Search Results (3,174)

The DHI data is crucial for monitoring the udder health of dairy cows during the breeding process. This study aimed to investigate the factors influencing milk production in dairy cows throughout this period. We analyzed DHI data from Holstein dairy cows in the Heilongjiang region, alongside the incidence of mastitis. The findings revealed that high-yielding cows demonstrated significantly higher peak milk yield days, peak milk yield, urea nitrogen levels, 305-day milk yield, and persistency (p < 0.0001) compared to their low-yielding counterparts. Conversely, high-yielding cows exhibited lower protein rates, fat-to-protein ratios, and milk fat rates (p < 0.0001). Additionally, the somatic cell count (SCC) in high-yielding cows was significantly lower than that in low-yielding cows (p < 0.0001). The multivariate linear regression analysis of the DHI data indicated that parity was the primary determinant affecting both milk yield and SCC. Statistical analysis of cows with clinical mastitis revealed that those experiencing a single episode of clinical mastitis during the lactation period were predominantly in their first and second parities, while recurrent cases were primarily observed in the second and third parities. These results suggest that as the number of lactations increases, the SCC also rises, reflecting the cumulative impact of parity on the udder health of dairy cows. Full article

In modern shock absorber development, the fatigue durability of shim-based clamped valve systems remains a critical factor influencing both performance and operational safety. In this study, the authors extend their previous research achievements by developing a fatigue life prediction methodology that integrates an established finite element framework with a strength-based fatigue model incorporating experimentally derived and validated Wöhler characteristics of the metal alloy used in the valve shims. The focus of this work is the validation of the proposed methodology for hydraulic shock absorbers equipped with shim stack valve systems, supporting the virtual pre-selection of valve configurations during the OEM design process. This approach enables substantial reductions in experimental testing and facilitates cost-effective development under realistic operating conditions. To address random-amplitude loading scenarios, the rainflow-counting algorithm was employed to convert complex load histories into equivalent constant-amplitude cycles, thereby accurately capturing material memory effects associated with stress–strain hysteresis. Experimental validation was conducted using a high-performance servo-hydraulic load frame tester. The validated model demonstrated a prediction uncertainty of 46% for random-amplitude lifetime estimation. Full article

Alkanolamine additives play a critical role in enhancing the early process performance of cement slurries, thereby improving construction efficiency and structural durability. This study systematically evaluates the effects of ethanolamine (EA), diethanolamine (DEA), and triethanolamine (TEA) on cement slurry properties, including the thickening time, rheology, density, shrinkage, and hydration kinetics. Clear structure–activity relationships are established based on the findings. The experimental analysis demonstrated that increasing the hydroxyl group count in the alkanolamines significantly accelerated cement hydration. At a dosage of 1.0%, the thickening time of the cement slurry was significantly shortened to 125 min (EA), 15 min (DEA), and 12 min (TEA), respectively. Concomitantly, a reduction in fluidity was observed, with flow diameters measuring 15.8 cm (EA), 14.6 cm (DEA), and 14.1 cm (TEA). The rheological analysis revealed that the alkanolamine additives significantly increased the consistency coefficient (K) and decreased the flowability index (n) of the slurry, with TEA exhibiting the most pronounced effect. The density measurements confirmed the enhanced settlement stability, as the density differences diminished to 0.1 g/cm³ at the optimal dosages (0.6% TEA and 0.8% DEA). The hydration degree analysis indicated a hydration rate acceleration of up to 32% relative to plain slurry, attributed to the hydroxyl-facilitated promotion of Ca(OH)₂ formation and C₃S dissolution. The XRD analysis confirmed that the alkanolamines modified the reaction kinetics without inducing phase transformation in the hydration products. Crucially, the hydroxyl group count governed the performance: a higher hydroxyl density intensified Ca²⁺/Al³⁺ complexation, thereby reducing ion mobility and accelerating setting. These findings establish a molecular design framework for alkanolamine-based additives that balances early process performance development with practical workability. The study advances sustainable cement technology by enabling targeted optimization of rheological and mechanical properties in high-demand engineering applications. Full article

This study focuses on the design, development and quality assessment of an innovative shelf-stable olive paste dip, aiming at the valorization of by-products of tomato processing and olive oil production (Product 1: OPD). Bioactive compounds (BACs), i.e., total carotenoids and phenolic components, were extracted from tomato and olive pomace, respectively. For further enrichment, BACs were incorporated in olive paste dips into a second product (OPDEnr) in encapsulated form (Product 2: OPD_Enr). The total carotenoids (TC) of OPD and OPD_Enr were 20.0 ± 2.0 and 30.2 ± 1.0 mg/kg, respectively. Similarly, the total phenolic content (TPC) and the antioxidant activity (AA) were 1.62 ± 0.08 and 3.05 ± 0.10 mg GAE/g, and 0.801 ± 0.075 and 0.976 ± 0.032 mg Trolox/g, respectively. The quality of the developed olive paste dip product prototypes was assessed using the Accelerated Shelf Life Testing (ASLT) methodology at a temperature range of 20–40 °C. Both OPD_Enr and OPD were microbiologically stable during storage (i.e., not exceeding 4 logCFU/g for total mesophilic counts), and no lipid oxidation evolution was observed (Peroxide Value, PV did not exceed 4 meq O₂/kg), while TC, TPC and AA values remained stable. The shelf life of OPD_Enr and OPD was determined based on the overall sensory quality and was found to be 120 and 211 d at 25 °C, respectively. OPD_Enr and OPD were characterized by a high quality (color and texture), with an overall sensory score of 8.0/9.0 and 9.0/9.0, respectively, in the acceptability–hedonic scale 1 (dislike extremely)-9 (like extremely), and they could potentially be consumed as an antioxidant-enriched olive paste dip. Full article

To address the challenges of narrow peak characteristics and low signal-to-noise ratio (SNR) detection in Laser-Induced Breakdown Spectroscopy (LIBS), in this paper, we combine the Sparse Bayesian Learning–Baseline Correction (SBL-BC) algorithm with residual skewness monitoring to propose a spectral estimation method tailored for LIBS. In LIBS spectra, discrete peaks are susceptible to baseline fluctuations and noise, while the Gaussian dictionary modeling and fixed convergence criterion of the existing SBL-BC lead to the inaccurate characterization of narrow peaks and high computational complexity. To overcome these limitations, we introduce a residual skewness dynamic tracking mechanism to mitigate residual negative skewness accumulation caused by positivity constraints under high noise levels, preventing traditional convergence criterion failure. Simultaneously, by eliminating the dictionary matrix and directly modeling the spectral peak vector, we transform matrix operations into vector computations, better aligning with LIBS’s narrow peak features and high-channel-count processing requirements. Through simulated and real spectral experiments, the results demonstrate that this method outperforms the SBL-BC algorithm in terms of spectral peak fitting accuracy, computational speed, and convergence performance across various SNRs. It effectively separates spectral peaks, baseline, and noise, providing a reliable approach for both quantitative and qualitative analysis of LIBS spectra. Full article

We propose a two-stage statistical learning framework to investigate how financial news headlines posted over weekends affect stock returns. In the first stage, Natural Language Processing (NLP) techniques are used to extract sentiment features from news headlines, including FinBERT sentiment scores and Impact Probabilities derived from Logistic Regression models (Binomial, Multinomial, and Bayesian). These Impact Probabilities estimate the likelihood that a given headline influences the stock’s opening price on the following trading day. In the second stage, we predict over-weekend log returns using various sets of covariates: sentiment-based features, traditional financial indicators (e.g., trading volumes, past returns), and headline counts. We evaluate multiple statistical learning algorithms—including Linear Regression, Polynomial Regression, Random Forests, and Support Vector Machines—using cross-validation and two performance metrics. Our framework is demonstrated using financial news from MarketWatch and stock data for Apple Inc. (AAPL) from 2014 to 2023. The results show that incorporating sentiment features, particularly Impact Probabilities, improves predictive accuracy. This approach offers a robust way to quantify and model the influence of qualitative financial information on stock performance, especially in contexts where markets are closed but news continues to develop. Full article

Natural sand (NS) is facing the problem of resource scarcity, while manufactured sand (MS) has become a favorable alternative resource due to its wide range of sources, superior performance, as well as economic and environmental protection. This study adopted MS to replace NS to prepare manufactured sand concrete (MSC). The water–cement ratio, replacement rate of MS, and stone powder content were systematically investigated for the damage evolution of rebar during bond–slip with MSC. Seven groups of specimens were tested using the center pull-out test to analyze the effects of different factors on the bond–slip characteristics (bond stress–slip curve, bond fracture energy, peak stress, and peak slip). Acoustic emission (AE) monitoring was also adopted to synchronously characterize the slip damage process of reinforced MSC. The results indicate that the water–cement ratio and replacement ratio of MS present significant influences on the bond strength of reinforced MSC, in which the smaller the water–cement ratio is, the stronger the bond strength of reinforced concrete. Further, the larger the replacement rate of MS is, the stronger the bond strength of reinforced concrete. The higher the stone powder content, the higher the bond strength, but the effect is small compared to the two variables mentioned above. In terms of AE, count and energy remain at low values in the first and middle stages, followed by larger values, proving that cracks were beginning to develop within the specimen, and then a very large signal and then splitting occurred. The information entropy is relatively stable in the first and middle stages of the test, then fluctuates with the generation of cracks, and finally fluctuates violently and then the specimen splits. The AE parameters are more active with an increasing water–cement ratio, while they are smoother with increases in the replacement rate of MS and stone powder content. Full article

This study developed a technology for restructured meat products (RMPs) from culled cow meat using the bioprotective culture Lactobacillus sakei (SafePro B-2, 10¹¹ CFU/g) and fortification with L-selenomethionine or zinc citrate. Four variants (Control, SafePro B-2, SafePro B-2 + Se, and SafePro B-2 + Zn) were produced under identical processing conditions and assessed for microbiological, physicochemical, textural, colorimetric, antioxidant, histological, mineral, and amino acid properties. Protein content remained high across all samples (up to 18.7%), while moisture increased by up to 1.4% compared to the control. The Zn-enriched sample showed the greatest cohesiveness and resistance to deformation (p < 0.05), with color stability under light exposure improving by up to 12.5%. Despite a reduction in FRAP antioxidant activity (up to 30.8% in buffer extract), the Zn-fortified product exhibited the highest levels of key essential amino acids, including leucine (12.9 mg/g) and lysine (12.6 mg/g). Microbiological analysis confirmed low total aerobic mesophilic counts (≤3.1 log CFU/g), with no detection of Salmonella spp. or Listeria monocytogenes. Histological evaluation revealed denser and more homogeneous protein matrices in fortified variants. Overall, L. sakei-driven bioprotection combined with Se/Zn fortification improved the safety and functional and nutritional characteristics of RMP from low-value beef, supporting sustainable and circular meat production. Full article

In recent years, ultrasound has been integrated into fermentation technology due to its activating effect on microorganisms, and the possible effects of ultrasound-assisted fermentation on the fermentation process, yield and quality of the final product have also attracted attention. This study aimed to reveal the effects of sonication applied before the fermentation on the fermentation process and the quality of fermented black carrot juice. The samples were sonicated at a frequency of 35 kHz and an amplitude of 60% for 0, 5, 15 or 30 min before the fermentation. During the fermentation, the pH, acidity, organic acid profile, ethanol and soluble solid content (SSC), color, turbidity, total lactic acid bacteria (LAB), total mesophilic aerobic bacteria (TMAB) and yeast counts were determined. The amount of SSC in the samples increased at the beginning of fermentation as the sonication time increased. Lactic, acetic and propionic acids were detected in the samples. The amount of lactic acid in all the samples treated with ultrasound was higher than in the control sample and the amounts of acetic acid, propionic acid and ethanol were lower. Ultrasound application caused an increase in the TMAB and yeast counts. A five-minute ultrasound application caused a decrease in the number of LAB, while 15- and 30-min applications caused an increase. Thirty minutes of ultrasound treatment resulted in the reddest fermented black carrot juices with the highest level of color saturation. The most appreciated sample in terms of taste, aroma and general acceptability was the sample subjected to a five-minute ultrasound application. As a result, ultrasound application before fermentation positively supports different quality parameters of fermented black carrot juice and the use of sonication in production can be recommended. Full article

The paper presents an FPGA-based, hardware-accelerated IEC 61850-9-2 Sampled Measured Values (SMV) subscriber—termed the high-speed SMV subscriber (HS3)—by integrating real-time energy-quality (EQ) analytics directly into the subscriber pipeline while preserving a deterministic, microsecond-scale operation under high stream counts. Building on a prior hardware decoder that achieved sub-3 μs SMV parsing for up to 512 subscribed svIDs with modest logic utilization (<8%), the proposed design augments the pipeline with fixed-point RTL modules for single-bin DFT frequency estimation, windowed true-RMS computation, and per-sample active power evaluation, all operating in a streaming fashion with configurable windows and resolutions. A lightweight software layer performs only residual scalar combinations (e.g., apparent power, form factor) on pre-aggregated hardware outputs, thereby minimizing CPU load and memory traffic. The paper’s aim is to bridge the gap between software-centric analytics—common in toolkit-based deployments—and fixed-function commercial firmware, by delivering an open, modular architecture that co-locates SMV subscription and EQ pre-processing in the same hardware fabric. Implementation on an MPSoC platform demonstrates that integrating EQ analytics does not compromise the efficiency or accuracy of the primary decoding path and sustains the latency targets required for protection-and-control use cases, with accuracy consistent with offline references across representative test waveforms. In contrast to existing solutions that either compute PQ metrics post-capture in software or offer limited in-FPGA analytics, the main contributions lie in a cohesive, resource-efficient integration that exposes continuous, per-channel EQ metrics at microsecond granularity, together with an implementation-level characterization (latency, resource usage, and error against reference calculations) evidencing suitability for real-time substation automation. Full article

Background: Acute spinal cord trauma management necessitates understanding the primary and secondary injury mechanisms at different timepoints. Objectives: To characterize the cell death process by examining the temporal and spatial distributions of necrosis and apoptosis in an experimental spinal cord injury model. Methods: Wistar male rats were divided into trauma (n = 30) and sham (n = 6) groups, and a 50 g/cm weight drop contusion design was used. The rats were sacrificed 1, 6, 24, 48, 72, and 168 h after the injury. Every 0.5 cm spinal cord segment was examined cranially and caudally up to a total of 2.5 cm for neuronal and glial damage via the apoptotic count and DNA damage index via morphology and immunohistochemistry using an anti-ssDNA antibody. The results were mapped to visualize the damage extent, intensity, and distribution. Results: The central zone underwent hemorrhage and necrosis one hour after the injury. The apoptotic cells and DNA damage index increased with time (p < 0.001), and specific spatial alterations were observed among the segments (p < 0.001). Mapping the apoptotic cells and DNA damage clearly reflected the injury’s severity and extent. Conclusion: The DNA damage and the apoptotic cell count increase over time were well correlated with the morphology and could easily be elucidated using ssDNA immunostaining. Full article

This paper presents an all-CMOS temperature sensor with low power consumption, wide temperature range, and high precision in a 180 nm CMOS process. Based on the I–V characteristics of MOSFETs in the subthreshold region and the negative exponential biasing current generated by the self-bootstrapped bias circuit, the proposed temperature-sensing front-end produces CTAT and PTAT voltages with high linearity and high sensitivity. The voltage-to-time converter (VTC) adopts a dual-comparator architecture to expand the time interval for improving resolution. The control logic unit is designed to count only within the time interval, eliminating interference during low-level periods and enhancing the accuracy of temperature measurement. The implemented sensor achieves an inaccuracy of −0.45 °C/+0.51 °C ($3\sigma$) from −40 °C to 130 °C after a two-point calibration with a resolution of 28 mK and consumes 503 nW at 27 °C when operating at 1 V, with an FoM of 7.9 pJ·K². 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

The nanoporous structure of coal is crucial for the occurrence and development of coalbed methane (CBM). This study, leveraging the combined characterization of atomic force microscopy (AFM) and Gwyddion software (v2.62), investigated six anthracite samples with varying degrees of metamorphism (R_o = 2.11–3.36%). It revealed the intrinsic relationships between their nanoporous structures, surface morphologies, fractal characteristics, and coalification processes. The research found that as R_o increases, the surface relief of coal decreases significantly, with pore structures evolving from being macropore-dominated to micropore-enriched, and the surface tending towards smoothness. Surface roughness parameters (R_a, R_q) exhibit a negative correlation with R_o. Quantitative data indicate that area porosity, pore count, and shape factor positively correlate with metamorphic grade, while mean pore diameter negatively correlates with it. The fractal dimensions calculated using the variance partition method, cube-counting method, triangular prism measurement method, and power spectrum method all show nonlinear correlations with R_o, moisture (M_ad), ash content (A_ad), and volatile matter (V_daf). Among these, the fractal dimension obtained by the triangular prism measurement method has the highest correlation with R_o, A_ad, and V_daf, while the variance partition method shows the highest correlation with M_ad. This study clarifies the regulatory mechanisms of coalification on the evolution of nanoporous structures and surface properties, providing a crucial theoretical foundation for the precise evaluation and efficient exploitation strategies of CBM reservoirs. Full article

The rapid advancement of general large models has significantly impacted and introduced new concepts to the traditional “one task, one model” research paradigm in construction automation. In this paper, we evaluate the performance of existing large models and those developed on large model platforms, using building material counting as an example. We compare three categories of large AI models for building material counting, including multimodal large models, purely visual large models, and secondary models developed on platforms. Through this research, we aim to explore the accuracy and practicality of these models in real-world construction scenarios. The results indicate that directly applying general large models faces challenges in processing photos with complex shapes or backgrounds, failing to provide accurate counting results. Additionally, while purely visual large models excel in instance segmentation tasks, their application to the specific counting of building materials requires additional programming work. To address these issues, this study explores solutions based on large model secondary development platforms and trains a model using EasyDL as an example. Leveraging deep learning techniques, this model achieves effective counting of building materials through five steps: data preparation, model type selection, model training, model validation, and model deployment. Although models developed based on large model platforms are presently less accurate than specialized models, they still represent a highly promising approach. Full article