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Occupational exoskeletons promise to alleviate musculoskeletal injuries among industrial workers. Knowledge of the usability of the exoskeleton designs with respect to the user device interaction points, and the problems in design features, functions and parts, evaluated and rated using design principles is still limited. Further, the usability of exoskeletons when assembling, donning, doffing and disassembling them, tasks that can be considered pre and post use tasks are also critical to evaluate, especially from a device design standpoint. We conducted a heuristic evaluation of the usability of three popular exoskeletons, a back support device, a shoulder support device, and a sit stand exoskeleton when assembling, donning, doffing and disassembling them. Seven evaluators used Nielsen and Shneiderman usability heuristics to evaluate the devices. Results indicate that none of the three exoskeletons had any catastrophic usability problems, but all three had major usability problems including accommodating diverse users, the assembly, donning and doffing being a two person operation, poor documentation, a lack of sequence indicators during assembly of the devices, presence of safety hazards while donning and doffing the devices, and manual strength requirements. Further, the assembly task is the most difficult task resulting in the most violations of usability heuristics. The exoskeleton human factors research community should include diverse users in their evaluations and conduct usability, accessibility, and safety evaluations of these devices to provide design feedback to device designers.

The Monte Carlo method is a thriving and mathematically beautiful numerical technique used extensively, nowadays, to deal with many demanding problems in diverse fields. Here, we present an iterative Monte Carlo algorithm to work out very general nonlinear second-order differential equations, with Dirichlet boundary conditions. An example of its usage is, also, reported., Comment: 8 pages, 2 figures

Recognizing unauthorized Unmanned Aerial Vehicles (UAVs) within designated no-fly zones throughout the day and night is of paramount importance, where the unauthorized UAVs pose a substantial threat to both civil and military aviation safety. However, recognizing UAVs day and night with dual-vision cameras is nontrivial, since red-green-blue (RGB) images suffer from a low detection rate under an insufficient light condition, such as on cloudy or stormy days, while black-and-white infrared (IR) images struggle to capture UAVs that overlap with the background at night. In this paper, we propose a new optical flow-assisted graph-pooling residual network (OF-GPRN), which significantly enhances the UAV detection rate in day and night dual visions. The proposed OF-GPRN develops a new optical fusion to remove superfluous backgrounds, which improves RGB/IR imaging clarity. Furthermore, OF-GPRN extends optical fusion by incorporating a graph residual split attention network and a feature pyramid, which refines the perception of UAVs, leading to a higher success rate in UAV detection. A comprehensive performance evaluation is conducted using a benchmark UAV catch dataset. The results indicate that the proposed OF-GPRN elevates the UAV mean average precision (mAP) detection rate to 87.8%, marking a 17.9% advancement compared to the residual graph neural network (ResGCN)-based approach., Comment: The article is accepted at July 08, 2024 with 13 pages and 10 figures in the Journal of Engineering Applications of Artificial Intelligence, Elsevier

Contribution: This article analyzes the learning and motivational impact of teacher-authored educational video games on computer science education and compares its effectiveness in both face-to-face and online (remote) formats. This work presents comparative data and findings obtained from 217 students who played the game in a face-to-face format (control group) and 104 students who played the game in an online format (experimental group). Background: Serious video games have been proven effective at computer science education, however, it is still unknown whether the effectiveness of these games is the same regardless of their format, face-to-face or online. Moreover, the usage of games created through authoring tools has barely been explored. Research Questions: Are teacher-authored educational video games effective in terms of learning and motivation for computer science students? Does the effectiveness of teacher-authored educational video games depend on whether they are used in a face-to-face or online format? Methodology: A quasi-experiment has been conducted by using three instruments (pre-test, post-test, and questionnaire) with the purpose of comparing the effectiveness of game-based learning in face-to-face and online formats. A total of 321 computer science students played a teacher-authored educational video game aimed to learn about software design. Findings: The results reveal that teacher-authored educational video games are highly effective in terms of knowledge acquisition and motivation both in face-to-face and online formats. The results also show that some students' perceptions were more positive when a face-to-face format was used., Comment: 10 pages, 3 figures. Accepted version of a journal article published in IEEE Transactions on Education

Recent attacks on federated learning (FL) can introduce malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This paper proposes a novel defense strategy, referred to as LayerCAM-AE, designed to counteract model poisoning in federated learning. The LayerCAM-AE puts forth a new Layer Class Activation Mapping (LayerCAM) integrated with an autoencoder (AE), significantly enhancing detection capabilities. Specifically, LayerCAM-AE generates a heat map for each local model update, which is then transformed into a more compact visual format. The autoencoder is designed to process the LayerCAM heat maps from the local model updates, improving their distinctiveness and thereby increasing the accuracy in spotting anomalous maps and malicious local models. To address the risk of misclassifications with LayerCAM-AE, a voting algorithm is developed, where a local model update is flagged as malicious if its heat maps are consistently suspicious over several rounds of communication. Extensive tests of LayerCAM-AE on the SVHN and CIFAR-100 datasets are performed under both Independent and Identically Distributed (IID) and non-IID settings in comparison with existing ResNet-50 and REGNETY-800MF defense models. Experimental results show that LayerCAM-AE increases detection rates (Recall: 1.0, Precision: 1.0, FPR: 0.0, Accuracy: 1.0, F1 score: 1.0, AUC: 1.0) and test accuracy in FL, surpassing the performance of both the ResNet-50 and REGNETY-800MF. Our code is available at: https://github.com/jjzgeeks/LayerCAM-AE

Occupational exoskeletons promise to reduce the incidence of musculoskeletal injuries; however, we do not know if their designs allow universal use by all workers. We also do not know how easy the tasks of assembling, donning, doffing, and disassembling exoskeletons are. The purpose of our study was to heuristically evaluate a back support, a shoulder support, a handgrip strength support, and a sit-stand exoskeleton for how well they are designed for universal use when assembling, donning, doffing, and disassembling the exoskeleton. Seven evaluators used universal design principles and associated criteria to independently evaluate and rate four exoskeletons when assembling, donning, doffing, and disassembling the devices. The rating scale was a Likert-type scale, where a rating of 1 represented not at all, and a rating of 5 represented an excellent design with respect to the universal design criteria for the task. The results indicate that providing perceptible information to the user, making the design equitable to use for a diverse set of users, making the design simple and intuitive to use with adequate feedback, and designing to prevent user errors, and when errors are made, allowing the user to recover quickly from the errors, were rated poorly. Assembling and donning tasks presented the most challenges.

Recent $\gamma$-ray and neutrino observations seem to favor the consideration of non-uniform diffusion of cosmic rays (CRs) throughout the Galaxy. In this study, we investigate the consequences of spatially-dependent inhomogeneous propagation of CRs on the fluxes of secondary CRs and antiprotons detected at Earth. A comparison is made among different scenarios in search of potential features that may guide us toward favoring one over another in the near future. We also examine both the influence of inhomogeneous propagation in the production of secondary CRs from interactions with the gas, and the effects of this scenario on the local fluxes of antiprotons and light antinuclei produced as final products of dark matter annihilation. Our results indicate that the consideration of an inhomogeneous diffusion model could improve the compatibility of the predicted local antiproton flux with that of B, Be and Li, assuming only secondary origin of these particles. In addition, our model predicts a slightly harder local antiproton spectrum, making it more compatible with the high energy measurements of AMS-02. Finally, no significant changes are expected in the predicted local flux of antiprotons and antinuclei produced from dark matter among the different considered propagation scenarios., Comment: 19 pages, 6 figures in the main text and 3 figures in appendices, 1 table, 2 appendices; based on MSc Thesis of \'Alvaro Tovar-Pardo. Comments are welcome

Unmanned Aerial Vehicle (UAV) swarms play an effective role in timely data collection from ground sensors in remote and hostile areas. Optimizing the collective behavior of swarms can improve data collection performance. This paper puts forth a new mean field flight resource allocation optimization to minimize age of information (AoI) of sensory data, where balancing the trade-off between the UAVs movements and AoI is formulated as a mean field game (MFG). The MFG optimization yields an expansive solution space encompassing continuous state and action, resulting in significant computational complexity. To address practical situations, we propose, a new mean field hybrid proximal policy optimization (MF-HPPO) scheme to minimize the average AoI by optimizing the UAV's trajectories and data collection scheduling of the ground sensors given mixed continuous and discrete actions. Furthermore, a long short term memory (LSTM) is leveraged in MF-HPPO to predict the time-varying network state and stabilize the training. Numerical results demonstrate that the proposed MF-HPPO reduces the average AoI by up to 45 percent and 57 percent in the considered simulation setting, as compared to multi-agent deep Q-learning (MADQN) method and non-learning random algorithm, respectively., Comment: 13 pages, 6 figures. arXiv admin note: substantial text overlap with arXiv:2312.09953

We introduce a high-order space-time approximation of the Shallow Water Equations with sources that is invariant-domain preserving (IDP) and well-balanced with respect to rest states. The employed time-stepping technique is a novel explicit Runge-Kutta (ERK) approach which is an extension of the class of ERK-IDP methods introduced by Ern and Guermond (SIAM J. Sci. Comput. 44(5), A3366--A3392, 2022) for systems of non-linear conservation equations. The resulting method is then numerically illustrated through verification and validation., Comment: 29 pages, 9 figures

The class of SHD spaces was recently introduced in [12]. The first part of this paper focuses on answering most of the questions presented in that article. For instance, we exhibit an example of a non-SHD Tychonoff space $X$ such that $\mathscr{F}[X]$, the Pixley-Roy hyperspace of $X$, $\beta X$, the Stone-\v{C}ech compactification of $X$, and $C_p(X)$, the ring of continuous functions over $X$ equipped with the topology of pointwise convergence, are SHD. In the second part of this work we will present some variations of the SHD notion, namely, the WSHD property and the OHD property. Furthermore, we will pay special attention to the relationships between $X$ and $\mathscr{F}[X]$ regarding these new concepts.

In the present letter, the solutions of the Klein Gordon equation in the space time of an infinite domain wall is studied. It is shown that the horizon is an special region, where initial conditions (scattering conditions) at asymptotic past for the field can not be defined. This is in harmony with the fact that this region can not be crossed, a possibility that was suggested in \cite{lanosa}. If these results are correct, then it makes no sense to impose boundary conditions at this location., Comment: 5 pages, no figures

Motivated by the main results of the articles by Hattori and Bouziad, we seek to answer the following questions about Hattori spaces. Let A be a subset of the real line, then: Given a compact set $K$ in the Euclidean topology, under what conditions is $K$ compact in the Hattori space $H(A)$? When is $H(A)$ a quasi-metrizable space? When is $H(A)$ a semi-stratifiable space? When is $C_p(H(A))$ a normal space? When is $C_p(H(A))$ a Lindel\"of space? We obtain complete answers for 3 out of these 5 questions, while the last ones remain with partial answers, among them: \ Theorem: If $\mathbb{R}\setminus A$ is analytic, then $C_p(H(A))$ is not normal. Moreover when we work on the Solovay Model we can improve the previous result to only require $\mathbb{R}\setminus A$ to be uncountable.