No abstract

We evaluated the moderating effects of firm size and leverage on the working capital finance (WCF)-profitability relationship among Chinese companies during 2000-2017. Applying the generalized method of moments (GMM) technique on panel data, we observed that firm size and leverage have strong moderating roles in the WCF-profitability relationship. We observed that small or low-leverage firms have an inverted U-shaped WCF-profitability relationship. However, this relationship is U-shaped for large or high-leverage firms. We report break-even points in these relationships that show the portion of short-term debt in working capital financing. The results reveal that the break-even point for all subgroups (small, large, low-leverage, and high-leverage firms) decreases compared to the break-even point of the full sample. This study shows how the break-even point of the WCF-profitability relationship shifts when a company expands or its leverage level changes. Managers can use this information for profit maximization.

Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.

Background: Alzheimer’s disease (AD) is a chronic devastating dysfunction of neurons in the brain leading to dementia. It mainly arises due to neuronal injury in the cerebral cortex and hippocampus area of the brain and is clinically manifested as a progressive mental failure, disordered cognitive functions, personality changes, reduced verbal fluency and impairment of speech. The pathology behind AD is the formation of intraneuronal fibrillary tangles, deposition of amyloid plaque and decline in choline acetyltransferase and loss of cholinergic neurons. Tragically, the disease cannot be cured but its progression can be halted. Various cholinesterase inhibitors available in the market like Tacrine, Donepezil, Galantamine, Rivastigmine, etc are being used to manage the symptoms of Alzheimer’s disease. Objective: The paper’s objective is to throw light not only on the cellular/genetic basis of the disease, but also on the current trends and various strategies of treatment including the use of phytopharmaceuticals and nutraceuticals. Materials & Methods: Enormous literature survey was conducted and published articles of PUBMED, Scifinder, Google Scholar, Clinical Trials.org and Alzheimer Association reports were studied intensively to consolidate the information on the strategies available to combat Alzheimer’s disease. Results & Conclusion: Currently, several strategies are being investigated for the treatment of Alzheimer’s disease. Immunotherapies targeting amyloid-beta plaques, tau protein and neural pathways are undergoing clinical trials. Moreover, antisense oligonucleotide methodologies are being approached as therapies for its management. Phytopharmaceuticals and nutraceuticals are also gaining attention in overcoming the symptoms related to AD. The present review article concludes that novel and traditional therapies simultaneously promise future hope for AD treatment.

Social network services allow a large population of end-users of software products to publicly share their concerns and experiences about software systems. From a software engineering perspective, such data can be collected and analyzed to help software development organizations to infer users’ emerging demands, receive their feedback, and plan the rapid evolution of software product lines. For the evolution of software product lines, organizations supplement emerging requirements in their products to meet user’s needs and also to retain their dominance in the market. Therefore, social network services, being a communication channel, have supported a number of software development activities such as requirements engineering. It has supported software development organizations to cope with numerous limitations of the traditional requirements engineering approaches by eliciting, prioritizing, and negotiating user requirements. However, these approaches do not consider eliciting requirements in terms of variability and commonality while identifying requirements. To address this issue, we have proposed a social network service-based requirement engineering process. It considers the attributes of users’ opinions to determine variability and commonality. In order to justify our proposed approach, a controlled experiment was conducted on a sample set of end-users on Facebook and Twitter. The experimental results show that the team using the proposed approach performed better in terms of efficiency and effectiveness than the team that used a traditional requirements engineering approach.