Deployment Challenges of Offshore Renewable Energy Systems for Sustainability in Developing Countries

This research aims to expose deployment challenges of offshore renewable energy systems in developing countries. The investigation of the deployment model covers climate conditions, economic conditions, necessary infrastructure services and wind power by considering the case of Cyprus Island which is one of the Small Island Developing States (SIDS) countries. The convenience of Offshore Energy Systems to the territory and their systematic proper work is an important issue. Because of that, the setting up of Offshore Wind Energy Tribunes in Cyprus, the planning process, structuring of cost values and necessary resources, the investigation of the geographic conditions for obtaining the energy flow and assessment of these conditions for Offshore Wind Tribunes are the prime objectives of this study. The orientation period and the applicable qualifications of the offshore energy systems were evaluated on the basis of the world wide references. The study is concluded by the estimation of the advantages and disadvantages of the system for Cyprus.

Systematic Analysis of Safety and Security Risks in Smart Homes

The revolution in Internet of Things(IoT)-based devices and applications has provided smart applications for humans.These applications range from healthcare to traffic-flow management,to communication devices,to smart security devices,and many others.In particular,government and private organizations are showing significant interest in IoT-enabled applications for smart homes.Despite the perceived benefits and interest,human safety is also a key concern.This research is aimed at systematically analyzing the available literature on smart homes and identifying areas of concern or risk with a view to supporting the design of safe and secure smart homes.For this systematic review process,relevant work in the most highly regarded journals published in the period 2016–2020(a section of 2020 is included)was analyzed.A final set of 99 relevant articles(journal articles,book sections,conference papers,and survey papers)was analyzed in this study.This analysis is focused on three research questions and relevant keywords.The systematic analysis results and key insights will help researchers and practitioners to make more informed decisions when dealing with the safety and security risks of smart homes,especially in emergency situations.

Study of the Effect of Food Habit, Lifestyle and Daily Trip on Physical and Mental Status of Subjects at Islamic University in Kushtia, Bangladesh

This is a qualitative (study that used framework analysis) to examine by interviews among University personnel who live inside and outside the Islamic University campus in Kushtia, Bangladesh. In the present study, we found that subjects who live inside of the University feel better physically and mentally than the nonresident subjects. It was found that residents are more fit and feel better during and after class time. Resident subjects have less stress and maintain balanced diet for which they have good average BMI value than the nonresident subjects. In addition nonresident subjects have suffered diseases comparatively much more than resident subjects.

基于社交媒体分析的手机缺陷识别

为了从社交媒体中获取用户反映手机产品缺陷的数据信息,在对现有社交媒体分析框架进行改进的基础上,结合不同的平台特征,构建了基于社交媒体分析的手机产品缺陷识别框架;在综合分析比较后采用信息增益作为特征评估函数,利用支持向量机分类方法对社交媒体数据进行情感识别,构建手机产品缺陷语料集;基于主题模型算法对手机产品缺陷进行聚类分析,形成不同的缺陷内容聚类。相关实验研究结果表明,该方法能够有效地识别出中文社交媒体中的手机缺陷,具有较高的准确率和召回率。

Pashto Characters Recognition Using Multi-Class Enabled Support Vector Machine

During the last two decades signicant work has been reported in the eld of cursive language’s recognition especially,in the Arabic,the Urdu and the Persian languages.The unavailability of such work in the Pashto language is because of:the absence of a standard database and of signicant research work that ultimately acts as a big barrier for the research community.The slight change in the Pashto characters’shape is an additional challenge for researchers.This paper presents an efcient OCR system for the handwritten Pashto characters based on multi-class enabled support vector machine using manifold feature extraction techniques.These feature extraction techniques include,tools such as zoning feature extractor,discrete cosine transform,discrete wavelet transform,and Gabor lters and histogram of oriented gradients.A hybrid feature map is developed by combining the manifold feature maps.This research work is performed by developing a medium-sized dataset of handwritten Pashto characters that encapsulate 200 handwritten samples for each 44 characters in the Pashto language.Recognition results are generated for the proposed model based on a manifold and hybrid feature map.An overall accuracy rates of 63.30%,65.13%,68.55%,68.28%,67.02%and 83%are generated based on a zoning technique,HoGs,Gabor lter,DCT,DWT and hybrid feature maps respectively.Applicability of the proposed model is also tested by comparing its results with a convolution neural network model.The convolution neural network-based model generated an accuracy rate of 81.02%smaller than the multi-class support vector machine.The highest accuracy rate of 83%for the multi-class SVM model based on a hybrid feature map reects the applicability of the proposed model.

A Decision-Based Hybrid Proxy Mobile IPv6 Scheme for Better Resources Utilization

Seamless mobility is always one of the major requirements of modern-day communication.In a heterogeneous and massive IoT environment,efficient network-based mobility protocol such as proxy mobile IPv6(PMIPv6),is potentially a good candidate for efficient mobility as well as resource utilization efficiency.Several extensions are devised for performance in the research domain.However,a multi-criterion decision-based resourceefficient PMIPv6 extension is required to achieve efficiency when network resources are overloaded.In this research,a multi-criterion decision-based PMIPv6 scheme is devised that provides better performance when the Local Mobility Anchor(LMA)or Mobile Access Gateway(MAG)is overloaded.The objective is achieved by monitoring the load status of MAG or LMA and based on their status,the proposed scheme adapts itself to provide seamless mobility in addition to optimal efficiency.The proposed scheme is compared with the existing LMA and MAG-based mobility management protocol extensions.Based on the analysis of the comparison,the obtained results prove that providing a decision-based PMIPv6 scheme is better for service continuity as well as optimal performance in the context of required buffering,handover efficiency,and necessary signaling cost.

Smart Object Detection and Home Appliances Control System in Smart Cities

During the last decade the emergence of Internet of Things(IoT)based applications inspired the world by providing state of the art solutions to many common problems.From traffic management systems to urban cities planning and development,IoT based home monitoring systems,and many other smart applications.Regardless of these facilities,most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets.In order to address this problem,this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.This hybrid model consists of;convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like;Raspberry Pi,Amazon Web services cloud,and GSM modems for remotely accessing and controlling of the home appliances.An android application is developed and deployed on Amazon Web Services(AWS)cloud for the remote monitoring of home appliances.A GSM device and Message queuing telemetry transport(MQTT)are integrated for communicating with the connected IoT devices to ensure the online and offline communication.For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model.The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light.Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34%compared to the conventional recognition model(k nearest neighbours)recognition rate of 76%.

Effective Video Summarization Approach Based on Visual Attention

Video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the frames that stand out visually are extracted as key frames based on humanattention modeling theories. The schemes for modeling visual attention haveproven to be effective for video summaries. Nevertheless, the high cost ofcomputing in such techniques restricts their usability in everyday situations.In this context, we propose a method based on KFE (key frame extraction)technique, which is recommended based on an efficient and accurate visualattention model. The calculation effort is minimized by utilizing dynamicvisual highlighting based on the temporal gradient instead of the traditionaloptical flow techniques. In addition, an efficient technique using a discretecosine transformation is utilized for the static visual salience. The dynamic andstatic visual attention metrics are merged by means of a non-linear weightedfusion technique. Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy. The experimental resultsof our proposed model indicate the efficiency and high standard in terms ofthe key frames extraction as output.

Does Knowledge Sharing and Technopreneurship Affect Entrepreneurial Outcomes in Developing Economies?Using The SME and ANOVA Approaches

This study on technopreneurship aims to reconfigure entrepreneurial outcomes,but how knowledge sharing moderates the interaction is an academic and practical concern.This study focuses on how knowledge sharing and technopreneurship affect the entrepreneurial outcome in Bangladeshi small and medium enterprises(SMEs).This research has established the following aims to establish a meaningful association between technopreneurship,knowledge sharing,and the outcomes of entrepreneurial endeavours.In pursuit of knowledge,data were collected through primary sources after establishing the questionnaire’s validity and reliability.Findings revealed that technopreneurship and knowledge sharing individually affected entrepreneurial outcomes.However,knowledge sharing could not significantly moderate the interaction between technopreneurship and entrepreneurial outcomes.Hence,the recommendation was anchored on improving knowledge sharing,technopreneurship,and managerial dexterity of owners and managers.

Regulatory Framework for e Health Data Policies in Zimbabwe: Measuring FAIR Equivalency

The FAIR Guidelines—that data should be Findable, Accessible, Interoperable and Reusable(FAIR)—aim to improve the management of digital data assets for improved decision making. FAIR comprises 15 elements(called facets) that explain how data should be able to be reused by researchers and policymakers. For this research, eight policy documents were reviewed from Zimbabwe’s Ministry of Health and Ministry of Information and Communication Technology(ICT) from 1999 to 2020. These were scrutinised to determine the mention of the FAIR Guidelines or FAIR Equivalent principles. The vision, mission statement and objectives of these documents were analysed relative to the 15 facets of FAIR. The research found that none of the policy documents in health/e Health or ICT in Zimbabwe explicitly mention the FAIR Guidelines, but all contain some FAIR Equivalent principles. Hence, the regulatory framework for health/e Health data management in Zimbabwe is aligned with the FAIR Guidelines and, therefore, a policy window is open for the adoption of FAIR Guidelines in relation to health/e Health data management.

How do board features and auditor characteristics shape key audit matters disclosures?Evidence from emerging economies

This study examines how corporate board features and auditor characteristics in Bangladesh influence the disclosure of key audit matters(KAM)in annual reports from 2018 to 2021.Using ordinary least squares(OLS)regressions,the study finds that factors such as chair gender,the presence of women on the board,audit committee(AC)size,auditor tenure,and client-auditor relationship significantly affect KAM disclosure.However,AC expertise,family CEO succession,and board political connections do not have significant effects.Notably,having a family member CEO with a long-tenured auditor has a negative association with KAM disclosure,while a politically connected family CEO has a positive association with such disclosures.Additionally,Big-4 auditors of important clients are negatively associated with KAM disclosure.