Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Bilateral Cochlear Implantation in Pediatric and Adult Patients with Profound Sensorineural Hearing Loss: A Retrospective Analysis in a Developing European Country

This retrospective study aimed to evaluate the outcomes of bilateral cochlear implantation in patients with severe-to-profound sensorineural hearing loss at the Timisoara Municipal Emergency Clinical Hospital ENT Clinic. The study involved 77 participants, divided into four groups based on their hearing loss characteristics and implantation history. Assessments were conducted pre- and post-implantation, focusing on speech perception, speech production, and reading achievement. Standard surgical procedures were performed, and participants were provided with a comprehensive rehabilitation program involving auditory training and communication therapy. The variables considered for analysis included demographic factors, implantation period, and quality of life assessment, with no statistically significant differences pre-implantation between the four study groups. Results revealed significant improvements in speech perception, speech production, and reading achievement after cochlear implantation. In adult patients, speech perception scores increased from 21.3% to 73.4% for WIPI and from 22.7% to 68.4% for HINT after 12 months of rehabilitation. Speech production scores improved from 33.5% to 76.8% and reading achievement scores increased from 76.2 to 106.3. Moreover, there was a significant improvement in patients’ quality of life following cochlear implantation, with mean scores increasing from 2.0 to 4.2. Although it is known that bilateral cochlear implantation significantly improves speech perception, speech production, reading achievement, and quality of life in patients with severe-to-profound sensorineural hearing loss, this is the first study of its kind from Romania. Further research is warranted to optimize patient selection and rehabilitation strategies to maximize outcomes and determine better policies towards funding and access of cochlear implants for a wider range of patients in need.
From Chip-Based to Chipless RFID Sensors: A Review

Radio frequency identification (RFID) sensors have received increasing attention in recent years due to their wireless battery-free operation, low profile, simplicity, low cost, and multimodality sensitivity. They have found their way to everyday applications, including smart packaging for food quality assurance, early noxious gas detection, smart bandages for chronic wound assessment, and structural health monitoring. This work aims to provide a review of chip-based and chipless RFID sensor technology and to depict a transition from the former to the later one. This is conducted by highlighting existing challenges for implementation of chipless RFIDs, possible solutions to overcome these problems, and recent development introduced in the literature. Then, it has been demonstrated how RFID sensors in conjunction with artificial intelligence and machine learning can be applied for enhancing RFID tag sensitivity and selectivity to a high degree of accuracy. The focus of this article is on chipless RFID sensors, and at the end, we present a direction for future research work.
Speech Enhancement:Using Burg-Based A Priori SNR Estimator to Eliminate Musical Noise Phenomenon

This paper presents a novel a priori SNR estimator for speech enhancement,which is applied to eliminate the musical noise phenomenon remaining in the results enhanced by spectral attenuation noise suppressor.For the purpose to find a proper estimator closest to the ideal one,we have studied the cause of musical noise and its suppression mechanism.With emphasis on estimator proper- ties of smoothness,accuracy and resolution,the burg-based method is built on the research of their relationships and influence on de-noi- sing.The experimental demonstrated that:if the proposed estimator is incorporated into the noise suppressor using minimum mean- square error(MMSE)method or log-spectral minimum mean-square error(LSA-MMSE)method,better results will be achieved with the musical noise greatly eliminated and distortion decreased further.
Terminal sliding mode observer based dynamic surface quasi‐synchronization control of multiple electro‐hydraulic systems under compositive outputs constraint

This paper focuses on the high accuracy synchronous control issue of multiple electro‐hydraulic systems (MEHS). For the parametric uncertainty and external load disturbance of electro‐hydraulic systems (EHS), a type of terminal sliding mode observer (TSMO) is introduced for MEHS in this paper, which able to estimate the parametric uncertainty and external load disturbance in finite time. Then, based on the proposed fast uncertainty estimation strategy, a synchronous controller is designed for MEHS, in which both dynamic surface design and barrier Lyapunov function (BLF) method has been adopted to realize a better tracking performance of MEHS. The feasibility and effectiveness of proposed algorithms in this paper are verified by simulation. Then, a series of comparative experimental results are provided to illustrate the advantages of both disturbance estimation capacity of TSMO and synchronous tracking control performance of proposed control strategy for MEHS.
Semiparametric Analysis of Network Formation

ABSTRACT We consider a statistical model for directed network formation that features both node-specific parameters that capture degree heterogeneity and common parameters that reflect homophily among nodes. The goal is to perform statistical inference on the homophily parameters while treating the node-specific parameters as fixed effects. Jointly estimating all parameters leads to incidental-parameter bias and incorrect inference. As an alternative, we develop an approach based on a sufficient statistic that separates inference on the homophily parameters from estimation of the fixed effects. The estimator is easy to compute and can be applied to both dense and sparse networks, and is shown to have desirable asymptotic properties under sequences of growing networks. We illustrate the improvements of this estimator over maximum likelihood and bias-corrected estimation in a series of numerical experiments. The technique is applied to explain the import and export patterns in a dense network of countries and to estimate a more sparse advice network among attorneys in a corporate law firm.
Reasoning for Public Transportation Systems Planning: Use of Dempster-Shafer Theory of Evidence

Policy-makers of today’s public transportation investment projects engage in debates in which the reasonableness and clarity of their judgment are tested many times. How to recommend the transportation system that achieves project’s goals and different stakeholders’ needs in a most logical and justifiable manner is the main question of this dissertation. This study develops a new decision-making approach, Belief Reasoning method, for evaluating public transportation systems in the planning process. The proposed approach applies a reasoning map to model how experts perceive and reason transportation alternatives to lead to the project’s goals. It applies the belief measures in the Dempster-Shafer theory of evidence as the mathematical mechanism to represent knowledge under uncertainty and ambiguity and to analyze the degree of achievement of stated goals. Three phases are involved in implementing the Belief Reasoning method. First, a set of goals, a set of characteristics of the alternatives, a set of performances and impacts are identified and the reasoning map, which connects the alternatives to the goals through a series of causal relations, is constructed. Second, a knowledge base is developed through interviewing the experts their degree of belief associated with individual premises and relations, and then aggregating the expert opinions. Third, the model is executed and the results are evaluated in three ways: (i) the transportation alternatives are evaluated based on the degree of belief for achieving individual goals; (ii) the integrity of the reasoning process is evaluated based on the measures of uncertainty associated with information used; and (iii) the critical reasoning chains that significantly influence the outcome are determined based on the sensitivity analysis.
“To Be Honest, You Probably Would Have to Read It 50 Times”: Stakeholders Views on Using the Opt-Out Approach for Vaccination in Jails

Abstract Background Despite national guidelines on infectious disease testing and vaccination in prisons, there is heterogeneity on the implementation of these practices in jails. We sought to better understand perspectives on the implementation of opt-out vaccination for infectious diseases in jails by interviewing a broad group of stakeholders involved in infectious diseases vaccination, testing, and treatment in Massachusetts jails. Methods The research team conducted semistructured interviews with people incarcerated in Hampden County Jail (Ludlow, Massachusetts), clinicians working in jail and community settings, corrections administrators, and representatives from public health, government, and industry between July 2021 and March 2022. Results Forty-eight people were interviewed, including 13 people incarcerated at the time of interview. Themes that emerged included the following: misunderstandings of what opt-out means, indifference to the way vaccines are offered, belief that using the opt-out approach will increase the number of individuals who receive vaccination, and that opt-out provides an easy way for vaccine rejection and reluctance to accept vaccination. Conclusions There was a clear divide in stakeholders’ support of the opt-out approach, which was more universally supported by those who work outside of jails compared to those who work within or are incarcerated in jails. Compiling the perspectives of stakeholders inside and outside of jail settings on the opt-out approach to vaccination is the first step to develop feasible and effective strategies for implementing new health policies in jail settings.
Quasar selection from combined radio and optical surveys using neural networks

The application of supervised artificial neural networks (ANNs) for quasar selection is investigated, using the list of candidates and their classification from White et al. (2000). The adopted architectures are 7:1 and 7:2:1, both with seven input parameters - optical and radio data from APM POSS-I (E, O plates) and VLA/FIRST, and a single output interpreted as a quasar probability. Both models were trained on samples of ∼ 800 sources and yielded similar performance on independent test samples, with reliability as large as 90 to 80% for completeness from 70 to 90%. For comparison, the quasar fraction from the original list of candidates was 56%. The accuracy found with ANNs is similar to that obtained by White et al. using oblique decision trees and training samples of similar size. In view of the large degree of overlapping between quasars and nonquasars in parameter space, this performance is probably the best that can be achieved with this database. Predictions of the probabilities for the 98 candidates without spectroscopic classification in White et al. are presented, showing a good agreement between the two ANN models and with the values obtained by White et al. Eight of these sources have recent spectroscopic classification from the NASA Extragalactic Database or from the Sloan Digital Sky Survey Data Release 2 and the classes are consistent with their probabilities, reinforcing the ability of ANNs to optimize the selection of quasars. This work presents the first analysis of the performance of ANNs for quasar selection and it shows that ANNs provide a promising technique to single out specific object types in astronomical databases. An article with the full description of this work has been accepted for publication in Monthly Notices of the Royal Astronomical Society ("Selection of quasar candidates from combined radio and optical survey", Carballo, Cofino and Gonzalez-Serrano. © 2004. The Royal Astronomical Society).
Factor Graph Framework for Smartphone Indoor Localization: Integrating Data-Driven PDR and Wi-Fi RTT/RSS Ranging

The classic fusion localization techniques based on the Kalman filter (KF) framework have been a focus of research community in the past decades, due to the limited computing power of mobile devices. However, with computing-efficient and sensor-rich smartphones now being commonplace, it is convenient and meaningful to provide more accurate positioning services for smartphones in an indoor environment. In this article, we design and develop a tightly coupled (TC) fusion platform of Wi-Fi round-trip time (RTT), received signal strength (RSS), and data-driven pedestrian dead reckoning (DPDR) based on factor graph optimization (FGO) for locating the consumer-grade smartphones in the indoor environment. Compared to the existing PDR solutions, including step model-based approaches and data-driven approaches, the proposed PDR solution with magnetic information (MI) constraint can track the relative position change of pedestrians at 20 Hz while supporting multiple smartphone usage poses. A comprehensive comparison between FGO, KF frame, and its variants is also performed. The experimental results demonstrate that the proposed fusion platform achieves an average positioning accuracy of 0.39 m. In addition, it also improves the accuracy of EFK and adaptive robust KF (ARKF) by 45.83% and 27.78%, respectively. The analysis shows that as the smartphone computing performance continues to improve, the FGO-based sensor fusion gradually replaces the KF frame and its variants.
Conditioning Strategies for Improving Handball Throwing Velocity: A Systematic Review and Meta-Analyses

Ball throwing velocity is essential for scoring goals in handball; the crucial question is how to develop throwing velocity in highly trained handball players. Therefore, this systematic review aims to summarize effective conditioning strategies to improve throwing velocity in elite male players and to perform a meta-analysis on which training system can provide the highest increase in throwing velocity. The literature was analyzed using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) in PubMed, Scopus, and Web of Science. Thirteen studies (sample n = 174) were included: five resistance training studies, one core training study, one study on repeated shuffle sprint training with small-sided games, and one on eccentric overload training. Effect size comparison showed that resistance training is the most effective strategy for improving throwing velocity in elite handball players (d > 0.7). Core training showed a small effect (d = 0.35). Small-sided game (SSG) training showed different results, from a significant positive effect (d = 1.95) to a negative effect (d = –2.03), and eccentric overload training showed a negative effect (d = –0.15). Resistance training is the most effective strategy for improving throwing velocity in elite handball players, while core training and SSGs can improve throwing velocity in youth athletes. Due to the small number of studies focusing on elite handball players, there is a need for more studies on advanced resistance training methods, e.g., contrast, complex, ballistic training, because much greater demands are placed on handball performance assumptions.
Using ChatGPT to navigate ambivalent and contradictory research findings on artificial intelligence

With the rapid development and integration of AI in various domains, understanding the nuances of AI research has become critical for policymakers, researchers, and practitioners. However, the results are vast and diverse and even can be contradictory or ambivalent, presenting a significant challenge for individuals seeking to grasp and synthesize the findings. This perspective paper discusses the ambivalent and contradictory research findings in the literature on artificial intelligence (AI) and explores whether ChatGPT can be used to navigate and make sense of the AI literature.
Scale-Context Perceptive Network for Crowd Counting and Localization in Smart City System

The task of crowd counting and localization is to predict the count and position of people in a crowd, which is a practical and essential subtask in crowd analysis and smart city systems. However, the inherent problems of scale variation and background disturbance restrain their performance. While recent research focus on studying counting and localization independently, a few works are capable of executing both tasks simultaneously. To this end, we propose a scale-context perceptive network (SCPNet) to jointly tackle the crowd counting and localization tasks in a unified framework. Specifically, a scale perceptive (SP) module with a local–global branch schema is designed to capture multiscale information. Meanwhile, a context perceptive (CP) module, by the channel-spatial self-attention mechanism, is derived to suppress the background disturbance. Furthermore, a novel hierarchical scale loss function that combines the Euclidean loss function and structural similarity loss function is designed to prompt the proposed model to fulfill the counting and localization simultaneously. Extensive experiments on challenging crowd data sets prove the superiority of the proposed SCPNet compared with the state-of-the-art competitors in both objective and subjective evaluations.
Responses to Stress among East Malaysian Students: Psychometric Properties of the Responses to Stress Questionnaire

Background Academic stress is part of a student’s life. Chronic stress may result in mental health problems, affecting the adolescent’s well-being in adulthood. However, not all types of stress result in a negative effect. Therefore, understanding how adolescents adapt to academic stress can lay the groundwork for preventive interventions. The Response to Stress Questionnaire (RSQ) for academic problems centred on a multidimensional model of responses to stress. However, it has not been tested among Malaysians. Thus, this study aimed to validate the questionnaire among Malaysians. Methods The questionnaire was translated into the Malay language using forward and backward translation. Data were collected via self-administered questionnaires at a secondary school in Kuching. A validity test was conducted using face and content validation by subject matter experts, and construct validation was performed using exploratory factor analysis (EFA). A reliability test was conducted by checking Cronbach’s alpha. Results Results showed that the questionnaire has good validity and reliability. The EFA resulted in only three dimensions of responses to stress among Malaysian adolescents in contrast to the five dimensions in the original RSQ for academic problems. The Cronbach’s alpha showed good reliability of the questionnaire. Conclusion The questionnaire measuring responses to stress was valid and reliable in assessing the responses of adolescents to academic stress.
To study the mode and mechanism of interaction of Angiopoietin II with receptor tyrosine kinase Tie-2 using molecular mechanics and molecular dynamics approach

BACKGROUD & OBJECTIVE: Angiopoietins are protein growth factors which play key role in Angiogenesis.Angiogenesis is the process of forming blood vessels from pre-existing ones. Angiopoietin-1 (Ang-1) and Angiopoietin-2 (Ang-2) have been identified as ligands of the endothelial receptor tyrosine kinase Tie-2. ANG-2 is a key regulator ofangiogenesis that exerts context-dependent effects on endothelial cell (ECs). ANG-2 binds the endothelial-specificreceptor TIE2 and acts as a negative regulator of ANG-1/TIE2 signaling during angiogenesis, thereby controlling theresponsiveness of ECs to exogenous cytokines. The transmembrane tyrosine kinase TIE-2 and the receptor forangiopoietins have been shown to be involved in angiogenic processes. They are also known to play a role in tumorangiogenesis. However, the mode of interactions between ANG-2 and TIE2 receptor is not known because of theabsence of high resolution co-crystal structure. Therefore in this study attempts were made to investigate the mode andmechanism of molecular interactions between Tie2 with Ang2 using molecular modeling and molecular dynamicsstudies. METHODOLOGY: In the present study, both Tie2 (PDB Id: 2GY5) and Angiopoietins (PDB Id: 2GY7) werefirst prepared using protein preparation wizard (Schrodinger package). Protein-protein interaction between both theproteins was studied using ZDock followed by refinement using Rdock. The best docked pose was then subjected toMolecular dynamics (MD) simulations to study the precise interaction between TIE2 (Receptor) and Angiopoietin-2(Ligand) over a specific time span using AMBER 11.0. The obtained MD trajectories were further used to estimate thebinding free energy of the complex using the molecular mechanics/Poisson Boltzmann surface area (MM-PBSA)method. RESULTS: The binding energy (∆Gbinding) between both the proteins, Tie2 and Ang2 was predicted to be -28.77kcal/mol using Rdock. The other energy parameters between Tie2 and APC interactions such as electrostatic (Eelec), vander Waals (Evdw) and desolvation (Esol) energy are -44.68 kcal/mol, -99.83 kcal/mol and 6.10 kacal/mol respectively,demonstrating modest interactions between them. The interacting surface area between Tie2 and Ang2 is 842:858Å2. CONCLUSION: Results obtained from this study revealed that both Ang2 and Tie2 bind with high affinity with modestinteracting surface area. Further the results guided us in designing specific experiments for biological evaluations.
Experimental investigation on microhardness, surface roughness, and white layer thickness of dry EDM

In this research, the environment-friendly dry electrical discharge machining (EDM) process is investigated to improve the microhardness, surface finish, and white layer thickness of the machined surfaces using graphite-argon gas as a dielectric medium. The graphite powder, mixed with compressed argon gas, has been used to replace the existing dielectric medium in the EDM process. Gas pressure, discharge current, pulse width, and gap voltage were working as input parameters to reduce surface roughness and enhance the microhardness and white layer thickness. The Taguchi L16 orthogonal array is applied to the design and analysis of the experimental results. The minimum surface roughness (2.23 μm) of the HN31 steel has been attained by increasing the gas pressure up to 1.0 MPa and the minimum values of pulse width (40 μs), gap voltage (40 V), and discharge current (6 A). The maximum microhardness (501.04 HV) has been obtained at 1.2 MPa of gas pressure, 120 μs of pulse width, 60 V of gap voltage, and 18 A of discharge current. The maximum white layer thickness (16.24 μm) is achieved by the maximum values of gas pressure (1.2 MPa), pulse width (160 μs), gap voltage (70 V) and discharge current (18 A). The SEM analysis had been done to reveal the white recast layer thickness and surface roughness of the machined surfaces of the dry EDM process. The SR is increased by the recast layer, pores, and microcracks on the machined surfaces. Finally, the multi-criteria optimization technique: Weight Product Method (WPM) is applied to predict optimum process parameter settings: GP: 1.2 MPa, PW: 120 μs, GV: 50 V, and DC: 18 A to meet the best machining performances (MH = 493.32 HV, WLT = 14.28 μm, and SR = 3.82 μm). The validation tests were done to confirm the predicted results obtained by both the Taguchi and WSM methods.