Search Results (369)

The typical ranges available for electric vehicles (EVs) may be considered by users to be inadequate when compared to long, real-life trips, and charging operations may need to be planned along journeys. To evaluate the compatibility between vehicle features and charging options for realistic journeys performed by car, a simulation approach is proposed here, using travel data collected from real vehicles to obtain trip chains for multiple consecutive days. Car travel activities, including stops with the option of charging, were simulated by applying an agent-based approach. Charging operations can be integrated into trip chains for user activities, assuming that they remain unchanged in the event that vehicles switch to electric. The energy consumption of the analyzed trips, disaggregated by vehicle type, was estimated using the average travel speed, which is useful for capturing the main route features (ranging from urban to motorways). Data were recorded for approximately 25,000 vehicles in the Turin Metropolitan Area for six consecutive days. Market segmentation of the vehicles was introduced to take into consideration different energy consumption rates and charging times, given that the electric power, battery size, and consumption rate can be related to the vehicle category. Charging activities carried out using public infrastructure during idle time between consecutive trips, as well as those carried out at home or work, were identified in order to model different needs. Full article

Context: Many patients at the end of life receive antibiotics to alleviate symptoms and improve quality of life; however, clear guidelines supporting decision making about the use of antibiotics are still lacking. Objectives: This study aimed to evaluate the benefits and harms of antibiotic use among patients under a palliative care community support team in Portugal. Methods: An observational, cross-sectional, retrospective study was conducted on 249 patients who died over a two-year period, having been followed for at least 30 days prior to their death. Data included patient demographics, clinical diagnoses, antibiotic prescriptions, and symptomatic outcomes. The effects of commonly prescribed antibiotics—amoxicillin + clavulanic acid, cefixime, ciprofloxacin, and levofloxacin—were compared using statistical analyses to assess survival, symptom intensity, and functional scales. Results: Adverse events, primarily infections and secretions, occurred in 57.8% of cases, with 33.7% receiving antibiotics. No significant difference in survival was observed across the antibiotic groups (p = 0.990). Symptom intensity significantly reduced after 72 h of treatment (p < 0.05), with ciprofloxacin demonstrating the greatest symptom control. The Palliative Outcome Scale decreased uniformly, with higher scores associated with amoxicillin + clavulanic acid (p = 0.004). The Palliative Performance Scale declined post-treatment, with significant changes noted for cefixime and ciprofloxacin (p < 0.05). Conclusions: Antibiotics may improve symptom control and quality of life in the end-of-life stage. While second-line antibiotics may offer additional benefits, the heterogeneity of the sample and limited adverse effect data underscore the need for further research to guide appropriate prescription practices in palliative care. Full article

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

The increasing prevalence of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) has led to a growing demand for kidney transplantation (KTx). Identifying risk factors that enable improved allograft survival through novel therapeutic agents, advanced biomarkers, and artificial intelligence (AI)-driven data integration are critical to addressing this challenge. Drugs, such as SGLT2 inhibitors and finerenone, have demonstrated improved outcomes in patients but lack comprehensive long-term evidence in KTx patients. The use of biomarkers, including circulating cytokines and transcriptomics, coupled with AI, could enhance early detection and personalized treatment strategies. Addressing patient self-management and addressing health access disparities may be more achievable using technologies used at home rather than traditional models of healthcare and thus lead to increased transplant success, both in terms of transplantation rates and allograft longevity. Full article

Despite notable advancements in smart home technologies, residential energy management continues to face critical challenges. These include the complex integration of intermittent renewable energy sources, issues related to data latency, interoperability, and standardization across diverse systems, the inflexibility of centralized control architectures in dynamic environments, and the difficulty of accurately modeling and influencing occupant behavior. To address these challenges, this study proposes an intelligent multi-agent system designed to accurately estimate and control energy consumption in residential buildings, with the overarching objective of optimizing energy usage while maintaining occupant comfort and satisfaction. The methodological approach employed is a hybrid framework, integrating multi-agent system architecture with system dynamics modeling and agent-based modeling. This integration enables decentralized and intelligent control while simultaneously simulating physical processes such as heat exchange, insulation performance, and energy consumption, alongside behavioral interactions and real-time adaptive responses. The system is tested under varying conditions, including changes in building insulation quality and external temperature profiles, to assess its capability for accurate control and estimation of energy use. The proposed tool offers significant added value by supporting real-time responsiveness, behavioral adaptability, and decentralized coordination. It serves as a risk-free simulation platform to test energy-saving strategies, evaluate cost-effective insulation configurations, and fine-tune thermostat settings without incurring additional cost or real-world disruption. The high fidelity and predictive accuracy of the system have important implications for policymakers, building designers, and homeowners, offering a practical foundation for informed decision making and the promotion of sustainable residential energy practices. Full article

Creole writer Sidonie de la Houssaye (1820–1894) registered the threat of anglophone dominance after the Civil War on behalf of a host of characters drawn from the geographies and ideologies in and around her home in Louisiana. Her little-known literary tales depict the period as a cultural and linguistic border zone. In addition to the texture of Louisiana French and Creole heritage, the tales depict the vexed social dynamics of prejudice and fragility. In the context of this special issue on good and evil, the poorly known children’s tales offer insight into these pernicious tensions that persisted under the surface of moral victory after the Civil War. La Houssaye’s lessons for children take up the moral panic of a Louisiana reckoning with its legacies of racial violence and cultural erasure. This article argues that morality in these tales takes shape in interpersonal practices that can be learned to heal social ills. What I have called La Houssaye’s “ethics of social life” relies on education rather than condemnation to redefine human bonds. If a broader lesson emerges from the stories taken together, it suggests that structural change is slow to heal cultural wounds. We must ourselves be the agents of a healthier community. Full article

Warm-mix recycled asphalt (WMA-R) technology for reclaimed asphalt pavement (RAP) significantly reduces energy consumption and environmental pollution while maintaining the performance of asphalt mixtures. Significant progress has been made at home and abroad in evaluating the impact of regenerated asphalt mixtures on the performance of regenerated asphalt. However, the performance improvement of WMA-R depends on the effective diffusion of regenerated agents and their interaction mechanism with aged asphalt, which has not been fully studied. This paper systematically studies the diffusion characteristics of biomimetic-based warm-mix regenerant in aged asphalt and its impact on the high- and low-temperature performance of asphalt mixtures through MD and experimental verification. The results show that biomimetic-based warm-mix regenerant can significantly improve the diffusion performance of aged asphalt. Through the rutting test and low-temperature bending test, the significant improvement of the biomimetic-based warm-mix regenerant in the rutting resistance and crack resistance of asphalt mixtures was verified. Full article

Breastmilk is known to provide optimal nutrition for infant growth and development. A cross-sectional analysis of nationally representative US data from 2016 to 2021 revealed that >90% of lactating mothers reported using breast pumps to express milk. We conducted a survey of n = 1049 lactating or recently lactating individuals from a US nationally representative population to explore breastmilk storage practices among this group. The data revealed that 83% of respondents store breastmilk in their homes, with 68% using freezers to do so for >1 month. The lowest available temperature in most household freezers is −20 °C, a temperature that is inadequate to maintain human milk’s emulsified structure, leading to separation, degradation of fats, loss of key vitamins, and changes in palatability. We developed a first-of-its-kind high-throughput screening platform to identify food-derived compounds and combinations of compounds that, when added to human breastmilk, preserve fat content, retain antioxidant capacity, and reduce production of rancid-associated free fatty acids during extended freezer storage. Our screening identified pectin (0.5% w/v) and ascorbic acid (100 μg/mL) as optimal preservation agents. Compared to untreated controls, this formulation reduced glycerol production by approximately 60% and maintained antioxidant capacity after 6 months of storage at −20 °C. Lysozyme and protease activity were maintained at >75% of the levels in fresh breastmilk. This formulation represents a lead for the development of safe and affordable frozen breastmilk shelf-life extenders for at-home use to increase the longevity of stored breastmilk. Full article

There is an ongoing debate about whether Christian theology has had positive or negative effects on the natural environment. Included in this debate is the role of Christian missions acting in colonial environments. This article investigates the relationship between Protestant agricultural missions and their environments, using the documents of the first World Missionary Conference (Edinburgh 1910) and the third World Missionary Conference (Tambaram 1938), as well as several related documents. Although the history of agricultural missions can be backtracked into the 19th century, they were not regarded as an independent branch of missions until the early twentieth century. In 1910, neither the home boards of Protestant missions nor the older generation of missionaries had any vision for agricultural missions, and traditional culture—including agriculture—was seen as superstitious and full of heathen beliefs. However, agricultural missions developed rapidly in the decades between Edinburgh and Tambaram and broadened into rural missions due to a change in vision. The deplorable rural areas of the younger Christian churches called for ‘rural reconstruction’, and rural missions were welcomed as the most important agents to undertake this challenge. The environment of the church and countryside was enlarged and, by 1938, included economic and social environments, known as the fourth dimension of the church and missions after preaching, education, and medical care. Full article

Canine distemper is caused by a morbillivirus similar to others that affect livestock and humans. The increase in host range and its persistence in wildlife reservoirs complicate eradication considerably. Canine distemper virus has been reported in wildlife in Mexico since 2007. Dogs were previously considered the main reservoirs, but high vaccination coverage in the USA has helped control the disease, and racoons (Procyon lotor) are now recognized as the main reservoirs of the agent in the USA, since they live in high densities in urban environments (peridomestic), where contact with domestic and wildlife species is common. Racoons are now considered to spread CDV in wildlife species and zoo animals. Mexico is home to at least two wildlife species that have been reported as carriers of the CDV infection in studies in the USA. Raccoons and Coyotes are distributed in several Mexican states and could play the same reservoir role as for the US. In addition, the increase in non-traditional pets expands the availability of susceptible individuals to preserve CDV in domiciliary and peri-domiciliary environments, contributing to the spread of the disease. Combined with incomplete vaccination coverage in domestic canids, this could contribute to maintaining subclinical infections. Infected pets with incomplete vaccination schedules could also spread CDV to other canines or wildlife coexisting species. In controlled habitats, such as flora and fauna sanctuaries, protected habitats, zoo collections, etc., populations of wildlife species and stray dogs facilitate the spread of CDV infection, causing the spilling over of this infectious agent. Restricting domestic pets from wildlife habitats reduces the chance of spreading the infection. Regular epidemiological surveillance and specific wildlife conservation practices can contribute to managing threatened species susceptible to diseases like CDV. This may also facilitate timely interventions in companion animals which eventually minimize the impact of this disease in both scenarios. Aim: The review discusses the circulation of CDV in wildlife populations, and highlights the need for epidemiological surveillance in wildlife, particularly in endangered wildlife species from Mexico. Through an extensive review of recent scientific literature about CDV disease in wildlife that has been published in local and international databases, the findings were connected with the current needs of information from a local to global perspective, and conclusions were made to broaden the context of Mexican epidemiological scenarios as closely related to the neighboring regions. Full article

In intelligent cluster systems, the spatio-temporal complexity of agent data collection and resource allocation, as well as the problems in collaborative organizations, present substantial challenges to efficient resource distribution. To address this, a novel self-organizing prediction method for spatio-temporal resource allocation is proposed. In the spatio-temporal modeling part, dilated convolution is applied for time modeling. Its dilation rate grows exponentially with the layer depth, allowing it to effectively capture the time trends of graph nodes and handle long time series data. For spatial modeling, an innovative dual-view dynamic graph convolutional network architecture is utilized to accurately explore the static and dynamic correlation information of the spatial layout of charging piles. Meanwhile, a composite self-organizing mechanism integrating a trust model is put forward. The trust model assists agents in choosing partners, and the Q-learning algorithm of the intelligent cluster realizes the independent evaluation of rewards and the optimization of relationship adaptation. In the experimental scenario of electric vehicle charging, considering charging piles as agents, under the home charging mode, the self-organizing charging scheduling can reduce the total load range by up to 90.37%. It effectively shifts the load demand from peak periods to valley periods, minimizes the total peak–valley load difference, and significantly improves the security and reliability of the microgrid, thus providing a practical solution for resource allocation in intelligent clusters. Full article

The deployment of autonomous AI agents in smart environments has accelerated the need for accurate and privacy-preserving human identification. Traditional vision-based solutions, while effective in capturing spatial and contextual information, often face challenges related to high deployment costs, privacy concerns, and susceptibility to environmental variations. To address these limitations, we propose IdentiFi, a novel AI-driven human identification system that leverages WiFi-based wireless sensing and contrastive learning techniques. IdentiFi utilizes self-supervised and semi-supervised learning to extract robust, identity-specific representations from Channel State Information (CSI) data, effectively distinguishing between individuals even in dynamic, multi-occupant settings. The system’s temporal and contextual contrasting modules enhance its ability to model human motion and reduce multi-user interference, while class-aware contrastive learning minimizes the need for extensive labeled datasets. Extensive evaluations demonstrate that IdentiFi outperforms existing methods in terms of scalability, adaptability, and privacy preservation, making it highly suitable for AI agents in smart homes, healthcare facilities, security systems, and personalized services. Full article

Background: Accelerating recovery and preventing the progression to more severe outcomes for patients with coronavirus disease 2019 (COVID-19) is of paramount importance. Non-steroidal anti-inflammatory agents (NSAIDs) have been widely adopted in the international recommendations for non-severe COVID-19 management. Among NSAIDs, evidence about the efficacy of ketoprofen lysin salt (KLS) in the treatment of non-severe COVID-19 has not been reported. Methods: This retrospective study compared the outcomes of 120 patients with mild to moderate COVID-19 treated at home with KLS between March 2021 and May 2023 compared with the outcomes of 165 patients who received corticosteroids. The outcomes included hospitalization, the need for oxygen supplementation, clinical recovery from acute COVID-19, and time to negative swabs. Results: Symptoms persisted in a lower percentage of patients in the KLS group compared to the corticosteroids group (p < 0.0001) and for a shorter period (p = 0.046). We found 6 patients (5%) in the KLS group were hospitalized compared to 45 (27%) in the corticosteroids group (p < 0.001). A higher percentage of patients in the corticosteroids group require oxygen administration (p < 0.001). In addition, patients taking corticosteroids showed a longer viral shedding period compared to those taking KLS (p = 0.004). A final multivariate analysis suggests that KLS might reduce hospitalization risk, the need for oxygen supplementation, and the persistence of post-COVID-19 symptoms when compared to an oral corticosteroid after adjusting for significant co-variables. Conclusions: KLS might have a positive effect on clinical recovery in non-severe COVID-19 patients. A comparison with other NSAIDs in terms of difference in efficacy and safety should be investigated in further trials. Full article

The rapid growth of smart home technologies, driven by the expansion of the Internet of Things (IoT), has introduced both opportunities and challenges in automating daily routines and orchestrating device interactions. Traditional rule-based automation systems often fall short in adapting to dynamic conditions, integrating heterogeneous devices, and responding to evolving user needs. To address these limitations, this study introduces a novel smart home orchestration framework that combines generative Artificial Intelligence (AI), Retrieval-Augmented Generation (RAG), and the modular OSGi framework. The proposed system allows users to express requirements in natural language, which are then interpreted and transformed into executable service bundles by large language models (LLMs) enhanced with contextual knowledge retrieved from vector databases. These AI-generated service bundles are dynamically deployed via OSGi, enabling real-time service adaptation without system downtime. Manufacturer-provided device capabilities are seamlessly integrated into the orchestration pipeline, ensuring compatibility and extensibility. The framework was validated through multiple use-case scenarios involving dynamic device discovery, on-demand code generation, and adaptive orchestration based on user preferences. Results highlight the system’s ability to enhance automation efficiency, personalization, and resilience. This work demonstrates the feasibility and advantages of AI-driven orchestration in realising intelligent, flexible, and scalable smart home environments. Full article

Engineered exosomes have emerged as transformative drug carriers, uniquely equipped to overcome biological barriers in central nervous system (CNS) disorders and cancer therapy. These natural extracellular vesicles, derived from cell membranes, offer inherent biocompatibility, low immunogenicity, and the ability to traverse physiological obstacles such as the blood–brain barrier (BBB) and dense tumor stroma. Recent advances in exosome engineering—including surface modification (e.g., ligand conjugation for receptor-mediated targeting) and cargo loading (siRNA, CRISPR-Cas systems, and chemotherapeutics)—have enhanced their precision and therapeutic utility. For CNS delivery, exosomes functionalized with brain-homing peptides (e.g., RVG or TfR ligands) have enabled the efficient transport of neuroprotective agents or gene-editing tools to treat Alzheimer’s disease or glioblastoma. In oncology, engineered exosomes loaded with tumor-suppressive miRNAs or immune checkpoint inhibitors exploit tumor microenvironment (TME) features, such as acidity or enzyme overexpression, for spatially controlled drug release. Furthermore, hybrid exosome–liposome systems and exosome–biomaterial composites are being explored to improve payload capacity and stability. Despite progress, challenges persist in scalable production, batch consistency, and regulatory standardization. This review critically evaluates engineering strategies, preclinical success, and translational hurdles while proposing innovations such as AI-driven exosome design and patient-derived exosome platforms for personalized therapy. By bridging nanotechnology and biomedicine, engineered exosomes can represent a paradigm shift in targeted drug delivery, offering safer and more effective solutions for historically intractable diseases. Full article