Search Results (17,324)

Monitoring sediment concentration in water bodies is crucial for assessing water quality, ecosystems, and environmental health. However, physical sampling and sensor-based approaches are labor-intensive and unsuitable for large-scale, continuous monitoring. This study employs machine learning models to estimate suspended sediment concentration using images captured in natural light, named RGB, and near-infrared (NIR) conditions. A controlled dataset of approximately 1300 images with SSC values ranging from 1000 mg/L to 150,000 mg/L was developed, incorporating temperature, time of image capture, and solar irradiance as additional features. Random forest regression and gradient boosting regression were trained on mean RGB values, red reflectance, time of captured, and temperature for natural light images, achieving up to 72.96% accuracy within a 30% relative error. In contrast, NIR images leveraged gray-level co-occurrence matrix texture features and temperature, reaching 83.08% accuracy. Comparative analysis showed that ensemble models outperformed deep learning models like Convolutional Neural Networks and Multi-Layer Perceptrons, which struggled with high-dimensional feature extraction. These findings suggest that using machine learning models and RGB and NIR imagery offers a scalable, non-invasive, and cost-effective way of sediment monitoring in support of water quality assessment and environmental management. Full article

Continuous and accurate biomass measurement is a critical enabler for control, decision making, and optimization in modern plant production systems. It supports the development of plant growth models for advanced control strategies like model predictive control, and enables responsive, data-driven, and plant state-dependent cultivation. Traditional biomass measurement methods, such as destructive sampling, are time-consuming and unsuitable for high-frequency monitoring. In contrast, image-based estimation using computer vision and deep learning requires frequent retraining and is sensitive to changes in lighting or plant morphology. This work introduces a low-cost, load-cell-based biomass monitoring system tailored for vertical farming applications. The system operates at the level of individual growing trays, offering a valuable middle ground between impractical plant-level sensing and overly coarse rack-level measurements. Tray-level data allow localized control actions, such as adjusting light spectrum and intensity per tray, thereby enhancing the utility of controllable LED systems. This granularity supports layer-specific optimization and anomaly detection, which are not feasible with rack-level feedback. The biomass sensor is easily scalable and can be retrofitted, addressing common challenges such as mechanical noise and thermal drift. It offers a practical and robust solution for biomass monitoring in dynamic, growing environments, enabling finer control and smarter decision making in both commercial and research-oriented vertical farming systems. The developed sensor was tested and validated against manual harvest data, demonstrating high agreement with actual plant biomass and confirming its suitability for integration into vertical farming systems. Full article

To further enhance the economic performance of fuel cell vehicles (FCVs), this study develops a model-adaptive model predictive control (MPC) strategy. This strategy leverages the dynamic relationship between proton exchange membrane fuel cell (PEMFC) output characteristics and temperature to track its optimal operating temperature (OOT), addressing challenges of temperature control accuracy and high energy consumption in the PEMFC thermal management system (TMS). First, PEMFC and TMS models were developed and experimentally validated. Subsequently, the PEMFC power–temperature coupling curve was experimentally determined under multiple operating conditions to serve as the reference trajectory for TMS multi-objective optimization. For MPC controller design, the TMS model was linearized and discretized, yielding a predictive model adaptable to different load demands for stack temperature across the full operating range. A multi-constrained quadratic cost function was formulated, aiming to minimize the deviation of the PEMFC operating temperature from the OOT while accounting for TMS parasitic power consumption. Finally, simulations under Worldwide Harmonized Light Vehicles Test Cycle (WLTC) conditions evaluated the OOT tracking performance of both PID and MPC control strategies, as well as their impact on stack efficiency and TMS energy consumption at different ambient temperatures. The results indicate that, compared to PID control, MPC reduces temperature tracking error by 33%, decreases fan and pump speed fluctuations by over 24%, and lowers TMS energy consumption by 10%. These improvements enhance PEMFC operational stability and improve FCV energy efficiency. Full article

Outdoor access, often referred to as pop holes, is widely used to improve the production and welfare of hens. Such cage-free environments present an opportunity for precision flock management via best environmental control practices. However, outdoor access disrupts the integrity of the indoor environment, including properly planned ventilation. Moreover, complaints exist that hens do not use the holes to access the outdoor environment due to the strong incoming airflow through the outdoor access, as they behave as uncontrolled air inlets in a negative pressure ventilation system. As the egg industry transitions to cage-free systems, there is an urgent need for validated computational fluid dynamics (CFD) models to optimize ventilation strategies that balance animal welfare, environmental control, and production efficiency. We developed and validated CFD models of a cage-free hen house with outdoor access by specifying real-world conditions, including two exhaust fans, sidewall ventilation inlets, wire-meshed pens, outdoor access, and plenum inlets. The simulations of four ventilation scenarios predict the measured air flow velocity with an error of less than 50% for three of the scenarios, and the simulations predict temperature with an error of less than 6% for all scenarios. Plenum-based systems outperformed sidewall systems by up to 136.3 air changes per hour, while positive pressure ventilation effectively mitigated disruptions to outdoor access. We expect that knowledge of improved ventilation strategy will help the egg industry improve the welfare of hens cost-effectively. Full article

This paper presents an annual, real-world evaluation of the performance and economics of a residential photovoltaic (PV) system coupled with a battery energy storage system (BESS) in southern Poland. The system, monitored with 5 min resolution, operated under time-of-use (TOU) electricity tariffs. Seasonal variation was significant; self-sufficiency exceeded 90% in summer, while winter conditions increased grid dependency. The hybrid system reduced electricity costs by over EUR 1400 annually, with battery operation optimized for high-tariff periods. Comparative analysis of three configurations—grid-only, PV-only, and PV + BESS—demonstrated the economic advantage of the integrated solution, with the shortest payback period (9.0 years) achieved with financial support. However, grid voltage instability during high PV production led to inverter shutdowns, highlighting limitations in the infrastructure. This study emphasizes the importance of tariff strategies, environmental conditions, and voltage control when designing residential PV-BESS systems. Full article

Space-Air-Ground Integrated Network (SAGIN), which is considered a network architecture with great development potential, exhibits significant cross-domain collaboration characteristics at present. However, most of the existing works ignore the matching and adaptability of differential tasks and heterogeneous resources, resulting in significantly inefficient task execution and undesirable network performance. As a consequence, we formulate a category attribute-oriented resource allocation and task offloading optimization problem with the aim of minimizing the overall scheduling cost. We first introduce a task–resource matching matrix to facilitate optimal task offloading policies with computation resources. In addition, virtual queues are constructed to take the impacts of randomized task arrival into account. To solve the optimization objective which jointly considers bandwidth allocation, transmission power control and task offloading decision effectively, we proposed a deep reinforcement learning (DRL) algorithm framework considering type matching. Simulation experiments demonstrate the effectiveness of our proposed algorithm as well as superior performance compared to others. Full article

Background: Pain is a complex phenomenon characterized by unpleasant experiences with profound heterogeneity influenced by biological, psychological, and social factors. According to the National Health Interview Survey, 50.2 million U.S. adults (20.5%) experience pain on most days, with the annual cost of prescription medication for pain reaching approximately USD 17.8 billion. Theranostic pain nanomedicine therefore emerges as an attractive analgesic strategy with the potential for increased efficacy, reduced side-effects, and treatment personalization. Theranostic nanomedicine combines drug delivery and diagnostic features, allowing for real-time monitoring of analgesic efficacy in vivo using molecular imaging. However, clinical translation of these nanomedicines are challenging due to complex manufacturing methodologies, lack of standardized quality control, and potentially high costs. Quality by Design (QbD) can navigate these challenges and lead to the development of an optimal pain nanomedicine. Our lab previously reported a macrophage-targeted perfluorocarbon nanoemulsion (PFC NE) that demonstrated analgesic efficacy across multiple rodent pain models in both sexes. Here, we report PFC-free, biphasic nanoemulsions formulated with a biocompatible and non-immunogenic plant-based coconut oil loaded with a COX-2 inhibitor and a clinical-grade, indocyanine green (ICG) near-infrared fluorescent (NIRF) dye for parenteral theranostic analgesic nanomedicine. Methods: Critical process parameters and material attributes were identified through the FMECA (Failure, Modes, Effects, and Criticality Analysis) method and optimized using a 3 × 2 full-factorial design of experiments. We investigated the impact of the oil-to-surfactant ratio (w/w) with three different surfactant systems on the colloidal properties of NE. Small-scale (100 mL) batches were manufactured using sonication and microfluidization, and the final formulation was scaled up to 500 mL with microfluidization. The colloidal stability of NE was assessed using dynamic light scattering (DLS) and drug quantification was conducted through reverse-phase HPLC. An in vitro drug release study was conducted using the dialysis bag method, accompanied by HPLC quantification. The formulation was further evaluated for cell viability, cellular uptake, and COX-2 inhibition in the RAW 264.7 macrophage cell line. Results: Nanoemulsion droplet size increased with a higher oil-to-surfactant ratio (w/w) but was no significant impact by the type of surfactant system used. Thermal cycling and serum stability studies confirmed NE colloidal stability upon exposure to high and low temperatures and biological fluids. We also demonstrated the necessity of a solubilizer for long-term fluorescence stability of ICG. The nanoemulsion showed no cellular toxicity and effectively inhibited PGE2 in activated macrophages. Conclusions: To our knowledge, this is the first instance of a celecoxib-loaded theranostic platform developed using a plant-derived hydrocarbon oil, applying the QbD approach that demonstrated COX-2 inhibition. Full article

Microfluidic platforms offer a powerful approach for ultimately replicating vascularization in vitro, enabling precise microscale control and manipulation of physical parameters. Despite these advances, the real-time ability to monitor and quantify mechanical forces—particularly pressure—within microfluidic environments remains constrained by limitations in cost and compatibility across diverse device architectures. Our work presents an advanced experimental module for quantifying pressure within a vascularizing microfluidic platform. Equipped with an integrated Arduino microcontroller and image monitoring, the system facilitates real-time remote monitoring to access temporal pressure and flow dynamics within the device. This setup provides actionable insights into the hemodynamic parameters driving vascularization in vitro. In-line pressure sensors, interfaced through I2C communication, are employed to precisely record inlet and outlet pressures during critical stages of microvasculature tubulogenesis. Flow measurements are obtained by analyzing changes in reservoir volume over time (dV/dt), correlated with the change in pressure over time (dP/dt). This quantitative assessment of various pressure conditions in a microfluidic platform offers insights into their impact on microvasculature perfusion kinetics. Data acquisition can help inform and finetune functional vessel network formation and potentially enhance the durability, stability, and reproducibility of engineered in vitro platforms for organoid vascularization in regenerative medicine. Full article

Finite-time thermodynamics (FTT) describes the study of thermodynamic processes that take place in finite time. Due to the finite-time requirement, in general the system cannot move from equilibrium state to equilibrium state. As a consequence, excess entropy is generated, available work is reduced, and/or the maximally achievable efficiency is not achieved; minimizing these negative side-effects constitutes an optimal control problem. Particularly challenging are processes and cycles that involve phase transitions of the working fluid material or the target material of a synthesis process, especially since most materials reside on a highly complex energy landscape exhibiting alternative metastable phases or glassy states. In this perspective, we discuss the issues and challenges involved in dealing with such materials when performing thermodynamic processes that include phase transitions in finite time. We focus on thermodynamic cycles with one back-and-forth transition and the generation of new materials via a phase transition; other systems discussed concern the computation of free energy differences and the general applicability of FTT to systems outside the realm of chemistry and physics that exhibit cost function landscapes with phase transition-like dynamics. Full article

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

Indoor horticulture requires a substantial quantity of electricity to meet crops extended photoperiodic requirements for optimal photosynthetic rate. Simultaneously, global electricity costs have grown dramatically in recent years, endangering the sustainability and profitability of indoor vertical farms and/or modern greenhouses that use artificial lighting systems to accelerate crop development and growth. This study investigates the growth rate and physiological development of cherry tomato plants cultivated in a pilot indoor vertical farm at the Agricultural University of Athens’ Laboratory of Farm Structures (AUA) under continuous and disruptive lighting. The leaf physiological traits from multiple photoperiodic stress treatments were analyzed and utilized to estimate the plant’s tolerance rate under varied illumination conditions. Four different photoperiodic treatments were examined and compared, firstly plants grew under 14 h of continuous light (C-14L10D/control), secondly plants grew under a normalized photoperiod of 14 h with intermittent light intervals of 10 min of light followed by 50 min of dark (NI-14L10D/stress), the third treatment where plants grew under 14 h of a load-shifted energy demand response intermittent lighting schedule (LSI-14L10D/stress) and finally plants grew under 13 h photoperiod following of a load-shifted energy demand response intermittent lighting schedule (LSI-13L11D/stress). Plants were subjected also under two different light spectra for all the treatments, specifically WHITE and Blue/Red/Far-red light composition. The aim was to develop flexible, energy-efficient lighting protocols that maintain crop productivity while reducing electricity consumption in indoor settings. Results indicated that short periods of disruptive light did not negatively impact physiological responses, and plants exhibited tolerance to abiotic stress induced by intermittent lighting. Post-harvest data indicated that intermittent lighting regimes maintained or enhanced growth compared to continuous lighting, with spectral composition further influencing productivity. Plants under LSI-14L10D and B/R/FR spectra produced up to 93 g fresh fruit per plant and 30.4 g dry mass, while consuming up to 16 kWh less energy than continuous lighting—highlighting the potential of flexible lighting strategies for improved energy-use efficiency. Full article

Climbing vines have recently induced increasing threats to forest growth under favourable environmental changes. In mangrove forests, the native vine Derris trifoliata became invasive and is now one of the main threats. Yet current management relies on manual removal with low efficiency. Exploring an alternative, cost-effective method is required. To assess the potential of a proposed biological control method, this study performed a pot-plant experiment using Cuscuta japonica to infect D. trifoliata and three common mangrove species in Beihai, China. Results showed that D. trifoliata had a higher infection rate and high host mortality (90%) than mangrove (0%). It also had significantly decreased moisture by 4%, nitrogen by 14%, phosphorus by 27%, potassium by 49% and increased soluble sugar by 49% and protein by 20%, whereas only moisture (2% reduction) and one or two minerals of Excoecaria agallocha and Aegiceras corniculatum were influenced. Only Kandelia obovata had neither effective haustoria nor any nutrients impact from the infection. This study indicated that C. japonica can cause more damage to D. trifoliata than to mangrove species and has the potential to be used as a biological control agent for the threatened mangrove forests of A. corniculatum and K. obovata with monitoring and control. Further field tests are required to bring this method into practice. Full article

Ceramic pastes used in additive manufacturing offer several advantages, including low production costs due to the availability of raw materials and efficient processing methods, as well as a reduced environmental footprint through minimized material waste, optimized resource use, and the inclusion of recyclable or sustainably sourced components. This study evaluates the effect of diatomite content in a ceramic paste composed of carboxymethyl cellulose, kaolinite, and feldspar on its extrusion behavior and thermal conductivity, with additional analysis of its implications for microstructure, mechanical properties, and thermal performance. Four ceramic pastes were prepared with diatomite additions of 0, 10, 30, and 60% by weight. Thermal conductivity, extrusion behavior, morphology, and distribution were examined using scanning electron microscopy (SEM), while thermal degradation was assessed through thermogravimetric analysis (TGA) and differential scanning calorimetry (DSC). The results show that increasing diatomite content leads to a reduction in thermal conductivity, which ranged from 0.719 W/(m·°C) for the control sample to 0.515 W/(m·°C) for the 60% diatomite sample, as well as an improvement in extrusion behavior. The ceramic paste demonstrated adequate extrusion performance for 3D printing at diatomite contents above 30%. These findings lay the groundwork for future research and optimization in the development of functional ceramic pastes for advanced manufacturing applications. Full article

Affecting over 50% of stroke patients, dysphagia is still challenging to diagnose and manage due to its complex multifactorial nature and can be the result of disruptions in the coordination of cortical and subcortical neural activity as reflected in electroencephalographic (EEG) signal patterns. Sample Entropy (SampEn), a signal complexity or predictability measure, could serve as a tool to identify any abnormalities associated with dysphagia. The present study aimed to identify quantitative dysphagia biomarkers using SampEn from EEG recordings in post-stroke patients. Sample entropy was calculated in the theta, alpha, and beta bands of EEG recordings in a repetitive swallowing task performed by three groups: 22 stroke patients without dysphagia (controls), 36 stroke patients with dysphagia, and 21 healthy age-matched individuals. Post-stroke patients, both with and without dysphagia, exhibited significant differences in SampEn compared to healthy subjects in the alpha and theta bands, suggesting widespread alterations in brain dynamics. These changes likely reflect impairments in sensorimotor integration and cognitive control mechanisms essential for effective swallowing. A significant cluster was identified in the left parietal region during swallowing in the beta band, where dysphagic patients showed higher entropy compared to healthy individuals and controls. This finding suggests altered neural dynamics in a region crucial for sensorimotor integration, potentially reflecting disrupted cortical coordination associated with dysphagia. The precise quantification of these neurophysiological alterations offers a robust and objective biomarker for diagnosing neurogenic dysphagia and monitoring therapeutic interventions by means of EEG, a non-invasive and cost-efficient technique. Full article

This study proposes a multi-objective optimization scheduling method for edible oil transportation in smart manufacturing, focusing on centralized control and addressing challenges such as complex pipelines and shared resource constraints. The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. Additionally, the transport time is calculated using the Hagen–Poiseuille law by considering the viscosity coefficients of different oil types. To minimize both cost and time, task execution sequences are optimized based on a Pareto front approach. A 3D digital model of the pipeline system was developed using C#, SolidWorks Professional, and the Helix Toolkit V2.24.0 to simulate a realistic production environment. This model is integrated with a 3D visual human–machine interface(HMI) that displays the status of each task before execution and provides real-time scheduling adjustment and decision-making support. Experimental results show that the proposed method improves scheduling efficiency by over 43% across various scenarios, significantly enhancing overall pipeline transport performance. The proposed method is applicable to pipeline scheduling and transportation management in digital factories, contributing to improved operational efficiency and system integration. Full article