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The irregular joint network unique to columnar joints separates the rock mass into several irregular polygonal prisms. Similar physical model specimens of columnar jointed rock mass (CJRM) were fabricated using a rock-like material. The effect of the irregularity of the joint network was considered in the horizontal plane, and the effect of the dip angle of the joint network was considered in the vertical plane. The strength and deformation moduli of the specimen were investigated using uniaxial compression tests. A total of four failure modes of regular columnar jointed rock mass (RCJRM) and irregular columnar jointed rock mass (ICJRM) were identified through the tests. The peak stress of the irregular columnar jointed rock mass specimen is reduced by 56.65%. The strength and deformation moduli of RCJRM were greater than those of ICJRM, while the anisotropic characteristics of ICJRM were stronger. The failure mode of CJRM was determined by the dip angle. With the increase in the dip angle, the strength and deformation moduli of irregular columnar jointed rock mass are a symmetrical “V” type distribution, 45° corresponds to the minimum strength, and 30° obtains the minimum deformation modulus. With the increase in the irregularity coefficient, the strength and deformation moduli of CJRM decreased first and then increased gradually. When the irregularity coefficient is 0.1, the linear deformation modulus reaches the minimum value. When the irregularity coefficient is 0.7, the median deformation modulus reaches the minimum value. The fitting function proposed in the form of the cosine function managed to predict the strength value of CJRM and showed the strength of the anisotropic characteristics caused by the change in the dip angle. Compared with the existing physical model test results, it is determined that the strength of the specimen is positively correlated with the addition amount of rock-like material and the loading rate, and negatively correlated with the water consumption. Full article

In smart ranch management, cattle behavior recognition and tracking play a crucial role in evaluating animal welfare. To address the issues of missed and false detections caused by inter-cow occlusions and infrastructure obstructions in the barn environment, this paper proposes a multi-object tracking method called YOLO-BoT. Built upon YOLOv8, the method first integrates dynamic convolution (DyConv) to enable adaptive weight adjustments, enhancing detection accuracy in complex environments. The C2f-iRMB structure is then employed to improve feature extraction efficiency, ensuring the capture of essential features even under occlusions or lighting variations. Additionally, the Adown downsampling module is incorporated to strengthen multi-scale information fusion, and a dynamic head (DyHead) is used to improve the robustness of detection boxes, ensuring precise identification of rapidly changing target positions. To further enhance tracking performance, DIoU distance calculation, confidence-based bounding box reclassification, and a virtual trajectory update mechanism are introduced, ensuring accurate matching under occlusion and minimizing identity switches. Experimental results demonstrate that YOLO-BoT achieves a mean average precision (mAP) of 91.7% in cattle detection, with precision and recall increased by 4.4% and 1%, respectively. Moreover, the proposed method improves higher order tracking accuracy (HOTA), multi-object tracking accuracy (MOTA), multi-object tracking precision (MOTP), and IDF1 by 4.4%, 7%, 1.7%, and 4.3%, respectively, while reducing the identity switch rate (IDS) by 30.9%. The tracker operates in real-time at an average speed of 31.2 fps, significantly enhancing multi-object tracking performance in complex scenarios and providing strong support for long-term behavior analysis and contactless automated monitoring. Full article

The aim of the study was to determine the effects of replacing soybean meal with insect meal on the body weight and the chemical composition of selected muscle groups of common pheasant females and males, including the mineral composition and the amino acid profile of the thigh and breast muscles. The study was conducted on three feeding groups, namely one control and two experimental groups. In the control group, plant feed components were used, which are commonly used to feed pheasants in confined breeding facilities. In the experimental groups, 100 g (group II) and 200 g (group III) portions of insect meal were introduced instead of the plant-protein components. The experiment used a preparation of insect larvae (Hermetia illucens) containing approximately 52% crude protein. The pheasant diet supplementation applied contributed to an increase in the proportion of muscles in the carcasses, with the highest effectiveness obtained for a 20% addition of insect meal. Lower and significant differences were noted in the feed conversion by birds from the experimental groups, as compared to the control group. The chemical composition of the birds’ muscles also changed. The experimental groups exhibited higher protein and fat contents and a lower water content. No significant changes in the amino acid profile or the mineral composition of the muscles were noted. The few exceptions concerned the methionine levels in both muscle groups and the isoleucine levels in the breast muscles. In most cases, the mineral composition did not vary significantly (p < 0.05). When supplementing the diet of breeding pheasants for improving meatiness, a 20% addition of insect meal is recommended, which affects the production effect of this trait while reducing feed consumption and maintaining the fatty acid profile. Full article

In this study, the effectiveness of Fe₃O₄-based clay as a cost-effective material for removing methylene blue (MB) dye from aqueous solutions was evaluated. The structural properties of the clay and Fe₃O₄-based clay were analyzed using SEM, XRF, BET, XRD, FTIR, and TGA techniques. In this research, the effects of various aspects, such as adsorbent amount, contact time, solution pH, adsorption temperature, and initial dye concentration, on the adsorption of Fe₃O₄-based clay are investigated. The experiments aimed at understanding the adsorption mechanism of Fe₃O₄-based clay have shown that the adsorption kinetics are accurately described by the pseudo-second order kinetic model, while the equilibrium data are well represented by the Langmuir isotherm model. The maximum adsorption capacity (qm) was calculated as 52.63 mg/g at 25 °C, 53.48 mg/g at 30 °C, and 54.64 mg/g at 35 °C. All variables affecting the MB adsorption process were systematically optimized in a controlled experimental framework. The effectiveness of the artificial neural network (ANN) model was refined by modifying variables such as the quantity of neurons in the latent layer, the number of inputs, and the learning rate. The model’s accuracy was assessed using the mean absolute percentage error (MAPE) for the removal and adsorption percentage output parameters. The coefficient of determination (R²) values for the dyestuff training, validation, and test sets were found to be 99.40%, 92.25%, and 96.30%, respectively. The ANN model demonstrated a mean squared error (MSE) of 0.614565 for the training data. For the validation dataset, the model recorded MSE values of 0.99406 for the training data, 0.92255 for the validation set, and 0.96302 for the test data. In conclusion, the examined Fe₃O₄-based clays offer potential as effective and cost-efficient adsorbents for purifying water containing MB dye in various industrial settings. Full article

Background/Objectives: Vaccines may improve the control and eradication of bovine tuberculosis. However, the evaluation of experimental candidates requires the assessment of the protection, excretion, transmission and biosafety. A natural transmission trial among likely infected animals was conducted. Methods: Seventy-four male heifers were randomly distributed (five groups) and vaccinated subcutaneously with attenuated strains (M. bovis Δmce2 or M. bovis Δmce2-phoP), a recombinant M. bovis BCG Pasteur (BCGr) or M. bovis BCG Pasteur. Then, they cohoused with a naturally infected bTB cohort under field conditions exposed to the infection. Results: A 23% of transmission of wild-type strains was confirmed (non-vaccinated group). Strikingly, first vaccination did not induce immune response (caudal fold test and IFN-gamma release assay). However, after 74 days of exposure to bTB, animals were re-vaccinated. Although their sensitization increased throughout the trial, the vaccines did not confer significant protection, when compared to the non-vaccinated group, as demonstrated by pathology progression of lesions and confirmatory tools. Besides, the likelihood of acquiring the infection was similar in all groups compared to the non-vaccinated group (p > 0.076). Respiratory and digestive excretion of viable vaccine candidates was undetectable. To note, the group vaccinated with M. bovis Δmce2-phoP exhibited the highest proportion of animals without macroscopic lesions, compared to the one vaccinated with BCG, although this was not statistically supported. Conclusions: This highlights that further evaluation of these vaccines would not guarantee better protection. The limitations detected during the trial are discussed regarding the transmission rate of M. bovis wild-type, the imperfect test for studying sensitization, the need for a DIVA diagnosis and management conditions of the trials performed under routine husbandry conditions. Re-vaccination of likely infected bovines did not highlight a conclusive result, even suggesting a detrimental effect on those vaccinated with M. bovis BCG. Full article

In computer vision, human action recognition is a hot topic, popularized by the development of deep learning. Deep learning models typically accept video input without prior processing and train them to achieve recognition. However, conducting preliminary motion analysis can be beneficial in directing the model training to prioritize the motion of individuals with less priority for the environment in which the action occurs. This paper puts forth a novel methodology for human action recognition based on motion information that employs transfer-learning techniques. The proposed method comprises four stages: (1) human detection and tracking, (2) motion estimation, (3) feature extraction, and (4) action recognition using a two-stream model. In order to develop this work, a customized dataset was utilized, comprising videos of diverse actions (e.g., walking, running, cycling, drinking, and falling) extracted from multiple public sources and websites, including Pexels and MixKit. This realistic and diverse dataset allowed for a comprehensive evaluation of the proposed method, demonstrating its effectiveness in different scenarios and conditions. Furthermore, the performance of seven pre-trained models for feature extraction was evaluated. The models analyzed were Inception-v3, MobileNet-v2, MobileNet-v3-L, VGG-16, VGG-19, Xception, and ConvNeXt-L. The results demonstrated that the ConvNeXt-L model yielded the most optimal outcomes. Furthermore, using pre-trained models for feature extraction facilitated the training process on a personal computer with a single graphics processing unit, achieving an accuracy of 94.9%. The experimental findings and outcomes suggest that integrating motion information enhances action recognition performance. Full article

Matrix factorization has demonstrated outstanding performance in machine learning. Recently, graph-based matrix factorization has gained widespread attention. However, graph-based methods are only suitable for handling small amounts of data. This paper proposes a fast semi-supervised learning method using only matrix factorization, which considers both global and local information. By introducing bipartite graphs into symmetric matrix factorization, the technique can handle large datasets effectively. It is worth noting that by utilizing tag information, the proposed symmetric matrix factorization becomes convex and unconstrained, i.e., the non-convex problem ${min}_x{(1-x^2)}^2$ is transformed into a convex problem. This allows it to be optimized quickly using state-of-the-art unconstrained optimization algorithms. The computational complexity of the proposed method is $O\left(nmd\right)$, which is much lower than that of the original symmetric matrix factorization, which is $O\left(n^{2}d\right)$, and even lower than that of other anchor-based methods, which is $O(nmd+m^{2}n+m^3)$, where n represents the number of samples, d represents the number of features, and $m\ll n$ represents the number of anchors. The experimental results on multiple public datasets indicate that the proposed method achieves higher performance in less time. Full article

Abstract: Sustainability and energy efficiency are now two pivotal goals that society aims towards. Green roofs and facades have gained significant attention in this direction for innovative, sustainable solutions for enhancing building energy performance. With a focus on sustainable urban development and energy-efficient building practices, this study delves into the intricate relationship between these green infrastructure elements and the overall energy dynamics of constructed environments. Furthermore, a range of case studies from diverse geographical locations are presented to provide valuable insights into their practical implications as emerging technologies that contribute to improved insulation, reduced heat transfer, regulating indoor temperatures, and mitigation of urban heat island effects, thus reducing the need for artificial heating and cooling and optimizing overall energy consumption. This comprehensive review serves as a dataset for understanding and highlighting all the research findings of the numerical and experimental investigations invested in the field of greenery systems to encourage their integration, which is crucial for combating climate change and pollution. Previous research is often focused on isolated, short-term, or single-climate analyses of consumption; therefore, by providing an inclusive description of their practical benefits in both temperate and extreme climates, the gap in previous articles is tackled. Full article

Corporate sustainability performance is gaining ever greater importance. The negative impact of climate change is manifested through heavy air, water and soil pollution. Polluting sectors, as the major players, are characterized by large amounts of emissions, waste and consumption of resources, and therefore have a larger negative impact on the environment. Companies operating in polluting sectors are recognized globally as the main sources of greenhouse gas emissions; thus, their performance is widely debated. Despite their character, such companies strive for higher profitability, better financial performance and operational efficiency. However, higher financial resources create the potential for innovation investments in companies. It is widely accepted that research and experimental development (R&D) expenditures enable new business ideas, models, products, services, and processes. However, while pursuing sustainability targets, financial results could be directed towards sustainability performance. The purpose of this paper is to analyze how the financial and innovation results of companies in polluting sectors interact with sustainability performance scores. For it, we have identified three essential pillars of sustainability: environmental, governance, and social. Using ordinary least squares (OLS) regressions, models were developed for each pillar of sustainability, including corporate financial performance indicators and R&D expenditures. The obtained results provide the insights that a company operating in polluting sector size and turnover significantly interacts with all pillars of sustainability. However, we also found that the corporate debt ratio, earnings ratio, and current liquidity have a significant relation only with environmental and social sustainability indicators. Full article

Wood is subject to various environmental conditions during its processing, with temperatures being one of the most relevant to the material’s behaviour. The heating process during drying is crucial for both the final quality of the dried wood and heat consumption. As the first stage of the drying process, it is essential to achieve adequate overheating in the middle of the thickness without causing damage. The present article focuses on the influence of pine wood thickness on the freezing process and heating during the warm-air drying process. Similarity theory was applied to the theoretical calculation of the time to heat the frozen wood, where Fourier and Biot’s criteria were used. The calculated times were confirmed by experimental measurements. Theoretical calculations of heating time for frozen wood align with measured values for larger thicknesses. For smaller thicknesses (<50 mm), the heating time was shown as unnecessarily long. The results showed that wood thickness significantly affected both freezing and heating processes. Specifically, the thickness of the samples had a notable impact on the heating of frozen samples, only after changing the water gradient in the wood from solid to liquid state. The optimal solution would be if the time and course of heating were regulated according to the actual measured temperature in the centre of the wood. Full article

The application of immobilized enzymes often plays a key role in successfully implementing an economically feasible biocatalytic process at an industrial scale. Designing an immobilized biocatalyst involves solving several tasks, from the selection of the carrier and immobilization method to the characterization of the kinetic properties of the immobilized enzyme. In this study, we focused on the kinetic properties of free and immobilized ß-N-acetylhexosaminidase (Hex), a promising enzyme for application in the field of biotechnology, especially for the synthesis of bioactive carbohydrates. Hex was immobilized via covalent binding in methacrylate particles. The effect of immobilizing Hex from Penicillium oxalicum into porous particles on kinetic properties was investigated, and mathematical and experimental modeling showed that the kinetic behavior of the enzyme was significantly influenced by diffusion in the particles. Along with the study on kinetics, a simple method was developed to investigate the reversible inhibition of the immobilized enzyme in a continuous-flow system. The method is suitable for application in cases where a chromogenic substrate is used, and here it was applied to demonstrate the inhibitory effects of N-acetyl-glucosaminyl thiazoline (NAG-thiazoline) and O-(2-Acetamido-2-deoxy-D-glucopyranosylidene)amino N-phenyl carbamate ((Z)-PugNAc) on Hex. Full article

High-resolution remote sensing imagery, reaching meter or sub-meter levels, provides essential data for extracting and identifying road information. However, rural roads are often narrow, elongated, and have blurred boundaries, with textures that resemble surrounding environments such as construction sites, vegetation, and farmland. These features often lead to incomplete extraction and low extraction accuracy of rural roads. To address these challenges, this study introduces the RC-MSFNet model, based on the U-Net architecture, to enhance rural road extraction performance. The RC-MSFNet model mitigates the vanishing gradient problem in deep networks by incorporating residual neural networks in the downsampling stage. In the upsampling stage, a connectivity attention mechanism is added after dual convolution layers to improve the model’s ability to capture road completeness and connectivity. Additionally, the bottleneck section replaces the traditional dual convolution layers with a multi-scale fusion atrous convolution module to capture features at various scales. The study focuses on rural roads in the Xiong’an New Area, China, using high-resolution imagery from China’s Gaofen-2 satellite to construct the XARoads rural road dataset. Roads were extracted from the XARoads dataset and DeepGlobe public dataset using the RC-MSFNet model and compared with some models such as U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. Experimental results showed that: (1) The proposed method achieved precision (P), intersection over union (IOU), and completeness (COM) scores of 0.8350, 0.6523, and 0.7489, respectively, for rural road extraction in Xiong’an New Area, representing precision improvements of 3.8%, 6.78%, 7.85%, 2.14%, 0.58%, and 2.53% over U-Net, FCN, SegNet, DeeplabV3+, R-Net, and RC-Net. (2) The method excelled at extracting narrow roads and muddy roads with unclear boundaries, with fewer instances of omission or false extraction, demonstrating advantages in complex rural terrain and areas with indistinct road boundaries. Accurate rural road extraction can provide valuable reference data for urban development and planning in the Xiong’an New Area. Full article

Sepsis is an exaggerated immune response resulting from systemic inflammation, which can damage tissues and organs. Acute kidney injury has been detected in at least one-third of patients with sepsis. Sepsis-associated acute kidney injury increases the risk of a secondary infection. Rapid diagnosis and appropriate initiation of antibiotics can significantly reduce mortality and morbidity. However, microorganisms are known to develop resistance to antibiotics. Estimations indicate that the annual casualties caused by microbial resistance will surpass cancer fatalities by 2050. The prevalence of bacterial infections and their growing antibiotic resistance has brought immediate attention to the search for novel treatments. Plant-derived supplements contain numerous bioactive components with therapeutic potential against a variety of conditions, including infections. Punica granatum peel is rich in phenolic compounds. The purpose of this study was to determine the anti-inflammatory and anti-bacterial properties of P. granatum peel extract (PGPE) on lipopolysaccharide (LPS)-induced acute kidney injury. Experimental groups were Control, LPS (10 mg/kg LPS, intraperitoneally), PGPE100, and PGPE300 (100 and 300 mg/mL PGPE via oral gavage, respectively, for 7 days). According to biochemical results, serum blood urea nitrogen (BUN), creatinine (Cr) and C-reactive protein (CRP), kidney tissue thiobarbituric acid reactive substances (TBARS), and reduced glutathione (GSH) levels significantly decreased in the PGPE groups compared to the LPS group. Histopathological and immunohistochemical findings revealed that toll-like receptor 4 (TLR4) level and nuclear factor kappa B (NF-κB) expression increased in the LPS group compared to the Control group. In addition, the anti-Gram-negative activity showed a dose-dependent effect on Acinetobacter baumannii, Escherichia coli, and Pseudomonas aeruginosa with the agar well diffusion method and the minimal inhibitory concentration (MIC). The MIC value was remarkable, especially on A. baumannii. We conclude that PGPE has the potential to generate desirable anti-bacterial and anti-inflammatory effects on LPS-induced acute kidney injury in rats. Full article

In this experiment, C₃N₅ was synthesized by pyrolysis of 3-amino-1,2,4 triazole material, and then 1% Co-C₃N₅, 3% Co-C₃N₅, 5% Co-C₃N₅, 7% Co-C₃N₅, and 9% Co-C₃N₅ were synthesized by varying the mass ratio of cobalt chloride to C₃N₅ by stirring and ultrasonic shaking. SEM, XPS, and XRD tests were performed on the synthesized materials. The experimental results showed that Co atoms were successfully doped into C₃N₅. The electrocatalytic reduction experiments were performed to evaluate their NH₃ yields and electrochemical properties. The results showed that the ammonia yield obtained by the electrolysis of the 9% Co-C₃N₅ catalyst as the working electrode in a mixed electrolytic solution of 0.1 mol/L KNO₃ and 0.1 mol/L KOH for 1 h at a potential of −1.0 V vs. RHE was 0.633 ± 0.02 mmol∙h⁻¹∙mg_cat⁻¹, and the Faraday efficiency was 65.98 ± 2.14%; under the same experimental conditions, the ammonia production rate and Faraday efficiency of the C₃N₅ catalyst were 0.049 mmol∙h⁻¹∙mg_cat⁻¹ and 16.41%, respectively, and the ammonia production rate of the C₃N₅ catalyst was nearly 13-fold worse than the 9% Co-C₃N₅, which suggests that Co can improve the Faraday efficiency and ammonia yield of the electrocatalytic reduction of NO₃⁻. This is due to the strong synergistic effect between the cobalt and C₃N₅ components, with C₃N₅ providing abundant and homogeneous sites for nitrogen coordination and the Co-N species present in the material being highly efficient active sites. The slight change in current density after five trials of 9% Co-C₃N₅ and the decrease in ammonia yield by about 12% in five repetitions of the experiment indicate that 9% Co-C₃N₅ can be recycled and work stably in electrocatalytic reactions and has good application prospects. Full article

The efficacy of visual Simultaneous Localization and Mapping (SLAM) diminishes in large-scale environments due to challenges in identifying distant landmarks, leading to a limited perception range and trajectory drift. This paper presents a practical method to enhance the accuracy of feature-based real-time visual SLAM for compact unmanned vehicles by constructing distant map points. By tracking consecutive image features across multiple frames, remote map points are generated with sufficient parallax angles, extending the mapping scope to the theoretical maximum range. Observations of these landmarks from preceding keyframes are supplemented accordingly, improving back-end optimization and, consequently, localization accuracy. The effectiveness of this approach is ensured by the introduction of the virtual map point, a proposed data structure that links relational features to an imaginary map point, thereby maintaining the constrained size of local optimization during triangulation. Based on the ORB-SLAM3 code, a SLAM system incorporating the proposed method is implemented and tested. Experimental results on drone and vehicle datasets demonstrate that the proposed method outperforms ORB-SLAM3 in both accuracy and perception range with negligible additional processing time, thus preserving real-time performance. Field tests using a UGV further validate the efficacy of the proposed method. Full article