bluuebunny/crossref_metadata_2025 huggingface dataset:
A Cross-Modal Semantic Alignment and Feature Fusion Method for Bionic Drone and Bird Recognition
Authors: Liu Hehao , Li Dong , Zhang Ming , Wan Jun , Liu Shuang , Zhu Hanying , Liu Qinghua
Published: August 2024

With the continuous progress in drone and materials technology, numerous bionic drones have been developed and employed in various fields. These bionic drones are designed to mimic the shape of birds, seamlessly blending into the natural environment and reducing the likelihood of detection. However, such a high degree of similarity also poses significant challenges in accurately distinguishing between real birds and bionic drones. Existing methods attempt to recognize both using optical images, but the visual similarity often results in poor recognition accuracy. To alleviate this problem, in this paper, we propose a cross-modal semantic alignment and feature fusion (CSAFF) network to improve the recognition accuracy of bionic drones. CSAFF aims to introduce motion behavior information as an auxiliary cue to improve discriminability. Specifically, a semantic alignment module (SAM) was designed to explore the consistent semantic information between cross-modal data and provide more semantic cues for the recognition of bionic drones and birds. Then, a feature fusion module (FFM) was developed to fully integrate cross-modal information, which effectively enhances the representability of these features. Extensive experiments were performed on datasets containing bionic drones and birds, and the experimental results consistently show the effectiveness of the proposed CSAFF method in identifying bionic drones and birds.
Spatial Feature Enhancement and Attention-Guided Bidirectional Sequential Spectral Feature Extraction for Hyperspectral Image Classification
Authors: Liu Yi , Jiang Shanjiao , Liu Yijin , Mu Caihong
Published: August 2024

Hyperspectral images have the characteristics of high spectral resolution and low spatial resolution, which will make the extracted features insufficient and lack detailed information about ground objects, thus affecting the accuracy of classification. The numerous spectral bands of hyperspectral images contain rich spectral features but also bring issues of noise and redundancy. To improve the spatial resolution and fully extract spatial and spectral features, this article proposes an improved feature enhancement and extraction model (IFEE) using spatial feature enhancement and attention-guided bidirectional sequential spectral feature extraction for hyperspectral image classification. The adaptive guided filtering is introduced to highlight details and edge features in hyperspectral images. Then, an image enhancement module composed of two-dimensional convolutional neural networks is used to improve the resolution of the image after adaptive guidance filtering and provide a high-resolution image with key features emphasized for the subsequent feature extraction module. The proposed spectral attention mechanism helps to extract more representative spectral features, emphasizing useful information while suppressing the interference of noise. Experimental results show that our method outperforms other comparative methods even with very few training samples.
Modeling the Long-Term Variability in the Surfaces of Three Lakes in Morocco with Limited Remote Sensing Image Sources
Authors: Haidu Ionel , El Orfi Tarik , Magyari-Sáska Zsolt , Lebaut Sébastien , El Gachi Mohamed
Published: August 2024

Satellite imagery has become a widespread resource for modeling variability in lake surfaces. However, the extended monitoring of a lake’s perimeter faces significant challenges due to atmospheric obstacles that cannot be rectified. Due to the atmosphere’s everchanging opacity, only half of the acquired satellite images have reliable qualitative accuracy making it possible to identify a lake’s contour. Consequently, approximately 50% of the monthly lake outline values can be determined using remote sensing methods, leaving the remaining 50% unknown. This situation is applicable to three lakes in Morocco (Abakhan, Ouiouan, and Tiglmanine), the subjects of the current research for the period between 1984 and 2022. What can we do if, during a period of time in which we monitored the evolution of the surface of a lake by satellite means, we obtain only about 50% of the possible images? Shall we just settle for this and stop the analysis? Although it may be challenging to believe, the present study introduces two statistical methods for interpolating and validating the monthly values of the lake outline: the iterative ratio method based on the autocorrelation of the monthly water balance and the Kalman filter. We estimated the reconstruction errors of the missing values and validated the methodology using an inverse philosophy, reconstructing the initial data from the table of the simulation results. Given that the difference between the initial values and the reconstructed initial values resembles white noise or an AR (1) process with a low coefficient, we deemed the methodological approach acceptable. Several comparison criteria between the two interpolation methods were employed, yet determining the more appropriate one remains challenging. Based on our surface reconstruction method, Lake Abakhan, with an average area of 22 hectares, experienced significant fluctuations, ranging from a maximum of 34 hectares in 2010 to a minimum of 0.8 hectares in 2022. Lake Ouiouan, with an average area of 14 hectares, displayed much lower variation, with a maximum of 17 hectares in 2020 and a minimum of 6.5 hectares in 1988. Lake Tiglmanine showed a pattern similar to that of Lake Abakhan but with less pronounced fluctuations. With an average area of 6.1 hectares, its maximum was 9.2 hectares in 2011 and its minimum was 4.1 hectares in 1984.
Stepwise Attention-Guided Multiscale Fusion Network for Lightweight and High-Accurate SAR Ship Detection
Authors: Wang Chunyuan , Cai Xianjun , Wu Fei , Cui Peng , Wu Yang , Zhang Ye
Published: August 2024

Many exceptional deep learning networks have demonstrated remarkable proficiency in general object detection tasks. However, the challenge of detecting ships in synthetic aperture radar (SAR) imagery increases due to the complex and various nature of these scenes. Moreover, sophisticated large-scale models necessitate substantial computational resources and hardware expenses. To address these issues, a new framework is proposed called a stepwise attention-guided multiscale feature fusion network (SAFN). Specifically, we introduce a stepwise attention mechanism designed to selectively emphasize relevant information and filter out irrelevant details of objects in a step-by-step manner. Firstly, a novel LGA-FasterNet is proposed, which incorporates a lightweight backbone FasterNet with lightweight global attention (LGA) to realize expressive feature extraction while reducing the model’s parameters. To effectively mitigate the impact of scale and complex background variations, a deformable attention bidirectional fusion network (DA-BFNet) is proposed, which introduces a novel deformable location attention (DLA) block and a novel deformable recognition attention (DRA) block, strategically integrating through bidirectional connections to achieve enhanced features fusion. Finally, we have substantiated the robustness of the new framework through extensive testing on the publicly accessible SAR datasets, HRSID and SSDD. The experimental outcomes demonstrate the competitive performance of our approach, showing a significant enhancement in ship detection accuracy compared to some state-of-the-art methods.
Open-Set Recognition Model for SAR Target Based on Capsule Network with the KLD
Authors: Jiang Chunyun , Zhang Huiqiang , Zhan Ronghui , Shu Wenyu , Zhang Jun
Published: August 2024

Synthetic aperture radar (SAR) automatic target recognition (ATR) technology has seen significant advancements. Despite these advancements, the majority of research still operates under the closed-set assumption, wherein all test samples belong to classes seen during the training phase. In real-world applications, however, it is common to encounter targets not previously seen during training, posing a significant challenge to the existing methods. Ideally, an ATR system should not only accurately identify known target classes but also effectively reject those belonging to unknown classes, giving rise to the concept of open set recognition (OSR). To address this challenge, we propose a novel approach that leverages the unique capabilities of the Capsule Network and the Kullback-Leibler divergence (KLD) to distinguish unknown classes. This method begins by deeply mining the features of SAR targets using the Capsule Network and enhancing the separability between different features through a specially designed loss function. Subsequently, the KLD of features between a testing sample and the center of each known class is calculated. If the testing sample exhibits a significantly larger KLD compared to all known classes, it is classified as an unknown target. The experimental results of the SAR-ACD dataset demonstrate that our method can maintain a correct identification rate of over 95% for known classes while effectively recognizing unknown classes. Compared to existing techniques, our method exhibits significant improvements.
Hyperspectral Data Can Differentiate Species and Cultivars of C3 and C4 Turf Despite Measurable Diurnal Variation
Authors: Cushnahan Thomas A. , Grafton Miles C. E. , Pearson Diane , Ramilan Thiagarajah
Published: August 2024

The ability to differentiate species is not adequate for modern forage breeding programs. The measurement of persistence is currently a bottleneck in the breeding system that limits the throughput of cultivars to the marketplace and prevents it from being selected as a trait. The use of hyperspectral data obtained through remote sensing offers the potential to reduce guesswork by identifying the distribution of pasture species, but only if such data alone can distinguish the subtle differences within species, i.e., cultivars. The implementation of this technology faces many challenges due to the spectral and temporal variability of species. To understand the spectral variability between and within species groups, differentiation using hyperspectral data from monoculture plots of turf species was utilized. Spectral data were collected over a year using an ASD FieldSpec® and canopy pasture probe (CAPP). The plots consisted of monocultures of various species, and cultivars (a total of 10 plots). Linear discriminant analysis (LDA) was conducted on the full spectrum and reduced band data. This technique successfully differentiated the species with high accuracy (>98%). We demonstrate the potential of hyperspectral data and analysis techniques to accurately separate differences down to cultivar level. We also show that diurnal variation is measurable in the spectra but does not preclude differentiation.
A Study on the Motion Behavior of Metallic Contaminant Particles in Transformer Insulation Oil under Multiphysical Fields
Authors: Wei Binbin , Wang Zhijuan , Qi Runze , Wang Xiaolong , Zhao Tong
Published: August 2024

When using transformer insulation oil as a liquid dielectric, the oil is easily polluted by the solid particles generated in the operation of the transformer, and these metallic impurity particles have a significant impact on the insulation performance inside the power transformer. The force of the metal particles suspended in the flow insulation oil is multidimensional, which will lead to a change in the movement characteristics of the metal particles. Based on this, this study explored the motion rules of suspended metallic impurity particles in mobile insulating oil in different electric field environments and the influencing factors. A multiphysical field model of the solid–liquid two-phase flow of single-particle metallic impurity particles in mobile insulating oil was constructed using the dynamic analysis method, and the particles’ motion characteristics in the oil in different electric field environments were simulated. The motion characteristics of metallic impurity particles under conditions of different particle sizes, oil flow velocities, and insulation oil qualities and influencing factors were analyzed to provide theoretical support for the detection of impurity particles in transformer insulation oil and enable accurate estimations of the location of equipment faults. Our results show that there are obvious differences in the trajectory of metallic impurity particles under different electric field distributions. The particles will move towards the region of high field intensity under an electric field, and the metallic impurity particles will not collide with the electrode under an AC field. When the electric field intensity and particle size increase, the trajectory of the metallic impurity particles between electrodes becomes denser, and the number of collisions between particles and electrodes and the motion speed both increase. Under the condition of a higher oil flow velocity, the number of collisions between metal particles and electrodes is reduced, which reduces the possibility of particle agglomeration. When the temperature of the insulation oil changes and the quality deteriorates, its dynamic viscosity changes. With a decrease in the dynamic viscosity of the insulation oil, the movement of the metallic impurity particles between the electrodes becomes denser, the collision times between the particles and electrodes increase, and the maximum motion speed of the particles increases.
Graphene–PbS Quantum Dot Heterostructure for Broadband Photodetector with Enhanced Sensitivity
Authors: Qing Jincheng , Wang Shicai , Gu Shuyi , Lin Lin , Xie Qinpei , Li Daming , Huang Wen , Guo Junxiong
Published: August 2024

Photodetectors converting light into electrical signals are crucial in various applications. The pursuit of high-performance photodetectors with high sensitivity and broad spectral range simultaneously has always been challenging in conventional semiconductor materials. Graphene, with its zero bandgap and high electron mobility, is an attractive candidate, but its low light absorption coefficient restricts its practical application in light detection. Integrating graphene with light-absorbing materials like PbS quantum dots (QDs) can potentially enhance its photodetection capabilities. Here, this work presents a broadband photodetector with enhanced sensitivity based on a graphene–PbS QD heterostructure. The device leverages the high carrier mobility of graphene and the strong light absorption of PbS QDs, achieving a wide detection range from ultraviolet to near-infrared. Employing a simple spinning method, the heterostructure demonstrates ultrahigh responsivity up to the order of 107 A/W and a specific detectivity on the order of 1013 Jones, showcasing significant potential for photoelectric applications.
Enhanced Safety in Autonomous Driving: Integrating a Latent State Diffusion Model for End-to-End Navigation
Authors: Chu De-Tian , Bai Lin-Yuan , Huang Jia-Nuo , Fang Zhen-Long , Zhang Peng , Kang Wei , Ling Hai-Feng
Published: August 2024

Ensuring safety in autonomous driving is crucial for effective motion planning and navigation. However, most end-to-end planning methodologies lack sufficient safety measures. This study tackles this issue by formulating the control optimization problem in autonomous driving as Constrained Markov Decision Processes (CMDPs). We introduce an innovative, model-based approach for policy optimization, employing a conditional Value-at-Risk (VaR)-based soft actor-critic (SAC) to handle constraints in complex, high-dimensional state spaces. Our method features a worst-case actor to ensure strict compliance with safety requirements, even in unpredictable scenarios. The policy optimization leverages the augmented Lagrangian method and leverages latent diffusion models to forecast and simulate future trajectories. This dual strategy ensures safe navigation through environments and enhances policy performance by incorporating distribution modeling to address environmental uncertainties. Empirical evaluations conducted in both simulated and real environments demonstrate that our approach surpasses existing methods in terms of safety, efficiency, and decision-making capabilities.
Enhancement of In Vitro Seed Germination, Growth, and Root Development in Two Sideritis Species through GA3 Application and Diverse LED Light Conditions
Authors: Sarropoulou Virginia , Grigoriadou Katerina , Maloupa Eleni , Chatzopoulou Paschalina
Published: August 2024

The Sideritis genus includes over 150 species primarily found in the Mediterranean basin, including the S. clandestina subsp. pelopponesiaca from the Peloponnese and S. scardica from North and Central Greece. In vitro seed germination has proven effective for conserving and amplifying the genetic diversity of endangered species such as Sideritis. This study aimed to optimize in vitro germination and seedling growth of S. scardica and S. clandestina subsp. pelopponesiaca under different lighting conditions at 22 °C, including white fluorescent lamps (WFL-BG-40) and LEDs (LED-BGYOR-40, LED-BR-40, LED-BR-80, LED-BR-120) all under a 16-h light/8-h dark photoperiod (WFL: white fluorescent light, B:blue, G:green, Y:yellow, O:orange, R:red, 40–80–120 μmol m−2 s−1), along with a 24-h dark treatment. The results indicated that LED-BR-80 combined with 250 mg L−1 GA3 in the MS medium promoted best germination (40%, day 55) and shoot proliferation in S. clandestina subsp. pelopponesiaca. Conversely, 5-year-old cold stratified S. scardica seeds showed higher germination rates (80%) and robust seedling growth under LED-BGYOR-40 with 250 mg L−1 GA3, particularly thriving in LED-BR-120 for increased shoot height and root number. This is the first report of the efficacy of LED technology in optimizing in vitro conditions for Sideritis species, crucial for their conservation and sustainable commercial cultivation.
Dynamics of Stress Biomarker in Cliff Divers during Official Competition
Authors: Perić Mia , Pavlinović Vladimir , Foretić Nikola , Nikolovski Zoran , Vrdoljak Dario
Published: August 2024

Stress is defined as a reaction of the body to any given stressor, external or internal. These stressors are common among participants in sports. Therefore, this study aimed to determine the dynamics of two stress biomarkers during an official cliff diving competition. The sample of participants included six professional cliff divers (three females and three males). Their salivary cortisol (C) and alpha-amylase (AA) samples were collected during a 3-day competition (four samples on day 1 and six samples on days 2 and 3). The analysis of the results showed a non-significant increase in AA from day one to day three. On the other hand, C has an increase in the first two days (0.58 ± 0.16 µg/mL–0.61 ± 0.23 µg/mL) and then a decrease in the last competition day (0.53 ± 0.23 µg/mL). Analysis of samples collected during each day demonstrates a fluctuation of the biomarkers. For AA, the sample after the training dive on the 2nd day has the highest value (326.34 ± 280.73 U/mL), whereas on day 3, the samples after training and before the competition dive are the highest (364.50 ± 287.13 U/mL; 466.49 ± 218.39 U/mL). Regarding C levels, the sample after the competition dive tends to show the highest values (0.66 ± 0.17 µg/mL; 0.89 ± 0.29 µg/mL; 0.76 ± 0.32 µg/mL, respectively). Furthermore, the correlation between the results of competition and biomarkers is not significant. These results demonstrate how cliff diving competition tends to have a high impact on the sympathetic nervous system, as observed mainly in AA dynamics. One might contend that even though there is no significant physical strain, cliff divers demonstrate heightened stress biomarker levels that could affect their performance and focus while diving.
Bringing Animals in-to Wildlife Tourism
Authors: Speiran Siobhan I. M. , Hovorka Alice J.
Published: August 2024

The objective of this paper is to highlight animal stakeholders, evidenced-based best practices, care ethics, and compassion as essential components of sustainable wildlife tourism. These tenets stem from an animal geography lens, which is well-positioned for studies of animal-based tourism and transspecies caregiving. As a conceptual contribution, this paper presents a theory synthesis that ‘stays with the trouble’ of wildlife tourism and identifies ways to ‘bring animals in’. Our approach could be described as multispecies, critical, and socio-ecological. We argue that the trouble with wildlife tourism writ large includes nonhuman suffering and biodiversity loss, unethical and unevidenced practices, gaps in the knowledge of wildlife welfare, and limited engagement with animals as stakeholders. We then present four ways to ‘bring animals in’ as co-participants in wildlife tourism research and practice. This involves enfranchising animals as stakeholders in wildlife tourism, buttressed by ethics of care, best practices, and a commitment to improved outcomes along the conservation-welfare nexus. Finally, we consider the extent to which wildlife sanctuary tourism serves as a further problem or panacea that balances the conservation and welfare of wild animals. The result of our theory synthesis is the promotion of a more care-full and compassionate paradigm for wildlife tourism, which draws from diverse scholarships that contribute, conceptually and practically, to the underserved niches of wildlife welfare, rehabilitation, and sanctuary research.
Sustainability as a Resilience Factor in the Agri-Food Supply Chain
Authors: Arimany-Serrat Núria , Montanyà Oriol , Amat Oriol
Published: August 2024

What factors have a positive impact on the resilience of supply chains? This question has been raised in several academic research papers, particularly in recent years, coinciding with a series of disruptions (healthcare, political and climate) that have shaken the reliability of a global logistics gear that seemed infallible. In this context, the motivation of this article is to provide new knowledge that helps identify the key elements of business management that guarantee greater reliability and security in the supply of products. More specifically, this research focuses on studying the causal relationship that exists between resilience and sustainability in the management of agri-food chains to generate valuable knowledge for the leaders of companies that have to deal with these two transformations. Through a qualitative comparative analysis methodology, taking a sample of eight Spanish distribution companies, the results obtained indicate that the strength of supply chains has a strong link to strategies to improve environmental, social and governance sustainability. This is a conclusion that provides interesting value, reaffirming that it is possible to design and execute supply chains that make sustainability and resilience compatible.
Ocean Plastics: Extraction, Characterization and Utilization of Macroalgae Biopolymers for Packaging Applications
Authors: Moore Evan , Colbert Declan
Published: August 2024

This review details the extraction, characterization and utilization of seaweed-derived biopolymers for future packaging applications. The review is contextualized within the broader scope of the challenge of plastic pollution and the current urgent need for more sustainable packaging materials. Macroalgae (or seaweed) has been highlighted as a promising source of biopolymers, most commonly sodium alginate, agar and carrageenan, for reasons such as a rapid growth rate and decreased environmental impact when compared with terrestrial plant life. Extraction methods detailed include traditional solvent-based extraction and more sustainable developments such as ultrasound-assisted extraction, microwave-assisted extraction and bead milling. This review additionally presents the characterization techniques most pertinent in determining the applicability of these biopolymers in packaging applications. Properties of key importance to the development of sustainable packaging materials such as thermal properties, mechanical strength, barrier properties and biodegradability are highlighted in comparison to conventional petroleum-based plastics. This review concludes by realistically identifying the challenges faced by implementing seaweed-based biopolymers into packaging structures, such as cost-effectiveness, scalability and performance while suggesting future directions to mitigate these issues and improve the commercial viability of these materials for the packaging industry.
Suitability of Residues from Seaweed and Fish Processing for Composting and as Fertilizer
Authors: Cabell Joshua , Eich-Greatorex Susanne , Ion Violeta Alexandra , Krogstad Tore , Matsia Sevasti , Perikli Maria , Salifoglou Athanasios , Løes Anne-Kristin
Published: August 2024

There is a need to find novel sources of fertilizers to meet the increasing food demands of a growing human population and alternatives to mined and synthetic fertilizers for the certified organic sector. Composting is a common method for processing and stabilizing organic residues for use in horticulture. To that end, a small-scale composting experiment with six combinations of dried and ground rockweed (Ascophyllum nodosum), algae fiber from chemically processed rockweed, ground bones and fishmeal from cod (Gadus morhua), and ground blue mussels (Mytilus edulis) was conducted in Dewar flasks to assess whether these residues are suitable for composting and have potential for use as fertilizers. Expanded clay aggregates were used as a bulking material. Physicochemical analyses were performed on the residues and their mixtures before and after composting, and the temperature in the flasks was monitored for 92 days. Suitability was determined by evaluating the temperature dynamics, changes in physiochemical parameters, and nutrient profiles. All treatments generated heat, with reductions in C/N ratio, weight, and volume, demonstrating suitability for composting. The treatments with algae fiber had a higher mean temperature (34.5 vs. 29.0 °C) and more degree days above the thermophilic range (mean = 176- vs. 19-degree days), the greatest reduction in volume (mean = 35% vs. 27%), and the lowest C/N ratios at the end of active composting (18 vs. 24) compared to the treatments with dried and ground seaweed. In terms of fertilizer value, none of the finished composts were balanced for use as fertilizers alone and, in some cases, contained too much Na, but contained sufficient concentrations of K, S, Mg, and Ca and could be a valuable source of these nutrients and organic matter in combination with other N- and P-rich sources.
Multi-Criteria Decision Analysis for Sustainable Oil and Gas Infrastructure Decommissioning: A Systematic Review of Criteria Involved in the Process
Authors: Wei Xin , Zhou Jin
Published: August 2024

The decommissioning of oil and gas (O&G, hereafter) facilities presents complex challenges when addressing the diverse needs of stakeholders. By synthesizing information from previous Multi-Criteria Decision Analysis (MCDA, hereafter) studies on decommissioning projects, this study aims to do the following: (a) formulate a structured set of criteria adaptable to MCDA for both offshore and onshore O&G decommissioning, (b) identify and analyze the evolving trends and regional disparities in MCDA for decommissioning, and (c) explore current O&G onshore decommissioning procedures and map specific criteria to these processes. Following a systematic literature review approach, this study analyzed 63 references across four stages from 2006 to 2024 and identified 158 criteria. These criteria were consolidated into a framework of 22 factors across dimensions comprising technical, environmental, societal, financial, health and safety considerations, and additional concerns from stakeholders. This study observed a significant focus shift from technical aspects to environmental considerations in decommissioning practices from 2011 onwards, reflecting growing awareness of sustainability. It also revealed regional differences, such as the technical emphasis in the North Sea and environmental concerns in Australia. Furthermore, this study refined O&G onshore decommissioning procedures and identified criteria gaps for further research, particularly in societal impact regarding public resource availability, recreational opportunities, and operating company reputation. The study provides a robust foundation for the development of future MCDA frameworks tailored to O&G infrastructure decommissioning projects, thus supporting long-term environmental and social sustainability.
Acceptance of an IoT System for Strawberry Cultivation: A Case Study of Different Users
Authors: Varela-Aldás José , Gavilanes Alex , Velasco Nancy , Del-Valle-Soto Carolina , Bran Carlos
Published: August 2024

The Internet of Things (IoT) has been impacting multiple industries worldwide for over a decade. However, less developed countries have yet to make the transition to these technologies. South America is among the regions with the least IoT influence in all sectors, indicating a need for studies to explore IoT acceptance among various users in this region. This study analyzes two different users of a monitoring and irrigation system for strawberry (Fragaria × ananassa) farming. Monitored variables include soil moisture, and ambient temperature and humidity, with irrigation performed via water pumping from a reservoir. The system is based on the M5Core2 development kit for the local station and the IoT platform ThingSpeak for remote access. It features a web user interface consisting of an application developed in HTML using a plugin on ThingSpeak. Thus, the system can be used locally via a touchscreen and remotely through a web browser. Measurements are cross-verified with commercial meters to ensure their reliability, and users are asked to fill out a Technology Acceptance Model (TAM) for IoT to gauge their acceptance level. Additionally, an interview is conducted that explores four critical factors, aimed at understanding their experience and interaction with the system after a period of usage. The findings confirm the validity of the monitored variables and demonstrate a global acceptance rate of slightly over 80%, albeit with varying user acceptance perspectives. Specifically, the technical user exhibits greater acceptance than the crop administrator, evidenced by a mean discrepancy of 1.85 points on the TAM scale.
Land Use Intensity Alters Ecosystem Service Supply and Demand as Well as Their Interaction: A Spatial Zoning Perspective
Authors: Yang Yunrui , Zhang Jiaying , Hu Yi’na
Published: August 2024

Clarifying the interaction between ecosystem service supply (ESS) and demand (ESD) and its response to land use change is essential for achieving ecological security. Previous research mainly focused on how land use change affects ESS, ignoring its impact on the interaction between ESS and ESD, making it difficult to guide regional sustainability in social–ecological coupled systems. Taking the Yangtze River Delta as the study area, after evaluating five types of ESS and ESD during 2000–2020, this study analyzed the interaction between changes in ESS and ESD using Geodetector from a spatial zoning perspective and subsequently explored the impact of land use intensity on ESS, ESD, and their interaction using Spearman correlation analysis and regression analysis. The results showed that there was obvious spatial–temporal heterogeneity in ESS and ESD. Different ecosystem services varied in their interaction between ESS and ESD, and the demand changes in water conservation and soil retention significantly influenced their supply changes, while supply changes in carbon sequestration were not influenced by its demand changes. The effect of ESD changes on ESS changes also showed spatial heterogeneity. All interactions between ESS changes and ESD changes, except carbon sequestration, were significant in the Supply Increases–Demand Increases zone. Soil retention and water conservation showed a significant supply–demand interaction in the Supply Decreases–Demand Decreases zone and the Supply Decreases–Demand Increases zone, respectively, while no ecosystem service showed significant supply–demand interaction in the Supply Increases–Demand Decreases zone. Land use intensity had linear and nonlinear effects on different ESS and ESD, and it was also found that the effect of ESD changes on corresponding ESS changes was relatively obvious in regions with dramatic land use change. Finally, this paper incorporated the changes in ESS and ESD, as well as their interaction, into land use management, which can provide implications for regional sustainability.
Investigation of Perception Differences in Shared Mobility between Driver’s License Holders and Nonholders: A Case Study of Seoul, Gyeonggi, and Incheon in South Korea
Authors: Baek Jiin , Shin Ju-Young
Published: August 2024

Shared mobility (SM) services are transitioning from the introduction stage to the growth stage, driven by the growth of the sharing economy, the promotion of smart cities, the diverse personal transportation modes, and the development of autonomous driving technologies. SM services, such as car sharing, car-hailing, shared bikes, and e-scooters, have emerged as solutions to address issues related to carbon neutrality and traffic congestion in densely populated areas. The purpose of this study is to investigate potential disparities in user perception and satisfaction among groups with or without driving experience when using SM services—through hypothesis testing using the two-proportion Z-test. Subsequently, a satisfaction analysis is conducted. This research creates foundational data for future SM services. The survey targeted 1041 residents living in Seoul, Gyeonggi Province, and Incheon, and was conducted over two weeks in March 2020. This study aims to derive associations between two groups using SM—those with and without driving experience. The results indicate that car sharing and bike sharing showed significant differences in user patterns based on driving experience, whereas e-scooters and car-hailing did not exhibit significant differences. This contradicts the assumption that people without a driver’s license would use SM more frequently. Moreover, the results of each SM’s analysis show different usage patterns and satisfaction between driver’s license holders and nonholders. This study will serve as foundational data for researching strategies to reduce personal car ownership through the promotion of public transportation and SM services. Furthermore, it can be a basis for suggesting policy recommendations to facilitate future mobility systems.
Research on Impact of Design Innovation Factors on Pure Technical Efficiency of Manufacturing Innovation
Authors: Xu Bing , Kong Siyuan , Ying Zhiyue , Chen Jiayang , Zhang Shihao , Yan Yuting , Xu Jun
Published: August 2024

Context: Improving the pure technical efficiency in manufacturing innovation is crucial to achieving sustainable development in the manufacturing industry. Objective: We aimed to explore the impact of design innovation factors on the pure technical efficiency of manufacturing innovation from 2011 to 2021, with industrial enterprises above the designated size in Zhejiang Province, China, taken as the research object. Method: The super-efficiency DEA model was used to calculate the pure technical efficiency of manufacturing innovation. Literature research, combined with the Pearson correlation coefficient, was employed to obtain five design innovation factors, including the number of policy regulations, growth rate of designers, number of design enterprises, number of patents granted, and number of design awards at the provincial level or above. Results: Based on the Tobit model, the influence of design innovation factors on the pure technical efficiency in manufacturing innovation was analyzed and demonstrated. Except for the number of policy regulations and growth rate of designers, the other three factors had a significant positive impact on the pure technical efficiency of manufacturing innovation. In general, design innovation exerts positive effects on the growth of pure technical efficiency. Conclusions: The results of this study provide helpful insights into the promotion of sustainable development in the manufacturing industry through design innovation.
Unraveling the Valorization Potential of Pineapple Waste to Obtain Value-Added Products towards a Sustainable Circular Bioeconomy
Authors: Paz-Arteaga Sarah L. , Cadena-Chamorro Edith , Goméz-García Ricardo , Serna-Cock Liliana , Aguilar Cristóbal N. , Torres-León Cristian
Published: August 2024

The pineapple (Ananas comosus) is one of the most commercialized tropical fruits worldwide. Its high processing and consumption generate huge quantities of organic waste and severe economic and environmental issues. Embracing the circular bioeconomy concept, this fruit waste can be applied as a bioresource (raw material) for the obtention of a wide range of high-valued biocompounds by applying innovative and ecofriendly technologies. In this paper, we critically describe pineapple-derived waste, from their chemical composition to their functional and biological properties, as well as the latest advances on valorization technologies, particular solid and submerged fermentations. Notably, this article highlights the possibility of using pineapple waste to obtain bioactive compounds such as bromelain, phenolic compounds, and dietary fiber, which have important biological properties such as antioxidant, anticancer, antimicrobial, and prebiotic capacities. Indeed, pineapple wastes can become valued materials by using green and biotechnological technologies that allow us to maximize their potential and might avoid wastage and environmental issues. Nevertheless, it is necessary to further investigate the biomolecules present in the waste derived from different pineapple varieties and their health beneficial effects as well as emerging technologies in order to obtain a full spectrum of natural value-added compounds that industries and society demand today.
Impact of Extracurricular Physical Activity on Achievement of the Sustainable Development Goals and Academic Performance: Mediating Cognitive, Psychological, and Social Factors
Authors: Zarazaga-Peláez Jorge , Barrachina Valentina , Gutiérrez-Logroño Alejandra , Villanueva-Guerrero Oscar , Roso-Moliner Alberto , Mainer-Pardos Elena
Published: August 2024

Physical activity has been recognized as an essential element for the health and well-being of children and adolescents. Therefore, this systematic review delves into the study of the relationship between extracurricular physical activity (PA) academic achievement (AA), and its alignment with the Sustainable Development Goals (SDGs) among students aged 3–16 years. This systematic review was guided by adhering to PRISMA guidelines. The review included studies that evaluated PA as an independent variable and its impact on academic performance (AP) through mediation models, structural equations, and multiple regression analyses. The key mediators identified between PA and AA include cognitive function, psychological well-being, and social factors. Cognitive self-regulation, attention, memory, self-efficacy, and self-esteem are key mediators between PA and AA as well as sleep patterns and a healthy lifestyle. In conclusion, extracurricular PA significantly influenced AA, and its influence was mediated by various cognitive, psychological, and social factors. These results support the inclusion and promotion of PA in educational settings, which aligns with the Sustainable Development Goals by improving AA and overall student well-being.
Research on Machine Learning-Based Method for Predicting Industrial Park Electric Vehicle Charging Load
Authors: Ma Sijiang , Ning Jin , Mao Ning , Liu Jie , Shi Ruifeng
Published: August 2024

To achieve global sustainability goals and meet the urgent demands of carbon neutrality, China is continuously transforming its energy structure. In this process, electric vehicles (EVs) are playing an increasingly important role in energy transition and have become one of the primary user groups in the electricity market. Traditional load prediction algorithms have difficulty in constructing mathematical models for predicting the charging load of electric vehicles, which is characterized by high randomness, high volatility, and high spatial heterogeneity. Moreover, the predicted results often exhibit a certain degree of lag. Therefore, this study approaches the analysis from two perspectives: the overall industrial park and individual charging stations. By analyzing specific load data, the overall framework for the training dataset was established. Additionally, based on the evaluation system proposed in this study and utilizing both Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) algorithms, a framework for machine learning-based load prediction methods was constructed to forecast electric vehicle charging loads in industrial parks. Through a case analysis, it was found that the proposed solution for the short-term prediction of the charging load in industrial park electric vehicles can achieve accurate and stable forecasting results. Specifically, in terms of data prediction for normal working days and statutory holidays, the Long Short-Term Memory (LSTM) algorithm demonstrated high accuracy, with R2 coefficients of 0.9283 and 0.9154, respectively, indicating the good interpretability of the model. In terms of weekend holiday data prediction, the Multilayer Perceptron (MLP) algorithm achieved an R2 coefficient of as high as 0.9586, significantly surpassing the LSTM algorithm’s value of 0.9415, demonstrating superior performance.
How Well Do German A-Level Students Understand the Scientific Underpinnings of Climate Change?
Authors: Schubatzky Thomas , Wackermann Rainer , Haagen-Schützenhöfer Claudia , Wöhlke Carina
Published: August 2024

Understanding the scientific underpinnings of climate change is crucial for informed citizenship and future decision-making. This study investigates the understanding of the scientific underpinnings of climate change among German A-level students, focusing on key content areas such as the atmosphere, the greenhouse effect, the carbon cycle, and the distinction between weather and climate. Using a validated climate change concept inventory (CCCI-422), we assessed 501 students from five urban secondary schools in North Rhine-Westphalia. Results indicate that students correctly answered on average 39% of the questions correctly, revealing significant knowledge gaps, particularly in areas like the greenhouse effect and atmospheric composition. We also identified several overarching ideas that many students demonstrate. This study underscores the importance of integrating comprehensive climate science education into national curricula and classroom instruction to foster scientifically literate future generations capable of addressing the global climate crisis.
Analysis of Regulation of Costs for Operating Buses in a Transport Company
Authors: Kurganov Valery , Gryaznov Mikhail , Aduvalin Andrey , Polyakova Liliya , Dorofeev Aleksey
Published: August 2024

The problem of increasing passenger traffic remains acute for municipal public transport. The value of this indicator is determined by the interest of citizens in this way of making their trips and determines the feasibility of the carrier’s operation. The authors conducted a study of the problems of public transport services in large- and medium-sized cities, which found that the population’s interest in public urban passenger transport has generally been significantly lost. More than 40% of the city population refuses to travel on public transport, half of the population has questions about the reliability of tariff formation, and the same number of people are not satisfied with the regular route network and schedule. City residents increasingly prefer personal vehicles or taxis for their trips, which negatively affects the revenue side of carriers, as well as the level of social comfort and the quality of life of citizens. Efforts to reduce the operating costs of the carrier are aimed at correcting the current situation with urban transport so that tariffs for transportation are more acceptable for passengers. The formation of tariffs for passenger transportation for transport companies is an urgent and complex task. It is necessary to formulate the tariff in such a way as to cover your own transportation costs in the near future and, at the same time, not exceed the psychological threshold for passengers so as not to cause their negative reaction. In addition, since the transportation of passengers by urban public transport is regulated by the authorities, it is also necessary to provide an economic justification for transportation tariffs. This is difficult in the absence of substantiated indicators of consumption rates of material resources in the transport process. To solve this problem, it is necessary to carefully analyze the current costs of operating the bus fleet, as well as forecast costs for future periods. At different periods, researchers have proposed various approaches for planning the cost of operating a bus fleet. The approach we propose is to use standardization of the consumption of material resources, considering the individual operating conditions of the bus fleet and the influence of various factors.
A Sustainable Approach Based on Sheep Wool Mulch and Soil Conditioner for Prunus domestica (Stanley Variety) Trees Aimed at Increasing Fruit Quality and Productivity in Drought Conditions
Authors: Gitea Manuel Alexandru , Borza Ioana Maria , Domuta Cristian Gabriel , Gitea Daniela , Rosan Cristina Adriana , Vicas Simona Ioana , Pasca Manuela Bianca
Published: August 2024

In the context of extreme climate change, experts in fruit production face a significant challenge in developing new strategies aimed at increasing the productivity of fruit tree crops. In order to investigate the changes in various horticultural indices (production, tree growth, and development) as well as the quality of plum fruits, sheep’s wool mulch, a cornstarch-based soil conditioner, and a combination of the two were applied in a Stanley plum orchard. In parallel, an experimental control variation was used. The results showed that the methods used had a substantial impact on fruit yield, size, and weight, with the best results obtained when mulching with sheep’s wool and soil conditioner. Plum fruits from mulching with sheep wool + soil conditioner exhibited the greatest total phenol concentration (1.30 ± 0.09 mg GAE/g dw), followed by the reference sample at 1.16 ± 0.09 mg GAE/g dw. The antioxidant capacity assessed using the three different methods provided favorable results for the experimental variant, sheep wool + soil conditioner. The results indicate that using the three experimental versions increased the fruit yield with 27% (sheep’s wool mulch) and with, 37% (sheep wool + soil conditioner) on average compared to that of the control group, while also improving the fruit quality. The fruit weight increased with 17.26% (cornstarch-based soil conditioner) and with 48.90% (sheep wool + soil conditioner) compared to that of the control, and the fruit size increased with 5% in two experiments (sheep’s wool mulch and a cornstarch-based soil conditioner) with 19% (sheep wool + soil conditioner), compared to the control group.