Role of adaptive leadership in learning organizations to boost organizational innovations with change self-efficacy

The present study investigates the direct impact of learning organizations on organizational innovations and investigates the mediating mechanism of change self-efficacy between learning organizations and organizational innovations. Furthermore, this study proposes adaptive leadership as a moderator between learning organizations, change self-efficacy, and organizational innovations. Three hundred seventy-three permanent employees from the pharmaceutical industry voluntarily participated. Data was collected using a simple random sampling technique through the temporal separation method (One-month interval between two temporal separations). SPSS v.25, AMOS v.22, and Smart-PLS were utilized to analyze reliability, validity, descriptive statistics, and correlations, and PROCESS-macro v3.4 was used for direct, indirect (mediation), and interaction (moderation) effects analysis. The study supports the hypothesized link between learning organizations and organizational innovations. In addition, change self-efficacy partially mediates the learning organizations - organizational innovations relationship. Moreover, adaptive leadership moderates the association between learning organization and organizational innovation, learning organizations and change self-efficacy, and change self-efficacy and organizational innovations relationship. The study's findings suggest that adaptive leadership is imperative not only for higher change self-efficacy of the individuals but also helps the organizations for organizational innovations with the utilization of learning organizations phenomenon. Additionally, this study highlights the importance of change self-efficacy, which plays a vital role in learning organizations for organizational innovations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-023-04669-z.

QoS-aware Web Services Recommendation Using Dynamic Clustering Algorithm:

Service-Oriented Computing (SOC) activates communication through web services to provide computing as a service for business applications in the Service-Oriented Architecture (SOA). To make SOC successful, finding a needed service to build a system directly depending on the collection of services is a critical confront, in this paper we planned the clustering-based approach called Dynamic Clustering (DCLUS). The novelty in DCLUS compared to static-based clustering technique is the use of dynamic clustering technique. In existing CLUS, the static various widths clustering method is exploited for the users and services clustering. However, due to the limitations of static clustering, we proposed dynamic clustering to optimize the performance of clustering using data mining to find the associations and patterns, for services, and also the prediction accuracy. The performance of the proposed DCLUS system will be implemented and evaluated facing the existing system in phases of precision, recall, and f-score performance metrics using the research dataset.

Semantic and Context understanding for sentiment analysis in Hindi Handwritten Character Recognition Using a Multiresolution Technique

The rapid growth of Web 2.0, which enables people to generate, communicate, and share information, has resulted in an increase in the total number of users. In developing countries, online users’ sentiment influences decision-making, social views, individual consumption decisions, and entity quality monitoring. As a result, more accurate sentiment analysis, particularly in their native language such as Hindi, is preferred over crude binary categorization. Because of the abundance of web-based data in Indian languages such as Hindi, Marathi, Kannada, Tamil, and so on. Analyzing this data and recovering valuable and relevant information from handwritten text has become extremely important. Despite years of research and development, no optical writing recognition (OCR) system has ever been certified as completely reliable. The first step in any pattern recognition system is feature selection. In many fields, feature selection is studied as a combinatorial optimization problem. The primary goal of feature selection is to reduce the number of redundant and ineffective traits in the recognition system. This feature selection is used to maintain or improve the performance of the classifier used by the recognition system: A support vector machine (SVM) technique could be used to solve this character recognition problem. The Hindi character recognition system recognizes Hindi characters by employing morphological operations, edge detection, HOG feature extraction, and an SVM-based classifier. The proposed model outperformed the current state-of-the-art method, achieving an accuracy of 96.77 %.

A scoping review of systems approaches for increasing physical activity in populations

Introduction

The past decade has increasingly seen systems approaches as a featured theme in public health studies and policy documents. This trend is evident in the area of physical activity, which is a significant global health risk factor that is addressed in WHO’s Global Action Plan on Physical Activity. We undertook a comprehensive scoping review to characterize the application of systems approaches to physical activity, to develop a typology of the objectives, themes and methods of research papers that purported to apply systems thinking to this issue.

Methods

We searched electronic databases (PubMed, Web of Science, Scopus and PsycINFO) for studies published during the period 2010–2021 that explicitly applied systems approaches or methods to investigate and/or address population physical activity. A framework using systems-based methodological approaches was adapted to classify physical activity studies according to their predominant approach, covering basic descriptive, complex analytical and advanced forms of practice. We selected case studies from retained studies to depict the current “state of the art”.

Results

We included 155 articles in our narrative account. Literature reporting the application of systems approaches to physical activity is skewed towards basic methods and frameworks, with most attention devoted to conceptual framing and predictive modelling. There are few well-described examples of physical activity interventions which have been planned, implemented and evaluated using a systems perspective. There is some evidence of “retrofitted” complex system framing to describe programmes and interventions which were not designed as such.

Discussion

We propose a classification of systems-based approaches to physical activity promotion together with an explanation of the strategies encompassed. The classification is designed to stimulate debate amongst policy-makers, practitioners and researchers to inform the further implementation and evaluation of systems approaches to physical activity.

Conclusion

The use of systems approaches within the field of physical activity is at an early stage of development, with a preponderance of descriptive approaches and a dearth of more complex analyses. We need to see movement towards a more sophisticated research agenda spanning the development, implementation and evaluation of systems-level interventions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12961-022-00906-2.

Is maternal defensiveness of Gyr cows (Bos taurus indicus) related to parity and cows’ behaviors during the peripartum period?

The maternal care of cows can influence both the milk production and the performance of their calves, making this a topic of important relevance for the production industry that uses zebu cattle. The aims of this study were to 1) investigate the effects of parity on the behaviors of Gyr cows during the peripartum period; 2) characterize the maternal defensiveness of primiparous and multiparous cows towards handlers during the first handling of their calves; and 3) evaluate the relationships between cows’ behaviors at the peripartum period and maternal defensiveness. Thirty-one Gyr cows (primiparous and multiparous), from Empresa de Pesquisa Agropecuária de Minas Gerais (Brazil), were used. The animals were placed in a maternity paddock monitored by video cameras. The behaviors of the animals were collected in four periods: Pre-calving, Post-calving, First handling of calf and Post-handling. Primiparous cows presented more pain signs, reflected in arched spine (P = 0.05), and tended to move more (P = 0.07) than the multiparous in the Pre-calving period. Trends were observed for both Maternal Composite Score (P = 0.06) and Maternal Protective Behavior score (P = 0.06), indicating that both primiparous and multiparous were protective, but only multiparous cows were aggressive toward the caretakers on the first handling of their calves. The most protective cows spent more time eating during the prepartum period (P = 0.03), while the least attentive cows spent more time lying down (P = 0.02) in the prepartum period. The cows who nursed and stimulated their calves more were also calmer (P = 0.02) and more attentive (P = 0.01). In conclusion, the peripartum behaviors of Gyr cows were related to maternal care and maternal defensiveness. Multiparous cows tended to be more aggressive than primiparous cows at the time of the first handling of their calves.

Operations research and analytics to combat human trafficking: A systematic review of academic literature

Human trafficking is a widespread and compound social, economic, and human rights issue occurring in every region of the world. While there have been an increasing number of anti-human trafficking studies from the Operations Research and Analytics domains in recent years, no systematic review of this literature currently exists. We fill this gap by providing a systematic literature review that identifies and classifies the body of Operations Research and Analytics research related to the anti-human trafficking domain, thereby illustrating the collective impact of the field to date. We classify 142 studies to identify current trends in methodologies, theoretical approaches, data sources, trafficking contexts, target regions, victim-survivor demographics, and focus within the well-established 4Ps principles. Using these findings, we discuss the extent to which the current literature aligns with the global demographics of human trafficking and identify existing research gaps to propose an agenda for Operations Research and Analytics researchers.

Distributed Agent-Based Orchestrator Model for Fog Computing

Fog computing is an extension of cloud computing that provides computing services closer to user end-devices at the network edge. One of the challenging topics in fog networks is the placement of tasks on fog nodes to obtain the best performance and resource usage. The process of mapping tasks for resource-constrained devices is known as the service or fog application placement problem (SPP, FAPP). The highly dynamic fog infrastructures with mobile user end-devices and constantly changing fog nodes resources (e.g., battery life, security level) require distributed/decentralized service placement (orchestration) algorithms to ensure better resilience, scalability, and optimal real-time performance. However, recently proposed service placement algorithms rarely support user end-device mobility, constantly changing the resource availability of fog nodes and the ability to recover from fog node failures at the same time. In this article, we propose a distributed agent-based orchestrator model capable of flexible service provisioning in a dynamic fog computing environment by considering the constraints on the central processing unit (CPU), memory, battery level, and security level of fog nodes. Distributing the decision-making to multiple orchestrator fog nodes instead of relying on the mapping of a single central entity helps to spread the load and increase scalability and, most importantly, resilience. The prototype system based on the proposed orchestrator model was implemented and tested with real hardware. The results show that the proposed model is efficient in terms of response latency and computational overhead, which are minimal compared to the placement algorithm itself. The research confirms that the proposed orchestrator approach is suitable for various fog network applications when scalability, mobility, and fault tolerance must be guaranteed.

Design of a Hybrid Fault-Tolerant Control System for Air–Fuel Ratio Control of Internal Combustion Engines Using Genetic Algorithm and Higher-Order Sliding Mode Control

Fault-tolerant control systems (FTCS) are used in safety and critical applications to improve reliability and availability for sustained operation in fault situations. These systems may be used in process facilities to reduce significant production losses caused by irregular and unplanned equipment tripping. Internal combustion (IC) engines are widely used in the process sector, and efficient air–fuel ratio (AFR) regulation in the fuel system of these engines is critical for increasing engine efficiency, conserving fuel energy, and protecting the environment. In this paper, a hybrid fault-tolerant control system has been proposed, being a combination of two parts which are known as an active fault-tolerant control system and a passive fault-tolerant control system. The active part has been designed by using the genetic algorithm-based fault detection and isolation unit. This genetic algorithm provides estimated values to an engine control unit in case of a fault in any sensor. The passive system is designed by using the higher-order sliding mode control with an extra fuel actuator in the fuel supply line. The performance of the system was tested experimentally in MATLAB/Simulink environment. Based on the simulation results, the designed system can sustain the AFR despite sensor failures. A new method of managing the AFR of an IC engine has been demonstrated in this study, and it is highly capable, robust, reliable, and highly effective. A comparison with the existing works found in the literature also proves its superior performance. By inserting the fault in each sensor, it was clearly observed that proposed HFTCS was much better than the existing model as it was more fault-tolerant due to its ability to work in both online and offline modes. It also provided an exact value of 14.6 of AFR without any degradation.

How to do (or not to do)…Using Causal Loop Diagrams for Health System Research in Low- and Middle-Income Settings

Causal loop diagrams (CLDs) are a systems thinking method that can be used to visualize and unpack complex health system behaviour. They can be employed prospectively or retrospectively to identify the mechanisms and consequences of policies or interventions designed to strengthen health systems and inform discussion with policymakers and stakeholders on actions that may alleviate sub-optimal outcomes. Whilst the use of CLDs in health systems research has generally increased, there is still limited use in low- and middle-income settings. In addition to their suitability for evaluating complex systems, CLDs can be developed where opportunities for primary data collection may be limited (such as in humanitarian or conflict settings) and instead be formulated using secondary data, published or grey literature, health surveys/reports and policy documents. The purpose of this paper is to provide a step-by-step guide for designing a health system research study that uses CLDs as their chosen research method, with particular attention to issues of relevance to research in low- and middle-income countries (LMICs). The guidance draws on examples from the LMIC literature and authors’ own experience of using CLDs in this research area. This paper guides researchers in addressing the following four questions in the study design process; (1) What is the scope of this research? (2) What data do I need to collect or source? (3) What is my chosen method for CLD development? (4) How will I validate the CLD? In providing supporting information to readers on avenues for addressing these key design questions, authors hope to promote CLDs for wider use by health system researchers working in LMICs.

Human Identification Using Electrocardiogram Signal as a Biometric Trait

Biometrics is an interesting study due to the incredible progress in security. Electrocardiogram (ECG) signal analysis is an active research area for diagnoses. Various techniques have been proposed in human identification system based on ECG. This work investigates in ECG as a biometric trait which based on uniqueness represented by physiological and geometrical of ECG signal of person.In this paper, a proposed non-fiducial identification system is presented with comparative study using Radial Basis Functions (RBF) neural network, Back Propagation (BP) neural network and Support Vector Machine (SVM) as classification methods. The Discrete Wavelet Transform method is applied to extract features from the ECG signal. The experimental results show that the proposed scheme achieves high identification rate compared to the existing techniques. Furthermore, the two classifiers RBF and BP are integrated to achieve higher rate of human identification.

Introducing Fog Computing (FC) Technology to Internet of Things (IoT) Cloud-Based Anti-Theft Vehicles Solutions

Securing vehicles, especially against theft, has become a significant concern. Smart antitheft solutions have emerged to provide better protection. However, most existing smart vehicle antitheft solutions use (GSM) and (GPS) technologies to track stolen vehicles and these technologies are not sufficiently efficient in tracking vehicles in real-time. Hence, there is a need to optimise solutions to incorporate new technologies such as Internet of Things (IoT), Fog Computing (FC), and Face Recognition (FR) technologies. This paper introduces the new concept of Fog Computing to existing tracking systems and presents the design and the development of the Internet of Things (IoT) Cloud-based vehicle anti-theft system to pinpoint the exact location of the stolen vehicle in real-time. The proposed system extends the existing tracking systems to include advanced features influenced by advanced computing technologies such as Fog, Cloud, IoT and FR. Furthermore, it sheds light on the benefits of using FC combined with Cloud Computing (CC) to provide a more accurate and reliable tracking system.

An algorithm for identifying reference signals under the environment of complex fuzzy sets

 In this paper, we have discussed some new operations and results of set theory for complex fuzzy sets (CFSs). Moreover, we developed the basic results of CFSs under the basic operations such as complex fuzzy simple difference, bounded sum, bounded difference, dot product, bounded product, union, intersection, and Cartesian product. We explored the CFSs and discussed the related properties with examples such as complex fuzzy bounded sum over the intersection, complex fuzzy dot product over the union, etc. Identifying the reference signals under the environment of CFSs have always been a challenging. Many algorithms based on set theoretic operations and distance measures have been proposed for identifying a reference signal using any common system. But linear time invariant (LTI) system is considered easy to analyze the linear and time-varying signals. We used CFSs in signals and systems. We developed an algorithm based on convolution product and LTI system under the complex fuzzy environment. We identified a high degree of resemblance (reference signal) of the received signals to the reference signal in a linear time-invariant (LTI) system that receives an input signal and produces an output signal.

Screening COVID-19 Suspected Cases and Determining the Associated Factors

Background

The incidence of corona-virus-positive persons in Africa, notably in Ethiopia, is rapidly increasing, leading to enhanced analyses. Even though the majority of people exhibit COVID-19's key symptoms, many refuse to go to the hospital to have the virus tested. This study aims to assess probable COVID-19 participants and the related characteristics among residents of Northwest Ethiopian municipal towns.

Methods

This project contains participants enlisted from Northwest Ethiopia municipal towns, and a cross-sectional data collection approach was employed. A total of 1,288 arbitrarily designated contestants accomplished an actively screening test questionnaire that was used to assess whether the participants were suspected of coronavirus. The statistical analysis Chi-square test and a binary logistic regression were implemented.

Results

Among the 1,288 designated contestants, 788 (61.2%) of them were men. About 77.5% of the participants were from orthodox religion and 12.2% live in the rural area permanently. As compared to female participants (45.9%), the number of suspected male participants (54.1%) was higher. As compared to societies in Woldya municipal town, populations in Bahir Dar (aOR = 0.101;95% CI = 0.065,0.156), Gondar (aOR = 0.072;95% CI = 0.043,0.122), and Debre Markos (aOR = 0.368;95% CI = 0.271,0.501) municipal town were less likely to be suspected of COVID-19. Equated to the employed contestants, unemployed contestants had lower odds of being suspected of COVID-19 (aOR = 0.147; 95% CI = 0.1160.186).

Conclusion

The prevalence of suspected cases of coronavirus in Northwest Ethiopia was considerably high. The city of residence, work status, hospital use, marital status, permanent residence, and source of information were important determinants of suspected cases of coronavirus. Thus, timely diagnosis of suspected cases of coronavirus and taking the appropriate remedial action help to reduce the spread and mortality rate.

Resilience in healthcare supply chains: a review of the UK’s response to the COVID19 pandemic

Purpose

The purpose of this explorative research is to analyse the resilience of the United Kingdom's (UK) healthcare supply chains from a customer’s perspective in the light of the coronavirus pandemic.

Design/methodology/approach

Using the capabilities of preparedness, robustness, recovery and adaptability as the foundational percept for supply chain resilience, 22 healthcare professionals in 17 of the UK's National Health Scheme (NHS) Trusts were interviewed to explore their personal and organisational approaches adopted relative to the provision of eye protection, gloves, gowns, aprons, masks and respirators. The Dynamic Capabilities View is mapped to the resilience capabilities and used to analyse the data from a transformational supply chain research perspective.

Findings

The supply chains were largely unprepared, which was not particularly surprising even though the availability of gloves was significantly better compared to the other personal protective equipment (PPE). Techniques adopted to ensure robustness and recovery revealed the use of unsanctioned methods such as extended use of PPE beyond recommended use, redefinition of guidelines, protocols and procedures by infection control and the use of expired PPE – all of which compromised customer well-being.

Research limitations/implications

As the paper views resilience through the lens of customers, it does not provide the perspectives of the supply chain practitioners as to the reasons for the findings and the challenges within these supply chains.

Practical implications

The compromise of the well-being of healthcare workers due to the vulnerabilities of healthcare supply chains is highlighted to managers and prescriptions for post-disruption adaptability are made.

Originality/value

This paper introduces transformative research to supply chain resilience research by uniquely looking at resilience from the customers' well-being perspective.

Machine Learning-Based Regression Framework to Predict Health Insurance Premiums

Artificial intelligence (AI) and machine learning (ML) in healthcare are approaches to make people’s lives easier by anticipating and diagnosing diseases more swiftly than most medical experts. There is a direct link between the insurer and the policyholder when the distance between an insurance business and the consumer is reduced to zero with the use of technology, especially digital health insurance. In comparison with traditional insurance, AI and machine learning have altered the way insurers create health insurance policies and helped consumers receive services faster. Insurance businesses use ML to provide clients with accurate, quick, and efficient health insurance coverage. This research trained and evaluated an artificial intelligence network-based regression-based model to predict health insurance premiums. The authors predicted the health insurance cost incurred by individuals on the basis of their features. On the basis of various parameters, such as age, gender, body mass index, number of children, smoking habits, and geolocation, an artificial neural network model was trained and evaluated. The experimental results displayed an accuracy of 92.72%, and the authors analyzed the model’s performance using key performance metrics.

Application of Deep Learning and Neural Network to Speeding Ticket and Insurance Claim Count Data

With the popularity of big data analysis with insurance claim count data, diverse regression models for count response variable have been developed. However, there is a multicollinearlity issue with multivariate input variables to the count response regression models. Recently, deep learning and neural network models for count response have been proposed, and a Keras and Tensorflow-based deep learning model has been also proposed. To apply the deep learning and neural network models to non-normal insurance claim count data, we perform the root mean square error accuracy comparison of gradient boosting machines (a popular machine learning regression tree algorithm), principal component analysis (PCA)-based Poisson regression, PCA-based negative binomial regression, and PCA-based zero inflated poisson regression to avoid the multicollinearity of multivariate input variables with the simulated normal distribution data and the non-normal simulated data combined with normally distributed data, binary data, copula-based asymmetrical data, and two real data sets, which consist of speeding ticket and Singapore insurance claim count data.

Analysis of critical success factors for blockchain technology implementation in healthcare sector

Purpose

Recently, blockchain technology (BT) has resolved healthcare data management challenges. It helps healthcare providers automate medical records and mining to aid in data sharing and making more accurate diagnoses. This paper attempts to identify the critical success factors (CSFs) for successfully implementing BT in healthcare.

Design/methodology/approach

The paper is methodologically structured in four phases. The first phase leads to identifying success factors by reviewing the extant literature. In the second phase, expert opinions were solicited to authenticate the critical success factors required to implement BT in the healthcare sector. Decision Making Trial and Evaluation Laboratory (DEMATEL) method was employed to find the cause-and-effect relationship among the third phase’s critical success factors. In phase 4, the authors resort to validating the final results and findings.

Findings

Based on the analysis, 21 CSFs were identified and grouped under six dimensions. After applying the DEMATEL technique, nine factors belong to the causal group, and the remaining 12 factors fall under the effect group. The top three influencing factors of blockchain technology implementation in the healthcare ecosystem are data transparency, track and traceability and government support, whereas; implementation cost was the least influential.

Originality/value

This study provides a roadmap and may facilitate healthcare professionals to overcome contemporary challenges with the help of BT.

Edge Computing Based on Federated Learning for Machine Monitoring

This paper focused on providing a general solution based on edge computing and cloud computing in IoT to machine monitoring in manufacturing of small and medium-sized factory. For real-time consideration, edge computing and cloud computing models were seamlessly cooperated to perform information capture, event detection, and adaptive learning. The proposed IoT system processed regional low-level features for detection and recognition in edge nodes. Cloud-computing including fog computing was responsible for mid- and high-level features by using the federated learning network. The system fully utilized all resources in the integrated deep learning network to achieve high performance operations. The edge node was implemented by a simple camera embedded on Terasic DE2-115 board to monitor machines and process data locally. Learning-based features were generated by cloud computing through the data sent from edge and the identification results could be obtained by combining mid- and high-level features with the nonlinear classifier. Therefore, each factory could monitor the real-time condition of machines without operators and keep its data privacy. Experimental results showed the efficiency of the proposed method when compared with other methods.

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard to attain due to the vulnerability of links (Wireless). IDC encompasses Polynomial Interpolations (PI) such as Lagrange, curve-fitting, and spline to provide security by implementing Integrated Key Management (IKM). The PI structure trusts all the available nodes in the network and randomly picks nodes for the security key generation. This paper presents a solution to the trust issues raised in Lagrange’s-PI (LI) utilizing an artificial neural network and attribute-based tree structure. The proposed structure not only improves the trust factor but also enhances the accuracy measures of LI to provide a sustainable network system. Throughput, PDR, noise, and latency have been increased by 47%, 50%, 34%, and 30%, respectively, by using LI and incorporating the aforementioned techniques.

The Effect of Cultural Orientations on Country Innovation Performance: Hofstede Cultural Dimensions Revisited?

Purpose. One of the perspectives that receives the most attention from studies in international business is cultural dimensions. This is due to the greater complexity and incidence of cultural aspects in economic performance. This paper explored the moderation effect of cultural orientations on the creation of innovation trajectories related to levels of innovation and their outcomes in countries from various geographical areas between 2011 and 2021. Design/Methodology/Approach. A growth trajectories model is conducted to achieve the research’s aim, considering the country’s cultural orientation, innovation inputs (institutions, human capital and research, infrastructure, market sophistication, and business sophistication), and impact on innovation output. The Global Innovation Index, Globe Project, and Global Entrepreneurship Index databases used this analysis, containing data from nations on different continents. The trajectories’ analysis approach is utilized to achieve the desired goal, which allows for the assessment of the variations in innovation trajectory across countries with cultural tendencies towards performance and humane orientation from 2011 to 2021. Findings. The literature affirms positive results for various innovation inputs, but the results show differences in innovation outputs. The difference is related to their inputs (institutions, human capital and research, infrastructure, market sophistication, business sophistication), institutions, and market sophistication. Additionally, a difference depends on the country’s performance culture, generating options to obtain higher outputs, such as knowledge and creative results. Research Limitations/Implications. Based on the results achieved, an attempt is made to provide a different perspective on innovation, especially evaluating the results over time and identifying decreasing trajectories that affect the innovation results in countries with different economic development conditions and cultural characteristics. Practical Implications. The results achieved make it possible to strengthen the analysis of the countries’ strategies regarding innovation, especially in the permanent evaluation of the results, which encourages changes in the execution of innovative activities to maintain their performance over time. Social Implications. The contributions allow us to understand the dynamics of innovation in countries’ knowledge and creative outputs over time. Originality/Value. The trajectory analysis used in the data analysis is perhaps one of the most robust techniques for a time series analysis. This allows for identifying trajectories for the study’s independent variables and their influence on a country’s innovation.

Load Frequency Control (LFC) Strategies in Renewable Energy-Based Hybrid Power Systems: A Review

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments.

Using Identity-Based Cryptography as a Foundation for an Effective and Secure Cloud Model for E-Health

Nowadays, one of the most popular applications is cloud computing for storing data and information through World Wide Web. Since cloud computing has become available, users are rapidly increasing. Cloud computing enables users to obtain a better and more effective application at a lower cost in a more satisfactory way. Health services data must therefore be kept as safe and secure as possible because the release of this data could have serious consequences for patients. A framework for security and privacy must be employed to store and manage extremely sensitive data. Patients' confidential health records have been encrypted and saved in the cloud using cypher text so far. To ensure privacy and security in a cloud computing environment is a big issue. The medical system has been designed as a standard, access of records, and effective use by medical practitioners as required. In this paper, we propose a novel algorithm along with implementation details as an effective and secure E-health cloud model using identity-based cryptography. The comparison of the proposed and existing techniques has been carried out in terms of time taken for encryption and decryption, energy, and power. Decryption time has been decreased up to 50% with the proposed method of cryptography. As it will take less time for decryption, less power is consumed for doing the cryptography operations.

Controlling Agronomic Variables of Saffron Crop Using IoT for Sustainable Agriculture

Saffron, also known as “the golden spice”, is one of the most expensive crops in the world. The expensiveness of saffron comes from its rarity, the tedious harvesting process, and its nutritional and medicinal value. Different countries of the world are making great economic growth due to saffron export. In India, it is cultivated mostly in regions of Kashmir owing to its climate and soil composition. The economic value generated by saffron export can be increased manyfold by studying the agronomical factors of saffron and developing a model for artificial cultivation of saffron in any season and anywhere by monitoring and controlling the conditions of its growth. This paper presents a detailed study of all the agronomical variables of saffron that have a direct or indirect impact on its growth. It was found that, out of all the agronomical variables, the important ones having an impact on growth include corm size, temperature, water availability, and minerals. It was also observed that the use of IoT for the sustainable cultivation of saffron in smart cities has been discussed only by very few research papers. An IoT-based framework has also been proposed, which can be used for controlling and monitoring all the important growth parameters of saffron for its cultivation.

Single and Interval-Valued Hybrid Enthalpy Fuzzy Sets and a TOPSIS Approach for Multicriteria Group Decision Making

The concept of entropy is one of the most important notions of the information theory. Entropy quantifies the amount of uncertainty involved in the value of a random variable or the outcome of a random process. Shannon’s entropy is one of the most useful entropy types. The notion of enthalpy is the information energy expressed by the complement of Shannon’s entropy. In this paper, we propose the concept of interval-valued hybrid enthalpy fuzzy set by modifying single and interval-valued fuzzy multisets. In this context, an interval-valued hybrid enthalpy fuzzy set contains information about both the data and their entropy. We also provide a cosine similarity measure between interval-valued hybrid enthalpy fuzzy sets. Using this cosine similarity measure, we propose a TOPSIS approach for multicriteria group decision making. Moreover, we apply the proposed TOPSIS method to a research assistant selection problem, and we compare the result with the result of a classical TOPSIS method.

The Blitz Canvas: A Business Model Innovation Framework for Software Startups

Software startups are temporary organizations created with the purpose of bringing a profitable business idea to life. In the initial stages, the commercial viability of any product concept is yet to be proven and until the startup can generate revenue, resources are always in short supply. To this end, this research proposes a process-oriented, competition-aware, metric-driven business model development and innovation framework. The proposed framework is designed to aid this process, by supporting the creation and validation of the business model. A web-based tool is created to demonstrate the working of the proposed model and validation is performed using survey data collected from the usage experience of participants. The data is used to evaluate the research questions and the ability of the proposed framework to overcome the shortcomings of the business model canvas. The results showed that the tool (and by extension, the framework) made the task of business model creation a quick and easy process, while at the same time covering all the required areas to create a holistic business model. The framework contributes to startup success by creating a structured approach to business development, helping to visualize the avenues for product differentiation and planning growth.

Construction and Demolition Waste Management Research: A Science Mapping Analysis

Construction and demolition waste treatment has become an increasingly pressing economic, social, and environmental concern across the world. This study employs a science mapping approach to provide a thorough and systematic examination of the literature on waste management research. This study identifies the most significant journals, authors, publications, keywords, and active countries using bibliometric and scientometric analysis. The search retrieved 895 publications from the Scopus database between 2001 and 2021. The findings reveal that the annual number of publications has risen from less than 15 in 2006 to more than 100 in 2020 and 2021. The results declare that the papers originated in 80 countries and were published in 213 journals. Review, urbanization, resource recovery, waste recycling, and environmental assessment are the top five keywords. Estimation and quantification, comprehensive analysis and assessment, environmental impacts, performance and behavior tests, management plan, diversion practices, and emerging technologies are the key emerging research topics. To identify research gaps and propose a framework for future research studies, an in-depth qualitative analysis is performed. This study serves as a multi-disciplinary reference for researchers and practitioners to relate current study areas to future trends by presenting a broad picture of the latest research in this field.

Deep Data Analysis-Based Agricultural Products Management for Smart Public Healthcare

Agricultural is an indispensably public healthcare industry for human beings at any time and smart management of it is of great significance. Since substantial technical advance relies on long-term efforts and continuous progress, reasonably scheduling the distribution of agricultural products acts as a key aspect of smart public healthcare. The most intuitive factor affecting the distribution of agricultural products is its dynamic price. Forecasting price fluctuations in advance can optimize the distribution of agricultural products and pave the way to smart public healthcare. Most researchers study the prices of various agricultural products separately, without considering the interaction of different agricultural products in the time dimension. This study introduces a typical deep learning model named graph neural network (GNN) for this purpose and proposes deep data analysis-based agricultural products management for smart public healthcare (named GNN-APM for short). The highlight of GNN-APM is to take latent correlations among multiple types of agricultural products into consideration when modeling evolving rules of price sequences. A case study is set up with the use of real-world data of the agricultural products market. Simulative results reveal that the designed GNN-APM functions well.

Correlations of Resource Bricolage and Exaptation With Low-Cost Breakthrough Innovations: Moderating Effect of Organizational Agility

The mechanism influencing resource bricolage driving low-cost breakthrough innovations remains unclear. By introducing exaptation and organizational agility, this study creates a regulated mediation model, explores effects of resource bricolage on low-cost breakthrough innovations, and analyzes the moderating effect of organizational agility and mediation effect of exaptation. The results revealed that resource bricolage exerted a significant positive impact on low-cost breakthrough innovations, and exaptation played a mediation role between resource bricolage and low-cost breakthrough innovations. In addition, both marketing agility and operational agility positively regulated the correlation between resource bricolage and exaptation. Further research revealed that the mediation effect of exaptation was positively regulated by marketing agility and operational agility, respectively. Overall, this study enriches the discussion of the impact mechanism of breakthrough innovations by resource bricolage and provides valuable enlightenment for enterprises to implement innovation-driven development strategies in the context of economic transformation.

Enhanced Parallel Sine Cosine Algorithm for Constrained and Unconstrained Optimization

The sine cosine algorithm’s main idea is the sine and cosine-based vacillation outwards or towards the best solution. The first main contribution of this paper proposes an enhanced version of the SCA algorithm called as ESCA algorithm. The supremacy of the proposed algorithm over a set of state-of-the-art algorithms in terms of solution accuracy and convergence speed will be demonstrated by experimental tests. When these algorithms are transferred to the business sector, they must meet time requirements dependent on the industrial process. If these temporal requirements are not met, an efficient solution is to speed them up by designing parallel algorithms. The second major contribution of this work is the design of several parallel algorithms for efficiently exploiting current multicore processor architectures. First, one-level synchronous and asynchronous parallel ESCA algorithms are designed. They have two favors; retain the proposed algorithm’s behavior and provide excellent parallel performance by combining coarse-grained parallelism with fine-grained parallelism. Moreover, the parallel scalability of the proposed algorithms is further improved by employing a two-level parallel strategy. Indeed, the experimental results suggest that the one-level parallel ESCA algorithms reduce the computing time, on average, by 87.4% and 90.8%, respectively, using 12 physical processing cores. The two-level parallel algorithms provide extra reductions of the computing time by 91.4%, 93.1%, and 94.5% with 16, 20, and 24 processing cores, including physical and logical cores. Comparison analysis is carried out on 30 unconstrained benchmark functions and three challenging engineering design problems. The experimental outcomes show that the proposed ESCA algorithm behaves outstandingly well in terms of exploration and exploitation behaviors, local optima avoidance, and convergence speed toward the optimum. The overall performance of the proposed algorithm is statistically validated using three non-parametric statistical tests, namely Friedman, Friedman aligned, and Quade tests. constrained optimization; metaheuristic; heuristic algorithm; OpenMP; parallel algorithms; SCA algorithm; unconstrained optimization

Image Retrieval Using Different Distance Methods and Color Difference Histogram Descriptor for Human Healthcare

As multimedia technology is developing and growing these days, the use of an enormous number of images and its datasets is likewise expanding at a quick rate. Such datasets can be utilized for the purpose of image retrieval. This research focuses on extraction of similar images established on its different features for the image retrieval purpose from huge dataset of images. In this paper initially, the query image is searched within the available dataset and, then, the color difference histogram (CDH) descriptor is employed to retrieve the images from database. The basic characteristic of CDH is that it counts the color difference stuck among two distinct labels in the L^∗a^∗b^∗ color space. This method is experimented on random images used for various medical purposes. Various unlike features of an image are extracted via different distance methods. The precision rate, recall rate, and F-measure are all used to evaluate the system's performance. Comparative analysis in terms of F-measure is also made to check for the best distance method used for retrieval of images.

Federated Learning Approach to Protect Healthcare Data over Big Data Scenario

The benefits and drawbacks of various technologies, as well as the scope of their application, are thoroughly discussed. The use of anonymity technology and differential privacy in data collection can aid in the prevention of attacks based on background knowledge gleaned from data integration and fusion. The majority of medical big data are stored on a cloud computing platform during the storage stage. To ensure the confidentiality and integrity of the information stored, encryption and auditing procedures are frequently used. Access control mechanisms are mostly used during the data sharing stage to regulate the objects that have access to the data. The privacy protection of medical and health big data is carried out under the supervision of machine learning during the data analysis stage. Finally, acceptable ideas are put forward from the management level as a result of the general privacy protection concerns that exist throughout the life cycle of medical big data throughout the industry.

Tools for Supporting the MCH Workforce in Addressing Complex Challenges: A Scoping Review of System Dynamics Modeling in Maternal and Child Health

Objectives

System Dynamics (SD) is a promising decision support modeling approach for growing shared understanding of complex maternal and child health (MCH) trends. We sought to inventory published applications of SD to MCH topics and introduce the MCH workforce to these approaches through examples to support further iteration and use.

Methods

We conducted a systematic search (1958–2018) for applications of SD to MCH topics and characterized identified articles, following PRISMA guidelines. Pairs of experts abstracted information on SD approach and MCH relevance.

Results

We identified 101 articles describing applications of SD to MCH topics. Approach: 27 articles present qualitative diagrams, 10 introduce concept models that begin to quantify dynamics, and 67 present more fully tested/analyzed models. Purpose: The most common purposes described were to increase understanding (n = 55) and support strategic planning (n = 26). While the majority of studies (n = 53) did not involve stakeholders, 40 included what we considered to be a high level of stakeholder engagement – a strength of SD for MCH. Topics: The two Healthy People 2020 topics addressed most frequently were early and middle childhood (n = 30) and access to health services (n = 26). The most commonly addressed SDG goals were “End disease epidemics” (n = 26) and “End preventable deaths” (n = 26).

Conclusions for Practice

While several excellent examples of the application of SD in MCH were found, SD is still underutilized in MCH. Because SD is particularly well-suited to studying and addressing complex challenges with stakeholders, its expanded use by the MCH workforce could inform an understanding of contemporary MCH challenges.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10995-022-03376-8.

Supply chain sustainability risk decision support model using integrated Preference Selection Index (PSI) method and prospect theory

Purpose

The purpose of this paper is to introduce the integrated model of the Preference Selection Index (PSI) and the prospect theory as new means to appraise the impact of supply chain sustainability risks based on five pillars of sustainability. Research has shown that sustainability risk assessment has a strong positive impact on improving the performance of enterprises.

Design/methodology/approach

This study adopts a new decision support model for assessing supply chain sustainability risk based on additional failure mode and effect analysis parameters and its integration with PSI methodology and prospect theory. A case example of the supply chain small and medium enterprise (SME) producing fashion have been used in this study.

Findings

The result of this study reveals some critical supply chain sustainability risks affecting the sustainability of enterprises under study.

Research limitations/implications

The use of a limited sample is often associated as a limitation in the research studies and this study is based on findings from SMEs in the fashion retail supply chain. This preliminary study provides academics and practitioners an exemplar of supply chain sustainability risk assessment using integration of the PSI method and prospect theory.

Practical implications

The result of this study is beneficial for practitioners, particularly owner–managers of SMEs who can use this study as guidance on how to consider risk behavior to identify and select the critical sustainability risks and plan mitigating strategies accordingly.

Originality/value

Scientific studies on using the PSI and its integration with prospect theory as means to assess the criticality of supply chain sustainability risks is very rare. To the best of the authors’ knowledge, this is the first paper that presents the integrated model of the PSI and prospect theory to rank supply chain sustainability risks based on five pillars of sustainability.

Statistical Growth Analysis of Rice Plants in Chhattisgarh Region Using Automated Pixel-Based Mapping Technique

The statistical growth analysis of field crop has become a great challenge in agriculture. Analyzing the growth of crop through automation provides extensive significance to the farmers for getting information about the problem arising in plants due to irregular growth monitoring. The idea behind this work is the importance of mapping with pixel-based clustering technique for growth analysis in terms of height calculation of rice crop (rice variety is MTU-1010). Height measurement plays a vital role in regular assessment for a healthy crop, and the approach proposed in this work achieves 97.58% accuracy of 14 sampled datasets taken from Indira Gandhi Agriculture University of Raipur, Chhattisgarh; a real-time dataset has been prepared. Proposed work is used for analyzing vertical as well as horizontal scaling technique. Vertical mapping provides the height of a single plant whereas horizontal mapping using k-means clustering provides an average height of the whole field. This work uses machine learning, and image processing techniques are used for this work.

Factors Impacting Defect Density in Software Development Projects

This study empirically examines the impact of technical and situational factors on the quality of software development. Defect density was used to measure the post-implementation quality of software projects. The non-parametric Kruskal-Wallis test and the parametric Welch T-test were used to test the differences in defect density for technological and situational factors related to these projects. Results suggest technological and situational factors significantly impact the quality of software. Empirical findings revealed the following factors result in lower defect density: (1) project enhancements versus new project development; (2) smaller projects versus larger projects; (3) using a development methodology; (4) using later generation programming languages; (5) developing projects with in-house teams; (6) using an iterative development methodology versus a waterfall development methodology; (7) using larger development teams versus smaller teams; (8) using CASE tools; and (9) developing projects on a standalone platform versus developing on a client\server multiplatform.

Use of Artificial Neural Network for Forecasting Health Insurance Entitlements

A number of numerical practices exist that actuaries use to predict annual medical claims expense in an insurance company. This amount needs to be included in the yearly financial budgets. Inappropriate estimating generally has negative effects on the overall performance of the business. This paper presents the development of Artificial Neural Network model that is appropriate for predicting the anticipated annual medical claims. Once the implementation of the neural network models were finished, the focus was to decrease the Mean Absolute Percentage Error by adjusting the parameters such as epoch, learning rate and neuron in different layers. Both Feed Forward and Recurrent Neural Networks were implemented to forecast the yearly claims amount. In conclusion, the Artificial Neural Network Model that was implemented proved to be an effective tool for forecasting the anticipated annual medical claims. Recurrent neural network outperformed Feed Forward neural network in terms of accuracy and computation power required to carry out the forecasting.

Inventory Management Sustainability

Inventory optimality is an option of energy utilization proportionality that can lessen carbon emanations and maximize profitability. This study proposes an inventory management model in which the stock volume is optimally decided to diminish energy per resource utilized in-other to reduce carbon emanations. This will likewise help in concluding renewal volume optimally. Consequently, the study utilized economic order quantity (EOQ) to decide inventory volumes in-other to decrease carbon emanations so as to augment profits of the inventory chain. Partial least square(PLS) was additionally utilized to examine the extent of inventory management frameworks on environmental sustainability. The study, therefore, shows its oddity and pertinency by utilizing economic order quantity(EOQ) and partial least square(PLS) to examine and optimize inventory respectively, as it gives a perspective of decreasing carbon emanations during inventory procedures.

Performance Accretion in Delay Compensation of Networked Control System Using Markov Approach-Based Randomness Estimation in Smith Predictor

By the second decade of the 21st century, there has been a multi-faceted technological development in the field of networked control system (NCS). This progression in NCS has not only revealed its significant applications in various areas but has also unveiled various difficulties associated with it that hampered the operations of networked control system. Network-induced delays are issues that promote many other issues like packet dropout and brevity in bandwidth utilization. In this research article, network-induced delay has been curtailed by using the harmony between Smith predictor and Markov approach. The error estimation of the Smith predictor controller used for the simulation is carried out through a Markov approach which allows the control of the system to operate smoothly by optimizing the control signal. To implement the proposed method, the authors have simulated a third order system in Matlab/Simulink software.

Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19

During this pandemic outbreak of COVID-19, the whole world is getting severely affected in respect of population health and economy. This novel virus has brought the whole world including the most developed countries to a standstill in a very short span like never before. The prime reason for this unexpected outburst of COVID-19 is lack of effective medicine and lack of proper understanding of the influencing factors. Here, the authors aim to find the effect of epidemiological factors that influence its spread using a fuzzy approach. For the same, a total of nine factors have been considered which are classified into risk and preventive factors. This fuzzy model supports to understand and evaluate the impact of these factors on the spread of COVID-19. Also, the model establishes a basis for understanding the effect of risk factors on preventive factors and vice versa. It is worth mentioning that this is the first attempt to analyze the effect of clinical and epidemiological factors with respect to COVID-19 using a fuzzy approach.

Primary Mobile Image Analysis of Human Intestinal Worm Detection

One among a lot of public health concerns in rural and tropical areas is the human intestinal parasite. Traditionally, diagnosis of these parasites is by visual analysis of stool specimens, which is usually tedious and time-consuming. In this study, the authors combine techniques in the Laplacian pyramid, Gabor filter, and wavelet to build a feature vector for the discrimination of intestinal worm in a low-resolution image captured with mobile devices. The dimension of the feature vector is reduced using principal component analysis, and the resultant vector is considered as input to the SVM classifier. The proposed framework was applied to the Makerere intestinal dataset. At its preliminary stage, the results demonstrate satisfactory classification with an accuracy rate of 65.22% with possible extension in future work.

Effective Selection of Entities From Heterogeneous and Large Resources Using a Cooperative Neuro-Fuzzy System

This paper focuses on using the cooperative neuro-fuzzy system for the effective and customised selection of entities from large and heterogeneous resources by presenting a general architecture. An experiment is carried out with the fast-moving consumer goods to prove the utility of the architecture. It is observed that most consumers go for the frequent purchase of fast-moving consumer items. Further, various brands, costs, discounts, schemes, quantities, and reviews might make it challenging. Hence, such decisions need to be intelligent and practically feasible in terms of time and effort. The paper discusses neural networks to categorise the entities, type-1 & 2 fuzzy membership functions with rules, training sets, and graphical views of the fuzzy rules and the experiment details. Besides the generic approach and experiment, the paper also discusses the work done so far with their limitations and applications in other domains. At the end, the paper presents the limitations and possible future enhancements.

Spatio-Temporal Graph for Improvement of Decision-Making in Risks Treatment

Failures that hinder the proper functioning of complex systems are characterised by frequencies of occurrence, rates of evolution and variables periodicities. Indeed, a good design of this kind of systems requires the elaboration of an adapted analysis and modelling approach, which takes into account the spatial-temporal dynamics of the systems elements dysfunction. This paper proposes a Spatio-temporal modelling approach integrated into a risk management methodology. This modelling allows both to describe the evolution of the system in space and in time, and to follow the spatial and temporal scope of a failure of the system in order to improve decision making in choosing the appropriate corrective action. The elements of the methodology are illustrated and validated by a case study of a wireless sensor network system.

Customized Rule-Based Model to Identify At-Risk Students and Propose Rational Remedial Actions

Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general.

Neuro-fuzzy based estimation of rotor flux for Electric Vehicle operating under partial loading

The primary objective of this work is to optimize the induction motor rotor flux so that maximum efficiency is attained in the facets of parameter and load variations. The conventional approaches based on loss model are sensitive to modelling accuracy and parameter variations. The problem is further aggravated due to nonlinear motor parameters in different speed regions. Therefore, this work introduces an adaptive neuro-fuzzy inference system-based rotor flux estimator for electric vehicle. The proposed estimator is an amalgamation of fuzzy inference system and artificial neural network, in which fuzzy inference system is designed using artificial neural network. The training data for neuro-fuzzy estimator is generated offline by acquiring rotor flux for different values of torque. The conventional fuzzy logic and differential calculation methods are also developed for comparative analysis. The efficacy of developed system is established by analyzing it under varying load conditions. It is revealed from the results that suggested methodology provides an improved efficiency i.e. 94.51% in comparison to 82.68% for constant flux operation.

Food Security and Sustainability: Discussing the Four Pillars to Encompass Other Dimensions

The unadjusted intake of food constitutes a real challenge for the several sustainability dimensions. In this perspective, the main objectives of this research are to characterise the current contexts of food security, its relationship with sustainability, and identify proposals and actions that may support the design of more adjusted policies in the future. In addition, it is intended to assess if the food security pillars properly address the sustainability goals and if the evolution of undernutrition is accompanied by sustainable frameworks. In this way, statistical information from the FAOSTAT database was considered for the several dimensions of food security over the period 2000-2020. These data were analysed through factor-cluster approaches and panel data methodologies, namely those related to quantile regressions. As main insights, we may refer that undernutrition is more impacted by the availability of food and nutrients and political stability than by the level of GDP-Gross Domestic Product (except for the extreme cases). This means that the level of development is not the primary explanation for the problems of nutrition. The main focus of the national and international policies must be to improve the agrifood supply chains and to support political stability, in order to mitigate undernutrition worldwide and ensure a global access to sustainable and healthy diets. In addition, it is suggested to rethink the four pillars of food security (availability, access, utilisation and stability), in order to encompass other dimensions, such as climate change.

Self-Correcting Virtual Current Sensor Based on the Modified Luenberger Observer for Fault-Tolerant Induction Motor Drive

Fault-tolerant control (FTC) solutions are increasingly being used in modern drive systems with AC motors. Such systems provide a higher degree of security and solutions that allow the on-line detection and localization of failures, as well as the switching of the control mode to a mode that allows us to continue the operation or safely stop the drive system. As the current sensors (CSs) are necessary to ensure precise control of the AC motors, in the event of their failure, one of two strategies can be used—hardware or software redundancy. The first strategy requires the use of additional measuring sensors. For this reason, the algorithmic solution, based on the Luenberger Observer (LO), has been proposed in this article as one of the software redundancy methods. In contrast to methods presented in the literature, the proposed solution allows one not only to compensate the stator current in a phase with a faulty CS, but also to adjust the correction of current estimation based on a measured signal in the other phase with a healthy CS. Extensive simulation studies in the direct rotor flux-oriented control (DRFOC) structure with the induction motor (IM) confirm the effectiveness of the proposed method. In addition, the proposed solution allows the drive system to be controlled even if all CSs are damaged.

Barriers in repurposing an existing manufacturing plant: a total interpretive structural modeling (TISM) approach

Repurposing of an existing manufacturing plant is an emerging field due to the increase in emergencies of the covid-19 pandemic with the need of rapid responses which has a wide range of potential applications in sustainment of the manufacturing plant in these unfavourable times and helping of the economy. It makes the manufacturing plant adaptable to changes, makes it productive by manufacturing products that are currently in demand, prevents the dissolution of the plant and thus harvests the maximum potential of the manufacturing plant in the need of an emergency. However, not many industries and plants are suited to make the appropriate changes and lack knowledge on how to proceed to do so. The paper identifies the barriers that are faced in the transition for repurposing a general manufacturing plant to a more suited plant for current emergencies that need rapid response. These barriers hinder the repurposing of the manufacturing plant and impact the business decisions to establish a manufacturing plant suited for emergency situations. Surveys and information from various experts in this field are used to identify these barriers and document their interdependencies and influence on one another. The data is graphed and analysed utilizing TISM (Total Interpretive Structural Modelling) and MICMAC (Cross-Impact Matrix Multiplication Applied to Classification) methodology to further examine by classifying and ranking the relationships. Analysing the relationships between barriers leads to effective decisions towards the successful adoption of repurposing of manufacturing plant. A contextual relationship based structural table called interpretive table and structural model is made to pinpoint influential barriers. Thus, the research explains and explores significant barriers to the adoption of repurposing in manufacturing plant and not only provides a strong methodological and contextual contribution with the help of TISM and MICMAC but also gives research a sense of links of the barriers across various levels. On a practical level, the study is immensely useful to help manufacturing plants overcome repercussions due to disruptions by modifying existing practice and business model to a new model which synchronizes with the new normal to increase the efficiency and survivability of the plant. The result of the research points out that strategical, cultural, technological, and innovation barriers are the most influential barrier in repurposing of manufacturing plant.

A hybrid approach to identifying and assessing interactions between climate action (SDG13) policies and a range of SDGs in a UK context

In 2015 the United Nations drafted the Paris Agreement and established the Sustainable Development Goals (SDGs) for all nations. A question of increasing relevance is the extent to which the pursuit of climate action (SDG 13) interacts both positively and negatively with other SDGs. We tackle this question through a two-pronged approach: a novel, automated keyword search to identify linkages between SDGs and UK climate-relevant policies; and a detailed expert survey to evaluate these linkages through specific examples. We consider a particular subset of SDGs relating to health, economic growth, affordable and clean energy and sustainable cities and communities. Overall, we find that of the 89 UK climate-relevant policies assessed, most are particularly interlinked with the delivery of SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities) and that certain UK policies, like the Industrial Strategy and 25-Year Environment Plan, interlink with a wide range of SDGs. Focusing on these climate-relevant policies is therefore likely to deliver a wide range of synergies across SDGs 3 (Good Health and Well-being), 7, 8 (Decent Work and Economic Growth), 9 (Industry, Innovation and Infrastructure), 11, 14 (Life Below Water) and 15 (Life on Land). The expert survey demonstrates that in addition to the range of mostly synergistic interlinkages identified in the keyword search, there are also important potential trade-offs to consider. Our analysis provides an important new toolkit for the research and policy communities to consider interactions between SDGs, which can be employed across a range of national and international contexts.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43621-021-00051-w.