bluuebunny/crossref_metadata_2025 huggingface dataset:
3D Visual Reconstruction as Prior Information for First Responder Localization and Visualization
Authors: Kaiser Susanna , Linkiewicz Magdalena , Meißner Henry , Baumbach Dirk
Published: September 2023

In professional use cases like police or fire brigade missions, coordinated and systematic force management is crucial for achieving operational success during intervention by the emergency personnel. A real-time situation picture enhances the coordination of the team. This situation picture includes not only an overview of the environment but also the positions, i.e., localization, of the emergency forces. The overview of the environment can be obtained either from known situation pictures like floorplans or by scanning the environment with the aid of visual sensors. The self-localization problem can be solved outdoors using the Global Navigation Satellite System (GNSS), but it is not fully solved indoors, where the GNSS signal might not be received or might be degraded. In this paper, we propose a novel combination of an inertial localization technique based on simultaneous localization and mapping (SLAM) with 3D building scans, which are used as prior information, for geo-referencing the positions, obtaining a situation picture, and finally visualizing the results with an appropriate visualization tool. We developed a new method for converting point clouds into a hexagonal prism map specifically designed for our SLAM algorithm. With this combination, we could keep the equipment for first responders as lightweight as required. We showed that the positioning led to an average accuracy of less than 1m indoors, and the final visualization including the building layout obtained by the 3D building reconstruction will be advantageous for coordinating first responder operations.
IoTSim: Internet of Things-Oriented Binary Code Similarity Detection with Multiple Block Relations
Authors: Luo Zhenhao , Wang Pengfei , Xie Wei , Zhou Xu , Wang Baosheng
Published: September 2023

Binary code similarity detection (BCSD) plays a crucial role in various computer security applications, including vulnerability detection, malware detection, and software component analysis. With the development of the Internet of Things (IoT), there are many binaries from different instruction architecture sets, which require BCSD approaches robust against different architectures. In this study, we propose a novel IoT-oriented binary code similarity detection approach. Our approach leverages a customized transformer-based language model with disentangled attention to capture relative position information. To mitigate out-of-vocabulary (OOV) challenges in the language model, we introduce a base-token prediction pre-training task aimed at capturing basic semantics for unseen tokens. During function embedding generation, we integrate directed jumps, data dependency, and address adjacency to capture multiple block relations. We then assign different weights to different relations and use multi-layer Graph Convolutional Networks (GCN) to generate function embeddings. We implemented the prototype of IoTSim. Our experimental results show that our proposed block relation matrix improves IoTSim with large margins. With a pool size of 103, IoTSim achieves a recall@1 of 0.903 across architectures, outperforming the state-of-the-art approaches Trex, SAFE, and PalmTree.
Spectrum Sensing Method Based on Residual Dense Network and Attention
Authors: Wang Anyi , Meng Qifeng , Wang Mingbo
Published: September 2023

To address the problems of gradient vanishing and limited feature extraction capability of traditional CNN spectrum sensing methods in deep network structures and to effectively avoid network degradation issues under deep network structures, this paper proposes a collaborative spectrum sensing method based on Residual Dense Network and attention mechanisms. This method involves stacking and normalizing the time-domain information of the signal, constructing a two-dimensional matrix, and mapping it to a grayscale image. The grayscale images are divided into training and testing sets, and the training set is used to train the neural network to extract deep features. Finally, the test set is fed into the well-trained neural network for spectrum sensing. Experimental results show that, under low signal-to-noise ratios, the proposed method demonstrates superior spectral sensing performance compared to traditional collaborative spectrum sensing methods.
Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System
Authors: Aziz Md Abdul , Rahman Md Habibur , Sejan Mohammad Abrar Shakil , Baik Jung-In , Kim Dong-Sun , Song Hyoung-Kyu
Published: September 2023

The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.
Determining the Spectral Requirements for Cyanobacteria Detection for the CyanoSat Hyperspectral Imager with Machine Learning
Authors: Matthews Mark W. , Kravitz Jeremy , Pease Joshua , Gensemer Stephen
Published: September 2023

This study determines an optimal spectral configuration for the CyanoSat imager for the discrimination and retrieval of cyanobacterial pigments using a simulated dataset with machine learning (ML). A minimum viable spectral configuration with as few as three spectral bands enabled the determination of cyanobacterial pigments phycocyanin (PC) and chlorophyll-a (Chl-a) but may not be suitable for determining cyanobacteria composition. A spectral configuration with about nine ideally positioned spectral bands enabled estimation of the cyanobacteria-to-algae ratio (CAR) and pigment concentrations with almost the same accuracy as using all 300 spectral channels. A narrower spectral band full-width half-maximum (FWHM) did not provide improved performance compared to the nominal 12 nm configuration. In conclusion, continuous sampling of the visible spectrum is not a requirement for cyanobacterial detection, provided that a multi-spectral configuration with ideally positioned, narrow bands is used. The spectral configurations identified here could be used to guide the selection of bands for future ocean and water color radiometry sensors.
Interactive Learning for Network Anomaly Monitoring and Detection with Human Guidance in the Loop
Authors: Yang Dong , Liu Ze , Wei Songjie
Published: September 2023

With the advancement in big data and cloud computing technology, we have witnessed tremendous developments in applying intelligent techniques in network operation and management. However, learning- and data-based solutions for network operation and maintenance cannot effectively adapt to the dynamic security situation or satisfy administrators’ expectations alone. Anomaly detection of time-series monitoring indicators has been a major challenge for network administrative personnel. Monitored indicators in network operations are characterized by multiple instances with high dimensions and fluctuating time-series features and rely on system resource deployment and business environment variations. Hence, there is a growing consensus that conducting anomaly detection with machine intelligence under the operation and maintenance personnel’s guidance is more effective than solely using learning and modeling. This paper intends to model the anomaly detection task as a Markov Decision Process and adopts the Double Deep Q-Network algorithm to train an anomaly detection agent, in which the multidimensional temporal convolution network is applied as the principal structure of the Q network and the interactive guidance information from the operation and maintenance personnel is introduced into the procedure to facilitate model convergence. Experimental results on the SMD dataset indicate that the proposed modeling and detection method achieves higher precision and recall rates compared to other learning-based methods. Our method achieves model optimization by using human–computer interactions continuously, which guarantees a faster and more consistent model training procedure and convergence.
OHO: A Multi-Modal, Multi-Purpose Dataset for Human-Robot Object Hand-Over
Authors: Stephan Benedict , Köhler Mona , Müller Steffen , Zhang Yan , Gross Horst-Michael , Notni Gunther
Published: September 2023

In the context of collaborative robotics, handing over hand-held objects to a robot is a safety-critical task. Therefore, a robust distinction between human hands and presented objects in image data is essential to avoid contact with robotic grippers. To be able to develop machine learning methods for solving this problem, we created the OHO (Object Hand-Over) dataset of tools and other everyday objects being held by human hands. Our dataset consists of color, depth, and thermal images with the addition of pose and shape information about the objects in a real-world scenario. Although the focus of this paper is on instance segmentation, our dataset also enables training for different tasks such as 3D pose estimation or shape estimation of objects. For the instance segmentation task, we present a pipeline for automated label generation in point clouds, as well as image data. Through baseline experiments, we show that these labels are suitable for training an instance segmentation to distinguish hands from objects on a per-pixel basis. Moreover, we present qualitative results for applying our trained model in a real-world application.
Unlocking the Power of Mentoring: A Comprehensive Guide to Evaluating the Impact of STEM Mentorship Programs for Women
Authors: Wolf Elke , Brenning Stefanie
Published: September 2023

Although mentoring programs for female STEM students are often carried out with a great deal of passion on the part of program managers and mentors, robust results on their effects are often missing. However, regular evaluations are indispensable for an efficient allocation of resources towards gender balances in STEM. To accomplish this requirement, empirically valid and easy-to-use evaluation concepts are needed. We therefore develop an evaluation concept which corresponds to a Logic Chart, capturing three levels of expected effects (output—outcome—impact). On each level of impact, we derive a set of success indicators that can be measured by qualitative methods. A major advantage of our evaluation design is that the effect of a mentoring program can be observed directly after the end of the program. Furthermore, the results provide information about different channels of impact (e.g., reduced stereotypes or increased self-efficacy) and hence offer concrete indications for the further development of the program.
The Application of AutoML Techniques in Diabetes Diagnosis: Current Approaches, Performance, and Future Directions
Authors: Zhuhadar Lily Popova , Lytras Miltiadis D.
Published: September 2023

Artificial Intelligence (AI) has experienced rapid advancements in recent years, facilitating the creation of innovative, sustainable tools and technologies across various sectors. Among these applications, the use of AI in healthcare, particularly in the diagnosis and management of chronic diseases like diabetes, has shown significant promise. Automated Machine Learning (AutoML), with its minimally invasive and resource-efficient approach, promotes sustainability in healthcare by streamlining the process of predictive model creation. This research paper delves into advancements in AutoML for predictive modeling in diabetes diagnosis. It illuminates their effectiveness in identifying risk factors, optimizing treatment strategies, and ultimately improving patient outcomes while reducing environmental footprint and conserving resources. The primary objective of this scholarly inquiry is to meticulously identify the multitude of factors contributing to the development of diabetes and refine the prediction model to incorporate these insights. This process fosters a comprehensive understanding of the disease in a manner that supports the principles of sustainable healthcare. By analyzing the provided dataset, AutoML was able to select the most fitting model, emphasizing the paramount importance of variables such as Glucose, BMI, DiabetesPedigreeFunction, and BloodPressure in determining an individual’s diabetic status. The sustainability of this process lies in its potential to expedite treatment, reduce unnecessary testing and procedures, and ultimately foster healthier lives. Recognizing the importance of accuracy in this critical domain, we propose that supplementary factors and data be rigorously evaluated and incorporated into the assessment. This approach aims to devise a model with enhanced accuracy, further contributing to the efficiency and sustainability of healthcare practices.
Factors Influencing Generation Z’s Pro-Environmental Behavior towards Indonesia’s Energy Transition
Authors: Wijaya Divine Ifransca , Kokchang Phimsupha
Published: September 2023

The energy transition is essential for combating climate change and achieving sustainability. Generation Z plays a vital role in taking responsibility for environmental concerns, and without rigorous action and support measures, they will be the group most affected by climate change. Their perceptions, attitudes, and actions are crucial to the success of the energy transition. Therefore, this study aims to explore the factors influencing Generation Z’s pro-environmental behavior in order to support the energy transition in Indonesia. The combination of the extended theory of planned behavior and the pro-environmental behavior concept was used as the main framework of this study. Statistical analysis was conducted using partial least square structural equation modeling with SmartlPLS 3.0 software. Quantitative data were collected using a snowball method online questionnaire that was distributed to 400 Generation Z individuals born between 1997 and 2012, who reside in Jakarta, Indonesia. The results indicate that environmental awareness and environmental concern have a positive influence on Generation Z’s attitude towards the energy transition in Indonesia. Attitude and perceived behavioral control positively influence the intention to support the energy transition, whereas perceived behavioral control and intention contribute positively to Generation Z’s pro-environmental behavior. Conversely, the subjective norm exerts no influence on intention. Interventions aimed at enhancing Generation Z’s support for the energy transition should address factors that positively influence pro-environmental behavior. These insights can aid governments, institutions, and stakeholders in devising interventions to increase Generation Z’s engagement in the energy transition and foster the adoption of pro-environmental behavior.
Blockchain Traceability for Sustainability Communication in Food Supply Chains: An Architectural Framework, Design Pathway and Considerations
Authors: Cao Shoufeng , Xu Henry , Bryceson Kim P.
Published: September 2023

The increasing demand for sustainable and ethically sourced food products has highlighted the importance of effective sustainability communication within the food supply chain. Existing sustainability communication approaches encounter limitations such as a lack of standardised frameworks, information overload, greenwashing, and an absence of transparent reporting. These challenges hinder their effectiveness and reliability in communicating sustainability efforts and commitments to businesses and consumers in a food chain. Blockchain technology, with its transparent, traceable, verifiable, and immutable features, offers a promising solution to address these limitations and facilitate effective sustainability communication. This paper explores the benefits of applying blockchain traceability to enhance sustainability communication in food supply chains. Using the system architecture approach, this paper proposes a high-level architectural framework, which can navigate the design and development of a blockchain-enabled solution for food sustainability communication. To assist with the translation of the architectural framework into a tailored solution, this paper further presents an action design pathway and discusses the design considerations around organisation, technology, governance, cost, and the user interface. The discussions and insights offered by this study can guide system developers and business analysts in the design and development of industry-oriented solutions, helping them make informed decisions before and during the design process. This paper contributes to advancing and expanding blockchain applications with a particular focus on sustainability communication in food supply chains.
The Relationship between Physical Activity Level and Sociodemographic Factors in Romanian Adults in the Post-COVID-19 Pandemic Period
Authors: Iconaru Elena Ioana , Tarcau Emilian , Ciucurel Manuela Mihaela , Draghici Liviu , Ciucurel Constantin
Published: September 2023

Background: This study examines how socio-demographic factors relate to post-pandemic physical activity patterns among Romanian adults. Methods: A cross-sectional study explores post-COVID-19 physical activity levels (PAL) and their correlation with socio-demographic factors in Romanian adults (n = 237, average age 28.23 ± 9.91 years). An online questionnaire covering constitutional, socio-demographic, and physical activity-related variables was administered for data collection. Data analysis involves descriptive and inferential statistics, including Kendall’s tau correlation, along with multinomial regression analyses. Results: Noteworthy correlations emerged, including a robust association (r = 0.79, p < 0.001) between testing and history of clinical signs of COVID-19; a significant moderate correlation between health status and PAL compared to the period before the pandemic (τ = 0.56, p < 0.001); and significant moderate correlation between health status and current PAL (τ = −0.51, p < 0.001). Multinomial regression underscores an intricate relationship; testing for COVID-19 relates to clinical sign severity, health status changes influence post-pandemic PAL, and self-perceived health associates with current PAL (p < 0.001). Conclusions: Revealing significant links between PAL and socio-demographic factors among adults in Romania’s post-pandemic landscape, this study emphasizes the interaction between health changes and activity involvement. It also highlights the potential to guide interventions for rehabilitation and healthier living.
Comprehensive Evaluation of Different Heating Modes in Northeast China
Authors: Gao Chengkang , You Huan , Tian Mingyan , Wu Yang
Published: September 2023

Given the freezing temperatures and extended winters, heating is essential for residents living in Northeast China. When selecting the optimum heating mode for the region, various factors are taken into consideration, such as the impact on the environment, financial cost and energy consumption. However, it is uncertain which heating mode can fulfill all conditions and requirements. In this study, seven heating modes were selected from the national and northeastern heating strategies. A comprehensive index system based on the life cycle theory was developed from the perspectives of energy, the environment, the economy, technology and society. Additionally, a comprehensive evaluation of the seven heating modes was performed using the analytical hierarchy process (AHP) and gray relation analysis. The results of this research showed that, from a complete standpoint, the ground-source heat pump heating mode (HMg) had the best comprehensive benefit, with a correlation of 0.8173, followed by the coal-fired cogeneration heating mode (HMcc), which had the highest correlation degree among the centralized heating modes at 0.6111. Based on the findings of our thorough examination, a recommended scheme made up of HMg, HMcc, the electro-thermal membrane heating mode (HMef), the natural gas boiler room regional central heating mode (HMgrc) and the natural gas boiler room decentralized heating mode (HMgrd) was established. The recommended scheme can provide a theoretical basis for the implementation of clean heating policies in Northeast China.
Urban Ecological Culture Construction and the Formation of Residents’ Green Living: Evidence from National Forest City Construction in China
Authors: Xie Hanjin , Yang Chunmei , Liu Yanping
Published: September 2023

Based on the data of 282 cities from 2006 to 2019, this paper conducts a quasi-natural experiment with National Forest City as a proxy variable for urban ecological culture construction (Cul), using the difference-in-differences model to verify its impact and mechanisms on residents’ green living. The results are described as follows. (1) Urban ecological culture construction helps form residents’ green living, specifically, reducing carbon emissions from electricity, transportation, and heating, and this conclusion still holds after a series of robustness tests. (2) Urban ecological culture construction helps form residents’ green living through two mediating channels: enhancing public environmental protection participation and forming green consumption awareness. (3) Confucianism plays a positive moderating role in the process of urban ecological culture construction promoting residents’ green living, while the moderating role of exotic culture is not significant. It is worth mentioning that the role of Confucianism remains unchanged even when the above two cultures coexist. In view of this, this paper argues that urban ecological culture construction matters in forming residents’ green living. The organic integration of modern urban ecological culture construction with the region’s excellent traditional culture is worth noting.
A Review of the Feasibility of Producing Polylactic Acid (PLA) Polymers Using Spent Coffee Ground
Authors: Mak Shu Lun , Wu Ming Yan Tanya , Chak Wai Ying , Kwong Wang Kei , Tang Wai Fan , Li Chi Ho , Lee Chi Chung , Li Chun Yin
Published: September 2023

Coffee is one of the most popular beverages in the world. Annual coffee consumption continues to increase, but at the same time, it generates a large amount of spent coffee grounds from the brewing process that give rise to environmental problems. An appropriate solution to manage these spent coffee grounds (SCGs) becomes crucial. Our project aims at discussing the feasibility of utilizing SCGs to synthesize polylactic acid (PLA) as a recycling application for SCGs. Producing PLA from SCGs offers the opportunity to contribute to the circular economy and sustainability due to the significant volume of coffee consumption. This approach reduces waste, provides environmental benefits, and promotes the use of renewable resources. Through repurposing SCGs into PLA, we can close the loop, minimize environmental impact, and create a more sustainable alternative to fossil-fuel-based plastics. This paper first discusses the properties and potential recycling applications of spent coffee grounds. The production of PLA from lactic acid and the potential processes for converting SCGs to lactic acid are then briefly discussed. From our review, it is feasible to utilize SCGs as the primary source for lactic acid production via bacteria fermentation and, further, to produce PLA via ring-opening polymerization. Possible ways to improve the yield and a corresponding cost analysis are also discussed.
Incorporating Heat Vulnerability into Local Authority Decision Making: An Open Access Approach
Authors: Ferranti Emma , Cook Samuel , Greenham Sarah Victoria , Grayson Nick , Futcher Julie , Salter Kat
Published: September 2023

High temperatures and heatwaves are becoming more frequent, but heat vulnerability is rarely considered within local authority city design and statutory land-use planning processes. Here, we describe an approach to assess heat vulnerability in Birmingham, the second largest city in the UK. The approach uses open access data and GIS techniques that are available for built environment practitioners. Heat vulnerability is assessed by combining four datasets: surface temperatures, Local Climate Zones, green space, and Indices of Multiple Deprivation. The assessment shows that central and eastern areas of Birmingham that have the most compact urban form, least green space, and highest levels of deprivation are most vulnerable to heat. We evaluated the approach against previous climate research, examined the approach and datasets at the local scale, and described how heat vulnerability can be (and is being) incorporated into decision making. This project combines three key innovations: (1) the decision-centric process that focuses the method on the decision that needs to be made, minimizing inertia related to scientific or modeling uncertainty and reducing resource-intensity; (2) the co-creation process with Birmingham City Council, who have statutory powers for planning within the city, thereby ensuring that heat vulnerability is embedded within decisions on the suitability, design, and location of sites for future development; and (3) the open access and technically appropriate methodology which can be applied to any urban area in the UK, using the open access datasets described here, or globally, using locally applicable data sources.
The Development of Green Ports in Emerging Nations: A Case Study of Vietnam
Authors: Le Son-Tung , Nguyen Trung-Hieu
Published: September 2023

The development of green ports is still limited, especially in developing nations, despite the fact that they are viewed as a significant answer to the problems of environmental pollution and climate change. The purpose of this study is to investigate the factors that promote and hinder the development of green ports in developing countries. Using 248 managers from 12 Vietnamese container ports as a sample, this study is one of the pioneers in using a quantitative methodology to investigate the factors affecting the development of green ports. We used SPSS 22.0 and AMOS 22.0 to perform principal component analysis (PCA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). Our results indicated that cooperation of involved parties and foreign capital has the most important role in green port development in developing countries, followed by environmental regulation. Our findings also showed that lack of initial capital and lack of technological advancement negatively affects the development of green ports in these countries. The results and implications of the study will be discussed in more detail.
A Study on the Green Building Trend in China—From 2001 to 2022, Focusing on Research Topic Words
Authors: Ren Weicheng , Kim Kyunghwan
Published: September 2023

The Chinese government has committed to achieving carbon neutrality by 2060. Consequently, a shift towards green building development in the construction industry is inevitable. The purpose of this study is to summarize and predict the development direction of China’s green buildings by understanding their changing trends over time. Accordingly, this study employed content analysis as the research method to analyze 69 research papers and their topic words published from 2001 to 2022, with further in-depth analysis from four sub-dimensions: technical, economic, policy, and systemic. Overall, since 2000, China’s green building research has mainly focused on energy-saving technology and economic aspects, with particular attention being paid to the role of incentive policies. Research on the energy-saving renovation of existing buildings is insufficient, and research on building energy-saving management systems has been scarce. This may be related to China’s emphasis on technology and its neglect of management functions such as operations and maintenance. Considering the important role of management in the construction industry, it is crucial for the academic community to strengthen research on a comprehensive and systematic green building management system and for the Chinese government to increase its support for green building management and practices.
Some Theoretical and Practical Aspects of Technical Efficiency—The Example of European Union Agriculture
Authors: Radlińska Kamila
Published: September 2023

The main objective of this article is to better understand the category of technical efficiency of agriculture. To achieve this goal, empirical research was carried out, the subject of which was to estimate the average technical efficiency of agriculture in the European Union in the years 2004–2020. The technical efficiency of agriculture was estimated using the Data Envelopment Analysis method–Cooper Rhodes Input Oriented model. In order to estimate the level of technical efficiency, the value of Total output was adopted as the output, and the following inputs were adopted: Total Utilized Agricultural Area, Fertilizers, Plant protection products, Total livestock units, Feed for grazing animals, Labor inputs, Machinery and equipment, and Gross expenditure on fixed assets. The conclusions from the literature review indicate that technical efficiency is the main determinant of economic efficiency. The conclusions from empirical research have shown that agriculture in the European Union is characterized by a high level of technical efficiency for 2004–2020, TE = 90.24%. Moreover, the technical efficiency of farms was checked according to classes of economic size. The obtained results only partially confirm that the increase in the economic size of a farm increases its technical efficiency, because the highest level of technical efficiency is characteristic of farms that belong to the classes very small and very large.
Optimization of the Liquid Desiccant Cooling Systems in Hot and Humid Areas
Authors: Zhang Yanling , Zhang Hao , Yang Hongxing , Chen Yi , Leung Chun Wah
Published: September 2023

Air-conditioning systems in hot and humid regions account for over 50% of total energy usage. Integrating an indirect evaporative cooling (IEC) and a liquid desiccant dehumidifier (LDD) as the liquid desiccant cooling system (LDCS) presents an energy-saving and emission-reducing solution to replace traditional mechanical vapor compression refrigeration (MVCR) systems. This integration overcomes the regional limitations of IEC in hot and humid areas. The newly developed LDCS uses exhaust air as the working air source and solar energy as the heat source for desiccant solution regeneration. This study aims to develop an empirical model for the outlet parameters of the LDCS, propose an optimization strategy for its operating parameters, and assess the potential and energy performance through parameter analysis and multifactor optimization. By conducting sensitivity analysis and optimizing six critical parameters based on a response surface model (RSM), the system outlet temperature, relative humidity, and coefficient of performance (COP) are improved as the optimization objectives. The regional capability is demonstrated in three selected hot and humid cities. The results indicate that the LDCS can significantly increase the COP by 57.3%. Additionally, it can meet the dehumidification demand when operating with 25% of the air extracted in the RIEC during months with high humidity and temperature. This study will facilitate the application of IEC and LDD technologies, guide the design and operation scheme of the system, and promote energy-saving and emission-reducing solutions in hot and humid regions.
The Spatio-Temporal Evolution and Driving Factors of High-Quality Development in the Yellow River Basin during the Period of 2010–2022
Authors: Zhang Mengna , Qi Shanzhong
Published: September 2023

The Yellow River Basin is an important ecological barrier and economic development area in China, but it faces some problems such as the degradation of its ecological quality and a lagging economic level. Promoting the high-quality development of the Yellow River Basin is the only way for China’s economic construction to enter into high-quality development, and an objective evaluation of the development quality of the study area is the premise for effectively improving this development quality. Based on panel data during the period of 2010–2022, a framework of drivers, pressures, state, impact, and a response model was used to build an index system. The index of high-quality development for each province in the Yellow River Basin was calculated using the entropy TOPSIS model. Further, the descriptive statistics method and standard deviation ellipse were applied to analyze the spatio-temporal characteristics of high-quality development in the study area, and the geographical detector and spatio-temporal geographical weighted regression model were employed to reveal the driving factors for this high-quality development in the Yellow River Basin. The results showed that (1) the high-quality development index of the Yellow River Basin was steadily improved over the study period, with an average annual growth rate of 3.024%. (2) The high-value area of the high-quality development level in the study area was distributed from northwest to southeast, with the high values of each subsystem tending to be spatially stable, as well as the spatial differences of the subsystems increasing. (3) The proportion of tertiary industry, per capita disposable income, rural–urban income ratio, per capita GDP, per capita highway mileage, and population were the main factors affecting the spatio-temporal evolution of high-quality development level in the Yellow River Basin, with average q values of 0.867, 0.938, 0.852, 0.781, 0.842, and 0.763, respectively. (4) Except for the negative effect of per capita GDP, the other five driving factors all had positive effects on the high-quality development level, with average values of 0.044, 0.068, 0.227, 0.064, and 0.215, respectively.
Adsorption of Zn(II), Pb(II), and Cu(II) by Residual Soil-Derived Zeolite in Single-Component and Competitive Systems
Authors: Wang Zhe , Li Wen , Wang Liling , Zhang Yi , Li Jiake , Liu Yuling
Published: September 2023

Using construction residual soil (RS) as the raw material, RS-derived zeolite (RSDZ) was prepared through a fusion-hydrothermal process. The adsorption performance and mechanisms of RSDZ for Pb2+, Zn2+, and Cu2+ were investigated in single-component and competitive systems. The strong RSDZ X-ray diffraction peaks at 2θ = 12.47, 17.73, 21.65, 28.18, and 33.44°, together with the results of scanning electron microscopy and Fourier transform-infrared spectroscopy (FT-IR) indicated that NaP1 zeolite (Na6Al6SiO32∙12H2O) was successfully synthesised. The Brunauer–Emmett–Teller surface area, average pore size, and cation exchange capacity increased from 9.03 m2∙g−1, 18.85 nm, and 0.12 meq∙g−1 to 47.77 m2∙g−1, 41.60 nm, and 0.61 meq∙g−1, respectively, after the fusion-hydrothermal process. The maximum Langmuir adsorption capacity of RSDZ for Zn2+, Pb2+, and Cu2+ in the unary systems was 0.37, 0.38, and 0.40 mmol·g−1, respectively. Increasing the initial solution pH facilitated the adsorption reaction, and the adsorption performance was stable when pH > 3. The distribution coefficients in the binary and ternary systems indicated that RSDZ had greater affinity for Pb2+ and Zn2+ than for Cu2+ due to the larger ionic radius and relative atomic weight of the former two cations. The relative affinity to Pb2+ and Zn2+ was related to their concentration, with more competitive adsorption of Pb2+ at concentrations higher than 0.4 mmol·L−1 in binary systems and 0.25 mmol·L−1 in ternary systems. X-ray photoelectron spectroscopy and FT-IR analyses indicated that ion exchange was the main mechanism involved in the adsorption of heavy metal ions by RSDZ, accompanied by ligand exchange.
Sustainable Ergonomic Workplace: Fostering Job Satisfaction and Productivity among Business Process Outsourcing (BPO) Workers
Authors: Gumasing Ma. Janice J. , Rendon Emil Renfred A. , German Josephine D.
Published: September 2023

The business process outsourcing (BPO) sector is important in managing business especially in a developing country like the Philippines. Call centers are one of the most common type of industries within the BPO sector as they generate 75.1% of its revenues. Given the lack of studies regarding the ergonomic factors in the BPO industry, the goal of this research is to examine the relationships of different ergonomic domains on the job satisfaction and overall productivity of the BPO agents in a customer service or call center industry. The data were gathered through an online survey and the approach used for this study was a variance-based partial least squares structural equation modeling (PLS-SEM) with maximum likelihood estimation. Through analysis, it was proven that macro-ergonomics and physical ergonomics have significant effects on the overall productivity of BPO workers, while cognitive and macro-ergonomics have a significant influence on job satisfaction. In addition to this, it was also proven in the study that overall productivity and job satisfaction were significantly associated. This is one of the first studies to investigate and examine the relationship between ergonomic domains and the productivity and job satisfaction of a Filipino BPO or call center worker. The results of this study could be used as a reference for further investigations by future researchers, especially on more ergonomic factors as well as the extrinsic variables that were not covered by the researchers of this paper. In addition to this, the proposed framework might also be adapted and used as a reference for further studies to produce more accurate and updated results.
Special Issue “From COP 26 to COP 27: Contributions of Systems Approaches to Address the Challenges Ahead”: An Editorial Commentary
Authors: Perissi Ilaria , Armenia Stefano , Pasqualino Roberto , Csala Denes
Published: September 2023

This Special Issue focuses on proposing and analyzing systemic interdisciplinary approaches to support collaborative strategies and agreed-upon global sustainability policies toward addressing the challenges that lie ahead for our planet’s future. The contributions target applications in system dynamics, systems thinking, discrete event simulation, agent-based modelling, and hybrid approaches and provide valuable qualitative and quantitative insights to guide the collaborative efforts of governments, institutions, organizations in general, and even the financial sector toward the next Conference of Parties (COP28).
An Adaptive Decision Tree Regression Modeling for the Output Power of Large-Scale Solar (LSS) Farm Forecasting
Authors: Kassim Nabilah Mat , Santhiran Sathiswary , Alkahtani Ammar Ahmed , Islam Mohammad Aminul , Tiong Sieh Kiong , Mohd Yusof Mohd Yusrizal , Amin Nowshad
Published: September 2023

The installation of large-scale solar (LSS) photovoltaic (PV) power plants continues to rise globally as well as in Malaysia. The data provided by LSS PV consist of five weather stations with seven parameters, a 22-unit inverter, and 1-unit PQM Meter Grid as a big dataset. These big data are rapidly changing every minute, they lack data quality when missing data, and need to be analyzed for a longer duration to leverage their benefits to prevent misleading information. This paper proposed the forecasting power LSS PV using decision tree regression from three types of input data. Case 1 used all 35 parameters from five weather stations. For Case 2, only seven parameters were used by calculating the mean of five weather stations. While Case 3 was chosen from an index correlation of more than 0.8. The analysis of the historical data was carried out from June 2019 until December 2020. Moreover, the mean absolute error (MAE) was also calculated. A reliability test using the Pearson correlation coefficient (r) and coefficient of determination (R2) was done upon comparing with actual historical data. As a result, Case 2 was proposed to be the best input dataset for the forecasting algorithm.
Sustainable Service-Learning through Massive Open Online Courses
Authors: Froehlich Dominik E.
Published: September 2023

In this opinion article, I discuss the role of online learning, especially Massive Open Online Courses (MOOCs), in making what I call “resource-intensive pedagogies” more sustainable. Specifically, I zero in on the framework of service-learning. I present a case study of a long-standing and successful service-learning project in the context of teacher education. This case-based evidence suggests that MOOCs can be highly instrumental in individualizing course content and increasing the course concept’s scalability. The article ends by suggesting three avenues for further research to explore this nexus of online learning and resource-intensive pedagogies.
Energy and Exergy Analysis of a Combined Cooling Heating and Power System with Regeneration
Authors: Jose Jobel , Parthasarathy Rajesh Kanna , Arumugam Senthil Kumar
Published: September 2023

Solar assisted trigeneration system has proved to be a potential method in generating power with net zero carbon emissions. The present work aims to address the potential ways to improve the efficiency of the solar energy-integrated carbon dioxide trigeneration system. A regeneration integrated combined cooling, heating, and power system is proposed. With a comprehensive thermodynamic model, the proposed system is simulated for various operating conditions. A component-level exergy analysis is also conducted to estimate the total irreversibility of the system. As the gas cooler exit temperature increases, the overall system irreversibility also increases. When the bleed mass is 20% of the total mass, the system has the lowest energy destruction rate. The potential component that contributes most to system irreversibility is the gas cooler, followed by the regenerator and expansion valve. The proposed system with regeneration yields 29% more COP than the conventional system when operating at lower compressor discharge pressure and a gas cooler exit temperature of 34 °C. It is inferred from the obtained results that to reduce the total irreversibility of the system, it is advised to operate the system at a lower compressor discharge pressure and gas cooler exit temperature.
Critical Technical Issues with a Voltage-Source-Converter-Based High Voltage Direct Current Transmission System for the Onshore Integration of Offshore Wind Farms
Authors: Koondhar Mohsin Ali , Kaloi Ghulam Sarwar , Saand Abdul Sattar , Chandio Sadullah , Ko Wonsuk , Park Sisam , Choi Hyeong-Jin , El-Sehiemy Ragab Abdelaziz
Published: September 2023

Long-distance offshore wind power transmission systems utilize multi-terminal high voltage direct current (MT-HVDC) connections based on voltage source converters (VSCs). In addition to having the potential to work around restrictions, the VSC-based MT-HVDC transmission system has significant technical and economic merits over the HVAC transmission system. Offshore wind farms (OWFs) will inevitably grow because of their outstanding resistance to climate change and ability to provide sustainable energy without producing hazardous waste. Due to stronger and more persistent sea winds, the OWF often has a higher generation capacity with less negative climate effects. The majority of modern installations are distant from the shore and produce more power than the early OWF sites, which are situated close to the shore. This paradigm shift has compelled industry and professional researchers to examine transmission choices more closely, specifically HVAC and HVDC transmission. This article conducts a thorough analysis of grid connection technologies for massive OWF integration. In comparison to earlier assessments, a more detailed discussion of HVDC and HVAC topologies, including HVDC based on VSCs and line-commutated converters (LCCs), and all DC transmission systems, is offered. Finally, a selection criterion for HVDC transmission is advised, and its use is argued to be growing.
Evaluating and Analyzing Urban Renewal and Transformation Potential Based on AET Models: A Case Study of Shenzhen City
Authors: Yao Kaizhong , Lu Yuefeng , Li Xiwen , Ruan Huaizhao , Gao Feng , Shao Shiwei , Sun Ying , Liu Yanru , Li Jing
Published: September 2023

With the development of the urban economy and population growth, the demand for urban construction land continues to rise. Addressing the challenges of land protection and ecological security calls for the exploration of new approaches. This study emphasizes a sustainable solution by emphasizing in-depth exploration of existing land resources, moving away from the traditional “expanding the pie” model of urban development. Specifically, we selected land parcels in Shenzhen city from 2010 to 2020 that met the criteria for urban renewal and transformation as evaluation units, further categorized into residential, industrial, and commercial land for transformation. From multiple perspectives, including geological conditions, building conditions, agglomeration, social factors, and the degree of completeness of public facilities, we constructed an evaluation system comprising 23 indicators to quantify the potential for urban renewal of these units. Through the AET (AHP-EWM-TOPSIS) model analysis for assessing the potential of urban renewal and transformation, we classified the transformation parcels into different potential levels based on the optimized solutions from the model. Finally, we validated the results using the planned land units implemented in Shenzhen from 2010 to 2020, achieving an accuracy of 81% in matching the spatial distribution of potential levels with the actual situation. The comprehensive evaluation results from the model provide a basis for optimizing and enhancing sustainable urban renewal. This research contributes to the formulation of informed decisions and strategic urban development planning, enabling a more cautious and resource-efficient approach to address the challenges of urban expansion.