Whole System Research Articles

On the integration of inspection data with seismic resilience assessment of corroded reinforced concrete structures

This research aims to improve the traditional statistical approach to evaluate the seismic resilience of concrete structures exposed to chloride attack. In the mentioned approach, the uncertain parameters are modeled using certain probability distribution functions (PDFs) selected from some standards such as fib. However, the mentioned PDFs should contain the actual conditions of the desired structure. This paper introduces a Bayesian framework for updating PDFs used in deterioration analysis, which is an important part of the life-cycle seismic resilience assessment process of corroded concrete structures. The proposed methodology was applied to a two-story reinforced concrete school in Iran that suffered from chloride corrosion for twenty years. The seismic resilience of the structure was investigated for a 50-year life cycle. The results showed that the updated model can significantly reduce the uncertainty of the influencing parameters in the seismic resilience evaluation process. Using the updated model resulted in a conservative seismic resilience index compared to the non-updated model. This research shows that the role of inspection data in periodically updating the seismic-resistance calculations of corroded concrete structures is not only necessary but also possible and beneficial.

Voluntary insurance for rapid growth digital banking: Seeking attentions for policy-design in Pakistan-economy

In today’s business-driven world, e-banking service is an important product in financial sectors of many countries and Pakistan is no exception. Many affect the delivery of these services, often unpredictable, factors and are fraught with serious pitfalls including those of psychological nature. The Voluntary Insurance scheme can help the country overcome existing difficulties and move closer to a cashless society, contributing to the growth of the banking sector and safety of the e-banking operations in Pakistan. The new e-banking legal products are expected to gratify consumers, make the banks stronger, and contribute to the overall social development; the proposed Voluntary Insurance is to guarantee their success. The Welfare Analyses have been used for setting the VI prices so that the customers’ efficiency-cost balance could become appealing. Once the policymakers and policy practitioners of Pakistan recognize the importance of the proposed VI product and make the necessary arrangements for its use in the e-banking services market, it may spread from bankers to their customers and thus over the whole economy. The process of life cycle of the VI product will follow the “S-curve” in progression. These developments will become drivers of economic growth and e-services will eventually replace traditional banking.

Introduction of ERAC (Environmental Risk Assessment and Control) Frame Work in Manufacturing to Enhance Sustainability

This paper introduces the Environmental Risk Assessment and Control (ERAC) framework as a comprehensive approach to managing environmental impacts throughout the lifecycle of product manufacturing processes. Drawing from a detailed case study conducted at a fiber cement manufacturing plant in Odisha, India, the study identifies environmental risks across various phases including raw material extraction, manufacturing processes, and product disposal. ERAC involves five key components: identification of environmental risks, rigorous risk assessment using tools such as environmental modeling and life cycle assessment, formulation of tailored control measures, implementation with robust monitoring, and continuous improvement. The case study demonstrates significant improvements in environmental performance indicators post-implementation, highlighting reductions in specific PM emissions, carbon footprint, waste generation, and energy consumption, as well as enhancements in productivity and workplace safety. The findings underscore ERAC's effectiveness in promoting sustainable manufacturing practices and environmental stewardship.

Micro and nanoplastics pollution: A review on global concern and its impacts on ecosystems

AbstractIn recent decades, the ubiquity of micro and nanoplastics (MNPs) pollution has emerged as a global concern due to its potential risks to both ecological and human health. The detrimental effects of MNPs have resulted in significant changes in the physicochemical and biological properties of terrestrial soil and benthic sediment. These alterations have led to a disrupted nutrient cycle, potential future climate hazards, and further impacts on ecosystem services. This review aims to discuss the possible origins, composition, abundance, life cycle, and transport processes of MNPs in aquatic and terrestrial ecosystems. It seeks to understand the potential ecological and human health risks associated with MNPs pollution and explores their implications for biodiversity conservation and ecosystem services. Moreover, this review highlights the advanced analytical techniques used to detect and quantify MNPs. It further suggests policy frameworks to combat this pollution in natural environments. The findings of this review are intended to assist environmentalists, microbiologists, hydrologists, and policymakers in identifying scientific gaps and pragmatic solutions to reduce MNPs pollution. Future research should investigate the intake mechanism and impact of MNPs on plants in different ecosystems, as well as their effects on food chains and human health. This will ensure that the ecological and human health risks associated with MNPs pollution can be timely mitigated.

科研人员科学数据元数据功能需求探索

In the context of data sharing and the advancement of open science, this study analyzes the functional requirements of scientific data metadata based on the requirements of scientific researchers for data resources, so as to provide valuable insights for the construction and enhancement of Chinese scientific data metadata standards. We gathered the functional requirements of scientific data metadata from 30 researchers in the research process during semi-structured interviews, and constructed a model for the functional requirements of scientific data metadata based on the information behavior theory. The findings show that the functional requirements of scientific data metadata are significantly affected by individual value perceptions and discipline differences, which can be reflected in the process of scientific data exchange and sharing. This paper provides insights and suggestions for the construction of scientific data metadata from three aspects: demands of researchers, information behavior process and data life cycle theory.

Hepatitis E virus ORF3 protein hijacking thioredoxin domain-containing protein 5 (TXNDC5) for its stability to promote viral particle release.

Hepatitis E virus (HEV) infection is the leading cause of acute viral hepatitis worldwide. After the synthesis and modification in the cells, the mature ORF3 protein is essential for HEV release. However, the host protein involved in this process has yet to be determined. Here, we reported a novel host protein, thioredoxin domain-containing protein 5 (TXNDC5), as a chaperone, contributing to HEV release by facilitating ORF3 protein stability in the endoplasmic reticulum through interacting with non-palmitoylated ORF3 protein. However, we also found that in the knockout-TXNDC5 stable cell lines, the HEV ORF3 protein may hijack other proteins for its stabilization. For the first time, our study demonstrated the involvement of TXNDC5 in viral particle release. These findings provide some new insights into the process of the HEV life cycle, the interaction between HEV and host factors, and a new direction for antiviral design.

Некоторые вопросы управления рисками реализации угроз безопасности информации

The article proposes an approach to assessing the risks of information security of automated technological process control systems, state information systems, municipal information systems, personal data information systems and critical information infrastructure facilities based on the requirements of the legislation of the Russian Federation in the field of protection of these types of information systems, the draft of State Standard R ISO/IEC 27005, planned to be adopted in the near future, and standards for risk assessment within the framework of processes in the life cycle State Standards 59329-59357 published in 2021. These standards are based on State Standard R 57193-2016, which describes the life cycle processes of human-made systems. The problem of processing residual risk in relation to unacceptable risks was considered, after the analysis of which another principle of safety management was proposed. A methodology for assessing confidence in the risk management system was obtained, with the help of which it is necessary to build a risk management system at the objects of critical information infrastructure.

A life cycle decision framework of China offshore wind turbines with ANP-Intuitionistic fuzzy TOPSIS method

In China, offshore wind energy is a popular source of green energy. Selecting offshore wind turbine design schemes is a decision problem based on multiple criteria. However, the selection is hardly made from the point of view of the life cycle due to the complex association of evaluation criteria, especially in the conceptual design stage. To solve this problem, a new multi-criteria decision-making framework for selecting the life cycle design scheme of offshore wind turbines is designed. The design information of the life cycle process of these turbines is expressed using Function-Structure-Material-Process-Transportation model in this framework. The life cycle evaluation index system of offshore wind turbines was established and mapped with this model to carry out rapid evaluation of various schemes. In light of this, an intuitive fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method based on analytic network process (ANP) was proposed to process fuzzy decision information and establish criteria correlation. Finally, a case study was conducted, and sensitivity and comparative analyses were carried out. The proposed method can effectively select the optimal scheme from six different design schemes for the life cycle of offshore wind turbines.

On the Interplay Between Business Process Management and Internet-of-Things

AbstractIn the last years, the increasing scientific and industrial interest in Business Process Management (BPM) approaches and methods on the one side, and Internet-of-Things (IoT) technologies and tools on the other. Are these fields complementary? What are their respective interplays and the research challenges to their realizations? The article presents a Systematic Literature Review (SLR) to gain in-depth insights into the maturity of existing approaches to IoT-aware BPM. The analysis of the retrieved studies, framed along the research questions addressed in the SLR, enables us to systematically evaluate the literature on IoT-aware BPM concerning the phases of the process life cycle covered by the different approaches, the specific topics addressed, the application domains involved, and the possibility to tackle the research challenges. Future research directions are also highlighted.

A Mini-Review on the Common Antiviral Drug Targets of Coronavirus.

Coronaviruses in general are a zoonotic pathogen with significant cross-species transmission. They are widely distributed in nature and have recently become a major threat to global public health. Vaccines are the preferred strategy for the prevention of coronaviruses. However, the rapid rate of virus mutation, large number of prevalent strains, and lag in vaccine development contribute to the continuing frequent occurrence of coronavirus diseases. There is an urgent need for new antiviral strategies to address coronavirus infections effectively. Antiviral drugs are important in the prevention and control of viral diseases. Members of the genus coronavirus are highly similar in life-cycle processes such as viral invasion and replication. These, together with the high degree of similarity in the protein sequences and structures of viruses in the same genus, provide common targets for antiviral drug screening of coronaviruses and have led to important advances in recent years. In this review, we summarize the pathogenic mechanisms of coronavirus, common drugs targeting coronavirus entry into host cells, and common drug targets against coronaviruses based on biosynthesis and on viral assembly and release. We also describe the common targets of antiviral drugs against coronaviruses and the progress of antiviral drug research. Our aim is to provide a theoretical basis for the development of antiviral drugs and to accelerate the development and utilization of commonly used antiviral drugs in China.

Proxy re-encryption with plaintext checkable encryption for integrating digital twins into IIoT

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT.

Intelligent Alzheimer's Diseases Gene Association Prediction Model Using Deep Regulatory Genomic Neural Networks.

Alzheimer's disease (AD) is an illness that affects the nervous system, leading to a loss in cognitive and logical abilities. Gene regulatory expressions, which are the complex language exhibited by DNA, serve several functionalities, including the physical and biological life cycle processes in the human body. The gene expression sequence affects the pathology experienced by an individual, its longevity, and potential for a cure. The transcription factors, from DNA to RNA conversion, and the binding process determine the gene expression, which varies for every human organ and disease. This study proposes Deep convolutional neural network model that reads the gene regulatory expression sequence through various convolutional layers encoded to detect positive spikes in transcription factors. This results in the prediction of disease conversion probability from mild cognitive impairment to AD which is the key-requisite for affected geriatric cohorts.

Exploring Quantitative Methodologies for Assessing the Environmental, Social, and Economic Impacts of Telemedicine: A Literature Review

The significant consumption of resources within the healthcare sector underscores the need to address both efficiency and sustainability concerns. Telemedicine has been identified as one of the most promising pathways for reducing the environmental impacts of the healthcare sector. However, a comprehensive sustainability assessment is still required. The main aim of the present study is to conduct a systematic literature review to explore approaches and methodologies employed for quantifying the environmental, social, and economic impacts of telemedicine. Moreover, the research seeks to determine whether the approaches focus on a single aspect or whether they allow for a comprehensive assessment including all three sustainability pillars. The searching phase was conducted in the Scopus and PubMed databases, considering last 10 years (i.e., 2013–2023). Keywords were related to remote care and sustainability impact fields. Following the PRISMA framework, out of 477 articles, 91 studies were included in the analysis. Primary findings highlighted that studies on telemedicine impacts predominantly focus on transport-related aspects, emphasizing direct emissions and associated costs that are avoided and time savings. Televisit emerged as the most investigated remote care activity. Database and conversion factors were mainly employed for analysis, while other methodologies were sporadically mentioned in the literature. Despite numerous papers addressing these issues, a standardized and comprehensive methodology still appears to be lacking. Future works should consider the entire life cycle process, including more stakeholders. A defined approach will be fundamental to move beyond theoretical discussions and provide actionable insights for healthcare practitioners, policymakers, and researchers.

Environmental Impacts of the Brazilian Egg Industry: Life Cycle Assessment of the Battery Cage Production System.

Brazil stands as one of the world's leading producers of animal protein, ranking sixth in global egg production. However, estimated growth in production demand, along with environmental impacts, represents a potential threat to the sustainability of the food system. Methods for assessing and quantifying the environmental impacts generated by Brazilian egg production remain scarce, lacking current reports on comparative effects or guiding standards. The objective of this study was to conduct a life cycle assessment from cradle to farm gate, adhering to ISO 14040 and 14044 standards, for the battery cage egg production systems and associated products in Brazil, with the aim of supporting and promoting sustainability improvements in the Brazilian egg industry. The entire life cycle modeling and process sustainability analysis were executed using the openLCA software, integrated with the Ecoinvent database. Emissions related to egg production yielded results of 65.06 kg SO2 eq., 27.74 kg N eq., 3086.71 kg CO2 eq., 75,152.66 CTUe, 2.75 × 10-5 CFC-11 eq., and 10,044.68 kg MJ eq. per ton of eggs produced. These findings can serve as comparative benchmarks for future studies and for analyzing data across different egg production systems in Brazil.

A Dusty Locale: evolution of galactic dust populations from Milky Way to dwarf-mass galaxies

ABSTRACT Observations indicate dust populations vary between galaxies and within them, suggesting a complex life cycle and evolutionary history. Here we investigate the evolution of galactic dust populations across cosmic time using a suite of cosmological zoom-in simulations from the Feedback in Realistic Environments project, spanning $M_{\rm vir}=10^{9-12}{M}_{\odot };\, M_{*}=10^{6-11}\, {M}_{\odot }$. Our simulations incorporate a dust evolution model that accounts for the dominant sources of dust production, growth, and destruction and follows the evolution of specific dust species. All galactic dust populations in our suite exhibit similar evolutionary histories, with gas–dust accretion being the dominant producer of dust mass for all but the most metal-poor galaxies. Similar to previous works, we find the onset of efficient gas–dust accretion occurs above a ‘critical’ metallicity threshold (Zcrit). Due to this threshold, our simulations reproduce observed trends between galactic D/Z and metallicity and element depletion trends in the interstellar medium. However, we find Zcrit varies between dust species due to differences in key element abundances, dust physical properties, and life cycle processes resulting in $Z_{\rm crit}\sim 0.05{\rm Z}_{\odot },\, 0.2{\rm Z}_{\odot },\, 0.5{\rm Z}_{\odot }$ for metallic iron, silicates, and carbonaceous dust, respectively. These variations could explain the lack of small carbonaceous grains observed in the Magellanic Clouds. We also find a delay between the onset of gas–dust accretion and when a dust population reaches equilibrium, which we call the equilibrium time-scale (τequil). The relation between τequil and the metal enrichment time-scale of a galaxy, determined by its recent evolutionary history, can contribute to the scatter in the observed relation between galactic D/Z and metallicity.

AN EMPIRICAL STUDY TO IDENTIFY AND WEIGH THE RISK ATTRIBUTES THROUGH THE APPLICATION OF FMEA IN THE SOFTWARE INDUSTRY WITH FOCUS ON MSME AND SME IT FIRMS IN BENGALURU REGION

Uncertain events can occur anytime during the lifecycle of software. An organization's readiness to deal with these events determines its ability to mitigate the potential losses. Risk management is an essential component of the lifecycle process and is given due diligence in a typical multi-tiered environment of the technological giants. On the contrary, small IT firms, especially the ones falling under the belt of SME and MSME sectors, often follow a reactive approach to risk management. Consequently, the probable losses incurred can be huge and sometimes even challenge the firm's existence in the market. Considering the above issue, we have put forward this paper after performing an empirical study targeting only the Bengaluru region's MSME/SME IT firms. The study was primarily conducted with two objectives (1) to identify the risk attributes and (2) to determine the effectiveness of FMEA techniques and weigh these attributes through the FMEA process. 43 failure modes were identified, which were ranked by a panel of 12 QA experts based on their past project experiences. The top ten failure modes identified by the process will be considered for future analysis and for determining the mitigation steps as a part of the risk management process, particularly for start-ups.

Process integration and systems optimization for the hydrodeoxygenation of 5-hydroxymethylfurfural to dimethylfuran

Most biomass reaction studies have focused on optimizing product yield without considering cost and emissions as key metrics; they consider pure feeds without accounting for process integration resulting from separation and recycling and often change one parameter at a time to maximize yield. Here, we propose a framework to overcome the above issues. We demonstrate it for the hydrodeoxygenation (HDO) of 5-hydroxymethylfurfural (HMF) to produce 2,5-dimethylfuran (DMF), a crucial reaction in making lignocellulosic biomass-based platform chemicals. We consider the impact of water in the feed stemming from the sugar dehydration reactor on the HDO in 2-pentanol over a Ru/C catalyst, guided by an active learning experimental design (NEXTorch toolkit), combined with Aspen Plus process flowsheet simulation, techno-economic analysis and life cycle assessment. We demonstrate that Bayesian optimization of process flowsheets significantly reduces production costs by 26% and greenhouse gas emissions by 15% after striking a balance between raw material usage, solvent loss, and utility consumption. Such reductions are strongly correlated to the product yield due to the dominant cost of the feed. Interestingly, a slight amount of water negatively impacts greenhouse gas emissions more than production costs, requiring relatively high purity in process integration.

A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.

The Effect of Slow Deep Breathing Exercises on Elderly Women With Hypertension

Background: The elderly or the elderly are the last stage of the human life cycle and experience degenerative processes in various aspects, especially in older women, which results in a higher risk of exposure to disease and ends in death, one of which is hypertension. Older women are closely related to the occurrence of hypertension when a woman experiences menopause. Treatment of hypertension can be done using non-pharmacological methods using slow deep breathing exercises. Purpose: To determine the effect of slow deep breathing exercises on older women with hypertension. Method: This study used the time series quasi-experimental method with one pre-test and post-test group. Results: The analysis of the Paired Sample T Test yielded p-values of 0.049 and 0.009 (p < 0.05), indicating that the alternative hypothesis (Ha) is supported. This implies that there is a significant impact of slow deep breathing exercises on blood pressure in older women when comparing the measurements before and after the intervention. Conclusion: Slow deep breathing exercises have an impact on the blood pressure of older women with hypertension.

Design, Development and Validation of a Digital Performance Based Appraisal System: A Planned Approach in Change Management

Objectives: In India, the professional performance of the academic faculty is assessed annually using Academic Performance Indicator (API) as per the University Grants Commission (UGC), Government of India. This is a manual model is cumbersome in filling, and time-consuming. As a planned change and a measure to introduce technology-based platforms in JSS Academy of Higher Education & Research (JSSAHER), Mysuru, India, we have designed, developed, and validated digital platform technology to evaluate the annual performance of faculty working in medical, dental, pharmacy, life sciences fraternities. Methods: Academic Performance Indicator (API) guideline as per the University Grants Commission (UGC), Government of India, was used to design and develop the digital performance-based appraisal system (d-PBAS), and its performance was validated (20% random sampling) by comparing the manual and digital data entered by the faculty from different disciplines of JSSAHER. Findings: The present manuscript demonstrates the designing and validation methodology of d-PBAS. Software Development Life Cycle (SDLC) process and Waterfall model was adopted to develop d-PBAS. The digital platform was assessed for the performance and ranking of the faculty. More than 96% of the users expressed satisfaction and 4% expressed dissatisfaction in d-PBAS. Novelty: For the first of its kind in a university set up in India, JSSAHER designed and validated the digital performance-based appraisal system to evaluate academic performance. Since d-PBAS was developed using the SDLC process and Waterfall model it is easy and consumes less time to enter the data. The most important highlight is d-PBAS helps to rank the faculty performance automatically. The design and validation of d-PBAS described in the present manuscript are simple, robust, cost-effective, and have a go-green value. Keywords: Change Management, Higher Education Institutions, Performance­based, Appraisal System, academic Performance Indicator, digital technology