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Two temperatures effect on wave propagation in microstretch thermoelastic medium with microtemperatures
Operational performance research of layered rotary tillage and ridging machine on for the sticky soil using DEM
Area efficient approximate multiplier based on novel 4:2 compressors and error correction logic
Abstract Multipliers are key components in arithmetic circuits, with their design having a significant impact on overall system performance. Approximate computing techniques seek to improve energy efficiency, processing speed and better use of hardware resources, particularly in applications where that can tolerate minimal accuracy loss. Achieving higher multiplier performance typically requires a careful trade-off between hardware complexity and computational precision. One widely adopted method for designing approximate multipliers involves replacing exact compressors with their approximate counterparts, resulting in a trade-off with accuracy. This paper introduces novel approximate multiplier architectures that partition the computation into three distinct regions: accurate, approximate, and lower region. Partial product compression in the approximate region is carried out using the proposed two 4:2 compressors combined with conventional arithmetic circuits like half adder, full adder and OR logic, to produce the final product. The proposed compressors are developed by analyzing the input occurrence probability of all possible combinations with trade-off between hardware efficiency and computational accuracy. To further improve accuracy, an error correction logic is developed to compensate for inaccuracies in specific input scenarios. Several benchmark error metrics and hardware synthesis using a 32-nm CMOS technology are evaluated for the proposed designs through simulations. Notably, the results of the proposed approximate multipliers shows an average improvements of 70.6% in accuracy, 60.4% in Energy-Delay Product, 30.9% in Power-Delay Product, and 41.6% in delay, outperforming all existing designs considered for comparison. Furthermore, real-time image multiplication experiments were performed using multiple benchmark image datasets, and the output quality was evaluated through the Similarity Index Metric (SSIM) and Peak Signal-to-Noise Ratio (PSNR). In addition, detailed error and heat-map visual analyses were conducted to examine the spatial distribution and intensity of computational errors across pixels. The results demonstrate that the proposed multiplier consistently achieves higher SSIM and PSNR values, along with significantly reduced error concentrations, outperforming existing approximate multiplier designs.
Unified gradation index (Sm) for shear mechanics and particle breakage at calcareous sand-structure interfaces
Safety evaluation in the weaving segment between minor roads and U-turns on arterial roadways
Growth factor supplementation modulates survival, morphology, and network activity of neurogenin-2 induced human neurons
miR-193a-5p–mediated Inhibition of the METTL1/COX-2 axis is critical for Astragalin-induced apoptosis in cervical cancer
Co-sputtered CuNi heteroatomic electrocatalyst for enhanced 5-hydroxymethylfurfural selective electrochemical conversion
Racial disparities in in-hospital outcomes and costs among U.S. patients on peritoneal dialysis: a 15-year national cohort study
Hybrid Fennec Fox–Sand Cat optimized cascaded ANFIS MPPT for enhanced control of DFIG-based WECS with grid support
Obesity concurrent with gestational diabetes mellitus dysregulates mitochondria-endoplasmic reticulum contacts in human placenta
Neonicotinoid-induced signature dysbiosis identified via metagenomic sequencing of the honey bee gut microbiome
Abstract The Western honey bee ( Apis mellifera ) plays an essential role in agriculture around the world. In Canada, honey bees contribute up to $7 billion in economic value annually by pollinating crops and producing honey. However, since 2006–2007 North American beekeepers have lost more than a quarter of their colonies each winter. In recent years, the losses have been up to 50% in some regions. The causes of losses are complex, including the interacting effects of nutrition, pathogens, and pesticides. Although the bee gut microbiome plays a crucial role in colony health and disease, studies on the effects of agricultural pesticides on the bee microbial community are sparse. We report the use of shotgun metagenomic sequencing to investigate bee gut microbiota changes, or dysbiosis, in response to two neonicotinoid insecticides, clothianidin and thiamethoxam. Common dysbiosis signatures included an increase in Bifidobacterium spp. after chronic sublethal exposure and an increase in Apibacter adventoris after short-term acute exposure. Other dysbiosis signatures were unique to each compound, such as an increase in Snodgrassella alvi for clothianidin and a decrease in Lactobacillus spp. for thiamethoxam. These findings enhance our understanding of how the honey bee gut microbiome responds to stressors and highlight identifiable microbial profile signatures which underscores the potential utility of gut microbiome profiling as a bee health diagnostic tool. Access to timely and accurate bee health diagnosis will inform regulatory actions to decrease and mitigate exposure to stressors and will facilitate managing and improving bee health.
Bistability and hysteresis in the proximity-based grouping of dot lattices
Effect of inoculation dose on infection kinetics and immune responses to Giardia
Optimization of Co60 gamma radiation dose for applying sterile insect technique and inherited sterility on Tuta absoluta (Meyrick) in Iran
Intestinal parasitic infections among school children in Shendi, Sudan (2021–2024): prevalence, risk factors, and diagnostic comparison
A manually driven centrifugal microfluidic LAMP platform for rapid visual detection of waterborne pathogens in aquatic sports
Magnetic field sensing of 3D printed Halbach arrays
Abstract This paper investigates magnetic field amplification in Halbach arrays. A Halbach array, composed of permanent magnets, is arranged to produce a strong magnetic field on one side and a weak field on the other. This configuration has numerous scientific and engineering applications. The literature review surveys representative implementations. In this work, we propose and validate a cost-effective approach for designing and fabricating Halbach magnet arrays. Specifically, in our experiments, we employ a low-cost Hall-effect sensor to measure the Halbach array’s magnetic flux density. Hall-effect sensors are well suited for measuring magnetic fields owing to their accuracy, ease of integration, low cost, and simplicity. Thus, analytical expressions for the magnetic flux density are derived from the magnetic scalar potential using the magnetostatic approximation to Maxwell’s equations and a Fourier-series expansion. We then determine and compare the magnetic flux density through experimental measurements, numerical simulations, and analytical calculations. Numerical simulations are performed using the open-source Python package Magpylib , followed by an exponential regression analysis of both experimental and simulated data. These procedures can be implemented without resorting to costly full three-dimensional magnetostatic simulations or specialized laboratory equipment and may be suited for imperfect physical models by inclusion of experimentally-fitted adjustment proportionality factor $$\xi$$ . Notably, the maximum relative error between the simulation and experimental results is approximately 11% for the Halbach array with large size permanent magnets.
Photovoltaic power interval prediction with conditional error dependency using Bayesian optimized deep learning
Abstract Accurate photovoltaic (PV) power forecasting serves as a critical foundation for economic dispatch and reliable grid operation. To address the inherent uncertainty in PV power generation, this study proposes a short-term PV power interval prediction method based on Bayesian-optimized CNN-BiLSTM-attention (BO-CNN-BiLSTM-attention) that accounts for conditional dependencies in prediction errors. The methodology comprises three main stages: first, PV output data undergoes preprocessing and feature selection. Second, a Bayesian-optimized CNN-BiLSTM-attention model achieves high-precision point forecasting for target time periods. Finally, the K-shape time series clustering algorithm matches point predictions with temporally similar historical data, while adaptive bandwidth kernel density estimation models the probability distribution of prediction errors from similar patterns, thereby enabling interval prediction. Experimental validation on a photovoltaic plant in Xinjiang, China demonstrates that the proposed method achieves superior prediction accuracy compared to various single and ensemble forecasting models, while outperforming multiple interval construction approaches in terms of prediction effectiveness.