Search Results (5,084)

Background: The GLA:D^® program is an evidence-based intervention widely used in Western countries to improve knee function and exercise adherence among individuals with osteoarthritis. However, its application in postoperative total knee arthroplasty (TKA) populations is limited, particularly in Asia. This study evaluated two modified GLA:D^® interventions tailored to the Korean clinical environment. Methods: Patients who underwent TKA participated in one of two programs. The GLA:D-M group received therapist-supervised neuromuscular training with structured progression. The GLA:D-C group received a modified GLA:D^® program with additional lower-limb strengthening, primarily conducted as home-based exercise. Outcomes at 3 and 6 months included functional performance, isokinetic strength, gait speed, and psychosocial measures. Statistical significance, minimal clinically important difference (MCID), and effect sizes were used to assess clinical relevance. Results: Both groups improved; however, recovery patterns differed. GLA:D-M demonstrated statistically significant (p < 0.05) and clinically meaningful improvements in functional and psychosocial outcomes, exceeding MCID thresholds with large effect sizes. GLA:D-C showed significant gains in lower-limb strength, but many changes did not meet the MCID and did not consistently translate into higher-level functional recovery. These findings suggest that supervised neuromuscular training may facilitate more comprehensive recovery than home-based strengthening alone. Conclusions: Adapting the GLA:D^® program for TKA patients in a Korean clinical setting was feasible and beneficial. Additionally, the delivery method, particularly therapist supervision, played a vital role in maximizing outcomes. Both program content and delivery format should be considered in rehabilitation models. Larger, long-term studies are warranted to confirm these findings and explore broader clinical applications. Full article

Modified biochars have emerged as effective adsorbents for remediating heavy metal-contaminated environments, yet variability in modification methods, feedstocks, and pyrolysis conditions has led to inconsistent findings. This study provides a quantitative meta-analysis of 173 peer-reviewed publications to systematically evaluate how modification strategies, feedstock types, and pyrolysis temperatures influence the adsorption of cadmium (Cd), lead (Pb), and copper (Cu). Six modification approaches were assessed (metal oxides, bases, strong acids, weak acids, hydrogen peroxide, and physical treatments), pyrolysis temperatures were grouped into three ranges (<400 °C, 400–550 °C, and >550 °C), and feedstocks were categorized as wood-, straw-, herbaceous-, and manure-based. Effect sizes were calculated to identify the most effective combinations of modification, feedstock, and thermal regime, providing a robust, data-driven framework for predicting biochar performance. Results show that metal oxide-treated biochars consistently exhibited the highest adsorption, while physical modifications were least effective. Moderate pyrolysis temperatures (400–550 °C) and wood-derived biochars also significantly enhanced adsorption across all three metals. These findings provide actionable guidance for designing tailored biochars, resolving inconsistencies in the literature, and supporting future studies aimed at optimizing biochar for heavy metal remediation and sustainable environmental applications. Full article

Titanium-containing nanoparticles have emerged as materials of significant technological importance due to their multifunctional properties and excellent performance. With their expanding applications, the amount of TiO₂ nanoparticles (TNPs) being released into the soil environment has increased significantly. This review addresses the gap in current research, which has predominantly focused on the environmental behavior of TNPs in aquatic systems while lacking systematic integration of the synergetic mechanism of adsorption–transformation–ecological effects in soil systems and its guiding value for practical applications. It deeply reveals the interaction mechanisms between TNPs and environmental pollutants. TNPs exhibit outstanding adsorption performance towards environmental pollutants such as heavy metals and organic compounds. Specifically, the maximum adsorption capacities of titanate nanowhiskers for the heavy metal ions Cu(II), Pb(II), and Cr(III) are 143.9 mg·g⁻¹, 384.6 mg·g⁻¹, and 190.8 mg·g⁻¹, respectively. Additionally, 1-hydroxydinaphthoic acid surface-modified nano-TiO₂ exhibits an adsorption rate of up to 98.6% for p-nitrophenol, with an enrichment factor of 50-fold. The transformation process of TNPs after pollutant adsorption profoundly affects their environmental fate, among which pH is a critical controlling factor: when the environmental pH is close to the point of zero charge (pHpzc = 5.88), TNPs exhibit significant aggregation behavior and macroscopic sedimentation. Meanwhile, factors such as soil solution chemistry, dissolved organic matter, and microbial activities collectively regulate the aggregation, aging, and chemical/biological transformation of TNPs. In the soil ecosystem, TNPs can exert both beneficial and detrimental impacts on various soil organisms, including bacteria, plants, nematodes, and earthworms. The beneficial effects include alleviating heavy metal stress, serving as a nano-fertilizer to supply titanium elements, and acting as a nano-pesticide to enhance plants’ antiviral capabilities. However, excessively high concentrations of TiO₂ can stimulate plants, induce oxidative stress damage, and impair plant growth. This review also highlights promising research directions for future studies, including the development of safer-by-design TNPs, strategic surface modifications to enhance functionality and reduce risks, and a deeper understanding of TNP–soil microbiome interactions. These avenues are crucial for guiding the sustainable application of TNPs in soil environments. Full article

This study investigates the relationship between economic growth and environmental performance in selected Middle East and North Africa (MENA) countries through the lens of the Environmental Kuznets Curve (EKC) hypothesis. Due to data availability constraints, our sample includes Algeria, Egypt, Lebanon, Mauritius, Morocco, and Oman, covering the period 1990–2022. Using annual panel data, we apply panel cointegration techniques alongside Fully Modified Ordinary Least Squares (FMOLS) and Dynamic Ordinary Least Squares (DOLS) estimators, complemented by Granger causality tests, to examine the interaction among GDP per capita, renewable energy consumption, and transport service exports in determining CO₂ emissions per unit of GDP. The empirical findings provide only partial support for the EKC: while the DOLS results confirm an inverted U-shaped income–emissions relationship, the FMOLS estimations contradict it, suggesting a more complex and nonlinear pattern. Beyond testing the EKC, this study contributes two novel dimensions to the literature. First, it shows that renewable energy exerts a statistically significant negative effect on carbon intensity in the long run, despite weak short-run causality, highlighting the delayed but durable environmental benefits of clean energy adoption. Second, it introduces transport service exports as a proxy for structural economic transformation, capturing the role of trade-driven diversification in reducing emissions. By embedding renewable energy deployment and service-based trade dynamics into the EKC framework, the study advances a more policy-relevant and region-specific understanding of the growth–environment nexus in the selected MENA economies. The results underscore the importance of scaling renewable energy, promoting low-carbon service sectors, and aligning trade and environmental policies to ensure that economic growth supports long-term climate objectives. Full article

Quantum computing is an emergent field promising the improvement of processing speed in key algorithms by reducing their exponential scaling to polynomial, thus enabling solutions to problems that exceed classical computational capabilities. Gate-based quantum computing is the most common approach but still faces high levels of noise and decoherence. Gates play the role of probability mixers codifying information settled in quantum systems. However, they are deviated from their programmed behaviour due to those decoherent effects as a hidden source modifies the desired probability flux. Their quantification of such unavoidable behaviours becomes crucial for quantum error correction or mitigation. This work presents an approach to decoherence in quantum circuits using the Lindblad master equation to model the impact of noise and thermalisation underlying the ideal programmed behaviour expected for processing gates. The Lindblad approach then provides a comprehensive tool to model both probability fluxes being present in the process, thus regarding the gate and the environment. It analyses the deviation of resulting noisy states from the ideal unitary evolution of some gates considered as universal, setting some operating regimes. Thermalisation considers a radiation bath where gates are immersed as a feasible model of decoherence. Numerical simulations track the information loss as a function of the decay rate magnitude. It also exhibits the minimal impact on decoherence coming from particular quantum states being processed, but a higher impact on the number of qubits being processed by the gate. The methodology provides a unified framework to characterise the processing probability transport in quantum gates, including noise or thermalisation effects. Full article

For sustainable greenhouse design, natural ventilation is a vital component; it depends on the local climate. Therefore, optimizing the greenhouse orientation and vent opening configuration is a critical issue that needs to be addressed for a specific location (e.g., the central region of Saudia Arabia). Experiments were conducted in winter, in a curved-roof, single-span, N-S oriented greenhouse that includes roof and side-wall vents. Five different vent opening arrangements were examined. The outside and inside greenhouse environmental parameters were measured, and the ventilation rate (kg·s⁻¹) and the number of air exchanges per hour (N_a) were estimated for each opening case using a modified energy balance equation. The results showed that the common wind directions are N-S and NW-SE. For effective ventilation, greenhouses should be oriented in the E-W or NE-SW directions. Opening the side-wall vents exhibited the highest wind-driven ventilation rate that is essential to control temperature and humidity at the crop level, while only opening the roof vents is not recommended. In the central region of Saudi Arabia, natural ventilation is sufficient for operating greenhouses (${\overline{N}}_a$ > 30). Opening the roof and side-wall vents (combined wind and buoyancy effects) is the most efficient as long as the greenhouse axis is aligned perpendicular to the wind direction. Such information is essential for sustainable greenhouse management in an arid environment. Full article

Adaptive filtering algorithms often suffer from biased parameter estimation and performance degradation in the presence of colored input noise and impulsive observation noise, both of which are common in practical sensor and communication systems. Existing bias-compensated least mean square (LMS) algorithms generally assume white Gaussian input noise, thereby limiting their applicability in real-world scenarios. This paper introduces a robust convex combination bias-compensated LMS (CC-BC-LMS) algorithm designed to address both colored Gaussian input noise and impulsive observation noise. The proposed algorithm achieves bias compensation through robust estimation of the input noise autocorrelation matrix and employs a modified Huber function to mitigate the influence of impulsive noise. A convex combination of fast and slow adaptive filters enables variable step-size adaptation, effectively balancing rapid convergence and low steady-state error. Extensive simulation results demonstrate that the proposed CC-BC-LMS algorithm provides substantial improvements in normalized mean square deviation (NMSD), surpassing state-of-the-art bias-compensated and robust adaptive filtering techniques by $4.48$ dB to $11.4$ dB under various noise conditions. These results confirm the effectiveness of the proposed approach for reliable adaptive filtering in challenging noisy environments. Full article

Visual odometry in dynamic environments is particularly challenging, as moving objects often cause incorrect data associations and large pose estimation errors. Traditional EKF-based VO methods rely on 1-point RANSAC to reject outliers under the assumption of a static world, thereby discarding dynamic landmarks as noise. However, in practice, outliers may arise not only from measurement errors but also from the motion of objects. To address this issue, we propose a modified 1-point RANSAC framework that detects dynamic objects and leverages both static and dynamic landmarks for ego-motion estimation. Inspired by adaptive strategies observed in biological vision systems, our approach integrates EKF-based state estimation with dynamic object tracking to achieve simultaneous ego-motion and object-motion estimation, improving robustness in complex and dynamic scenes. Full article

Accurate determination of opacity is critical for understanding radiation transport in both astrophysical and laboratory plasmas. We employ atomic data from R-Matrix calculations to investigate radiative properties in high-energy-density (HED) plasma sources, focusing on opacity variations under extreme plasma conditions. Specifically, we analyze environments such as the base of the convective zone (BCZ) of the Sun ($2\times {10}^6$ K, $N_e={10}^{23}$/cc), and radiative opacity data collected using the inertial confinement fusion (ICF) devices at the Sandia Z facility ($2.11\times {10}^6$ K, $N_e=3.16\times {10}^{22}$/cc) and the Lawrence Livermore National Laboratory National Ignition Facility. We calculate Rosseland Mean Opacities (RMO) within a range of temperatures and densities and analyze how they vary under different plasma conditions. A significant factor influencing opacity in these environments is line and resonance broadening due to plasma effects. Both radiative and collisional broadening modify line shapes, impacting the absorption and emission profiles that determine the RMO. In this study, we specifically focus on electron collisional and Stark ion microfield broadening effects, which play a dominant role in HED plasmas. We assume a Lorentzian profile factor to model combined broadening and investigate its impact on spectral line shapes, resonance behavior, and overall opacity values. Our results are relevant to astrophysical models, particularly in the context of the solar opacity problem, and provide insights into discrepancies between theoretical calculations and experimental measurements. In addition, we investigate the equation-of-state (EOS) and its impact on opacities. In particular, we examine the “chemical picture” Mihalas–Hummer–Däppen EOS with respect to level populations of excited levels included in the extensive R-matrix calculations. This study should contribute to improving opacity models of HED sources such as stellar interiors and laboratory plasma experiments. Full article

Selenium nanoparticles (Se NPs) have received increasing attention as a new alternative source to other forms of selenium in nutritional dietary supplements; however, the limited stability and pronounced tendency of selenium nanoparticles (Se NPs) to aggregate in aqueous environments have significantly constrained their practical applications. In this study, Poria cocos polysaccharide-modified Se NPs (PCP-Se NPs) were synthesized by the selenite/ascorbic acid chemical reduction method. PCP-Se NPs exhibited a uniformly dispersed spherical morphology with an average particle size of 66.64 ± 0.30 nm, and displayed an amorphous crystal structure. Compared to unmodified Se NPs, the PCP-Se NPs exhibited low Se release (8.83 ± 0.73%) after simulated gastrointestinal digestion, and they had excellent storage stability and salt ion stability. PCP-Se NPs exhibited potent antioxidant activity manifested by the effective scavenging of DDPH and ABTS radicals. PCP-Se NPs were efficiently internalized by RAW264.7 cells and released into the cytoplasm by a lysosomal escape mechanism, thereby effectively reducing intracellular inflammatory factor levels (the levels of MPO, NO, iNOS, TNF-α, IL-1β, and IL-10 in the PCP-Se NPs treatment group were 0.38 ± 0.013-fold, 0.26 ± 0.02-fold, 0.36 ± 0.02-fold, 0.57 ± 0.03-fold, 0.35 ± 0.02-fold, and 2.07 ± 0.16-fold that of the LPS group, respectively), alleviating oxidative stress (the levels of CAT, SOD, GSH, and MDA in the PCP-Se NP-treated group were 2.48 ± 0.02-fold, 1.91 ± 0.11-fold, 3.16 ± 0.28-fold, and 0.46 ± 0.03-fold that of the LPS group, respectively), and maintaining mitochondrial membrane potential stability. This study provides a basis and reference for improving the stability of Se NPs and developing novel selenium-enriched dietary supplements. Full article

With the rapid development of unmanned aerial vehicle (UAV) technology, quadrotor UAVs have demonstrated extensive application potential in various fields. However, due to parameter uncertainties and strong coupling, the flight attitude of quadrotors is prone to external disturbances, posing challenges for achieving precise control and stable flight. In this paper, we address the tracking control problem under unknown command rate variations by proposing a Modified Linear Active Disturbance Rejection Control (LADRC) strategy, aiming to enhance flight stability and anti-disturbance capability in complex environments. First, based on the attitude dynamics model of quadrotors, an LADRC technique is adopted to realize three-channel decoupling-free control. By integrating a parameter estimator, the proposed method can compensate unknown disturbances in real time, thereby improving the system’s anti-disturbance ability and dynamic response performance. Second, to further enhance system robustness, a linear extended state observer (LESO) is designed to accurately estimate the tracking error rate and total disturbances. Additionally, a Levant differentiator is introduced to replace the traditional differentiation component for optimizing the response speed of command rate. Finally, a modified LADRC controller incorporating error rate estimation is constructed. Simulation results validate that the proposed scheme maintains good tracking accuracy under high-frequency disturbances, providing an effective solution for stable UAV flight in complex scenarios. Compared with traditional control methods, the modified LADRC strategy exhibits significant advantages in tracking performance, anti-disturbance capability, and dynamic response. This research not only offers a novel perspective and solution for quadrotor control problems but also holds important implications for improving UAV performance and reliability in practical applications. Full article

Organisms that physically modify their environment, known as ecosystem engineers, can influence resource availability, species interactions and the structure of soil communities. However, the specific effect of ecosystem engineers like ants on the abundance and diversity of non-engineering soil organisms remains understudied. To address this knowledge gap, we conducted a survey of a multi-taxon belowground community of soil microarthropods—Collembola, Mesostigmata, Oribatida and Actinedida—in urban areas, comparing nest mounds of the ant species Lasius niger and Lasius flavus with areas without ant-nesting activity (control). We hypothesised differences in abundance and distribution patterns of different soil microarthropod taxa between ant mounds and the control soil. We also hypothesised that ant-induced soil disturbance is species-specific, and may result in different patterns of diversity and composition of soil microarthropod assemblages within trophic levels, such as among detritivores (e.g., Collembola) and predators (e.g., Mesostigmata). Our results reveal how ecological filters shape different soil microarthropod groups’ responses to ant-driven changes in their environment. As we expected, soil disturbance caused by ant nest-building activity significantly influenced the abundance, distribution patterns and diversity of soil microarthropods, especially in the assembly of detritivorous—but not predatory—guilds of soil microarthropods. Full article

The growing penetration of photovoltaic (PV) generation in multi-energy microgrids has amplified the challenges of maintaining real-time operational efficiency, reliability, and safety under conditions of renewable variability and forecast uncertainty. Conventional rule-based or optimization-based strategies often suffer from limited adaptability, while purely data-driven reinforcement learning approaches risk violating physical feasibility constraints, leading to unsafe or economically inefficient operation. To address this challenge, this paper develops a Physics-Informed Reinforcement Learning (PIRL) framework that embeds first-order physical models and a structured feasibility projection mechanism directly into the training process of a Soft Actor–Critic (SAC) algorithm. Unlike traditional deep reinforcement learning, which explores the state–action space without physical safeguards, PIRL restricts learning trajectories to a physically admissible manifold, thereby preventing battery over-discharge, thermal discomfort, and infeasible hydrogen operation. Furthermore, differentiable penalty functions are employed to capture equipment degradation, user comfort, and cross-domain coupling, ensuring that the learned policy remains interpretable, safe, and aligned with engineering practice. The proposed approach is validated on a modified IEEE 33-bus distribution system coupled with 14 thermal zones and hydrogen facilities, representing a realistic and complex multi-energy microgrid environment. Simulation results demonstrate that PIRL reduces constraint violations by 75–90% and lowers operating costs by 25–30% compared with rule-based and DRL baselines while also achieving faster convergence and higher sample efficiency. Importantly, the trained policy generalizes effectively to out-of-distribution weather conditions without requiring retraining, highlighting the value of incorporating physical inductive biases for resilient control. Overall, this work establishes a transparent and reproducible reinforcement learning paradigm that bridges the gap between physical feasibility and data-driven adaptability, providing a scalable solution for safe, efficient, and cost-effective operation of renewable-rich multi-energy microgrids. Full article

Electrochemical detection is widely used in environmental, health, and food analysis due to its portability, low cost, and high sensitivity. However, when analytes with similar redox potentials coexist, overlapping voltammetric signals often occur, which compromises detection accuracy and sensitivity. In this study, a simple second-derivative image sharpening (IS) algorithm is applied to the electrochemical detection of chlorophenol (CP) isomers with similar redox behaviors. Specifically, a graphene-modified electrode was employed for the electrochemical detection of two chlorophenol isomers: ortho-CP (o-CP) and meta-chlorophenol (m-CP) in the range from 1.0 to 10.0 μmol/L. After image-sharpening, the peak potential difference between o- and m-CP increased from 0.08 V to 0.12 V. The limits of detection (LOD) for o-CP and m-CP decreased from 0.6 to 0.9 μmol/L to 0.12 and 0.31 μmol/L, respectively. The corresponding sensitivities also improved from 0.92 to 1.35 A/(mol L⁻¹) to 4.11 and 3.71 A/(mol L⁻¹), respectively. Moreover, the sharpened voltammograms showed enhanced peak resolution, facilitating visual discrimination of the two isomers. These results demonstrate that image sharpening can significantly improve peak shape, peak separation, sensitivity, and detection limit in electrochemical analysis. The obtained algorithm is computationally efficient (<30 lines of C++ (Version 6.0)/OpenCV, executable in <1 ms on an ARM-M0 microcontroller) and easily adaptable to various programming environments, offering a promising approach for data processing in portable electrochemical sensing systems. Full article

Background: Burnout is a common occupational health risk among healthcare professionals. While the phenomenon has been studied in physicians and nurses, limited evidence exists regarding pharmacists and public health inspectors despite their critical role in ensuring safe medication use and protecting population health. The study aims to identify and analyze the influence of work environment characteristics on the level of professional burnout among master pharmacists, assistant pharmacists, and public health inspectors. Methods: A cross-sectional anonymous survey was conducted among 491 healthcare professionals (221 master pharmacists, 151 assistant pharmacists, and 119 public health inspectors). Burnout was assessed using the validated Maslach Burnout Inventory–Human Services Survey (MBI-HSS (MP)), covering three dimensions: emotional exhaustion (EE), depersonalization (DP), and personal accomplishment (PA). Work environment characteristics were examined across four domains: work tasks, stressors, occupational risks, and social environment. Data were analyzed using descriptive statistics and chi-square tests with IBM SPSS. Results: High levels of EE (66.6%) and DP (53%) were reported, while low PA was less frequent (6.7%). Significant factors associated with EE included time constraints (χ² = 9.985; p < 0.01), workflow disruptions (χ² = 23.987; p < 0.001), insufficient information (χ² = 22.890; p < 0.001), and lack of recognition (χ² = 16.498; p < 0.001). The social environment demonstrated the broadest impact, influencing all three burnout dimensions. Conclusions: The study found a risk of professional burnout among the surveyed groups which is associated with modifiable work environment characteristics. Preventive interventions aimed at promoting a supportive work environment could help mitigate this risk. Full article