Search Results (4,884)

This research analyzes the wavy, axisymmetric flow of a Ree–Eyring non-Newtonian nanofluid, infused with motile microorganisms, within a porous, tapered cylindrical channel under a transverse magnetic field. This investigation presents a theoretical framework that may inform the improvement of energy efficiency and thermal management in biomedical engineering applications, such as drug delivery systems and microfluidic biosensors. The work provides an extended insight by a contribution to the evaluation of entropy generation, explicitly considering the influence of motile microorganisms, thereby bridging a gap in the existing literature. The comprehensive physical model further incorporates the combined effects of Joule heating, viscous dissipation, nonlinear thermal radiation, and chemical reactions. Methodologically, the governing nonlinear equations of the system were rendered tractable under long-wavelength and low-Reynolds-number assumptions and subsequently solved using the numerical Runge–Kutta–Fehlberg technique. The key conclusion is that, based on the present numerical model, careful selection of magnetic field strength and microorganism motility parameters may reduce irreversible energy losses, potentially improving the net usable work in advanced nanofluid transport systems for biomedical applications, subject to experimental validation. The most significant finding reveals that the magnetic field serves as a dual-purpose control parameter: increasing its strength boosts total entropy generation by 20–30% while simultaneously raising the Bejan number, confirming heat transfer as the dominant irreversibility mechanism in the system. Additionally, nanoparticle concentration diminishes substantially with elevated chemical reaction rates and Schmidt numbers, while microorganism density is highly sensitive to the Péclet number, which causes flow disruptions. Full article

Surface solar ultraviolet (UV) radiation represents an essential component of shortwave solar radiation, with important implications for atmospheric chemistry and climate studies. Reliable, high-quality records of surface solar UV radiation are essential for UV-related research and applications; however, ground-based UV observations remain sparse worldwide. This study presents a novel broadband parametric model, based on physical principles, for estimating solar UV irradiance ($0.280–0.400 \mu \mathrm{m}$) under clear-sky conditions. The model is computationally efficient and suitable for practical applications. The proposed approach is based on the SMARTS2 spectral radiative transfer model and employs an interdependent integration scheme to derive broadband UV irradiance from spectrally resolved shortwave radiation. The model performance is evaluated against high-quality measurements from the Baseline Surface Radiation Network (BSRN) and compared with an established parameterization. The proposed model demonstrates improved performance at both validation sites, reducing the mean nRMSE from 8.88% to 7.64% at Izaña and from 60.69% to 29.24% at Payerne, while also substantially decreasing the bias under more challenging atmospheric conditions, although the nRMSE at Payerne remains relatively high. These results highlight the potential of the proposed approach as an efficient and physically consistent tool for clear-sky UV irradiance estimation. Full article

Accurate solar irradiance forecasting is important for efficient planning of solar energy systems, renewable energy integration, and data-driven energy management in smart cities. This becomes more essential in regions with limited measured data availability and varying climatic conditions, where reliable forecasting can support urban energy planning and smart grid operation. Pakistan faces a scarcity of available solar data and has varying climatic conditions, which makes it ideal for such a study. This study utilizes nine geographically diverse stations to develop a benchmark framework for direct one-step-ahead hourly solar irradiance forecasting. The dataset was subjected to data preprocessing, feature engineering, and multi-model evaluation. A staged approach was adopted for feature selection, starting from a base model comprising three input variables: extraterrestrial radiation, solar zenith angle, and relative humidity. Features were added in an incremental order, which resulted in an optimized four-variable input set through the addition of a lagged clearness index to the base model. The forecasting models evaluated in this study, using these input variables, were ANN, NAR, NARX, LSTM, GRU, SARIMA, and Prophet. Deep learning models outperformed the other considered approaches, with LSTM showing the best overall benchmark performance with an average RMSE of 92.93 W/m², MAE of 66.56 W/m², and R-Squared of 0.872. The performance trends were broadly consistent across the evaluated stations, indicating stable behaviour within the adopted dataset and experimental setup. The study shows that a compact and physically interpretable input feature set, used with recurrent deep learning models, provides an effective solution for hourly solar irradiance forecasting, especially in locations with varying climatic conditions. The proposed benchmark can support smart city applications related to distributed solar generation, energy-aware urban planning, and intelligent operation of renewable-rich power systems. Full article

The NaI(TI) detector is highly favored in gamma radiation detection and widely applied in fields such as environmental radiation monitoring, nuclear medicine, and laboratory gamma-ray spectroscopy. Its detection performance determines the results of quantitative gamma-ray detection, making it a crucial indicator in detector design and development. This study employs the Monte Carlo method and utilizes TopMC 1.0 software to establish a NaI(TI) detector model. First, the effects of crystal size, ray energy, cladding thickness, and distance on the detector’s detection efficiency were investigated. Subsequently, the energy resolution and peak-to-total ratio of the detector were simulated and calculated, with comparisons made to experimental values. The results indicate that all three detection efficiencies of the NaI(TI) detector are positively correlated with crystal size and exhibit an initial increase followed by a decrease with rising gamma-ray energy. Both the absolute detection efficiency and full-energy peak detection efficiency first increase and then decrease with increasing cladding thickness, while showing a negative correlation with detection distance. The intrinsic detection efficiency is almost unaffected by cladding thickness and initially rises before declining with increasing detection distance. The simulated values of energy resolution closely match experimental values, improving with higher gamma-ray energy. The deviation between simulated and experimental values for different source peak-to-total ratios remains within 6.25%, verifying the model’s reliability and the accuracy of simulation data. These findings provide valuable references and guidance for optimizing detection performance, conducting source-free efficiency calibration, and structural design of NaI(TI) detectors. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Background: Nuclear medicine healthcare professionals (NMHP) sustain chronic occupational exposure to iodine-131 (I-131), conferring an elevated risk of radiation-induced solid thyroid cancer. Established radiobiological prediction tools derive risk coefficients from atomic bomb survivor data but are not configured for rapid individualized risk assessment in occupational exposure settings. This study examined whether machine learning algorithms can serve as high-precision computational surrogates for excess relative risk estimation in NMHP. Aim: The study aimed to (i) develop and validate three machine learning algorithms for predicting the excess relative risk per unit absorbed dose for radiation-induced solid thyroid cancer (ERR/Gy.RST), (ii) characterize relationships between dosimetric and demographic features and predicted risk, and (iii) identify the optimal algorithm for deployment in occupational health surveillance. Methods: A dataset of 4657 observations was constructed from Life Span Study-derived ERR/Gy parameters, adapted to occupational low-dose conditions, using a dose-and-dose-rate effectiveness factor of 2.0, per ICRP Publication 103. Five features (gender, age at exposure, current age, distance from the I-131 source, and cumulative absorbed dose in the thyroid) were used to train a decision tree regressor (dtcr), a random forest regressor (rfr), and a multilayer perceptron (MLP) neural network algorithm. Results: Cumulative absorbed dose in the thyroid correlated positively with ERR/Gy.RST (r = 0.63, p < 0.01), while radiation source distance demonstrated a strong inverse association (r = −0.38, p < 0.01). The MLP algorithm achieved R² score = 0.999, MSE = 0.002, and MAE = 0.010, substantially outperforming the rfr (R² score = 0.700, MSE = 0.410, MAE = 0.295) and the dtcr (R² score = 0.510, MSE = 0.654, MAE = 0.289). Conclusions: The MLP algorithm provides a high-fidelity surrogate for established ERR/Gy.RST projection tools in the NMHP context, enabling computationally efficient, feature-integrated occupational radiation-induced thyroid cancer risk quantification. These findings suggest that machine learning-based surrogate modeling is a practical, scalable complement for occupational health practitioners and radiation protection officers to support individualized surveillance of radiation-induced thyroid cancer risk in nuclear medicine departments. Full article

Integrating semi-transparent photovoltaics (STPV) into greenhouse structures offers an effective approach to optimizing the Food–Energy Nexus and maximizing sustainable land-use efficiency. However, a knowledge gap remains regarding how specific STPV spectral signatures drive plant morpho-physiological acclimation across multiple cultivation cycles. This study presents a 19-month multi-cycle, proof-of-concept evaluation of the structural growth dynamics and physiological responses of generative (tomato) and vegetative (lettuce) crops under greenhouse prototypes with two distinct thin-film STPV technologies: Cadmium Telluride (CdTe) and amorphous Silicon (a-Si), compared to an unshaded transparent control. Biometric monitoring revealed that morphological acclimation (Shade-Avoidance Syndrome) was highly plastic, driven by the interplay between spectral filtering and seasonal irradiance limits. While structural adaptations, such as foliar expansion and stem elongation under the a-Si spectrum, were pronounced during specific transitional seasons (e.g., early spring), these morphological differences largely homogenized across treatments during periods of extreme high or low natural irradiance. Despite the shading penalty, this morphological acclimation successfully sustained agronomic fresh mass. Systemic efficiency, quantified by the Land Equivalent Ratio (LER) as a relative biophysical synergy index, demonstrated notably crop-specific synergies. Under an extended single fruiting cycle, the CdTe prototype showed potential to improve yield, achieving a maximum LER of 1.66 for the high-light-demanding tomato (Y_crop = 1.40). Conversely, the a-Si module excelled with the shade-tolerant lettuce during early vegetative stages in high-radiation periods, achieving peak LERs up to 1.55. These findings provide a biophysical baseline to help guide future scalability assessments prior to full-scale commercial agrivoltaic (APV) implementation for sustainable food systems. Full article

Objectives: To evaluate the diagnostic performance of automated vertebral trabecular Hounsfield unit (HU) measurements derived from routine fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) for identifying low bone density. Methods: This retrospective study included 131 consecutive women (mean age, 53.5 ± 9.6 years) undergoing health screening with FDG PET/CT and dual-energy X-ray absorptiometry (DXA) between January 2020 and December 2024. A deep learning-based model (TotalSegmentator) automatically segmented the lumbar vertebrae (L1–L4). HU-based metrics in trabecular regions were calculated, and their correlations with DXA-derived bone mineral density (BMD) were assessed. Diagnostic performance was evaluated using receiver operating characteristic analysis. A multivariable logistic regression model incorporating mean HU, age, and body mass index was developed and internally validated using bootstrap resampling. Results: According to WHO criteria, 47 of 131 participants (35.9%) had low bone density. Mean HU demonstrated strong diagnostic performance (area under the curve [95% confidence interval]: L1, 0.861 [0.800–0.923]; L2, 0.852 [0.788–0.915]; L3, 0.861 [0.800–0.921]; L4, 0.845 [0.781–0.909]). L1 mean HU provided the most balanced performance (sensitivity, 0.851; specificity, 0.750); L3 mean HU was slightly inferior. L1 mean HU was strongly correlated with BMD (r = 0.821, p < 0.001). In multivariable analysis, mean HU independently predicted low bone density (odds ratio: 0.949, p < 0.001). The model achieved an accuracy of 0.786 and demonstrated favorable calibration performance. Conclusions: The automated assessment of vertebral trabecular HU from routine FDG PET/CT provides a reliable and highly efficient method for screening low bone density without additional radiation exposure or cost. Full article

The chemical evolution of simple molecules into higher-order structures, such as amino acids, is a fundamental process occurring throughout the cosmos. Methane (CH₄) serves as a key precursor in this evolutionary sequence and is prevalent on planetary bodies like Mars and Saturn. In these environments, CH₄ is frequently ionized by cosmic radiation, forming the methane radical cation (CH₄⁺). In this study, the ion-molecule reactions between CH₄⁺ and neutral CH₄ (CH₄⁺ + CH₄ → products) were investigated using direct ab initio molecular dynamics (AIMD) simulations to elucidate the underlying reaction mechanisms. Our calculations demonstrate that proton transfer (PT) occurs efficiently, yielding the methanium ion (CH₅⁺) and the highly reactive methyl radical (CH₃): CH₄⁺ + CH₄ → CH₅⁺ + CH₃. Furthermore, the reaction outcomes exhibit a strong dependence on the impact parameter (b). Collisions at very low impact parameters (b = 0–0.2 Å) resulted in non-reactive, billiard-ball-like scattering. Within the range of b = 0.2–3.0 Å, the formation of a long-lived complex, [CH₅-CH₃]⁺, was observed. In the intermediate range of b = 3.0–5.0 Å, a proton-stripping mechanism predominated in PT channel, while collisions at b > 5.0 Å were exclusively non-reactive. The reaction mechanism was qualitatively discussed. These findings provide a detailed atomistic picture of the collision dynamics governing methane-derived molecular evolution in celestial environments. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

Agrivoltaics is a promising technique, especially in view of the rapid population growth associated with the expansion of cultivated areas to satisfy the food demands of the population, and the increase in solar power plants, which require considerable space to supply the population with energy. Thus, the transition from agricultural to agrivoltaics systems and the transition from PV power plants to agrivoltaics systems can enable more efficient use of land for energy and agricultural production. However, the configuration of agrivoltaics systems, namely panel elevation, spacing between panels and between rows of panels, and panel size, defines the amount of material used. As a result, configuration can have a major impact on the environment. The aim of this study is to highlight the environmental impact from converting 1 ha of land used entirely for agricultural production to 1 ha of an agrivoltaic system, and from converting 1 ha of land used entirely for solar photovoltaic energy production to 1 ha of an agrivoltaic system through a life cycle assessment. Three different configurations of agrivoltaics systems are considered to assess the environmental potential of agrivoltaics configurations. This analysis is performed with SimaPro 9.4 software, using the ReCiPe Midpoint (H) method and the Eco-invent database. The study determined impacts on global warming, stratospheric ozone depletion, ionizing radiation, ozone formation, mineral resource scarcity, fossil resource scarcity, water consumption, and land use through the determination of the Land Equivalent Ratio (LER). The results show that impacts are highest for PV power plants, followed by the agrivoltaic system with the largest PV panels for all indicators, except for stratospheric ozone depletion, where impacts are highest for agrivoltaics and agricultural use systems. The results of the land evaluation showed that the agrivoltaic system Case 3 gave the best performance, with a Land Equivalent Ratio of 148.7%. Full article

Microwave-assisted polymerization is a transformative technique for synthesizing conductive polymers such as polypyrrole (PPy). Unlike conventional chemical or electrochemical methods that rely on external heating or electrode mediated oxidation, microwave irradiation induces volumetric and selective heating through dipole orientation and ionic conduction, which leads to faster reaction kinetics, improved uniformity and higher yields. This review highlights the fundamental mechanisms governing microwave polymer interactions, compares conventional and microwave-assisted polymerization routes and traces the evolution of pyrrole polymerization. Special emphasis is placed on the microwave-synthesized PPy composites and their superior electrochemical performance in energy storage, sensing and biomedical applications. Case studies of graphene/PPy, PPy–metal oxide (e.g., SnO₂@PPy nanotubes) and magnetic ferrite hybrids (e.g., BaFe₁₂O₁₉/PPy) nanocomposites demonstrate enhanced electrical conductivity, specific capacitance and more uniform nanostructures. Beyond energy storage, microwave polymerization techniques have led to the development of PPy composites that are used for sensing, antimicrobial activity and photothermal cancer therapy, highlighting the technique’s versatility across biomedical sciences. Reactor scale up, temperature and pressure control under sealed conditions, reproducibility and deeper mechanism understanding of how microwave radiation influences nucleation, chain growth, doping and charge transport were identified as the outstanding challenges that must be addressed to transform microwave-assisted synthesis from pilot to industrial scale. Overall, microwave-assisted polymerization is on its way to becoming a mainstream, energy efficient method for manufacturing high performance polymer composite materials. Full article

Probiotic lactic acid bacteria are widely recognized for their health-promoting effects. However, the use of live microorganisms may pose safety concerns and stability limitations. Consequently, postbiotics, defined as inactivated microbial cells and/or their components, have emerged as a promising alternative. This study integrates genome-guided evaluation of probiotic potential, experimental validation of in silico predictions and process optimization for the production of inactivated Lactiplantibacillus plantarum DM1 and KK1 cells as postbiotics. Genome mining identified genes and gene clusters associated with metabolic versatility, antimicrobial activity, gastrointestinal stress tolerance, adhesion and prebiotic substrate utilization. Building on these findings, to generate postbiotics, the efficiency of thermal, enzymatic, mechanical and radiation-based inactivation methods was evaluated in bacterial suspensions prepared in three dairy by-product matrices: milk permeate, sweet whey and sour whey. Complete inactivation of both strain cells was achieved by thermal treatment (3 min pasteurization), γ-irradiation (3 kGy), and combined lysozyme–pasteurization treatment, whereas other treatments showed partial and matrix-dependent effects. Matrix composition significantly influenced treatment efficacy, suggesting a protective role of food components used. These findings highlight the importance of combining genome mining for potential probiotic strain characterization with robust, matrix-adapted inactivation strategies for the development of stable postbiotic formulations. Full article

The preparation of high-performance cadmium zinc telluride (CZT) radiation detector materials requires efficient ingot-level quality assessment before full downstream wafer testing. This study proposes a machine learning framework that predicts the product-level yield of test wafers from IV and double-sided spectral measurements of a limited number of standardized evaluation wafers from the same ingot. To address the small number of ingots and wafer-level variability, ingot-level aggregate, A/B-side consistency, threshold-ratio, and distributional features were combined with intra-ingot bootstrap augmentation. Among the evaluated regression models, Random Forest achieved the best held-out test performance under a leakage-safe protocol, with an MSE of 0.021, an MAE of 0.125, and a Pearson correlation coefficient of 0.646; XGBoost showed comparable performance, with an MSE of 0.023, an MAE of 0.128, and a Pearson correlation coefficient of 0.601. In a top-22% screening experiment, the average true yield of ingots selected by Random Forest and XGBoost reached 63.71% and 60.40%, respectively, exceeding the empirical Rule_IV_Abs baseline of 59.08%. These results indicate that the proposed framework can provide useful ranking and prioritization support for early CZT ingot screening, while remaining a decision-support tool rather than a replacement for wafer-level inspection. Full article

The growing use of wireless technology significantly increases the exposure of all living organisms to radiofrequency electromagnetic fields (RF-EMF). However, the physiological effects of RF-EMF on plants have not yet been sufficiently researched. In this study, we investigated the effects of RF-EMF radiation in the frequency ranges 1890–1900 MHz (DECT) and 2.4 GHz plus 5 GHz (Wi-Fi) on photosynthetic performance of Tilia plants (Tilia tomentosa). The recorded fast chlorophyll fluorescence transients were used to analyze the structure and function of PSII by the JIP-test. The analysis of the fluorescence of chlorophyll a showed that the RF-EMF interfered with the electron transport processes of photosynthesis. Tilia plants exposed to RF-EMF induced decrease in photosynthetic efficiency (F_V/F_M) and inactivation of part of PSII reaction centers (RC/CS_O). Observations of leaf senescence and lifespan over a period of 102 days showed that RF-EMF-exposed Tilia plants exhibited accelerated aging. Full article