bluuebunny/crossref_metadata_2025 huggingface dataset:
Trends in Antimicrobial Usage on Swiss Pig Farms from 2018 to 2021: Based on an Electronic Treatment Journal
Authors: Wissmann Ramona , Kümmerlen Dolf , Echtermann Thomas
Published: September 2024

(1) Background: The aim of this retrospective observational study was to observe the trends in antimicrobial usage (AMU) from 2018 to 2021 in Swiss pigs based on an electronic treatment journal used nationwide by farmers. Thus, for the first time, standardized, longitudinal comparisons of AMU between the years could be analyzed, as well as the influence of targeted interventions, on farms with higher consumption. (2) Methods: The data was evaluated by different indicators, such as the amount of active ingredient in kilograms, treatment days per farm (ATI) and treatment incidence (TI) based either on animal-defined daily doses (TIADD) or used daily doses (TIUDD). Calculations were performed across the following five age categories: suckling piglets, weaners, fattening pigs, and gestating and lactating sows, and the proportions of antimicrobial classes were evaluated for each age category. (3) Results: The highest amount of the active ingredient was administered to the group of fattening pigs, while the suckling piglets received the lowest amount of the active ingredient. In 2021, there was a significant decrease in active ingredient consumption per pig, but a significant increase in ATI, TIADD and TIUDD compared to 2018. The largest proportion of AMU was attributed to penicillins each year, followed by sulfonamides and tetracyclines. The “Highest Priority Critically Important Antimicrobials” represented a proportion of overall usage, declining from 5.2% in 2018 to 3.1% in 2021, while polypeptides were the most used class of critical antimicrobials. Interventions on high-usage farms showed that some farms decreased their AMU in the following year while others did not. (4) Conclusions: This study reveals a decrease in the overall usage measured in kilograms per pig of antimicrobials in Swiss pigs between 2019 and 2021 through the monitoring of AMU, but, at the same time, there was an increase in treatment days or incidence per farm. Critical antimicrobials can be reduced regardless of the indicator. The significance and quality of interventions should be investigated in future studies.
A Retrospective Analysis of Intravenous Push versus Extended Infusion Meropenem in Critically Ill Patients
Authors: Johnson Emory G. , Maki Ortiz Kayla , Adams David T. , Kaur Satwinder , Faust Andrew C. , Yang Hui , Alvarez Carlos A. , Hall Ronald G.
Published: September 2024

Meropenem is a broad-spectrum antibiotic used for the treatment of multi-drug-resistant infections. Due to its pharmacokinetic profile, meropenem’s activity is optimized by maintaining a specific time the serum concentration remains above the minimum inhibitory concentration (MIC) via extended infusion (EI), continuous infusion, or intermittent infusion dosing strategies. The available literature varies regarding the superiority of these dosing strategies. This study’s primary objective was to determine the difference in time to clinical stabilization between intravenous push (IVP) and EI administration. We performed a retrospective pilot cohort study of 100 critically ill patients who received meropenem by IVP (n = 50) or EI (n = 50) during their intensive care unit (ICU) admission. There was no statistically significant difference in the overall achievement of clinical stabilization between IVP and EI (48% vs. 44%, p = 0.17). However, the median time to clinical stability was shorter for the EI group (20.4 vs. 66.2 h, p = 0.01). EI administration was associated with shorter hospital (13 vs. 17 days; p = 0.05) and ICU (6 vs. 9 days; p = 0.02) lengths of stay. Although we did not find a statistically significant difference in the overall time to clinical stabilization, the results of this pilot study suggest that EI administration may produce quicker clinical resolutions than IVP.
Correction: Abd El-Fattah et al. Immobilization of ZnO-TiO2 Nanocomposite into Polyimidazolium Amphiphilic Chitosan Film, Targeting Improving Its Antimicrobial and Antibiofilm Applications. Antibiotics 2023, 12, 1110
Authors: Abd El-Fattah Wesam , Alfaifi Mohammad Y. , Alkabli Jafar , Ramadan Heba A. , Shati Ali A. , Elbehairi Serag Eldin I. , Elshaarawy Reda F. M. , Kamal Islam , Saleh Moustafa M.
Published: September 2024

In the original publication [...]
Detection of Antimicrobial Proteins/Peptides and Bacterial Proteins Involved in Antimicrobial Resistance in Raw Cow’s Milk from Different Breeds
Authors: Piras Cristian , De Fazio Rosario , Di Francesco Antonella , Oppedisano Francesca , Spina Anna Antonella , Cunsolo Vincenzo , Roncada Paola , Cramer Rainer , Britti Domenico
Published: September 2024

Proteins involved in antibiotic resistance (resistome) and with antimicrobial activity are present in biological specimens. This study aims to explore the presence and abundance of antimicrobial peptides (AMPs) and resistome proteins in bovine milk from diverse breeds and from intensive (Pezzata rossa, Bruna alpina, and Frisona) and non-intensive farming (Podolica breeds). Liquid atmospheric pressure matrix-assisted laser desorption/ionization (LAP-MALDI) mass spectrometry (MS) profiling, bottom-up proteomics, and metaproteomics were used to comprehensively analyze milk samples from various bovine breeds in order to identify and characterize AMPs and to investigate resistome proteins. LAP-MALDI MS coupled with linear discriminant analysis (LDA) machine learning was employed as a rapid classification method for Podolica milk recognition against the milk of other bovine species. The results of the LAP-MALDI MS analysis of milk coupled with the linear discriminant analysis (LDA) demonstrate the potential of distinguishing between Podolica and control milk samples based on MS profiles. The classification accuracy achieved in the training set is 86% while it reaches 98.4% in the test set. Bottom-up proteomics revealed approximately 220 quantified bovine proteins (identified using the Bos taurus database), with cathelicidins and annexins exhibiting higher abundance levels in control cows (intensive farming breeds). On the other hand, the metaproteomics analysis highlighted the diversity within the milk’s microbial ecosystem with interesting results that may reflect the diverse environmental variables. The bottom-up proteomics data analysis using the Comprehensive Antibiotic Resistance Database (CARD) revealed beta-lactamases and tetracycline resistance proteins in both control and Podolica milk samples, with no relevant breed-specific differences observed.
Challenging Sarcopenia: Exploring AdipoRon in Aging Skeletal Muscle as a Healthspan-Extending Shield
Authors: Selvais Camille M. , Davis-López de Carrizosa Maria A. , Versele Romain , Dubuisson Nicolas , Noel Laurence , Brichard Sonia M. , Abou-Samra Michel
Published: September 2024

Sarcopenia, characterized by loss of muscle mass, quality, and function, poses significant risks in aging. We previously demonstrated that long-term treatment with AdipoRon (AR), an adiponectin receptor agonist, alleviated myosteatosis and muscle degeneration in middle-aged obese mice. This study aimed to determine if a shorter AR treatment could effectively offset sarcopenia in older mice. Two groups of old mice (20–23 months) were studied, one untreated (O) and one orally-treated with AR (O-AR) at 50 mg/kg/day for three months, compared with control 3-month-old young mice (Y) or 10-month-old young-adult mice (C-10). Results showed that AR remarkably inversed the loss of muscle mass by restoring the sarcopenia index and fiber count, which were greatly diminished with age. Additionally, AR successfully saved muscle quality of O mice by halving the accumulation of tubular aggregates and aberrant mitochondria, through AMPK pathway activation and enhanced autophagy. AR also bolstered muscle function by rescuing mitochondrial activity and improving exercise endurance. Finally, AR markedly curbed muscle fibrosis and mitigated local/systemic inflammation. Thus, a late three-month AR treatment successfully opposed sarcopenia and counteracted various hallmarks of aging, suggesting AR as a promising anti-aging therapy for skeletal muscles, potentially extending healthspan.
The Effect of Light Intensity during Cultivation and Postharvest Storage on Mustard and Kale Microgreen Quality
Authors: Gudžinskaitė Ieva , Laužikė Kristina , Pukalskas Audrius , Samuolienė Giedrė
Published: September 2024

Microgreens are vegetable greens that are harvested early while they are still immature and have just developed cotyledons. One of the disadvantages and a challenge in production is that they exhibit a short shelf life and may be damaged easily. In seeking to prolong the shelf life, some pre- and postharvest interventions have been investigated. Here, kale and mustard microgreens were grown in a controlled-environment walk-in chamber at +21/17 °C, with ~65% relative air humidity, while maintaining the spectral composition of deep red 61%, blue 20%, white 15%, and far red 4% (150, 200, and 250 µmol m−2 s−1 photosynthetic photon flux density (PPFD)). Both microgreens seemed to exhibit specific and species-dependent responses. Higher PPFD during growth and storage in light conditions resulted in increased contents of TPC in both microgreens on D5. Additionally, 150 and 250 PPFD irradiation affected the α-tocopherol content by increasing it during postharvest storage in kale. On D0 150 for kale and 200 PPFD for mustard microgreens, β-carotene content increased. D5 for kale showed insignificant differences, while mustard responded with the highest β-carotene content, under 150 PPFD. Our findings suggest that both microgreens show beneficial outcomes when stored in light compared to dark and that mild photostress is a promising tool for nutritional value improvement and shelf-life prolongation.
Distributed Cooperative Dispatch Method of Distribution Network with District Heat Network and Battery Energy Storage System Considering Flexible Regulation Capability
Authors: Fu Xin , Yu Shunjiang , He Qibo , Wang Long , Chen Changming , Niu Chengeng , Lin Zhenzhi
Published: September 2024

Flexible resources, including district heat networks (DHN) and battery energy storage systems (BESS), can provide flexible regulation capability for distribution networks (DN), thereby increasing the absorption capacity for renewable energy. In order to improve the operation economy of DN and ensure the information privacy of different operators, a distributed cooperative dispatch method of DN with DHN and BESS considering flexible regulation capability is proposed. First, a distributed cooperative dispatch framework of DN-DHN-BESS is constructed. Then, an optimal dispatch model of DHN under constant flow-variable temperature control strategy is established in order to utilize the heat storage capacity to provide flexible regulation capability for DN. Next, the optimal dispatch models of BESS and DN are established with the objective of minimizing the operation cost, respectively. Finally, a solution method based on the alternating direction multiplier method of distributed cooperative dispatch for DN-DHN-BESS is proposed. Case studies are performed on a system consisting of a 33-node DN and a 44-node DHN, and simulation results demonstrate that the proposed method differs from the centralized dispatch method by only 0.52% in the total system cost, and the proposed method reduces the total system cost by 34.5% compared to that of the independent dispatch method.
Development and Usability Evaluation of Augmented Reality Content for Light Maintenance Training of Air Spring for Electric Multiple Unit
Authors: Kim Kyung-Sik , Kim Chul-Su
Published: September 2024

The air spring for railway vehicles uses the air pressure inside the bellows to absorb vibration and shock to improve ride comfort and adjust the height of the underframe with a leveling valve to control stable driving of the train. This study developed augmented reality content that proposes a novel visual technology to effectively support the training of air spring maintenance tasks. In this study, a special effect algorithm that displays the dispersion and diffusion of fluid, and an algorithm that allows objects to be rotated at various angles, were proposed to increase the visual learning effect of fluid flow for maintenance. The FDG algorithm can increase the training effect by visualizing the leakage of air at a specific location when the air spring is damaged. In addition, the OAR algorithm allows an axisymmetric model, which is difficult to rotate by gestures, to be rotated at various angles, using a touch cube. Using these algorithms, maintenance personnel can effectively learn complex maintenance tasks. The UMUX and CSUQ surveys were conducted with 40 railway maintenance workers to evaluate the effectiveness of the developed educational content. The results showed that the UMUX, across 4 items, averaged as score of 81.56. Likewise, the CSUQ survey score, consisting of 19 questions in 4 categories, was very high, at 80.83. These results show that this AR content is usable for air spring maintenance and field training support.
An Autonomous Intelligent Liability Determination Method for Minor Accidents Based on Collision Detection and Large Language Models
Authors: Chen Junbo , Lu Shunlai , Zhong Lei
Published: September 2024

With the rapid increase in the number of vehicles on the road, minor traffic accidents have become more frequent, contributing significantly to traffic congestion and disruptions. Traditional methods for determining responsibility in such accidents often require human intervention, leading to delays and inefficiencies. This study proposed a fully intelligent method for liability determination in minor accidents, utilizing collision detection and large language models. The approach integrated advanced vehicle recognition using the YOLOv8 algorithm coupled with a minimum mean square error filter for real-time target tracking. Additionally, an improved global optical flow estimation algorithm and support vector machines were employed to accurately detect traffic accidents. Key frames from accident scenes were extracted and analyzed using the GPT4-Vision-Preview model to determine liability. Simulation experiments demonstrated that the proposed method accurately and efficiently detected vehicle collisions, rapidly determined liability, and generated detailed accident reports. The method achieved the fully automated AI processing of minor traffic accidents without manual intervention, ensuring both objectivity and fairness.
Assessing the Fatigue Stress Behavior of Starch Biodegradable Films with Nanoclay Using Accelerated Survival Test Methods
Authors: Frangopoulos Theofilos , Dimitriadou Sophia , Ozuni Joanis , Marinopoulou Anna , Goulas Athanasios , Petridis Dimitrios , Karageorgiou Vassilis
Published: September 2024

A destructive degradation model was applied on films made from different concentrations of starch, glycerol and nanoclay using various elongation levels as a stress variable at different stress times and stretch cycles. The log tensile quotient (logarithm of the tensile strength to the corresponding break cycle) was recorded as the response variable. The log tensile quotient increased, and the log exact break time decreased, as the elongation level increased. The treatment containing the highest starch and nanoclay and lowest glycerol content proved to be the most resistant to stress conditions and the most versatile in relation to the varying log tensile quotients, while the treatments containing the lowest nanoclay and highest glycerol contents, regardless of the starch concentration, manifested the lowest log tensile quotient at higher levels of log exact break time. According to multiple regression findings, the break cycle governed mostly the stress conditions in the degradation model, followed by the sample ID and the log exact break time. The term log tensile quotient, attempted for the first time on data concerning biodegradable films enhanced with nanoclay, seems very promising for deeper research due to its ability to retrieve predictive information from survival equations and to discriminate the difference between film structures.
Simulation Study on Attosecond Inverse Compton Scattering Source from Laser Wakefield Acceleration with Near-Threshold Ionization Injection
Authors: Deng Aihua , Li Yan , Weng Yugan , Luo Zhiling , Yu Xitao , Zeng Jiaolong
Published: September 2024

We present the generation of attosecond gamma rays via inverse Compton scattering within the framework of laser wakefield acceleration through 2D Particle-In-Cell simulations. Utilizing the near-threshold ionization injection mechanism, an attosecond micro-bunched electron beam characterized by a comb-like current density profile can be achieved with a linearly polarized laser at an intensity of a0 = 1.5. The micro-bunched beam provides a beam energy of approximately 300 MeV and achieves a minimum relative energy spread of about 1.64% after undergoing 2 mm of acceleration. In the inverse Compton scattering scheme, these attosecond electron micro-bunches interact with the reflected driving laser pulse, resulting in the attosecond gamma-ray radiation exhibiting similar structures. Individual spatial-separated gamma-ray pulses exhibit a length of approximately 260–300 as, with a critical energy of 2.0 ± 0.2 MeV. The separated attosecond gamma-ray source owns a peak brilliance of ~1022 photons s−1 mm−2 mrad−2 0.1% BW. This brilliance is competitive in a laboratory for multi-MeV γ-ray sources with a laser intensity of I = 5 × 1018 W/cm2. Such attosecond gamma-ray radiation offers promising applications requiring ultrashort X-ray/gamma ray sources.
Attention-Based Spatiotemporal-Aware Network for Fine-Grained Visual Recognition
Authors: Ren Yili , Lu Ruidong , Yuan Guan , Hao Dashuai , Li Hongjue
Published: September 2024

On public benchmarks, current macro facial expression recognition technologies have achieved significant success. However, in real-life scenarios, individuals may attempt to conceal their true emotions. Conventional expression recognition often overlooks subtle facial changes, necessitating more fine-grained micro-expression recognition techniques. Different with prevalent facial expressions, weak intensity and short duration are the two main obstacles for perceiving and interpreting a micro-expression correctly. Meanwhile, correlations between pixels of visual data in spatial and channel dimensions are ignored in most existing methods. In this paper, we propose a novel network structure, the Attention-based Spatiotemporal-aware network (ASTNet), for micro-expression recognition. In ASTNet, we combine ResNet and ConvLSTM as a holistic framework (ResNet-ConvLSTM) to extract the spatial and temporal features simultaneously. Moreover, we innovatively integrate two level attention mechanisms, channel-level attention and spatial-level attention, into the ResNet-ConvLSTM. Channel-level attention is used to discriminate the importance of different channels because the contributions for the overall presentation of micro-expression vary between channels. Spatial-level attention is leveraged to dynamically estimate weights for different regions due to the diversity of regions’ reflections to micro-expression. Extensive experiments conducted on two benchmark datasets demonstrate that ASTNet achieves performance improvements of 4.25–16.02% and 0.79–12.93% over several state-of-the-art methods.
Residual Dense Optimization-Based Multi-Attention Transformer to Detect Network Intrusion against Cyber Attacks
Authors: Alsulami Majid H.
Published: September 2024

Achieving cyber-security has grown increasingly tricky because of the rising concern for internet connectivity and the significant growth in software-related applications. It also needs a robust defense system to defend itself from multiple cyberattacks. Therefore, there is a need to generate a method for detecting and classifying cyber-attacks. The developed model can be integrated into three phases: pre-processing, feature selection, and classification. Initially, the min-max normalization of original data was performed to eliminate the impact of maximum or minimum values on the overall characteristics. After that, synthetic minority oversampling techniques (SMOTEs) were developed to reduce the number of minority attacks. The significant features were selected using a Hybrid Genetic Fire Hawk Optimizer (HGFHO). An optimized residual dense-assisted multi-attention transformer (Op-ReDMAT) model was introduced to classify selected features accurately. The proposed model’s performance was evaluated using the UNSW-NB15 and CICIDS2017 datasets. A performance analysis was carried out to demonstrate the effectiveness of the proposed model. The experimental results showed that the UNSW-NB15 dataset attained a higher precision, accuracy, F1-score, error rate, and recall of 97.2%, 98.82%, 97.8%, 2.58, and 98.5%, respectively. On the other hand, the CICIDS 2017 achieved a higher precision, accuracy, F1-score, and recall of 98.6%, 99.12%, 98.8%, and 98.2%, respectively.
Maximizing the Absorbing Performance of Rectangular Sonic Black Holes
Authors: Červenka Milan , Bednařík Michal
Published: September 2024

This study examines the absorption performance of rectangular sonic black holes (SBHs), which are designed to provide broadband anechoic termination for rectangular waveguides. The SBHs explored in this work consist of a series of opposing rib pairs embedded within the waveguide, where the distance between the ribs in each pair decreases towards the end of the structure according to a specific profile. A computationally efficient mathematical model, combined with an evolutionary optimization algorithm, is employed to determine the optimal geometrical parameters, including the SBH profile, which maximize absorption performance over a broad frequency range. As the optimal geometries feature very fine internal structures, which pose challenges for practical implementation, micro-perforated plates are incorporated to introduce additional losses. Numerical simulations and optimizations are again utilized to identify the geometrical and physical parameters that maximize the absorption performance of these modified structures. The results demonstrate superior absorption performance, even with internal structures compatible with contemporary manufacturing processes. The results of the numerical simulations are validated via a comparison with detailed and accurate mathematical model.
Enhanced Defect Management in Strawberry Processing Using Machine Vision: A Cost-Effective Edge Device Solution for Real-Time Detection and Quality Improvement
Authors: Jovanović Rodoljub , Djordjevic Aleksandar , Stefanovic Miladin , Eric Milan , Pajić Nemanja
Published: September 2024

Managing defects in agricultural fruit processing is crucial for maintaining quality and sustainability in the fruit market. This study explores the use of edge devices, web applications, and machine vision algorithms to improve defect reporting and classification in the strawberry processing sector. A software solution was developed to utilize edge devices for detecting and managing strawberry defects by integrating web applications and machine vision algorithms. The study shows that integrating built-in cameras and machine vision algorithms leads to improved fruit quality and processing efficiency, allowing for better identification and response to defects. Tested in small organic and conventional strawberry processing enterprises, this solution digitizes defect-reporting systems, enhances defect management practices, and offers a user-friendly, cost-effective technology suitable for wider industry adoption. Ultimately, implementing this software enhances the organization and efficiency of fruit production, resulting in better quality control practices and a more sustainable fruit processing industry.
Vehicle Ego-Trajectory Segmentation Using Guidance Cues
Authors: Mihalea Andrei , Florea Adina Magda
Published: September 2024

Computer vision has significantly influenced recent advancements in autonomous driving by providing cutting-edge solutions for various challenges, including object detection, semantic segmentation, and comprehensive scene understanding. One specific challenge is ego-vehicle trajectory segmentation, which involves learning the vehicle’s path and describing it with a segmentation map. This can play an important role in both autonomous driving and advanced driver assistance systems, as it enhances the accuracy of perceiving and forecasting the vehicle’s movements across different driving scenarios. In this work, we propose a deep learning approach for ego-trajectory segmentation that leverages a state-of-the-art segmentation network augmented with guidance cues provided through various merging mechanisms. These mechanisms are designed to direct the vehicle’s path as intended, utilizing training data obtained with a self-supervised approach. Our results demonstrate the feasibility of using self-supervised labels for ego-trajectory segmentation and embedding directional intentions within the network’s decisions through image and guidance input concatenation, feature concatenation, or cross-attention between pixel features and various types of guidance cues. We also analyze the effectiveness of our approach in constraining the segmentation outputs and prove that our proposed improvements bring major boosts in the segmentation metrics, increasing IoU by more than 12% and 5% compared with our two baseline models. This work paves the way for further exploration into ego-trajectory segmentation methods aimed at better predicting the behavior of autonomous vehicles.
The Influence of Effective Mass on the Striking Force of Lead Jab and Rear Cross Punches of Boxers
Authors: Mosler Dariusz , Kacprzak Jakub , Wąsik Jacek
Published: September 2024

Background: Modern combat sports, including boxing, categorize participants by body mass to ensure fairness and safety. The effective mass, or the ability to maximize body mass during a punch, significantly impacts striking force. This study aims to explore the relationship between effective mass and striking force in lead jab and rear cross punches of boxers. Material and methods: Thirteen male boxers with an average body mass of 90.6 kg and average height of 184 cm participated. The study employed an AMTI MC12-2K force plate (AMTI, Watertown, MA, USA) and Noraxon Ultium EMG sensors (Noraxon, Scottsdale, AZ, USA) to measure impact force and acceleration. Each boxer performed five maximum-force strikes with both lead jab and rear cross techniques. Results: The rear cross punch generated significantly higher ground reaction force (1709.28 ± 486.62 N) compared to the lead jab (1182.56 ± 250.81 N). However, effective mass values were similar for both punches: lead jab (18.95 ± 5.29 kg, 21.51% of body mass) and rear cross (18.50 ± 5.56 kg, 21.04% of body mass). Higher body mass and longer training tenure positively correlated with higher effective mass. An inverse relationship was found between fist acceleration and effective mass. Conclusions: Effective mass plays a crucial role in punch biomechanics, with similar utilization between lead jab and rear cross punches despite the latter’s higher force. Training focused on optimizing body mass utilization and refining punch techniques can enhance punch effectiveness.
Observations and Simulations of the Winds at a Bridge in Hong Kong during Two Tropical Cyclone Events in 2023
Authors: Lo Ka-Wai , Chan Pak-Wai , Lai Kai-Kwong
Published: September 2024

The impact of tropical cyclones on the operation of bridges and railways are mostly dependent on the forecast wind speed trend (upward, downward or staying steady) at present in Hong Kong. There are requests to forecast the exceedance of wind speed thresholds for such operations with a lead time of many hours ahead. This study considers the technical feasibility of forecasting the wind speed along a recently built bridge in Hong Kong with a coupled mesoscale numerical weather prediction (NWP)–computational fluid dynamics (CFD) model. This bridge features numerous anemometers where the coupled model can be verified. It is found that these two tropical cyclone cases are very challenging, especially in representing the wind structure of the cyclone because of its relatively small circulation. As such, the timing of the maximum wind could be 2 to 4 h earlier than the actual observation. The maximum wind speed from CFD modeling could be higher than that from the NWP model alone by 5 to 10 m/s, which shows that CFD modeling could add value in the forecasting of wind speed exceedance, although the maximum simulated value is still lower than the actual observation by as much as 10 m/s. As a result, while the general wind speed trend may be forecast, the exceedance of definite values of wind speed limits is still practically rather challenging, given the present two cases of tropical cyclones.
Integrating the Finite Element Method with Python Scripting to Assess Mining Impacts on Surface Deformations
Authors: Dudek Mateusz , Mrocheń Dawid , Sroka Anton , Tajduś Krzysztof
Published: September 2024

Mining operations disrupt the structure of rock layers, leading to surface deformations and potential mining damage. This issue has been extensively studied since the 19th century using various analytical, geometric-integral, and stochastic methods. Since the 1990s, numerical methods have been increasingly applied to determine changes in the stress and strain states of rock masses due to mining activities. These methods account for numerous additional factors influencing surface deformation, offering significant advantages over classical approaches. However, modelling rock masses presents challenges, particularly in calibrating the mechanical parameters of rock layers, an area extensively researched with numerous publications. In this study, we determined the mechanical parameter values of rock layers at the advancing mining front using a custom Python script and Finite Element Method (FEM) numerical models. We also introduced a modification to evaluate the error of the estimated parameter values. Numerical analyses were conducted for the Piast–Ziemowit mine region in Poland, utilizing mining, geological, and surveying data. Our results demonstrate that accurate calibration of mechanical parameters is crucial for reliable predictions of surface deformations. The proposed methodology enhances the precision of numerical models, providing a more robust framework for assessing the impact of mining activities on rock layers.
Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus Algorithm
Authors: Wang Hongbin , Binti Mansor Nurulafiqah Nadzirah , Mokhlis Hazlie Bin
Published: September 2024

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.
Entity Extraction of Key Elements in 110 Police Reports Based on Large Language Models
Authors: Xing Xintao , Chen Peng
Published: September 2024

With the rapid advancement of Internet technology and the increasing volume of police reports, relying solely on extensive human labor and traditional natural language processing methods for key element extraction has become impractical. Applying advanced technologies such as large language models to improve the effectiveness of police report extraction has become an inevitable trend in the field of police data analysis. This study addresses the characteristics of Chinese police reports and the need to extract key elements by employing large language models specific to the public security domain for entity extraction. Several lightweight (6/7b) open-source large language models were tested as base models. To enhance model performance, LoRA fine-tuning was employed, combined with data engineering approaches. A zero-shot data augmentation method based on ChatGPT and prompt engineering techniques tailored for police reports were proposed to further improve model performance. The key police report data from a certain city in 2019 were used as a sample for testing. Compared to the base models, prompt engineering improved the F1 score by approximately 3%, while fine-tuning led to an increase of 10–50% in the F1 score. After fine-tuning and comparing different base models, the Baichuan model demonstrated the best overall performance in extracting key elements from police reports. Using the data augmentation method to double the data size resulted in an additional 4% increase in the F1 score, achieving optimal model performance. Compared to the fine-tuned universal information extraction (UIE) large language model, the police report entity extraction model constructed in this study improved the F1 score for each element by approximately 5%, with a 42% improvement in the F1 score for the “organization” element. Finally, ChatGPT was employed to align the extracted entities, resulting in a high-quality entity extraction outcome.
Research on Indirect Influence-Line Identification Methods in the Dynamic Response of Vehicles Crossing Bridges
Authors: Zhou Yu , Shi Yingdi , Di Shengkui , Han Shuo , Wang Jingtang
Published: September 2024

The bridge influence line can effectively reflect its overall structural stiffness, and it has been used in the studies of safety assessment, model updating, and the dynamic weighing of bridges. To accurately obtain the influence line of a bridge, an Empirical and Variational Mixed Modal Decomposition (E-VMD) method is used to remove the dynamic component from the vehicle-induced deflection response of a bridge, which requires the preset fundamental frequency of the structure to be used as the cutoff frequency for the intrinsic modal decomposition operation. However, the true fundamental frequency is often obtained from the picker, and the testing process requires the interruption of traffic to carry out the mode decomposition. To realize the rapid testing of the influence lines of bridges, a new method of indirectly identifying the operational modal frequency and deflection influence lines of bridge structures from the axle dynamic response is proposed as an example of cable-stayed bridge structures. Based on the energy method, an analytical solution of the first-order frequency of vertical bending is obtained for a short-tower cable-stayed bridge, which can be used as the initial base frequency to roughly measure the deflection influence line of the cable-stayed bridge. The residual difference between the deflection response and the roughly measured influence line under the excitation of the vehicle is operated by Fast Fourier Transform, from which the operational fundamental frequency identification of the bridge is realized. Using the operational fundamental frequency as the cutoff frequency and comparing the influence-line identification equations, the empirical variational mixed modal decomposition, and the Tikhonov regularization to establish a more accurate identification of the deflection influence line, the deflection influence line is finally identified. The accuracy and practicality of the proposed method are verified by real cable-stayed bridge engineering cases. The results show that the relative error between the recognized bridge fundamental frequency and the measured fundamental frequency is 0.32%, and the relative error of the recognized deflection influence line is 0.83%. The identification value of the deflection influence line has a certain precision.
Machine Learning Platform for Disease Diagnosis with Contrast CT Scans
Authors: Jin Jennifer , Kim Mira , Kim Soo Dong , Jin Daniel
Published: September 2024

Machine learning has gained significant recognition as a powerful approach for medical diagnosis using medical images. Among various medical imaging modalities, contrast-enhanced CT (CECT) is utilized to obtain additional diagnostic information that improves visualization and evaluation of certain abnormalities in the human body, as well as to observe temporal changes in lesions and tumors across different time phases. However, developing such medical diagnostic systems presents two significant challenges: high technical complexity and substantial development effort. This paper presents a software platform that effectively addresses these challenges. Specifically, we propose a unified software process that fully automates contrast-enhanced CT (CECT)-specific disease diagnosis, with key tasks performed by leveraging task-specific machine learning models to enhance accuracy. The platform incorporates a suite of specialized machine learning models into the diagnostic process, enabling precise diagnosis of lesions, malignancies, tumors, tumor characteristics, and temporal changes over phases. Moreover, the platform has been designed according to the Open–Closed Principle, allowing it to be applicable to a wide range of CECT-based diagnostic systems. The platform has been implemented in Python using the Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a hepatocellular carcinoma (HCC) diagnosis system has been implemented.
Spatiotemporal Variation Patterns of Drought in Liaoning Province, China, Based on Copula Theory
Authors: Wu Jiayu , Li Yao , Zhang Xudong , Cai Huanjie
Published: September 2024

Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change.
The Complex and Changing Genus Bacillus: A Diverse Bacterial Powerhouse for Many Applications
Authors: Blanco Crivelli Ximena , Cundon Cecilia , Bonino María Paz , Sanin Mariana Soledad , Bentancor Adriana
Published: September 2024

For years, the Bacillus genus has encompassed a heterogeneous group of individuals whose main common trait was their ability to sporulate in the presence of oxygen. This criterion has been revised, resulting in the reclassification of several species into new genera and to a redefinition of the characteristics of the members of this taxon. Currently, the species of the genus are grouped into the Subtilis clade and the Cereus clade. The former, called Bacillus sensu stricto, initially composed of B. subtilis, B. licheniformis, B. pumilus, and B. amyloliquefaciens, has subsequently incorporated new species related to these. The Cereus clade, Bacillus cereus sensu lato, consists of pathogenic species (B. anthracis, B. cereus, and B. thuringiensis) as well as others of significance in agriculture and industry. Furthermore, identifying these individuals remains complex, requiring alternatives to 16S rRNA sequencing. The ability to form spores resistant to stressful conditions provides a significant advantage over other genera, with observable differences in sporulation rates and spore structure among different species. Additionally, Bacillus spp. are known for their capacity to produce antimicrobial substances, lytic enzymes, and volatile organic compounds, each with diverse applications. Some species are even used as probiotics. This review delves into aspects related to the taxonomy and identification of microorganisms belonging to the genus Bacillus, which often present challenges. The aim is to provide a comprehensive overview of the topic. In addition, it highlights the characteristics and applications of the genus, emphasizing its importance in biotechnology and microbiology.
Improvement of the Chemical Quality of Cachaça
Authors: Marcondes Pereira Amanda de Andrade , Acevedo Maria Soledad M. S. F. , Alcarde André Ricardo
Published: September 2024

The objective of this study was to determine the chemical composition of sugarcane spirits and commercial Cachaças, comparing them with the limits established by national legislation and with studies conducted in previous periods. Previous studies have shown that 50% of the samples of this distillate were above the contaminant limits allowed by national legislation, constituting one of the main factors responsible for the low volume of exports. In this research, 531 Cachaça samples were analyzed in order to verify whether they complied with the limits of contaminants and volatile compounds required by Brazilian legislation. The results obtained indicate that Brazilian producers have adapted to the use of good manufacturing practices during the production process, ensuring the standardization of this distilled beverage and consequent compliance with legislation.
Calibration and Modeling of the Semmes–Weinstein Monofilament for Diabetic Foot Management
Authors: Castro-Martins Pedro , Pinto-Coelho Luís , Campilho Raul D. S. G.
Published: September 2024

Diabetic foot is a serious complication that poses significant risks for diabetic patients. The resulting reduction in protective sensitivity in the plantar region requires early detection to prevent ulceration and ultimately amputation. The primary method employed for evaluating this sensitivity loss is the 10 gf Semmes–Weinstein monofilament test, commonly used as a first-line procedure. However, the lack of calibration in existing devices often introduces decision errors due to unreliable feedback. In this article, the mechanical behavior of a monofilament was analytically modeled, seeking to promote awareness of the impact of different factors on clinical decisions. Furthermore, a new device for the automation of the metrological evaluation of the monofilament is described. Specific testing methodologies, used for the proposed equipment, are also described, creating a solid base for the establishment of future calibration guidelines. The obtained results showed that the tested monofilaments had a very high error compared to the 10 gf declared by the manufacturers. To improve the precision and reliability of assessing the sensitivity loss, the frequent metrological calibration of the monofilament is crucial. The integration of automated verification, simulation capabilities, and precise measurements shows great promise for diabetic patients, reducing the likelihood of adverse outcomes.
Vaccinium Species—Unexplored Sources of Active Constituents for Cosmeceuticals
Authors: Kukula-Koch Wirginia , Dycha Natalia , Lechwar Paulina , Lasota Magdalena , Okoń Estera , Szczeblewski Paweł , Wawruszak Anna , Tarabasz Dominik , Hubert Jane , Wilkołek Piotr , Halabalaki Maria , Gaweł-Bęben Katarzyna
Published: September 2024

The genus Vaccinium is represented by shrubs growing in a temperate climate that have been used for ages as traditional remedies in the treatment of digestive problems, in diabetes, renal stones or as antiseptics due to the presence of polyphenols (anthocyanins, flavonoids and tannins) in their fruits and leaves. Recent studies confirm their marked potential in the treatment of skin disorders and as skin care cosmetics. The aim of this review is to present the role of Vaccinium spp. as cosmetic products, highlight their potential and prove the biological properties exerted by the extracts from different species that can be useful for the preparation of innovative cosmetics. In the manuscript both skin care and therapeutic applications of the representatives of this gender will be discussed that include the antioxidant, skin lightening, UV-protective, antimicrobial, anti-inflammatory, and chemopreventive properties to shed new light on these underestimated plants.
Biological Relevance of Dual Lysine and N-Terminal Methyltransferase METTL13
Authors: Boulter Mullen , Biggar Kyle K.
Published: September 2024

The dual methyltransferase methyltransferase-like protein 13, also referred to as METTL13, or formerly known as FEAT (faintly expressed in healthy tissues, aberrantly overexpressed in tumors), has garnered attention as a significant enzyme in various cancer types, as evidenced by prior literature reviews. Recent studies have shed light on new potential roles for METTL13, hinting at its promise as a therapeutic target. This review aims to delve into the multifaceted biology of METTL13, elucidating its proposed mechanisms of action, regulatory pathways, and its implications in disease states, as supported by the current body of literature. Furthermore, the review will highlight emerging trends and gaps in our understanding of METTL13, paving the way for future research efforts. By contextualizing METTL13 within the broader landscape of cancer biology and therapeutics, this study serves as an introductory guide to METTL13, aiming to provide readers with a thorough understanding of its role in disease phenotypes.