bluuebunny/crossref_metadata_2025 huggingface dataset:
Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach
Authors: Yfantidou Anastasia , Zoka Melpomeni , Stathopoulos Nikolaos , Kokkalidou Martha , Girtsou Stella , Tsoutsos Michail-Christos , Hadjimitsis Diofantos , Kontoes Charalampos
Published: September 2023

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.
An Optimized Arabic Multilabel Text Classification Approach Using Genetic Algorithm and Ensemble Learning
Authors: Alzanin Samah M. , Gumaei Abdu , Haque Md Azimul , Muaad Abdullah Y.
Published: September 2023

Multilabel classification of Arabic text is an important task for understanding and analyzing social media content. It can enable the categorization and monitoring of social media posts, the detection of important events, the identification of trending topics, and the gaining of insights into public opinion and sentiment. However, multilabel classification of Arabic contents can present a certain challenge due to the high dimensionality of the representation and the unique characteristics of the Arabic language. In this paper, an effective approach is proposed for Arabic multilabel classification using a metaheuristic Genetic Algorithm (GA) and ensemble learning. The approach explores the effect of Arabic text representation on classification performance using both Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) methods. Moreover, it compares the performance of ensemble learning methods such as the Extra Trees Classifier (ETC) and Random Forest Classifier (RFC) against a Logistic Regression Classifier (LRC) as a single and ensemble classifier. We evaluate the approach on a new public dataset, namely, the MAWQIF dataset. The MAWQIF is the first multilabel Arabic dataset for target-specific stance detection. The experimental results demonstrate that the proposed approach outperforms the related work on the same dataset, achieving 80.88% for sentiment classification and 68.76% for multilabel tasks in terms of the F1-score metric. In addition, the data augmentation with feature selection improves the F1-score result of the ETC from 65.62% to 68.80%. The study shows the ability of the GA-based feature selection with ensemble learning to improve the classification of multilabel Arabic text.
Uniaxial Testing of Soil–Cement Composites to Obtain Correlations to Be Used in Numerical Modeling
Authors: Jończyk-Szostek Małgorzata , Kanty Piotr , Rybak Jarosław , Saloni Jakub , Trybocka Karolina
Published: September 2023

This paper presents the results of laboratory testing of samples obtained from soil–cement composite columns produced on a real-world test site as part of a research and development project. The introduction presents the motivation of the research and the goals that guided the layout of a research program. The general geotechnical conditions, initially assumed methodology of soil–cement composite sampling, and finally, the methodology of strength tests were presented. In tests conducted with the measurement of the strain-stress path, the strength and stiffness of the material were determined in various modes of the stress–strain path, including unloading/reloading cycles. The test results were presented in the form of graphs presenting soil–cement composite stiffness in the function of material strength and subjected to a short critical discussion against the background of reference samples from composites prepared in laboratory conditions. This allowed for their qualitative and quantitative assessment and the formulation of conclusions and guidelines, concerning the execution of works and especially the potential practical outcomes (benefits for numerical modeling), juxtaposed in the summary. Some prospects and needs for future developments were also formulated on the basis of actual experience.
Research on the Influence of Turbulent Flow Induced by Dunes on AUVs
Authors: Guo Yu , Li Peng , Qin Hongde , Lin Zhongyu , Guo Jiaqi
Published: September 2023

The demand for oceanic resource exploration and development is increasing, and autonomous underwater vehicles (AUVs) have emerged as potential tools for ocean exploration. To meet specific operational requirements, AUVs are required to operate in close proximity to the seafloor. However, complex and changeable seafloor terrains have complex spatiotemporal characteristics, which produce unsteady hydrodynamic interference on AUVs, affecting their safety and stability. In order to investigate the hydrodynamic characteristics of AUVs in turbulent flow fields near seafloor terrains, this study selects a typical dune terrain. Based on the computational fluid dynamics (CFD) method, the flow field around an AUV near the seafloor is simulated, and the flow field is solved using the large eddy simulation (LES) method, calculating the AUV’s hydrodynamic instantaneous and average characteristics, as well as flow field characteristics at different inflow velocities and distances from the seafloor. The results indicate that when the AUV is situated within the turbulent flow field caused by the terrain, its drag, lift, and pitch moment performance present significant fluctuations. Variations in total resistance are primarily caused by variations in pressure resistance. Variations in lift and pitch moment are more sensitive to variations in the flow field structure. The AUV also affects the development of the turbulent flow field near the terrain. When the AUV is in close proximity to the seafloor, it hampers the development of vortices below it. These results will offer guidance for the maneuverability and control of AUVs in practical engineering applications.
Using Kernel Density Estimation in Knowledge Distillation to Construct the Prediction Model for Bipolar Disorder Patients
Authors: Tseng Yu-Shiang , Yang Meng-Han
Published: September 2023

Bipolar disorder is a severe mood disorder and is one of the top 20 causes of disability in the world. Although there have been numerous studies based on machine learning models for the detection of bipolar disorder patients, these works have limitations. This study used a kernel density estimation algorithm to generate distributions of the input data, which can make knowledge distillation work and can improve prediction performances of the machine learning models for bipolar disorder. To the best of our knowledge, this is the first attempt to apply kernel density estimation to knowledge distillation. Another main contribution is that we used medical history information that was readily available from the electronic health record system, trying to improve the limitation of previous studies that needed to use special instruments to collect input data. Furthermore, in view of the fact that most previous studies have sample sizes of less than 1000, we collected tens of thousands of data samples to improve the representativeness of the constructed prediction models. Finally, the generated data distributions helped the decision tree algorithm to select the appropriate branching attributes to construct the prediction models. These branching attributes can be mapped back to specific diseases that are all associated with bipolar disorder.
Circular Orbit Flip Trajectories Generated by E-Sail
Authors: Quarta Alessandro A. , Bassetto Marco , Mengali Giovanni
Published: September 2023

An Electric Solar Wind Sail (E-sail) is a propellantless propulsion concept that extracts momentum from the high-speed solar wind stream to generate thrust. This paper investigates the performance of such a propulsion system in obtaining the transition from a prograde to a retrograde motion. The spacecraft is assumed to initially trace a circular heliocentric orbit of given radius. This particular trajectory, referred to as Circular Orbit Flip Trajectory (COFT), is analyzed in a two-dimensional mission scenario, by exploiting the capability of a medium-high performance E-sail to change the spacecraft angular momentum vector during its motion in the interplanetary space. More precisely, the paper describes a procedure to evaluate the E-sail optimal performance in a set of COFTs, by calculating their minimum flight times as a function of the sail reference propulsive acceleration. It is shown that a two-dimensional COFT can be generated by means of a simple steering law in which the E-sail nominal plane has a nearly fixed attitude with respect to an orbital reference system, for most of the time interval of the interplanetary transfer.
Advancements in Electrical Steels: A Comprehensive Review of Microstructure, Loss Analysis, Magnetic Properties, Alloying Elements, and the Influence of Coatings
Authors: Elgamli Elmazeg , Anayi Fatih
Published: September 2023

Electrical steels play a crucial role in modern electrical devices and power systems due to their exceptional magnetic properties. This comprehensive review delves into the advancements in the field of electrical steels, focusing on key aspects such as microstructure, loss analysis, magnetic properties, alloying elements, and the influence of coatings. The microstructural characteristics of electrical steels are explored in relation to their impact on magnetic behaviour and overall performance. Loss analysis techniques are discussed, highlighting the importance of minimizing energy dissipation in applications. The intricate relationship between magnetic properties and material composition, including the role of alloying elements, is examined to elucidate the mechanisms governing magnetic behaviour. Furthermore, the influence of coatings on the performance of electrical steels is investigated, considering both protection against environmental factors and their impact on magnetic properties. Through a comprehensive synthesis of recent research, this review provides valuable insights into the multifaceted nature of electrical steels and sets the stage for further advancements in their design and application.
Pockels Effect at the Interface between Water and Ti Electrode
Authors: Tanimoto Shiou , Okada Akihiro , Kobayashi Takayoshi , Tokunaga Eiji
Published: September 2023

The Pockels coefficient of interfacial water between bulk water and a Ti electrode was estimated from the electroreflectance spectra (ΔR/R) to be r13≈−150 pm/V as the maximum value of magnitude, which is comparable in magnitude to the largest coefficient for electrode interfacial water, i.e., 200 pm/V for interfacial water on a transparent oxide electrode. This Pockels signal increased by a factor of about ±3 by applying a DC bias voltage of ±1 V. The reflectance (R) of the Ti electrode had a dip structure in the UV region (3.5–4.5 eV) due to the interference of a 14 nm thick surface oxide film, and the ΔR/R spectra in aqueous electrolyte solution showed a large reflectance change in the UV region with a dispersive shape due to the contribution of the TiO2 film. The reproducibility of the electroreflectance experiment was high, suggesting that the surface oxide film contributes to the large Pockels effect of interfacial water and the robustness of the electrode.
A Purely Entity-Based Semantic Search Approach for Document Retrieval
Authors: Sidi Mohamed Lemine , Gunal Serkan
Published: September 2023

Over the past decade, knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purposes, the problem of how to effectively leverage entity-based representation still needs to be resolved. This paper proposes a Purely Entity-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method and (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach, which is tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method on the Robust collection, for the queries whose terms are all annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.00). Also, it is shown that using PESS4IR alongside another document retrieval method would improve performance, unless that method alone achieves the maximum nDCG@5 score for those highly annotated queries.
Chirped-Pulse Amplification in an Echo-Enabled Harmonic-Generation Free-Electron Laser
Authors: Zeng Li , Wang Xiaofan , Liang Yifan , Yi Huaiqian , Zhang Weiqing , Yang Xueming
Published: September 2023

The field of ultrafast science has experienced significant growth over the last decade, largely attributed to advancements in optical and laser technologies such as chirped-pulse amplification and high-harmonic generation. The distinctive characteristics of intense ultrafast free-electron lasers (FELs) have introduced novel prospects for investigating molecular dynamics, as well as providing an opportunity to gain deeper insights into nonlinear processes in materials. Therefore, high-power ultrafast FELs can be widely used for both fundamental research and practical applications. This study presents a novel approach for producing high-power femtosecond FEL pulses, utilizing chirped-pulse amplification in echo-enabled harmonic generation. Chirped seed pulses are employed to induce frequency-chirped energy modulation in the electron beam. The generated FEL pulse, which inherits the chirped frequency, can be compressed through the gratings in the off-plane mount geometry to provide ultraintense ultrafast pulses. The numerical modeling results indicate that peak power exceeding 20 GW and a pulse duration in the order of several femtoseconds can be achieved.
Structural Assessment of Endodontic Files via Finite Element Analysis
Authors: Kökan Eyüp Can , Atik Abdulkadir Yasin , Özüpek Şebnem , Podnos Evgeny
Published: September 2023

A methodology for the structural assessment of Nickel-Titanium (Ni-Ti) endodontic files and a novel approach to predict their fatigue behavior using finite element method (FEM) were proposed. ProTaper-Universal F1 and F2 endodontic files were selected due to availability of extensive test data needed for the validation of the methodology. Bending and torsional loadings were analyzed since these provide essential data for the structural integrity assessment for the endodontic files. High-definition FEM models and their computationally efficient idealized versions were developed. The results for the bending and torsional stiffness of the F1 endodontic file agreed with the literature data validating the proposed methodology. Hysteresis energy density was shown to give promising results as a predictor of low cycle fatigue failure. The predictions with the idealized models matched those of the high-definition models, justifying the proposed idealizations. The validated models demonstrated that F2 has 60% higher bending and torsion resistance and 7% higher hysteresis energy density per cycle with respect to F1, leading to the conclusion that F1 has a lower structural stiffness but a longer fatigue life as compared to F2. In summary, the developed methodology allows for the structural and durability evaluation of various design parameters for Ni-Ti endodontic files.
A Fracture Model for Dynamic Sliding Safety Evaluation of a Concrete Dam Subjected to Seismic Excitation
Authors: Lee Jeeho , Lim Hyeong Seok
Published: September 2023

The Sliding Safety Factor (SSF) is a crucial criterion for the sliding stability evaluation of concrete dam structures. A concrete gravity dam subjected to strong earthquakes undergoes progressive fractures, in addition to pre-existing fractures, at the dam–foundation interface, which causes a reduction in the shear strength against sliding. In this study, a new SSF is suggested to take account of the progressive fractured area at the dam–foundation interface. A contact and sliding model for the dam–foundation system is also suggested to compute the dynamically varying normal forces and sliding motions for the suggested SSF. To investigate the effect of the progressively fractured area on the sliding safety evaluation, the conventional, improved, and newly suggested SSFs are compared using the dynamic seismic analysis results of a concrete gravity dam. The conventional formulation of the SSF, in which the fractured area is not represented, yields extremely overestimated sliding safety judgements when a dam is subjected to strong earthquakes. On the other hand, the newly suggested SSF with the proposed contact–sliding model provides more realistic and conservative sliding safety evaluation results than the others.
Who Cares about the Weather? Inferring Weather Conditions for Weather-Aware Object Detection in Thermal Images
Authors: Johansen Anders Skaarup , Nasrollahi Kamal , Escalera Sergio , Moeslund Thomas B.
Published: September 2023

Deployments of real-world object detection systems often experience a degradation in performance over time due to concept drift. Systems that leverage thermal cameras are especially susceptible because the respective thermal signatures of objects and their surroundings are highly sensitive to environmental changes. In this study, two types of weather-aware latent conditioning methods are investigated. The proposed method aims to guide two object detectors, (YOLOv5 and Deformable DETR) to become weather-aware. This is achieved by leveraging an auxiliary branch that predicts weather-related information while conditioning intermediate layers of the object detector. While the conditioning methods proposed do not directly improve the accuracy of baseline detectors, it can be observed that conditioned networks manage to extract a weather-related signal from the thermal images, thus resulting in a decreased miss rate at the cost of increased false positives. The extracted signal appears noisy and is thus challenging to regress accurately. This is most likely a result of the qualitative nature of the thermal sensor; thus, further work is needed to identify an ideal method for optimizing the conditioning branch, as well as to further improve the accuracy of the system.
Distribution Characteristics of Meteor Angle of Arrival in Mohe and Wuhan, China
Authors: Du Xiaoyong , Yin Wenjie , Du Zhitao , Zhou Yufeng , Feng Jian , Xu Bin , Xu Tong , Deng Zhongxin , Zhao Zhengyu , Zhang Yuqiang , Zhou Chen , Zhu Jiawei , Liu Yi
Published: September 2023

Meteor radar is one of the key tools for studying the atmospheric dynamics in the mesosphere and lower thermosphere. The physical parameters obtained by meteor radar inversion can provide important statistical information for research. The daily and annual variations in meteor azimuth distribution detected by meteor radars contain information about meteor source regions and patterns related to the rotation and revolution of the Earth. Using the meteor parameters from two meteor radars located in Mohe (53.5° N, 122.3° E) and Wuhan (30.6° N, 114.4° E), this study calculates the variation patterns in the meteor azimuth distribution over the two sites over 1 year. Additionally, this study introduces the variable, Max_Azi, to describe the position of the peak of azimuth distribution. The peak value of azimuth distribution is calculated by Gaussian fitting to quantify the variation patterns in azimuth distribution. This study provides complementary information on the azimuth distribution in high and middle latitudes. The results indicated that the azimuth distribution variation for the Mohe meteor radar is consistent with the Earth’s revolution model.
Necessary and Sufficient Conditions for Commutator of the Calderón–Zygmund Operator on Mixed-Norm Herz-Slice Spaces
Authors: Zhang Lihua , Zhou Jiang
Published: September 2023

We obtain the separability of mixed-norm Herz-slice spaces, establish a weak convergence on mixed-norm Herz-slice spaces, and get the boundedness of the Calderón–Zygmund operator T on mixed-norm Herz-slice spaces. Moreover, we get the necessary and sufficient conditions for the boundedness of the commutator [b,T] on mixed-norm Herz-slice spaces, where b is a locally integrable function.
Investigation of the Oscillatory Properties of Solutions of Differential Equations Using Kneser-Type Criteria
Authors: Alnafisah Yousef , Moaaz Osama
Published: September 2023

This study investigates the oscillatory properties of a fourth-order delay functional differential equation. This study’s methodology is built around two key tenets. First, we propose optimized relationships between the solution and its derivatives by making use of some improved monotonic features. By using a comparison technique to connect the oscillation of the studied equation with some second-order equations, the second aspect takes advantage of the significant progress made in the study of the oscillation of second-order equations. Numerous applications of functional differential equations of the neutral type served as the inspiration for the study of a subclass of these equations.
Dynamics of a Prey–Predator Model with Group Defense for Prey, Cooperative Hunting for Predator, and Lévy Jump
Authors: Chen Hengfei , Liu Ming , Xu Xiaofeng
Published: September 2023

A stochastic predator–prey system with group cooperative behavior, white noise, and Lévy noise is considered. In group cooperation, we introduce the Holling IV interaction term to reflect group defense of prey, and cooperative hunting to reflect group attack of predator. Firstly, it is proved that the system has a globally unique positive solution. Secondly, we obtain the conditions of persistence and extinction of the system in the sense of time average. Under the condition that the environment does not change dramatically, the intensity of cooperative hunting and group defense needs to meet certain conditions to make both predators and preys persist. In addition, considering the system without Lévy jump, it is proved that the system has a stationary distribution. Finally, the validity of the theoretical results is verified by numerical simulation.
Calculation of Thermodynamic Quantities of 1D Ising Model with Mixed Spin-(s,(2t − 1)/2) by Means of Transfer Matrix
Authors: Akın Hasan
Published: September 2023

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with the nearest neighbors and the external magnetic field. We establish the partition function of the model using the transfer matrix. We compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the iterative equations associated with the model, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.
Effectiveness of Systemic Insecticide Dog Treatment for the Control of Chagas Disease in the Tropics
Authors: Fiatsonu Edem , Deka Aniruddha , Ndeffo-Mbah Martial L.
Published: September 2023

Chagas disease, caused by Trypanosoma cruzi and transmitted by triatomines, can lead to severe cardiac issues and mortality in many mammals. Recent studies have shown that systemic insecticide treatment of dogs is highly effective in killing triatomines. Here, we assessed the impact of dog treatment on T. cruzi transmission. We developed a mathematical model of T. cruzi transmission among triatomines, dogs, humans, and rodents. We used the model to evaluate the impact of dog treatment regimens on T. cruzi transmission dynamics to determine their effectiveness in reducing T. cruzi infection among hosts. We show that a 3-month treatment regimen may reduce T. cruzi incidence among humans by 59–80% in a high transmission setting, and 26–82% in a low transmission setting. An annual treatment may reduce incidence among humans by 49–74% in a high transmission setting, and by 11–76% in a low transmission setting. However, dog treatment may substantially increase T. cruzi prevalence among dogs if dog consumption of dead triatomines increases. Our model indicates that dog treatment may reduce T. cruzi infections among humans, but it may increase infections in dogs. Therefore, a holistic approach targeting different hosts is necessary for Chagas elimination.
Pharmacology of Adenosine Receptors: Recent Advancements
Authors: Vincenzi Fabrizio , Pasquini Silvia , Contri Chiara , Cappello Martina , Nigro Manuela , Travagli Alessia , Merighi Stefania , Gessi Stefania , Borea Pier Andrea , Varani Katia
Published: September 2023

Adenosine receptors (ARs) are widely acknowledged pharmacological targets yet are still underutilized in clinical practice. Their ubiquitous distribution in almost all cells and tissues of the body makes them, on the one hand, excellent candidates for numerous diseases, and on the other hand, intrinsically challenging to exploit selectively and in a site-specific manner. This review endeavors to comprehensively depict the substantial advancements witnessed in recent years concerning the development of drugs that modulate ARs. Through preclinical and clinical research, it has become evident that the modulation of ARs holds promise for the treatment of numerous diseases, including central nervous system disorders, cardiovascular and metabolic conditions, inflammatory and autoimmune diseases, and cancer. The latest studies discussed herein shed light on novel mechanisms through which ARs exert control over pathophysiological states. They also introduce new ligands and innovative strategies for receptor activation, presenting compelling evidence of efficacy along with the implicated signaling pathways. Collectively, these emerging insights underscore a promising trajectory toward harnessing the therapeutic potential of these multifaceted targets.
Deep Medullary Vein and MRI Markers Were Related to Cerebral Hemorrhage Subtypes
Authors: Wang Dan , Xiang Yayun , Peng Yuling , Zeng Peng , Zeng Bang , Chai Ying , Li Yongmei
Published: September 2023

Background: To explore the performance of deep medullary vein (DMV) and magnetic resonance imaging (MRI) markers in different intracerebral hemorrhage (ICH) subtypes in patients with cerebral small vessel disease (CSVD). Methods: In total, 232 cases of CSVD with ICH were included in this study. The clinical and image data were retrospectively analyzed. Patients were divided into hypertensive arteriopathy (HTNA)-related ICH, cerebral amyloid angiopathy (CAA)-related ICH, and mixed ICH groups. The DMV score was determined in the cerebral hemisphere contralateral to the ICH. Results: The DMV score was different between the HTNA-related and mixed ICH groups (p < 0.01). The MRI markers and CSVD burden score were significant among the ICH groups (p < 0.05). Compared to mixed ICH, HTNA-related ICH diagnosis was associated with higher deep white matter hyperintensity (DWMH) (OR: 0.452, 95% CI: 0.253–0.809, p < 0.05) and high-degree perivascular space (PVS) (OR: 0.633, 95% CI: 0.416–0.963, p < 0.05), and CAA-related ICH diagnosis was associated with increased age (OR: 1.074; 95% CI: 1.028–1.122, p = 0.001). The DMV score correlated with cerebral microbleed (CMB), PVS, DWMH, periventricular white matter hyperintensity (PWMH), and CSVD burden score (p < 0.05) but not with lacuna (p > 0.05). Age was an independent risk factor for the severity of DMV score (OR: 1.052; 95% CI: 0.026–0.076, p < 0.001). Conclusion: DMV scores, CSVD markers, and CSVD burden scores were associated with different subtypes of ICH. In addition, DMV scores were associated with the severity of CSVD and CSVD markers.
Data Mining of Microarray Datasets in Translational Neuroscience
Authors: O’Connor Lance M. , O’Connor Blake A. , Zeng Jialiu , Lo Chih Hung
Published: September 2023

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.
Personal Workplace Relationships: Unifying an Understudied Area of Organizational and Personal Life
Authors: Chory Rebecca M. , Horan Sean M.
Published: September 2023

Relationships that cross the work–life domain have long been of interest to scholars in multiple disciplines, including Communication, Management, and Psychology. Close relationships that span work–life borders are called personal workplace relationships. Personal workplace relationships are voluntary informal relationships between two members of the same organization. These relationships are mutual and consensual and have a relatively strong emotional component. They involve the partners knowing and communicating with each other as unique individuals. The goal of this Special Issue (“Personal Workplace Relationships: Implications for Work and Life in a Rapidly Changing Society”) is to explore this specific form of work–life intersection. To that end, we present the scholarly work of researchers from diverse backgrounds who share the goal of better understanding workplace relationships. In this opening essay, we describe how we began to study this area, we preview the articles in this Special Issue, and we conclude with recommendations for future research on personal workplace relationships.
Shyness and Socio-Emotional Adjustment among Young Chinese Children: The Moderating Role of Screen Time
Authors: Zhu Jingjing , McVarnock Alicia , Polakova Laura , Xiang Shuhui , Li Yan , Coplan Robert J.
Published: September 2023

The primary aim of the present study was to examine the potential moderating role of screen time in the links between shyness and indices of socio-emotional adjustment in young Chinese children. Participants were N = 211 children (112 boys, 99 girls) ages 43–66 months (M = 58.84 months, SD = 5.32) recruited from two public kindergartens in Shanghai, People’s Republic of China. Mothers completed assessments of children’s shyness and screen time, and both mothers and teachers completed measures of indices of children’s socio-emotional functioning (prosocial, internalizing problems, learning problems). Among the results, shyness was positively associated with internalizing problems and negatively associated with prosocial behavior, whereas screen time was positively associated with internalizing problems. However, several significant shyness × screen time interaction effects were observed. The pattern of these results consistently revealed that at higher levels of screen time, links between shyness and indices of socio-emotional difficulties were exacerbated. Results are discussed in terms of the implications of shyness and screen time in early childhood.
Electroencephalogram-Based Subject Matching Learning (ESML): A Deep Learning Framework on Electroencephalogram-Based Biometrics and Task Identification
Authors: Xu Jin , Zhou Erqiang , Qin Zhen , Bi Ting , Qin Zhiguang
Published: September 2023

An EEG signal (Electroencephalogram) is a bioelectric phenomenon reflecting human brain activities. In this paper, we propose a novel deep learning framework ESML (EEG-based Subject Matching Learning) using raw EEG signals to learn latent representations for EEG-based user identification and tack classification. ESML consists of two parts: one is the ESML1 model via an LSTM-based method for EEG-user linking, and one is the ESML2 model via a CNN-based method for EEG-task linking. The new model ESML is simple, but effective and efficient. It does not require any restrictions for EEG data collection on motions and thinking for users, and it does not need any EEG preprocessing operations, such as EEG denoising and feature extraction. The experiments were conducted on three public datasets and the results show that ESML performs the best and achieves significant performance improvement when compared to baseline methods (i.e., SVM, LDA, NN, DTS, Bayesian, AdaBoost and MLP). The ESML1 model provided the best precision at 96% with 109 users and the ESML2 model achieved 99% precision at 3-Class task classification. These experimental results provide direct evidence that EEG signals can be used for user identification and task classification.
Distribution Path Optimization of Fresh Products in Cold Storage Considering Green Costs
Authors: Feng Qun , Zhao Ge , Li Wenjing , Shi Xuejun
Published: September 2023

With the continuous improvement of people’s income level and consumption level, the demand for fresh products is driven by the strong demand, and at the same time, low-carbon and green development puts forward new requirements for the cold chain logistics industry. Starting from the perspective of considering the green cost, by constructing a distribution path optimization model of fresh cold chain products considering green cost with the optimal total distribution cost as the research objective, energy saving and emission reduction are integrated into the path optimization problem so that we can explore how to protect the environment while realizing the benefits of the enterprise, and the model is solved by using the ant colony algorithm. By observing the cold chain logistics distribution path arrangement before and after optimization, it is found that the fresh cold chain product distribution path optimization considering green cost can effectively reduce the transportation cost, refrigeration cost, carbon emission cost, and cargo damage cost in the distribution process. Under the optimal distribution strategy, the total cost is reduced by about 16.6% compared to the original route, and the environmental cost is reduced while reducing the distribution cost. It shows that this strategy can improve transportation efficiency and customer satisfaction while saving resources and protecting the environment. And this study comprehensively considers the actual operation of logistics enterprises, so this study has a certain significance of reference value for the green transformation of enterprises. It further promotes the sustainable development of the cold chain industry and reduces the distribution costs of cold chain logistics companies. It also provides a certain degree of inspiration and reference for other cold chain logistics companies to realize the unification of economic and environmental benefits in actual operation.
Multiresolution Modeling of a Modular Building Design Process Based on Design Structure Matrix
Authors: Wang Yu , Bian Suping , Dong Ling , Li Hongxian
Published: September 2023

Currently, modular building is undergoing vigorous development in China, and has provided a significant advantage in many fields, but its quality is difficult to guarantee. The key is to improve the design process, shorten the development cycle, and enhance competitiveness. Therefore, it is necessary to establish and optimize the design process of modular buildings. This article was based on the DSM and adopted a parallel design to model the modular building design process. Based on this, the design process was analyzed and optimized to shorten the development cycle and reduce costs. At the same time, considering the differences in attributes between different stages of the modular building design process, a DSM model with different resolutions was constructed based on the content and interrelationships of modular building design. The schedule and cost of the design process were evaluated through simulation, the differences between different resolution models were compared, and a more reliable estimate of the schedule and cost of the design process was obtained. Considering the workload and difficulty of high-resolution modeling, an approximate estimation method based on low-resolution modeling was proposed to alleviate the dependence on high-resolution models and modeling costs to some extent, thereby effectively improving the predictability of the design process and providing necessary reference for product design management personnel.
Investigation on Behaviours along Weak Axes of Steel Beam under Low Velocity Impact Loading: Experimental and Numerical
Authors: Nasery Mohammad Manzoor
Published: September 2023

This study examines the behaviors of structural members in their most vulnerable state (fixed along their weak axes) against sudden loads such as impact loads. To do so, eight supported rectangular hollow steel beam samples were constructed, and a circular head hammer weighing 75 kg was dropped as a free fall along the weak axes of these beams. Each fall had a different drop height (ranging from 250 mm to 2000 mm) and different amounts of energy (ranging from 183 J to 1471 J) in order to compare the structural behaviors of the beams against low velocity impacts with different drop heights and energy levels. Additionally, finite element analyses were conducted to investigate the performances of the rectangular hollow steel beams against impact loading along the weak axes numerically as well as experimentally. Initially, six model verification and mesh convergence analyses were performed to determine the optimum mesh size. After that, eight finite element models were developed with verified data in order to calculate the displacements, accelerations, plastic denting values, distributed stresses, and plastic equivalent strains. Consequently, impact load factors for static analysis were calculated mathematically and compared with the experimental and numerical results. Overall, the results obtained from the FE analyses were in line with the results obtained from experimental and mathematical studies. To sum up, the increases in the drop height of the impact force and the amount of energy in beams placed along their weak axes will increase the amounts of acceleration, maximum deflection, internal stress, residual displacement, and plastic denting in such beams.
Antitumor Activity of Metformin Combined with Locoregional Therapy for Liver Cancer: Evidence and Future Directions
Authors: Choksi Eshani J. , Elsayed Mohammad , Kokabi Nima
Published: September 2023

This article aimed to examine the effect of metformin use on improving outcomes after liver-directed therapy in patients with HCC and identify future directions with the adjuvant use of and potential therapeutic agents that operate on similar mechanistic pathways. Databases were queried to identify pertinent articles on metformin’s use as an anti-cancer agent in HCC. Eleven studies were included, with five pre-clinical and six clinical studies. The mean overall survival (OS) and progression-free survival were both higher in the locoregional therapy (LRT) + metformin-treated groups. The outcome variables, including local tumor recurrence rate, reduction in HCC tumor growth and size, tumor growth, proliferation, migration and invasion of HCC cells, HCC cell apoptosis, DNA damage, and cell cycle arrest, showed favorable outcomes in the LRT + metformin-treated groups compared with LRT alone. This systemic review provides a strong signal that metformin use can improve the tumor response after locoregional therapy. Well-controlled prospective trials will be needed to elucidate the potential antitumor effects of metformin and other mTOR inhibitors.
Update in Immunotherapy for Advanced Non-Small Cell Lung Cancer: Optimizing Treatment Sequencing and Identifying the Best Choices
Authors: Roque Katia , Ruiz Rossana , Mas Luis , Pozza Daniel Humberto , Vancini Marina , Silva Júnior José Antônio , de Mello Ramon Andrade
Published: September 2023

The introduction of immunotherapy has brought about a paradigm shift in the management of advanced non-small cell lung cancer (NSCLC). It has not only significantly improved the prognosis of patients but has also become a cornerstone of treatment, particularly in those without oncogenic driver mutations. Immune checkpoint inhibitors (ICIs) play a crucial role in the treatment of lung cancer and can be classified into two main groups: Anti-cytotoxic T lymphocyte antigen-4 (Anti-CTLA-4) and anti-T-cell receptor programmed cell death-1 or its ligand (Anti-PD-1 and Anti-PD-L1). Certainly, the landscape of approved first line immunotherapeutic approaches has expanded to encompass monotherapy, immunotherapy-exclusive protocols, and combinations with chemotherapy. The complexity of decision-making in this realm arises due to the absence of direct prospective comparisons. However, a thorough analysis of the long-term efficacy and safety data derived from pivotal clinical trials can offer valuable insights into optimizing treatment for different patient subsets. Moreover, ongoing research is investigating emerging biomarkers and innovative therapeutic strategies that could potentially refine the current treatment approach even further. In this comprehensive review, our aim is to highlight the latest advances in immunotherapy for advanced NSCLC, including the mechanisms of action, efficacy, safety profiles, and clinical significance of ICI.
Salvage High-Dose-Rate Interventional Radiotherapy (Brachytherapy) Combined with Surgery for Regionally Relapsed Head and Neck Cancers
Authors: Soror Tamer , Paul Justina , Melchert Corinna , Idel Christian , Rades Dirk , Bruchhage Karl-Ludwig , Kovács György , Leichtle Anke
Published: September 2023

(1) Background: to report on the use of high-dose-rate (HDR) interventional radiotherapy (brachytherapy, IRT) as a salvage treatment for patients with regionally relapsed head and neck cancers. (2) Methods: A retrospective study of 60 patients treated with HDR-IRT for loco-regionally relapsed head and neck cancers at our institution (2016–2020). Treatment procedure, results, and related toxicities were collected. Local and overall survival outcomes were analyzed. (3) Results: The median follow-up was 22.4 months. Twenty-nine (48.3%) patients had locoregional recurrences with a median time of 28.9 months. The local-recurrence free-survival was 88.1% and 37.3% at 3 years and 5 years. At the last follow-up, 21 patients were alive and the median time to death was 24 months. The overall survival was 39.2% and 16.6% at 3 years and 5 years. Collectively, there were 28 events of grade ≥ 3 late toxicities recorded in 21 patients (35%). (4) Conclusions: Salvage HDR-IRT combined with surgery offers a second-line curative treatment option for regionally relapsed head and neck cancers with acceptable outcomes and toxicities.
Bispecific Antibodies in Hematological Malignancies: A Scoping Review
Authors: Omer Mohamed H. , Shafqat Areez , Ahmad Omar , Alkattan Khaled , Yaqinuddin Ahmed , Damlaj Moussab
Published: September 2023

Bispecific T-cell engagers (BiTEs) and bispecific antibodies (BiAbs) have revolutionized the treatment landscape of hematological malignancies. By directing T cells towards specific tumor antigens, BiTEs and BiAbs facilitate the T-cell-mediated lysis of neoplastic cells. The success of blinatumomab, a CD19xCD3 BiTE, in acute lymphoblastic leukemia spearheaded the expansive development of BiTEs/BiAbs in the context of hematological neoplasms. Nearly a decade later, numerous BiTEs/BiAbs targeting a range of tumor-associated antigens have transpired in the treatment of multiple myeloma, non-Hodgkin’s lymphoma, acute myelogenous leukemia, and acute lymphoblastic leukemia. However, despite their generally favorable safety profiles, particular toxicities such as infections, cytokine release syndrome, myelosuppression, and neurotoxicity after BiAb/BiTE therapy raise valid concerns. Moreover, target antigen loss and the immunosuppressive microenvironment of hematological neoplasms facilitate resistance towards BiTEs/BiAbs. This review aims to highlight the most recent evidence from clinical trials evaluating the safety and efficacy of BiAbs/BiTEs. Additionally, the review will provide mechanistic insights into the limitations of BiAbs whilst outlining practical applications and strategies to overcome these limitations.
The Role of CXCR1, CXCR2, CXCR3, CXCR5, and CXCR6 Ligands in Molecular Cancer Processes and Clinical Aspects of Acute Myeloid Leukemia (AML)
Authors: Korbecki Jan , Kupnicka Patrycja , Barczak Katarzyna , Bosiacki Mateusz , Ziętek Paweł , Chlubek Dariusz , Baranowska-Bosiacka Irena
Published: September 2023

Acute myeloid leukemia (AML) is a type of leukemia known for its unfavorable prognoses, prompting research efforts to discover new therapeutic targets. One area of investigation involves examining extracellular factors, particularly CXC chemokines. While CXCL12 (SDF-1) and its receptor CXCR4 have been extensively studied, research on other CXC chemokine axes in AML is less developed. This study aims to bridge that gap by providing an overview of the significance of CXC chemokines other than CXCL12 (CXCR1, CXCR2, CXCR3, CXCR5, and CXCR6 ligands and CXCL14 and CXCL17) in AML’s oncogenic processes. We explore the roles of all CXC chemokines other than CXCL12, in particular CXCL1 (Gro-α), CXCL8 (IL-8), CXCL10 (IP-10), and CXCL11 (I-TAC) in AML tumor processes, including their impact on AML cell proliferation, bone marrow angiogenesis, interaction with non-leukemic cells like MSCs and osteoblasts, and their clinical relevance. We delve into how they influence prognosis, association with extramedullary AML, induction of chemoresistance, effects on bone marrow microvessel density, and their connection to French–American–British (FAB) classification and FLT3 gene mutations.