bluuebunny/crossref_metadata_2025 huggingface dataset:
Design and Characterization of Ocular Inserts Loaded with Dexamethasone for the Treatment of Inflammatory Ophthalmic Disease
Authors: Guadarrama-Escobar Omar Rodrigo , Valdés-Alvarez Cassandra Araceli , Constantino-Gonzalez Karla Stella , Serrano-Castañeda Pablo , Peña-Juárez Ma. Concepción , Morales-Florido Miriam Isabel , Salgado-Machuca Mariana , Rodríguez-Pérez Betsabe , Rodriguez-Cruz Isabel Marlen , Vargas-Estrada Dinorah , Mercado-Márquez Crisóforo , Vázquez-Durán Alma , Méndez-Albores Abraham , Anguíano-Almazán Ericka , Escobar-Chavez José Juan
Published: February 2024

The short precorneal residence time of ophthalmic drops is associated with their low absorption; therefore, the development of ocular inserts capable of prolonging and controlling the ophthalmic release of drugs is an interesting option in the design and development of these drugs. A surface response design was developed, specifically the Central Composite Design (CCD), to produce ophthalmic films loaded with Dexamethasone (DEX) by the solvent evaporation method having experimental levels of different concentrations of previously selected polymers (PVP K-30 and Eudragit RS100.). Once optimization of the formulation was obtained, the in vivo test was continued. The optimal formulation obtained a thickness of 0.265 ± 0.095 mm, pH of 7.11 ± 0.04, tensile strength of 15.50 ± 3.94 gF, humidity (%) of 22.54 ± 1.7, mucoadhesion strength of 16.89 ± 3.46 gF, chemical content (%) of 98.19 ± 1.124, release of (%) 13,510.71, and swelling of 0.0403 ± 0.023 g; furthermore, in the in vivo testing the number and residence time of PMN cells were lower compared to the Ophthalmic Drops. The present study confirms the potential use of polymeric systems using PVPK30 and ERS100 as a new strategy of controlled release of ophthalmic drugs by controlling and prolonging the release of DEX at the affected site by decreasing the systemic effects of the drug.
Automation’s Impact on Agriculture: Opportunities, Challenges, and Economic Effects
Authors: Bazargani Khadijeh , Deemyad Taher
Published: February 2024

Automation and robotics are the key players in modern agriculture. They offer potential solutions for challenges related to the growing global population, demographic shifts, and economic status. This review paper evaluates the challenges and opportunities of using new technologies and the often-missed link between automation technology and agricultural economics. Through a systematic analysis of the literature, this study explores the potential of automation and robotics in farming practices, as well as their socio-economic effects, and provides strategic recommendations for those involved. For this purpose, various types of robots in different fields of agriculture and the technical feasibility and challenges of using automation have been discussed. Other important factors, including demographic shifts, labor market effects, and economic considerations, have been analyzed. Furthermore, this study investigates the social effects of automation, particularly in terms of employment and workforce adaptation. It finds that, while automation boosts productivity and sustainability, it also causes labor displacement and demands considerable technological investment. This thorough investigation fills a crucial gap by assessing economic sustainability, labor market evolution, and the future of precision agriculture. It also charts a course for further research and policy-making at the intersection of agricultural technology and socio-economic fields and outlines a future roadmap for further research and policy.
Comparative Analysis of GF-5 and Sentinel-2A Fusion Methods for Lithological Classification: The Tuanjie Peak, Xinjiang Case Study
Authors: Chi Yujin , Zhang Nannan , Jin Liuyuan , Liao Shibin , Zhang Hao , Chen Li
Published: February 2024

This study investigates the application of hyperspectral image space–spectral fusion technology in lithologic classification, using data from China’s GF-5 and Europe’s Sentinel-2A. The research focuses on the southern region of Tuanjie Peak in the Western Kunlun Range, comparing five space–spectral fusion methods: GSA, SFIM, CNMF, HySure, and NonRegSRNet. To comprehensively evaluate the effectiveness and applicability of these fusion methods, the study conducts a comprehensive assessment from three aspects: evaluation of fusion effects, lithologic classification experiments, and field validation. In the evaluation of fusion effects, the study uses an index analysis and comparison of spectral curves before and after fusion, concluding that the GSA fusion method performs the best. For lithologic classification, the Random Forest (RF) classification method is used, training with both area and point samples. The classification results from area sample training show significantly higher overall accuracy compared to point samples, aligning well with 1:50,000 scale geological maps. In field validation, the study employs on-site verification combined with microscopic identification and comparison of images with actual spectral fusion, finding that the classification results for the five lithologies are essentially consistent with field validation results. The “GSA+RF” method combination established in this paper, based on data from GF-5 and Sentinel-2A satellites, can provide technical support for lithological classification in similar high-altitude regions.
Bubble Sensors for Temperature Measurements through a Colorimetric Approach
Authors: Trigona Carlo , Panebianco Sara , Galvagno Rosaria , Gueli Anna Maria
Published: February 2024

This paper introduces an innovative sensor utilizing bubbles coated with thermochromic paint, aiming to facilitate temperature measurements in challenging-to-reach locations without the requirement of an external power source. The research conducted is innovative in terms of both methodology and application. The characterization of the thermochromic properties of paints was, in fact, performed using spectroradiometric measurements by selecting a temperature range useful for applications in various fields including preventive conservation. The study encompasses two main objectives: (1) analyzing the color characteristics of thermochromic paint and plastic resin that forms the bubbles, and (2) assessing a temperature sensor comprising a thermochromic paint-coated bubble subjected to temperature variations. The thermochromic paint exhibits reversible color modifications in response to temperature changes, making it an ideal candidate for applications of this nature. The color characterization phase involves measurements using a spectroradiometer to compare the spectral reflectance factor (SRF%) of the colored plastic resin spread on canvas with that of the inflated bubbles. The sensor characterization entails evaluating color changes of the thermochromic paint on the bubble surface with varying temperatures. Experimental results indicate that the combination of a red (R) bubble and blue (B) thermochromic paint produces quantifiable color variations suitable for the proposed applications, whereas the alternative combination under examination, namely a blue bubble and red thermochromic paint, yields less accurate results. Considering that for both thermochromic paints the color change temperature is 35 °C, it is possible to see how, for B bubble with R thermochromic paint, the chromatic coordinates change value: C* = 3.14 ± 0.14 and h = 289.54 ± 11.58 at room temperature, while C* = 2.96 ± 0.12 and h = 304.20 ± 12.17 at 35 °C. The same is true for R bubble with B thermochromic paint where C* = 25.31 ± 1.01 and h* = 285.05 ± 11.40 at room temperature, while C* = 20.87 ± 0.85 and h = 288.37 ± 11.53 at 35 °C. The study demonstrates the potential of the approach and suggests further investigations into reproducibility and expanded color combinations. The results provide a promising basis for future improvements in temperature monitoring with thermochromic bubble sensors.
Low-Cost Plant-Protection Unmanned Ground Vehicle System for Variable Weeding Using Machine Vision
Authors: Dong Huangtao , Shen Jianxun , Yu Zhe , Lu Xiangyu , Liu Fei , Kong Wenwen
Published: February 2024

This study presents a machine vision-based variable weeding system for plant- protection unmanned ground vehicles (UGVs) to address the issues of pesticide waste and environmental pollution that are readily caused by traditional spraying agricultural machinery. The system utilizes fuzzy rules to achieve adaptive modification of the Kp, Ki, and Kd adjustment parameters of the PID control algorithm and combines them with an interleaved period PWM controller to reduce the impact of nonlinear variations in water pressure on the performance of the system, and to improve the stability and control accuracy of the system. After testing various image threshold segmentation and image graying algorithms, the normalized super green algorithm (2G-R-B) and the fast iterative threshold segmentation method were adopted as the best combination. This combination effectively distinguished between the vegetation and the background, and thus improved the accuracy of the pixel extraction algorithm for vegetation distribution. The results of orthogonal testing by selected four representative spraying duty cycles—25%, 50%, 75%, and 100%—showed that the pressure variation was less than 0.05 MPa, the average spraying error was less than 2%, and the highest error was less than 5% throughout the test. Finally, the performance of the system was comprehensively evaluated through field trials. The evaluation showed that the system was able to adjust the corresponding spraying volume in real time according to the vegetation distribution under the decision-making based on machine vision algorithms, which proved the low cost and effectiveness of the designed variable weed control system.
Exploring Interference Issues in the Case of n25 Band Implementation for 5G/LTE Direct-to-Device NTN Services
Authors: Pastukh Alexander , Tikhvinskiy Valery , Devyatkin Evgeny
Published: February 2024

This paper delves into an interference analysis, focusing on the forthcoming Starlink Generation 2 satellites, stated to operate within the 1990–1995 MHz frequency band. The aim is to assess the potential interference from this Starlink system to the satellite receivers of mobile satellite systems (MSSs), which are set to function within the 1980–2010 MHz range, and satellite receivers of the NTN systems, which are planned to operate in the n256 bands, defined by the 3GPP specifications. Through simulation-based evaluations, both single-entry and aggregate interference levels from Starlink to MSSs and NTN systems are comprehensively explored. To estimate the interference impact, several protection criteria were used. The study is in line with the Recommendations of International Telecommunication Union (ITU-R) and common approaches that are used when performing compatibility studies between satellite systems. The findings of this study demonstrate the feasibility of utilizing the n25 band for NTN direct-to-device services.
Cloud-Based Machine Learning Methods for Parameter Prediction in Textile Manufacturing
Authors: Chang Ray-I , Lin Jia-Ying , Hung Yu-Hsin
Published: February 2024

In traditional textile manufacturing, downstream manufacturers use raw materials, such as Nylon and cotton yarns, to produce textile products. The manufacturing process involves warping, sizing, beaming, weaving, and inspection. Staff members typically use a trial-and-error approach to adjust the appropriate production parameters in the manufacturing process, which can be time consuming and a waste of resources. To enhance the efficiency and effectiveness of textile manufacturing economically, this study proposes a query-based learning method in regression analytics using existing manufacturing data. Query-based learning allows the model training to evolve its decision-making process through dynamic interactions with its solution space. In this study, predefined target parameters of quality factors were first used to validate the training results and create new training patterns. These new patterns were then imported into the solution space of the training model. In predicting product quality, the results show that the proposed query-based regression algorithm has a mean squared error of 0.0153, which is better than those of the original regression-related methods (Avg. mean squared error = 0.020). The trained model was deployed as an application programing interface (API) for cloud-based analytics and an extensive auto-notification service.
Machine Learning Method and Hyperspectral Imaging for Precise Determination of Glucose and Silicon Levels
Authors: Wawerski Adam , Siemiątkowska Barbara , Józwik Michał , Fajdek Bartłomiej , Partyka Małgorzata
Published: February 2024

This article introduces an algorithm for detecting glucose and silicon levels in solution. The research focuses on addressing the critical need for accurate and efficient glucose monitoring, particularly in the context of diabetic management. Understanding and monitoring silicon levels in the body is crucial due to its significant role in various physiological processes. Silicon, while often overshadowed by other minerals, plays a vital role in bone health, collagen formation, and connective tissue integrity. Moreover, recent research suggests its potential involvement in neurological health and the prevention of certain degenerative diseases. Investigating silicon levels becomes essential for a comprehensive understanding of its impact on overall health and well-being and paves the way for targeted interventions and personalized healthcare strategies. The approach presented in this paper is based on the integration of hyperspectral data and artificial intelligence techniques. The algorithm investigates the effectiveness of two distinct models utilizing SVMR and a perceptron independently. SVMR is employed to establish a robust regression model that maps input features to continuous glucose and silicon values. The study outlines the methodology, including feature selection, model training, and evaluation metrics. Experimental results demonstrate the algorithm’s effectiveness at accurately predicting glucose and silicon concentrations and showcases its potential for real-world application in continuous glucose and silicon monitoring systems.
Anomaly Detection Methods in Autonomous Robotic Missions
Authors: Chirayil Nandakumar Shivoh , Mitchell Daniel , Erden Mustafa Suphi , Flynn David , Lim Theodore
Published: February 2024

Since 2015, there has been an increase in articles on anomaly detection in robotic systems, reflecting its growing importance in improving the robustness and reliability of the increasingly utilized autonomous robots. This review paper investigates the literature on the detection of anomalies in Autonomous Robotic Missions (ARMs). It reveals different perspectives on anomaly and juxtaposition to fault detection. To reach a consensus, we infer a unified understanding of anomalies that encapsulate their various characteristics observed in ARMs and propose a classification of anomalies in terms of spatial, temporal, and spatiotemporal elements based on their fundamental features. Further, the paper discusses the implications of the proposed unified understanding and classification in ARMs and provides future directions. We envisage a study surrounding the specific use of the term anomaly, and methods for their detection could contribute to and accelerate the research and development of a universal anomaly detection system for ARMs.
FV-MViT: Mobile Vision Transformer for Finger Vein Recognition
Authors: Li Xiongjun , Feng Jin , Cai Jilin , Lin Guowen
Published: February 2024

In addressing challenges related to high parameter counts and limited training samples for finger vein recognition, we present the FV-MViT model. It serves as a lightweight deep learning solution, emphasizing high accuracy, portable design, and low latency. The FV-MViT introduces two key components. The Mul-MV2 Block utilizes a dual-path inverted residual connection structure for multi-scale convolutions, extracting additional local features. Simultaneously, the Enhanced MobileViT Block eliminates the large-scale convolution block at the beginning of the original MobileViT Block. It converts the Transformer’s self-attention into separable self-attention with linear complexity, optimizing the back end of the original MobileViT Block with depth-wise separable convolutions. This aims to extract global features and effectively reduce parameter counts and feature extraction times. Additionally, we introduce a soft target center cross-entropy loss function to enhance generalization and increase accuracy. Experimental results indicate that the FV-MViT achieves a recognition accuracy of 99.53% and 100.00% on the Shandong University (SDU) and Universiti Teknologi Malaysia (USM) datasets, with equal error rates of 0.47% and 0.02%, respectively. The model has a parameter count of 5.26 million and exhibits a latency of 10.00 milliseconds from the sample input to the recognition output. Comparison with state-of-the-art (SOTA) methods reveals competitive performance for FV-MViT.
Block Partitioning Information-Based CNN Post-Filtering for EVC Baseline Profile
Authors: Choi Kiho
Published: February 2024

The need for efficient video coding technology is more important than ever in the current scenario where video applications are increasing worldwide, and Internet of Things (IoT) devices are becoming widespread. In this context, it is necessary to carefully review the recently completed MPEG-5 Essential Video Coding (EVC) standard because the EVC Baseline profile is customized to meet the specific requirements needed to process IoT video data in terms of low complexity. Nevertheless, the EVC Baseline profile has a notable disadvantage. Since it is a codec composed only of simple tools developed over 20 years, it tends to represent numerous coding artifacts. In particular, the presence of blocking artifacts at the block boundary is regarded as a critical issue that must be addressed. To address this, this paper proposes a post-filter using a block partitioning information-based Convolutional Neural Network (CNN). The proposed method in the experimental results objectively shows an approximately 0.57 dB for All-Intra (AI) and 0.37 dB for Low-Delay (LD) improvements in each configuration by the proposed method when compared to the pre-post-filter video, and the enhanced PSNR results in an overall bitrate reduction of 11.62% for AI and 10.91% for LD in the Luma and Chroma components, respectively. Due to the huge improvement in the PSNR, the proposed method significantly improved the visual quality subjectively, particularly in blocking artifacts at the coding block boundary.
Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain
Authors: Phan Trung C. , Pranata Adrian , Farragher Joshua , Bryant Adam , Nguyen Hung T. , Chai Rifai
Published: February 2024

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.
Different Scenarios of Autonomous Operation of an Environmental Sensor Node Using a Piezoelectric-Vibration-Based Energy Harvester
Authors: Bouhedma Sofiane , Bin Taufik Jawad , Lange Fred , Ouali Mohammed , Seitz Hermann , Hohlfeld Dennis
Published: February 2024

This paper delves into the application of vibration-based energy harvesting to power environmental sensor nodes, a critical component of modern data collection systems. These sensor nodes play a crucial role in structural health monitoring, providing essential data on external conditions that can affect the health and performance of structures. We investigate the feasibility and efficiency of utilizing piezoelectric vibration energy harvesters to sustainably power environmental wireless sensor nodes on the one hand. On the other hand, we exploit different approaches to minimize the sensor node’s power consumption and maximize its efficiency. The investigations consider various sensor node platforms and assess their performance under different voltage levels and broadcast frequencies. The findings reveal that optimized harvester designs enable real-time data broadcasting with short intervals, ranging from 1 to 3 s, expanding the horizons of environmental monitoring, and show that in case the system includes a battery as a backup plan, the battery’s lifetime can be extended up to 9 times. This work underscores the potential of vibration energy harvesting as a viable solution for powering sensor nodes, enhancing their autonomy, and reducing maintenance costs in remote and challenging environments. It opens doors to broader applications of sustainable energy sources in environmental monitoring and data collection systems.
Learning in Transit: Crossing Borders, Waiting, and Waiting to Cross
Authors: Bellino Michelle J. , Gluckman Maxie
Published: February 2024

Recent U.S. policy changes have contributed to longer waiting periods for migrant families in Mexican border cities. This study centers on four Honduran families enrolled in the Migrant Protection Protocols (MPP) policy, also referred to as ‘Remain in Mexico,’ while undergoing prolonged waiting periods in the Mexican border town of Monterrey, Nuevo Léon. Centering on young people’s voices, we ask what they learn during this prolonged period of transit. Through ethnographic and digital participatory storytelling interviews, we illustrate how children learned about the politics of border crossing through fraught interactions with im/migration officials, prolonged periods of immobility, and evolving understandings of legality. Building on theories of ‘border thinking’ and ‘politicized funds of knowledge,’ we highlight ways that young people employed their evolving understandings of national borders and the legal contours of their transborder asylum process, while protecting themselves and their families from danger and discrimination. We argue that transit is not simply time that young people are forced to endure; rather, the experience of forced transit is constitutive of young people’s learning about state power and their evolving understanding of borders, rights, and belonging.
A Method to Facilitate the Regeneration of Human Resources: A Sustainability Perspective
Authors: Chen Ningning , Zhao Xinan , Guo Baorong , Sun Chenxi
Published: February 2024

Within the sustainable development framework, organizations are tasked with creating strategies that ensure the enduring provision of value through human capital for the future. Our study emphasizes employee development and training, adopting a people-centric approach aligned with sustainability principles. By leveraging techniques for the identification of Characteristics of Individual Strengths (CIS), Agent Evaluation, and composite decision making, we introduce a novel approach to formulating personalized employee training strategies. This approach is structured around three pivotal steps: identifying CIS, assessing employee roles within the organization based on CIS, and analyzing training strategies. Demonstrated through illustrative examples, our method validates its applicability in real-world settings. This research provides organizations with an innovative pathway for effectively fostering employee skills and securing a steady influx of high-quality, diverse talent.
Mapping Urban Expansions along China–Europe Railway Express with the 30 m Time-Series Global Impervious Surface Area (GISA-2) Data from 2010 to 2019
Authors: Guo Xian , Pei Yujie , Xu Hong , Wang Yang
Published: February 2024

The China–Europe Railway Express (CRE) plays a crucial role in promoting local economic development and bolstering regional connectivity. Quantitative assessments of urban changes along the CRE are essential for understanding its influence on the local eco-environment and the economy, which is a cornerstone for fostering sustainable growth. Urban boundaries are vital indicators of city development; however, accurately extracting these boundaries over multiple years at a large scale and fine granularity remains challenging. On the basis of a global impervious surface area (GISA2) dataset from 2010 to 2019, this study extracted fine-scaled city boundaries annually for European countries and analyzed the spatiotemporal patterns of urbanization in areas along and not along the express during the past decade. The generated enhanced urban boundaries (EUBs) show strong agreement with the Landsat time-series data and align well with existing global urban time-series products (GUB, GAUD, and NTL). From 2010 to 2019, urban areas and clusters expanded across the entire region, with notable disparities observed between areas along the express (noted as ON-CRE) and those not along (noted as OFF-CRE). ON-CRE cities experienced more substantial urban growth near the express, resulting in new mega urban agglomerations and expansion in small and moderate cities. In contrast, most OFF-CRE cities exhibited stable development with limited urban sprawl. Dynamic proportions of the impervious surface area (ISA) within the EUB indicated more compact impervious areas within ON-CRE boundaries compared to OFF-CRE. This study contributes to understanding urban expansions driven by the long-term CRE operations. The EUB dataset developed herein provides a valuable resource for future investigations into the impacts of urbanization on biotic stress and urban health in European countries, thus supporting the achievement of sustainable development goals.
Navigating the Efficiency Landscape: A Data Envelopment Analysis of Tourist Resorts in Jiangsu Province for Optimized Socio-Economic Benefits
Authors: Chu Guang , Yang Liangjian , Zhang Jinhe , Wang Tian , Dong Yingjia , Qian Zhangrui
Published: February 2024

Tourist resorts stand out as a focal point in the academic discourse on tourism, garnering significant attention within the tourism academic community. Assessing the efficiency of these resorts serves as a crucial tool for steering their management strategies, optimizing resource allocation, and contributing to regional economic development. This study centers on tourist resorts in Jiangsu Province, employing the data envelopment analysis method to gauge their tourism efficiency. The research delves into the impact of decomposing the efficiency of tourist resorts and investigates the spatiotemporal dynamic patterns of various efficiencies. Key findings indicate that: (1) The overall tourism efficiency of tourist resorts in Jiangsu Province registers as low, with an average of only 0.119, signaling ample room for improvement towards optimal levels. Among different efficiencies, scale efficiency exhibits the highest average value, followed by pure technical efficiency, with comprehensive efficiency ranking the lowest. (2) The comprehensive efficiency of tourist resorts in Jiangsu Province is influenced by the combined effects of various decomposition efficiencies. Notably, pure technical efficiency plays a more substantial role in overall efficiency compared to scale efficiency. (3) Spatial differentiation in efficiency values is evident among tourist resorts in Jiangsu Province. High-efficiency areas, particularly the southern Jiangsu region, display concentrated clusters, emphasizing a pronounced agglomeration of scale efficiency. In contrast, the central and northern regions of Jiangsu witness a rising number of tourist resorts demonstrating pure technical efficiency and high overall efficiency. (4) Over the research period, the focus of various efficiency factors in tourist resorts shifted towards the north, albeit without significant deviation. Simultaneously, the standard deviation ellipse area of various efficiencies exhibits a general trend of expansion. Drawing from these research outcomes, the article recommends practical measures such as enhancing the diversity of vacation resort services, establishing interactive mechanisms, and attracting management talent. These suggestions aim to provide actionable guidance for the development of tourist resorts, contributing to their sustained growth and success.
Biomass Polygeneration Systems Integrated with Buildings: A Review
Authors: Di Fraia Simona , Shah Musannif , Vanoli Laura
Published: February 2024

Biomass is widely acknowledged as a plentiful and easily accessible source of renewable energy. Unlike many other renewable sources, biomass offers a consistent and predictable power supply without significant concerns about energy and environmental impacts. When used as a fuel in polygeneration systems designed to produce multiple outputs such as electricity, heat, chemicals, and synthetic fuels, biomass greatly enhances overall system efficiency by minimizing energy losses. These systems gain further advantages when integrated in a decentralized manner with energy-intensive applications like buildings. This review article aims to shift the focus of readers from generic biomass-based systems to polygeneration systems tailored for specific applications, such as buildings. The overview will discuss various biomass resources, systematic approaches, technologies, successful case studies, potential benefits, and limitations of such systems integrated into real-life building applications. It also categorizes studies based on different conversion processes such as combustion, gasification, and anaerobic digestion, with combustion-based polygeneration systems being the most prevalent. The review also explores the use of standalone and hybrid biomass-based energy systems. Taking a multidisciplinary approach, the analysis considers energy, exergy, economic, and environmental perspectives. Parameters such as the primary energy savings (PES), exergy efficiency, simple payback (SPB) period, and CO2 emission reductions are commonly used in system analyses. The review underscores how polygeneration systems integrated into the building sector can enhance efficiency, resilience, and environmental sustainability. This synthesis aims to address current gaps, particularly in the domain of polygeneration systems connected with buildings, offering essential insights for researchers and specialists in the field.
A Study on the Changes of Green Total Factor Productivity in Chinese Cities under Resource and Environmental Constraints
Authors: Fu Lei , Zhang Siyuan , Guo Sidai
Published: February 2024

Confronting the dual challenges of excessive resource consumption and environmental pollution, the traditional extensive economic development pattern significantly impeded the high-quality development of the Chinese economy. Examining variations in green total factor productivity across different types of cities holds substantial practical significance for promoting coordinated regional development and facilitating the green transformation of urban economies. Panel data from 283 cities in China spanning the years 2006 to 2020 were selected for analysis. The window-Malmquist–Luenberger index model, incorporating a mixed distance function, was employed to assess changes in green total factor productivity among the sample cities. The results were then categorized and analyzed based on different city attributes. The findings indicate that (1) the variation in green total factor productivity across China’s four major regions from 2006 to 2020 is generally characterized by an initial decline followed by an increase; (2) the proportion of cities with significantly improved green total factor productivity decreases from the east to the central, western, and northeastern regions; (3) the increase in green total factor productivity is positively correlated with city size, suggesting that larger cities experience higher growth in green total factor productivity; (4) first- and second-tier cities exhibit a relatively high mean value of green total factor productivity growth, while third-, fourth-, and fifth-tier cities demonstrate relatively lower growth.
Impact of Probabilistic Modeling Alternatives on the Seismic Fragility Analysis of Reinforced Concrete Dual Wall–Frame Buildings towards Resilient Designs
Authors: Martinez Ivanna , Gallegos Marco F. , Araya-Letelier Gerardo , Lopez-Garcia Diego
Published: February 2024

Demands to advance toward more resilient and sustainable cities in terms of reducing casualties, economic losses, downtime, and environmental impacts derived from earthquake-induced damage are becoming more frequent. Indeed, accurate evaluations of the seismic performance of buildings via numerical simulations are crucial for the sustainable development of the built environment. Nevertheless, performance estimations could be influenced by alternative probabilistic methods that can be chosen throughout the procedure of building-specific risk assessment, specifically in the construction and validation of fragility functions. This study evaluates the numerical impacts of selecting different probabilistic models on seismic risk metrics for reinforced concrete dual wall–frame buildings. Specifically, alternative probabilistic models are implemented and evaluated for (i) the identification and elimination of unusual observations within the simulated data (i.e., outliers); (ii) the selection and implementation of different Probability Distribution Functions (PDFs) to estimate fragility functions at different limit states (LSs); and (iii) the application of goodness-of-fit tests and information criteria to assess the validity of proposed PDFs. According to the results, the risk measures showed large variability at the extreme building LS (collapse). On the other hand, for a lower LS (service level), the measures remain similar in all the cases despite the methods selected. Further, the variability observed in the collapse response is up to two times that after eliminating data outliers. Finally, the large variability obtained with the evaluated alternative probabilistic modeling methods suggests re-opening the technical discussion over the state of the practice often used in earthquake engineering to improve the decision-making process, mitigating earthquake-induced consequences in an environmentally, economically, and socially beneficial manner.
Student-Led Campus Happiness Lunchboxes: Paying for Positive Impact
Authors: Wang Ze-Yung , Chen Kuo-Wei
Published: February 2024

In the aftermath of the 2022 Russian–Ukrainian war, Taiwan experienced economic shocks that prompted the government to initiate the happiness lunchbox program, aimed at fostering sustainable development and zero hunger concerns. Despite these efforts, economically disadvantaged university students faced challenges due to the unconventional outsourcing of campus meals. This study, conducted by leveraging campus culinary facilities, adopts an inferred value approach as opposed to the subjective willingness-to-pay (WTP) method, providing a more conservative assessment of students’ willingness to contribute. Through regression analysis, this study highlights the positive correlation between student engagement in charitable activities and WTP for student-led events. This involvement not only enhances food safety and hygiene but also reflects a genuine commitment to supporting financially challenged students. The comprehensive nature of this approach effectively tackles issues related to campus nutrition, emphasizing the significance of establishing a sustainable campus environment to achieve objectives such as “zero hunger” and “responsible consumption and production” on campus.
5-E Levers: A New Conceptual Model for Achieving Carbon Neutrality in Cities
Authors: Mazon Jordi
Published: February 2024

This article proposes a conceptual model for integrating and categorizing urban projects aimed at achieving carbon neutrality. This model comprises five interconnected levers: energy efficiency, renewable energy production and consumption, electrification of end use, circular economy, and CO2 ensnaring (capture). Each lever encompasses projects and initiatives capable of directly or indirectly capturing urban CO2 and accelerating the reduction of greenhouse gas emissions. These levers are interlinked, providing a road map for constructing a coherent and sustainable municipal model. Referred to as the “5-E levers”, this conceptual framework derives its name from the fact that all levers begin with the letter “E”, facilitating memorization and dissemination among policymakers.
The Effect of Cognitive Load on Learning Memory of Online Learning Accounting Students in the Philippines
Authors: Abeysekera Indra , Sunga Emily , Gonzales Avelino , David Raul
Published: February 2024

Before COVID-19, universities in the Philippines sparingly used online learning instructional methods. Online learning is now widely known, and universities are increasingly keen to adopt it as a mainstream instructional method. Accounting is a popular discipline of study undertaken by students, but its online adoption is less well known. This study investigated university accounting students’ perceptions of the cognitive load of learning and how it influences their effect on learning memory at a university in the Philippines. During the COVID-19 period, after introducing online learning, 482 university undergraduate accounting students provided their perceptions using a five-point Likert scale survey questionnaire. The study measured teaching quality, learning content quality, and learning management system (LMS) quality, representing the cognitive load of learning. It measured electronic learning (e-learning) quality, learner satisfaction, and behavioral intentions to adopt online learning, continually representing the learning memory framework. The data analyzed using a structural equation model showed that students managing their cognitive load positively influenced their short-term learning. Learning content, teaching, and LMS quality positively influenced e-learning quality and student satisfaction. Student satisfaction positively influenced, but e-learning quality did not influence, students’ continued willingness for online learning. The findings were largely consistent across the second- and third-year enrolments. Findings from the first-year students showed that teaching quality did not influence student satisfaction and e-learning quality. This is the first study to test the influence of the cognitive load of learning on the learning memory of accounting students in an online learning environment.
Life Cycle Assessment of Using Firewood and Wood Pellets in Slovenia as Two Primary Wood-Based Heating Systems and Their Environmental Impact
Authors: Topić Božič Jelena , Fric Urška , Čikić Ante , Muhič Simon
Published: February 2024

Sustainable use of biomass energy sources can reduce dependency on fossil fuels. Wood biomass is the primary source for heating in Slovenia, with firewood and wood pellets having the highest share. Slovenia’s largest consumers of wood fuels are households primarily using wood from their forests or imported wood pellets. This research used a life cycle assessment to analyze and evaluate the environmental impacts of using firewood and wood pellets for household heating in Slovenia for the first time. The results showed that wood logs have a considerably greater effect on stratospheric ozone depletion, ozone formation, and fine particulate matter (PM) formation. The impact on global warming was lower due to short transportation distances and using log boilers with high combustion efficiency (0.016 and 0.041 kg CO2 eq for wood log and wood pellet combustion, respectively). An increase in transportation distance from 100 km to 1000 km resulted in an 84.9% increase in the values for the categories ozone formation and human health, a 120.4% increase for fossil resource scarcity, and a 102.4% increase in global warming, supporting the premise that short distribution routes are necessary for more sustainable use of the energy source.
Risk Assessment and Attribution Analysis of Potentially Toxic Elements in Soil of Dongdagou, Baiyin, Gansu Province, China
Authors: Zhang Lirui , Wang Bo , Zhang Songlin
Published: February 2024

Analyzing the cause is crucial for recognizing the risks associated with potentially harmful substances found in soil, such as toxic elements. These substances can have adverse effects on both the ecological environment and human health, as they can migrate and transform within food chain networks. Therefore, it is imperative to address and prioritize the risks associated with these elements. Dongdagou, Baiyin City, Gansu Province, is a typical area of potentially toxic element pollution in farmland soil, which has attracted much attention and urgently needs to be controlled. Therefore, the main objective of this investigation is to analyze the concentrations of As, Cd, Pb, Hg, Cu, and Zn in the agricultural soil found in Dongdagou. Using statistical analysis, ecological and human health risk, principal component analysis, and the PMF model, we found that (1) there are varying degrees of accumulation in the soil in the study area, with Cu being the main component. (2) The soil in the study area has high and extremely high concentrations of Cd, posing significant risks. On the other hand, Hg presents mild and medium risks. However, there are no risks associated with As, Pb, Cu, and Zn. Overall, the ecological risks in the study area’s soil due to potentially toxic elements are predominantly extremely high (49.65%) and high (38.25%). A small proportion of the soil exhibits low risks (2.76%) and medium risks (9.33%). (3) As has a moderate acceptable carcinogenic risk for local residents, Cd has a moderate acceptable carcinogenic risk for local children, and other potentially toxic elements do not have carcinogenic or non-carcinogenic risks. (4) The source analysis shows that Cd in the soil in the study area mainly comes from agricultural activities and sewage irrigation, As mainly comes from industrial production, and Zn, Cu, Pb, and Hg are multiple sources. We recommend adopting targeted and differentiated safety utilization and control measures based on the pollution level and potential risks of potentially toxic elements in the research area, combined with the sources of potentially toxic elements.
Evaluation of the Environmental Impact and Energy Utilization Efficiency of Wastewater Treatment Plants in Tumen River Basin Based on a Life Cycle Assessment + Data Envelopment Analysis Model
Authors: Liu Jiaxin , Sun Bo , Piao Wenhua , Jin Mingji
Published: February 2024

The environmental impacts from energy consumption account for a high percentage of the environmental impacts of wastewater treatment plants (WWTPs) throughout their life cycle; therefore, controlling energy use in WWTPs could bring substantial benefits to the environment. In this study, according to the different percentages of electricity generation from renewable energy compared to fossil energy, the global warming, acidification, eutrophication, human toxicity, and photochemical smog, the environmental impacts of WWTP operation were considered. Furthermore, to explore a more sustainable way of operating WWTPs under the “dual-carbon” strategic decision, the environmental impacts and energy utilization efficiency of different power allocation scenarios at present and in the next 40 years were compared based on the LCA+DEA integrated model. The study revealed that in scenarios 1–5, as the proportion of renewable energy power generation gradually increased, all LCA results showed a gradual decrease, of which GWP decreased by 83.32% and human toxicity decreased by 93.34%. However, in scenarios 2–5, the contribution ratio (proportion) of gas and electricity to GWP and POCP gradually increased, reaching 77.11% and 59.44%, respectively, in scenario 5. The contribution ratio (proportion) of biomass generation to AP and EP gradually increased as well, reaching 65.22% and 68.75%, respectively, in scenario 5. Meanwhile, the combined technical efficiency in energy utilization in the five scenarios showed a decreasing trend; only scenario 1 was fully efficient, and the combined efficiency was 1. The values of combined technical efficiency in scenarios 2, 3, 4, and 5 gradually decreased and were 0.7386, 0.4771, 0.2967, and 0.1673, respectively. This study discusses whether the use of renewable energy in place of fossil energy power elicits an environmental impact in WWTPs. We explore the feasibility of achieving energy savings and emission reductions in WWTPs within the Tumen River Basin, to provide a theoretical basis for their sustainable development.
Eliminating Non-Value-Added Activities and Optimizing Manufacturing Processes Using Process Mining: A Stock of Challenges for Family SMEs
Authors: Laghouag Abderrazak , Zafrah Faiz bin , Qureshi Mohamed Rafik Noor Mohamed , Sahli Alhussain Ali
Published: February 2024

Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding leadership type, human resource management practices, innovation orientation, change management, information and communication technology deployment, process maturity, and resource availability. These differences present challenges when leading any change. Process mining (PM) tools can optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”. The present study investigates how a PM project is implemented in an FSME operating in the agri-food sector, focusing on challenges faced in every project phase to extract the most appropriate process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle methodology combined with quality and lean management tools such as the fishbone diagram, Pareto diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing process for an FSME operating in the agri-food sector. To achieve theoretical production capacity (TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle methodology allowed for the identification of five critical causes. An action plan to eliminate causes was proposed to the FSME managers.
Estimating the Social Value of Digital Signage Landmarks as Sustainable Tourist Attractions
Authors: Quan Lihua , Hong Insu , Lee Taejun , Yoo Changsok
Published: February 2024

As urban tourism increases, digital signage landmarks are frequently utilized to develop and enhance the attractiveness of cities for tourism. However, the benefits of this development for local residents have not been fully explored from a sustainability perspective. Thus, this study aims to quantitatively analyze local residents’ perceptions of digital signage landmarks in urban areas using one of the prominent icons, the Samseong-dong free display zone in Seoul, Korea. To measure the overall value of the landmarks, this study used a double-bounded dichotomous choice contingent valuation method and spike model. Based on the surveys of 600 respondents in Korea, the results show that a household’s willingness to pay to support the landmark annually is KRW 5401 (USD 4) on average in the form of income tax. The perceived annual value for the landmark is about KRW 790 million (USD 60 million), surpassing that of typical tourism attractions in Korea.