Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison and fusion of retrieval schemes based on different structures, similarity measures and weighting schemes

Many retrieval models and techniques can be applied to retrieve theses that are most relevant to certain queries or concepts. It has been found that different retrieval methods often retrieve different sets of relevant documents. It is therefore anticipated that a particular retrieval method will usually retrieve some relevant theses not retrieved by other methods. Therefore in this study, different methods are used in the theses retrieval, based on different thesis structures, different similarity measures and different weighting schemes. The theses used in this study are collected from FSKSM postgraduate library. Many operations have been applied on the collected theses such as digitizing, stop words removal, stemming and building index. The results from these operations are stored in a database. In this study, 85 theses and 30 queries are used. The comparisons between query and theses were made using five similarity measures with seven weighting schemes using different thesis structures. The results show that the use of bibliography gives poorer results compared to the use of title and abstract alone. In the weighting schemes combinations, the results show that weighting schemes using Cosine and Tanimoto perform well individually but did not do well in the combinations and weighting schemes using Forbes and Russell similarity measures do not do well individually but did well in the combination. In the similarity measures combinations, the results show that the best combination was Cosine using LTU weighting scheme with Russell using LOGG weighting scheme using title structure but using abstract structure, the best combination was Cosine using TFIDF weighting scheme with Forbes using ATFA weighting scheme but it has less performance than the combination of Cosine using LTU weighting scheme with Russell using LOGG weighting scheme using title structure. The overall results show that the best thesis structure is title and the best similarity measure is Cosine with LTU weighting scheme.
Fatty Acid Binding Protein 5 (FABP5) Promotes Aggressiveness of Gastric Cancer Through Modulation of Tumor Immunity

Purpose Gastric cancer (GC) is the second most lethal cancer globally and is associated with poor prognosis. Fatty acid-binding proteins (FABPs) can regulate biological properties of carcinoma cells. FABP5 is overexpressed in many types of cancers; however, the role and mechanisms of action of FABP5 in GC remain unclear. In this study, we aimed to evaluate the clinical and biological functions of FABP5 in GC. Materials and Methods We assessed FABP5 expression using immunohistochemical analysis in 79 patients with GC and evaluated its biological functions following in vitro and in vivo ectopic expression. FABP5 targets relevant to GC progression were determined using RNA sequencing (RNA-seq). Results Elevated FABP5 expression was closely associated with poor outcomes, and ectopic expression of FABP5 promoted proliferation, invasion, migration, and carcinogenicity of GC cells, thus suggesting its potential tumor-promoting role in GC. Additionally, RNA-seq analysis indicated that FABP5 activates immune-related pathways, including cytokine-cytokine receptor interaction pathways, interleukin-17 signaling, and tumor necrosis factor signaling, suggesting an important rationale for the possible development of therapies that combine FABP5-targeted drugs with immunotherapeutics. Conclusions These findings highlight the biological mechanisms and clinical implications of FABP5 in GC and suggest its potential as an adverse prognostic factor and/or therapeutic target.
Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.
Research on Evalution Method Based on Improvement of Degree of Grey Slope Incidence

An improvement model on degree of grey slope incidence is developed and analyse its nature.The improved one can reflect the positive and negative relation of sequence.It has some characters,such as symmetry,uniqueness,comparability and non-dimensionalized order preservation.It is found that this method makes better use of the information of datas,and the result of grey incidence analysis by this method is more scientific than before.At the end of this paper,some examples are utilized to prove the effectiveness and practicality of this method.
Observer‐based neural adaptive fixed‐time tracking control for multi‐input multi‐output nonlinear systems with actuator faults

This study developed a new strategy for adaptive fixed‐time output feedback control of multi‐input multi‐output (MIMO) nonlinear systems involving unmeasurable external disturbances and actuator faults. Two actuator faults, loss of effectiveness and lock‐in‐place were simultaneously considered. Furthermore, a composite observer was used in MIMO systems to estimate unmeasurable states and external disturbances simultaneously, and radial basis function neural networks were employed to identify unknown internal nonlinearities. Additionally, the command‐filtered backstepping approach was applied to avoid tedious analytic calculations of the backstepping framework, and a compensation item was constructed to attenuate the filter error. A fault‐tolerant controller was developed to ensure that the overall states of the controlled system were practically fixed‐time bounded while the tracking error was regulated to the equilibrium region having a fixed‐time convergence ratio. Illustrative studies showed the validity and practicability of the proposed theory.
On Dispersion Compensation for GAW-Based Structural Health Monitoring

Guided acoustic waves (GAW) have proven to be a useful tool for structural health monitoring (SHM). However, the dispersive nature of commonly used Lamb waves compromises the spatial resolution making it difficult to detect small or weakly reflective defects. Here we demonstrate an approach that can compensate for the dispersive effects, allowing advanced algorithms to be used with significantly higher signal-to-noise ratio and spatial resolution. In this paper, the sign coherence factor (SCF) extension of the total focusing method (TFM) algorithm is used. The effectiveness is examined by numerical simulation and experimentally demonstrated by detecting weakly reflective layers with a highly dispersive A0 mode on an aluminum plate, which are not detectable without compensating for the dispersion effects.
Tell me how you feel, I will tell you what you look at: Impact of mood and craving on alcohol attentional bias in binge drinking

Background: Alcohol-related attentional bias (AB) is thought to play a key role in the emergence and maintenance of excessive alcohol use. Recent models suggest that AB, classically considered as a permanent feature in alcohol use disorders, is rather modulated by temporary motivational states. Aims: We explored the influence of current mood and craving on AB in binge drinking, through a mood induction procedure combined with eye-tracking measures of AB. Methods: In Experiment 1, we measured AB (visual probe task with eye-tracking measures) among binge drinkers (n = 48) and light drinkers (n = 32) following positive, negative and neutral mood inductions. Participants reported subjective craving and mood before/after induction. In Experiment 2, we measured AB among the same binge drinkers compared with 29 moderate drinkers following alcohol-related negative, non-alcohol-related negative and neutral mood inductions. Results: In Experiment 1, induced negative mood and group positively predicted subjective craving, which was positively associated with AB. We found no effect of induced positive mood nor a direct mood-AB association. In Experiment 2, the relationships AB presented with both induced negative mood and group were again mediated by craving. Inducing alcohol-related negative mood did not modify the mood-craving association. Conclusions: Alcohol-related AB is not a stable binge drinking characteristic but rather varies according to transient motivational (i.e., craving) and emotional (i.e., negative mood) states. This study provides important insights to better understand AB in subclinical populations and emphasizes the importance of considering motivational and affective states as intercorrelated, to offer multiple ways to reduce excessive alcohol use.
Toiling from the homespace, longing for the workplace: gendered workplace imaginaries in an (in)flexible work scenario

ABSTRACT Flexible work arrangements (FWAs) promise both emancipation and loss: freedom from workplace constraints also strips away protective work-life boundaries. To date, very little is understood about how flexible work shapes workplace imaginaries. Drawing on a sample of 44 managers from a Middle Eastern firm, we explore their evolving internal representations of the workplace and domestic space under (in)flexible conditions. Findings suggest that (in)flexible work arrangements fashion and amplify subjective, organizational, and gender-specific workplace imaginaries. While both genders in this study imagined the workplace as a site of ‘salvation,’ it became a haven from domestic labor for the women, while for the men it provided a crucial extension of individual managerial identity. Such distinctions provide glimpses into how (in)flexible scenarios influence and shape neoliberal workplace imaginaries that may sustain gendered career trajectories and the perpetuation of FWAs as a misleading panacea for individual freedom and happiness in the workplace.
Chaotic and fractal dynamics

A fascinating and timely look at the applications of chaotic dynamics in science and technology, this volume reflects the collected research of the world's foremost physicists, chemists, mathematicians, engineers, and computer scientists. An outgrowth of the EPRI-sponsored International Workshop on Applications of Chaos, this work reveals how the concepts and language of chaos link these disparate fields together . Subjects covered include the dynamics of electrocardiograph data, the instability of conveyor belts, time series modelling, the control of chaos, and more.
Assessment of Dorsiflexion Ability across Tasks in Persons with Subacute SCI after Combined Locomotor Training and Transcutaneous Spinal Stimulation

In people with spinal cord injury (SCI), transcutaneous spinal stimulation (TSS) has an immediate effect on the ability to dorsiflex the ankle, but persistent effects are not known. Furthermore, TSS has been associated with improved walking, increased volitional muscle activation, and decreased spasticity when combined with locomotor training (LT). In this study, the persistent impact of combined LT and TSS on dorsiflexion during the swing phase of walking and a volitional task in participants with SCI is determined. Ten participants with subacute motor-incomplete SCI received 2 weeks of LT alone (wash-in phase), followed by 2 weeks of either LT + TSS (TSS at 50 Hz) or LT + TSSSham (intervention phase). There was no persistent effect of TSS on dorsiflexion during walking and inconsistent effects on the volitional task. There was a strong positive correlation between the dorsiflexor ability for both tasks. There was a moderate effect of 4 weeks of LT on increased dorsiflexion during the task (d = 0.33) and walking (d = 0.34) and a small effect on spasticity (d = −0.2). Combined LT + TSS did not show persistent effects on dorsiflexion ability in people with SCI. Four weeks of locomotor training was associated with increased dorsiflexion across tasks. Improvements in walking observed with TSS may be due to factors other than improved ankle dorsiflexion.
Evaluation of design procedure for steel self-centering moment frames

Self-centering moment-resisting frames (SC-MRFs) have the potential to eliminate structural damage under a design basis earthquake and return to their original vertical position following a major earthquake. The objective of this study is to validate an existing design procedure for SC-MRFs. This study examines the response of a 3, 9, and 20 story SC-MRF subject to one thousand artificially generated design (DBE) and maximum considered (MCE) ground motions. The predicted maximum roof drift, story drift, and connection rotation are compared, in probabilistic terms, to the demand obtained in the nonlinear analyses. This data is used to recommend means to improve the existing design procedure and to generate fragility curves for the structure. The results will be used to develop a reliability-based seismic design procedure for these SC-MRF connection details.
CoBRA without experts: New paradigm for software development effort estimation using COCOMO metrics

Software development effort estimation (SDEE) is a critical activity in developing software. Accurate effort estimation in the early phases of software design life cycle has important effects on the success of software projects. COCOMO (Constructive Cost Model) is a parametric data‐driven SDEE model whose parameters must be calibrated with an organization's local data for accurate estimation. Such data are scarce for most organizations. On the other hand, CoBRA (Cost estimation, Benchmarking, and Risk Assessment) is one of the powerful hybrid methods that need a small number of local historical data for effort estimation. However, data gathering in CoBRA is time‐consuming and costly. To ease the use of CoBRA, in this paper, we design a methodology that extracts CoBRA‐required data from COCOMO datasets. By the proposed method, data collected for COCOMO would be used in CoBRA. Using CoBRA, a more accurate estimation of the required effort would be achieved with fewer number of historical data than what is required to calibrate the COCOMO model. We apply the proposed method on six well‐known public COCOMO datasets and use them in CoBRA. Obtained results depict an increase in the accuracy of estimations in comparison with other existing methods.
Shining the Light on Astrocytic Ensembles

While neurons have traditionally been considered the primary players in information processing, the role of astrocytes in this mechanism has largely been overlooked due to experimental constraints. In this review, we propose that astrocytic ensembles are active working groups that contribute significantly to animal conduct and suggest that studying the maps of these ensembles in conjunction with neurons is crucial for a more comprehensive understanding of behavior. We also discuss available methods for studying astrocytes and argue that these ensembles, complementarily with neurons, code and integrate complex behaviors, potentially specializing in concrete functions.
Investigation of AFGSM Methods for Some Electromagnetic Bandgap Structures: From Analysis to Applied Antenna Design

Electromagnetic Bandgap (EBG) structures that exhibit various performances, such as preventing the electromagnetic wave propagation and reflecting an incident wave within the stopband, have unique electromagnetic characterization. Due to this reason, accurate analysis and design of the EBG structures are crucial to enhance the integrated system performance. This paper concentrates on the characterization of some planar EBGs using the Auxiliary Functions of Generalized Scattering Matrix (AFGSM) methods, with particular importance on an in- depth consideration of its bandgaps. The AFGSM method is applied to the planar EBG structures in the literature for the first time. The well-known kinds (symmetric and asymmetric cases) of mushroom type and multilayer EBG structures are considered to verify the presented method. Analysis results are compared with the Conventional Eigenvalue Equation (C-EIV) and the Generalized Scattering Matrix based Eigenvalue Equation (GSM-EIV) methods. Low computation load and accurate results are obtained to analyze the planar EBG structures with the AFGSM method due to using transmission line model. In addition, a design methodology is proposed for a chosen planar EBG structure using the AFGSM methods. Geometrical parameters of interested EBG problems are determined for acquiring the stopband frequency region of interest using the scattering parameters of unit cell configuration. The mushroom EBG model along one and two-dimensional axes is used in an antenna application to decrease mutual coupling between antenna elements. Three different scenarios are simulated in the HFSS electromagnetic simulation design environment to understand the effect of mutual coupling reduction in the antenna problem of the designed EBG structure via the AFGSM method. All designed antennas are manufactured, and the measurement results are in good agreement with the simulation results. The measurement results of the fabricated antenna application example including designed EBG using the proposed AFGSM method are compared with the existing similar problems with the same and different EBG models. It has been demonstrated that bandgap analysis, design of the planar EBG structures and integration of considered EBG model to a design application can be accurately and quickly achieved with the given methodology using the AFGSM method.
Natural Language Processing Approach for Classification of Archetypes Using Text on Business Environments

: Organizations increasingly offer resources to improve performance, minimize costs, and achieve better re-sults. An organization is the individuals who work or provide services in it. Therefore, good organizational performance directly results from the good work of its collaborators. Identifying the archetype in the business environment can combine individuals with companies, which can improve the organizational environment and enhance the development of the individual. A person leaves traces of his behavior in what he produces, such as videos and texts. Some studies point to the possibility of identifying a behavioral profile from a textual production. In this work, we seek to identify the archetype of individuals within the business environment based on their curriculum texts. We combine the behavioral profile assessment (BPA) archetypes (Planner, Analyst, Communicator, and Executor) with 26,636 curriculum to apply machine learning models. For this task, we used classification and regression approaches. The main algorithm used for the approaches was the SVM. The results suggest that the archetypes are better modeled using regression techniques, obtaining an MSE of 4.49 in the best case. We also provide a visual explanation example to understand the model outputs.
Multi-scale Modeling and Numerical Analysis of Thermal Behavior of Cu-Al Interface Structure in Micro/nano Manufacturing

A multi-scale model is proposed for interfacial thermal conduction of Cu-Al interface in this paper. A TTM-MD model is adopted hereby to explore the electron-phonon scattering at the interface of different metals. The simulation results show that the temperature distribution is non-uniform along the interface, and there exist large temperature gradient some places and even sever plastic deformation. It is concluded that the stress near the interface is caused by dissimilarity of material. The present atomic-based multi-scale model is computationally efficient for interfacial thermal conduction on micro/nano scales, and the present theory can be easily extended to other interfacial behaviors of dissimilar materials in micro/nano manufacturing.
Prediction and variable selection in sparse ultrahigh dimensional additive models

The advance in technologies has enabled many fields to collect datasets where the number of covariates (p) tends to be much bigger than the number of observations (n), the so-called ultrahigh dimensionality. In this setting, classical regression methodologies are invalid. There is a great need to develop methods that can explain the variations of the response variable using only a parsimonious set of covariates. In the recent years, there have been significant developments of variable selection procedures. However, these available procedures usually result in the selection of too many false variables. In addition, most of the available procedures are appropriate only when the response variable is linearly associated with the covariates. Motivated by these concerns, we propose another procedure for variable selection in ultrahigh dimensional setting which has the ability to reduce the number of false positive variables. Moreover, this procedure can be applied when the response variable is continuous or binary, and when the response variable is linearly or non-linearly related to the covariates. Inspired by the Least Angle Regression approach, we develope two multi-step algorithms to select variables in sparse ultrahigh dimensional additive models. The variables go through a series of nonlinear dependence evaluation following a Most Significant Regression (MSR) algorithm. In addition, the MSR algorithm is also designed to implement prediction of the response variable. The first algorithm called MSR-continuous (MSRc) is appropriate for a dataset with a response variable that is continuous. Simulation results demonstrate that this algorithm works well. Comparisons with other methods such as greedy-INIS by Fan et al. (2011) and generalized correlation procedure by Hall and Miller (2009) showed that MSRc not only has false positive rate that is significantly less than both methods, but also has accuracy and true positive rate comparable with greedy-INIS. The second algorithm called MSR-binary (MSRb) is appropriate when the response variable is binary. Simulations demonstrate that MSRb is competitive in terms of prediction accuracy and true positive rate, and better than GLMNET in terms of false positive rate. Application of MSRb to real datasets is also presented. In general, MSR algorithm usually selects fewer variables while preserving the accuracy of predictions.