Search Results (5,617)

Although considered relatively sustainable, mushroom production generates significant waste at the end of cultivation. This study investigated the reuse of Spent Mushroom Substrate (SMS) to formulate new substrates for Pleurotus ostreatus cultivation. Substrates with high (higher bran content) and low (lower bran content) nitrogen levels were prepared and supplemented with 5%, 10%, or 20% SMS across three successive cycles P. ostreatus crops. Cultivation performance was evaluated based on biological efficiency, number of mushrooms, fresh weight, and number of clusters. Substrates were chemically characterized for total nitrogen, carbon, C/N ratio, electrical conductivity, and pH. The inclusion of SMS, along with reduced bran content, did not improve P. ostreatus yield and led to lower productivity compared to control substrates. No consistent correlations were observed between chemical variables and yield, although high-N substrates generally performed better. SMS reuse, under these conditions, is not viable, but results encourage further research. Full article

Background: Proximal hamstring tendinopathy (PHT) is a challenging overuse injury, particularly in athletes, characterized by deep buttock pain localized to the ischial tuberosity and often exacerbated by sports activities. This condition can impact an athlete’s performance, limiting high-level athletic activity. Return to sport (RTS) thus becomes a medical, physical, athletic, and economic necessity. Previous research has explored several conservative and injection-based therapies, but evidence regarding the efficacy of porcine collagen injections remains limited. Therefore, this study aims to compare the results obtained from ultrasound-guided porcine collagen injections versus a structured rehabilitation program in reducing time to return to sport (RTS) and improving Victorian Institute of Sport Assessment—Hamstring (VISA-H) scores with respect to athletes with clinically diagnosed PHT. Conservative approaches for PHT treatments include various options, such as physiotherapy, corticosteroids, plasma-rich-platelet, shockwave therapy, and collagen injection. Collagen demonstrated to be a validated option for tendinopathies treatment due its regenerative and restorative mechanism of action. Methods: Retrospective data were collected from twenty-eight athletes with a clinical diagnosis of PHT, confirmed based on pain provocation tests (Puranen–Orava, bent-knee, and modified bent-knee tests), who were divided into two groups: COL and REHAB. The VISA-H outcomes were recorded for all subjects. The COL group received three ultrasound-guided collagen injections at weekly intervals, plus standard care instructions. The REHAB group completed a progressive exercise program targeting hamstring and lumbopelvic stabilization. The primary outcomes were RTS time (days) and VISA-H scores at baseline and 8 weeks. Adverse effects were recorded. Results: The two groups of treatment were very homogeneous and showed parametric distribution concerning the biological and pathophysiological conditions. No adverse events were reported. The mean times to RTS were 57 and 72 days for COL and REHAB, respectively (p = 0.0083). The VISA-H results revealed better improvement for the COL group than the REHAB treatment (p < 0.0001), and the log-rank test showed a higher odds ratio (HR) for RTS, 5.35 (p = 0.0008), for the COL athletes. Conclusions: Ultrasound-guided porcine collagen injections, combined with standard care, significantly reduced RTS time and improved VISA-H scores compared with rehabilitation alone in athletes with PHT. However, a larger cohort of athletes might be needed to gather more information about this conservative treatment in PHT pathology. Full article

Power modules in silicon carbide (SiC)-based modular multilevel converters (MMCs) suffer from notably severe thermal imbalance and localized overheating. This paper puts forward an asymmetric SiC power module with direct integration of a vapor chamber (VC), designed to balance the thermal distribution inside MMC SMs. Specifically, the chips on the lower side of the HBSM are soldered onto a VC, which is additionally mounted on the direct bonding copper (DBC). Endowed with merits such as favorable temperature uniformity, exceptional thermal conductivity, compact size, flexible design, high integration level, and reasonable cost, the VC serves as an outstanding heat diffuser significantly expanding the effective thermal conduction area and reducing thermal resistance. Moreover, in this structure, the VC also functions as a conductor for device current. Finite element method (FEM) simulation results reveal that the proposed structure can notably reduce the hotspot temperature (from 109 $°\mathrm{C}$ to 71.8 $°\mathrm{C}$), the maximum temperature difference among chips (from 45 $°\mathrm{C}$ to 13.89 $°\mathrm{C}$), and the low-frequency temperature swing (TSL) (from 68 $°\mathrm{C}$ to 38 $°\mathrm{C}$). Consequently, the issues of localized overheating and thermal imbalance in SiC-MMC SMs are effectively addressed. Lifetime analysis further indicates that the proposed structure can reduce the annual damage rate of the chip solder layer by 92.6%. Full article

This study evaluated serum neurofilament light chain (NfL) as a biomarker for spinal cord diseases in dogs, including 46 healthy dogs and 76 with conditions, such as intervertebral disc herniation (IVDH), syringomyelia (SM), fibrocartilaginous embolism (FCE), and acute non-compressive nucleus pulposus extrusion (ANNPE). There was a significant difference in serum NfL levels between healthy dogs (12.55 pg/mL) and those with spinal cord diseases (91.10 pg/mL; p < 0.0001). The NfL level in dogs with SM (50.7 pg/mL) was significantly lower than that in dogs with IVDH (99.3 pg/mL; p = 0.012) and those with other diseases, including FCE and ANNPE (241.0 pg/mL; p = 0.002). The area under the curve for differentiating between dogs with spinal cord diseases and healthy dogs was 0.91, with an optimal NfL cutoff value of 30.31 pg/mL (sensitivity of 80.68%; specificity of 91.30%). For dogs with IVDH treated solely with medication, the serum NfL levels in the Poor and Static group (180.0 pg/mL) were significantly higher than those in the Partial and Good group (81.30 pg/mL) (p = 0.03). Serum NfL is a promising biomarker for neuroaxonal injury, aiding in differentiating SM from other spinal cord diseases and evaluating treatment response. Full article

Gliomas are aggressive brain tumours with diverse histological and molecular features, complicating accurate diagnosis and treatment. Dysregulated lipid metabolism contributes to glioma progression, and analysing lipid profiles in plasma and tissue may enhance diagnostic and prognostic accuracy. This study investigated lipid dysregulation to identify key lipid signatures that distinguish glioma from other brain diseases and examined the associations between lipid biomarkers in glioma tissue and plasma. Biospecimens from 11 controls and 72 glioma patients of varying grades underwent lipidomic profiling using liquid chromatography-mass spectrometry. Univariate and multivariate analyses identified differentially abundant lipids, and correlation analysis evaluated the associations between tissue and plasma biomarkers. Lipidomic analysis revealed distinct lipid profiles in the tissues and plasma of glioma patients compared to those of controls. Prominent lipid metabolites in glioma tissues included LPC 21:3 (AUC = 0.925), DG 43:11 (AUC = 0.906), and PC 33:1 (AUC = 0.892), which served as effective biomarkers. Conversely, in plasma, lipid metabolites such as phosphatidylethanolamine (PE 21:3, AUC = 0.862), ceramide-1-phosphate (CerP 26:1, AUC = 0.861), and sphingomyelin (SM 24:3, AUC = 0.858) were identified as the most promising lipid biomarkers. Significant positive and negative correlations were observed between the tissue and plasma lipid biomarkers of glioma patients. Lipidomic profiling revealed aberrant lipid classes and pathways in glioma tissues and plasma, enhancing understanding of glioma heterogeneity and potential clinical applications. Full article

Recent advancements in large language models (LLMs) have increased the necessity of alignment and safety mechanisms. Despite these efforts, jailbreak attacks remain a significant threat, exploiting vulnerabilities to elicit harmful responses. While white-box attacks, such as the Greedy Coordinate Gradient (GCG) method, have demonstrated promise, their efficacy is often limited by non-smooth optimization landscapes and a tendency to converge to local minima. To mitigate these issues, we propose Spatial Momentum GCG (SM-GCG), a novel method that incorporates spatial momentum. This technique aggregates gradient information across multiple transformation spaces—including text, token, one-hot, and embedding spaces—to stabilize the optimization process and enhance the estimation of update directions, thereby more effectively exploiting model vulnerabilities to elicit harmful responses. Experimental results on models including Vicuna-7B, Guanaco-7B, and Llama2-7B-Chat demonstrate that SM-GCG significantly enhances the attack success rate in white-box settings. The method achieves a 10–15% improvement in attack success rate over baseline methods against robust models such as Llama2, while also exhibiting enhanced transferability to black-box models. These findings indicate that spatial momentum effectively mitigates the problem of local optima in discrete prompt optimization, thereby offering a more powerful and generalizable approach for red-team assessments of LLM safety. Warning: This paper contains potentially offensive and harmful text. Full article

The power and electricity (P & E) sector experiences a substantial number of occupational accidents, including in Nigeria. The implementation of a safety management system (SMS) to promote safety performance and mitigate occupational risks in this sector remains underreported. Therefore, we aimed to explore the factors influencing the safety performance of Nigeria’s P & E distribution companies by applying McGrath’s input–process–output model as a theoretical framework. We used SmartPLS 3.0 for structural equation modelling and SPSS Version 23 for preliminary data analysis. We included a sample of 222 organizations and found that management commitment to safety, safety communication, safety champions, and government regulations influence working conditions and safety performance to varying degrees. Employee involvement, safety training, and working conditions were significant factors affecting safety performance. Management commitment, employee involvement, safety communication, safety champions, and government regulations had significant indirect effects on safety performance through their influence on working conditions. Organizational and regulatory elements played a crucial role in shaping safety performance in high-risk environments. The results highlight vital areas to be considered when developing interventions to address P & E occupational accidents. The results can aid stakeholders in developing and implementing measures to improve workplace safety, including examining current SMSs and considering working conditions when implementing safety interventions. Full article

Sensors in power systems utilize the detection results of fault signals to guide subsequent fault handling procedures. However, the traditional phase-shift restart strategy exhibits limitations such as power interruptions, reactive power redundancy, and intersystem fault clearance failures when addressing faults in the hybrid AC/DC transmission system. To address these shortcomings, a power compensation-based fault ride-through (FRT) scheme and a protection-control cooperation FRT scheme are proposed, taking into account the operational characteristics of the symmetric monopole LCC-HVDC (SM-LCC-HVDC). The power compensation-based FRT scheme actively compensates for power, mitigating the impact of reactive power redundancy on the AC-side bus during faults. The protection-control cooperation FRT scheme is activated after sufficient AC components are detected on the DC side. It leverages the adjustability of the DC system to proactively activate protection for AC transmission lines. An electromagnetic transient simulation model of the hybrid AC/DC same-tower transmission system was developed in PSCAD/EMTDC. Simulation results validate the effectiveness and superiority of the proposed methods. Full article

Satellite Precipitation Products (SPPs) play a crucial role in hydrological modeling, particularly in data-scarce and climate-sensitive basins such as the Magat River Basin (MRB), Philippines—one of Southeast Asia’s most typhoon-prone and infrastructure-critical watersheds. This study presents the first full-cycle evaluation of nine widely used multi-source precipitation products (2000–2024), integrating raw validation against rain gauge observations, bias correction using quantile mapping, and post-correction re-ranking through an Entropy Weight Method–TOPSIS multi-criteria decision analysis (MCDA). Before correction, SM2RAIN-ASCAT demonstrated the strongest statistical performance, while CHIRPS and ClimGridPh-RR exhibited robust detection skills and spatial consistency. Following bias correction, substantial improvements were observed across all products, with CHIRPS markedly reducing systematic errors and ClimGridPh-RR showing enhanced correlation and volume reliability. Biases were decreased significantly, highlighting the effectiveness of quantile mapping in improving both seasonal and annual precipitation estimates. Beyond conventional validation, this framework explicitly aligns SPP evaluation with four critical hydrological applications: flood detection, drought monitoring, sediment yield modeling, and water balance estimation. The analysis revealed that SM2RAIN-ASCAT is most suitable for monitoring seasonal drought and dry periods, CHIRPS excels in detecting high-intensity and erosive rainfall events, and ClimGridPh-RR offers the most consistent long-term volume-based estimates. By integrating validation, correction, and application-specific ranking, this study provides a replicable blueprint for operational SPP assessment in monsoon-dominated, data-limited basins. The findings underscore the importance of tailoring product selection to hydrological purposes, supporting improved flood early warning, drought preparedness, sediment management, and water resources governance under intensifying climatic extremes. Full article

This paper focuses on the efficient fine-tuning of large language models within the federated learning framework. To address the performance bottlenecks caused by multi-source heterogeneity and structural inconsistency, a structure-aware federated fine-tuning method is proposed. The method incorporates a graph representation module (GRM) to model internal structural relationships within text and employs a segmentation mechanism (SM) to reconstruct and align semantic structures across inputs, thereby enhancing structural robustness and generalization under non-IID (non-Independent and Identically Distributed) settings. During training, the method ensures data locality and integrates structural pruning with gradient encryption (SPGE) strategies to balance privacy preservation and communication efficiency. Compared with representative federated fine-tuning baselines such as FedNLP and FedPrompt, the proposed method achieves consistent accuracy and F1-score improvements across multiple tasks. To evaluate the effectiveness of the proposed method, extensive comparative experiments are conducted across tasks of text classification, named entity recognition, and question answering, using multiple datasets with diverse structures and heterogeneity levels. Experimental results show that the proposed approach significantly outperforms existing federated fine-tuning strategies on most tasks, achieving higher performance while preserving privacy, and demonstrating strong practical applicability and generalization potential. Full article

As electric vehicles (EVs) continue to advance, the impact of their charging on the power grid is receiving increasing attention. This study evaluates the efficiency of EV charging piles in performing peak shaving and valley filling for power grids, a critical function for integrating Renewable Energy Sources (RESs). Utilising a high-resolution dataset of over 240,000 charging transactions in China, the research classifies charging volumes into “inputs” (charging during peak grid load periods) and “outputs” (charging during off-peak, low-price periods). The Vector Autoregression (VAR) model is used to analyse interrelationships between charging periods. The methodology employs a Slack-Based Measure (SBM) Data Envelopment Analysis (DEA) model to calculate overall efficiency, incorporating charging variance as an undesirable output. A Malmquist index is also used to analyse temporal changes between charging periods. Key findings indicate that efficiency varies significantly by charging pile type. Bus Stations (BS) and Expressway Service Districts (ESD) demonstrated the highest efficiency, often achieving optimal performance. In contrast, piles at Government Agencies (GA), Parks (P), and Shopping Malls (SM) showed lower efficiency and were identified as key targets for optimisation due to input redundancy and output shortfall. Scenario analysis revealed that increasing off-peak charging volume could significantly improve efficiency, particularly for Industrial Parks (IP) and Tourist Attractions (TA). The study concludes that a categorised approach to the deployment and management of charging infrastructure is essential to fully leverage electric vehicles for grid balancing and renewable energy integration. Full article

This study investigated the transformative potential of Compressive Sensing (CS) for optimizing multimodal biomedical signal fusion in Wireless Body Sensor Networks (WBSN), specifically targeting challenges in data storage, power consumption, and transmission bandwidth. Through a Systematic Mapping Study (SMS) and Systematic Literature Review (SLR) following the PRISMA protocol, significant advancements in adaptive CS algorithms and multimodal fusion have been achieved. However, this research also identified crucial gaps in computational efficiency, hardware scalability (particularly concerning the complex and often costly adaptive sensing hardware required for dynamic CS applications), and noise robustness for one-dimensional biomedical signals (e.g., ECG, EEG, PPG, and SCG). The findings strongly emphasize the potential of integrating CS with deep reinforcement learning and edge computing to develop energy-efficient, real-time healthcare monitoring systems, paving the way for future innovations in Internet of Medical Things (IoMT) applications. Full article

Conductive hydrogels (CHs) have attracted significant attention in the fields of flexible electronics, human–machine interaction, and electronic skin (e-skin) due to their self-adhesiveness, environmental stability, and multi-stimuli responsiveness. However, integrating these diverse functionalities into a single conductive hydrogel system remains a challenge. In this study, polyvinyl alcohol (PVA) and polyacrylamide (PAM) were used as the dual-network matrix, lithium chloride and MXene were added, and a simple immersion strategy was adopted to synthesize a multifunctional MXene-based conductive hydrogel in a glycerol/water (1:1) binary solvent system. A subsequent investigation was then conducted on the hydrogel. The prepared PVA/PAM/LiCl/MXene hydrogel exhibits excellent tensile properties (~1700%), high electrical conductivity (1.6 S/m), and good self-healing ability. Furthermore, it possesses multimodal sensing performance, including humidity sensitivity (sensitivity of −1.09/% RH), temperature responsiveness (heating sensitivity of 2.2 and cooling sensitivity of 1.5), and fast pressure response/recovery times (220 ms/230 ms). In addition, the hydrogel has successfully achieved real-time monitoring of human joint movements (elbow and knee bending) and physiological signals (pulse, breathing), as well as enabled monitoring of spatial pressure distribution via a 3 × 3 sensor array. The performance and versatility of this hydrogel make it a promising candidate for next-generation flexible sensors, which can be applied in the fields of human health monitoring, electronic skin, and human–machine interaction. Full article

A limited supply and price shortages of fishmeal with the expansion of aquaculture make it necessary to seek alternative protein sources. Soybean meal (SM) has been the widely preferred replacer for fishmeal in fish diets. Nevertheless, this substitution, especially when given at high doses, potentially shows adverse impact on fish intestinal health. This study aimed to investigate the effect of replacing fishmeal with SM on intestinal health in olive flounder (Paralichthys olivaceus). A 56-day feeding trial was conducted with 450 juvenile fish (initial weight: 6.32 ± 0.01 g) randomly allocated to five diets with graded SM replacement: 0% (FM), 12% (SM12), 24% (SM24), 36% (SM36), and 48% (SM48). The results demonstrated that concentrations of glucose, total triglyceride, and low-density lipoprotein cholesterol increased, whereas total protein and high-density lipoprotein cholesterol contents, and lysozyme activity decreased in serum with increasing dietary SM levels. Meanwhile, total antioxidant capacity and superoxide dismutase activity significantly decreased at replacement levels exceeding 24%, accompanied by elevated malondialdehyde concentration (p < 0.05). Compared with the FM group, the SM24, SM36, and SM48 groups showed significantly reduced VH and increased lamina propria width (p < 0.05). Increasing dietary SM levels upregulated expression of genes related to endoplasmic reticulum stress (ERS) (chop, perk, and grp78), inflammation (tnf-α and il-6), and apoptosis (bax, casp3, casp6, and casp9), while downregulated anti-inflammatory cytokines (il-10 and tgf-β1) and tight junction-related genes (zo-1, zo-2, claudin-5, ocln, muc-13, and muc-15) in the intestine (p < 0.05). There were significant differences in the abundances of intestinal microbiota at both the phylum and genus levels among the FM, SM24, and SM36 groups (p < 0.05), but the clusters and microbiota composition of the SM24 group were more similar to those of the FM group. In conclusion, replacing 24% of fishmeal with SM induced intestinal dysfunction through evoking ERS, inflammation, barrier disruption, and microbial dysbiosis in olive flounder. Full article

Rare-earth transition metal-based double perovskite ceramics E₂TMO₆ (where E = rare-earth metals, T = transition metals, and M = metal) have received impressive attention lately for cryogenic applications as a result of their intrinsic physical features such as multiferroicity, dielectric features, and adjustable magnetic transition temperature. However, determination and enhancement of magnetic transition temperature of E₂TMO₆ ceramic are subject to experimental procedures and processes with a significant degree of difficulties and cumbersomeness. This work proposes an extreme learning machine (ELM)-based intelligent method of determining magnetic transition temperature of E₂TMO₆ ceramics with activation function sigmoid (SM) and sine (SE) at varying magnetic field. The outcomes of the SE-ELM and SM-ELM models were compared with genetically optimized support vector regression (GEN-SVR) predictive models using RMSE, CC, and MAE metrics. Using the testing samples of E₂TMO₆ ceramics, SE-ELM predictive model outperforms GEN-SVR with a superiority of 6.3% (using RMSE metric) and 15.7% (using MAE metric). The SE-ELM predictive model further outperforms the SM-ELM model, with an improvement of 5.3%, using CC computed with training ceramic samples. The simplicity of the employed descriptors, coupled with the outstanding performance of the developed predictive models, would potentially strengthen E₂TMO₆ ceramics exploration for low-temperature cryogenic applications and circumvent energy challenges in different sectors. Full article