Articles

This charge-ahead paper suggests that transitioning the automotive industry towards a zero-carbon ecosystem from material to end-of-life can be accomplished through disruptive zero-carbon manufacturing in the broad area of all-electric vehicle production technology. To accomplish zero carbon emission automotive manufacturing in the vehicle assembly domain, future paradigms must converge on the decoupling of carbon dioxide emissions from automobile manufacturing and use the design, processing, and manufacturing conditions. The envisioned zero carbon emission vehicle manufacturing domain consists of two complementary components: (a) making more efficient use of energy and (b) reducing carbon in energy use. This paper presents the status of key scientific and technological advancements to bring the manufacturing model of today to a zero-carbon ecosystem for the entire automotive industry of tomorrow. This paper suggests the groundbreaking application of dynamic and distributed predictive scheduling algorithms and open sensing and visualization technology to meet the zero carbon emission vehicle manufacturing goals. Power-aware high-performance computing clusters have recently become a viable solution for sustainable production. Advances in scalable and self-adaptive monitoring, predictive analytics, timeline-based machine learning, and digital replica of cyber-physical systems are also seen co-evolving in the zero carbon manufacturing future. These methods are inspired by initiatives to decouple gross domestic product growth and energy-related carbon dioxide emissions. Stakeholders could co-design and implement shared roadmaps to transition the automotive manufacturing sector with relevant societal and environmental benefits. The automated mobility sector offers a program, an industry-leading example of transforming an automotive production facility to carbon neutrality status. The conclusions from this paper challenge automotive manufacturers to engage in industry offsetting and carbon tax programs to drive continuous improvement and circular vehicle flows via a multi-directional zero-carbon smart grid.

Advances in web-centric cloud computing have facilitated the establishment of an integrated cloud environment connecting a wide variety of clinical trial stakeholders. A web-centric cloud framework is proposed for real-time monitoring and risk prediction during clinical trials. The framework focuses on identifying relevant datasets, developing a data-management interface, and implementing machine-learning algorithms for data analysis. Detailed descriptions of the data-management interface and the machine-learning processes are provided, targeting active clinical trials with therapeutic uses in cancer. Demonstrations utilize publicly available clinical-trial data from the ClinicalTrials.gov repository. The real-time monitoring and risk prediction systems were assessed by developing five supervised-classification-machine-learning models for trial-status prediction and six unsupervised models for patient-safety-profile assessment, each representing a different phase of the clinical-trial process. All supervised models yielded high accuracy and area-under-the-curve values at the testing stage, while the unsupervised models demonstrated practical applicability. The results underscore the advantages of using the trial-status algorithm, the patient-safety-profile model, and the proposed framework for performing real-time monitoring and risk prediction of clinical trials.

<b>Purpose</b>: The model of CSR devised by the American scientist ″Archie B. Carroll in 1991″ - which became well- known in academic circles as Carroll′s CSR pyramid, has been criticized by academics and researchers. The researcher firmly believes that one of the most important reasons that led to the emergence of these criticisms was Carroll's adoption of the idea of the pyramid as a form to illustrate his idea of CSR. The content of the pyramid does not match the accompanying explanations given by Carroll. Carroll's CSR pyramid does not reflect the concept of simultaneous CSRs. Also, Carroll's CSR pyramid does not reflect the logical interaction and interrelationships among CSRs. It is also noted that, Carroll's pyramid reflects the expectations of stakeholders and does not reflect the expectations of companies from their commitment to their social responsibilities. This study aimed to design a model for CSR, in which the content of its figure matches the explanations attached to it, and reflects the concept of simultaneous CSRs, in addition to the logical interaction and the interrelationship among those responsibilities. <b>Method</b>: The researcher used the concept of precedence as an approach to explain the logical interaction and interrelationships among CSRs. The idea of precedence is clearly found only in the railway industry. The interaction of CSRs with each other can be likened to the interaction of the components of a classic train (a cockpit and coaches). Therefore, the researcher adopted the idea of the classic train as an innovative model to address some of the shortcomings of Carroll ′s pyramid. Battal's CSR train as an innovative model- in this study, can reflect the notion of simultaneous CSRs, the logical interaction and interrelationships among CSRs, and expectations and aspirations of both stakeholders and companies. <b>Result</b>: By adopting the idea of the classic train work and its components, this study was able to produce a model of CSR as an alternative model for Carroll′s pyramid (1991). <b>Originality</b>/<b>Value</b>: Battal ′s CSR train is an educational model that is designed to address some of the shortcomings of Carroll's CSR pyramid "as a figure and content." The content of Battal′s CSR train matches the attached explanation, and this will help to understand how CSRs interact with each other, as well as the concept of simultaneous CSRs. It is an enrichment of the thought of CSR.