Search Results (323)

The article focuses on forecasting 7-day daily natural gas consumption for a healthcare facility in Slovakia during the winter season (1 October–30 April). The goal is to optimise operational costs while maintaining user comfort and considering economic and environmental indicators. The prediction is based on historical gas consumption and temperature data from eleven heating seasons (taking into account external factors such as COVID-19 and geopolitical conflicts). Linear regression and counting of residuals, Wavelet decomposition and Long Short-Term Memory (LSTM) neural networks were used. Two approaches were tested: firstly, data augmentation using Wavelet decomposition and creating an LSTM model and secondly, individual prediction of wavelet components by LSTM and combining the best-performing models. The second approach, which forecasted each wavelet component separately and then reconstructed the final prediction, yielded the best accuracy (nMAE = 5.71%, NRMSE = 7.80%). The results showed that using predicted temperatures slightly reduced accuracy. Overall, the Wavelet-LSTM model proved to be the most effective method for forecasting gas consumption in healthcare facilities during winter. Full article

Background: The treatment of methicillin-resistant Staphylococcus aureus (MRSA) infections is extremely challenging due to its antibiotic resistance, and the combination of plant active ingredients with antibiotics represents a potential strategy to address this issue. Methods: We determined the combinatorial relationship between baicalin (BA) and kanamycin (KM) using the checkerboard dilution method. The antibacterial activity of the baicalin–kanamycin (BA/KM) combination was evaluated through growth curve determination assays and scanning electron microscopy (SEM). The effects of the BA/KM combination on the cell membrane and cell wall of MRSA were analyzed using reactive oxygen species (ROS) detection assays, intracellular protein leakage experiments, alkaline phosphatase (AKP) activity assays, laser scanning confocal microscopy (LSCM) observations, and molecular docking simulations. The antibiofilm activity and related mechanisms of the BA/KM combination were elucidated via crystal violet staining, MTT assay, phenol-sulfuric acid method, congo red staining, staphyloxanthin determination assays, and quantitative real-time polymerase chain reaction (qPCR). The safety of the BA/KM combination was assessed through hemolytic activity analysis, and its anti-MRSA efficacy was evaluated on lettuce. Results: BA/KM combination showed a synergistic antibacterial effect on MRSA USA300. Mechanistic studies revealed that BA may interact with amino acid residues of peptidoglycan synthetase PBP2a to hinder peptidoglycan synthesis, thereby facilitating KM penetration through the cell wall. Subsequently, BA binds to amino acid residues of the membrane transporter NorA, leading to disruption of cell membrane homeostasis and enhancing KM’s ability to induce intracellular ROS accumulation in MRSA. Furthermore, the BA/KM combination reduced MRSA biofilm formation by 77.85% and decreased the metabolic activity of biofilm cells by 42.93% through inhibiting the synthesis of biofilm components EPS and PIA. Additionally, this combination suppressed the synthesis of staphyloxanthin and downregulated the expression of agrA and agrC genes. When 1/8 MIC BA was combined with 1/4 MIC KM, the count of MRSA on lettuce surfaces was reduced by 0.88 log CFU/cm², an effect comparable to that of 0.2% (v/v) hydrogen peroxide. Conclusions: According to these findings, the BA/KM combination may offer a promising option for enhancing antibacterial efficacy through synergism, reducing antibiotic usage concentrations, and limiting MRSA transmission in fresh agricultural products. Full article

Corn, as a globally significant food crop, faces significant yield reductions due to competitive growth from weeds. Precise detection and efficient control of weeds are critical technical components for ensuring high and stable corn yields. Traditional deep learning object detection models generally suffer from issues such as large parameter counts and high computational complexity, making them unsuitable for deployment on resource-constrained devices such as agricultural drones and portable detection devices. Based on this, this paper proposes a lightweight corn weed detection model, AGRI-YOLO, based on the YOLO v11n architecture. First, the DWConv (Depthwise Separable Convolution) module from InceptionNeXt is introduced to reconstruct the C3k2 feature extraction module, enhancing the feature extraction capabilities for corn seedlings and weeds. Second, the ADown (Adaptive Downsampling) downsampling module replaces the Conv layer to address the issue of redundant model parameters; The LADH (Lightweight Asymmetric Detection) detection head is adopted to achieve dynamic weight adjustment while ensuring multi-branch output optimization for target localization and classification precision. Experimental results show that the AGRI-YOLO model achieves a precision rate of 84.7%, a recall rate of 73.0%, and a mAP50 value of 82.8%. Compared to the baseline architecture YOLO v11n, the results are largely consistent, while the number of parameters, G FLOPs, and model size are reduced by 46.6%, 49.2%, and 42.31%, respectively. The AGRI-YOLO model significantly reduces model complexity while maintaining high recognition precision, providing technical support for deployment on resource-constrained edge devices, thereby promoting agricultural intelligence, maintaining ecological balance, and ensuring food security. Full article

The surface quality of optical components plays a decisive role in advanced imaging, precision manufacturing, and high-power laser systems, where even defects can induce abnormal scattering and degrade system performance. Addressing the limitations of conventional single-view inspection methods, this study presents a panoramic multi-angle scattered light field acquisition approach integrated with deep learning-based recognition. A hemispherical synchronous imaging system is designed to capture complete scattered distributions from surface defects in a single exposure, ensuring both structural consistency and angular completeness of the measured data. To enhance the interpretation of complex scattering patterns, we develop a tailored lightweight network, SFD-YOLO, which incorporates the PSimam attention module for improved salient feature extraction and the Efficient_Mamba_CSP module for robust global semantic modeling. Using a simulated dataset of multi-width scratch defects, the proposed method achieves high classification accuracy with strong generalization and computational efficiency. Compared to the baseline YOLOv11-cls, SFD-YOLO improves Top-1 accuracy from 92.5% to 95.6%, while reducing the parameter count from 1.54 M to 1.25 M and maintaining low computational cost (Flops 4.0G). These results confirm that panoramic multi-angle scattered imaging, coupled with advanced neural architectures, provides a powerful and practical framework for optical surface defect detection, offering valuable prospects for high-precision quality evaluation and intelligent defect inversion in optical inspection. Full article

Total Mixed Ration (TMR) particle size significantly impacts dairy cow health and productivity. This study investigated the effects of TMR particle size tertiles on rumen pH, dry matter intake (DMI), and milk characteristics in Simmental cows by continuous pH monitoring (Moonsyst Ltd., Kilkenny, Republic of Ireland) and particle separation by 19, 8, 4 mm sieves and pad using the Wasserbauer particle separator, along with regular milk and DMI measurements. Data were analyzed by IBM SPSS 26.0 with ANOVA, Pearson correlations and statistically significant differences between tertiles by post hoc Tukey HSD test were performed (p < 0.05). Tertiles by frequency analysis were used to categorize particle size proportions into three groups, each containing an equal number of observations. Principal component analysis (PCA) and heatmaps by SRplot were generated. Moderate particle size distributions (second tertiles of 19 mm, 8 mm, 4 mm sieves, and pad as the fraction of TMR particles that pass through the all sieves and are collected in the bottom pan) optimized rumen pH stability, reducing time below 6.2 (SARA risk) or above 6.8, and correlated with milk β-hydroxybutyrate (BHB), oleic acid, and acetone levels. Moreover, milk production was maximized with a combination of coarser (19 mm and 8 mm, third tertiles) and finer (4 mm, first tertile) particles, milk fat peaked in both the finest pad fraction (third tertile) and coarsest larger sieves (first tertiles), and milk protein in the first tertiles of 19 mm and 8 mm sieves. Similarly, DMI positively correlated with coarser particles, but sometimes negatively with milk quality. In addition, PCA showed fine particle groups clustering with higher milk fat-to-protein ratios, somatic cell counts, and urea. In conclusion, mid-range TMR particle sizes (second tertiles) consistently provided the most benefits across ruminal, metabolic, and production parameters, underscoring TMR structure as a crucial precision feeding tool. Full article

With the continuous advancement of on-orbit services and space intelligence sensing technologies, the efficient and accurate identification of spacecraft components has become increasingly critical. However, complex lighting conditions, background interference, and limited onboard computing resources present significant challenges to existing segmentation algorithms. To address these challenges, this paper proposes a lightweight spacecraft component segmentation framework for on-orbit applications, termed M4MLF-YOLO. Based on the YOLOv5 architecture, we propose a refined lightweight design strategy that aims to balance segmentation accuracy and resource consumption in satellite-based scenarios. MobileNetV4 is adopted as the backbone network to minimize computational overhead. Additionally, a Multi-Scale Fourier Adaptive Calibration Module (MFAC) is designed to enhance multi-scale feature modeling and boundary discrimination capabilities in the frequency domain. We also introduce a Linear Deformable Convolution (LDConv) to explicitly control the spatial sampling span and distribution of the convolution kernel, thereby linearly adjusting the receptive field coverage range to improve feature extraction capabilities while effectively reducing computational costs. Furthermore, the efficient C3-Faster module is integrated to enhance channel interaction and feature fusion efficiency. A high-quality spacecraft image dataset, comprising both real and synthetic images, was constructed, covering various backgrounds and component types, including solar panels, antennas, payload instruments, thrusters, and optical payloads. Environment-aware preprocessing and enhancement strategies were applied to improve model robustness. Experimental results demonstrate that M4MLF-YOLO achieves excellent segmentation performance while maintaining low model complexity, with precision reaching 95.1% and recall reaching 88.3%, representing improvements of 1.9% and 3.9% over YOLOv5s, respectively. The mAP@0.5 also reached 93.4%. In terms of lightweight design, the model parameter count and computational complexity were reduced by 36.5% and 24.6%, respectively. These results validate that the proposed method significantly enhances deployment efficiency while preserving segmentation accuracy, showcasing promising potential for satellite-based visual perception applications. Full article

Airborne and surface-residing microorganisms in indoor environments pose potential risks for infectious disease transmission. To address this issue, we developed a composite device combining a generator of vaporized free chlorine components with a fine particle removal filter. Field tests were conducted in occupied university classrooms to evaluate the device’s efficacy in reducing airborne bacterial loads. Airborne bacteria were sampled under three operational conditions [Electrolyzed (+)/Filter (+), Electrolyzed (−)/Filter (+), and Electrolyzed (−)/Filter (−)]. Significant reductions in bacterial counts were observed in the Electrolyzed (+)/Filter (+) condition, with a residual rate of 14.5% after 2.25 h (p = 0.00001). Additionally, surface contact tests demonstrated that vaporized free chlorine components, primarily consisting of hypochlorous acid (HOCl), reduced viable counts of E. coli, P. aeruginosa, and S. aureus by 59.0–99.7% even at a distance of 8.0 m. The concentrations of vaporized free chlorine components during operation remained within safe exposure limits (0–19 ppb), consistent with the effective range reported in prior literature (10–50 ppb). Computational fluid dynamics simulations confirmed the diffusion of vaporized free chlorine components throughout the room, including distant sampling points. These findings suggest the combined use of a vaporized free chlorine generator and a particulate filter effectively reduces microbial contamination in indoor environments, providing a promising approach for infection control in residential and public settings. Full article

Multipliers are crucial components in digital processing and the arithmetic logic unit (ALU) of central processing unit (CPU) design. As the data bit length increases, the number of partial products in the multiplication process increases, resulting in an increased summation time for the partial products. Consequently, the speed of the multiplier circuit is adversely affected by increased time delays. In this article, we present a combined radix-4 Booth encoding module that employs metal–oxide–semiconductor (MOS) transistors that share common control signals to reduce the transistor count. In HSPICE simulations, the functionality of the proposed circuit architecture was verified, and the number of transistors used was successfully reduced. Full article

Pea protein isolate (PPI) is a plant protein with high nutritional value, but its application in food is limited by an unpleasant beany flavor. This study aimed to investigate the feasibility of improving the flavor of PPI through fermentation with Enterococcus faecalis 07. PPI was subjected to fermentation by E. faecalis 07 for different durations (0 H, 24 H, 48 H, and 72 H). After fermentation, pH, viable cell counts, free amino acid contents, electronic tongue analysis, and volatile organic compounds were determined. The results showed that fermentation significantly reduced the bitterness of PPI and enhanced its umami intensity. A total of 64 volatile organic compounds were identified in the fermented samples, 42 more than in the unfermented sample. Quantitative analysis revealed that hexanal (grass-like odor) decreased by 92% after 72 h of fermentation, 1-octen-3-ol (mushroom-like odor) decreased from 6.94 mg/kg to 1.73 mg/kg, and trans-2-octenal decreased to 0.59 mg/kg; meanwhile, aromatic compounds such as esters and ketones were produced. Along with changes in the physicochemical properties, organic acids, and free amino acid composition of PPI, correlation analysis between electronic tongue data and volatile compounds further indicated that changes in volatile components simultaneously affected the perception of five taste attributes of PPI (bitterness, sourness, sweetness, saltiness, and umami). In conclusion, this study demonstrated the feasibility of fermenting PPI with E. faecalis 07, which effectively improved its sensory attributes and physicochemical properties to a certain extent. Full article

In this study, oyster fermentation broth (OFB) was prepared by fermenting oysters with yeast, and its effects on oxidative stress and reproductive damage induced by tripterygium glycosides (TG) in male rats were investigated. Component analysis revealed that OFB contained bioactive substances including proteins (1.19 g/L), taurine (0.76 g/L), organic acids (2.30 mg/mL), polyphenols (123.00 mg GAE/L), flavonoids (1.97 mg RE/L), and zinc (1.10 mg/L). In vitro study revealed that OFB exhibited notable antioxidant activity, with a total antioxidant capacity of 1.28 U/mL, and DPPH, ABTS, and hydroxyl radical scavenging rates of 55.80%, 69.54%, and 48.36%, respectively. Animal experiments showed that, compared with the TG-induced model group, rats administered both low-dose (5 mL/kg) and high-dose (10 mL/kg) OFB showed significantly increased testis and seminal vesicle + prostate indices, sperm count, and serum testosterone (T) levels and decreased sperm malformation rate (p < 0.01 for all). Histological analysis of the testis revealed an increased number of spermatogenic cells and sperm within the seminiferous tubules, along with ameliorated pathological conditions compared to the model group. Potential mechanisms might be related to OFB increasing the activities of catalase (CAT), superoxide dismutase (SOD), and glutathione peroxidase (GSH-PX) enzymes and reducing levels of malondialdehyde (MDA) in testis (p < 0.01). The findings demonstrated that OFB successfully alleviated TG-induced reproductive damage in male rats, which might be attributed to its excellent antioxidant effect. The study offers valuable insights for producing functional foods from oysters and further validates OFB’s efficacy in promoting reproductive function. Full article

This paper presents a computational design method for generating discrete shell structures using sets of equivalent discrete members. This study addresses the challenge of reducing the geometrical variety in discrete shell elements by introducing a method to design and optimize constituent members considering their similarity, approximation of the double-curved architectural surface, and buildability. First, we employed a relaxation-based computational form-finding method to generate a discrete topology with planar quad faces and an approximated smooth, double-curved surface. Then, we perform clustering and optimization based on face similarities concerning the minimization of deviations from the smooth surface approximation, and the dihedral angle between the planes of neighboring elements and their optimal intersection plane. The proposed approach can reduce the geometrical differences in discrete shell elements while satisfying the user-defined error threshold. We demonstrated the viability of our method on various structured topologies with different boundary conditions, support settings, and total face counts, while explicitly controlling inter-element facing angles for assembly ready contacts. This enables mold-based prefabrication with repeatable components. Full article

Space-based on-orbit servicing missions impose stringent requirements for precise identification and localization of satellite components, while existing detection algorithms face dual challenges of insufficient accuracy and excessive computational resource consumption. This paper proposes SLD-YOLO, a lightweight satellite component detection model based on improved YOLO11, balancing accuracy and efficiency through structural optimization and lightweight design. First, we design RLNet, a lightweight backbone network that employs reparameterization mechanisms and hierarchical feature fusion strategies to reduce model complexity by 19.72% while maintaining detection accuracy. Second, we propose the CSP-HSF multi-scale feature fusion module, used in conjunction with PSConv downsampling, to effectively improve the model’s perception of multi-scale objects. Finally, we introduce SimAM, a parameter-free attention mechanism in the detection head to further improve feature representation capability. Experiments on the UESD dataset demonstrate that SLD-YOLO achieves measurable improvements compared to the baseline YOLO11s model across five satellite component detection categories: mAP50 increases by 2.22% to 87.44%, mAP50:95 improves by 1.72% to 63.25%, while computational complexity decreases by 19.72%, parameter count reduces by 25.93%, model file size compresses by 24.59%, and inference speed reaches 90.4 FPS. Validation experiments on the UESD_edition2 dataset further confirm the model’s robustness. This research provides an effective solution for target detection tasks in resource-constrained space environments, demonstrating practical engineering application value. Full article

Whether on greenfield or brownfield sites, new buildings need land. The locations of additional dwellings in England, whether provided through a standard planning process or a light-touch approach, have recently been criticised for not impacting affordability and for being in the wrong places. More sustainable means of raising the stock of abodes in England, including repurposing dilapidated or underused property, land, or infrastructure; reducing the demolition rate; and reducing the time an existing dwelling is left idle, do not consume additional land for building. Although the National Planning Policy Framework for additional dwellings places a duty on each district planning authority to find more land for housing, alternatives to new builds are included in the count. This paper examines the spatial concentrations of the components that can add to the habitable stock of real estate. It examines their take-up over recent years. This is important for land-use planning and the preservation of green spaces in the face of increasing housing pressures. Using a simple, innovative approach to assessing collocation, the paper considers whether there are similarities in spatial concentrations. The approach is used to infer whether builders converting existing property add units in areas where new builds are in more modest supply. Although alternative means of adding to the housing stock may be more sustainable, and more likely to be found in areas of greater need, the numbers are too low to be anything other than a supplement to new builds. Full article

This study aimed to examine the relationship between a high somatic cell count (SCC) in cows and milk quality during the hot season in different breeds. Milk samples from 500 cows in the hot season and 431 in the cold season of 2022 were collected across 39 farms in Santa Catarina, Brazil. The samples were analyzed for SCC, milk composition, and physical attributes. Cows were also evaluated for udder depth, udder clearance, teat-end condition, and leg and udder cleanliness. Based on the SCC levels, cows were categorized as low (≤200,000 cells/mL), medium (>200,000 and ≤615,000), or high (>615,000). Data were analyzed by ANOVA with a statistical model that included the effects of the SCC class, season, days in milk, parity, genetic group, and the interaction of the SCC level and season. The results showed that cows with a high SCC produced less milk with lower component levels but higher chloride content. Milk from the hot season had lower acidity and reduced component levels. The impact of SCC on the physical traits of milk did not vary with season. Furthermore, cows with deeper udders and lower udder clearance were more likely to have a high SCC, regardless of genetics. Both a high SCC and hot temperatures independently compromised milk yield and quality, thereby increasing the risk of culling. Therefore, improving udder conformation and avoiding cows with deep udders may help to reduce SCC levels. Full article

Mastitis is a common disease for dairy cows that exerts tremendously detrimental impacts on the productivity of cows and economic viability of pasture. Dihydromyricetin (DMY) is a flavonoid monomeric compound that possesses anti-inflammatory and antioxidant activity. This study aimed at dissecting the effects of DMY on the lactation performance, blood parameters, gut microbiota, and metabolite profiles of dairy cows with subclinical mastitis (SM). The results showed that dietary supplementation with DMY resulted in a reduction in milk somatic cell count, an increase in serum T-AOC and CAT activity, as well as a decrease in serum MDA content. DMY significantly enhanced the prevalence of Coprococcus and Roseburia and reduced the proportion of Cyanobacteria, Proteobacteria, and Dehalobacterium. The amino acid degradation, antibiotic resistance, and O-antigen building blocks biosynthesis (E. coli) capacity of gut microbes were notably diminished by DMY supplementation in cows with SM. Moreover, fecal and plasma metabolomic analysis revealed that DMY intervention reduced the abundance of pro-inflammatory metabolites including arachidonic acid analogues, ω-6 PUFA, and structural components of bacteria. Nevertheless, the levels of anti-inflammatory and antioxidant metabolites involving secondary bile acids, antioxidant vitamins, specific amino acid analogues, etc. were elevated by DMY administration. Overall, DMY might ameliorate SM via enhancing antioxidant capacity and improving the structure of the hindgut microbial community and metabolite profiles in dairy cows. These findings underscore the potential of DMY as a valuable dietary supplement for the improvement of mammary inflammatory diseases in dairy cows. Full article