bluuebunny/crossref_metadata_2025 huggingface dataset:
Response of Soil Fungal Community in Coastal Saline Soil to Short-Term Water Management Combined with Bio-Organic Fertilizer
Authors: Xiao Meng , Chen Cheng , Yao Rongjiang , Wang Xiuping , Liu Guangming
Published: July 2024

This study aimed to elucidate the response of soil microbial communities to saline soil amelioration via biological organic fertilizer. A year-long experiment was conducted on coastal saline soil, employing water and fertilizer strategies. Three treatments were compared: dry field (control, CK), paddy field (W), and combined dry and irrigated fields with biological organic fertilizer (BW). Soil DNA was extracted and sequenced using high-throughput methods, revealing significant reductions in soil electrical conductivity (EC) and pH with W and BW treatments. Moreover, the BW treatment notably increased soil organic carbon content by 17.2%, as well as soil urease and alkaline phosphatase activity. Fungal community richness increased, with the BW treatment showing a 36% rise in the ACE index and a 24% increase in the Shannon index, while the Simpson index decreased by 59%. Dominant fungal phyla were Ascomycota, Mortierellomycota, and Basidiomycota, with Basidiomycota prevailing at the genus level. Redundancy analysis (RDA) indicated that soil pH, EC, and organic carbon were key determinants of fungal community distribution, with the BW treatment correlating negatively with pH and salt and positively with soil organic carbon (SOC). Fungal functional groups varied among treatments, with saprophytic fungi predominating, but the BW treatment showed a higher relative abundance of animal pathogenic fungi. In summary, the integration of biological organic fertilizer with flooding ameliorates soil properties and influences the changes in soil fungal community structure and function in the short term. These results could enhance the scientific basis for the efficient utilization and development of saline soil resources in coastal areas.
Dynamic and Postural Changes in Forelimb Amputee Dogs: A Pilot Study
Authors: Rodriguez Oliver , Regueiro-Purriños Marta , Figueirinhas Pedro , Gonzalo-Orden José Manuel , Prada Iván , Vilar José Manuel , Millán Lorena , Rodríguez-Altónaga José
Published: July 2024

The amputation of a limb in quadrupeds can overload the remaining limbs, especially the contralateral one. The compensatory effort is especially high if it is a forelimb. It is, therefore, important to objectively know the changes in weight redistribution that occur in the animal while walking and standing still. With this objective, static (postural) and dynamic kinetic examinations were carried out on five French bulldogs with an amputated forelimb and five intact French bulldogs. For this examination, force and pressure platforms were used. The results were statistically compared using the student t-test. The parameters derived from the ground reaction forces were significantly higher in the amputee group. Surprisingly, postural examination showed that amputated dogs reached the same stability as healthy ones. Tripedal support in dogs does not objectively imply a loss of balance in quantitative terms; although the increase in force used by the remaining limb, as well as its altered cranial disposition during the support phase, may potentially predispose the animal to additional injuries in the future due to an overuse of different musculoskeletal units.
Analysing the Impact of Spirulina Intake Levels on Performance Parameters, Blood Health Markers and Carcass Traits of Broiler Chickens
Authors: Spínola Maria P. , Costa Mónica M. , Prates José A. M.
Published: July 2024

This systematic review examines the impact of varying Spirulina (Limnospira platensis) intake levels on broiler chickens, focusing on growth performance, blood health markers and carcass traits. The data revealed cumulative Spirulina intakes from 3.13 g to 521 g per bird (total feed consumed multiplied by its proportion in the diet) establish a cubic relationship between dosage and growth outcomes. Initial benefits peak and diminish with increased intake, with the optimal threshold for growth performance identified at 45 g per bird. Lower intakes between 14 g and 29 g per bird enhance blood health markers, improving lipid profiles and antioxidant capacity. Similarly, cumulative intakes of 14 g to 37 g per bird optimise meat quality, resulting in better dressing percentages, breast and thigh yields and meat tenderness while minimizing undesirable traits like abdominal fat and cooking loss. These findings underscore the importance of precisely calibrated Spirulina supplementation strategies to maximise growth, health and meat quality benefits while avoiding adverse effects at higher doses. Future research should focus on identifying optimal dosage and duration, assessing long-term implications, elucidating mechanisms of action and ensuring safety and regulatory compliance. Comparative studies with other feed additives could further establish Spirulina’s effectiveness and economic viability in poultry production.
The Potential of Infrared Thermography for Early Pregnancy Diagnosis in Nili-Ravi Buffaloes
Authors: Riaz Umair , Idris Musadiq , Ahmed Mehboob , Ali Farah , Farooq Umer , Yang Liguo
Published: July 2024

This study was designed to explore the potential of infrared thermography (IRT) as an alternate approach for early pregnancy diagnosis in buffaloes. The surface temperature (ST) of different regions (eyes, muzzle, flanks, and vulva) was determined in 27 buffaloes using IRT from the day of artificial insemination (AI; Day 0), and measurement was repeated every fourth day until Day 24 post-AI. From all regions, the ST in each thermograph was recorded at three temperature values (maximum, average, minimum). Pregnancy status was confirmed through ultrasonography on Day 30, and animals were retrospectively grouped as pregnant or non-pregnant for analysis of thermographic data. In pregnant buffaloes, all three values of ST were significantly greater (p ≤ 0.05) for the left flank, while, in the left eye and vulva, only the maximum and average values were significantly greater. By contrast, the maximum ST of the muzzle was significantly lower (p ≤ 0.05) in pregnant buffaloes compared to non-pregnant buffaloes. However, the ST of the right eye and right flank did not show significant temperature variation at any value. These findings suggest that IRT has the potential to identify thermal changes associated with pregnancy in buffaloes at an early stage.
A Bead-Based Nonradioactive Immunoassay for Autoantibody Testing in a Mouse Model of Myasthenia Gravis
Authors: Bahauddin Afrin , Curtis Kyra , Guptarak Jutatip , Huda Ruksana
Published: July 2024

Serological testing for anti-acetylcholine receptor (AChR) autoantibodies is not only crucial for the diagnosing, disease monitoring, and treatment management of patients with myasthenia gravis (MG) but also for preclinical studies utilizing MG disease models. However, there are no specific guidelines on which methods to use in clinical diagnostic or research laboratories to detect or quantify any MG-specific autoantibodies. Conventional autoantibody assays, particularly those for anti-AChR antibodies, are varied and mostly laboratory-specific. Here, we report our new nonradioactive immunoprecipitation–immunoblotting method for assessing autoantibodies (anti-AChR antibodies) in a mouse model of MG. This simple, efficient, reproducible, and cost-effective assay appears superior to the enzyme-linked immunosorbent assay but comparable to the radioimmunoprecipitation or cell-based assay in specificity and sensitivity. Thus, the newly developed assay can serve as a valuable alternative to classical assays and is suitable for routine testing of AChR-specific autoantibodies in preclinical studies. The further optimization of our assay may facilitate its application in the diagnosis and therapeutic management of patients with MG.
A Study on the Effect of Quaternization of Polyene Antibiotics’ Structures on Their Activity, Toxicity, and Impact on Membrane Models
Authors: Omelchuk Olga , Tevyashova Anna , Efimova Svetlana , Grammatikova Natalia , Bychkova Elena , Zatonsky George , Dezhenkova Lyubov , Savin Nikita , Solovieva Svetlana , Ostroumova Olga , Shchekotikhin Andrey
Published: July 2024

Polyene antibiotics have been used in antifungal therapy since the mid-twentieth century. They are highly valued for their broad spectrum of activity and the rarity of pathogen resistance to their action. However, their use in the treatment of systemic mycoses often results in serious side-effects. Recently, there has been a renewed interest in the development of new antifungal drugs based on polyenes, particularly due to the emergence of highly dangerous pathogenic strains of fungi, such as Candida auris, and the increased incidence of mucormycosis. Considerable understanding has been established regarding the structure–biological activity relationships of polyene antifungals. Yet, no previous studies have examined the effect of introducing quaternized fragments into their molecular structure. In this study, we present a series of amides of amphotericin B, nystatin, and natamycin bearing a quaternized group in the side chain, and discuss their biological properties: antifungal activity, cytotoxicity, and effects on lipid bilayers that mimic fungal and mammalian cell membranes. Our research findings suggest that the nature of the introduced quaternized residue plays a more significant role than merely the introduction of a constant positive charge. Among the tested polyenes, derivatives 4b, 5b, and 6b, which contain a fragment of N-methyl-4-(aminomethyl)pyridinium in their structure, are particularly noteworthy due to their biological activity.
A New Guanidine-Core Small-Molecule Compound as a Potential Antimicrobial Agent against Resistant Bacterial Strains
Authors: Morata-Moreno Noelia , Pérez-Tanoira Ramón , del Campo-Balguerias Almudena , Carrillo-Hermosilla Fernando , Hernando-Gozalo Marcos , Rescalvo-Casas Carlos , Ocana Ana V. , Segui Pedro , Alonso-Moreno Carlos , Pérez-Martínez Francisco C. , Molina-Alarcón Milagros
Published: July 2024

The guanidine core has been one of the most studied functional groups in medicinal chemistry, and guanylation reactions are powerful tools for synthesizing this kind of compound. In this study, a series of five guanidine-core small molecules were obtained through guanylation reactions. These compounds were then evaluated against three different strains of Escherichia coli, one collection strain from the American Type Culture Collection (ATCC) of E. coli ATCC 35218, and two clinical extended-spectrum beta-lactamase (ESBL)-producing E. coli isolates (ESBL1 and ESBL2). Moreover, three different strains of Pseudomonas aeruginosa were studied, one collection strain of P. aeruginosa ATCC 27853, and two clinical multidrug-resistant isolates (PA24 and PA35). Among Gram-positive strains, three different strains of Staphylococcus aureus, one collection strain of S. aureus ATCC 29213, and two clinical methicillin-resistant S. aureus (MRSA1 and MRSA2) were evaluated. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) experiments were reported, and the drop plate (DP) method was used to determine the number of viable suspended bacteria in a known beaker volume. The results from this assessment suggest that guanidine-core small molecules hold promise as therapeutic alternatives for treating infections caused by clinical Gram-negative and Gram-positive bacteria, highlighting the need for further studies to explore their potential. The results from this assessment suggest that the chemical structure of CAPP4 might serve as the basis for designing more active guanidine-based antimicrobial compounds, highlighting the need for further studies to explore their potential.
Therapeutic Potential of Mangosteen Pericarp Extract-Loaded Liposomes against Superficial Skin Infection Caused by Staphylococcus pseudintermedius in a Murine Model
Authors: Kim Seong-Yeop , Park Seong-Yong , Lee Jung-Hwa , Kim Nayeong , Oh Ha-Na , Yoo So-Young , Lee Dae-Sung , Lee Je-Chul
Published: July 2024

α-mangostin (α-MG) demonstrates antibacterial activity against Staphylococcus species. Therefore, this study aimed to explore the antibacterial activity of α-MG-rich mangosteen pericarp extract (MPE)-loaded liposomes against Staphylococcus isolates from companion animal skin diseases in vitro and evaluated their therapeutic potential in a murine model of superficial skin infection caused by S. pseudintermedius. α-MG-rich extract was purified from mangosteen pericarp and then complexed with γ-cyclodextrin (γ-CD), forming the inclusion complexes. Nanoliposomes containing MPE and γ-CD complexes were prepared by adding lecithin and casein. Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) of MPE-loaded liposomes were determined using agar dilution and broth microdilution methods. The therapeutic potential of MPE-loaded liposomes was evaluated in vivo on tape-stripped skin lesions infected with S. pseudintermedius. Purified MPE and MPE-loaded liposomes contained 402.43 mg/g and 18.18 mg/g α-MG, respectively. MPE-loaded liposomes showed antibacterial activity against clinical Staphylococcus isolates in vitro but did not show antibacterial activity against Gram-negative bacterial isolates. MPE-loaded liposomes demonstrated consistent MICs and MBCs against Staphylococcus isolates. These liposomes significantly reduced bacterial numbers and lesional sizes in a superficial skin infection model. Moreover, they reconstructed the epidermal barrier in skin lesions. The therapeutic concentrations of MPE-loaded liposomes did not induce cytotoxicity in canine progenitor epidermal keratinocyte cells. In conclusion, MPE-loaded liposomes hold promise for the development of a prospective topical formulation to treat superficial pyoderma in companion animals.
Integrating Multi-Omics Using Bayesian Ridge Regression with Iterative Similarity Bagging
Authors: Almutiri Talal Morizig , Alomar Khalid Hamad , Alganmi Nofe Ateq
Published: July 2024

Cancer research has increasingly utilized multi-omics analysis in recent decades to obtain biomolecular information from multiple layers, thereby gaining a better understanding of complex biological systems. However, the curse of dimensionality is one of the most significant challenges when handling omics or biological data. Additionally, integrating multi-omics by transforming different omics types into a new representation can reduce a model’s interpretability, as the extracted features may lose the biological context. This paper proposes Iterative Similarity Bagging (ISB), assisted by Bayesian Ridge Regression (BRR). BRR serves as a domain-oriented supervised feature selection method, choosing essential features by calculating the coefficients for each feature. Despite this, the BRR output datasets contain many features, leading to complexity and high dimensionality. To address this, ISB was introduced to dynamically reduce dimensionality and complexity without losing the biological integrity of the omics data, which often occurs with transformation-based integration approaches. The evaluation measures employed were Root Mean Square Error (RMSE), the Pearson Correlation Coefficient (PCC), and the coefficient of determination (R2). The results demonstrate that the proposed method outperforms some current models in terms of regression performance, achieving an RMSE of 0.12, a PCC of 0.879, and an R2 of 0.77 for the CCLE. For the GDSC, it achieved an RMSE of 0.029, a PCC of 0.90, and an R2 of 0.80.
Utilize Educational Robots to Design Logistic Systems
Authors: Kovalčík Jakub , Straka Martin , Trojanowska Justyna
Published: July 2024

The use of educational robots is new in terms of their utilization for designing logistic systems. The current methodology for designing logistic systems is not directly oriented towards the use of educational robots. This indicates the need to develop a methodology is directly oriented toward the use of educational robots in logistic system design. The aim of this article is to highlight the possibilities of creating a new methodology for designing logistic systems applying using educational robots or small physical models in general. This article brings a new perspective on the possibilities of designing logistic systems. We consider educational robots, infinite conveyor belts, and so on, such as small physical models. From these elements, we can assemble a functional logistic system for study. This method is necessary solution for the accredited field of study industrial logistics.
Influence of Land Use and Land Cover on the Quality of Surface Waters and Natural Wetlands in the Miranda River Watershed, Brazilian Pantanal
Authors: Carvalho Wesley dos Santos , Filho Fernando Jorge Corrêa Magalhães , Rodrigues Lucia Ribeiro , Calheiros Cristina Sousa Coutinho
Published: July 2024

Water quality assessment stands as a fundamental step in water resource management. In addition to substances and organisms present in the water, land use and land cover are two factors also affecting water quality. This study analyzed the correlation and influence between water quality and land use and land cover in the Miranda River Basin (MRB) and the natural wetlands in the Pantanal Biome. Using a watershed-wide approach, results suggested that water quality management policies can be considered in terms of sub-basins (smaller planning units). The Water Quality Index (WQI) was considered GOOD or EXCELLENT throughout the MRB. It was observed that the natural wetlands have the capacity to contribute to improving the WQI, with reductions in thermotolerant coliforms and turbidity; however, high values were found for thermotolerant coliforms, a temporal increasing trend of biochemical oxygen demand (BOD), and a reduction in dissolved oxygen and total phosphorus. Conversely, in these flooded areas, trends of a decrease in dissolved oxygen and an increase in BOD levels were found. Natural wetlands play important hydrological and ecological roles in water quality, storing, removing, and cycling nutrients. They are highly relevant areas for defining watershed management and conservation strategies, environmental protection, and providing ecosystem services.
Low Speed Impact of an Elastic Ball with Tapes and Clay Court
Authors: Akhan Ahmet F. , Marghitu Dan B.
Published: July 2024

The study aims to investigate the normal and oblique impact of an elastic sphere (tennis ball) on a granular surface (clay) and two different plastic tape lines. In this research, we model the impact force with a mathematical elastoplastic force model, and a differential approach is used. The model is applied for an impact with granular material (green clay) and plastic surfaces (line tapes). We investigated the normal and oblique impact dynamics of a sphere (tennis ball). The impact duration is divided into two phases: compression with an elastoplastic force and restitution with an elastic force. The laboratory experiments in various configurations are recorded with a high frame-per-second camera and analyzed using image processing methods. The mathematical model for the impact with rebounds is verified with the experimental set-up for the considered surfaces. The viscoelastic and elastic forces agree well with the experimental data. The impact parameters of the granular surface and plastic tapes are compared. The ANOVA test suggests robust statistical significance in the coefficient of restitution between granular surfaces and plastic tapes. Our force model for impact performs well, and the impact responses of the sphere on the granular surface and the plastic line tapes are significantly different.
The Efficiency of Binaural Beats on Anxiety and Depression—A Systematic Review
Authors: Baseanu Ionut Cristian Cozmin , Roman Nadinne Alexandra , Minzatanu Diana , Manaila Adina , Tuchel Vlad Ionut , Basalic Elena Bianca , Miclaus Roxana Steliana
Published: July 2024

Anxiety and depression are two of the most impactful diseases on quality of life and cause significant disability to patients. It burdens the medical system even as a stand-alone pathology or a secondary condition. These conditions can occur secondarily after a patient suffers from other illnesses, such as upper motor neuron lesions. Binaural beats are a new and emerging type of technology that can be used as an adjunct therapy for anxiety and depression as well as in neurologic conditions to some extent. We searched through the MEDLINE, PsychINFO, EMBASE, CENTRAL, ISRCTN, and ICTRP databases to identify studies using binaural beats as therapy for anxiety and depression. Twelve articles were declared eligible to be included in this review. Binaural beats, whether used in the form of pure beats or masked by another sound, have shown better results in alleviating symptoms of anxiety and depression compared to control conditions such as no music or the use of noise-canceling headphones alone. The results suggest that using binaural beats could be a promising and easy-to-use method to help alleviate the symptoms of anxiety and depression.
Application of Dimension Extending Technique to Unified Hardening Model
Authors: Chen Qian , Zheng Hong , Tian Dongshuai
Published: July 2024

This paper provides the process of incremental constitutive integration for the unified hardening model combined with the transformation stress method. The dimension-extending technique takes the hardening function of the hardening/softening model as the same position as the stress components, so that the constitutive integration of the plasticity can be reduced to an initial value problem of differential–complementarity equations, which is solved using the Gauss–Seidel algorithm-based Projection–Correction for the mixed complementarity problem. The Gauss–Seidel based Projection–Correction algorithm does not require the calculation of the Jacobean matrix of the potential function, making it relatively easy to implement in programming. The unified hardening model is proposed based on the modified Cam–Clay model and the sub-loading surface model, and the elastic properties are pressure-dependent. Two processing methods, backward Euler integration and exact elastic property, are used for the variable elasticity properties. The constitutive integration of the increased dimensional unified hardening model is reduced to a special mixed complementarity problem and solved by the proposed algorithm, which does not need to calculate the Jacobean matrix of the potential function, and greatly simplifies the derivation process. Several numerical examples are given to verify the feasibility of the incremental constitutive integration in the unified hardening model, including the single integral point and the boundary value problems. The research results have expanded the scope of use of the Gauss–Seidel based Projection–Correction algorithm.
The Development of a Named Entity Recognizer for Detecting Personal Information Using a Korean Pretrained Language Model
Authors: Jang Sungsoon , Cho Yeseul , Seong Hyeonmin , Kim Taejong , Woo Hosung
Published: July 2024

Social network services and chatbots are susceptible to personal information leakage while facilitating language learning without time or space constraints. Accurate detection of personal information is paramount in avoiding such leaks. Conventionally named entity recognizers commonly used for this purpose often fail owing to errors of unrecognition and misrecognition. Research in named entity recognition predominantly focuses on English, which poses challenges for non-English languages. By specifying procedures for the development of Korean-based tag sets, data collection, and preprocessing, we formulated directions on the application of entity recognition research to non-English languages. Such research could significantly benefit artificial intelligence (AI)-based natural language processing globally. We developed a personal information tag set comprising 33 items and established guidelines for dataset creation, later converting it into JSON format for AI learning. State-of-the-art AI models, BERT and ELECTRA, were employed to implement and evaluate the named entity recognition (NER) model, which achieved an 0.943 F1-score and outperformed conventional recognizers in detecting personal information. This advancement suggests that the proposed NER model can effectively prevent personal information leakage in systems processing interactive text data, marking a significant stride in safeguarding privacy across digital platforms.
Multimodal Machine Learning for Prognosis and Survival Prediction in Renal Cell Carcinoma Patients: A Two-Stage Framework with Model Fusion and Interpretability Analysis
Authors: Yan Keyue , Fong Simon , Li Tengyue , Song Qun
Published: July 2024

Current medical limitations in predicting cancer survival status and time necessitate advancements beyond traditional methods and physical indicators. This research introduces a novel two-stage prognostic framework for renal cell carcinoma, addressing the inadequacies of existing diagnostic approaches. In the first stage, the framework accurately predicts the survival status (alive or deceased) with metrics Accuracy, Precision, Recall, and F1 score to evaluate the effects of the classification results, while the second stage focuses on forecasting the future survival time of deceased patients with Root Mean Square Error and Mean Absolute Error to evaluate the regression results. Leveraging popular machine learning models, such as Adaptive Boosting, Extra Trees, Gradient Boosting, Random Forest, and Extreme Gradient Boosting, along with fusion models like Voting, Stacking, and Blending, our approach significantly improves prognostic accuracy as shown in our experiments. The novelty of our research lies in the integration of a logistic regression meta-model for interpreting the blending model’s predictions, enhancing transparency. By the SHapley Additive exPlanations’ interpretability, we provide insights into variable contributions, aiding understanding at both global and local levels. Through modal segmentation and multimodal fusion applied to raw data from the Surveillance, Epidemiology, and End Results program, we enhance the precision of renal cell carcinoma prognosis. Our proposed model provides an interpretable analysis of model predictions, highlighting key variables influencing classification and regression decisions in the two-stage renal cell carcinoma prognosis framework. By addressing the black-box problem inherent in machine learning, our proposed model helps healthcare practitioners with a more reliable and transparent basis for applying machine learning in cancer prognostication.
Developing a Platform Using Petri Nets and GPenSIM for Simulation of Multiprocessor Scheduling Algorithms
Authors: Dirdal Daniel Osmundsen , Vo Danny , Feng Yuming , Davidrajuh Reggie
Published: July 2024

Efficient multiprocessor scheduling is pivotal in optimizing the performance of parallel computing systems. This paper leverages the power of Petri nets and the tool GPenSIM to model and simulate a variety of multiprocessor scheduling algorithms (the basic algorithms such as first come first serve, shortest job first, and round robin, and more sophisticated schedulers like multi-level feedback queue and Linux’s completely fair scheduler). This paper presents the evaluation of three crucial performance metrics in multiprocessor scheduling (such as turnaround time, response time, and throughput) under various scheduling algorithms. However, the primary focus of the paper is to develop a robust simulation platform consisting of Petri Modules to facilitate the dynamic representation of concurrent processes, enabling us to explore the real-time interactions and dependencies in a multiprocessor environment; more advanced and newer schedulers can be tested with the simulation platform presented in this paper.
An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference
Authors: Tian Zhaoxu , Cheng Yongmei , Yao Shun
Published: July 2024

In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph optimization algorithm is proposed. The simulation results demonstrate that the factor-graph-based integrated navigation algorithm consistently yields high-precision navigation outcomes even amidst dynamic changes in sensor validity and the presence of asynchronous and delayed sensor measurements. Notably, the AFIS factor graph optimization algorithm significantly enhances real-time performance compared to traditional Incremental Smoothing (IF) algorithms, while maintaining comparable real-time accuracy.
Simplified Tunnel–Soil Model Based on Thin-Layer Method–Volume Method–Perfectly Matched Layer Method
Authors: Wang Yu , Zhou Mengfan , Cao Yanmei , Wang Xiaoxi , Li Zhe , Ma Meng
Published: July 2024

In order to analyze the ground vibration responses induced by the dynamic loads in a tunnel, this paper proposes a new simplified tunnel–soil model. Specifically, based on the basic theory of the thin-layer method (TLM), the basic solution of three-dimensional layered foundation soil displacement was derived in the cylindrical coordinate system. The perfectly matched layer (PML) boundary condition was applied to the TLM. Subsequently, a tunnel–soil dynamic interaction analysis model was established using the volume method (VM) in conjunction with the TLM-PML method. The displacement frequency response function of the foundation soil around the tunnel foundation was derived. Finally, a ground vibration test under an impact load in a tunnel was carried out. The test and calculated results were compared. The comparison results show that the ground vibration acceleration response values within 25 m from the load are similar. Compared with the test results, the theoretical calculation results exhibit a decreasing trend in the range of 40–80 Hz between 25 and 60 m, with the maximum reduction being approximately one order of magnitude. In addition, the experimental comparison demonstrates that the model can be used to analyze the ground vibrations caused by underground loads.
How Does Digital Technology Inspire Global Fashion Design Trends? Big Data Analysis on Design Elements
Authors: Lee Nahyun , Suh Sungeun
Published: July 2024

Digital technology has changed every process of the fashion industry significantly. Using big data analysis methods such as text-mining, network, CONCOR, and content analyses, this study aims to understand the impact of digital technology trends from the fashion design perspective. The influence of digital technology on fashion design elements (e.g., color, print and graphic, textiles, and style and details) was evident through various keywords related to digital technology, humans, and nature, and the relationships between these keywords were confirmed. The analysis of the implicit meanings and directions of the derived keywords resulted in four clusters: (1) human- and nature-oriented design in the digital world as a new reality; (2) new textiles reflecting digital technology; (3) sustainable design technology; and (4) new utility fashion in the digital space. This study proposed a new design research methodology in which big data were incorporated and could be applied to educational curricula, allowing students to derive practical design elements through big data analysis and serving as a guide for planning and developing technology-inspired designs. Practically, it provided specific information on the direction of digital-technology-inspired fashion design trends, which could assist fashion designers and aspiring entrepreneurs in planning.
A Multi-Objective Evaluation Method for Smart Highway Operation and Management
Authors: Li Li , Long Yixin , Peng Chongmei
Published: July 2024

Smart highways represent a novel highway concept in the era of big data, emphasizing the synergy among people, vehicles, road facilities, and the environment. However, the operation and management of smart highways have become more intricate, surpassing the adaptability of traditional highway evaluation and management methods. This study integrates the distinctive characteristics of smart highway facilities and operational objectives to enhance and modernize the existing highway evaluation system. Drawing from research on smart highway construction projects, a smart highway evaluation system encompassing facility structure, electromechanical facilities, and operation services is formulated based on a hierarchical analysis method. The quantitative evaluation of each indicator is achieved by combining existing specifications and expert questionnaire solicitation. The group decision-making method is initially employed to optimize subjective weights, followed by the calculation of combined weights using both the entropy weight method and critic method in objective evaluation. Finally, a comprehensive evaluation model is established and validated through engineering projects. The results demonstrate that the evaluation system effectively highlights the advantages and disadvantages in the operation and management of smart highways, thereby fostering the advancement of smart highway iteration.
Towards Enhanced Autonomous Driving Takeovers: Fuzzy Logic Perspective for Predicting Situational Awareness
Authors: Ferenc Goran , Timotijević Dragoje , Tanasijević Ivana , Simić Danijela
Published: July 2024

This paper investigates the application of fuzzy logic to enhance situational awareness in Advanced Driver Assistance Systems (ADAS). Situational awareness is critical for drivers to respond appropriately to dynamic driving scenarios. As car automation increases, monitoring situational awareness ensures that drivers can effectively take control of the vehicle when needed. Our study explores whether fuzzy logic can accurately assess situational awareness using a set of 14 critical predictors categorized into time decision, criticality, eye-related metrics, and driver experience. We based our work on prior research that used machine learning (ML) models to achieve high accuracy. Our proposed fuzzy logic system aims to match the predictive accuracy of ML models while providing additional benefits in terms of interpretability and robustness. This approach emphasizes a fresh perspective on situational awareness within ADAS, potentially improving safety and efficiency in real-world driving scenarios.
A Comparative Study on Soil-Crop Selenium Characteristics in High-Incidence Areas of Keshan Disease in Chinese Loess and Black Soil
Authors: Zhao Jun , Rao Zhu , Liu Siwen , Wang Lei , Wang Peng , Yang Tao , Bai Jin
Published: July 2024

With the gradual emphasis on health by people, the research on the pathogenesis of endemic diseases has become increasingly in-depth. Through analyzing the environmental selenium characteristics and conducting a comparative study in typical areas of Chinese loess and black soil in this paper, it is concluded that the environmental selenium in the two regions has different characteristics. The soil in the loess area has the characteristics of high alkalinity, low selenium, and relatively high selenium availability, and the crops are selenium-deficient, while the soil in the black soil area has the characteristics of high organic matter, low selenium availability, and relatively high selenium in crops. The research concluded that the environmental occurrence mechanism of Keshan disease in the loess area and the black soil area is different. Keshan disease can be induced in both low-selenium and sufficient-selenium environments, and environmental selenium should be one of the inducing factors of Keshan disease. This research provides a reference for predicting the areas where Keshan disease occurs and for disease prevention, and it can also serve in the prevention and control of endemic diseases.
The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
Authors: Lambert Cause Joan , Solé Morillo Ángel , García-Naranjo Juan C. , Stiens Johan , da Silva Bruno
Published: July 2024

Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments.
Parametric Study on the Buckling of Unbraced Steel Frames under Fire Situation
Authors: Silva Thiago , Couto Carlos , Vila Real Paulo , Lopes Nuno
Published: July 2024

A parametric investigation of several unbraced steel frames with regular and irregular geometry subjected to elevated temperatures is carried out in this study to determine the most accurate procedure and buckling lengths to be considered during the structural design under a fire situation. In such conditions, the stiffness and strength of steel decrease considerably due to high temperatures, and uncertainty remains in the application of the fire design rules of Eurocode 3 Part 1-2 (EN 1993-1-2) for unbraced frames as no information is given regarding the treatment of the deformed geometry (the so-called second-order effects). More precisely, it is unclear in the norm whether the verification based on the buckling length concept could be used or if a second-order analysis to calculate the internal forces is sufficient to ensure the stability of the frame in case of fire. Based on the linear buckling analysis of the steel frames accounting for the temperature development during a fire, recommendations for the appropriate buckling lengths to be used are given. Finally, it is demonstrated that using the recommended buckling lengths together with the design rules of EN 1993-1-2 leads to results in favor of safety when compared to the results obtained with the finite element method. On the other hand, it is concluded that using second-order internal forces and the real length of the columns as the buckling length, as suggested for room temperature design, yielded results outside safety when compared with the finite element method, and this analysis and verification procedure should not be used for the case of fire.
Research on User Default Prediction Algorithm Based on Adjusted Homogenous and Heterogeneous Ensemble Learning
Authors: Lu Yao , Wang Kui , Sun Hui , Qu Hanwen , Chen Jiajia , Liu Wei , Chang Chenjie
Published: July 2024

In the field of risk assessment, the traditional econometric models are generally used to assess credit risk. And with the introduction of the “dual-carbon” goals to promote the development of a low-carbon economy, the scale of green credit in China has rapidly expanded. But with the advent of the big data era, due to the poor interpretability of a traditional single machine learning model, it is difficult to capture nonlinear relationships, and there are shortcomings in prediction accuracy and robustness. This paper selects the adjusted ensemble learning model based on the homogeneous and heterogeneous factors for user default prediction, which can efficiently process large quantities of high-dimensional data. This article adjusts each model to adapt to the task and innovatively compares various models. In this paper, the missing value filling method, feature selection, and ensemble model are studied and discussed, and the optimal ensemble model is obtained. When comparing the predictions of single models and ensemble models, the accuracy, sensitivity, specificity, F1-Score, Kappa, and MCC of Categorical Features Gradient Boosting (CatBoost) and Random undersampling Boosting (RUSBoost) all reach 100%. The experimental results prove that the algorithm based on adjusted homogeneous and heterogeneous ensemble learning can predict the user default efficiently and accurately. This paper also provides some references for establishing a risk assessment index system.
Study of Urban Unmanned Aerial Vehicle Separation in Free Flight Based on Track Prediction
Authors: Zhang Jian , Li Zongxiao , Luo Xinyue , Zhao Yifei , Lu Fei
Published: July 2024

In recent years, the application prospect of urban logistics unmanned aerial vehicles has attracted extensive attention. The high-density operation of UAVs requires autonomous separation maintenance capability. To achieve autonomous separation maintenance, it is necessary to conduct autonomous track prediction and formulate the required separation accordingly. Based on the target level of safety requirements for UAV operation, aiming at the autonomous separation maintenance ability of UAVs and considering the accuracy of track prediction, a method to calculate the required separation between UAVs is proposed. This study consists of two parts. Firstly, based on historical data, the position prediction error of the flight track is investigated. Using a machine learning model, a two-stage track prediction method, which involves classification followed by prediction, is proposed for urban logistics UAV track data. Subsequently, based on the track prediction error distribution, by designing a gas model and a position error probability model, a separation-formulating model for urban logistics UAVs in free flight is proposed in which UAV maneuverability is considered. By applying this model, the required separation is formulated for UAVs. When the required separation is set to 48.5 m, the overall collision risk meets the TLS requirements. The research provides a feasible method for establishing autonomous separation for urban logistics UAVs.
High-Quality Multi-Emitter LED-Based Retrofits for Incandescent Photometric A Illuminant Reliability of R2 Evaluation
Authors: Błaszczak Urszula J. , Gryko Łukasz
Published: July 2024

This research deals with the design problems of LED-based spectrally tunable light sources (LSTLSs). The study aims to assess the reliability of popular models for the spectral modeling of LED radiation and a typically used curve-fitting criterion (R2) in the development of high-quality multi-emitter LED retrofits for incandescent photometric illuminant. The research methodology involves modeling each LED channel using Lorentz and Gaussian functions and combining multiple channels to approximate the desired spectral power distribution (SPD). After the optimization, 20 various LED sets were designed, which allowed us to replicate the SPD of CIE illuminant A with a very high R2 value. Two sets were constructed and measured to recognize the reliability of the simulation approach. The results suggest that for planning the LSTLS for photometric applications, these models are unreliable as they do not reflect the real effect of changes in the characteristics of the components nor reveal the share of various spectral ranges. Therefore, the decisions made on these criteria may not be the best solutions in the context of specific applications.
Synergistic Impact of ARSB, TP53, and Maspin Gene Expressions on Survival Outcomes in Colorectal Cancer: A Comprehensive Clinicopathological Analysis
Authors: Kovacs Zsolt , Banias Laura , Osvath Eva , Gurzu Simona
Published: July 2024

(1) Background: Colorectal cancer (CRC) remains a significant cause of morbidity and mortality worldwide, with its prognosis influenced by genetic and clinicopathological factors. This study investigates the associations between the gene expressions of Arylsulfatase B (ARSB), TP53, and Maspin, alongside traditional clinicopathological features, and their impact on CRC survival outcomes. (2) Methods: 70 consecutive CRC cases were analyzed for ARSB, TP53, and Maspin gene expression using RT-qPCR, and their protein levels were assessed through immunohistochemistry. Clinicopathological parameters—age, gender, tumor localization, macroscopic and microscopic aspects, lymph node ratio, pT stage, and tumor budding—were evaluated for their prognostic significance. Kaplan–Meier survival analysis with Cox proportional hazards regression was used to determine their impact on overall survival. (3) Results: No significant survival differences were observed based on age, gender, tumor localization, and macroscopic aspect. The microscopic aspect and pT stage showed significant associations with survival, with poorer outcomes in G3 and pT3/pT4 stages, respectively. Immunohistochemical positivity for ARSB and Maspin indicated a longer survival, while TP53 protein expression alone did not significantly impact the prognosis. Dual high gene expression (ARSB + TP53, TP53 + Maspin) and triple high gene expression (ARSB + TP53 + Maspin) were significantly associated with better survival outcomes. (4) Conclusions: The combined gene expression profile of ARSB, TP53, and Maspin presents a novel prognostic marker in CRC, offering insights into the molecular dynamics of cancer cells and potential therapeutic targets. These findings emphasize the importance of integrating molecular markers with traditional clinicopathological factors for a more accurate prognostication and personalized treatment approach in CRC.
Formulation and Stability Assessment of Bakery Snacks Enriched with Encapsulated Phenolic Compounds from Lemnian Tomatoes and Pumpkin (Cucurbita moschata)
Authors: Michalaki Afroditi , Karantonis Haralabos C.
Published: July 2024

In recent years, the health-promoting properties of plant-derived compounds have garnered increasing scientific interest. Notably, tomatoes and pumpkins (Cucurbita moschata), renowned for their abundant phytochemicals and associated biological activities, have become focal points of research. This study investigated the extraction of phenolic compounds from tomatoes and pumpkins cultivated on Lemnos, an Aegean Island, aiming to enhance the nutritional profiles of food products. We established an extraction protocol for total phenolics and evaluated the antioxidant capacity using response surface methodology. Utilizing a central composite design, we optimized the extraction parameters, with time and ethanol concentration identified as critical factors (p < 0.05). The optimized extracts demonstrated substantial phenolic content (9.47 ± 0.08 and 4.52 ± 0.05 mg GAE/g for tomatoes and pumpkins, respectively) and antioxidant capabilities as determined by DPPH (7.65 ± 0.08 and 5.78 ± 0.05 μmol TE/g, respectively), ABTS (9.27 ± 0.02 and 3.95 ± 0.04 μmol TE/g, respectively), FRAP (5.25 ± 0.09 and 2.99 ± 0.03 μmol TE/g, respectively), and CUPRAC assays (2.3 ± 0.04 and 1.25 ± 0.03 μmol TE/g, respectively). Following extraction, the phenolic compounds were encapsulated using maltodextrin and subsequently freeze-dried, yielding high encapsulation efficiency. In alignment with a comprehensive strategy aimed at fostering functional snacks that enhance local economic and public health outcomes, vegetables sourced from local farms were employed to develop a savory cereal bar enriched with tomato extract and a sweet cookie infused with pumpkin extract.