Search Results (54)

Background: Foster care alumni face increased health challenges across the domains of mental and physical health, yet there is a paucity of research examining the associations between care experiences, health, and sleep quality in alumni aged 30 and above. Objectives: Our exploratory mixed-method study examined the sleep quality of North American group care leavers aged 30+ to understand whether sleep quality in adulthood is associated with earlier child welfare system experiences during childhood and adolescence. Secondly, we examined the association between sleep quality and overall concurrent health. Methods: Using a convenience sample of 41 alumni of care aged 30–85 and 16 qualitative interviews, we explored the intricate connections between group care leavers’ developmental trauma, sleep quality, and health. Linear regression and qualitative content analysis were utilized to understand how sleep was related to well-being in aging care alumni. Results: Adult sleep was significantly associated with the perceived quality of their youth out-of-home placement experiences (β = 0.421, p < 0.01), controlling for friendship support networks and demographic variables. Adult sleep quality was a significant predictor of overall health (β = −0.328, p < 0.05). Qualitative interviews elucidated insights into the importance and linkages of child welfare system experiences, adult sleep, and well-being. Conclusions: Our research highlights the enduring association between child welfare placement experiences, and sleep functioning well into adulthood, even when accounting for contemporaneous social support and other demographic indicators. Practitioners should be inquiring directly about sleep, and future longitudinal research should delve deeper into the nature of sleep difficulties and their association with health and well-being. Full article

Background: Heat stress challenges small ruminants in semi-arid regions, requiring integrative multi-modeling approaches to identify adaptive thermotolerance traits. This study aimed to identify phenotypic biomarkers and explore the relationships between thermoregulatory responses and hematological, behavioral, morphometric, carcass, and meat traits in lambs. Methods: Twenty 4-month-old non-castrated male lambs, with an average body weight of 19.0 ± 5.11 kg, were evaluated under natural heat stress. Results: Thermoregulatory variables were significantly associated with non-carcass components (p = 0.002), carcass performance (p = 0.027), commercial meat cuts (p = 0.032), and morphometric measures (p = 0.029), with a trend for behavioral responses (p = 0.078). The main phenotypic traits related to thermoregulation included idleness duration, cold carcass weight, blood, liver, spleen, shank, chest circumference, and body length. Exploratory factor analysis reduced the significant indicators to seven latent domains: carcass traits, commercial meat cuts, non-carcass components, idleness and feeding behavior, and morphometric and thermoregulatory responses. Bayesian network modeling revealed interdependencies, showing carcass traits influenced by morphometric and thermoregulatory responses and non-carcass traits linked to ingestive behavior. Thermoregulatory variables were not associated with meat quality or hematological traits. Conclusions: These findings highlight the complex biological relationships underlying heat adaptation and emphasize the potential of combining phenomic data with computational tools to support genomic selection for climate-resilient and welfare-oriented breeding programs. Full article

The realization of forest ecological functions value is an important path for implementing the “Two Mountains” theory. Improving the efficiency of forest ecological functions and benefits value realization faces several challenges, such as an underdeveloped value evaluation system that makes it difficult to quantify ecological value, a weak policy system lacking effective incentive mechanisms, and unclear ecological property rights leading to unfair benefits distribution. Forest new quality productive drivers are a key factor in promoting high-quality forestry development, and can effectively address several issues hindering the efficiency of forest ecological functions and benefits value realization. Forest ecological functions and benefits are divided into tangible forest products and intangible ecological services, with the efficiency of realizing their economic and welfare values reflecting the input–output status of forest ecological value. This paper constructs an indicator system for assessing the modern productive capacity in forestry and the efficiency of forest ecological value realization, and uses a two-stage network DEA model and a double fixed effects model for empirical analysis. The study finds that the advanced drivers of forestry productivity significantly enhance the efficiency of forest ecological economic value realization but constrain the efficiency of ecological welfare value realization, with significant regional differences. As a moderating variable, enhancing the resilience of the industry chain can significantly deepen the effect throughout the process, while improving the informatization level of residents can weaken the constraints of forest new quality productive drivers on the efficiency of forest ecological welfare value realization. Therefore, this paper offers targeted recommendations aimed at providing theoretical support and practical guidance for optimizing the efficiency of forest ecological value realization. Full article

The rapid development of unmanned aerial vehicles (UAVs) has had a significant impact on the growth of the economic, industrial, and social welfare of society. The possibility of reaching places that are difficult and dangerous for humans to access with minimal use of third-party resources increases the efficiency and quality of maintenance of construction structures, agriculture, and exploration, which are carried out with the help of drones with a predetermined trajectory. The widespread use of UAVs has caused problems with the control of the drones’ correctness following a given route, which leads to emergencies and accidents. Therefore, UAV monitoring with video cameras is of great importance. In this paper, we propose a Yolov12 architecture with positive–negative pulse-based optimization algorithms to solve the problem of drone detection on video data. Self-attention-based mechanisms in transformer neural networks (NNs) improved the quality of drone detection on video. The developed algorithms for training NN architectures improved the accuracy of drone detection by achieving the global extremum of the loss function in fewer epochs using positive–negative pulse-based optimization algorithms. The proposed approach improved object detection accuracy by 2.8 percentage points compared to known state-of-the-art analogs. Full article

The Belt and Road Initiative (BRI) is the first and currently the most expansive global infrastructure initiative, notably for its scale and emphasis on connectivity. In response, alternative initiatives such as the Partnership for Global Infrastructure and Investment (PGII) and Free and Open Indo-Pacific Strategy (FOIP), including their components the Blue Dot Network (BDN) and Partnership for Quality Infrastructure (PQI), as well as Global Gateway (GG) and the Three Seas Initiative (3SI), have emerged to counterbalance the BRI’s influence and promote more transparent, sustainable, and rules-based infrastructure frameworks. This review investigates how global and regional infrastructure initiatives—namely PGII/BDN, GG, FOIP/PQI, and 3SI—compare with the BRI in terms of development objectives, implementation models, institutional structures, and implications for developing economies. Adopting an inductive approach, this review identifies key themes from the literature to evaluate these initiatives across seven dimensions: (1) infrastructure objectives, (2) the quality and transparency of investments, (3) investment policy orientation, (4) trade policy orientation, (5) inclusivity and regional integration, (6) coordination mechanisms, and (7) environmental sustainability. While PGII/BDN, GG, FOIP/PQI, and 3SI appear well-positioned to address some of BRI’s shortcomings, the evidence does not clearly favour one model over another in terms of achieving welfare-enhancing outcomes and bridging development gaps. Nonetheless, strategic competition and complementarities among the connectivity policies of multiple initiatives can ultimately contribute to more accountable, multidimensionally sustainable, and socially inclusive infrastructure development. We also illustrate how stated preference methods, i.e., willingness to pay (WTP) and willingness to accept (WTA), can be used to quantify the value of soft infrastructure, particularly public preferences for sustainable investment and norm diffusion, which are central to evaluating the social welfare gains from participating in these initiatives. Full article

Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. Likewise, computer vision and deep learning models are driven by CNNs to act intelligently in improving productivity and animal management for sustainable animal husbandry. In animal husbandry, CNNs play a vital role in the management and monitoring of livestock’s health and productivity due to their high-performance accuracy in analyzing images and videos. Monitoring animals’ health is important for their welfare, food abundance, safety, and economic productivity. This paper aims to comprehensively review recent advancements and applications of relevant models that are based on CNNs for livestock health monitoring, covering the detection of their various diseases and classification of their behavior, for overall management gain. We selected relevant articles with various experimental results addressing animal detection, localization, tracking, and behavioral monitoring, validating the high-performance accuracy and efficiency of CNNs. Prominent anchor-based object detection models such as R-CNN (series), YOLO (series) and SSD (series), and anchor-free object detection models such as key-point based and anchor-point based are often used, demonstrating great versatility and robustness across various tasks. From the analysis, it is evident that more significant research contributions to animal husbandry have been made by CNNs. Limited labeled data, variation in data, low-quality or noisy images, complex backgrounds, computational demand, species-specific models, high implementation cost, scalability, modeling complex behaviors, and compatibility with current farm management systems are good examples of several notable challenges when applying CNNs in animal husbandry. By continued research efforts, these challenges can be addressed for the actualization of sustainable animal husbandry. Full article

Metritis is one of the most common postpartum diseases in dairy cows, associated with impaired reproductive performance and substantial economic losses. In this study, we investigated the potential of machine learning (ML) techniques applied to physiological, behavioural, and milk quality parameters for the early detection of metritis in dairy cows during the postpartum period. A total of 2707 daily observations were collected from 94 cows in early lactation, of which 11 cows (275 records) were diagnosed with metritis. The dataset included daily measurements of body weight, rumination time, milk yield, milk composition (fat, protein, lactose), somatic cell count (SCC), and feed intake. Five classification models—partial least squares discriminant analysis (PLS-DA), random forest (RF), support vector machine (SVM), neural network (NN), and an Ensemble model—were developed using standardised features and stratified 80/20 training/test splits. To address class imbalance, model loss functions were adjusted using class weights. Models were evaluated based on accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV), area under the receiver operating characteristic (ROC) area under the curve (AUC), and Matthews correlation coefficient (MCC). The NN model demonstrated the highest overall performance (accuracy = 96.1%, AUC = 96.3%, MCC = 0.79), indicating strong capability in distinguishing both healthy and diseased animals. The SVM achieved the highest sensitivity (90.9%), while RF and Ensemble models showed high specificity (>98%) and PPV. This study provides novel evidence that ML methods can effectively detect metritis using routinely collected, non-invasive on-farm data. Our findings support the integration of neural and Ensemble learning models into automated health monitoring systems to enable earlier disease detection and improved animal welfare. Although external validation was not performed, internal cross-validation demonstrated consistent performance across models, suggesting suitability for application in multi-farm settings. To the best of our knowledge, this is among the first studies to apply ML for early metritis detection based exclusively only automated herd data. Full article

Animal welfare in barns is strongly influenced by air quality, with gaseous emissions like ammonia posing significant respiratory health risks. However, current state-of-the-art ammonia monitoring systems are labor-intensive and expensive. Metal Oxide Semiconductor (MOS) sensors offer a promising alternative due to their compatibility with sensor networks, enabling high-resolution ammonia monitoring across spatial and temporal scales. While MOS sensors exhibit high sensitivity to various volatile compounds, temperature-cycled operation is commonly employed to enhance selectivity, effectively creating virtual sensor arrays. This study aims to improve ammonia detection by designing a virtual sensor array through a cyclic data-driven approach, integrating machine learning with solid-state sensor modeling. The results of a two-week dataset with measurements of four different pig barns demonstrate ammonia sensing with a sampling rate of about 2/min and a range of 1–30 ppm. The method is robust and exhibits a 10% increase in normalized RMSE when comparing testing results of an unseen sensor module with results of the training dataset. A filter membrane boosts accuracy and prevents data loss due to contamination, such as flyspecks. Overall, the used MOS sensor BME688 is effective and economical for widespread continuous ammonia monitoring and localization of ammonia sources in pig barns. Full article

This work focuses on optimizing the scheduling of virtual power plants (VPPs)—as implemented in the Portuguese national project New Generation Storage (NGS)—to maximize social welfare and enhance energy trading efficiency within modern energy grids. By integrating distributed energy resources (DERs), including renewable energy sources and energy storage systems, VPPs represent a pivotal element of sustainable urban energy systems. The scheduling problem is formulated as a Mixed-Integer Linear Programming (MILP) task and addressed by using a parallelized simulated annealing (SA) algorithm implemented on high-performance computing (HPC) infrastructure. This parallelization accelerates solution space exploration, enabling the system to efficiently manage the complexity of larger DER networks and more sophisticated scheduling scenarios. The approach demonstrates its capability to align with the objectives of smart cities by ensuring adaptive and efficient energy distribution, integrating dynamic pricing mechanisms, and extending the operational lifespan of critical energy assets such as batteries. Rigorous simulations highlight the method’s ability to reduce optimization time, maintain solution quality, and scale efficiently, facilitating real-time decision making in energy markets. Moreover, the optimized coordination of DERs supports grid stability, enhances market responsiveness, and contributes to developing resilient, low-carbon urban environments. This study underscores the transformative role of computational infrastructure in addressing the challenges of modern energy systems, showcasing how advanced algorithms and HPC can enable scalable, adaptive, and sustainable energy optimization in smart cities. The findings demonstrate a pathway to achieving socially and environmentally responsible energy systems that align with the priorities of urban resilience and sustainable development. Full article

The Welfair^® certificate has become an important part of food chain integrity for animal welfare assessment in several countries, relying on a rigorous audit that verifies compliance with legislation and assesses animal welfare through the Welfare Quality Protocol (WQP). Dairy cattle farmers are encouraged to conduct internal audits beforehand to self-assess the farm’s animal welfare level. Since early 2023, the Welfair^® scheme has proposed simplified audits to shorten the time needed for internal audits. Ten measures are selected from the WQP, five of which must always be assessed: body condition, water provision, lameness, integument alterations, and pain management in disbudding. The main objective of this study was to determine whether analyzing the results of these five key indicators helps in identifying welfare problems, ultimately leading to a better final score. To test this, seven Portuguese commercial dairy farms were randomly selected to conduct a simplified internal audit followed by a certification audit. Considering the circumstances of our study, the visits proved essential to promoting better welfare practices, which positively influenced the final classification. However, areas that require improvement (such as the lack of an accurate risk analysis of the simplified audits provided by the Welfair^® scheme) were identified and are discussed. Full article

(1) Background: Prenatal depression, maternal anxiety, puerperal psychosis, and suicidal thoughts affect child welfare and development and maternal health and mortality. Women in low-income countries suffer maternal mental health issues in 25% of cases during pregnancy and 20% of cases thereafter. However, MMH screening, diagnosis, and reporting are lacking. The primary goals of the present study are twofold, as follows: firstly, to evaluate the importance of screening maternal mental health to alleviate perinatal depression and maternal anxiety, and, secondly, to analyze research patterns and propose novel approaches and procedures to bridge the current research gap and aid practitioners in enhancing the quality of care offered to women exhibiting symptoms of perinatal depression. (2) Methods: We conducted a bibliometric analysis to analyze the research topic, using the bibliometric tools Biblioshiny and VOSviewer, as well as the R statistical programming language. To accomplish our goal, we obtained a total of 243 documents from the Scopus and PubMed databases and conducted an analysis utilizing network, co-occurrence, and multiple correlation approaches. (3) Results: Most of the publications in the field were published between the years 2021 and 2024. The results of this study highlight the significance of shifting from conventional screening methods to digital ones for healthcare professionals to effectively manage the symptoms of maternal mental health associated with postpartum depression. Furthermore, the results of the present study suggest that digital screening can prevent maternal physical morbidity, contribute to psychosocial functioning, and enhance infant physical and cognitive health. (4) Conclusions: The research indicates that it is crucial to adopt and include a computerized screening practice to efficiently and immediately detect and clarify the signs of prenatal to neonatal depression. The introduction of digital screening has led to a decrease in scoring errors, an improvement in screening effectiveness, a decrease in administration times, the creation of clinical and patient reports, and the initiation of referrals for anxiety and depression therapy. Full article

Recent reports have indicated a slowdown in global progress towards compliance with the 2030 Agenda and a setback in some objectives. This has prompted the development of research to identify the factors contributing to some countries moving faster than others in achieving the goals. Until now, the literature has emphasized the role of economic and institutional factors in achieving the 2030 Agenda, making it necessary to investigate the effects that other political or social factors may generate. To contribute to this purpose, this article aims to identify the effect of the quality of government, democracy, and well-being on aggregate compliance with the 2030 Agenda. Through a quantitative analysis that uses the level of achievement of the 2030 Agenda as a dependent variable and six independent variables related to the quality of government, democracy status, and well-being, we found that the effectiveness of government, the welfare regime, subjective well-being, and democracy status positively influence the achievement of sustainable development objectives. These findings have practical implications, as they suggest that countries with solid and effective government institutions, social safety networks, high subjective well-being, and healthy democracy have greater potential for meeting the goals of the 2030 Agenda, emphasizing the urgency of our collective efforts. Full article

Although direct behavioural observations are widely used, they are time-consuming, prone to error, require knowledge of the observed species, and depend on intra/inter-observer consistency. As a result, they pose challenges to the reliability and repeatability of studies. Automated video analysis is becoming popular for behavioural observations. Sleep is a biological metric that has the potential to become a reliable broad-spectrum metric that can indicate the quality of life and understanding sleep patterns can contribute to identifying and addressing potential welfare concerns, such as stress, discomfort, or health issues, thus promoting the overall welfare of animals; however, due to the laborious process of quantifying sleep patterns, it has been overlooked in animal welfare research. This study presents a system comparing convolutional neural networks (CNNs) with direct behavioural observation methods for the same data to detect and quantify dogs’ sleeping patterns. A total of 13,688 videos were used to develop and train the model to quantify sleep duration and sleep fragmentation in dogs. To evaluate its similarity to the direct behavioural observations made by a single human observer, 6000 previously unseen frames were used. The system successfully classified 5430 frames, scoring a similarity rate of 89% when compared to the manually recorded observations. There was no significant difference in the percentage of time observed between the system and the human observer (p > 0.05). However, a significant difference was found in total sleep time recorded, where the automated system captured more hours than the observer (p < 0.05). This highlights the potential of using a CNN-based system to study animal welfare and behaviour research. Full article

The introduction of 5G technologies has enabled the possibility of designing and building several new classes of networked information systems that were previously impossible to implement due to limitations on data throughput or the reliability of transmission channels. Among them, one of the most interesting and successful examples with a highly positive impact in terms of the quality of urban environments and societal and economical welfare is a system of semi-autonomous connected vehicles, where IoT devices, data centers, and fleets of smart vehicles equipped with communication and computational resources are combined into a heterogeneous and distributed infrastructure, unifying hardware, networks, and software. In order to efficiently provide various services (e.g., patrolling, pickup and delivery, monitoring), these systems typically rely on collecting and broadcasting large amounts of data (e.g., sensor data, GPS traces, or maps), which need to be properly collected and processed in a timely manner. As is well documented in the literature, one of the most effective ways to achieve this purpose, especially in a real-time context, is to adopt a graph model of the data (e.g., to model communication networks, roads, or interactions between vehicles) and to employ suitable graph algorithms to solve properly defined computational problems of interest (e.g., shortest paths or distributed consensus). While research in this context has been extensive from a theoretical perspective, works that have focused on the implementation, deployment, and evaluation of the practical performance of graph algorithms for real-world systems of autonomous vehicles have been much rarer. In this paper, we present a study of this kind. Specifically, we first describe the main features of a real-world information system employing semi-autonomous connected vehicles that is currently being tested in the city of L’Aquila (Italy). Then, we present an overview of the computational challenges arising in the considered application domain and provide a systematic survey of known algorithmic results for one of the most relevant classes of computational problems that have to be addressed in said domain, namely, pickup and delivery problems. Finally, we discuss implementation issues, adopted software tools, and the deployment and testing phases concerning one of the algorithmic components of the mentioned real-world system dedicated to handling a specific problem of the above class, namely, the pickup and delivery multi-vehicle problem with time windows. Full article

Urban street greening, a key component of urban green spaces, significantly impacts residents’ physical and mental well-being, contributing substantially to the overall quality and welfare of urban environments. This paper presents a novel framework that integrates street greenery with accessibility, enabling a detailed evaluation of the daily street-level greenery visible to residents. This pioneering approach introduces a new measurement methodology to quantify the quality of urban street greening, providing robust empirical evidence to support its enhancement. This study delves into Nanjing’s five districts, employing advanced image semantic segmentation based on machine learning techniques to segment and extract green vegetation from Baidu Street View (BSV) images. Leveraging spatial syntax, it analyzes street network data sourced from OpenStreetMap (OSM) to quantify the accessibility values of individual streets. Subsequent overlay analyses uncover areas characterized by high accessibility but inadequate street greening, underscoring the pressing need for street greening enhancements in highly accessible zones, thereby providing valuable decision-making support for urban planners. Key findings revealed that (1) the green view index (GVI) of sampled points within the study area ranged from 15.79% to 38.17%, with notably better street greening conditions observed in the Xuanwu District; (2) the Yuhua District exhibited comparatively lower pedestrian and commuting accessibility than the Xuanwu District; and (3) approximately 139.62 km of roads in the study area demonstrated good accessibility but lacked sufficient greenery visibility, necessitating immediate improvements in their green landscapes. This research utilizes the potential of novel data and methodologies, along with their practical applications in planning and design practices. Notably, this study integrates street greenery visibility with accessibility to explore, from a human-centered perspective, the tangible benefits of green landscapes. These insights highlight the opportunity for local governments to advance urban planning and design by implementing more human-centered green space policies, ultimately promoting societal equity. Full article