bluuebunny/crossref_metadata_2025 huggingface dataset:
Point2Tree(P2T)—Framework for Parameter Tuning of Semantic and Instance Segmentation Used with Mobile Laser Scanning Data in Coniferous Forest
Authors: Wielgosz Maciej , Puliti Stefano , Wilkes Phil , Astrup Rasmus
Published: July 2023

Inthis study, we introduce Point2Tree, a modular and versatile framework that employs a three-tiered methodology, inclusive of semantic segmentation, instance segmentation, and hyperparameter optimization analysis, designed to process laser point clouds in forestry. The semantic segmentation stage is built upon the Pointnet++ architecture and is primarily tasked with categorizing each point in the point cloud into meaningful groups or ’segments’, specifically in this context, differentiating between diverse tree parts, i.e., vegetation, stems, and coarse woody debris. The category for the ground is also provided. Semantic segmentation achieved an F1-score of 0.92, showing a high level of accuracy in classifying forest elements. In the instance segmentation stage, we further refine this process by identifying each tree as a unique entity. This process, which uses a graph-based approach, yielded an F1-score of approximately 0.6, signifying reasonable performance in delineating individual trees. The third stage involves a hyperparameter optimization analysis, conducted through a Bayesian strategy, which led to performance improvement of the overall framework by around four percentage points. Point2Tree was tested on two datasets, one from a managed boreal coniferous forest in Våler, Norway, with 16 plots chosen to cover a range of forest conditions. The modular design of the framework allows it to handle diverse pointcloud densities and types of terrestrial laser scanning data.
MFSFNet: Multi-Scale Feature Subtraction Fusion Network for Remote Sensing Image Change Detection
Authors: Huang Zhiqi , You Hongjian
Published: July 2023

Change detection plays a crucial role in remote sensing by identifying surface modifications between two sets of temporal remote sensing images. Recent advancements in deep learning techniques have yielded significant achievements in this field. However, there are still some challenges: (1) Existing change feature fusion methods often introduce redundant information. (2) The complexity of network structures leads to a large number of parameters and difficulties in model training. To overcome these challenges, this paper proposes a Multi-Scale Feature Subtraction Fusion Network (MFSF-Net). It comprises two primary modules: the Multi-scale Feature Subtraction Fusion (MFSF) module and the Feature Deep Supervision (FDS) module. MFSF enhances change features and reduces redundant pseudo-change features. FDS provides additional supervision on different scales of change features in the decoder, improving the training efficiency performance of the network. Additionally, to address the problem of imbalanced samples, the Dice loss strategy is introduced as a means to mitigate this issue. Through comprehensive experiments, MFSF-Net achieves an F1 score of 91.15% and 95.64% on LEVIR-CD and CDD benchmark datasets, respectively, outperforming six state-of-the-art algorithms. Moreover, it attains an improved balance between model complexity and performance, showcasing the efficacy of the proposed approach.
Radar Target Characterization and Deep Learning in Radar Automatic Target Recognition: A Review
Authors: Jiang Wen , Wang Yanping , Li Yang , Lin Yun , Shen Wenjie
Published: July 2023

Radar automatic target recognition (RATR) technology is fundamental but complicated system engineering that combines sensor, target, environment, and signal processing technology, etc. It plays a significant role in improving the level and capabilities of military and civilian automation. Although RATR has been successfully applied in some aspects, the complete theoretical system has not been established. At present, deep learning algorithms have received a lot of attention and have emerged as potential and feasible solutions in RATR. This paper mainly reviews related articles published between 2010 and 2022, which corresponds to the period when deep learning methods were introduced into RATR research. In this paper, the current research status of radar target characteristics is summarized, including motion, micro-motion, one-dimensional, and two-dimensional characteristics, etc. This paper reviews the progress of deep learning methods in the feature extraction and recognition of radar target characteristics in recent years, including space, air, ground, sea-surface targets, etc. Due to more and more attention and research results published in the past few years, it is hoped that this review can provide potential guidance for future research and application of deep learning in fields related to RATR.
Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers
Authors: Zhao Yuxin , Feng Hancong , Jiang Kaili , Tang Bin
Published: July 2023

The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.
A Measurement Method for Cislunar Spacecraft Based on Connected Element Interferometry and BeiDou-3 Interplanetary Link in Future Lunar Exploration
Authors: Gao Zefu , Yang Wenge , Ma Hongbin , Teng Fei , Li Chao , Li Xuejian , Wang Yuxin , Jiao Yiwen
Published: July 2023

To meet the urgent need for high-precision tracking and reliable cataloging of non-cooperative targets in the Earth–Moon space, this paper proposes a GNSS Inter-Satellite Link and Connected Element Interferometry (CEI)-based measurement method for high-value cislunar space targets. Firstly, the general flow and basic scenario of the proposed method are given, followed by the mathematical model which, mainly includes four parts: (i) dynamical constraint equations for targets; (ii) GNSS-based interplanetary link for irradiation of targets; (iii) transmission loss equation of GNSS inter-satellite link signal in Earth–Moon space; (iv) CEI-based precision measurements of targets. On this basis, the full process link budget analysis is carried out, followed by the performance evaluation, which includes the reception performance of CEI receiving arrays and the measurement accuracy of targets. The feasibility of the proposed method is evaluated and verified in experiments, and it is illustrated that (i) for inter-satellite link visibility analysis, at least 20 satellites can simultaneously provide inter-satellite link signals to the Earth–Moon space targets, with a single GEO satellite up to 8.5 h continuously, while the chain access can be available at up to 73,000 km, with the angle ranging from −80∘ to 360∘; (ii) the Max Duration of Chain Access for BD3-lunarprobe-CEI (from 24 March 2023 04:00:00.000 to 31 March 2023 10:00:00.000) is 50,998.804 s/day, with a Total Duration of 358,408.797 s in 7 days; (iii) for link budget and measurement accuracy analysis, even beyond the farthest Earth–Moon Lagrangian point, the C/N0 will be above 56.1 dBHZ, while even approaching the distances of 4.5×105km, the σDLL and σFLL will be below 5.345 m and 3.475×10−4 m/s, respectively, and the final measurement error will remain at 62.5 m with the proposed method. The findings of this paper could play a key role in future increasingly serious space missions, such as Earth–Moon space situational awareness, and will have a broad application prospect, if put into actual testing and operations.
A Novel Imaging Algorithm for Wide-Beam Multiple-Receiver Synthetic Aperture Sonar Systems
Authors: Zhang Jiafeng , Cheng Guangli , Tang Jinsong , Xie Zhimin , Wu Haoran
Published: July 2023

In existing imaging algorithms for wide-beam multiple-receiver synthetic aperture sonar (SAS) systems, the double-square-root (DSR) range history of each receiver is generally converted into the sum of a single-square-root (SSR) range history and an error term using displaced phase center aperture (DPCA) approximation. Therefore, before imaging, each receiver’s error term needs to be individually compensated in the azimuth frequency domain, which is computationally expensive. As a result, a novel wide-beam multiple-receiver SAS system algorithm with low complexity and high precision is suggested. First, the translation relationship between the range histories of the reference receiver and other receivers is used to derive an SSR approximation range history that takes into account the azimuth variance of the non-stop-hop-stop time while ignoring differential range curvature (DRC) between the range histories from different receivers. Then, using the principle of stationary phase (POSP), the two-dimensional (2-D) spectrum of the point target is obtained. Finally, the multiple-receiver data are transformed into monostatic SAS-equivalent data for imaging after phase correction, time delay correction, and azimuth reconstruction. The range-Doppler (RD) algorithm is used as an example to explain the specific steps of the proposed approach. Simulation data and ChinSAS data experiments verify that the proposed algorithm achieves an imaging performance that is comparable to that of the existing wide-beam algorithm, but with much higher computational efficiency, making it suitable for real-time imaging.
Monitoring of Damages to Cultural Heritage across Europe Using Remote Sensing and Earth Observation: Assessment of Scientific and Grey Literature
Authors: Cuca Branka , Zaina Federico , Tapete Deodato
Published: July 2023

This research is part of a wider framework of index literature studies that have been conducted in the past few years. Some of these have had a focus on specific remote sensing (RS) technologies, while others have tackled specific threats to cultural heritage and landscapes. By considering both damages to heritage sites and technologies used for documentation and the monitoring of such occurrences, this paper unveils the current trends on a global scale in the study of the threats to heritage caused by both human-induced and natural hazards. Papers published by Europe-based researchers over the last 20 years using RS and Earth Observation (EO) techniques were surveyed alongside recommendations and programmatic documents issued by institutions in charge of heritage protection and management of several countries in Europe. Around 300 documents, including scientific articles (published from 2000 until 2022) and Grey literature (from 2008 and 2022), were analysed. The data collection and analysis were undertaken by a working group that was intentionally composed to bring together diverse perspectives and expertise, i.e., requirements of heritage professionals using RS and EO technologies, knowledge on technologies and their use in the field, and expertise in methodology implementation to support heritage management. The results highlight the type of hazards considered the most and the geographical distribution of the archaeological sites and monuments targeted by these studies; the countries the researchers are affiliated with; the types of RS and specifically satellite-based technologies used (and hence the type of data used); the tendencies of satellite data usage—visual interpretation, image processing, employment of machine learning, and AI; the technologies most applied by public institutions and practitioners; and many others. Recommendations and future trajectories are then outlined to efficiently reframe discrepancies between types of damage that have received the greatest attention in the literature and the most impactful ones in terms of the number of sites damaged.
Spatial Heterogeneity of Watershed Ecosystem Health and Identification of Its Influencing Factors in a Mountain–Hill–Plain Region, Henan Province, China
Authors: Wei Hejie , Han Qing , Yang Yi , Li Ling , Liu Mengxue
Published: July 2023

A watershed ecosystem is a compound ecosystem composed of land and rivers, and its health is closely related to the sustainable development of the region it is located in. The Yihe River Basin (YRB) in central China’s Henan province, which is located in the north–south transition zone and has a mountain–hill–plain landscape from the upstream to the downstream, is adopted as the research area in this study. A watershed ecosystem health assessment system is constructed based on an ecosystem vigor–organization–resilience–service supply and demand harmony (EVORSH) framework and utilized to assess the ecosystem health in the YRB by taking a 3 km × 3 km grid as the evaluation unit. Thirteen factors are selected from natural and human social factors, and from them, the factors that influence watershed ecosystem health through the generation of spatial heterogeneity are identified using the geographical detector model. The following findings are obtained. (1) The mean value of ecosystem health levels in the YRB is 0.65 and at the good level. The ecosystem health has considerable spatial heterogeneity. The areas with high–high concentration are distributed in the mountains in the upper reaches of the YRB, and the areas with low–low concentration are mainly distributed in the plain areas in the middle reaches of the YRB. (2) The geographical detector result shows that 9 of 13 factors have a considerable impact on the spatial distribution of the YRB’s ecosystem health. The interaction between two factors is enhanced synergically. The decisive power of population density, rainfall, and potential evapotranspiration are more than 0.5, so these three are the main factors that influence the distribution of ecosystem health in the YRB. (3) The EVORSH framework is suitable for the measurement of ecosystem health in the YRB. The evaluation result is consistent with the actual situation in the YRB. A 3 km × 3 km grid is used as the basic research unit, and it can more accurately and scientifically express the spatial heterogeneity of ecosystem health in the YRB compared with the macro evaluation unit. This study can provide a scientific basis for ecological protection and high-quality development planning in the YRB. By integrating multi-dimensional data and methods, the EVORSH framework proposed in this study can quickly and scientifically assess the status of ecosystem health, identify the influencing factors of spatial heterogeneity, and could be applied in other similar watersheds.
Robust and Reconfigurable On-Board Processing for a Hyperspectral Imaging Small Satellite
Authors: Langer Dennis D. , Orlandić Milica , Bakken Sivert , Birkeland Roger , Garrett Joseph L. , Johansen Tor A. , Sørensen Asgeir J.
Published: July 2023

Hyperspectral imaging is a powerful remote sensing technology, but its use in space is limited by the large volume of data it produces, which leads to a downlink bottleneck. Therefore, most payloads to date have been oriented towards demonstrating the scientific usefulness of hyperspectral data sporadically over diverse areas rather than detailed monitoring of spatio-spectral dynamics. The key to overcoming the data bandwidth limitation is to process the data on-board the satellite prior to downlink. In this article, the design, implementation, and in-flight demonstration of the on-board processing pipeline of the HYPSO-1 cube-satellite are presented. The pipeline provides not only flexible image processing but also reliability and resilience, characterized by robust booting and updating procedures. The processing time and compression rate of the simplest pipeline, which includes capturing, binning, and compressing the image, are analyzed in detail. Based on these analyses, the implications of the pipeline performance on HYPSO-1’s mission are discussed.
Isolation and Structural Elucidation of Unreported Prenylhydroquinone Glycoside from Sedum kamtschaticum Leaves and Its Effect on Hyperphosphorylated Tau Production in Aβ1–42-Treated SH-SY5Y Cells
Authors: Lee Seung-Eun , Jeong Se Yun , Jang Yoon Seo , Cho Kwang-Jin , Lee Jeonghoon , Lee Yunji , Kim Ki Hyun
Published: July 2023

Sedum kamtschaticum Fischer, of the Crassulaceae family, is a perennial and medicinal plant used in Asian folk medicine to alleviate inflammatory disease and improve blood circulation. As part of our ongoing exploration into natural products, seeking to identifying bioactive compounds, we characterized, identified, and isolated an unreported bioactive compound, prenylhydroquinone glycoside (1), which we named kirinchoside from S. kamtschaticum leaves. Using high-resolution (HR)-ESIMS, NMR spectroscopic data, and enzymatic hydrolysis, followed by LC–MS analysis, we determined the structure of this isolated compound. Despite a previous report on the planar structure of compound 1 (kirinchoside), the absolute configuration of 1 had not been verified. We investigated the effects of kirinchoside on hyperphosphorylated tau (p-tau) accumulation, a hallmark of Alzheimer’s disease (AD) progression. We observed that treatment with 5 μM kirinchoside suppressed p-tau levels by 16.9% in amyloid β (Aβ)1–42-treated SH-SY5Y cells, compared to the negative control. These findings indicate that kirinchoside, an unreported prenylhydroquinone glycoside found in S. kamtschaticum leaves, could be a candidate preventive agent against AD via inhibition of p-tau accumulation.
A Review of Sustainability Standards and Ecolabeling in the Textile Industry
Authors: Plakantonaki Sofia , Kiskira Kyriaki , Zacharopoulos Nikolaos , Chronis Ioannis , Coelho Fernando , Togiani Amir , Kalkanis Konstantinos , Priniotakis Georgios
Published: July 2023

Environmental damage and the resulting global warming are two of the most serious threats to living species. These problems are the result of industrialization in all fields. The textile and fashion industries bear a negative impact on the environment and contribute significantly to water, air, and solid waste pollution. Over the last decades, consumer buying habits have shifted, and clothing purchases have increased dramatically. The manufacturing process of these textiles, from pretreatment to dyeing and finishing, involves the use of numerous chemicals that are harmful to both humans and the planet. Textiles have been identified as unsustainable products due to their entire life cycle, from raw material cultivation to manufacturing, and generate a large amount of toxic waste and greenhouse gases. Therefore, embedding sustainability in strategy is essential to meet evolving investor pressure, consumer demand, and regulatory requirements. More alternatives are available, such as ecofriendly textiles. Governments are promoting the idea of ecolabels and sustainability standards that endorse the textile’s “ecofriendliness”. Ecolabeling stimulates consumers and manufacturers to buy and produce ecotextiles, simultaneously allowing consumers to compare the various products. Consumers are gradually requesting more ecofriendly products. To save our environment and future generations, the textile industry must become more sustainable. Major brands should implement sustainable manufacturing practices. This review paper investigates the requirements of ecofriendly textiles, restricted substances, and ecolabeling in the textile industry and highlights the need to enhance the expertise and information existing in the design process with regard to the sustainability of finished products in order to create a more sustainable textile sector. Such a shift is only feasible if the designers are guided by a clear vision of design for sustainability.
Effect of Compost and Vermicompost Amendments on Biochemical and Physiological Responses of Lady’s Finger (Abelmoschus esculentus L.) Grown under Different Salinity Gradients
Authors: Suhani Ibha , Srivastava Vaibhav , Megharaj Mallavarapu , Suthar Surindra , Garg Vinod Kumar , Singh Rajeev Pratap
Published: July 2023

In the coming decades, the pressure to use saline water will increase as most of the natural resources with good water quality are being depleted. In order to avoid more stress on the soil plant system, a better understanding of the type of amendments and their integration with the irrigational water quality of any location-specific region is essential. Utilizing salt-affected lands in the best way possible will facilitate food security for the growing human population. An experiment was conducted with the Abelmoschus esculentus L. plant, irrigated with saline water having different NaCl gradients (0, 50, 100 and 150 mM), to evaluate the biochemical and physiological responses under different salinity gradients. Additionally, the effect of compost and vermicompost amendments in soil on plant responses to the changing salinity of irrigated water was observed. The results suggested that the addition of compost and vermicompost in soil not only suppressed the adverse impact of salinity in plants but also increased soil nutrients (TKN, OC, avail. P, avail. K and avail. Ca contents). Moreover, some biochemical parameters and plant growth parameters showed better traits in such manure-amended setups. The enhancement of proline, phenol, ascorbic acid and lipid peroxidation contents in the leaves of Abelmoschus esculentus L. under high salinity levels suggests some secondary metabolite-mediated response possibly due to stress caused by soil salt accumulations. In summary, crop production could be efficiently maintained in saline water-irrigated areas after amending the soils with appropriate organic manure.
Population Mobility and Urban Air Quality: Causal Inference and Impact Measurement
Authors: Huang Fu , Wu Qiang , Wang Pei
Published: July 2023

This paper treats the lockdown of Hubei Province during the outbreak of COVID-19 in early 2020 as a quasi-experiment, and uses the prefecture-level data of 328 cities in China to identify the causal effects of population mobility and urban air quality. This paper uses the DID model to eliminate the ‘Spring Festival effect’ with data from the same period of the lunar calendar in 2019 as the control group, and finds the reduction in population mobility has a clear causal impact on the improvement of urban air quality. The vast majority of air pollutants decreased, but ozone, which has a special generation mechanism, increased. This paper also constructs 29-day panel data of 328 prefecture-level cities from January to February in 2020 to quantitatively estimate the impact of population flow on urban air quality. After controlling for fixed effects, the results reveal that 1% increases in intra-city and inter-city population flows correspond to respective increases of 0.433% and 0.201% in the urban air quality index. Compared with inter-city flow, intra-city population flow increases air pollution more severely.
Water Management Education in the East African Region: A Review of the Challenges to Be Addressed
Authors: Nalumenya Brian , Rubinato Matteo , Kennedy Michael , Catterson Jade , Bakamwesiga Hilary , Blackett Matthew
Published: July 2023

Increased urbanisation coupled with inadequate awareness of the public on the issue of freshwater resource management has affected the use and the availability of freshwater resources in urban areas of Uganda, Kenya, and Tanzania. Lake Victoria has been the clearest example, with the water level decreasing 0.005 m/year from 1993 to 2016 causing an overall drop of 0.115 m. In order to develop sustainable methods for addressing these issues, this paper critically reviews the different legal frameworks used in each country (Uganda, Kenya, and Tanzania) adopted to manage the water resources and identifies the challenges faced by each legal framework applied. It also analyses the education systems implemented within these three nations to educate students at various levels about water resources and identifies the challenges involved in each system. Finally, suggestions are made for future research to be conducted to obtain specific benefits for better management of water resources in East Africa.
E-Nose-Driven Advancements in Ammonia Gas Detection: A Comprehensive Review from Traditional to Cutting-Edge Systems in Indoor to Outdoor Agriculture
Authors: Moshayedi Ata Jahangir , Sohail Khan Amir , Hu Jiandong , Nawaz Abdullah , Zhu Jianxiong
Published: July 2023

Ammonia (NH3) represents a perilous gas that poses a substantial hazard to both human well-being and the environment, particularly within agricultural regions. Agricultural activities constitute a primary source of ammonia emissions. Thus, effective monitoring and measurement of ammonia sources in agriculture are imperative for mitigating its adverse impact. However, not all existing ammonia detection methods are suitable for discerning the low concentrations typically encountered in agricultural ammonia volatilizing (ranging from 0.01 to 5 parts per million). Consequently, curtailing ammonia volatilization from farmland assumes paramount importance, with real-time monitoring serving as a crucial mechanism for assessing environmental contamination and minimizing agricultural ammonia losses. Deploying appropriate detection methodologies ensures that requisite measures are taken to safeguard human health and the environment from the deleterious repercussions of ammonia exposure. The present paper introduces a comprehensive approach to detecting and analyzing ammonia in agricultural settings. It elucidates the merits and demerits of conventional indoor and outdoor ammonia detection methods, juxtaposing them with the innovative technology of Electronic nose (E-nose). Within the paper, seven widely employed ammonia detection methods in farmland are scrutinized and compared against traditional techniques. Additionally, the constructional aspects and distinct components of E-nose are meticulously delineated and appraised. Ultimately, the paper culminates in a comprehensive comparative analysis encompassing all the aforementioned methodologies, elucidating the potential and limitations of E-nose in facilitating ammonia detection endeavors within agricultural contexts.
Ukraine’s Healthcare Crisis: Sustainable Strategies for Navigating Conflict and Rebuilding for a Resilient Future
Authors: Goniewicz Krzysztof , Burkle Frederick M. , Dzhus Marta , Khorram-Manesh Amir
Published: July 2023

The protracted war in Ukraine, sparked in 2014 and persistently escalating, has significantly impacted the country’s healthcare system. It has amplified existing challenges, such as shortages of medical supplies and personnel, outdated infrastructure, and restricted access to healthcare services. Destruction of medical facilities, non-adherence to prevention and treatment standards, lack of medical personnel and supplies, and logistical disruptions are hampering the provision of timely healthcare to the Ukrainian population. This study employed a rapid review methodology to synthesize available research concerning the healthcare crisis in Ukraine, with an explicit focus on the war’s sustained impact on the healthcare system. Through this review, a series of critical challenges facing Ukraine’s healthcare system were identified. These included the shortage of medical supplies and personnel, insufficient infrastructure, and funding deficits. Additionally, the review brought to light various interventions and strategies aimed at counteracting these challenges. These comprised international aid and support, initiatives enhancing the resilience of the Ukrainian health system, and others. Given the ongoing war, this review highlighted the pressing need for continuous support and investment in the Ukrainian healthcare system. This commitment is essential to guarantee access to quality healthcare for the Ukrainian population during the war and in its aftermath. The findings presented here can serve as a guide for policymakers and healthcare professionals working not only in Ukraine but also in other regions impacted by war. These insights can inform the development and implementation of effective strategies and interventions tailored to such challenging contexts.
Decarbonization Strategies in the UAE Built Environment: An Evidence-Based Analysis Using COP26 and COP27 Recommendations
Authors: Locke Jasmina , Dsilva Jacinta , Zarmukhambetova Saniya
Published: July 2023

The urgency of addressing climate change is increasingly evident through the rise in devastating natural disasters and significant shifts in global temperatures. With the urbanization of rural landscapes to accommodate population growth, the built environment has emerged as a major contributor to climate change, accounting for approximately 40% of natural resource consumption and carbon emissions. In pursuit of tackling climate challenges, countries have united under the United Nations Framework Convention on Climate Change (UNFCCC) to develop strategies for climate action and adaptation, through the Conference of the Parties (COP). The UAE has been an active member of the COP and has been at the forefront of implementing decarbonization strategies. This paper aims to provide a comprehensive analysis of the decarbonization recommendations presented during COP26 and COP27, specifically focusing on the built environment sector. The primary objective is to highlight how recommendations were effectively incorporated into the UAE’s built environment sector, employing a case study approach further highlighting the specific implementation strategies adopted in the G+2 SEE Institute building while demonstrating how COP26, COP27, and the UAE’s National Climate Change Plan 2017–2050 recommendations were translated into practical measures. The study places particular emphasis on the areas of energy, water and waste management, investigating how these strategies were integrated to promote decarbonization efforts. By examining the G+2 SEE Institute building case, this research attempts to provide valuable insights on aligning built environment practices with climate change mitigation objectives. The planning of the building structure employed a systems thinking approach, while assessments were conducted to identify materials and designs that would enable the building to achieve net-zero status. Real-time data analysis was employed for comprehensive analysis. The findings of this study will contribute to the body of knowledge on sustainable construction practices and serve as a guide for stakeholders, including developers, policymakers, and practitioners, in adopting effective strategies in reducing carbon emissions and fostering environmental sustainability in line with the Paris Agreement.
The Effect of Local Government Environmental Concern on Corporate Environmental Investment: Evidence from China
Authors: Yu Dan , Hu Kewei , Hao Yugui
Published: July 2023

This paper uses machine learning tools to construct local government environmental concern indicators and empirically examines the impact of local government environmental concern on corporate environmental investment. From the resource endowment perspective, corporate resources’ moderating role is also verified. The major findings are as follows: (1) local government environmental concern has a significant positive effect on the environmental investment of corporations in their jurisdictions; (2) corporations with fewer financial and political resources will pay more attention to the local government’s intention when making environmental investment decisions, and the promotion effect of local government environmental concern on the environmental investment of such corporations is more prominent. Further analysis shows that this promotion effect is more significant in regions with a high intensity of environmental regulation and high levels of economic development, and is more effective for key regulated corporations. This paper verifies the effect of local government on micro-corporations in environmental governance from the perspective of environmental concern, broadens the boundary of research on the relationship between government and corporate environmental responsibility fulfillment, and enriches the study of factors influencing corporate environmental investment behavior. It also provides important empirical evidence for central and local governments to implement green development and build a government–business collaborative environmental governance system.
Research on Learning Concentration Recognition with Multi-Modal Features in Virtual Reality Environments
Authors: Hu Renhe , Hui Zihan , Li Yifan , Guan Jueqi
Published: July 2023

Learning concentration, as a crucial factor influencing learning outcomes, provides the basis for learners’ self-regulation and teachers’ instructional adjustments and intervention decisions. However, the current research on learning concentration recognition lacks the integration of cognitive, emotional, and behavioral features, and the integration of interaction and vision data for recognition requires further exploration. The way data are collected in a head-mounted display differs from that in a traditional classroom or online learning. Therefore, it is vital to explore a recognition method for learning concentration based on multi-modal features in VR environments. This study proposes a multi-modal feature integration-based learning concentration recognition method in VR environments. It combines interaction and vision data, including measurements of interactive tests, text, clickstream, pupil facial expressions, and eye gaze data, to measure learners’ concentration in VR environments in terms of cognitive, emotional, and behavioral representation. The experimental results demonstrate that the proposed method, which integrates interaction and vision data to comprehensively represent the cognitive, emotional, and behavioral dimensions of learning concentration, outperforms single-dimensional and single-type recognition results in terms of accuracy. Additionally, it was found that learners with higher concentration levels achieve better learning outcomes, and learners’ perceived sense of immersion is an important factor influencing their concentration.
Advocacy, Ecotourism, and Biopolitics of Whale Conservation in Ecuador
Authors: Tatar Bradley
Published: July 2023

Whale-watching tourism in Ecuador thrives through the spectacular image of a flagship species, the humpback whale. Seemingly, it is an example of an industry regulated and managed in accordance with sustainable principles of nature conservation, thanks to the work of Ecuadorian scientists who advocate for policies to protect whales from harmful exploitation. However, does the use of the whale as an icon of conservation result in its utilization as a mere commodity for profit? Through ethnographic fieldwork including interviews, observations, and textual analysis, it is shown that the Ecuadorian practices of whale conservation have resulted in the whale becoming a subject of governance, by which the wild animals are recognized as entities worthy of ethical treatment. Using the humpback whale as a flagship species, the Ecuadorian scientists practice biopolitics through the strategies of categorizing, monitoring, and regulating human interactions with the whale population. The success of this approach to wildlife governance highlights the role of NGO-affiliated scientists as knowledge producers and policy advocates.
Low-Carbon City Building and Green Development: New Evidence from Quasi Natural Experiments from 277 Cities in China
Authors: Chen Wanzhe , Liu Jiaqi , Ning Xuanwei , Du Lei , Zhang Yang , Wu Chengliang
Published: July 2023

As a high-quality and sustainable growth model, green development has different economic, ecological, and social dimensions and is strategically important for the realization of modern city construction and the sustainable development of human society. The low-carbon city pilot policy (LCCP) is an innovative initiative for promoting green urban development and building a harmonious society in China. Based on balanced panel data from 277 prefecture-level cities from 2007 to 2020, this paper measures the level of urban green development in terms of three dimensions: green economic growth, ecological welfare enhancement, and social welfare increase. This paper also adopts a multi-period difference-in-differences (DID) method for investigating the impact of LCCP on green development with the panel dataset. The results of the study show that: (1) LCCP is generally beneficial to urban green development, and the results still hold after a series of robustness check analyses. (2) The results of the mechanism analysis show that the construction of low-carbon cities has improved the level of green technology innovation, thereby promoting the level of regional green development. Environmental regulation has a masking effect between low-carbon city construction and green development in this study. When environmental regulation is controlled for, the coefficient of the effect of LCCP on green development increases, reflecting that environmental regulation also plays an important role between the two. (3) According to the geographical location, whether it is a resource-based city, and the city cluster, we found that the low-carbon city pilot policy has a significant positive role in promoting green development in the central region, non-resource-based cities, and the Jing-Jin-Ji, but not in the eastern region, the western region, the Yangtze River Delta and Pearl River Delta. We also found that in resource-based cities, this effect presents a significant negative relationship. The above findings enrich the literature on low-carbon city pilot policies and green development and provide Empirical evidence for relevant countries and regions to carry out low-carbon city pilots.
Examining the Role of Motivation, Attitude, and Self-Efficacy Beliefs in Shaping Secondary School Students’ Academic Achievement in Science Course
Authors: İnce Murat
Published: July 2023

The aim of this study is to examine the effect of motivation, attitude, and self-efficacy beliefs of secondary school students towards science course on their academic achievement. The relational survey model was used to conduct the study. A total of 635 students, 319 females, and 316 males participated in the study. “Motivation Scale for Learning Science”, “Self-Efficacy Scale for Science”, “Attitude Scale for Science Course” were used as data collection tools. In the study, independent t-test was used to make comparisons in terms of gender, and path analysis using structural equation modeling principles was conducted to determine the direction and level of the relationship between the variables. In the results of the research, significant differences were found in students’ attitudes, motivation, and self-efficacy in terms of the gender variable. It was concluded that students’ attitudes and motivation directly affect their academic achievement. There was a strong positive relationship between attitude and motivation, a low positive relationship between self-efficacy and attitude, and a moderate positive relationship between self-efficacy and motivation. In line with the results of this research, and considering the direct effects of academic achievement on students’ motivation in science course, it is recommended that necessary information and training activities be provided to teachers in order to carry out studies on increasing students’ motivation.
Drought Characterization and Potential of Nature-Based Solutions for Drought Risk Mitigation in Eastern Ethiopia
Authors: Sintayehu Dejene W. , Kassa Asfaw Kebede , Tessema Negash , Girma Bekele , Alemayehu Sintayehu , Hassen Jemal Yousuf
Published: July 2023

Drought is one of the challenges in Ethiopia that affects the agricultural production. Ecosystems can mitigate the effects of climate-related hazards including drought. For this reason, nature-based solutions (NbS) are becoming more prominent to manage climate-related impacts in developing countries; however, there is still limited empirical evidence, which would inform NbS policy and practices. Thus, the aim of this study was to characterize droughts in Eastern Ethiopia (the case of Erer Sub-basin) and assess the effectiveness of various NbS for drought. The temporal changes in soil moisture deficit index (SMDI) (agricultural drought) and standard precipitation evapotranspiration index (SPEI) (meteorological drought) at various timescales (1, 3, and 6 months) between 1981 and 2020 were analyzed. Qualitative analysis was used to categorize and evaluate the effectiveness of NbS to mitigate drought risk by adopting a Living Labs approach. Overall, the study revealed that droughts showed an increasing trend with more frequency, longevity, and severity. The drought analysis results showed that SPEI’s variants were less reliable than SMDI0–5 and SMDI5–100. With an increase in rainfall, SPEI showed stronger relationships with SMDI0–5 at one and two-month delays between May and July. SPEI and SMDI5–100 performed better in terms of capturing actual drought occurrences than SMDI0–5. The results of focus group discussions (FGD) suggested NbS such as habitat restoration, structural restoration, reforestation, rehabilitation, revegetation, land enclosures, conservancies, and locally managed areas have been practiced as a strategy to manage drought risks. Overall, the study indicated that a new, comprehensive approach through nature-based solutions to reduce the risk of drought is urgently needed.
Decision-Making Approach in Sustainability Assessment in Steel Manufacturing Companies—Systematic Literature Review
Authors: Depczyński Radosław , Secka Jim , Cheba Katarzyna , D’Alessandro Carlotta , Szopik-Depczyńska Katarzyna
Published: July 2023

Sustainability is understood as a balanced integration of economic, ecological, and social aspects. Sustainable manufacturing can be considered one of the most important issues to address in the pursuit of sustainable development. The main purpose of the paper is to identify the most important directions of research to date and to indicate new and emerging areas of research concerned with the usage of decision-making methods in sustainability assessment in steel manufacturing companies. A systematic review was based on the publications indexed in the Web of Science and Scopus databases. In the analysis, the correspondence analysis and log-linear model were applied. To sum up, this study examines decision-making modalities and sustainable performance in terms of approaches that influence sustainability in steel manufacturing. Firstly, the study determined key decision-making aspects, such as the appropriate material identification for the manufacturing process and material labeling for the manufacturing process, which is essential for sustainable metal products. Secondly, the identification of sustainability indicators is considered vital in the hierarchical process as their integration is essential for the decision-making process and its outcome. Furthermore, system efficiency and productivity, alongside increasing attention to environmental protection, have led to significant changes in all production systems. In addition, many factors play an important role in the selection of suppliers, such as increasing the importance of the environment, increasing the recycling rate of the products, and ensuring sustainability performance. However, companies in the steel manufacturing industry rely little on the application of decision-making to assess sustainability.
Multi-Objective Optimization of the Structural Design of a Combustion Chamber of a Small Agricultural Diesel Engine Fueled with B20 Blend Fuel at a High Altitude Area
Authors: Shi Zhipeng , Wang Jun , Guo Xiangchi , Liu Xueyuan
Published: July 2023

This study focuses on a small agricultural diesel engine fueled with B20 (20% biodiesel and 80% diesel by volume) blend fuel in a plateau area. The combustion chamber’s structural parameters and fuel injection angle were taken as variables at peak torque conditions. First, a full factorial design was used for experimental design. Second, the back-propagation (BP) neural network was employed to predict the indicated thermal efficiency and the indicated specific NOx emission. Third, the non-dominated sorting genetic algorithm-II (NSGA-II) was utilized to optimize the indicated thermal efficiency and the indicated specific NOx emission. Finally, the technique for order of preference by similarity to ideal solution (TOPSIS) method was applied to obtain optimal solutions, and a three-dimensional numerical simulation was conducted to verify the optimization results. The optimization results indicate that the shape characteristics of the combustion chamber have a certain influence on the engine’s performance. The optimized design significantly reduces NOx emissions, by 22.83%, compared to the original engine, whilst maintaining the engine’s performance.
A Study on the Recycling Classification Behavior of Express Packaging Based on UTAUT under “Dual Carbon” Targets
Authors: Zhan Ying , Sun Yue , Xu Junfei
Published: July 2023

The rapid development of e-commerce has brought about a sharp increase in the demand for express packaging materials, which in turn has led to problems such as resource waste and environmental pollution. Under the target of “dual carbon”, the urgency and importance of effective classification and recycling of express packaging waste cannot be ignored. Based on the unified theory of technology acceptance and use (UTAUT) model and the characteristics of express packaging, this study selected performance expectancy, effort expectancy, social influence and perceived value as core variables, and gender, age and frequency as moderating variables, and established a structural equation through survey and analysis to study the factors influencing consumers’ willingness to recycle express packaging. The results of the study showed that performance expectancy, effort expectancy, social influence and perceived value had a significant positive effect on consumers’ willingness to classify. Classification willingness had a significant positive effect on consumers’ classification behavior. In addition to directly influencing classification intention, performance expectancy, effort expectancy and social influence also have mediating effects. In addition, gender played a moderating role in the effects of performance expectancy and social influence on classification intention. Based on the findings, this study proposes targeted measures to promote the green and healthy development of the express delivery industry.
Business Model Innovation toward Sustainability and Circularity—A Systematic Review of Innovation Types
Authors: Brenner Barbara , Drdla Daria
Published: July 2023

Organizations increasingly build on business model innovation (BMI) to reinvent their business models in sustainable and circular ways. This is reflected by a surge in academic research and business practice on sustainable and circular business model innovation. In this article, we take stock of the current literature to clarify which types of innovations contribute to the transformation to sustainable and circular business models. Building on a systematic literature review on sustainable and circular business model innovation using Systematic Reviews and Meta-Analysis (PRISMA), our primary contributions include (1) the identification, categorization, and discussion of various innovation types that lead to sustainable and circular business model innovation, (2) the identification of a research gap, and (3) avenues for future research.