Search Results (7,424)

Millions of puppies are welcomed into the homes of families around the world each year. However, understanding the ways in which puppies are bred and raised by their breeders, as well as the perspectives and perceptions underpinning these practices, is still in its infancy. The current study administered an online survey to 200 Australian dog breeders to investigate their breeding program characteristics, breeding dog selection, understanding of the importance of early experiences in puppyhood, and the extent and diversity of their puppy rearing and socialisation practices. Results indicated that breeders were motivated by breed improvement and producing dogs for themselves rather than providing companion dogs, despite most of their puppies being placed in companionship roles. The participating breeders also acknowledged the important role they play in shaping puppies’ behaviour and temperament, which was reflected in both their breeding dog selection and in their rearing and socialisation practices. The majority of breeders housed their litters within their residence for the initial weeks of life but the socialisation experiences they provided were variable in type and frequency. Longer-term breeders and those with larger, more intensive programs reported providing human-focused socialisation experiences less frequently, though the correlational nature of these findings require cautious interpretation. Whilst future research should endeavor to explore these results more comprehensively among a more diverse sample, these findings provide valuable insight into the breeding, rearing, and socialisation process undertaken by dog breeders in Australia. Full article

Graph anomaly detection aims at identifying rare, unusual entities in attributed networks with respect to their patterns or structures that deviate significantly from the majority within a graph. Over the years, extensive efforts in this field have been dedicated to the powerful capability of attributed networks to model real-world systems. Given the scarcity of labeled anomalies, current research primarily emphasizes model design via unsupervised learning. Graph autoencoders have been widely utilized for such purposes, leveraging the outstanding capabilities of Graph Neural Networks to model graph structured data. However, most existing graph autoencoder-based anomaly detectors do not exploit the nodes’ local subgraph information, limiting their ability to comprehensively understand the network for better representation learning. Moreover, these methods place greater emphasis on the attribute reconstruction process while neglecting the structure reconstruction aspect. This paper proposes an enhanced graph autoencoder framework for graph anomaly detection tasks that incorporates a subgraph extraction and aggregation preprocessing stage to utilize the nodes’ local topological information for enhanced embedding generation and to induce an additional node–subgraph view through model learning. A graph structure learning-based decoder is introduced as the structure decoder for better relationship learning. Finally, during the anomaly scoring stage, a node neighborhood selection technique is applied to enhance the detection performance. The effectiveness of the proposed framework is demonstrated through comprehensive experiments conducted on six commonly used real-world datasets. Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Coffee is one of the most widely consumed beverages globally, with over 60% of Americans drinking it daily. This review examines coffee’s multifaceted impact on health and well-being, drawing on decades of research. Overall, the consensus is that moderate coffee intake is more beneficial than harmful across a wide range of health outcomes. Numerous large-scale, prospective cohort studies from around the world have consistently shown that moderate coffee consumption—typically three to five cups per day—is associated with reduced overall mortality and lower risk of major diseases such as cardiovascular diseases, diabetes, stroke, respiratory conditions, cognitive decline, and potentially several types of cancer, including liver and uterine cancers. Both caffeinated and decaffeinated coffee have shown benefits. The addition of sugar and cream to coffee may attenuate coffee’s positive health effects. Despite historical concerns, coffee consumption is not linked to increased risks of cancer, hypertension, or arrhythmia. However, some concerns remain. For pregnant women, coffee consumption should be limited to lower amounts, such that the daily intake of caffeine does not exceed 200 mg/day. Also, excessive caffeinated coffee intake may cause anxiety or sleep disturbances. Coffee’s health-promoting mechanisms include improved glucose balancing, increased physical activity, increased fat oxidation, improved lung function, and reduced inflammation. Beyond mortality and chronic diseases, coffee consumption affects many aspects of well-being: it supports hydration, boosts mental acuity, enhances physical performance, and may aid bowel recovery after surgery. While the field is well-studied via long-term observational cohorts, future research should focus on randomized controlled trials, Mendelian randomization studies, and granular analyses of coffee types and additives. Full article

Background: Fixed-dose combinations of rosuvastatin and ezetimibe are increasingly used in clinical practice, but real-world data on their effectiveness and safety in large populations remain limited. Methods: This prospective, single-group, open-label, non-interventional observational study was conducted in the Republic of Korea to evaluate the effectiveness and safety of Rovazet^® (a fixed-dose combination of rosuvastatin and ezetimibe). Patients were prospectively enrolled from 235 institutions (50 general hospitals and 185 private clinics) as part of routine clinical practice over a five-year period. Lipid profiles and medication compliance questionnaire results were collected at baseline, 12 weeks, and 24 weeks of treatment. Results: A total of 5527 patients with dyslipidemia, the majority were men (53.0%), and the mean age was 60.4 years. Rovazet^® significantly reduced low-density lipoprotein cholesterol (LDL-C) by 23.5% at 12 weeks (from 117.47 ± 50.65 mg/dL to 81.14 ± 38.20 mg/dL; p < 0.0001) and by 27.4% at 24 weeks (from 117.47 ± 50.65 mg/dL to 74.52 ± 33.36 mg/dL; p < 0.0001). Total cholesterol was significantly reduced by 17.7% at 12 weeks and by 19.8% at 24 weeks. Rovazet^® treatment reduced triglycerides by 4.1% at 12 weeks and by 7.2% at 24 weeks. High-density lipoprotein cholesterol increased by 4.5% at 12 weeks and by 7.9% at 24 weeks following Rovazet^® treatment. These changes in lipid profiles were consistent, regardless of cardiovascular risk profiles. By 24 weeks of treatment with Rovazet^®, 91.8% of patients had reached their target LDL-C goals. Adverse drug reactions were reported in 2.81% of patients, most of which were minor, indicating that Rovazet^® was well tolerated. Conclusions: Rovazet^® was effective in improving lipid profiles and well tolerated in Korean adults with dyslipidemia. Full article

The global energy transition and increasingly rigorous legal regulations aimed at climate protection are driving the search for alternative energy sources, including renewable energy sources (RESs) and waste heat. However, the mismatch between supply and demand presents a significant challenge. Thermal energy storage (TES) technologies, particularly mobile thermal energy storage (M-TES), offer a potential solution to address this gap. M-TES can not only balance supply and demand but also facilitate the transportation of heat from the source to the recipient. This paper reviews the current state of M-TES technologies, focusing on their technology readiness level, key operating parameters, and advantages and disadvantages. It is found that M-TES can be based on sensible heat, latent heat, or thermochemical reactions, with the majority of research and projects centered around latent heat storage. Regarding the type of research, significant progress has been made at the laboratory and simulation levels, while real-world implementation remains limited, with few pilot projects and commercially available systems. Despite the limited number of real-world M-TES implementations, currently existing M-TES systems can store up to 5.4 MWh in temperatures ranging from 58 °C to as high as 1300 °C. These findings highlight the potential of the M-TES and offer data for technology selection, simultaneously indicating the research gaps and future research directions. Full article

Background: Increasing regulatory pressure for maritime decarbonization (e.g., IMO CII, FuelEU) drives adoption of low-carbon fuels and prompts reassessment of regional ports’ competitiveness. This study aims to evaluate the economic and environmental viability of rerouting deep-sea container services to regional ports in a decarbonizing world. Methods: A scenario-based analysis is used to evaluate total costs and CO₂ emissions across the entire container shipping supply chain, incorporating deep-sea shipping, port operations, feeder services, and inland rail/road transport. The Port of Liverpool serves as the primary case study for rerouting Asia–Europe services from major ports. Results: Analysis indicates Liverpool’s competitiveness improves with shipping lines’ slow steaming, growth in hinterland shipment volume, reductions in the emission factors of alternative low-carbon fuels, and an increased modal shift to rail matching that of competitor ports (e.g., Southampton). A dual-port strategy, rerouting services to call at both Liverpool and Southampton, shows potential for both economic and environmental benefits. Conclusions: The study concludes that rerouting deep-sea services to regional ports can offer cost and emission advantages under specific operational and market conditions. Findings on factors and conditions influencing competitiveness and the dual-port strategy provide insights for shippers, ports, shipping lines, logistics agents, and policymakers navigating maritime decarbonization. Full article

Urban population growth is intensifying pressure on mobility systems, with road transportation contributing to environmental and sustainability challenges. Policymakers must navigate complex uncertainties in addressing rising mobility demand while pursuing sustainability goals. Advanced technologies offer promise, but their real-world effectiveness in urban contexts remains underexplored. This meta-review comprised three complementary studies: a broad analysis of sustainable mobility with Norwegian case studies, and systematic literature reviews on digital twins and Big Data/AI applications in urban mobility, covering the period of 2019–2024. Using structured criteria, we synthesised findings from 72 relevant articles to identify major trends, limitations, and opportunities. The findings show that mobility policies often prioritise technocentric solutions that unintentionally hinder sustainability goals. Digital twins show potential for traffic simulation, urban planning, and public engagement, while machine learning techniques support traffic forecasting and multimodal integration. However, persistent challenges include data interoperability, model validation, and insufficient stakeholder engagement. We identify a hierarchy of mobility modes where public transit and active mobility outperform private vehicles in sustainability and user satisfaction. Integrating electrification and automation and sharing models with data-informed governance can enhance urban liveability. We propose actionable pathways leveraging Big Data and AI, outlining the roles of various stakeholders in advancing sustainable urban mobility futures. Full article

This study presents an extended approach to compartmental modeling of infectious disease spread, focusing on regional heterogeneity within affected areas. Using classical SIS, SIR, and SEIR frameworks, we simulate the dynamics of COVID-19 across two major regions of Ukraine—Dnipropetrovsk and Kharkiv—during the period 2020–2024. The proposed mathematical model incorporates regionally distributed subpopulations and applies a system of differential equations solved using the classical fourth-order Runge–Kutta method. The simulations are validated against real-world epidemiological data from national and international sources. The SEIR model demonstrated superior performance, achieving maximum relative errors of 4.81% and 5.60% in the Kharkiv and Dnipropetrovsk regions, respectively, outperforming the SIS and SIR models. Despite limited mobility and social contact data, the regionally adapted models achieved acceptable accuracy for medium-term forecasting. This validates the practical applicability of extended compartmental models in public health planning, particularly in settings with constrained data availability. The results further support the use of these models for estimating critical epidemiological indicators such as infection peaks and hospital resource demands. The proposed framework offers a scalable and computationally efficient tool for regional epidemic forecasting, with potential applications to future outbreaks in geographically heterogeneous environments. Full article

The widespread adoption of liquefied natural gas (LNG) as a marine fuel has driven the development of LNG bunkering operations in global ports. Major international hubs, such as Shenzhen Port, have implemented ship-to-ship (STS) bunkering practices. However, this process entails unique safety risks, particularly hazards associated with vapor cloud dispersion caused by bunkering hose releases. This study employs the Phast software developed by DNV to systematically simulate LNG release scenarios during STS operations, integrating real-world meteorological data and storage conditions. The dynamic effects of transfer flow rates, release heights, and release directions on vapor cloud dispersion are quantitatively analyzed under daytime and nighttime conditions. The results demonstrate that transfer flow rate significantly regulates dispersion range, with recommendations to limit the rate below 1500 m³/h and prioritize daytime operations to mitigate risks. Release heights exceeding 10 m significantly amplify dispersion effects, particularly at night (nighttime dispersion area at a height of 20 m is 3.5 times larger than during the daytime). Optimizing release direction effectively suppresses dispersion, with vertically downward releases exhibiting minimal impact. Horizontal releases require avoidance of downwind alignment, and daytime operations are prioritized to reduce lateral dispersion risks. Full article

Osteoarthritis (OA) remains a major contributor to pain and disability; however, the current management is largely reactive, focusing on symptoms rather than preventing irreversible cartilage loss. This review first examines the mechanistic foundations for pharmacological chondroprotection—illustrating how conventional agents, such as glucosamine sulfate and chondroitin sulfate, can potentially restore extracellular matrix (ECM) components, may attenuate catabolic enzyme activity, and might enhance joint lubrication—and explores the delivery challenges posed by avascular cartilage and synovial diffusion barriers. Subsequently, a practical “What–How–When” framework is introduced to guide community pharmacists in risk screening, DMOAD selection, chronotherapeutic dosing, safety monitoring, and lifestyle integration, as exemplified by the CHONDROMOVING infographic brochure designed for diverse health literacy levels. Building on these strategies, the P4–4P Chondroprotection Framework is proposed, integrating predictive risk profiling (physicians), preventive pharmacokinetic and chronotherapy optimization (pharmacists), personalized biomechanical interventions (physiotherapists), and participatory self-management (patients) into a unified, feedback-driven OA care model. To translate this framework into routine practice, I recommend the development of DMOAD-specific clinical guidelines, incorporation of chondroprotective chronotherapy and interprofessional collaboration into health-professional curricula, and establishment of multidisciplinary OA management pathways—supported by appropriate reimbursement structures, to support preventive, team-based management, and prioritization of large-scale randomized trials and real-world evidence studies to validate the long-term structural, functional, and quality of life benefits of synchronized DMOAD and exercise-timed interventions. This comprehensive, precision-driven paradigm aims to shift OA care from reactive palliation to true disease modification, preserving cartilage integrity and improving the quality of life for millions worldwide. Full article

An increasing global population, rising energy demands, and the shift toward a circular bioeconomy are driving the need for more resource-efficient waste management. The increase in the world population—now exceeding 8 billion as of 2024—results in an increased need for alternative proteins, both human and feed grade proteins, as well as for biopolymers and bioenergy. As such, agricultural, forest, and marine waste biomass represent a valuable feedstock for production of food and feed ingredients, biopolymers, and bioenergy. However, the lack of integrated and efficient valorization strategies for these diverse biomass sources remains a major challenge. This literature review aims to give a systematic approach on the recent research status of agricultural, forest, and marine waste biomass valorization, focusing on cascade processing (a sequential combination of processes such as pretreatment, extraction, and conversion methods). Potential products will be identified that create the most economic value over multiple lifetimes, to maximize resource efficiency. It highlights the challenges associated with cascade processing of waste biomass and proposes technological synergies for waste biomass valorization. Moreover, this review will provide a comprehensive understanding of the potential of waste biomass valorization in the context of sustainable and circular bioeconomy. Full article

The majority of the challenges faced in power system engineering are presented as constrained optimization functions, which are frequently characterized by their complicated architectures. Metaheuristics are mathematical techniques used to solve complicated optimization problems. One such technique, Artificial Rabbits Optimization (ARO), has been designed to address global optimization challenges. However, ARO has limitations in terms of search functionality, restricting its efficiency in dealing with constrained optimization environments. To improve ARO’s compatibility with a variety of challenging problems, this work proposes implementing the Cauchy mutation operator into the position-updating procedure during the exploration stage. Furthermore, a novel multi-mode control parameter is developed to facilitate a smooth transition between exploration and exploitation phases. The enhancements may boost the performance and serve as an effective optimization tool for tackling complex engineering tasks. The improved version is known as Cauchy Artificial Rabbits Optimization (CARO). The proposed CARO’s performance is evaluated using eleven power system challenges as part of the CEC2020 competition’s test set of real-world constrained problems. The experimental results demonstrate the practical applicability of the proposed CARO in engineering applications and provide areas for future investigation. Full article

Diabetic nephropathy affects approximately 30–40% of individuals with diabetes mellitus (DM) and is a major contributor to end-stage renal disease (ESRD). While angiotensin II receptor blockers (ARBs) have long served as a standard treatment, sodium-glucose cotransporter-2 inhibitors (SGLT2i) have recently gained attention for their renal and cardiovascular benefits. However, comparative real-world data on their long-term renal effectiveness remain limited. We conducted a retrospective, longitudinal study over a 2-year period to compare the impact of ARB monotherapy versus SGLT2i and angiotensin-converting enzyme inhibitor (ACEi) combination therapy on the progression of chronic kidney disease (CKD) in patients with DM. A total of 126 patients were included and grouped based on treatment regimen. Renal biomarkers were analyzed using t-tests and ANOVA (p < 0.01). Albuminuria was qualitatively classified via urinalysis as negative, level 1 (+1), level 2 (+2), or level 3 (+3). The ARB group demonstrated higher estimated glomerular filtration rate (eGFR) and lower serum creatinine (sCr) levels than the combination therapy group, with glycated hemoglobin (HbA1c), potassium (K⁺), and blood pressure remaining within normal limits in both cohorts. Albuminuria remained stable over time, with 60.8% of ARB users and 73.1% of combination therapy users exhibiting persistently or on-average negative results. Despite the expected additive benefits of SGLT2i/ACEi therapy, ARB monotherapy was associated with slightly more favorable renal function markers and a lower incidence of severe albuminuria. These findings suggest a need for further controlled studies to clarify the comparative long-term renal effects of these treatment regimens. Full article

Background: Faced with the profound impact of major infectious diseases on public life and economic development, humans have long sought to understand disease transmission and intervention strategies. To better explore the impact of individuals’ different coping behaviors—triggered by changes in their psychological security due to public information and external environmental changes—on the spread to infectious diseases, the model will place greater emphasis on quantifying psychological factors to make it more aligned with real-world situations. Methods: To better understand the interplay between information dissemination and disease transmission, we propose a two-layer network model that incorporates psychological safety factors. Results: Our model reveals key insights into disease transmission dynamics: (1) active defense behaviors help reduce both disease spread and information diffusion; (2) passive resistance behaviors expand disease transmission and may trigger recurrence but enhance information spread; (3) high-timeliness, low-fuzziness information reduces the peak of the initial infection but does not significantly curb overall disease spread, and the rapid dissemination of disease-related information is most effective in limiting the early stages of transmission; and (4) community structures in information networks can effectively curb the spread of infectious diseases. Conclusions: These findings offer valuable theoretical support for public health strategies and disease prevention after government information release. Full article