bluuebunny/crossref_metadata_2025 huggingface dataset:
Farm Monitoring System with Drones and Optical Camera Communication
Authors: Kondo Shinnosuke , Yoshimoto Naoto , Nakayama Yu
Published: September 2024

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On–Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10−3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture.
A Recommendation System for Trigger–Action Programming Rules via Graph Contrastive Learning
Authors: Kuang Zhejun , Xiong Xingbo , Wu Gang , Wang Feng , Zhao Jian , Sun Dawen
Published: September 2024

Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as “IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed”. As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods.
Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model
Authors: Alhoraibi Lamia , Alghazzawi Daniyal , Alhebshi Reemah
Published: September 2024

Advancements in wireless communication and automation have revolutionized mobility systems, notably through autonomous vehicles and unmanned aerial vehicles (UAVs). UAV spatial coordinates, determined via Global Positioning System (GPS) signals, are susceptible to cyberattacks due to unencrypted and unauthenticated transmissions with GPS spoofing being a significant threat. To mitigate these vulnerabilities, intrusion detection systems (IDSs) for UAVs have been developed and enhanced using machine learning (ML) algorithms. However, Adversarial Machine Learning (AML) has introduced new risks by exploiting ML models. This study presents a UAV-IDS employing AML methodology to enhance the detection and classification of GPS spoofing attacks. The key contribution is the development of an AML detection model that significantly improves UAV system robustness and security. Our findings indicate that the model achieves a detection accuracy of 98%, demonstrating its effectiveness in managing large-scale datasets and complex tasks. This study emphasizes the importance of physical layer security for enhancing IDSs in UAVs by introducing a novel detection model centered on an adversarial training defense method and advanced deep learning techniques.
Comparative Study of Lightweight Target Detection Methods for Unmanned Aerial Vehicle-Based Road Distress Survey
Authors: Xu Feifei , Wan Yan , Ning Zhipeng , Wang Hui
Published: September 2024

Unmanned aerial vehicles (UAVs) are effective tools for identifying road anomalies with limited detection coverage due to the discrete spatial distribution of roads. Despite computational, storage, and transmission challenges, existing detection algorithms can be improved to support this task with robustness and efficiency. In this study, the K-means clustering algorithm was used to calculate the best prior anchor boxes; Faster R-CNN (region-based convolutional neural network), YOLOX-s (You Only Look Once version X-small), YOLOv5-s, YOLOv7-tiny, YOLO-MobileNet, and YOLO-RDD models were built based on image data collected by UAVs. YOLO-MobileNet has the most lightweight model but performed worst in accuracy, but greatly reduces detection accuracy. YOLO-RDD (road distress detection) performed best with a mean average precision (mAP) of 0.701 above the Intersection over Union (IoU) value of 0.5 and achieved relatively high accuracy in detecting all four types of distress. The YOLO-RDD model most successfully detected potholes with an AP of 0.790. Significant or severe distresses were better identified, and minor cracks were relatively poorly identified. The YOLO-RDD model achieved an 85% computational reduction compared to YOLOv7-tiny while maintaining high detection accuracy.
TAMC: Textual Alignment and Masked Consistency for Open-Vocabulary 3D Scene Understanding
Authors: Wang Juan , Wang Zhijie , Miyazaki Tomo , Fan Yaohou , Omachi Shinichiro
Published: September 2024

Three-dimensional (3D) Scene Understanding achieves environmental perception by extracting and analyzing point cloud data with wide applications including virtual reality, robotics, etc. Previous methods align the 2D image feature from a pre-trained CLIP model and the 3D point cloud feature for the open vocabulary scene understanding ability. We believe that existing methods have the following two deficiencies: (1) the 3D feature extraction process ignores the challenges of real scenarios, i.e., point cloud data are very sparse and even incomplete; (2) the training stage lacks direct text supervision, leading to inconsistency with the inference stage. To address the first issue, we employ a Masked Consistency training policy. Specifically, during the alignment of 3D and 2D features, we mask some 3D features to force the model to understand the entire scene using only partial 3D features. For the second issue, we generate pseudo-text labels and align them with the 3D features during the training process. In particular, we first generate a description for each 2D image belonging to the same 3D scene and then use a summarization model to fuse these descriptions into a single description of the scene. Subsequently, we align 2D-3D features and 3D-text features simultaneously during training. Massive experiments demonstrate the effectiveness of our method, outperforming state-of-the-art approaches.
Performance Enhancement for B5G/6G Networks Based on Space Time Coding Schemes Assisted by Intelligent Reflecting Surfaces with Higher Modulation Orders
Authors: El-Hussien Mariam , Abdelhamid Bassant , Elbadawy Hesham , El-Hennawy Hadia , Ahmed Mehaseb
Published: September 2024

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.
Adult Children’s Marital Timing and Upward Intergenerational Financial Transfers: Variation across Gender, Race, and Ethnicity
Authors: Zhang Xing , Hammersmith Anna M.
Published: September 2024

Marriage is a key adult milestone yet is also considered a greedy institution that monopolizes resources, jeopardizing adult children’s financial transfers to aging parents. As the age at which marriage shifts later into adulthood, this study examines whether marriage still operates as a greedy institution, considering the role of marital timing on upward financial transfers to aging parents. Using data from Waves I–V of Add Health and Wave I of the Add Health Parent Questionnaire from 1994 to 2018, we examined whether timing of marriage (never, early, on time, and late) was associated with adult children’s upward financial transfers to their parents, accounting for variation across gender, race, and ethnicity. Adult children who never married were the largest group who gave upward financial transfers to their aging parents (33%). Results suggested that marriage or timing of marriage was a greedy institution among men, women, white, Hispanic, and Asian adults, but not Black adults, suggesting heterogeneity in the role of marriage on family support.
Social Determinants of School-to-School Differences in Opportunity to Learn (OTL): A Cross-National Study
Authors: Xu Shangmou , Kelly Sean
Published: September 2024

Do some students learn more than others because they attend School A instead of School B? Or is educational inequality generated elsewhere, from common processes within schools, or outside of formal schooling? Within these paradigmatic questions, this study investigates the social determinants of school-to-school differences in STEM course-taking experiences, a key component of Opportunity to Learn (OTL), in a cross-national setting. Drawing on an internal-development model and social stratification theories, we examine whether observed school-to-school differences in OTL can more clearly be attributed to functional or dysfunctional sources, using a large cross-national sample from TIMSS with 278 observations across 67 countries/regions, dating from 1995 to 2019. Results from Generalized Multilevel Linear Models indicate that variation in school-level OTL comes primarily from variation in school readiness in a given country. Yet, we also observe evidence that supports conflict forces of differentiation. This paper contributes to existing cross-national studies of educational inequality by tracing the fundamental origins of inequality in OTL, locating potentially dysfunctional sources of OTL between schools.
Does Green Finance Reduce Carbon Emissions? Global Evidence Based on System Generalized Method of Moments
Authors: Baştürk Meryem Filiz
Published: September 2024

Global environmental awareness has increased with the adoption of the sustainable development goals (SDGs) and the Paris Agreement. Global climate change has been a focal point in these international frameworks, with an emphasis on addressing environmental issues and setting specific targets for solutions. Financing plays a vital role in attaining goals established in international frameworks. The limitations of conventional finance have highlighted the need for green finance. There is a scarcity of studies in the literature examining the impact of green finance on carbon emissions, and a definitive conclusion has yet to be reached. This research aims to enhance the current literature by presenting empirical findings on how green finance influences carbon emissions globally. By employing the system generalized method of moments (GMM), developed by Arellano and Bover (1995) and Blundell and Bond (1998), this study investigated how green finance influences carbon emissions. Covering the span of 2017–2022, this study encompassed 48 countries across the globe. Green finance was found to have a negative and statistically significant impact on carbon emissions. Issuing green bonds to represent green finance, when increased by 1%, reduces carbon emissions by −0.012%.
Factors Affecting the Happiness of Learners in Higher Education: Attitude, Grade Point Average, and Time Management
Authors: Thongsri Nattaporn , Seksan Jariya , Warintarawej Pattaraporn
Published: September 2024

Student well-being is essential for academic achievement and personal growth. Fostering happiness among university students is crucial for individual development, strong family bonds, a harmonious society, and national progress. This study aimed to identify key determinants of student happiness in higher education. Eight factors, including GPA, workload, family support, university environment, attitude, motivation, time management, and social relationships, were examined among 388 Thai students using an online survey. Students were categorized into distinct groups based on these factors using k-means clustering. ANOVA was employed to assess whether these factors significantly differentiated the groups, and significant factors were further analyzed using regression analysis to confirm their impact on student happiness. A neural network analysis was also utilized to evaluate the relative importance of each factor. The results revealed that attitude, GPA, and time management significantly affected student happiness. A positive attitude fosters a sense of opportunity and achievement, a high GPA reflects academic success and enhances self-confidence, and effective time management reduces stress while allowing more time for enjoyable activities.
Research on Composting of Garden Waste and Its Application in Cultivation Substrates
Authors: Yao Xu , Wang Xin , Zheng Shengyang , Zhao Haitao , Ju Jing , Wang Chenzhe
Published: September 2024

To achieve the resource utilization of garden waste, in this study, we used garden waste as the main raw material and conducted static composting with high-temperature aerobic treatment and forced ventilation by adding appropriate external additives. Our results showed that during the composting process, the pH was weakly alkaline, and the electrical conductivity was between 1.42 and 1.50 mS/cm. The E4/E6 (an important indicator of the quality or degree of condensation and aromatization of humic acid) gradually decreased, and nitrogen, phosphorus, and potassium contents increased. The germination index gradually rose and ultimately exceeded 80%. Treatment (T2) with a final C/N ratio of 25:1 and the addition of 0.3% bacterial agent resulted in the highest nutrient content and the best degree of compost maturity. All indicators met the requirements of the Chinese “Technical Requirements for Urban Landscape Waste Resource Recycling and Deep Processing (GB/T 40199-2021)”. When using a composite substrate of garden waste and other horticultural substrates for planting, a membership function was executed for comprehensive evaluation. The V (T2):V (peat):V (vermiculite):V (vermiculite) = 135:135:30:00 substrate treatment resulted in optimal lettuce growth and quality. In summary, combining the compost products of garden waste with traditional cultivation substrates at a specific ratio shows favorable applicative prospects.
Multi-Objective Optimization of Building Envelope Retrofits Considering Future Climate Scenarios: An Integrated Approach Using Machine Learning and Climate Models
Authors: Ding Zhikun , Li Jinze , Wang Zhan , Xiong Zhaoyang
Published: September 2024

The intensification of global climate change has exacerbated building energy consumption issues, presenting a significant challenge in retrofitting existing buildings to meet current environmental requirements while adapting to future climate scenarios. A multi-objective optimization design process for building envelope retrofits was developed in this study, utilizing random forest (RF) models and atmospheric circulation models to generate future weather data. Building Information Modeling (BIM) and performance simulations were employed to obtain data under various renovation scenarios. A BP neural network model related envelope design parameters to performance indicators, while the NSGA-III algorithm optimized retrofit strategies. The proposed method demonstrates improved efficiency and validity in developing energy-saving solutions that consider future climate scenarios. Results highlight the importance of incorporating climate change factors in retrofit designs, providing valuable insights and methodological support for decision-makers in building energy conservation practices.
Probabilistic Seismic Risk Assessment of Metro Tunnels in Soft Soils
Authors: Huang Zhongkai , Deng Xingmian , Lei Chong , Cheng Yixin , Zhang Chenlong , Sun Qiangqiang
Published: September 2024

Tunnels are of significant importance in the sustainable development of global urban areas, particularly in metropolitan areas. It is of the utmost importance to evaluate the seismic performance of tunnels across a wide spectrum of earthquake intensities. In order to address this, our study presents a framework for the assessment of seismic risk in tunnels. This study employs the city of Shanghai’s urban metro tunnels as case studies. The nominal values of seismic risk for the three main damage states—minor, moderate, and major—were calculated. Furthermore, the influence of utilizing disparate fragility functions on expected seismic risk assessments was investigated. In this framework, the probability density functions of the different fragility curve models are employed to treat the probability values associated with them as random variables. This approach aims to facilitate the propagation of IMV in seismic risk assessments. The results demonstrate that the Bayesian framework efficiently incorporates the full range of input model variability into risk estimation. The findings of this study offer a foundation for decision-making processes, seismic risk assessments, and the resilience management of urban infrastructure.
Trends in CO, CO2, CH4, BC, and NOx during the First 2020 COVID-19 Lockdown: Source Insights from the WMO/GAW Station of Lamezia Terme (Calabria, Southern Italy)
Authors: D’Amico Francesco , Ammoscato Ivano , Gullì Daniel , Avolio Elenio , Lo Feudo Teresa , De Pino Mariafrancesca , Cristofanelli Paolo , Malacaria Luana , Parise Domenico , Sinopoli Salvatore , De Benedetto Giorgia , Calidonna Claudia Roberta
Published: September 2024

In 2020, the COVID-19 outbreak led many countries across the globe to introduce lockdowns (LDs) that effectively caused most anthropic activities to either stop completely or be significantly reduced. In Europe, Italy played a pioneeristic role via the early introduction of a strict nationwide LD on March 9th. This study was aimed at evaluating, using both chemical and meteorological data, the environmental response to that occurrence as observed by the Lamezia Terme (LMT) GAW/WMO station in Calabria, Southern Italy. The first 2020 lockdown was therefore used as a “proving ground” to assess CO, CO2, CH4, BC, and NOx concentrations in a rather unique context by exploiting the location of LMT in the context of the Mediterranean Basin. In fact, its location on the Tyrrhenian coast of Calabria and local wind circulation both lead to daily cycles where western-seaside winds depleted in anthropogenic pollutants can be easily differentiated from northeastern-continental winds, enriched in anthropogenic outputs. In addition to this, the first Italian LD occurred during the seasonal transition from winter to spring and, consequently, summer, thus providing new insights on emission outputs correlated with seasons. The findings clearly indicated BC and, in particular, CO as strongly correlated with average daily temperatures, as well as possibly domestic heating. CO2’s reduction during the lockdown and consequent increase in the post-lockdown period, combined with wind data, allowed us to constrain the local source of emissions located northeast from LMT. NOx reductions during specific circumstances were consistent with hypotheses from previous research, which linked them to rush hour traffic and other forms of transportation emissions. CH4’s stable patterns were consistent with livestock, landfills, and other sources assumed to be nearly constant during LD periods.
Promoting Plant-Based Sustainable Diet to Support Future Development: Emotional Design Card Development
Authors: Fang Xiaochen , Zhu Jinwei
Published: September 2024

The quest for a plant-based sustainable diet has significant value for promoting future development, posing novel challenges for designers. This study involved a five-step design process, encompassing a case study, an experimental study, prototype conception, user testing, and design refinement, with the aim of developing a “Plant-based Sustainable Diet 3P Emotional Design Method” card set that enables individuals to devise personalized sustainable diet plans. The results demonstrated that the instinctive level originates from the product itself, signifying the efficacy of sensory design cues for plant-based foods. The behavioral level stems from the interactive content generated by the product, denoting the efficacy of design cues and highlighting the advantages of transitioning to healthier ingredients for the body. The reflective level arises from the reflection and contemplation of the product, signifying the efficacy of value perception and design cues pertaining to economic, environmental, and social sustainability knowledge. The study analyzed the influencing factors of diet choices through emotional design and provided insights into the underlying psychological mechanisms. The theoretical contribution of this study lies in the novel integration of emotional design and sustainable diet research, while its practical contribution is the introduction of methods and tools that facilitate the adoption of plant-based sustainable diet practices at the individual level.
Domestic vs. Foreign Institutional Investors: Who Improves ESG and Value of Chinese Companies?
Authors: Yoo Jae Wook , Chang Yu Jin
Published: September 2024

Recent years have seen the influence of both institutional investors and corporate social responsibility strengthen in the Chinese capital market. However, research on the impact of these market changes on corporate activities and values has been insufficient. To address this gap, this study analyzes the impact of foreign and domestic institutional investors who invest in Chinese A-share listed companies on corporate value through environmental, social, and governance (ESG) policies. The results of the analysis are as follows: First, the shareholding of both foreign institutional investors (FIIs) and domestic institutional investors (DIIs) enhances corporate value. Second, the shareholding of FIIs strengthens the company’s ESG, while that of DIIs does not significantly affect it. Third, ESG has a positive impact on corporate value. Fourth, ESG partially mediates the positive relationship between the shareholding of FIIs and corporate value. The research findings provide academic implications for the causal relationship between corporate governance, sustainable management, and performance, as well as practical implications for the development of the Chinese capital market and corporate sustainability.
Digital Transformation in the Construction Sector: A Digital Twin for Seismic Safety in the Lifecycle of Buildings
Authors: Lauria Massimo , Azzalin Maria
Published: September 2024

The construction sector is currently undergoing a deep digital transformation resulting from the prioritization of emerging technologies, among which are digital twins. New goals and opportunities are appearing that minimize the impact on a building’s lifecycle, reduce economic, environmental, and extra-social costs, optimize energetic performance, decrease energy consumption and emissions, and enhance the durability and service life of buildings and their components. Among the research activities that have led to the development of a maintenance management model (MMM), this paper deals with the digital-twin approach, considering it instrumental to the innovative governance of the building environment from a lifecycle-based and sustainable perspective. It includes paying attention to efficiency in terms of resource use, energy consumption, and the energy performance of buildings, supporting decarbonization processes, and environmental vulnerability due to natural disasters, extreme weather, and seismic events. Its current implementation is presented here. In this scenario, the authors, operating at BIG srl, an academic spinoff of the Mediterranean University of Reggio Calabria, Italy, working together with the startup Sysdev, based in Torino, Italy, the company Berna Engineering srl, based in Reggio Calabria, Italy, and ACCA Software spa, based in Avellino, Italy, introduce the experimental application of the DT4SEM for safety and well-being in buildings, which is specifically oriented to seismic behavior monitoring. The proposal, while highlighting the innovative character of DT approaches, responds to the need for reliable data for increasingly effective forecasts and the control of the seismic behavior of buildings, facilitating informed decision-making for building management while also optimizing maintenance schedules.
Analysing Travel Patterns at Beirut Arab University, Lebanon: An In-Depth Characterization of Travel Behavior on Campus
Authors: Joumblat Rouba , Jawad Hadi , Elkordi Adel
Published: September 2024

Understanding the travel patterns of university campus visitors is crucial for developing effective transportation strategies. Existing research predominantly focuses on student commuting within specific regions, often overlooking the diverse needs of faculty and staff and varying campus contexts. This study addresses a significant gap in the literature by investigating travel behaviors at Beirut Arab University (BAU), which has not been previously studied in this context. BAU’s unique situation, with campuses in both urban and rural zones, presents distinct transportation challenges, particularly for those traveling between these areas. Through a comprehensive survey of students, faculty, and staff, this research explores differences in transportation modes, travel distances, durations, and patterns. Statistical techniques, including one-way analysis of variance (ANOVA), Chi-Squared, and McNemar-Bowker tests, reveal significant variations among traveler groups. The findings highlight specific needs, such as improvements in bus services, car-sharing programs, and parking facilities, essential for creating sustainable campus environments. By examining these travel behaviors, the study offers valuable insights into the complexities of campus transportation, contributing new perspectives to the field. The originality of this research lies in its focus on an underexplored area, providing a deeper understanding of how diverse university environments impact transportation choices. This work not only fills a critical void in campus transportation research but also offers practical recommendations for enhancing transportation systems in similar settings.
Tourist Crowding versus Service Quality: Impacting Mechanism of Tourist Satisfaction in World Natural Heritage Sites from the Mountain Sanqingshan National Park, China
Authors: Nian Sifeng , Chen Min , Yan Jia , Du Yongcun , Su Xiaojie
Published: September 2024

World Heritage Sites (WHS) possess outstanding universal value (OUV) centered on science and aesthetics, and the large scale of tourism has a certain influence on sustainable development, which will have some degree influence on the quality of tourist service and experience. Taking the World Natural Heritage Site Mount Sanqingshan National Park in China as a case, we collected 535 samples of tourists and used structural equation modeling as a methodology to construct a theoretical framework from the perspective of tourists’ perception, including tourism crowding (functional crowding, personal crowding, and social crowding), service quality (interpretation, goods, commuting, accommodation, and catering), tourists’ satisfaction, and OUV attractiveness as intermediary variables. The results were: (1) tourist crowding has a substantial negative influence on satisfaction, and the negative influence on OUV attractiveness is not tenable; (2) service quality has a substantial positive impact on OUV attractiveness and satisfaction; (3) the tourists’ perception of OUV attractiveness has a mediating influence on service quality and satisfaction, but there is no mediating impact on tourism crowding and satisfaction; (4) this paper puts forward the SCA-S (service, crowding, attractiveness, and satisfaction) framework of WHS, and explores impact factors and mechanisms of visitor satisfaction of WHS from different aspects. The relevant research conclusions have some theoretical value and practical significance for the interpretation and display of heritage value, improved service quality, and tourism experience, and they are conducive to protecting WHS.
A Review on Liquid Hydrogen Storage: Current Status, Challenges and Future Directions
Authors: Naquash Ahmad , Agarwal Neha , Lee Moonyong
Published: September 2024

The growing interest in hydrogen (H2) has motivated process engineers and industrialists to investigate the potential of liquid hydrogen (LH2) storage. LH2 is an essential component in the H2 supply chain. Many researchers have studied LH2 storage from the perspective of tank structure, boil-off losses, insulation schemes, and storage conditions. A few review studies have also been published considering LH2 storage; however, most are simply collections of previous articles. None of these review articles have critically evaluated the research articles. In this review study, recent reports, conceptual studies, and patents have been included and critically discussed. Further, challenges and recommendations have been listed based on the literature review. Our results suggest that the multi-layer insulation scheme and integrated refrigeration system can effectively reduce boil-off losses. However, boil-off losses from storage tanks during transportation are the least discussed and must be addressed. The cost of an LH2 storage tank is high, but it can be reduced with advancements in materials and the utilization of latest technologies. The present challenges and future directions for LH2 storage include minimizing and utilizing boil-off losses, improving insulation schemes, and ensuring cost-effective large-scale LH2 storage. This review study can be fundamental for process engineers and new academic researchers to design energy-efficient and cost-effective LH2 storage systems.
Ground Surface Effect of Earth Pressure Balance Tunnelling in Deltaic Deposits: A Case Study of Line 9 of the Barcelona Metro
Authors: Yubero Maria Teresa , Bonet E.
Published: September 2024

The 47.8 km long Line 9 of the Barcelona Metro is one of Europe’s longest urban metro lines. Its southern section connects the city to the airport, being entirely excavated through soft deltaic deposits, promoting more sustainable mobility by reducing significant road traffic. This study identifies the most accurate method for predicting surface settlements caused by tunnel excavation using ground movement monitoring data. Several methodologies were assessed, with the Mean Absolute Error (MAE) and Mean Relative Error (MRE) calculated to evaluate their performances. The methods considered were Peck’s Gaussian curve method, Sagaseta’s method, and Verruijt and Booker’s method, with MAE values of 0.66 mm, 0.50 mm, and 0.48 mm and MRE values of 49%, 45%, and 36%, respectively. Verruijt and Booker’s method proved the most effective for predicting settlement, minimising surface impacts, improving building sustainability, and reducing environmental contamination from chemical injections. A sensitivity analysis was also conducted by comparing the monitoring data from Line 9 with data from 45 other tunnels excavated worldwide in deltaic soils. This analysis aimed to develop rapid predictive models applicable to different locations. The methodologies proposed for estimating ground settlements relied on specific parameters, particularly the K value, which was consistent across all deltaic soil locations, with values ranging from 0.45 to 0.55.
Identification of Opportunities for Capitalizing on Tourist Potential in Hunedoara County through Rural Tourism Activities and Supporting the Development of the Local Community
Authors: Mănescu Camelia , Mateoc-Sîrb Nicoleta , Adamov Tabita Cornelia , Marin Diana , Moisa Sebastian , Gordan Marius-Ionuț
Published: September 2024

The need to diversify economic activities in rural areas is essential for stimulating sustainable development. In this regard, rural tourism can be an effective tool for supporting the development in rural areas. The purpose of this article was to identify the favorability of Hunedoara County for rural tourism activities, which would contribute to the sustainable development of tourism. Rural tourism activities would generate economic and social benefits both for local communities and for tourists, and at the same time preserve and protect the cultural and natural values of the area. The research methods used to carry out this study are both qualitative (documentary study) and quantitative (a questionnaire applied in order to identify the perceptions of tourists with respect to the tourist development potential of Hunedoara County through rural tourism). The main results of the research consist in the identification of some directions for future development of Hunedoara County. These directions would, in the future, lead to an increase in the visibility of local rural communities; to the preservation of local traditions and customs, while promoting sustainability and respect for the environment; and to the implementation of a plan for sustainable development through rural tourism in the studied area.
Impact of Big Data on Carbon Emissions: Empirical Evidence from China’s National Big Data Comprehensive Pilot Zone
Authors: Liu Yali , Li Zhi , Chen Haonan , Cui Xiaoning
Published: September 2024

Big data is a pivotal factor in propelling the digital economy forward and emerges as a novel driver in realizing the goals of carbon peaking and carbon neutrality. This study focuses on a quasi-natural experiment, namely national big data comprehensive pilot zones (NBD-CPZs), and employs a multi-period difference-in-differences (DID) model to identify the influence of big data on carbon emissions. The findings of this study are as follows. Overall, big data significantly reduces carbon emissions within the pilot zones. Mechanism analysis shows that big data reduces urban carbon emissions by promoting green innovation, optimizing energy structure, mitigating capital mismatch and improving public awareness of environmental protection. Heterogeneity analysis shows that the carbon reduction effect of big data are more pronounced in cities with high levels of digital economy, non-resource-based cities, cities with strong intellectual property rights protection and the Guizhou Province. Spatial effect analysis indicates that within a radius of 400–500 km, the NBD-CPZ increases urban carbon emissions, signifying a significant siphoning effect; within a radius of 500–900 km, the NBD-CPZ reduces urban carbon emissions, signifying a significant spillover effect, and beyond a distance of 900 km, the spatial effect of the NBD-CPZ is not significant. Based on the above conclusions, this study puts forward several policy recommendations to effectively exert the carbon emission reduction effect of big data.
Learning to Score: A Coding System for Constructed Response Items via Interactive Clustering
Authors: Luo Lingjing , Yang Hang , Li Zhiwu , Pedrycz Witold
Published: September 2024

Constructed response items that require the student to give more detailed and elaborate responses are widely applied in large-scale assessments. However, the hand-craft scoring with a rubric for massive responses is labor-intensive and impractical due to rater subjectivity and answer variability. The automatic response coding method, such as the automatic scoring of short answers, has become a critical component of the learning and assessment system. In this paper, we propose an interactive coding system called ASSIST to efficiently score student responses with expert knowledge and then generate an automatic score classifier. First, the ungraded responses are clustered to generate specific codes, representative responses, and indicator words. The constraint set based on feedback from experts is taken as training data in metric learning to compensate for machine bias. Meanwhile, the classifier from responses to code is trained according to the clustering results. Second, the experts review each coded cluster with the representative responses and indicator words to score a rating. The coded cluster and score pairs will be validated to ensure inter-rater reliability. Finally, the classifier is available for scoring a new response with out-of-distribution detection, which is based on the similarity between response representation and class proxy, i.e., the weight of class in the last linear layer of the classifier. The originality of the system developed stems from the interactive response clustering procedure, which involves expert feedback and an adaptive automatic classifier that can identify new response classes. The proposed system is evaluated on our real-world assessment dataset. The results of the experiments demonstrate the effectiveness of the proposed system in saving human effort and improving scoring performance. The average improvements in clustering quality and scoring accuracy are 14.48% and 18.94%, respectively. Additionally, we reported the inter-rater reliability, out-of-distribution rate, and cluster statistics, before and after interaction.
Research on Convergence Media Ecological Model Based on Blockchain
Authors: Hu Hongbin , Wang Yongbin , Song Guohui
Published: September 2024

Currently, the media industry is in the rapid development stage of media integration, which has brought about great changes in content production mode, presentation form, communication mechanism, operation and maintenance management, etc. At the same time, it is also faced with problems such as difficult information traceability, declining industry credibility, low data circulation quality and efficiency, difficult data security and user privacy protection, etc. Utilizing blockchain’s characteristics can solve these problems that the media industry is currently facing. This paper designs a convergence media ecology model based on blockchain (CMEM-BC), focusing on the basic elements of the model, node operation and maintenance system, node management mechanism, value circulation mechanism, and storage mechanism, trying to establish a decentralized, traceable, and immutable convergence media ecosystem. On this basis, this paper summarizes the ecological framework and ecological model of CMEM-BC. Finally, the paper describes the verification of the effectiveness of CMEM-BC in key links through simulation experiments, verifying that CMEM-BC has high originality and is more suitable for the application of convergence media ecology through model analysis and comparison.
A Methodological Framework for New Product Development in Fuzzy Environments
Authors: Yang Chun-Ming , Li Shiyao , Chen Kuen-Suan , Li Mingyuan , Lo Wei
Published: September 2024

New product development (NPD) is crucial for helping companies to maintain competitive advantages. In this study, a methodological framework is presented combining a novel Kano model and fuzzy axiomatic design (FAD) for improving the product development capability in the whole NPD process. In the Kano model, a novel mixed-class classification method is presented to classify each evaluation indicator agreed on by the majority, and to calculate the affiliation value based on category strength (CS) to display the degree to which the indicator belongs to a certain attribute. A new importance ratio is also proposed to adjust the importance of each indicator attribute. This helps to achieve higher customer satisfaction and improve the attractiveness of the product or service. FAD is then used to measure the gap between customer satisfaction and the company’s expected levels of satisfaction in terms of product functions. This enables the company to obtain more comprehensive information for decision-making. A case study is provided to verify the practicability of the proposed method. Sensitivity analysis proves the robustness of the results based on the number of respondents. Finally, comparative analysis with existing approaches demonstrates the strengths of the proposed method.
Characteristics and Mechanism of Hematite Dissolution and Release on Arsenic Migration in Heterogeneous Materials
Authors: Li Zheying , Shan Huimei , Rong Wanyue , Zhao Zhicheng , Ma Kexin , Peng Sanxi , Wei Song
Published: September 2024

The migration of arsenic in groundwater is influenced by the heterogeneity of the medium, and the presence of iron minerals adds complexity and uncertainty to this effect. In this study, a stratified heterogeneous sand column with an embedded hematite lens at the coarse-to-medium sand interface was designed. We introduced an arsenic-laden solution and controlled groundwater flow to investigate the spatiotemporal characteristics of arsenic migration and the impact of hematite dissolution. The results showed that the medium structure significantly influenced the arsenic migration and distribution within the lens-containing sand column. The clay layers directed the lateral migration of arsenic, and the arsenic concentrations in deeper layers were up to seven times greater than those on the surface. The extraction experiments of solid-phase arsenic revealed that the main adsorption modes on quartz sand surfaces were the specific adsorption (F2) and adsorption on weakly crystalline iron–aluminum oxides (F3), correlating to the specific and colloidal adsorption modes, respectively. Monitoring the total iron ions (Fe(aq)) revealed rapid increases within the first 14 days, reaching a maximum on day 15, and then gradually declining; these results indicate that hematite did not continuously dissolve. This study can aid in the prevention and control of arsenic contamination in groundwater.
A Monoclonal Antibody with a High Affinity for Ricin Isoforms D and E Provides Strong Protection against Ricin Poisoning
Authors: Lequesne Loïs , Dano Julie , Rouaix Audrey , Kropp Camille , Plaisance Marc , Gelhaye Stéphanie , Lequesne Marie-Lou , Piquet Paloma , Avril Arnaud , Becher François , Orsini Delgado Maria Lucia , Simon Stéphanie
Published: September 2024

Ricin is a highly potent toxin that has been used in various attempts at bioterrorism worldwide. Although a vaccine for preventing ricin poisoning (RiVax™) is in clinical development, there are currently no commercially available prophylaxis or treatments for ricin intoxication. Numerous studies have highlighted the potential of passive immunotherapy using anti-ricin monoclonal antibodies (mAbs) and have shown promising results in preclinical models. In this article, we describe the neutralizing and protective efficacy of a new generation of high-affinity anti-ricin mAbs, which bind and neutralize very efficiently both ricin isoforms D and E in vitro through cytotoxicity cell assays. In vivo, protection assay revealed that one of these mAbs (RicE5) conferred over 90% survival in a murine model challenged intranasally with a 5 LD50 of ricin and treated by intravenous administration of the mAbs 6 h post-intoxication. Notably, a 35% survival rate was observed even when treatment was administered 24 h post-exposure. Moreover, all surviving mice exhibited long-term immunity to high ricin doses. These findings offer promising results for the clinical development of a therapeutic candidate against ricin intoxication and may also pave the way for novel vaccination strategies against ricin or other toxins.
Ultrasound-Guided Botulinum Toxin-A Injections into the Masseter Muscle for Both Medical and Aesthetic Purposes
Authors: Popescu Marius Nicolae , Beiu Cristina , Iliescu Carmen Andrada , Racoviță Andreea , Berteanu Mihai , Iliescu Mădălina Gabriela , Stănescu Ana Maria Alexandra , Radaschin Diana Sabina , Popa Liliana Gabriela
Published: September 2024

With the increasing use of Botulinum toxin type A (BoNT-A) injections in the masseter muscles for both medical and aesthetic purposes, there is a constant need to continually enhance the efficacy of these treatments and reduce the risk of potential adverse events. This review provides an in-depth analysis of the masseter muscle’s anatomical structure and essential landmarks and emphasizes the advantages of ultrasound (US) guidance in improving the precision of BoNT-A injections compared to conventional blind methods. The review is supplemented with comprehensive figures, including graphics, clinical images, and ultrasound visuals, to support the discussion. Potential complications such as paradoxical bulging, inadvertent injections into the risorius muscle or parotid gland, facial paralysis, and the risk of bone resorption are examined. Future research should aim at refining injection techniques and assessing the long-term effects of repeated treatments to ensure optimal patient care and safety.