Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Network of Industrial Positron Images on Memory Module

PET (Positron Emission Computed Tomography) imaging is a challenge due to the ill-posed nature and the low data of photo response lines. Generative adversarial networks have been widely used in computer vision and made great success recently. In our paper, we trained an adversarial model to improve the industrial positron images quality based on the attention mechanism. The innovation of the proposed method is that we build a memory module that focuses on the contribution of feature details to interested parts of images. We use an encoder to get the hidden vectors from a basic dataset as the prior knowledge and train the nets jointly. We evaluate the quality of the simulation positron images by MS-SSIM and PSNR. At the same time, the real industrial positron images also show a good visual effect.
Frequency sensitivity analysis of dynamic demand response in wind farm integrated power system

Demand response (DR) has emerged as a very important part of the future smart grid to enhance the power system reliability and frequency stability. This study presents the benefits of DR control and battery energy storage (BES) on frequency regulation in wind farm integrated power system. In this study, a generalised load frequency control model of a hybrid hierarchical DR control mechanism is developed by incorporating the inherent system parameters such as frequency dead-band, aggregated generations/devices and communication time delay and so on. The mathematical formulation for frequency stability and sensitivity analysis are carried out with respect to the frequency change coefficient for switching OFF the DR devices and frequency regulation service shared by conventional supplementary control, DR and BES. The extensive simulation results are presented for system frequency response (SFR) to support the theoretical analysis. It is found that DR has a significant effect to stabilise SFR. The observations drawn from analysis can be helpful for system operators/regional transmission organisation to investigate or plan for deciding the frequency threshold, minimum turn OFF time for individual DR devices, frequency coefficient factor and percentage share of frequency regulation services to restore the frequency quickly.
Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks

The purpose of this paper is the development of data science models for the detection of empty on-street parking spaces in urban road networks based on data provided by in-vehicle cameras that are already, or soon will be, a standard vehicle equipment. A rolling spatial interval is used to identify the existence of an on-street parking space and the properties of empty spaces are used to determine the availability of the parking space. Convolutional neural networks are developed, trained, and evaluated with the use of images from a moving vehicle camera. The images are preprocessed and converted to suitable matrices, so that only the useful information for the empty on-street parking space detection problem is preserved. The optimized convolutional networks, in terms of structural and learning parameters, provided predictions for the detection of empty on-street parking spaces with approximately 90% average accuracy. The proposed model performs better than the relatively complex SVMs, which supports its appropriateness as an approach. Finally, the implementation of a framework, which integrates the developed models to produce meaningful parking information for drivers in real time, is discussed.
Deep Reinforcement Learning-Based Real-Time Joint Optimal Power Split for Battery–Ultracapacitor–Fuel Cell Hybrid Electric Vehicles

Hybrid energy storage systems for hybrid electric vehicles (HEVs) consisting of multiple complementary energy sources are becoming increasingly popular as they reduce the risk of running out of electricity and increase the overall lifetime of the battery. However, designing an efficient power split optimization algorithm for HEVs is a challenging task due to their complex structure. Thus, in this paper, we propose a model that jointly learns the optimal power split for a battery/ultracapacitor/fuel cell HEV. Concerning the mechanical system of the HEV, two propulsion machines with complementary operation characteristics are employed to achieve higher efficiency. Additionally, to train and evaluate the model, standard driving cycles and real driving cycles are employed as input to the mechanical system. Then, given the inputs, a temporal attention long short-term memory model predicts the next time step velocity, and through that velocity, the predicted load power and its corresponding optimal power split is computed by a soft actor–critic deep reinforcement learning model whose training phase is aided by shaped reward functions. In contrast to global optimization techniques, the local velocity and load power prediction without future knowledge of the driving cycle is a step toward real-time optimal energy management. The experimental results show that the proposed method is robust to different initial states of charge values, better allocates the power to the energy sources and thus better manages the state of charge of the battery and the ultracapacitor. Additionally, the use of two motors significantly increases the efficiency of the system, and the prediction step is shown to be a reliable way to plan the HESS power split in advance.
Extended AHP Approach with Latent Factor and Stratum in Prioritizing and Positioning of OTOP Thailand’s Program for Elderly Market

We proposed a modified AHP technique to prioritize and position the OTOP product for the elderly consumer market. This research developed and extended the methodology proposed by R. Suttipong to account for both latent and stratum factors. The product preferences defined as the latent factor are included in the model. This paper applied the AHP with the latent factor of product preferences inserted into the calculation. The prioritized weights are computed overall and at each stratum classified by gender. This stratum has been statistically found significant by S. Phanphet based on the large survey result of product preference and affects the latent factor of the preferences. This proposed method leads to new findings on the implementation plan to promote the OTOP product among different sectors attributed to the latent factor. This newly proposed analysis technique was used to set the strategic action plan for product development for each gender group for the northern Thailand OTOP development program. This proposed technique can justify and prioritize the group of OTOP for efficient product development.
Multiobjective Optimization Model and Algorithm Based on Differential Brain Storm for Service Path Constructing

Network function virtualization (NFV) can provide the resource according to the request and can improve the flexibility of the network. It has become the key technology of the next-generation communication. Resource scheduling for virtual network function service chain (VNF-SC) mapping is the key issue of the NFV. Aiming at previous research primarily focused on constructing service paths with a single objective, for example, latency minimization, cost minimization, or load balance, which ignored the overall performance of constructed service paths, a multiobjective model, which minimizes benefit, expense, time delay, and load balance, to solve the service path constructing problem, is established. In this model, VNF-SCs are divided into two classes, i.e., part of the required VNFs in each VNF-SC is dependent, and others are independent. To solve this multiobjective model, an algorithm based on discrete difference brain storm (MO2DBS) in the framework of MOEA/D was proposed. In the new algorithm, a two dimensional integer coding is designed. In addition, a difference mutation was used to replace the original Gaussian mutation to adjust the mutation step adaptively. Simulation experiments show that the proposed algorithms can obtain higher benefit, load balance, lower expenses, and time delay than the compared algorithms.
Finite Element Analysis of a Flexible Tactile Sensor with Circular Pattern

Additive manufacturing technology presents new opportunities and challenges for tactile sensor fabrication and packaging. Specifically, aerosol jet printing technology enables on-demand deposition of a wide selection of materials onto flexible substrates with potentially uneven surfaces, and feature sizes in the micron scale. One of the applications of aerosol jet printing is integrated tactile sensors onto robotic and mechatronic devices. In this paper, we present a design and simulation study of a new tactile sensor that is compatible with the aerosol jet printing process on customized, flexible printed circuit (FPC) substrates, featuring a strain gauge with a circular pattern. The tactile sensor is packaged in between the cover and bedding - two pieces of elastomer material that give the sensor space to comply and deform. A dimple and a cavity were added to the cover and bedding respectively to help the sensor concentrate external forces onto the location where strain is detected. Finite element analysis (FEA) was conducted to study the performance of the proposed design, with respect to the relative sizes of the dimple and the cavity on the circular sensor pattern. Simulation results show the feasibility of finding the best combination of the dimple and cavity size, which can be used to optimize our sensor design.
From Grave to Cradle - Development of Weft Knitted Fabrics Based on Hybrid Yarns from Recycled Carbon Fibre Reclaimed by Solvolytic Process from of EOL-Components

Carbon fibre (CF) is widely used in CF reinforced plastic (CFRP) components. However, waste CF, CFRP and the end-of-life (EOL) CFRP structures will cause an even bigger problem in the next years because of strict environmental regulations. Currently, recycling is carried out almost entirely by the use of pyrolysis to regain CF as a valuable resource. This high temperature process is very energy consuming and the resulting fibres are brittle. Hence, not suitable for textile processing into yarns or fabrics. To enable a grave to cradle circle, a new approach based on a solvolytic recovery of CF and the subsequent spinning process to obtain a hybrid yarn suitable for weft knitting processing is the focus of the international research project IGF/CORNET 256EBR “3D-r-CFRP”.
Evaluation of Beamsteering Performance in MultiuserMIMO Unmanned Aerial Base Stations Networks

Future wireless communication networks can benefit from Unmanned Aerial Base Stations (UABSs) to provide enhanced capacity to ground users (GU) in large and remote locations. Connecting UABSs to the terrestrial network presents several challenges, such as the limited gain traditional antennas need to maintain suitable wireless links between the core network, UABS, and GU. A convenient solution is to use MaMIMO (Massive Multiple Input Multiple Output) since it improves spectral and energy efficiency, thus providing high data rates while reducing power consumption. This paper proposes a Multi-user MIMO (MuMIMO) model for UABS aided networks to increase service range and served capacity. It uses hybrid beamforming and beamsteering on Terrestrial Base Stations (TBSs) and UABSs to grant access to mobile GU in a bicycle race scenario. Results show that using the mobile operators’ locations will benefit the backhaul network’s performance by duplicating the capacity compared to using a private operator. Furthermore, user coverage increases by 400% if MuMIMO is used, compared to a single beam MaMIMO network. The proposed scenario could achieve a channel efficiency of 6.5 bit/s/Hz in the access network and 8.6 bit/s/Hz in the backhaul network. Finally, the average UABS transmitted power is reduced by 2/3, increasing the number of used beams.
Strategic Framework for Inclusion of Persons with Disabilities in Online (Platform) Work

The COVID-19 crisis has accelerated digital transformation and expanded the usage of advanced ICT solutions globally. Moreover, the expansion of remote work platforms and the tendency of modern businesses to outsource part of their business operations to individuals in remote locations creates significant potential for finding employment. According to available data sources, it is estimated that about 700,000 persons in Serbia face some form of disability. Of that number, only about 13% are employed. As a consequence of facing discrimination, persons with disabilities have a greater tendency towards long-term unemployment and complete withdrawal from the labor market. The Internet and advanced digital technologies provide equal opportunities for persons with disabilities to get involved in the digital labor market and increase their chances to find employment and generate income. This paper aims to discuss the potential of digital labor platforms for lowering the long-term unemployment of persons with disabilities in Serbia. Accordingly, the paper provides data on the level of digital competencies of persons with disabilities, since they are fundamental for obtaining employment on digital labor platforms. For that purpose, the standard desk-research method and secondary data analysis of relevant literature, strategic documents and public policies have been performed. The research results provide a deeper insight into the current level of digital competencies of persons with disabilities and suggest a potential solution for improving their chances of obtaining employment and generating income.
Plasmonic nanobiosensors for detection of different targets

Plasmonic nanobiosensors have an enormous application range. It has the capacity to detect a wide variety of substances including metal, protein and even nucleic acids due to the superiority of SPR and LSPR. Plasmonic biosensors have been widely applied in the field of disease diagnosis, environmental conservation and food safety, eliminating barriers of traditional diagnosis methods and providing sensitive, quick and label-free devices. The applications of plasmonic biosensors in detection of many concerned diseases like cancer and SARS-CoV-2 are making an improvement on our medical condition. In the field of environmental protection, plasmonic-based biosensors also show great potential. They can efficiently detect two main types of contaminants, inorganic heavy metals involving Pb, Cd, As and Hg, and organic pollutants like polycyclic aromatic hydrocarbons (PAHs). Plasmonic biosensors could also overcome challenges on food allergen detection. This paper mainly focusses on SPR and LSPR-based nanobiosensors’ application in environmental protection, food safety and health-care.
LIMITATIONS OF CURRENT OF THE THREE-PHASE ACTIVE POWER FILTER IN THE CONDITIONS OF OVERLOAD AND SHORT CIRCUIT

Purpose. The purpose of the work is to develop a method of limiting the maximum allowable level of current of a threephase active power filter in conditions of overload or short circuit and a system for the implementation of the method. Methodology. For research purposes, the provisions of the pq-theory of instantaneous power, the method of the theory of automatic control in systems with relay controllers, and the methods of simulation in the visual programming environment were used. Results. Both the overloading mode and the short circuit emergency mode, using the proposed solution, do not lead to significant changes in the voltage level on the accumulation capacitor, thus maintaining the stability of the power part of the active power filter. Originality. In the case of temporary overloads of current and short circuits at the network node to which a active power filter is connected, the current's limiting is performed by scaling the current to the level allowed by normal operation of the semiconductor elements of the device, which allows the basic operating algorithm to be implemented in the specified modes. Practical value. The proposed solution can be used as a mean to protect the power part of the device in case of overload, which, in the event of emergencies, and their elimination will automatically restore the normal mode of the device. References 11, tables 2, figures 5.
A survey on UML model smells detection techniques for software refactoring

Bad smells tend to have a negative impact on software by degrading its quality. It is beneficial to detect model smells to avoid their propagation to later stages of software development. The objective of this paper is to present the state‐of‐the‐art research on techniques for detecting UML model bad smells. The detection techniques are compared and evaluated using a proposed evaluation framework. The framework consists of two parts. The first part of the framework compares the techniques in terms of the implemented approach, the investigated model, and the explored model smells, while the experimental design is explored in the second part of the framework. We found that the detection of bad smells in class and sequence diagrams is accomplished via design patterns, software metrics, and predefined rules, while model smells in use cases are detected using metrics and predefined rules. We also found that the class diagram is the most investigated UML model in the context of model smell detection, whereas there is a lack of work on other UML models. In addition, there is a scarcity of independent studies on sequence diagrams. Furthermore, the studies investigating class diagrams are mostly validated, whereas use case diagrams and sequence diagrams are rarely validated.
Globalization, Political Institutions, and Redistribution in Central and Eastern Europe

We examine the role of political institutions in mediating the effects of globalization on economic redistribution in Central and Eastern Europe. The region represents a least-likely case of welfare state resilience in the face of global economic pressures given its reliance on foreign capital and considerable domestic elite support for neoliberal recipes during the postcommunist transition. Yet, contrary to the race-to-the-bottom hypothesis and consistent with the compensation perspective, we find that economic openness is on average related to higher redistribution. Using the high-quality European Union Statistics on Income and Living Conditions database for 2004–2018, we find that this effect is particularly pronounced in institutional environments where votes are more accurately translated into legislative seats (low disproportionality) and where governments face greater scrutiny both during elections (vertical accountability) and between them (diagonal accountability). Thus, even in a region that adopted “competitive deregulation,” the downward pressures of globalization were limited by electoral pressures for economic redistribution.
An Adaptive Geometry Transformation and Repair Method for Hybrid Manufacturing

Hybrid manufacturing has become particularly attractive for refurbishing of high-value freeform components. Components may experience unique geometric distortions and/or wear-driven material loss in service, which require the use of part-specific, adaptive repair strategies. The current work presents an integrated adaptive geometry transformation method for additive/subtractive hybrid manufacturing based on rigid and nonrigid registrations of parent region material and geometric interpolation of the repair region material. In this approach, rigid registration of nominal part geometry to actual part geometry is accomplished using iterative alignment of profiles in the parent material. Nonrigid registration is used to morph nominal part geometry to actual part geometry by transformation of the profile mean line. Adaptive additive and subtractive tool paths are then used to add material based on constant stock margin requirements, as well as to produce blend repairs with smooth transition between parent and repair regions. A range of part deformation conditions due to profile twist and length changes are evaluated for the case of a compressor blade/airfoil geometry. Accuracy of the resulting adaptive geometry transformation method were quantified by (1) surface comparisons of actual and transformed nominal geometry and (2) blend region surface accuracy. Performance of the adaptive repair strategy relative to a naïve strategy is evaluated by the consideration of material efficiency and process cycle time. It is shown that the adaptive repair strategy resulted in an increase in material efficiency by 42.2% and a decrease in process time by 17.8%, depending on the initial deformation imposed on the part geometry.
Applications and Challenges of Task Mining: A Literature Review

Task mining is a technological innovation that combines current developments in process mining and data mining. Using task mining, the interactions of workers with their workstations can be recorded, processed, and linked with the business data of the organization. The approach can provide a holistic picture of the business processes and related tasks. Currently, there is no overview of application scenarios and the challenges of task mining. In our work, we reflect application scenarios as well as technological, legal, and organizational challenges of task mining using a structured literature review. The application areas include discovery of automation potentials, monitoring, as well as optimization of business processes. The challenges include the cleansing, collection, data protection, explainability, merging, organization, processing, and segmentation of task mining data.
A Group Videoconferencing Intervention (C@nnected) to Improve Maternal Sensitivity: Protocol for a Randomized Feasibility Trial

Background Early childhood development is highly dependent on the sensitive care provided by caregivers, and interventions focused on supporting parents to improve their sensitivity have shown to be effective. The COVID-19 pandemic has had a significant impact on mental health, with pregnant women and mothers of infants being an especially vulnerable group and maternal sensitivity particularly affected. However, access to face-to-face interventions is restricted; thus, it is important to have remote interventions to support this group of mothers. Objective The objective of this study is to evaluate the feasibility and acceptability of C@nnected, a group videoconferencing intervention to improve maternal sensitivity aimed at mother-infant dyads attending primary health care centers in vulnerable areas of Santiago, Chile. Methods This is a randomized feasibility single-masked (outcome assessor) study with a qualitative component. It will involve a block randomization procedure to generate a 3:2 allocation ratio (with more people allocated to the intervention arm). The intervention consists of 4 group videoconferencing sessions adapted from a face-to-face intervention with proven effectiveness. The control group will receive treatment as usual, along with educational brochures. The feasibility and acceptability of this study will be quantitatively and qualitatively assessed. Changes in clinical outcomes relating to maternal sensitivity, depressive symptoms, postpartum maternal attachment, and infant socioemotional development will also be evaluated. Results We finished adapting the face-to-face intervention to the videoconferencing format in July 2021. The study began recruitment in August 2021, and enrollment is expected to end in August 2022, with final study results expected in December 2022. Conclusions This study will contribute evidence for the use of eHealth interventions to promote maternal sensitivity. It will also inform the design and implementation of a future randomized clinical trial. Trial Registration ClinicalTrials.gov NCT04904861; https://clinicaltrials.gov/ct2/show/NCT04904861 International Registered Report Identifier (IRRID) DERR1-10.2196/35881