Search Results (361)

S-Methylmethionine (SMM), also called vitamin U, shows antihistamine, anti-inflammatory, radioprotective and anti-irritant activity, as well as enhanced wound healing. In addition, vitamin U affects the regeneration and renewal of the skin hydrolipid mantle and offers some UVB-protective effects on the skin. Since there are currently no reports in the scientific literature concerning the release of vitamin U from cosmetic formulations, the present study was undertaken as a preliminary and exploratory pilot investigation. The research focused on evaluating the release behavior of SMM (synthetic methylmethionine), a compound recognized for its potential skin-regenerating, soothing, and protective properties, from several commonly used topical delivery systems. Therefore, the various topical formulations, including oil-in-water (O/W) and water-in-oil (W/O) emulsions, as well as hydrogel formulations, were evaluated as potential, effective vitamin U skin delivery systems. Vitamin U-loaded emulsions (O/W and W/O) differing in droplet size in the internal phase and a hydrogel were prepared. The physicochemical properties of the formulations, such as emulsion type, stability, viscosity, pH and droplet size, were evaluated. The study of vitamin U release was performed in thermostatic diffusion chambers at a temperature of T = 32 °C using the Spectra/Por Standard Regenerated Cellulose dialysis membrane. A phosphate buffer (PBS) with pH 7.4 was used as the receptor solution. The concentration of the released S-Methylmethionine was analyzed with ninhydrin-based spectrophotometric assays. The obtained results showed that the type of the formulation significantly influenced the SMM release. The highest release of SMM was observed from hydrogel and O/W emulsions. Full article

Accurate design and performance assessment of solar thermal domestic hot water systems coupled with a heat pump auxiliary typically requires transient simulation, as the system’s behavior depends on multiple interactions among collector characteristics, storage stratification, control logic, weather, and draw-off timing. Monthly methods such as the f-chart are useful for first-pass estimates, but they do not resolve stratification, thermostat operation, or demand timing, and they may become inaccurate for stratified thermostat-controlled systems. Direct comparisons of locally inspectable symbolic and black-box surrogate families for this system class remain limited. A 10,982-case development dataset was generated from minute-resolved annual MATLAB simulations, parameterized by collector area, optical efficiency, and first- and second-order loss coefficients. Three surrogate families were benchmarked under a unified protocol, random forest-assisted shape-constrained symbolic regression (SR), feed-forward artificial neural network (ANN) models, and Automatic Learning of Algebraic Models for Optimization (ALAMO), with the f-chart used as a monthly reference method. The targets were the 12 monthly solar fractions under the direct solar heat definition and the corresponding annual mean solar fraction, evaluated on the same independent 991-case test set. SR achieved the lowest average error (mean absolute percentage error, MAPE = 0.82%; root mean square error, RMSE = 0.006), followed by the ANN (MAPE = 2.07%, RMSE = 0.028) and ALAMO (MAPE = 3.67%, RMSE = 0.060), with Nash–Sutcliffe efficiency (NSE) values above 0.98 for all models. Evaluation times were 0.0026–0.124 s per target, compared with about 1000 s for one full-year simulation. These results define the study as a common protocol benchmark within the studied simulator-defined envelope. SR gives the strongest accuracy with local symbolic inspectability, the ANN remains the flexible retrainable option, and ALAMO provides compact algebraic evaluation with the shortest learned model runtime. Full article

This paper presents a thermal and power characterization of the NVIDIA Jetson AGX Orin deployed in a realistic outdoor edge AI enclosure, integrated with power supplies, a Long Term Evolution (LTE) module, and a thermostat-controlled fan, across an ambient temperature range from −20 °C to +40 °C. The device was tested in a climate chamber under two workloads: a synthetic CPU and GPU stress test, and a YOLOv8s inference workload (TensorRT FP16, 640 × 640 input). Internal temperatures were recorded using four calibrated platinum Resistance Temperature Detectors (RTD)—PT100/PT1000, while Jetson chip temperatures and power consumption were logged via tegrastats and jtop. At sub-zero ambient temperatures, heat dissipated by the device itself kept all components within their operating ranges down to −20 °C. At the +40 °C setpoint, the stress test triggered Jetson thermal throttling at GPU and CPU temperatures of +95.6 °C and +99.0 °C, respectively. Under the same conditions, the YOLOv8s inference workload sustained 108.8 frames per second (FPS) at 19.1 W average power, approximately half of the 36.5 W consumed under the stress test, with chip temperatures well below the throttling threshold. These findings indicate that synthetic stress tests substantially overestimate the thermal and power demands of the tested inference workload, and that the enclosure retains sufficient thermal headroom for outdoor edge device deployment. Full article

Providing optimal temperatures in greenhouses is essential for cultivating high-temperature-demand crops in winter. Therefore, this study aimed to investigate the feasibility of utilizing a flat plate solar collector (FPC) for heating greenhouses. A field experiment was conducted, complemented by simulations using the PolySun V2023.11 software. The FPC system comprised two collectors, each with an aperture area of 2.24 m², connected to a 300 L hot water tank. The water tank had an internal electric backup heater (2 kW) and a thermostat to regulate the hot water temperature. The experiment consisted of two greenhouses, each with an area of 50 m². The first unheated greenhouse (UHGH) was used as the control, while the second heated greenhouse (HGH) was heated by a closed-loop system comprising copper pipes installed along the internal perimeter. Results revealed that the FPC significantly increased air temperature by 2.7 °C, and reduced relative humidity by 9.7% in the HGH compared to the UHGH. Simulated results showed that the annual generated energy of the FPC was 4830 kWh with a reduction of CO₂ emission by ≈2.9 tones. The average thermal efficiency of the FPC was 44%, with a payback period of 8.5 years. In conclusion, the FPC could protect plants from low temperatures in winter. Full article

Accurate day-ahead estimation of residential demand response (DR) potential is essential for load aggregators participating in electricity markets. It is also difficult to estimate because public residential datasets rarely contain observed DR event labels and household flexibility is shaped by heterogeneous, weather-sensitive consumption behavior. This paper proposes an appliance-agnostic two-stage framework for forecasting residential DR potential from aggregate hourly load and weather data. In the first stage, a thermal-response model estimates household heating and cooling sensitivities and converts thermostat-setback assumptions into synthetic DR-potential targets. Because these targets are model-derived proxies rather than measured DR events, the reported forecasting errors should be interpreted in terms of accuracy against a physically motivated synthetic target. In the second stage, the synthetic target sequence is forecast using a mixture of KAN experts (MoKE). The architecture combines Wavelet-KAN, Fourier-KAN, and RBF-KAN experts through sparse top-k routing with reversible instance normalization, allowing the model to represent local irregularities, recurrent daily/seasonal structure, and smooth nonlinear response regimes in the same forecasting layer and these forecasting characteristics are absent from traditional deep learning forecasting models. The framework is evaluated on the UMass residential dataset, which contains hourly electricity and meteorological measurements from 114 apartments collected during 2015 and 2016, using a 24 h day-ahead forecasting horizon. Across both winter and summer evaluation windows, the proposed model achieves the lowest error among all benchmark methods, outperforming TimesNet, Informer, N-HiTS, FEDformer, PatchTST, and TCN across MAE, MAPE, RMSE, and sMAPE. In particular, MoKE attains MAE values of 3.19 in winter and 3.18 in summer, demonstrating stable predictive accuracy under seasonally distinct operating conditions. These results show that heterogeneous KAN experts offer a feasible method for residential DR forecasting when appliance-level metering and observed event-level DR measurements are unavailable. Full article

The genesis of the conducted research was the intention to determine how the rheological properties of engine oil change upon the addition of a coolant. The aim of the study was to assess the effect of coolant addition to engine oil on its dynamic viscosity. The experiments were carried out for six different fresh engine oils and six of the same oils sampled during routine oil replacement after a vehicle mileage of 10,000 km. Two oils were selected from each of the three primary categories: synthetic, semi-synthetic, and mineral oils. Then, mixtures of engine oils with the addition of coolant were prepared at 0, 5, 10, 20, 30, 40 and 50% (v/v), respectively. The study involved measuring the dynamic viscosity of the samples at 1000 s⁻¹, covering temperatures from 0 to 50 °C. The tests were conducted using a measurement setup equipped with a RHEOLABQC rotational rheometer manufactured by Anton Paar GmbH (Ostfildern, Germany). To investigate the temperature dependence of dynamic viscosity, a GRANT thermostatic bath was coupled with the rheometer. The studies demonstrated that dynamic viscosity strongly depends on temperature, as well as on the type and condition of the engine oil. At 0 °C, the dynamic viscosity of fresh oils ranged from approximately 500 to 700 mPa·s, whereas at 50 °C it decreased to approximately 100 mPa·s. The addition of 5% (v/v) coolant to engine oil resulted in only a slight change in dynamic viscosity. In contrast, a substantial decrease in dynamic viscosity was observed when 50% (v/v) coolant was added to the tested engine oils. The results indicate that coolant, which may enter the engine oil in the event of an engine failure, can significantly deteriorate the rheological properties of this lubricant. Full article

Internal thermal load fluctuations and variations in occupant density affect the performance of Hybrid Geothermal Heat Pump (HGHP) systems. Traditional control strategies cannot provide the rapid adjustments needed to operate efficiently in real time and can be inefficient, leading to increased energy consumption and reduced thermal comfort. A data-driven fuzzy logic control framework is developed in this paper to dynamically adjust the performance of an HGHP system in real time as a function of occupancy and environmental conditions (e.g., temperature and humidity differences). The controller analyzes input data related to real-time outdoor ambient conditions like temperature, humidity and occupied spaces; a real-time flow sensor attached to the occupants of the building (a count of the number of occupants currently in each occupied space); and the coefficient of performance (COP) of the HGHP system, and uses the analysis to generate a “smart” control decision for the following device types: variable speed drive (VSD), fan number, operating modes, system control and valve positions. The controller also controls the overall system. The model was developed and simulated in MATLAB Simulink^®, with realistic system parameters, and validated and calibrated using operational data from an HGHP system at a university, based on operating conditions. The simulation results indicate that our fuzzy controller achieves higher energy efficiency for thermal comfort than traditional thermostat-based controls, with COP improvements ranging from 7.36% to 11.76% and power consumption reductions between 4.13% and 8.55% across various occupancy scenarios. The improved COP also demonstrates the device’s responsiveness and effectiveness, even under frequent changes in occupancy patterns (dynamic occupancy), making it suitable for use in automated climate control systems in modern buildings. Full article

This study presents the metrological validation of encapsulated DS18B20 digital temperature sensors. Eight units were tested, and seven were analysed (sensor 8 was excluded owing to a systematic failure). The evaluation was performed using a standard comparison calibration, where T_ref was defined as the mean of two calibrated Pt-100 probes in a Julabo DYNEO DD 601F thermostatic bath, following the TH-001 procedure of the Spanish Centre of Metrology (CEM). Four validation tests were performed: Test 1 (E1, 20 to 75 °C), Test 2 (E2, 20 to 72 °C), and with an extended range, Test 3 (E3, −12 to 86 °C) and Test 4 (E4, −12 to 86 °C; repetition to assess reproducibility relative to E3), with 10 steady-state readings per setpoint. Erroneous readings were defined and removed (probe 3, Test 4), and set points without valid readings from probe 4 above 68 °C were excluded. Without data processing, the errors were consistent with the manufacturer’s stated ±0.5 °C, despite an inter-probe bias. Several correction models were evaluated (offset, affine linear, polynomial, and segmented); the probe-specific affine linear model provided the best overall compromise, reducing MAE (Mean Absolute Error) to 0.046 to 0.130 °C and RMSE (Root Mean Square Error) to 0.057 to 0.169 °C. The process uncertainty is dominated by the traceability of the Pt-100 probes and the effective nonuniformity of the isothermal volume, which limits the achievable accuracy. The results support the use of individually calibrated DS18B20 sensors for continuous monitoring, provided that the effective operating range is maintained. Full article

This study analyzes four alternative cycle configurations for the traditional vapor compression system used in conventional, hybrid, and electric vehicles, taking low-GWP alternatives for the substitution of R134a. These are cycle with an internal heat exchanger and thermostatic expansion valve (IHX + TEV); cycle with an internal heat exchanger and short tube (IHX + ST); cycle with an ejector (EC); and cycle with an ejector and internal heat exchanger (EC + IHX). Similarly, the energy, exergy, exergoeconomic, and environmental impact of these configurations were analyzed using synthetic refrigerants with a GWP of less than 150. The results indicate that, using the EC + IHX configuration, the COP for refrigerants R1234yf, R1234ze(E), R1243zf, and R516A is the highest, increasing by more than 20%. Using R1243zf in the EC configuration can reduce the total cost ratio compared to other refrigerants. On the other hand, the use of IHX cycle configurations with R444A and R445A decreases the exergy efficiency and increases the total cost ratio by up to 35% and 70%, respectively. Additionally, the Total Equivalent Warming Impact (TEWI) analysis showed reductions up to 20% for ejector cycle configurations using R1234ze(E), R1234yf, R1243zf, and R516A. Full article

Buildings account for a substantial share of global energy demand, and decisions made during conceptual design strongly influence long-term operational consumption. This study presents an open, simulation-derived dataset to support early-stage estimation of residential energy use in a hot–arid context (New Cairo, Egypt). A parametric Rhino/Grasshopper workflow coupled with EnergyPlus was used to generate 12,000 annual simulations. The simulations were produced by systematically sampling key geometric, envelope, glazing, and operational variables, including building dimensions, orientation, window-to-wall ratio, envelope construction options, glazing properties, internal loads (lighting and equipment), and thermostat setpoints. For each case, annual end-use outputs (heating, cooling, lighting, and equipment energy) are reported alongside the corresponding input features, enabling design-space exploration, sensitivity analysis, and the development of surrogate and machine-learning models for rapid decision support. Verification checks and plausibility screening were applied to confirm successful simulation execution and consistent data extraction. In addition, dataset-level sampling diagnostics (marginal balance and correlation screening) are reported to support robust reuse in surrogate and machine-learning studies. The resulting dataset and documentation provide a reusable resource for researchers and practitioners investigating energy-informed residential design under hot-climate boundary conditions. Full article

The increasing demand for the energy-efficient and occupant-centred operation of educational buildings requires accurate and interpretable models capable of predicting indoor environmental conditions under real operating constraints. This study proposes a residual learning-enhanced grey-box modelling framework for predicting indoor air temperature and assessing indoor environmental quality indicators in a KNX-enabled educational building operating under simple thermostatic heating control. The approach combines a reduced-order discrete-time RC thermal model with a data-driven machine learning component trained to model the next-step residual between measured and simulated indoor temperatures. High-resolution KNX monitoring data were recorded at a 5 min sampling interval over three consecutive months (October–December) during the heating season. Using a chronological 70/30 train–test split, the identified RC grey-box model achieved a pooled test RMSE of 0.269 °C, an MAE of 0.126 °C, and an R² of 0.987. The proposed hybrid formulation achieved RMSE = 0.343 °C, MAE = 0.106 °C, and R² = 0.978 across 62,456 test samples. While the pooled RMSE remains influenced by occasional larger deviations in a small number of rooms, the hybrid model yields a consistent reduction in absolute error (≈16% MAE reduction) and reduced inter-room variability compared to the physics-based baseline. These results indicate that residual learning can enhance predictive robustness under decentralized thermostatic operation and limited sensing, while preserving physical interpretability. The proposed framework provides a practical and scalable solution for indoor temperature prediction and IEQ assessment in educational buildings using existing KNX automation data. Full article

Increased global energy consumption contributes to higher operational costs in the energy sector and results in environmental deterioration. This study evaluates the effectiveness of integrating Internet of Things (IoT) sensors and machine learning techniques to predict adaptive thermostat setpoints to support behavior-aware Heating, Ventilation, and Air Conditioning (HVAC) operation in residential buildings. The dataset was collected over two years from 2080 IoT devices installed in 370 zones in two buildings in Halifax, Canada. Specific categories of real-time information, including indoor and outdoor temperature, humidity, thermostat setpoints, and window/door status, shaped the dataset of the study. Data preprocessing included retrieving data from the MySQL database and converting the data into an analytical format suitable for visualization and processing. In the machine learning phase, deep learning (DL) was employed to predict adaptive threshold settings (“from” and “to”) for the thermostats, and a gradient boosted trees (GBT) approach was used to predict heating and cooling thresholds. Standard metrics (RMSE, MAE, and R²) were used to evaluate effective prediction for adaptive thermostat setpoints. A comparative analysis between GBT ”from” and “to” models and the deep learning (DL) model was performed to assess the accuracy of prediction. Deep learning achieved the highest performance, reducing the MAE value by about 9% in comparison to the strongest GBT model (1.12 vs. 1.23) and reaching an R² value of up to 0.60, indicating improved predictive accuracy under real-world building conditions. The results indicate that IoT-driven setpoint prediction provides a practical foundation for behavior-aware thermostat modeling and future adaptive HVAC control strategies in smart buildings. This study focuses on setpoint prediction under real operational conditions and does not evaluate automated HVAC control or assess actual energy savings. Full article

This study investigates the incorporation of a standardised flexibility protocol within a physics-based models to enable controllable demand-side flexibility in residential energy systems. A heating subsystem is developed using MATLAB/Simulink and Simscape, serving as a testbed for protocol-driven control within a Multi-Energy System (MES). A conventional thermostat controller is first established, followed by the implementation of an OpenADR event engine in Stateflow. Simulations conducted under consistent boundary conditions reveal that protocol-enabled control enhances system performance in several respects. It maintains a more stable and pronounced indoor–outdoor temperature differential, thereby improving thermal comfort. It also reduces fuel consumption by curtailing or shifting heat output during demand-response events, while remaining within acceptable comfort limits. Additionally, it improves operational stability by dampening high-frequency fluctuations in mdot_fuel. The resulting co-simulation pipeline offers a modular and reproducible framework for analysing the propagation of grid-level signals to device-level actions. The research contributes a simulation-ready architecture that couples standardised demand-response signalling with a physics-based MES model, alongside quantitative evidence that protocol-compliant actuation can deliver comfort-preserving flexibility in residential heating. The framework is readily extensible to other energy assets, such as cooling systems, electric vehicle charging, and combined heat and power (CHP), and is adaptable to additional protocols, thereby supporting future cross-vector investigations into digitally enabled energy flexibility. Full article

A one-dimensional analytical model for a proton exchange membrane fuel cell (PEMFC) engine is presented. The model is structured into three main subsystems: the fuel cell stack, the intake and exhaust system, and the thermal management system. The modeling of the thermal management system specifically encompasses key components such as the expansion tank, thermostat, pump, fan, and radiator. The heat transfer and fluid flow within key thermal management components—primarily fans and radiators—are analyzed via three-dimensional modeling. A porous media model represents the unit parallel-flow radiator, where the complex fin structures are replaced by a homogenized medium. This allows for the efficient calculation of 3D thermal and flow fields once the necessary constitutive parameters are identified. Ultimately, the one-dimensional (1D) thermal management system is coupled with the three-dimensional (3D) flow field analysis. This integrated 1D-3D co-simulation framework is implemented to enhance the computational fidelity of the PEMFC engine’s thermal management model. Full article

Smart thermostats are a key technology for reducing residential energy consumption in smart cities, but their real-world effectiveness depends on the interaction between automation, occupant behavior, and the design of behavioral interventions. This study presents a physics-informed assessment of thermostat strategies across Luxembourg’s single-family home stock, using an aggregate thermal model calibrated to eight years of hourly national heating demand and meteorological data. We simulate five categories of behavioral scenarios: dynamic thermostat adjustments, heat-wasting window-opening behavior, flexible comfort models, occupancy-based automation, and a portfolio of four probabilistic nudges (social comparison, real-time feedback, pre-commitment, and gamification). Results show that occupancy-based automation delivers the largest energy savings at 12.9%, by aligning heating with presence. In contrast, behavioral savings are highly fragile, as a stochastic window-opening behavior significantly erodes the 9.8% savings from eco-nudges, reducing the net gain to 7.6%. Among nudges, only social comparison yields significant savings, with a mean reduction of 7.6% (90% confidence interval: 5.3% to 9.8%), by durably lowering the thermal baseline. Real-time feedback and pre-commitment fail, achieving less than 0.5% savings, because they are misaligned with high-consumption periods. Thermal comfort, the psychological state of satisfaction with the thermal environment drives a large share of residential energy use. These findings demonstrate that effective smart thermostat design must prioritize robust, presence-responsive automation and interventions that reset default comfort norms, offering scalable, policy-ready pathways for residential energy reduction in urban energy systems. Full article