Miracle-dz/UniFAR-FTUs huggingface dataset:
Determining Movement Measures for Trust Assessment in Human-Robot Collaboration Using IMU-Based Motion Tracking

Close-proximity human-robot collaboration (HRC) requires an appropriate level of trust from the operator to the robot to maintain safety and efficiency. Maintaining an appropriate trust level during robot-aided production requires non-obstructive real-time human-robot trust assessment. To this end we performed an experiment with 20 participants performing two types of HRC tasks in close proximity to a Kuka KR 300 R2500 ultra robot. The two tasks involved collaborative transport of textiles and collaborative draping, respectively. During the experiment we performed full body motion tracking and administered human-robot trust questionnaires in order investigate the correlation between trust and operator movement patterns. From the initial per-session analyses we see the effects of task types on movement patterns, but the correlations with trust are weak overall. Further analysis at higher temporal resolution and with correction for participants’ base movement patterns are required.
Effects of forest on water circle on the Loess Plateau

From the research results on soil hydrology, the effects of forest hydrology on the total runoff and the surface water transferred into ground water were analyzed. It was found out that there exits an underlying drying layer of soil underneath forestland at a depth of more than 1 to 3 m from the soil surface. The distribution of the drying layer and causes why it exits are expounded, as related to ecological attributes of the Loess Plateau. It was believed that the drying layer is in great water deficit caused by evapotranspiration, which prevents gravitational infiltration of water and the replenishment of ground water. It is determined that a forestland on the Loess Plateau reduces the total runoff as the results of the increased retention of runoff and of the reduced recharged into ground water.
A Multistage Deep Learning Network for Trace Explosive Residues Detection in SERS Chips

To address the challenges of relying on specialized personnel and incurring significant time costs in qualitative and quantitative analysis using surface-enhanced Raman scattering (SERS) technology for explosive residue detection, this article proposes a detection method for explosive residues based on a multistage deep learning network and SERS chip. To improve the qualitative analysis performance of the SERS spectrum, a novel fusion attention module-based residual neural (FAB-ResNet) is constructed through the integration of a modified attention mechanism into the ResNet network. In addition, for proper processing of long sequential data, the nested long short-term memory (NLSTM) network is selected for quantitative analysis with its powerful global information aggregating capability. Consequently, the NLSTM is incorporated into FAB-ResNet to construct a multistage hybrid network. Extensive experiments are carried out to prove the effectiveness of the proposed hybrid network. The qualitative results demonstrated the superiority of the proposed FAB-ResNet with its outstanding classification accuracy (100%). Meanwhile, by comparing quantitative results, the NLSTM network provides promising performance ( ${R} ^{{2}}$ = 0.9835, root mean square error (RMSE) = 0.1653, mean absolute error (MAE) = 0.0916, and mean absolute relative error (MARE) = 2.7488%). Furthermore, the comparative results among other state-of-the-art networks confirmed the effectiveness of the proposed method as a means of explosive residue detection and analysis, which shows the potential for further application of SERS technology in explosive site detection.
Trait and state anxiety in healthcare professionals of intensive care unit.

OBJECTIVE To estimate trait and state anxiety levels among intensivecare professionals during the Covid-19 pandemic. METHOD Cross-sectional, analytical study, conducted from April to July 2022, in a teaching hospital in southern Brazil, using the State-Trait Anxiety Inventory scale. RESULTS Trait and state anxiety were present in more than half of the sample, being associated with gender, age group, having children, weekly workload, time working in the hospital and ICU (p<0.05) for the state; and associated with gender, having children, profession, daily and weekly workload, and time working in the hospital (p<0.05) for the trait. CONCLUSION Trait and state anxiety were medium to high level for women, young, without children, non-nurses, with a daily shift of 9 to 18 working hours, a weekly workload of more than 40 hours, with less than five years of experience in intensive care and with more than five years of experience working in the institution.
Structural Origin for the Enhanced Photocatalysis of Zr–Ti Oxide Composites from Machine Learning

: Multi-element oxides are notorious for their structural complexity, offering ample room for structure engineering toward applications. Here we demonstrate, via comprehensive machine learning (ML) based techniques to explore millions of likely structures on Zr-Ti oxide global potential energy surface, that the Zr-Ti oxide with varied Zr:Ti ratios can cover a full range of composite structure types, including the cation-disordered solid solution, the cation-ordered homogeneous crystals, and heterophase junctions. We show that the three-phase junctions are the global minima at x=0.375 and 0.625 of Zr x Ti 1-x O 2 , constituted by a monoclinic ZrO 2 phase, a thin layer of orthorhombic ZrTi 2 O 6 phase, and an anatase TiO 2 phase. This three-phase junction has a
Perspective-taking for promoting prosocial behaviors through robot-robot VR task

Perspective-taking, which enables individuals to consider the thoughts and objectives of another, is well established to be a successful strategy for encouraging pro-social behavior in human-computer interactions. Nowadays, perspective-taking is no longer limited to text; it is now more frequently used in virtual reality (VR). However, most previous research has focused on simulating human-human interactions in the real world in VR by providing participants with experiences connected to different moral tasks. In this study, we investigated whether participants’ prosocial behaviors toward robots would change if they experienced an altruistic VR task involving robots from the perspective of different robots. Our findings show that participants who had the help-receiver-view exhibited more altruistic behaviors toward a robot than those who had the help-provider-view one in a dictator game. We believe that this work is the first attempt to investigate the relationship between perspective-taking in a VR environment and changes in prosocial behavior in human-robot interaction.
Barriers to learning at a U3A in Lebanon: A structurationist perspective

This study examines the barriers older learners experience at a University for the Third Age (U3A) in Lebanon. Contemporary literature often categorizes these barriers into dispositional, situational, and institutional realms, arising as individual or structural phenomena. This article envisages barriers as the (un)intended consequences of (inter)actions among different institutional agents — namely, learners, teachers, and administrators — within the learning environment. Following Anthony Giddens’ dualistic understanding of agency and structure, the article aims to transcend the typical dichotomic approach in understanding barriers older persons face when engaging in lifelong learning. Shedding light on this new perspective on barriers as (un)intended consequences of agents’ (inter)actions at the U3A, this work raises two research questions: (i) what barriers confront older learners when engaging in non-formal learning? Moreover, (ii) taking older learners’ perspective, how are these barriers (re)produced in the (inter)actions of different institutional agents? Following a reflexive deductive thematic analysis of interview data with ten members at a U3A in Lebanon, this article generates two types of barriers. First, barriers as outcomes of interactions involving learners with teachers and administrators (curricula issues, teachers and teaching methods, language of instruction, class protocol, and accessibility). Second, barriers as outcomes of interactions involving learners (unwillingness and inability to socialize, as well as social bias and prejudice). This paper concludes that the actions of institutional agents at the U3A (re)produce its modus vivendi and modus operandi and calls for the promotion of continuous dialog and reflexivity as countermeasures against bias and exclusion to enhance the U3A’s age-friendliness.
An Addition to Binomial Array Antenna Theory

: This research extends the theory of binomial array antennas. A closed-form expression for the half-power beamwidth of the array factor of an array antenna is presented. The expression is correct for the ratios of element spacing to wavelength greater than or equal to one quarter. Also, exact equations for determining the half-power beamwidth for main-beam steering are derived. A comparison with an approximate formula for the half-power beamwidth of binomial array antennas is carried out.
MFFNet: A multi-frequency feature extraction and fusion network for visual processing

Convolutional neural networks have achieved tremendous success in the areas of image processing and computer vision. However, they experience problems with low-frequency information such as semantic and category content and background color, and high-frequency information such as edge and structure. We propose an efﬁcient and accurate deep learning framework called the multi-frequency feature extraction and fusion network (MFFNet) to perform image processing tasks such as deblurring. MFFNet is aided by edge and attention modules to restore high-frequency information and overcomes the multiscale parameter problem and the low-efﬁciency issue of recurrent architectures. It handles information from multiple paths and extracts features such as edges, colors, positions, and differences. Then, edge detectors and attention modules are aggregated into units to reﬁne and learn knowledge, and efﬁcient multi-learning features are fused into a ﬁnal perception result. Experimental results indicate that the proposed framework achieves state-of-the-art deblurring performance on benchmark datasets.
Diversity recovery and probiotic shift of gastric microbiota in functional dyspepsia patients after Helicobacter pylori eradication therapy

The effects of Helicobacter pylori eradication on gastric mucosa-colonizing microbes in patients with functional dyspepsia (FD) remain unclear. Here, we explored microbial variation induced by H. pylori infection and eradication treatment in FD patients. Gastric microbial abundance and diversity were significantly reduced in the H. pylori-infected FD patients. Eradication treatment increased alpha and beta diversity of gastric mucosa-colonizing microbes, and promoted the expansion of several probiotic microbes, such as Leuconostoc mesenteroides, which exhibited a matched antagonistic performance against H. pylori. Significant variation was observed in gastric mucosa-colonizing microbes between H. pylori-positive and H. pylori-negative FD patients. Eradication treatment induced microbial diversity recovery and may provide sufficient nutrition and space for probiotic microbes, such as Leuconostoc mesenteroides.
Postoperative Seizure Prophylaxis in Meningioma Resection: A Systematic Review and Meta-Analysis

Background: Seizures in the early postoperative period may impair patient recovery and increase the risk of complications. The aim of this study is to determine whether there is any advantage in postoperative seizure prophylaxis following meningioma resection. Methods: This systematic review was conducted in accordance with PRISMA guidelines. PUBMED, Web of Science, Embase, Science Direct, and Cochrane were searched for papers until April 2023. Results: Among nine studies, a total of 3249 patients were evaluated, of which 984 patients received antiepileptic drugs (AEDs). No significant difference was observed in the frequency of seizure events between patients who were treated with antiepileptic drugs (AEDs) and those who were not. (RR 1.22, 95% CI 0.66 to 2.40; I2 = 57%). Postoperative seizures occurred in 5% (95% CI: 1% to 9%) within the early time period (<7 days), and 9% (95% CI: 1% to 17%) in the late time period (>7 days), with significant heterogeneity between the studies (I2 = 91% and 97%, respectively). In seizure-naive patients, the rate of postoperative seizures was 2% (95% CI: 0% to 6%) in the early period and increased to 6% (95% CI: 0% to 15%) in the late period. High heterogeneity led to the use of random-effects models in all analyses. Conclusions: The current evidence does not provide sufficient support for the effectiveness of prophylactic AED medications in preventing postoperative seizures in patients undergoing meningioma resection. This underscores the importance of considering diagnostic criteria and conducting individual patient analysis to guide clinical decision-making in this context.
Secrecy Performance Analysis and Optimization for UAV-Relay-Enabled WPT and Cooperative NOMA MEC in IoT Networks

This paper investigates a system for unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and non-orthogonal multiple access (NOMA) mobile edge computing (MEC) in the Internet of Things (IoT) - UWNMI, wherein a UAV equipped with an energy transmitter (ET) acts as a relay. In particular, we consider a situation involving two clusters of IoT devices (IDs) that have limited resources and therefore are unable to compute their own tasks and must instead offload them to a base station (BS) via the UAV relay (UR) in the presence of a passive eavesdropper. In the UWNMI system, the effects of imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) are considered, along with the usage of artificial noise (AN) to improve the physical layer security (PLS) of the system. We derive closed-form expressions for the successful computation probability (SCP) and secrecy outage probability (SOP) under the Nakagami- $\mathfrak {m}$ fading channel model in order to evaluate the performance of the system. Moreover, we present optimization problems with the objectives of optimizing the position and height of the UR, the time switching ratio (TSR), and the power allocation for the AN in order to maximize the SCP and minimize the SOP. These problems are solved using a particle swarm optimization (PSO)-based algorithm. In addition, Monte Carlo simulation results are presented to validate the accuracy of our analysis based on simulations of the system performance under different values of the system parameters, including the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and height of the UR.
SNR enhancement of magnetic fields measurement with the diamond NV center using a compound filter system

Nitrogen-vacancy (NV) centers in diamond are progressively favored for room-temperature magnetic field measurement. The signal to noise ratio (SNR) optimization for NV diamond magnetometry generally concentrates on signal amplitude enhancement rather than efficient noise processing. Here, we report a compound filter system combining a wavelet denoising method and an adaptive filter for the realization of an efficient weak magnetic measurement with a high SNR. It allows enhanced magnetic field measurement with an average SNR enhancement of 17.80 dB at 50 nT within 500 mHz to 100 Hz and 14.76 dB at 500 mHz within 50 nT to 1100 nT. The introduction of this system in NV diamond magnetometry is aimed to improve signal quality by effectively eliminating the noise and retaining ideal signals.
Advancements in Adsorption Techniques for Sustainable Water Purification: A Focus on Lead Removal

The long-term sustainability of the global water supply, with a paramount emphasis on cleanliness and safety, stands as a formidable challenge in our modern era. In response to this pressing issue, adsorption techniques have emerged as pivotal and widely recognized solutions for the removal of hazardous pollutants, with a particular emphasis on lead adsorption from wastewater. This comprehensive review explores the relentless advancements made in the adsorption domain, highlighting innovations using separation and purification techniques that surpass traditional metal oxide-based adsorbents. Of particular note is the growing exploration of alternative materials, such as starch, chitosan, nanoscale structures like zeolites and metal-organic frameworks, magnetic materials, and carbon-based substances for the development of inorganic adsorbents. These materials, with their remarkable capacity for nanoscale structural adjustment, possess extraordinary capabilities for effective contaminant removal, facilitating swift water purification. The literature survey for this review was conducted using the Google Scholar engine, with “adsorbents for lead remediation” as the starting keywords, resulting in approximately 6000 papers. The search was refined to focus on the last three years and specifically targeted review papers which are most relevant to lead remediation. More than 100 papers were analysed to investigate various techniques, surface modifications, and adsorbent materials for managing inorganic pollutants in water. This review also illuminates research limitations, with a specific focus on starch-based adsorbents in lead remediation. As we progress towards practical commercial applications, this review identifies challenges associated with the development of inorganic adsorbents and provides invaluable insights into future prospects. Surface modification emerges as a promising path, with the potential to substantially enhance adsorption capacity, potentially doubling or even quadrupling it. Moreover, the adsorbents demonstrate impressive regenerative capabilities, maintaining up to 90% regeneration efficiency after multiple cycles. In conclusion, starch-based adsorbents show considerable potential as effective agents for lead purification from aquatic environments. Nevertheless, the need for further research persists, emphasizing the optimization of the adsorption process and exploring the long-term stability of starch-based adsorbents in real-world scenarios.
Development of a Real-Time Analysis System for Concrete Compaction Positioning and a Construction Site Trial

—The environment surrounding the construction industry in Japan is drastically changing with advances in IT (Information Technology). The central reason for this change is the development of AI (Artificial Intelligence) technologies such as deep learning. On the other hand, in concrete works, which are often used in the construction industry, the system of carrying fresh concrete produced at a concrete plant to a construction site using an agitator car, placing concrete in a form, compacting with a vibrator, finishing the surface, and curing the finished concrete surface has hardly changed in more than 30 years. It was confirmed in this study that this system can be used to carry out more effective compaction management by shifting from qualitative management using individual know-how based on the experience of conventional workers to quantitative management based on AI analysis using images.
New and little-known bryozoans from Monte León Formation (early Miocene, Argentina) and their paleobiogeographic relationships

Abstract. The bryozoan fauna from the South American Cenozoic is poorly known. The study of new material collected in the Monte León Formation (early Miocene), gave us the opportunity to describe four new species: Valdemunitella canui n. sp. , Foveolaria praecursor n. sp. , Neothoa reptans n. sp. , and Calyptotheca santacruzana n. sp. Two of them (V. canui and C. santacruzana) were first recorded by F. Canu and interpreted as recent species from the Australian bryozoan fauna, but are herein described as new species. The stratigraphic range of Otionella parvula (Canu, 1904) is extended to the early Miocene. The present study emphasizes the close relationships between the South American Neogene bryozoan faunas and those of other Gondwanan sub-continents such as New Zealand and Australia.
Watershed Prioritization and Reservoir Sedimentation Using Remote Sensing Data

Abstract To limit siltation, it is essential that soil conservation measures are undertaken in the drainage basin upstream of the reservoir. In this study, the catchment for the Ramganga reservoir has been divided into nine sub‐watersheds to determine the sub‐watershed most prone to soil erosion. Also, temporal IRS‐1B LISS‐III images between years 2000-2001 are used on ILWIS image processing and GIS software for the assessment of reservoir sedimentation in Ramganga catchment. An approach, based on land surface factors mainly responsible for erosion, which include slope, landuse, brightness and greeneness etc., are used in this study. The catchment of reservoir is divided into 9 sub‐watersheds to assess the sub‐watershed contributing maximum sediments to the reservoir. Palain sub‐watershed of Ramganga catchment is identified as being most susceptible to sedimentation. The LISS III images are used to compute the water index, while the GIS system is used to analyse the topography. The integrated effect of all the parameters is evaluated to find sedimentation rate in the reservoir.
Clinician Perceptions of Barriers and Facilitators for Delivering Early Integrated Palliative Care via Telehealth

Simple Summary For patients with a life-limiting illness like advanced cancer, early engagement with palliative care services in the ambulatory care setting can provide several benefits, including improved symptom management and quality of life. Telehealth may be a viable way to deliver these services, especially for populations with limited access to palliative care in a traditional outpatient clinic. We surveyed clinicians who provide palliative care services to patients with advanced lung cancer regarding the barriers, facilitators, and benefits of using telehealth for delivering early integrated palliative care. Our findings show that policies and interventions targeting patient-, organizational-, and systems-based levels are needed to support the use of telehealth for palliative care. Abstract Early integrated palliative care (EIPC) significantly improves clinical outcomes for patients with advanced cancer. Telehealth may be a useful tool to deliver EIPC sustainably and equitably. Palliative care clinicians completed a survey regarding their perceptions of the barriers, facilitators, and benefits of using telehealth video visits for delivering EIPC for patients with advanced lung cancer. Forty-eight clinicians across 22 cancer centers completed the survey between May and July 2022. Most (91.7%) agreed that telehealth increases access to EIPC and simplifies the process for patients to receive EIPC (79.2%). Clinicians noted that the elderly, those in rural areas, and those with less-resourced backgrounds have greater difficulty using telehealth. Perceived barriers were largely patient-based factors, including technological literacy, internet and device availability, and patient preferences. Clinicians agreed that several organizational factors facilitated telehealth EIPC delivery, including technological infrastructure (85.4%), training (83.3%), and support from study coordinators (81.3%). Other barriers included systems-based factors, such as insurance reimbursement and out-of-state coverage restrictions. Patient-, organization-, and systems-based factors are all important to providing and improving access to telehealth EIPC services. Further research is needed to investigate the efficacy of telehealth EIPC and how policies and interventions may improve access to and dissemination of this care modality.