Search Results (113,451)

Geopolymers, as a novel class of low-carbon and eco-friendly cementitious material, exhibit outstanding durability and promote the resource utilization of industrial solid wastes. However, as a promising alternative to ordinary Portland cement, its susceptibility to carbonation-induced degradation may limit its widespread application. To address this challenge, this study systematically examined the effects of magnesium oxide (MgO) content and the metakaolin-to-fly ash ratio on the carbonation performance, mechanical properties, pH value, and microstructures of metakaolin–fly ash-based (MF-based) geopolymer pastes. The findings revealed that an increase in the fly ash ratio correlated with a decline in the compressive strength of MF-based geopolymer pastes. Conversely, the incorporation of MgO significantly enhanced the compressive strength, with higher fly ash ratios leading to more substantial improvements in strength. Furthermore, the addition of MgO and fly ash effectively mitigated the penetration of carbonation and the associated decrease in the pH value of the MF-based geopolymer pastes. Specifically, compared to the control group without MgO (M8F2-0%), MF-based geopolymer pastes with 4% and 8% MgO additions exhibited reductions in carbonation depth of 69.4% and 80.4%, respectively, after 28 days of carbonation, while pH values were observed to be 1.22 and 1.15 units higher, respectively. Additionally, microscopic structural analysis revealed that the inclusion of MgO resulted in a reduction in pore size, porosity, and mean pore diameter within the geopolymer pastes. This improvement was mainly attributed to the promotion of hydration processes by MgO, leading to the formation of fine Mg(OH)₂ crystals within the high-alkalinity pore solution, which enhances microstructural densification. In conclusion, the incorporation of MgO significantly improves the carbonation resistance and mechanical performance of MF-based geopolymers. It is recommended that future studies explore the long-term performance under combined environmental actions and evaluate the economic and environmental benefits of MgO-modified geopolymers for large-scale applications. Full article

Floating photovoltaic (FPV) systems are promising for coastal aquaculture where reliable electricity is essential for pumping, oxygenation, sensing, and control. A sustainable FPV–storage hybrid tailored to monsoon-prone sites is developed, with emphasis on energy efficiency and structural resilience. The prototype combines dual-axis solar tracking with a spray-cooling and cleaning subsystem and an active wind-protection strategy that automatically flattens the array when wind speed exceeds 8.0 m/s. Temperature, wind speed, and irradiance sensors are coordinated by an Arduino-based supervisor to optimize tracking, thermal management, and tilt control. A 10 W floating module and a fixed-tilt reference were fabricated and tested outdoors in Penghu, Taiwan. The FPV achieved a 25.17% energy gain on a sunny day and a 40.29% gain under overcast and windy conditions, while module temperature remained below 45 °C through on-demand spraying, reducing thermal losses. In addition, a hybrid energy storage system (HESS), integrating a 12 V/10 Ah lithium-ion battery and a 12 V/24 Ah lead-acid battery, was validated using a priority charging strategy. During testing, the lithium-ion unit was first charged to stabilize the control circuits, after which excess solar energy was redirected to the lead-acid battery for long-term storage. This hierarchical design ensured both immediate power stability and extended endurance under cloudy or low-irradiance conditions. The results demonstrate a practical, low-cost, and modular pathway to couple FPV with hybrid storage for coastal energy resilience, improving yield and maintaining safe operation during adverse weather, and enabling scalable deployment across cage-aquaculture facilities. Full article

Background/Objectives: Trigger finger is a common cause of hand pain and functional limitation. Although open A1 pulley release remains the standard surgical treatment, ultrasound-guided percutaneous needle release (UGPR) has emerged as a minimally invasive alternative. This study aimed to compare the clinical effectiveness and safety of UGPR with open surgery. Methods: In this prospective, randomized controlled trial, 146 patients with Green stage 2–4 trigger finger were randomly assigned to UGPR (n = 75) or open release (n = 71). Pain (VAS), functional status (QuickDASH), and symptom severity (Nirschl Phase Rating) were assessed preoperatively and at postoperative day 3, 1, 6, and 12 months. Grip strength was measured with a digital pinchmeter, and ultrasonographic evaluation of A1 pulley and flexor tendon thickness was performed preoperatively and at 12 months. Subgroup analyses were conducted to address the imbalance in thumb distribution. Results: Both groups showed significant postoperative improvements in VAS, QuickDASH, and Nirschl scores (p < 0.05 for intragroup comparisons), with no significant differences between groups at 12 months (p > 0.05). At the one-year follow-up, grip strength was significantly greater in the UGPR group (p = 0.008). Ultrasonographic evaluation revealed greater MCP tendon thickness in UGPR, without clinical impact. Subgroup analyses confirmed comparable functional outcomes in thumb-only and non-thumb cases. Four revisions occurred in the UGPR group (incomplete release, recurrent tenosynovitis, flexor tendon rupture, and neurovascular injury), while none were observed in the open group. Conclusions: UGPR and open release provide comparable long-term outcomes in the treatment of trigger finger. UGPR offers the advantages of being minimally invasive and preserving grip strength, although it carries a small risk of incomplete release and procedure-related complications. Patient preference, surgeon expertise, and digit type should guide treatment selection. Full article

In recent years, advances in medicine have been evident thanks to technological growth and interdisciplinary research, which has allowed the integration of knowledge, for example, of engineering into medical fields. This integration has generated developments and new methods that can be applied in alternative situations, highlighting, for example, aspects related to post-stroke therapies, Multiple Sclerosis (MS), or Spinal Cord Injury (SCI) treatments. One of the methods that has stood out and is gaining more acceptance every day is Brain–Computer Interfaces (BCIs), through the acquisition and processing of brain electrical activity, researchers, doctors, and scientists manage to transform this activity into control signals. In turn, there are several methods for operating a BCI, this work will focus on motor imagery (MI)-based BCI and three types of acquisition paradigms (traditional arrow, picture, and video), seeking to improve the accuracy in the classification of motor imagination tasks for naive subjects, which correspond to a MI task for both the left and the right hand. A pipeline and methodology were implemented using the CAR+CSP algorithm to extract the features and simple standard and widely used models such as LDA and SVM for classification. The methodology was tested with post-stroke (PS) subject data with BCI experience, obtaining 96.25% accuracy for the best performance, and with the novel paradigm proposed for the naive subjects, 97.5% was obtained. Several statistical tests were carried out in order to find differences between paradigms within the collected data. In conclusion, it was found that the classification accuracy could be improved by using different strategies in the acquisition stage. Full article

The joint adoption of agronomic practices has often been employed to maximize the efficiency of production inputs, especially water and nutrients. Potato (Solanum tuberosum) is a highly demanding crop in both water and nutrients. This study aimed to determine the most appropriate strategy for irrigation frequency and planting fertilization depth in potato cultivated in amended soil, in order to maximize plant growth, tuber yield, and tuber quality. Field experiments were conducted over two growing seasons, with irrigation frequencies of daily irrigation and irrigation every 4, 7, and 10 days, and planting fertilization depths of 10 and 20 cm. Irrigation frequency significantly affected agronomic traits, water consumption, potato growth, and tuber quality. Treatments did not influence root development across different soil layers. Irrigation intervals of 1 and 4 days promoted greater plant growth. A 7-day irrigation interval enhanced specific gravity and soluble solids in tubers, while a 10-day interval increased tuber dry matter content by up to 18% compared to daily irrigation (IF1). Decreasing irrigation frequency reduced the irrigation depth without affecting yield and average tuber mass, and improved water productivity. Water productivity increased by up to 32% under the 10 day irrigation interval (IF10) compared to IF1. Therefore, reducing irrigation frequency is a promising strategy to improve water use efficiency in potato cultivation. Full article

Dynamic equivalence of active distribution networks (ADNs) is emerging as one of the most important issues for the backbone network security analysis due to high penetration of distributed generations (DGs) and electricity vehicles (EVs). The multiscale and multimodal fusion deep learning (MMFDL) method proposed in this paper contains two modalities, one of which is a CNN + attention module to simulate Newton Raphson power flow calculation (NRPFC) for the important feature extraction of a power system caused by disturbance, which is motivated by the similarities between NRPFC and convolution network computation. The other is a long short-term memory (LSTM) + fully connected (FC) module for load modeling based on the fact that LSTM + FC can represent a load′s differential algebraic equations (DAEs). Moreover, to better capture the relationship between voltage and power, the multiscale fusion method is used to aggregate load modeling models with different voltage input sizes and combined with CNN + attention, merging as MMFDL to represent the dynamic behaviors of ADNs. Then, the Kepler optimization algorithm (KOA) is applied to automatically tune the adjustable parameters of MMFLD (called KOA-MMFDL), especially the LSTM and FC hidden layer number, as they are important for load modeling and there is no human knowledge to set these parameters. The performance of the proposed method was evaluated by employing different electric power systems and various disturbance scenarios. The error analysis shows that the proposed method can accurately represent the dynamic response of ADNs. In addition, comparative experiments verified that the proposed method is more robust and generalizable than other advanced non-mechanism methods. Full article

Background: The Coma Recovery Scale–Revised (CRS-R) is the gold standard for diagnosing chronic disorders of consciousness (DoC); however, its clinical utility is limited by lengthy administration and the need for specialized training. The Simplified Evaluation of Disorders of Consciousness (SECONDs) provides a faster and more user-friendly alternative. Objective: This study aims to evaluate the validity and reliability of the Argentine adaptation of the SECONDs scale in adults with chronic DoC due to acquired brain injury. Methods: Twenty-nine patients were evaluated over two consecutive days by three blinded raters. On day one, rater A administered the SECONDs (A1) and rater B administered the CRS-R (B) to assess concurrent validity. On day two, rater A repeated the SECONDs (A2), and rater C performed an additional SECONDs assessment (C), permitting evaluation of intra-rater (A1 vs. A2) and inter-rater (A vs. C) reliability. Results: The SECONDs demonstrated excellent intra-rater (ICC = 0.98) and inter-rater (ICC = 0.86) reliability. Concurrent validity with the CRS-R was strong (r = 0.73, p < 0.001). Diagnostic agreement was high between A1 and B (κ = 0.75) and between both A1-A2 and A1-C (κ = 0.82). The median administration time was significantly shorter for the SECONDs (10 vs. 15 min; p < 0.001). Conclusion: The Argentine SECONDs is a valid, reliable, and efficient tool for the clinical assessment of DoC patients in rehabilitation settings. Full article

Background/Objectives: Stage 5 chronic kidney disease (CKD), or end-stage renal disease (ESRD), requires renal replacement therapy, commonly hemodialysis (HD). This treatment necessitates dietary changes due to impaired excretory function and protein-energy wasting (PEW). A structured diet with adequate energy, protein, electrolytes, and fluids is essential. The aim was to characterize habitual dietary intake in adults on HD relative to KDOQI and ESPEN recommendations. Methods: In this cross-sectional study, 50 adults on maintenance HD at the Medical University of Warsaw completed a validated Food Frequency Questionnaire (55 items, nine frequency categories). The study was questionnaire-based and did not collect, link, or analyze dialysis efficacy indices, residual diuresis, or anthropometric measurements; all dietary estimates are independent of these clinical parameters. Estimated intakes of energy, macronutrients, fiber, electrolytes (Na, K, Ca, P), and fluids were compared with KDOQI 2020 and ESPEN 2021 recommendations. Sensitivity analyses included deterministic scenarios and Monte Carlo simulations. Results: Mean intakes were 2696.9 ± 1392.7 kcal and 87.7 ± 35.3 g protein; 64% and 82% met reference values. Sensitivity analyses revealed per-kg shortfalls in heavier patients (>75 kg): Monte Carlo medians were 37.8 kcal/kg/day and 1.28 g/kg/day. Diets were fat-dominant (~46%E), with low carbohydrates (~40%E) and low fiber, about 8 g per 1000 kcal. Sodium and phosphorus were elevated, about 1119 mg and 498 mg per 1000 kcal, while calcium was low (~346 mg/1000 kcal). Conclusions: Despite adequate mean intake, sensitivity analyses revealed per-kg energy/protein deficits and elevated sodium and phosphorus. Individualized counseling with electrolyte and fluid management, greater dietary diversity, and psychosocial support is warranted in HD. Full article

This paper presents a day-ahead forecasting approach for discharge profiles of a 0.5 MW battery energy storage system connected to the power grid, utilizing historical daily discharge profiles collected over one year to capture key operational patterns and variability. Two forecasting techniques are employed: a Kalman filter for dynamic state estimation and Holt’s exponential smoothing method enhanced with adaptive alpha to capture trend changes more responsively. These methods are applied to generate next-day discharge forecasts, aiming to support better battery scheduling, improve grid interaction, and enhance overall energy management. The accuracy and robustness of the forecasts are evaluated against real operational data. The results confirm that combining model-based and statistical techniques offers a reliable and flexible solution for short-term battery discharge prediction in real-world grid applications. Full article

Introduction: Nowadays, there is evidence regarding a beneficial effect of heat on neuromuscular strength and muscle hypertrophy development. The aim of this study is to evaluate the effects of a 4-week passive sauna bathing program to extreme heat (100 ± 2 °C) as a support for a resistance strength training program on maximal strength and body composition. Methods: 30 young male subjects participated in the study. They were randomly assigned to a Hyperthermia group (HG, n = 14; age: 20.48 (19.12–22–30) years; weight: 76.30 (71.00–79.00) Kg; BMI: 23.92 (22.93–24.87) Kg/m²), or to a Normothermia group (NG, n = 15; age: 19.95 (19.10–21–94) years; weight: 61.70 (59.45–72.90) Kg; BMI: 21.56 (20.42–23.26) Kg/m²). All participants followed the same lower limb strength training program (2 exercises; 4 sets of 10 repetitions at 75% 1RM with progressive loading). Additionally, HG underwent two weekly sessions of exposure to extreme heat in a sauna (100 ± 2 °C and 24 ± 1% relative humidity, four sets of 10 min, 2 days per week). The intervention lasted for 4 weeks, followed by a 4-week deconditioning period. Maximum isometric knee flexion-extension strength, maximum counter-resistance strength, as well as body composition and anthropometric variables were assessed. Results: The HG group significantly increased body weight (p < 0.05) and muscle mass (p < 0.05), while their sum of six skinfolds (Σ6 skinfolds) significantly decreased (p < 0.05). Both groups improved their 1RM squat performance following the intervention program (p < 0.05; HG: r = 0.86; NG: r = 0.89). However, only the HG group continued to improve their squat 1RM after the deconditioning period (p < 0.001; r = 0.93), as well as their leg press 1RM (p < 0.01; r = 0.94). Maximal isometric strength increased only in the NG group at the end of the training program, with a significant increase in knee flexion torque (p < 0.05; r = 0.76). In contrast, the HG group showed significant increases in isometric strength after the deconditioning period in both knee extension (p < 0.05; r = 0.76) and knee flexion (p < 0.05; r = 0.75). Conclusions: A four-week period of passive sauna bathing at extreme heat appears to alter the chronology of strength responses. It also seems to induce favorable responses in terms of strength development and body composition. Full article

Amid growing urban climate uncertainty and complex water demand, conventional standalone rainwater harvesting (RWH) systems often fail to ensure supply reliability and overflow control. Most existing studies focus on single-function building clusters, leaving a gap in understanding how functionally diverse groups with complementary demand patterns can be coordinated. This study addresses this gap by applying an hourly water balance model to compare decentralized and coordinated modes for an integrated RWH system serving a campus and adjacent student dormitories in Kitakyushu, Japan. Five performance metrics were evaluated: potable water supplementation, reliability, non-potable replacement rate, overflow volume, and overflow days. The results show that coordinated operation reduced annual potable supplementation by 14.1%, improved overall reliability to 81.7% (a 9.6% gain over decentralized operation), and increased the replacement rate to 87.9%. Overflow volume decreased by 295 m³ and overflow days by five, with pronounced benefits during summer rainfall peaks. Differential heatmaps further revealed distinct spatiotemporal advantages, though temporary disruptions occurred under extreme events. Overall, the study demonstrates that cross-functional coordination can enhance system resilience and operational stability, while highlighting the need for adaptive scheduling and real-time information systems for broader urban applications. Full article

The study aimed to evaluate the effect of betanin—a bioactive, natural pigment found in beetroot and prickly pear—on cognitive function, motor performance, and neurotransmission in a mouse model of Parkinson’s disease (PD). Aged mice with PD-like symptoms induced by 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) were pretreated with betanin (50 or 100 mg/kg b.w./day) via drinking water. Behavioural tests assessed motor skills, anxiety-related behaviour, and spatial memory. Biochemical analyses of central nervous system structures were conducted using high-performance liquid chromatography (HPLC) to determine neurotransmitter levels and metabolites. Betanin improved motor and cognitive functions in MPTP-treated mice. While learning ability remained unchanged, the 50 mg/kg dose alleviated spatial memory deficits. Biochemically, betanin moderately limited dopamine depletion and significantly influenced dopamine metabolism and serotonin levels. These findings suggest that betanin, as a functional food component, may exert neuroprotective effects and support cognitive and motor function in neurodegenerative conditions such as PD. Full article

High-molecular-weight polycyclic aromatic hydrocarbons (PAHs), such as pyrene, are persistent environmental pollutants that threaten soil health and agricultural productivity due to their resistance to degradation. This study evaluated the efficacy of domesticated bacteria isolated from contaminated farmland soil and activated sludge, used alone and in combination with corn (Zea mays L.), to remove pyrene from soil, enhance plant growth, improve tolerance, and ensure crop safety. Six bacterial strains were isolated: three from polluted farmland soil (WB1, WB2, and WF2) and three from activated sludge (WNB, WNC, and WH2). High-throughput 16S rRNA amplicon sequencing profiled bacterial communities after 30 days of treatment. Analytical tools, including LEfSe, random forest, and ZiPi analyses, identified biomarkers and core bacteria associated with pyrene degradation, assessing their correlations with plant growth, tolerance, and pyrene accumulation in corn straw. Bacteria from activated sludge (WNB, WNC, and WH2) outperformed farmland soil-derived strains and the inoculant strain ETN19, with WH2 and WNC achieving 65.06% and 87.69% pyrene degradation by days 15 and 30, respectively. The corn–bacteria consortium achieved up to 97% degradation. Activated sewage sludge (ASS)-derived bacteria were more effective at degrading pyrene and enhancing microbial activity, while soil-derived bacteria better promoted plant growth and reduced pyrene accumulation in straw. Microbial communities, dominated by Proteobacteria, exhibited high species richness and resilience, contributing to xenobiotic degradation. The corn-domesticated bacteria consortia effectively degraded pyrene, promoted plant growth, and minimized pollutant accumulation in crops. This remediation technology offers a promising strategy for rapid and sustainable bioremediation of agricultural soils contaminated with organic compounds such as PAHs or other complex pollutants, while promoting the development of efficient bacterial communities that enhance crop growth. Full article

Adverse weather events have a negative impact on the productivity of construction site activities. Understanding these effects is essential for developing realistic construction schedules. The influence of weather is shaped by both environmental factors (climate, geography, topography) and construction-related aspects such as technologies, materials, equipment, and site exposure. This paper proposes a model to quantify the influence of adverse weather by estimating monthly intervals of expected days with reduced construction productivity, based on data regarding specific weather events, including precipitation, wind, extreme temperatures, snow cover, fog, and high humidity. Data analysis employs the inclusion–exclusion principle, a combinatorial technique, alongside confidence interval estimation, a standard statistical approach. The model was applied in three Croatian cities to demonstrate its practicality and accuracy. Contractors with extensive on-site experience reviewed the results, providing insights into weather-sensitive activities and organizational practices. Full article

To capture bus passenger flow fluctuations and address the problems of slow convergence and high error in machine learning parameter optimization, this paper develops an improved Whale Optimization Algorithm (IWOA) integrated with a Bidirectional Gated Recurrent Unit (BiGRU). First, a Logistic–Tent chaotic mapping is introduced to generate a diverse and high-quality initial population. Second, a hybrid mechanism combining elite opposition-based learning and Cauchy mutation enhances population diversity and reduces premature convergence. Third, a cosine-based adaptive convergence factor and inertia weight strategy improve the balance between global exploration and local exploitation. Based on the correlation analysis between bus passenger flow and weather condition data in Harbin, and combined with the fluctuation characteristics of bus passenger flow, the data were divided into windows with a 7-day weekly cycle and processed by fuzzy information granulation to obtain three groups of fuzzy granulated window data, namely LOW, R, and UP, representing the fluctuation trend and spatial characteristics of bus passenger flow. The IWOA was employed to optimize and solve parameters such as the hidden layer weights and bias vectors of the BiGRU, thereby constructing a bus passenger flow fluctuation trend and spatial prediction model based on FIG-IWOA-BiGRU. Simulation experiments with 21 benchmark functions and real bus data verified its effectiveness. Results show that IWOA significantly improves optimization accuracy and convergence speed. For bus passenger flow forecasting, the average MAE, RMSE, and MAPE of LOW, R, and UP data are 2915, 3075, and 8.1%, representing improvements over existing classical models. The findings provide reliable decision support for bus scheduling and passenger travel planning. Full article