Feasible Framework Research Articles

A Comprehensive Framework for Feasibility of Ccus Deployment: A Meta-Review of Literature on Factors Impacting Ccus Deployment

A Comprehensive Framework for Feasibility of Ccus Deployment: A Meta-Review of Literature on Factors Impacting Ccus Deployment

Ensemble Nonlinear Support Vector Machine Approach for Predicting Chronic Kidney Diseases

Urban living in large modern cities exerts considerable adverse effects on health and thus increases the risk of contracting several chronic kidney diseases (CKD). The prediction of CKDs has become a major task in urbanized countries. The primary objective of this work is to introduce and develop predictive analytics for predicting CKDs. However, prediction of huge samples is becoming increasingly difficult. Meanwhile, MapReduce provides a feasible framework for programming predictive algorithms with map and reduce functions. The relatively simple programming interface helps solve problems in the scalability and efficiency of predictive learning algorithms. In the proposed work, the iterative weighted map reduce framework is introduced for the effective management of large dataset samples. A binary classification problem is formulated using ensemble nonlinear support vector machines and random forests. Thus, instead of using the normal linear combination of kernel activations, the proposed work creates nonlinear combinations of kernel activations in prototype examples. Furthermore, different descriptors are combined in an ensemble of deep support vector machines, where the product rule is used to combine probability estimates of different classifiers. Performance is evaluated in terms of the prediction accuracy and interpretability of the model and the results.

Relevance of developing public health service cadres alongside a more prevention oriented Indian health system: an overview

The relevance of public health has been emphasized in the wake of the global epidemic COVID-19. There are several success stories that we often tend to forget, such as the fight against various infectious illnesses like smallpox, poliomyelitis, and current human-immunodeficiency virus (HIV) prevention, to name a few, public health has played a significant impact. Diarrheal diseases, for example, which contributes significantly to India's under-five mortality rate and is one of the leading causes of malnutrition, can be effectively handled by improving access to safe water and sanitation. Because public health encompasses more than just health, we require a workforce with managerial and leadership skills as well as training in public health as a specialty. This paper explores some of the successes and lessons learned from systematic investments in public health in the Indian state of Tamil Nadu, namely The Tamil Nadu model and other countries, as well as the system's flaws. In India, a feasible framework for establishing dedicated public health cadres has also been explored. Evidence was acquired from PubMed, Google Scholar, newspaper stories, and publicly released government orders and papers. The recruitment of cadres may resemble that of the Indian economic/statistical services (IES/ ISS) by the UPSC. Another area to emphasize for health professionals interested in public health is training. Starting from frontline workers, block level workers to district and state we need dedicated public health workforce. Moreover, the need of the hour is to establish such a system which will work alongside pre-existing clinical fields.

NN model-based evolved control by DGM model for practical nonlinear systems

This article develops an evolutionary bat algorithm (EBA) based fuzzy neural network (NN), which is applied to the adaptive controller in a plant, for the purpose of ensuring the asymptotic stability and heightening the stability of the vehicle. With a gray signal predictor, the Lyapunov theory as well as the backstepping method are adopted to ensure the non-linearity of the controlled system and derive the evolutionary control law of the signal tracking. The discrete gray model (DGM) (2,1) is used to predict the future motion of the nonlinear system, so that the fuzzy controller can ensure the Lyapunov stability and the feasibility of the parallel distributed compensation (PDC) scheme through the Lyapunov type lemma. The controller design is demonstrated in practical nonlinear systems, for a mechanical elastic wheel (MEW), as a feasible mathematical framework for the matching of this circular object revolving on an axle.

Distributed Task Scheduling for Wireless Powered Mobile Edge Computing: A Federated-Learning-Enabled Framework

Sixth generation (6G) wireless communication networks are expected to provide ultra-low latency and energy-efficient services for global users. The paradigm of wireless powered mobile edge computing (WP-MEC) provides a feasible framework to real-ize the above purposes by leveraging the advantage of both wireless power transfer and MEC. Howev-er, massive network traffic and control information overburden the centralized server. In this article, we integrate federated learning and deep reinforcement learning with WP-MEC to jointly optimize computing and communication resources. Specif-ically, we design an online learning framework to schedule computation tasks of mobile clients, where the learning model can be distributed over edge servers, and parameter synchronization happens on the cloud. The superiority of the designed frame-work is evaluated and compared with benchmarks in terms of average task completion delay and task completion ratio. Finally, we discuss some research challenges and opportunities for future WP-MEC.

Coupling complementary strategy to flexible graph neural network for quick discovery of coformer in diverse co-crystal materials

Cocrystal engineering have been widely applied in pharmaceutical, chemistry and material fields. However, how to effectively choose coformer has been a challenging task on experiments. Here we develop a graph neural network (GNN) based deep learning framework to quickly predict formation of the cocrystal. In order to capture main driving force to crystallization from 6819 positive and 1052 negative samples reported by experiments, a feasible GNN framework is explored to integrate important prior knowledge into end-to-end learning on the molecular graph. The model is strongly validated against seven competitive models and three challenging independent test sets involving pharmaceutical cocrystals, π–π cocrystals and energetic cocrystals, exhibiting superior performance with accuracy higher than 96%, confirming its robustness and generalization. Furthermore, one new energetic cocrystal predicted is successfully synthesized, showcasing high potential of the model in practice. All the data and source codes are available at https://github.com/Saoge123/ccgnet for aiding cocrystal community.

Fire safety management in public health-care buildings: issues and possible solutions

PurposeFrequent fire incidences in various hospitals across the globe with devastating effects on humans and other resources have multiplied. Stakeholders in the health-care sector are worried because of the yearly fire outbreak increase. However, fire safety management has been proved as a successful platform to mitigate fire in health-care facilities. How far regarding public health-care facilities is yet to receive in-depth studies in Malaysia. This paper aims to investigate the issues associated with fire safety management and proffers possible solutions to improve safety in public health-care facilities from the operators’ perception.Design/methodology/approachThe objectives were achieved through a combination of case studies of five selected Malaysia’s public hospitals and a qualitative approach. Thematic analysis with the assistance of MAXQDA (software program designed for computer-assisted qualitative and mixed methods data) 2018, a type of qualitative data analysis software was used to analyse the collated data which emerged from the knowledgeable participants.FindingsLax implementation of safety policy, inadequate water pressure, poor maintenance, inadequate communication system, amongst others, emerged as the issues. Findings proffer five main measures to possibly improve fire safety management in public health-care buildings. This includes a feasible institutional framework, improve the emergency response team, improve the occupational health and safety system and others.Research limitations/implicationsThis paper is limited to exploring the issues associated with fire safety management and proffer possible solutions to improve safety in public health-care facilities from the operators’ perception. Future research is needed to engage useful information in advancing fire safety management in public hospital buildings and possibly developing a qualitative model using a generic fire response model. Also, to investigate the level of compliance with the requirements of a fire safety management plan and possibly develop a well-detailed fire safety plan in Malaysia’s public health-care buildings.Practical implicationsAs part of the paper implications, the paper concludes that improved fire protection systems via modern technologies and training of key staff members in accident response and recovery during fire emergencies should be encouraged. This can be achieved through the enforcement and implementation of a fire safety management plan. Thus, this study is encouraging the implementation and sustainability of a fire safety management plan for health-care buildings across Malaysia.Originality/valueTo the best of this paper’s knowledge, this is possibly the first comprehensive paper on fire safety management in public health-care facilities that engaged operators in Malaysia. Moreover, this paper proffers feasible policy solutions to improve the fire safety management plan in public health-care buildings.

Unpacking Peer Collaborative Experiences in Pre-Class Learning of Flipped Classroom With a Production-Oriented Approach

In flipped learning (FL), the pre-class section plays an important role and determines whether meaningful and deep-level learning can take place in the following in-class section. However, previous studies focus on in-class learning and research little on the pre-class section. In order to explore an interactive mode which can ensure the effect of pre-class learning, a production-oriented peer collaboration FL framework was proposed and applied to the Business English course. Mixed research methods including questionnaire and focus group interview were used to test the effectiveness of the framework and explore effective forms of interaction. Data analysis shows that the FL framework improved significantly social interaction self-efficacy, help-seeking, and self-evaluation in self-regulated learning, thus ensured quality pre-class learning. As to interaction, eight collaboration forms were found through thematic analysis, namely comparison, correction, reaching agreement, supervision, inspiration, complementation, negotiation, and emotional support. Among them, comparison is the most beneficial and frequently mentioned collaborative behavior. This research provides an empirical case for peer interaction based on online learning tasks. It helps enrich interactive research in FL and provides teaching reference for practitioner teachers by offering a feasible framework.

Structural Covariance of Depth-Dependent Intracortical Myelination in the Human Brain and Its Application to Drug-Naïve Schizophrenia: A T1w/T2w MRI Study.

Aberrations in intracortical myelination are increasingly being considered as a cardinal feature in the pathophysiology of schizophrenia. We investigated the network-level distribution of intracortical myelination across various cortex depths. We enrolled 126 healthy subjects and 106 first-episode drug-naïve schizophrenia patients. We used T1w/T2w ratio as a proxy of intracortical myelination, parcellated cortex into several equivolumetric surfaces based on cortical depths and mapped T1w/T2w ratios to each surface. Non-negative matrix factorization was used to generate depth-dependent structural covariance networks (dSCNs) of intracortical myelination from 2 healthy controls datasets-one from our study and another from 100-unrelated dataset of the Human Connectome Project. For patient versus control comparisons, partial least squares approach was used; we also related myelination to clinical features of schizophrenia. We found that dSCNs were highly reproducible in 2 independent samples. Network-level myelination was reduced in prefrontal and cingulate cortex and increased in perisylvian cortex in schizophrenia. The abnormal network-level myelination had a canonical correlation with symptom burden in schizophrenia. Moreover, myelination of prefrontal cortex correlated with duration of untreated psychosis. In conclusion, we offer a feasible and sensitive framework to study depth-dependent myelination and its relationship with clinical features.

Applicability of Robotic Process Automation in the Supply Chain & Logistics Industry in Sri Lanka

As technology transforms the business world, the supply chain and logistics sector need to embrace automation that promise rebuilding and integrating sustainable supply chains. Robotic process automation is the application of automating the business processes using software bots. These software bots can enter data into software applications, manipulate data, and communicate with other software applications. However, most robotic process automation literature focuses on sectors such as finance, but little research focuses on supply chain and logistics sector applications. Besides, robotic process automation in the Sri Lankan supply chain context is less studied. Therefore, the study's main objectives are to identify the applicability of robotic process automation in supply chain and logistics industry in Sri Lanka. Furthermore, the study aims to identify employee perception towards robotic process automation implementations in the supply chain logistics industry in Sri Lanka.
 This study uses qualitative and quantitative research methods to collect data. Initially, a case study was conducted in one Logistics Company. A comprehensive feasibility framework was developed to identify the most feasible process. The AS-IS process and data flow mapping were developed to in-depth study the applicability of robotic process automation of the selected process. Findings confirm that many tasks of the selected process can be automated using robotic process automation. Moreover, a survey was conducted to identify employee perception towards robotic process automation implementations. The findings reflect that the young employees who have higher education and income levels are more likely to view robotic process automation as a positive impact on their jobs in the supply chain and logistics industry in Sri Lanka.
 Keywords: Robotic Process Automation, Supply Chain and Logistics Industry in Sri Lanka, Employee Perception, Feasibility Framework

A systemic perspective for the Sustainable Energy and Climate Action Plan (SECAP)

ABSTRACT The voluntary-based initiative Covenant of Mayors (CoM) focused on the active role of local authorities and increased its targets by 2016, through the Sustainable Energy and Climate Action Plans (SECAPs) in terms of GHG reduction from 20% to 40% by 2030. In actual fact, the SECAP, intended as an urban planning tool, has the potential to improve the resilience of the EU Municipalities to climate-change, developing adaptation/mitigation actions and increasing the ‘environmental awareness’ of the involved actors. From a planning point of view, we affirm that the CoM started a new season of urban planning in Europe that covers the planning demand in the domain of implementing sustainable territorial development objectives. It unlocks the current ineffective planning systems and represents a remarkable practice promoting feasible framework projects for public and private investments. This paper proposes a methodological proposal to exploit this spread of SECAPs in the EU, including the systemic strategic planning approach in the process of SECAP design and ensuring an integrated vision of city development over the list of actions per sector required by the Global CoM procedures. Besides the methodological proposal, a review of the major EU energy policies is provided and relevant Best Practices are presented.

Exploring Coaches’ Perceptions of the Feasibility of a Movement-Oriented Games-Based Assessment Within “Made to Play” Programs

Coaches can provide an appropriate environment for children to develop a range of movement skills, but there is a dearth of research exploring the creation of appropriate resources to support the coach in developing and assessing children’s Complex Movement Skills. There is also a lack of research around coaches’ perceived feasibility of the limited resources in this area. Therefore, the purpose of this study was to design and then assess the feasibility of a Movement-Oriented Games-Based Assessment (MOGBA) for children aged 8–12 years, to be used by coaches within “Made to Play” programs. Thirteen coaches from across the United States and the United Kingdom used pilot materials to assess the feasibility of MOGBA over a 9-week period. Individual, paired, and focus group interviews were structured and data were thematically analyzed using Bowen et al.’s feasibility framework. Findings suggested that MOGBA provided a welcomed and much needed enhancement to their programs, with effective use of technology-enhanced coaching. Coaching involved notions of pedagogy and assessment, with issues emerging around class size and complexity of assessment. Coaches often used MOGBA covertly and flavored the resource to the sport being delivered, and this revealed to coaches the capability of children not viewed before.

Means and Mechanism of the Realization of Linguistic Subjectivity from the Perspective of Philosophical Cognition

This study discusses the linguistic subjectivity (LS) phenomenon from the perspective of philosophical cognition. The purpose of this study is to explore the human’s cognitive styles and ways of existence in language and to deepen the understanding of communicative functions of language. An interactive geometric research framework of LS was constructed and the means and mechanism of its realization were explored at six levels: language system level, language use level, propositional meaning level, non-propositional meaning level, speaker’s encoded meaning level and hearer’s decoded meaning level. Also, three dimensions were covered: lexical dimension, syntactic dimension, and discourse dimension. Results show that humans’ cognitive styles and ways of existence encoded in language can be more profoundly dug by exploring LS from the perspective of philosophical cognition. The study can provide a feasible analytical framework for systematically and comprehensively probing of LS phenomenon. Thus, the study facilitates a deeper understanding of LS and communicative functions of language. The study also thoroughly mines subjective factors behind the speaker’s words, utterances or discourse, such as attitudes, emotions and feelings, which also belong to the cognition category, indicating the speaker’s existence in language.

A feasible framework to downscale NPP-VIIRS nighttime light imagery using multi-source spatial variables and geographically weighted regression

The cloud-free monthly composite of global nighttime light (NTL) data of the Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) day/night band (DNB) provides indispensable indications of human activities and settlements. However, the coarse spatial resolution (15 arc sec) of NTL imagery greatly restricts its application potential. This study proposes a feasible framework to downscale NPP-VIIRS NTL using muti-source spatial variables and geographically weighted regression (GWR) method. High-resolution auxiliary variables were acquired from the Landsat 8 OLI/ TIRS and social media platforms. GWR-based downscaling procedures were consequently implemented to obtain NTL at a 100-m resolution. The downscaled NTL data were validated against Loujia1-01 imagery based on the coefficient of determination (R2) and root-mean-square error (RMSE). The results suggest that the data quality was suitably improved after downscaling, yielding higher R2 (0.604 vs. 0.568) and lower RMSE (8.828 vs. 9.870 nW/cm2/sr) values than those of the original NTL data. Finally, the NTL was extendedly applied to detect impervious surfaces, and the downscaled NTL had higher accuracy than the original NTL. Therefore, this study facilitates data quality improvement of NPP-VIIRS NTL imagery by downscaling, thus enabling more accurate applications.

Variability in and mixtures among residential vacancies at granular levels: Evidence from municipal water consumption data

Unprecedented urbanization in China has directly resulted in residential vacancies, which has seriously stunted sustainable development, a part of China's new-type urbanization plan. Understanding the various types and mixes of residential vacancies is critical for the advancement of our knowledge of speculative urbanism and for devising vacancy-mitigation policies, but this issue remains insufficiently studied. Using municipal water consumption data, this study proposes a feasible and general-purpose framework for providing innovative insights into the variability in residential vacancies at the household level and the mixture of residential vacancies at the building level. This framework was applied to the city of Changshu, China, and four categories of vacant residences at the household level were identified: seasonally vacant residences, long-term vacant residences, newly built residences and occasionally vacant residences. The first category is closely related to tourism and seasonal industries, while the last three exhibit a Matthew effect. In addition to revealing significant and intensifying spatial clustering and three patterns of changes in vacancy mixtures (i.e., emergence, disappearance, and increases or decreases), the results identify particular types of vacant residences at the building level (e.g., extremely low-entropy long-term multihousehold buildings). The insights from this study can contribute to devising customized policies for alleviating residential vacancies.

Preparing Future Doctors for Telemedicine: An Asynchronous Curriculum for Medical Students Implemented During the COVID-19 Pandemic.

The COVID-19 pandemic led to changes in both the clinical environment and medical education. The abrupt shift to telemedicine in March 2020, coupled with the recommendation that medical students pause in-person clinical rotations, highlighted the need for student training in telemedicine. To maintain students' ability to participate in clinical encounters and continue learning in the new virtual environment, a telemedicine curriculum for clinical students was rapidly developed at Harvard Medical School (HMS) focusing on the knowledge and skills needed to conduct live video encounters. Curriculum leads created an interactive, flexible curriculum to teach students clinical skills, regulatory issues, professionalism, and innovations in telemedicine. This 5-module curriculum was delivered using various primarily asynchronous modalities including webinar-style presentations, prerecorded videos of physical exams from different disciplines, shadowing a synchronous telemedicine visit, peer discussions in small groups, and quizzes with both multiple-choice and open-ended questions. During May 2020, 252 clerkship and postclerkship medical students at HMS completed the telemedicine curriculum. All students completed a precourse survey and 216 (85.7%) completed the postcourse survey. Students' self-rated knowledge of telemedicine increased, on average, from 38 (15.1%) reporting being fairly/very knowledgeable over 4 domains before the course to 182 (84.3%) afterward (P < .001). The course was highly rated, with 176/205 (85.9%) students reporting that it met their learning needs and 167/205 (81.5%) finding the delivery methods to be effective. Of 101 (45.3%) students who answered an open-ended postcourse survey question, 91 (90.1%) reported asynchronous learning to be a positive experience. As telemedicine becomes increasingly and likely permanently integrated into the health care system, providing medical students with robust training in conducting care virtually will be essential. This curriculum provides a promising and feasible framework upon which other schools can apply these emerging competencies to design their own telemedicine curricula.

Blinding quantum computation using alternative sources

Quantum computation has seen rapid progress in the past years. Due to implementation complexity and cost, cloud-based quantum computing is strongly believed to be the most feasible framework for individual users. Universal blind quantum computing (UBQC) provides the protocol for secure delegation of arbitrary quantum computations and has received significant attention. However, one of the major challenges in UBQC is a secure and reliable transmission of a quantum state over a long distance. Herein, we improve the efficiency of UBQC using alternative photon sources. Our analysis shows that a heralded single-photon source can significantly improve the transmitted distance of UBQC and a modified coherent source can reduce the required transmitted number of pulses. Furthermore, we study the finite-data-size effect, which is a crucial for real-world applications. Our proposed scheme can be a promising alternative for the future development of UBQC.

Wealth Inequality: A Hybrid Approach Toward Multidimensional Distributional National Accounts In Europe

This article proposes a practically feasible framework for compiling Multidimensional Distributional National Accounts (MDINAs) serving two functions: a comprehensive measure of (components of) net worth and their distribution, and a link to macroeconomic statistics. I break down 12 components of marketable wealth by wealth and income groups, and three functions of wealth for Austria, Finland, France, Germany, and Spain. MDINA complemented by summary indicators reveal large heterogeneity in the degree of inequality, and shed light on differences in the structure of wealth portfolios across and within countries. I combine data collected in the largely harmonized HFCS survey and adjust for remaining differences in survey modes regarding the treatment of the top tail using (Generalized) Pareto models estimated from rich lists or top wealth shares derived from tax data and leaked information on wealth held in offshore tax havens. Measured inequality increases strongest in countries where surveys refrain from appropriate top‐tail corrections.

Towards enhancement of gas-liquid mass transfer in bioelectrochemical systems: Validation of a robust CFD model.

Mass transfer has been identified as a major bottleneck in gas fermentation and microbial conversion of carbon dioxide to chemicals. We present a pragmatic and validated Computational Fluid Dynamics (CFD) model for mass transfer in bioelectrochemical systems. Experiments were conducted to measure mixing times and mass transfer in a Duran bottle and an H-cell. An Eulerian-Eulerian framework with a simplified model for the bubble size distribution (BSD) was developed that utilized only one additional equation for the bubble number density while including the breakup and coalescence. Validations of the CFD model for mixing times showed that the predictions were within the confidence intervals of the measurements, verifying the model's capability in simulating the hydrodynamics. Further validations were performed using constant and varying bubble diameters for the mass transfer. The results showed the benefits of a simplified BSD model, as it yielded improvements of seven and four times in accuracy when assessed against the experimental data for the Duran bottle and H-cell, respectively. Modeling of the H-cell predicted that a lower stirring rate improves mass transfer compared with higher stirring rates, which is of great importance when designing microbial cultivation processes. The model offers a feasible framework for advanced modeling of gas fermentation and microbial electrosynthesis.

Research on the process planning and multi-method integration strategy of product conceptual design with cognitive mechanisms.

Innovative product design is essentially an activity involving creative cognitive thinking. Therefore, research on the innovative design process and of methods, computer-aided innovation tools should be conducted based on systematic exploration based on the principles of innovative cognitive thinking. We aim to uncover some general principles that can serve as a systematic thinking framework for product designers, provide a feasible framework and method of innovative thinking for designers, and provide theoretical and methodological support for further development of computer-aided innovation platforms. This paper summarizes the sources and content of studies on the idea generation and design processes behind product innovation. Specifically, we break down the general innovative design process of products, outlining the cognitive mechanisms and propose an integrated application strategy that incorporates multiple methods. And taking "the solution program to the coking of oily sludge pyrolysis equipment" as an example, this paper demonstrated and verified the effectiveness and practicability of the method and strategy proposed. Based on the results on improvements in the equipment, the designers put forward some creative and implementable design schemes. The entire thinking process was relatively smooth and efficient. The example presented in this paper shows that the proposed method can effectively guide/assist/motivate designers to think creatively.