Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Lecture Model with WhatsApp Group (WaG) and Google Classroom (GC) Integration

The impact of Covid-19 has made educational institutions carry out online learning processes; thus, they must provide innovative learning media so that students continue to get material explanations like in face-to-face learning. This study aims to design and evaluate an online lecture model by integrating WaG and GC. Using descriptive method, this study involves Economic Education students at Universitas Indraprasta PGRI for the academic year 2020/2021 who received 276 scientific writing courses. This study consists of (1) designing WaG and GC media, (2) providing the lecture material on the learning media, (3) implementing the media in lecture activities, and (4) evaluating the effectiveness of the media. The questionnaire shows that learning media by integrating WaG and GC is effective to be applied. This is indicated by the percentage of students who strongly agree (54%), agree (32.5%), quite agree (11.2%), disagree (1.6%), and strongly disagree (0.7%). The evaluation includes ease of access, affordability, understanding of the material, two-way communication, media updates, and ease of using technology.
Pectin graft copolymer-montmorillonite composite: Synthesis, swelling and divalent metal ion adsorption

ABSTRACT A novel graft copolymer gel made up of pectin (Pec), 2-(methacryloyloxyethyl)trimethylammonium chloride (METAC), 2-acrylamido-2-methyl-1-propane sulfonic acid (AMPS) and its composite with montmorillonite (MMT) were prepared using methylenebisacrylamide (MBA) as cross-linker via microwave irradiation and characterized using Fourier transform infrared (FTIR), Thermogravimetric Analysis (TGA) and Scanning electron microscopy (SEM) techniques. Swelling studies were carried out under different pH conditions. The graft copolymer gel and its composite showed maximum swelling in neural medium (pH 7.1) and the swelling process followed second order kinetics. The mechanism of water transport is found to be a Less Fickian diffusion process. The adsorption capacities of the graft copolymer gel and the composite towards divalent metal ions (Cu2+, Pb2+ and Hg2+) were evaluated. The adsorption capacity of the representative samples, Pec-g-poly(METAC-co-AMPS)-A5 and Pec-g-poly(METAC-co-AMPS)/MMT-C2 respectively for Cu2+ are 30.71 and 39.18 mg/g; for Pb2+ are 58.06 and 79.78 mg/g and for Hg2+ are 12.16 and 19.58 mg/g. The re-usability of the materials was also evaluated. The % recovery for the above two systems towards metals ion are 87.91 and 63.46 for Cu2+, 32.13 and 58.30 for Pb2+ and 78.53 and 51.92 for Hg2+. respectively. The adsorption isotherm studies indicated the adsorption of Pb2+, Cu2+ and Hg2+ in both samples is explained best by the Freundlich model except of Hg2+ by Pec-g-poly(METAC-co-AMPS)-A5, which is best explained by the Langmuir model.
Text Adversarial Examples Generation and Defense Based on Reinforcement Learning

: In recent years, the neural networks are widely used in image processing, natural language processing and other fields. But there are new security issues-the adversarial examples. Crafted adversarial examples can make a trouble for the neural network, which leads to the mis-classification. Text classification is one of the basic tasks of the natural language processing. This paper is concerned about the generation and defense of text adversarial examples. The main contributions of this research are as follows: This paper explores a new type of adversarial example and applies reinforcement learning to generate the adversarial examples; a training set composed of adversarial examples is constructed. To build a more robust classifier, a new defense framework is established. In order to eliminate the influence of noise, well-designed predetector and reformer were implemented, which helps the neural networks to resist adversarial examples and reduce coupling.
Inverse Identification of the Ductile Failure Law for Ti6Al4V Based on Orthogonal Cutting Experimental Outcomes

Despite the prevalence of machining, tools and cutting conditions are often chosen based on empirical databases, which are hard to be made, and they are only valid in the range of conditions tested to develop it. Predictive numerical models have thus emerged as a promising approach. To function correctly, they require accurate data related to appropriate material properties (e.g., constitutive models, ductile failure law). Nevertheless, material characterization is usually carried out through thermomechanical tests, under conditions far different from those encountered in machining. In addition, segmented chips observed when cutting titanium alloys make it a challenge to develop an accurate model. At low cutting speeds, chip segmentation is assumed to be due to lack of ductility of the material. In this work, orthogonal cutting tests of Ti6Al4V alloy were carried out, varying the uncut chip thickness from 0.2 to 0.4 mm and the cutting speed from 2.5 to 7.5 m/min. The temperature in the shear zone was measured through infrared measurements with high resolution. It was observed experimentally, and in the FEM, that chip segmentation causes oscillations in the workpiece temperature, chip thickness and cutting forces. Moreover, workpiece temperature and cutting force signals were observed to be in counterphase, which was predicted by the ductile failure model. Oscillation frequency was employed in order to improve the ductile failure law by using inverse simulation, reducing the prediction error of segmentation frequency from more than 100% to an average error lower than 10%.
Is digital heroin creating disturbing behaviors among children?

Addiction of cell phones, tabletsand computers is creating disturbing behaviors among society members universally. The purpose of this study is to describe the influence of digital heroin on youngsters that are students of top Montessori schools and how their parent’s addictive habits acts as a moderator. This research includes the survey of four hundred and fifty respondents, most of them are the parents of children aged between 3 to 8 years in Lahore, Pakistan. Gymboree (a Montessori academy by Beaconhouse), Lahore Grammar School and Choueifat was included in this survey. The results of the survey exhibit that overwhelming usage of digital gadgets has sudden and significant influence on the youngsters’ health; the greater the addiction of parents towards smart phones, greater is the tendency of a child to demonstrate disturbing behavior. Children of this age are having more interaction with digital technology but there is a very fine line between healthy and addictive usage. There are a lot of reasons why parents let their children use smart gadgets for a longer period of time, one of the reasons of which is easy parenting. This research can play a significant role for social science as well as for technological companies who need to realize that the real game lies in long term goodwill and not the short-term profitability through considering the drivers of youngsters’s psychological problem.
Traces of a Swedish army camp from 1644 revealed at Uppåkra by extensive magnetometer survey

In the framework of an archaeological prospection case study conducted at the Swedish Iron Age site of Uppåkra near Lund, a large number of anomalies caused by buried archaeological remains were detected using extensive magnetic surveys. Written sources report that the Swedish army under Field marshal Gustav Horn had established a camp near the village of Uppåkra in autumn 1644, awaiting the approaching Danish army. Magnetic anomalies of two large, square structures, numerous pits and several pit alignments have been interpreted as possible traces of buried remains of this army camp. We present archaeological prospection data from Uppåkra with regard to the events that took place during Horn's war in 1644.
The influence of affect on the production of referring expressions

ABSTRACT Many studies have provided evidence for the influence of affect on cognitive processing. However, experimental investigations of the relationship between affect and speech production are scarce. This study explores whether a speaker’s affective state influences the production of referring expressions. In two experiments, affective states were elicited using film excerpts, after which speakers referred to target stimuli in a way that differentiated them from distractors. The affective states were opposites, either in terms of valence (happiness versus sadness) or approach-avoidance motivation (anger versus disgust). Affective conditions were then compared with respect to the frequency with which participants referred to a target’s affect, whether this affect was congruent with the speaker's affective state, the number of modifiers per expression, the ambiguity of referring expressions, and overspecification. Results revealed no differences between different affective states concerning these factors, suggesting that a speaker’s affective state does not influence the production of referring expressions.
Graph Convolutional Sparse Subspace Coclustering With Nonnegative Orthogonal Factorization for Large Hyperspectral Images

Sparse subspace clustering (SSC) is a representative data clustering paradigm that has been broadly applied in the unsupervised classification of hyperspectral images (HSIs). Existing SSC methods usually produce a subspace affinity matrix between representations of hyperspectral pixels first, followed by spectral clustering for the affinity matrix. To this end, the separated framework fails to exploit the dualities contained in both features and pixels or higher order entities at the same time, and thus, it is difficult to compute coclusters simultaneously. In addition, SSC methods often require expensive computational consumption and memory capacity to approximate the spectral decomposition of the affinity matrix, thus hindering the applicability of SSC methods for large HSIs. To overcome these limitations, we propose a novel graph convolutional sparse subspace coclustering (GCSSC) model with nonnegative orthogonal factorization for large HSIs in which affinity matrix learning and spectral coclustering are integrated into a unified optimizing model to obtain the optimal clustering results. Specifically, to form a more compact self-representation, the superpixel-based adaptive dictionary construction strategy is proposed instead of the global dictionary to precisely represent the pixels. To explore the spatial–contextual and spectral neighboring characteristics between dictionary atoms, graph convolution is incorporated into the dictionary atoms to aggregate the local neighborhood information, and the affinity matrix in the proposed coclustering framework is constructed under a joint sparsity constrained representation model. To reduce high computational consumption and memory capacity, a nonnegative orthogonal factorization constraint is proposed to offer an alternative spectral clustering for hyperspectral pixels and dictionary atoms simultaneously. The clustering performance of the proposed method is evaluated for three classical HSIs, and the experimental results illustrate that the proposed method is memory and computationally efficient and outperforms the state-of-the-art HSI clustering methods.
Objective falls-risk prediction using wearable technologies amongst patients with and without neurogenic gait alterations: a narrative review of clinical feasibility.

Objectives The present narrative review aims to collate the literature regarding the current use of wearable gait measurement devices for falls-risk assessment in neurological and non-neurological populations. Thereby, this review seeks to determine the extent to which the aforementioned barriers inhibit clinical use. Background Falls contribute a significant disease burden in most western countries, resulting in increased morbidity and mortality with substantial therapeutic costs. The recent development of gait analysis sensor technologies has enabled quantitative measurement of several gait features related to falls risk. However, three main barriers to implementation exist: accurately measuring gait-features associated with falls, differentiating between fallers and non-fallers using these gait features, and the accuracy of falls predictive algorithms developed using these gait measurements. Methods Searches of Medline, PubMed, Embase and Scopus were screened to identify 46 articles relevant to the present study. Studies performing gait assessment using any wearable gait assessment device and analysing correlation with the occurrence of falls during a retrospective or prospective study period were included. Risk of Bias was assessed using the Centre for Evidence Based Medicine (CEBM) Criteria. Conclusions Falls prediction algorithms based entirely, or in-part, on gait data have shown comparable or greater success of predicting falls than existing stratification scoring systems such as the 10-meter walk test or timed-up-and-go. However, data is lacking regarding their accuracy in neurological patient populations. Inertial measurement units (IMU) have displayed competency in obtaining and interpreting gait metrics relevant to falls risk. They have the potential to enhance the accuracy and efficiency of falls risk assessment in inpatient and outpatient setting.
Constrained-Group-Delay-Optimized Multiple-Resonator-Based Harmonic Analysis

: Recently, a multiple-resonator structure was proposed as a robust and computationally efficient tool for harmonic analysis. Two trivial cases have been previously observed. The first case exhibits good out-of-band suppression and elimination of unwanted harmonics, but with a high latency. In the second case, a phase frequency response around the passband centre is zero-flat, that provides fast estimates. However, in this case, resonant peaks at the ends of the passband and high interharmonic gains cause large overshoots. In general, the basic algorithm performance requirements: selectivity and speed, are contradictory which makes it impossible to completely fulfil both of them. In this paper, the Constrained Linear Least-Squares (CLLS) optimization method is used to obtain a compromise solution. As a result, the resonant peaks in the passband are avoided and side lobes are mitigated, simultaneously minimizing the group delay in the middle of the passband. Performed simulations confirmed the effectiveness of the proposed algorithm. algorithm
Administration of Cannabis extracts causes alteration in brain and plasma nitric oxide concentration in rats

Nitric oxide is a biological messenger molecule that plays important roles in controlling neurotransmitter release, neurodevelopment, memory function, and regulation of gene expression. In this study, the time course effects of varying dose of Cannabis extract on nitric oxide in the brain and plasma was investigated. Twenty four hours after the last treatment, animals were sacrificed under anaesthesia, the brain of the animals were harvested, mopped dry and stored on ice. Nitric oxide activity was assayed according to Griess reaction assay. In the plasma, the results shows that at 4 weeks, there was a significant increase (p < 0.05) in the concentration of nitric oxide in all doses with compared with control. 25 mg/kg dose has a higher concentration compared with 12.5 mg/kg and 50 mg/kg body weight dose. However, at 8 weeks, there was a decrease in concentration of nitric oxide in all the doses compared to 4 weeks, but when compared with the control group, only 12 mg/kg dose significantly decreased. In the brain however, 12.5 mg/kg dose shows no significant difference in NO concentration through the duration of exposure compared to the control group. However, 25 mg/Kg and 50 mg/Kg body weight dose of Cannabis significantly decrease NO concentration in the brain of rat. The results suggest that at high doses, NO is reduced in the brain and at low dose its increased. The decrease in NO concentration may affect the regulatory function of NO in the brain as nitric oxide affect serotonergic transmission which could explain the imopaired cognitive function in chronic Cannabis users.
High-efficiency compact SRR–CSRR–SIW antenna based on CRLH-TL

A high-efficiency compact split-ring resonator (SRR)–complimentary split-ring resonator (CSRR)–substrate integrated waveguide (SIW) rectangular resonator antenna based on a composite right/left-handed transmission line is presented in this paper. A four-element rectangular CSRR structure is etched on the front of the antenna, and a four-element rectangular SRR structure is loaded on the back of the metal to correspond with each other, thus forming a composite right/left-handed transmission line structure. By meandering technique, a rectangular CSRR is etched on the front of the metal surface to reduce the frequency and realize the miniaturization of the antenna. Combined with SIW rectangular resonator, the high-efficiency compact antenna is finally realized. Simulation and experimental results show that the −10 dB impedance bandwidth of the SRR–CSRR–SIW rectangular resonator antenna is 5.26∼5.29 GHz (1.14%) and 9.07∼9.15 GHz (0.88%). The resonance frequency is 5.26 and 9.11 GHz. The gains are 5.33 and 5.25 dBi. The antenna can be widely used in WLAN and WiMAX.
Social media within the contexts of public sphere and civic engagement: the case of ‘My Oga at the Top'

ABSTRACT YouTube readers’ comments have evolved into the public sphere, allowing readers to participate in the public sphere and civic engagement. This case study explores public reactions generated from a YouTube video, ‘My Oga at the Top’, which became viral as a result of the inability of Mr. Obafaiye Shem to name the website of the Nigeria Security and Civil Defence Corps (NSCDC). He claimed the information about the website can only come from his top boss ‘my oga at the top’. Using Habermas’s public sphere as a framework and qualitative critical analysis as a method, the YouTube comments were analysed and five major themes emerged: (a) humour; (b) embarrassment and disgrace; (c) illiteracy and poor grammar; (d) empathy; and (e) incompetence and corruption. The findings indicate that commenters used social media to partake in civic engagement and public discourse as they vented their feelings toward the Nigerian government’s shortcomings.
A Novel Method for Harmonic Current Injection with Sensor Supported Adaptation on Permanent Magnet Synchronous Machines

To reduce torque oscillations in electric motors, harmonic current injection (HCI) has been used in industry for some time. For this purpose, higher harmonic currents calculated in advance are injected into the machine. Since the general conditions for the machine can change during its life cycle, this article presents a method that makes it possible to change the parameters of HCI during operation. For this purpose, sensor signals are used to detect the reaction of the electric motor to small variations of the HCI parameters. The knowledge gained in this way is used to make further suitable variations. FEM simulations were used to verify the effectiveness of the approach. The results show that the algorithm can independently optimize the HCI parameters during runtime and reduces the amplitude of the 6th harmonic in the torque by 87% for a permanent magnet synchronous machine.
Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.
International marketing capabilities development: The role of firm cultural intelligence and social media technologies

ABSTRACT The purpose of this study is to analyze how firm cultural intelligence and social media technologies influence the international marketing capabilities of multinational enterprise (MNE) regional offices. The data used in this study were obtained from MNE regional offices located in the Emirate of Dubai, which were analyzed using structural equation modeling. The results of the study indicate that firm cultural intelligence and social media technologies have unique and complementary contributions to the development of international marketing capabilities, and these capabilities play an essential role in firm performance by lessening the adverse effects of foreign market turbulence.
Could meditation make part of psychiatric treatment?

This is an integrative review relating the effects of meditation in the brain and its possible use as preventive alternative medicine and an adjuvant in the psychiatric treatment. Studies have associated meditation and spirituality with increased life quality for being linked to emotional control and self-preservation behaviors, such as ethical practices, emotional regulation, attention improvement, stress and anxiety reduction and cognitive functions improvement in a general fashion, which is also evidenced by studies that analyzed morphological changes in correlated brain areas. This work proposes an integrative review in order to prove the hypothesis that meditation could be used as a practice in alternative and preventive medicine aiming to diminish the harmful effects of diseases and psychopathologies in general.
Influences of North Pacific anomalies on Indian summer monsoon onset

This study finds a significantly decreasing (increasing) trend (1979–2018) of sea level pressure (SLP) over coastal East Asia (far North Pacific) during May and June, which resembles the negative (positive) Pacific Decadal Oscillation (PDO) (North Pacific Oscillation, NPO)‐like pattern. Thus, we examine May composite differences between early and late Indian summer monsoon (ISM) onset years. Sea surface temperature (SST) differences show that early (late) ISM onset is accompanied by a negative (positive) PDO‐like state. Whereas, in SLP, an intense low pressure over northwestern India, the Arabian Sea to Southeast Asia (far North Pacific, East Asia) is associated with the early (late) onset. During the early onset, warm surface air temperature (SAT) anomalies over southeast Russia propagate towards Central Asia to the Middle East and the northwest Indian subcontinent, strengthening the land‐sea thermal contrast, which subsequently strengthens the monsoon low. We thus show how the positive NPO, negative PDO, and high spring Bering Sea ice link with the SAT anomalies. In the early onset, more wave packets generated in response to these North Pacific anomalies propagate towards the Atlantic and European region, reaching and converging over the northwest Indian subcontinent. The warm surface anomalies possibly draw eastward‐propagating waves towards northwest India, thus intensifying the monsoon low there. Meanwhile, in the CAM5 PDO simulation, we observe a similar pattern of atmospheric responses, where warm SAT anomalies associated with stationary Rossby wave trains propagate from the North Pacific towards the North Atlantic to Central Asia and the northwest Indian subcontinent, strengthening the monsoon low. Terefore, our study highlights the crucial role of the North Pacific anomalies in modulating the ISM onset processes via atmospheric pathways.