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The industrial internet of things (IIoT) has undergone rapid growth in recent years, which has resulted in an increase in the number of threats targeting both IIoT devices and their connecting technologies. However, deploying tools to counter these threats involves tackling inherent limitations, such as limited processing power, memory, and network bandwidth. As a result, traditional solutions, such as the ones used for desktop computers or servers, cannot be applied directly in the IIoT, and the development of new technologies is essential to overcome this issue. One approach that has shown potential for this new paradigm is the implementation of intrusion detection system (IDS) that rely on machine learning (ML) techniques. These IDSs can be deployed in the industrial control system or even at the edge layer of the IIoT topology. However, one of their drawbacks is that, depending on the factory's specifications, it can be quite challenging to locate sufficient traffic data to train these models. In order to address this problem, this study introduces a novel IDS based on the TabPFN model, which can operate on small datasets of IIoT traffic and protocols, as not in general much traffic is generated in this environment. To assess its efficacy, it is compared against other ML algorithms, such as random forest, XGBoost, and LightGBM, by evaluating each method with different training set sizes and varying numbers of classes to classify. Overall, TabPFN produced the most promising outcomes, with a 10–20% differentiation in each metric. The best performance was observed when working with 1000 training set samples, obtaining an F1 score of 81% for 6-class classification and 72% for 10-class classification. [ABSTRACT FROM AUTHOR]

Background: The effectiveness of eLearning in enhancing healthcare professionals' capacity has received substantial attention globally. This review sought to synthesis evidence on the effectiveness of various types of eLearning programmes, and the facilitators and barriers to its use. Methods: The review was guided by Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Four main databases (PubMed, Web of Science, JSTOR, and Scopus) in July 2023 and 44 articles met the eligibility criteria and were included in the review. The JBI critical appraisal checklist was used to appraise the methodological quality of the studies. The data were examined using narrative review to determine the effectiveness of the intervention as well as the barriers and facilitators to its use. Results: This review found that asynchronous, synchronous, blended, and self-learning methodologies are effective eLearning approaches for continuous professional development. Previous positive experiences, user-friendly interfaces and relevance of the eLearning content to daily practice are critical elements that facilitate eLearning usage. Poor computer competence and literacy, lack of personal computers and high family duties were the main personal factors that hindered eLearning use. Some systemic barriers included; heavy workloads, shortage of specialised eLearning facilitators poor management involvement, and technical inadequacies within the ICT departments. Environmental issues such as poor infrastructure, including limited internet and frequent power outages acted as barriers. Conclusion: The review highlights the effectiveness of various eLearning approaches among health professionals and presents the disparities between developing and developed economies in relation to the facilitators and barriers. [ABSTRACT FROM AUTHOR]

The integration of a blockchain-supported serverless computing framework enhances the performance of computational and analytical operations and the provision of services within internet-based data centers, rather than depending on independent desktop computers. Therefore, in the present research paper, a blockchain-assisted serverless framework for geospatial data visualizations is implemented. The proposed BS-GeoEduNet 1.0 framework leverages the capabilities of AWS Lambda for serverless computing, providing a reliable and efficient solution for data storage, analysis, and distribution. The proposed framework incorporates AES encryption, decryption layers, and queue implementation to achieve a scalable approach for handling larger files. It implements a queueing mechanism during the heavier input/output processes of file processing by using Apache KAFKA, enabling the system to handle large volumes of data efficiently. It concludes with the visualization of all geospatial-enabled NIT/IIT details on the proposed framework, which utilizes the data fetched from MongoDB. The experimental findings validate the reliability and efficiency of the proposed system, demonstrating its efficacy in geospatial data storage and processing. [ABSTRACT FROM AUTHOR]

Shape modeling is a dynamic area in computer graphics with significant applications in computer-aided design, animation, architecture, and entertainment. Virtual sculpting, a key paradigm in free-form modeling, has traditionally been performed on desktop computers where users manipulate meshes with controllers and view the models on two-dimensional displays. However, the advent of Extended Reality (XR) technology has ushered in immersive interactive experiences, expanding the possibilities for virtual sculpting across various environments. A real-time virtual sculpting system implemented in a Virtual Reality (VR) setting is introduced in this paper, utilizing quasi-uniform meshes as the foundational structure. In our innovative sculpting system, we design an integrated framework encompassing a surface selection algorithm, mesh optimization technique, mesh deformation strategy, and topology fusion methodology, which are all tailored to meet the needs of the sculpting process. The universal, user-friendly sculpting tools designed to support free-form topology are offered in this system, ensuring that the meshes remain watertight, manifold, and free from self-intersections throughout the sculpting process. The models produced are versatile and suitable for use in diverse fields such as gaming, art, and education. Experimental results confirm the system's real-time performance and universality, highlighting its user-centric design. [ABSTRACT FROM AUTHOR]

The point cloud provides viewers with intuitive geometric understanding but requires a huge amount of data. Moving Picture Experts Group (MPEG) has developed video‐based point‐cloud compression in the range of 300–700. As the compression rate increases, the complexity increases to the extent that it takes 101.36 s to compress one frame in an experimental environment using a personal computer. To realize real‐time point‐cloud compression processing, the direct patch projection (DPP) method proposed herein simplifies the complex patch segmentation process by classifying and projecting points according to their geometric positions. The DPP method decreases the complexity of the patch segmentation from 25.75 s to 0.10 s per frame, and the entire process becomes 8.76 times faster than the conventional one. Consequently, this proposed DPP method yields similar peak signal‐to‐noise ratio (PSNR) outcomes to those of the conventional method at reduced times (4.7–5.5 times) at the cost of bitrate overhead. The objective and subjective results show that the proposed DPP method can be considered when low‐complexity requirements are required in lightweight device environments. [ABSTRACT FROM AUTHOR]

In the contemporary era, dizziness is a prevalent ailment among patients. It can be caused by either vestibular neuritis or a stroke. Given the lack of diagnostic utility of instrumental methods in acute isolated vertigo, the differentiation of vestibular neuritis and stroke is primarily clinical. As a part of the initial differential diagnosis, the physician focuses on the characteristics of nystagmus and the results of the video head impulse test (vHIT). Instruments for accurate vHIT are costly and are often utilized exclusively in healthcare settings. The objective of this paper is to review contemporary methodologies for accurately detecting the position of pupil centers in both eyes of a patient and for precisely extracting their coordinates. Additionally, the paper describes methods for accurately determining the head rotation angle under diverse imaging and lighting conditions. Furthermore, the suitability of these methods for vHIT is being evaluated. We assume the maximum allowable error is 0.005 radians per frame to detect pupils' coordinates or 0.3 degrees per frame while detecting the head position. We found that for such conditions, the most suitable approaches for head posture detection are deep learning (including LSTM networks), search by template matching, linear regression of EMG sensor data, and optical fiber sensor usage. The most relevant approaches for pupil localization for our medical tasks are deep learning, geometric transformations, decision trees, and RASNAC. This study might assist in the identification of a number of approaches that can be employed in the future to construct a high-accuracy system for vHIT based on a smartphone or a home computer, with subsequent signal processing and initial diagnosis. [ABSTRACT FROM AUTHOR]

Background: Ultra high molecular weight polyethylene composites are fiber based composites used in armor applications. While some characterization has been conducted experimentally, this study varies multiple parameters simultaneously to investigate material response under a wide range of conditions. Objective: This work focuses on characterizing the response of Dyneema® HB26 hard laminate composites under high-speed impacts to examine the influence of plate diameter, clamping pressure, and plate spacing on target performance. Additionally, micro Computer Tomography scans are used to nondestructively evaluate the damage evolution in the targets. Methods: These scan results are used in concert with more traditional armor performance metrics to evaluate the effect of various parameters using the method of orthogonal array analysis. This technique allows for multiple variables to be investigated in the same test series, saving time and budget while still providing quality results across a range of variables and variable values. Results: We conclude that of the parameters investigated, the plate spacing parameter has the largest effect on performance, followed by the plate diameter. Bolt torque was found to not have a significant impact on results, indicating that an edge clamping pressure is not critical to material response. Additionally, by examining the high resolution scans, we can quantify the damage with an effective damage angle and that this angle is a good predictor of performance. Conclusion: Finally a damage theory involving the effective bending strength of the plates is discussed as an explanation for all of the results observed in this test series. [ABSTRACT FROM AUTHOR]

Copyright of Baghdad Science Journal is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new system design tool for the simulation of a full hybrid rocket engine is developed in this article. This tool enables to simulate the behaviour of the engine at different conditions/configurations in several minutes from a desktop computer by keeping a balance between accuracy and computation time. This makes its use especially attractive for the pre-design phases of the engine. The main components of the hybrid rocket engine from our laboratory-characterized by a catalytic injection of the oxidizer-, are modelled and implemented in the tool: the feed/injection sub-system through 0-D models of a mass flow rate regulator and a catalyst; the combustion chamber, with a 1.5-D model; and the nozzle through a 1-D model. An iterative method is employed to reach the convergence of pressure in the combustion chamber between these sub-systems. The corresponding set of equations is solved by a Newton–Raphson technique. Seven experiments performed on our lab-scale engine were used to validate the system design tool. In five of the simulations, the relative differences of the main physical quantities with the experiment were below 30%, being the largest errors found in the fuel regression rate and mixture ratio. The best agreements with the experiments were retrieved for the cases with the largest oxidizer mass fluxes (above 230 kg/ m 2 /s) and mixture ratios closest to stoichiometry, defining thus, the range of applicability of the system design tool. The results presented in this article were issued of a Ph.D. thesis. • A 1.5-D combustion chamber model of a hybrid rocket engine was developed and validated. • 0-D/1-D mass flow regulator, catalyst and nozzle models were developed and validated. • A system design tool of a hybrid rocket engine implementing these models was developed. • The tool is adapted to pre-design phases, with a few minutes of computation time. • Errors between 8%–40% in fuel regression rate and below 10% in thrust and chamber pressure. [ABSTRACT FROM AUTHOR]

Identification of different species of animals has become an important issue in biology and ecology. Ornithology has made alliances with other disciplines in order to establish a set of methods that play an important role in the birds' protection and the evaluation of the environmental quality of different ecosystems. In this case, the use of machine learning and deep learning techniques has produced big progress in birdsong identification. To make an approach from AI-IoT, we have used different approaches based on image feature comparison (through CNNs trained with Imagenet weights, such as EfficientNet or MobileNet) using the feature spectrogram for the birdsong, but also the use of the deep CNN (DCNN) has shown good performance for birdsong classification for reduction of the model size. A 5G IoT-based system for raw audio gathering has been developed, and different CNNs have been tested for bird identification from audio recordings. This comparison shows that Imagenet-weighted CNN shows a relatively high performance for most species, achieving 75% accuracy. However, this network contains a large number of parameters, leading to a less energy efficient inference. We have designed two DCNNs to reduce the amount of parameters, to keep the accuracy at a certain level, and to allow their integration into a small board computer (SBC) or a microcontroller unit (MCU). [ABSTRACT FROM AUTHOR]

Transformer‐based models have revolutionized single cell RNA‐seq (scRNA‐seq) data analysis. However, their applicability is challenged by the complexity and scale of single‐cell multi‐omics data. Here a novel single‐cell multi‐modal/multi‐task transformer (scmFormer) is proposed to fill up the existing blank of integrating single‐cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large‐scale single‐cell multimodal data and heterogeneous multi‐batch paired multi‐omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell‐type labels from single‐cell transcriptomics to proteomics data. Using COVID‐19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well‐suited for spatial multi‐omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single‐cell multi‐omics data. [ABSTRACT FROM AUTHOR]

Nowadays, establishing security in data transmission is essential, and it is achieved by cryptography. Encryption of still or video images in specific applications such as Internet of Things, medical and satellite imaging, in applications requiring high-speed encryption, or even in applications where a personal computer is unavailable or cannot be used needs special hardware. In this paper, an image encryption algorithm based on chaos theory named Parallel Chaotic Checksum-based Image Encryption or PCCIE algorithm is proposed that has been able to provide a fast, efficient and secure algorithm with a hardware perspective on the design and parallel system structure. Using high-level synthesis, PCCIE is implemented on a Field Programmable Gate Array (FPGA). The proposed algorithm using small and independent local image buffers solved FPGA internal memory limitation for encryption of large images. Ultimately, a Hexa-core crypto-processor with single-precision floating-point and fixed-point precision has been designed, capable of encrypting 256 × 256 and Full HD images in 2.13 and 59.52 milliseconds, respectively, at 469 and 16 frames per second. [ABSTRACT FROM AUTHOR]

Background: Since cytokine receptor-like factor 1 (CRLF1) has been implicated in tissue regeneration, we hypothesized that CRLF1 released by mesenchymal stem cells can promote the repair of osteochondral defects. Methods: The degree of a femoral osteochondral defect repair in rabbits after intra-articular injections of bone marrow-derived mesenchymal stem cells (BMSCs) that were transduced with empty adeno-associated virus (AAV) or AAV containing CRLF1 was determined by morphological, histological, and micro computer tomography (CT) analyses. The effects of CRLF1 on chondrogenic differentiation of BMSCs or catabolic events of interleukin-1beta-treated chondrocyte cell line TC28a2 were determined by alcian blue staining, gene expression levels of cartilage and catabolic marker genes using real-time PCR analysis, and immunoblot analysis of Smad2/3 and STAT3 signaling. Results: Intra-articular injections of BMSCs overexpressing CRLF1 markedly improved repair of a rabbit femoral osteochondral defect. Overexpression of CRLF1 in BMSCs resulted in the release of a homodimeric CRLF1 complex that stimulated chondrogenic differentiation of BMSCs via enhancing Smad2/3 signaling, whereas the suppression of CRLF1 expression inhibited chondrogenic differentiation. In addition, CRLF1 inhibited catabolic events in TC28a2 cells cultured in an inflammatory environment, while a heterodimeric complex of CRLF1 and cardiotrophin-like Cytokine (CLC) stimulated catabolic events via STAT3 activation. Conclusion: A homodimeric CRLF1 complex released by BMSCs enhanced the repair of osteochondral defects via the inhibition of catabolic events in chondrocytes and the stimulation of chondrogenic differentiation of precursor cells. [ABSTRACT FROM AUTHOR]

Our previous studies discussed the potential of measuring virtual trees using computational virtual measurement (CVM). CVM is a general methodology that employs observational techniques in lieu of mathematical processing. The advantage of CVM lies in its ability to circumvent mathematical assumptions of tree shapes at the algorithmic level. However, due to the current computational limitations of desktop computers, the previously developed CVM application, namely, virtual water displacement (VWD), could only act as a primary theoretical testimonial using an idealized point cloud of a tree. The key problem was that simulating a massive number of virtual water molecules (VMMs) consumed most of the computational resources. As a consequence, an unexpected empirical formula for volume calibration had to be applied to the output measurement results. Aiming to create a more realistic simulation of what occurs when water displacement is used to measure tree volume in the real world, in this study, we developed a new physical scenario for VWMs. This new scenario, namely, a flood area mechanism (FAM), employed footprints of VWMs instead of quantifying VWM counts. Under a FAM, the number of VMMs was reduced to a few from several thousands, making the empirical mathematical process (of the previously developed physical scenario of VWMs) unnecessary. For the same ideal point clouds as those used in our previous studies, the average volume overestimations were found to be 6.29% and 2.26% for three regular objects and two artificial stems, respectively. Consequently, we contend that FAM represents a closer approximation to actual water displacement methods for measuring tree volume in nature. Therefore, we anticipate that the VWD method will eventually utilize the complete tree point cloud with future advancements in computing power. It is necessary to develop methods such as VWD and more CVM applications for future applications starting now. [ABSTRACT FROM AUTHOR]

The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system's efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions. [ABSTRACT FROM AUTHOR]

This study investigates how 5-year-old children perceive and understand Artificial Intelligence (AI). The participants of the study were 45 children from a private kindergarten in South Korea. The research employs drawings and small group interviews to explore the children's conceptualizations of AI. Research results were as follows. First, 73.3% of children associated AI with positive feelings. Although they generally viewed AI as a convenience in their lives, they also expressed concerns related to health and costs. Secondly, they indicated household items, smart devices, robot and machinery, personal computer and other things as images of AI. The most frequently indicated images of AI included items that they had used themselves or seen people using in daily lives. Children primarily view AI as a tool for adult convenience and entertainment, with their perceptions shaped by their developmental stage and daily interactions with technology. These findings underscore the need for a more comprehensive approach to AI education in early childhood, one that addresses ethical considerations and is tailored to children's developmental needs. This study aims to contribute to the ongoing dialogue and development of strategies for effectively introducing AI to young learners in a way that supports their growth and development. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Oil Crop Sciences is the property of Oil Crops Research Institute of Chinese Academy of Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The study examined how three polishing methods, using equipment from NTI CeraGlaze (NTI), Komet Dental (Komet), and EVE Diacera (EVE) and employing either wet or dry grinding, affect the texture (roughness) and phase composition of Y-PSZ dental crowns. Dental crowns made from VITA's 3Y-/4Y-/5Y-partly stabilized zirconia (Y-PSZ; YZ-HT/ST/XT), utilizing a standard CAD/CAM process, underwent both wet or dry grinding and polishing. The effects of distinct polishing treatments on Y-PSZ surface phase content were investigated using X-ray diffraction (XRD) and Rietveld refinement, the grain size was measured by field emission scanning electron microscopy (FE-SEM), and confocal scanning laser microscopy (CLSM) was used to determine the surface roughness as the arithmetical mean height (Sa). To analyse the different mode of action, the components of the polishers were analysed using XRD, along with micro X-ray computer tomography (µXCT), FE-SEM, and CLSM for microstructural examination. The Komet and NTI polishing regimes reduced roughness significantly better than the EVE regime for the 3Y and all wet specimens, but caused a rhombohedral phase fraction. A possible explanation for this result is the overall finer structure of the EVE coarse polisher (abrasive particle size and content, texture density), which probably results in a lower force on the Y-PSZ surface. Therefore, the rhombohedral phase boundary would not be reached. Due to rhombohedral phase having larger volume expansion and shear than the monoclinic phase, it may result in enhanced transformation toughening or detrimental low-temperature degradation effects. [ABSTRACT FROM AUTHOR]

Cloud computing alludes to the on-demand availability of personal computer framework resources, primarily information storage and processing power, without the customer's direct personal involvement. Cloud computing has developed dramatically among many organizations due to its benefits such as cost savings, resource pooling, broad network access, and ease of management; nonetheless, security has been a major concern. Researchers have proposed several cryptographic methods to offer cloud data security; however, their execution times are linear and longer. A Security Key 4 Optimization Algorithm (SK4OA) with a non-linear run time is proposed in this paper. The secret key of SK4OA determines the run time rather than the size of the data as such is able to transmit large volumes of data with minimal bandwidth and able to resist security attacks like brute force since its execution timings are unpredictable. A data set from Kaggle was used to determine the algorithm's mean and standard deviation after thirty (30) times of execution. Data sizes of 3KB, 5KB, 8KB, 12KB, and 16 KB were used in this study. There was an empirical analysis done against RC4, Salsa20, and Chacha20 based on encryption time, decryption time, throughput and memory utilization. The analysis showed that SK4OA generated lowest mean non-linear run time of 5.545±2.785 when 16KB of data was executed. Additionally, SK4OA's standard deviation was greater, indicating that the observed data varied far from the mean. However, RC4, Salsa20, and Chacha20 showed smaller standard deviations making them more clustered around the mean resulting in predictable run times. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) can be a promising tool to simulate reality in various settings but the real impact of this technology on the human mental system is still unclear as to how VR might (if at all) interfere with cognitive functioning. Using a computer, we can concentrate, enter a state of flow, and still maintain control over our surrounding world. Differently, VR is a very immersive experience which could be a challenge for our ability to allocate divided attention to the environment to perform executive functioning tasks. This may also have a different impact on women and men since gender differences in both executive functioning and the immersivity experience have been referred to by the literature. The present study aims to investigate cognitive multitasking performance as a function of (1) virtual reality and computer administration and (2) gender differences. To explore this issue, subjects were asked to perform simultaneous tasks (span forward and backward, logical–arithmetic reasoning, and visuospatial reasoning) in virtual reality via a head-mounted display system (HDMS) and on a personal computer (PC). Our results showed in virtual reality an overall impairment of executive functioning but a better performance of women, compared to men, in visuospatial reasoning. These findings are consistent with previous studies showing a detrimental effect of virtual reality on cognitive functioning. [ABSTRACT FROM AUTHOR]

The development of a high-performance, low-cost, and simply fabricated flexible three-dimensional (3D) force sensor is essential for the future development of electronic skins suitable for the detection of normal and shear forces for several human motions. In this study, a sandwich-structured flexible 3D force tactile sensor based on a polyethylene-carbon composite material (velostat) is presented. The sensor has a large measuring range, namely, 0–12 N in the direction of the normal force and 0–2.6 N in the direction of the shear force. For normal forces, the sensitivity is 0.775 N−1 at 0–1 N, 0.107 N−1 between 1 and 3 N, and 0.003 N−1 at 3 N and above. For shear forces, the measured sensitivity is 0.122 and 0.12 N−1 in x- and y-directions, respectively. Additionally, the sensor exhibits good repeatability and stability after 2500 cycles of loading and releasing. The response and recovery times of the sensor are as fast as 40 and 80 ms, respectively. Furthermore, we prepared a glove-like sensor array. When grasping the object using the tactile glove, the information about the force applied to the sensing unit can be transmitted through a wireless system in real-time and displayed on a personal computer (PC). The prepared flexible 3D force sensor shows broad application prospects in the field of smart wearable devices. [ABSTRACT FROM AUTHOR]

Ad avoidance (e.g., "blinding out" digital ads) is a substantial problem for advertisers. Avoiding mobile banner ads differs from active ad avoidance in nonmobile (desktop) settings, because mobile phone users interact with ads to avoid them: (1) They classify new content at the bottom of their screens; if they see an ad, they (2) scroll so that it is out of the locus of attention and (3) position it at a peripheral location at the top of the screen while focusing their attention on the (non-ad) content in the screen center. Introducing viewport logging to marketing research, we capture granular ad-viewing patterns from users' screens (i.e., viewports). While mobile users' ad-viewing patterns are concave over the viewport (with more time at the periphery than in the screen center), viewing patterns on desktop computers are convex (most time in the screen center). Consequently, we show that the effect of viewing time on recall depends on the position of an ad in interaction with the device. An eye-tracking study and an experiment show that 43% to 46% of embedded mobile banner ads are likely to suffer from ad avoidance, and that ad recall is 6 to 7 percentage points lower on mobile phones (versus desktop). [ABSTRACT FROM AUTHOR]

We model the effect of online information search across mobile (smartphone and tablet) and nonmobile (personal computer [PC], both desktop and laptop) platforms on frequency of purchasing per online shopping session. Using clickstream data from a multinational retailer, we find that device modality drives purchase frequency, likely due to the differential ease of use of PCs, tablets, and smartphones. In particular, frequency of completed orders is highest when information search and purchase completion are highly convenient, such as when shopping via tablet. We also determine that information search in the form of reading online product reviews has no effect on mobile platforms, while it does on other platforms. These findings contribute to information search theory, suggesting that information search increases purchase likelihood when it is goal directed, extensive, and easy to conduct. Thus, the broad role of digital advertising should be to make the information search process easier and more convenient for consumers to stimulate purchases. These findings help digital advertisers understand information search patterns across device modalities. Implications for digital advertisers on electronic commerce (e-commerce) platforms are offered. [ABSTRACT FROM AUTHOR]

Rates of obesity continue to rise in the United States and across the globe. Obesity is a risk factor for developing insulin resistance, type 2 diabetes, and cardiovascular disease. For clinicians, other health care providers, and educators, providing patients with accurate and meaningful information about obesity, including lifestyle (diet and exercise) interventions and symptom monitoring, is challenging because of infrequent contact, methods of communication, a lack of effective patient education resources, and inefficient patient feedback methods. Evidence suggests that significantly more patients are now getting their health care information online from general medical websites, disease-specific network communities, and social media. Thus, harnessing the power of technologies, including personal computers and smartphones, with attention to social media may equip health care providers with methods to serve their patients better by addressing challenges, improving indirect patient contact, and enhancing health outcomes. This article aims to provide an overview of technology with a focus on social media use in obesity education and outreach. Practical information is provided related to creating content, delivering content, and managing the social media space for the novice creator. [ABSTRACT FROM AUTHOR]

Several digital timers and clocks for desktop computers were developed in present study. Digital timers are used for time control in scientific experiments, dissertation defensing, work debriefing, academic lecture, student teaching, etc. Digital timer-counter and clocks are used for field sampling, field observation, ecological experiments, etc. All digital timers and clocks were packed and can be freely downloaded. [ABSTRACT FROM AUTHOR]

The objective of this manuscript is to investigate the determinants influencing the selection of over-the-top (OTT) platforms as opposed to traditional television mediums—cable, Internet protocol television (IPTV), and satellite broadcasting—for the consumption of content such as television shows and films. Employing data extracted from the 2020 Media Panel comprising 423,851 observations garnered from personal media diaries, this study scrutinizes the impacts of individual attributes, environmental conditions, and temporal factors on platform choice. The findings reveal a temporal influence characterized by a "Friday effect" and a heightened preference for OTT platforms during early afternoon (12:00–16:00) and late-night hours (00:00–04:00). Notably, the likelihood of selecting OTT platforms is significantly augmented during the late-night period in comparison to other time frames. In relation to individual characteristics, variables such as male gender, younger age, higher educational attainment, and elevated income levels were positively correlated with a predilection for OTT platforms. Additionally, environmental variables such as possession of an unlimited data plan and ownership of a tablet personal computer also emerged as significant predictors for OTT preference. Furthermore, the presence of a beam projector during late-night hours and residing in a household with multiple occupants during afternoon hours also served as contributing factors for OTT utilization. In conclusion, the study offers critical insights for stakeholders in both traditional television and burgeoning OTT markets, providing data-driven recommendations for the strategic allocation of resources in consideration of day-of-week and time-of-day variables. [ABSTRACT FROM AUTHOR]

Learning physics can be seen by many as a problem, as the standard method of learning tends to focus on remembering and using concepts that fail to construct meaning. To overcome this problem in teaching rectilinear motion, we implemented multiple representations using a cyber–physical system that enables interaction between a physical model and the real world. We did so by using a microcomputer system housed inside a ball, including motion and force sensors. This system communicated with the teacher's laptop in order to display the corresponding data via a projector. The study was conducted with 49 tenth-grade students across five sessions on rectilinear motion. Using a pre- and post-test, we observed that the experimental group performed significantly better than the control group, both in terms of learning as well as in the development of creativity (fluency and flexibility). With guidance from the teacher, the multiple representations allowed the students to improve their learning and creativity by connecting various forms of representation. In other words, the students were able to connect both abstract and concrete views through a real-world, physical experience. Our study reveals the potential of cyber–physical systems within the teaching–learning process for physics, specifically rectilinear motion, and how such a system supports multiple representations. [ABSTRACT FROM AUTHOR]

Global neural dynamics emerge from multi-scale brain structures, with nodes dynamically communicating to form transient ensembles that may represent neural information. Neural activity can be measured empirically at scales spanning proteins and subcellular domains to neuronal assemblies or whole-brain networks connected through tracts, but it has remained challenging to bridge knowledge between empirically tractable scales. Multi-scale models of brain function have begun to directly link the emergence of global brain dynamics in conscious and unconscious brain states with microscopic changes at the level of cells. In particular, adaptive exponential integrate-and-fire (AdEx) mean-field models representing statistical properties of local populations of neurons have been connected following human tractography data to represent multi-scale neural phenomena in simulations using The Virtual Brain (TVB). While mean-field models can be run on personal computers for short simulations, or in parallel on high-performance computing (HPC) architectures for longer simulations and parameter scans, the computational burden remains red heavy and vast areas of the parameter space remain unexplored. In this work, we report that our HPC framework, a modular set of methods used here to implement the TVB-AdEx model for the graphics processing unit (GPU) and analyze emergent dynamics, notably accelerates simulations and substantially reduces computational resource requirements. The framework preserves the stability and robustness of the TVB-AdEx model, thus facilitating a finer-resolution exploration of vast parameter spaces as well as longer simulations that were previously near impossible to perform. Comparing our GPU implementations of the TVB-AdEx framework with previous implementations using central processing units (CPUs), we first show correspondence of the resulting simulated time-series data from GPU and CPU instantiations. Next, the similarity of parameter combinations, giving rise to patterns of functional connectivity, between brain regions is demonstrated. By varying global coupling together with spike-frequency adaptation, we next replicate previous results indicating inter-dependence of these parameters in inducing transitions between dynamics associated with conscious and unconscious brain states. Upon further exploring parameter space, we report a nonlinear interplay between the spike-frequency adaptation and subthreshold adaptation, as well as previously unappreciated interactions between the global coupling, adaptation, and propagation velocity of action potentials along the human connectome. Given that simulation and analysis toolkits are made public as open-source packages, this framework serves as a template onto which other models can be easily scripted. Further, personalized data-sets can be used for for the creation of red virtual brain twins toward facilitating more precise approaches to the study of epilepsy, sleep, anesthesia, and disorders of consciousness. These results thus represent potentially impactful, publicly available methods for simulating and analyzing human brain states. [ABSTRACT FROM AUTHOR]

This paper is an attempt to provide a dual-source inverter, an intelligent inverter topology that links two isolated DC sources to a single three-phase output through single-stage conversion. The converter is designed to be utilized in hybrid photovoltaic fuel cell systems, among other renewable energy applications. The proposed dual-source inverter employs a single DC-AC converter, as opposed to conventional dual-source hybrid inverters which make use of several input DC-DC modules to obtain the voltage formed across the inverter's input DC-link. In the proposed topology, the semiconductor count is low, which leads to improved efficiency, cost, complexity, and reliability. The proposed topology makes use of two impedance networks connected by transformers, diodes, and capacitors. The regulation of the electrical power generated by primary sources and the independence of the converter on key factors like voltage and frequency are essential parameters in multi-input converters. This feature becomes highly prominent when the control algorithm is implemented by conventional processors. Viewed from this perspective, the control method described in this paper is worthy of consideration. The research work describes a 220-W/50 Hz prototype that employs Simple Boost-SPWM. Experimental results verify the analyses and corroborate the satisfactory performance of the suggested converter. [ABSTRACT FROM AUTHOR]

Background: This prospective study aimed to evaluate the influence of the computer type (tablet or desktop) on accuracy and tracing time of cephalometric analyses. Methods: Dental students used a web-based application specifically developed for this purpose to perform cephalometric analyses on tablet and desktop computers. Landmark locations and timestamps were exported to measure the accuracy, successful detection rate and tracing time. Reference landmarks were established by six experienced orthodontists. Statistical analysis included reliability assessment, descriptive statistics, and linear mixed effect models. Results: Over a period of 8 semesters a total of 277 cephalometric analyses by 161 students were included. The interrater reliability of the orthodontists establishing the reference coordinates was excellent (ICC > 0.9). For the students, the mean landmark deviation was 2.05 mm and the successful detection rate for the clinically acceptable threshold of 2 mm suggested in the literature was 68.6%, with large variations among landmarks. No effect of the computer type on accuracy and tracing time of the cephalometric analyses could be found. Conclusion: The use of tablet computers for cephalometric analyses can be recommended. [ABSTRACT FROM AUTHOR]

There is no question about the innovation force and the economic potential of blockchain technology. As the basis for new currencies, financial services, and smart contracts, blockchain technology can be seen as the fifth disruptive computing paradigm, after mainframes, personal computers, the Internet, and mobile devices. However, there are questions about its ethical implications, which have the potential to also impact the economic success of blockchain technology. This article aims to provide ethical guidance on blockchain technology. In order to reach this goal, the focus of the ethical analysis will first concentrate on the unique characteristics of blockchain technology compared to other technology-based innovations. The unique nucleus of blockchain technology can be defined as a move from the trust in people to a trust in math, as a move from an internet of information to an internet of value, or—as I would propose—a shift from an intermediated network to an immediate network. Second, the ethical opportunities (e.g., transparency, participation, global access to services) and risks (e.g., ecological impact, lack of legal monitoring and enforcement) associated with this unique nucleus of blockchain technology will be discussed. Third, an outlook on possible concrete solutions will be provided. [ABSTRACT FROM AUTHOR]

An analytical solution was obtained for seismic wave fields in a spherically symmetric Earth. Asymptotics is used for stable calculation of wave fields. It is shown that the classical asymptotics in the case of a sphere of large (in wavelengths) dimensions gives an error in the solution. The original asymptotics is used for efficient calculation of a solution without errors with great detail. Software has been created that makes it possible to carry out calculations for high-frequency (1 Hz and more) teleseismic wave fields in a discrete (layered) sphere of planetary dimensions. Calculations can be carried out on personal computers with OpenMP parallelization. V.Yu. Burmin (2010, 2019) proposed a spherically symmetric model of the Earth. It is characterized by the fact that the outer core in this model has viscosity and, therefore, an effective shear modulus other than zero. For this model of the Earth, a highly detailed calculation was carried out with a carrier frequency of 1 Hz. As a result of the analytical calculation, it was found that low-amplitude high-frequency oscillations, so-called "precursors," appear ahead of the PKP waves. An analytical calculation showed that the theoretical seismograms for this model of the Earth is in many respects similar to the experimental data. This confirms the correctness of the ideas that form the basis of the model. [ABSTRACT FROM AUTHOR]

A prototype system based on a combination of 4π sensitive gamma imaging and simultaneous localization and mapping (SLAM) was developed to find unknown radiation source(s) rapidly. The system consisted of a spherical visible camera, a three-dimensional light detection and ranging, a CdTe pixel array detector for 4π gamma imaging, a portable battery, and a control laptop personal computer; these were mounted on an unmanned vehicle. In a search area of 16 × 16 m2 with a height of 3 m, the prototype system successfully demonstrated the finding of a 137Cs point source with an activity of 0.67 MBq in the surrounding thin walls. [ABSTRACT FROM AUTHOR]

Although many user-friendly workflows exist for identifications of peptides and proteins in mass-spectrometry-based proteomics, there is a need of easy to use, fast, and accurate workflows for identifications of microorganisms, antimicrobial resistant proteins, and biomass estimation. Identification of microorganisms is a computationally demanding task that requires querying thousands of MS/MS spectra in a database containing thousands to tens of thousands of microorganisms. Existing software can't handle such a task in a time efficient manner, taking hours to process a single MS/MS experiment. Another paramount factor to consider is the necessity of accurate statistical significance to properly control the proportion of false discoveries among the identified microorganisms, and antimicrobial-resistant proteins, and to provide robust biomass estimation. Recently, we have developed Microorganism Classification and Identification (MiCId) workflow that assigns accurate statistical significance to identified microorganisms, antimicrobial-resistant proteins, and biomass estimation. MiCId's workflow is also computationally efficient, taking about 6–17 minutes to process a tandem mass-spectrometry (MS/MS) experiment using computer resources that are available in most laptop and desktop computers, making it a portable workflow. To make data analysis accessible to a broader range of users, beyond users familiar with the Linux environment, we have developed a graphical user interface (GUI) for MiCId's workflow. The GUI brings to users all the functionality of MiCId's workflow in a friendly interface along with tools for data analysis, visualization, and to export results. [ABSTRACT FROM AUTHOR]

Introduction: The memory conformity effect occurs when people witness a given incident and then talk to each other about it, and the statement of one person affects the memory account of another person with respect to that incident. The main objectives of this experiment were (1) to examine the effectiveness of a modified version of the MORI-v technique in inducing the memory conformity effect and (2) to investigate how the manner in which participants discuss the observed event influences the magnitude of this effect. In general, the modified online MORI-v technique consists of the following main elements: (1) original material, that is, two versions of a short film which are identical except for certain critical details; for example, in one version, a thief puts on a red cap, but in the other version it is black; (2) the collaborative recognition test, that is, a discussion about the original material which leads to mutual misinformation; and (3) an individual recognition test that checks the effect of the discussion on the memory account of the original material. Methods: A total of 72 participants (36 pairs) aged 18-54 took part in the research. Participants were tested using the online MORI-v technique: They were familiarized with the original material on their computers at home, and then they talked about it via a video communication app and completed an individual recognition test on their computers. Importantly, the discussions were recorded and analyzed in detail after the experimental session. Results and discussion: Using the online MORI-v technique, the effect of memory conformity was demonstrated, that is, in the individual recognition test, the proportion of correct answers to questions about discussed details (related to misinformation) was lower than the proportion of correct answers to questions about non-discussed details. It was also demonstrated that if one participant introduced misinformation during the discussion about a particular item and the other did not question it, the latter's answer to that item during the individual recognition test was most often incorrect. However, if one participant introduced misinformation during the discussion about an item and the other questioned it, the latter's answer about that item during the individual recognition test was most often correct. [ABSTRACT FROM AUTHOR]

To determine near-surface winds within and above vegetation canopies for operational environmental applications, a wind model must run at high-resolution (O(1-10 m)), in a few minutes, using limited input information, and requiring minimal computing resources (e.g., personal computers). Current research models simulate large domains at coarse resolution or small domains at fine scale, but canopy simulations can take days. Fast- modeling approaches are used to solve large complex wind fields, but they oversimplify the roughness elements' distribution impact on momentum exchanges. To overcome these deficits, the fast-running wind model QUIC-URB (Quick Urban and Industrial Complex) was augmented with a high-resolution canopy wind solver. The wind model includes a non-local factor that describes how momentum propagates through the canopy and how sub-canopy jets appear under certain conditions. QUIC-URB was also coupled with the mesoscale WRF (Weather Research and Forecasting) model to downscale wind fields from a few kilometers to a meter. The new QUIC Canopy Model resolves 3-D wind fields over hundreds of millions of cells in less than 30 s per time step on a personal computer. It was compared to two canopy models for real quasi-homogeneous and heterogeneous canopies. An error analysis shows that the model was relatively accurate with a normalized root-mean-square error of about 0.2 m s-1in the quasi-homogeneous canopy, and a mean absolute error of 0.3 m s-1. The new model is suitable for coupling with pollution dispersion, wildfire spread, and numerical weather prediction models over weakly complex terrain, defined here as a mildly undulating environment with gradual changes in elevation and a heterogeneous distribution of plants. [ABSTRACT FROM AUTHOR]

Computer simulations used to study food purchasing behavior can be separated into low immersion virtual environments (LIVE), which use personal computers and standard monitors to display a scene, and high immersion virtual environments (HIVE) which use virtual reality technology such as head-mounted displays to display a scene. These methods may differ in their ability to create feelings of presence or cybersickness that would influence the usefulness of these approaches. In this present study, thirty-one adults experienced a virtual supermarket or fast-food restaurant using a LIVE system or a HIVE system. Feelings of presence and cybersickness were measured using questionnaires or physiological responses (heart rate and electrodermal activity). The participants were also asked to rate their ability to complete the set task. The results of this study indicate that participants reported a higher sense of presence in the HIVE scenes as compared to the LIVE scenes (p < 0.05). The participant's heart rate and electrodermal activity were significantly higher in the HIVE scene treatment when compared to the LIVE scene (p < 0.05). There was no difference in the participant's ability to complete tasks in the different scenes. In addition, feelings of cybersickness were not different between the HIVE and LIVE scenes. [ABSTRACT FROM AUTHOR]

In this study, we developed a multi-walled carbon nanotube (MWCNT)-based field-effect transistor (MWCNT-FET) sensor with high sensitivity and selectivity for microcystin-LR (MC-LR). Carboxylated MWCNTs were activated with an MC-LR-targeting aptamer (MCTA). Subsequently the bioactivated MWCNTs were immobilized between interdigitated drain (D) and source (S) electrodes through self-assembly. The top-gated MWCNT-FET sensor was configured by dropping the sample solution onto the D and S electrodes and immersing a Ag/AgCl electrode in the sample solution as a gate (G) electrode. We believe that the FET sensor's conduction path arises from the interplay between the MCTAs, with the applied gate potential modulating this path. Using standard instruments and a personal computer, the sensor's response was detected in real-time within a 10 min time frame. This label-free FET sensor demonstrated an impressive detection capability for MC-LR in the concentration range of 0.1–0.5 ng/mL, exhibiting a lower detection limit of 0.11 ng/mL. Additionally, the MWCNT-FET sensor displayed consistent reproducibility, a robust selectivity for MC-LR over its congeners, and minimal matrix interferences. Given these attributes, this easily mass-producible FET sensor is a promising tool for rapid, straightforward, and sensitive MC-LR detection in freshwater environments. [ABSTRACT FROM AUTHOR]

Cloud lifecycles are influenced by interactions among clouds and mixing with cloud‐free environments. Cloud shapes dictate the length of cloud boundaries, which affects these interactions. This study uses a large eddy simulation to examine the relationships between the shallow cumulus cloud shapes, processes at their boundaries, and lifecycles. To characterize the shape of clouds, two metrics are newly defined: one reflects the overall horizontal cloud shape, and the other defines the cloud edge irregularity. Simulated clouds experience increased irregularity, but little changes in aspect ratio. Irregularity‐driven cloud perimeter growth strongly indicates cloud splitting, with a more pronounced effect than aspect ratio‐driven perimeter growth. Smaller cloud‐shell gradients of properties across cloud boundaries are associated with more irregular clouds, suggesting enhanced mixing of clouds with the cloud‐free environment. These findings indicate that the shallow cumulus evolution is partly driven by the irregularity of the cloud edge through lateral mixing and cloud splitting. Plain Language Summary: This study uses computer simulations of small fluffy clouds to examine how the shape of these clouds impacts their evolution. As clouds evolve, their edges become more jagged, but their overall shape does not change much. Clouds with more jagged edges are more likely to split in two. Furthermore, the jaggedness of the cloud edge affects mixing with the surrounding clear air, which influences cloud growth and decay. These findings highlight the importance of irregularity on the edge of the cloud for the evolution of entire clouds. Key Points: Shallow cumulus clouds develop more irregular edges while maintaining a similar overall shape as they ageThere is more perimeter growth before clouds split than merge, led by more irregular edges than by changes in overall shapeThe growth of the cloud perimeter led by irregular edges relates to small differences with nearby clear air attributed to lateral mixing [ABSTRACT FROM AUTHOR]

This paper presents a method that enables a small car to reach its destination in an indoor environment using ultrawideband technology. For this purpose, we used four anchors and one tag. The tag can move because it is fixed on the car. The anchors and the tag are DWM1001 modules. The configuration of the five DW1001 modules is performed through a smartphone application provided by the manufacturer. The car has been built by us. The controller of the car is a Raspberry Pi microcomputer which controls the driver of the car DC motors by generating PWM signals. In addition, Raspberry Pi communicates with the tag by the UART interface. Thus, our goal was to control the car to reach a certain destination (xd, yd) requested by the user via Wi-Fi. Because the car hosts the tag, it continuously updates its position according to the information received from the four anchors. We implemented two scenarios, considering the initial (x, y) position of the car. In the first scenario, which is simpler, the car moved to its destination either along the x-axis or along the y-axis. In the second scenario, the car moves along one of the axes and then turns 90 degrees to reach its destination. Experimental results that show the precision of the positioning system are presented. [ABSTRACT FROM AUTHOR]

Augmented and Virtual Reality (AR/VR) environments require user interaction concepts beyond the traditional mouse-and-keyboard setup for seated desktop computer usage. Although advanced input modalities such as hand or gaze tracking have been developed, they have yet to be widely adopted in available hardware. Modern smartwatches have been shown to provide a powerful and intuitive means of input, thereby overcoming the limitation of the current AR/VR headsets. They typically offer a set of interesting input modalities, such as a touchscreen, rotary buttons, and an Inertial Measurement Unit (IMU), which can be used for mid-air gesture recognition. Compared to other input devices, they have the benefit that they are hands-free as soon as the user stops interacting since they are attached to the wrist. As many concepts have been proposed, comparative evaluations of their effectiveness and user-friendliness are still rare. In this paper, we evaluate the usability of two commonly found approaches for using a smartwatch as an interaction device, specifically in immersive environments provided by AR/VR HMDs: using the physical inputs of the watch (touchscreen, rotary buttons) or mid-air gestures. We conducted a user study with 20 participants, where they tested both of the interaction methods, and we compared them in their usability and performance. Based on a prototypical AR application, we evaluated the performance and user experience of these two smartwatch-based interaction concepts. We have found that the input using a touchscreen and buttons was generally favored by the participants and led to shorter task completion times. [Display omitted] • We assessed smartwatch interaction: buttons, touchscreens, and gestures for AR/VR. • We implemented AR app with common concepts, ran a user study (20 participants). • Users preferred touchscreens over gestures; they're faster and less taxing. [ABSTRACT FROM AUTHOR]

Background: Because of the wide use of information and communication technologies (ICT) in healthcare, medical students' knowledge and skills of modern ICT have been considered essential for their successful learning and future careers. According to Bandura's self-efficacy, enhancing ICT self-efficacy, which might be affected by technology experience, could be a pathway to improving ICT literacy and competence, which should be one focus of medical educationalists. However, there is a lack of suitable measurements of medical students' selfefficacy and a clear understanding of its relationship with technology experience. Materials and methods: We conducted a literature review and direct consultation with an expert panel to identify potential items for the ICT self-efficacy scale. Based on the data collected in a survey of 486 first-year medical students in China, the exploratory factor analysis (EFA) was employed to confirm the structure of the final version. Furthermore, we used linear regressions to quantify the association between ICT self-efficacy and technology experience measured by the age of first access to the Internet, the age at first ownership of a personal computer (PC) or a laptop, and that of a smartphone. Results: The EFA results derived 15 items of four factors, with 67.02% of the total variance explained: Privacy and Safety, Differencing, Communication, and Learning and Application. The Cronbach's alphas for the four subscales and the overall scale ranged from 0.78 to 0.89. Regression results demonstrated a significant association of ICT self-efficacy with age at first ownership of a personal computer (PC) and the mediation role of the general self-efficacy in the ICT self-efficacy's association with the age at first ownership of a personal smartphone. Conclusion: The ICT scale developed is a reliable and valid task-specific measure to assess ICT Self-Efficacy for medical students. In addition, enhancing students' technology experience might improve their ICT self-efficacy. [ABSTRACT FROM AUTHOR]