Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Control of Mobile Robots with ANN Assisted Improved Q-learning: IQ-CRL Algorithm

This paper presents a novel algorithm, IQ-CRL (Improved Q-learning using Classification and Regression with ANN), which is a control architecture that uses the existing Q-learning algorithm and integrates it with Artificial Neural Networks (ANNs). The proposed algorithm addresses the limitations of traditional Q-learning by incorporating an adaptive mechanism that leverages ANN to find an optimal control policy that is more accurate and at lower computational expense. IQ-CRL is designed to be used efficiently with dynamical systems such as mobile robots. The objective of this study is to evaluate the performance of IQ-CRL in comparison to classical Q-learning and PID controller. The results show that the IQ-CRL algorithm outperforms both the classical Q-learning and PID controller in terms of learning efficiency, control accuracy and computing complexity.
Proposed Method for Threat Detection Using User Behavior Analysis

The development of a unique Web-Based platform to identify malicious user activity is the motivation of this research. Paper has applied analytics to the application-layer logs and proposed a Random Forest Algorithm-based model to certain metrics to identify malicious users. The paper shows collected IP addresses and device IDs from web application logs and has then applied the random forest algorithm to establish the behavior of users based on their browsing patterns. In this paper, the identification of the behavior is based on the number of requests made by the user, the time spent on the web application, and the frequency of requests. The patterns with the highest number of requests, the longest time spent, and the highest frequency of requests is the most probable cases of malicious user behavior. have created a Password manager web application and introduced vulnerabilities in training data intentionally for this purpose. The paper shows hosted setup on the LAMP stack based on the AWS EC2 cloud. These patterns would be used to sort incoming traffic and flag any IP addresses that would be the most probable cases of unauthorized login attempts.
Multifinger Robotic Gripper: A Review

The capabilities of robots beyond the humans have rapidly increased its demand as well as expansion due to its potential in the field of hazardous environment, research work, space, medical, agriculture to various household works. The main focus of the robots has been the end effector or gripper of the hand as it is the part which connects the operator to the working environment and handle the work object. These days, most robots are equipped with grippers which enables it to perform variety of tasks and shows enhanced skills. Despite years of research, we are still not able to mimic the human hand behaviour to robotic grippers in order to deploy the versatility and dexterity with required amount of stability to achieve a successful grasping of objects and hence, a very interesting topic in the field of robotics is human hand and its performance. In this paper we review various robotic Multi finger grippers in terms of their geometrical information, degrees of freedom, grasping system and usability for the understanding of andromorphic hand. We also try to explore latest technological development and experiments in the field of robotic gripper in real world. The later part covers the limitation and the problem being faced at present research works that still need rectification for greater deployment of the robotic grippers.
Self-Sustainable Multi-IRS-Aided Wireless Powered Hybrid TDMA-NOMA System

Intelligent reflecting surface (IRS) has been recently integrated with emerging communication technologies to meet the demanding requirements of communication systems. This paper investigates the deployment of multi-IRS units in a hybrid time-domain multiple access (TDMA) and non-orthogonal multiple access (NOMA) system, referred to as a multi-IRS-aided hybrid TDMA-NOMA system. In particular, a self-sustainable scenario is proposed, in which the IRS units can harvest energy from the radio frequency signal to feed them-self with the required energy. With this self-sustainable IRS-aided hybrid TDMA-NOMA system, the available time is fragmented into a set of time slots, in which an IRS unit is assigned to serve a cluster of users during each time slot. Meanwhile, the remaining unassigned (i.e., idle) IRS units harvest energy to feed themselves with the required energy, which addresses the energy limitation challenge related to conventional communication systems. Specifically, we propose an efficient algorithm to group the users in clusters and, thus, assign an appropriate IRS unit for each cluster. To examine the capabilities of the proposed self-sustainable multi-IRS-aided hybrid TDMA-NOMA system, a resource allocation framework is formulated aiming to minimize the transmit power at the base station under a set of quality-of-service (QoS) constraints. Such QoS constraints include the minimum required rate for each user and the minimum harvested energy at each IRS. However, since the considered optimization problem is not convex, and the coupled nature of the design parameters (i.e., the per-user power allocation and per-IRS reflection phase matrix), solving such a problem is challenging. Thus, we develop an efficient iterative algorithm, based on the sequential convex approximation, to solve the original optimization problem. Simulation results reveal that the proposed self-sustainable IRS-aided TDMA-NOMA system with the proposed clustering approach and IRS assignment consumes less power while achieving the sustainability of IRS units compared to benchmark approaches.
Prevalence of Elevated ALT in Adolescents in the US 2011–2018

Objective: The objective of this study is to characterize suspected nonalcoholic fatty liver disease (NAFLD) using elevated alanine aminotransferase (ALT) in a diverse and nationally representative cohort of adolescents and to characterize higher ALT elevation in adolescents with obesity. Methods: Data from the National Health and Nutrition Examination Survey 2011–2018 were analyzed for adolescents 12–19 years. Participants with causes for elevated ALT other than NAFLD were excluded. Race and ethnicity, sex, body mass index (BMI), and ALT were examined. Elevated ALT was defined as >22 U/L (females) and >26 U/L (males) using the biologic upper normal limit (ULN). Elevated ALT thresholds up to 2X-ULN were examined among adolescents with obesity. Multivariable logistic regression was used to determine the association of race/ethnicity and elevated ALT, adjusting for age, sex, and BMI. Results: Prevalence of elevated ALT in adolescents was 16.5% overall and 39.5% among those with obesity. For White, Hispanic, and Asian adolescents, prevalence was 15.8%, 21.8%, and 16.5% overall, 12.8%, 17.7%, and 27.0% in those with overweight, and 43.0%, 43.5%, and 43.1% in those with obesity, respectively. Prevalence was much lower in Black adolescents (10.7% overall, 8.4% for overweight, 20.7% for obesity). Prevalence of ALT at 2X-ULN was 6.6% in adolescents with obesity. Hispanic ethnicity, age, male sex, and higher BMI were independent predictors of elevated ALT. Conclusions: Prevalence of elevated ALT in U.S. adolescents is high, affecting 1 in 6 adolescents during 2011–2018. The risk is highest in Hispanic adolescents. Asian adolescents with elevated BMI may comprise an emerging risk group for elevated ALT.
Potential of 3D Hierarchical Porous TiO2-Graphene Aerogel (TiO2-GA) as Electrocatalyst Support for Direct Methanol Fuel Cells

Fuel cells have already demonstrated their potential for green energy generation. However, the low reaction performance becomes an obstacle in terms of large-scale commercial manufacturing. Accordingly, this work focuses on a new unique fabrication of three-dimensional pore hierarchy TiO2-graphene aerogel (TiO2-GA) supporting PtRu catalyst for anodic catalyst direct methanol fuel cell, which is facile, ecologically benign, and economical. In this work, a hydrothermal technique was used, followed by a freeze-drying technique and a microwave-assisted ethylene reduction technique. The structural properties of the studied materials were confirmed by UV/visible spectroscopy, XRD, Raman spectrum, FESEM TEM, and XPS. Based on existing structural advantages, the performance of PtRu/TiO2-GA has been investigated on DMFC anode catalysts. Furthermore, electrocatalytic stability performance with the same loading (~20%) was compared to commercial PtRu/C. Experimental outcomes show that the TiO2-GA support offered a significantly high surface area value of 68.44 m2g−1, mass activity/specific activity (608.17 mAmg−1/0.45 mA/cm2 PtRu) that is higher than commercial PtRu/C (79.11 mAmg−1/0.19 mA/cm2 PtRu). In passive DMFC mode, PtRu/TiO2-GA showed a maximum power density of 3.1 mW cm−2, which is 2.6 times higher than that of the PtRu/C commercial electrocatalyst. This suggests that PtRu/TiO2-GA has a promising possibility for methanol oxidation and may be used as an anodic element in DMFC.
A3C-Based and Dependency-Aware Computation Offloading and Service Caching in Digital Twin Edge Networks

The combination of Mobile Edge Computing (MEC) and Digital Twin (DT) is anticipated to enhance the quality of mobile application services in the 6G era. However, current research often overlooks service caching and task dependency, which may deteriorate system performance. Moreover, Edge Servers (ESs) have limited computing resources and caching capacities, which require collaboration to meet user demands. To address these challenges, we propose a DT-empowered MEC architecture that supports Mobile Users (MUs) offloading dependency-aware tasks, while considering service caching and edge collaboration. The objective is to jointly optimize computation offloading and resource allocation to minimize the system’s energy consumption. Hence, this problem can be formulated as a Mixed Integer Non-linear Programming (MINLP) problem and addressed by utilizing the Asynchronous Advantage Actor-Critic (A3C)-based method. Extensive simulation results demonstrate that our approach outperforms other benchmark algorithms under various scenarios, significantly reducing energy consumption.
Uniform and Adaptive (Re)Meshing of Surfaces

Triangle meshes are frequently used for computer representations of surfaces, with applications in computer graphics, visualization, and finite element analysis. Many geometry processing operations require meshes with high-quality triangles as input, but tend to degrade the quality of the output. This drives the need for remeshing algorithms, which take a poor mesh as input, and improve the sizes and shapes of the triangles while keeping the geometry the same. While a wide variety of geometry processing operations operate only on meshes, many surfaces are acquired with different underlying representations. For example, isosurfaces are often acquired from CT and MRI scans, and point set surfaces are acquired from laser range scans. This drives the need for generating meshes from other surface representations. This dissertation presents novel methods for meshing and remeshing surfaces in both uniform and adaptive ways. A novel parameterization-based method for generating highly-uniform remeshes is presented. This method considers the fully general problem of creating a map between two arbitrary triangle meshes. Whereas previous approaches compose parameterizations over a simpler intermediate domain, the presented method directly creates and optimizes a continuous map between the meshes. The distortion of the map is measured with a new symmetric metric, and is minimized during interleaved coarse-to-fine refinement of both meshes. By explicitly favoring low intersurface distortion, maps are obtained that naturally align corresponding shape elements. Typically, the user need only specify a handful of feature correspondences for initial registration, and even these constraints can be removed during optimization. The method robustly satisfies hard constraints if desired. This general intersurface mapping framework can be applied to parameterize surfaces onto simplicial domains, such as coarse meshes, and octahedral and toroidal domains. These parameterizations can be used to create high-quality remeshes. This dissertation also presents a novel surface meshing algorithm that is closely related to surface reconstruction techniques, and requires no explicit parameterization. This approach is based on the advancing front paradigm, augmented with a guidance field to both adapt the triangle sizes to the surface curvature and bound rate at which they can change. Simple and intuitive user controls are given for the size and adaptivity of the triangles. The method can be used to mesh surfaces with a wide variety of underlying definitions, including isosurfaces, point set surfaces, as well as other meshes. It is accurate, fast, robust, and suitable for use with interactive mesh processing applications that require local remeshing. A number of applications are shown, including Boolean operations between surfaces with different underlying definitions, extraction of large out-of-core isosurfaces that do not fit in working memory, and reconstruction of point set surfaces with sharp features.
A Comparison of Chaotic Circuits from a Statistical Approach

In this paper, we have investigated the chaotic structure of two different nonlinear circuits by a statistical approach and compared the results of analyses for these systems. We have utilized time history technique to identify the dynamic system. According to the results of the analysis using the BDS test, it has been found that each time series data belonging to circuits indicate chaotic behavior in some cases.
Internal Variability of the Climate System Mirrored in Decadal‐Scale Trends of Surface Solar Radiation

Decadal‐scale unforced climate variability associated with low‐frequency ocean (or coupled) modes can be identified within the time‐evolving sea surface temperature (SST) pattern. We seek to find a relationship between 12 well‐known climate modes of variability (El Niño–Southern Oscillation, Pacific Decadal Oscillation, Atlantic Multidecadal Oscillation, etc.), which are reflected in the SST pattern, and decadal trends in downwelling shortwave radiation at Earth's surface (surface solar radiation, surface shortwave radiation [SSR]). The analysis is performed using the pre‐industrial control runs (piControl) of 57 models from the Coupled Model Intercomparison Project—Phase 6. We find that regional SSR trends occur during periods when a climate mode is transitioning and show a mirroring pattern (opposite sign SSR trends) when the mode transitions in the opposite direction. Unforced trends in clear‐sky SSR are mostly driven by SST‐induced water vapor changes, while all‐sky SSR trends show a complex spatial structure with trends of different signs in different regions. Unforced SSR trends are generally weaker in simulations with climatological SSTs. The role of natural aerosols as another potentially relevant factor for SSR variability is briefly addressed and we find that constantly higher aerosol loads result in increased variability for clear‐sky conditions, but not for all‐sky conditions. The results from this study suggest that all‐sky dimming and brightening in different parts of the world are enhanced rather than suppressed by internal variability related to an SST‐pattern with the exception of India and China, where both effects are present. A practical application can be the planning of photovoltaic energy facilities given areas where internal variability is generally smaller or affects SSR in opposite directions.
Reaction time as a predictor of trust: empirical evidence

Automation Trust is created when human operator interacts with machines. It is more obvious when machines work in automation more. Operators interact and at times machines fails to take decision on their own and require human intervention. Reaction Time (RT) acts a predictor of trust. This paper has tried to study the relationship between trust and reaction in two conditions i.e. 90% reliability condition and 25% reliability condition. The result reveals that, there is a high RT in former condition vis-a-vis latter condition.
QbD-Optimized, Phospholipid-Based Elastic Nanovesicles for the Effective Delivery of 6-Gingerol: A Promising Topical Option for Pain-Related Disorders

In this study, elastic nanovesicles, constructed of phospholipids optimized by Quality by Design (QbD), release 6-gingerol (6-G), a natural chemical that may alleviate osteoporosis and musculoskeletal-related pain. A 6-gingerol-loaded transfersome (6-GTF) formulation was developed using a thin film and sonication approach. 6-GTFs were optimized using BBD. Vesicle size, PDI, zeta potential, TEM, in vitro drug release, and antioxidant activity were evaluated for the 6-GTF formulation. The optimized 6-GTF formulation had a 160.42 nm vesicle size, a 0.259 PDI, and a −32.12 mV zeta potential. TEM showed sphericity. The 6-GTF formulation’s in vitro drug release was 69.21%, compared to 47.71% for the pure drug suspension. The Higuchi model best described 6-G release from transfersomes, while the Korsmeyer–Peppas model supported non-Fickian diffusion. 6-GTF had more antioxidant activity than the pure 6-G suspension. The optimized transfersome formulation was converted into a gel to improve skin retention and efficacy. The optimized gel had a spreadability of 13.46 ± 4.42 g·cm/s and an extrudability of 15.19 ± 2.01 g/cm2. The suspension gel had a 1.5 μg/cm2/h ex vivo skin penetration flux, while the 6-GTF gel had 2.71 μg/cm2/h. Rhodamine B-loaded TF gel reached deeper skin layers (25 μm) compared to the control solution in the CLSM study. The gel formulation’s pH, drug concentration, and texture were assessed. This study developed QbD-optimized 6-gingerol-loaded transfersomes. 6-GTF gel improved skin absorption, drug release, and antioxidant activity. These results show that the 6-GTF gel formulation has the ability to treat pain-related illnesses effectively. Hence, this study offers a possible topical treatment for conditions connected to pain.
Modelling and Design of Pre-Equalizers for a Fully Operational Visible Light Communication System

Nowadays, Visible Light Communication (VLC) has gained much attention due to the significant advancements in Light Emitting Diode (LED) technology. However, the bandwidth of LEDs is one of the important concerns that limits the transmission rates in a VLC system. In order to eliminate this limitation, various types of equalization methods are employed. Among these, using digital pre-equalizers can be a good choice because of their simple and reusable structure. Therefore, several digital pre-equalizer methods have been proposed for VLC systems in the literature. Yet, there is no study in the literature that examines the implementation of digital pre-equalizers in a realistic VLC system based on the IEEE 802.15. 13 standard. Hence, the purpose of this study is to propose digital pre-equalizers for VLC systems based on the IEEE 802.15. 13 standard. For this purpose, firstly, a realistic channel model is built by collecting the signal recordings from a real 802.15. 13-compliant VLC system. Then, the channel model is integrated into a VLC system modeled in MATLAB. This is followed by the design of two different digital pre-equalizers. Next, simulations are conducted to evaluate their feasibility in terms of the system’s BER performance under bandwidth-efficient modulation schemes, such as 64-QAM and 256-QAM. Results show that, although the second pre-equalizer provides lower BERs, its design and implementation might be costly. Nevertheless, the first design can be selected as a low-cost alternative to be used in the VLC system.
IX Draconis, ER UMa-type dwarf nova

Abstract We present results of our world-wide observing campaign which was dedicated to one of the most active dwarf novae, IX Draconis. We investigated photometric behaviour of the system to derive its basic parameters. This study is important in the context of our understanding of the accretion process of IX Draconis, as well as other active cataclysmic variables, especially those from the still poorly studied type of ER UMa stars.
Maintaining cyberhygiene in the Internet of Things (IoT): An expert consensus study of requisite user behaviours

The Internet of Things (IoT) connects computing devices embedded in everyday objects via the internet, enabling them to send and receive data. Little is known about behaviours required to protect IoT users. The study sought to develop expert consensus on the key protective behaviours, risk behaviours, and threats for IoT cybersecurity. An online, three-round Delphi consensus study was conducted with IoT experts. In Round One, experts’ responses to open-ended questions were analysed using inductive and content analyses to categorise them into behavioural categories. In Round Two, experts rated the importance of protective behaviours, and the likelihood that risk behaviours and threats would lead to IoT breaches. In Round Three experts re-evaluated their responses based on their own and the group’s responses. Experts agreed that 28 protective behaviours, one risk behaviour, and six threats were critical for IoT cyberhygiene. Five of the top 10 protective behaviours for conventional computing were also deemed important for IoT, i.e. ‘_Limit sharing of your personal information with devices’_,_ ‘Keep your IoT devices updated’_;_ ‘Read articles about IoT security, safety and privacy risks’, ‘Use a strong firewall’, and ‘Use strong passwords on devices, networks and services_’. The study provided information on the key behaviours and threats for IoT settings, and the extent to which recommendations for conventional computing settings may also be suitable for IoT settings. These findings can inform the development of tailored behaviour change interventions to improve cybersecurity.
On generalised convex mathematical programming

Abstract The sufficient optimality conditions and duality results have recently been given for the following generalised convex programming problem: where the funtion f and g satisfy for some η: X0 × X0 → ℝn It is shown here that a relaxation defining the above generalised convexity leads to a new class of multi-objective problems which preserves the sufficient optimality and duality results in the scalar case, and avoids the major difficulty of verifying that the inequality holds for the same function η(. , .). Further, this relaxation allows one to treat certain nonlinear multi-objective fractional programming problems and some other classes of nonlinear (composite) problems as special cases.
Transience, Nonlinearity, and Eddy Feedback in the Remote Response to El Niño

Abstract A dry primitive equation model is used to investigate the remote response to a fixed tropical heat source. The basic forcing for the model takes the form of time-independent terms added to the prognostic equations in two configurations. One produces a perturbation model, in which anomalies grow on a fixed basic state. The other gives a simple GCM, which can be integrated for a long time and delivers a realistic climate simulation with realistic storm tracks. A series of experiments is performed, including 15-day perturbation runs, ensemble experiments, and long equilibrium runs, to isolate different dynamical influences on the fully developed Pacific–North American (PNA) type response to an equatorial heating anomaly centered on the date line. The direct linear response is found to be very sensitive to changes in the basic state of the same order as the atmosphere’s natural variability, and to the natural progression of the basic state over the time period required to set up the response. However.