Miracle-dz/UniFAR-FTUs huggingface dataset:
Trust Recommendation Based on Deep Deterministic Strategy Gradient Algorithm

Trust recommendation is a vital recommendation system application based on social networks. It can recommend items based on the trust between users, which can alleviate data sparseness and enhance the interpretability of results. A large number of recommendation algorithms have been proposed, but most of them believe that trust is fixed, ignoring the changes of trust in the process of interaction. In addition, deep learning models are good at solving complex tasks and processing high-dimensional data, and they can model recommendation algorithms, but they are insufficient in capturing changes in user preferences timely. Therefore, given the shortcomings of existing research, we propose a DDPG-TR algorithm based on deep reinforcement learning to capture the changes in user preferences and update the trust between users. The algorithm uses deep deterministic policy gradient algorithm DDPG to model the user-item interaction process. Firstly, we improve a state representative structure to express the user’s state, which is convenient to capture the changes in user preferences. Then when the user accepts the recommendation, algorithm combines trust and similar information to predict item score, as well as calculates the difference of the score. Finally, Agent gets the score feedback and uses the difference to update trust. Experiments are conducted on three datasets, and they are verified that the DDPG-TR algorithm can provide more accurate recommendation results, compared with other recommendation algorithms.
Supply chain risk identification: a real-time data-mining approach

PurposeThe global pandemic COVID-19 unveils transforming the supply chain (SC) to be more resilient against unprecedented events. Identifying and assessing these risk factors is the most significant phase in supply chain risk management (SCRM). The earlier risk quantification methods make timely decision-making more complex due to their inability to provide early warning. The paper aims to propose a model for analyzing the social media data to understand the potential SC risk factors in real-time.Design/methodology/approachIn this paper, the potential of text-mining, one of the most popular Artificial Intelligence (AI)-based data analytics approaches for extracting information from social media is exploited. The model retrieves the information using Twitter streaming API from online SC forums.FindingsThe potential risk factors that disrupt SC performance are obtained from the recent data by text-mining analyses. The outcomes carry valuable insights about some contemporary SC issues due to the pandemic during the year 2021. The most frequent risk factors using rule mining techniques are also analyzed.Originality/valueThis study presents the significant role of Twitter in real-time risk identification from online SC platforms like “Supply Chain Dive”, “Supply Chain Brain” and “Supply Chain Digest”. The results indicate the significant role of data analytics in achieving accurate decision-making. Future research will extend to represent a digital twin for identifying potential risks through social media analytics, assessing risk propagation and obtaining mitigation strategies.
Femto power-delay(FPD)super threshold level shifter for network on chip (NoC)

ABSTRACT This paper presents a novel architecture of femto power-delay super threshold voltage level shifter (LS) for network on-chip voltage control, developed using feedback topology for error aware interconnections data transmission. The proposed LS utilises eight MOS transistors with low aspect ratios for level up or level down. The developed LS can shift as low as 0.12–1.25 V with a tremendous reduction in delay and power consumption. Implemented in 65 nm CMOS technology, the post-layout simulation substantiates the achievement of voltage translation. The proposed LS incurs energy per cycle of 44 fJ during up shift; the average of level up and level down static power consumption is 3.61 nW, while VDDL is 0.3 V and VDDH is 1.2 V at a frequency of 1 MHz. The layout area of the proposed LS is 3.21 µm × 2.13 µm.
Plant disease and insect pest identification based on vision transformer

With the rapid development of precision agriculture and smart agriculture, the need to build an automatic identification and detection system for diseases and insect pests is increasing. Using computers to correctly label plant diseases and insect pests is an important prerequisite for achieving accurate classification of plant diseases and insect pests and ensuring system performance. In order to improve the accuracy of computer classification of plant pests and diseases, this paper proposes an automatic pest identification method based on the Vision Transformer (ViT). In order to avoid training overfitting, the plant diseases and insect pests data sets are enhanced by methods such as Histogram Equalization, Laplacian, Gamma Transformation, CLAHE, Retinex-SSR, and Retinex-MSR. Then use the enhanced data set to train the constructed ViT neural network, so as to realize the automatic classification of plant diseases and insect pests. The simulation results show that the constructed ViT network has a test recognition accuracy rate of 96.71% on the plant disease and insect pest public data set Plant_Village, which is about 1.00% higher than the Plant disease and pest identification method based on traditional convolutional neural networks such as GoogleNet and EfficentNetV2.
Tumor Lysis Syndrome in the Chronic Phase of Chronic Myeloid Leukemia Following COVID-19 Infection: A Case Report

Tumor lysis syndrome (TLS) is a hematological emergency. This syndrome is characterized by metabolic derangements such as hyperkalemia and hypocalcemia, which result from rapid lysis of cells, especially rapidly growing tumors, after the initiation of chemotherapy. It is rarely seen in chronic myeloid leukemia (CML) and has not been previously reported to be triggered by coronavirus disease 2019 (COVID-19) infection. We report a case of a 45-year-old male, a known case of CML in the chronic phase, who presented with fatigue and abdominal pain for four days. Initial laboratory results were consistent with leukocytosis and positive COVID-19 antigen. The patient was started on intravenous fluids and hydroxyurea; however, over the next few days, he deteriorated quickly and developed oliguric acute kidney injury (AKI) with electrolyte disturbance consistent with TLS. The patient was shifted to the intensive care unit and underwent one sustained low-efficiency dialysis (SLED) session and received rasburicase. Over the next few days, the patient started to improve and was discharged in good shape. Although CML rarely presents with TLS, physicians should monitor their patients closely, especially those who have concurrent COVID-19 infection, as this condition may result in lethal sequelae such as AKI, severe arrhythmias, and multiorgan failure. Additionally, early detection and treatment lead to a better prognosis.
Efficiency scaling method of gasoline engines for different geometries and the application in hybrid vehicle simulation

This work presents a scalable model of a naturally aspirated gasoline engine forecasting the effective efficiency map for varying cylinder displacements. Engine test bench measurements and a global nonlinear hybrid optimization method were used to calibrate the engine model. The validation showed a good prediction of engine efficiency by the scaling model with a mean error of 2% compared with the measurements. A pure scaling of the cylinder displacement led to overall small changes in the effective engine efficiency map. In addition to the development of a scalable engine model, a forward-looking hybrid vehicle simulation model was used in order to evaluate the impact of different engine cylinder displacements on fuel consumption. For this purpose, simulations for varying cylinder displacements were performed in a series–parallel hybrid drivetrain of an A-class vehicle in two driving cycles. The simulation results showed a small influence of different engine cylinder displacements on fuel consumption for the given configuration.
Efficient Uncertainty Quantification and Variance-Based Sensitivity Analysis in Epidemic Modelling using Polynomial Chaos

The use of epidemic modelling in connection with spread of diseases plays an important role in understanding dynamics and providing forecasts for informed analysis and decision-making. In this regard, it is crucial to quantify the effects of uncertainty in the modelling and in model-based predictions to trustfully communicate results and limitations. We propose to do efficient uncertainty quantification in compartmental epidemic models using the generalized Polynomial Chaos (gPC) framework. This framework uses a suitable polynomial basis that can be tailored to the underlying distribution for the parameter uncertainty to do forward propagation through efficient sampling via a mathematical model to quantify the effect on the output. By evaluating the model in a small number of selected points, gPC provides illuminating statistics and sensitivity analysis at a low computational cost. Through two particular case studies based on Danish data for the spread of Covid-19, we demonstrate the applicability of the technique. The test cases consider epidemic peak time estimation and the dynamics between superspreading and partial lockdown measures. The computational results show the efficiency and feasibility of the uncertainty quantification techniques based on gPC, and highlight the relevance of computational uncertainty quantification in epidemic modelling.
Interaction and meaning making in basic adult education for immigrants the case of Swedish for immigrants in Sweden (SFI)

Abstract In this article, focus is on students’ oral production in two classrooms in Swedish for immigrants (SFI). The study focuses on practices with interaction patterns where students are involved in negotiation of meaning. Theoretical basis is the importance of interpersonal interaction for language development, with a focus on students’ use of varied speaker roles and speech actions. Data were obtained through observations, field notes, and audio and video recordings. The examples presented here demonstrate that students were involved in the negotiation of meaning and had space to try different speaker roles and speech actions, such as (among others) initiating, agreeing, dissenting, arguing, interrupting, and taking the floor. These examples only constituted a restricted part of class time, and most of the teaching was of a type where little oral or written interaction took place. Overall, we think that SFI education could be improved by developing teaching that stimulates and allows for negotiations of meaning and language production and also allows for use of different types of digital media, both for oral and written interaction.
Modeling Human Resource Experience Evolution for Multiobjective Project Scheduling in Large Scale Software Projects

The software project scheduling (SPS) is a project-scheduling problem where limited human resources are assigned to the tasks in multi-team project settings. Besides other dynamic events, employees experience evolution has direct influence in completing large-scale projects within budget and time. In this paper, a new SPS model is developed as a dynamic multi-objective optimization problem, which incorporates employees experience evolution with their learning ability over time. The experimental results on 24 problem instances (including six real-world instances) show that the developed SPS model reduces project duration by 40% while being within budget. The results provide evidence that consideration of experience evolution while tasks reallocation under dynamic events significantly optimizes project schedules. Moreover, the developed SPS model is evaluated with six state-of-the-art algorithms as bi-criterion evolution (BCE), NSGA-II, NSGA-III, Two_Arch2, OMOPSO, speed-constrained multi-objective particle swarm optimization (SMPSO) where BCE demonstrated distinct superiority for 63% data instances.
The Association Between Physical Activity Level and Severity of Knee Osteoarthritis: A Single Centre Study in Saudi Arabia

Background and aim Knee osteoarthritis (OA) is a disabling joint disease that results in degeneration of the joint cartilage. Many studies demonstrate the risk factors and complications of knee osteoarthritis, but how physical activity impacts the osteoarthritis severity needs to be studied. The study aimed to assess the impact and association of physical activity level with the severity of knee osteoarthritis among patients attending a single center in the Aseer region, southwest Saudi Arabia. Methods An analytical cross-sectional study was conducted targeting all patients clinically diagnosed with knee osteoarthritis in the Aseer region from June 2021 to September 2021. Data was collected using a pre-structured online questionnaire. The questionnaire included patients' socio-demographic data and clinical data on knee osteoarthritis. The level of physical activity was assessed using the International Physical Activity Questionnaire (IPAQ). The severity of knee osteoarthritis was assessed using the index of severity for osteoarthritis of the knee. Results Out of 473 patients clinically diagnosed with knee OA, only 145 patients met our inclusion criteria. Patients' ages ranged from 35 years to 71 years, with a mean age of 44.3 (±12.9) years. A total of 122 (84.1%) patients were females. Sixty-five (44.8%) patients complained of knee OA for less than two years, 46 (31.7%) for two to five years, and 34 (23.4%) were diagnosed for more than five years. A total of 96 (66.2%) had a low level of physical activity, 32 (22.1%) had a moderate level of physical activity, while 17 (11.7%) had a high level of physical activity. Thirty-eight (26.2%) cases complained of extremely severe knee OA, 37 (25.5%) had very severe knee OA, 28 (19.3%) complained of severe knee OA, 36 (24.9%) had mild to moderate knee OA, while six (4.1%) had minimal knee OA. There was a significant beneficial effect of physical activity on the severity of knee OA. Conclusion The study revealed that physical activity, especially at high levels, was associated with lower knee OA severity which means a beneficial effect. Most of the patients included in the study had severe to extremely severe knee OA with low physical activity levels.
Ultrasound process‐enhanced removal of the toxic disinfection by‐product bromate from water by aluminum: A comparative study

As bromate removal and reduction can be also achieved using metals, aluminum (Al) appears as the most promising one for reduction of bromate because Al is abundant element and exhibits a high reduction power. Reactions between bromate and Al shall be even enhanced through ultrasound (US) process because US can facilitate mass transfer on liquid/solid interfaces and clean surfaces via generating microscale turbulence to facilitate reactions. Therefore, the aim of this study is for the first time to investigate the effect of US on bromate removal by Al metal. Specifically, Al particle would be treated by HCl to afford HCl‐treated Al (HCTAL), which is capable of removing bromate and even reducing it to bromide. Such a mechanism is also validated by density function theory calculation through determining adsorption energy as −152.8 kJ/mole, and oxygen atoms of bromate would be extracted and reacted with Al atoms, releasing bromide ion. US not only facilitated bromate removal by further increasing removal capacity under the acidic condition but also suppressed the inhibitive effect from basicity at relatively high pH. The spent HCTAL could still remove bromate and convert it to bromide after regeneration. These features indicate that US considerably enhances bromate removal by Al.
Estimating Path Dependence in Energy Transitions

How can an economy transition from dirty to clean inputs? When transitional dynamics exhibit strong path dependence, a temporary shock to input composition can trigger permanent structural change. This paper examines whether such dynamics characterize the U.S. energy sector’s use of coal - the most climate-damaging energy input - over the 20th century. Exploiting local coal transport distance shocks driven by the changing regional accessibility of subsurface coal resources, I find increasing imbalance in the coal composition of electricity capital lasting ten decades following a shock. Additional tests detect increasing returns to scale as the underlying mechanism. To inform energy transitions more broadly, I develop a model of scale-driven structural change to map reduced-form estimates onto a key parameter found across a class of structural change models. Calibrated model simulations further characterize conditions under which a temporary climate policy can trigger a permanent future transition towards clean energy.
A study of multistep model predictive direct current control for dynamic drive applications with high switching frequency

In this work a study of multistep model predictive direct current control in high switching frequency range will be presented. The optimization problem underlying this control approach is represented by an integer least-square problem, which can be solved by the sphere decoding algorithm. In this paper, the system performance, both in transient and steady-state will be analysed in such a way, that a direct current controller can be obtained for an induction motor operated by a two-level voltage source inverter in high dynamic drive applications, which achieves higher dynamics when compared to the classic modulation based schemes and lower current distortion than other direct control approaches.
IoT Middleware Platforms for Smart Energy Systems: An Empirical Expert Survey

Middleware platforms are key technology in any Internet of Things (IoT) system, considering their role in managing the intermediary communications between devices and applications. In the energy sector, it has been shown that IoT devices enable the integration of all network assets to one large distributed system. This comes with significant benefits, such as improving energy efficiency, boosting the generation of renewable energy, reducing maintenance costs and increasing comfort. Various existing IoT middlware solutions encounter several problems that limit their performance, such as vendor locks. Hence, this paper presents a literature review and an expert survey on IoT middleware platforms in energy systems, in order to provide a set of tools and functionalities to be supported by any future efficient, flexible and interoperable IoT middleware considering the market needs. The analysis of the results shows that experts currently use the IoT middleware mainly to deploy services such as visualization, monitoring and benchmarking of energy consumption, and energy optimization is considered as a future application to target. Likewise, non-functional requirements, such as security and privacy, play vital roles in the IoT platforms’ performances.
Switched Controller Design for Robotic Manipulator via Neural Network-Based Sliding Mode Approach

In this brief, a novel adaptive switched controller of uncertain industry robotic manipulator (IRM) systems with switching loads is designed for trajectory tracking by utilizing switching neural network-based sliding mode control (SNNSMC) scheme. However, the error accumulation and system instability of the IRM may be caused by switching loads, while the existing single adaptive controller may not effectively deal with this issue. To do so, the IRM with switching loads is modeled as a switched system with multi-modal. Besides, the individual sub-controller with adaptive gain algorithm is devised to replace the single adaptive controller with fixed-gain for multi-modality system. Then, the radial basis function NN (RBFNN) is utilized for approaching to the plant, which avoids the limitation of the accurate model for the IRM system with switching loads. The SMC gain is designed as an adaptive adjustment value, which can be adjusted in real time to enhance the robustness of the system in spite of unknown disturbances and uncertainties. Subsequently, based on average dwell time (ADT) principle, the trajectory tracking error being close to zero is verified by the multi-Lyapunov function method. Finally, simulation results show that the provided method can both track the preset trajectory accurately, and attenuate chattering and high-speed switching of the plant effectively.
Detecting and Localizing Wireless Spoofing Attacks

Wireless networks are vulnerable to spoofing attacks, which allows for many other forms of attacks on the networks. Although the identity a node can be verified through cryptographic authentication, the authentication is not always possible because it requires key management and additional infrastructural overhead. In this project we propose a method for detecting spoofing attacks and locating the positions of adversaries which performs the attacks. At first we introduce an attack detector for wireless spoofing that utilizes K-means cluster analysis. Here we describe how integrate our attack detector into a real time indoor localization system, which is capable of localizing the positions of the intruders. Then we indicate the position of the attackers can be localized using either area-based or point-based localization algorithms with the same relative errors as in the normal case. Here we evaluated our methods through experimentation using both an 802.11 (WiFi) network and 802.15. 4 (ZigBee) network. Our results show that is possible to detect wireless spoofing with both a high detection rate and a low false positive rate, thereby providing strong evidence of the effectiveness of the K-means spoofing detector as well as the attack localizer.
Neurophysiological Measures in Hospitality and Tourism: Review, Critique, and Research Agenda

This paper provides a critical review of studies using neurophysiological measures in tourism and hospitality. Among 145 articles covering 20 years of research, 16 studies applied either electroencephalography (EEG), functional magnetic resonance imaging (fMRI), or skin conductance (SC) measures in tourism and hospitality settings. Results show that, in general, (1) EEG studies investigated the relationships between EEG components and attention/emotion induced by destination advertisements; (2) fMRI studies examined the correlation between brain area activation and behavior (e.g., visit intention); and (3) SC studies focused on emotional responses to tourism stimuli. Neurophysiological techniques are theoretically and practically useful in tourism and hospitality: these tools uncover subjects’ objective, unbiased, and real-time responses to provide academic insight and guide industry practitioners’ decisions. Directions for future research are proposed along with solutions to address the current limitations of neuroscience measures in tourism and hospitality applications.
Effect of Oreganum onites L. Aromatic Water Supplemented to Whole Milk on Performance, Blood Metabolites and Oxidative Status of Holstein Calves During the Pre-Weaning Period

In intensive growing systems, the growth and yield of livestock are suppressed due to the oxidative stress to which they are also exposed. In the present study, it was investigated whether Oreganum onites L. aromatic water (OW) could serve as an agent of reducing the effects of oxidative stress while improving the growth performance of suckling Holstein calves. Twenty-eight newborn calves (n=7, in each group) were randomly selected and assigned to the following four treatments: Control, 40, 60 and 80 ml OW supplemented whole milk (WM) per day. The applied 60 and 80 ml OW tended to improve the antioxidative stress mechanism of calves while supporting their growth (e.g., total and daily weight gain, body measurements). The findings of the present study showed that Oreganum onites L. aromatic water, a by-product, can be used safely in alleviating the problems such as oxidative stress that may occur in intensive growing systems.