Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealing the Sound Transmission Loss Capacities of Sandwich Metamaterials with Re-Entrant Negative Poisson’s Ratio Configuration

Due to the influence of mass law, traditional lightweight sandwich structures have struggled to surpass solid structures in sound insulation performance. To this end, we propose an acoustic metamaterial structure with a sandwich configuration based on the re-entrant negative Poisson’s ratio (NPR) structure and systematically investigate its sound transmission loss (STL) performance under incident plane wave conditions. We used the acoustic impedance tube method to experimentally study the sound insulation performance of the re-entrant NPR sandwich structure under free boundary conditions, and then established an acoustic analysis simulation model based on COMSOL Multiphysics software, which verified that the results obtained by the experiment and the numerical simulation were in good agreement. The results show that the sandwich structure exhibits excellent sound transmission loss performance in the studied frequency range (250–4000 Hz), and the overall sound insulation performance exceeds the curve of the mass theorem, basically achieving more than 20 dB when the sandwich thickness is 2 cm. Finally, we conduct parametric studies to establish a correlation between the geometric design of NPR sandwich structures and their sound transmission loss performance. The research shows that the changes of the length of the ribs, the distance from the ribs to the center of the unit, and the length of the upper wall and the lower wall have a significant impact on the sound insulation performance of the re-entrant NPR sandwich structure, while the change of the wall thickness basically will not affect the sound insulation performance of the sandwich structure. This research can provide practical ideas for the engineering application of noise suppression designs of lightweight structures.
Differentially Private Federated Multi-Task Learning Framework for Enhancing Human-to-Virtual Connectivity in Human Digital Twin

Ensuring reliable update and evolution of a virtual twin in human digital twin (HDT) systems depends on any connectivity scheme implemented between such a virtual twin and its physical counterpart. The adopted connectivity scheme must consider HDT-specific requirements including privacy, security, accuracy and the overall connectivity cost. This paper presents a new, secure, privacy-preserving and efficient human-to-virtual twin connectivity scheme for HDT by integrating three key techniques: differential privacy, federated multi-task learning and blockchain. Specifically, we adopt federated multi-task learning, a personalized learning method capable of providing higher accuracy, to capture the impact of heterogeneous environments. Next, we propose a new validation process based on the quality of trained models during the federated multi-task learning process to guarantee accurate and authorized model evolution in the virtual environment. The proposed framework accelerates the learning process without sacrificing accuracy, privacy and communication costs which, we believe, are non-negotiable requirements of HDT networks. Finally, we compare the proposed connectivity scheme with related solutions and show that the proposed scheme can enhance security, privacy and accuracy while reducing the overall connectivity cost.
Association between female circulating heavy metal concentration and abortion: a systematic review and meta-analysis

Objective This study aimed to evaluate the association between blood heavy metal (zinc (Zn), copper (Cu), lead (Pb), and cadmium (Cd)) concentrations and spontaneous abortion (SA) and recurrent pregnancy loss (RPL) and explore the possible endocrine dysfunction associated with it. Methods A literature search was performed in the PubMed, Embase, Cochrane Library, and Web of Science databases up to April 2023. The overall effects were expressed as the standard mean difference (SMD). Subgroup analysis was performed according to the type of abortion (SA or RPL). Stata 16.0 was utilized for data analysis. Results Based on the integrated findings, abortion women showed significantly lower Zn (SMD = −1.05, 95% CI: −1.74 to −0.36, p = 0.003) and Cu concentrations (SMD = −1.42, 95% CI: −1.97 to −0.87, p <0.001) and higher Pb (SMD = 1.47, 95% CI: 0.89–2.05, p <0.001) and Cd concentrations (SMD = 1.15, 95% CI: 0.45–1.85, p = 0.001) than normal pregnant women. Subgroup analysis showed that Zn and Cu deficiency and Cd and Pb exposure were significantly (p <0.05) associated with RPL, whereas Cu deficiency and Cd and Pb exposure were significantly (p <0.05) associated with SA. Conclusion Zn and Cu deficiencies and Pb and Cd exposure were associated with abortion. Endocrine dysfunction, such as insulin resistance, vitamin D insufficiency, and abnormal thyroid and sex hormone concentrations, is thought to be involved in heavy metal-related abortion.
TIPOS DE PESQUISA CIENTÍFICA E A ANÁLISE DE DADOS NO ÂMBITO DA PESQUISA NA ÁREA DA ADMINISTRAÇÃO

Este artigo apresenta uma análise dos principais tipos de pesquisa científica que podem ser empregados na área da administração. Qual é o significado da pesquisa? Qual é a principal estrutura para uma boa pesquisa? Quais elementos devem existir para um bom desenvolvimento da pesquisa? Com o intuito de responder a essas questões, os objetivos são os seguintes: apresentar a concepção de pesquisa, destacar a estrutura básica para uma pesquisa inicial, fomentar a visão de alguns teóricos sobre essa estrutura de pesquisa, explicitar a pesquisa qualitativa e quantitativa, descrever alguns pressupostos da pesquisa organizacional, sendo essa mais direcionada para a área administrativa, apontar a visão de Enise Teixeira sobre pesquisa em estudos organizacionais. Para tentar elucidar as questões norteadoras e os objetivos, optou-se por uma pesquisa de revisão sistemática e bibliográfica. Os teóricos que enriquecem este trabalho são: Garcia (1998), Hartmann (2021), Menezes (2001), Gil (2017), Popper (2004). Foi possível evidenciar a necessidade de conhecer o mais diversificado tipo de pesquisa para melhor contribuir de forma epistêmica na área da administração e em outras áreas também, pois a pesquisa é um manancial de investigação de teoria e prática para o desenvolvimento da vida.
Frequency Adaptive Disturbance Observer for Sensorless Control of a 3 × 3-Phase PMA-SynRM Driven by Mono-Inverter

Sensorless control methods based on flux observer have been widely used in motor drives with medium-high speed operation. However, the conventional methods have some shortcomings in estimation accuracy due to the dc bias caused by the integration, measurement, and parameter uncertainty. In this article, a new frequency adaptive disturbance observer with band-pass filter characteristics is proposed. It is insensitive to dc bias and high-frequency noise, hence improving the robustness of the system. Since the proposed method has no phase delay at any synchronous rotor frequency and can amplify the amplitude of the estimated active flux, it does not need a phase compensation module and can obtain a wider operating range. Moreover, an adaptive extended state observer is added to estimate the disturbance caused by parameter mismatch. The estimated disturbance is fed back to the estimated active flux, which further improves the accuracy of the system. Finally, experiments based on a triple redundant permanent magnet-assisted synchronous reluctance motor are accomplished to verify the effectiveness and accuracy of the proposed observer.
THE MODERATING EFFECT OF ECONOMIC VIABILITY BETWEEN SUSTAINABLE TOURISM DIMENSIONS AND TOURIST SATISFACTION IN ETHIOPIA USING PARTIAL LEAST SQUARES

Visitors' contentment is essential to the development of sustainable tourism and economic development. In order to examine the moderating role of economic value between environmental sustainable tourism, a destination's local prosperity, socio-cultural issues, and the sustainability and satisfaction of tourists, this study was conducted using empirical research design. 382 valid replies for the current investigation were used for further analysis. Overseas travellers were asked to fill the questionnaires at Harar, Gondar, Bahardar and Dire Dawa that are the Ethiopia's most popular tourist destinations. The proposed hypotheses were tested using partial least squares (PLS) after careful screening of the data. Results from this study show that economic ability is an important moderator of local prosperity, environmental sustainability, socio-cultural stability and tourist satisfaction. As a result, the nation's economy must be strengthened in order to provide a healthy environment and a positive visitor experience, both of which appear to be directly linked. La satisfacción de los visitantes es esencial para el desarrollo del turismo sostenible y el desarrollo económico. Para examinar el papel moderador del valor económico entre el turismo ambiental sostenible, la prosperidad local de un destino, los problemas socioculturales y la sostenibilidad y satisfacción de los turistas, este estudio se realizó utilizando un diseño de investigación empírico. Se utilizaron un total de 382 respuestas válidas para la investigación actual para un análisis posterior. Se pidió a los viajeros extranjeros que cumplimentaran los cuestionarios en Harar, Gonder, Bahirdar y Dire Dawa, que son los destinos turísticos más populares de Etiopía. Las hipótesis propuestas se probaron utilizando mínimos cuadrados parciales (PLS) después de una cuidadosa selección de los datos. Los resultados de este estudio muestran que la capacidad económica es un moderador importante de la prosperidad local, la sostenibilidad ambiental, la estabilidad sociocultural y la satisfacción turística. Como resultado, la economía de la nación debe fortalecerse para brindar un ambiente saludable y una experiencia positiva para los visitantes, los cuales parecen estar directamente relacionados.
Exploring the Potential of Lapatinib, Fulvestrant, and Paclitaxel Conjugated with Glycidylated PAMAM G4 Dendrimers for Cancer and Parasite Treatment

Fulvestrant (F), lapatinib (L), and paclitaxel (P) are hydrophobic, anticancer drugs used in the treatment of estrogen receptor (ER) and epidermal growth factor receptor (EGFR)-positive breast cancer. In this study, glycidylated PAMAM G4 dendrimers, substituted with F, L, and/or P and targeting tumor cells, were synthesized and characterized, and their antitumor activity against glioma U-118 MG and non-small cell lung cancer A549 cells was tested comparatively with human non-tumorogenic keratinocytes (HaCaT). All cell lines were ER+ and EGFR+. In addition, the described drugs were tested in the context of antinematode therapy on C. elegans. The results show that the water-soluble conjugates of G4P, G4F, G4L, and G4PFL actively entered the tested cells via endocytosis due to the positive zeta potential (between 13.57–40.29 mV) and the nanoparticle diameter of 99–138 nm. The conjugates of G4P and G4PFL at nanomolar concentrations were the most active, and the least active conjugate was G4F. The tested conjugates inhibited the proliferation of HaCaT and A549 cells; in glioma cells, cytotoxicity was associated mainly with cell damage (mitochondria and membrane transport). The toxicity of the conjugates was proportional to the number of drug residues attached, with the exception of G4L; its action was two- and eight-fold stronger against glioma and keratinocytes, respectively, than the equivalent of lapatinib alone. Unfortunately, non-cancer HaCaT cells were the most sensitive to the tested constructs, which forced a change in the approach to the use of ER and EGFR receptors as a goal in cancer therapy. In vivo studies on C. elegans have shown that all compounds, most notably G4PFL, may be potentially useful in anthelmintic therapy.
Tissue and Circulating MicroRNAs 378 and 142 as Biomarkers of Obesity and Its Treatment Response

Promising approaches to the treatment of obesity include increasing energy expenditure and slowing down fibrogenesis of adipose tissue. The neurotransmitter reuptake inhibitor sibutramine affects appetite and activates lipolysis in a catecholaminergic way. MicroRNAs (miRs) are considered as biomarkers of molecular genetic mechanisms underlying various processes. The profile of a number of miRs is altered in obesity, both in the circulation and in adipose tissue. The aim of this study was to assess the expression levels of miRs (hsa-miR-378a-3p, hsa-miR-142-3p) by real-time polymerase chain reaction in subcutaneous adipose tissue (SAT) and in plasma in patients with different degrees and duration of obesity and during sibutramine therapy. This study included 51 obese patients and 10 healthy subjects with normal weight who formed a control group. The study found that, before treatment, obese patients had no significant difference in the expression level of miR-378 in SAT and plasma compared to the control group, while the expression of miR-142 was significantly decreased in SAT and increased in plasma. A significant elevation in miR-378 expression level was noted in patients with first-degree obesity and duration of less than 10 years, and the decline in miR-142 increased with the duration of obesity. These data indicate a maximal increase in the expression of the adipogenesis inducer miR-378 in the early stages of obesity, a progressive decrease in the expression of the fibrogenesis inhibitor miR-142 in SAT with growth of duration of obesity and the likely presence of antifibrogenic effects of sibutramine realized through miR-142 activation.
MAT-WGCN: Traffic Speed Prediction Using Multi-Head Attention Mechanism and Weighted Adjacency Matrix

Traffic prediction is important in applications such as traffic management, route planning, and traffic flow optimization. Traffic speed prediction is an important part of traffic forecasting, which has always been a challenging problem due to the complexity and dynamics of traffic systems. In order to predict traffic speed more accurately, we propose a traffic speed prediction model based on a multi-head attention mechanism and weighted adjacency matrix: MAT-WGCN. MAT-WGCN first uses GCN to extract the road spatial features in the weighted adjacency matrix, and it uses GRU to extract the correlation between speed and time from the original features. Then, the spatial features extracted by GCN and the temporal features extracted by GRU are fused, and a multi-head attention mechanism is introduced to integrate spatiotemporal features, collect and summarize spatiotemporal road information, and realize traffic speed prediction. In this study, the prediction performance of MAT-WGCN was tested on two real datasets, EXPY-TKY and METR-LA, and compared with the performance of traditional methods such as HA and SVR that do not combine spatial features, as well as T-GCN, A3T-GCN, and newer methods such as GCN and NA-DGRU that combine spatial features. The experimental results demonstrate that MAT-WGCN can capture the temporal and spatial characteristics of road conditions, thus enabling accurate traffic speed predictions. Furthermore, the incorporation of a multi-head attention mechanism significantly enhances the robustness of our model.
Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain–Computer Interface Performance

(1) Background: in the field of motor-imagery brain–computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.
Understanding corporate social responsibility, employee psychological capital and work performance: The perspective of tourism in the emerging economy

Corporate social responsibility (CSR) improves business performance in a significant way (Manzoni & Islam, 2015). This study examines how travel and tourism firms handled their CSR during the COVID-19 pandemic and how it affected employee satisfaction (EST) with corporate responses on employee psychological capital (PsyCap) that was measured through self-efficacy (SEC), hope (HOP), resilience (RES) and optimism (OPT). This study also examines PsyCap’s impact on employee work performance (WPM). An online questionnaire was used to obtain data from Indian travel industry personnel to support the study’s conceptual model. A total of 301 valid and usable responses were obtained for the final analysis, which was analyzed using Statistical Package for the Social Sciences (SPSS) 20 and Analysis of Moment Structures (AMOS) 22 software. The model underwent validation through confirmatory factor analysis (CFA) and structural equations modeling (SEM). CSR strongly affects EST, which positively impacts all four variables of employee PsyCap. A positive association of SEC, HOP, and OPT with WPM has also been found, while the association between RES and WPM is insignificant. These variables explained about 53% (R2 = 53.2) of the variance in work performance. This study offers academia and industry practitioners valuable insights into CSR during natural disasters and health hazards.
Wearable Sensors for Respiration Monitoring: A Review

This paper provides an overview of flexible and wearable respiration sensors with emphasis on their significance in healthcare applications. The paper classifies these sensors based on their operating frequency distinguishing between high-frequency sensors, which operate above 10 MHz, and low-frequency sensors, which operate below this level. The operating principles of breathing sensors as well as the materials and fabrication techniques employed in their design are addressed. The existing research highlights the need for robust and flexible materials to enable the development of reliable and comfortable sensors. Finally, the paper presents potential research directions and proposes research challenges in the field of flexible and wearable respiration sensors. By identifying emerging trends and gaps in knowledge, this review can encourage further advancements and innovation in the rapidly evolving domain of flexible and wearable sensors.
Predictive modelling of moisture movement with soil cover systems for saline/sodic overburden piles

The research presented in this thesis describes the application of the computer modeling program Soil Cover 2000 as a tool for predicting the soil-atmosphere fluxes and associated moisture movement in a variety of soil cover systems. The four systerns examined for this thesis are used to reclaim a saline-sodic shale overburden deposit located at the Syncrude Canada Limited mine site, 40 km North of Fort McMurray, Alberta, Canada. The research represents the second phase of a cover instrumentation and modeling research program. Characterization of the soil cover materials and field responses was carried out during phase one research conducted by Boese (2003) and Meiers (2002). The models \vere made to simulate field conditions by using multi-modal soil-water characteristic curves and hydraulic conductivity functions, and by estimating the growth of the plant species found on the covers. Computed and measured field response patterns for the four cover systems matched reasonably well for a five month period from May 19 to October 22, 2000. The models were then applied ~o predict the field measurements for the same period during 2001. Only two adjustments needed to be made to the model pararneters in order to simulate the 2001 data; namely changing the dominant vegetation (and related growth parameters) and; adjusting the saturated hydraulic conductivity to 1:natch Meiers (2002) field measurements. The calibrated model inputs were used to simulate five cover designs to test their performance during extreme climate conditions. The main objective was to ascertain whether a thinner cover system than the currently recommended cover thickness of 1 m could be effective at the mine. The results indicate that the peat layer is required to minimize the amount of runoff and to decrease the potential for saturated conditions· forming at the base of the cover. For a peat-over-till cover system to work effectively, the peat layer needs to be thicker than 30 em to further reduce the potential for saturated conditions forming at the base. However, thinning the till layer is acceptable sinc:e the results show that a thinner till layer has little impact on the performance of the cover. The overall cover thickness needs to be greater than 60 em to improve plant survival.
Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Introduction Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface. Methods To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model. Results Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed. Discussion The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.