bluuebunny/crossref_metadata_2025 huggingface dataset:
An Ionospheric Anomaly Monitor Based on the One Class Support Vector Algorithm for the Ground-Based Augmentation System
Authors: Gao Zhen , Fang Kun , Zhu Yanbo , Wang Zhipeng , Guo Kai
Published: October 2021

An ionospheric anomaly is the irregular change of the ionosphere. It may result in potential threats for the ground-based augmentation system (GBAS) supporting the high-level precision approach. To counter the hazardous anomalies caused by the steep gradient in ionospheric delays, customized monitors are equipped in GBAS architectures. A major challenge is to rapidly detect the ionospheric gradient anomaly from environmental noise to meet the safety-critical requirements. A one-class support vector machine (OCSVM)-based monitor is developed to clearly detect ionospheric anomalies and to improve the robust detection speed. An offline-online framework based on the OCSVM is proposed to extract useful information related to anomalous characteristics in the presence of noise. To validate the effectiveness of the proposed framework, the influence of noise is fully considered and analyzed based on synthetic, semi-simulated, and real data from a typical ionospheric anomaly event. Synthetic results show that the OCSVM-based monitor can identify the anomaly that cannot be detected by other commonly-used monitors, such as the CCD-1OF, CCD-2OF and KLD-1OF. Semi-simulation results show that compared with other monitors, the newly proposed monitor can improve the average detection speed by more than 40% and decrease the minimum detectable gradient change rate to 0.002 m/s. Furthermore, in the real ionospheric anomaly event experiment, compared with other monitors, the OCSVM-based monitor can improve the detection speed by 16%. The result indicates that the proposed monitor has encouraging potential to ensure integrity of the GBAS.
Potentials and Limitations of WorldView-3 Data for the Detection of Invasive Lupinus polyphyllus Lindl. in Semi-Natural Grasslands
Authors: Schulze-Brüninghoff Damian , Wachendorf Michael , Astor Thomas
Published: October 2021

Semi-natural grasslands contribute highly to biodiversity and other ecosystem services, but they are at risk by the spread of invasive plant species, which alter their habitat structure. Large area grassland monitoring can be a powerful tool to manage invaded ecosystems. Therefore, WorldView-3 multispectral sensor data was utilized to train multiple machine learning algorithms in an automatic machine learning workflow called ‘H2O AutoML’ to detect L. polyphyllus in a nature protection grassland ecosystem. Different degree of L. polyphyllus cover was collected on 3 × 3 m2 reference plots, and multispectral bands, indices, and texture features were used in a feature selection process to identify the most promising classification model and machine learning algorithm based on mean per class error, log loss, and AUC metrics. The best performance was achieved with a binary classification of lupin-free vs. fully invaded 3 × 3 m2 plot classification with a set of 7 features out of 763. The findings reveal that L. polyphyllus detection from WorldView-3 sensor data is limited to large dominant spots and not recommendable for lower plant coverage, especially single plant detection. Further research is needed to clarify if different phenological stages of L. polyphyllus as well as time series increase classification performance.
Near-Surface Air Temperature Retrieval Using a Deep Neural Network from Satellite Observations over South Korea
Authors: Choi Sungwon , Jin Donghyun , Seong Noh-Hun , Jung Daeseong , Sim Suyoung , Woo Jongho , Jeon Uujin , Byeon Yugyeong , Han Kyung-soo
Published: October 2021

Air temperature (Ta), defined as the temperature 2 m above the land’s surface, is one of the most important factors for environment and climate studies. Ta can be measured by obtaining the land surface temperature (LST) which can be retrieved with the 11- and 12-µm bands from satellite imagery over a large area, and LST is highly correlated with Ta. To measure the Ta in a broad area, we studied a Ta retrieval method through Deep Neural Network (DNN) using in-situ data and satellite data of South Korea from 2014 to 2017. To retrieve accurate Ta, we selected proper input variables and conditions of a DNN model. As a result, Normalized Difference Vegetation Index, Normalized Difference Water Index, and 11- and 12-µm band data were applied to the DNN model as input variables. And we also selected proper condition of the DNN model with test various conditions of the model. In validation result in the DNN model, the best accuracy of the retrieved Ta showed an correlation coefficient value of 0.98 and a root mean square error (RMSE) of 2.19 K. And then we additional 3 analysis to validate accuracy which are spatial representativeness, seasonal analysis and time series analysis. We tested the spatial representativeness of the retrieved Ta. Results for window sizes less than 132 × 132 showed high accuracy, with a correlation coefficient of over 0.97 and a RMSE of 1.96 K and a bias of −0.00856 K. And in seasonal analysis, the spring season showed the lowest accuracy, 2.82 K RMSE value, other seasons showed high accuracy under 2K RMSE value. We also analyzed a time series of six the Automated Synoptic Observing System (ASOS) points (i.e., locations) using data obtained from 2018 to 2019; all of the individual correlation coefficient values were over 0.97 and the RMSE values were under 2.41 K. With these analysis, we confirm accuracy of the DNN model was higher than previous studies. And we thought the retrieved Ta can be used in other studies or climate model to conduct urban problems like urban heat islands and to analyze effects of arctic oscillation.
Impacts of the Kuroshio and Tidal Currents on the Hydrological Characteristics of Yilan Bay, Northeastern Taiwan
Authors: Hsu Po-Chun , Lee Hung-Jen , Lu Ching-Yuan
Published: October 2021

Yilan Bay is in the northeast corner of Taiwan at the junction of the East China Sea (ECS) and the Pacific Ocean. This study clarified the composition of water masses adjacent to Yilan Bay. The upper seawater in the bay is characterized by Kuroshio surface water, Taiwan warm current water, and shelf mixed water masses. The flow field in this area is mainly determined by the inter-actions among the northeastern Taiwan countercurrent, Kuroshio Current (KC), and tidal currents. The fall season is the main rainfall period in Yilan Bay, which causes a large amount of river runoff and a further increase in chlorophyll concentration, and the salinity of the upper water layer is observed much lower than other seasons. Water with a high chlorophyll concentration can flow into the ECS with ebb currents and the KC with ebb and flood currents. Combining hourly geosynchronous ocean color imager data and numerical simulation flow field helps us understand short-term changes of chlorophyll concentration. The trajectories of the drifters and virtual particle simulations help us understand the sources and movement of ocean currents in Yilan Bay. The seasonal swing of the KC path outside the bay is an important factor affecting the flow field and hydrological characteristics.
Geographic Graph Network for Robust Inversion of Particulate Matters
Authors: Li Lianfa
Published: October 2021

Although remote sensors have been increasingly providing dense data and deriving reanalysis data for inversion of particulate matters, the use of these data is considerably limited by the ground monitoring samples and conventional machine learning models. As regional criteria air pollutants, particulate matters present a strong spatial correlation of long range. Conventional machine learning cannot or can only model such spatial pattern in a limited way. Here, we propose a method of a geographic graph hybrid network to encode a spatial neighborhood feature to make robust estimation of coarse and fine particulate matters (PM10 and PM2.5). Based on Tobler’s First Law of Geography and graph convolutions, we constructed the architecture of a geographic graph hybrid network, in which full residual deep layers were connected with graph convolutions to reduce over-smoothing, subject to the PM10–PM2.5 relationship constraint. In the site-based independent test in mainland China (2015–2018), our method achieved much better generalization than typical state-of-the-art methods (improvement in R2: 8–78%, decrease in RMSE: 14–48%). This study shows that the proposed method can encode the neighborhood information and can make an important contribution to improvement in generalization and extrapolation of geo-features with strong spatial correlation, such as PM2.5 and PM10.
Spectral-Spatial Offset Graph Convolutional Networks for Hyperspectral Image Classification
Authors: Zhang Minghua , Luo Hongling , Song Wei , Mei Haibin , Su Cheng
Published: October 2021

In hyperspectral image (HSI) classification, convolutional neural networks (CNN) have been attracting increasing attention because of their ability to represent spectral-spatial features. Nevertheless, the conventional CNN models perform convolution operation on regular-grid image regions with a fixed kernel size and as a result, they neglect the inherent relation between HSI data. In recent years, graph convolutional networks (GCN) used for data representation in a non-Euclidean space, have been successfully applied to HSI classification. However, conventional GCN methods suffer from a huge computational cost since they construct the adjacency matrix between all HSI pixels, and they ignore the local spatial context information of hyperspectral images. To alleviate these shortcomings, we propose a novel method termed spectral-spatial offset graph convolutional networks (SSOGCN). Different from the usually used GCN models that compute the adjacency matrix between all pixels, we construct an adjacency matrix only using pixels within a patch, which contains rich local spatial context information, while reducing the computation cost and memory consumption of the adjacency matrix. Moreover, to emphasize important local spatial information, an offset graph convolution module is proposed to extract more robust features and improve the classification performance. Comprehensive experiments are carried out on three representative benchmark data sets, and the experimental results effectively certify that the proposed SSOGCN method has more advantages than the recent state-of-the-art (SOTA) methods.
Bone Mineral Density Screening System Using CMOS-Sensor X-ray Detector
Authors: Maneerat Areerat , Visitsattapongse Sarinporn , Pintavirooj Chuchart
Published: October 2021

This research concerns a design and construction of a bone mineral density (BMD) and bone mineral content (BMC) measurement system based on dual energy X-ray absorptiometry (DEXA). An indirect X-ray detector is designed by optical coupling CMOS sensor with image on the intensifying screen. A dedicated microcontroller X-ray apparatus is used as an X-ray source to capture two energy level X-ray of middle phalanges bone of middle finger. The captured image is processed based on modified Beer-Lambert law to compute bone mineral density. Bone mineral content is also computed by determining the area of the phalanges bone using active contour. The designed bone mineral density (BMD) and bone mineral content (BMC) measurement system is low-cost and hence can be distributed at district hospital for screening purposes of Osteoporosis of the elderly. Compared with BMD measured from commercial model, BMD measurement of our system acquires linear relation with R2 equals 0.969. The mean square error between the normalized BMD value and that of the commercial model is 0.0000981.
An Energy-Friendly Scheduler for Edge Computing Systems
Authors: Llorens-Carrodeguas Alejandro , G. Sagkriotis Stefanos , Cervelló-Pastor Cristina , P. Pezaros Dimitrios
Published: October 2021

The deployment of modern applications, like massive Internet of Things (IoT), poses a combination of challenges that service providers need to overcome: high availability of the offered services, low latency, and low energy consumption. To overcome these challenges, service providers have been placing computing infrastructure close to the end users, at the edge of the network. In this vein, single board computer (SBC) clusters have gained attention due to their low cost, low energy consumption, and easy programmability. A subset of IoT applications requires the deployment of battery-powered SBCs, or clusters thereof. More recently, the deployment of services on SBC clusters has been automated through the use of containers. The management of these containers is performed by orchestration platforms, like Kubernetes. However, orchestration platforms do not consider remaining energy levels for their placement decisions and therefore are not optimized for energy-constrained environments. In this study, we propose a scheduler that is optimised for energy-constrained SBC clusters and operates within Kubernetes. Through comparison with the available schedulers we achieved 23% fewer event rejections, 83% less deadline violations, and approximately a 59% reduction of the consumed energy throughout the cluster.
Convolutional Autoencoding and Gaussian Mixture Clustering for Unsupervised Beat-to-Beat Heart Rate Estimation of Electrocardiograms from Wearable Sensors
Authors: Zhong Jun , Hai Dong , Cheng Jiaxin , Jiao Changzhe , Gou Shuiping , Liu Yongfeng , Zhou Hong , Zhu Wenliang
Published: October 2021

Heart rate is one of the most important diagnostic bases for cardiovascular disease. This paper introduces a deep autoencoding strategy into feature extraction of electrocardiogram (ECG) signals, and proposes a beat-to-beat heart rate estimation method based on convolution autoencoding and Gaussian mixture clustering. The high-level heartbeat features were first extracted in an unsupervised manner by training the convolutional autoencoder network, and then the adaptive Gaussian mixture clustering was applied to detect the heartbeat locations from the extracted features, and calculated the beat-to-beat heart rate. Compared with the existing heartbeat classification/detection methods, the proposed unsupervised feature learning and heartbeat clustering method does not rely on accurate labeling of each heartbeat location, which could save a lot of time and effort in human annotations. Experimental results demonstrate that the proposed method maintains better accuracy and generalization ability compared with the existing ECG heart rate estimation methods and could be a robust long-time heart rate monitoring solution for wearable ECG devices.
Support Vector Machine Classifiers Show High Generalizability in Automatic Fall Detection in Older Adults
Authors: Alizadeh Jalal , Bogdan Martin , Classen Joseph , Fricke Christopher
Published: October 2021

Falls are a major cause of morbidity and mortality in neurological disorders. Technical means of detecting falls are of high interest as they enable rapid notification of caregivers and emergency services. Such approaches must reliably differentiate between normal daily activities and fall events. A promising technique might be based on the classification of movements based on accelerometer signals by machine-learning algorithms, but the generalizability of classifiers trained on laboratory data to real-world datasets is a common issue. Here, three machine-learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were trained to detect fall events. We used a dataset containing intentional falls (SisFall) to train the classifier and validated the approach on a different dataset which included real-world accidental fall events of elderly people (FARSEEING). The results suggested that the linear SVM was the most suitable classifier in this cross-dataset validation approach and reliably distinguished a fall event from normal everyday activity at an accuracy of 93% and similarly high sensitivity and specificity. Thus, classifiers based on linear SVM might be useful for automatic fall detection in real-world applications.
FaTEMa: A Framework for Multi-Layer Fault Tolerance in IoT Systems
Authors: Melo Mário , Aquino Gibeon
Published: October 2021

Fault tolerance in IoT systems is challenging to overcome due to its complexity, dynamicity, and heterogeneity. IoT systems are typically designed and constructed in layers. Every layer has its requirements and fault tolerance strategies. However, errors in one layer can propagate and cause effects on others. Thus, it is impractical to consider a centralized fault tolerance approach for an entire system. Consequently, it is vital to consider multiple layers in order to enable collaboration and information exchange when addressing fault tolerance. The purpose of this study is to propose a multi-layer fault tolerance approach, granting interconnection among IoT system layers, allowing information exchange and collaboration in order to attain the property of dependability. Therefore, we define an event-driven framework called FaTEMa (Fault Tolerance Event Manager) that creates a dedicated fault-related communication channel in order to propagate events across the levels of the system. The implemented framework assist with error detection and continued service. Additionally, it offers extension points to support heterogeneous communication protocols and evolve new capabilities. Our empirical results show that introducing FaTEMa provided improvements to the error detection and error resolution time, consequently improving system availability. In addition, the use of Fatema provided a reliability improvement and a reduction in the number of failures produced.
Perspectives on Topical Medical Research in the COVID-19 Era
Authors: McAleer Michael
Published: October 2021

The SARS-CoV-2 virus that causes COVID-19 has wreaked havoc on the global community in terms of every imaginable parameter. The research output on COVID-19 has been nothing short of phenomenal, especially in the medical and biomedical sciences, where the search for a potential vaccine has been conducted in earnest. Much of the advanced research has been distributed in leading medical journals, including the Journal of the American Medical Association (JAMA), where the latest research is distributed on a daily basis. The purpose of this paper is to provide some perspectives on 44 interesting and highly topical research papers that have been published in JAMA, at the time of writing, within the past two weeks. The diverse topics include public health, general medicine, internal medicine, oncology, pediatrics, geriatrics, and biostatistics.
A Classification of Different Approaches to Green Finance and Green Monetary Policy
Authors: Dziwok Ewa , Jäger Johannes
Published: October 2021

In recent years, green finance has emerged as a commonly used strategy for dealing with environmental problems. However, it still remains to be seen whether green finance deals effectively with current global environmental problems. More recently, proposals regarding greening monetary policy have emerged. The goal of this paper is to provide a conceptual framework that helps to distinguish between different forms of green finance and monetary policy. We systematically analyse forms, tools and measures of green finance and monetary policy against different theoretical backgrounds. In so doing, we fill a research gap by providing an appropriate classification that is intended to facilitate future academic research. We provide different categories and distinguish, on an abstract level, between neoliberal, reformist and progressive forms of green finance. Furthermore, we provide sub-categories on a more concrete level of abstraction. With this, we focus on both financial market regulation and monetary policy strategies. Against the background of our categorisation, the different focuses on green finance and green monetary policy and the (often implicitly) underlying theoretical assumptions become transparent. The classification has significant implications for the evaluation of different perspectives and is, therefore, important for academic debate. The classification also potentially represents a basis for policy related discussions. We conclude that neoliberal forms of green finance and monetary policy that rely on the assumption of the effectiveness of markets for contributing to sustainability tend to neglect or abstract from potentially adverse distributional effects. Reformist forms of green finance and monetary policy are more skeptical of the effectiveness of market processes and, therefore, consider a greater role for government policies. In addition, reformist approaches are more concerned about the potentially adverse distributional effects of environmental policies. Finally, progressive green finance and monetary policy adopts a more global perspective on environmental issues and links the discussion intrinsically with questions of global inequality and socio-ecological transformation. Moreover, progressive approaches are skeptical of global capitalism at a systemic level and therefore demand global rules and financial and monetary regimes that allow for solutions of environmental problems based on global solidarity and a democratic economic governance.
Seasonal and Temporal Influence on Polycyclic Aromatic Hydrocarbons in the Red Sea Coastal Water, Egypt
Authors: El-Naggar Mohamed , Hanafy Said , Younis Alaa M. , Ghandour Mahmoud A. , El-Sayed Abdel-Aziz Y.
Published: October 2021

This study investigated seasonal variation, spatial distribution, sources, composition and potential ecological risks of 16 polycyclic aromatic hydrocarbons (PAHs) in Red Sea coastal water. Surface seawater samples were collected at fourteen different locations. The sum of 16 PAHs concentrations in Red Sea showed clear variation between seasons, ranged from 1.08–6.10, 0.79–50.86, 1.37–54.47 and 0.21–7.18 μg/L in summer, autumn, winter, and spring respectively. PAHs levels in Red Sea coastal waters were relatively high at certain sites during autumn and winter seasons. They could be classified as highly polluted by PAHs contaminants (>10 μg/L). According to the diagnostic ratios measured, the majority of PAHs in this study originated from pyrogenic sources, while minor amounts originated from petrogenic sources. Four rings PAHs accounted for more than 64% of the total PAHs studied. PAHs pose a high ecological risk along the period of study, according to the ecological risk assessment by Risk quotients (RQNCs and RQMPCs).
Importance and Performance of Value-Based Maintenance Practices in Hospital Buildings
Authors: Wong Wai Fang , Olanrewaju AbdulLateef , Lim Poh Im
Published: October 2021

After two decades of privatization of building maintenance service in government hospitals in Malaysia, evidence of under-maintained hospital buildings suggests a need to raise the level of hospital maintenance service delivery. This study identified the critical success factors to enhance the value outcomes of hospital maintenance service. A total of 66 questionnaire survey responses from maintenance personnel in public hospitals were analyzed using the Importance-Performance Matrix Analysis (IPMA) in the SmartPLS3.0 software. The Importance versus the Performance of value-based practices was mapped to identify the critical areas that require greater considerations to improve maintenance service delivery. The findings revealed four critical success factors: Responsive to Needs, Integrated Service Solutions, Innovative Improved Practices, and Value for Money. These practices were found to be the impetus that can bring significant enhancement to hospital building maintenance service delivery. Although the findings are based on data derived from public hospitals in Malaysia, the outcomes are applicable to private hospitals both in and outside of Malaysia.
Estimating the Artificial Intelligence Learning Efficiency for Civil Engineer Education: A Case Study in Taiwan
Authors: Chiang Tzuping
Published: October 2021

The civil engineering educators focused on implementing interdisciplinary learning in artificial intelligence (AI) based on a more innovative application of construction requirements. However, only a few pieces of literature discussed the educational learning efficiency and feedback for this trend. Hence, this study surveyed the 237 data from eight universities that issued the interdisciplinary courses. The factors were modified from the scales in science, technology, engineering, and mathematics education. Further, the descriptive analysis was used to explain this situation in Taiwan. A novel approach based on data envelopment analysis and Mahalanobis distance approaches was proposed to solve this problem. The advantages of the proposed approach were discussed and compared with traditional method. Based on the student gains in the interdisciplinary courses, three groups were clustered and compared. The feedback of a high-input and low-efficiency student group was suggested for improving learning strategies. The sensitivity analysis of this special group showed that effective teaching practice is the key factor in the artificial intelligence courses for civil engineering students. These students may increase technical efficiency by 37% by paying 21% inputs. Therefore, this paper provided a useful and easy approach to make learning strategies for non-informatics students in AI learning.
The Impact of Foreign Direct Investment on China’s Carbon Emissions
Authors: Hou Fangmiao , Su Haiying , Li Yuelu , Qian Weicong , Xiao Jianxiu , Guo Shuhui
Published: October 2021

China is world renowned for its significant achievements in several fields. China’s economic growth has been prominent, with the country’s GDP ranking second globally. China’s Foreign Direct Investment (FDI) inflows have been significant, with the country now known as the second largest economy in the world. FDI however does have some negative consequences on China’s environment. While attracting FDI promotes economic growth through industrial upgrading, the deleterious impacts of the latter on the environment cannot be ignored. The current study analyses the actual use of FDI and carbon emissions in China from 1997 to 2018. Quantitative analysis was employed to analyze the trends of FDI and carbon emissions in China as a whole and in the respective regions, namely the eastern, central and western regions. Regression analysis was then conducted to analyze the impact of FDI on carbon emissions in China on the national level and regional levels, i.e., in the eastern, central and western regions. The conclusion of this article is that FDI will play a positive role in China’s overall carbon emissions. The study has important implications for policy. We recommend that the corresponding investment policies need to be formulated according to the different levels of economic development among the regions.
Female Labour Force Participation: What Prevents Sustainable Development Goals from Being Realised in Iran?
Authors: Taheri Elham , Güven Lisaniler Fatma , Payaslioğlu Cem
Published: October 2021

Elimination of the gender gap in labour force participation has become one of the main challenges of achieving the U.N.’s Sustainable Development Goals (SDGs) in the 2030 Agenda for Sustainable Development. The rapid increase in university education and decrease in fertility rate in Iran over the past decades might affect the achievement of the SDGs. However, the country’s female labour force participation (FLFP) rate stagnates at a superficial level. The stagnating FLFP rate is inconsistent with basic labour economics supply-side explanations while drawing attention to demand-side explanations, namely, female labour demand constraints, such as occupational segregation and labour market discrimination. This study empirically investigates the effect on FLFP of primary labour supply factors along with household conditions and gender norms and attitudes for 2 specific years: 2008 and 2018. The aim is to shed light on the causes of stagnation at the FLFP level by using the Household Expenditure and Income Survey conducted annually by the Statistical Center of Iran. The empirical findings suggest gender norms and attitudes towards working women and gendered division of work associated with family care as the driving forces of stagnation in FLFP in Iran.
Smallholder Farmers’ Perceived Climate-Related Risk, Impact, and Their Choices of Sustainable Adaptation Strategies
Authors: Mamun Abdullah Al , Roy Susmita , Islam Abu Reza Md. Towfiqul , Alam G. M. Monirul , Alam Edris , Chandra Pal Subodh , Sattar Md. Abdus , Mallick Javed
Published: October 2021

Bangladesh is one of the most climate-vulnerable countries on the globe. The country is frequently affected by numerous climatic events including floods, droughts, cyclones, etc., which damages the farm household’s livelihood and socio-economic condition. Therefore, this work intends to appraise the smallholder farmers’ perceived climate-related risk, impact, and the factors that influence their choices of adaptation strategies to cope with the adverse impact of the climatic extreme events in northern Bangladesh. Survey data were collected from 300 respondents from two drought-prone districts of northern Bangladesh in January–February 2020. The climate-related risk perception index (CRRPI) was constructed to assess the farmers’ perceived risk. The multinominal logit (MNL) model was employed to explore the factors influencing farmers’ choices of adaptation strategies. Farmers’ perceptions of climate change risk were found to be consistent with meteorological data in the research area. Results of the CRRPI revealed that flood, drought, riverbank erosion, and heat waves were the critical risks perceived by the farmers. Farmers used a variety of adaptation measures to deal with these climatic threats, including agricultural diversification, changes in fertilizer and insecticides, and crop and seed changes. The MNL model results showed that the factors that influenced a farmer’s choices of adaptation strategies were age, education level, family members, income level, year of farming experience, and the farmer’s information on climate change. This study also demonstrated that water scarcity in the dry period and the frequency of crop diseases were the major limiting factors experienced by the farmers whilst undertaking adaptation strategies. Thus, awareness and capacity building through training and support to adopt the adaptation strategies are essential to enhance the resilience of the farmers.
Can Public Procurement Requirements for Railway Transport Promote Economic and Social Sustainability in South Africa?
Authors: Fourie David , Malan Cornel
Published: October 2021

When considering sustainability from a socioeconomic and environmental point, the South African public transport industry was not an integral part of such planning until the last decade. Various well-intended and well-defined public procurement and environmental regulatory policies and instruments were put in place over the years, however, they appear to be failing in achieving the goals of local job creation and alleviation of unemployment. This exploratory literature review study of various public documents, regulatory instruments, and review reports aims to evaluate current knowledge pertaining to the current level of sustainable transport from a life-cycle approach, in support of overall sustainable goals in South Africa. The research set out to assess the status of public procurement in the revitalising of public rail freight transport towards identifying areas of excellence that could be used to address under-performance in other areas. The documentary analysis found various areas where the public procurement policies within South Africa, which aims to promote local participation, are facing difficulties due to lack of access to modern technology as well as inadequate skills levels to be able to partake in the ventures. In exploring South African challenges and current solutions, the article recommends a holistic and integrated approach to provide for a sustained manufacturing and service supply stream within the local railway transport environment.
Dynamic Electric Dispatch for Wind Power Plants: A New Automatic Controller System Using Evolutionary Algorithms
Authors: Marcelino Carolina G. , Avancini João V. C. , Delgado Carla A. D. M. , Wanner Elizabeth F. , Jiménez-Fernández Silvia , Salcedo-Sanz Sancho
Published: October 2021

In this paper, we use an evolutionary swarm intelligence approach to build an automatic electric dispatch controller for an offshore wind power plant (WPP). The optimal power flow (OPF) problem for this WPP is solved by the Canonical Differential Evolutionary Particle Swarm Optimization algorithm (C-DEEPSO). In this paper, C-DEEPSO works as a control system for reactive sources in energy production. The control operation takes place in a daily energy dispatch, scheduled into 15 min intervals and resulting in 96 operating test scenarios. As the nature of the optimization problem is dynamic, a fine-tuning of the initialization parameters of the optimization algorithm is performed at each dispatch interval. Therefore, a version of the C-DEEPSO algorithm has been built to automatically learn the best set of initialization parameters for each scenario. For this, we have coupled C-DEEPSO with the irace tool (an extension of the iterated F-race (I/F-Race)) by using inferential statistic techniques. The experiments carried out showed that the methodology employed here is robust and able to tackle this OPF-like modeling. Moreover, the methodology works as an automatic control system for a dynamic schedule operation.
A Study on the Sustainable Relationship among the Green Finance, Environment Regulation and Green-Total-Factor Productivity in China
Authors: Liu Yanhong , Lei Jia , Zhang Yihua
Published: October 2021

Exploring the mechanism and constraints of Green Finance on high-quality economic development is of great significance to achieve the strategic goal of carbon peak and carbon neutral. Based on the panel data of 30 provinces in China from 2009 to 2019, this paper uses the epsilon-based measure model and entropy method to measure the total factor rate of green economy and the development level of green finance. It then brings green finance, technological innovation, industrial structure upgrading, environmental supervision and high-quality economic development into a unified research framework for the first time. By constructing a panel two-way fixed effect model, regulatory intermediary effect model and threshold effect model, this paper empirically tests the action mechanism and constraints between green finance and high-quality economic development. The results show that: (1) The spatial evolution of green finance in China presents a gradient decreasing pattern from east to middle to west, coastal to inland, and the spatial evolution presents an obvious southwest-northeast pattern. (2) Green finance does have a significant role in promoting high-quality economic development, in which technological innovation and industrial structure upgrading play a part of the intermediary role. This conclusion is still valid under the robustness test of lagged explanatory variables and after the possible endogenous problems are alleviated by the difference-in-difference model (DID). (3) Environmental regulation plays a non-linear regulatory role in the relationship between green finance and high-quality economic development, and there is a single threshold value. Too high intensity of environmental regulation will weaken green finance, resulting in the innovation compensation effect being more diminutive than the circular cost effect. At this time, the high-quality economic development presents a state of diminishing marginal benefits.
Systematic Literature Review of Supply Chain Relationship Approaches amongst Business-to-Business Partners
Authors: Jamaluddin Faridzah , Saibani Nizaroyani
Published: October 2021

Managing a business-to-business (B2B) supply chain relationship is an endless challenge. Many recent systematic literature review studies have discussed supply chain relationships from various perspectives. However, a comprehensive analysis, summarising the existing research, explicitly identified the implemented B2B supply chain relationships and found the effects of these relationships on supply chain performance remain lacking. To address the gap, this article presents a systematic literature review based on the PRISMA approach regarding the nature of the supply chain relationships between B2B partners and their effect on supply chain performance. Web of Science and Scopus were used in the compilation of studies published between 2000 and 2020. Findings indicate that the majority of B2B partners use a collaborative relationship approach and that the impacts are marked on the operational, financial, innovation, environmental, social and economic performance of their supply chain. This study seeks to contribute to the existing literature on B2B supply chain relationships by conducting a thorough and unbiased review of previous studies, drawing more general conclusions about the adopted supply chain relationships between B2B partners and providing insights for future research.
Numerical and Experimental Investigation of a Thermoelectric-Based Radiant Ceiling Panel with Phase Change Material for Building Cooling Applications
Authors: Seyednezhad Mohadeseh , Najafi Hamidreza , Kubwimana Benjamin
Published: October 2021

The present paper investigates the performance of a thermoelectric (TE)-based radiant ceiling panel with an additional layer of phase change material (PCM) for building cooling application through numerical and experimental analyses. The design of the ceiling panel consisted of an aluminum sheet with TE modules installed on the back to maintain a relatively low ceiling temperature that provided cooling through radiation and convection. A three-dimensional model was developed in COMSOL Multiphysics, and the system’s performance in several different configurations was assessed. The effect of the number of TE modules, as well as incorporating different amounts of PCM under transient conditions, was investigated for two modes of operation: startup and shutdown. It was shown that for a 609.6 mm × 609.6 mm ceiling panel, the use of four TE modules reduced the average surface temperature down to the comfort range in less than 5 min while producing a relatively uniform temperature distribution across the ceiling panel. It was also shown that the addition of a 2 mm thick PCM layer to the back of the ceiling panel enhanced the system’s performance by elongating the time that it took for the ceiling panel’s temperature to exceed the comfort range when the system shut down, which in turn reduced the number of on/off cycling of the system. The numerical results demonstrated a good agreement with the experimental data. The results from this study can be used for the optimal design of a TE-based radiant ceiling cooling system as a promising technology for smart buildings.
Life Cycle Assessment of a Vapor Compression Cooling System Integrated within a District Cooling Plant
Authors: Hampo Chima Cyril , Ya Hamdan Haji , Majid Mohd Amin Abd , Mokhtar Ainul Akmar , Kalpani Rasangika Ambagaha Hewage Dona , Muhammed Musa
Published: October 2021

In standard district cooling (DC) plants, central chillers produce cold energy for space cooling throughout the district network. In recent times, the integration of the vapor compression system, which includes the functionalities of vapor compression chillers (VCC), and thermal energy storage (TES) tanks in the DC setup, has gained more implementation across the globe. This is due to the possibility of load shifting by using the VCC to produce chilled water for charging the TES tanks during off peak periods. Since the environmental implications of various energy intensive systems are largely determined by the amount of material and energy consumed throughout their life cycle, it is critical to conduct a sustainability assessment of these systems in terms of environmental contributions, and suggest design options to reduce these impacts. A cradle to grave life cycle assessment (LCA) model is created in response to these issues and in order to meet the project’s objectives. The life cycle impact assessment (LCIA) results of the analysis reveal that the carbon footprint per 1 RTh of the produced chilled water is estimated at 0.72 kg CO2 eq/RTh. The operation phase of the system’s life cycle accounted for the most impact, about 98%, with other life cycle phases having negligible contributions. In substantiating the study’s investigation, the environmental performance based on several design options were discussed and compared to the case study. Among the several scenarios considered, incorporating the Sweden mix technology provided the case study with the most significant environmental savings, of about 94%.
COVID-19 and the New Normal of Organizations and Employees: An Overview
Authors: Raghavan Aarthi , Demircioglu Mehmet Akif , Orazgaliyev Serik
Published: October 2021

The COVID-19 pandemic has hit organizations and employees in every sector worldwide in unprecedented ways. It became extremely difficult for organizations and employees across sectors to operate under increased mobility restrictions. The pandemic effectively disrupted previous operational models and imbued changes such as telework and digital adoption that are pervasive and may potentially last beyond the pandemic. Amid these circumstances, it was essential to ask how organizations and employees will sustain themselves in the post-COVID-19 ”new normal”. Although so much research is conducted about COVID-19, there is no comprehensive view of the changes at the meso (organizational) and micro (individual) levels. This article aims to explain this using the emergency-learning-institutionalization-new normal (ELIN) framework, which is based on the timeline of the pandemic. The article aims to bring forth the overall trends in how organizations and employees are adapting to the pandemic, the lessons they have learned, and how they will change and adapt in a post-COVID-19 “new normal”. We have analyzed existing policy papers, articles published in business, public administration, nonprofit journals, and other studies to achieve this. We find an increasing trend towards the adoption of telework and digital tools at both meso- and micro-levels. The effective implementation of telework policies and digital transformation plans at the meso-level will ensure the sustainability of organizations and jobs in the new normal. Although these trends vary across sectors and within and across countries, there is an overall increase in the flexibility of organizations and employees in adopting new solutions, making them more open to innovation. The article makes important recommendations for organizations to make these transitions more sustainable in the medium and long term.
Soft Is Better: Determinants of Preferences for Non-Motorized Forms of Transportation in Urban Tourism Destinations
Authors: Bi Yahua , Romão João
Published: October 2021

Non-motorized forms of transportation are increasingly perceived as an option that can contribute to reducing the ecological impact of the transportation of tourists while offering satisfactory opportunities to appreciate the urban environment. Walking, bicycles, and other forms of non-motorized transport are increasingly used in urban contexts, both by residents and tourists. By looking into the characteristics and trip motivations of international tourists visiting the city of Barcelona, our analysis identifies the groups of tourists more oriented to these soft forms of mobility and also takes into consideration how they obtained previous information about the destination. Based on an extensive survey conducted in the city, the results of our multinomial logistic regressions reveal a slight generational divide when looking at sources of information (with retired tourists less oriented to digital tools) and a strong generational divide when observing transport choices (younger tourists more oriented to both soft or collective forms of mobility). Our results also show that tourists traveling in groups and/or with trips organized by travel companies and other organizations tend to prefer the utilization of private cars. These results can be used to promote the non-motorized mobility of tourists in other urban destinations.
Widespread Dietary Patterns (Healthy and Balanced Diet, Western Diet, and Vegan and Vegetarian Diets) Compared for Water Consumption: Which Is the Winner?
Authors: Vettori Virginia , Bronzi Bianca , Lorini Chiara , Cavallo Giuseppe , Bonaccorsi Guglielmo
Published: October 2021

An increasing number of people are interested in following a dietary pattern that is environmentally sustainable and water saving. However, consumers are mostly unable to recognize evidence-based information on food and nutrition and efficiently manage their diet. We conducted a narrative review aiming to support professionals to guide their assisted people that decide to follow a dietary pattern chosen for environmental or ethical reasons. We reviewed three scientific databases and Google Scholar, searching for papers that deal with the water consumption of widespread and well known dietary patterns (Healthy and Balanced Diet, Western Diet, and Vegan and Vegetarian diets). Our results suggest that moving toward a low-animal-based diet favors a reduction in water consumption, but current evidence is not sufficient to establish which is the winner in terms of water saving by comparing Vegetarian and Vegan diets.
Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer
Authors: El-Sehiemy Ragab , Elsayed Abdallah , Shaheen Abdullah , Elattar Ehab , Ginidi Ahmed
Published: October 2021

Typically, the main control on alternating current (AC) power systems is performed by the scheduling of rotary machines of synchronous generators and static machines of on-load tap changer (OLTC) transformers and volt-ampere reactive (VAR) sources. Large machines of synchronous generators can be managed by utilizing terminal voltage control when synchronized in parallel to the power system. These machines are typically terminal voltage regulated. In addition, substation on-load tap changer (OLTC) transformers improve system voltage management by controlling variable turn ratios that are adjusted in different levels known as taps along either the primary or secondary winding. Moreover, volt-ampere reactive (VAR) sources of static VAR compensators (SVCs), which are automated impedance devices connected to the AC power network, are designed for voltage regulation and system stabilization. In this paper, scheduling of these machines is coordinated for optimal power system operation (OPSO) using a recent algorithm of social network search optimizer (SNSO). The OPSO is performed by achieving many optimization targets of cost of fuel, power losses, and polluting emissions. The SNS is a recent optimizer that is inspired from users in social networks throughout the different moods of users such as imitation, conversation, disputation, and innovation mood. The SNSO is developed for handling the OPSO problem and applied on an IEEE standardized 57-bus power system and real Egyptian power system of the West Delta area. The developed SNSO is used in various assessments and quantitative analyses with various contemporary techniques. The simulated findings prove the developed SNSO’s solution accuracy and resilience when compared to other relevant techniques in the literature.