Search Results (1,278)

The combination of gold nanoparticles (AuNPs) with hydrogels has drawn significant interest in the design of smart materials as advanced platforms for biomedical applications. These systems endow light-responsiveness enabled by the AuNPs localized surface plasmon resonance (LSPR) phenomenon. In this study, we propose a nanocomposite hydrogel in which gold nanorods (AuNRs) are included in an agarose–carbomer–hyaluronic acid (AC-HA)-based hydrogel matrix to study the correlation between light irradiation, local temperature increase, and drug release for potential light-assisted drug delivery applications. The gel is obtained through a facile microwave-assisted polycondensation reaction, and its properties are investigated as a function of both the hyaluronic acid molecular weight and ratio. Afterwards, AuNRs are incorporated in the AC-HA formulation, before the sol–gel transition, to impart light-responsiveness and optical properties to the otherwise inert polymeric matrix. Particular attention is given to the evaluation of AuNRs/AC-HA light-induced heat generation and drug delivery performances under near-infrared (NIR) laser irradiation in vitro. Spatiotemporal thermal profiles and high-resolution thermal maps are registered using fiber Bragg grating (FBG) sensor arrays, enabling accurate probing of maximum internal temperature variations within the composite matrix. Lastly, using a high-steric-hindrance protein (BSA) as a drug mimetic, we demonstrate that moderate localized heating under short-time repeated NIR exposure enhances the release from the nanocomposite hydrogel. Full article

Ethanolamine (EA) is a widely used yet toxic volatile organic compound (VOC). However, existing gas sensors for EA detection face persistent challenges in achieving exceptional sensitivity and low detection limits at room temperature (RT). In this study, a novel and high-performance EA sensor based on the MoO₃/BiOI composite was prefabricated using hydrothermal and cyclic impregnation methods. The response value toward 100 ppm EA reached 861.3, which was 3.5-times higher compared to that of pure MoO₃. In addition, the MoO₃/BiOI composite exhibited a low detection limit (0.13 ppm), excellent selectivity, short response/recovery times, exceptional repeatability and long-term stability. The outstanding gas sensing performance of the MoO₃/BiOI is attributed to the formation of a p-n heterojunction, synergistic effects between the two materials, abundant adsorbed oxygen species and superior charge transfer efficiency. The sensor developed in this work effectively addresses the long-standing challenges, demonstrating unprecedented practical application potential for EA gas detection. Simultaneously, this study provides a novel strategy, a new approach and a promising material for the subsequent development of advanced amine sensors. Full article

Tungsten is one of the critical materials with important applications in many areas. Electrolysis of Na₂WO₄-based salt is a short and green process for the production of tungsten metal and alloys. The conventional process for producing Na₂WO₄ is expensive and time-consuming. Scheelite (CaWO₄) is becoming the most important resource for the extraction of tungsten. Based on thermodynamic calculations and phase equilibrium studies, a novel process is proposed to prepare Na₂WO₄-based salt directly from scheelite through a high-temperature process. By reacting with silica and sodium oxide, immiscible layers of liquid salt and slag are formed from scheelite between 1200 and 1300 °C. High-density salt containing Na₂WO₄ is separated from the silicate slag, which is composed of impurities and fluxes. The effects of fluxing agents, smelting temperature, and reaction time on the direct yield of WO₃ and purity of sodium tungsten are investigated in combination with thermodynamic calculations and high-temperature experiments. The salt containing up to 99% Na₂WO₄ is obtained directly in a single process, which can be used for the production of other tungsten chemicals. This study provides a novel research method and detailed information to produce low-cost sodium tungstate directly from scheelite. Full article

Accurate short-term forecasting of solar photovoltaic (PV) power output is essential for efficient grid integration and energy management, especially given the widespread global adoption of PV systems. To address this research gap, the present study introduces a scalable, interpretable ensemble learning model of PV power prediction with respect to a large PVOD v1.0 dataset, which encompasses more than 270,000 points representing ten PV stations. The proposed methodology involves data preprocessing, feature engineering, and a hybrid ensemble model consisting of Random Forest, XGBoost, and CatBoost. Temporal features, which included hour, day, and month, were created to reflect the diurnal and seasonal characteristics, whereas feature importance analysis identified global irradiance, temperature, and temporal indices as key indicators. The hybrid ensemble model presented has a high predictive power, with an R² = 0.993, a Mean Absolute Error (MAE) = 0.227 kW, and a Root Mean Squared Error (RMSE) = 0.628 kW when applied to the PVOD v1.0 dataset to predict short-term PV power. These findings were achieved on standardized, multi-station, open access data and thus are not in an entirely rigorous sense comparable to previous studies that may have used other datasets, forecasting horizons, or feature sets. Rather than asserting numerical dominance over other approaches, this paper focuses on the real utility of integrating well-known tree-based ensemble techniques with time-related feature engineering to derive real, interpretable, and computationally efficient PV power prediction models that can be used in smart grid applications. This paper shows that a mixture of conventional ensemble methods and extensive temporal feature engineering is effective in producing consistent accuracy in PV forecasting. The framework can be reproduced and run efficiently, which makes it applicable in the integration of smart grid applications. Full article

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels. Full article

Hylocereus undatus growth is limited by long-term heat stress, and heat shock protein 70 (Hsp70) is crucial in the plant’s heat stress (HS) response. In a previous study, transcriptomic data revealed that Hsp70 family members in pitaya seedlings respond to temperature changes. This study identified 27 HuHsp70 genes in pitaya, analyzed their physicochemical properties (such as molecular weight and isoelectric point), and divided them into five subfamilies with conserved gene structures, motifs (short conserved sequence patterns), and cis-acting elements (regulatory DNA sequences). The Ks value (synonymous substitution rate) ranged from 0.93~3.54, and gene duplication events occurred between 71.17 and 272.19 million years ago (Mya). Under HS, eight and nine differentially expressed genes (DEGs) were detected at 24 h and 48 h, respectively. Quantitative real-time PCR (qRT-PCR, a method for measuring gene expression) verified the expression trends, with HuHsp70-11 expression increasing with heat shock duration, indicating that HuHsp70-11 is a key candidate. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses revealed that HuHsp70s, especially HuHsp70-11, play key roles in responding to high temperatures (HT) in H. undatus seedlings. A potential model by which HuHsp70-11 removes excess reactive oxygen species (ROS) and enhances cell membrane permeability was constructed. These results provide new perspectives for exploring the HS response mechanisms and adaptability of H. undatus plants to heat stress. Full article

High-precision prediction of the crystal diameter during the growth of electronic-grade silicon single crystals is a critical step for the fabrication of high-quality single crystals. However, the process features high-temperature operation, strong nonlinearities, significant time-delay dynamics, and external disturbances, which limit the accuracy of conventional mechanism-based models. In this study, mechanism-based models denote physics-informed heat-transfer and geometric models that relate heater power and pulling rate to diameter evolution. To address this challenge, this paper proposes a hybrid deep learning model combining a convolutional neural network (CNN), a bidirectional long short-term memory network (BiLSTM), and self-attention to improve diameter prediction during the shoulder-formation and constant-diameter stages. The proposed model leverages the CNN to extract localized spatial features from multi-source sensor data, employs the BiLSTM to capture temporal dependencies inherent to the crystal growth process, and utilizes the self-attention mechanism to dynamically highlight critical feature information, thereby substantially enhancing the model’s capacity to represent complex industrial operating conditions. Experiments on operational production data collected from an industrial Czochralski (Cz) furnace, model TDR-180, demonstrate improved prediction accuracy and robustness over mechanism-based and single data-driven baselines, supporting practical process control and production optimization. Full article

Liquid gallium (Ga) enables low-temperature transient liquid phase bonding (TLPB), but optimizing microstructure and joint performance remains challenging. Here, we developed a copper (Cu)-powder/liquid-Ga composite paste for Cu/Cu interconnects and systematically studied the effects on the interconnect joint performance of Cu powder particle size (Cu_PS, 10–20, 20–30 and 30–40 μm) and Cu mass fraction (Cu_MF, 10–30 wt%). The microstructure, electrical conductivity, and shear strength of the joint were evaluated, followed by an assessment of bonding temperature, pressure, and time. Under bonding conditions of 220 °C, 5 MPa and 720 min, a dense intermetallic compound (IMC) microstructure predominantly composed of Cu₉Ga₄ and CuGa₂ was formed, yielding an electrical conductivity of approximately 1.1 × 10⁷  S·m⁻¹ and a shear strength of 52.2 MPa, thereby achieving a synergistic optimization of electrical and mechanical properties; even under rapid bonding conditions of 220 °C, 5 MPa and 1 min, the joint still attained a shear strength of 39.2 MPa, demonstrating the potential of this process for high-efficiency, short-time interconnection applications. These results show that adjusting the composite paste formulation and dosage enables Cu–Ga TLPB joints that combine high conductivity with robust mechanical integrity for advanced packaging. Full article

The Czochralski method is the dominant technique for producing power-electronics-grade silicon crystals. At the beginning of the seeding stage, an excessively high (or low) temperature at the solid–liquid interface can cause the time required for the seed to reach the specified length to be too long (or too short). However, the time taken for the seed to reach a specified length is strictly controlled in semiconductor crystal growth to ensure that the initial temperature is appropriate. An inappropriate initial temperature can adversely affect crystal quality and production yield. Accurately evaluating whether the current temperature is appropriate for seeding is therefore essential. However, the temperature at the solid–liquid interface cannot be directly measured, and the current manual evaluation method mainly relies on a visual inspection of the meniscus. Previous methods for detecting this temperature classified image features, lacking a quantitative assessment of the temperature. To address this challenge, this study proposed using the duration of the seeding stage as the target variable for evaluating the temperature and developed an improved multimodal fusion regression network. Temperature signals collected from a central pyrometer and an auxiliary pyrometer were transformed into time–frequency representations via wavelet transform. Features extracted from the time–frequency diagrams, together with meniscus features, were fused through a two-level mechanism with multimodal feature fusion (MFF) and channel attention (CA), followed by masking using spatial attention (SA). The fused features were then input into a random vector functional link network (RVFLN) to predict the seeding duration, thereby establishing an indirect relationship between multi-sensor data and the seeding temperature achieving a quantification of the temperature that could not be directly measured. Transfer comparison experiments conducted on our dataset verified the effectiveness of the feature extraction strategy and demonstrated the superior detection performance of the proposed model. Full article

Environmental monitoring systems generate large volumes of multivariate time series data from heterogeneous sensors, including those measuring soil, weather, and air quality parameters. However, sensor malfunctions and transmission failures frequently lead to missing values, compromising the performance of downstream analytical and predictive models. To address this challenge, this study presents a comprehensive and systematic evaluation of previously proposed hybrid architecture that interleaves Long Short-Term Memory (LSTM) layers with a Multi-Head Attention mechanism in a “sandwiched” setting (LSTM–Attention–LSTM) for robust multivariate data imputation in environmental IoT datasets. The first LSTM layer captures short-term temporal dependencies, the attention layer emphasises long-range relationships among correlated features, and the second LSTM layer re-integrates these enriched representations into a coherent temporal sequence. The model is evaluated using multiple environmental datasets of soil temperature, meteorological (precipitation, temperature, wind speed, humidity), and air quality data across missingness levels ranging from 10% to 90%. Performance is compared against baseline methods, including K-Nearest Neighbour (KNN) and Bidirectional Recurrent Imputation for Time Series (BRITS). Across all datasets, the Hybrid model consistently outperforms baseline methods, achieving MAE reductions exceeding 50% and reaching over 80% in several scenarios, along with RMSE reductions of up to approximately 85%, particularly under moderate to high missingness conditions. An ablation study further examines the contribution of each layer to overall model performance. Results demonstrate that the proposed Hybrid model achieves superior accuracy and robustness across datasets, confirming its effectiveness for environmental sensor data imputation under varying missing data conditions. Full article

The conventional reactive cooling strategy, which relies on static thresholds, has become inadequate for managing dynamically changing heat loads, often resulting in energy inefficiency and increased risk of local hot spots. In this study, we develop a data center cooling optimization system that integrates distributed sensor arrays for predictive analysis. By deploying high-density temperature and humidity sensors both inside and outside server racks, a real-time, high-fidelity three-dimensional digital twin of the data center’s thermal environment is constructed. Time-series analysis combined with Long Short-Term Memory algorithms is employed to forecast temperature and humidity based on the extensive environmental data collected, achieving high predictive accuracy with a root mean square error of 0.25 and an R² value of 0.985. Building on these predictions, a proactive cooling control strategy is formulated to dynamically adjust fan speeds and the opening degree of chilled-water valves in computer room air conditioning units, changing the cooling approach from passive to preemptive prevention of overheating. Compared with conventional proportional–integral–differential control, the developed system significantly reduces overall energy consumption and maintains all equipment within safe operating temperatures. Specifically, the framework has reduced the energy consumption of the cooling system by 37.5%, lowered the overall power usage effectiveness of the data center by 12% (1.48 to 1.30), and suppressed the cumulative hotspot duration (temperature 27 °C) by nearly 96% (from 48 to 2 h). Full article

This study employed tree-based machine learning (ML) algorithms to predict low-level wind shear (LLWS) at Jeju International Airport (ICAO: RKPC). Hourly meteorological data from 47 observation stations across Jeju Island, collected between 2019 and 2023, were split into training (60%), validation (20%), and test (20%) sets to develop individual prediction models for lead times ranging from 1 to 6 h. A probabilistic prediction model was developed by assigning weights to individual models according to their true skill statistic performance. Validation using an independent 2024 dataset showed that the light gradient boosting machine-based probabilistic model exhibited the highest predictive performance, achieving an area under the receiver operating characteristic curve of 0.883. The Shapley additive explanation analysis identified wind components (U, V) as key variables, contributing over 50%, with the significance of pressure and temperature slightly increasing over long-term prediction times (4–6 h). In addition, spatial analysis revealed that nearby airport stations were more influential for short-term prediction times (1–2 h), whereas Mount Halla and offshore stations north of the airport gained greater influence for medium-to long-term prediction times (3–6 h). The ML-based LLWS prediction model offers high accuracy and interpretability, supporting stepwise warning systems and aiding aviation decision-making. Full article

This work examines resistance drift in FPGA I/O paths subjected to combined electrical and thermal stress, using a Xilinx Spartan-6 device as a representative platform. A multiplexed measurement approach was employed, in which multiple I/O pins were externally shorted and sequentially activated, enabling precise tracking of voltage, current, and effective series resistance over time, under controlled bias conditions. Two accelerated stress modes were investigated: high-temperature dwell in the range of 80–120 °C and thermal cycling between 80 and 140 °C. Both stress modes exhibited similar sub-linear (power-law) time dependence on resistance change, indicating cumulative degradation behavior. However, Arrhenius analysis revealed a strong contrast in effective activation energy: approximately 0.62 eV for high-temperature dwell and approximately 1.3 eV for thermal cycling. This divergence indicates that distinct physical mechanisms dominate under each stress regime. The lower activation energy is consistent with electrically and thermally driven on-die degradation within the FPGA I/O macro, including bias-related aging of output drivers and pad-level structures. In contrast, the higher activation energy observed under thermal cycling is characteristic of diffusion- and creep-dominated thermo-mechanical damage in package-level interconnects, such as solder joints. These findings demonstrate that resistance-based monitoring of FPGA I/O paths can discriminate between device-dominated and package-dominated aging mechanisms, providing a practical foundation for reliability assessment and self-monitoring methodologies in complex electronic systems. Full article

Acoustic telemetry monitoring for tagged sharks in nearshore waters has become an important tool for beach safety management; however, detection performance can vary widely in shallow, high-energy nearshore environments where management decisions are often most time-sensitive. Real-time acoustic telemetry buoys offer the potential to deliver live detections and system diagnostics, but their performance relative to autonomous bottom-mounted receivers remains poorly evaluated under realistic coastal conditions. We compared the detection efficiency of real-time buoy-mounted acoustic receivers and autonomous bottom-mounted receivers across five nearshore sites in southern California. Using paired long-term reference tag deployments and short-term range tests, we quantified detection probability, effective detection range, and the influence of environmental conditions and receiver placement. Detection performance was evaluated in relation to wind speed, water temperature, receiver tilt, and signal-to-noise ratio. Both buoy-mounted and bottom-mounted receivers maintained high long-term detection efficiency, recovering 77–99% of expected transmissions at 82–250 m. Range tests indicated greater effective detection distances for buoy-mounted receivers, with 50% detection probabilities occurring at approximately 471 m compared to 282 m for bottom-mounted receivers. Receiver placement strongly influenced performance, with surface-mounted receivers outperforming bottom-mounted units regardless of receiver model. Environmental effects on detections were site-specific and variable. Detection probability varied predictably with environmental conditions. Higher SNR increased detection success, particularly for bottom/substrate mounted receivers, while warm water significantly reduced detection probability across placement configuration. These results demonstrate that real-time acoustic telemetry buoys provide reliable detection performance in dynamic nearshore environments while offering key operational advantages, including immediate data access and system diagnostics. The observed relationships demonstrate that receiver performance is dynamic rather than fixed, and that real-time buoy systems therefore represent a practical tool for coastal monitoring programs that require timely information to support adaptive management, public safety, and conservation decision making. Full article

The presented study aims to experimentally investigate the potential of flash sanitation (short time exposure to hot air stream) for wheat seeds to control surface contamination and protect against fungal diseases. Experiments were conducted at the laboratory scale using very short residence times (2–4 s) and higher temperature range (150–350 °C) of dry air stream at two different flow rates (280 L/min and 557 L/min). The goal was to identify thermal conditions that provide high sanitation efficiency while maintaining seed viability. A design of the experiment approach, employing central-composite design and face-centred response surface methodology, was used to evaluate the effects of the thermal treatment on seed surface temperature, sanitation efficiency, and germination capabilities. Higher air flow rate (557 L/min) significantly increased post-treatment seed surface temperatures (42.1–122.7 °C) compared to the flow rate of 280 L/min (36.7–80.5 °C). Pronounced germination drops were observed with air temperatures above 175 °C. Satisfactory sanitation efficiency >90% was achieved only with high-temperature air >250 °C, which, however, caused unacceptable germination loss. Extending residence time beyond the experimental plan is unlikely to yield significant benefits, as the factor was identified as weak and insignificant compared to temperature. Higher flow rates improve heat transfer but require strict control to prevent variability affecting seed quality. The heating media flow rate should be considered an essential factor in thermal treatment studies. Dry air has not proven to be appropriate for seeds’ flash sanitation within the selected experimental condition framework. Full article