Revised and extended social commerce technology adoption in e-business of Pakistan

Social commerce is a new perspective change in modern electronic business procedures bringing together individuals on social media sites and opens up another electronic social marketing channel to establish business. In developing countries like Pakistan, e-business can help organizations using social commerce and social marketing intelligently on particular social network sites to grasp their business clients while they are interacting with their online community on the social media sites. This study investigated consumers’ behavior towards adoption of social commerce and introduced a revised and extended social commerce technology model. The investigation proved the proposed model is valid by confirming loading factor, Kaiser-Mayer-Olkin (KMO), reliability analysis, and structural equation modeling approach for hypotheses tests. The study found Perceived Ease of Use (PEU), Perceived Usefulness (PU), Social Media Influence (SMI), and Risk (RI) have significant impact on social commerce adoption in e-business of Pakistan. More, Trust (TR) and Web experience (WXP) were insignificant that revealed the preventive behavior towards adoption of social commerce in e-business of Pakistan

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.This article belongs to the Special Issue Sustainability Assessmen

A game engine designed to simplify 2D video game development

In recent years, the increasing popularity of casual games for mobile and web has promoted the development of new editors to make video games easier to create. The development of these interactive applications is on its way to becoming democratized, so that anyone who is interested, without any advanced knowledge of programming, can create them for devices such as mobile phones or consoles. Nevertheless, most game development environments rely on the traditional way of programming and need advanced technical skills, even despite today’s improvements. This paper presents a new 2D game engine that reduces the complexity of video game development processes. The game specification has been simplified, decreasing the complexity of the engine architecture and introducing a very easy-to-use editing environment for game creation. The engine presented here allows the behaviour of the game objects to be defined using a very small set of conditions and actions, without the need to use complex data structures. Some experiments have been designed in order to validate its ease of use and its capacity in the creation of a wide variety of games. To test it, users with little experience in programming have developed arcade games using the presented environment as a proof of its easiness with respect to other comparable software. Results obtained endorse the concept and the hypothesis of its easiness of use and demonstrate the engine potential

Damage of reinforced concrete beams consisting modified artificial polyethylene aggregate (MAPEA) under low impact load

The impact damage of reinforced concrete beams subjected to low velocity impact loading at the ultimate load range are explored. In this study, an impact tests is carried out on reinforced concrete beam consisting Modified Artificial Polyethylene Aggregate (MAPEA), where, an approximately 100 kg of impact weight were dropped three times onto the beam specimens until its fails. The waste plastic bags, that encapsulated by glass powder as known as MAPEA were used as the replacement of coarse aggregate. There are twelve beam specimens of size 120 mm x 150 mm x 800 mm are categorized into three groups, where each group consists of 4 specimens. The three groups denoted as normal reinforced concrete (NRC), reinforced concrete with MAPEA concrete block infill (RCAI) and reinforced concrete with 9% of MAPEA as a coarse aggregate (RC9A). All specimens were tested under low velocity impact loads under 0.32 m and 1.54 m (2.5 m/s & 5.5 m/s velocities) drop height of impact weight. The comparisons were made between the three types of beams under the aspect of failure (shear and flexural) and its final displacement. The result of the laboratory test showed that the RC9A beams produced less crack and low value of residual displacement

Performance Evaluation of Smart Decision Support Systems on Healthcare

Medical activity requires responsibility not only from clinical knowledge and skill but also on the management of an enormous amount of information related to patient care. It is through proper treatment of information that experts can consistently build a healthy wellness policy. The primary objective for the development of decision support systems (DSSs) is to provide information to specialists when and where they are needed. These systems provide information, models, and data manipulation tools to help experts make better decisions in a variety of situations. Most of the challenges that smart DSSs face come from the great difficulty of dealing with large volumes of information, which is continuously generated by the most diverse types of devices and equipment, requiring high computational resources. This situation makes this type of system susceptible to not recovering information quickly for the decision making. As a result of this adversity, the information quality and the provision of an infrastructure capable of promoting the integration and articulation among different health information systems (HIS) become promising research topics in the field of electronic health (e-health) and that, for this same reason, are addressed in this research. The work described in this thesis is motivated by the need to propose novel approaches to deal with problems inherent to the acquisition, cleaning, integration, and aggregation of data obtained from different sources in e-health environments, as well as their analysis. To ensure the success of data integration and analysis in e-health environments, it is essential that machine-learning (ML) algorithms ensure system reliability. However, in this type of environment, it is not possible to guarantee a reliable scenario. This scenario makes intelligent SAD susceptible to predictive failures, which severely compromise overall system performance. On the other hand, systems can have their performance compromised due to the overload of information they can support. To solve some of these problems, this thesis presents several proposals and studies on the impact of ML algorithms in the monitoring and management of hypertensive disorders related to pregnancy of risk. The primary goals of the proposals presented in this thesis are to improve the overall performance of health information systems. In particular, ML-based methods are exploited to improve the prediction accuracy and optimize the use of monitoring device resources. It was demonstrated that the use of this type of strategy and methodology contributes to a significant increase in the performance of smart DSSs, not only concerning precision but also in the computational cost reduction used in the classification process. The observed results seek to contribute to the advance of state of the art in methods and strategies based on AI that aim to surpass some challenges that emerge from the integration and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to quickly and automatically analyze a larger volume of complex data and focus on more accurate results, providing high-value predictions for a better decision making in real time and without human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações que os especialistas podem consistentemente construir uma política saudável de bem-estar. O principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações, modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores decisões em diversas situações. A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de diferentes fontes em ambientes de e-saúde, bem como sua análise. Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que podem suportar. Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional utilizado no processo de classificação. Os resultados observados buscam contribuir para o avanço do estado da arte em métodos e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados em inteligência artificial é possível analisar de forma rápida e automática um volume maior de dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana