bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Crosslink Density on Electrical Performance and Rheological Properties of Crosslinked Polyethylene
Authors: Di Linting , Qin Chenyuan , Wang Wenying , Huang Anping , Wei Fuqing , Xu Huifang , Yang Shiyuan
Published: March 2024

To investigate the influence of the crosslinked polyethylene (XLPE) structure on electrical performance, various analytical methods were employed to study polyethylene structures with different degrees of crosslinking. Dynamic rheological analysis was conducted to determine material shear viscosity, dynamic viscosity, storage modulus (G′), loss modulus (G″), and other rheological parameters. Additionally, the electrical performance of the material was analyzed by studying the phenomenon of space charge accumulation under direct current voltage. The results indicate that with an increasing mass fraction of the crosslinking agent, the crosslink density of crosslinked polyethylene initially increases and then decreases. When the dicumyl peroxide (DCP) content exceeds 1.0 wt.%, there is an accumulation of like-polarity space charges. The best rheological processing performance of crosslinked polyethylene is observed when the DCP content is in the range of 1.0–1.5 wt.%.
Influence of the Trailing Edge Shape of Impeller Blades on Centrifugal Pumps with Unsteady Characteristics
Authors: Song Zhengkai , Chen Yuxuan , Yu Tao , Wang Xi , Cao Haifeng , Li Zhiqiang , Lang Xiaopeng , Xu Simeng , Lu Shiyi , Jiang Chenxing
Published: March 2024

The flow field structure and pressure pulsation characteristics in two series of trailing edges of a centrifugal pump are investigated using the SST k-w turbulence model. Series 1 involves varying the impeller exit angle, and Series 2 involves varying the impeller exit shape. The entropy generation rate analysis method is used to evaluate the numerical simulation results. Vortex cores within the flow field are identified by applying the Ω criterion. The influence of different trailing edge configurations on the energy loss characteristics of the pumps is explored. The dynamic mode decomposition (DMD) method is used to analyze pressure pulsations at the volute considering unsteady flows in centrifugal pumps with different trailing edge shapes. The findings suggest that different trailing edge shapes can be used to adjust the energy loss proportions in various components of the pump. In Series 1, the efficiency remains nearly constant with changes in the outlet angle on both sides of the trailing edge. In Series 2, the efficiency is enhanced by 1.18% with the elliptical edge shape on both sides (EBS) compared to the original trailing edge (OTE) shape. In Series 1 and Series 2, greater entropy generation rates are accompanied by greater pressure pulsations at the pump outlet. The DMD results demonstrate a noticeable impact of the different trailing edges on the pressure distribution of various modes within the volute. Moreover, the impeller outlet pressure inhomogeneity coefficient changes under different modes. This study holds great significance for selecting the appropriate trailing edges for centrifugal pumps.
Comparison of Sodium Hydroxide and Sodium Carbonate as Alkali Catalysts in Ethanol Organosolv Treatment of Cotton Stalks for the Release of Hydroxycinnamates
Authors: Papadimitriou Georgia , Zarnavalou Vassiliki , Chatzimitakos Theodoros , Palaiogiannis Dimitrios , Athanasiadis Vassilis , Lalas Stavros I. , Makris Dimitris P.
Published: March 2024

Cotton stalks are residual biomass resulting from cotton bud harvesting, and they are composed primarily of lignocellulosic material. This material could be a source of functional polyphenols. To investigate this prospect, this study was undertaken with the view to examining whether an ethanol-based organosolv treatment could be suitable for producing extracts enriched in polyphenolic compounds. To this end, alkali catalysis was employed, and two catalysts, sodium hydroxide and sodium carbonate, were tested. The initial approach based on treatment severity showed that both catalysts may be equally effective in the recovery of polyphenols, yet in most cases studied, no clear trend between treatment severity and total polyphenol yield was recorded. The following study, based on response surface methodology, provided optimized conditions for both treatments, sodium hydroxide and sodium carbonate, where the recommended catalyst concentrations were 0.67 and 4%, respectively. Under a constant temperature of 90 °C and residence time of 300 min, the treatments with sodium hydroxide and sodium carbonate afforded total polyphenol yields of 18.4 ± 1 and 15.6 ± 1.9 mg CAE g−1 DM, respectively, which showed no significant statistical difference (p > 0.05). However, high-performance liquid chromatography analyses revealed that the sodium carbonate-catalyzed treatment produced extract particularly enriched in two hydroxycinnamate derivatives, ferulic and p-coumaric acid. This extract also exhibited increased antioxidant activity. The outcome of this study strongly suggests cotton stalks as a bioresource of functional substances, while mild alkali-catalyzed ethanol organosolv treatment appears to be a very promising technique for effectively delivering hydroxycinnamate-enriched extracts.
A Review on the Recovery and Separation of Gallium and Indium from Waste
Authors: Kluczka Joanna
Published: March 2024

Gallium and indium are crucial metals in various industries, such as the medical and telecommunication industries. They can find applications as pure metals, alloys and alloy admixtures, oxides, organometallic compounds, and compounds with elements such as nitrogen or arsenic. Recovery of these two metals from waste is an important issue for two main reasons. First, gallium and indium are scattered in the Earth’s crust and their minerals are too rare to serve as a primary source. Second, e-waste contributes to the rapidly growing problem of Earth littering, as its amount increased significantly in recent years. Therefore, it is essential to develop and implement procedures that will enable the recovery of valuable elements from waste and limit the emission of harmful substances into the environment. This paper discusses technological operations and methods that are currently used or may be used to produce pure gallium and indium or their oxides from waste. The first step was described—waste pretreatment, including disassembly and sorting in several stages. Then, mechanical treatment as well as physical, chemical, and physicochemical separations were discussed. The greatest emphasis was placed on the hydrometallurgical methods of gallium and indium recovery, to be more precise on the extraction and various sorption methods following the leaching stage. Methods of obtaining pure metals or metal oxides and their refining processes were also mentioned.
Sugarcane Yield Estimation Using Satellite Remote Sensing Data in Empirical or Mechanistic Modeling: A Systematic Review
Authors: de França e Silva Nildson Rodrigues , Chaves Michel Eustáquio Dantas , Luciano Ana Cláudia dos Santos , Sanches Ieda Del’Arco , de Almeida Cláudia Maria , Adami Marcos
Published: March 2024

The sugarcane crop has great socioeconomic relevance because of its use in the production of sugar, bioelectricity, and ethanol. Mainly cultivated in tropical and subtropical countries, such as Brazil, India, and China, this crop presented a global harvested area of 17.4 million hectares (Mha) in 2021. Thus, decision making in this activity needs reliable information. Obtaining accurate sugarcane yield estimates is challenging, and in this sense, it is important to reduce uncertainties. Currently, it can be estimated by empirical or mechanistic approaches. However, the model’s peculiarities vary according to the availability of data and the spatial scale. Here, we present a systematic review to discuss state-of-the-art sugarcane yield estimation approaches using remote sensing and crop simulation models. We consulted 1398 papers, and we focused on 72 of them, published between January 2017 and June 2023 in the main scientific databases (e.g., AGORA-FAO, Google Scholar, Nature, MDPI, among others), using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. We observed how the models vary in space and time, presenting the potential, challenges, limitations, and outlooks for enhancing decision making in the sugarcane crop supply chain. We concluded that remote sensing data assimilation both in mechanistic and empirical models is promising and will be enhanced in the coming years, due to the increasing availability of free Earth observation data.
Aboveground Biomass Estimation and Time Series Analyses in Mongolian Grasslands Utilizing PlanetScope Imagery
Authors: Jargalsaikhan Margad-Erdene , Ichikawa Dorj , Nagai Masahiko , Indree Tuvshintogtokh , Katiyar Vaibhav , Munkhtur Davaagerel , Dashdondog Erdenebaatar
Published: March 2024

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems.
Spatial Resolution Enhancement of Vegetation Indexes via Fusion of Hyperspectral and Multispectral Satellite Data
Authors: Alparone Luciano , Arienzo Alberto , Garzelli Andrea
Published: March 2024

The definition and calculation of a spectral index suitable for characterizing vegetated landscapes depend on the number and widths of the bands of the imaging instrument. Here, we point out the advantages of performing the fusion of hyperspectral (HS) satellite data with the multispectral (MS) bands of Sentinel-2 to calculate such vegetation indexes as the normalized area over reflectance curve (NAOC) and the red-edge inflection point (REIP), which benefit from the availability of quasi-continuous pixel spectra. Unfortunately, MS data may be acquired from satellite platforms with very high spatial resolution; HS data may not. Despite their excellent spectral resolution, satellite imaging spectrometers currently resolve areas not greater than 30 × 30 m2, where different thematic classes of landscape may be mixed together to form a unique pixel spectrum. A way to resolve mixed pixels is to perform the fusion of the HS dataset with the same dataset produced by an MS scanner that images the same scene with a finer spatial resolution. The HS dataset is sharpened from 30 m to 10 m by means of the Sentinel-2 bands that have all been previously brought to 10 m. To do so, the hyper-sharpening protocol, that is, m:n fusion, is exploited in two nested steps: the first one to bring the 20 m bands of Sentinel-2 all to 10 m, the second one to sharpen all the 30 m HS bands to 10 m by using the Sentinel-2 bands previously hyper-sharpened to 10 m. Results are presented on an agricultural test site in The Netherlands imaged by Sentinel-2 and by the satellite imaging spectrometer recently launched as a part of the environmental mapping and analysis program (EnMAP). Firstly, the excellent match of statistical consistency of the fused HS data to the original MS and HS data is evaluated by means of analysis tools, existing and developed ad hoc for this specific case. Then, the spatial and radiometric accuracy of REIP and NAOC calculated from fused HS data are analyzed on the classes of pure and mixed pixels. On pure pixels, the values of REIP and NAOC calculated from fused data are consistent with those calculated from the original HS data. Conversely, mixed pixels are spectrally unmixed by the fusion process to resolve the 10 m scale of the MS data. How the proposed method can be used to check the temporal evolution of vegetation indexes when a unique HS image and many MS images are available is the object of a final discussion.
Two-Step Accuracy Improvement for Multitarget Detection in Complex Environment Using UWB Radar
Authors: Liang Zhihuan , Jin Yanghao , Yang Degui , Liang Buge , Mo Jinjun
Published: March 2024

Detecting multiple human targets in indoor scenarios using ultra-wideband (UWB) radar usually involves false detection results caused by the secondary reflections, which might reduce the target detection accuracy and cause a more severe deterioration when the number of targets increases. This article proposed a two-step accuracy improvement method for multitarget detection in environments with multiple human targets of more than three and strong secondary reflections by the surroundings, especially the walls. Based on the rough detection results acquired by the modified CA-CFAR (MCA-CFAR) processing, the first step achieves the primary false alarm suppression using a short-window accumulation in the time domain. Then, the second step applies the decision confidence on the detection results from the first step to assess the reliability of results for improved accuracy. The two-step accuracy improvement could thus have a higher accuracy through cascading false alarm suppression. The effectiveness and accuracy of the proposed algorithm are verified based on the experimental results.
Performance Comparison of Deep Learning (DL)-Based Tabular Models for Building Mapping Using High-Resolution Red, Green, and Blue Imagery and the Geographic Object-Based Image Analysis Framework
Authors: Hossain Mohammad D. , Chen Dongmei
Published: March 2024

Identifying urban buildings in high-resolution RGB images presents challenges, mainly due to the absence of near-infrared bands in UAVs and Google Earth imagery and the diversity in building attributes. Deep learning (DL) methods, especially Convolutional Neural Networks (CNNs), are widely used for building extraction but are primarily pixel-based. Geographic Object-Based Image Analysis (GEOBIA) has emerged as an essential approach for high-resolution imagery. However, integrating GEOBIA with DL models presents challenges, including adapting DL models for irregular-shaped segments and effectively merging DL outputs with object-based features. Recent developments include tabular DL models that align well with GEOBIA. GEOBIA stores various features for image segments in a tabular format, yet the effectiveness of these tabular DL models for building extraction still needs to be explored. It also needs to clarify which features are crucial for distinguishing buildings from other land-cover types. Typically, GEOBIA employs shallow learning (SL) classifiers. Thus, this study evaluates SL and tabular DL classifiers for their ability to differentiate buildings from non-building features. Furthermore, these classifiers are assessed for their capacity to handle roof heterogeneity caused by sun exposure and roof materials. This study concludes that some SL classifiers perform similarly to their DL counterparts, and it identifies critical features for building extraction.
An Efficient Hybrid CNN-Transformer Approach for Remote Sensing Super-Resolution
Authors: Zhang Wenjian , Tan Zheng , Lv Qunbo , Li Jiaao , Zhu Baoyu , Liu Yangyang
Published: March 2024

Transformer models have great potential in the field of remote sensing super-resolution (SR) due to their excellent self-attention mechanisms. However, transformer models are prone to overfitting because of their large number of parameters, especially with the typically small remote sensing datasets. Additionally, the reliance of transformer-based SR models on convolution-based upsampling often leads to mismatched semantic information. To tackle these challenges, we propose an efficient super-resolution hybrid network (EHNet) based on the encoder composed of our designed lightweight convolution module and the decoder composed of an improved swin transformer. The encoder, featuring our novel Lightweight Feature Extraction Block (LFEB), employs a more efficient convolution method than depthwise separable convolution based on depthwise convolution. Our LFEB also integrates a Cross Stage Partial structure for enhanced feature extraction. In terms of the decoder, based on the swin transformer, we innovatively propose a sequence-based upsample block (SUB) for the first time, which directly uses the sequence of tokens in the transformer to focus on semantic information through the MLP layer, which enhances the feature expression ability of the model and improves the reconstruction accuracy. Experiments show that EHNet’s PSNR on UCMerced and AID datasets obtains a SOTA performance of 28.02 and 29.44, respectively, and is also visually better than other existing methods. Its 2.64 M parameters effectively balance model efficiency and computational demands.
Low-Power and Wireless Communication Research on Underground Displacement Three-Dimensional Monitoring System
Authors: Shentu Nanying , Zhang Xianyang , Li Qing , Tong Renyuan , Qiu Guohua
Published: March 2024

Underground displacement monitoring is a crucial means of preventing geological disasters. Compared to existing one-dimensional methods (measuring only horizontal or vertical displacement), the underground displacement three-dimensional measurement method and monitoring system proposed by the author’s research team can more accurately reflect the internal movement of rock and soil mass, thereby improving the timeliness and accuracy of geological disaster prediction. To ensure the reliability and long-term operation of the underground displacement three-dimensional monitoring system, this article further introduces low-power design theory and Bluetooth wireless transmission technology into the system. By optimizing the power consumption of each sensing unit, the current during the sleep period of a single sensing unit is reduced to only 0.09 mA. Dynamic power management technology is employed to minimize power consumption during each detection cycle. By using Bluetooth wireless transmission technology, the original wired communication of the system is upgraded to a relay-type wireless network communication, effectively solving the problem of the entire sensing array’s operation being affected when a single sensing unit is damaged. These optimized designs not only maintain monitoring accuracy (horizontal and vertical displacement errors not exceeding 1 mm) but also enable the monitoring system to operate stably for an extended period under harsh weather conditions.
Augmented Reality-Based Real-Time Visualization for Structural Modal Identification
Authors: Carter Elliott , Sakr Micheal , Sadhu Ayan
Published: March 2024

In the era of aging civil infrastructure and growing concerns about rapid structural deterioration due to climate change, the demand for real-time structural health monitoring (SHM) techniques has been predominant worldwide. Traditional SHM methods face challenges, including delays in processing acquired data from large structures, time-intensive dense instrumentation, and visualization of real-time structural information. To address these issues, this paper develops a novel real-time visualization method using Augmented Reality (AR) to enhance vibration-based onsite structural inspections. The proposed approach presents a visualization system designed for real-time fieldwork, enabling detailed multi-sensor analyses within the immersive environment of AR. Leveraging the remote connectivity of the AR device, real-time communication is established with an external database and Python library through a web server, expanding the analytical capabilities of data acquisition, and data processing, such as modal identification, and the resulting visualization of SHM information. The proposed system allows live visualization of time-domain, frequency-domain, and system identification information through AR. This paper provides an overview of the proposed technology and presents the results of a lab-scale experimental model. It is concluded that the proposed approach yields accurate processing of real-time data and visualization of system identification information by highlighting its potential to enhance efficiency and safety in SHM by integrating AR technology with real-world fieldwork.
Intraoperative Angle Measurement of Anatomical Structures: A Systematic Review
Authors: Cruz João , Gonçalves Sérgio B. , Neves Manuel Cassiano , Silva Hugo Plácido , Silva Miguel Tavares
Published: March 2024

Ensuring precise angle measurement during surgical correction of orientation-related deformities is crucial for optimal postoperative outcomes, yet there is a lack of an ideal commercial solution. Current measurement sensors and instrumentation have limitations that make their use context-specific, demanding a methodical evaluation of the field. A systematic review was carried out in March 2023. Studies reporting technologies and validation methods for intraoperative angular measurement of anatomical structures were analyzed. A total of 32 studies were included, 17 focused on image-based technologies (6 fluoroscopy, 4 camera-based tracking, and 7 CT-based), while 15 explored non-image-based technologies (6 manual instruments and 9 inertial sensor-based instruments). Image-based technologies offer better accuracy and 3D capabilities but pose challenges like additional equipment, increased radiation exposure, time, and cost. Non-image-based technologies are cost-effective but may be influenced by the surgeon’s perception and require careful calibration. Nevertheless, the choice of the proper technology should take into consideration the influence of the expected error in the surgery, surgery type, and radiation dose limit. This comprehensive review serves as a valuable guide for surgeons seeking precise angle measurements intraoperatively. It not only explores the performance and application of existing technologies but also aids in the future development of innovative solutions.
Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts
Authors: Wei Yu , He Xiaozheng
Published: March 2024

Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies.
Context-Based e2e Autonomous Operation in B5G Networks
Authors: Wang Shaoxuan , Ruiz Marc , Velasco Luis
Published: March 2024

The research and innovation related to fifth-generation (5G) networks that has been carried out in recent years has decided on the fundamentals of the smart slice in radio access networks (RANs), as well as the autonomous fixed network operation. One of the most challenging objectives of beyond 5G (B5G) and sixth-generation (6G) networks is the deployment of mechanisms that enable smart end-to-end (e2e) network operation, which is required for the achievement of the stringent service requirements of the envisioned use cases to be supported in the short term. Therefore, smart actions, such as dynamic capacity allocation, flexible functional split, and dynamic slice management need to be performed in tight coordination with the autonomous capacity management of the fixed transport network infrastructure. Otherwise, the benefits of smart slice operation (i.e., cost and energy savings while ensuring per-slice service requirements) might be cancelled due to uncoordinated autonomous fixed network operation. Notably, the transport network in charge of supporting slices from the user equipment (UE) to the core expands across access and metro fixed networks. The required coordination needs to be performed while keeping the privacy of the radio and fixed network domains, which is important in multi-tenant scenarios where both network segments are managed by different operators. In this paper, we propose a novel approach that explores the concept of context-aware network operation, where the slice control anticipates the aggregated and anonymized information of the expected slice operation that is sent to the fixed network orchestrator in an asynchronous way. The context is then used as the input for the artificial intelligence (AI)-based models used by the fixed network control for the predictive capacity management of optical connections in support of RAN slices. This context-aware network operation aims at enabling accurate and reliable autonomous fixed network operation under extremely dynamic traffic originated by smart RAN operation. The exhaustive numerical results show that slice context availability improves the benchmarking fixed network predictive methods (90% reduction in prediction maximum error) remarkably in the foreseen B5G scenarios, for both access and metro segments and in heterogeneous service demand scenarios. Moreover, context-aware network operation enables robust and efficient operation of optical networks in support of dense RAN cells (>32 base stations per cell), while the benchmarking methods fail to guarantee different operational objectives.
Deep Reinforcement Learning-Based Energy Consumption Optimization for Peer-to-Peer (P2P) Communication in Wireless Sensor Networks
Authors: Yuan Jinyu , Peng Jingyi , Yan Qing , He Gang , Xiang Honglin , Liu Zili
Published: March 2024

The fast development of the sensors in the wireless sensor networks (WSN) brings a big challenge of low energy consumption requirements, and Peer-to-peer (P2P) communication becomes the important way to break this bottleneck. However, the interference caused by different sensors sharing the spectrum and the power limitations seriously constrains the improvement of WSN. Therefore, in this paper, we proposed a deep reinforcement learning-based energy consumption optimization for P2P communication in WSN. Specifically, P2P sensors (PUs) are considered agents to share the spectrum of authorized sensors (AUs). An authorized sensor has permission to access specific data or systems, while a P2P sensor directly communicates with other sensors without needing a central server. One involves permission, the other is direct communication between sensors. Each agent can control the power and select the resources to avoid interference. Moreover, we use a double deep Q network (DDQN) algorithm to help the agent learn more detailed features of the interference. Simulation results show that the proposed algorithm can obtain a higher performance than the deep Q network scheme and the traditional algorithm, which can effectively lower the energy consumption for P2P communication in WSN.
The Impacts of the African Growth Opportunity Act on the Economic Performances of Sub-Saharan African Countries: A Comprehensive Review
Authors: Tadesse Bedassa
Published: March 2024

The African Growth Opportunity Act (AGOA) has been a crucial trade and development initiative, offering preferential access to qualified Sub-Saharan African (SSA) countries to the United States market since its enactment in 2000. This paper presents a comprehensive review of scholarly articles and policy reports that analyze the impact of AGOA on the economic performance of SSA countries. Employing various econometric methods and data analysis techniques, researchers have investigated the effects of AGOA on trade flows, foreign direct investment (FDI) inflows, employment, economic growth, and poverty levels. The findings reveal that AGOA has positively affected the region’s trade, particularly in apparel, textiles, and agriculture. However, its influence on promoting export diversification and attracting FDI is nuanced, with substantial heterogeneity among the beneficiary countries and industries within each country. While some SSA countries have experienced substantial export growth and FDI inflows, others have not fully leveraged the benefits of AGOA due to absorptive capacity constraints and governance challenges. AGOA’s effectiveness in promoting broad-based employment, GDP growth, and poverty reduction remains an active area of inquiry, necessitating further research to understand the policy’s sustained impact and inform future trade policy designs for SSA countries.
Inclusive Teaching in Higher Education: Challenges of Diversity in Learning Situations from the Lecturer Perspective
Authors: Svendby Rannveig Beito
Published: March 2024

This paper contributes to the dialogue around how to increase inclusion in higher education, taking the lecturer perspective as the point of departure. Theoretically, disability is understood as an interaction, which means that lecturers partake in the constitution of dis/ability in learning situations. Two qualitative interviews were conducted with an interdisciplinary lecturer employed in an institution of higher education in Norway. These data are used for this single case study to illustrate and reflect on the challenges of diversity in learning situations. Findings suggest that the lecturer struggles to encounter an increasingly diverse student population inclusively. Overall, her experiences unpack the outcome of a structural lack of prioritization to ensure accessibility for disabled students at an institutional level at the university where she is employed. This article emphasizes that the responsibility to ensure an inclusive teaching practice in higher education must be recognized and treated as an institutional obligation. To signal its priority, all institutions of higher education should make inclusive training obligatory for lecturers, as well as assigning hours to work on enhancing the development of inclusive skills in this group.
Virtual Worlds for Learning in Metaverse: A Narrative Review
Authors: Damaševičius Robertas , Sidekerskienė Tatjana
Published: March 2024

As digital technologies continue to evolve, they offer unprecedented opportunities to transform traditional educational paradigms. Virtual worlds offer a dynamic and immersive platform for fostering sustainability education, bridging the gap between theoretical knowledge and practical application. In these interactive environments, students can engage with complex ecological systems and sustainability challenges in a risk-free setting, allowing for experimentation and exploration that would be impractical or impossible in the real world. This study aims to investigate the application of various types of virtual worlds in educational settings, examine their characteristics and potential, and explore how they foster critical 21st-century skills like critical thinking, creativity, communication, and collaboration. This paper comprehensively explores various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—assessing their impact on educational processes and outcomes. Adventure Worlds, with narrative-driven quests, engage students in exploratory learning within a story context. Simulation Worlds replicate real-world environments, allowing students to practice and hone practical skills in a risk-free setting. Creative Worlds provide open-ended, sandbox-like environments where innovation and imagination are paramount. Role-Playing Worlds facilitate empathy and perspective-taking through character-driven scenarios, while Collaborative Worlds emphasize teamwork and problem-solving in group projects. The narrative review methodology was adopted for the comprehensive analysis and synthesis of the literature to assess the impact and integration of virtual worlds in education, focusing on identifying trends, challenges, and opportunities within this domain. The evaluation methodology used in this study incorporates a mix of the Theory of Inventive Problem Solving (TRIZ), Concept-Knowledge (C-K) theory, Structure-behavior-function (SBF) modeling, the Framework for 21st Century Learning (P21), and Universal Design for Learning (UDL) to evaluate the characteristics and educational potential of different virtual world types. Findings indicate that virtual worlds effectively support critical thinking, creativity, communication, and collaboration skills, presenting a comprehensive analysis of how these environments can support, supplement, or transform traditional educational models. The main outcome of the study is the comprehensive exploration of various types of virtual worlds—Adventure World, Simulation World, Creative World, Role-Playing World, and Collaborative World—in education, demonstrating their significant potential to enhance learning experiences and outcomes through immersive, interactive environments that foster critical thinking, creativity, communication, and collaboration skills.
Synergism of Life Cycle Assessment and Sustainable Development Goals Techniques to Evaluate Downflow Hanging Sponge System Treating Low-Carbon Wastewater
Authors: Anang Samuel , Nasr Mahmoud , Fujii Manabu , Ibrahim Mona G.
Published: March 2024

While recent researchers have focused on optimizing the operational conditions of low-carbon wastewater treatment processes, they have not sufficiently evaluated the sustainability of such systems. This study is the first to assess the performance of a low-carbon wastewater treatment facility using an integration of techno-economic and life cycle assessment (LCA) criteria accompanied by several sustainability indicators. A downflow hanging sponge (DHS) reactor was operated at a hydraulic retention time of 3.4 h, an organic loading rate of 3.8 kg COD/m3 sponge/d, and 24–35 °C (scenario_1). Another two DHSs were operated in parallel, i.e., a 50% influent bypass (scenario_2) and 260 mg/L charcoal addition (scenario_3), providing carbonaceous matter to maintain the nitrification/denitrification pathway. Employing the DHS’s scenario_3 could fulfill most of the SDGs regarding the environmental (e.g., COD and nitrogen removals) and socio-economic (e.g., reliability, labor, and health and safety) targets. The LCA tool also confirmed the superior environmental benefits of scenario_3, concerning effluent quality, GHG emissions, and sludge generation. The synergistic interaction of LCA and SDGs approaches ranked the proposed DHS modifications as scenario_3 > scenario_1 > scenario_2. Hence, the current study provided an innovative strategy that could be employed to assess the sustainability of wastewater treatment systems worldwide.
Making Space: A New Way for Community Engagement in the Urban Planning Process
Authors: Gearin Elizabeth , Hurt Carletta S.
Published: March 2024

The current research shifts from the tradition of researcher-generated and directed community study and explores a new model for data collection based on citizen-directed participation and restorative economies. This is important because the scholarship of urban planning is often separated from the practice, in which scholars undertake the bulk of the academic research and professional planners work in the field, engaging with the community and creating and implementing plans around local issues and opportunities. While urban planning trends support diverse, equitable, and inclusive engagement in visioning and shaping local development, few operationalized examples exist for citizen-led scholarship with local community application. The work outlined herein explicitly partners with local residents to collect data on both community and academic interests, and it also promotes a citizen sense of agency. Community leaders worked with the local public university to design and implement a study to solicit park use ideas and visions among three groups: youth; adults; and returning citizens. Results identify an overall theme of a lack of engagement with the community, relative to other local parks, and they also highlight four common areas of thought between the three populations—concerns about safety among park users, functional use of park space, demand for widespread park access, and desire for natural environment conservation. This work represents a viable local engagement approach including demonstrated investment in a local community and resultant increased trust; also, this work contributes valuable new knowledge about local history and the use of a community resource to inform land use planning and policy around community sustainability.
Utilising eDNA Methods and Interactive Data Dashboards for Managing Sustainable Drinking Water
Authors: Watson Sophie E. , Taylor Charlotte H. , Bell Veronica , Hooper Annalise S. , Bellamy Thomas R. , Kille Peter , Perkins Rupert G.
Published: March 2024

Generating rapid, easy-to-interpret community data for drinking reservoirs as a means of tackling water quality management is of increasing demand within the water industry. Taste and odour (T&O) is one of many increasing concerns to water companies worldwide, incurring huge costs as customer complaints accumulate and additional treatment and resource management are required. However, there remains a two-fold issue in addressing T&O management: firstly, predicting the initial onset of a T&O event relies on a highly complex understanding of environmental considerations and their interaction with T&O-related taxa, and secondly, there remains a lag between the notification of a T&O event and the resolution of the issue by reservoir management staff. This is partly due to slow, low-resolution methods of detecting and reliably identifying problem taxa in samples. These methods are unable to provide information on the huge plethora of taxa related to T&O metabolite production and often cannot provide data in a timely enough manner for an opportune management response. This means the water industry is often forced to use a reactive, rather than proactive, approach to water quality monitoring. Here, we present methods for implementing a high-throughput sequencing approach to monitoring drinking reservoirs for water quality and improving the sustainability of water supplies, as well as methods for presenting these data on easy-to-interpret data dashboards that can be updated rapidly as new data are generated. Our methods and dashboarding approaches are currently being trialled and tested within the UK water industry, and so here, we show anonymised examples of those data presentations. We propose that these methods can greatly aid reservoir management teams in their approach to T&O monitoring and can be used to implore more sustainable management pipelines, safeguarding future water sources.
An Empirical Evaluation of a New Heuristic Method for Identifying Safety Improvement Sites on Rural Highways: An Oregon Case Study
Authors: Dhakal Bishal , Al-Kaisy Ahmed
Published: March 2024

Road safety is considered one of the fundamental factors for sustainable mobility, which requires establishing effective highway safety management programs and processes. Identifying safety improvement sites (network screening) is a critical step in the state roadway safety programs. The overall effectiveness of these programs largely relies on the robustness of the network screening method in identifying sites with high potential for safety improvements. This study investigates the network screening performance of a new proposed methodology that employs heuristic scoring schemes to account for roadway and roadside characteristics, crash history, and traffic exposure. The new method, originally proposed for use on rural low-volume roads, is simplistic in that it does not require exact and detailed information on traffic and geometric characteristics and can still be applied in the absence of crash data. The performance evaluation is conducted using a sample of 1495 miles of rural two-lane highway segments in Oregon. The effectiveness of the proposed method is assessed using observed crash history and compared to the well-established Empirical Bayes method as a reference. The effect of traffic level on the performance of the proposed method was evaluated using separate analyses for lower and higher traffic volume segments. The study findings indicate that the effectiveness of the proposed method in identifying sites with potential safety improvements is overall high and slightly exceeded that of the more sophisticated and data-intensive Empirical Bayes method. When data were stratified by traffic volume, the proposed method was found to be more effective for lower volume roads; however, the Empirical Bayes method outperformed the proposed method for higher volume roads.
Community-Centred Energy Planning: Within and beyond Administrative Borders
Authors: Costa Valentina , Campanini Federico
Published: March 2024

Since its introduction in 2008, the Covenant of Mayors (CoM) has helped municipalities meet EU Climate and Energy goals and thresholds through Sustainable Energy and Climate Action Planning (SECAP). The engagement of local communities holds particular significance for smaller municipalities, which can leverage collective strategies to mutually contribute to climate change mitigation efforts, thereby optimizing results. In the realm of communities, Renewable Energy Communities (REC) have emerged as a potential tool for SECAP implementation, but although they target common objectives and constitute subsequent steps of the same planning flow, their interaction is poor. This study aimed to investigate similar tools’ integration by focusing on administrative and technical boundaries whose overlaps often hinder their interoperability. To this aim, the Italian framework was chosen due to the representation of its signatories in the CoM. Municipalities that have undertaken actions related to RECs within their Joint SECAPs have been compared through an analysis of the CoM datasets. Finally, two Italian case studies were selected to evaluate the impact of different territorial and institutional configurations on these initiatives, aiming to face climate change and achieve a green transition. This helped the authors propose practical recommendations and policy implications concerning this kind of community-centred energy planning solution as outlined in the concluding section.
Sustainability in Mountain Viticulture: Insights from a Case Study in the Portuguese Douro Region
Authors: Martins José António , Marta-Costa Ana , Lucas Maria Raquel , Santos Mário
Published: March 2024

Evaluations of the sustainability of the viticulture associated with wine production are still scarce in the literature. Usually, the ‘carbon footprint’ assumes the environmental dimension, while the economic pillar is focused on market orientation. In the present work, the integration of both was tested using a case study supported in a six-year series (2015–2021) of primary data from a farm in the Região Demarcada do Douro (Douro Demarcated Region, hereafter the RDD). Economic and environmental inputs and outputs were collected from ‘pruning to harvest’. Profitability was calculated based on the market prices and environmental impacts using the AgriBalyse database, which is available in OpenLCA 1.10.3. In the scope of the economic results, the following costs stand out: (1) ‘human labor’; (2) use of machinery; and (3) plant protection products. Concerning the environmental impacts, the greatest weight resulted from the use of fuels, and no relation was found between the grape production variation and environmental factor variation. The indicators studied were considered valuable for comparing production systems (conventional, organic, and biodynamic, among others) and might support stakeholders’ decision making. We highlight the importance of replication in further studies to better understand the complex world of viticulture’s sustainability.
Properties of RDF after Prolonged Storage
Authors: Romaszewski Tomasz , Fitas Jakub
Published: March 2024

Increasing production of municipal solid waste (MSW) drives the need for its disposal in a manner that is safe for the environment and human health. However, this may require short- or long-term storage before it can be properly processed. Similarly, a way of processing waste material is necessary for the re-cultivation of dump sites. This article presents the results of an investigation into the effects of long-term open-air storage upon waste material to be turned into refuse-derived fuel (RDF) by standard methods for the assessment of MSW and RDF pellet quality including bomb calorimetry, sieve analysis, furnace drying/burning for water/ash content assessment, and pellet expansion measurements. Results of the investigation indicate that such a form of storage bears no notable negative effect on the quality of the material; the pellet expansion coefficient, heat of combustion, and ash content were all found to be approximate to pre-storage values, with positive implications for the storage of solid waste and the prospects of its subsequent processing into solid fuel. It is shown that such material can be stored in open-air conditions for prolonged periods without the loss of desired parameters. In addition, a discussion of differences between the properties of material drawn from varying depths of the pile is provided and the potential impact of the findings in the context of the production and the storage of refuse-derived fuel is assessed.