Miracle-dz/UniFAR-FTUs huggingface dataset:
Parent's acceptance: Humanoid-robot intervention for Malay autistic children

Past studies have shown the related research on the acceptance of direct interaction among autistic children and teachers with humanoid-robot on how they accept or not if humanoid-robot is used in the therapeutic sessions. Humanoid robot is used as a mediator without neglecting the involvement of therapists because autistic children are found to be more attracted to respond to the technology rather than to human. The issue arises when there is lack of information on the parental acceptance and which factors influence their approval in accepting humanoid-robot use in their child’s therapy. This study represents an identification of criteria that affect parents’ acceptance if humanoid-robot intervention is used in therapeutic sessions. The finding is then need to produce a conceptual model of parents’ acceptance towards humanoid-robot intervention in therapeutic sessions. This research was held in Seksyen 7, Shah Alam by approaching Malay parents who have autistic children within ages 2 to 9 years old. The research has used Snowball sampling and quantitative method was applied by distributing the questionnaires to 43 parents. It contains 13 questions excluding the demographic profile of the respondents. A theoretical framework of parental acceptance proposed from past research was also adapted and modified in this study. The results of multiple linear regressions stated that learning opportunities and subjective norm were the criteria that affects the parental acceptance with significant value of . 033 and . 049 respectively. It brings significance to the robot system developer in making enhancement to the current robot system for autistic children.
Hi-Loc: Hybrid Indoor Localization via Enhanced 5G NR CSI

Fifth-generation (5G) networks have been massively deployed in commerce. In addition, the new features introduced by 5G networks have been recognized to be beneficial to wireless localization. In this study, we investigate the performance of indoor localization with commercial 5G new radio (NR) signals, and the channel state information (CSI) is used for localization, which is acquired from the synchronization signal block (SSB) of the downlink physical channel. A hybrid indoor localization system named Hi-Loc has been developed. Initially, a feature enhancement module is designed to generate new features from the SSB CSI, and a data construction module is applied to prepare the dataset for subsequent localization algorithms. By introducing the dual-attention mechanism deep network with convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM), a feature-based attention mechanism and a sample-based attention mechanism are designed to extract the implicit spatial and time information of CSI, respectively. Finally, in the online stage, by applying a fully connected neural network (FCNN), the 2-D coordinates are estimated based on the corresponding CSI features. We carry out indoor field tests in the typical office and corridor scenario. The results show that Hi-Loc achieves mean absolute errors of 0.65 and 1.03 m in the internal test cases of the office scenario and the corridor scenario, respectively, and 1.93 and 3.10 m in the external test cases of the office scenario and the corridor scenario, respectively, indicating its superiority over the compared CSI fingerprint-based methods in localization accuracy.
Molecular Structure, Spectroscopic, Quantum Computational, and Molecular Docking Investigations on Propyl Gallate

Abstract Propyl gallate was characterized by utilizing quantum computational investigations using the DFT technique and B3LYP/6-311++G (d, p) basis set using GAUSSIAN 16 W software. The FT-IR, FT-RAMAN, UV-Vis, and NMR chemical shifts of propyl gallate were studied theoretically and experimentally. The estimated vibrational wavenumbers were scaled using a suitable scaling factor after the optimized geometrical parameters were determined. FMO and the HOMO-LUMO energy gap were calculated using the TD-DFT method. NBO and NLO properties were also calculated. The reactive sites were predicted using MEP, ELF, LOL, and RDG. Propyl gallate follows Lipinski's rule and has good drug-likeness, according to the bioactivity evaluation. Molecular docking was performed and the protein-ligand binding properties were discussed and compared to pharmaceuticals already in use against COVID-19, which showed good suppression of SARS-CoV-2. Propyl gallate assay studies were used to predict antibacterial and anti-inflammatory properties. These discoveries could help researchers figure out how to employ the title compound to prevent and cure SARS-CoV-2.
Noncovalent interactions in model molecular clusters containing the tetrel atoms Si and Ge

An MP2 computational study was undertaken for the XF···LiF-TF3CN (X = Li, H, Cl: T = Si, Ge) and LiF-TF3CN···XF (T = Si, Ge; XF = BeF2, BF3, SF2, PH2F, SiH3F, LiF, HF, ClF) series of complexes involving a strongly bound LiF-TF3CN molecular subunit, characterised by a strong bonding motif with an Si-F (or Ge-F) bond perpendicular to the LiF molecule. The effect of attachment of XF, to either the LiF or the CN end of the LiF-TF3CN moiety, was investigated and rationalised with the aid of the natural bond orbital (NBO) analyses and localised molecular orbital-energy decomposition analyses (LMO-EDA). These analyses allow for a comparison of the bonding features in these complexes and the relative strengths of the noncovalent interactions introduced by the model Lewis acids XF. GRAPHICAL ABSTRACT
Cellular Traffic Prediction Using Deep Convolutional Neural Network with Attention Mechanism

Predictive analysis on cellular traffic is important for the control and monitoring of wireless networks. Cellular traffic prediction is a challenging problem due to the non- stationarity and dynamic spatial-temporal correlation of the traffic. In this paper, we address the problem of accurate traffic prediction in a base station by proposing a deep neural network called RAConv. Its structure includes residual network, attention mechanism, and deep convolutional network. In the proposed architecture, a deep 3D residual convolutional network (ResConv3D) with three residual blocks are employed to learn the local spatial-temporal features. An attention-aided convolutional long short-term memory network (AConvLSTM) is then used to capture the long-term spatial-temporal dependencies. The use of the attention modules enable the network to focus on the most important spatial-temporal information. We evaluate the performance of the proposed RAConv network using a dataset provided by a Canadian wireless service provider. We consider the traffic prediction on two time scales (i.e., hourly and daily), which exhibit different spatial-temporal dependency patterns. Experimental results show that the proposed RAConv network can achieve accurate prediction under both time scales. Results also show that our proposed network provides a lower root-mean-square error (RMSE) than the conventional ConvLSTM baseline scheme.
Dysbiotic human oral microbiota alters systemic metabolism via modulation of gut microbiota in germ-free mice

ABSTRACT Background The effect of oral microbiota on the intestinal microbiota has garnered growing attention as a mechanism linking periodontal diseases to systemic diseases. However, the salivary microbiota is diverse and comprises numerous bacteria with a largely similar composition in healthy individuals and periodontitis patients. Aim We explored how health-associated and periodontitis-associated salivary microbiota differently colonized the intestine and their subsequent systemic effects. Methods The salivary microbiota was collected from a healthy individual and a periodontitis patient and gavaged into C57BL/6NJcl[GF] mice. Gut microbial communities, hepatic gene expression profiles, and serum metabolites were analyzed. Results The gut microbial composition was significantly different between periodontitis-associated microbiota-administered (PAO) and health-associated oral microbiota-administered (HAO) mice. The hepatic gene expression profile demonstrated a distinct pattern between the two groups, with higher expression of lipid and glucose metabolism-related genes. Disease-associated metabolites such as 2-hydroxyisobutyric acid and hydroxybenzoic acid were elevated in PAO mice. These metabolites were significantly correlated with characteristic gut microbial taxa in PAO mice. Conversely, health-associated oral microbiota were associated with higher levels of beneficial serum metabolites in HAO mice. Conclusion The multi-omics approach used in this study revealed that periodontitis-associated oral microbiota is associated with the induction of disease phenotype when they colonized the gut of germ-free mice.
Chiral symmetry breaking beyond BCS and theorem on the width of scalars

We review chiral symmetry breaking at the BCS level, in the framework of chiral invariant quark models and in the Schwinger‐Dyson formalism. We revisit the π mass problem beyond the BCS level. We show a theorem on the masses, on the widths and on the qq content of the scalar mesons σ and f0.
Investigating the Roles of the Four Sources of Self-Efficacy Beliefs in an EFL Listening Context

Research in the academic context has revealed the positive roles of self-efficacy in teaching and learning, showing that a thorough understanding of self-efficacy is essential. In this study, the relationships between the four principal sources and the formation of self-efficacy belief proposed by Bandura (1997) are examined in order to further this understanding. Based on this, several empirical studies have attempted to explore these relationships in various fields, but the relevant research appears to have produced insufficient empirical data in the field of language learning to support the theory. Therefore, this study aims to investigate how these sources affect basic self-efficacy (BSSE) and advanced skill self-efficacy (ASSE) classified according to the difficulty of listening tasks in English. As many as 107 Korean university students participated in the study and mediation analysis was employed to examine the relationships. The results show that all four sources act as mediators of BSSE, and all but physiological and emotional states serve as mediators of ASSE. The findings support Bandura’s hypothesis and the pedagogical implications are discussed.
Modulatory Effects of Arctostaphylos uva-urs Extract In Ovo Injected into Broiler Embryos Contaminated by Aflatoxin B1

Simple Summary The poultry industry is of great importance worldwide; however, mycotoxins contamination, especially aflatoxins, has harmful effects on poultry production. Therefore, finding out new strategies to alleviate aflatoxin contamination is an important trend of research. In our study, we focused on the in ovo injection techniques using the methanolic extract of brae beery “Arctostaphylos uva-ursi” (Ar. uu) to modulate the aflatoxinB1 (AFB1) adverse effects in broiler embryos. As a result, 0.1 g of Ar. uu in ovo injection alleviates AFB1 embryotoxicity by enhancing chicks’ physiological responses and optimizing antioxidant status, which is reflected in a low mortality rate and heavier relative weight of the hatched chicks. The in ovo injection of AFB1 + Ar. uu significantly declined AFB1-induced toxicity in embryos and increased broiler chicks embryo’s survival by 62.5%, increased relative embryo weight by ∼65.25% compared to eggs injected with AFB1 alone. Regarding immune responses, the in ovo injection of Ar. uu enhances the embryo’s humoral immune responses and regulates oxidative stress biomarkers. In general, the in ovo injection of Ar. uu modulates broiler chicks’ embryotoxicity caused by AFB1. Abstract In ovo injection of nutrients can modulate the embryo’s physiological responses against aflatoxin B1 (AFB1) embryotoxicity. This hypothesis was tested using in ovo injection of Arctostaphylos uva-ursi (Ar. uu.) methanolic extract. The total polyphenols, total flavonoids, total antioxidant capacity, and GC-MS analysis were all assessed in the Ar. uu. methanolic extract. A total of 180 ten-day-old embryonated eggs were distributed into six groups of 30 replicates each. The first group was used as a control (non-injected), and the second, third, fourth, fifth, and sixth groups were injected with 10 µ double-distilled water (DDW), 500 µL methanol, 0.01 g Ar. uu. /500 µL methanol, 50 ng AFB1/10 µL DDW, and 50 ng AFB1 in 10 µ DDW + 0.01 g Ar. uu. /500 µL methanol, respectively. The relative embryo weight, residual yolk sac weight, tibia length and weight, and survival were recorded. Total and differential leukocytes, oxidative stress, and humoral immune responses were observed. The residual yolk sac was lower (p < 0.05) in the Ar. uu. group than other groups. The embryonic growth (tibia weight and length) was enhanced in AFB1 + Ar. uu. -injected embryos compared with those injected with AFB1 alone. In conclusion, in ovo injection of Arctostaphylos uva-ursi could modulate AFB1-induced toxicity in chicken embryos.
Entrainment to speech prosody influences subsequent sentence comprehension

ABSTRACT Speech processing is subserved by neural oscillations. Through a mechanism termed entrainment, oscillations can maintain speech rhythms beyond speech offset. We here tested whether entrainment affects higher-level language comprehension. We conducted four online experiments on 80 participants each. Our paradigm combined acoustic entrainment to repetitive prosodic contours with subsequent visual presentation of ambiguous target sentences (e.g. “Max sees Tom and Karl laughs”). We aimed to elicit faulty segmentations through the duration of the preceding contour (e.g. the segment “Max sees Tom and Karl” leads to an error at “laughs”). Across experiments, self-paced reading data showed that participants employed the duration of the initial prosodic contour to predict the duration of the upcoming segments. Prosody entrainment may thus serve a predictive function during language comprehension, not only helping the reader to segment the current speech input, but also inducing temporal predictions about upcoming segments.
Exploring the educational needs of patients with systemic vasculitis using the educational needs assessment tool

Abstract Objectives Knowledge and health literacy enable patients to monitor symptoms and disease impact. Educational needs have previously been explored in rheumatology, but scarcely for patients with ANCA-associated vasculitis (AAV). The aim of the study was to assess the educational needs among patients with AAV using the educational needs assessment tool (ENAT). Methods This was a cross-sectional observational study including adults with AAV. Educational needs were captured by ENAT. Total ENAT (0–117 points, with higher numbers indicating higher educational need) and the seven domains (managing pain, movement, feelings, disease process, treatment, self-management and support systems) were explored regarding sex, age, education, diagnosis, disease duration and disease activity. To compare domains, a percentage response (0–100%) was calculated. Results One hundred and seventy-eight individuals (50% men; 34% with disease duration ≤2 years) were included. The total ENAT mean was 66.5 (s.d. 26.6; 57%), with domains as follows: disease process, 78%; self-management, 69%; treatments, 64%; feelings, 56%; managing pain, 48%; support systems, 47%; and movement, 41%. Higher educational needs were found among women in the domains movement, feelings and disease process and in total ENAT (all P < 0.04) compared with men. Higher educational needs were also seen in patients with disease duration ≤2 years regarding disease process, self-management and support systems and in total ENAT compared with patients with longer disease duration (all P < 0.03). Conclusion This study revealed great educational needs among AAV patients. Some groups expressed higher needs (women and those with shorter disease duration). Increased education for patients with AAV might lead to improved self-care and treatment adherence.
Investigating cohort effects of early foreign language learning

ABSTRACT With the rapid implementation of early foreign language programmes in the state of North-Rhine-Westphalia, Germany, first for Grade 3 (ages 8–9 years) in 2003 and then from Grade 1 (ages 6–7 years) in 2008, primary school teachers had to adapt to teaching a foreign language in Grade 1 quickly. Teachers had little experience with language teaching to very young learners, and curricula and materials had not been tested prior to implementation. This study investigates the development of receptive English proficiency across three large cohorts (N = 7,289) . The first cohort started in Grade 3 , the second cohort was the first to start in Grade 1, and the third cohort started in Grade 1, six years after the initial implementation. Propensity scores were used to compare sampling weights of cohorts without the influence of confounding variables. Results confirmed a slight advantage for an earlier start in primary school for students’ receptive proficiency in Grade 5. The results further indicate that proficiency scores did not improve from the first cohort of students starting in Grade 1 to one six years later. Systemic changes in teacher education for language specialists in primary education may not yet have been able to affect student outcomes.
An acousto‐ultrasonics approach for probabilistic modelling and inference based on Gaussian processes

In recent years, there has been an increasing interest in employing and analysing complex ultrasonic waves for the purpose of designing health monitoring systems for guaranteeing the safe structural operation by means of early damage prognosis strategies and reducing the costs of maintenance and downtimes. For this reason, monitoring systems are essential and require highly efficient techniques providing a probabilistic interpretation of their diagnostics. On that account, a Bayesian framework within the context of Gaussian processes is adopted in the present work for the purpose of probabilistic data‐driven modelling and damage detection. A network of permanently installed piezoelectric sensors in a pitch‐catch configuration is used for collecting the propagated ultrasonic waves together with the discrete wavelet transform for feature extraction purposes and matrix unfolding procedures such as the ones used in the monitoring of batch processes for sensor data fusion. The effectiveness of the proposed methodology is experimentally evaluated in three different composite structures. Receiver operating characteristics are used as a statistical measure to assess the damage detection capabilities of the presented method.
Inorganic‐Rich and Flexible Solid‐Electrolyte Interphase Formed Over Dipole‐Dipole Interaction for Highly Stable Lithium‐Metal Anodes

The uncontrollable lithium (Li) dendrite growth and unstable solid electrolyte interfaces (SEI) hinder the practical application of Li metal batteries (LMBs). Herein, a PAN/CNTs‐based SiO2‐modified vertical‐cavity film (PCS‐VCF) with a network structure is prepared by a simple spin‐coating process to address these challenges. This new current collector with a polar lithiophilic network stabilizes the Li metal anodes by regulating the chemical environment in carbonate electrolytes. The dipole interaction between the CN groups and the CO groups reduces the high reactivity of the carbonates in the electrolyte, forming an SEI layer with higher inorganic components and enhancing the stability of the electrode/electrolyte interface. The special vertical‐cavity network structure inside the current collector can decrease local current density and suppress the huge volume expansion of Li metal during the cycling process. Consequently, a high‐performance Li metal anode with dendrite‐free morphology is achieved (over 1500 cycles with low overpotential at 20 mA cm−2 in symmetric cells). Furthermore, full cell with the LiNi0.8Co0. 1Mn0.1O2 cathode delivers a stable capacity of 164.5 mA h g−1 for 500 cycles at 5 C, with a capacity decay rate of 0.077% per cycle.
Meta-analysis, military expenditures and growth

This article conducts a meta-analysis of the effect of military expenditures on growth within a structured analytic framework. We extend the pioneering study of Aynur Alptekin and Paul Levine, by using a much larger sample of studies. Like them we confine our attention to studies that use the share of military expenditure in GDP, the military burden, as the independent variable, but unlike them we include not just those that use the military burden directly, what we call the core sample, but also those that use other functions of it, such as logarithms, differences, etc., which we call the remaining sample. We also consider an overall sample which pools all results. The t-statistic on the coefficient of military burden is used as the dependent variable. Our null hypothesis is that military expenditure has no significant effect on growth and we explain why this is plausible. The estimates are sensitive to the sample and type of data used, estimation method adopted, and the controls included. Overall, the results are consistent with the hypothesis of no effect: the average effect across all studies is close to zero. Certain study characteristics appear significant determinants of the effect of military expenditure on growth, but there does not appear to be a simple pattern and different characteristics were significant in the three samples. This might be a result of data mining to produce a significant result. However, there does not appear to be strong evidence of publication bias towards positive or negative results, perhaps because there is no strong a priori belief in the direction of the effect.
Intelligent Fingerprinting to Detect Data Leakage Attacks on Spectrum Sensors

Data confidentiality protection is a must for IoT and crowdsensing platforms, and a challenge due to the constrained nature of their sensors. Currently, the combination of device fingerprinting and anomaly detection systems based on Machine and Deep Learning (ML/DL) techniques is one of the most promising approaches to detect zero-day cyberattacks. However, most of existing work is not suitable for resource-constrained devices or does not deal with cyberattacks affecting data confidentiality of spectrum sensors. Thus, this paper proposes a framework that monitors network interface events of sensors, uses unsupervised learning to create fingerprints, and detects anomalies produced by such cyberattacks. The framework validation has been performed in the crowdsensing platform ElectroSense, where a sensor has been infected by a backdoor leaking different sensitive data during an experiment. A set of unsupervised learning algorithms has been evaluated, being Autoencoder the one showing the best balance when detecting normal behavior and data leakages of different sizes and at frequencies, while providing a reduced detection time and sensor resources consumption.