Search Results (10,779)

Against the backdrop of an urgent energy crisis, solar energy has attracted sufficient attention as one of the most inexhaustible and friendly types of environmental energy. Faced with long service and harsh environment, the poor performance ratios of photovoltaic arrays and safety hazards are frequently boosted worldwide. In particular, the hot spot effect plays a vital role in weakening the power generation performance and reduces the lifetime of photovoltaic (PV) modules. Here, our research reports a spatial–temporal hot spot management system integrated with fiber Bragg grating (FBG) temperature sensor arrays and cooling hydrogels. Through finite element simulations and indoor experiments in laboratory conditions, a superior cooling effect of hydrogels and photoelectric conversion efficiency improvement have been demonstrated. On this basis, field tests were carried out in which the FBG arrays detected the surface temperature of the PV module first, and then a classifier based on an optimized artificial neural network (ANN) recognized hot spots with an accuracy of 99.1%. The implementation of cooling hydrogels as a feedback mechanism achieved a 7.7 °C reduction in temperature, resulting in a 5.6% enhancement in power generation efficiency. The proposed strategy offers valuable insights for conducting predictive maintenance of PV power plants in the case of hot spots. Full article

Dam monitoring tracks environmental variables (water level, temperature) and structural responses (deformation, seepage, and stress) to assess safety and performance. Structural health monitoring (SHM) refers to the systematic observation and analysis of the structural condition over time, and it is essential in maintaining the safety, functionality, and long-term performance of dams. This review examines monitoring data applications, covering structural health assessment methods, historical motivations, and key challenges. It discusses monitoring components, data acquisition processes, and sensor roles, stressing the need to integrate environmental, operational, and structural data for decision making. Key objectives include risk management, operational efficiency, safety evaluation, environmental impact assessment, and maintenance planning. Methodologies such as numerical modeling, statistical analysis, and machine learning are critically analyzed, highlighting their strengths and limitations and the demand for advanced predictive techniques. This paper also explores future trends in dam monitoring, offering insights for engineers and researchers to enhance infrastructure resilience. By synthesizing current practices and emerging innovations, this review aims to guide improvements in dam safety protocols, ensuring reliable and sustainable dam operations. The findings provide a foundation for the advancement of monitoring technologies and optimization of dam management strategies worldwide. Full article

Obesity, type 2 diabetes mellitus (T2DM) and steatohepatitis associated with metabolic dysfunction (MASLD) are on the rise and pose serious health challenges worldwide. In recent years, researchers have gained a better understanding of the important role of the gut microbiota in the development and progression of these diseases. Intestinal dysbiosis can contribute to the occurrence of increased intestinal permeability, inflammation and reduced numbers of commensal bacteria. In obesity, these changes contribute to chronic low-grade inflammation and deregulated metabolism. In MASLD, gut microbiota dysbiosis can promote liver fibrosis and impair bile acid metabolism, while in T2DM, they are associated with impaired glycemic control and insulin resistance. Regular physical activity has a positive effect on the composition of the gut microbiota, increasing its diversity, modulating its metabolic functions, strengthening the intestinal barrier and reducing inflammation. These findings suggest that exercise and microbiota-targeted interventions may play an important role in the prevention and treatment of metabolic diseases. Full article

Sexually transmitted infections (STIs) are a significant global health concern, affecting millions of people worldwide, particularly sexually active adolescents and young adults. These infections, caused by various pathogens, including bacteria, viruses, parasites, and fungi, can have profound implications for women’s reproductive health and fertility. This review explores the role of vaginal and uterine infections in women’s infertility, focusing on the most common pathogens and their impact on reproductive outcomes. Bacterial infections, such as those caused by intracellular bacteria (Mycoplasma, Ureaplasma, and Chlamydia), Neisseria gonorrhoeae, and bacterial vaginosis, are among the most prevalent causes of infertility in women. Studies have shown that these infections can lead to pelvic inflammatory disease, tubal occlusion, and endometrial damage, all of which can impair fertility. Mycobacterium tuberculosis, in particular, is a significant cause of genital tuberculosis and infertility in high-incidence countries. Viral infections, such as Human papillomavirus (HPV) and Herpes simplex virus (HSV), can also affect women’s fertility. While the exact role of HPV in female infertility remains unclear, studies suggest that it may increase the risk of endometrial implantation issues and miscarriage. HSV may be associated with unexplained infertility. Parasitic infections, such as trichomoniasis and schistosomiasis, can directly impact the female reproductive system, leading to infertility, ectopic pregnancy, and other complications. Fungal infections, such as candidiasis, are common but rarely have serious outcomes related to fertility. The vaginal microbiome plays a crucial role in maintaining reproductive health, and alterations in the microbial balance can increase susceptibility to STIs and infertility. Probiotics have been proposed as a potential therapeutic strategy to restore the vaginal ecosystem and improve fertility outcomes, although further research is needed to establish their efficacy. In conclusion, vaginal and uterine infections contribute significantly to women’s infertility, with various pathogens affecting the reproductive system through different mechanisms. Early diagnosis, appropriate treatment, and preventive measures are essential to mitigate the impact of these infections on women’s reproductive health and fertility. Full article

Widespread antibiotic residues are accumulating in the environment, potentially causing adverse effects for humans, animals, and the ecosystem, including an increase in antibiotic-resistant bacteria, resulting in worldwide concern. There are various commonly used physical, chemical, and biological treatments for the degradation of antibiotics. However, the elimination of toxic end products generated by physicochemical methods and the need for industrial applications pose significant challenges. Hence, environmentally sustainable, green, and readily available approaches for the transformation and degradation of these antibiotic compounds are being sought. Herein, we review the impact of sustainable fungal laccase-based bioremediation strategies. Fungal laccase enzyme is considered one of the most active enzymes for biotransformation and biodegradation of antibiotic residue in vitro. For industrial applications, the low laccase yields in natural and genetically modified hosts may constitute a bottleneck. Methods to screen for high-laccase-producing sources, optimizing cultivation conditions, and identifying key genes and metabolites involved in extracellular laccase activity are reviewed. These include advanced transcriptomics, proteomics, and metagenomics technologies, as well as diverse laccase-immobilization technologies with different inert carrier/support materials improving enzyme performance whilst shifting from experimental assays to in situ monitoring of residual toxicity. Still, more basic and applied research on laccase-mediated bioremediation of pharmaceuticals, especially antibiotics that are recalcitrant and prevalent, is needed. Full article

Prostate cancer (PC) remains a leading cause of malignancy in men worldwide, with current diagnostic methods such as prostate-specific antigen (PSA) testing and tissue biopsies facing limitations in specificity, invasiveness, and ability to capture tumor heterogeneity. Liquid biopsy, especially analysis of circulating tumor DNA (ctDNA), has emerged as a transformative tool for non-invasive detection, real-time monitoring, and treatment selection for PC. This review examines the role of ctDNA in both localized and metastatic PCs, focusing on its utility in early detection, risk stratification, therapy selection, and post-treatment monitoring. In localized PC, ctDNA-based biomarkers, including ctDNA fraction, methylation patterns, fragmentation profiles, and mutations, demonstrate promise in improving diagnostic accuracy and predicting disease recurrence. For metastatic PC, ctDNA analysis provides insights into tumor burden, genomic alterations, and resistance mechanisms, enabling immediate assessment of treatment response and guiding therapeutic decisions. Despite challenges such as the low ctDNA abundance in early-stage disease and the need for standardized protocols, advances in sequencing technologies and multimodal approaches enhance the clinical applicability of ctDNA. Integrating ctDNA with imaging and traditional biomarkers offers a pathway to precision oncology, ultimately improving outcomes. This review underscores the potential of ctDNA to redefine PC management while addressing current limitations and future directions for research and clinical implementation. Full article

Rabies remains a significant global public health concern, causing an estimated 59,000–69,000 human fatalities annually. Despite being entirely preventable through vaccination, rabies continues to impose substantial economic burdens worldwide. This study presents a scoping review of the economic research on rabies to determine overlaps and gaps in knowledge and inform future research strategies. We selected 150 studies (1973–2024) to analyze. The review categorizes the literature based on geographic distribution, species focus, and type of study. Findings indicate that economic studies are disproportionately concentrated in developed countries, such as the United States and parts of Europe, where rabies risk is low, while high-risk regions, particularly in Africa and Asia, remain underrepresented. Most studies focus on dog-mediated rabies, reflecting its dominant role in human transmission, while fewer studies assess the economic impacts of wildlife and livestock-mediated rabies. Case studies and modeling approaches dominate the literature, whereas cost–benefit and cost–effectiveness analyses—critical for informing resource allocation—are limited. The review highlights the need for more economic evaluations in rabies-endemic regions, expanded research on non-dog reservoirs, and broader use of economic methods. Addressing these gaps will be crucial for optimizing rabies control and supporting global initiatives to eliminate dog-mediated rabies by 2030. Full article

Understanding how energy use and economic activity shape carbon emissions is pivotal for achieving global climate targets. This study quantifies the dynamic nexus between disaggregated energy consumption, economic growth, and CO₂ emissions in India and China—two economies that together account for more than one-third of global emissions. Using annual data from 1990 to 2021, we implement Long Short-Term Memory (LSTM) neural networks, which outperform traditional linear models in capturing nonlinearities and lagged effects. The dataset is split into training (1990–2013) and testing (2014–2021) intervals to ensure rigorous out-of-sample validation. Results reveal stark national differences. For India, coal, natural gas consumption, and economic growth are the strongest positive drivers of emissions, whereas renewable energy exerts a significant mitigating effect, and nuclear energy is negligible. In China, emissions are dominated by coal and petroleum use and by economic growth, while renewable and nuclear sources show weak, inconsistent impacts. We recommend retrofitting India’s coal- and gas-plants with carbon capture and storage, doubling clean-tech subsidies, and tripling annual solar-plus-storage auctions to displace fossil baseload. For China, priorities include ultra-supercritical upgrades with carbon capture, utilisation, and storage, green-bond-financed solar–wind buildouts, grid-scale storage deployments, and hydrogen-electric freight corridors. These data-driven pathways simultaneously cut flagship emitters, decouple GDP from carbon, provide replicable models for global net-zero research, and advance climate-resilient economic growth worldwide. Full article

Lung cancer remains one of the most prevalent and lethal malignancies worldwide. Although conventional treatments such as surgery, chemotherapy, and radiotherapy have modestly improved patient survival, their overall efficacy remains limited, and the prognosis is generally poor. In recent years, immunotherapy, particularly immune checkpoint inhibitors, has revolutionized cancer treatment. Nevertheless, the immunosuppressive tumor microenvironment, tumor heterogeneity, and immune escape mechanisms significantly restrict the clinical benefit, which falls short of expectations. Within this context, cancer vaccines have emerged as a promising immunotherapeutic strategy. By activating the host immune system to eliminate tumor cells, cancer vaccines offer high specificity, low toxicity, and the potential to induce long-lasting immune memory. These advantages have positioned them as a focal point in cancer immunotherapy research. This paper provides a comprehensive overview of recent clinical advances in lung cancer vaccines, discusses the major challenges impeding their clinical application, and explores potential strategies to overcome these barriers. Full article

The transportation sector, as a major source of carbon emissions, plays a crucial role in the realization of dual carbon goals worldwide. In this study, an improved least absolute shrinkage and selection operator (LASSO) is used to identify six key factors affecting transportation carbon emissions (TCEs) in China. Aiming at the spatio-temporal characteristics of transportation carbon emissions, a CNN-BiLSTM neural network model is constructed for the first time for prediction, and an improved whale optimization algorithm (EWOA) is introduced for hyperparameter optimization, finding that the prediction model combining spatio-temporal characteristics has a more significant prediction accuracy, and scenario forecasting was carried out using the prediction model. Research indicates that over the past three decades, TCEs have demonstrated a rapid growth trend. Under the baseline, green, low-carbon, and high-carbon scenarios, peak carbon emissions are expected in 2035, 2031, 2030, and 2040. The adoption of a low-carbon scenario represents the most advantageous pathway for the sustainable progression of China’s transportation sector. Consequently, it is imperative for China to accelerate the formulation and implementation of low-carbon policies, promote the application of clean energy and facilitate the green transformation of the transportation sector. These efforts will contribute to the early realization of dual-carbon goals with a positive impact on global sustainable development. Full article

Metagenomic studies have provided deeper insights into the complex interactions between the skin and its microbiota. However, limited research has been conducted on the skin microbiota of Brazilian women. Given that Brazil ranks as the fourth-largest consumer of cosmetics worldwide, the development of new tools to analyze skin microbiota is crucial for formulating cosmetic products that promote a healthy microbiome. Skin samples were analyzed using the Illumina platform. Biometrology assessments were applied. The results showed pH variations were more pronounced in the older age group, along with higher transepidermal water loss values. Metagenomic analysis showed a predominance of Actinobacteria (83%), followed by Proteobacteria (7%), Firmicutes (9%) and Bacteroidetes (1%). In the older group (36–45 years old), an increase in Actinobacteria (87%) was observed and a decrease in Proteobacteria (6%). Moreover, the results differ from the international literature, since an increase in proteobacteria (13.9%) and a decrease in actinobacteria (46.7%) were observe in aged skin. The most abundant genus identified was Propionibacterium (84%), being the dominant species. Interestingly, previous studies have suggested a decline in Cutibacterium abundance with aging; although there is no significant difference, it is possible to observe an increasing trend in this genus in older skin. These studies can clarify many points about the skin microbiota of Brazilian women, and these findings could lead to the development of new cosmetics based on knowledge of the skin microbiome. Full article

Diabetes mellites (DM) is a chronic disease with increasing prevalence worldwide and multiple health implications. Among them, sarcopenia is a metabolic disorder characterized by loss of muscle mass and function. The two age-related diseases, DM and sarcopenia, share underlying pathophysiological pathways. This narrative literature review aims to provide an overview of the existing evidence on metabolomic studies evaluating DM associated with sarcopenia. Advancements in targeted and untargeted metabolomics techniques could provide better insight into the pathogenesis of sarcopenia in DM and describe their entangled and fluctuating interrelationship. Recent evidence showed that sarcopenia in DM induced significant changes in protein, lipid, carbohydrate, and in energy metabolisms in humans, animal models of DM, and cell cultures. Newer metabolites were reported, known metabolites were also found significantly modified, while few amino acids and lipids displayed a dual behavior. In addition, several therapeutic approaches proved to be promising interventions for slowing the progression of sarcopenia in DM, including physical activity, newer antihyperglycemic classes, D-pinitol, and genetic USP21 ablation, although none of them were yet validated for clinical use. Conversely, ceramides had a negative impact. Further research is needed to confirm the utility of these findings and to provide potential metabolomic biomarkers that might be relevant for the pathogenesis and treatment of sarcopenia in DM. Full article

Skin cancer is the most common cancer worldwide, for which early detection is crucial to improve survival rates. Visual inspection and biopsies have limitations, including being error-prone, costly, and time-consuming. Although several deep learning models have been developed, they demonstrate significant limitations. An interpretable and improved transfer learning model for binary skin cancer classification is proposed in this research, which uses the last convolutional block of VGG16 as the feature extractor. The methodology focuses on addressing the existing limitations in skin cancer classification, to support dermatologists and potentially saving lives through advanced, reliable, and accessible AI-driven diagnostic tools. Explainable AI is incorporated for the visualization and explanation of classifications. Multiple optimization techniques are applied to avoid overfitting, ensure stable training, and enhance the classification accuracy of dermoscopic images into benign and malignant classes. The proposed model shows 90.91% classification accuracy, which is better than state-of-the-art models and established approaches in skin cancer classification. An interactive desktop application integrating the model is developed, enabling real-time preliminary screening with offline access. Full article

This research examines the compounding risks of global environmental change, including climate change, environmental law, biodiversity loss, and pollution, which threaten the stability of economic systems worldwide. While digital technology and global governance regulation are increasingly being proposed as solutions, their synergistic potential in advancing economic sustainability has been less explored. How can these technologies mitigate environmental risks while promoting sustainable and equitable development, aligning with the Sustainable Development Goals? We analyze policy global environmental data from the World Bank and the United Nations, as well as literature reviews on digital interventions, artificial intelligence, and smart databases. Global environmental change presents economic stability and rule of law threats, and innovative governance responses are needed. This study evaluates the potential for digital technology to be leveraged to enhance climate resilience and regulatory systems and address key implementation, equity, and policy coherence deficits. Policy recommendations for aligning economic development trajectories with planetary boundaries emphasize that proactive digital governance integration is indispensable for decoupling growth from environmental degradation. However, fragmented governance and unequal access to technologies undermine scalability. Successful experiences demonstrate that integrated policies, combining incentives, data transparency, and multilateral coordination, deliver maximum economic and environmental co-benefits, matching digital innovation with good governance. We provide policymakers with an action plan to leverage technology as a multiplier of sustainability, prioritizing inclusive governance structures to address implementation gaps and inform legislation. Full article

Based on a comprehensive review of the current research status of ecological compensation both domestically and internationally, combined with field survey data, this study delves into the issue of multi-stakeholder participation in the ecological compensation mechanisms of the Xin’an River Basin. This research reveals that the joint participation of multiple stakeholders is crucial to achieving the goals of ecological compensation in river basins. The government plays a significant role in macro-guidance, financial support, policy guarantees, supervision, and management. It promotes the comprehensive implementation of ecological environmental protection by formulating relevant laws and regulations, guiding the public to participate in ecological conservation, and supervising and punishing pollution behaviors. The public, serving as the main force, forms strong awareness and behavioral habits of ecological protection through active participation in environmental protection, monitoring, and feedback. As participants, enterprises contribute to industrial transformation and green development by improving resource utilization efficiency, reducing pollution emissions, promoting green industries, and participating in ecological restoration projects. Scientific research institutions, as technology enablers, have effectively enhanced governance efficiency through technological research and innovation, ecosystem value accounting to provide decision-making support, and public education. Social organizations, as facilitators, have injected vitality and innovation into watershed governance by extensively mobilizing social forces and building multi-party collaboration platforms. Communities, as supporters, have transformed ecological value into economic benefits by developing characteristic industries such as eco-agriculture and eco-tourism. Based on the above findings, further recommendations are proposed to mobilize the enthusiasm of upstream communities and encourage their participation in ecological compensation, promote the market-oriented operation of ecological compensation mechanisms, strengthen cross-regional cooperation to establish joint mechanisms, enhance supervision and evaluation, and establish a sound benefit-sharing mechanism. These recommendations provide theoretical support and practical references for ecological compensation worldwide. Full article