bluuebunny/crossref_metadata_2025 huggingface dataset:
MULTI-AGENT SIMULATION MODEL OF INFECTIOUS DISEASE SPREAD
Authors: Ivashchenko Daria , Kutsenko Oleksandr
Published: July 2024

The aim of the research is to develop a multi-agent simulation model for predicting the spread of infectious diseases, particularly COVID-19. In the context of the COVID-19 pandemic, there emerged an urgent need to create tools for forecasting and analyzing the dynamics of epidemics, as well as for evaluating the effectiveness of management decisions. The use of mathematical models in this process allows for an adequate description of the infection spread dynamics, which is essential for making informed decisions. The article discusses traditional approaches to epidemic modeling, such as the predator-prey model and the compartmental SIR (Susceptible-Infectious-Recovered) model. The predator-prey model describes the interaction between two species in an ecosystem using differential equations, which allows for modeling population dynamics. The compartmental SIR model divides the population into three groups: susceptible, infected, and recovered, which enables the analysis of the spread of infectious diseases. However, these models have limitations, particularly due to assumptions about population homogeneity and constant parameters. To more accurately model complex epidemic processes, a multi-agent simulation model was developed. In this model, agents interact within a defined area, mimicking real conditions of infection spread. Agents are divided into three classes: healthy, infected, and recovered. The movement of agents is modeled using random walk in a two-dimensional space, taking into account the possibility of contact between them, which can lead to infection. Infected agents transition to the recovered class after a certain period of illness and can no longer be infected. Modeling results showed that the multi-agent model allows for more accurate prediction of infection spread dynamics. Numerous experiments were conducted, demonstrating the model's adequacy in replicating the infection process, peak infection rates, and recovery periods. The influence of various parameters, such as the duration of illness, on the epidemic dynamics was investigated. The obtained results confirm that considering individual characteristics and behavioral traits of agents improves the accuracy of modeling. This allows the multi-agent simulation model to be used for developing effective control strategies and predicting the spread of infectious diseases, which can be useful for making management decisions in real pandemic conditions.
FORECASTING CHANGE IN THE LEVEL OF FOREST COVERAGE USING THE GLOBAL FOREST WATCH SERVICE AND THE PROGRAMMING AND DATA ANALYSIS LANGUAGE R
Authors: Melnykov Oleksandr , Denysenko Viktoriia
Published: July 2024

The problem of calculating the level of forest cover is considered, including forecasting changes in forest cover in individual forestry. It is stated that the authors previously developed software for calculating forest cover and processing information about forest plantations using the example of the village of Spivakivka in the Izyum district of the Kharkiv region. A comparison of forest cover over a number of years was also made using the Global Forest Watch resource. From this resource, images of Prydonetsk Forestry were taken with conventional designations: areas where new forest plantations are being planted are shown in blue, and areas where cutting is taking place are shown in pink. It is proposed to divide each of the uploaded images of the selected forestry into squares, and then analyze the data for each square. The pink color saturation was calculated and stored in the table. It is noted that forecasting the change in forest stands on the selected site, that is, the change in the percentage of felling, can be done in different ways. First, use regression analysis - apply the regression equation separately to the values of each square, as well as to the entire forestry. Secondly, to form a list of input factors containing indicators on the selected plot in the two previous years and the same indicators on neighboring plots. Thus, the number of factors will be equal to 27: 26 input and 1 output (values on the studied square). Such a forecasting problem can be solved either by the method of multivariate linear regression or by the method of artificial neural networks. The R programming and data analysis language was used to perform calculations using both methods. A script was created that performs calculations by constructing regression lines and an artificial neural network, and also allows determining the best architecture of a neural network and a more effective method of its training for a certain data set. The calculation of the felling dynamics in the entire forestry (the forecast for the last year provides an error of 1 %) and the calculation of the felling dynamics in the selected square (the forecast for the last year provides an error of 3.5 %) are given. After many runs of the script, it was found that the best result is provided by a perceptron with two hidden layers and two neurons in each layer. The results of the calculations indicate a high correlation of the data for determining the percentage of forest that will be cut down in a certain square. Application of this perceptron for forecasting for the last year showed an error of 3 %.
Evaluating The Effectiveness of Wastewater Treatment In the Reusable Packaging Industry: A Case Study of ALNER’S WWTP In Indonesia
Authors: Ekananda Bintang , Farida Anif , Sumendar Rian
Published: July 2024

The widespread use of single-use plastic packaging has become a significant environmental issue, particularly in the Global South. This study examines the innovative approach of the startup Alner, which employs a combination of electrochemical treatment and Moving Bed Biofilm Reactor (MBBR) technology in its wastewater treatment plant (WWTP) to manage wastewater generated from the cleaning of reusable packaging. Wastewater samples were collected before and after treatment, and analyzed for key parameters such as Chemical Oxygen Demand (COD), Biological Oxygen Demand (BOD5), Total Suspended Solids (TSS), oil and grease, ammonia, surfactants, and phosphates. The results demonstrated a significant reduction in all measured pollutants, with COD reduced by 99.58% and BOD5 by 99.56%. Post-treatment values for all parameters were well within the regulatory limits set by the Ministry of Environment and Forestry Regulation No. P68/MenLHK/Setjen/Kum.1/8/2016. This study underscores the potential of reuse models in reducing single-use plastic waste and highlights the importance of effective wastewater treatment in maintaining environmental sustainability. Alner's approach provides a scalable and environmentally friendly solution, setting a benchmark for similar initiatives in the industry, and supports the feasibility and sustainability of integrating advanced wastewater treatment technologies in the reusable packaging sector, offering a comprehensive solution to plastic pollution.
Identification of Priorities for Colorectal Cancer Screening Interventions Among US Hispanic/Latino Populations
Authors: Buchalter R. Blake , Stern Mariana C. , Mendez Joel Sanchez , Kim Michelle K. , Rose Johnie , Meade Cathy D. , Gwede Clement K. , Figueiredo Jane C. , Schmit Stephanie L.
Published: July 2024

Objectives. To identify nationwide census tract‒level areas where improving colorectal cancer (CRC) screening uptake via targeted local preventive intervention may benefit Hispanic or Latino/a (H/L) groups defined by region or country of origin.

Methods. Using 2021 Centers for Disease Control and Prevention PLACES and American Community Survey data, we applied geographically weighted regression and Getis-Ord Gi* hot spot procedures to identify CRC screening priority zones for H/L groups in the United States. Priority zones can be conceptualized as census tracts with strong inverse associations between percentage of a particular H/L group in the population and CRC screening rate, after adjusting for socioeconomic deprivation and lack of insurance.

Results. We identified 6519, 3477, 3522, 1069, and 1424 census tract CRC screening priority zones for H/L communities of Mexican, Puerto Rican, Central/South American, Dominican, and Cuban heritage, respectively. Priority zones for H/L groups had strong spatial heterogeneity, and overlap of geographic patterns among H/L groups varied by region.

Conclusions. Our findings and interactive web map may serve as a translational tool for public health authorities, policymakers, clinicians, and other stakeholders to target investment and interventions to increase guideline-concordant CRC screening uptake benefitting specific H/L communities in the United States. ( Am J Public Health. 2024;114(S6):S515–S524. https://doi.org/10.2105/AJPH.2024.307733 )

[Formula: see text]
Lateral Approach With and Without Posterior Stabilization for Lumbar Pyogenic Spondylodiscitis Treatment: A Retrospective Study With an Average Follow-up of 7 Years
Authors: Bassani Julio , Terrasa Sergio , Kido Gonzalo , Gruenberg Marcelo , Petracchi Matías , Solá Carlos , Duarte Matías Pereira
Published: July 2024

Objective: The aim of this study was to present the results after an average of 7-year follow-up in patients diagnosed with pyogenic spondylodiscitis (PS) treated with lumbar discectomy and fusion through minimally invasive lateral lumbar interbody fusion (LLIF), with and without posterior stabilization.Methods: In this retrospective analysis, patients diagnosed with PS treated with minimally invasive LLIF were studied. Variables included C-reactive protein (CRP), blood cultures, and findings on magnetic resonance imaging, computed tomography, and x-ray examinations. The classification of PS according to Pola was recorded.Results: Twenty-one patients were male (70%) and 9 were female (30%), with an average age of 67.6 years (range, 33–85 years). The average follow-up was 83.2 months (range, 12–160 months). In 7 out of the 30 cases, treatment consisted exclusively of discectomy and anterior fusion through LLIF. Bone consolidation was observed in 27 out of 30 cases (90%), with an average postoperative CRP level of 5.77±5.00 mg/L at the end of follow-up.Conclusion: The treatment of lumbar PS through debridement and minimally invasive LLIF, with or without posterior stabilization, resulted in infection control over an average follow-up of 83 months.
Response of an Avifaunal Community to the La Tuna Fire in the Verdugo Mountains of Southern California
Authors: Jordan Shane E. , Rachman Richard
Published: July 2024

In California’s coastal sage scrub and chaparral, fire reshapes the distribution of vegetation and the composition of bird communities, although the direction of the response in various habitats remains unclear. We investigated changes in the bird community at one site following the 2017 La Tuna Fire in the Verdugo Mountains of southern California. We found that the community’s diversity and evenness did not change, but the structure of foraging guilds did. Counts of omnivores and aerial and generalist foragers increased significantly, whereas counts of granivores and shrub foragers decreased. The number of insectivores decreased slightly, and ground foragers appeared unaffected. Principal components analysis suggests positive, neutral, and negative responses of birds associated with these guilds were driving differences in their abundances after the fire. We show how post-fire bird distributions may be related to fire severity, vegetation structure, and interspecific interactions. However, differences between our results and those of other studies suggest that patterns of bird community response to wildfire may be dictated by factors both related and unrelated to the fire over broader time scales.
Subcellular Systems Observe Onsager Reciprocity
Authors: Horowitz Arie
Published: July 2024

The complexity of prokaryotic and, exceedingly, of eukaryotic cells, challenges the implemen-tation of methodologies to determine causality and quantify intracellular dynamics. Although stochastic thermodynamics can be used to effectively quantify the dynamics of single proteins or complexes, there is no statistical approach to generalize subcellular microstates to deter-mine the macrostates of a whole cell. Onsager reciprocity, a coupling between thermody-namic flows, has not been used systematically to quantify and establish causality among cel-lular dynamics. A prototypical thermodynamic profile of the cell is formulated as a model sys-tem to enable representation of its dynamics as force-driven thermodynamic flows. The ex-planatory potential of the methodology of thermodynamic reciprocity is demonstrated on mo-lecular mechanisms in which coupling between internal fluxes has been evident. Its predictive potential is then tested against mechanisms wherein coupling is unacknowledged, ruling out reciprocity between the contractile ring of the bacterial ortholog of tubulin that facilitates the separation of dividing Escherichia coli cells, and the bacterial actin ortholog, which polymer-izes along the cell walls. In contrast, reciprocity is identified, albeit with a large margin of error, between the actomyosin ring that facilitates the division of fission yeast cells and treadmilling longitudinal microtubules. Directly pertinent to the definition of the cell's prototypical thermody-namic profile, the reciprocity that was suggested between AMP-regulated kinase (AMPK) and mechanistic target of rapamycin (mTORC1) in the regulation of the cell cycle is confirmed. These findings support wider implementation of thermodynamic reciprocity to quantify and es-tablish causal relationships among cellular dynamics.
A Multilingual BERT-based classification of reviews for enhanced visitors’ experience analysis
Authors: RICCIARDI RICCARDO , Manisera Marica
Published: July 2024

Cultural organizations today can rely on online platforms to study users’ opinions and the most discussed topics related to both general and specific cultural offerings. Despite data acquisition tools, managing unstructured databases remains a hurdle. To overcome this, we propose a classification model that transforms unorganized data into a structured thematic database.
The specific case pertains to the Italian city of Brescia. We build a language model that classifies online reviews into four semantic areas defined by the key attractions of the city. We fine-tuned the pre-trained Multilingual BERT model in a multiclassification task. The model shows promising results based on traditional performance metrics. Additionally, clusters of reviews have been detected by applying the HDBSCAN algorithm on their vector representations produced by the model. As a transformation of the chi-square statistic, the Keyness statistic has been employed to extract cluster-specific keywords, which have proven to be highly consistent with the characteristics and offerings of the key cultural attractions, further confirming the good performance of the model. Results show that the proposed model can be profitably employed by policymakers and managers of cultural tourism institutions to understand textual data and derive relevant insights about visitors’ experience at specific attractions of interest.
Integrated CBL and PBL Teaching with Standardized Patients for Internal Medicine Education
Authors: Shao Pengfei , Ran Junpeng , Long Qin , Li Wei
Published: July 2024

Objective
This study evaluates the effectiveness and efficiency of combining Case-Based Learning (CBL) utilizing Standardized Patients with Problem-Based Learning (PBL) in enhancing clinical education for internal medicine students.
Methods
A retrospective cohort study was performed involving 240 internal medicine students trained in our department between September 2021 and June 2023.Initially, students were divided into a traditional teaching group (n = 120) and a combined PBL-CBL group (n = 120). Baseline calculations revealed significant gender differences between the two groups. Subsequently, propensity score matching was employed to create a matched traditional teaching group (n = 93) and a PBL-CBL combined group (n = 120). Independent sample t-tests were utilized to compare the teaching performance between the two groups.
Results
Post propensity score matching, the integrated PBL-CBL group demonstrated significant enhancements (p < 0.05) across various metrics, including end-of-course assessment scores, skill assessments, dOPS scores, theoretical knowledge, practical skills, doctor-patient communication, self-directed learning, perceived learning burden, and overall competence.
Conclusion
Integrating CBL and PBL teaching methods with Standardized Patients proves to be an effective approach for enhancing the overall performance and clinical skills of medical students and resident physicians.
Proteogenomic analysis reveals Arp 2/3 complex as a common molecular mechanism in high risk pancreatic cysts and pancreatic cancer
Authors: Mahmud AKM Firoj , Aly Dina Gamaleldin Mansour , Zhao Yelin , Benson Mikael , Smelik Martin , Sysoev Oleg , Wang Hui , Li Xinxiu
Published: July 2024

Pancreatic cysts, particularly intraductal papillary mucinous neoplasms (IPMNs), pose a potential risk for progressing to pancreatic cancer (PC). This study investigates the genetic architecture of benign pancreatic cysts and its potential connection to PC using genome-wide association studies (GWAS). The discovery GWAS identified significant genetic variants associated with benign cysts, specifically the rs142409042 variant near the OPCML gene. A pairwise GWAS comparing PC to benign cysts revealed the rs7190458 variant near the BCAR1 and CTRB1 genes. Further analysis with identified GWAS genes highlighted the Actin Related Protein (Arp) 2/3 complex as a potentially important molecular mechanism connecting benign cysts and PC. The Arp2/3 complex-associated genes were significantly upregulated in PC, suggesting their role in the malignant transformation of pancreatic cysts. Differential expression of these genes was observed across various cell types in PC, indicating their involvement in the tumor microenvironment. These findings suggest that the Arp2/3 complex-associated genes can serve as potential biomarkers for predicting the malignant transformation of pancreatic cysts, opening new avenues for targeted therapies and early detection strategies.
The m6A reader IGF2BP3 promotes LIHC progression by enhancing MCM10 stability
Authors: Zhao Lianwu , Huang Hongyan , Luo Linfei , Huang Zixiang , Wu Zhengqiang , Wang Fenfen , Wen Zhili
Published: July 2024

Abnormal N6-methyladenosine (m6A) modifications were associated with the occurrence, development, and metastasis of cancer. However, the functions and mechanisms of m6A regulators in cancer remained largely elusive and should be explored. Here, we identified that IGF2BP3 was specifically overexpressed and associated with poor prognosis in LIHC. Importantly, IGF2BP3 promoted liver hepatocellular carcinoma (LIHC) cell progression in an m6A-dependent manner, IGF2BP3 silencing significantly inhibited proliferation and migratory ability of tumor cells in vitro. Mechanistically, IGF2BP3 interacted with minichromosomal maintenance complex component (MCM10) mRNAs to prolong stability of m6A-modified RNA. Therefore, our findings indicated that m6A reader IGF2BP3 contributed to tumorigenesis and poor prognosis, providing a potential prognostic biomarker and therapeutic target for LIHC.
Catechin promotes endoplasmic reticulum stress-mediated gastric cancer cell apoptosis via NOX4-induced reactive oxygen species
Authors: Jiang Jun , Li Deming , Li Fan , Li Huanqing , Zhang Xiaohong , Feng Li
Published: July 2024

Background
Catechin, a polyphenolic compound in various foods and beverages, shows strong anticancer effects against gastric cancer (GC) cells. This study explored the effect of Catechin on GC cell apoptosis and endoplasmic reticulum (ER) stress.
Methods
GC cells were treated with different catechin concentrations to assess effects on cell viability, LDH release, invasion, migration, apoptosis, intracellular calcium (Ca²⁺), ER stress markers, and reactive oxygen species (ROS). siRNA knockdown targeted GRP78, PERK, CHOP, and NOX4 to examine their roles in catechin-induced ER stress and apoptosis.
Results
Catechin treatment significantly reduced GC cell viability, increased LDH release, and induced apoptosis dose-dependently. Catechins elevated intracellular Ca²⁺ and ER stress markers. Co-treatment with thapsigargin (TG) intensified these effects, implicating ER stress in apoptosis. Knocking down GRP78, PERK, and CHOP mitigated catechin-induced apoptosis and restored viability. Additionally, catechins raised ROS levels, while co-treatment with Diphenyleneiodonium (DPI) or N-acetylcysteine (NAC) lowered ROS, cell damage, and ER stress markers. NOX4 knockdown countered catechin-induced viability loss and upregulated CHOP and cleaved caspase-3.
Conclusion
Catechin induce apoptosis in GC cells through ER stress and ROS generation. Key mediators include GRP78, PERK, CHOP, and NOX4, suggesting potential therapeutic targets for enhancing catechin efficacy in GC treatment.
A Matrix Product State Model for SimultaneousClassification and Generation
Authors: Mossi Alex , Flouris Kyriakos , Zunkovic Bojan
Published: July 2024

Quantum machine learning (QML) is a rapidly expanding field that merges the principles of quantum computing with the techniques of machine learning. One of the powerful mathematical frameworks in this domain is tensor networks. These networks are used to approximate high-order tensors by contracting tensors with lower ranks. Originally developed for simulating quantum systems, tensor networks have become integral to quantum computing and, by extension, to QML. Their ability to efficiently represent and manipulate complex, high-dimensional data makes them suitable for various machine learning tasks within the quantum realm. Here, we present a matrix product state (MPS) model, where the MPS functions as both a classifier and a generator. The dual functionality of this novel MPS model permits a strategy that enhances the traditional training of supervised MPS models. This framework is inspired by generative adversarial networks and is geared towards generating more realistic samples by reducing outliers. Additionally, our contributions offer insights into the mechanics of tensor network methods for generation tasks. Specifically, we discuss alternative embedding functions and a new sampling method from non-normalized MPSs.
Pharmacy Students' Insights on Assessment Tools and Their Predictive Value in Evaluating Clinical Competencies
Authors: Netere Adeladlew Kassie , Sendekie Ashenafi Kibret , Chanie Gashaw Sisay , Belachew Eyayaw Ashete
Published: July 2024

Introduction:
The assessment of clinical competency models is tailored to evaluate the academic performance and organization capabilities and whether the purpose is formative or summative. Competency-based curriculum for pharmacy students, focusing on core competencies with formative and summative evaluations. This study aimed to explore pharmacy students’ insights on assessment tools and the predictive value in evaluating clinical competencies at the University of Gondar.
Method
A cross-sectional survey was conducted to explore the students' attitudes and insights towards assessment approaches in pharmacy education. Comparative descriptive was used in describing students' perception towards the assessment approaches and instruments utilized in the modularized competency-based curriculum for pharmacy students. A comparative repeated measure was conducted across multiple assessment instruments to evaluate perceived difficulty, fairness, degree of learning and preferred frequency of use, using the Friedman test.
Result
A total of 135 students approached, and 118 participated in this study (response rate 87.4%). About 64%) were regular and fifth-year clerkship undergraduate students. The formative assessment approach was perceived to enhance confidence (59%) and motivation to study (50%). Conversely, more than half (53%) of students reported that question-spotting was their primary strategy for preparing for written exams. Additionally, more than 47% believed that study materials required for written exams were excessive and they felt that module grades often overly rely on single, one-time written exams. Also, many students (68%) agreed with the utilization and objectivity of OSCE in assessing clinical competencies, they rated it as difficult to perform each OSCE.
Conclusion
This survey revealed students prefer formative assessments for improving performance, motivation, and confidence, while summative assessments determine grades through one-time exams. The OSCE is effective yet challenging for clinical competency assessment, guiding the selection of assessment methods and highlighting potential difficulties and acceptability of assessment approaches.
Modeling and Experimental Study of the Force and Surface Topography in Cylindrical Grinding of GH4169
Authors: Li Zhipeng , Zhang Quanli , Wang Bao , Ding Wenfeng , Du Kai , Wang Yongfei
Published: July 2024

GH4169 superalloy is an outstanding material widely used in aerospace, and precision grinding is widely applied to machine GH4169 superalloy, where the grinding force plays essential role on the ground surface quality, and the achieved surface topography. In this paper, the material removal mechanism under different machining parameters was analyzed in cylindrical grinding of GH4169 superalloy. The interaction process between abrasive grain and workpiece material is investigated to establish the simulation model of material removed based on the kinematics. The random wheel topography is constructed statistically using the probability density function that characterizes the distribution of the grains. The simulated grinding force and surface topography are agreement with the experimental results, where both the grinding force and surface roughness increased with decreasing grinding speed, as well as growing feed rate and grinding depth. The simulation model and calculation results could provide certain theoretical basis to explore the material removal mechanisms and improve the achieved surface quality in cylindrical grinding of GH4169 superalloy.
Improving Detectability of Illegal Fishing Activities Across Supply Chains
Authors: Oyanedel Rodrigo , Gelcich Stefan , Milner-Gulland E. J. , Wilcox Chris
Published: July 2024

Improving detectability (i.e., enforcers’ capacity to detect illegal fishing activities) is crucial for fisheries management, food security and livelihoods. Identifying factors associated with higher probabilities of illegal activities and their detection across supply chains are necessary for effective interventions. Here, we developed a Bayesian Hierarchical Model to assess the detectability of illegal fishing activities. We use a large fisheries enforcement dataset from Chile covering all supply chain actors (e.g., fishers, traders, restaurants). Our model allowed evaluation of determinants of detectability and probability of violation across supply chain actors, species, regulations and effort predictors. Our results show an overall detectability rate of illegal fishing activities at 7%, with this rate varying significantly across supply chain actors. Notably, those positioned higher in the supply chain, such as processors and restaurants, which are also those receiving less enforcement effort, exhibit markedly higher detection rates. This study provides relevant management insights to improve detectability of infringements of fisheries regulations in Chile and more broadly. Our approach complements recent technological advances (e.g., satellite monitoring), and can support improved targeting of enforcement across supply chains, particularly in situations where capacity exists, but budgets are constrained. Improving detectability of rules violations is a key component of reducing illegal fishing and promoting compliance.
Mobile money, Entrepreneurship, and Informality
Authors: Malkova Alina
Published: July 2024

This paper investigates the impact of mobile money access on informal businesses in Sub-Saharan Africa. While mobile money can improve business performance and facilitate transactions, it may also enable labor informality. I examine this trade-off by studying solo entrepreneurs and small business owners in Kenya, Nigeria, Tanzania, and Uganda between 2013–2015. Leveraging changes in cellular coverage as a natural experiment, I employ a fuzzy spatial regression discontinuity design to analyze the impact of mobile money on informality rates. Findings suggest that living within GSM coverage areas increases the formalization of employment for entrepreneurs in Kenya and Tanzania but not necessarily in Nigeria. The paper also explores the broader effects of mobile money access. Mobile money is associated with lower poverty levels, particularly for informal entrepreneurs, and promotes higher formalization rates for previously unbanked entrepreneurs. Additionally, the study reveals network effects, where neighbors’ mobile money adoption influences individual entrepreneurs’ formalization decisions.
JEL codes: G21, O17, G51, J62
Influence of Soft Surface Soil on Site Seismic Response in Tianjin Area
Authors: Cao Ying , Gao Wuping , Wang Zhisheng , Yan Chengguo , Yang Fei
Published: July 2024

Soft soil is a prevalent type of special soil, boasting mechanical characteristics like low strength, significant deformation, and heightened sensitivity. Engineering-wise, it often poses hazards such as settlement and uneven subsidence when subjected to seismic forces. Moreover, the substantial impact of soft soil on seismic events has been widely recognized. Investigations into the seismic response characteristics of soft soil primarily concentrate on two key aspects: the presence of soft soil in the form of interlayers and soft soil sites. By conducting a statistical analysis of dynamic parameters pertaining to soft soil in the Tianjin area, this study provides recommended values for such parameters in the area. Subsequently, ten distinct models of soft soil strata with varying layer thickness are established to examine the influence of soft surface soil’s thickness on characteristic parameters of site ground response spectra. This research furnishes valuable insights for seismic design within the study area.
Analysis of the structure and properties of Ni-based alloys on the surface of 42CrMo steel with plasma cladding
Authors: Liu Hongmei , Li Chao , Cao Xiuquan , He Yao , Wang Lin , Li Xinyang
Published: July 2024

As a kind of structural alloy steel, 42CrMo steel is widely used in a variety of mechanical components, especially in high-load conditions. The surface of 42CrMo steel is very unlikely to work properly in lasting bad conditions. The failure is mostly because the steel wears. And sometimes the steel cracks or even breaks. Since it is difficult to repair the steel once it fails, this paper proposes a surface repair method for 42CrMo steel based on plasma cladding technology. Firstly, using plasma cladding technology, Ni-based cladded layers were prepared on the surface of 42CrMo steel. Then, the microstructure, hardness and wear of the cladded layers were observed through specialized equipment. Finally, the effect of process parameters on the temperature of the molten pool was analyzed with the simulation software. It has been found out that the substrate is mainly compose of ferrite and pearlite and the zone affected by heat is mainly composed of coarse grains and strip grains. It has also been found that the cladded layer is composed of-Ni and-(Fe,Ni) phases, which results in a significant increase of hardness, namely from 282 HV0.2 to 516 HV0.2. The average friction coefficients of the substrate and the cladded layer are 0.910 and 0.569, respectively. Correspondingly, the wear amounts are 5.39E-6 mm3·N-1·m-1 and 2.24E-6 mm3·N-1·m-1, and the wear volumes are 3.88E-3 mm3 and 1.61E-3 mm3. This means that the wear resistance of the cladded layer is higher than that of the substrate. With the help of analysis of simulation, it is concluded that the width and depth of the molten pool of the cladded layer grows when the current grows. The width and depth of the molten pool of the cladded layer decreases when the speed gets higher.
Capability of chatbots powered by large language models to support the screening process of scoping reviews: a feasibility study
Authors: Nordmann Kim , Schaller Michael , Sauter Stefanie , Fischer Florian
Published: July 2024

The recent surge in publications increases the screening time required to maintain up-to-date and high-quality literature reviews. One of the most time-consuming phases is the screening of titles and abstracts. With the support of machine learning tools, this process has been semi-automated for systematic reviews, with limited success for scoping reviews. ChatGPT, a large language model, might support scoping review screening with its ability to identify key concepts and themes within texts. We hypothesise that ChatGPT’s performance in abstract screening surpasses that of the semi-automated tool Rayyan, increasing efficiency at acceptable costs while maintaining a low type II error. In our retrospective analysis, ChatGPT 4.0 decided upon 15 306 abstracts, vastly outperforming Rayyan. ChatGPT demonstrated high levels of accuracy (68%), specificity (67%) and sensitivity (88–89%) and a negative predictive value of 99% when compared to human researchers’ decisions. The workload savings were at 64% reasonable costs. Despite the promising results, human oversight remains paramount, as ChatGPT’s decisions resulted in a 11% false negative rate. A hybrid screening approach combining human raters and ChatGPT might ensure accuracy and quality while enhancing efficiency. Further research on ChatGPT’s parameters, the prompts and screening scenarios is necessary in order to validate these results and to develop a standardised approach.
Video Game Addiction, Anxiety, and the Moderating Role of Sociodemographic Factors in Peruvian Adolescents
Authors: Dávila-Arenas José Alfredo , Ramos-Cuzque Roberto Carlos , Reyes-Perez Moises David , Acosta-Enriquez Benicio Gonzalo , Facho-Cornejo Jhoselit Lisset , Arbulu-Castillo Julie Catherine , Fernández-Altamirano Antony Esmit Franco , Cabanillas-Palomino Roxana , Piedra Dina Calonge Calonge De la
Published: July 2024

Background Video game addiction and anxiety are growing concerns among adolescents worldwide, particularly in the context of the COVID-19 pandemic. This study aimed to investigate the relationship between video game addiction and anxiety in Peruvian adolescent students, considering the moderating role of sociodemographic factors. Method The study employed a quantitative, correlational, cross-sectional approach with a sample of 202 students aged 12 to 17 years. The TDV Dependence Test and the Lima Anxiety Scale were used to measure video game addiction and anxiety, respectively. Data analysis included normality tests, Mann-Whitney U tests, Kruskal-Wallis tests, Spearman's rho correlation, and Fisher's confidence intervals. Results The findings revealed a highly significant relationship between video game addiction and anxiety (p < 0.01). However, the confidence intervals suggested that this relationship might be due to chance or may not truly exist in the studied population. High levels of video game addiction (28.2% very high, 13.9% high) and anxiety (24.8% severe, 20.3% moderate) were identified, with males being more affected by addiction and females by anxiety. Abstinence was significantly related to general anxiety and social phobia, while abuse and tolerance were related to agoraphobia. Sociodemographic factors did not moderate the relationship between video game addiction and anxiety (p > 0.05). Conclusion This study provides new evidence on the relationship between video game addiction and anxiety in Peruvian adolescents, highlighting the importance of analyzing confidence intervals for accurate interpretation. The significant relationships found between specific dimensions of video game addiction and anxiety could be theoretically explained by neurobiological, cognitive, and focused internet use models. Preventative and intervention strategies differentiated by gender are recommended to manage abstinence, abuse, tolerance, and associated anxious symptoms in adolescents.
Exosomal miR-146a-5p Derived from HSCs Accelerates Sepsis-induced Liver Injury by Suppressing KLF-4
Authors: Sheng Ziyi , Song Hua , Gao Xianzhi , Shu Bian , You Yu , Liu Zuojin
Published: July 2024

Background
This study aims to investigate whether and how LPS-activated hepatic stellate cells (HSCs) could regulate macrophage activity, as well as to explore the impact of microRNA(miRNA) in exosomes from HSCs in this process.
Methods
Mice subjected to lipopolysaccharide (LPS) or Cecal Ligation and Puncture (CLP) were used to explore sepsis-induced liver injury. Liver injury was evaluated by HE staining, and AST and ALT levels were measured. LPS-Exo or N-LPS-Exo from HSCs were added to hepatic macrophages, and the expression of iNOS, IL-1β, and TNF-α was detected by Western Blotting. miRNA microarray analysis and PCR were used to evaluate differentially expressed miRNAs between LPS-Exo and N-LPS-Exo. Target genes were screened using the TargetScan database and verified by luciferase assays and WB. Inflammation and macrophage activity were observed in vivo by HE and CD86 staining in mice injected with PKH67-labeled LPS-Exo or N-LPS-Exo.
Results
Sepsis-related liver injury activates hepatic stellate cells, which regulate macrophage activity through exosomes. Specifically, exosomal miR-146a-5p secreted by hepatic stellate cells targets KLF-4, regulating the macrophage inflammatory response via the JNK signaling pathway.
Conclusion
Exosomes containing miRNA-146a-5p released from HSCs following LPS treatment may increase macrophage sensitivity to LPS and trigger an inflammatory response. Exosomal miR-146a-5p derived from HSCs accelerates sepsis-induced liver injury by suppressing KLF-4 expression.
Dynamic Response Characteristics Analysis of Biomimetic Foot during Ground Contact Instant
Authors: Song Mengjun , Zhang Liping , Wei Jinggong
Published: July 2024

In this paper, the dynamic substructure method is applied to analyze the touch instant response characteristics of commonly used materials for foot mechanism, and the touch instant of quadrupedal mechanism is taken as an example for corresponding analysis. This paper firstly takes a walking mechanism as the research object, simplifies the contact model, and introduces the dynamic substructure solving method to construct the corresponding numerical model; then the dynamic substructure method is used to solve the transient corresponding characteristics of the leg components under different materials and different loading conditions, and obtain the corresponding transient corresponding characteristics of the collision force, etc. At the same time, the material characteristics are synthesized to analyze the transient response characteristics of different materials for the bionic mechanism, so as to provide a comprehensive analysis for the transient response characteristics of different materials for the bionic mechanism. At the same time, in order to analyze the transient response characteristics of different materials, the material characteristics are synthesized, so as to provide a basis for the study of transient mechanical properties of different materials of the bionic mechanism. Finally, the transient characteristics of foot-ground contact are experimentally verified by a high-speed camera system. Finally, this paper provides some theoretical support and methodological reference for the mitigation of transient response of foot-ground contact.
Enhancement of Long-Horizon Task Planning via Active and Passive Modification in Large Language Model
Authors: Hori Kazuki , Suzuki Kanata , Ogata Tetsuya
Published: July 2024

This study proposes a method for generating complex, long-horizon off-line task plans using large language models (LLMs). Although several studies have been conducted in recent years on robot task planning using LLMs, the planning results are often simple. In the proposed method, the LLM actively collects missing information by asking questions, and the task plan is upgraded with a one-shot dialog example. In addition, the robustness of the task planning is enhanced by using the method that modifies the plan results based on human instructions. The effectiveness of the proposed method is demonstrated through dialogue experiments using a cooking task as the subject. Furthermore, the proposed method can be applied to instructions that include images.
Yangtze finless porpoise sexual behavior object detection algorithm based on YOLOv8
Authors: Chen Huiyan , Ge Panyuanyuan , Hao Yujiang
Published: July 2024

Traditional studies on Yangtze finless porpoise behavior rely on manual observation mainly, posing challenges such as low efficiency, high labor costs and visual fatigue. To address these issues, the Baiji Dolphinarium at the Institute of Hydrobiology, Chinese Academy of Sciences, has deployed a monitoring platform supporting accelerated playback to enhance observation efficiency and conduct researches on Yangtze finless porpoise sexual behaviors leading to reproduction and conservation. The plantform gives a new way to monitor Yangtze finless porpoises` behavior by using computer vision, which means real-time object detection algorithm on recognizing sexual behaviors of Yangtze finless porpoises is essential. However, existing models suffer from imbalance categories problem in finless porpoise sexual behavior dataset, background noise in images, occlusion and overlap sections of Yangtze finless porpoises.. In this paper, we established the first Yangtze Finless Porpoise Sexual Behavior dataset (YFPSB) in artificial rearing environments, consisting of 4900 images in different camera views for reaserchers to train and test new vision algorithms. We also propose an improved method based on YOLOv8 to tackle theseproblems. Specifically, we introduce Expanded Window Multi-Head Self-Attention (EW-MHSA) into the backbone network to enhance the model's spatial awareness. EW-MHSA also lightened origin model and meet the demand of capturing long-distance dependency in the images with 9.7% decrease of the model parameters, smaller than the smallest model in yolov8 series. Through experiments and tests, our model achieves the result of 96.6% mAP, demonstrating its accuracy and potential application in marine ecological monitoring and conservation.
Exploring the Bond, Flexure, and Shear Behaviour of Bundled Bars in Concrete Structures
Authors: Ponni M , Rajkumar Shobha
Published: July 2024

Bundled bars, also known as bundled reinforcement, involve grouping multiple steel bars together to form a single unit, which is then embedded within concrete structures. We aim to comprehensively understand the behavior of bundled bars in reinforced concrete structures. This study investigates the behavior of bundled reinforcement bars in concrete beams, aiming to assess their influence on structural performance. A series of experiments were conducted, including pullout tests on bundled bars and flexural and shear tests on concrete beams with and without stirrups. The study evaluated various parameters such as peak load, displacement, ductility, stiffness, and energy absorption capacity for different bundling configurations. Results indicate that bundling enhances the peak load capacity and energy absorption of beams, with minimal impact on displacement and stiffness. However, beams without stirrups exhibit higher stiffness compared to those with stirrups, suggesting a trade-off between shear capacity and structural rigidity. Overall, the findings provide insights into the behavior of bundled reinforcement bars in concrete structures, informing design practices for enhanced structural performance and resilience. Contrary to IS code recommendations, findings from this study indicate that such recommendations are not essential and could even be counterproductive.
Anesthetic Challenges of Caring for an Adult with Moebius Syndrome: A Case Report
Authors: Vegunta Geetasravya , DO Giovanna Patafio , Gade Swata , Awuku Kwame , Kalyoussef Evelyne , Shah Shridevi Pandya
Published: July 2024

Background Moebius syndrome is a rare congenital disorder with facial nerve palsies and craniofacial malformations, increasing the risk of a difficult airway during anesthesia. We report a difficult airway in an adult with Moebius syndrome during an elective procedure, which is unique as most reports of Moebius syndrome are in children.
Case Presentation Our patient was a 30-year old male diagnosed with Moebius syndrome at birth, and presented with craniofacial and orthopedic malformations, intellectual disability, and facial nerve palsies. Although our patient had prior successful intubations, their airway exam deteriorated from a Mallampati Class II to IV after many surgeries. After fiberoptic intubation with nebulized lidocaine to maintain spontaneous ventilation failed, an attempt with dexmedetomidine and video laryngoscopy was successful.
Conclusion Our case demonstrates the necessity of preparing for a difficult airway and strategies for airway management in an adult with Moebius Syndrome.
A mixed sample data augmentation method based on sparse adversarial attack
Authors: Xu Xiaojun , Zhang Xiaolin , Wang Yongping , Liu Lixin , Xu Enhui
Published: July 2024

To enhance the generalization ability of deep neural networks in image classification tasks, advanced data augmentation strategies have been widely studied. Information deletion guides the model to focus on less discriminative parts by removing regions of the image, but this can lead to the loss of important information from the training samples. Recent studies have explored randomly cutting and mixing patches and their labels between training images. However, this random patch selection strategy may not accurately represent relevant information about the corresponding objects. Using saliency maps to guide patch selection also has drawbacks, as it may not fully align with the model's internal decision-making mechanisms. To solve those problems, we propose a novel data augmentation method, AdvMix.First, AdvMix accurately finds the most sensitive pixels in the decision-making process of the model through sparse adversarial attacks, selects the points as the center, and expands them into sensitive areas. Next, equally important sensitive regions from a patch image are used to replace the corresponding sensitive regions in the original image. The mixed samples generate new images. AdvMix forces the model to find other relevant features when the most discriminative content is modified, thereby enhancing the model's generalization ability. Experimental results across various datasets and image classification models validate that this method outperforms previous data augmentation techniques, significantly improving the performance of classification models.