Miracle-dz/UniFAR-FTUs huggingface dataset:
Placement of FCS Considering Power Loss, Land Cost, and EV Population

Electric vehicles (EVs) have recently gotten a lot of attention from the government and the auto industry. This is because EVs produce less CO2 and cost less to run and maintain. However, as EV adoption rises, the pressure on the distribution network increases due to changes in power loss, voltage profile, etc. So, EV fast charging stations (FCSs) must be put in the right places for the distribution network to work well. As a result, a two-stage technique is suggested for the deployment of FCSs in this paper. The Land Cost Index (LCI) and the EV Population Index have been taken into consideration while introducing the Charging Station Investor Decision Index (CSIDI) in the first stage (EVPI). Further, the CSIDI was developed to determine the location of FCS in the electrical distribution system while minimizing the cost of land and maximizing the EV population. The distribution system restrictions are considered while formulating an optimization problem in the following step to minimize the overall active power loss. The improved version of the bald eagle search (IBES) algorithm has also been used to solve the minimization issue, and the outcomes have been contrasted with those of the particle swarm optimization (PSO) technique.
Traffic Aware Routing with Round Robin Technique for Equating the Load in Software Defined WSN

Multiple uses of Wireless Sensor Networks (WSNs) in the real world would need the probable dispersion of a large number of sensor nodes. The adaptability and flexibility is very difficult in the traditional WSNs which uses balancing load technology. Software-defined networking (SDN) is a good choice because it lets you see what resources are used and also set up boundaries. In WSN, the highest traffic load causes an overflow of the data queue. As a result, important data should be lost. Also, the sensor node’s energy is rapidly drained, and the whole network’s lifespan is damaged. Traffic-aware routing with round robin technique (TARR) for equating the load in a software-defined WSN is proposed to solve this problem. The network contains two significant functions, namely, the formation of the network and the action of the network. This approach recognizes the traffic by obtaining or forwarding packets via control and data messages. In this approach, the round-robin technique is used during congestion situations. Thus, the data is forwarded constantly. As a result, the recipient node receives all data packets efficiently. The results of the simulation demonstrate that the TARR technique boosts network throughput while simultaneously reducing both packet loss and latency in a software-defined WSN.
Characteristics Study of Capacitive Sensor to Identify Native Breed Cow Milk

The predominant genotype in Indian native breeds of cows and buffaloes is A2A2, meaning they produce A2 milk. Cow milk has calcium, phosphorus, rich fats, potassium which helps to maintain blood pressure. This work focuses on the capacitive sensor method to identify the native breed cow milk from different varieties of milk. The milk variety taken for study are Holstein Friesian(HF), Girr, Jersey and Native breed. This study is carried out using parallel plate capacitor and cylindrical type capacitor. The parameters taken for study are capacitance, magnitude and phase value for both the type of capacitors. The results show that parallel plate capacitor characteristics provides differentiation of milk samples in capacitance at 1kHz frequency and in magnitude values of impedance at 100Hz and 120Hz frequencies. The characteristics of the cylindrical capacitor shows clear identification of native breed in magnitude of impedance at all the frequency from 100Hz to 100kHz but in capacitance value the difference is very low at all the frequency range. Hence, by considering capacitance as a parameter, parallel plate capacitor characteristics provide better results at 1kHz frequency compared to the cylindrical capacitor.
Biometric Aided Intelligent Security System Built using Internet of Things

Protection is critical for information, property, and even living beings. The usage of IoT and biometrics for security is becoming increasingly common as technology progresses. Facial recognition is widely accepted as a universally applicable, aggregated, and easily accessible biometric technology. Facial biometrics employ the same indicators that humans use to differentiate between persons. This study has investigated the face recognition technology in order to create a dependable door lock system. The biometric face recognition system developed using ESP32-CAM. When a face comes in front of the camera, the system instantly detects and recognizes it. A prototype was created and tested to evaluate the security functionality, effectiveness, and dependability. The findings indicate the result may be utilized effectively improve person's existence and safety, as well as for industrial and residential applications.
Design and optimization of MobileNet neural network acceleration system based on FPGA

Aiming at the problem of low computational efficiency when convolutional neural network is deployed in embedded devices, this work proposes a convolution neural network acceleration architecture based on FPGA, and designs and implements a MobileNet neural network acceleration system based on FPGA. Firstly, the method of dynamic fixed-point quantization and operator fusion is designed to optimize the model. Secondly, the method of convolution block, parallel convolution calculation and data stream optimization is designed based on FPGA to improve the efficiency of convolution operation and system throughput. The system is tested on Xilinx PYNQ-Z2 platform. The average frame rate of the system is 6 fps and the power consumption is 2.45w. The system has the characteristics of low power consumption and low delay, which can effectively improve the operation efficiency of convolutional neural network algorithm in embedded devices.
Capacity Evaluation of MIMO System:with and without Successive Interference Cancellation

MIMO is a technique that enhances system performance through antenna arrays at source side and at receiver. These methods can increase the wireless networks' capacity, reach, and dependability. MIMO systems can also be used to mitigate the effects of multipath fading, which is a common problem in wireless communications. MIMO technology is widely used in various wireless schemes, such as Wi-Fi, cellular, and satellite communications. Despite the benefits of MIMO, there are still research challenges that need to be addressed for MIMO systems to be more effective, efficient, and reliable. Overall, the capacity of a MIMO system is a complex and multi-faceted metric that can be evaluated using various techniques (different receiver), depending on the specific operating conditions and requirements. Understanding the performance of a MIMO is the crucial for the design, deployment, and optimization of wireless networks that use this technology. Successive Interference Cancellation (SIC) is a receiver technique has been used for separate and decode multiple signals that are transmitted simultaneously in MIMO which inherently improves the capacity. Hence, this work is focused on estimating the capacity of MIMO with different receiver structures such as; MMSE, Zero Forcing Equalizers incorporated with SIC. The performance of MIMO system has been measured in terms of capacity and compared with MIMO having SIC and MIMO with out SIC.
Test Design for Combining Tests at Multiple Product Levels

SUMMARY & CONCLUSIONSIn reliability validation test practice, the component of same type can be tested at multiple product levels such as component, subsystem, system, and product. All those tests contribute something to the component reliability validation. Instead of treating these tests completely independent, there are significant benefits to combine these tests together for the test design and the component reliability performance evaluation. [1] provides a method with the underlying assumption that the same stress level is applied to the component in both component level test and system level test. This paper extends the results in [1] to much general and useful cases in practice. First, consider the components of same type are tested at two product levels in component and system separately with the stress of same type applied for the targeted failure modes. However, the stress levels and therefore the acceleration factors in two tests are different. The goal is to validate the overall reliability requirement with the tests at the component level and the system level combined. The equations for the life test designs are derived and the test design methods are described. Then, the result for the tests at the two product levels is extended to the more general case with more than two product levels. Examples are given to illustrate the developed methods.
Cooperative Task Offloading in Cybertwin-Assisted Vehicular Edge Computing

Vehicular Edge Computing (VEC) is a computing paradigm that brings Mobile Edge Computing (MEC) to the road and vehicular scenarios by providing low-latency and high-efficiency computation services. One key technology of VEC is task offloading, which allows vehicles to send computation tasks to surrounding Roadside Units (RSUs) for execution, thereby reducing service delay. However, the existing task offloading schemes face the important challenges because the vehicles with time-varying trajectories and limited computing resources need to process massive data with high complexity and diversity. In this paper, we propose a Cooperative Task Qffloading Scheme (CTOS) based on Cybertwin-assisted VEC. Specially, a novel Cybertwin-assisted VEC network architecture is established by applying the combination of the Digital-Twins (DT) and the Generative Adversarial Network (GAN). With the powerful prediction capability of GAN, the data of DT is advanced with the physical entity, which is an effective assistant for task offloading. Then, we leverage the distributed Deep Reinforcement Learning (DRL) to make offloading decisions, which consider the limited resources of RSUs and the cooperation of vehicles. The simulation results demonstrate that the proposed scheme can achieve excellent performance in terms of system stability and efficiency.
Deep Learning-Based Approach for State-of-Health Estimation of Lithium-Ion Battery in the Electric Vehicles

State-of-health (SoH) of the battery is crucial for ensuring the long-term safe, reliable and robust operation of electric vehicles. The health condition of the battery cannot be quantified directly using some measurement tools; therefore, estimation has to be done based on measurable quantities readily obtained from the battery management system. SoH estimation is possible either by analyzing the electrochemical process of the battery, which is highly nonlinear and unpredictable or by using data-driven techniques to trace the behavior pattern of the battery with aging. The latter stated approach is adopted here because it depends on historic data and does not require specific knowledge of material properties. The main objective is to build an accurate state-of-health estimation approach for lithium-ion batteries using the following algorithms and compare the proposed model’s performance. Thus, the three most widely used data-driven approaches, i.e., back propagation neural network (BPNN), support vector regression (SVR) and deep long short-term memory (LSTM) are applied for SoH estimation. The proposed algorithm can be used for onboard applications concerning processing and memory restrictions or used remotely by utilizing cloud data technology.
Retinal Glaucoma Detection from Digital Fundus Images using Deep Learning Approach

A disorder called glaucoma that damages the optic nerve can result in either a partial or whole loss of vision. As a reason, it is critical to start glaucoma screening at a young age. Glaucoma symptoms do not manifest until the condition is advanced and the patient has already experienced considerable vision loss. The bulk of early diagnostic methods rely on careful feature engineering. Fundus images are particularly useful in the clinical context for the early detection of vision problems.Because of its superior performance, In related fields including image synthesis, disease segmentation, biomarker segmentation, and illness identification, deep learning is being employed more and more often. Convolutional neural networks have lately been used to diagnose glaucoma and other eye problems in ophthalmology. They have been effective in the early diagnosis of several disorders. Many layers of highly connected neural networks. The study approach makes use of models that were previously trained on ImageNet using the dristi dataset with the accuracy of almost 97% that was achieved, it is evident that the built classification model can accurately diagnose glaucoma.
Sampling sufficiency for mechanical properties of wood

Based on most recently published studies, there is a large variability in both the mechanical properties of wood and sample sizes selected to evaluate them. This study aims to define sampling sufficiency for some mechanical properties of wood, which were bending strength, bending modulus, compressive strength, compressive modulus, hardness, and shear strength. The mechanical tests were carried out according to an American standard procedure on wood samples cut from clonal Eucalyptus planted in southern Brazil. Sampling sufficiency was determined by an intensive computational method based on resampling of original data using Monte Carlo simulations. The experimental tests data conformed to the normal distribution and most of the obtained sufficient sample sizes determined by Monte Carlo simulation were above those sample sizes used in most already published studies. Furthermore, properties related to wood stiffness presented smaller variabilities than their respective properties associated with wood strength, leading to smaller sample sizes for the former cases.
Status and Trend of the Main Allergenic Pollen Grains and Alternaria Spores in the City of Rome (2003–2019)

Today a large part of the European population is exposed to levels of air pollution exceeding the standards recommended by the World Health Organization. Moreover, air pollution and the seasonal emission of allergenic pollen are progressively affecting human health and can cause severe allergic reactions, particularly when air pollution combines with pollen allergen peaks. Unlike atmospheric pollutants of anthropogenic origin, pollen sources have a pulsating trend that leads to high values in the flowering period and values close to, or equal to, zero in the rest of the year. This aspect makes essential the definition of data coverage standards for the main allergenic taxa. For air quality assessment detailed classification criteria for monitoring stations are defined by international standards, not the same from the European Standards for the Sampling and analysis of airborne pollen grains and fungal spores. This paper describes the status and the air concentration trends of the main allergenic pollen and the Alternaria spore measured in Rome from 2003 to 2019 by the Aerobiological Monitoring Center of Tor Vergata (Rome) and calculated by the Seasonal Kendall test with the open-source OpenAir R package. The analysis was carried out on the daily concentrations of the most widespread allergenic taxa in Italy: Asteraceae, Betulaceae, Corylaceae, Cupressaceae/Taxaceae, Poaceae, Oleaceae, Urticaceae and the Alternaria spores.
Optical SED models of galaxy mergers

Abstract I discuss recent work in which we construct models of poststarburst galaxies by combining fully three-dimensional hydrodynamic simulations of galaxy mergers with radiative transfer calculations of dust attenuation. The poststarburst signatures can occur shortly after a bright starburst phase in gas-rich mergers, and thus offer a unique opportunity to study the formation of bulges and the effects of feedback. Several additional applications of spatially-resolved spectroscopic models of interacting galaxies include multi-wavelength studies of AGN/starburst diagnostics, mock integral field unit data to interpret the evolution of ULIRGs, and the ‘Green Valley’. Optical spectra of simulated major gas-rich galaxy mergers can be found at http://www.cfa. harvard.edu/~gsnyder
Intrusiveness and Emotional Manipulation as Facets of Parental Psychological Control: A Culturally and Developmentally Sensitive Reconceptualization

Over 750 studies have examined parental psychological control (PPC) in different cultures. However, the conceptualization of PPC remains unclear, and operationalizations of PPC have been inconsistent. Herein we review and refine conceptual models of PPC, focusing on intrusiveness and emotional manipulation as two core facets of PPC. Guided by the Social Domain Theory, we relate intrusiveness to the boundaries of the child’s personal domain, which can vary by culture and age group. We describe how our conceptual model of PPC can clarify the disagreement in the literature about whether PPC may be arguably less damaging in interdependent cultures than it is in independent cultures or not; operationalizing PPC as mainly emotional manipulation – inducing guilt – might have contributed to this argument, and testing PPC with both intrusiveness and emotional manipulation can show both universal and culture-specific consequences of PPC. We conclude with recommendations for applying our conceptual model in future studies.
“The PTA intimidates”: racially segregated parent organizations in a dual immersion school

ABSTRACT Previous research highlights how schools value white, middle-class modes of parental involvement, we know less about Latinx parents’ involvement in their children’s schools. This article compares the participatory patterns of Latinx and non-Latinx white parents whose children attend a Spanish/English dual-immersion school in Los Angeles. Drawing from 66 interviews and 20 months of participant observation, I find that all parents participate actively but are channeled into racially segregated Parent Teacher Organizations (PTO) based on the reputation, demographic composition and informal culture of each of these organizations. This organizational segregation disadvantages Latinxs – working-class Latinx parent efforts garner less power and recognition and middle-class Latinx are less active in PTOs, as they feel they do not ‘fit’ in either organization. This article illustrates how school-based parental involvement is stratified in ways that are not fully captured by solely focusing on participation, and demonstrates how parent organizations can function as racialized organizations that perpetuate inequality.
Atmospheric Boundary Layer Wind Profile Estimation Using Neural Networks, Mesoscale Models, and LiDAR Measurements

This paper introduces a novel methodology that estimates the wind profile within the ABL by using a neural network along with predictions from a mesoscale model in conjunction with a single near-surface measurement. A major advantage of this solution compared to other solutions available in the literature is that it requires only near-surface measurements for prediction once the neural network has been trained. An additional advantage is the fact that it can be potentially used to explore the time evolution of the wind profile. Data collected by a LiDAR sensor located at the University of León (Spain) is used in the present research. The information obtained from the wind profile is valuable for multiple applications, such as preliminary calculations of the wind asset or CFD modeling.
Brief Paper - Input-to-state stability of non-linear impulsive and switched delay systems

This study addresses the input-to-state stability (ISS) of non-linear impulsive and switched delay systems. Based on the piecewise Lyapunov-Krasovskii functional method, the authors show that when only some of the constituent subsystems are ISS, the ISS property still can be retained for the non-linear impulsive and switched delay system, if the dwell time of the ISS subsystems satisfies a lower bound condition and the activation time of the non-ISS subsystems satisfies an upper bound condition, respectively. In common parlance, some of the intervals must be overly long, whereas the others must be short enough between impulses. Compared with existing results on related problems, the theory can be applied to a larger class of systems. The proposed approach also enables us to give the analysis of systems involving actuator failure, controller failure or temporary uncertain switching signal. As an example, the authors employ the method to analyse the problem of reliable control for a mechanical rotational cutting process.
Factors affecting online self-learning acceptance among older adults after the pandemic: living arrangements and gender as mediators

ABSTRACT Research on acceptance of online self-learning is regularly based on the technology acceptance model (TAM), but less has been discussed on older adults. This article expands TAM by taking living arrangements and gender as moderators after the pandemic. Utilizing a structural equation model based on the partial least squares technique to examine how living arrangements and gender differences influence the acceptance of online self-learning among older adults above 55 in China. Findings show that male older adults favor status and value orientations, so their self-learning intention was significantly affected by perceived usefulness and social influence. Female ones prefer social and utilitarian orientations; therefore and their online self-learning behavior was affected by perceived usefulness, perceived ease to use, social influence, trust, and perceive financial cost. Types of living arrangements moderated TAM significantly among older adults. This article deepens the theoretical discussion and cognition of the TAM model in the online self-learning of older adults.
Hypersoft separation axioms

In this manuscript, we continue to study the hypersoft topological space (for short, HSTS) by presenting hypersoft (HS) separation axioms, called HS Ti-spaces for i = 0, 1, 2, 3, 4. The notions of HS regular and HS normal spaces are explained in detail. We discuss the connections between them and present numerous examples to help clarify the interconnections between the different types of these spaces. We point out that HS Ti-axioms imply HS Ti? 1 for i = 1, 2, 3, and with the help of an example we show that HS T4-space need not be HS T3-space. We also clarify that the property that an HS space being HS Ti-spaces (i = 0, 1, 2, 3) is HS hereditary. Finally, we provide a diagram to illustrate the relationships between our proposed axioms.