Forming Group Identity through Shared Hashtag on Facebook: A Preliminary Study on Malaysian Universities

The hashtag has become an essential part of communication for those who Tweet and use Facebook. It allows media users to share information and interact with each other as part of a series of overlapping social networks. This article examines how Malay, Chinese and Indian Malaysian universities students use the hashtag to form a group identity. The analysis is based on the used and sharing of hashtags by students in Facebook during several catastrophic events which took place between 2014 and 2015 in Malaysia. Social categorization, social identification, and social comparison were analysed in this research. This study analysed data from a survey of 255 Malaysian universities students. Findings disclosed that most of the Malay, Chinese and Indian students were aware of the official hashtag used on Facebook and majority they agree that it shows their support to the group when posting with the official hashtag. Most of the Malay, Chinese and Indian students felt that they are part of the group when they use the official hashtag, and as the in-group, the use of official hashtag was part of the shared information, being supportive, and cooperative to the community

Meta-level Control in Multi-Agent Systems.

Abstract Sophisticated agents operating in open environments must make decisions that efficiently trade off the use of their limited resources between dynamic deliberative actions and domain actions? This is the meta-level control problem for agents operating in resource-bounded multi-agent environments. Control activities involve decisions on when to invoke and the amount to effort to put into scheduling and coordination of domain activities. The focus of this paper is how to make effective meta-level control decisions. We show that meta-level control with bounded computational overhead allows complex agents to solve problems more efficiently than current approaches in dynamic open multi-agent environments. The meta-level control approach that we present is based on the decision-theoretic use of an abstract representation of the agent state. This abstraction concisely captures critical information necessary for decision making while bounding the cost of meta-level control and is appropriate for use in automatically learning the meta-level control policies

Bending Behavior of Splice Connection for Mengkulang Glued Laminated Timber Beam

This paper presents the experimental bending strength of steel dowelled splice glulam timber made of 'Mengkulang' species. Bending tests were conducted under a four-point bending load. Six (6) glulam specimens with 45mm x 90mm x 1800mm were loaded. Three (3) specimens were full beams as the control and three (3) splice beams dowelled with grade 8.8; 20 mm diameter steel rod. The embedded length of the steel dowel was 60mm and glued at 2mm thickness on both sides. Results show that the bending strength of the glulam control beam performed 74.18% higher than the splice beams with an increment of 58.26% displacement. Keywords: Structural material, flexural strength, failure mode, dowelled connection eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI:https://doi.org/10.21834/ebpj.v6iSI4.302

Pre-exposure hydroxychloroquine prophylaxis for COVID-19 in healthcare workers: a retrospective cohort

Background: While several trials are ongoing for treatment of Corona virus 2019 (COVID-19), scientific research on chemoprophylaxis is still lacking even though it has potential to flatten the curve allowing us time to complete research on vaccines.Methods: This retrospective cohort study explores the potential of hydroxychloroquine (HCQ) as a pre- exposure prophylaxis for COVID-19 among 106 health care workers (HCW) exposed to COVID-19 patients, at a tertiary care hospital in India where there was an abrupt cluster outbreak within on duty personnel. HCWs who had voluntarily taken HCQ prior to exposure were considered one cohort while those who had not were considered to be the Control group. All participants with a verifiable high-risk contact history were tested for COVID-19 by RT- PCR.Results: The two cohorts were comparable in terms of age, gender, co-morbidity and exposure. The primary outcome was incidence rates of RT-PCR positive COVID-19 infection among HCQ users and Controls.106 HCW were examined of whom 54 were HCQ users. The comparative analysis of incidence of infection between the two groups demonstrated that voluntary HCQ usage was associated with lesser likelihood of developing severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection (4 out of 54 HCW), compared to those who were not on it (20 out of 52 HCW), χ2=14.59, p<0.001. None of the HCQ users noted any serious adverse effects.Conclusions: The study demonstrated that voluntary pre- exposure HCQ prophylaxis by HCWs is associated with a statistically significant reduction in risk of SARS-CoV-2.

Adaptive PVD Steganography Using Horizontal, Vertical, and Diagonal Edges in Six-Pixel Blocks

The traditional pixel value differencing (PVD) steganographical schemes are easily detected by pixel difference histogram (PDH) analysis. This problem could be addressed by adding two tricks: (i) utilizing horizontal, vertical, and diagonal edges and (ii) using adaptive quantization ranges. This paper presents an adaptive PVD technique using 6-pixel blocks. There are two variants. The proposed adaptive PVD for 2×3-pixel blocks is known as variant 1, and the proposed adaptive PVD for 3×2-pixel blocks is known as variant 2. For every block in variant 1, the four corner pixels are used to hide data bits using the middle column pixels for detecting the horizontal and diagonal edges. Similarly, for every block in variant 2, the four corner pixels are used to hide data bits using the middle row pixels for detecting the vertical and diagonal edges. The quantization ranges are adaptive and are calculated using the correlation of the two middle column/row pixels with the four corner pixels. The technique performs better as compared to the existing adaptive PVD techniques by possessing higher hiding capacity and lesser distortion. Furthermore, it has been proven that the PDH steganalysis and RS steganalysis cannot detect this proposed technique

Data pre-processing for the preterm prediction study MFMU dataset

Preterm birth is a major public health problem with profound implications on society. There would be extreme value in being able to identify women at risk of preterm birth during the course of their pregnancy. Previous research has largely focused on individual risk factors correlated with preterm birth (e.g. prior preterm birth, race, and infection) and less on combining these factors in a way to understand the complex etiologies of preterm birth. We attempt to address this gap by conducting a deeper analysis of the preterm prediction study data collected by the NICHD Maternal Fetal Medicine Units (MFMU) Network, a high-quality data for over 3,000 singleton pregnancies having detailed study visits and biospecimen collection at 24, 26, 28 and 30 weeks gestation. Reports from this dataset used relatively straightforward biostatitistical methodologies such as relative risk assessments to measure associations between risk factors and PTB (Maternal Fetal Medicine Units Net- work. Biostatistical Coordinating Center NICHD Networks, 1995). These methods include descriptive statistics, Pearson correlation, Fisher’s exact tests and linear/logistic regression where risk factors are studied independent of each other. In order to perform detailed experiments on this data using non-linear Support Vector Machines and other machine learning (ML) methodologies, it is necessary to complete several pre-processing steps that we describe in this report

Effect of seed invigoration with inorganic nanoparticles on seed yield in chilli (Capsicum annum)

An experiment was conducted to study the effect of seed invigoration with inorganic nanoparticles on plant growth and seed yield of chilli. Seed invigoration with nano particles of ZnO and TiO2 was found to be beneficial in improving yield and yield attributes. Seed treatment with nano ZnO @ 1300 mg kg-1 of seed and nano TiO2 @ 900 mg kg-1 exhibited increased plant height, fruits per plant, fruit length, fruit yield, number of seeds per fruit and seed yield

Challenges in Migrating Imperative Deep Learning Programs to Graph Execution: An Empirical Study

Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code that supports symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development tends to produce DL code that is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, less error-prone imperative DL frameworks encouraging eager execution have emerged at the expense of run-time performance. While hybrid approaches aim for the best of both worlds, the challenges in applying them in the real world are largely unknown. We conduct a data-driven analysis of challenges—and resultant bugs—involved in writing reliable yet performant imperative DL code by studying 250 open-source projects, consisting of 19.7 MLOC, along with 470 and 446 manually examined code patches and bug reports, respectively. The results indicate that hybridization: (i) is prone to API misuse, (ii) can result in performance degradation—the opposite of its intention, and (iii) has limited application due to execution mode incompatibility. We put forth several recommendations, best practices, and anti-patterns for effectively hybridizing imperative DL code, potentially benefiting DL practitioners, API designers, tool developers, and educators