirth

Purpose Reproductive health rights became an issue of interest long before the 1990s. Several policies were created to guarantee reproductive health rights, including birth rights. Women have the right to give birth in a way that is respectful and free from obstetric violence. Policymaking must, of course, be based on empirical evidence, so it is necessary to map studies on human rights in the context of childbirth. This study aims to investigate the viewpoint on human rights during childbirth across time by using a bibliometric methodology. Design/methodology/approach Bibliometric analysis was conducted using the VOSviewer application. The analysis was sourced from 301 articles on human rights during delivery for the 1976–2023 period from the Scopus database. Findings The studies concerning reproductive health rights encompass various topics, including reproductive health policy, human rights during childbirth, child marriage and human rights, pandemics and health care related to human rights. Research limitations/implications Studies related to human rights in reproductive health (especially childbirth) must be increased. Remember, policymaking must be based on the evidence found. Apart from that, empirical information about harmful actions during childbirth must be revealed so that it becomes a concern. While there has been a significant increase in the establishment of human rights during childbirth, there is a lack of research on this topic specifically in the Asian context. Researchers might consider this to be a matter of concern, given the implications for human rights. Two themes that have citations but a small number relate to obstetric violence and respectful maternity care during childbirth, so this could be a concern for other researchers for further studies. Originality/value This paper provides an overview of the most cited topics of human rights during childbirth, contributions by the distribution of studies throughout time, distribution of studies by country, the highest number of publications and citations.

Translating Violence

This paper examines the relationship between regional concepts of manhood and the experience of wartime sexual violence for the sub-group that is highly at risk of its victimization and its perpetration, the combatant-male. The violence witnessed by sub-Saharan Africa in the wake of its independence movements and the instability caused by rival factions not only displayed incidents of sexual violence but also highlighted the social value tied to “manhood” by these regions and their populations such that the concept of masculinity was weaponized at that time. Additionally, this paper explores the more intimate sphere of the combatant’s unit and group dynamics to discuss the formation of “hyper-masculinities” that drive its members to increased aggression and the employment of harmful actions, such as sexual crimes, as a mark of manliness. As such, this paper argues that the idea of the “hegemonic male” and the social value tied to such men present a twin-edge opportunity where committing sexual crimes serves to increase the prestige of the perpetrator, while at the same time, inflicting harm upon its victim with consequences beyond the initial incident of violence. Insights and data were drawn from existing literature on masculinities and the experience of militarized persons, with the intent of adding to existing knowledge on masculinity.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

Machine Learning Applications in Offense Type and Incidence Prediction

In today's rapidly evolving world, detrimental behaviourhas undeniably emerged as a significant factor leading to the downfall of individuals and communities. The rising prevalence of such behaviourcreates substantial disruptions within a country's population, affecting social stability and economic progress. To mitigate the impact of these harmful actions, it is crucial to identify and address them promptly and effectively. This study evaluates specific patterns of detrimental behaviour using data from Kaggleto predict and analyzeprevalent negative behaviours. Recent incidents of theft, for example, have underscored the importance of understanding the most common types of misconduct, as well as their timing and locations. We can develop targeted strategies to prevent and respond to such incidents by analyzing these patterns. Artificial Intelligence (AI) techniques encompass variouscomputational methods and algorithms designed to enable machines to perform tasks that typically require human intelligence. These techniques are used in various applications, fromnatural language processing to image recognition, and offer powerful tools for behavioral analysis. This project employs advanced AI techniques, such as Naive Bayes, to model and identify patterns in detrimental behavior. Naive Bayes, a probabilistic classifier based on Bayes' theorem, is particularly effective in handling large datasets and making accurate predictions. By applying this algorithm, the study achieves a high level of precision in predicting various types of detrimental behavior, enabling a better understanding of their underlying patterns. This knowledge can inform the development of more effective prevention and intervention strategies, ultimately contributing to the reduction of harmful behaviors and the enhancement of community well-being.

Enhancing UAV‐HetNet security through functional encryption framework

SummaryIn the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

The Intergenerational Transmission of Pro-Environmental Behaviours: The Role of Moral Judgment in Primary School-Age Children

The environmental crisis poses a critical issue for current and future generations, driving research to investigate the key factors and psychological characteristics that motivate individuals to engage in pro-environmental behaviours (PEBs) from an early age. In this context, intergenerational transmission—which refers to how parents influence their children’s behaviour—plays a crucial role in initiating and promoting eco-friendly practices. From a children-centred perspective, the current study focused on the intergenerational transmission of PEBs, addressing the moderating role of children’s moral judgment. This latter was evaluated considering general moral judgment (i.e., moral transgressions, social-conventional transgressions, and non-harmful personal choices) and domain-specific environmental moral judgment (i.e., harmful actions with no specific victim, harmful actions to animals, and harmful actions to plants/trees). This study was carried out with 229 triads of Italian children (Mage = 8.54 years; SDage = 1.46 years; rangeage 6–11 years, 130 girls and 99 boys), fathers (Mage = 45.73 years; SDage = 5.07 years; rangeage 29–64 years), and mothers (Mage = 42.56 years; SDage = 4.67 years; rangeage 28–57 years). Results revealed that only the moral evaluations on harmful actions directed at animals (B = 0.32, SE = 0.15, t = 2.18, CI 95% = [0.030, 0.612]) and those towards plants/trees (B = 0.19, SE = 0.08, t = 2.49, CI 95% = [0.369, 0.342]) moderated the association between parents’ PEBs and children’s PEBs, boosting the intergenerational transmission of PEBs. Overall, this research yielded novel evidence on the main factors affecting the intergenerational transmission of PEBs, suggesting moral judgment as a critical mechanism in nurturing pro-environmental practices in school-age children. Implications, limitations, and future research directions are discussed.

From Offer to Breach: The Empirical Study on Quid Pro Quo Cyber Attack

Quid pro quo attacks are a sophisticated form of insider threat where malicious individuals exploit the human element within an organization to gain sensitive information or unauthorized access. Unlike traditional cyberattacks that focus on technical system flaws, these attacks rely on social engineering techniques to deceive people. Attackers often use ransomware, encrypting a victim's data and demanding payment, typically in cryptocurrency, for the decryption key. The term "quid pro quo" highlights the exchange involved, indicating that the attacker seeks something in return for stopping their harmful actions. To effectively understand and defend against quid pro quo attacks, a comprehensive approach is necessary. The primary goal of this research is to examine how these attacks operate and to propose effective solutions and preventive measures. This will be achieved by studying incidents involving affected victims. The research aims to identify the specific methods used in these attacks, analyze the vulnerabilities that allow them to succeed, and offer practical recommendations for improving cybersecurity defenses. By learning from the experiences of past victims, this research seeks to provide valuable insights that can be used to develop proactive strategies and security protocols to prevent or mitigate the impact of quid pro quo attacks in the future.

Guide to Citrus Nutritional Deficiency and Toxicity Identification

This document explains the importance of accurately identifying symptoms of various disorders in citrus trees, which can affect their health, vigor, and productivity. It emphasizes the challenges in distinguishing nutritional deficiencies or toxicities from other issues, such as herbicide or fungicide damage and physiological stress. Nutritional disorders, particularly those involving micronutrients like zinc, manganese, and iron, can manifest differently on different parts of the tree and may not always match descriptions in publications. The text advises seeking expert advice when symptoms are unclear to avoid costly and potentially harmful remedial actions. First published Oct. 2000.

Under the Shadows of Authority: Unveiling Age and Gender Disparities in Obedience

Why is it that many people obey when they feel coerced? People focus so much on being good followers that they become unaware of the consequences of their actions. Various experiments about “obedience to authority” have been taken place in the past decades. Stanley Milgram, a social psychologist conducted his famous obedience experiments in the early 1960s. His aim was to study the willingness of participants to obey authority figures, even when it involved harmful actions against others. The current study aimed to investigate the difference of age and gender differences in relation to obedience to authority. Age factor was not examined in Milgram’s original experiment. The research regarding age differences in obedience is suggestive of demarcation in level of obedience. This work aims to show a new way to understand gender difference in obedience, and wish to open a gate of age differences in obedience study. Are males more likely to obey to authority? Will gender affect obedience ? The research in existence is mixed point of view in relation to males and females related to obedience. Thus with this aim in mind present study was carried out on 150 working employees ( Non-teaching staff); males (n=75) and females (n=75), with the age range of 34-64 (M=40.88 age), (SD=5.37); early middle age (34–44) and late middle age (45- 64) across Punjabi University, Patiala. The aim of the study was to investigate the difference between age, gender and obedience, examining whether obedience vary across different age groups and gender wise. The Organizational Obedience Scale (Capan & Uzuncarsili, 2022) was administered to the working staff with the Cronbach alpha is .79. The questionnaire was translated into native language Punjabi and was certified and validated by the experts (Dept. of Psychology, Punjabi University, Patiala) with the reliability of .78. The obtained data was analysed with the latest version IBM SPSS 29. The data collected was analysed with descriptive analysis, and t- test. There was significant differences in scores of male and female on the basis of obedience. Another findings there was significant difference due to age in obedience. Later middle aged people more obey. By examining the interplay between age, gender, and obedience, this work aims to inform develop interventions that mitigate potential risks associated with destructive obedience.

La simbolica dello straniero nel Salterio

Who is a foreigner in the Hebrew Psalter? The article answers this question in two sections. First, seven terms referring to “foreigner” are examined to determine their individual characteristicsin the Psalms and their correspondence to biblical texts outside the Psalter. Second, other literary motifs are analyzed in a cursory reading of Psalms 105, 106 and 137. The Psalter outlines a partial picture of the foreigner, which is subordinated to theological and symbolic issues. The Psalms primarily recall the adventures of Israel, which had to frequently change its place of residence for various reasons. As a foreigner, deprived of land and permanent residence, he experienced the ups and downs of migrant life and ultimately strove to integrate into a new socio-cultural environment. Defending one’s identity and maintaining national and religious integrity is portrayed as a difficult process, at risk. Another type of foreigner in the Psalms is the non-Jew, individually or communally, who enjoys the social and legal protection to which the poor are entitled. Either he is a well-integrated member of society, or he remains unassimilated. In the latter case, his false words, harmful actions and physical hostility toward Israel come to the fore. Finally, in the Book of Psalms, terms associated with “foreigner” are part of figurative language. The vocabulary sometimes either evokes non-literal associations with foreign countries, nations and foreign gods, or denotes an emotional state of suffering and isolation. Moreover, key lexemes symbolize the transitory human condition, emphasizing the marginalization and even segregation of the petitioner from the household.

Identification of predictive patient characteristics for assessing the probability of COVID-19 in-hospital mortality.

As the world emerges from the COVID-19 pandemic, there is an urgent need to understand patient factors that may be used to predict the occurrence of severe cases and patient mortality. Approximately 20% of SARS-CoV-2 infections lead to acute respiratory distress syndrome caused by the harmful actions of inflammatory mediators. Patients with severe COVID-19 are often afflicted with neurologic symptoms, and individuals with pre-existing neurodegenerative disease have an increased risk of severe COVID-19. Although collectively, these observations point to a bidirectional relationship between severe COVID-19 and neurologic disorders, little is known about the underlying mechanisms. Here, we analyzed the electronic health records of 471 patients with severe COVID-19 to identify clinical characteristics most predictive of mortality. Feature discovery was conducted by training a regularized logistic regression classifier that serves as a machine-learning model with an embedded feature selection capability. SHAP analysis using the trained classifier revealed that a small ensemble of readily observable clinical features, including characteristics associated with cognitive impairment, could predict in-hospital mortality with an accuracy greater than 0.85 (expressed as the area under the ROC curve of the classifier). These findings have important implications for the prioritization of clinical measures used to identify patients with COVID-19 (and, potentially, other forms of acute respiratory distress syndrome) having an elevated risk of death.

E-Watcher: insider threat monitoring and detection for enhanced security

AbstractInsider threats refer to harmful actions carried out by authorized users within an organization, posing the most damaging risks. The increasing number of these threats has revealed the inadequacy of traditional methods for detecting and mitigating insider threats. These existing approaches lack the ability to analyze activity-related information in detail, resulting in delayed detection of malicious intent. Additionally, current methods lack advancements in addressing noisy datasets or unknown scenarios, leading to under-fitting or over-fitting of the models. To address these, our paper presents a hybrid insider threat detection framework. We not only enhance prediction accuracy by incorporating a layer of statistical criteria on top of machine learning-based classification but also present optimal parameters to address over/under-fitting of models. We evaluate the performance of our framework using a real-life threat test dataset (CERT r4.2) and compare it to existing methods on the same dataset (Glasser and Lindauer 2013). Our initial evaluation demonstrates that our proposed framework achieves an accuracy of 98.48% in detecting insider threats, surpassing the performance of most of the existing methods. Additionally, our framework effectively handles potential bias and data imbalance issues that can arise in real-life scenarios.

Valence-Dependent Implicit Action Generalization Among Group Members.

People implicitly generalize the actions of known individuals in a social group to unknown members. However, actions have social goals and evaluative valences, and the extent to which actions with different valences (helpful and harmful) are implicitly generalized among group members remains unclear. We used computer animations to simulate social group actions, where helping and hindering actions were represented by aiding and obstructing another's climb up a hill. Study 1 found that helpful actions are implicitly expected to be shared among members of the same group but not among members of different groups, but no such effect was found for harmful actions. This suggests that helpful actions are more likely than harmful actions to be implicitly generalized to group members. This finding was replicated in Study 2 by increasing the group size from three to five. Study 3 found that the null effect for generalizing harmful actions among group members is not due to the difficulty of detecting action generalization, as both helpful and harmful actions are similarly generalized within particular individuals. Moreover, Study 4 demonstrated that weakening social group information resulted in the absence of implicit generalization for helpful actions, suggesting the specificity of group membership. Study 5 revealed that the generalization of helping actions occurred when actions were performed by multiple group members rather than being repeated by one group member, showing group-based inductive generalization. Overall, these findings support valence-dependent implicit action generalization among group members. This implies that people may possess different knowledge regarding valenced actions on category-based generalization.

MILITARY ECOCIDE IN UKRAINE AS A DESTRUCTIVE CONSEQUENCE OF THE USE OF RUSSIAN MISSILES AND PROJECTILES

Effectively, russia’s aggression against Ukraine can be considered the first case of intentional ecocide during wartime in this century. Through its actions, russia has caused massive destruction of the animal and plant world, pollution of the atmosphere, Ukrainian fertile lands, and water resources. According to calculations by the working group at the State Environmental Inspection, as of January 2023, the ecological damage to Ukraine during the 11 months of russian military aggression amounts to over 1 trillion 743 billion hryvnias, or over 47.6 billion dollars. This sum includes hundreds of thousands of square kilometers of ruined soil and devastated lands, forest fires, emissions of poisonous substances into the atmosphere, destruction of animals, alterations to the migratory paths of birds, and more. The top five regions most affected by ecocide in Ukraine are Dnipropetrovsk, Kharkiv, Mykolaiv, Donetsk, and Zaporizhzhia regions.
 The study of war ecocide crimes allows to systematise them by the following features: air pollution, soil pollution, destruction of green spaces, destruction of wildlife, water pollution, unauthorized use of subsoil, and disruption of ecosystems Since the beginning of the full-scale armed aggression by russian occupation forces on objects within the 71 territory of Ukraine, almost 6,500 missile strikes and nearly 3,500 aerial strikes have been carried out, resulting in environmental damage.
 Missiles launched onto Ukrainian territory carry hundreds and thousands of kilograms of chemical substances. In case of complete combustion and dispersion of remnants into the surrounding environment, they pose significant pollution and are toxic to all living beings.
 The destruction of the upper fertile layer of soil, which has been forming over centuries, occurs as a result of missile explosions, artillery shells of various types, cluster bombs, drones, mines, and various types of multiple rocket launchers.
 A significant problem is the contamination of soil with chemical substances, leading to a reduction in fertility and causing harm to both humans and animals living in polluted areas. In substantial areas of Ukraine, there is considerable damage to the surface layer of the soil due to the burning of munitions. The detonation of missiles, artillery shells, cluster bombs results in the release of carbon monoxide, carbon dioxide, water vapor, nitrogen oxides, formaldehyde, hydrogen cyanide, nitrogen, as well as a large amount of toxic organic compounds, which then enter the soil.
 According to assessments from the Kyiv School of Economics and Zoï Environment Network, a total of 186,000 km2 of land, nearly 31% of Ukraine's territory, is at risk of damage and pollution. Among them, over 20,000 km2 are damaged by more than 75% In addition to missiles, the aggressor country employs projectiles of various calibers and cluster bombs, the detonation of which causes environmental destruction, including damage to buildings, structures, industrial facilities, soil, wildlife, and ecosystems. Ukrainian land has transformed into a horrifying testing ground for various types of weaponry, including long-range artillery, salvo fire systems, phosphorus bombs, and guided missiles.
 A threatening issue is the replacement of territories, particularly agricultural lands. A mined area loses its intended purpose and cannot be utilized for a long period. Additionally, the demining of such territory requires substantial expenses.
 Therefore, russian missiles and projectiles pose a deadly threat to humans, animals, plants, harm the environment, devastate soil, water bodies, forests, destroy infrastructure, alter landscapes, and dismantle ecosystems.
 Thus, the investigation of war ecocide crimes involving russian missiles and projectiles allows the conclusion that these are extremely harmful actions resulting from military aggression. Those who perpetrate them are well aware of the serious and irreversible long-term damage inflicted on the environment.