Search Results (594)

The interface between high-performance thermoelectric materials and electrodes critically governs the conversion efficiency and long-term reliability of thermoelectric generators under high-temperature operation. Here, we propose Al_xCoCrFeNi high-entropy alloys (HEA) as barrier layers to bond Cu-W electrodes with p-type skutterudite (p-SKD) thermoelectric materials. The HEA/p-SKD interface exhibited excellent chemical bonding with a stable and controllable reaction layer, forming a dense, defect-free (Fe,Ni,Co,Cr)Sb phase (thickness of ~2.5 μm) at the skutterudites side. The interfacial resistivity achieved a low value of 0.26 μΩ·cm² and remained at 7.15 μΩ·cm² after aging at 773 K for 16 days. Moreover, the interface demonstrated remarkable mechanical stability, with an initial shear strength of 88 MPa. After long-term aging for 16 days at 773 K, the shear strength retained 74 MPa (only 16% degradation), ranking among the highest reported for thermoelectric materials/metal joints. Remarkably, the joint maintained a shear strength of 29 MPa even after 100 continuous thermal cycles (623–773 K), highlighting its outstanding thermo-mechanical stability. These results validate the Al_xCoCrFeNi high-entropy alloys as an ideal interfacial material for thermoelectric generators, enabling simultaneous optimization of electrical and mechanical performance in harsh environments. Full article

Modern manufacturing strives for peak efficiency while facing pressing demands for environmental sustainability. Balancing these often-conflicting objectives represents a fundamental trade-off in modern manufacturing, as traditional methods typically address them in isolation, leading to suboptimal outcomes. Process mining offers operational insights but often lacks dynamic environmental indicators, while standard Life Cycle Assessment (LCA) provides environmental evaluation but uses static data unsuitable for real-time optimization. Frameworks integrating real-time data for dynamic multi-objective optimization are scarce. This study proposes a comprehensive, data-driven, cloud-based framework that overcomes these limitations. It uniquely combines three key components: (1) real-time Process Mining for actual workflows and operational KPIs; (2) dynamic LCA using live sensor data for instance-level environmental impacts (energy, emissions, waste) and (3) Multi-Objective Optimization (NSGA-II) to identify Pareto-optimal solutions balancing efficiency and sustainability. TOPSIS assists decision-making by ranking these solutions. Validated using extensive real-world data from a tube manufacturing facility processing over 390,000 events, the framework demonstrated significant, quantifiable improvements. The optimization yielded a Pareto front of solutions that surpassed baseline performance (87% efficiency; 2007.5 kg CO₂/day). The optimal balanced solution identified by TOPSIS simultaneously increased operational efficiency by 5.1% and reduced carbon emissions by 12.4%. Further analysis quantified the efficiency-sustainability trade-offs and confirmed the framework’s adaptability to varying strategic priorities through sensitivity analysis. This research offers a validated framework for industrial applications that enables manufacturers to improve both operational efficiency and environmental sustainability in a unified manner, moving beyond the limitations of disconnected tools. The validated integrated framework provides a powerful, data-driven tool, recommended as a valuable approach for industrial applications seeking continuous improvement in both economic and environmental performance dimensions. Full article

Objectives: With the development of artificial intelligence technology in medicine, an intelligent deep learning-based embryo scoring system (iDAScore) has been developed on full-time lapse sequences of embryos. It automatically ranks embryos according to the likelihood of achieving a fetal heartbeat with no manual input from embryologists. To ensure its performance, external validation studies should be performed at multiple clinics. Methods: A total of 6291 single vitrified–thawed blastocyst transfer cycles from 2018 to 2021 at the Reproductive Medicine Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology were retrospectively analyzed by the iDAScore model. Patients with two or more blastocysts transferred and blastocysts that were not cultured in a time-lapse incubator were excluded. Blastocysts were divided into four comparably sized groups by first sorting their iDAScore values in ascending order and then compared with the clinical, perinatal, and neonatal outcomes. Results: Our results showed that clinical pregnancy, miscarriage, and live birth significantly correlated with iDAScore (p < 0.001). For perinatal and neonatal outcomes, no significant difference was shown in four iDAScore groups, except sex ratio. Uni- and multivariable logistic regressions showed that iDAScore was significantly positively correlated with live birth rate (p < 0.05). Conclusions: In conclusion, the objective ranking can prioritize embryos reliably and rapidly for transfer, which could allow embryologists more time for processes requiring hands-on procedures. Full article

Background: The Fit-Model in bodybuilding is a relatively new category designed for women seeking a balanced physique, avoiding excessive muscularity and extreme leanness. This study examined the dietary strategies, body composition changes, and plasma leptin fluctuations of Fit-Model athletes during a seven-week pre-competition phase. Methods: Twelve females (age: 27.6 ± 4.4 years, body mass: 60.0 ± 6.2 kg) preparing for a national championship were monitored for energy and macronutrient intakes, total, lean, and fat mass, plasma leptin levels, and menstrual cycle characteristics. The five highest-ranked athletes were selected to compete at the world championship, allowing for comparisons between national and international athletes. Results: Low carbohydrate intake was reported, and total energy intake decreased from 1700 to 1520 kcal/day approaching the contest day. Athletes experienced an average body mass loss of 4.2 kg, with no clear relationship between final weight or fat mass and competitive success. Plasma leptin levels were markedly low during all 7 weeks of preparation with a further decline before the contest, but did not correlate with either changes in body composition and weight or energy or macronutrient intakes. Menstrual cycle disturbances were prevalent, with only two athletes maintaining regular cycles by the end of the preparation. Conclusions: Fit-Model athletes undergo a considerable decline in body weight and fat mass during the final weeks before the contest, yet these changes do not appear to be decisive for performance outcomes. Persistently low leptin levels and menstrual irregularities call for strategies that balance physique optimization with endocrine health to support both the performance and well-being of athletes. Full article

The UI GreenMetric World University Ranking has become a widely adopted instrument for benchmarking institutional sustainability performance; nevertheless, empirically grounded evidence from universities in diverse regional contexts remains scarce. This study undertakes a rigorous appraisal of the extent to which Mahasarakham University (MSU) has institutionalized the United Nations Sustainable Development Goals (SDGs) within its pedagogical offerings, research portfolio, community outreach, and governance arrangements during the 2021–2024 strategic cycle. Employing a mixed-methods design and guided by the 2024 UI GreenMetric Education and Research indicators, this investigation analyzed institutional datasets pertaining to curriculum provision, ring-fenced research funding, 574 peer-reviewed sustainability publications, student-led community initiatives, and supporting governance mechanisms; the analysis was interpreted through a Plan–Do–Check–Act management lens. The number of sustainability-oriented academic programs expanded from 49 to 58. Student participation in community service activities strongly recovered following the COVID-19 pandemic, and MSU’s GreenMetric score increased from 7575 to 8475, thereby elevating the institution to the 100th position globally. These gains were facilitated by strategic SDG-aligned investment, cross-sector collaboration, and the consolidation of international partnerships anchored in Thailand’s Isaan region. The MSU case provides a transferable model for universities—particularly those operating in resource-constrained contexts—endeavoring to align institutional development with the SDGs and internationally recognized quality benchmarks. The findings substantiate the capacity of transformative education and applied research to engender enduring societal and environmental benefits. Full article

Problem: Agriculture in India faces pressing challenges related to water scarcity, excessive pesticide use, and inefficient energy consumption, impacting both economic sustainability and environmental health. Methodology: This study integrates Life Cycle Assessment (LCA), Data Envelopment Analysis (DEA), Intelligent Management Models (IMMs), and Multi-Criteria Decision Analysis (MCDA) to assess the economic and environmental benefits of UAV-based spraying in Indian agriculture. Data were collected from UAV service providers and field trials in Punjab, Haryana, and Rajasthan. Results: UAV spraying achieved a 70% reduction in water use, 40% reduction in pesticide consumption, and a 50% reduction in CO₂ emissions compared to conventional spraying. DEA results showed higher efficiency scores for UAVs, while IMM optimization achieved 95% pesticide coverage and reduced drift by 80%. Implications: MCDA ranked government subsidies as the most effective policy intervention. These findings support UAV spraying as a viable, scalable solution for climate-smart agriculture in India, offering both productivity and sustainability gains. Full article

Background: Bladder cancer ranks ninth among the most commonly diagnosed cancers, with urothelial carcinoma (UC) accounting for more than 90% of all cases. Given the high recurrence rate and progression risk of bladder cancer, investigating alternative adjunct therapies is imperative. One potential candidate is isoliensinine, which has shown antitumor potential in various cancers; however, the effectiveness of isoliensinine on UC is largely unknown. Methods: In the present study, the effects of isoliensinine on UC cells were examined in a variety of in vitro experiments, including MTT assays, colony formation assays, flow cytometry assays, RNA sequencing analysis, and Western blotting. Results: The isoliensinine-treated T24 and UMUC3 UC cell lines showed cell growth inhibition and proliferation in the MTT and colony formation assays and an apoptotic effect in the flow cytometry assays. RNA sequencing analysis, performed to explain the underlying mechanisms, revealed a significant regulation of cell functions, including apoptosis, the cell cycle, hypoxia-inducible factor 1 (HIF-1) signaling, tumor necrosis factor (TNF) signaling, and ferroptosis. Subsequent Western blotting results verified all these findings. Conclusions: Overall, our data indicate that isoliensinine inhibits UC cell growth and proliferation by inducing apoptosis through alterations in the TNF and HIF1 pathways and ferroptosis. Overall, isoliensinine shows potential for use in new or combined adjunct therapies for the treatment of bladder cancer. Full article

Pinus radiata D. Don is planted in South Central Chile on a wide range of sites using genetically improved genotypes for timber production. As drought events are expected to increase with ongoing climatic change, the variability in gas exchange, which could impact growth and water use, needs to be evaluated. In this study, we assessed the genotypic variability of leaf-level light-saturated photosynthesis (A_sat), stomatal conductance (g_s), transpiration (E), intrinsic water use efficiency (iWUE), and Chlorophyll a fluorescence (OJIP-test parameters) among 30 P. radiata genotypes (i.e., full-sib families) from third-cycle parents at age 6 years on three sites in Central Chile. We also evaluated tree height (HT), diameter at breast height (DBH), and stem index volume (VOL). Families were ranked for HT as top-15 and bottom-15. In the OJIP-test parameters we observed differences at the family level for the maximum quantum yield of primary PSII photochemistry (Fv/Fm), the probability that a photon trapped by the PSII reaction center enters the electron transport chain (ψ_Eo), and the potential for energy conservation from photons captured by PSII to the reduction in intersystem electron acceptors (PI_ABS). Fv/Fm, PI_ABS, and ψ_Eo ranged from 0.82 to 0.87, 45 to 95, and 0.57 to 0.64, respectively. Differences among families for growth and not for leaf-level physiology were detected. DBT, H, and VOL were higher in the top-15 families (12.6 cm, 8.4 m, and 0.10 m³, respectively) whereas A_sat, g_s, E, and iWUE were similar in both the top-15 and bottom-15 families (4.0 μmol m⁻² s⁻¹, 0.023 mol m⁻² s⁻¹, 0.36 mmol m⁻² s⁻¹, and 185 μmol mol m⁻² s⁻¹, respectively). However, no family by site interaction was detected for growth and leaf-level physiology. The results of this study suggest that highly improved genotypes of P. radiata have uniformity in leaf-level physiological rates, which could imply uniform water use at the stand-level. The family variation found in PI_ABS suggests that this parameter could be incorporated to select genotypes tolerant to environmentally stressful conditions. Full article

This study presents CORE-ReID V2, an enhanced framework built upon CORE-ReID V1. The new framework extends its predecessor by addressing unsupervised domain adaptation (UDA) challenges in person ReID and vehicle ReID, with further applicability to object ReID. During pre-training, CycleGAN is employed to synthesize diverse data, bridging image characteristic gaps across different domains. In the fine-tuning, an advanced ensemble fusion mechanism, consisting of the Efficient Channel Attention Block (ECAB) and the Simplified Efficient Channel Attention Block (SECAB), enhances both local and global feature representations while reducing ambiguity in pseudo-labels for target samples. Experimental results on widely used UDA person ReID and vehicle ReID datasets demonstrate that the proposed framework outperforms state-of-the-art methods, achieving top performance in mean average precision (mAP) and Rank-k Accuracy (Top-1, Top-5, Top-10). Moreover, the framework supports lightweight backbones such as ResNet18 and ResNet34, ensuring both scalability and efficiency. Our work not only pushes the boundaries of UDA-based object ReID but also provides a solid foundation for further research and advancements in this domain. Full article

Cement production significantly contributes to global warming, resource depletion, environmental degradation, and environmental pollution. Identifying sustainable alternatives is critical but requires balancing multiple, often conflicting, factors. The objective of this study is to determine the most preferred cement alternative produced in South Africa using an integrated life cycle assessment (LCA) and multi-criteria decision-making (MCDM) framework. The LCA quantified the environmental impacts of seven different cement-type alternatives across 18 midpoint impact categories. The LCA results showed that slag cement-based CEM III/A achieved a 50% reduction in global warming potential (GWP) compared to traditional CEM I (0.57 vs. 0.99 kg CO₂ eq. This study also used the entropy-weighted, COPRAS and ARAS methodologies to evaluate and rank cement types based on their environmental impacts and weighted sustainability criteria and the results showed that fly ash-based CEM II/B-V demonstrated the highest overall sustainability, with utility scores of 100.00 (COPRAS) and 0.7257 (ARAS) in MCDM ranking. These results highlight that fly ash-based cement offers substantial environmental benefits over traditional CEM I, particularly in reducing greenhouse gas emissions and resource consumption. The integrated LCA–MCDM method enables robust prioritization by linking quantitative environmental impacts with objective ranking criteria. Although this analysis focuses on South African cement formulations, the methodology and findings are applicable to other regions with similar production profiles and SCM availability. The framework offers a practical tool for policymakers and industry to support environmentally responsible decision-making in cement production. Full article

Multi-criteria Decision Making (MCDM) presents a novel approach towards truly holistic green sustainability, particularly within the context of chemical process plants (CPPs). ASPEN Plus v12.0 was utilised for two representative CPP cases: isopropanol (IPA) production via isopropyl acetate, and green ammonia (NH₃) production. An integrated Fuzzy Analytic Hierarchy Process (FAHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was modelled in MATLAB v24.1 to prioritise the holistically green and sustainable pathways. Life cycle assessments (LCAs) were employed to select the pathways, and the most suitable sub-criteria per the four criteria are as follows: social, economic, environmental, and technical. In descending order of optimality, the pathways were ranked as follows for green NH₃ and IPA, respectively: Hydropower (HPEA) > Wind Turbine (WGEA) > Biomass Gasification (BGEA)/Solar Photovoltaic (PVEA) > Nuclear High Temperature (NTEA), and Propylene Indirect Hydration (IAH) > Direct Propylene Hydration (PH) > Acetone Hydrogenation (AH). Sensitivity analysis evaluated the FAHP–TOPSIS framework to be overall robust. However, there are potential uncertainties within and/or among sub-criteria, particularly in the social dimension, due to software and data limitations. Future research would seek to integrate FAHP with VIKOR and the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II). Full article

Plain concrete specimens and FRP(Fiber Reinforced Polymer)-reinforced concrete specimens were fabricated to investigate concrete’s mechanical and surface degradation behaviors reinforced with carbon, basalt, glass, and aramid fiber-reinforced polymer under coupled sulfuric acid and freeze–thaw cycles. The compressive strength of fully wrapped FRP cylindrical specimens and the flexural load capacity of prismatic specimens with FRP reinforced to the pre-cracked surface, along with the dynamic elastic modulus and mass loss, were evaluated before and after acid–freeze cycles. The degradation mechanism of the specimens was elucidated through analysis of surface morphological changes captured in photographs, scanning electron microscopy (SEM) observations, and energy-dispersive spectroscopy (EDS) data. The experimental results revealed that after 50 cycles of coupled acid–freeze erosion, the plain cylindrical concrete specimens showed a mass gain of 0.01 kg. In contrast, after 100 cycles, a significant mass loss of 0.082 kg was recorded. The FRP-reinforced specimens initially demonstrated mass loss trends comparable to those of the plain concrete specimens. However, in the later stages, the FRP confinement effectively mitigated the surface spalling of the concrete, leading to a reversal in mass loss and subsequent mass gain. Notably, the GFRP(Glassfiber Reinforced Polymer)-reinforced specimens exhibited the most significant mass gain of 1.653%. During the initial 50 cycles of acid–freeze erosion, the prismatic and cylindrical specimens demonstrated comparable degradation patterns. However, in the subsequent stages, FRP reduced the exposed surface area-to-volume ratio of the specimens in contact with the acid solution, resulting in a marked improvement in their structural integrity. After 100 cycles of acid–freeze erosion, the compressive strength loss rate and flexural load capacity loss rate followed the ascending order: CFRP-reinforced < BFRP(Basalt Fiber Reinforced Polymer)-reinforced < AFRP(Aramid Fiber Reinforced Polymer)-reinforced < GFRP-reinforced < plain specimens. Conversely, the ductility ranking from highest to lowest was AFRP/GFRP > control group > BFRP/CFRP. A probabilistic analysis model was established to complement the experimental findings, encompassing the quantification of hazard levels and reliability indices. Full article

Background: Synthetic and pseudosynthetic images can be used to extend colonoscopy datasets, which, in turn, are used to train AI-detection models, yet their clinical acceptability depends on whether medical professionals can still recognize non-real content. Aim: To quantify the ability of practicing gastroenterologists to discriminate real, pseudosynthetic, and synthetic polyp images and to determine how training level and synthesis method impact detection. Materials and Methods: A total of 32 Romanian gastroenterologists (18 residents and 14 seniors) reviewed 24 images (8 real, 8 augmented, 4 CycleGAN, and 4 diffusion) via an online form. Classification accuracy, 95% confidence intervals (CI), class sensitivity and precision, 3 × 3 confusion matrices, and Fleiss’ κ were calculated. Resident vs. senior differences were tested with Pearson χ²; CycleGAN versus diffusion detectability was analyzed with the Wilcoxon signed-rank test (α = 0.05). Results: Overall accuracy was 61.2% (95% CI 57.7–64.6). Residents and seniors performed similarly (62.3% vs. 59.8%; χ²₁ = 0.38, p = 0.54). Sensitivity/precision were 70.7%/62.2% for real, 51.6%/58.9% for augmented, and 61.3%/62.1% for synthetic images. Collapsing to “real vs. non-real” yielded 70.7% sensitivity and 78.5% specificity for real images. CycleGAN images were always recognized as synthetic (128/128; 97.1–100% CI), whereas diffusion images were correctly classified only 22.7% of the time (16.3–30.6%; Wilcoxon p < 0.001). The training level did not impact detection performance (χ²₂ < 1.2, p > 0.5). Inter-rater agreement was fair (κ = 0.30, 95% CI 0.15–0.43). Conclusions: Clinicians detect non-real colonoscopy images only slightly above chance, irrespective of experience. The diffusion synthesis method creates images that escape human scrutiny, suggesting the need for automated authenticity safeguards before synthetic datasets are applied in clinical or AI-validation contexts. Full article

Circadian rhythms (CRs) are a key biological system regulating physiological processes such as metabolism, cell growth, DNA repair, and immunity, adapting to environmental changes like the light/dark cycle. Governed by internal clocks, it modulates gene expression through feedback loops involving Clock Genes (CGs), with the cycle initiated by CLOCK–BMAL1 and NPAS2–BMAL1 heterodimers. Disruptions in circadian rhythms have been linked to diseases including metabolic disorders, neurodegeneration, and cancer. CIPC (CLOCK-interacting pacemaker) has been studied as a negative regulator of the CLOCK–BMAL1 complex, focusing on its role in cancer, particularly leukemias. Public datasets and bioinformatics tools were used to examine CIPC gene expression in healthy patients and acute myeloid leukemia (AML) samples. Our analysis revealed significant overexpression of CIPC in AML compared to healthy tissues (p < 0.0001 ****). Additionally, survival analysis indicated significant differences in overall survival based on CIPC expression, with a log-rank test p-value = 0.014, suggesting that CIPC expression may affect overall patient survival. Altered CIPC expression may contribute to leukemogenesis by inhibiting circadian genes, which are often disrupted in leukemia. Furthermore, CIPC interacts with oncogenic pathways, including the MAPK/ERK pathway, which is essential for cell proliferation. Additional studies are needed to validate these findings and explore the detailed role of CIPC in cancer development. Full article

The construction industry urgently requires sustainable alternatives to conventional concrete to reduce its environmental impact. This study addresses this challenge by developing machine learning-optimized geopolymer concrete (GPC) using industrial waste fly ash as cement replacement. An integrated Taguchi–Grey relational analysis (GRA) and artificial neural network (ANN) approach was developed to simultaneously optimize mechanical properties and environmental performance. The methodology analyzes over 1000 data points from 83 studies to identify key mix parameters including fly ash content, NaOH/Na₂SiO₃ ratio, and curing conditions. Results indicate that the optimized FA-GPC formulation achieves a 78% reduction in CO₂ emissions, decreasing from 252.09 kg/m³ (GRC rank 1) to 55.0 kg/m³, while maintaining a compressive strength of 90.9 MPa. The ANN model demonstrates strong predictive capability, with R² > 0.95 for strength and environmental impact. Life cycle assessment reveals potential savings of 3941 tons of CO₂ over 20 years for projects using 1000 m³ annually. This research provides a data-driven framework for sustainable concrete design, offering practical mix design guidelines and demonstrating the viability of fly ash-based GPC as high-performance, low-carbon construction material. Full article