Search Results (3,209)

Back-support exoskeletons have emerged as promising ergonomic interventions to assist workers during repetitive manual handling tasks, particularly when task redesign or automation is not feasible. However, limited research has examined the evolution of lumbar biomechanical parameters throughout the task. This study aimed to evaluate the effects of the CORFOR^® soft-back exoskeleton on lumbar loads at L5-S1, muscle activity, and perceived exertion during a repetitive sagittal-plane manual handling task in eleven young shealthy male participants over a 4-min protocol. Results showed a significant increase in perceived lower back exertion over time. However, no significant difference in perceived exertion was found between EXO and FREE conditions despite reductions of 11–13%. Lumbar loads at L5-S1 were significantly reduced when using the soft-back exoskeleton in both lowering and lifting phases (−14% for peak flexion–extension moment, −6% for compression force, and −10% for shear force). In addition, peak flexion–extension moment and peak compression force significantly decreased over time. Significant interactions between exoskeleton use and time were observed for peak L5-S1 flexion–extension moment and right ES muscle activity. Overall, these findings partially support our hypothesis, as the CORFOR^® reduced lumbar mechanical demands ES muscle activity, with some additional time-dependent effects observed. Full article

The inherent drying shrinkage of geopolymers restricts their widespread application in concrete crack repair, particularly for medium-to-wide cracks that demand stringent workability and penetrability. This study systematically investigates the effects of three single-component expansive agents (MgO, CaO, and CSA) on the fresh properties, mechanical performance, and microstructural evolution of a slag-fly ash-based geopolymer. The optimal modified formulation was subsequently evaluated for remediating preinduced concrete cracks (2.0, 2.5 and 3.0 mm apertures) and benchmarked against ordinary Portland cement and epoxy resin. The results indicate that while CaO and CSA severely compromise paste fluidity and induce rapid setting, MgO modification provides an exceptional operational window. An 8 wt.% MgO dosage (MG8) induces only a marginal 3.73% reduction in paste fluidity and maintains stable initial and final setting times, thereby preserving excellent workability retention and enabling precise construction scheduling. Microstructural analyses (XRD, SEM, and MIP) reveal that the precipitation of micro expansive Mg(OH)₂ effectively suppresses the 28-day drying shrinkage to 0.23%, while facilitating the attainment of a robust compressive strength of 44.1 MPa and preserving a highly favorable strength development trajectory. In the structural repair phase, the MG8 demonstrated outstanding compressive strength recovery, peaking at 28.80 MPa for 2.0 mm cracks, which significantly outperformed both the cement and epoxy resin repaired groups. Conversely, the epoxy resin repaired specimens exhibited superior splitting tensile strength due to the inherent elongation properties of the flexible macromolecular polymer. Comprehensive durability assessments revealed that the MG8 repair system exhibits exceptional resistance against freeze–thaw cycles and sulfate/chloride attacks, ensuring long-term structural integrity that significantly outperforms conventional materials. Overall, this work presents a viable and durable geopolymer-based alternative to traditional materials, aiming to ensure timely and reliable remediation concrete cracks that do not cause structural damage. Full article

With the rapid growth of the medicinal cannabis sector, there is a growing concern regarding its environmental impact and sustainability. In recent years, life cycle assessment (LCA) studies on medicinal cannabis cultivation and processing have been conducted since 2021. However, there is a lack of comprehensive LCA studies that include all stages of medicinal cannabis cultivation and processing. In this systematic review, various LCA studies conducted from 2021 to 2025 using the ISO 14040/44 methodology are reviewed and discussed in terms of their goal and scope, life cycle inventory (LCI), life cycle impact assessment (LCIA), and result interpretation. Various environmental impact indicators are considered in this review, such as greenhouse gas emissions, energy demand, water usage, eutrophication, acidification, and resource depletion. All of these impact indicators point to a significant environmental impact of indoor cultivation in terms of greenhouse gas emissions, which vary from 2.3 × 10³ to 5.2 × 10³ kg CO₂ eq kg⁻¹ of dried cannabis product. Nevertheless, it is important to note that this is significantly influenced by regional electricity sources. Low-carbon-based electricity sources, especially hydro-based sources, can reduce emissions to a significant level. Cultivation outdoors presents significantly lower emissions of (60–110 kg CO₂ eq kg⁻¹), but fertilizers and substrates used in cultivation contribute significantly to emissions. Also, outdoor plants use 22.7 L plant⁻¹ d⁻¹ water at peak growth, while indoor plants use 9–11 L plant⁻¹ d⁻¹ water. Improvements in the life cycle of cannabis cultivation can be achieved through renewable energy use, water and fertilizers, substrate use and reuse, and inventories for post-harvesting activities like drying and extraction. Botanical parameters including genotype, planting density, and harvesting frequency are identified as significant but under-characterized determinants of LCA outcomes. Ethical and legal barriers are shown to be structural drivers of the LCA data gap. A SWOT analysis contextualizes the opportunities and constraints of the sector. Future research should focus on cradle-to-grave LCA and incorporate socio-economic factors for sustainability in the medicinal cannabis sector. Full article

Steel moment-resisting frames exhibiting vertical geometric irregularities, particularly those with setback configurations, experience increased seismic demands due to stiffness discontinuities and complex dynamic interactions. These conditions present significant challenges for conventional vibration control strategies. This study introduces a performance-based optimization framework that utilizes the Circle-Inspired Optimization Algorithm (CIOA) to enhance the design of tuned liquid dampers (TLDs) in irregular steel structures. Structural responses are simulated in OpenSees, with a rheological model based on the Housner method employed to accurately capture fluid–structure interaction. Seismic performance is evaluated using a suite of real subduction-type ground motions, selected to represent the seismic hazard level of Armenia, Colombia, in accordance with the Conditional Scenario Spectra (CSS) methodology and the National Seismic Risk Model for Colombia. The optimization process considers the mean response across multiple ground-motion records to ensure robustness against seismic variability. Multiple time-domain objective functions are examined, including peak interstory drift, maximum displacement, and peak acceleration. The results indicate that objective functions related to interstory drift and displacement provide the most effective, stable, and consistent reductions in seismic demand across all scenarios, while acceleration-based objectives display greater sensitivity to record-to-record variability. These outcomes underscore the importance of objective function selection in determining both optimization stability and control effectiveness. The CIOA demonstrates rapid convergence, numerical robustness, and reliable performance, confirming its suitability as a computationally efficient and resilient optimization tool for the design of passive control systems in irregular steel structures exposed to high seismic hazard. Full article

Liuchun Lake area, located in the high-elevation and topographically complex western region of Zhejiang Province, exhibits temperature variability strongly influenced by terrain-induced dynamics and local microclimates. The Zhejiang Operational Consensus Forecasts (ZJOCF) model shows pronounced systematic biases in this area, making it difficult to meet the demand for short-term, fine-scale forecasts in cultural-tourism applications. Using observational data from four stations at different elevations, this study analyzes how ZJOCF temperature forecast errors vary with altitude, develops a station-level machine-learning temperature bias-correction model, and evaluates its performance in terms of accuracy, mean absolute error (MAE), error distribution, and control of extreme errors. Results show that the accuracy of the raw forecasts decreases significantly with increasing elevation, with high-altitude sites exhibiting distinct warm biases and strong fluctuations. After correction, the 72 h forecast accuracy at the four stations increases to 69–71% (up to 40.8% at the mountaintop station), MAE is reduced by more than 60% on average, extreme-error cases decrease by 40–60%, and the error distribution shifts from a scattered multi-peak pattern to a concentrated single-peak structure. These findings demonstrate that station-level machine-learning correction can effectively mitigate structural errors in ZJOCF temperature forecasts over complex terrain, providing a reliable technical pathway for refined meteorological services in mountainous regions. Full article

The widespread residual ciprofloxacin (CIP) poses severe environmental and health risks, demanding efficient detection methods. Herein, a Zr–Tb bimetallic MOF (ZTM) was green-synthesized via a room-temperature aqueous route with disodium terephthalate as ligand, and developed as a ratiometric fluorescent probe for CIP detection. Structural characterization confirmed Tb³⁺ was successfully incorporated into the Zr-MOF framework, endowing ZTM with high stability and excellent luminescence. The absorption edge of ZTM (320–330 nm) overlapped with CIP’s 330 nm absorption peak, so 327 nm was selected as the excitation wavelength. Under this excitation, ZTM showed a strong Tb³⁺ emission at 657 nm; upon CIP addition, the 657 nm peak was quenched, while the 491 nm emission was enhanced, realizing a distinct ratiometric response. The ratio I₄₉₁/I₆₅₇ was linear with CIP concentration (0.5–25 μM, R² = 0.992), with a limit of detection far below the statutory 30 μM limit (0.16 μM). ZTM also exhibited excellent selectivity, good pH tolerance (5.0–8.0) and rapid response (1 min). Mechanism analysis revealed that the response was mainly due to the inner filter effect (IFE) between ZTM and CIP. This work provides a green-synthesized MOF probe for sensitive and selective CIP detection in environmental samples. Full article

Power systems with high renewable penetration impose multi-dimensional demands on energy storage (ES) regulation. Short-duration ES is required for power balance and frequency support, while medium- and long-duration ES is essential for daily, weekly, and seasonal peak shaving and energy time-shifting. Aiming at the challenge of multi-timescale configuration of hybrid energy storage (HES) in the initial planning stage of carbon-neutral transition, this paper proposes an optimal configuration strategy combining STL-PDM-VMD. First, the seasonal and trend decomposition using Loess (STL) is used to extract quarterly trends of annual net power for seasonal ES configuration. Then, the Past Decomposable Mixing (PDM) module in the time-mixer model is applied to decouple and mix multi-scale features of the detrended power curve for monthly and weekly configurations. Finally, an improved Variational Mode Decomposition (VMD) is adopted to decompose daily net power fluctuations and optimize intra-day energy storage schemes. Based on actual data from a carbon-neutral transition region, simulations are carried out and compared with the VMD method with decomposition layers optimized by Gurobi. The results show that the proposed STL-PDM-VMD multi-timescale hybrid energy storage configuration strategy can effectively capture the multi-timescale fluctuation characteristics of net load, significantly improve the Renewable Energy (RE) penetration rate, and ensure the power and energy balance of the new power system at multiple timescales. penetration, and maintain power and energy balance in the new-type power system. Full article

The rapid growth of residential photovoltaic (PV) installations has increased interest in electrical storage units (ESUs) as a means of enhancing self-consumption and reducing surplus electricity fed into the grid. However, in temperate climates characterized by strong seasonal variability in solar generation, the economic viability of residential battery storage remains uncertain. This study examines whether ESUs provide measurable financial benefits under such climatic conditions, particularly after the transition from net-metering to net-billing schemes. The analysis combines empirical household electricity consumption data with simulation-based modeling of PV–battery operation. Periods of surplus energy production during high solar generation were taken into account, as well as periods of increased energy demand in the winter season and technical limitations related to energy storage, including the difference between actual and nominal capacity of energy storage systems. The results indicate that although battery storage increases self-consumption and reduces grid injection during peak generation periods, its economic performance is limited by the seasonal mismatch between electricity production and demand. Consequently, under net-billing conditions, residential ESUs do not automatically ensure economic profitability in temperate climates. Full article

This study quantified nutrient accumulation and partitioning among the kernel, shell, husk, rachis, and leaves during fruit development in three macadamia cultivars. Racemes and leaves were sampled at biweekly intervals until kernel maturity. The shell and rachis ceased to accumulate biomass earlier in the season than the husk or kernel. Nitrogen (N) and potassium (K) were the dominant nutrients accumulated in the fruit. Despite declining concentrations between 80 and 140 DAF, total kernel nutrient content continued to increase, indicating sustained nutrient import during this critical period. The kernel was the primary sink for N, phosphorus (P), sulfur (S), and magnesium (Mg), with peak accumulation occurring during rapid kernel growth at 80–175 days after flowering (DAF). In contrast, the accumulation of calcium (Ca) and manganese (Mn) into the kernel ceased earlier, suggesting limited late-stage mobility. The husk accumulated more K than the kernel and remained an active sink for K, S, Mg, Ca, and Mn until maturity, while N, P, and boron (B) accumulation slowed after ~107 DAF. The shell contributed minimally to nutrient demand, with N, zinc (Zn), and B accumulation ceasing after shell hardening (90–110 DAF). The cultivars exhibited consistent temporal patterns, differing mainly in magnitude. Nutrient partitioning efficiency among- the fruit components was highest for cv. A38. The rachis acted as a transient sink early in development before declining in mobile nutrients, while leaf nutrient dynamics did not reflect fruit demand. Full article

Background: Plantar fasciitis is a leading cause of heel pain, affecting approximately 10% of the population. Despite its prevalence, treatments may result in symptom recurrence and chronicity, which can significantly increase patient dissatisfaction. Google Trends provides insights into public interest through search volume analysis. This study examines global and USA trends in plantar fasciitis, focusing on temporal, seasonal, and income-based variations. Methods: Google Trends data for “Plantar Fasciitis” (2004–2024) were analyzed for both global and USA search trends. Monthly and seasonal search volumes were grouped by time and location. Regression and post hoc tests were conducted to identify significant patterns. Comparisons were made between high- and low-income states in the USA. Results: Public interest in plantar fasciitis increased significantly over time, both globally (R² = 0.871, p < 0.001) and in the USA (R² = 0.854, p < 0.001). Interest peaked in summer and declined in winter, with seasonal differences significant worldwide (p < 0.05). Monthly variations were significant only in the USA. No significant difference was found between high- and low-income states (p > 0.05). Conclusions: Interest in plantar fasciitis has grown steadily, reflecting its prevalence and impact. The findings emphasize the need for accessible, high-quality information to address public demand. These insights can guide healthcare professionals and policymakers in developing targeted resources. Full article

Home Energy Management Systems (HEMS) are increasingly positioned at the center of residential flexibility, particularly as homes integrate photovoltaics, battery storage, electric vehicles, and responsive loads. This systematic review examines recent advances in optimization and multi-asset coordination for HEMS. Searches were conducted in Scopus, Web of Science, IEEE Xplore, and ScienceDirect for studies published between 2020 and 2025; after screening and eligibility assessment, 90 studies were included. The findings indicates that deterministic optimization remains well suited to structured scheduling problems, whereas metaheuristic, hybrid, and learning-based methods are better able to address nonlinearity, uncertainty, and real-time adaptation. Across the reviewed literature, multi-asset integration generally improves cost, peak demand, self-consumption, and, in some cases, user comfort and emissions. Yet the field remains dominated by simulation-based validation. Future progress of HEMS will depend on real-world validation, interoperable system design, explainable control, and stronger alignment with user behavior, communication constraints, and regulatory frameworks. Full article

This research introduces a novel methodology that combines Building Information Modelling (BIM) and Economic Multi-Criteria Decision-Making (EMCDM) with Neural Networks to optimize hybrid renewable energy systems in small communities. Its core aim is to improve sustainability, technical performance, and financial vokiability through integrated modelling and decision-making. The approach is applied to a hydropower site, evaluating five Scenarios (IDs 1–5) under a Community and Industry model. Financial benchmarks include a 10% Minimum Required Return and a 7-year payback period. ID3—hydropower, solar, and wind—proves most effective, with ANPV of €10,905 (wet) and €4501 (dry), and ROI of 155%/64%. Its ROIA/MRA Index peaks at 539%, and Payback/N ratios remain within acceptable limits (55%/96%). LCOE stays stable in average conditions (0.042–0.046 €/kWh), rising in dry years (0.07–0.10 €/kWh). Profitability differences primarily stem from demand and curtailment, rather than production costs. The NARX neural network reliably models SS% values from renewable inputs with low error across scenarios. The integrated BIM–EMCDM framework ensures transparent, sustainable, and risk-balanced energy system decisions for long-term autonomy. Full article

Influenced by factors such as residents’ living habits, commuting patterns, and commercial activity cycles, the generation of domestic waste exhibits a distinct double-peak distribution. To meet the high demand during peak periods, collection companies typically deploy excess transportation capacity, which leads to severe resource idleness during off-peak periods, imposing significant economic and environmental burdens. To address this issue, this study develops a dynamic smart waste collection routing model aimed at minimizing the coordinated collection cost between self-owned and outsourced multi-compartment vehicles, and designs a two-phase algorithm to solve it. In the pre-optimization phase, an improved Harris Hawks Optimization algorithm integrated with multiple heuristic algorithms is employed to generate initial collection routes. In the re-optimization phase, a hybrid strategy combining periodic and continuous re-optimization is used to dynamically update collection routes. Finally, the effectiveness of the proposed model and algorithm is validated through case studies. Furthermore, a systematic sensitivity analysis is conducted to investigate the impact of key parameters, yielding practical insights for waste collection management. Full article

As power-electronic-interfaced renewable generation displaces synchronous machines, modern power systems face coupled day-ahead challenges: net-load variability demands peak shaving, while declining inertia necessitates explicit frequency-regulation scheduling. In sequential security-constrained unit commitment (SCUC) and Security-Constrained Economic Dispatch (SCED), the reserve procured in SCUC may lose deliverability after redispatch because the same storage bandwidth is reassigned to energy service. This paper proposes a two-stage day-ahead framework that addresses both challenges for low-inertia systems with high inverter-based resource (IBR) penetration. Stage I embeds Rate-of-Change of Frequency (RoCoF), frequency nadir, and quasi-steady-state (QSS) constraints in SCUC, with a piecewise-linear outer approximation for the non-convex nadir limit. Stage II strictly inherits the SCUC commitment and reserve reservation, and it applies bandwidth deduction to prevent peak-shaving redispatch from consuming committed frequency reserve. A technology-aware partition further assigns fast-response Lithium Iron Phosphate (LFP) batteries to sub-second frequency support and long-duration Vanadium Redox Flow Batteries (VRFBs) to energy shifting. Evaluated under the adopted reduced-order frequency-response framework and disturbance representation, tests on a modified IEEE 39-bus system under an extreme-wind scenario demonstrate that explicit frequency constraints eliminate all post-contingency violations, the inheritance mechanism closes a 23.85 MW reserve gap after redispatch, and heterogeneous storage partitioning preserves essentially the same disturbance sensitivity while increasing the peak-shaving ratio to 45.85%, lowering the day-ahead cost to CNY $10.483\times {10}^6$ and reducing the average system price to 209.33 CNY/MWh. Full article

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article