Search Results (39)

Background: Youth suicide remains a significant public health concern in Japan, driven by multifaceted factors such as academic pressures, social isolation, bullying, and family dysfunction. Recent societal changes, including the rise of internet addiction and subcultural influences from anime, manga, and gaming, have further shaped the psychological landscape of Japanese youth. The COVID-19 pandemic has exacerbated these challenges, intensifying feelings of loneliness and anxiety about the future. Methods: This study explores the impact of these factors on youth suicide risk through a systematic review of existing literature and statistical data, focusing on trends from 2000 to 2024. Results: In 2023, 513 school-aged youth in Japan died by suicide, marking persistently high rates. High school students accounted for the majority of cases, followed by middle and elementary school students. Key risk factors include intense academic expectations, cyberbullying, and internet addiction, which are often compounded by cultural stigmas surrounding mental health. Subcultures offer both solace and potential alienation, influencing youth emotions in complex ways. The COVID-19 pandemic has also worsened mental health issues and heightened suicide risks among this vulnerable group. Conclusions: The findings highlight the urgent need for comprehensive mental health support systems tailored to Japanese cultural contexts. Recommendations include enhancing access to school-based counseling, promoting family-based interventions, and implementing policies to regulate harmful online content. Additionally, efforts must address cultural attitudes that stigmatize mental health care. Collaborative societal and policy-level interventions are crucial for mitigating youth suicide and fostering a supportive environment for young people in Japan. Full article

The impacts of climate change on agricultural production are becoming more severe, leading to increased food insecurity. Adopting more progressive methodologies, like smart farming instead of conventional methods, is essential for enhancing production. Consequently, livestock production is swiftly evolving towards smart farming systems, propelled by rapid advancements in technology such as cloud computing, the Internet of Things, big data, machine learning, augmented reality, and robotics. A Digital Twin (DT), an aspect of cutting-edge digital agriculture technology, represents a virtual replica or model of any physical entity (physical twin) linked through real-time data exchange. A DT conceptually mirrors the state of its physical counterpart in real time and vice versa. DT adoption in the livestock sector remains in its early stages, revealing a knowledge gap in fully implementing DTs within livestock systems. DTs in livestock hold considerable promise for improving animal health, welfare, and productivity. This research provides an overview of the current landscape of digital transformation in the livestock sector, emphasizing applications in animal monitoring, environmental management, precision agriculture, and supply chain optimization. Our findings highlight the need for high-quality data, comprehensive data privacy measures, and integration across varied data sources to ensure accurate and effective DT implementation. Similarly, the study outlines their possible applications and effects on livestock and the challenges and limitations, including concerns about data privacy, the necessity for high-quality data to ensure accurate simulations and predictions, and the intricacies involved in integrating various data sources. Finally, the paper delves into the possibilities of digital twins in livestock, emphasizing potential paths for future research and progress. Full article

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management. Full article

This review highlights the critical role of software sensors in advancing biosystem monitoring and control by addressing the unique challenges biological systems pose. Biosystems—from cellular interactions to ecological dynamics—are characterized by intrinsic nonlinearity, temporal variability, and uncertainty, posing significant challenges for traditional monitoring approaches. A critical challenge highlighted is that what is typically measurable may not align with what needs to be monitored. Software sensors offer a transformative approach by integrating hardware sensor data with advanced computational models, enabling the indirect estimation of hard-to-measure variables, such as stress indicators, health metrics in animals and humans, and key soil properties. This article outlines advancements in sensor technologies and their integration into model-based monitoring and control systems, leveraging the capabilities of Internet of Things (IoT) devices, wearables, remote sensing, and smart sensors. It provides an overview of common methodologies for designing software sensors, focusing on the modelling process. The discussion contrasts hypothetico-deductive (mechanistic) models with inductive (data-driven) models, illustrating the trade-offs between model accuracy and interpretability. Specific case studies are presented, showcasing software sensor applications such as the use of a Kalman filter in greenhouse control, the remote detection of soil organic matter, and sound recognition algorithms for the early detection of respiratory infections in animals. Key challenges in designing software sensors, including the complexity of biological systems, inherent temporal and individual variabilities, and the trade-offs between model simplicity and predictive performance, are also discussed. This review emphasizes the potential of software sensors to enhance decision-making and promote sustainability in agriculture, healthcare, and environmental monitoring. Full article

Rural residents in China are still at risk of malnutrition, and increasing dietary diversity is crucial to improving their health. This study empirically analyzed the impact of non-farm employment on the dietary diversity of rural left-behind family members based on the China Land Economy Survey (CLES) 2020–2021 panel data at the farm and village levels. Dietary diversity was measured using the dietary diversity score (DDS) and the Chinese Food Guide Pagoda Score (CFGPS). The empirical results show that non-farm employment significantly enhances the dietary diversity of rural left-behind household members, including animal food diversity and plant food diversity. This result verifies the altruism phenomenon of non-farm employment in family diet. Mechanism analysis shows that non-farm employment enhances the dietary diversity of rural left-behind family members by increasing the level of family income, Internet accessibility, and family education. Heterogeneity analysis shows that non-farm employment does not enhance the dietary diversity of rural empty nesters and even has a negative impact. This reminds us that the nutritional health of rural empty nesters needs attention in the context of rapid urbanization and aging. Full article

The livestock industry is undergoing significant transformation with the integration of intelligent technologies aimed at enhancing productivity, welfare, and sustainability. This review explores the latest advancements in intelligent systemization (IS), including real-time monitoring, machine learning (ML), and the Internet of Things (IoT), and their impacts on livestock farming. The aim of this study is to provide a comprehensive overview of how these technologies can address industry challenges by improving animal health, optimizing resource use, and promoting sustainable practices. The methods involve an extensive review of the current literature and case studies on intelligent monitoring, data analytics, automation in feeding and climate control, and renewable energy integration. The results indicate that IS enhances livestock well-being through real-time health monitoring and early disease detection, optimizes feeding efficiency, and reduces operational costs through automation. Furthermore, these technologies contribute to environmental sustainability by minimizing waste and reducing the ecological footprint of livestock farming. This study highlights the transformative potential of intelligent technologies in creating a more efficient, humane, and sustainable livestock industry. Full article

Introduction: Antibiotic resistance poses a significant threat to public health, that can lead to reduced effectiveness of many therapies, increased morbidity, longer hospitalization times, increased deaths, and additional costs for health care systems. Unreasonable use of antibiotics may result from a lack of adequate knowledge about antibiotic therapy and a lack of knowledge of the risks associated with antibiotic resistance, both among medical personnel and patients. Aim. The primary objective of the study was to verify the opinion of medical personnel on the risks associated with antibiotic resistance. Material and Methods: The study was conducted in 2023 among 605 Polish sanitary workers. An anonymous survey designed specifically for the purpose of the study was used. The survey was made available on the Internet through the Trade Unions of Pharmacy Workers and directly to hospitals with the support of local authorities. Results: The majority of respondents were women (77.36%). The largest group consisted of individuals over 40 years of age (55.04%). More than half of the respondents were nurses (56.20%), and every fourth of the respondents was a physician (23.64%). Most respondents consider antibiotic resistance to be a very serious (24.13%) or extremely serious (30.75%) problem. The problem of antibiotic resistance on a global scale was mentioned, especially in the opinions of physicians and nurses (p < 0.01), people working in the profession for over a year (p < 0.01), and people with a specialization or undergoing specialist training (p = 0.00). Similarly, these groups most often indicated that antibiotic resistance poses a problem in their workplace. The main problems of antibiotic resistance were the use of antibiotics in farm animals (36.69%), the pressure on patients to take antibiotics (38.84%), and the prophylactic use of antibiotics (43.15%). Conclusions: Medical personnel consider antibiotic resistance a somewhat serious problem, although not all agree in this regard. The risk of antibiotic resistance is much more seriously assessed by physicians and nurses, as well as by people with specializations or undergoing specialization training. Knowledge about antibiotic resistance should be further spread among all groups of medical personnel. Full article

Soil quality is vital for ecosystem stability, impacting human, plant, and animal health. Traditional soil quality assessments are labor-intensive and costly, making them unsuitable for smart agriculture. To overcome this, Internet of Things (IoT) and artificial intelligence (AI) technologies are employed for sustainable agriculture, enabling real-time data collection and analysis, trend identification, and soil health optimization. The Western Greece Soil Information System (WΕSIS) offers open-access data and services for soil health and sustainability. It includes modules for soil quality indicators, sustainable fertilization management zones, soil property distribution, prediction, mapping, statistical analysis, water management, land use maps, digital soil mapping, and crop health calculation. Integrating the IoT and AI allows for real-time and remote monitoring of soil conditions, managing soil interventions adaptively and in a data-driven way, enhancing soil resources’ efficiency and sustainability, and increasing crop yield and quality. AI algorithms assist farmers and regional stakeholders in optimizing production lines, methodologies, and field practices, reducing costs and increasing profitability. This promotes a circular economy, a soil- and climate-resilient future, biodiversity protection targets, and enhanced soil fertility and productivity. The proposed IoT/AI technical architecture can underpin the development of soil health monitoring platforms, integrating data from various sources, automating data collection, and providing decision support tools. Full article

Herein, we examined the transformative potential of artificial intelligence (AI) and machine learning (ML) as new fronts in addressing some of the pertinent challenges posed by food integrity to human and animal health. In recent times, AI and ML, along with other Industry 4.0 technologies such as big data, blockchain, virtual reality, and the internet of things (IoT), have found profound applications within nearly all dimensions of the food industry with a key focus on enhancing food safety and quality and improving the resilience of the food supply chain. This paper provides an accessible scrutiny of these technologies (in particular, AI and ML) in relation to food integrity and gives a summary of their current advancements and applications within the field. Key areas of emphasis include the application of AI and ML in quality control and inspection, food fraud detection, process control, risk assessments, prediction, and management, and supply chain traceability, amongst other critical issues addressed. Based on the literature reviewed herein, the utilization of AI and ML in the food industry has unequivocally led to improved standards of food integrity and consequently enhanced public health and consumer trust, as well as boosting the resilience of the food supply chain. While these applications demonstrate significant promise, the paper also acknowledges some of the challenges associated with the domain-specific implementation of AI in the field of food integrity. The paper further examines the prospects and orientations, underscoring the significance of overcoming the obstacles in order to fully harness the capabilities of AI and ML in safeguarding the integrity of the food system. Full article

Due to the increasing risk of extreme events caused by climate change (i.e., floods, fires and hurricanes) or wars, European veterinary public health may need some improvement. Utilizing a mix of qualitative (participatory observation) and quantitative methods (Internet mining), we analyzed the Greek media’s responses to the millennial flood in Thessaly (September 2023), focusing on animal health (including wild, companion animals and livestock) and public sentiment towards epizootic/epidemic threats. The study revealed a gap in crisis management plans regarding veterinary-related issues, emphasizing the need for comprehensive emergency response strategies. Our findings show how (i) the lay referral system is projecting the perception of epidemic threats into the population; (ii) the emotional load of images of animal carcasses is misused by media creators aiming for a big audience; and (iii) pets’ owners are creating online communities for the searching and treatment of their pets. Our results stress the importance of integrating crisis communication in consecutive phases of the discourse, such as the following: (i) weather change; (ii) acute flood; (iii) recovery; and (iv) outbreaks, into veterinary practices to better prepare for such disasters. Full article

The autonomous identification of animal births has a significant added value, since it enables for a prompt timely human intervention in the process, protecting the young and the mothers’ health, without requiring continuous human surveillance. Wearable inertial sensors have been employed for a variety of animal monitoring applications, thanks to their low cost and the fact that they allow less invasive monitoring process. Alarms triggered by the occurrence of events must be generated close to the events to avoid delays caused by communication latency, which is why this type of mechanism is typically implemented at the network’s edge and integrated with existing auxiliary mechanisms on the Internet. Although the detection of births in cattle has been carried out commercially for some years, there is no solution for small ruminants, especially goats, where the literature does not even report any attempts. The current work consisted of a first attempt at developing an automatic birth monitor using inertial sensing, as well as detection techniques based on Machine Learning, implemented in a network edge device to assure real-time alarm triggering. Thus, two concept drift detection techniques and seven kidding detection mechanisms were developed using data classification models. The work also includes the testing and comparison of learning results, both in terms of accuracy and of computational costs of the detection module, for algorithms implemented. The results revealed that, despite their simplicity, concept drift algorithms do not allow kidding detection, whereas classification-algorithm-based static learning models do, despite the unbalanced character of the dataset and its reduced size. The learning findings are quite promising in terms of computational cost and its suitability for deployment on edge devices. The algorithm demonstrates behavior changes four hours before kidding and allows for the identification of the kidding hour with an accuracy of 61%, as well as the capacity to improve the overall learning process with a larger dataset. Full article

The term “Internet of Underwater Things (IoUT)” refers to a network of intelligent interconnected underwater devices designed to monitor various underwater activities. The IoUT allows for a network of autonomous underwater vehicles (AUVs) to communicate with each other, sense their surroundings, collect data, and transmit them to control centers on the surface at typical Internet speeds. These data serve as a valuable resource for various tasks, including conducting crash surveys, discovering shipwrecks, detecting early signs of tsunamis, monitoring animal health, obtaining real-time aquatic information, and conducting archaeological expeditions. This paper introduces an additional set of alternative simulation tools for underwater networks. We categorize these tools into open-source and licensed simulator options and recommend that students consider using open-source simulators for monitoring underwater networks. There has not been widespread deployment or extensive research on underwater 5G-based networks. However, simulation tools provide some general insights into the challenges and potential issues associated with evaluating such networks, based on the characteristics of underwater communication and 5G, by surveying 5G-based underwater networks and 5G key aspects addressed by the research community in underwater network systems. Through an extensive review of the literature, we discuss the architecture of both Internet of Underwater application-assisted AUVs and Internet of Underwater Things communications in the 5G-based system. Full article

Milk production plays an essential role in the global economy. With the development of herds and farming systems, the collection of fine-scale data to enhance efficiency and decision-making on dairy farms still faces challenges. The behavior of animals reflects their physical state and health level. In recent years, the rapid development of the Internet of Things (IoT), artificial intelligence (AI), and computer vision (CV) has made great progress in the research of precision dairy farming. Combining data from image, sound, and movement sensors with algorithms, these methods are conducive to monitoring the behavior, health, and management practices of dairy cows. In this review, we summarize the latest research on contact sensors, vision analysis, and machine-learning technologies applicable to dairy cattle, and we focus on the individual recognition, behavior, and health monitoring of dairy cattle and precise feeding. The utilization of state-of-the-art technologies allows for monitoring behavior in near real-time conditions, detecting cow mastitis in a timely manner, and assessing body conditions and feed intake accurately, which enables the promotion of the health and management level of dairy cows. Although there are limitations in implementing machine vision algorithms in commercial settings, technologies exist today and continue to be developed in order to be hopefully used in future commercial pasture management, which ultimately results in better value for producers. Full article

Wearable touch sensors, which can convert force or pressure signals into quantitative electronic signals, have emerged as essential smart sensing devices and play an important role in various cutting-edge fields, including wearable health monitoring, soft robots, electronic skin, artificial prosthetics, AR/VR, and the Internet of Things. Flexible touch sensors have made significant advancements, while the construction of novel touch sensors by mimicking the unique properties of biological materials and biogenetic structures always remains a hot research topic and significant technological pathway. This review provides a comprehensive summary of the research status of wearable touch sensors constructed by imitating the material and structural characteristics in nature and summarizes the scientific challenges and development tendencies of this aspect. First, the research status for constructing flexible touch sensors based on biomimetic materials is summarized, including hydrogel materials, self-healing materials, and other bio-inspired or biomimetic materials with extraordinary properties. Then, the design and fabrication of flexible touch sensors based on bionic structures for performance enhancement are fully discussed. These bionic structures include special structures in plants, special structures in insects/animals, and special structures in the human body. Moreover, a summary of the current issues and future prospects for developing wearable sensors based on bio-inspired materials and structures is discussed. Full article

This study presents internet of things (IOT) and artificial intelligence technologies that are critical in reducing the harmful effects of this illness and assisting its recovery. It explores COVID-19’s economic impacts before learning about new technologies and potential solutions. The research objective was to propose a solution for self-diagnosis, self-monitoring, and self-management of COVID-19 with personal mobiles and personal data using cloud solutions and mobile applications with the help of an intelligent IoT system, artificial intelligence, machine learning, and 5G technologies. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics by using handsets only is an innovative approach. Since the COVID-19 outbreak, the global social, economic, religious, and cultural frameworks and schedules have been affected adversely. The fear and panic associated with the new disease, which the world barely knew anything about, amplified the situation. Scientists and epidemiologists have traced the first outbreak of COVID-19 at Wuhan, China. A close examination of the genetic makeup of the virus showed that the virus is zoonotic, meaning that the virus changed hosts from animals to humans. The uncertainty associated with the above features and characteristics of the virus, as well as the high mortality rates witnessed in many parts of the globe, significantly contributed to the widespread global panic that brought the world to a standstill. Different authorities and agencies associated with securing the public have implemented different means and methods to try and mitigate the transmission of the infection as scientists and medical practitioners work on remedies to curb the spread of COVID-19. Owing to different demographics, different parts of the globe have attempted to effectively implement locally available resources to efficiently fight and mitigate the adverse effects of the COVID-19 pandemic. The general framework provided by the World Health Organization (WHO) has been implemented or enhanced in different parts of the globe by locally available resources and expertise to effectively mitigate the impact of COVID-19. There is currently no effective vaccine for COVID-19, but new technology can be available within weeks to reduce the spread of the disease; current approaches such as contact tracing and testing are not secure, and the cost of testing is high for end users. The proposed solution based on self-diagnosis without any security risk for users’ data with low cost of cloud-based data analytics functions by using an intelligent internet of things (IOT) system for collecting sensors data and processing them with artificial intelligence to improve efficiency and reduce the spread of COVID-19. Full article