Search Results (254)

This review analyzes the economic, social, and environmental dimensions of water buffalo (Bubalus bubalis) production and its contribution to the Sustainable Development Goals (SDGs). A scoping review following PRISMA-ScR guidelines was conducted using the Web of Science (2020–2026), resulting in 225 included studies. Buffalo production is a multipurpose system that generates value through milk, meat, hides, manure, draft power, and animal-assisted services, with greater longevity than most livestock species. Economically, it supports income diversification, resource efficiency, and functions as a financial asset that can be sold to cover unexpected expenses. Socially, it enhances food security by providing nutrient-dense products, particularly milk with bioactive compounds associated with potential health benefits, and promotes women’s participation in livestock management and household economies. Environmentally, buffalo systems efficiently utilize low-quality forages, are adapted to marginal conditions, contribute to wetland conservation, and provide ecosystem services. These contributions align with several SDGs (1, 2, 5, 8, 12, 13, and 15). However, sector expansion is constrained by limitations in nutrition, management, veterinary services, and reproductive efficiency, as well as environmental challenges related to methane emissions and life cycle impacts. While global methane emissions from buffalo are lower due to their smaller population, emission intensity remains system-dependent and represents a critical challenge. In conclusion, water buffalo production represents a multifunctional and context-dependent system with significant potential to support sustainable development, although targeted innovations are required to improve productivity and address environmental challenges. Future research should integrate One Health and One Welfare approaches, develop long-term studies, and expand research under diverse experimental and field conditions to better characterize the potential health implications of buffalo-derived products. In addition, strengthening circular economy strategies, including region-specific diets to reduce emissions, remains a priority. Full article

Accurate extreme rainfall representation is critical for resilient hydrological design and sustainable water management in tropical regions. This study evaluates the GPM IMERG product across three diverse watersheds in East Java (Welang, Kedak, and Grindulu) using Extreme Value Theory (EVT). By employing Generalized Extreme Value (GEV) and Peaks Over Threshold (POT) methods, the research assesses the reliability of satellite estimates in characterizing the extreme events that safeguard community security and infrastructure longevity. Results indicate that while GPM IMERG excels at monthly scales, it lacks the daily precision required for effective flash flood mitigation, particularly in small basins. Crucially, GEV analysis reveals a structural mismatch: ground observations exhibit heavy-tailed (Fréchet) distributions, while GPM IMERG follows bounded (Weibull) distributions. Consequently, the satellite product underestimates high-magnitude events at long return periods, the exact events that define the design limits of adaptive hydraulic structures. Complementary POT analysis identifies scale-dependent biases across catchments. These findings suggest that while GPM IMERG is robust for regional monitoring, it requires distribution-specific bias correction to support disaster-resilient engineering. Addressing these gaps is essential for achieving climate-responsive sustainable development in data-scarce regions. Full article

Accurate mortality prediction is essential to actuarial practice as it is directly linked to insurance pricing, reserving, and the management of longevity risk. This study proposes a deep neural network (DNN) model for the mortality rates of multiple populations; it is composed of long short-term memory (LSTM) and convolutional neural network (CNN) components. As mortality trends evolve over long time horizons, and as capturing the complex dependencies among mortality rates across countries or regions with a linear model is challenging, the LSTM and CNN were applied to mortality modeling. The former can automatically learn long-term dependencies of sequential data, whereas the latter can extract local features from grid or sequential data. Formulated as a nonlinear generalization of the Lee–Carter decomposition, the model maps the log-mortality matrix $logM$ to future $logm(x,t)$ end-to-end and generates multi-step forecasts through dynamic recursive prediction. Then, the DNN and baseline models were used to fit mortality data of 21 countries from the Human Mortality Database (HMD), which were divided into training and test sets with the year 2000 as the split point. Extensive numerical experiments from the perspectives of accuracy, stability, and reliability of long-term forecasting revealed that DNN models yield better predictive performance, particularly the LSTM-CNN model. It combines the LSTM, CNN, and fully connected network (FCN) layers and thus exploits each deep neural network to fit nonlinear age, period, and cohort effects as well as their interactive terms to achieve better predictive performance. However, the CNN still outperformed other models for certain groups. In addition, the conclusions hold for remaining life expectancy. Full article

The present work examines a severe, long-lived bow echo in South China during 12–13 April 2016 and investigates the favorable factors for its strength and longevity using a series of 20 cloud-resolving ensemble experiments. Analysis of observational data indicated that this system developed near a surface front under unstable and favorable conditions with dynamic uplifting by approaching troughs at 500–700 hPa. After formation, it propagated rapidly toward the east–southeast across South China and made landfall in Southern Taiwan. The ensemble used four different datasets as initial and boundary conditions and started at five different initial times, whereby comparing the better-performing members with worse ones, four key factors promoting its strength and longevity were identified: (1) A stronger and moister low-level southwesterly flow to the south of the front to enhance convergence and moisture flux at the leading edge—where a stronger inflow with higher equivalent potential temperature (θ_e) values could feed into the bow echo—leading to a stronger and taller updraft and overall more abundant hydrometeors and rainfall; (2) stronger northwesterly to westerly winds near 700 hPa and thus stronger low-level vertical wind shear, resulting in a stronger rear inflow jet (RIJ), bookend vortices behind the bow apex, and, eventually, a faster propagation speed; (3) a deeper low to the northeast of the bow echo near 850 hPa, where its circulation also helped to bring in low-θ_e air from farther away and enhance the RIJ and cold pool; and (4) a convective initiation location farther to the east in a more favorable environment, with higher θ_e and a faster speed to remain in such a better environment. Helped by the above factors, the bow echo in the present case could reach the observed severity and long duration (~15 h) through interactions and reinforcement among its structural components, including the tilted updraft/downdraft, the low-level inflow and stratiform region, the RIJ and bookend vortices, and the cold pool and gust front. Full article

The global shift toward decentralized generation has established standalone energy supply systems as a vital solution for remote regions. However, the integration of intermittent renewable sources and the inherent lack of rotational inertia in power electronic interfaces create significant challenges for frequency stability. This study addresses these issues by introducing an original Two-Tier Fractional-Order PID (TTFOPID) controller designed for robust Load Frequency Control (LFC) in a hybrid system comprising solar, diesel, biodiesel, and battery energy storage (BESS). The research utilizes the Multi-Objective Imperialist Competitive Algorithm (MOICA), enhanced with an attractive and repulsive assimilation phase, to navigate the high-dimensional parameter space. A unique framework is established to simultaneously tune controller gains and high-level system parameters, specifically BESS sizing and droop settings. Results demonstrate that the MOICA-tuned TTFOPID provides superior performance, achieving a 72% improvement in the Integral of Time-Weighted Absolute Error (ITAE) compared to NSGA-II and a 56% improvement in the Integral of the Square of Control (ISC) compared to MOPSO. Furthermore, robustness analysis validates the controller’s stability against significant parametric variations. The study concludes that the integrated TTFOPID-MOICA approach provides a superior pathway for stabilizing autonomous energy supply systems while protecting hardware longevity through optimized control effort. Full article

Regenerative medicine is becoming more widely integrated with longevity-oriented and preventive care as populations age and chronic degenerative diseases burden healthcare systems. Mesenchymal stem cell (MSC) therapies have progressed from experimental interventions to approved products, yet scalability, safety, cost, and regulatory complexity constrain widespread implementation in medical wellness contexts. The predominant therapeutic effects of MSCs are mediated via paracrine mechanisms, leading to cell-free approaches based on the MSC secretome—a complex mixture of bioactive factors including all types of biomolecules and assemblies thereof, such as exosomes. These acellular products offer compelling advantages: multiple batches from single-donor sources, standardized dosing, reduced allogeneic cell risks, and shorter outpatient-compatible administration. Preclinical and clinical data indicate that secretome-based products exert potent regenerative effects in osteoarthritis, chronic wounds, stroke, traumatic brain injury, and neurodegenerative diseases. This review examines the evolution from cell-based to cell-free regenerative strategies, focusing on human umbilical cord Wharton’s jelly MSC secretome for precision longevity medicine. It compares MSC therapies with secretome- and exosome-based formulations across mechanistic, manufacturing, safety, practical and regulatory dimensions. Regional perspectives highlight Southeast Asia, and especially Thailand, as an emerging regenerative-longevity hub. Finally, it outlines the preventive patient journey integrating cell-free interventions within multi-modal programs aimed at extending healthspan. Full article

Drug-resistant epilepsy (DRE) has a significant burden on children and their families that extends beyond seizure management. Surgery can be a curative treatment but is sometimes not an option for certain generalized epilepsies or epilepsy in an eloquent region. Neuromodulation therapies (vagus nerve stimulation–VNS, deep brain stimulation–DBS, and responsive neurostimulation–RNS) have emerged as effective palliative treatments to mitigate seizure burden. Only VNS is FDA-approved for use in certain pediatric populations for epilepsy, but all are used off-label to treat pediatric drug-resistant epilepsy. This review provides an overview of these therapies, the perioperative considerations related to their implantation, and the perioperative considerations related to managing a device in situ. Care must be taken to avoid unintentional harm to the device, the leads, and the generator. Procedures must be cognizant of possible physiological changes that can occur intraoperatively and anatomic restrictions due to lead/generator placement. Although there is still a need for more long-term safety data regarding the use of neuromodulation devices in children, the current data demonstrate good efficacy and safety thus far. More children are likely to receive these devices for treatment, and so continued training and education will be needed for health care providers to maintain device longevity and safety. Full article

This study examines the impact of family multidimensional poverty on cognitive function among older adults in China using the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS). Filling a critical gap in the existing literature, we construct a multidimensional poverty index (MPI) based on the Alkire-Foster methodology to evaluate cognitive decline within the context of China’s post-poverty-eradication landscape. Utilizing quantile regression analysis, our findings demonstrate that multidimensional poverty exerts a significant, negative effect on cognitive function, which is more pronounced among individuals at lower cognitive quantiles, consistent with the cumulative disadvantage theory. Furthermore, we identify substantial urban–rural and regional disparities, revealing unique socio-economic inequalities. By linking multidimensional poverty to elderly cognitive health through psychosocial pathways, this study provides empirical evidence that reducing multidimensional deprivation among older adults is integral to achieving both SDG1 and SDG3 in China’s post-eradication context, demonstrating that income-based metrics alone are insufficient to capture the full burden of poverty on elderly cognitive health. Full article

Flower senescence is a key physiological constraint on the ornamental and commercial longevity of cut roses. Although abscisic acid (ABA) is recognized as a promoter of this process, the molecular circuitry through which ABA operates, particularly the specific contributions of MYB transcription factors, remains largely unexplored. In this study, we identify RcMYB002 as a negative regulator of rose flower senescence. Transient overexpression of RcMYB002 significantly delays senescence, preserves anthocyanin accumulation, and modulates antioxidant enzyme activities in a time-dependent manner, consequently attenuating ABA-triggered oxidative stress. In contrast, silencing RcMYB002 accelerates senescence-associated phenotypes. At the molecular level, ABA suppresses RcMYB002 transcript accumulation, while yeast one-hybrid (Y1H) assays indicate that RcMYB002 interacts with the promoter regions of senescence-associated genes SAG12 and SAG21, consistent with a role in their transcriptional regulation. Taken together, our results support a model in which ABA promotes flower senescence by downregulating RcMYB002, thereby derepressing downstream senescence-executing genes. This work provides a molecular basis for understanding flower senescence and offers a potential target for extending rose vase life. Full article

The present study investigates the thermo-mechanical behaviour and fatigue life associated with the shrink-fit process of shrink-fit tool holders. These holders are an indispensable component of high-precision and high-speed machining processes in modern manufacturing industries. Shrink-fit holders are subjected to elevated levels of stress as a consequence of repeated heating and cooling cycles, which can result in clamping fatigue over time. In this study, a three-dimensional finite element model (FEM) of a holder manufactured from H13 tool steel in accordance with BT40 standards was created using ANSYS software. The numerical analyses included transient thermal and structural analyses, consisting of a 4.5-s induction heating stage at 10 kW power, followed by a 1200-s cooling process. The analysis yielded results that were corroborated by the experimental data. It was established that, upon the conclusion of the heating process, the temperature in the conical region of the holder attained a range of approximately 388–417 °C. Furthermore, it was ascertained that a radial expansion of approximately 17.2–22 µm, which is required for the successful insertion of the cutting tool into the inner bore, was achieved. The fatigue life prediction, which constitutes the main focus of the study, applied the Soderberg criterion and evaluated two basic loading scenarios: the first tool assembly and repeated tool assembly cycles. The calculations yielded a life estimate of approximately 12,407 cycles for the first tool assembly cycle and approximately 19,400 cycles for the repeated tool assembly cycle. Accordingly, the repeated tool assembly condition exhibited a longer fatigue life than the first tool assembly condition. The enhanced longevity observed in the repeated tool assembly scenario is attributed to the stress cycle not fully reaching zero during this process, resulting in a lower stress amplitude. Full article

The leg structure of swine is closely related to their robustness and longevity. Animals with sound legs generally have longer productive lifespans and higher reproductive efficiency, whereas leg defects can markedly impair performance and shorten service life. To address the high subjectivity, low efficiency, and poor consistency of traditional leg-structure evaluation by humans, this study developed an automatic scoring system for swine leg structure based on 3D point clouds. The hardware components of the system include the acquisition channel, a multi-view time-of-flight (ToF) depth camera array, an industrial computer, and a star-type synchronization hub. The core algorithm modules include point cloud preprocessing, leg segmentation, geometric feature extraction, and structure-based scoring. Body orientation was corrected using principal component analysis (PCA). An adaptive limb region segmentation method was proposed that combines iterative cropping with geometric verification. Two point cloud tasks were performed: key structural points were extracted via multi-scale curvature analysis, and angular and symmetry parameters of the fore- and hindlimbs were computed in the sagittal and coronal planes. Following a “classify first, then score” strategy, a nine-level linear scoring model was constructed. Field validation showed that the classification accuracy exceeded 90%, the scores were significantly negatively correlated with the degree of structural deviation, and multi-frame resampling yielded good repeatability. The processing time per animal ranged from 1.6 s to 3.0 s, which met the requirements for real-time applications. These results demonstrated that the proposed method could automatically identify and quantitatively evaluate swine leg structure, providing efficient and reliable technical support for objective selection and smart pig farming. Full article

Wireless Sensor Networks (WSNs) commonly employ clustering to improve scalability and energy efficiency; however, cluster heads (CHs) located near the base station (BS) often suffer from excessive relay traffic, leading to rapid energy depletion and reduced network lifetime. This article proposes an Energy-Efficient Distance-Controlled Clustering (EEDC) scheme that adjusts CH density and transmission power according to each node’s distance from the BS. In EEDC, a higher number of CHs is deployed near the BS to balance forwarding loads, while fewer CHs are selected in distant regions to conserve energy. Additionally, CHs adapt their transmission power to enable distance-proportional communication. A mathematical model is developed to analyze the relationship between CH distribution, transmission power, and overall energy consumption. Performance evaluation is conducted through simulations and compared with LEACH, HEED, DEEC, SEP, and EECS. The results show that EEDC improves the stability period by up to 42%, extends network lifetime by 23%, increases average residual energy by 13–29%, enhances throughput by 16–44%, and achieves 23–61% higher packet delivery efficiency. Moreover, cumulative CH energy consumption is reduced by 5–21%, leading to more balanced energy distribution. These findings indicate that distance-controlled CH selection and adaptive transmission power effectively alleviate the BS energy bottleneck and enhance the energy efficiency and operational longevity of clustered WSNs. Full article

Low-cost air quality sensors, such as PurpleAir monitors, have rapidly expanded fine particulate matter (PM2.5) monitoring across the United States, providing dense, hyper-local measurements. While prior research has focused largely on sensor accuracy and calibration, less is known about where these sensors are deployed and whether they persist long enough to support multi-year analyses relevant to exposure assessment and policy. Using publicly available PurpleAir data, we characterized the geographic distribution, deployment longevity, and persistence of outdoor sensors across the United States from 2016 to 2025. We quantified deployment duration as the time between first and last publicly available observations and summarized patterns nationally, by U.S. Census region, and by state. Most publicly shared sensors remained deployed for more than three years, indicating substantial potential for multi-year applications, particularly in the western United States, where sensor density and longevity were highest. As an illustrative component, we present descriptive summaries of PM2.5 concentrations in four high-coverage states (California, Minnesota, Pennsylvania, and Texas) by deployment duration and urban–rural classification to demonstrate the types of analyses enabled by these networks. These results establish a national baseline of sensor availability and temporal continuity. By focusing on deployment patterns, this study provides foundational context for future exposure modeling, epidemiologic studies, and targeted expansion of community air quality monitoring networks. Full article

Background: Hyaluronic acid (HA) hydrogels are the most widely used injectable fillers for facial rejuvenation. A new generation of HA fillers crosslinked with poly(ethylene glycol) diglycidyl ether (PEGDE) has been developed to enhance cohesiveness, tissue integration, stability, and longevity while minimizing swelling and immunogenicity. Owing to their distinct viscoelastic properties, PEGDE-crosslinked HA fillers may require product-specific selection and adapted injection techniques. Objective: The objective of this study is to provide practical, expert-based recommendations for the safe and effective use of PEGDE-crosslinked HA hydrogels in facial aesthetic treatments. Methods: A multidisciplinary panel of nine Italian experts in aesthetic medicine, each with more than 15 years of experience using HA fillers, developed consensus recommendations based on clinical practice and available evidence. A pre-meeting questionnaire informed structured discussions during a face-to-face meeting held in Paris in January 2024. The nominal group technique was applied, with consensus defined as agreement by at least 80% of panel members. Results: Consensus was reached on product selection, injection planes, delivery devices, techniques, and typical treatment volumes for PEGDE-HA hydrogels across multiple facial regions, including the forehead, temples, midface, nasolabial folds, chin, jawline, and lips. Recommended injection techniques included microbolus, macrobolus, and retrograde linear threading, with placement ranging from superficial subcutaneous to supraperiosteal planes depending on the anatomical area and clinical indication. Typical injection volumes generally ranged from 0.1 to 0.5 mL per side. Optional ultrasound mapping was considered beneficial in selected high-risk or superficial procedures to improve anatomical safety. Conclusions: These expert consensus recommendations provide practical guidance for clinicians using PEGDE-crosslinked HA hydrogels in facial rejuvenation. Tailoring product characteristics to injection depth, technique, and regional anatomy may help optimize clinical outcomes and procedural safety. Future research priorities include prospective comparative studies with other crosslinking technologies, standardized reporting of adverse events, long-term outcome registries, and further evaluation of ultrasound-guided injection strategies. Full article

The preservation of littoral zones faces escalating challenges from global thermal forcing and anthropogenic pressure, particularly in vulnerable basins like the Mediterranean. This research proposes an expanded Coastal Vulnerability Index to assess 253 km of beaches across the Valencian Community (Spain), integrating sediment durability parameters for the first time. Methodologically, an arithmetic mean of ten physical, biotic, and sedimentological indicators was employed, including a novel Sediment Quality Classification Index (SQCI) derived from accelerated particle wear tests as a key resilience indicator. Results reveal that 40.9% of the coastline exhibits high or very high risk, reaching critical levels in the province of Valencia due to key driving factors such as a 91.8% proportion of fine sediments and the severe degradation of its dune systems. Conversely, the presence of Posidonia oceanica meadows in Alicante acts as a key resilience factor. It is possible to conclude that the mechanical quality of sediment is a determinant for the longevity of protection measures, highlighting the necessity of transitioning from rigid infrastructures towards ecosystem-based adaptation to enhance regional resilience. Full article