The development of efficient wave energy converters (WECs) is essential for harnessing marine renewable energy, particularly in regions with low wave energy flux. This study investigates a floating WEC with an internal eccentric rotor designed to enhance energy capture efficiency. The device consists of a floating body for wave energy absorption, an internal rotor for mechanical-to-hydraulic energy conversion, and a mooring system for stability. A numerical model was developed and validated against wave tank experiments, showing good agreement in peak values and amplitudes. Frequency-domain analysis examined the effects of structural parameters, draft, and center of gravity offset on hydrodynamic characteristics, while time-domain analysis evaluated the impact of rotor mass and power take-off (PTO) damping on energy capture. Multi-parameter optimization led to an improved structural design, increasing instantaneous power output by 150% and total power output by 108%. These findings provide a basis for further optimization of WECs in low-energy wave environments.
With global broiler production reaching 103 million tons in 2024—a 1.5% increase over 2023—the poultry industry continues to grow rapidly. However, traditional broiler segmentation methods struggle to meet modern demands for speed, precision, and adaptability. First, this study proposes an improved lightweight image segmentation algorithm based on YOLOv8-seg and integrates the Segment Anything Model (SAM) for semi-automatic annotation, achieving precise mask segmentation of broiler parts. Subsequently, Key geometric features (e.g., area, perimeter, axes) were extracted using image processing techniques, with enhancements from HSV color transformation, convex hull optimization, and ellipse fitting. Furthermore, Image calibration was applied to convert pixel data to physical dimensions, enabling real-sample validation. Using these features, multiple regression models—including CNNs—were developed for carcass quality prediction. Finally, by analyzing the broiler segmentation process, machine vision techniques were effectively integrated with quality grading algorithms and applied to intelligent broiler segmentation production lines, providing technical support for the intelligent and efficient processing of poultry products. The improved YOLOv8-seg model achieved mAP@0.5:box scores of 99.2% and 99.4%, and the CNN model achieved R2 values of 0.974 (training) and 0.953 (validation). Compared to traditional systems, the intelligent broiler cutting line reduced failure rates by 11.38% and improved operational efficiency by over 3%, offering a reliable solution for automated poultry processing.
Extreme flooding events are increasing in frequency and severity due to climate change, challenging the effectiveness of traditional, infrastructure-centric flood management strategies. A key gap remains in the lack of spatially explicit and process-based frameworks for assessing and enhancing flood resilience at the watershed scale, which hinders the development of integrated and adaptive management solutions. This study proposes a conceptual framework for evaluating watershed flood resilience (WFR) by integrating resilience theory with the “source-flow-sink” paradigm from landscape ecology. It applies it to the post-disaster reconstruction of the Sishui River Basin following the 2021 Zhengzhou flood in China. The framework quantifies WFR through pre-event resistance capacity and intra-event adaptive capacity using hydrological modeling and loss curves. It systematically analyzes the effects of targeted interventions across source, flow, and sink areas. The results demonstrate that the proposed approach significantly improves WFR in the Sishui River Basin, with source interventions generally outperforming flow and sink interventions in the simulated cases, and compensatory effects observed among different intervention types. The findings confirm the operational feasibility and effectiveness of the proposed framework, including nature-based solutions and spatial planning in watershed management, which could provide support for future holistic and adaptive flood resilience strategies addressing climate change.
As perceptions of happiness and well-being shift throughout life, expectations for the future may influence subjective well-being (SWB) differently depending on age. Younger individuals, particularly in uncertain social and economic contexts, may place greater emphasis on anticipated life satisfaction than on their present circumstances. Generational differences are important in exploring how people form and are affected by future expectations, as well as the psychological and contextual factors involved. Such research may deepen our understanding of age-specific pathways to well-being and inform more effective strategies for supporting mental health across different life stages. This paper emphasizes that expectations of future life satisfaction represent a valid and distinct construct that plays an important role in shaping SWB, particularly among younger individuals. Unlike present satisfaction, which reflects current circumstances, anticipated satisfaction functions as a forward-looking cognitive resource, offering accountability in developmental contexts where the current quality of life may not align with optimism or happiness.
Despite the ambitious national visions, Qatar is facing many challenges regarding the notion of sustainability. In this context, a considerable emphasis has been placed on the notion of Circular Economy (CE) to address suitability issues. Despite such an emphasis, the actual implementation of CE notions is still facing several obstacles present in, but not limited to, the Qatari context, such as heavy reliance on landfilling, water scarcity, and a heavy reliance on the oil and gas sectors. Our contention is that CE is an important factor in the sustainability equation and works towards meeting Qatar’s vision of becoming an environmentally sustainable country. by using a qualitative approach, predominantly adopting case study, document and content analysis, this paper explores the notion of CE and its implementation in light of the Qatar National Vision 2030. the challenges facing CE implementation, such as resources, qualified personnel, access to technology, and coordination between different areas of the economy, should be of prime importance for policymakers in Qatar. in order to ensure a sustainable circular city model in Qatar, the challenges related to CE implementation must be addressed accordingly. To this end, the paper suggests several policy recommendations, including the provision of adequate resources and personnel, the use of clean technology to improve the environmental quality of economic activities, in addition to the provision of adequate support and funding for the development of sustainable economic practices. These solutions will help to ensure sustainable economic development based on the concept of CE.
This article explores the environmental implications of electrification and artificial intelligence (AI) infrastructure, emphasizing the importance of aligning technological development with climate goals. There is a lack of academic literature that explains and analyses such issues. Section 1 assesses the climate efficacy of promoting electric vehicles (EVs) and electric heating in regions where electricity is primarily coal-based. While electrification offers substantial climate benefits when powered by clean energy, lifecycle analyses reveal that EVs in coal-reliant grids may emit more greenhouse gases than internal combustion engine vehicles. Similarly, the climate performance of electric heat pumps depends on the carbon intensity of electricity sources. The section advocates for integrated policies that simultaneously promote electrification and grid decarbonization, enhancing emissions reductions and public health while mitigating the negative impacts of increased demand on polluting power plants. Section 2 uses Saudi Arabia as a case study and examines the environmental impact of AI data centers in the context of Saudi Arabia’s energy and climate policies. It highlights AI infrastructure’s energy and water intensity and its potential to strain environmental resources. To align AI development with national sustainability goals, the article recommends policies such as siting data centers near renewable energy sources, enforcing environmental efficiency standards, fostering R&D partnerships, mandating sustainability reporting, and expanding power purchase agreements and demand response participation. These measures aim to ensure responsible AI growth within climate-aligned frameworks. The implications of this study are that electrification and AI infrastructure can significantly reduce emissions and improve efficiency if powered by clean energy, but they also risk increasing environmental strain unless technological growth is carefully aligned with climate and sustainability goals.
Xinjiang, a “genetic crossroads” of Eurasia, offers critical insights into transcontinental population migrations, cultural exchanges, and genetic fusion. This review synthesizes ancient genomic data from about 200 individuals (from the Bronze Age to the Historical Era), alongside archaeological, paleo microbial, and environmental evidence. Key findings include: (1) Bronze Age genetic heterogeneity: Tarim Basin populations (e.g., Xiaohe culture) retained high Ancient North Eurasian (ANE) ancestry, while northern Xinjiang groups exhibited tripartite admixture among ANE, Afanasievo pastoralists, and Baikal hunter-gatherers; (2) Stratified admixture patterns emerged during the Iron Age to Historical Era, shaped by Xinjiang’s “barrier-and-connection” geography traits; (3) Trans-Eurasian interactions were propelled by technological diffusion (e.g., metallurgy), socio-political transformations (e.g., Silk Road governance), and adaptive strategies (e.g., lactose fermentation), positioning Xinjiang as a nexus of Eurasian mutil-connectivity. Our synthesis bridges genetics, archaeology, and environmental clues, highlighting Xinjiang’s critical role in Eurasian population dynamics. Future research should employ high-resolution spatiotemporal sampling and interdisciplinary approaches to unravel genetic-societal coupling during vital historical phases (e.g., Xiongnu expansion) and molecular mechanisms of environmental adaptation.
Systemic sclerosis (SSc) is an autoimmune disease characterized by widespread fibrosis affecting multiple organ systems. There is clinical heterogeneity among patients with SSc in terms of the organs affected. However, the pathophysiology of the disease remains elusive. The NLRP3 inflammasome is upregulated in SSc and exerts its fibrotic effects through activation of caspase-1, which in turn activates a fibrotic signaling cascade, resulting in increased collagen deposition and myofibroblast transition. Recently, IL-11 has been shown to be elevated in disease and has been shown to participate in downstream signaling via the NLRP3 inflammasome. A significant number of patients with SSc will develop pulmonary involvement, termed interstitial lung disease (SSc-ILD). Though this type of pulmonary involvement is distinct from other types of pulmonary fibrosis (such as idiopathic pulmonary fibrosis), it may be a valuable model to study mechanisms of fibrosis that could apply to other fibrotic diseases. Here, we discuss recent advances in understanding the mechanisms of the NLRP3 inflammasome and IL-11 in SSc pulmonary fibroblasts. We tie together some of the recent findings, such as senescence, the unfolded protein response, and reactive oxygen species, that contribute to fibrotic pathology via modulating NLRP3 activation, possibly leading to IL-11 expression.
Offshore photovoltaic (PV) systems encounter challenges due to high humidity and salt spray environments. The PV connectors on the DC input side of inverters are particularly susceptible to increased contact resistance and local overheating caused by environmental corrosion. This paper introduces a novel thermal fault location method utilizing a multiple model estimator (MME). The approach develops a lumped thermal model and an abnormal overheating disturbance model for the PV connectors. By combining a Kalman filter with a probability fusion algorithm, the method effectively detects thermal faults. Simulation and experimental results demonstrate that this approach can accurately locate faults while requiring only a minimal number of thermal sensors, thereby enhancing the reliability of offshore PV systems.
Through the molecular structure design, first starting from the molecular structure of the monomer, the monomer of the synthetic structure continues to polymerize with propanesulfonolactone, and finally reacts with quaternary ammonium salts to obtain polyimide containing biswitterionic groups. In this study, a hydrophilic polyimide membrane with a quaternary ammonium salt structure was synthesized. Then, the sulfonate hydrophilic structure was introduced into the polyimide film by electrospinning and the stencil method. Hydrophilic groups were introduced by introducing propane sulfonate, and the PI membrane was prepared by electrospinning and the template method. The results show that introduced sulfonic acid groups reduce the contact angle of polyimide membrane from 85° to 30°. The water permeability, porosity and mechanical strength of the membrane were tested and analyzed, and the membrane showed excellent oil-water separation performance.