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Innovative machine learning approaches for indoor air temperature forecasting in smart infrastructure
AbstractEfficient energy management and maintaining an optimal indoor climate in buildings are critical tasks in today’s world. This paper presents an innovative approach to surrogate modeling for predicting indoor air temperature (IAT) in buildings, leveraging advanced machine learning techniques. At the core of this study is the application of Long Short-Term Memory (LSTM) networks for time-series modeling, which significantly enhances the capture of temporal dependencies in temperature predictions. The proposed LSTM with RWCV (Rolling Window Cross-Validation) offers significant advantages over a usual LSTM in time-series tasks, particularly due to its ability to adapt to new data trends through the rolling window mechanism. It provides more robust and generalizable forecasts in dynamic environments, prevents overfitting through dropout and cross-validation, and improves model evaluation with temporal integrity. In contrast, traditional LSTM models are better suited for static, non-evolving datasets and may not handle dynamic time-series data effectively. To rigorously assess model performance, a comprehensive evaluation framework is developed, incorporating metrics such as mean square error (MSE) and the coefficient of determination (R²). Additionally, a novel cumulative error analysis method is introduced enabling real-time monitoring and model adjustment to maintain predictive accuracy over time. Test results demonstrate that model losses on the test dataset are only marginally higher than those on the training dataset, indicating robust generalization capabilities. Loss values range from 0.0004709 to 0.02819861, depending on building operating conditions. A comparative analysis reveals that Adaboost and Gradient Boosting models outperform linear regression, highlighting their potential for achieving energy-efficient and comfortable indoor climate management in buildings. The findings underscore the efficacy of the proposed approach for IAT prediction and point towards further research possibilities in dataset expansion and model optimization to enhance building climate management and energy conservation.
A cost-utility analysis of adding SGLT2 inhibitors for the management of type 2 diabetes with chronic kidney disease in Thailand
Physiological premature aging of ovarian blood vessels leads to decline in fertility in middle-aged mice
Investigating the relationship between built environment and urban vitality using big data
Distribution and risk assessment of microplastics in a source water reservoir, Central China
AbstractThe current researches on microplastics in different water layers of reservoirs remains limited. This study aims to investigate the microplastics in different water layers within a source water reservoir. Results revealed that the abundance of microplastics ranged from 2.07 n/L to 14.28 n/L (reservoir, water) and 3 to 7.02 n/L (river, water), while varied from 350 to 714 n/kg(dw) (reservoir, sediment) and 299 to 1360 n/kg(dw) (river, sediment). The average abundance in surface, middle, and bottom water were 6.83 n/L, 6.30 n/L, and 6.91 n/L respectively. Transparent fibrous smaller than < 0.5 mm were identified as the predominant fraction with Polypropylene and Polyethylene being the prevalent polymer types. Additionally, the pollution load index, hazard index, and pollution risk index were calculated for different layers and sediments. Results showed that surface water exhibited a moderate level of risk while the sediments posed a low level of risk. Both the middle and bottom water showed elevated levels of risk due to higher concentrations of polymers with significant toxicity indices. This study presents novel findings on the distribution of microplastics in different water layers, providing crucial data support for understanding the migration patterns of microplastics in source water reservoirs and facilitating pollution prevention efforts.
Helical dislocation-driven plasticity and flexible high-performance thermoelectric generator in α-Mg3Bi2 single crystals
The brain prioritizes the basic level of object category abstraction
Comparison of oxidative stress status in the kidney tissue of male rats treated with paraquat and nanoparaquat
Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics
Abstract Identifying pharmacological probes for human proteins represents a key opportunity to accelerate the discovery of new therapeutics. High-content screening approaches to expand the ligandable proteome offer the potential to expedite the discovery of novel chemical probes to study protein function. Screening libraries of reactive fragments by chemoproteomics offers a compelling approach to ligand discovery, however, optimising sample throughput, proteomic depth, and data reproducibility remains a key challenge. We report a versatile, label-free quantification proteomics platform for competitive profiling of cysteine-reactive fragments against the native proteome. This high-throughput platform combines SP4 plate-based sample preparation with rapid chromatographic gradients. Data-independent acquisition performed on a Bruker timsTOF Pro 2 consistently identified ~23,000 cysteine sites per run, with a total of ~32,000 cysteine sites profiled in HEK293T and Jurkat lysate. Crucially, this depth in cysteinome coverage is met with high data completeness, enabling robust identification of liganded proteins. In this study, 80 reactive fragments were screened in two cell lines identifying >400 ligand-protein interactions. Hits were validated through concentration-response experiments and the platform was utilised for hit expansion and live cell experiments. This label-free platform represents a significant step forward in high-throughput proteomics to evaluate ligandability of cysteines across the human proteome.
Wavelength-tunable infrared metasurfaces with chiral bound states in the continuum
Prognosis of invasive encapsulated follicular variant and classical papillary thyroid carcinoma: a propensity score-matched study using the SEER database
Harnessing orbital Hall effect in spin-orbit torque MRAM
AbstractSpin-Orbit Torque (SOT) Magnetic Random-Access Memory (MRAM) devices offer improved power efficiency, nonvolatility, and performance compared to static RAM, making them ideal, for instance, for cache memory applications. Efficient magnetization switching, long data retention, and high-density integration in SOT MRAM require ferromagnets (FM) with perpendicular magnetic anisotropy (PMA) combined with large torques enhanced by Orbital Hall Effect (OHE). We have engineered a PMA [Co/Ni]3 FM on selected OHE layers (Ru, Nb, Cr) and investigated the potential of theoretically predicted larger orbital Hall conductivity (OHC) to quantify the torque and switching current in OHE/[Co/Ni]3 stacks. Our results demonstrate a ~30% enhancement in damping-like torque efficiency with a positive sign for the Ru OHE layer compared to a pure Pt layer, accompanied by a ~20% reduction in switching current for Ru compared to pure Pt across more than 250 devices, leading to more than a 60% reduction in switching power. These findings validate the application of Ru in devices relevant to industrial contexts, supporting theoretical predictions regarding its superior OHC. This investigation highlights the potential of enhanced orbital torques to improve the performance of orbital-assisted SOT-MRAM, paving the way for next-generation memory technology.