bluuebunny/crossref_metadata_2025 huggingface dataset:
Automatic Building Roof Plane Extraction in Urban Environments for 3D City Modelling Using Remote Sensing Data
Authors: Campoverde Carlos , Koeva Mila , Persello Claudio , Maslov Konstantin , Jiao Weiqin , Petrova-Antonova Dessislava
Published: April 2024

Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–‘t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–‘t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.
Enhancing Soil Moisture Active–Passive Estimates with Soil Moisture Active–Passive Reflectometer Data Using Graph Signal Processing
Authors: Garcia-Cardona Johanna , Rodriguez-Alvarez Nereida , Munoz-Martin Joan Francesc , Bosch-Lluis Xavier , Oudrhiri Kamal
Published: April 2024

The Soil Moisture Active–Passive (SMAP) mission has greatly contributed to the use of remote sensing technologies for monitoring the Earth’s land surface and estimating geophysical parameters that influence the climate system. Since the SMAP mission switched its radar receiver to allow the reception of Global Positioning System (GPS) signals, Global Navigation Satellite System Reflectometry (GNSS-R) configuration has been enabled, providing full polarimetric forward scattering measurements of the Earth’s surface, also known as SMAP Reflectometry or SMAP-R. Polarimetric GNSS-R is beneficial for sensing land surface properties, especially for more accurate estimations of soil moisture (SM) in densely vegetated areas. In this study, we explore the opportunity to enhance SMAP mission soil moisture estimates using reflected GNSS signals. We achieve this by interpolating the sparse reflectivity data with terrain information to disaggregate radiometer brightness temperatures. Our main objective is to present a novel algorithm based on Graph Signal Processing (GSP) that uses reflectometry data to enhance SMAP radiometer observations and ultimately improve SM retrievals. By implementing methods from the GSP field, we formulate the reflectivity interpolation problem as a signal reconstruction on a graph, where the weights of the edges between the nodes are chosen as a function of geophysical information. Subsequently, using the retrieved reflectivity maps, we increase the resolution of the brightness temperature data, leading to an improvement in the SM estimates. Initial findings indicate that our GSP method presents a promising alternative for analyzing sparse remote sensing observations, leveraging Earth’s surface geophysical information. This approach results in a notable improvement, with a reduced Root Mean Square Error (RMSE) of 11.8% compared to SMAP data and a reduction in unbiased RMSE (uRMSE) by 14.7% over vegetated areas.
The Relationship between Indoor and Outdoor Fine Particulate Matter in a High-Rise Building in Chicago Monitored by PurpleAir Sensors
Authors: Wenner Megan M. , Ries-Roncalli Anna , Whalen Mena C. R. , Jing Ping
Published: April 2024

In urban areas like Chicago, daily life extends above ground level due to the prevalence of high-rise buildings where residents and commuters live and work. This study examines the variation in fine particulate matter (PM2.5) concentrations across building stories. PM2.5 levels were measured using PurpleAir sensors, installed between 8 April and 7 May 2023, on floors one, four, six, and nine of an office building in Chicago. Additionally, data were collected from a public outdoor PurpleAir sensor on the fourteenth floor of a condominium located 800 m away. The results show that outdoor PM2.5 concentrations peak at 14 m height, and then decline by 0.11 μg/m3 per meter elevation, especially noticeable from midnight to 8 a.m. under stable atmospheric conditions. Indoor PM2.5 concentrations increase steadily by 0.02 μg/m3 per meter elevation, particularly during peak work hours, likely caused by greater infiltration rates at higher floors. Both outdoor and indoor concentrations peak around noon. We find that indoor and outdoor PM2.5 are positively correlated, with indoor levels consistently remaining lower than outside levels. These findings align with previous research suggesting decreasing outdoor air pollution concentrations with increasing height. The study informs decision-making by community members and policymakers regarding air pollution exposure in urban settings.
Multistatic Integrated Sensing and Communication System Based on Macro–Micro Cooperation
Authors: Wang Xiaoyun , Han Zixiang , Jin Jing , Xi Rongyan , Wang Yajuan , Han Lincong , Ma Liang , Lou Mengting , Gui Xin , Wang Qixing , Liu Guangyi
Published: April 2024

A novel multistatic integrated sensing and communication (ISAC) system based on macro–micro cooperation for the sixth-generation (6G) mobile network is proposed. Instead of using macrosites at both the transmitter and receiver sides, microsites are considered as receivers in cooperative sensing. This system is important since microsites can be deployed more flexibly to reduce their distances to the sensing objects, providing better coverage for sensing service. In this work, we first analyze the deployment problem of microsites, which can be deployed along the radius and azimuth angle to cover macrosite cells. The coverage area of each microsite is derived in terms of its position in the cell. Then, we describe an efficient estimating approach for obtaining the position and velocity of sensing objects in the macrosite cell. By choosing multiple microsites around the targeted sensing area, joint data processing with an efficient optimization method is also provided. Simulation results show that the multistatic ISAC system employing macro–micro cooperation can improve the position and velocity estimation accuracy of objects compared to systems employing macrosite cooperation alone, demonstrating the effectiveness and potential for implementing the proposed system in the 6G mobile network.
Easy-to-Use MOX-Based VOC Sensors for Efficient Indoor Air Quality Monitoring
Authors: Pietraru Radu Nicolae , Nicolae Maximilian , Mocanu Ștefan , Merezeanu Daniel-Marian
Published: April 2024

This research paper presents a case study on the application of Metal Oxide Semiconductor (MOX)-based VOC/TVOC sensors for indoor air quality (IAQ) monitoring. This study focuses on the ease of use and the practical benefits of these sensors, drawing insights from measurements conducted in a university laboratory setting. The investigation showcases the straightforward integration of MOX-based sensors into existing IAQ monitoring systems, highlighting their user-friendly features and the ability to provide precise and real-time information on volatile organic compound concentrations. Emphasizing ease of installation, minimal maintenance, and immediate data accessibility, this paper demonstrates the practicality of incorporating MOX-based sensors for efficient IAQ management. The findings contribute to the broader understanding of MOX sensor capabilities, providing valuable insights for those seeking straightforward and effective solutions for indoor air quality monitoring. This case study outlines the feasibility and benefits of utilizing MOX-based sensors in various environments, offering a promising avenue for the widespread adoption of user-friendly technologies in IAQ management.
Sensing Utilities of Cesium Lead Halide Perovskites and Composites: A Comprehensive Review
Authors: Shellaiah Muthaiah , Sun Kien Wen , Thirumalaivasan Natesan , Bhushan Mayank , Murugan Arumugam
Published: April 2024

Recently, the utilization of metal halide perovskites in sensing and their application in environmental studies have reached a new height. Among the different metal halide perovskites, cesium lead halide perovskites (CsPbX3; X = Cl, Br, and I) and composites have attracted great interest in sensing applications owing to their exceptional optoelectronic properties. Most CsPbX3 nanostructures and composites possess great structural stability, luminescence, and electrical properties for developing distinct optical and photonic devices. When exposed to light, heat, and water, CsPbX3 and composites can display stable sensing utilities. Many CsPbX3 and composites have been reported as probes in the detection of diverse analytes, such as metal ions, anions, important chemical species, humidity, temperature, radiation photodetection, and so forth. So far, the sensing studies of metal halide perovskites covering all metallic and organic–inorganic perovskites have already been reviewed in many studies. Nevertheless, a detailed review of the sensing utilities of CsPbX3 and composites could be helpful for researchers who are looking for innovative designs using these nanomaterials. Herein, we deliver a thorough review of the sensing utilities of CsPbX3 and composites, in the quantitation of metal ions, anions, chemicals, explosives, bioanalytes, pesticides, fungicides, cellular imaging, volatile organic compounds (VOCs), toxic gases, humidity, temperature, radiation, and photodetection. Furthermore, this review also covers the synthetic pathways, design requirements, advantages, limitations, and future directions for this material.
Wind-Induced Vibration Monitoring of High-Mast Illumination Poles Using Wireless Smart Sensors
Authors: Shaheen Mona , Li Jian , Bennett Caroline , Collins William
Published: April 2024

This paper describes the use of wireless smart sensors for examining the underlying mechanism for the wind-induced vibration of high-mast illumination pole (HMIP) structures. HMIPs are tall, slender structures with low inherent damping. Video recordings of multiple HMIPs showed considerable vibrations of these HMIPs under wind loading in the state of Kansas. The HMIPs experienced cyclic large-amplitude displacements at the top, which can produce high-stress demand and lead to fatigue cracking at the bottom of the pole. In this study, the natural frequencies of the HMIP were assessed using pluck tests and finite element modeling, and the recorded vibration frequencies were obtained through computer vision-based video analysis. Meanwhile, a 30.48 m tall HMIP with three LED luminaires made of galvanized steel located in Wakeeney, Kansas, was selected for long-term vibration monitoring using wireless smart sensors to investigate the underlying mechanism for the excessive wind-induced vibrations. Data analysis with the long-term monitoring data indicates that while vortex-induced vibration occurs frequently at relatively low amplitude, buffeting-induced vibration was the leading cause of the excessive vibrations of the monitored HMIP. The findings provide crucial information to guide the design of vibration mitigation strategies for these HMIP structures.
The Methodology for Evaluating the Operating State of SF6 HVCBs Based on IDDA
Authors: Bai Tong , Sun Chenhao , Feng Wenqing , Liu Yajing , Zhang Huanzhen , Wang Yujia
Published: April 2024

To enhance the precision of evaluating the operational status of SF6 high-voltage circuit breakers (HVCBs) and devise judicious maintenance strategies, this study introduces an operational state assessment method for SF6 HVCBs grounded in the integrated data-driven analysis (IDDA) model. The relative degradation weight (RDW) is introduced as a metric for quantifying the relative significance of distinct indicators concerning the operational condition of SF6 HVCBs. A data-driven model, founded on critical factor stability (CFS), is formulated to convert environmental indicators into quantitative computations. Furthermore, an optimized fuzzy inference (OFI) system is devised to streamline the system architecture and enhance the processing speed of continuous indicators. Ultimately, the efficacy of the proposed model is substantiated through validation, and results from instance analyses underscore that the presented approach not only attains heightened accuracy in assessment compared to extant analytical methodologies but also furnishes a dependable foundation for prioritizing maintenance sequences across diverse components.
Experimental Evaluation of Pedestrian-Induced Multiaxial Gait Loads on Footbridges: Effects of the Structure-to-Human Interaction by Lateral Vibrating Platforms
Authors: Castillo Bryan , Marulanda Johannio , Thomson Peter
Published: April 2024

The introduction of resistant and lightweight materials in the construction industry has led to civil structures being vulnerable to excessive vibrations, particularly in footbridges exposed to human-induced gait loads. This interaction, known as Human–Structure Interaction (HSI), involves a complex interplay between structural vibrations and gait loads. Despite extensive research on HSI, the simultaneous effects of lateral structural vibrations with fundamental frequencies close to human gait frequency (around 1.0 Hz) and wide amplitudes (over 30.0 mm) remain inadequately understood, posing a contemporary structural challenge highlighted by incidents in iconic bridges like the Millennium Bridge in London, Solferino Bridge in Paris, and Premier Bridge in Cali, Colombia. This paper focuses on the experimental exploration of Structure-to-Human Interaction (S2HI) effects using the Human–Structure Interaction Multi-Axial Test Framework (HSI-MTF). The framework enables the simultaneous measurement of vertical and lateral loads induced by human gait on surfaces with diverse frequency ranges and wide-amplitude lateral harmonic motions. The study involved seven test subjects, evaluating gait loads on rigid and harmonic lateral surfaces with displacements ranging from 5.0 to 50.0 mm and frequency content from 0.70 to 1.30 Hz. A low-cost vision-based motion capture system with smartphones analyzed the support (Tsu) and swing (Tsw) periods of human gait. Results indicated substantial differences in Tsu and Tsw on lateral harmonic protocols, reaching up to 96.53% and 58.15%, respectively, compared to rigid surfaces. Normalized lateral loads (LL) relative to the subject’s weight (W0) exhibited a linear growth proportional to lateral excitation frequency, with increased proportionality constants linked to higher vibration amplitudes. Linear regressions yielded an average R2 of 0.815. Regarding normalized vertical load (LV) with respect to W0, a consistent behavior was observed for amplitudes up to 30.0 mm, beyond which a linear increase, directly proportional to frequency, resulted in a 28.3% increment compared to rigid surfaces. Correlation analyses using Pearson linear coefficients determined relationships between structural surface vibration and pedestrian lateral motion, providing valuable insights into Structure-to-Human Interaction dynamics.
Inspection Robot Navigation Based on Improved TD3 Algorithm
Authors: Huang Bo , Xie Jiacheng , Yan Jiawei
Published: April 2024

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long–short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics.
Design of a Self-Measuring Device Based on Bioelectrical Impedance Analysis for Regular Monitoring of Rheumatoid Arthritis
Authors: Jeong JuYoung , Park Yun Soo , Lee Eunchae , Choi SeoYoun , Lim Dokshin , Kim Jiho
Published: April 2024

Rheumatoid arthritis (RA) is a chronic disease, in which permanent joint deformation is largely preventable with the timely introduction of appropriate treatment strategies. However, there is no consensus for patients with RA to monitor their progress and communicate it to the rheumatologist till the condition progresses to remission. In response to this unmet need, we proposed the design of a self-measuring device based on bioelectrical impedance analysis (BIA) for regular monitoring of inflammation levels. Twenty joints of both hands were measured to monitor trends in inflammation levels. Three electrodes were used to measure two joints of each finger. A central electrode was used for two consecutive measurements. A suitable form factor for the device was proposed for the vertical placement of the hand. To ensure the stability of measurements, an air cushion was incorporated into the back of the hand, hand containers were designed on both sides, and a mobile application was designed. We conducted a convergence-assessment experiment with five air pressures to validate the consistency and convergence of bioimpedance measurements. A heuristic evaluation of the usability around the product and mobile application was conducted in parallel by six subject matter experts and validated the design. This study underscores the significance of considering patients’ disease activity during intervals between hospital visits and introduces a novel approach to self-RA care.
Editorial for the Special Issue “Sensing Brain Activity Using EEG and Machine Learning”
Authors: Rogelj Peter
Published: April 2024

Sensing brain activity to reveal, analyze and recognize brain activity patterns has become a topic of great interest and ongoing research [...]
Resource-Efficient Multicast URLLC Service in 5G Systems
Authors: Krasilov Artem , Lebedeva Irina , Yusupov Ruslan , Khorov Evgeny
Published: April 2024

Many emerging applications, such as factory automation, electric power distribution, and intelligent transportation systems, require multicast Ultra-Reliable Low-Latency Communications (mURLLC). Since 3GPP Release 17, 5G systems natively support multicast functionality, including multicast Hybrid Automatic Repeat Request and various feedback schemes. Although these features can be promising for mURLLC, the specifications and existing studies fall short in offering guidance on their efficient usage. This paper presents the first comprehensive system-level evaluation of mURLLC, leveraging insights from 3GPP specifications. It points out (i) how mURLLC differs from traditional multicast broadband wireless communications, and (ii) which approaches to provide mURLLC require changing the paradigm compared with the existing solutions. Finally, the paper provides recommendations on how to satisfy strict mURLLC requirements efficiently, i.e., with low channel resource consumption, which increases the capacity of 5G systems for mURLLC. Simulation results show that proper configuration of multicast mechanisms and the corresponding algorithms for mURLLC traffic can reduce resource consumption up to three times compared to the baseline solutions proposed for broadband multicast traffic, which significantly increases the system capacity.
Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon
Authors: Vien Benjamin Steven , Kuen Thomas , Rose Louis Raymond Francis , Chiu Wing Kong
Published: April 2024

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water’s Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model’s accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model’s predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors.
Experimental Study on SPR Array Sensing Chip Integrated with Microvalves
Authors: Chen Wanwan , Wang Peng , Li Bin
Published: April 2024

This paper discusses a microfluidic system designed for surface plasmon resonance (SPR) sensing, incorporating integrated microvalves. This system is built from a layered structure of polydimethylsiloxane (PDMS) and polymethylmethacrylate (PMMA). The functionality of the microvalves is verified through a conductance method involving electrodes positioned at the microfluidic channels’ inlets and outlets. These microvalves can fully close at a control pressure of 0.3 MPa, with their operation depending on the duration of the applied pressure. The study further explores the coordinated operation of multiple microvalves to regulate the sequential flow of samples and reagents in the system. In SPR detection experiments, the microfluidic system is integrated with an SPR array sensing system to control the injection of NaCl solutions via the microvalves, and the observation of phase change curves in different chip regions are observed. The findings validate the microvalves’ dependability and suitability for use in SPR array sensing.
Comparative Study and Evaluation of Sediment Deposition and Migration Characteristics of New Sustainable Filter Media in Micro-Irrigation Sand Filters
Authors: Song Lei , Cai Jiumao , Zhai Guoliang , Feng Junjie , Fan Yongshen , Han Jinzhao , Hao Pingping , Ma Ning , Miao Faqiang
Published: April 2024

The quartz sand filter medium used in micro-irrigation media filters has the disadvantages of short filtration cycle, surface filtration, and mining pollution. Selecting resources as new filter media is essential to improve the performance of the media filter and boost sustainable development. In this study, the traditional quartz sand filter medium and two new filter media were selected, and their corresponding filtration performances were comparatively studied. The influence of the type, particle size, and height of the filter medium on filtration performance was evaluated. The sediment content and distribution based on the size of particles in quartz sand, crushed glass, and glass bead filter layers was measured and analyzed. The hydraulic performance of different filter columns was analyzed. The results showed that for a given particle size, quartz sand exhibits the best sediment retention ability. This promoted the aggregation of small sediment particles into larger ones, whereas the crushed glass and bead glass filter layers promoted the splitting of large sediment particles into smaller ones, which enabled the reduction of blockage during the micro-irrigation process. The filtration rate of the quartz sand filter column exhibited the least fluctuation relative to crushed glass and glass bead filter media, and the pressure in each column exhibited a linear incremental change. In summary, glass microbeads are not suitable as filter material, crushed glass is suitable for general micro-irrigation systems, and quartz sand is suitable for micro-irrigation systems with elaborate filtration requirements. The findings of this study can provide theoretical guidance for the selection of the micro-irrigation filter material.
Research on Energy Conservation and Emission-Reduction Effects of Green Finance: Evidence from China
Authors: Jiang Runnan , Jin Chengxiao , Wang Haoyu
Published: April 2024

The energy-saving and low-carbon development model is one of the important symbols of high-quality economic development. This article attempts to study the environmental effects of green finance from both theoretical and empirical perspectives, that is, to test whether green finance policies contribute to achieving energy conservation and emission reduction. This article is based on provincial panel data from 2007 to 2020 in China and constructs a dynamic spatial Durbin model to examine the impact of green finance on environmental pollution and energy intensity. The results indicate that (1) green finance can achieve a dual effect of energy conservation and emission reduction simultaneously and has a significant promoting effect on energy conservation and emission reduction in neighboring regions. This conclusion is still valid after conducting robustness tests. (2) The energy-saving and emission-reduction effects of green finance exhibit significant regional heterogeneity, indicating that the performance of green finance is more outstanding in the eastern region with a higher level of economic development. (3) Mechanism testing has found that green finance can achieve energy-saving and emission-reduction effects through four channels: environmental regulation, credit allocation, enterprise profits, and enterprise innovation. Therefore, in order to further promote high-quality economic development, we need to build a comprehensive and multi-level green finance system, enrich the green finance policy toolbox, and smooth the transmission channels of green finance to promote green and stable economic development.
Adding Machine-Learning Functionality to Real Equipment for Water Preservation: An Evaluation Case Study in Higher Education
Authors: Kondoyanni Maria , Loukatos Dimitrios , Arvanitis Konstantinos G. , Lygkoura Kalliopi-Argyri , Symeonaki Eleni , Maraveas Chrysanthos
Published: April 2024

Considering that the fusion of education and technology has delivered encouraging outcomes, things are becoming more challenging for higher education as students seek experiences that bridge the gap between theory and their future professional roles. Giving priority to the above issue, this study presents methods and results from activities assisting engineering students to utilize recent machine-learning techniques for tackling the challenge of water resource preservation. Cost-effective, innovative hardware and software components were incorporated for monitoring the proper operation of the corresponding agricultural equipment (such as electric pumps or water taps), and suitable educational activities were developed involving students of agricultural engineering. According to the evaluation part of the study being presented, the implementation of a machine-learning system with sufficient performance is feasible, while the outcomes derived from its educational application are significant, as they acquaint engineering students with emerging technologies entering the scene and improve their capacity for innovation and cooperation. The study demonstrates how emerging technologies, such as IoT, ML, and the newest edge-AI techniques can be utilized in the agricultural industry for the development of sustainable agricultural practices. This aims to preserve natural resources such as water, increase productivity, and create new jobs for technologically efficient personnel.
Blended Learning Delivery Methods for a Sustainable Learning Environment: A Delphi Study
Authors: Alammary Ali Saleh
Published: April 2024

The outbreak of COVID-19 necessitated social distancing. Universities around the world were left with two options: a total suspension or a major reduction in students’ attendance. While the nature of many courses made it very difficult to teach them online, blended learning was the most suitable approach to teach these types of courses. Academics and educational institutions have realized the significance of blended learning not only as a response to immediate disruptions but also as a pivotal element in fostering a sustainable learning environment. However, designing successful blended learning courses requires making challenging decisions regarding the selection of the most appropriate delivery methods to achieve learning outcomes. To support higher education adoption of blended learning, this study identifies and rates the importance of the delivery methods that need consideration when designing a blended learning course. The aim is to assist academics to prioritize their delivery options and provide adaptable and resilient educational models. A Delphi study of two rounds was conducted to identify and rate the delivery methods. An expert panel of 19 academics with extensive experience in course design and online delivery was recruited. The findings indicate that online collaborative work and face-to-face collaborative work should be academics’ first delivery choices. Interestingly, face-to-face instructor-led learning was at the bottom of the list and rated well below all the other delivery methods.
Conservation, Sustainability, Conflict and Coexistence: Key Themes in Wildlife Management
Authors: Liordos Vasilios
Published: April 2024

Human activities have shaped the Earth’s biotic and abiotic elements for millennia [...]
Environmental Policy Overlays and Urban Pollution and Carbon Reduction—Evidence from China
Authors: Wan Kai , Bu Yanjun
Published: April 2024

The in-depth promotion of environmental pollution prevention and control is a must for China to move towards green development, and the effectiveness of urban environmental pollution control largely depends on the selection of these environmental policies and the synergistic application of these policies. This paper empirically tests three environmental policies’ mixed and synergistic effects using Chinese urban data from 2000 to 2017. This study found that (1) the three environmental policies significantly curb urban environmental pollution and show synergistic effects. (2) The three types of environmental policies can have a superimposing effect by strengthening financial inputs and increasing environmental concern, as well as a complementary effect by improving the efficiency of environmental protection enforcement and the institutional environment. The synergy between the central environmental protection inspection policy and the environmental information disclosure policy is more of a superposition effect than a complementary effect due to the similarity of the policy objectives. (3) The synergistic effect of environmental policies is affected by factors such as the age of the principal officials and financial pressure. The younger the age of the principal officials and the cities with less financial pressure are more capable of utilizing the synergistic effect of environmental policies to form a situation where the market, the government, the public, and the enterprises work together to govern the city. This paper provides new ideas for exploring the synergies and mechanisms of environmental policies and theoretical references for the design, implementation, and effects of environmental policy portfolios in the 14th Five-Year Plan period.
Analysis of Technical and Operational Requirements of Alternative Drive Systems by Transport Companies: The Case of the Overhead Contact Line Truck
Authors: Linke Regina , Öztürk Özgür , Kassens-Noor Eva
Published: April 2024

A change to zero-emission drive systems in road freight transport is required to achieve climate protection targets. Therefore, alternative drive systems for road freight transport are being tested in field trials in Germany. One such technology is the eHighway system. The eHighway system combines an overhead contact line hybrid truck (OH truck) and an overhead contact line infrastructure. The objective of this study was to evaluate technical and operational success factors and barriers influencing the adoption of the eHighway system by freight transport companies. We evaluated expert interviews, written surveys, and vehicle data. We found that transport companies had no major restrictions concerning the technical equipment of the OH trucks. Furthermore, we found no operational restrictions in the use of OH trucks while the OH trucks can be integrated into different transport operations.
Assessing the Degree of Sustainability in Extractive Reserves in the Amazon Biome Using the Fuzzy Logic Tool for Decision Making
Authors: Lopes Raimundo Valdan Pereira , Tejerina-Garro Francisco Leonardo , Leite Jandecy Cabral , Nascimento Manoel Henrique Reis , Nascimento Aline Santos do
Published: April 2024

The Extractive Reserve (RESEX) was designed to protect rubber tapping communities and their livelihoods, thus guaranteeing environmental health. This study was carried out between 2021 and 2023 and aimed to propose a methodology based on the fuzzy logic method to assess the degree of sustainability in RESEXs in the state of Amazonas, Brazil. For this assessment, 10 indicators were used, represented through input variables in the fuzzy inference systems represented by the Environmental Subsystem (ES), Economic Subsystem (ECS), Social Subsystem (SS), and Institutional Subsystem (IS), with performances that converged so that the Sustainability System in the RESEX (SRE) system reached a performance value of 30.0, on a scale of 0 to 100, which translates into low sustainability in these spaces in the state of Amazonas. The methodology’s ability to represent the main phenomena that impact sustainability in the RESEX studied through linguistic variables and weight them in their complexities, as well as inferring a set of decision rules that reflect the knowledge of experts and which aim to quantitatively contextualise sustainability under uncertainty and imprecision in these areas, makes it a viable instrument to be applied and used by managers and decision-makers in the management of these spaces.
Are Regions Conducive to Photovoltaic Power Generation Demonstrating Significant Potential for Harnessing Solar Energy via Photovoltaic Systems?
Authors: Bao Jiayu , Li Xianglong , Yu Tao , Jiang Liangliang , Zhang Jialin , Song Fengjiao , Xu Wenqiang
Published: April 2024

To achieve the goals of carbon peak and carbon neutrality, Xinjiang, as an autonomous region in China with large energy reserves, should adjust its energy development and vigorously develop new energy sources, such as photovoltaic (PV) power. This study utilized data spatiotemporal variation in solar radiation from 1984 to 2016 to verify that Xinjiang is suitable for the development of PV power generation. Then, the averages of the solar radiation, sunshine duration, and other data in the period after 2000 were used to assess the suitability of Xinjiang, based on spatial principal component analysis (SPCA). Finally, the theoretical power generation potential, fossil fuel reduction, and CO2 emissions reduction were estimated. The results are as follows: (1) In terms of temporal variation, the solar radiation in Xinjiang decreased (1984–2002), increased (2002–2009), and decreased again (2009–2016), but the fluctuations were not statistically significant. In terms of spatial distribution, the Kunlun Mountains in southern Xinjiang had the highest solar radiation during the span of the study period. Hami and Turpan, in eastern Xinjiang, had sufficiently high and stable solar radiation. (2) The area in Xinjiang classed as highly suitable for solar PV power generation is about 87,837 km2, which is mainly concentrated in eastern Xinjiang. (3) In the situation where the construction of PV power plants in Xinjiang is fully developed, the theoretical potential of annual solar PV power generation in Xinjiang is approximately 8.57 × 106 GWh. This is equivalent to 2.59 × 109 tce of coal. Furthermore, 6.58 × 109 t of CO2 emissions can be reduced. PV power generation potential is approximately 27 times the energy consumption of Xinjiang in 2020. Through the suitability assessment and calculations, we found that Xinjiang has significant potential for PV systems.
Application of Multi-Criteria Decision-Making (MCDM) to Select the Most Sustainable Power-Generating Technology
Authors: Parvaneh Fatemeh , Hammad Ahmed
Published: April 2024

In response to the growing importance of sustainability and regulatory pressures, companies are increasingly engaging in sustainable projects to mitigate environmental and social harm. Therefore, it is crucial to incorporate sustainability considerations during selecting construction projects in the feasibility phase. This study aims to identify a comprehensive set of sustainability criteria and sub-criteria to help the owners of power-generating plants to select the most sustainable technology for their new projects. Sixteen criteria are identified and categorized under the pillars of sustainability: economic, social, and environmental, plus the technical category. To illustrate practical application, a case study demonstrates the use of these essential sustainability criteria through a hybrid multi-criteria decision-making (MCDM) model for power-generating technology ranking. The results suggest that when stakeholders’ perspectives are weighted approximately equally, considering all sustainability pillars, natural gas with carbon capture is favored for sustainability. A three-scenario sensitivity analysis was performed involving expert opinions from one of the largest power-generating companies in Canada. This integrated generic model can be utilized by industry experts to apply multi-dimensional rational decision-making techniques to solve the complex problem of selecting the most sustainable alternative in construction projects.
Review on Sustainable Construction and Demolition Waste Management—Challenges and Research Prospects
Authors: Islam Noushin , Sandanayake Malindu , Muthukumaran Shobha , Navaratna Dimuth
Published: April 2024

(1) Background: Globally, numerous development projects are being undertaken to expand and improve urban infrastructure facilities, which result in around 30% to 40% of construction and demolition (C&D) waste in the total waste generation. Due to its detrimental impacts on the environment and human health, several researchers have been striving to find effective methods to manage these large amounts of C&D wastes to minimise environmental impacts and maximise the economic and social benefits. Despite these efforts, limited studies have focused on comprehensively reviewing the integrated sustainable management of C&D waste. Thus, this study aims to conduct a comprehensive review of the published literature to identify barriers that hinder sustainable C&D waste management practices and map future research areas for effective C&D waste management. (2) Methods: A two-step systematic approach was adopted where the first step involved a bibliometric assessment of the published literature from 2002 to 2022, and then the most significant publications were reviewed from the bibliometric findings to achieve this study aim. (3) Results: The review findings identifies several research gaps which were categorised according to seven themes, including limitations in operational aspects, the lack of monitoring of legislation and regulations, an inadequate market for recycled products, inefficient stakeholder engagement and management, limitations of effective framework/models for sustainable C&D waste management, limited research on social performance, and inadequate integration of information technology in C&D waste management. This study’s findings are invaluable for researchers, industry professionals, and policymakers for in-depth understanding of the boundaries and potential future research areas to enhance sustainable C&D waste management practices.
A Coordinated Control Strategy of Multi-Type Flexible Resources and Under-Frequency Load Shedding for Active Power Balance
Authors: Zhang Jian , Wang Jiaying , Cao Yongji , Li Baoliang , Li Changgang
Published: April 2024

With the increasing expansion of power systems, there is a growing trend towards active distribution networks for decentralized power generation and energy management. However, the instability of distributed renewable energy introduces complexity to power system operation. The active symmetry and balance of power systems are becoming increasingly important. This paper focuses on the characteristics of distributed resources and under-frequency load shedding, and a coordinated operation and control strategy based on the rapid adjustment of energy storage power is proposed. The characteristics of various controllable resources are analyzed to explore the rapid response capabilities of energy storage. The energy storage types are categorized based on the support time, and the final decision is achieved with power allocation and adjustment control of the energy storage system. Additionally, a comprehensive control strategy for under-frequency load shedding and hierarchical systems is provided for scenarios with insufficient active support. The feasibility of the proposed model and methods is verified via a multi-energy system case.
Cyanobacteria and Macroinvertebrate Relationships in Freshwater Benthic Communities beyond Cytotoxicity
Authors: Ubero-Pascal Nicolás , Aboal Marina
Published: April 2024

Cyanobacteria are harmful algae that are monitored worldwide to prevent the effects of the toxins that they can produce. Most research efforts have focused on direct or indirect effects on human populations, with a view to gain easy accurate detection and quantification methods, mainly in planktic communities, but with increasing interest shown in benthos. However, cyanobacteria have played a fundamental role from the very beginning in both the development of our planet’s biodiversity and the construction of new habitats. These organisms have colonized almost every possible planktic or benthic environment on earth, including the most extreme ones, and display a vast number of adaptations. All this explains why they are the most important or the only phototrophs in some habitats. The negative effects of cyanotoxins on macroinvertebrates have been demonstrated, but usually under conditions that are far from natural, and on forms of exposure, toxin concentration, or composition. The cohabitation of cyanobacteria with most invertebrate groups is long-standing and has probably contributed to the development of detoxification means, which would explain the survival of some species inside cyanobacteria colonies. This review focuses on benthic cyanobacteria, their capacity to produce several types of toxins, and their relationships with benthic macroinvertebrates beyond toxicity.
Antifungal Activity of Ribosome-Inactivating Proteins
Authors: Iglesias Rosario , Citores Lucía , Gay Claudia C. , Ferreras José M.
Published: April 2024

The control of crop diseases caused by fungi remains a major problem and there is a need to find effective fungicides that are environmentally friendly. Plants are an excellent source for this purpose because they have developed defense mechanisms to cope with fungal infections. Among the plant proteins that play a role in defense are ribosome-inactivating proteins (RIPs), enzymes obtained mainly from angiosperms that, in addition to inactivating ribosomes, have been studied as antiviral, fungicidal, and insecticidal proteins. In this review, we summarize and discuss the potential use of RIPs (and other proteins with similar activity) as antifungal agents, with special emphasis on RIP/fungus specificity, possible mechanisms of antifungal action, and the use of RIP genes to obtain fungus-resistant transgenic plants. It also highlights the fact that these proteins also have antiviral and insecticidal activity, which makes them very versatile tools for crop protection.