Miracle-dz/UniFAR-FTUs huggingface dataset:
Position Tracking Control of Fully-Actuated Underwater Vehicles With Constrained Attitude and Velocities

This article presents a redundant control strategy of fully-actuated underwater vehicles. The proposed strategy offers vehicles more control flexibility for addressing the position tracking problem under the attitudes and velocities constraints. In kinematics, position tracking is formulated as a convex optimization problem, where the velocity constraints are considered as a feasible region and the attitudes are bounded via a barrier function. Then, a conservative boundary is determined in the context of stability analysis of the dynamic controller with a disturbance observer and an auxiliary system so that kinematic variables lie in the constraints even with the velocity tracking error. Also, it is proven that the position tracking error can converge to a neighborhood about zero, which can be made arbitrarily small. The simulations and experimental results through multiple scenarios have illustrated the efficiency of the proposed strategy.
Quasi-globally Optimal and Real-time Visual Compass in Manhattan Structured Environments

We present a drift-free visual compass to estimate the three degrees of freedom (DoF) rotational motion of a camera by recognizing structural regularities in a Manhattan world (MW), which posits that the major structures conform to three orthogonal principal directions. Existing Manhattan frame (MF) estimation approaches are based on either data sampling or parameter search, and they fail to guarantee accuracy and efficiency at the same time. To overcome these limitations, we propose a novel approach to hybridize these two strategies, achieving quasi-global optimality and high efficiency. We first compute the two DoF of the camera orientation by detecting and tracking a vertical dominant direction from a depth camera or an IMU, and then search for the optimal third DoF with the image lines through the proposed Manhattan Mine-and-Stab (MnS) approach. Once we find an initial rotation estimate of the camera, we refine the absolute camera orientation by minimizing the average orthogonal distance from the endpoints of the lines to the MW axes. We compare the proposed algorithm to other state-of-the-art approaches on a variety of real-world datasets including data from a drone flying in an urban environment, and demonstrate that the proposed method outperforms them in terms of accuracy, efficiency, and stability. Code is available at the project page: {\textcolor{magenta}{\url{https://github.com/PyojinKim/MWMS}}}
Comparative Analysis of Banking Financial Performance Pre and Post Covid-19 Pandemic

Covid-19 struck the Indonesian banking industry in particular ASEAN, through the weaker economic growth, which resulted in a slowdown in credit growth and eventually reduce profitability. This study aimed to analyze the financial performance of banks before and after the occurrence of a covid-19 pandemic and formulate alternative strategies to improve the financial performance of Indonesian banks. The study sample consisted of four banks with saturated sampling method (census) are owned banks (State Bank) listed on the Stock Exchange Indonesia. The data in this research is secondary data obtained from the bank's annual report period 2019 until the second quarter of 2020 which is accessed via the IDX website. Performance is measured using the six financial ratios namely ROA, BOPO, NPL, NIM, CAR and LDR with different test analysis method (Paired T-Test). The study found that in the form of financial ratios ROA, BOPO, CAR and LDR pre and post Covid-19 pandemics have significantly different values, while the NPL and NIM did not differ significantly.
Evaluation of a human urine barbiturate test to screen for pentobarbital euthanasia of dogs and cats

Pentobarbital is used commonly to euthanize animals. Occasionally during a death investigation, it is necessary to determine whether a cat or dog was euthanized via pentobarbital overdose. Screening for the detection of barbiturates including pentobarbital can be performed using commercial immunochromatographic tests. We used a commercial immunochromatographic test for barbiturates in humans to screen for barbiturates in urine collected postmortem from 20 dogs and 20 cats to determine whether they had been euthanized with pentobarbital. Additionally, we analyzed the urine for pentobarbital using liquid chromatography–mass spectrometry as a confirmatory test. Screening and confirmation testing revealed 100% agreement between the tests and with the euthanasia status of each animal. Our results support the use of the immunochromatographic test for the screening of urine collected postmortem to assess for the presence of barbiturates, specifically pentobarbital, used for euthanasia.
The Impact of Covid 19 on Agile Software Development: A Systematic Literature Review

In 2020, the world changed due to the Covid 19 pandemic. Containment measures to reduce the spread of the virus were planned and implemented by many countries and companies. Worldwide, companies sent their employees to work from home. This change has led to signiﬁcant challenges in teams that were co-located before the pandemic.
High voltage transmission line fault classification based on fuzzy logic and Support Vector Machines

Accurate fault types recognition for high voltage transmission line is the basis of fault location and accident analysis, it is significant to explore efficient and practical fault recognition techniques. On the basis of analyzing the principles and methods of accomplishing high voltage transmission line fault types classification, this paper presented to combine Support Vector Machines (SVM) with strong generalization ability for small samples and fuzzy set theory of being suitable for solving uncertainty linear division relations to perform the recognition task for high voltage transmission line fault types, so as to obtain the effect of optimum classification with expected risk minimization. The simulation results show that the proposed method has the characteristic of simple and clear recognition process, is able to identify fault types correctly and is fit for any model structures of high voltage transmission line, and therefore the generality and practicability can be ensured. Consequently the recognition method for high voltage transmission line fault types based on fuzzy logic and SVM technique completely overcomes the limitations by using conventional linear classification methods, achieves nonlinear partition for transmission line fault mode space, solves the essential problem for high voltage transmission line fault types recognition.
Vestibular rehabilitation for individuals with mal de debarquement syndrome: a systematic review

Abstract Background Mal de debarquement syndrome (MdDS) is a pathological condition characterized by rocking, swaying, and/or a sense of imbalance. The two types of MdDS are known as motion triggered MdDS and spontaneous or other onset MdDS. Objectives The purpose of this systematic review was to evaluate the effectiveness of vestibular rehabilitation on individuals experiencing the symptoms of MdDS using valid and reliable outcome measures. Methods MEDLINE, ProQuest Medical Database, and Web of Science were the databases utilized in the search process. The search terms included ‘mal de debarquement’ AND ‘intervention’ OR ‘management’ OR ‘rehabilitation’ OR ‘therapy’ OR ‘treatment’. The Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence was utilized in evaluating the evidence level for all of the included articles, and a 10-item tool created by Medlicott and Harris was utilized in evaluating the methodological rigor for all of the included articles. Results Seven articles met the eligibility requirements based upon the inclusion and exclusion criteria, and these articles were qualitatively analyzed. The intervention focused on traditional vestibular rehabilitation in two of the studies and on optokinetic stimulation in the other five studies. Optokinetic stimulation is designed to induce re-adaptation of the vestibulo-ocular reflex. This systematic review found that optokinetic stimulation required a much shorter treatment duration than traditional vestibular rehabilitation to significantly improve the balance and decrease the symptoms of individuals affected with MdDS. Conclusions Clinicians should consider using optokinetic stimulation when treating individuals diagnosed with MdDS, especially those with the motion-triggered subtype.
Partial Discharge Diagnostics: Data Cleaning and Feature Extraction

Detection of partial discharge (PD) in switchgears requires extensive data collection and time-consuming analyses. Data from real live operational environments pose great challenges in the development of robust and efficient detection algorithms due to overlapping PDs and the strong presence of random white noise. This paper presents a novel approach using clustering for data cleaning and feature extraction of phase-resolved partial discharge (PRPD) plots derived from live operational data. A total of 452 PRPD 2D plots collected from distribution substations over a six-month period were used to test the proposed technique. The output of the clustering technique is evaluated on different types of machine learning classification techniques and the accuracy is compared using balanced accuracy score. The proposed technique extends the measurement abilities of a portable PD measurement tool for diagnostics of switchgear condition, helping utilities to quickly detect potential PD activities with minimal human manual analysis and higher accuracy.
Designing the Metaverse

The Metaverse, a term coined in science fiction, is now being discussed seriously as a new form of infrastructure. The Metaverse is intended to make possible thematically interconnected immersive experiences. In this paper, we conceptualize the Metaverse as a meta design space. Within this space, designers create various interconnected design spaces. We highlight how the key dimensions of human experience (time, space, actors, and artifacts) each introduce tensions for making decisions in those design spaces
Effect of Impurity Adsorption on the Electronic and Transport Properties of Graphene Nanogaps

Graphene stands out as a versatile material with several uses in fields that range from electronics to biology. In particular, graphene has been proposed as an electrode in molecular electronics devices that are expected to be more stable and reproducible than typical ones based on metallic electrodes. In this work, we study by means of first principles, simulations and a tight-binding model the electronic and transport properties of graphene nanogaps with straight edges and different passivating atoms: Hydrogen or elements of the second row of the periodic table (boron, carbon, nitrogen, oxygen, and fluoride). We use the tight-binding model to reproduce the main ab-initio results and elucidate the physics behind the transport properties. We observe clear patterns that emerge in the conductance and the current as one moves from boron to fluoride. In particular, we find that the conductance decreases and the tunneling decaying factor increases from the former to the latter. We explain these trends in terms of the size of the atom and its onsite energy. We also find a similar pattern for the current, which is ohmic and smooth in general. However, when the size of the simulation cell is the smallest one along the direction perpendicular to the transport direction, we obtain highly non-linear behavior with negative differential resistance. This interesting and surprising behavior can be explained by taking into account the presence of Fano resonances and other interference effects, which emerge due to couplings to side atoms at the edges and other couplings across the gap. Such features enter the bias window as the bias increases and strongly affect the current, giving rise to the non-linear evolution. As a whole, these results can be used as a template to understand the transport properties of straight graphene nanogaps and similar systems and distinguish the presence of different elements in the junction.
Painting A Holistic Picture of Trust in and Adoption of Conversational Agents: A Meta-Analytic Structural Equation Modeling Approach

With their human-like nature, conversational agents (CAs) introduce a social component to human-computer interaction. Numerous studies have previously attempted to integrate this social component by incorporating trust into models such as the technology acceptance model (TAM) to decipher the adoption mechanisms related to CAs. Given the heterogeneity of these previous works, the aim of this paper is to integrate empirical evidence on the role and influence of trust within the nomological network of the TAM. For this purpose, we conduct a meta-analytic structural equation modeling approach based on 45 studies comprising k = 155 correlations, and N = 13,786 observations. Our findings highlight the multifaceted role of trust as a mediator transmitting the effects of the technology-related perceptions that drive the intention to use CAs. Our results present a comprehensive overview in a thriving research field that can guide both future theory building and the designs of more trustworthy CAs.
In Vitro Methods for Measuring the Permeability of Cell Monolayers

Cell monolayers, including endothelial and epithelial cells, play crucial roles in regulating the transport of biomolecules to underlying tissues and structures via intercellular junctions. Moreover, the monolayers form a semipermeable barrier across which leukocyte transmigration is tightly regulated. The inflammatory cytokines can disrupt the epithelial and endothelial permeability, thus the reduced barrier integrity is a hallmark of epithelial and endothelial dysfunction related with numerous pathological conditions, including cancer-related inflammation. Therefore, the assessment of barrier function is critical in in vitro models of barrier-forming tissues. This review summarizes the commercially available in vitro systems used to measure the permeability of cellular monolayers. The presented techniques are separated in two large groups: macromolecular tracer flux assays, and electrical impedance measurement-based permeability assays. The presented techniques are briefly described and compared.
Comparative Analysis of Root Canal Dentin Removal Capacity of Two NiTi Endodontic Reciprocating Systems for the Root Canal Treatment of Primary Molar Teeth. An In Vitro Study

The objective of the present study was to evaluate and compare the dentin removal capacity of Endogal Kids and Reciproc Blue NiTi alloy endodontic reciprocating systems for root canal treatments in primary second molar teeth via a micro-computed tomography (micro-CT) scan. Materials and Methods: Sixty root canal systems in fifteen primary second molar teeth were chosen and classified into one of the following study groups: A: EK3 Endogal Kids (n = 30) (EDG) and B. R25 Reciproc Blue (n = 30) (RB). Preoperative and postoperative micro-CT scans were uploaded into image processing software to analyze the changes in the volume of root canal dentin using a mathematical algorithm that enabled progressive differentiation between neighboring pixels after defining and segmenting the root canal systems in both micro-CT scans. Volumetric variations in the root canal system and the root canal third were calculated using a t-test for independent samples or a nonparametric Mann–Whitney–Wilcoxon test. Results: Statistically significant differences (p = 0.0066) in dentin removal capacity were found between the EDG (2.89 ± 1.26 mm3) and RB (1.22 ± 0.58 mm3) study groups for the coronal root canal third; however, no statistically significant differences were found for the middle (p = 0.4864) and apical (p = 0.6276) root canal thirds. Conclusions: Endogal and Reciproc Blue NiTi endodontic reciprocating systems showed similar capacity for the removal of root canal dentin, except for the coronal root canal third, in which the Reciproc Blue NiTi endodontic reciprocating system preserved more root canal dentin tissue.
Occupational Stressors and Access to COVID-19 Resources among Commuting and Residential Hispanic/Latino Farmworkers in a US-Mexico Border Region

Hispanic/Latino and migrant workers experience high degrees of occupational stress, constitute most of California’s agricultural workforce, and were among the most impacted populations by the COVID-19 pandemic. However, relatively little is known about the occupational stress experienced by farmworkers who commute daily between the US and Mexico. Occupational stress is considered an imbalance between the demands at work and the capabilities to respond in the context of the workforce. The goal of this study is to determine the type and severity of stressors in daytime and resident farmworkers and how COVID-19 vaccination status contributes to these stressors. Interviews containing the Migrant Farmworker Stress Inventory (MSWSI) were administered to a sample of 199 Hispanic/Latino farmworkers in Imperial County, a multi-billion-dollar agriculture sector in the US. Principal factor analysis differentiated latent factors in the MFSWI. Simple linear regression models and correlations identified associations between MFWSI scores and sample characteristics. The MFWSI reduced to five stressor domains: Health and Well-Being Vulnerabilities, Inadequate Standards of Living/Unknown Conditions of Living, Working Conditions, Working Environment, and Language Barriers. Approximately 40 percent of the respondents reported significant stress levels, with foreign-born (p = 0.014) and older respondents (p = 0.0415) being more likely to experience elevated stress regardless of their nighttime residence. We found that Spanish-language COVID-19 outreach might have been particularly effective for workers who reported high stress from English-language communication (p = 0.001). Moreover, our findings point to the importance of worker and human rights to mitigate the high-stress foreign-born workers who live in Mexico and the US experience.
Variation of amphibian and reptile composition in forest fragments of Veracruz highlands, Mexico

The central mountain region of Veracruz is one of the richest areas of herpetofauna in Mexico. The steep topographic gradient of this region is associated with climatic gradients that enable the occurrence of highly diverse herpetofauna communities. We tested differences among habitats to herpetofauna in an urban area, pine forest, oak forest, and riparian forest of municipality Camerino Z. Mendoza, Veracruz, Mexico. We conducted two-day monthly surveys between 2015 and 2017 in 69 sites from fragments of an urban area, riparian forest, Quercus forest, and Quercus-Pinus forest and tested for differences in herpetofauna species among these fragments. We found a total of 11 amphibian and 33 reptile species, and a high composition dissimilarity and species replacement among the sites. Our results amplified significantly the species-records within Camerino Z. Mendoza, and showed a great variation of amphibian and reptile composition among sites, highlighting the current role of these forests as a reservoir for herpetofauna and their importance for future conservation strategies in the region.
Dual-bottleneck feature pyramid network for multiscale object detection

Abstract. Multiscale object detection is a challenging task due to the multiscale and multiclassification nature of different objects. Convolutional neural networks are commonly used to extract the features. However, continuous downsampling operations and spatial position quantization result in the loss of detailed information. Subsequently, the high-level feature maps cannot distinguish the features of adjacent objects, and in particular, small objects are ignored. To solve the above problems, we propose a model for multiscale object detection. First, we design a dual-bottleneck subconvolutional network to extract shallow features from the multiscale image pyramid. The subnetwork provides more spatial information for feature maps of the backbone network. Then, we introduce a new downsampling method to avoid losing a large amount of detailed information during the downsampling operations. Finally, we use a loss function to alleviate the foreground–background class imbalance. In the ablation study, we research the matching between the image pyramid module and the backbone network, and we compare different feature fusion modules. We evaluate our proposed model on two publicly available large-scale datasets and observe that our model achieves an 8.5% improvement compared with the classical feature pyramid network and exhibits acceptable detection performance for the occluded and diverse objects.
Joint projection learning and structured analysis-synthesis dictionary pair learning for pattern classification

Abstract. Analysis-synthesis dictionary pair learning has attracted much attention in the field of pattern classification. To reduce the negative effect of trivial information contained in raw training samples and improve the computation efficiency, most existing dictionary pair learning methods first learn a projection matrix to project raw training samples into a low-dimensional subspace and then use the dimensionality-reduced samples for dictionary pair learning. However, the separation of projection learning (PL) and dictionary pair learning may make the dimensionality-reduced training samples not fit well for dictionary pair learning. To address this issue, we proposed a joint PL and structured analysis-synthesis dictionary pair learning (PLSDPL) method for pattern classification. Specifically, PLSDPL integrates PL and dictionary pair learning into a unified framework, so the dimensionality-reduced training samples are more suitable for the structured analysis-synthesis dictionary pair learning, thus making the learned analysis-synthesis dictionary pair have powerful discrimination capability. In addition, PLSDPL also imposes a low-rank constraint on each synthesis sub-dictionary to reduce the negative effect of noise contained in training samples. Experimental results on several image datasets show that PLSDPL is effective for pattern classification tasks.
Wi-Fi Direct Multi-group Networks

The added value of Device-to-Device (D2D) commu­ nication amounts to an efficient content discovery mechanism that enables users to steer their requests toward the node most likely to satisfy them. In paper, we address the implementation of content-centric routing in a D2D architecture for Android devices based on WiFi Direct, a protocol recently standardised by the Wi­ Fi Alliance. After discussing the creation of multiple D2D groups, we introduce novel paradigms featuring intra- and inter-group bidirectional communication. We then present the primitives involved in content advertising and requesting among members of the multi-group network. Finally, we evaluate the performance of our architecture in a real testbed involving Android devices in different group configurations. We also compare the results against the ones achievable exploiting Bluetooth technologies.