Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Multimodal Wearable Sensors for Quantifying Affective States and Depression With Neural Networks

With the increasing proliferation of embedded sensors in wearable devices, there is potential for modeling individual emotional and mental state variations. The popular measure for the quantification of emotions outlines the affective states of arousal and valences, with high and low being the discrete categories of interest. Recent works explore the discernability of digital behavior differences between groups with and without mental disorders. However, the interaction between physiological states and affective states within a predominantly depressive population remains to be studied with the aid of wearables. Despite the pervasiveness of emotional state inference through the tracking of ubiquitous physiological trackers, such as heart rate, blood volume pulse, skin conductance, and motion, a dearth of work is noted in the exploration of physiological markers in single-modal and multimodal settings. This work provides an extensive evaluation of a convolutional neural network with an attention mechanism ensembled with a random forest algorithm to effectively leverage multiple raw signal-to-image transformations as feature inputs to predict depression severity and affective state. The proposed models are assessed on the Daily Ambulatory Psychological and Physiological recording for Emotion Research (DAPPER) dataset and achieve the sensitivity: specificity scores of 58.75%:45.59%, 62.34%:43.41%, and 49.43%:51.70% for predicting depression, valence, and arousal with a mixture of unimodality and bimodality applying continuous wavelet transforms and short-time Fourier transform to motion and skin-conductance readings, respectively. This work is envisioned as a preliminary study to contribute toward the monitoring of affective states among a depressed population by utilizing low-frequency sensor recordings with the DAPPER dataset.
WiFi round-trip time (RTT) fingerprinting: an analysis of the properties and the performance in non-line-of-sight environments

ABSTRACT Indoor positioning systems based on WiFi round-trip time (RTT) measurement were reported to deliver sub-metre-level accuracy using trilateration, under ideal indoor conditions. However, the performance of WiFi RTT positioning in complex, non-line-of-sight environment remains an open research question. To this end, this article investigates the properties of WiFi RTT in several real-world indoor environments on heterogeneous smartphones. We present three datasets collected on a large-scale building floor, an office room and an apartment. The datasets contain both RTT and received signal strength (RSS) signal measures with correct ground-truth labels for further research. Our results indicated that in a complex indoor environment, RTT fingerprinting system delivered an accuracy of 0.6 m which was 107% better than traditional RSS fingerprinting and 6 m better than RTT trilateration which failed to deliver sub-metre accuracy as claimed.
SUBSAMPLlNG INFERENCE IN CUBE ROOT ASYM PTOTICS WITH AN APPLICATION TO MANSKI'S MAXIMUM SCORE ESTIMATOR

---------------------------------------------------------------------------------------------------------------------Kim and Pollard (1990) showed that a general class of M-estimators converge at rate n l/3 rather than at the standard rate n 1/2• Many times, this situation arises when the objective function is non-smooth. The limiting distribution is the (almost surely unique) random vector that maximizes a certain Gaussian process and is difficult to analyze analytically. In this paper, we propose the use of the subsampling method for inferential purposes. The general method is then applied to Manski' s maximum score estimator and its small sample performance is highlighted via a simulation study.
Human-in-the-Loop Consensus Tracking Control for UAV Systems via an Improved Prescribed Performance Approach

In some crucial consensus tracking missions, the sign of the tracking error is not allowed to change during the tracking process. The existing tracking control schemes are unable to satisfy this requirement. To solve this issue, this work improves the conventional prescribed performance control (PPC) approach and applies it to the cooperative control of unmanned aircraft vehicle (UAV) systems. Moreover, the flight controller design incorporates human decision-making. This hybrid active interaction approach implies that humans and UAVs can execute complex and variable flight missions as a collaborative team. Considering that equipped motors are prone to faults when operating in harsh environments, this article presents an adaptive fuzzy fault compensation mechanism to ensure the flight safety of UAVs. Finally, the simulation study validates the effectiveness and control advantage of the proposed approach.
An investigation of what protective individual- and community-level factors are associated with life satisfaction in middle-aged and older family carers in Ireland

Background Family care plays an essential role in providing care in society. However, caring can cause stress, and mental and physical responses to caring vary widely. Different outcomes for carers may reflect different approaches or adaptability to caring and their ability to maintain or recover their mental health and wellbeing following an adverse event (psychosocial resilience). We aim to identify factors that may promote psychosocial resilience, conceptualized as maintaining or recovering subjective wellbeing and operationalized as satisfaction with life, among carers. Methods Data were from 6 Waves (2009–2021) of The Irish Longitudinal Study on Aging (TILDA), a prospective biennial nationally representative longitudinal study of older adults aged ≥50 in Ireland. Family caregiving was assessed in Waves 3–6. Participants were asked if they cared for someone, their relationship to the recipient, and the number of hours per week that they provided care. We used growth mixture modeling to identify latent trajectories of satisfaction with life (SWL) before and after caring was initiated. Regression modeling was then used to identify protective factors (at the individual, family, and community levels) associated with resilient trajectories. Results Overall, 731 (12.2%) participants became carers during follow-up. We identified three trajectories in SWL in carers following initiation of caring, namely, Resilient-Stable (81%), Resilient-Recovery (12%), and Non-recovery (6%). Membership in Resilient-Stable and Resilient-Recovery trajectories was associated with fewer depressive symptoms (OR = 0.86, 95% CI 0.78, 0.94) and chronic conditions (OR = 0.21, 95% CI 0.06, 0.74), larger social networks (OR = 2.03, 95% CI 1.06, 3.86), more close friends and relatives (OR = 1.15, 95% CI 1.01, 1.32), and caring for someone other than a child (OR = 0.19, 95% CI 0.07, 0.51) compared to the Non-recovery group. Conclusion Becoming a family carer was associated with a decline in SWL over time in some carers. However, most carers either did not experience a decline in SWL or recovered their SWL over time. We found that both individual and community-level supports may be protective for carers' wellbeing. These results will inform the priorities for social and community-level services and support for older carers and contribute to the design of new projects and programs to meet these needs.
Selected Aspects of Household Savings in Germany: Evidence from Micro-Data

This paper uses household level data from the German Socio-Economic Panel (GSOEP) over the period 1991 to 2008 to analyse the driving factors of movements in the German household savings rate. Specifically, it analyses the impact of the precautionary savings motive and the impact of the 2002 private pension reform (the so-called Riester reform) on households’ savings rate as these factors are among the most discussed in the German context. There is evidence for both factors at work: First, households with a more volatile income stream tend to save more and the extent to which they do depends on their (subjectively assessed) risk aversion. Second, the introduction of the Riester pension scheme in 2002 was associated with a general increase in the household savings rate, both for households that signed up for private pension contracts and for those that did not. This effect is not found for low-income households, thus confirming the findings of other studies. Principaux aspects de l'epargne des menages en Allemagne : indications fournies par les microdonnees Ce document s’appuie sur des donnees recueillies aupres des menages par le German Socio-Economic Panel (GSOEP) sur la periode 1991-2008 afin d’analyser les determinants des variations du taux d’epargne des menages allemands. Il analyse en particulier l’impact du desir d’epargne de precaution et l'impact de la reforme des retraites privees de 2002 (« reforme Riester ») sur le taux d’epargne des menages, ces facteurs etant parmi ceux qui presentent le plus d’interet dans le cas de l’Allemagne. Il apparait que ces deux elements jouent un role : En premier lieu, les menages dont le revenu est plutot irregulier epargnent generalement plus et ce, en fonction de leur degre d’aversion au risque (evalue subjectivement). En second lieu, la mise en place du regime de retraite Riester en 2002 s’est accompagnee d’une hausse generale du taux d’epargne des menages, aussi bien pour les signataires de contrats de retraite prives que pour les autres. Cet effet n’apparait pas pour les menages a faible revenu, ce qui confirme les resultats des autres etudes.
Using GPU Accelerators for Parallel Simulations in Material Physics

This work is focused on parallel simulation of electron-electron interactions in materials with non-trivial topological order (i.e. Chern insulators). The problem of electron-electron interaction systems can be solved by diagonalizing a many-body Hamiltonian matrix in a basis of configurations of electrons distributed among possible single particle energy levels – the configuration interaction method. The number of possible configurations exponentially increases with the number of electrons and energy levels; 12 electrons occupying 24 energy levels corresponds to the dimension of Hilbert space about 10. Solving such a problem requires effective computational methods and highly efficient optimization of the source code. The work is focused on many-body effects related to strongly interacting electrons on flat bands with non-trivial topology. Such systems are expected to be useful in study and understanding of new topological phases of matter, and in further future they can be used to design novel nanomaterials. Heterogeneous architecture based on GPU accelerators and MPI nodes will be used for improving performance and scalability in parallel solving problem of electron-electron interaction systems.
On Utilization of NEXRAD Scan Strategy Information to Infer Discrepancies Associated With Radar and Rain Gauge Surface Volumetric Rainfall Accumulations

To evaluate the Tropical Rainfall Measuring Mission (TRMM) monthly Ground Validation (GV) rain map, 42 quality controlled tipping bucket rain gauge data (1 minute interpolated rain rates) were utilized. We have compared the gauge data to the surface volumetric rainfall accumulation of NEXRAD reflectivity field, (converting to rain rates using a 0.5 dB resolution smooth Z-R table). The comparison was carried out from data collected at Melbourne, Florida during the month of July 98. GV operational level 3 (L3 monthly) accumulation algorithm was used to obtain surface volumetric accumulations for the radar. The gauge records were accumulated using the 1 minute interpolated rain rates while the radar Volume Scan (VOS) intervals remain less than or equal to 75 minutes. The correlation coefficient for the radar and gauge totals for the monthly time-scale remain at 0.93, however, a large difference was noted between the gauge and radar derived rain accumulation when the radar data interval is either 9 minute, or 10 minute. This difference in radar and gauge accumulation is being explained in terms of the radar scan strategy information. The discrepancy in terms of the Volume Coverage Pattern (VCP) of the NEXRAD is being reported where VCP mode is ascertained using the radar tilt angle information. Hourly radar and gauge accumulations have been computed using the present operational L3 method supplemented with a threshold period of +/- 5 minutes (based on a sensitivity analysis). These radar and gauge accumulations are subsequently improved using a radar hourly scan weighting factor (taking ratio of the radar scan frequency within a time bin to the 7436 total radar scans for the month). This GV procedure is further being improved by introducing a spatial smoothing method to yield reasonable bulk radar to gauge ratio for the hourly and daily scales.
Availability of non- and low-fluoride paediatric toothpastes in Nigeria: a need for indigenous affordable formulations

Objective: Some studies in Nigeria have shown a high prevalence of fluorosis. This study looks at toothpastes manufactured locally to determine the availability and affordability of low- fluoride paediatric toothpastes in Nigeria.Method: Toothpastes manufactured locally in Nigeria which bear the Nigerian Agency for Food and Drug Administration (NAFDAC) approval number were purchased from open markets, reputable supermarkets and pharmacies. The cost price and fluoride content were recorded for each of the toothpastes.Result: Twenty-one toothpastes manufactured locally in Nigeria were identified. Thirteen toothpastes had sodium fluoride as the active fluoride compound while five had sodium monofluorophosphate. Seventeen toothpastes had optimum fluoride content (1100 – 1450 ppm fluoride or 0.306 – 0.32% w/w sodium fluoride or 0.76 – 0.85% w/w Sodium monofluorophosphate), one had low-fluoride content (500ppm or 0.1106% w/w sodium fluoride) while three were non-fluoride toothpastes. The low-fluoride toothpaste was designated as paediatric while the non-fluoride toothpastes were herbal formulations. A cost analysis of the toothpastes showed that the lone paediatric low-fluoride toothpaste was sold at more than three times the cost of the optimum-fluoride toothpastes.Conclusion: The majority of toothpastes manufactured locally in Nigeria have optimum fluoride content, the non-fluoride toothpastes are herbal formulations while only one toothpaste is a low-fluoride paediatric toothpaste. The present cost of the paediatric toothpaste makes it unaffordable and unavailable to the average child in Nigeria. Companies which manufacture toothpastes locally are encouraged to produce more brands of paediatric low-fluoride toothpastes for the Nigerian market at affordable prices.
Beneficial effects of red rice bran extract on brown adipose tissue whitening, inflammation, and oxidative stress in mice with obesity induced by a high-fat diet

Red rice bran extract (RRBE) has been demonstrated to have anti-lipid accumulation, antioxidant, and anti-inflammatory effects on the livers of obese animals and/or in macrophages. However, the molecular mechanisms driving these effects have not yet been investigated in brown adipose tissue (BAT). Thus, the present study aimed to investigate the beneficial effects of RRBE on BAT in mice with obesity induced by a high-fat diet (HFD). Six weeks after induction of obesity, the obese mice were fed continuously with HFD, with or without RRBE treatment for 6 weeks. Body weight and food intake were recorded weekly. At the end of the feeding experiment, BAT was collected for determination of expressions of genes associated with liporegulation, inflammation, and oxidative stress, as well as histological examination. Compared with untreated obese mice, RRBE-treated mice had reductions in body weight, food efficiency ratio, and white-like adipocytes in BAT, accompanied by down-regulated expression of nuclear receptor subfamily 1 group H member 3 (NR1H3). Moreover, RRBE decreased the levels of nitric oxide (NO), reactive oxygen species (ROS), and malondialdehyde (MDA) in BAT, which were associated with modulated expressions of target genes for nuclear factor-kappa B and nuclear factor erythroid 2-related factor 2. RRBE produced beneficial effects against HFD-induced abnormalities in BAT by mitigating whitening, inflammation, and oxidative stress.
Antisymmetric Lamb Wave Simulation Study Based on Electromagnetic Acoustic Transducer with Periodic Permanent Magnets

Due to its multi-mode and dispersion characteristics, Lamb waves cause interference to signal processing, which profoundly limits their application in nondestructive testing. To resolve this issue, firstly, based on the traditional EMAT, a horizontal polarization periodic permanent magnet electromagnetic acoustic transducer (HP-PPM-EMAT) was proposed. A 2-D finite element model was then developed to compare magnetic flux density, Lorentz force, and signal strength between the traditional EMAT and the HP-PPM-EMAT. The simulation results show that the HP-PPM-EMAT enhances the A0 mode Lamb wave (A0 wave) and suppresses the S0 mode Lamb wave (S0 wave). Finally, the influence of structural parameters of the HP-PPM-EMAT on the total displacement amplitude ratio of A0 and S0 was investigated using orthogonal test theory, and the width of magnet units was improved based on the orthogonal test. The results show that the total displacement amplitude ratio of A0 to S0 of the improved HP-PPM-EMAT can be improved by a factor of 7.74 compared with that of the traditional Lamb wave EMAT, which can produce higher-purity A0 mode Lamb waves.
Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network

Brain tumor segmentation from magnetic resonance imaging (MRI) scans is critical for the diagnosis, treatment planning, and monitoring of therapeutic outcomes. Thus, this research introduces a novel hybrid approach that combines handcrafted features with convolutional neural networks (CNNs) to enhance the performance of brain tumor segmentation. In this study, handcrafted features were extracted from MRI scans that included intensity-based, texture-based, and shape-based features. In parallel, a unique CNN architecture was developed and trained to detect the features from the data automatically. The proposed hybrid method was combined with the handcrafted features and the features identified by CNN in different pathways to a new CNN. In this study, the Brain Tumor Segmentation (BraTS) challenge dataset was used to measure the performance using a variety of assessment measures, for instance, segmentation accuracy, dice score, sensitivity, and specificity. The achieved results showed that our proposed approach outperformed the traditional handcrafted feature-based and individual CNN-based methods used for brain tumor segmentation. In addition, the incorporation of handcrafted features enhanced the performance of CNN, yielding a more robust and generalizable solution. This research has significant potential for real-world clinical applications where precise and efficient brain tumor segmentation is essential. Future research directions include investigating alternative feature fusion techniques and incorporating additional imaging modalities to further improve the proposed method’s performance.
Leveraging AI‐enabled 6G‐driven IoT for sustainable smart cities

Many scholastic researches have begun around the globe about the competitive technological interventions like 5G communication networks and its challenges. The incipient technology of 6G networks has emerged to facilitate ultrareliable and low‐latency applications for sustainable smart cities which are infeasible with the existing 4G/5G standards. Therefore, the advanced technologies like machine learning (ML), block chain, and Internet of Things (IoT) utilizing 6G network are leveraged to develop cost‐efficient mechanisms to address the issues of excess communication overhead in the present state of the art. Initially, the authors discussed the key vision of 6G communication technologies, its core technologies (such as visible light communication [VLC] and THz), and the existing issues with the existing network generations (such as 5G and 4G). A detailed analysis of benefits, challenges, and applications of blockchain‐enabled IoT devices with application verticals like Smart city, smart factory plus, automation, and XR that form the key highlights for 6G wireless communication network is also presented. In addition, the key applications and latest research of artificial intelligence (AI) in 6G are discussed facilitating the dynamic spectrum allocation mechanism and mobile edge computing. Lastly, an in‐depth study of the existing open issues and challenges in green 6G communication network technology, as well as review of solutions and potential research recommendations are also presented.
DENTAL RESTORATION WITH OCCLUSAL ISLAND: A FINITE ELEMENT METHOD STUDY

: When a posterior tooth has its functionality compromised, it must be repaired with a restorative material. In order to verify the potential use of types of occlusal islands for the recovery of regions in occlusal contact, the present study compared the stress distribution when applying a central load of 291.36 N, simulating centric bruxism (BC), in a sound maxillary 2nd premolar tooth (HC), with a Class 1 restoration in Filtek bulk fill posterior resin without (RC) and with (RICC) occlusal islands in Vita Enamic hybrid ceramic in two regions of occlusal contact in cusps. BC loading was also compared on the supporting cusp of a sound mandibular second premolar (HV) with a sloped restoration in Filtek posterior bulk fill resin (RV) and in Vita Enamic hybrid ceramic (ICV). The MSC.Patran program was used in the construction of the six finite element models, the processing was carried out in the MSC.Nastran, and the von Mises stress distribution, principal maximum and maximum shear stress were evaluated. The RC and RICC models in relation to the HC reduced the tensile stresses in the remaining enamel and at the enamel/resin interface, but caused an increase in the tensile stresses in the region between the islands. The ICV model compared to the RV model reduced the tensile stresses in the remaining enamel and in the island/enamel interface. It is concluded that
Ex Vivo Chromosomal Radiosensitivity Testing in Patients with Pathological Germline Variants in Breast Cancer High-Susceptibility Genes BReast CAncer 1 and BReast CAncer 2

Background: Individual radiosensitivity is an important factor in the occurrence of undesirable consequences of radiotherapy. The potential for increased radiosensitivity has been linked to highly penetrant heterozygous mutations in DNA repair genes such as BRCA1 and BRCA2. By studying the chromosomal radiosensitivity of BRCA1/2 mutation carriers compared to the general population, we study whether increased chromosomal radiation sensitivity is observed in patients with BRCA1/2 variants. Methods: Three-color-fluorescence in situ hybridization was performed on ex vivo-irradiated peripheral blood lymphocytes from 64 female patients with a heterozygous germline BRCA1 or BRCA2 mutation. Aberrations in chromosomes #1, #2 and #4 were analyzed. Mean breaks per metaphase (B/M) served as the parameter for chromosomal radiosensitivity. The results were compared with chromosomal radiosensitivity in a cohort of generally healthy individuals and patients with rectal cancer or breast cancer. Results: Patients with BRCA1/2 mutations (n = 64; B/M 0.47) overall showed a significantly higher chromosomal radiosensitivity than general healthy individuals (n = 211; B/M 0.41) and patients with rectal cancer (n = 379; B/M 0.44) and breast cancer (n = 147; B/M 0.45) without proven germline mutations. Chromosomal radiosensitivity varied depending on the locus of the BRCA1/2 mutation. Conclusions: BRCA1/2 mutations result in slightly increased chromosomal sensitivity to radiation. A few individual patients have a marked increase in radiation sensitivity. Therefore, these patients are at a higher risk for adverse therapeutic consequences.