Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of universal masking for source control and personal protection from simulated cough and exhaled aerosols in a room

Abstract Face masks reduce the expulsion of respiratory aerosols produced during coughs and exhalations (“source control”). Factors such as the directions in which people are facing (orientation) and separation distance also affect aerosol dispersion. However, it is not clear how the combined effects of masking, orientation, and distance affect the exposure of individuals to respiratory aerosols in indoor spaces. We placed a respiratory aerosol simulator (“source”) and a breathing simulator (“recipient”) in a 3 m × 3 m chamber and measured aerosol concentrations for different combinations of masking, orientation, and separation distance. When the simulators were front-to-front during coughing, masks reduced the 15-min mean aerosol concentration at the recipient by 92% at 0.9 and 1.8 m separation. When the simulators were side-by-side, masks reduced the concentration by 81% at 0.9 m and 78% at 1.8 m. During breathing, masks reduced the aerosol concentration by 66% when front-to-front and 76% when side-by-side at 0.9 m. Similar results were seen at 1.8 m. When the simulators were unmasked, changing the orientations from front-to-front to side-by-side reduced the cough aerosol concentration by 59% at 0.9 m and 60% at 1.8 m. When both simulators were masked, changing the orientations did not significantly change the concentration at either distance during coughing or breathing. Increasing the distance between the simulators from 0.9 m to 1.8 m during coughing reduced the aerosol concentration by 25% when no masks were worn but had little effect when both simulators were masked. During breathing, when neither simulator was masked, increasing the separation reduced the concentration by 13%, which approached significance, while the change was not significant when both source and recipient were masked. Our results show that universal masking reduces exposure to respiratory aerosol particles regardless of the orientation and separation distance between the source and recipient.
Generating and selecting resilient and maintainable locators for Web automated testing

Web user interface (UI) test automation strategies have been dominated by programmable and record–playback approaches. Of these, record–playback allows creating automation tests easily and reduces the cost of test generation. However, this approach increases the cost of test maintenance due to its unstable generated locators for identifying UI objects during playback. In this paper, we propose a new approach to generating and selecting resilient and maintainable locators. Our approach consists of two parts, a new XPath construction method and selecting the best XPath to locate the target element. Our XPath construction method relies on semantic structures of Web pages to locate the target element using its neighbors. We conducted an experiment on 15 popular websites. The results show that our approach outperforms the state‐of‐the‐practice/art Selenium IDE and Robula+ in locating target elements by effectively avoiding wrong locators. It also produces more readable XPaths (hence more maintainable tests) than do these approaches.
Distributed frameworks for detecting distributed denial of service attacks: A comprehensive review, challenges and future directions

A distributed denial of service (DDoS) attack is a significant threat to web‐based applications and hindering legitimate traffic (denies access to benign users) by overwhelming the victim system or its infrastructure (service, bandwidth, networking devices, etc.) with a large volume of attack traffic. It leads to a delay in responses or sometimes a crash victim system. Even a few moments of pause in web‐based applications lead to a huge monetary loss and a bad reputation in the market. Several approaches available in the literature to protect websites from different types of DDoS attacks. However, incidents and volume sizes of DDoS attacks are growing quarter by quarter. Further, various challenges in the traditional framework based defense mechanisms: itself becoming a victim of attacks while analyzing a massive amount of traffic, require more time for detection process, no coordination among the modules, etc. This paper presents a comprehensive DDoS defense deployment taxonomy and critically reviewed existing distributed frameworks based DDoS attack detection systems. Further, characterized several existing distributed processing frameworks to select an appropriate one for deploying DDoS attack detection mechanisms. Finally, several evaluation metrics, open issues, discussion on available datasets including their limitations, and future directions are presented.
Research on pull-type multi-AGV system dynamic path optimization based on time window

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.
“Aging-and-Tech Job Vulnerability”: A proposed framework on the dual impact of aging and AI, robotics, and automation among older workers

As the aging population and workforce constitute a worldwide concern, it is becoming necessary to predict how the dual threat of aging and technology at work increases the job vulnerability of older workers and jeopardizes their employability and permanence in the labor market. The objective of this paper is twofold: (1) to analyze perceptions of artificial intelligence, robotics, and automation in work settings and the expected impact of these technologies on older workers to contextualize this emergent phenomenon; and (2) to propose a general model related to “Aging-and-Tech Job Vulnerability” to explain and predict the combined effect of aging and AI/robotics/automation on job insecurity and additional outcomes among older workers. The propositions of the Age-and-Tech Job Vulnerability model developed in this paper seek to present a first approach for the conceptual advance and research on this emerging phenomenon and entails several theoretical and practical implications for organizational psychology.
Design Automation for Tree-based Nearest Neighborhood–aware Placement of High-speed Cellular Automata on FPGA with Scan Path Insertion

Cellular Automata (CA) is attractive for high-speed VLSI implementation due to modularity, cascadability, and locality of interconnections confined to neighboring logic cells. However, this outcome is not easily transferable to tree-structured CA, since the neighbors having half and double the index value of the current CA cell under question can be sufficiently distanced apart on the FPGA floor. Challenges to meet throughput requirements, seamlessly translate algorithmic modifications for changing application specifications to gate level architectures and to address reliability challenges of semiconductor chips are ever increasing. Thus, a proper design framework assisting automation of synthesizable, delay-optimized VLSI architecture descriptions facilitating testability is desirable. In this article, we have automated the generation of hardware description of tree-structured CA that includes a built-in scan path realized with zero area and delay overhead. The scan path facilitates seeding the CA, state modification, and fault localization on the FPGA fabric. Three placement algorithms were proposed to ensure maximum physical adjacency amongst neighboring CA cells, arranged in a multi-columnar fashion on the FPGA grid. Our proposed architectures outperform implementations arising out of standard placers and behavioral designs, existing tree mapping strategies, and state-of-the-art FPGA centric error detection architectures in area and speed.
Efficient One-pass Synthesis for Digital Microfluidic Biochips

Digital microfluidics biochips are a promising emerging technology that provides fluidic experimental capabilities on a chip (i.e., following the lab-on-a-chip paradigm). However, the design of such biochips still constitutes a challenging task that is usually tackled by multiple individual design steps, such as binding, scheduling, placement, and routing. Performing these steps consecutively may lead to design gaps and infeasible results. To address these shortcomings, the concept of one-pass design for digital microfluidics biochips has recently been proposed—a holistic approach avoiding the design gaps by considering the whole synthesis process as large. But implementations of this concept available thus far suffer from either high computational effort or costly results. In this article, we present an efficient one-pass solution that is runtime efficient (i.e., rarely needing more than a second to successfully synthesize a design) while, at the same time, producing better results than previously published heuristic approaches. Experimental results confirm the benefits of the proposed solution and allow for realizing really large assays composed of thousands of operations in reasonable runtime.
Automated segmentation algorithm with deep learning framework for early detection of glaucoma

Early stage of diagnosis of eye diseases through automatic analysis in the retinal image is the emerging technology in the area of retinopathy. Glaucoma is the primary reason for the loss of visibility in people around the world. The separation of the disc and the cup in the optic region is the technique used to identify glaucoma in the human retinal image. In this paper, superpixel segmentation, followed by Modified Kernel Fuzzy C‐Means (MKFCM) algorithm is used to segment the optic disc and optic cup. The proposed segmentation method achieves a maximum average of F‐score as 0.979, an average boundary distance as 10.016 pixels, and an average correlation coefficient of 0.949. To train convolutional neural networks (CNN), the segmented images obtained by the MKFCM segmentation algorithm is given as the input for the identification of glaucoma. This CNN uses the gray level co‐occurrence matrix features calculated from the segmented image. The experiment used for this study demonstrates that CNN gives superior categorization correctness and requires fewer figures of knowledge iterations than the original CNN. The accuracy obtained by this proposed method is 94.2%. The model will help to identify the proper class of severity of glaucoma in retinal images.
Assessing the Impact of Sequential Data Presentation on Specific Learning Disabilities Identification Decisions Using Patterns of Strengths and Weaknesses Methods

Patterns of strengths and weaknesses represent relatively novel methods for identifying specific learning disabilities (SLD) with proponents asserting that the incorporation of multiple sources of assessment data and professional judgment play a key role in their utility. In this study, we examined if the sequential presentation of assessment data impacted school psychologists’ ratings as to whether or not hypothetical students depicted in special education evaluation vignettes should be identified with SLD. Results showed that when participants viewed vignettes that were indicative of SLD (i.e., SLD positive), SLD likelihood ratings increased with the additional presentation of assessment data sources over time. However, when participants viewed vignettes that were indicative of a student not having SLD (i.e., SLD negative), SLD likelihood ratings were relatively consistent over time. Moreover, participants demonstrated relatively high levels of confidence in their SLD identification decisions, and in SLD negative vignettes, confidence increased after the fourth assessment data source was presented. Implications for SLD identification are discussed.
testar – scriptless testing through graphical user interface

Covering all the possible paths of the graphical user interface (GUI) with test scripts would take too much effort and result in serious maintenance issues. We propose complementing scripted testing with scriptless test automation using the open‐source testar tool. This paper gives a comprehensive overview of testar and its latest extensions together with the ongoing and future research. With this paper, we hope we can help and encourage other researchers to use testar for their GUI testing‐related research and pave the way for an international research agenda in GUI testing built upon stable and open‐source infrastructure.
A Practical Approach to Screening and Surveillance of Barrett’s Esophagus

Barrett’s esophagus (BE) can progress to Esophageal Adenocarcinoma (EAC), which has associated high morbidity and mortality. As such, societal guidelines suggest endoscopic screening in select individuals with multiple BE risk factors. However, cheaper and less invasive new technologies may allow for more widespread BE screening practices in the future. In patients with established BE, endoscopic surveillance is recommended with intervals based primarily on histology and to a lesser degree, BE segment length. Similar to BE screening, endoscopic surveillance can further be optimized with improved techniques, innovative technology, and further understanding of risk stratification for EAC.
Real-Time Imaging of Short-Wave Infrared Luminescence Lifetimes for Anti-counterfeiting Applications

Rare-earth doped nanoparticles (RENPs) have been widely used for anti-counterfeiting and security applications due to their light frequency conversion features: they are excited at one wavelength, and they display spectrally narrow and distinguished luminescence peaks either at shorter wavelengths (i.e., frequency/energy upconversion) or at longer wavelengths (frequency/energy downconversion). RENPs with a downconversion (DC) photoluminescence (PL) in short-wave infrared (SWIR) spectral range (~1,000–1,700 nm) have recently been introduced to anti-counterfeiting applications, allowing for multilevel protection based on PL imaging through opaque layers, due to a lesser scattering of SWIR PL emission. However, as the number and spectral positions of the discrete PL bands exhibited by rare-earth ions are well-known, it is feasible to replicate luminescence spectra from RENPs, which results in a limited anti-counterfeiting security. Alternatively, lifetime of PL from RENPs can be used for encoding, as it can be finely tuned in broad temporal range (i.e., from microseconds to milliseconds) by varying type of dopants and their content in RENPs, along with the nanoparticle morphology and size. Nevertheless, the current approach to decoding and imaging the RENP luminescence lifetimes requires multiple steps and is highly time-consuming, precluding practical applications of PL lifetime encoding for anti-counterfeiting. Herein, we report the use of a rapid lifetime determination (RLD) technique to overcome this issue and introduce real-time imaging of SWIR PL lifetime for anti-counterfeiting applications. NaYF4:20% Yb, x% Er (x = 0, 2, 20, 80)@NaYF4 core@shell RENPs were synthesized and characterized, revealing DC PL in SWIR region, with maximum at ~1,530 nm and PL lifetimes ranging from 3.2 to 6 ms. Imaging of the nanoparticles with different lifetimes was performed by the developed time-gated imaging system engaging RLD method and the precise manipulation of the delay between the excitation pulses and camera gating windows. Moreover, it is shown that imaging and decrypting can be performed at a high rate (3–4 fps) in a cyclic manner, thus allowing for real-time temporal decoding. We believe that the demonstrated RLD-based fast PL lifetime imaging approach can be employed in other applications of photoluminescent RENPs.
A nonbile acid farnesoid X receptor agonist tropifexor potently inhibits cholestatic liver injury and fibrosis by modulating the gut–liver axis

Tropifexor (TXR) is a novel nonbile acid that acts as an agonist of farnesoid X receptor (FXR). TXR is currently in Phase 2 trials for the treatment of non‐alcoholic steatohepatitis (NASH). Herein, we report the impact of TXR on in a piglet model in which cholestatic liver damage and fibrosis where induced by bile duct ligation (BDL).
Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

One of the foremost issues deliberated by the cloud infrastructure providers is to minimize the costs by reducing energy consumption. Though many heuristic algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and so on, have been existed for task scheduling or server consolidation to reduce energy consumption, more energy savings in data centers is required. In this paper, a combination of two distinctive heuristic algorithms, namely Particle Swarm Optimization (PSO) technique for proficient task scheduling and Ant Colony System (ACS) for effective server consolidation are proposed. In PSO, each particle in the swarm finds a task scheduling solution and updates the best solution. It is faster than many meta‐heuristic algorithms. In ACS, each ant in the ant colony tries to find the best destination PMs for migrating VMs from overloaded or underloaded PMs. The proposed PSO‐ACS algorithm is experimented with PlanetLab real workload traces available in the CloudSim toolkit. The algorithm provides reduction in energy consumption, number of VM migrations, and Energy SLA Violation (ESV) metrics while maintaining QoS. In the best‐case scenario, the proposed method reduces the ESV metric by 28%, 27%, and 18% in comparison with other algorithms like LR, ACS, and PSO‐LR.
Lipid Nanoparticles as Carriers for Bioactive Delivery

Nanotechnology has made a great impact on the pharmaceutical, biotechnology, food, and cosmetics industries. More than 40% of the approved drugs are lipophilic and have poor solubility. This is the major rate-limiting step that influences the release profile and bioavailability of drugs. Several approaches have been reported to administer lipophilic drugs with improved solubility and bioavailability. Nanotechnology plays a crucial role in the targeted delivery of poorly soluble drugs. Nanotechnology-based drug delivery systems can be classified as solid lipid nanoparticulate drug delivery systems, emulsion-based nanodrug delivery systems, vesicular drug delivery systems, etc. Nanotechnology presents a new frontier in research and development to conquer the limitations coupled with the conventional drug delivery systems through the formation of specific functionalized particles. This review presents a bird's eye view on various aspects of lipid nanoparticles as carriers of bioactive molecules that is, synthesis, characterization, advantage, disadvantage, toxicity, and application in the medical field. Update on recent development in terms of patents and clinical trials of solid lipid nanoparticles (SLNs) and nanostructure lipid carriers (NLCs) have also been discussed in this article.
Preceding vehicle following algorithm with human driving characteristics

This paper proposes a new approach of using reinforcement learning (RL) to train an agent to perform the task of vehicle following with human driving characteristics. We refer to the ideal of inverse reinforcement learning to design the reward function of the RL model. The factors that need to be weighed in vehicle following were vectorized into reward vectors, and the reward function was defined as the inner product of the reward vector and weights. Driving data of human drivers was collected and analyzed to obtain the true reward function. The RL model was trained with the deterministic policy gradient algorithm because the state and action spaces are continuous. We adjusted the weight vector of the reward function so that the value vector of the RL model could continuously approach that of a human driver. After dozens of rounds of training, we selected the policy with the nearest value vector to that of a human driver and tested it in the PanoSim simulation environment. The results showed the desired performance for the task of an agent following the preceding vehicle safely and smoothly.
Mechanisms of Cultural Evolution in the Songs of Wild Bird Populations

Young songbirds draw the source material for their learned songs from parents, peers, and unrelated adults, as well as from innovation. These learned songs are used for intraspecific communication, and have well-documented roles for such functions as territory maintenance and mate attraction. The songs of wild populations differ, forming local “dialects” that may shift over time, suggesting that cultural evolution is at work. Recent work has focused on the mechanisms responsible for the cultural evolution of bird songs within a population, including drift, learning biases (such as conformity and rare-form copying), and selection (including sexual selection). In many songs or song repertoires, variability is partitioned, with some songs or song segments being stable and consistent, while others vary within the population and across time, and still others undergo population-wide transitions over time. This review explores the different mechanisms that shape the cultural evolution of songs in wild populations, with specific reference to a long-term investigation of a single population of philopatric Savannah sparrows. Males learn a single four-segment song during their 1st year and sing the same song thereafter. Within this song, the buzz segment is a population marker, and may be stable for decades – variant forms occur but eventually disappear. In contrast, the middle segment is highly variable both within the population and over time; changes in relative prevalence of different forms may be due to cultural drift or a rare-form learning bias. Within the introductory segment, a high note cluster was replaced by a click train between 1982 and 2010, following an S-shaped trajectory characteristic of both selective sweeps in population genetics and the replacement of one form by another in human language. In the case of the Savannah sparrows, this replacement may have been due to sexual selection. In subsequent generations, the number of clicks within trains increased, a form of cultural directional selection. In contrast to the narrowing of a trait's range during directional selection in genetic systems, variation in the number of clicks in a train increased as the mean value shifted because improvisation during song learning allowed the range of the trait to expand. Thus, in the single short song of the Savannah sparrow, at least four different mechanisms appear to contribute to three different types of cultural evolutionary outcomes. In the future, it will be import to explore the conditions that favor the application of specific (and perhaps conditional) learning rules, and studies such as the ongoing song seeding experiment in the Kent Island Savannah sparrow population will help in understanding the mechanisms that promote or repress changes in a population's song.