bluuebunny/crossref_metadata_2025 huggingface dataset:
Glycemic Control Assessed by Intermittently Scanned Glucose Monitoring in Type 1 Diabetes during the COVID-19 Pandemic in Austria
Authors: Secco Katharina , Baumann Petra Martina , Pöttler Tina , Aberer Felix , Cigler Monika , Elsayed Hesham , Harer Clemens Martin , Weitgasser Raimund , Schütz-Fuhrmann Ingrid , Mader Julia Katharina
Published: July 2024

Objective: The aim of this analysis was to assess glycemic control before and during the coronavirus disease (COVID-19) pandemic. Methods: Data from 64 (main analysis) and 80 (sensitivity analysis) people with type 1 diabetes (T1D) using intermittently scanned continuous glucose monitoring (isCGM) were investigated retrospectively. The baseline characteristics were collected from electronic medical records. The data were examined over three periods of three months each: from 16th of March 2019 until 16th of June 2019 (pre-pandemic), from 1st of December 2019 until 29th of February 2020 (pre-lockdown) and from 16th of March 2020 until 16th of June 2020 (lockdown 2020), representing the very beginning of the COVID-19 pandemic and the first Austrian-wide lockdown. Results: For the main analysis, 64 individuals with T1D (22 female, 42 male), who had a mean glycated hemoglobin (HbA1c) of 58.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration 13.5 years (5.5 to 22.0 years) were included in the analysis. The time in range (TIR[70–180mg/dL]) was the highest percentage of measures within all three studied phases, but the lockdown 2020 phase delivered the best data in all these cases. Concerning the time below range (TBR[<70mg/dL]) and the time above range (TAR[>180mg/dL]), the lockdown 2020 phase also had the best values. Regarding the sensitivity analysis, 80 individuals with T1D (26 female, 54 male), who had a mean HbA1c of 57.5 mmol/mol (51.0 to 69.3 mmol/mol) and a mean diabetes duration of 12.5 years (5.5 to 20.7 years), were included. The TIR[70–180mg/dL] was also the highest percentage of measures within all three studied phases, with the lockdown 2020 phase also delivering the best data in all these cases. The TBR[<70mg/dL] and the TAR[>180mg/dL] underscored the data in the main analysis. Conclusion: Superior glycemic control, based on all parameters analyzed, was achieved during the first Austrian-wide lockdown compared to prior periods, which might be a result of reduced daily exertion or more time spent focusing on glycemic management.
Review of Microwave Near-Field Sensing and Imaging Devices in Medical Applications
Authors: Origlia Cristina , Rodriguez-Duarte David O. , Tobon Vasquez Jorge A. , Bolomey Jean-Charles , Vipiana Francesca
Published: July 2024

Microwaves can safely and non-destructively illuminate and penetrate dielectric materials, making them an attractive solution for various medical tasks, including detection, diagnosis, classification, and monitoring. Their inherent electromagnetic properties, portability, cost-effectiveness, and the growth in computing capabilities have encouraged the development of numerous microwave sensing and imaging systems in the medical field, with the potential to complement or even replace current gold-standard methods. This review aims to provide a comprehensive update on the latest advances in medical applications of microwaves, particularly focusing on the near-field ones working within the 1–15 GHz frequency range. It specifically examines significant strides in the development of clinical devices for brain stroke diagnosis and classification, breast cancer screening, and continuous blood glucose monitoring. The technical implementation and algorithmic aspects of prototypes and devices are discussed in detail, including the transceiver systems, radiating elements (such as antennas and sensors), and the imaging algorithms. Additionally, it provides an overview of other promising cutting-edge microwave medical applications, such as knee injuries and colon polyps detection, torso scanning and image-based monitoring of thermal therapy intervention. Finally, the review discusses the challenges of achieving clinical engagement with microwave-based technologies and explores future perspectives.
YPL-SLAM: A Simultaneous Localization and Mapping Algorithm for Point–line Fusion in Dynamic Environments
Authors: Du Xinwu , Zhang Chenglin , Gao Kaihang , Liu Jin , Yu Xiufang , Wang Shusong
Published: July 2024

Simultaneous Localization and Mapping (SLAM) is one of the key technologies with which to address the autonomous navigation of mobile robots, utilizing environmental features to determine a robot’s position and create a map of its surroundings. Currently, visual SLAM algorithms typically yield precise and dependable outcomes in static environments, and many algorithms opt to filter out the feature points in dynamic regions. However, when there is an increase in the number of dynamic objects within the camera’s view, this approach might result in decreased accuracy or tracking failures. Therefore, this study proposes a solution called YPL-SLAM based on ORB-SLAM2. The solution adds a target recognition and region segmentation module to determine the dynamic region, potential dynamic region, and static region; determines the state of the potential dynamic region using the RANSAC method with polar geometric constraints; and removes the dynamic feature points. It then extracts the line features of the non-dynamic region and finally performs the point–line fusion optimization process using a weighted fusion strategy, considering the image dynamic score and the number of successful feature point–line matches, thus ensuring the system’s robustness and accuracy. A large number of experiments have been conducted using the publicly available TUM dataset to compare YPL-SLAM with globally leading SLAM algorithms. The results demonstrate that the new algorithm surpasses ORB-SLAM2 in terms of accuracy (with a maximum improvement of 96.1%) while also exhibiting a significantly enhanced operating speed compared to Dyna-SLAM.
Mixed Micro/Macro Cache for Device-to-Device Caching Systems in Multi-Operator Environments
Authors: Rim Minjoong
Published: July 2024

In a device-to-device (D2D) caching system that utilizes a device’s available storage space as a content cache, a device called a helper can provide content requested by neighboring devices, thereby reducing the burden on the wireless network. To enhance the efficiency of a limited-size cache, one can consider not only macro caching, which is content-based caching based on content popularity, but also micro caching, which is chunk-based sequential prefetching and stores content chunks slightly behind the one that a nearby device is currently viewing. If the content in a cache can be updated intermittently even during peak hours, the helper can improve the hit ratio by performing micro caching, which stores chunks that are expected to be requested by nearby devices in the near future. In this paper, we discuss the performance and effectiveness of micro D2D caching when there are multiple operators, the helpers can communicate with the devices of other operators, and the operators are under a low load independently of each other. We also discuss the ratio of micro caching in the cache area when the cache space is divided into macro and micro cache areas. Good performance can be achieved by using micro D2D caching in conjunction with macro D2D caching when macro caching alone does not provide sufficient performance, when users are likely to continue viewing the content they are currently viewing, when the content update cycle for the cache is short and a sufficient number of chunks can be updated for micro caching, and when there are multiple operators in the region.
Physics Constrained High-Precision Data-Driven Modeling for Multi-Path Ultrasonic Flow Meter in Natural Gas Measurement
Authors: Cai Haohui , Liu Wensi , Zhou Kaixi , Wang Xin , Lin Kunwei , Tang Xiao-Yu
Published: July 2024

Ultrasonic flow meters are crucial measuring instruments in natural gas transportation pipeline scenarios. The collected flow velocity data, along with the operational conditions data, are vital for the analysis of the metering performance of ultrasonic flow meters and analysis of the flow process. In practical applications, high requirements are placed on the modeling accuracy of ultrasonic flow meters. In response, this paper proposes an ultrasonic flow meter modeling method based on a combination of data learning and industrial physics knowledge. This paper builds ultrasonic flow meter flow velocity prediction models under different working conditions, combining pipeline flow field velocity distribution knowledge for data preprocessing and loss function design. By making full use of the characteristics of the physics and data learning, the prediction results are close to the real acoustic path flow velocity distribution; thus, the model has high accuracy and interpretability. Experiments are conducted to prove that the prediction error of the proposed method can be controlled within 1%, which can meet the needs of ultrasonic flow meter modeling and subsequent performance analysis in actual production.
Network Topology Reconfiguration-Based Blind Equalization over Sensor Network
Authors: Sulin Chi , Tetsuya Shimamura
Published: July 2024

Distributed in-network processing has garnered much attention due to its capability to estimate the unknown parameter of interest from noisy measurements based on a set of cooperating sensor nodes. In previous studies, distributed in-network processing mainly focused on short-distance communication systems, wherein sensor nodes collect certain parameters of interest within their maximum communication distance. In addition, the estimation of certain parameter vectors of interest from noisy measurements, relying heavily on training signals, is achieved with a non-blind distributed estimation algorithm. However, in some applications, acquiring knowledge of training signals beforehand is difficult. Therefore, it is necessary to perform distributed estimation algorithms for receivers without training signals, a concept known as blind distributed estimation. In this paper, the generalized Sato algorithm is used to design the blind equalizer for the signal estimation. In addition, we consider extending the short-distance communication system to a long-distance communication system for an unmanned aerial vehicle (UAV) cooperating with sensor nodes in the wireless sensor network (WSN). In this scenario, the data signal is transmitted from a UAV to the WSN and is received by sensor nodes. However, the performance of the blind equalizer is susceptible to the transmission channel in long-distance communication systems. Here, we present a network topology reconfiguration approach to address the issue of distributed blind equalization. The objective of the proposed method is to discard the influence of ill-channels on the other sensor nodes by detecting ill-channels and redesigning the sensor node weights. Through computer simulation experiments, we evaluated the performance of the blind equalizer using the average mean square error (MSE) and average symbol error rate (SER). In the results of the computer simulation experiments, the blind equalizer using the proposed method outperformed the conventional methods in terms of prediction accuracy and convergence speed.
Site-Independent Mapping of Clay Content in Vineyard Soils via Mobile Proximal Gamma-Ray Spectrometry and Machine Learning Calibrations
Authors: Wehrle Ralf , Pätzold Stefan
Published: July 2024

Vineyards hold considerable soil variability between regions and plots, and there is frequently large soil heterogeneity within plots. Clay content in vineyard soils is of interest with respect to soil management, environmental monitoring, and wine quality. However, spatially resolved clay mapping is laborious and expensive. Gamma-ray spectrometry (GS) is a suitable tool for predicting clay content in precision agriculture when locally calibrated, but it has scarcely been tested site-independently and in vineyards. This study evaluated GS to predict clay content with a site-independent calibration and four machine learning algorithms (Support Vector Machines, Random Forest, k-Nearest Neighbors, and Bayesian regulated neuronal networks) in eight vineyards from four German vine-growing regions. Clay content in the studied soils ranged from 62 to 647 g kg−1. The Random Forest calibration was most suitable. Test set evaluation revealed good model performance for the entire dataset with RPIQ = 4.64, RMSEP = 56.7 g kg−1, and R2 = 0.87; however, prediction quality varied between the sites. Overall, GS with the Random Forest model calibration was appropriate to predict the clay content and its spatial distribution, even for heterogeneous geopedological settings and in individual plots. Therefore, GS is considered a valuable tool for soil mapping in vineyards, where clay content and product quality are closely linked.
Covalently Modified Molecular-Recognition-Capable UV-Transparent Microplate for Ultra-High-Throughput Screening of Dissolved Zn2+ and Pb2+
Authors: Ádám Bálint Árpád , Kis-Tót Bálint , Jávor Bálint , László Szabolcs , Vezse Panna , Huszthy Péter , Tóth Tünde , Golcs Ádám
Published: July 2024

Zn2+ has a crucial role both in biology and the environment, while Pb2+ presents serious hazards in the same areas due to its toxicity, and the need for their analysis often exceeds available instrumental capacity. We report, herein, a new high-throughput optochemical screening method for Zn2+ and Pb2+ in various solutions. Moreover, we also introduced a new and generalizable three-step-microplate-modification technique, including plasma treating, linker-docking and photocatalytic copolymerization. The surface of a commercially available 96-well-cycloolefin-microplate was treated with atmospheric plasma, and then, the bottoms of the wells were covered by covalently attaching a methacrylate-containing linker-monolayer. Finally, the preactivated microplate wells were covalently functionalized by immobilizing bis(acridino)-crown ether-type sensor molecules, via photocatalytic copolymerization, to a polymethacrylate backbone. This sensing tool can be used in all microplate readers, is compatible with liquid handling platforms and provides an unprecedently fast monitoring (>1000 samples/hour, extrapolated from the time required for 96 measurements) of dissolved Zn2+ and Pb2+ among recent alternatives above the detection limits of 8.0 × 10−9 and 3.0 × 10−8 mol/L, respectively, while requiring a sample volume of only 20 µL.
An In-Depth Examination into How Genotype, Planting Density, and Time of Sowing Affect Key Phytochemical Constituents in Nigella sativa Seed
Authors: Thani Parbat Raj , Johnson Joel B. , Bhattarai Surya , Trotter Tieneke , Walsh Kerry , Broszczak Daniel , Naiker Mani
Published: July 2024

Nigella sativa, also known as black cumin, is esteemed for its rich reservoir of health-benefitting phytoconstituents nestled within its seeds. The composition of its seeds can be influenced by factors such as genotype diversity and agricultural practices. Understanding these dynamics is important for maximizing the nutritional and medicinal attributes of the seeds. This study investigated how different genotypes, growing densities, and sowing times affect oil yield and phytoconstituents of Nigella seeds in Northern Australia. The aim was to find the optimal combination of these factors to maximize desirable compounds. Our findings revealed variability in oil yield and phytoconstituents among different genotypes, growing densities, and sowing times. No single genotype stood out as having elevated levels of all desired compounds. For instance, genotype AVTKS#5 had high total phenolic content (TPC) and antioxidant capacity, while AVTKS#8 and AVTKS#7 excelled in thymoquinone (TQ) and polyunsaturated fatty acids (PUFAs) and monounsaturated fatty acids (MUFAs), respectively. Planting density had a nuanced impact, with no significant effect on oil yield and CUPRAC values, but higher densities decreased TPC, FRAP, and TQ. Interestingly, seeds cultivated at 20 and 30 plants/m2 had higher ratios of MUFAs/SFAs, PUFAs/SFAs, and (MUFAs + PUFAs)/SFAs, indicating the importance of planting density in shaping fatty acid profiles. Sowing times also had a noticeable effect, with late sowing leading to a decrease in oil yield from 19% to 14%. May-sown seeds had higher TPC, FRAP, CUPRAC, and fatty acid ratios, while TQ levels peaked in June-sown seeds. Our study highlighted positive correlations among TPC, FRAP, CUPRAC, and TQ, emphasizing their collective contribution to the nutritional and medicinal potency of Nigella seeds. Fatty acids, on the other hand, showed no significant correlation with these parameters, indicating independent regulation. In summary, our comprehensive analysis provides insights into the factors (genotype and agronomic practice) that shape the phytochemical profile of Nigella seeds, and suggests better genotype, planting density, and time of sowing for the cultivation and quality production.
Reviewing the Perspectives on the Relationship between Religious Beliefs and Sex Work: A Qualitative Systematic Review
Authors: Winter Mokhwelepa Leshata , Olivia Sumbane Gsakani
Published: July 2024

The intersection of religious beliefs and sex work has long been a subject of debate, with varying perspectives on the moral, social, and legal implications. This qualitative systematic review aimed to review the nuanced relationship between religious beliefs and sex work by synthesizing the existing literature and providing insights into the diverse perspectives and experiences. The rationale for this research is to understand how religious beliefs influence societal attitudes, policies, and the experiences of sex workers in order to inform more effective and culturally sensitive interventions and support mechanisms. This review focused on articles pertaining to female-identified sex workers, as the gendered discussion happens heavily in religious interpretations and in current anti-trafficking legislation, which now genders men as customers and not as sex workers. Five stages of systematic review were followed in this review. The following peer-reviewed electronic databases from 2000 to 2023 were utilized: Google Scholar, Scopus database, Science Direct, and PubMed. The search strategy focused on identifying studies that investigated the impact of religious beliefs on various aspects of sex work, including attitudes towards sex work within religious communities and the influence of religious teachings on the behavior and practices of sex workers. Inclusion criteria were defined based on population, exposure, study design, and outcomes, while exclusion criteria were established to ensure the relevance and rigor of the included studies. Only 25 relevant studies resulted from this research. Four themes emerged from this study: (1) religious attitudes towards sex work; (2) experiences of sex workers, including stigmatization and marginalization within religious contexts; (3) social control; and (4) empowerment and advocacy. The relationship between religious beliefs and sex work is complex and multifaceted, influenced by cultural, social, and historical factors. Recognizing and understanding these perspectives is essential for developing more inclusive and effective interventions and policies that address the needs and rights of sex workers within the context of their religious beliefs and communities.
“The Adultery of a Woman Is a Very Serious Attack on the Honor and Dignity of a Man.” Perceptions about Gender Violence of Judges and Inmates
Authors: Frois Catarina , Lima Antónia
Published: July 2024

Crossing the analysis of court rulings on domestic violence produced in Portuguese courts and semi-structured interviews of men convicted of this same crime, this article emphasizes the transversality of gender categories as social markers in the different dimensions of Portugal’s social and institutional life, as well as the processes through which an inseparability between gender and state is built. From the comparative analysis of our research, we have identified the presence and relevance of these moral models that ascribe meanings and expectations to gender-based violence in different contexts—in courts and in prisons, with judges and inmates.
Neurodevelopmental Impairments Prediction in Premature Infants Based on Clinical Data and Machine Learning Techniques
Authors: Ortega-Leon Arantxa , Gucciardi Arnaud , Segado-Arenas Antonio , Benavente-Fernández Isabel , Urda Daniel , Turias Ignacio J.
Published: July 2024

Preterm infants are prone to NeuroDevelopmental Impairment (NDI). Some previous works have identified clinical variables that can be potential predictors of NDI. However, machine learning (ML)-based models still present low predictive capabilities when addressing this problem. This work attempts to evaluate the application of ML techniques to predict NDI using clinical data from a cohort of very preterm infants recruited at birth and assessed at 2 years of age. Six different classification models were assessed, using all features, clinician-selected features, and mutual information feature selection. The best results were obtained by ML models trained using mutual information-selected features and employing oversampling, for cognitive and motor impairment prediction, while for language impairment prediction the best setting was clinician-selected features. Although the performance indicators in this local cohort are consistent with similar previous works and still rather poor. This is a clear indication that, in order to obtain better performance rates, further analysis and methods should be considered, and other types of data should be taken into account together with the clinical variables.
A Green Marketing and Operations Management Decision-Making Approach Based on QFDE for Photovoltaic Systems
Authors: Fargnoli Mario , Salvatori Emilio , Tronci Massimo
Published: July 2024

Today, to properly address circular economy practices, strategic decisions encompassing all the various life cycle stages of products or services have become critically important in the market. However, companies still have difficulties in balancing the technical and environmental requirements of their offerings, and numerous studies outline the need for more research on ecodesign tools to support them in decision-making. To reduce such a research gap, a decision-making framework based on the integrated use of the quality function deployment for the environment (QFDE), analytic hierarchy process (AHP), strengths, weaknesses, opportunities, and threats (SWOT), and TOWS matrix methods was developed through a case study related to the provision of photovoltaic solar systems for domestic use. The results achieved show that to better enhance the company’s offering of ensuring customer satisfaction and green compliance, a shift towards a product–service system (PSS) approach is required, and practical implementation strategies are suggested. Overall, this study contributes to the environmental research literature by streamlining marketing strategy planning decision-making through a novel QFD-based approach that aligns customer requirements with environmental concerns and improvement options. Thus, it provides both academics and practitioners with a useful framework to better address the implementation of circular economy practices.
Evaluating Experts’ Perceptions on Regional Competitiveness Based on the Ten Key Factors of Assessment
Authors: Kouskoura Amalia , Kalliontzi Eleni , Skalkos Dimitris , Bakouros Ioannis
Published: July 2024

In the contemporary landscape, the foundation of competitive advantage lies in sustainability, particularly within the context of regional competitiveness. This topic explores the impact of sustainability on regional competitiveness. In this study, we assessed experts’ opinions on the impact of the main ten key factors of evaluation on regional competitiveness, namely the economy, labor market, poverty and social inclusion, health, education, environmental and energy considerations, transport infrastructure, science and technology, high-tech industries, and innovation. The study aims to understand how these factors influence regional competitiveness. A sample of 93 experts participated in a questionnaire survey through the Google platform from October to December 2023. This methodology was chosen to gather diverse expert opinions efficiently. The collected data were analyzed with statistical tools, combining cross and chi-square tests. Statistical analysis methods such as cross and chi-square tests were employed to analyze the collected data. Our findings indicate that experts recognize the economy (up to a positive level of 69.1%), the labor market (up to 64.7%), and the health infrastructure (up to 52.9%) as the main impact factors on regional competitiveness. Collective efforts and improvements in these factors are essential to alleviate poverty and social exclusion (up to 50.8%). Investments in education (up to 41.9%) and technological science and the digital society (up to 39.2%) are considered key after the main factors to promoting prosperity and competitiveness. Following them, there is a growing call for creating a greener, healthier, and more sustainable future, reflected in the focus on environmental and energy issues (up to 36.7%) and toward transport developments (up to 35.7%). At the bottom of the list are high-tech industry (up to 32.5%) and the innovation factor (up to 28.2%). These factor groups are highlighted as critical aspects for promoting prosperity and addressing social challenges in the region. The findings underscore the multifactorial nature of regional competitiveness, emphasizing both the important and less critical factors. It becomes apparent that the interpretation of regional competitiveness requires a comprehensive analysis of multiple factors, each contributing differently to overall competitiveness. Further examination and analysis are necessary to fully understand these dynamics.
Navigating Digital Transformation and Technology Adoption: A Literature Review from Small and Medium-Sized Enterprises in Developing Countries
Authors: Díaz-Arancibia Jaime , Hochstetter-Diez Jorge , Bustamante-Mora Ana , Sepúlveda-Cuevas Samuel , Albayay Isidora , Arango-López Jeferson
Published: July 2024

This article examines digital transformation and technology adoption within small and medium-sized enterprises (SMEs) in developing countries. It focuses on identifying the mechanisms that facilitate or hinder the integration of new technologies in these contexts, often constrained by specific socioeconomic and cultural factors. The research employed a systematic literature review, adhering to a structured protocol to explore existing studies. Critical stages of this review included defining research questions, developing a comprehensive search strategy, and systematically selecting and analyzing relevant studies from significant databases. The review reveals a significant emphasis on basic information systems as the primary form of technology adoption in SMEs within developing countries, with more advanced technologies like cloud computing and artificial intelligence being less common. Factors such as the entrepreneurial ecosystem and external social influences, including government and private sector initiatives, play pivotal roles in shaping these adoption patterns. This study underscores the critical role of community-driven efforts in promoting technology adoption over initiatives by the public and private sectors. The findings suggest a need for future research to develop tailored technological adoption models that accommodate the unique challenges of SMEs in developing countries. Additionally, more inclusive approaches that consider sociocultural dynamics could further enhance the effectiveness of technology integration strategies in these regions.
Historic Preservation as Sustainable Urban Development in African Cities: A Technical and Technological Framework
Authors: Teklemariam Nathan
Published: July 2024

Africa is currently undergoing rapid urbanization, which has placed a significant strain on its already insufficient infrastructure. Many cities struggle to meet the needs of their residents through sustainable urban development. This has put urban Africa to a pressing dilemma: the tension between preserving historic buildings and sites, and the drive to modernize and redevelop cities. Addis Ababa, the capital of Ethiopia, exemplifies this struggle, as it is currently undergoing a rapid urban transformation underpinned by an urban renewal and infrastructure development project, which has included the demolition of historic buildings in its oldest district, Piazza. This article uses Addis Ababa as a case study to demonstrate how historic preservation can be integrated into urban development strategies. It utilizes Zerrudo’s methodology for historic preservation, encompassing the following four phases: awareness, appreciation, protection, and utilization. It further integrates Geographic Information Systems (GIS) technology throughout each phase, enhancing the precision and effectiveness of preservation efforts. It argues that historic preservation not only safeguards cultural identity and heritage, but also brings significant economic benefits, including tourism and job creation. This integrated approach ensures that development and preservation are not mutually exclusive, but rather are complementary aspects of sustainable urban development in Africa.
Workplace Flexibility and Participation in Adult Learning
Authors: Radovan Marko
Published: July 2024

Understanding the relationship between job characteristics and participation in adult learning is essential for fostering sustainable development. This study explores how employment factors such as job characteristics, work autonomy, and required skills influence participation in adult learning, which is important for economic resilience and social cohesion. The research utilizes data from the 2021 Labor Force Survey (LFS) in Slovenia, examining adults aged 25–64 and their participation in formal and non-formal education. Findings reveal significant correlations between employment status, type of contract, company size, and adult learning participation. Specifically, individuals in larger companies and those with flexible working hours are more likely to engage in educational activities. Additionally, the ability to work from home is strongly associated with higher participation rates in adult education, highlighting the role of flexible work arrangements in promoting lifelong learning. These insights underscore the importance of creating supportive job environments and equitable access to educational resources to achieve sustainable economic growth and reduce inequalities. By addressing the factors that influence adult learning, policymakers and organizations can develop strategies to enhance workforce adaptability and lifelong learning, ultimately contributing to the broader goals of sustainable development.
Improving Order-Picking Performance in E-Commerce Warehouses through Entropy-Based Hierarchical Scattering
Authors: Pawar Nilendra Singh , Rao Subir S. , Adil Gajendra K.
Published: July 2024

The high service expectations of e-commerce customers are placing unprecedented demands on e-commerce warehouse workers, leading to higher fatigue and health-related disorders among these workers. Order picking in retail e-commerce fulfilment warehouses (REFWs) is highly labour-intensive and physically demanding activity. This is mainly due to the prevalence of single-unit orders and the expectation of quick order servicing. One strategy to reduce picking effort is the adoption of a scattered storage assignment policy, which spreads the inventory of each product across the entire warehouse. This paper proposes a new, hierarchical approach for the scattering of stock, along with an entropy-based measure for scattering. This measure overcomes some significant limitations of the existing scattering measures and captures the extent of scattering more effectively. We developed a storage assignment heuristic for the scattering of stock and conducted a simulation study to demonstrate its effectiveness in reducing the order-picking effort. Some valuable managerial insights were obtained using a simulation with different warehouse designs and operating parameters. This research also illustrates that the adoption of scattered storage requires careful consideration of the nature of the demand pattern in the warehouse.
Can the Digital Economy Empower Low-Carbon Transition Development? New Evidence from Chinese Resource-Based Cities
Authors: Xu Hongxia , Li Honghe , Yan Xiang-Wu , Cui Xinghua , Liang Xiaoyan , Xu Ning
Published: July 2024

Existing research lacks a systematic and comprehensive analysis of the digital economy (DE)’s impact on the low-carbon transformation of resource-based cities. This study utilizes panel data from 114 of these cities in China from 2006 to 2019 to construct a DE measurement system. Based on the global SBM directional distance function and the Malmquist–Luenberger index (SBM-DDF-GML), we calculated the total factor carbon productivity (TFCP), decomposed the carbon inefficiency value (CIV), and examined DE’s impact, mechanism, and heterogeneity on low-carbon transition development (LCTD) during distinct growth phases of resource-based cities. Based on this examination, we found the following: (1) The DE effectively reduced carbon intensity and inefficiency and improved the total factor carbon productivity in resource-based cities. These findings remained robust after a series of robustness tests. (2) The DE empowered LCTD by improving energy efficiency, upgrading industrial structure, and optimizing innovation factor allocation. Finally, (3) this effect varied across the different city stages, being most significant in mature cities and weakest in declining ones. The research findings provide empirical evidence for the LCTD of resource-based cities.
The Conceptualization of a Modular Residential Settlement Project Emerging in a Displacement Situation due to War in the Context of Sustainable Development Requirements
Authors: Wierzbicka Anna Maria , Trębacz Paweł , Jóźwik Renata , Duda Magdalena
Published: July 2024

Modular housing represents an opportunity for the mass provision of housing of an appropriate standard, implemented sustainably. We analyzed the theoretical basis of this type of housing for refugee populations. We identified several generations of housing provision, from tented camps to container settlements, and ultimately to units resembling traditional housing estates. The latter is feasible due to technological advancements. Our research contributes to the implementation of the principles of the New European Bauhaus initiative, which underpins the European Green Deal policy on space design. Using the Research through Design (RtD) method within the framework of a settlement project for the Ukrainian population migrating from war zones (Modular Refugees Settlement Project “ProModSe”), we tested the suitability of these principles in extreme and critical war-related situations. The most important criteria were speed of implementation, the prospect of long-term residence, and ensuring safety. We analyzed the social, environmental, and aesthetic–functional aspects, comparing them with existing practices in the design of settlements for displaced persons, modular settlements, and quality standards in residential environments. The results demonstrate, at multiple scales, the critical interdependence of sustainable development, aesthetics and functionality, and community creation. We emphasize the need to include security as an essential fourth axis within the New European Bauhaus concept. This study contributes to addressing the complex housing problem for refugee populations. The generalizations formulated based on empirical research complement the knowledge of housing and the functioning of the sociotechnological system.
Effect of the Connectivity of Weak Rock Zones on the Mining-Induced Deformation of Rock Slopes in an Open-Pit Mine
Authors: Zhang Cheng , Clement Amagu amagu , Kodama Jun-ichi , Sainoki Atsushi , Fujii Yoshiaki , Fukuda Daisuke , Wang Shuren
Published: July 2024

Geological structures significantly influence mining-induced deformations in open-pit mines, with their variations and interactions adding complexity to the excavation process and introducing uncertainties in deformation outcomes. This study utilized numerical simulations to analyze the impact of weak rock zones in a specific open-pit limestone quarry in Japan on mining-induced deformation. The simulation results were both qualitatively and quantitatively validated against field measurements, enhancing the reliability of the findings. Subsequently, four conceptual models were developed based on the characteristics of the quarry to investigate the mechanisms by which weak rock zones affect rock slope deformations. Our analyses demonstrated that slip deformation occurred exclusively when two weak rock zones were connected. This deformation was associated not only with shear failure in the upper weak rock zone but also with the contraction and bending of the lower weak rock zone. Furthermore, the simulation results were consistent with field data and supported by the conceptual models, confirming that the proposed sliding mechanisms can effectively explain the observed deformation behaviors. The insights gained from these models provide valuable references for managing similar geological challenges in other open-pit mines.
Prediction of the Potentially Suitable Areas of Actinidia latifolia in China Based on Climate Change Using the Optimized MaxEnt Model
Authors: Wang Zhi , Luo Minmin , Ye Lixia , Peng Jue , Luo Xuan , Gao Lei , Huang Qiong , Chen Qinghong , Zhang Lei
Published: July 2024

Actinidia latifolia, with the highest vitamin C content in its genus, is a unique wild relative of kiwifruit that could be important for genetic breeding research. Climate change significantly influences the distribution range of wild plants. Accurately assessing the potential distribution of wild kiwifruit and its response to climate change is crucial for the effective protection and sustainable utilization of its germplasm resources. In this study, we utilized the optimized MaxEnt model to predict the potential habitats of A. latifolia in China, employing the jackknife test to assess the importance of environmental variables in our modeling process. The results showed that annual precipitation (Bio12) and temperature annual range (Bio7) emerged as the most influential environmental variables affecting the distribution of this kiwifruit wild relative. As radiative forcing and time increase, the potential habitats of A. latifolia in China are projected to shrink southward, thereby exacerbating habitat fragmentation. This research offers significant scientific references for the investigation, protection, cultivation, and application of wild relatives of the kiwifruit.
Snow Depth Estimation and Spatial and Temporal Variation Analysis in Tuha Region Based on Multi-Source Data
Authors: Yang Wen , He Baozhong , Luo Xuefeng , Ma Shilong , Jiang Xing , Song Yaning , Du Danying
Published: July 2024

In the modelling of hydrological processes on a regional scale, remote-sensing snow depth products with a high spatial and temporal resolution are essential for climate change studies and for scientific decision-making by management. The existing snow depth products have low spatial resolution and are mostly applicable to large-scale studies; however, they are insufficiently accurate for the estimation of snow depth on a regional scale, especially in shallow snow areas and mountainous regions. In this study, we coupled SSM/I, SSMIS, and AMSR2 passive microwave brightness temperature data and MODIS, TM, and Landsat 8 OLI fractional snow cover area (fSCA) data, based on Python, with 30 m spatially resolved fractional snow cover area (fSCA) data obtained by the spatio-temporal dynamic warping algorithm to invert the low-resolution passive microwave snow depths, and we developed a spatially downscaled snow depth inversion method suitable for the Turpan–Hami region. However, due to the long data-processing time and the insufficient arithmetical power of the hardware, this study had to set the spatial resolution of the result output to 250 m. As a result, a day-by-day 250 m spatial resolution snow depth dataset for 20 hydrological years (1 August 2000–31 July 2020) was generated, and the accuracy was evaluated using the measured snow depth data from the meteorological stations, with the results of r = 0.836 (p ≤ 0.01), MAE = 1.496 cm, and RMSE = 2.597 cm, which are relatively reliable and more applicable to the Turpan–Hami area. Based on the spatially downscaled snow depth data produced, this study found that the snow in the Turpan–Hami area is mainly distributed in the northern part of Turpan (Bogda Mountain), the northwestern part of Hami (Barkun Autonomous Prefecture), and the central part of the area (North Tianshan Mountain, Barkun Mountain, and Harlik Mountain). The average annual snow depth in the Turpan–Hami area is only 0.89 cm, and the average annual snow depth increases with elevation, in line with the obvious law of vertical progression. The annual mean snow depth in the Turpan–Hami area showed a “fluctuating decreasing” trend with a rate of 0.01 cm·a−1 over the 20 hydrological years in the Turpan–Hami area. Overall, the spatially downscaled snow depth inversion algorithm developed in this study not only solves the problem of coarse spatial resolution of microwave brightness temperature data and the difficulty of obtaining accurate shallow snow depth but also solves the problem of estimating the shallow snow depth on a regional scale, which is of great significance for gaining a further understanding of the snow accumulation information in the Tuha region and for promoting the investigation and management of water resources in arid zones.
An Extended Car-Following Model Considering Lateral Gap and Optimal Velocity of the Preceding Vehicle
Authors: Zhang Zhiyong , Tang Wu , Feng Wenming , Liu Zhen , Huang Caixia
Published: July 2024

The car-following model (CFM) utilizes intelligent transportation systems to gather comprehensive vehicle travel information, enabling an accurate description of vehicle driving behavior. This offers valuable insights for designing autonomous vehicles and making control decisions. A novel extended CFM (ECFM) is proposed to accurately characterize the micro car-following behavior in traffic flow, expanding the stable region and improving anti-interference capabilities. Linear stability analysis of the ECFM using perturbation methods is conducted to determine its stable conditions. The reductive perturbation method is used to comprehensively describe the nonlinear characteristics of traffic flow by solving the triangular shock wave solution, described by the Burgers equation, in the stable region, the solitary wave solution, described by the Korteweg–de Vries (KdV) equation, in the metastable region, and the kink–antikink wave solution, described by the modified Korteweg–de Vries (mKdV) equation, in the unstable region. These solutions depict different traffic density waves. Theoretical analysis of linear stability and numerical simulation indicate that considering both the lateral gap and the optimal velocity of the preceding vehicle, rather than only the lateral gap as in the traditional CFM, expands the stable region of traffic flow, enhances the anti-interference capability, and accelerates the dissipation speed of disturbances. By improving traffic flow stability and reducing interference, the ECFM can decrease traffic congestion and idle time, leading to lower fuel consumption and greenhouse gas emissions. Furthermore, the use of intelligent transportation systems to optimize traffic control decisions supports a more efficient urban traffic management, contributing to sustainable urban development.
Understanding Why Farmers Leave: Validating Key Indicators for Farm Exit in İzmir, Türkiye
Authors: Türkekul Berna , Abay Canan Fisun
Published: July 2024

This study analyses the factors that affect farmers’ decisions to quit farming, using the İzmir region of Türkiye as a case study. A sample of 195 farmers was surveyed to identify the factors influencing this decision. Factors such as the farm type, farm size, agricultural subsidies, land ownership, and age of farmers were found to significantly affect the decisions of the farmers. Sensitivity analysis was performed to examine the changes in the selected variables. The sensitivity analysis showed that young farmers, under financial pressure and tenancy uncertainty, are likely to stay in farming when they receive agricultural subsidies. Thus, targeted financial subsidies can help sustain the livelihood of young farmers efficiently. This study reveals the important relationship between policy interventions and the long-term economic viability of farming as a livelihood. This relationship is crucial for the overall viability of agriculture and the well-being of rural communities. Overall, the findings from this study will inform the development of policies to strengthen the resilience and sustainability of rural farming communities in Türkiye.
Cervical Necrotizing Fasciitis in Adults: A Life-Threatening Emergency in Oral and Maxillofacial Surgery
Authors: de Leyva Patricia , Dios-Díez Paula , Cárdenas-Serres Cristina , Bueno-de Vicente Ángela , Ranz-Colio Álvaro , Sánchez-Jáuregui Eduardo , Almeida-Parra Fernando , Acero-Sanz Julio
Published: July 2024

Necrotizing fasciitis (NF) is a life-threatening soft-tissue infection affecting the deep fascia and subcutaneous tissue. It is characterized by a fulminant course and high mortality rates. NF of the head and neck is very rare, with most cases being odontogenic in origin. The purpose of this study is to comprehensively review the most important features of cervical necrotizing fasciitis (CNF) in adults and add our experience in the management of this entity. The most common isolated organisms are Streptococcus spp. and Staphylococcus spp. If the infection progresses to descending mediastinitis, the prognosis becomes very poor. Since the initial clinical features can be similar to those of a non-necrotizing deep cervical infection, a high degree of suspicion is critical for an early diagnosis. A computed tomography scan is essential for the diagnosis and to define the extent of the infection/rule out descending mediastinitis. Early and aggressive surgical debridement of all compromised tissue and antibiotic therapy and fluid resuscitation are essential and should not wait for bacterial culture results. Despite prompt and adequate treatment, the mortality of CNF can be as high as 35%.
Study of Morphology of Gas–Liquid Interfaces in Tank with Central Column in CSS under Different Gravity Conditions
Authors: Chen Zhewen , Duan Li , Chen Shangtong , Li Ce , Yang Chao , Hu Liang , Zhang Pu , Wu Di , Zhang Yuhao , Pang Huan , Zhao Yifan , Kang Qi
Published: July 2024

Most space shuttle fuel tanks use a center column to hold the Propellant Management Device (PMD). This paper analyzes the gas–liquid interface state in the tanks with a central column during microgravity experiments conducted in the Chinese Space Station. It launches an extended study to investigate the gas–liquid interface state under different gravity conditions. Using the perturbation method and boundary layer theory, we numerically calculated the morphology of the gas–liquid interface under varying gravity conditions based on the Young–Laplace equation. The results were then compared to those obtained from existing commercial software and were found to be consistent. Based on this, the study develops two types of calculation procedures. The first procedure generates the corresponding shape of the liquid surface by inputting the height of the liquid surface endpoints and the gravity level. The second procedure is based on the targeting method and generates the corresponding liquid surface by inputting the volume of the liquid in the storage tank and the gravity level. The procedures were used to analyze the variation of gas–liquid interface properties under different gravity conditions. This study offers theoretical support for liquid management in aerospace engineering fuel tanks.