Harm People Research Articles

A vast increase in heat exposure in the 21st century is driven by global warming and urban population growth

Over the 21st century, human-caused climate change is projected to vastly increase the occurrence of severe heat, which has deleterious health, economic, and societal impacts. Over the same period, global human population is expected to increase from 7.8 to 10.9 billion, placing more people in harm's way. Here, we combine projections of sustained heat from climate models with spatially explicit population projection scenarios. We find that: (1) by 2090, high climate change and population growth scenarios show a ~5-, ~10-, and ~100–1000-fold increase in the population exposed to a mean hottest monthly temperature of 30 ºC, 35 ºC, and 40 ºC, respectively; (2) globally, population growth, warming, and their interaction, are the major drivers for the increase in exposure at milder, harsher, and extreme, temperatures, respectively; and (3) differences between population growth scenarios show that policy can potentially reduce the level of increase in exposure by up to 70%. Based on our analyses, the major driver for the increased heat exposure is the dangerous combination between global warming and population growth in already-warm cities in regions like Africa, India, and the Middle East.

Home is where the safer ground is: the need to promote affordable housing laws and policies in receiving communities.

Increasingly frequent and intense extreme weather events are pushing communities to confront difficult decisions in order to protect people and infrastructure sitting in harm’s way. One decision is around managed retreat or the voluntary movement and transition of individuals and communities away from coastal and other climate-vulnerable areas. “Receiving communities” and other low-risk areas where people may choose to relocate must adopt a multi-pronged strategy that addresses the full breadth of services and resources that relocating residents will need in order to adapt to their new communities, such as in areas of health, education, and workforce development. A critical consideration for receiving communities is the provision of adequate and affordable housing for new residents, many of whom have been driven to relocate due to housing insecurity caused by climate change impacts. Climate change can contribute to housing insecurity in multiple ways, such as when insurance rates increase for low-income homeowners living in flood-prone areas, or when disasters destroy existing affordable housing stock. These impacts can trigger the displacement of individual households or entire communities, requiring local governments to plan ahead for community-centered housing solutions. This article (1) explores the affordable housing crisis in the USA and the compounding impacts of climate change; (2) analyzes the concept of receiving communities and the unique considerations for preparing and investing in affordable housing; and (3) recommends how local governments can use new and existing planning, funding, and legal tools to ensure an effective and inclusive housing strategy for receiving communities.

СПОСОБИ ВИКОРИСТАННЯ НЕЙРОННИХ МЕРЕЖ ТА МАШИННОГО НАВЧАННЯ В КОМП’ЮТЕРНИХ ІГРАХ

This article discusses the main ways of using neural networks and machine learning methods of various types in computer games. Machine learning and neural networks are hot topics in many technology fields. One of them is the creation of computer games, where new tools are used to make games more interesting. Remastered and modified games with neural networks have become a new trend. One of the most popular ways to implement artificial intelligence is neural networks. They are used in everything from medicine to the entertainment industry. But one of the most promising areas for their development is games. The game world is an ideal platform for testing artificial intelligence without the danger of harming nature or people. Making bots more complex is just a small part of what neural networks can do. They are also actively used in game development, and in some areas they already make people feel uncomfortable. Research is ongoing on color and light correction, real-time character animation and behavior control. The main types of neural networks that can learn such functions are considered. Neural networks learn (and self-learn) very quickly. The more primitive the task, the faster the person will become unnecessary. This is already noticeable in the gaming industry, but will soon spread to other areas of life, because games are just a convenient platform for experimenting with artificial intelligence before its implementation in real life. The main problem faced by scientists is that it is difficult for neural networks to copy the mechanics of the game. There are some achievements in this direction, but research continues. Therefore, in the future, real specialists will be required for the development of games for a long time, although AI is already coping with some tasks.

THE PROBLEMS OF URBAN TRANSPORT IN MAJOR CITIES

Cities, being a place with a high level of accumulation and concentration of population and economic activity, are complex spatial structures, which are supported by transport systems. Transportation, being the most important factor of economic and social development, contributes to the competitiveness of the economy. Transport development inexorably leads to increased pollution of ecology and nature, the concept of sustainable transport development is being developed all over the world. Globally, transportation accounted for 24 % of global CO2 emissions in 2019 [1]. Unsustainable transportation can cause air pollution, noise, accidents and other negative impacts that harm people and the environment, which is especially true in urban areas. Today, about 50 % of the world’s population of 7.5 billion people live in cities. According to the UN, this proportion will rise to almost 85 % in 2050, when the planet’s population is expected to increase to 9 billion people. Today, cities account for nearly two-thirds of the world’s energy consumption and more than 70 % of CO2 emissions. Thus, the sustainability of urban transportation is a serious problem, and its importance will only grow.

Temps dip deeper: Temporary employment and the midlife nadir in human well-being

Temporary employees rank lower than permanent employees on various measures of mental and physical health, including well-being. In parallel, much research has shown that the relationship between age and well-being traces an approximate U-shape, with a nadir in midlife. Temporary employment may well have different associations with well-being across the lifespan, likely harming people in midlife more than at the start of their working lives. Using over twenty years of the German Socio-economic panel (SOEP), this investigation considers the relationship between temporary employment, age and well-being. In doing so, it both sheds new light on the relationship between temporary employment and well-being, and explores systematic differences for the oft-found U-shaped relationship between age and well-being. The results show that temporary employment deepens the U-shape in midlife, and that this result holds when many socioeconomic factors as well as the industry, region, cohort, personality, worries about the present job and about being able to finding another job are taken into account. Furthermore, the investigation considers transitions between permanent and temporary employment and uses these to assess causation and selection.

The effect of various urban design parameter in alleviating urban heat island and improving thermal health-a case study in a built pedestrianized block of China.

Increasing urban heat island and global warming have aroused serious thermal environmental problems and even harm people's thermal health. Because of the importance in people's daily life, a commercial pedestrianized block represents a symbol of a city or metropolis; therefore, focusing the attention on the thermal environment in such regions is very necessary. Most of the researches on the urban thermal environment are calculated by remote sensing data; limited by the low spatial resolution of remote sensing image, it may not obviously reflect the true thermal environment of the research site, especially in some microscale regions. Based on this, the new software ENVI-met is developed to research the thermal environment and forecast people's thermal sensation in a microscale region. Therefore, the objective of this study aims at conducting field measurement and numerical simulation to assess the thermal environment of a typical commercial pedestrianized space in southern China and assess the different urban design parameters in ameliorating the urban heat island effect. Our final results demonstrate a quantitative evidence for establishing a comprehensive standard for improving the thermal environment in a microscale region, and this study also can be a supplementary in the research field about improving thermal health.

Prediction of BLEVE blast loading using CFD and artificial neural network

Boiling Liquid Expanding Vapour Explosions (BLEVEs) are extreme explosions driven by nonlinear physical processes associated with explosively expanded vapour and flashed liquid. Blast loading generated from BLEVEs may severely harm structures and people. Prediction of such strong explosions is not currently feasible using simple tools. Physics-based Computational Fluid Dynamics (CFD) methods are commonly utilized to predict the blast loading of BLEVE by going through many empirical formulas that map input variables to the target progressively. The calculation is often time-consuming, and it is therefore impractical to apply these methods to predict explosion loads from BLEVE in normal design analysis. Thinking of the composition of empirical relations in CFD models as a complex and nonlinear function, it is necessary to find an approximation of this function that can be efficiently calculated. The Artificial Neural Network (ANN) is a data-driven computational model that is capable of approximating any functions by learning from training data. Once properly trained, ANN can produce accurate predictions even for unseen inputs. This article presents the development of an ANN model to predict blast loading of BLEVEs in an open environment. A rigorous validation process is presented for the design of ANN structure, and the selected ANN is trained using validated simulation data from CFD models. Extensive evaluation of the network predictive performance is conducted, and it shows that the developed ANN can reproduce the result of CFD models effectively and efficiently, not only on simulation data but also on real experimental data. The prediction of ANN has a percentage error around 6 % and R2 value over 0.99 with the result of CFD simulated data. It speeds up the processing time from hours to seconds and only increases the error from 26.3 %–27.6 %, compared to the CFD simulations of real experimental data. Therefore, the developed ANN model can be potentially applied in the process engineering to generate a large number of reliable data for safety and risk assessment of BLEVEs in a more efficient way.

Short circuit fault analysis and solution research of equipment electric gas cabinet under conductive dust pollution environment

The production of conductive dust not only pollute the environment, harm people's health, but also bring great harm to the safe operation of electrical equipment. Such as shorting electrical equipment, electrical switch according to touch bad, poor ventilation, etc., it is easy to cause equipment electrical cabinets failure problems, in order to strengthen the reliability of enterprise production, for the timely and effective maintenance of the equipment failure, and take effective measures to guarantee with the safe operation of the equipment electrical cabinets in conductive dust environment. A great deal of work has been done in this paper to prevent the harm of dust to the electrical equipment and remarkable results have been obtained.

Pilgrimage, mountain worshiping, and human – Non-human entities’ interactions in the South Andes: A case study from the North Calchaquí Valley (Salta, Argentina)

Indigenous peoples of the Andes have shared their territories with a variety of animated and sacred non-human entities, with whom they have maintained complex interactions. Because these entities are providers of the vital elements that human communities need to guarantee their survival and reproduction, people must treat them with respect and affection. This entails reattributing with rituals, offerings, and attention, the goods and materials these entities supply them. Denial and neglect make these entities sad and angry, and willing to harm people in different ways. In this paper, we discuss how Diaguita-Kallchakí communities from the North Calchaquí Valley (Argentina) interacted with the sacred high-altitude territory of Nevado de Cachi during pre-Hispanic times, with the wak’as that dwelled in it, and how this interaction was completely transformed once the Inkas conquered and settled in the region around CE 1400/1450. We show that the Inkas not only reorganized pilgrimage and ritual activities in this area, but they also intentionally marked their presence in order to represent themselves as superior entities capable of dealing, without risk, with non-human forces.

The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI

The rapid spread of artificial intelligence (AI) systems has precipitated a rise in ethical and rights-based frameworks intended to guide the development and use of these technologies. Despite the proliferation of these principles, there is mounting public concern over the influence that the AI systems have in our society, and coalitions in all sectors are organizing to resist harmful applications of AI worldwide. Responses from peoples everywhere, from workers protesting unethical conduct and applications of AI, to student's protesting MIT's relationships with donor, sex trafficker, and pedophile Jeffery Epstein, to the healthcare community, to indigenous people addressing “the twin problems of a lack of reliable data and information on indigenous peoples and biopiracy and misuse of their traditional knowledge and cultural heritage”, to smart city stakeholders, to many others. Like corporations, governments around the world have adopted strategies for becoming leaders in the development and use of Artificial Intelligence, fostering environments congenial to AI innovators. Neither corporations nor policymakers have sufficiently addressed how the rights of children fit into their AI strategies or products. The role of artificial intelligence in children’s lives—from how children play, to how they are educated, to how they consume information and learn about the world—is expected to increase exponentially over the coming years. Thus, it’s imperative that stakeholders evaluate the risks and assess opportunities to use artificial intelligence to maximize children’s wellbeing in a thoughtful and systematic manner. This paper discusses AI and children's rights in the context of social media platforms such as YouTube, smart toys, and AI education applications. The Hello Barbie, Cloud Pets, and Cayla smart toys case studies are analyzed, as well as the ElsaGate social media hacks and education's new Intelligent Tutoring Systems and surveillance of students apps. Though AI has valuable benefits for children, it presents some particular challenges around important issues including child safety, privacy, data privacy, device security and consent. Technology giants, all of whom are heavily investing in and profiting from AI, must not dominate the public discourse on responsible use of AI. We all need to shape the future of our core values and democratic institutions. As artificial intelligence continues to find its way into our daily lives, its propensity to interfere with our rights only gets more severe. Many of the issues mentioned in this examination of harmful AI are not new, but they are greatly exacerbated and threatened by the scale, proliferation, and real-life impact that artificial intelligence facilitates. The potential of artificial intelligence to both help and harm people is much greater than earlier technologies. Continuing to examine what safeguards and structures can address AI’s problems and harms, including those that disproportionately impact marginalized people, is a critical activity. There are assumptions embedded in the AI algorithms that will shape how our world is realized. Many of these algorithms are wrongful and biased, they must get locked-in. Our best human judgment is needed to contain AI's harmful impacts. Perhaps one of the greatest contributions of AI will be to make us ultimately understand how important human wisdom truly is in life on earth.

AI Soft Law and the Mitigation of Product Liability Risk

Artificial intelligence systems present various types of risks, but are generally not subject to comprehensive government regulation at this time. Soft law measures such as private standards have been developed to fill this governance gap, but a major limitation of such standards is that they are not directly enforceable, and so many companies may not comply with them. This article examines the thesis that compliance with soft law standards may provide some protection from product liability, thus providing additional incentive for companies to adopt and comply with such standards. Using U.S. case law as the focus of analysis, compliance with private standards does not provide an absolute shield against liability, but can provide persuasive evidence of due care that can diminish liability risks in most U.S. state jurisdictions. Conversely, while failure to abide by soft law standards does not automatically result in liability, it can provide probative evidence that a company has not exercised due care, and thus should be held liable. Moreover, compliance with soft law standards can provide an effective defense against punitive damages in many States if a company’s AI system does harm people or property and is held liable for compensating such damages. These findings suggest that protecting against such liability provides a powerful incentive for companies to comply with soft law standards.

The straight categorization bias: A motivated and altruistic reasoning account.

For 70 years, the field of social perception has concluded that perceivers can determine others' social category memberships with remarkable accuracy. However, it has become increasingly clear that accuracy is only part of the story, as social category judgments are often systematically biased toward one category over another. For example, when categorizing sexual orientation, perceivers label others as straight more often than gay. This straight categorization bias is reliable, has an effect size larger than that for accuracy, and is not exclusively driven by the low base rate of sexual minorities in the population, yet we know little about its proximal causes. Here, we argue that one facet of this bias is a motivated reasoning process that avoids applying stigmatizing labels to unknown others. Specifically, we propose that perceivers ascribe heavy consequences to incorrect gay categorizations, compelling them to gather and integrate available information in a manner that favors straight categorizations. Studies 1 and 2 tested the dynamic nature of the bias, exploring decision ambivalence and the real-time accrual of visible evidence about a target throughout the perceptual process using mouse-tracking and diffusion modeling. Studies 3-5 tested motivational determinants for the bias, revealing that perceivers associate high costs with incorrect gay categorizations because those errors put other people in harm's way. Studies 6-9 tested the cognitive mechanisms perceivers engage as they search for information that allows them to avoid costly decision errors. Collectively, these studies provide a new framework for understanding a well-documented but poorly understood response bias in social categorization. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Mountain Biodiversity Is Central to Sustainable Development in Mountains and Beyond

Rich biological diversity in mountains provides ample benefits to people locally and beyond. However, mountain biodiversity is decreasing rapidly, often as a result of geographically distant demands. We can achieve the transition to sustainable development only by accounting for the drivers and benefits of mountain biodiversity from a local to global level.

Rhetoric and the Creation of Hysteria

The narrative is “us versus them.” Rightful U.S. citizens versus the invading masses. These contrasting depictions have been a national obsession since the election of Donald Trump in 2016, and his unrelenting verbal war against immigrants. Trump began his presidential bid by targeting Mexican immigrants, and since taking office, he has made rhetoric concerning tough immigration policies a purported top priority for his administration. Trump has and continues to use this sort of theater to create nationalistic fervor amongst U.S. citizens, and to demonize the so-called invasion at our borders. Indeed, Trump’s “us versus them” anti-immigrant political rhetoric likely won him the 2016 presidential election. Thus, this sort of rhetoric can serve as a powerful, if not dangerous, tool. Anthropologist Leo Chavez defines political rhetoric as speech and images “that often rely on emotion-laden messages to ‘accuse, denounce and actually harm people. They can also flatter, promote, and benefit those same people.’” “Political rhetoric as part of the social and cultural environment can [thus create] strong emotions.” President Trump has taken full advantage of political rhetoric with stoking fear “by calling Mexican immigrants drug dealers, criminals, and rapists,” and garnering an almost cult-like following from his supporters. But President Trump is not alone. Across the hemisphere, national leaders have taken a hardline stance on immigration, some even before Trump did. A hemispheric “wall” of rejection is being built to keep the immigrant “other” away.

COVID-19: The New Immune Challenge

Do you want to know more about Coronavirus infection? What has actually happened and why now? How big is the virus? How does it harm people? Where did the virus come from, and how did it spread to humans? Or maybe you want to know how your body defends itself from the virus. Well, you can find the answers to all these questions in this short article. We hope you will enjoy reading.

Personal Stories Can Shift Climate Change Beliefs and Risk Perceptions: The Mediating Role of Emotion

Sharing personal stories of how climate change is already harming people is a promising communication strategy to engage diverse and even skeptical audiences. Using two experiments, we test the effects of a radio story on the climate change beliefs and risk perceptions of political moderates and conservatives. The radio story, which aired on hundreds of stations across the U.S., is a North Carolina sportsman’s personal account of how climate change has already affected the places he loves. Both experiments found positive effects on global warming beliefs and risk perceptions. Additionally, Study 2 found these effects were mediated by emotional reactions of worry and compassion. These studies suggest personal stories can be a persuasive communication strategy.

COVID-19 pandemic and derogation to human rights.

Under international human rights law, States can limit the exercise of most human rights if it is necessary to protect the rights of others or collective interests. The exceptional circumstances brought by the COVID-19 global pandemic lead to more extensive, on both their scope and their duration, restrictions of human rights than in usual times. This article introduces the States’ specific right to derogate to human rights in circumstances of public emergency and the conditions of a legitimate derogation in the context of COVID-19. It argues that States must ensure that the general measures they adopt to face the crisis do not disproportionally harm vulnerable people.

Conservation opportunities and challenges emerge from assessing nuanced stakeholder attitudes towards the Asian elephant in tea estates of Assam, Northeast India

Interactions between wildlife and people lie at the core of conservation planning in heterogeneous landscapes. Understanding stakeholder perspectives towards wildlife is a key endeavour in this regard. In particular, it can be useful to separate notional or generic attitudes towards wildlife, from those that pertain to more practical considerations at localised scales. We assessed nuances in stakeholder attitudes and underlying beliefs towards the endangered Asian elephant Elephas maximus––a wide-ranging species that needs landscape-scale conservation, while also being an animal that is both culturally revered and conflict-prone. We instrumented semi-structured questionnaire surveys using a 5-point Likert score, to 2252 respondents representing tea estate labour and management across 17 estates in the Kaziranga–Karbi Anglong landscape of Assam, Northeast India. Respondents were overwhelmingly positive (80–98%) towards elephants notionally. In our landscape, this stemmed more from cultural links and beliefs about the animal’s intrinsic right to persist, rather than utilitarian benefits in terms of ecosystem health. At localised scales, responses were more varied with issues relating to safety concerns and crop loss maximally inciting non-positive responses. Similarly, stakeholder attitudes towards elephant conservation at localised scales were varied. Elephant use of lands outside forests, for instance, incited equivocal responses. Interestingly, while safety concerns clearly limit the potential for human–elephant co-occurrence, stakeholders still believed that elephants do not harm people unprovoked; this highlights the opportunities a culture of tolerance provides for stakeholder support of, and engagement with, wildlife conservation. Ultimately, understanding stakeholder attitudes can determine our ability to encourage ‘wildlife-friendly’ behavioural change and shape human–wildlife interactions into the future.

COVID-19 and Psychosocial Issues: Israeli/Middle East Perspective

The number of confirmed cases and fatalities in the Middle East countries has been relatively low in comparison to some countries in Europe and the United States, in spite of the economic, political, cultural, and medical differences. One explanation may be the closed borders and lack of migration in the Middle East, with the exception of Iran, largely to its close economic ties with China and mismanagement. The distribution of the infected cases in Israel reveals two distinct populations with disproportionately high infection rate: the elderly people leaving in nursery homes (like in the rest of the world) and ultra-orthodox Jews. Ultra-orthodox Jews comprise about 12% of Israel's population, but they accounted for more than one-third of the confirmed cases of COVID-19 and as much as 60%–70% of Israel's COVID-19 cases in major hospitals. Ultra-orthodox communities initially resisted physical-distancing measures regarding the closure of synagogues, religious schools, and prayer services, due to their shared belief that practicing the religious services and rituals as usual will protect them from harm. This seemed to be helpful in alleviating feelings of stress and anxiety from the pandemic on one hand but placed people in harm on the second hand. Thus, social factors such as sense of belonging, social support and religious beliefs, known to increase resilience, and coping with adversity in uncontrolled disasters turned to have a harmful effect on coping with the COVID-19 pandemic, a disaster that its consequences can be partially controlled. This is an interesting social phenomenon worth further study.

Price Discrimination, Algorithmic Decision-Making, and European Non-Discrimination Law

Our society can benefit immensely from algorithmic decision-making and similar types of artificial intelligence. But algorithmic decision-making can also have discriminatory effects. This paper examines that problem, using online price differentiation as an example of algorithmic decision-making. With online price differentiation, a company charges different people different prices for identical products, based on information the company has about those people. The main question in this paper is: to what extent can non-discrimination law protect people against online price differentiation? The paper shows that online price differentiation and algorithmic decision-making could lead to indirect discrimination, for instance harming people with a certain ethnicity. Indirect discrimination occurs when a practice is neutral at first glance, but ends up discriminating against people with a protected characteristic, such as ethnicity. In principle, non-discrimination law prohibits indirect discrimination. The paper also shows, however, that non-discrimination law has flaws when applied to algorithmic decision-making. For instance, algorithmic discrimination can remain hidden: people may not realise that they are being discriminated against. And many types of unfair – some might say discriminatory – algorithmic decisions are outside the scope of current non-discrimination law. price discrimination, price differentiation, personalised pricing, dynamic pricing, algorithmic decision-making, big data, artificial intelligence, fairness, law, equality, non-discrimination law, human rights, fundamental rights.