Search Results (84)

Background/Objectives: Antimicrobial agents have revolutionized disease management in humans and animals; however, their misuse and overuse have accelerated the emergence and spread of antimicrobial resistance (AMR) and antimicrobial resistance genes (ARGs). Dairy farms are recognized as potential hotspots for ARG dissemination, particularly through Escherichia coli, which acts as a reservoir and vector of ARGs, enabling their horizontal transfer via plasmids and other mobile genetic elements. This study aimed to characterize the genomic diversity, ARG profiles, plasmid content, and phylogenetic relationships of E. coli isolated from dairy farm environments and milk using whole-genome sequencing. Methods: A total of 31 E. coli isolates recovered from soil, effluent, cow dung, and milk samples underwent deoxyribonucleic acid extraction, library preparation, and sequencing on the Illumina MiSeq platform, followed by comprehensive bioinformatic analysis. Results: The E. coli isolates exhibited 20 distinct sequence types, including one novel sequence type. Plasmids were detected in 71% of the isolates, with the IncF plasmid family being the most predominant. Furthermore, 12 ARG groups were identified, with β-lactam resistance genes detected in 67.7% of isolates. Notably, blaCTX-M genes were identified in all phenotypically confirmed extended-spectrum β-lactamase-producing isolates. Additional ARGs, including those conferring resistance to tetracyclines (tet(A), tetX4), quinolones (qnrS1), aminoglycosides (aph, aad, ant), and folate pathway inhibitors (dfr and sul), were widely distributed throughout the samples. Phylogenetic analysis revealed clustering of isolates from different sample types, particularly among ST58 isolates, suggesting cross-environmental transmission. Conclusions: This study demonstrates that E. coli from dairy farm environments harbor diverse ARGs and plasmids, confirming their role as reservoirs of AMR. These findings underscore the importance of prudent antimicrobial use, routine genomic surveillance, and enhanced biosecurity measures to limit cross-environmental transmission. Full article

The lightweight design of box-type double-girder overhead crane main girders is important for improving load-carrying capacity, reducing energy consumption, and enhancing transportation efficiency. However, the structural optimization of crane main girders involves multiple constraints and strong nonlinearity, which often leads to slow convergence and premature stagnation when using traditional optimization methods. To address these issues, a multi-strategy improved dung beetle optimization algorithm (MSIDBO) is proposed for the lightweight design of overhead crane main girders. First, the search mechanism and inherent limitations of the standard dung beetle optimization (DBO) algorithm are analyzed. Subsequently, several enhancement strategies are introduced, including hybrid chaotic population initialization; reflective boundary handling; adaptive quantum jump updating; adaptive hybrid updating; and a staged control strategy for search intensity. These strategies are designed to enhance population diversity and achieve a better balance between global exploration and local exploitation. The performance of MSIDBO was evaluated on 29 CEC2017 benchmark functions. The results show that MSIDBO generally converges faster on 25 functions and reaches the global optimum on 24 functions among the compared algorithms. Finally, based on mechanical analysis and design specifications of overhead crane main girders, a constrained structural optimization model is established. The lightweight design optimization is carried out, and finite element simulations were conducted using ANSYS Workbench to verify the effectiveness and engineering feasibility of the optimized design. The results show that the proposed MSIDBO algorithm exhibits enhanced stability and convergence performance, achieving a weight reduction of 19.4% in the main girder under the specified design configuration, meeting satisfying strength and safety requirements. Full article

An archaeological exposure near Hachum, featuring a ditch profile interpreted as part of a Neolithic earthwork, was characterized using DNA analyses of bacterial 16S rRNA from soil samples. The NGS data from 13 sampling points at different positions and depths within the trench profile were compared with regard to the percentage distribution of phyla and the frequency of occurrence of individual bacterial types (genera or operational taxonomic units, OTUs). Characteristic differences between parts of the trench profile became apparent based on correlations of OTU abundances as well as the occurrence of specific types. In particular, a high similarity in bacterial communities was observed among samples from intermediate trench depths, while a markedly different composition was found in the area of the central trench bottom. These findings indicate that the trench must have remained open for a certain period of time and was later filled relatively homogeneously. The results showed that the middle and lower parts of the ditch fill could be clearly distinguished from each other and from the surrounding area based on the composition of soil bacterial DNA. Genera detected predominantly in the lower part of the ditch suggest that, after the ditch was completed, organic matter, animal dung, and possibly even human feces were accumulated at the bottom. The investigations demonstrate that analyses of soil bacterial communities can provide valuable insights into the history and function of a Neolithic earthwork and, more generally, represent an important additional source of information for interpreting archaeological contexts that are devoid of or poor in finds. Full article

An Integrated Energy System (IES) integrates electricity, heat, and natural gas, optimizing energy use and management efficiency. These systems connect to a Virtual Power Plant (VPP) for demand response dispatch in the electricity market. However, the impact of VPP load on the IES is often overlooked, which can limit the IES’s effective market participation and stability. To address this issue, this study introduces a two-layer collaborative model to coordinate VPP scheduling for multiple IES units, aiming to improve collaboration efficiency. The upper level involves the VPP setting electricity prices based on load conditions, guiding IES units to adjust their market strategies. At the lower level, the model encourages integration and optimization of different energy types within the IES through enhanced energy interactions. Additionally, the application of the Shapley value method ensures fair benefit distribution among all IES members. This approach supports equitable economic outcomes for all participants in the energy market. The model employs a multi-strategy improved Dung Beetle Optimizer (FSGDBO) combined with commercial solver techniques for efficient problem-solving. Experimental results demonstrate that the model significantly enhances the VPP’s peak-shaving and valley-filling capabilities while preserving the economic interests of the IES alliances, thereby boosting overall energy management effectiveness. Full article

To address the issues of insensitivity to high-impedance ground faults and difficulty in identifying reflected wavefronts in single-ended traveling-wave fault location methods for asymmetric ground faults in high-voltage AC transmission lines, this paper proposes a single-ended fault location method based on the modal amplitude ratio and deep learning. First, based on the dispersion characteristics of traveling waves, an approximate formula is derived between the fault distance and the amplitude ratio of the sum of the initial transient voltage traveling-wave 1-mode and 2-mode to 0-mode at the measurement point. Simulation verifies that the fault distance x from the measurement point at the line head is unaffected by transition resistance and fault inception angle, and that a nonlinear positive correlation exists between the distance x and the modal amplitude ratio. The multi-scale wavelet modal maximum ratio of the sum of 1-mode and 2-mode to 0-mode is used to characterize the amplitude ratio. This ratio serves as the input for a Residual Bidirectional Long Short-Term Memory (BiLSTM) network, which is optimized using the Dung Beetle Optimizer (DBO). The DBO-Res-BiLSTM model fits the nonlinear mapping between the fault distance x and the amplitude ratio. Simulation results demonstrate that the proposed method achieves high location accuracy. Furthermore, it remains robust against variations in fault type, location, transition resistance, and inception angle. Full article

This study examines how farm workers working with cattle talk to and interact with these non-human animals. This study presents linguistic animal studies and multi-species pragmatics, and it is based on fieldwork, interviews, and video recordings from several types of Finnish dairy farms. This study concentrates especially on one facet of human–cattle interaction: how humans use dung pushers and other sticks when communicating with cows. Thus, it draws on the materiality of language. It is shown how objects, bodies, and spaces, as well as words and linguistic constructions, are meaningful in human–animal interaction. Videoed recordings are analysed with multimodal conversation analysis. It is shown how dung pushers and snow stakes are used when steering cows, making them stand up, and pointing at things. It is then shown how these objects become ‘meaning-carriers’ for humans and for cows. For example, the dung pusher acquires four different meaning qualities for the human participants in the cattle barns: floor-cleaner quality, shepherd’s-crook quality, pointer quality, and weapon quality. The study examines how the cows’ and humans’ Umwelts, the subjective meaning universes of these species and their constituent individuals, influence interaction on farms and how and why the dung pusher becomes a semiotic resource. Full article

This study investigates the selective dissolution of manganese from a manganiferous iron ore using nitric acid (HNO₃) in the presence of various organic reductants. A series of leaching experiments was performed to evaluate the effects of temperature, reductant type, and leaching time on Mn recovery, with particular emphasis on biomass (horse dung) and tartaric acid as novel reducing agents. The dissolution behaviour of Fe, Mn, Mg, Ca, and Al was systematically examined, revealing that Mn extraction was strongly enhanced in the presence of reductants, while Fe dissolution remained below 10% under all conditions. The maximum Mn dissolution exceeded 90% at 90 °C using biomass and reached nearly 85%–90% with tartaric acid at elevated temperatures. Kinetic studies were conducted by applying reaction order models and the shrinking core model. The results indicated that Mn dissolution in HNO₃ medium is predominantly controlled by surface chemical reaction, with Arrhenius analysis yielding activation energies of 27.74 kJ/mol for biomass and 21.26 kJ/mol for tartaric acid. These relatively low values confirm the efficiency of organic reductants in facilitating Mn reduction and dissolution. To sum up, comparison of reductant efficiency revealed that, at the lowest concentrations, the dissolution of Mn followed the sequence glucose > sucrose > oxalic acid > tartaric acid > maleic acid > biomass > citric acid > acetic acid. At the highest concentrations, the trend shifted, with citric acid emerging as the most effective, followed by tartaric acid > oxalic acid > glucose > sucrose > maleic acid > biomass > acetic acid. Full article

This study investigates the relationship between anchor cable drilling parameters and roadway roof strata properties. The goal is to enable rapid and accurate rock type identification. Field-measured drilling data were processed using data cleaning and wavelet transform noise reduction. Four recognition models were developed and compared: LSTM (Long Short-Term Memory), BiLSTM (Bidirectional Long Short-Term Memory), DBO-LSTM (Dung Beetle Optimizer), and DBO-BiLSTM. The results demonstrate a strong correlation between vibration, pressure signals and rock strength, enabling the effective differentiation of rock types. All models performed exceptionally for coal seams with distinct features, achieving 100% accuracy, precision, recall, and F1 scores. Model performance improved with increased complexity for strata with subtle differences, such as sandstone and mudstone. The DBO-BiLSTM model outperformed others, showing significant improvements in accuracy, recall, and F1 score compared to LSTM, BiLSTM, and DBO-LSTM models. Specifically, accuracy improved by up to 9%, recall by 12.48%, and F1 score by 13.06%. These findings highlight the DBO-BiLSTM model’s superior recognition capability for roof strata drilling signals. This method provides a robust technical foundation for lithology identification in Measurement While Drilling (MWD) systems. It supports more precise and efficient roadway design in complex geological conditions. Full article

As the core device for protecting the safety of the pressure-bearing system, the spring full-open safety valve is prone to various forms of valve seat sealing surface damage after long-term opening and closing impact, corrosion, and medium erosion, which may lead to internal leakage. In view of the problems that the high-frequency acoustic emission signal of the internal leakage of the safety valve has, namely, a large number of energy-overlapping areas in the frequency domain, the overall signal presents broadband characteristics, large noise content, and no obvious time–frequency characteristics. A composite denoising method, IWTD, improved wavelet threshold function with dual adjustable factors, and the improved VMD algorithm is proposed. In view of the problem that the optimal values of the dual adjustment factors a and b of the function are difficult to determine manually, an improved dung beetle optimization algorithm is proposed, with the maximum Pearson coefficient as the optimization target; the optimization is performed within the value range of the dual adjustable factors a and b, so as to obtain the optimal value. In view of the problem that the key parameters K and α in VMD decomposition are difficult to determine manually, the maximum Pearson coefficient is taken as the optimization target, and the improved dung beetle algorithm is used to optimize within the value range of K and α, so as to obtain the IVMD algorithm. Based on the IVMD algorithm, the characteristic decomposition of the internal leakage acoustic emission signal occurs after the denoising of the IWTD function is performed to further improve the denoising effect. The results show that the Pearson coefficients of all types of internal leakage acoustic emission signals after IWTD-IVMD composite noise reduction are greater than 0.9, which is much higher than traditional noise reduction methods such as soft and hard threshold functions. Therefore, the IWTD-IVMD composite noise reduction method can extract more main features out of the measured spring full-open safety valve internal leakage acoustic emission signals, and has a good noise reduction effect. Feature recognition after noise reduction can provide a good evaluation for the safe operation of the safety valve. Full article

During the dry machining of 6061 aluminum alloy, cemented carbide tools often suffer from severe wear and built-up edge (BUE) formation, which significantly shortens tool life. Inspired by the non-smooth surface structure of dung beetles, this study proposes an elliptical dimple–groove composite bionic micro-texture, applied to the rake face of cemented carbide tools to enhance their cutting performance. Four types of tools with different surface textures were designed: non-textured (NT), single-groove texture (PT), circular dimple–groove composite texture (AKGC), and elliptical dimple–groove composite texture (TYGC). The cutting performance of these tools was analyzed through three-dimensional finite element simulations using the Deform-3D (version 11.0, Scientific Forming Technologies Corporation, Columbus, OH, USA) software program. The results showed that, compared to the NT tool, the TYGC tool exhibited the best performance, with a reduction in the main cutting force of approximately 30%, decreased tool wear, and significantly improved chip-breaking behavior. Based on the simulation results, a response surface model was constructed to optimize key texture parameters, and the optimal texture configuration was obtained. In addition, a theoretical model was developed to reveal the mechanism by which the micro-texture reduces interfacial friction and temperature rises by shortening the effective contact length. To verify the accuracy of the simulation and theoretical analysis, cutting experiments were further conducted. The experimental results were consistent with the simulation trends, and the TYGC tool demonstrated superior performance in terms of cutting force reduction, smaller adhesion area, and more stable cutting behavior, validating both the simulation model and the proposed texture design. This study provides a theoretical foundation for the structural optimization of bionic micro-textured cutting tools and offers an in-depth exploration of their friction-reducing and wear-resistant mechanisms, showing promising potential for practical engineering applications. Full article

Direct Position Estimation (DPE) is an alternative GNSS positioning method that models received satellite signals as a function of the receiver’s navigation state, allowing for the direct estimation of position, velocity, and time within the navigation domain. However, existing DPE algorithms face significant challenges due to the non-convex nature of the optimization problem and the large solution space, resulting in high computational complexity. To address these challenges, this paper introduces a framework for searching for navigation solutions in DPE through swarm intelligence algorithms, combined with a low-complexity correlation approach. Furthermore, an adaptive Dung Beetle Optimization (ADBO) algorithm is developed. By leveraging insights from fitness landscape analysis, the ADBO algorithm dynamically adjusts subpopulation proportions and the convergence factor while incorporating hybrid mutation strategies for effective adaptation to various types of optimization problems. Benchmark function tests demonstrate that the ADBO algorithm achieves superior convergence performance compared with other popular swarm intelligence algorithms. Both extensive simulations and real GNSS data experiments further validate that the proposed framework, incorporating the ADBO algorithm, achieves improved positioning accuracy compared to traditional positioning methods while outperforming traditional search algorithms and other swarm intelligence algorithms in both accuracy and computational efficiency. Full article

Dung beetles are important ecosystem engineers, as they utilize the excrement produced by animals. For nearly 60 years, several species of dung beetle have been introduced to Australia to help mitigate the problems caused by the accumulation of livestock dung. The twenty-three successfully established species directly contribute to reducing the environmental impacts from dung accumulation, providing improvements to soil health, pasture productivity and pest fly reduction. Despite this success, there are still geographical and seasonal gaps in dung beetle activity, causing dung to remain on the soil surface. The continued importation of new dung beetle species is warranted to fill these gaps. One of the significant remaining gaps is during spring in southern Australia. Four spring-active dung species from Morocco were selected for a new importation program (2018–2022): Euonthophagus crocatus, Onthophagus vacca, Onthophagus marginalis subsp. andalusicus and Gymnopleurus sturmi. These species were surveyed at four sites in Morocco on an altitudinal gradient to assess their seasonal activity. The four species were found at all sites during spring, but in varying abundances, with different species dominating different sites. This is most likely due to differences in local conditions such as soil type. Seasonal activity varied depending on elevation. Gymnopleurus sturmi was found to be active later in the season and should be considered as a summer species. The four species selected will be, if they establish, a useful addition to the already introduced and established dung beetle fauna in Australia. Full article

Grassland soil carbon stocks contain substantial amounts of organic carbon and play a crucial role in the global carbon cycle. Grazing is one of the most primary land use types in grasslands. However, few studies have focused on the impact of three grazing behaviors (mowing (M), trampling (T), and dung and urine addition (D)) on the soil organic carbon (SOC) of mountain meadows. In this experiment, we simulated three grazing behaviors to explore the impacts of grazing behaviors on plant characteristics with plant growth, soil physicochemical properties, soil aggregate, and analyzed the main factors influencing the changes in SOC. After six years of treatment, the experimental results showed that M significantly decreased plant height, density, and aboveground biomass and significantly decreased soil organic carbon (SOC) (no M vs. M, −3.64%). T significantly increased soil bulk density, the proportion of macroaggregates, the organic carbon of microaggregates, and silt and clay aggregates and significantly increasing SOC (no T vs. T, +3.17%). D significantly increased plant density, soil total nitrogen and the organic carbon of macroaggregates, significantly increasing SOC (no D vs. D, +9.74%). Correlation and principal component analyses indicated that SOC was significantly negatively correlated with soil bulk density and plant coverage and significantly positively correlated with soil total nitrogen, soil C/N, microaggregate proportion, and the organic carbon of macroaggregates. Redundancy analysis indicated that the proportion of microaggregates and the organic carbon of macroaggregates were the main factors influencing SOC. The following conclusions were drawn: SOC responds differently to three types of grazing behaviors, D primarily increases the organic carbon in macroaggregates, while T mainly enhances the organic carbon in microaggregates and silt and clay aggregates, thereby affecting the SOC in mountain meadows. Full article

Gas Insulated Switchgear (GIS) is a type of critical substation equipment in the power system, and its safe and stable operation is of great significance for ensuring the reliability of power system operation. To accurately identify partial discharge in GIS, this paper proposes an acoustic identification method based on improved mel frequency cepstral coefficients (MFCC) and dung beetle algorithm optimized random forest (DBO-RF) based on the ultrasonic detection method. Firstly, three types of typical GIS partial discharge defects, namely free metal particles, suspended potential, and surface discharge, were designed and constructed. Secondly, wavelet denoising was used to weaken the influence of noise on ultrasonic signals, and conventional, first-order, and second-order differential MFCC feature parameters were extracted, followed by principal component analysis for dimensionality reduction optimization. Finally, the feature parameters after dimensionality reduction optimization were input into the DBO-RF model for fault identification. The results show that this method can accurately identify partial discharge of typical GIS defects, with a recognition accuracy reaching 92.2%. The research results can provide a basis for GIS insulation fault detection and diagnosis. Full article

The dichotomy of striking a balance between sustainable food crop production for the skyrocketing human population and ensuring agricultural practices that mitigate environmental degradation has prompted much research into sustainable crop production methods. The application of amendments has become an integral part of arable soil management in restoring declining soil fertility for sustainable and high-quality crop production. This study was conducted on lettuce and carrot cultivated on soil treated with three different amendments: cow dung, sewage sludge, and nitrogen–phosphorus–potassium (NPK) mineral fertilizer. The vegetables were harvested at maturity at 60 and 110 days for lettuce and carrot, respectively, dried in a hot air oven, crushed, and then digested to obtain an aliquot sample. The level of macronutrients was quantified from the aliquots using inductively coupled plasma optical emission spectrometry (ICP-OES), Avio 550 Max, PerkinElmer, USA. It was observed that both soil treatment and types of vegetables significantly impacted the level of mineral contents in the vegetables. The highest values of 58.00 ± 8.36 mg/kg and 72.97 ± 12.53 mg/kg were recorded for Na and P in carrots from soil treated with sewage sludge, respectively. The highest values of 247.97 ± 17.07 mg/kg and 104.72 ± 4.12 mg/kg were recorded for Ca and Mg in lettuce from sewage sludge-treated soil, respectively. Similarly, the highest value of 546.75 ± 76.44 mg/kg for K was also recorded in lettuce, but from cow dung-treated soil. The overall pattern of mineral accumulation by vegetables shows that carrots accumulate more Na than lettuce, and lettuce accumulates more Mg, Ca, and K than carrots, while there was no significant difference in the level of P in both lettuce and carrots. The findings reveal that lettuce and carrots from soil treated with organic manure cow dung and sewage sludge accumulated higher mineral contents of most of the investigated minerals. It was also observed that lettuce accumulated higher contents of most of the minerals investigated. This study therefore concludes that organic manures are better alternatives to mineral fertilizers for vegetable production, which supports the effort to strike a balance between sustainable and ecofriendly agriculture. Full article