Gut microbiome and metabolic responses of adult zebrafish (Danio rerio) to the co-exposure of polyethylene microplastics and levofloxacin.

Integrated metabolomics and flow cytometry assessment reveals immune-metabolic modulation of Crassostrea (=Magallana) gigas hemolymph under hypoxic and elevated temperature conditions.

Engineering electronic interaction between cobalt single atom and polymeric carbon nitride for hydrogen peroxide efficient photosynthesis.

Deletion of electron-bifurcating [FeFe]-hydrogenase enhances H2-driven CO2 fixation and ethanol production in Clostridium ljungdahlii.

Rheumatoid arthritis (RA) causes persistent symptoms that limit individuals' ability to engage in meaningful daily occupations. Occupational therapy plays a key role in integrating lifestyle and behavioral strategies into comprehensive RA management. To examine the efficacy of a client-centered, modular lifestyle intervention (LI) on occupational performance and health outcomes of individuals with RA. A randomized controlled trial. Rheumatology outpatient clinic of a university hospital. Forty-five individuals diagnosed with RA were randomly assigned to an LI group (n = 23) or a control group (n = 22). The LI group received an eight-session, occupation-based LI; the control group received twice-weekly well-being check phone calls over 4 wk. The primary outcome was the Canadian Occupational Performance Measure (COPM) Performance score. Secondary outcomes included COPM Satisfaction, Disease Activity Score-28, visual analog scale for pain, Bristol Rheumatoid Arthritis Fatigue Multi-Dimensional Questionnaire, and Rheumatoid Arthritis Quality of Life Scale scores. Assessments were conducted at baseline, postintervention, and 6-mo follow-up. Compared with the control group, the LI group demonstrated significantly greater improvements in the primary outcome (COPM Performance) as well as in secondary outcomes including COPM Satisfaction, pain, fatigue, and quality of life. A delayed reduction in disease activity was observed at the 6-mo follow-up. The LI produced durable improvements in occupational performance and patient-reported outcomes, supporting its integration as a sustainable adjunct to pharmacological care for individuals with RA. Plain-Language Summary: People with rheumatoid arthritis often have difficulty completing daily activities because of pain, fatigue, and limitations in movement. This study explored whether a structured lifestyle program based on occupational therapy principles could help improve these challenges. All participants continued their usual medical treatment throughout the study. In addition, one group received an eight-session lifestyle intervention focused on meaningful daily activities, healthy habits, energy conservation, and participation in social and leisure activities. The control group received their usual medical treatment along with brief well-being check phone calls. Participants who received the lifestyle program showed greater improvements in performing and enjoying daily activities and reported lower fatigue levels than those in the control group. These findings suggest that adding an occupation-based lifestyle program to standard medical care may provide meaningful benefits for people living with rheumatoid arthritis.

Energy conservation and pressure relaxation in an extended two-temperature model for copper with an electron temperature-dependent interaction potential

Targeted design of polyurethane modified asphalt based on Click reaction: Application for enhancing interfacial bonding properties of ultra-thin overlays

Physics-informed synergy regional co-seismic landslide size prediction: A novel data-driven approach for improved reliability and interpretability

Extreme weather poses serious challenges to wildlife, often forcing animals to alter their behaviour with potential population-level consequences. Following contemporary climate change, the number and intensity of Arctic storms are increasing, but the responses of Arctic species to wind speed and episodic storms are poorly documented. We aimed to quantify behavioural responses (i.e. changes in movement modes and habitat selection patterns) of muskoxen (Ovibos moschatus) to wind speed and to compare estimates from before, during and after storm to hurricane-level wind events (wind speeds ≥24.5 m/s on the Beaufort scale). Hourly positions of Global Positioning System (GPS)-collared adult muskoxen (N = 61) tracked in northeast Greenland between 2013 and 2024 were georeferenced with data on wind speed, precipitation class (with or without), elevation, terrain ruggedness and vegetation type. Statistical movement modes were estimated using unsupervised hidden Markov models, and habitat selection patterns were quantified using step selection functions for summer (June-August) and winter (September-May). Results showed that wind speed influenced the allocation of time between movement modes. Increasing wind speed reduced the amount of time spent in states characterized by intermediate step lengths and turning angles, suggesting a decrease in foraging activity. Changes in state-time budgets were most pronounced under wet conditions during summer, likely due to reduced fur insulation and increased thermoregulatory behaviour. Habitat selection patterns during calm conditions (e.g. selection for dense vegetation) were reinforced at increasing wind speeds. State-time budgets of muskoxen affected and unaffected by storms were comparable between days before and after episodic storm events. During storms, however, the allocation of time among movement modes shifted more towards states indicative of resting at the expense of states indicative of foraging and relocating, which differed from muskoxen unaffected by storms. Muskoxen adopt a simple energy conservation strategy of bedding down in dense vegetation habitat to buffer against negative impacts of high wind speed, without actively compensating for potential lost foraging opportunities during episodic storm events. However, as the Arctic climate continues to change, the energetic implications of increasing wind speed and frequency of storms on individual fitness and population performance represent a valuable area for future research.

The secondary network supply water temperature is critical to heat transmission and distribution from heating systems to end users, and its prediction accuracy directly determines heating quality and energy utilization efficiency. At present, most heating systems rely solely on single-source meteorological data for regulation and suffer from pronounced prediction lag, creating an urgent need for intelligent prediction methods that integrate multi-dimensional features. Therefore, this paper adopts Ensemble Empirical Mode Decomposition (EEMD) for multi-scale time series decomposition, employs Convolutional Neural Network (CNN) for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for temporal dependency mining, and introduces Dynamic Weight Allocation (DWA) to realize adaptive fusion of dual-model outputs. On this basis, an EEMD-DWA-CNN-BiLSTM multi-scale feature fusion prediction framework is constructed. We trained and validated the proposed model using 1776 sets of secondary network supply water temperature data collected from a heat exchange station in Qiqihar City, Heilongjiang Province, spanning from December 2024 to February 2025. The proposed EEMD-DWA-CNN-BiLSTM combined model achieved excellent prediction performance with a Mean Absolute Error (MAE) of 0.31, a Mean Absolute Percentage Error (MAPE) of 0.68, a Root Mean Square Error (RMSE) of 0.38, and a coefficient of determination (R2) of 88.54. This algorithm exhibits good prediction accuracy and reliable generalization ability, which provides solid technical support for heating enterprises to achieve energy conservation, emission reduction and precise heating regulation.

Under the circumstances where the green credit regulatory system has been implemented and the large-scale application of industrial green microgrids has occurred, the institutional constraints and clean energy technologies jointly reshape the emission reduction choices of high-tech manufacturing enterprises. This study constructs an evolutionary game model involving the government, research and development institutions, and high-tech manufacturing enterprises. Through this model, the stable equilibrium of the system's evolution is sought, and the evolution patterns of key parameters are explored through numerical simulation. The conclusions are as follows: (1) The various input costs of the three parties constitute the threshold for emission reduction costs. The increase in costs significantly hinders green behaviors such as supervision, research and development, and active emission reduction. (2) Market-based benefits serve as the intrinsic driving force for collaborative emission reduction. The increase in technology transfer benefits and the improvement in enterprises' green operation benefits can lead to a spontaneous shift towards a low-carbon economy. (3) The government's policy tools exhibit differentiated effects, and research subsidies facilitate the breakthroughs in green microgrids technology. Excessive rewards can lead to a policy-dependent situation within the enterprise. Appropriate fines can rely on green credit constraints to compel enterprises to comply with emission reduction regulations. This study fills the analytical gap regarding the technical supply side in multi-party collaborative emission reduction. The conclusions provide a theoretical basis and practical reference for improving the collaborative emission reduction system, promoting the green transformation of high-tech manufacturing enterprises, and facilitating regional green and low-carbon development.

The welfare of free-roaming, captive wild or domestic animals housed outdoors is often questioned. How subjects perceive climatic conditions is not always well detected as it is highly subjective, depending on species, age and experience. Hence, decisions on housing rely mostly on human anthropomorphic perception. In particular, indicators of comfort and positive emotions remain poorly documented. Acoustic signals, however, offer a promising window into animals' internal states. In the present study, we hypothesized that acoustic signals could be used to identify positive or negative perceptions of environmental conditions in horses. Horses possess a diverse acoustic repertoire and the snort produced by nostril vibration, has been identified as a likely indicator of calm, positive emotions. Over 3 weeks in early spring, acoustic production and time budget were recorded for 20 adult horses living outdoors on pasture. Meteorological conditions and pasture composition were monitored simultaneously. Results showed that snorts were the most frequent acoustic signal and that their production varied considerably across observation sessions. Snort production was highly correlated with temperature: horses produced more snorts and showed more relaxed behaviours when the temperature ranged between 15 and 20°C. In contrast, lower temperatures around 5-8°C were associated with more "static" behaviours (reflecting energy conservation) and less snorts. Finally, snorts were more frequent immediately after horses entered a new pasture with higher food diversity than just before leaving, when food diversity had become more restricted. These findings suggest that acoustic production may help identify the subjective perception that animals have from their environment, allowing more individualized and adapted management practices.

The evaluation and control of energy efficiency in machine tools are crucial to achieving energy conservation and emission reduction in manufacturing. However, existing energy efficiency models primarily focus either on the energy efficiency of material removal or on the inherent energy efficiency of machine tools themselves, without considering the impact of speed loss caused by low-load operation on the overall energy efficiency of the machining process. To address this gap, this paper establishes a novel load-energy efficiency model for machining systems to specifically assess the impact of speed loss on machining energy efficiency. Furthermore, a statistical process control method for load-energy efficiency is proposed to monitor energy use. Finally, a case study of load-energy efficiency modelling and control for the CK6153i lathe is conducted, demonstrating the effectiveness of the proposed method through a reduction in energy loss of 417.54 kJ and an increase in load-energy efficiency by 4.59%. The proposed method reveals previously untapped opportunities for energy saving in machining processes. The study provides a more nuanced understanding of the relationship between speed loss and machining energy efficiency, thereby offering practical insights for machining energy optimisation.

Image pixel density is a key factor limiting image clarity, while the scalar spatial bandwidth product (SBP) is the critical parameter determining the resolution of acoustic holographic imaging. Under finite SBP conditions, achieving high-resolution imaging remains a significant challenge for current acoustic meta-hologram techniques due to the constraints imposed by the diffraction limit of sound waves. To address the resolution and data density limitations of acoustic holography at the diffraction limit, we propose a super-resolution and high data density holographic imaging method at low SBP based on amplitude-phase coupling modulation mechanisms. By revealing the relationship between amplitude-phase coupling and acoustic energy conservation, we derive a high-resolution focused phase distribution suitable for low SBP under acoustic energy conservation constraints. Introducing coupled focused phases into the hologram enables super-resolution acoustic field reconstruction and high-data-density information transmission at low SBP, providing a theoretical basis for overcoming the diffraction limit. We demonstrate the proposed concept of super-resolution and high-data-density hologram through theoretical derivation, numerical simulation, and experimental validation. This super-resolution holographic imaging method, based on amplitude-phase coupling modulation under acoustic energy conservation constraints, provides a conceptually advanced technical approach for achieving high-resolution acoustic hologram under low data density.

Integrated control of redox and energy metabolism by the membrane-bound and soluble transhydrogenases of Pseudomonas putida across metabolic regimes.

The dynamics of an eccentric droplet impacting a sessile droplet on flat, convex, and concave superhydrophobic surfaces are investigated using three-dimensional Lattice Boltzmann simulations. We analyze the coupled effects of Weber number, impact eccentricity, and substrate curvature on collision outcomes. Results show that convex curvature amplifies spreading and accelerates droplet shedding through divergent centrifugal effects, whereas concave curvature imposes geometric confinement that converges momentum inward, thereby effectively suppressing splashing. A regime map identifies four distinct rebound modes, with curvature significantly shifting transition boundaries. Furthermore, a theoretical scaling law is developed based on energy conservation, incorporating an effective driving Weber number and a curvature correction factor. This model successfully collapses data onto a unified master curve for maximum spreading, predicting the interplay between inertia and geometry. A deviation is observed only in the high-eccentricity "glancing" regime, where shear-driven elongation prevails over pressure-driven spreading. These findings offer theoretical guidance for optimizing droplet-based technologies on nonplanar surfaces.

Distinguishing energy conservation from mechanical energy conservation using a bouncing ball: undergraduate laboratory experiment

Abstract The steady, two-dimensional magnetohydrodynamic (MHD) boundary layer flow and heat transfer of a piezoviscous fluid over a permeable stretching or contracting wedge are investigated in this study. Unlike classical Newtonian fluids, the dynamic viscosity of the working fluid is assumed to be exponentially dependent on the local mechanical pressure; consequently, the fluid’s rheological response is fundamentally altered, and the deviatoric stress is closely coupled to the pressure field. A transverse magnetic field is incorporated, and variable thermal conductivity is accounted for in the analysis, thereby enhancing the physical fidelity of the heat transfer model. The complex, highly coupled governing partial differential equations—representing the conservation of mass, momentum, and thermal energy—are simplified using Prandtl’s boundary layer approximations. Due to the explicit streamwise dependence of the piezoviscous viscosity, a global similarity solution cannot be achieved. Instead, appropriate local similarity transformations are employed to reduce the partial differential equations into a system of coupled, nonlinear ordinary differential equations. These equations, generalized forms of the classical Falkner–Skan problem, are solved numerically using a robust Runge–Kutta integration scheme. The effects of the pressure–viscosity coefficient, magnetic interaction parameter, wedge angle, and variable thermal conductivity on the velocity and temperature profiles, as well as on key engineering quantities like the skin friction coefficient and local Nusselt number, are systematically analyzed. Critical insights into the thermo-fluidic behavior of fluids under extreme pressure environments, where the assumption of constant viscosity is physically restrictive, are provided by these findings.

Long-COVID is a heterogenous, episodic, and multisystemic condition which can result following infection with a novel pathogen, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Whilst a precise pathophysiological cause is unknown, several mechanisms are hypothesised, each with plausible scientific rationale. Most people experiencing persistent symptoms following COVID-19 infection recover, yet some experience severe and debilitating illness for years after infection. Strategies to manage the sequelae of Long-COVID can improve the lives of sufferers. To describe the physiotherapy management of Long-COVID based on current evidence. Those with 'invisible illness' (illness without outwardly visible signs), such as Long-COVID, often report stigmatisation and scepticism from healthcare systems. Validation of the experience of those with Long-COVID is therefore crucial to ensure patient-centred care. Thorough patient assessment is required to provide tailored management approaches given the diversity of Long-COVID presentations. Red flags that may contraindicate certain rehabilitation approaches (particularly exercise-based interventions) or that warrant further investigation should be considered. Assessments of fatigue, post-exertional malaise, respiratory symptoms, neurocognitive symptoms (i.e., brain fog), physical function, and orthostatic intolerance are strongly recommended. Management strategies may involve pacing and energy conservation techniques, pulmonary rehabilitation, inspiratory muscle training, dysfunctional breathing retraining, lifestyle and dietary strategies to manage orthostatic intolerance, and return-to-work planning. Physiotherapists are well positioned to deliver individualised, patient-centred, and validating care based on best available evidence.

The deep terrestrial biosphere is the vast biome beneath the soil layer that contains the majority of the Earth's prokaryotic biomass, yet it is one of the least investigated communities. Although, estimates of deep biosphere biomass suggest biofilm cells outnumber the planktonic biomass by several orders of magnitude, most investigations target planktonic communities captured from groundwaters. This multi-year study used 16S rRNA gene sequencing to compare planktonic and biofilm communities attached to natural granitic rock, demonstrating that biofilm formation selected for taxa with distinct relative abundances and exhibited temporal development. The biofilm communities also showed a decreasing influence of introduced populations on the natural rock surfaces (macadam) present at the onset of the incubations. After two- and four-years of biofilm incubation, a community developed that was dominated by sulfur/sulfate reducing Desulfocapsaceae, Desulfobacteraceae, and BM004 along with the families UBA5619, Rhodocyclaceae, Profunditerraquicolaceae, and UBA2206. This long-term community included populations predicted to be host-associated ultra-small cells. This contrasted with previous studies of early biofilm development in deep Fennoscandian Shield groundwaters that suggested biofilm initiation was mediated by lithotrophic carbon and nitrogen fixing populations. However, metabolic predictions based upon the 16S rRNA gene-based communities also showed an autotrophic and diazotrophic community including sulfur cycling in line with the previous studies. In conclusion, this study showed long-term biofilm composition to be dissimilar to the planktonic communities with a consistent strategy for energy conservation similar to previous studies of early biofilm formation from these groundwaters.