Deep Level Analysis of Legitimacy in Bengali News Sentences

The tremendous increase in the growth of misinformation in news articles has the potential threat for the adverse effects on society. Hence, the detection of misinformation in news data has become an appealing research area. The task of annotating and detecting distorted news article sentences is the immediate need in this research direction. Therefore, an attempt has been made to formulate the legitimacy annotation guideline followed by annotation and detection of the legitimacy in Bengali e-papers. The sentence-level manual annotation of Bengali news has been carried out in two levels, namely “Level-1 Shallow Level Classification” and “Level-2 Deep Level Classification” based on semantic properties of Bengali sentences. The tagging of 1,300 anonymous Bengali e-paper sentences has been done using the formulated guideline-based tags for both levels. The validation of the annotation guideline has been done by applying benchmark supervised machine learning algorithms using the lexical feature, syntactic feature, domain-specific feature, and Level-2 specific feature in both levels. Performance evaluation of these classifiers is done in terms of Accuracy, Precision, Recall, and F-Measure. In both levels, Support Vector Machine outperforms other benchmark classifiers with an accuracy of 72% and 65% in Level-1 and Level-2, respectively.

Government and innovation performance: evidence from the ICT enterprising community

Purpose The purpose of this paper will be on finding and analyzing the impact of government on the information and communication technology (ICT) companies’ innovation performance. The study aims to conduct in-depth research about the government as an integrated actor of the innovation ecosystem (IE), not a sole member. This would be the core finding toward doing further research about the impact of the innovation ecosystem in the ICT sector in Kosovo. Design/methodology/approach Quantitative research has been considered the most suitable data collection method. Furthermore, in this study, it is used convenience sampling as a technique of the sampling process. The sample size of this study is 106 participants. The participants are owners or representatives of ICT companies in Kosovo. Since the study is conducted using the deductive approach, the questionnaire is considered to be the most suitable instrument for gathering data. Findings This paper provides empirical insights that the company’s size and the dedicated department for research and development have no impact on how the company takes advantage of public funding from the government. Furthermore, the authors of the paper found out that government has a positive impact on companies’ introduction of new products and services, while the impact of the government on a company’s financial performance was insignificant. Research limitations/implications The future research direction should be firstly on studying other IE actors and their impact on companies’ innovation performance and secondly on measuring the IE actors as a set of actors to have a broader picture on how IE impacts the companies’ innovation performance. Practical implications The scientific contribution of this study will be on mapping, analyzing and proposing government policies in accordance with the findings of this study that would lead to a more comprehensive and sustainable IE in Kosovo. Originality/value This study tries to fulfill an identified need to study the impact of interconnected actors of an innovation ecosystem and to show how they affect each other. In this study, it is taken for research one fragment of a set of actors.

Global Research Trends on the Use of Nanotechnology to Boost Meat Production: A Scientometric Analysis

Meat production plays a vital socioeconomic role for sustainable development and for promoting food security in most countries. However, not much is known about research agendas done globally and the advancement of knowledge-generating networks in this area of study. The present study aims to reveal and analyze scientific research outputs on meat production linked with recent nanotechnology research work done till date. A compilation of research advancement and development within the sphere was realized through a scientometric study to comprehend the trend of research outputs, scientific impacts, authors' involvement, collaboration networks, and the advancement of knowledge gaps for future research endeavors on the current subject matter. Scholarly published articles were retrieved from the web of science (WOS) and Scopus databases from 1985 to 2020 and they were merged together using bibliometric package in R studio. All duplicated articles (438) from both data bases were excluded. A combination of terms (nano* AND (livestock* OR meat* OR beef* OR mutton* OR pork* OR chevon* OR chicken* OR turkey*)), and conversely analyzed for scientometric indices. A collection of 656 peer-reviewed, research articles were retrieved for the study period and authored by 2,133 researchers with a collaboration index of 3.31. The research outputs were highest in the year 2020 with total research outputs of 140 articles. The topmost three authors' keywords commonly used by authors were nanoparticles, meat, and chitosan with a respective frequency of 75, 62, and 57. China, Iran, and India ranked top in terms of meat production research outputs linked to nanotechnology and total citation with respective article productivity (total citations) of 160 (3,193), 111 (1,765), and 37 (552). Our findings revealed an increasing trend in research (with an annual growth rate of 25.18%) tending toward advancing meat production with the use of nanotechnology. Likewise, there is an increasing pointer to the fact that research work on nanotechnology and meat production has the prospect to influence positively, decision-making on research direction, and collaborations, hereby increasing the production of meat and its products in the future.

A Review on Precision Polishing Technology of Single-Crystal SiC

Single-crystal SiC is a typical third-generation semiconductor power-device material because of its excellent electronic and thermal properties. An ultrasmooth surface with atomic surface roughness that is scratch free and subsurface damage (SSD) free is indispensable before its application. As the last process to reduce the surface roughness and remove surface defects, precision polishing of single-crystal SiC is essential. In this paper, precision polishing technologies for 4H-SiC and 6H-SiC, which are the most commonly used polytypes of single-crystal SiC, such as chemical mechanical polishing (CMP), photocatalytic chemical mechanical polishing (PCMP), plasma-assisted polishing (PAP), electrochemical mechanical polishing (ECMP), and catalyst-referred etching (CARE), were reviewed and compared with emphasis on the experimental setup, polishing mechanism, material removal rate (MRR), and surface roughness. An atomically smooth surface without SSD can be obtained by CMP, PCMP, PAP, and CARE for single-crystal SiC. However, their MRRs are meager, and the waste treatment after CMP is difficult and expensive. Moreover, PAP’s operation is poor due to the complex polishing system, plasma generation, and irradiation devices. A high MRR can be achieved by ECMP. In addition, it is an environmentally friendly precision polishing process for single-crystal SiC since the neutral salt solution is generally used as the electrolyte in ECMP. However, the formation of the egglike protrusions at the oxide/SiC interface during anodic oxidation would lead to a bigger surface roughness after ECMP than that after PAP is processed. The HF solution used in CARE was toxic, and Pt was particularly expensive. Ultrasonic vibration-assisted single-crystal SiC polishing and electrolyte plasma polishing (EPP) were discussed; furthermore, the research direction of further improving the surface quality and MRR of single-crystal SiC was prospected.

(α, β)−Pythagorean Fuzzy Numbers Descriptor Systems

By using pythagorean fuzzy sets and T-S fuzzy descriptor systems, the new (α, β)-pythagorean fuzzy descriptor systems are proposed in this paper. Their definition is given firstly, and the stability of this kind of systems is studied, the relation of (α, β)-pythagorean fuzzy descriptor systems and T-S fuzzy descriptor systems is discussed. The (α, β)-pythagorean fuzzy controller and the stability of (α, β)-pythagorean fuzzy descriptor systems are deeply researched. The (α, β)-pythagorean fuzzy descriptor systems can be better used to solve the problems of actual nonlinear control. The (α, β)-pythagorean fuzzy descriptor systems will be a new research direction, and will become a universal method to solve practical problems. Finally, an example is given to illustrate effectiveness of the proposed method.

How academic sabbaticals used and how they contribute to research – a small-scale study of the University of Cambridge using interviews and analysis of administrative data

Background: Academic sabbaticals are seen as an important aspect of academic life and require considerable resources, however, little research has been done into how they are used and whether their effects can be measured. We explored these issues at the University of Cambridge. Methods: A mixed method approach including 24 interviews with academics, eight interviews with administrators; alongside analysis of administrative and publication data between 2010 and 2019. Results: Academics underline the importance of sabbaticals in providing uninterrupted time for research that is used to think, explore new ideas, master new techniques, develop new collaborations, draw together previous work, set work in a wider context, and provide personal discretion in research direction. They also highlight sabbaticals’ contributions in allowing the beneficial effects of combining teaching and research, while mitigating some of the disadvantages. However, it is difficult to detect the effect of sabbaticals on publications using a time series approach. Conclusions: Sabbaticals provide manifold contributions to academic research at the University of Cambridge; however, detecting and quantifying this contribution, and extending these findings requires wider and more detailed investigation.

Environmental and Safety Impacts of Vehicle-to-Everything Enabled Applications: A Review of State-of-the-Art Studies

With the rapid development of communication technology, connected vehicles (CV) have the potential, through the sharing of data, to enhance vehicle safety and reduce vehicle energy consumption and emissions. Numerous research efforts have been conducted to quantify the impacts of CV applications, assuming instant and accurate communication among vehicles, devices, pedestrians, infrastructure, the network, the cloud, and the grid, collectively known as V2X (vehicle-to-everything). The use of cellular vehicle-to-everything (C-V2X), to share data is emerging as an efficient means to achieve this objective. C-V2X releases 14 and 15 utilize the 4G LTE technology and release 16 utilizes the new 5G new radio (NR) technology. C-V2X can function without network infrastructure coverage and has a better communication range, improved latency, and greater data rates compared to older technologies. Such highly efficient interchange of information among all participating parts in a CV environment will not only provide timely data to enhance the capacity of the transportation system but can also be used to develop applications that enhance vehicle safety and minimize negative environmental impacts. However, before the full benefits of CV can be achieved, there is a need to thoroughly investigate the effectiveness, strengths, and weaknesses of different CV applications, the communication protocols, the varied results with different CV market penetration rates (MPRs), the interaction of CVs and human driven vehicles, the integration of multiple applications, and the errors and latencies associated with data communication. This paper reviews existing literature on the environmental, mobility and safety impacts of CV applications, identifies the gaps in our current research of CVs and recommends future research directions. The results of this paper will help shape the future research direction for CV applications to realize their full potential benefits.

Evaluation Algorithm for Team Strength Based on the Collected Healthcare Data through IoT and Smart Devices

In the past, the fans used to evaluate the strength of the team according to the victory and defeat ranking or according to their own intuition and preferences, however, the strength of the team is difficult to measure in analytical figures. The team’s winning rate is not the only factor to be considered to determine the strength of the team. There are many factors to be considered for determining the strength of the team. According to the variation coefficient of basketball scoring frequency, the paper designs the principal model of basketball players’ pitching target system. The data is captured by IoT devices and smart devices. The algorithm sets the number of the frequency of Gabor filter transformation features, controls the error accumulation, extracts the cascade features of basketball score video, constructs the video conversion discrimination rules, detects the basketball target, and obtains the tracking target contour to frame information. Finally, it realizes the target tracking detection of the team based on the team strength using an evaluation algorithm. The aim of this research work is to determine the strength of the team based on the healthcare data, team cohesiveness, and variance coefficient of basketball score frequency. The study on the coefficient of variation for basketball score frequency in teams can provide a theoretical research direction for team strength evaluation and meet the real-time needs of the coefficient of variation of basketball score frequency in teams. The empirical results show that the designed algorithm has the optimal execution time, more successful evaluation targets, high efficiency, and more reliability in evaluating the strength of the team.

Electrochemical Devices to Monitor Ionic Analytes for Healthcare and Industrial Applications

Recent advances in electrochemical devices have sparked exciting opportunities in the healthcare, environment, and food industries. These devices can be fabricated at low costs and are capable of multiplex monitoring. This overcomes challenges presnted in traditional sensors for biomolecules and provides us a unique gateway toward comprehensive analyses. The advantages of electrochemical sensors are derived from their direct integration with electronics and their high selectivity along with sensitivity to sense a wide range of ionic analytes at an economical cost. This review paper aims to summarize recent innovations of a wide variety of electrochemical sensors for ionic analytes for health care and industrial applications. Many of these ionic analytes are important biomarkers to target for new diagnostic tools for medicine, food quality monitoring, and pollution detection. In this paper, we will examine various fabrication techniques, sensing mechanisms, and will also discuss various future opportunities in this research direction.