Miracle-dz/UniFAR-FTUs huggingface dataset:
Affect as Anaesthetic: how emotional contexts modulate the processing of counterintuitive concepts

ABSTRACT In popular narratives, minimally counterintuitive concepts (MCIs), which violate one category of our real-world knowledge (e.g. talking trees), are frequently embedded in emotional contexts. To assess the impact of emotion on MCI processing, we presented micro-narratives with negative or neutral contents before target sentences. We compared electrophysiological correlates of semantic processing elicited by MCIs, common semantic expectancy violations, and intuitive concepts, presented as critical within-sentence words and as images after the sentences. Results show that emotional contexts play a critical role for MCI processing. N400 effects in neural responses to MCIs that we observed after neutral contexts were not found after negative contexts, suggesting that the synergy between emotional context and MCI saliency enhances the processing of narratives at the cost of critical semantic processing. This finding is relevant for neurocognitive models of language comprehension in high-level contexts, for our understanding of the attraction of counterintuitive concepts and rhetorical strategies.
Artificial magnetic conductor‐backed dual‐band shared‐aperture antenna array for base station applications

A low‐profile dual‐band dual‐polarized base station antenna array is proposed. The dual‐band array is backed by an artificial magnetic conductor (AMC) surface. The AMC achieves profile reduction to 0.159 λL0 in the low band (LB), but it acts as a bandpass metasurface in the high band (HB). The nested design of AMC unit cell creates two out‐of‐phase induced currents with lower intensities, enabling suppression and cancelation of HB scattered fields. The back cavities establish clear and independent radiating environments for the LB and HB antennas. They aid in further radiation pattern restoration and antenna profile reduction. The proposed antenna array was fabricated and experimentally measured. The simulated and measured results are presented. The operating principles of the proposed AMC and back cavities are also investigated.
Image Retrieval Method Combining Bayes and SVM Classifier Based on Relevance Feedback with Application to Small-scale Datasets

: A vast amount of images has been generated due to the diversity and digitalization of devices for image acquisition. However, the gap between low-level visual features and high-level semantic representations has been a major concern that hinders retrieval accuracy. A retrieval method based on the transfer learning model and the relevance feedback technique was formulated in this study to optimize the dynamic trade-off between the structural complexity and retrieval performance of the small-and medium-scale content-based image retrieval (CBIR) system. First, the pretrained deep learning model was fine-tuned to extract features from target datasets. Then, the target dataset was clustered into the relative and irrelative image library by exploring the Bayes classifier. Next, the support vector machine (SVM) classifier was used to retrieve similar images in the relative library. Finally, the relevance feedback technique was employed to update the parameters of both classifiers iteratively until the request for the retrieval was met. Results demonstrate that the proposed method achieves 95.87% in classification index F 1 - Score, which surpasses that of the suboptimal approach DCNN-BSVM by 6.76%. The performance of the proposed method is superior to that of other approaches considering retrieval criteria as average precision, average recall, and mean average precision. The study indicates that the Bayes + SVM combined classifier accomplishes the optimal quantities more efficiently than only either Bayes or SVM classifier under the transfer learning framework. Transfer learning skillfully excels training from scratch considering the feature extraction modes. This study provides a certain reference for other insights on applications of small-and medium-scale CBIR systems with inadequate samples.
Improving the Diversity of Bootstrapped DQN by Replacing Priors With Noise

Q-learning is one of the most well-known reinforcement learning algorithms. There have been tremendous efforts to develop this algorithm using neural networks. Bootstrapped deep Q-learning network is amongst them. It utilizes multiple neural network heads to introduce diversity into Q-learning. Diversity can sometimes be viewed as the amount of reasonable moves an agent can take at a given state, analogous to the definition of the exploration ratio in RL. Thus, the performance of bootstrapped deep Q-learning network is deeply connected with the level of diversity within the algorithm. In the original research, it was pointed out that a random prior could improve the performance of the model. In this article, we further explore the possibility of replacing priors with noise and sample the noise from a Gaussian distribution to introduce more diversity into this algorithm. We conduct our experiment on the Atari benchmark and compare our algorithm to both the original and other related algorithms. The results show that our modification of the bootstrapped deep Q-learning algorithm achieves significantly higher evaluation scores across different types of Atari games. Thus, we conclude that replacing priors with noise can improve bootstrapped deep Q-learning's performance by ensuring the integrity of diversities.
Index Checkpoints for Instant Recovery in In-Memory Database Systems

We observe that the time bottleneck during the recovery phase of an IMDB (In-Memory DataBase system) shifts from log replaying to index rebuilding after the state-of-art techniques for instant recovery have been applied. In this paper, we investigate index checkpoints to eliminate this bottleneck. However, improper designs may lead to inconsistent index checkpoints or incur severe performance degradation. For the correctness challenge, we combine two techniques, i.e. , deferred deletion of index entries, and on-demand clean-up of dangling index entries after recovery, to achieve data correctness. For the efficiency challenge, we propose three wait-free index checkpoint algorithms, i.e., ChainIndex, MirrorIndex, IACoW , for supporting efficient normal processing and fast recovery. We implement our proposed solutions in HiEngine, an IMDB being developed as part of Huawei's next-generation cloud-native database product. We evaluate the impact of index checkpoint persistence on recovery and transaction performance using two workloads ( i.e. , TPC-C and Microbench). We analyze the pros and cons of each algorithm. Our experimental results show that HiEngine can be recovered instantly ( i.e. , in ~10 s) with only slight ( i.e. , 5% - 11%) performance degradation. Therefore, we strongly recommend integrating index checkpointing into IMDBs if recovery time is a crucial product metric.
A Proposal of Fuzzy Rule Partition and Its Application to Controller Design Based on Affine T‐S Fuzzy Model

When designing a controller based on a fuzzy model composed of fuzzy rules, in most cases controllers are designed only based on the consequent parts of the fuzzy rules, equally meaning the information of the antecedent parts is merely ignored. This paper first partitions all of the rules into two groups based on the information of the antecedent parts, in which one contains certain rules around the origin, and the other is a group of rules away from the origin. While being able to make the former arbitrarily small, each rule of the latter leads to a favorable quadratic inequality, which will be useful in controller design by the help of the S‐procedure. On the other hand, while the affine T‐S fuzzy model is much more effective for describing the dynamics of the system concerned in comparison with the regular T‐S fuzzy model, it encounters some difficulties when designing a controller based on the model because of the affine terms within. This paper makes clear that the difficulties can be overcame by using the quadratic inequalities based on the fuzzy rule partition. In addition, the effectiveness of the approach is demonstrated by applying it to the CSTR for an exothermic irreversible reaction. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
FACTORS AFFECTING JOB STRESS AND IMPACT OF STRESS ON ABSENTEEISM: A STUDY ON THE BANKING SECTOR IN BANGLADESH

The purpose of the study is to explore the factors affecting stress and the impact of stress on absenteeism in the public and private banking sector in Bangladesh. An attempt was also made to investigate how stress is related to absenteeism. The number of samples was 272. With help of the structured questionnaire data was collected. The research applied the confidence interval, univariate, bivariate, multivariate analysis and t-test analysis for analyzing the collected data. It is found that the degree of stress is high in this sector, and stress factors (workload, role characteristics, time pressure & deadline and non-work pressure) are positively related to employee absenteeism. Whereas it has also found that, stress level is almost same for male and female employees in the banking sector. This study is advantageous to human resource practitioners and managers in developing workplace strategies and in improving positive workplace outcomes from the workplace by reducing stress.
2D Relative Pose and Scale Estimation with Monocular Cameras and Ranging

Cooperative swarms of robots equipped with cameras are robust against failures and can explore Global Navigation Satellite System‐denied environments efficiently. Applying Visual Simultaneous Localization and Mapping (VSLAM) techniques, vehicles can estimate their trajectories and simultaneously reconstruct the map of the environment using visual cues. Due to constraints on payload size, weight, and costs, many Visual Simultaneous Localization and Mapping applications must be based on a single camera. The associated monocular estimation of the trajectory and map is ambiguous by a scale factor. This work shows that by exploiting sparse range measurements between a pair of dynamic rovers in planar motion, the correct scale factors of both cameras and the relative position, as well as the relative attitude between the rovers, can be estimated. Neither images nor feature vectors are required to be transmitted over the communication channel for the proposed method, which is a significant advantage in practice.
Free electron harmonic generation in heavily doped semiconductors: the role of the materials properties

Heavily doped semiconductors have emerged as low-loss and tunable materials for plasmonics at mid-infrared frequencies. We analyze the nonlinear optical response of free electrons and show how nonlinear optical phenomena associated with high electron concentration are influenced by the intrinsic properties of semiconductors, namely background permittivity and effective mass. We apply our recently developed hydrodynamic description that takes into account nonlinear contributions up to the third order, usually negligible for noble metals, to compare third-harmonic generation from InP, Ge, GaAs, Si, ITO and InSb. We show how free electron nonlinearities may be enhanced with a proper choice of the semiconductor.
Antioxidant Properties of Synthesized Bicyclic Thiazolopyrimidine Derivatives as Possible Therapeutic Agents

Oxidant/antioxidant imbalance in the body has been implicated as one of the pathophysiological mechanisms leading to disease development. Therefore, we searched for a good antioxidant candidate that can be used as a therapeutic agent alone or in combination with other reported drugs. Earlier, we reported the A2A receptor antagonist properties of 7-Imino-3-substituted-2-thioxo-3,7-dihydro-2H-thiazolo[4,5-d]pyrimidin-6-yl)-urea derivatives (compounds 1–12) and the neuroprotective effect of compound 2. Therefore, in the present work, the antioxidant potential of compounds 1–12 was studied. Compounds 1–12 were screened using different (red/ox) tests, such as the Ferric Reducing Antioxidant Power (FRAP) assay, to determine total antioxidant activity, redox status tests (with and without prooxidants) such as Advanced Oxidation Protein Products (AOPP) and Total Oxidative Status (TOS) which measures H2O2 and lipid hydroperoxides, Paraoxonase-1 Enzyme Activity (PON1), Total SH-groups content, and Total Antioxidative Status (TAS) for antioxidant determination. The Prooxidative Score, Antioxidative Score, and Oxy Score were also calculated. From the obtained results, compounds 6 (8720 FRAP value and 39.31 Oxy Score) and 12 (7866 FRAP value and 36.41 Oxy Score) were found to possess significant antioxidant activity with reasonable potential for therapeutic activity.
Bound orbit domains in the phase space of the Kerr geometry

We derive the conditions for a non-equatorial eccentric bound orbit to exist around a Kerr black hole in two-parameter spaces: the energy, angular momentum of the test particle, spin of the black hole, and Carter's constant space ($E$, $L$, $a$, $Q$), and eccentricity, inverse-latus rectum space ($e$, $\mu$, $a$, $Q$). These conditions distribute various kinds of bound orbits in different regions of the ($E$, $L$) and ($e$, $\mu$) planes, depending on which pair of roots of the effective potential forms a bound orbit. We provide a prescription to select these parameters for bound orbits, which are useful inputs to study bound trajectory evolution in various astrophysical applications like simulations of gravitational wave emission from extreme-mass ratio inspirals, relativistic precession around black holes, and the study of gyroscope precession as a test of general relativity.
Coordinated Participation of Electric Vehicles and Generating Units in Primary Frequency Control in the Presence of Renewables

With the integration of renewables into the grid, the ability to perform primary frequency control (PFC) by the conventional generating units decreases in terms of frequency quality and stability. Also, the wear and tear of the generating units responsible for the PFC increases. In this article, a hierarchical PFC scheme is proposed that combines the coordinated actions of hydro-generation units and electric vehicles (EVs) such that the stability is maintained for large and small disturbances, while at the same time, frequency quality is improved and wear and tear of the generating units is reduced. The control parameters of the proposed scheme are tuned by solving the proposed optimization problem with proper objective functions and constraints that are representative of frequency stability and quality, wear and tear of the generating units, and comfort of EVs owners. The scheme is complemented by a new combination of frequency filters for EVs and generating units to more effectively reduce the effect of small frequency fluctuations on the wear and tear of generating units. Comparative simulation studies show the effectiveness of the proposed method in ameliorating the problems raised by the integration of renewables.
Study the Impacts of Aggregate’s Geometry and Cement Contents on Fresh and Hardened Properties of Concrete

The effects of gravel’s geometry and cement content are one of the most important parameters in the construction industry. Two types of aggregates, sharp corners and rounded corners with different sizes, and several samples of various cement contents were prepared. Then the workability, strength, and elastic modulus tests were performed. Experimental results indicate that workability and compressive strength increase with increasing cement content as expected (12% and 22% for workability and 64% and 69% for compressive strength with regard to sharp and rounded aggregate, respectively). Also, in most cases, seismic parameters improve too (maximum of 120% and 107% for energy absorption regarding sharp and rounded aggregate, respectively). The results demonstrate that concrete samples with the largest grain size of 12.5 mm have the most compressive strength, elastic modulus, energy absorption, and ductility in comparison with other samples (maximum 24% and 18% for elastic modulus with reference to sharp and rounded aggregate, respectively). Finally, the results show that the sharp corner aggregates have greater strength and better seismic parameters in comparison with rounded corner (maximum 16% for energy absorption). In contradiction to the above statement, the rounded corners aggregates have more workability than sharp corner ones (up to 18%), and also the workability increases by reducing the gravel size (26% and 19% for sharp and rounded aggregate, respectively). A statistical approach was used to determine the optimal shape and size of aggregate and organize the results scattering.
Comparison of advanced non‐parametric models for wind turbine power curves

To continuously assess the performance of a wind turbine (WT), accurate power curve modelling is essential. Various statistical methods have been used to fit power curves to performance measurements; these are broadly classified into parametric and non-parametric methods. In this study, three advanced non-parametric approaches, namely: Gaussian Process (GP); Random Forest (RF); and Support Vector Machine (SVM) are assessed for WT power curve modelling. The modelled power curves are constructed using historical WT supervisory control and data acquisition, data obtained from operational three bladed pitch regulated WTs. The modelled power curve fitting performance is then compared using suitable performance, error metrics to identify the most accurate approach. It is found that a power curve based on a GP has the highest fitting accuracy, whereas the SVM approach gives poorer but acceptable results, over a restricted wind speed range. Power curves based on a GP or SVM provide smooth and continuous curves, whereas power curves based on the RF technique are neither smooth nor continuous. This study highlights the strengths and weaknesses of the proposed non-parametric techniques to construct a robust fault detection algorithm for WTs based on power curves.
Review on Bovine Tuberculosis: An Emerging Disease Associated with Multidrug-Resistant Mycobacterium Species

Bovine tuberculosis is a serious infectious disease affecting a wide range of domesticated and wild animals, representing a worldwide economic and public health burden. The disease is caused by Mycobacterium bovis and infrequently by other pathogenic mycobacteria. The problem of bovine tuberculosis is complicated when the infection is associated with multidrug and extensively drug resistant M. bovis. Many techniques are used for early diagnosis of bovine tuberculosis, either being antemortem or postmortem, each with its diagnostic merits as well as limitations. Antemortem techniques depend either on cellular or on humoral immune responses, while postmortem diagnosis depends on adequate visual inspection, palpation, and subsequent diagnostic procedures such as bacterial isolation, characteristic histopathology, and PCR to reach the final diagnosis. Recently, sequencing and bioinformatics tools have gained increasing importance for the diagnosis of bovine tuberculosis, including, but not limited to typing, detection of mutations, phylogenetic analysis, molecular epidemiology, and interactions occurring within the causative mycobacteria. Consequently, the current review includes consideration of bovine tuberculosis as a disease, conventional and recent diagnostic methods, and the emergence of MDR-Mycobacterium species.
A semi-automated hybrid approach to identify radicalization on social digital platform

The digital social platform is an important medium for sharing or communicating a message from one person to another or one to many. The growth of internet users and social media use has also led to many adverse consequences. Such a platform is also used for radical activity by spreading the radical message in public. The detection of such a message is impossible by human monitoring. Many researchers are continually working on automatic detection of such activity to find a way to stop it. Automatic identification is also not possible due to the massive amount of data present and ambiguity in messages. The proposed work presents a framework for detecting the radical message and taking action by automatically blocking it. A dataset of 33k tweets has been fetched from twitter based on radical words. Two machine learning models, first countervectorizer and Logistic regression-based and second convolutional neural networks (CNN) have been applied yielding 96.97% accuracy. The provision of human intervention is also given in doubt cases which helps further to improve the accuracy of overall model. The framework gives very good results in a simulated environment.
Improved microwave attenuator topology minimizing the number of control voltages

This letter presents an improved circuit topology to minimize the number of external control voltages that are required to set the state of an N‐bit microwave digital attenuator. An alternative method to connect the control signal is proposed. Typically, the control signal is connected to the gate terminal of the field effect transistor while the source terminal is DC grounded. Here, the control signal is applied to the source terminal of the field effect transistor while the gate is connected to a fixed reference voltage. In this way, only one control voltage is necessary to set the state of single attenuator cell vs two control voltages required in the typical case. The proposed method becomes particularly beneficial as the number of bits increases (ie, >4). The topology has been validated through design, realization, and testing of a 5‐bit Monolithic Microwave Integrated Circuit (MMIC) attenuator operating over the full C‐band (4‐8 GHz).
Development and Performance Evaluation of a New Energy-Efficient Double Cluster-Head Routing (EEDCR) Protocol for Wireless Sensor Networks

Utilization of energy and the lifetime increment are the big issues in designing of routing algorithms for wireless sensor networks (WSNs). Many routing algorithms have been developed by various researchers to achieve energy efficiency and to improve the lifetime of the network. But, the way to route the information from the sensor node (SN) to the base station (BS) and vice versa is an important issue, because of resource constraints. In this paper, we have proposed a low energy consumed, cluster-based routing protocol named an energy-efficient and double cluster-head routing (EEDCR) protocol, to increase the network lifetime and minimize the end-to-end delay. Selection of the cluster head (CH) is depending on random selection method, energy method, the total number of nodes, and its energy level. The performances of developed protocol were assessed using simulations and found that it provides successful outcomes in terms of energy-efficient and lifetime increment in routing for WSNs.