System Environment Research Articles

요양보호사의 근무환경이 직무만족에 미치는 영향

Objectives The purpose of this study is to analyze the mediating effect of job enthusiasm in the relationship between the work environment and job satisfaction of the nursing care workers who work in long-term care homes for the elderly. Methods This study conducted a survey on 330 caregivers working at long-term care facilities for the elderly in Yeongdong-gun, and analyzed the collected questionnaire data using SPSS 24.0 and AMOS 24.0 statistical programs. Results First, out of the working environment factors of nursing care workers at long-term care homes, all of them, namely the human environment, physical environment, reward systems, and job expertise factors, had a significant and positive impact on job enthusiasm. Second, of the work environment factors of the nursing care workers of long-term care homes, the physical environment and reward system factors had a significant and positive impact on job satisfaction. Third, the job enthusiasm of the nursing care workers at long-term care homes turned out to have a similarly significant and positive impact on job satisfaction. Fourth, in the relationship between work environment factors of nursing care workers at long-term care homes, such as the human environment, physical environment, reward system, job expertise, and job satisfaction, it was shown that, upon all of these factors, job enthusiasm had a significant mediating effect. Fifth, with regard to the groups where the work experience was less than five years and those of five years or longer, the difference between these two groups was confirmed in the paths of [reward system→job enthusiasm], [reward system→job satisfaction], [job enthusiasm→job satisfaction]. Conclusions This study is significant in that it identified the working environment of nursing care workers and sought specific action plans to increase job satisfaction by increasing job enthusiasm.

Movement-driven modelling reveals new patterns in disease transmission networks.

Interspecific interactions are highly relevant in the potential transmission of shared pathogens in multi-host systems. In recent decades, several technologies have been developed to study pathogen transmission, such as proximity loggers, GPS tracking devices and/or camera traps. Despite the diversity of methods aimed at detecting contacts, the analysis of transmission risk is often reduced to contact rates and the probability of transmission given the contact. However, the latter process is continuous over time and unique for each contact, and is influenced by the characteristics of the contact and the pathogen's relationship with both the host and the environment. Our objective was to assess whether a more comprehensive approach, using a movement-based model which assigns a unique transmission risk to each contact by decomposing transmission into contact formation, contact duration and host characteristics, could reveal disease transmission dynamics that are not detected with more traditional approaches. The model was built from GPS-collar data from two management systems in Spain where animal tuberculosis (TB) circulates: a national park with extensively reared endemic cattle, and an area with extensive free-range pigs and cattle farms. In addition, we evaluated the effect of the GPS device fix rate on the performance of the model. Different transmission dynamics were identified between both management systems. Considering the specific conditions under which each contact occurs (i.e. whether the contact is direct or indirect, its duration, the hosts characteristics, the environmental conditions, etc.) resulted in the identification of different transmission dynamics compared to using only contact rates. We found that fix intervals greater than 30 min in the GPS tracking data resulted in missed interactions, and intervals greater than 2 h may be insufficient for epidemiological purposes. Our study shows that neglecting the conditions under which each contact occurs may result in a misidentification of the real role of each species in disease transmission. This study describes a clear and repeatable framework to study pathogen transmission from GPS data and provides further insights to understand how TB is maintained in multi-host systems in Mediterranean environments.

Transforming experimental radiology: Design and implementation of an innovative ePACS image storage system for AI imaging research environments

Introduction and purpose: We present the needs, design, development, implementation, and accessibility of a crafted experimental PACS (ePACS) system to securely store images, ensuring efficiency and ease of use for AI processing, specifically tailored for research scenarios, including phantoms, animal and human studies and quality assurance (QA) exams. The ePACS system plays a crucial role in any medical imaging departments that handle non-care profile studies, such as protocol adjustments and dummy runs. By effectively segregating non-care profile studies from the healthcare assistance, the ePACS usefully prevents errors both in clinical practice and storage security. Methods and results: The developed ePACS system considers the best practices for management, maintenance, access, long-term storage and backups, regulatory audits, and economic aspects. Moreover, key aspects of the ePACS system include the design of data flows with a focus on incorporating data security and privacy, access control and levels based on user profiles, internal data management policies, standardized architecture, infrastructure and application monitorization and traceability, and periodic backup policies. A new tool called DicomStudiesQA has been developed to standardize the analysis of DICOM studies. The tool automatically identifies, extracts, and renames series using a consistent nomenclature. It also detects corrupted images and merges separated dynamic series that were initially split, allowing for streamlined post-processing. Discussion and conclusions: The developed ePACS system encompasses a successful implementation, both in hospital and research environments, showcasing its transformative nature and the challenging yet crucial transfer of knowledge to industry. This underscores the practicality and real-world applicability of our innovative approach, highlighting the significant impact it has on the field of experimental radiology.

The Relationship between Urbanization and the Water Environment in the Chengdu-Chongqing Urban Agglomeration

Ensuring the harmonization between urbanization and water environment systems is imperative for fostering sustainable regional development in the future. With urban agglomerations and metropolitan areas increasingly dominating urbanization trends in China, it is crucial to explore the interdependent relationship between urbanization and the water environment. Such exploration holds significant implications for water resource management and the formulation of urbanization policies. This study utilizes a comprehensive index system encompassing urbanization and the water environment. It examines the coupled and coordinated spatial and temporal dynamics of these systems within the Chengdu-Chongqing Urban Agglomeration from 2011 to 2019. This analysis employs the Coupled Coordination Degree model alongside the spatial autocorrelation model. The results show that there is still much room for improving the urbanization development level and the water environment quality. During the study period, a nonlinear and nearly U-shaped evolutionary trajectory was observed between the two systems. The results suggest that there is a progression from basic to more advanced coordination between urbanization and water environment at the city cluster scale. Urbanization appears to generally lag behind the water environment in terms of coordination. At the municipal scale, there is a gradient in which some cities show better coordination compared to others. Spatially, the coupling and coordination of this region exhibited dual-core development characteristics centered around Chengdu and Chongqing. The region is in the transition stage towards a core-type networked and decentralized development mode, which has not yet formed an integrated pattern. This offers a theoretical and technical framework for harmonizing water environments and urbanization in similar regions globally.

Multi-Scenario Research on the Coupled and Coordinated Development of the Economic–Energy–Environmental (3E) System under the Reconstruction of the Power System—New Exploration Based on the “Dual Triangle” Theory

Under the background of building a new-type power system, as the core of the energy system, the restructuring of the power system will inevitably have a linked impact on the coordinated development of the economy–energy–environment system. A “Dual Triangle” theoretical framework consisting of the “Energy Impossibility Triangle” and “Sustainable Development Triangle” was constructed in this paper. By combining historical statistical analysis with scenario predictive analysis, the historical characteristics of the “Dual Triangle” and future trends under different scenarios were analyzed, and the coupled interaction relationship of the “Dual Triangle” was also studied. Conclusions were as follows: The energy system gradually inclined towards economy and cleanliness from 2010 to 2022, and the operation status of the “Dual Triangle” was stable. A positive coupling relationship within the “Dual Triangle” in vigorously promoting renewable energy scenarios is close, which can be regarded as the preferred path for the reconstruction of the power system. The security and low-carbon phased development scenario is a development model that dynamically mitigates the challenges of the “Energy Impossibility Triangle”. Security has become the core contradiction of the “Energy Impossibility Triangle”. This paper conducts an integrated and innovative application of the “Dual Triangle” theory and provides a case basis for how to achieve the coupled interaction of the “Dual Triangle”.

TRNet: Two-level Refinement Network leveraging speech enhancement for noise robust speech emotion recognition

One persistent challenge in Speech Emotion Recognition (SER) is the ubiquitous environmental noise, which frequently results in deteriorating SER performance in practice. In this paper, we introduce a Two-level Refinement Network, dubbed TRNet, to address this challenge. Specifically, a pre-trained speech enhancement module is employed for front-end noise reduction and noise level estimation. Later, we utilize clean speech spectrograms and their corresponding deep representations as reference signals to refine the spectrogram distortion and representation shift of enhanced speech during model training. Experimental results validate that the proposed TRNet substantially promotes the robustness of the proposed system in both matched and unmatched noisy environments, without compromising its performance in noise-free environments.

Bridging computer and education sciences: A systematic review of automated emotion recognition in online learning environments

Emotions play an important role in the learning process. With intelligent technology support, identification and intervention of learners’ cognition have made great achievement, but the care of emotion has been in the absence for a long time. In recent years, the use of affective computing technology to solve affective loss in online education has become a key research topic. To date, a growing number of studies have investigated automated emotion recognition (AER) in online environments. However, AER has been mainly studied from the perspective of computer science focusing on technical characteristics of developing AI technology while its pedagogical value and educational application has been overlooked. Therefore, this systematic literature review aimed to bring together educational and technical aspects of AER. Following PRISMA methodology, a comprehensive search of AER research from 2010 to 2024 in three databases (Web of Science, Science Direct and IEEE Xplore) identified 117 studies that met inclusion criteria. The articles were coded for report characteristics, educational characteristics (tech platform, pedagogy, assessment, content), technical characteristics (emotion model, emotion category, emotion measurement channel, database, algorithm model) and outcome characteristics (technical result, educational application). We found that the primary purpose of these studies was to develop and evaluate systems for AER, rather than implementing these systems in real online learning environments. Furthermore, our findings indicated a lack of integration between computer science and educational science in the realm of AER. Despite the fact that most algorithm models demonstrated high accuracy in AER, the interpretability of the results was significantly constrained by the quality of the databases used, along with the scarcity of studies focusing on the effective and real-time application of AER results. These findings provide essential guidance for shaping future research and development pathways in this field.

A bi-level model for coal power decarbonization via biomass co-firing considering CO[formula omitted] emission trading system

Faced with the challenges posed by the Paris Agreement targets, biomass co-firing offers an efficient measure to decarbonize coal-fired power generation. Although economic burden of biomass co-firing has severely limited the large-scale application, effective CO2 emission trading system has the capability to subsidize and incentivize. Existing studies related to biomass co-firing under CO2 emission trading system fail to consider multiple decision makers and uncertain decision-making environment. To address these limitations, this paper proposes a bi-level model for coal power decarbonization via biomass co-firing considering CO2 emission trading system in an uncertain decision-making environment. It considers the interactive relationship between multiple decision makers. The upper-level model focuses on the formulation of CO2 emission trading system, and the lower-level model decides biomass co-firing and emission reduction. Fuzzy set theory is employed to describe the uncertain parameters and convert them into exact values. To calculate the model, a bi-level interactive method based on satisfactory solution is developed. A case study is conducted to illustrate the effectiveness and practicality of the proposed model. Results show that the price of initial quotas fluctuates between 38.01 and 48.7 CNY/tonne, and the lowest carbon intensity is 0.741 kg/kWh. Management recommendations are provided to support CO2 emission reduction.

Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments

Many stochastic continuous-state dynamical systems can be modeled as probabilistic programs with nonlinear non-polynomial updates in non-nested loops. We present two methods, one approximate and one exact, to automatically compute, without sampling, moment-based invariants for such probabilistic programs as closed-form solutions parameterized by the loop iteration. The exact method applies to probabilistic programs with trigonometric and exponential updates and is embedded in the Polar tool. The approximate method for moment computation applies to any nonlinear random function as it exploits the theory of polynomial chaos expansion to approximate non-polynomial updates as the sum of orthogonal polynomials. This translates the dynamical system to a non-nested loop with polynomial updates, and thus renders it conformable with the Polar tool that computes the moments of any order of the state variables. We evaluate our methods on an extensive number of examples ranging from modeling monetary policy to several physical motion systems in uncertain environments. The experimental results demonstrate the advantages of our approach with respect to the current state-of-the-art.

Determination of irrigation requirement for tomato using FAO-CROPWAT model in the Arid Region

Food security presents a significant challenge in arid regions, exacerbated by water scarcity due to climate change. Tomato is one of the top agricultural crops around the world, in which it is needed to investigate its irrigation water requirements for each region, especially in the arid regions. This work mainly aims to estimate the irrigation water requirements and irrigation schedules for cultivating tomato in Ras Al Khaimah (RAK), United Arab Emirates (UAE). This would be done by utilizing the FAO-CROPWAT 8.0 Model and CLIMWAT 2.0. The findings of this research support efficient water use by utilizing the advanced modeling tools. The outcomes will inform and enhance irrigation water management practices for farmers, farm owners, and policymakers, contributing significantly to sustainable development goals and the resilience of agricultural systems in water-limited environments.

Clean energy technologies and energy systems for industry and power generation: Current state, recent progress and way forward

This vision article accompanies a Special Issue of Applied Thermal Engineering dedicated to the Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series held during 2022, including the 5th SEE SDEWES Conference Vlore, 3rd LA SDEWES Conference Sao Paulo, and 17th SDEWES Conference Paphos. The article discusses a selection of papers presented at these conferences and selected for publication in Applied Thermal Engineering, all connected by a focus on clean energy technologies and systems for the advancement of sustainable thermal energy systems. This research area covers a wide range of technologies but is primarily focused on the power generation sector, energy storage and utilization, efficiency improvements, sustainable technical solutions, and the facilitation of the robust integration of renewable energy resources into wider energy systems. Additional work on previously established research topics is also present, but new directions have crystallized, especially research into thermal management for power devices and microelectronics. Based on the recent contributions to the research field presented in the included papers, this vision article discusses the development and application of cutting-edge technological solutions and identifies the challenges that require focused attention. Current shortcomings of developing and established technologies will be developed by the underlying fundamental research, reduction of prohibitively high costs, expansion of operating conditions, and the inclusion of advanced control approaches, sophisticated combinations of renewable energy sources, and modeling tools for system investigations. Scientists, researchers, and engineers can benefit from the summary of the topics at the forefront of the research field and find guidelines and ways forward for future research activities.

A high sensitivity digital giant magneto-impedance (GMI) sensor for magnetic communication

Abstract A high sensitivity Magneto-Impedance (GMI) sensor for magnetic communication systems in harsh environments is presented. In this system, the receiving coil of a conventional magnetic communication system is replaced by a high sensitivity GMI sensor and its digital electronic conditioning as well as digital signal processing. The whole architecture of the sensor is described in detail. It achieves two digital amplitude demodulations to recover, in real time, the binary sequence of the message sent by the transmitter. The demonstration and the validation of the reliable functioning of the system are illustrated through the transmission of messages using On-Off-Keying modulation method. The measured equivalent magnetic noise of the sensor was about noise 2 p T / Hz at 60 kHz, which is largely lower than the noise of other potential types of magnetic sensors such as Anisotropic Magneto-Resistive sensors for this application. This noise level suggests a potential higher communication range. When compared with an inductive coil, the developed sensor also presented an almost flat and sufficiently wide bandwidth compatible with magnetic communication. This bandwidth could potentially be extended according to the application.

Is the Economy, Environment and Energy (3E) System Sustainable?—An Analysis of the Coordination Degree of Carbon Decoupling and Energy Equity in the Yangtze River Economic Belt

As the climate crisis intensifies, the low-carbon transition seems to be the necessary path to maintain the sustainability of the 3E system. However, does it no longer pose potential threats to sustainability? In the context of the low-carbon transition, this study reveals hidden worries about the sustainability of the 3E system from an energy equity perspective, specifically focusing on the coordination between carbon decoupling and energy equity. This study innovatively calculates the level of carbon decoupling and energy equity in China’s Yangtze River Economic Belt from 2008 to 2019 and explores the degree of coordinated development between carbon decoupling and energy equity by employing the coupling coordination model and bivariate local Moran index. The results show the following: (1) from 2008 to 2019, the energy equity in the Yangtze River Economic Belt showed steady improvement, but the overall level was still not high, being below 0.5; (2) the upstream and downstream regions showed more obvious resistance in maintaining the coordinated development of carbon decoupling and energy equity, but there are structural differences among them; (3) inter-basin differences were an important feature of the low-carbon economy and equitable energy development in the Yangtze River Economic Belt in the past. This study provides policy implications and research insights for promoting the sustainability of the 3E system in transition countries from an energy equity perspective.

Thermodynamic performance assessment of a solar-driven desiccant evaporative air cooling system (SDEAC) for outdoor environments- a dynamic study

In this work, a novel dynamic model of a solar-driven desiccant evaporative air cooling system for outdoor environments is proposed, namely the SDEAC system. The effects of operating and designing parameters on the performance of the SDEAC system are analyzed. The results indicate that the sensible cooling capacity, the latent cooling capacity, and the COP decrease monotonically as the rotational speed of the desiccant wheel increases. When the ratio of dehumidification area to total area (r1) is 0.5, the performance of the SDEAC system is optimal under the condition of sufficient solar radiation. The ratio of the dehumidified air flowrate to the regenerated air flowrate has a small effect on the cooling capacity but has a large effect on the COP. The SDEAC system has a higher sensible cooling capacity and total cooling capacity compared to the EAC system. Moreover, although the COP of the SDEAC is lower than that of the EAC system, it is still maintained at a relatively high level. The SDEAC system with a supply air flowrate of 670 m3/h to cool the outdoor air saves about 1283∼1617 kWh of electricity and 788 kg–1139 kg of carbon emission reduction compared to a conventional vapor compression air conditioning system in different cities during the period of June to August.

Integrated Neural Network Approach for Enhanced Vital Signal Analysis Using CW Radar

This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, require direct contact and impose constraints on specific scenarios. Conversely, our study primarily focused on non-contact measurement techniques, particularly those using CW radar, which is known for its simplicity but faces challenges such as noise interference and complex signal processing. To address these issues, we propose a temporal convolutional network (TCN)-based framework that seamlessly integrates noise removal, demodulation, and fast Fourier transform (FFT) processes into a single neural network. This integration minimizes cumulative errors and processing time, which are common drawbacks of conventional methods. The TCN was trained using a dataset comprising preprocessed in-phase and quadrature (I/Q) signals from the CW radar and corresponding heart rates measured via ECG. The performance of the proposed method was evaluated based on the L1 loss and accuracy against the moving average of the estimated heart rates. The results indicate that the proposed approach has the potential for efficient and accurate non-contact vital signal analysis, opening new avenues in health monitoring and medical research. Additionally, the integration of CW radar and neural networks in our framework offers a robust and scalable solution, enhancing the practicality of non-contact health monitoring systems in diverse environments. This technology can be leveraged in healthcare robots to provide continuous and unobtrusive monitoring of patients’ vital signs, enabling timely interventions and improving overall patient care.

Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Accurate fault diagnosis in transmission lines is crucial to ensure the reliability and stability of power grids. Conventional approaches often rely on expert knowledge or complex feature extraction methods, which are subjective and time-consuming. Additionally, many existing approaches use separate sub-algorithms for fault classification and localization. These are operating independently and sequentially. This research work proposes an innovative method for fault classification and localization in transmission lines using phasor measurement unit (PMU) data. The proposed method employs a Weighted Extreme Learning Machine (WELM) algorithm, which uses the variable data distribution across different fault classes through a weighted approach. The PMU data is generated by simulation using an IEEE 9-bus test system in the MATLAB simulation environment. The Maximal Overlap Discrete Wavelet Transform-based feature extraction technique is applied to derive input feature data to facilitate fault classification and localization. The WELM classifier is also optimized using the Grey Wolf Optimization (GWO) algorithm. The resulting GWO-optimized WELM (GWO-WELM) model, when trained on PMU data, achieves a remarkable fault classification accuracy of 99.83 % and fault localization accuracy of 95.48 %, respectively. These results demonstrate that the GWO-WELM model outperforms commonly used classifiers. Moreover, the proposed model shows robustness by accurately classifying the noisy data with a signal-to-noise ratio (SNR) of 10 dB (achieving 91.5 % accuracy in classification and 88.1 % accuracy in localization, respectively).

Reliability analysis and preventive maintenance policy for consecutive k$k$‐out‐of‐n:F$n: F$ balanced system under failure criterion operating in shock environment

AbstractThis paper presents a consecutive ‐out‐of‐: balance system in a shock environment, where the state transition of component is induced by external shocks. If a predetermined threshold number of effective shocks are applied to component in a critical state, the component will fail. The state of the system is defined by the number of consecutive failing component groups in the system, which leads to system failure when a critical number of consecutive failing components is reached. To minimize maintenance costs, we propose a preventive maintenance method with an optimization model. We use finite Markov chain imbedding and Phase‐type distribution to calculate component group failure rates and associated probability functions in discrete and continuous time, respectively. The validity and accuracy of the model are confirmed by numerical examples and Monte Carlo simulations.

FLBC-IDS: a federated learning and blockchain-based intrusion detection system for secure IoT environments

FLBC-IDS: a federated learning and blockchain-based intrusion detection system for secure IoT environments

The impact of risk factors on the sustainability of food distribution chain functioning in Kazakhstan

In conditions of risk and uncertainty, food security is ensured taking into account the requirements of isolation, safety and health, which requires changes to the existing supply chain (SC) system in the food environment, as well as the creation of more sustainable food supply chains. The aim of the study is to assess the conditions and factors of the functioning of the food commodity distribution chain (FCDC) of the agri-food market in conditions of risk, in particular, the COVID-19 pandemic. To formulate a conceptual framework reflecting the structure of the SC elements, the method of systematic review and generalization of literature was used; the method of expert survey of participants in the SC of food products (FP). During the analysis, seven significant factor constraints (technological, financial, economic, institutional, social, epidemiological, geographical) were revealed, the environment and operating conditions of the FCDC associated with the impact of the pandemic were identified. The risk factors of the environment and operating conditions of the FCDC and their possible effects on the SC are evaluated. Practical recommendations are given to reduce risks and improve the efficiency of the agro-food commodity distribution SC. The study attempts to study the influence of various factors caused by the restriction of the functioning of the food environment and affecting the stability of the SC of perishable products. The results could also be used to inform SC participants about the measures needed to create more sustainable SC foods in the future.

The Influence of the Work Environment and Work System on the Work Motivation of Business Actors at Cipadu Market, Tangerang City, Banten Province

This research aims to determine the influence of the work environment and work system on the work motivation of entrepreneurs at Cipadu Market. This type of research is descriptive research with a quantitative approach using multiple regression analysis methods, which was carried out at Cipadu Market. The partial research results show that the work environment and work system have a significant influence on the work motivation of entrepreneurs at Cipadu Market. The work environment has a significant influence on the work motivation of entrepreneurs because it is related to factors such as emotional support, market facilities, the role of market leaders, the level of competition, as well as economic conditions and textile market trends which play a role in influencing their motivation. The work system has a significant influence on the work motivation of business actors because it involves important aspects such as funding sources, product promotion, management staff and business financial management. Suggestions for business actors in the Cipadu market: create a positive work culture, promote team collaborati