bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Running Surface Using Advanced Footwear Technology Spikes on Middle- and Long-Distance Running Performance Measures
Authors: Alda-Blanco Alejandro , Rodríguez-Barbero Sergio , Rodrigo-Carranza Víctor , Valero Fernando , Chico Patricia , González-Mohíno Fernando
Published: December 2024

Objective: This study evaluated the effects of advanced footwear technology (AFT) spikes on running performance measures, spatiotemporal variables, and perceptive parameters on different surfaces (track and grass). Methods: Twenty-seven male trained runners were recruited for this study. In Experiment 1, participants performed 12 × 200 m at a self-perceived 3000 m running pace with a recovery of 5 min. Performance (time in each repetition), spatiotemporal, and perceptive parameters were measured. In Experiment 2, participants performed 8 × 5 min at 4.44 m/s while energy cost of running (W/kg), spatiotemporal, and perceptive parameters were measured. In both experiments the surface was randomized and mirror order between spike conditions (Polyether Block Amide (PEBA) and PEBA + Plate) was used. Results: Experiment 1: Runners were faster on the track (p = 0.002) and using PEBA + Plate spike (p = 0.049). Experiment 2: Running on grass increased energy cost (p = 0.03) and heart rate (p < 0.001) regardless of the spike used, while PEBA + Plate spike reduced respiratory exchange ratio (RER) (p = 0.041). Step frequency was different across surfaces (p < 0.001) and spikes (p = 0.002), with increased performance and comfort perceived with PEBA + Plate spikes (p < 0.001; p = 0.049). Conclusions: Running on the track surface with PEBA + Plate spikes enhanced auto-perceived 3000 m running performance, showed lower RER, and improved auto-perceptive comfort and performance. Running on grass surfaces increased energy cost and heart rate without differences between spike conditions.
Pilot Assessment of Knowledge and Compliance with the Sustainable Development Goals as Drivers for Strengthening BioTrade
Authors: Cabrera Camila , Cuenca Pablo
Published: December 2024

The Sustainable Development Goals (SDGs) and BioTrade are fundamental to address socio-environmental challenges and promote sustainable development. This study assessed the level of knowledge of the SDGs among students, faculty, and administrative staff of the Universidad Regional Amazónica Ikiam (URAI) using a validated survey and also analyzed Ikiam’s contribution to the fulfillment of the SDGs. Through a qualitative analysis of interviews with experts, a SWOT matrix was designed to understand the status of BioTrade, and strategies were proposed to improve the understanding of the relationship between BioTrade and SDG compliance. The results showed that there is limited knowledge of the SDGs, with significant variations between groups. Also, it was shown that the SDGs to which the university population is most related, according to the area to which they are linked, are SDG 3, SDG 4, SDG 6, and SDG 11, while the greatest contribution of Ikiam to the SDGs is made through research and outreach projects. On the other hand, it was shown that the main limitation faced by BioTrade is the lack of knowledge of this initiative in the population, so it is important that academia promotes the strategies proposed to promote the fulfillment of SDGs through effective tools such as BioTrade.
The Use of Plastic Waste as Replacement of Coarse Aggregate in Concrete Industry
Authors: Mohamedsalih Moustafa Abdulrahim , Radwan Ahmed E. , Alyami Saleh H. , Abd El Aal Ahmed K.
Published: December 2024

Recycling solid waste is one of the most important ways to reduce carbon footprints and put sustainability into practice. This research aims to bridge the gap between the results of previous studies and the effectiveness and sustainability benefits of using plastic waste (PW) in concrete mixes by partially replacing coarse aggregate with PW. Furthermore, we examine the suitability of the concrete produced for use as a construction material. The research methodology is based on studying the physical and mechanical behavior of concrete produced by partially replacing coarse aggregate with 0%, 2.5%, 5%, 7.5%, 10%, and 12.5% PW. For the conventional concrete–CC mix of 0% PW, the design strength, fcu, was 35 MP, with a slump of 100 mm, using a water–cement ratio of 0.5, a M.A.S of coarse aggregate of 20 mm, and a sand F.M. of 2.2. According to British standards, BS, slump and density tests were carried out for concrete samples produced in their fresh state and strength tests, ultrasonic testing, etc. for concrete samples after hardening. The results indicated that there is no significant difference between the dry density and bulk density of concrete produced at all its age stages, regardless of the percentage of PW that replaces the aggregate. It also indicated that the compressive strength, the flexural strength, and the splitting tensile strength of the produced concrete decrease steadily and significantly when aggregates are replaced by PW by more than 2.5%. It was found that the decrease in compressive strength does not exceed 1% for concrete with 2.5% PW compared to the strength of CC, while the compressive strength of concrete with 5% PW decreases by 24%. The maximum reduction rate of the flexural strength and splitting tensile strength was 40% and 32%, respectively, for concrete at 12.5% PW compared to the strength of CC. Therefore, PW concrete can retain its strength when used in small quantities of up to 2.5% and can be applied in structural works.
Ecological Dynamics and Conservation Strategies for Mediterranean Salt Marshes: Insights from a Pilot Study of Biodiversity and Environmental Drivers in the Palud Marsh, Croatia
Authors: Sertić Perić Mirela , Dražina Tvrtko , Žutinić Petar , Rubinić Josip , Kuczyńska-Kippen Natalia , Zhang Chen , Špoljar Maria
Published: December 2024

Salt marshes support high biodiversity and provide essential ecosystem services, yet are increasingly threatened by climate change, reduced freshwater input, and human activities. This pilot study examined the physico-chemical characteristics and biodiversity of the Palud salt marsh in Istria, Croatia, during July 2021, focusing on phytoplankton, zooplankton, and macrozoobenthos communities across different habitats. The results show that while parameters like water temperature and pH remained stable, dissolved oxygen and nutrient levels fluctuated significantly. Water depth decreased due to drought and tidal oscillations, affecting salinity and nutrient concentrations. Phytoplankton communities were dominated by Bacillariophyta, with Cryptophyta, particularly Cryptomonas sp., having the highest abundance. Zooplankton communities were dominated by Rotifera, especially Brachionus plicatilis, while the macrozoobenthos communities were dominated by Ostracoda, with some variation in gastropod and chironomid abundance across sites. These findings indicate the dynamic nature of plankton and benthos communities in response to changing water levels in the Palud marsh. This study highlights the need for targeted conservation strategies to preserve the ecological integrity of Mediterranean salt marshes. Sustainable management practices must account for water level fluctuations, the role of aquatic vegetation, and the resilience of these ecosystems, emphasizing their importance in supporting biodiversity and ecosystem services in the face of environmental change.
The Effects of Different Management and Processing Methods on the Carbon Footprint of Chinese Hickory
Authors: Xu Ruizhi , Shi Haitao , Dai Wenzhe , Chen Yangen , Huang Sha , Zhou Guomo , Zhou Yufeng , Shi Yongjun
Published: December 2024

Amidst the global shift towards a low-carbon development trajectory, the hickory industry in Lin’an District is progressively embracing green, low-carbon, and sustainable practices. This study, leveraging the life cycle assessment (LCA) methodology, meticulously scrutinizes the carbon footprint of the hickory industry by segmenting its life cycle into two distinct subsystems: the plantation and the factory. Through comprehensive, year-long monitoring of soil greenhouse gas (GHG) emissions in hickory plantations, our findings underscore that while total GHG emissions from eco-complex management (ECM) surpassed those of intensive management (IM) by 10.7% (p < 0.001), ECM significantly mitigated the carbon footprint per kilogram of hickory produced, achieving a reduction of 1.0495 kgCO2eq. This advantage is primarily attributable to ECM’s diminished reliance on chemical fertilizers. Within the factory subsystem, when processing 1 kg of hickory, the digital factory incurred a carbon footprint of 2.5923 kgCO2eq kg−1, whereas family workshops exhibited a lower footprint of 1.9544 kgCO2eq kg−1. Notably, the processing and packaging stages collectively accounted for over 90% of the factory subsystem’s carbon emissions, with natural gas being the primary contributor during processing. To estimate the carbon emission reduction potential within the hickory industry, this research draws parallels with emission reduction strategies employed by other industries and outlines tailored strategies to propel its low-carbon development. By advocating for the widespread adoption of ECM and enhancing the energy efficiency of processing enterprises, the hickory industry can effectively diminish its carbon footprint and steer towards a green, low-carbon, and high-quality development paradigm.
Developing a Competency Model for Human Resource Directors (HRDs) in Exponential Organizations Undergoing Digital Transformation
Authors: Zhang Xuming , Wang Ping , Peng Ling
Published: December 2024

The rapid digitalization of organizations has positioned the digital transformation of Human Resources (HR) as a critical trend for the future. In particular, exponential organizations—characterized by their ability to leverage digital technology to achieve disproportionately large impacts relative to their size—require a fundamental shift in the role and competencies of HR professionals. This study focuses on the competencies required of Human Resource Directors (HRDs) within these highly adaptive and scalable organizations, using Shandong Province as a case sample. Grounded in the Resource-Based View (RBV), which emphasizes that a firm’s internal resources are essential for sustaining competitive advantage, we argue that HRDs competencies are critical resources for organizations undergoing digital transformation. Through the application of methods such as questionnaire surveys, structural equation modeling, and t-tests, we identified and validated 11 key competency indicators for HRDs in the context of digital transformation. These indicators are grouped into four dimensions based on Dave Ulrich’s framework: Strategic Partner, Change Agent, Administrative Expert, and Employee Champion. Our research organizes these 11 competencies within the four dimensions, providing a structured framework for evaluating the evolving role of HRDs in the digital era. These 11 competencies offer organizations valuable insights, particularly in organizational development, talent acquisition and deployment, employee training and upskilling, and performance evaluation, contributing to a more comprehensive understanding of the competencies needed for HR leadership in the digital age.
Research on the Driving Paths and Evolution of Digital Innovation in Intelligent Connected Vehicle Enterprises—A Configurational Analysis Based on Dynamic fsQCA
Authors: Zhu Guiju , Peng Yuxing , Shi Changfa , Liu Sha , Liu Haiyun
Published: December 2024

Intelligent connected vehicles (ICVs) are increasingly emerging as a pivotal driving force for promoting sustainable development in global transportation due to their outstanding performance in mitigating carbon emissions, alleviating traffic congestion, and enhancing travel efficiency. Enhancing the digital innovation capability of ICV enterprises is an imperative path to explore high-quality industry development and effectively respond to the latest technological revolution and industrial transformation. Based on the panel data from 20 listed companies in the ICV industry in China from 2019 to 2022, this study utilizes a fixed-base index and fuzzy set qualitative comparative analysis (fsQCA) method within the framework of technology–organization–environment (TOE) theory to investigate the influencing factors, driving paths, and temporal trends of digital innovation in ICV enterprises in China. The results show the following: (1) No single factor can constitute a sufficient condition for achieving a high level of digital innovation. (2) The level of digital innovation of ICV enterprises in China showed a fluctuating growth trend during 2019–2022. Digital infrastructure and market competition are the two critical factors for digital innovation, while technology and organizational conditions have become bottlenecks. (3) There are five driving paths of digital innovation in ICV enterprises in China, including the Entrepreneurial Navigation + Technology Accumulation Driving Path, R&D Advantage + Environment Driving Path, Application Oriented + Environment Driving Path, Knowledge Absorption Driving Path, and Total Factor Driving Path. (4) From 2019 to 2022, the digital innovation of ICV enterprises in China underwent two stages of evolution, namely “Knowledge Absorption + Government Promotion” to “Technology Embedded + Market Driven”. (5) A substitutive relationship exists between digital resource acquisition capabilities and digital infrastructure, which promotes digital innovation through different paths leading to the same goal. The findings of this study have important theoretical and practical implications for ICV enterprises in making scientific and reasonable digital innovation decisions, thereby fostering the sustainable development of the automotive industry.
Sustainable Consensus Algorithms Applied to Blockchain: A Systematic Literature Review
Authors: Pineda Magda , Jabba Daladier , Nieto-Bernal Wilson , Pérez Alfredo
Published: December 2024

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.
Sustainable Weaving: Guano Knotted Carpets as a Tourism Experience Contributing to Territorial Development
Authors: Maldonado-Erazo Claudia Patricia , Montúfar-Guevara Silvia Patricia , Verdugo-Bernal Catalina Margarita , Durán-Sánchez Amador
Published: December 2024

Sustainable tourism is a management model that can easily be applied in the territories declared as “Pueblo Mágico”, promoting economic strengthening, as well as opportunities for environmental and cultural preservation, by means of tourism through the use of the historical–cultural characteristics of these territories. One of the main challenges is to find a balance between promoting tourism and conserving intangible cultural heritage. In July 2024, Guano, located in the province of Chimborazo, Ecuador, was re-accredited within the Magical Towns of Ecuador Program. Within this process, one of the main elements to reach this milestone was creating a meaningful tourism experience based on one of the cultural manifestations certified as part of the intangible cultural heritage of Ecuador: the weaving technique used to create Guano knotted carpets. This article examines how this technique was integrated into tourism development by focusing on knowledge transfer and symbolic and historical value, avoiding tourist overexploitation, which can lead to the erosion of traditions and loss of their cultural significance. The methodology used is a participatory action research methodology. This experience not only offers an opportunity for tourists to connect with local culture, but it can also be a driver of sustainable development. By integrating this tradition into a responsible tourism model, economic, social, and environmental benefits can be generated, which strengthen communities and preserve their cultural heritage. To sum up, sustainable tourism in Guano, Ecuador, can balance tourism promotion and cultural heritage preservation, and the integration of the knotted carpet weaving technique as a tourism experience strengthens local identity and generates economic, social, and environmental benefits, promoting sustainable development and avoiding cultural commodification.
Fostering Sustainable Development Through Women’s Entrepreneurship: Psychosocial Factors and Attitudinal Mediation in Colombian Higher Education
Authors: Arbulú Ballesteros Marco Agustín , Ramos Farroñán Emma Verónica , García Juárez Hugo Daniel , Ganoza-Ubillús Lucila María , Arbulú Castillo Julie Catherine , Saavedra Torres Isaac , Reyes-Perez Moises David , Pérez Pertuz Johemir Jesús , Martínez-Vargas Agustín María
Published: December 2024

University female entrepreneurship has seen significant growth in Colombia in recent years. This study aimed to examine the psychosocial factors that determine the entrepreneurial intent of Colombian university women, specifically analyzing how entrepreneurial attitude mediates the relationship between these factors and the intent to engage in entrepreneurship using a structural equation modeling approach (SEM). A total of 409 university students from various higher education institutions in Colombia participated by completing an online survey. The results demonstrated that extrinsic motivation (β = 0.178 moderate intensity), family environment (β = 0.127 moderate intensity), innovation (β = 0.462 very intense), and intrinsic motivation (β = 0.117 low intensity) positively influence entrepreneurial attitude, which in turn has a significant positive effect on the intent to engage in entrepreneurship (β = 0.709 very intense). Furthermore, innovation, extrinsic motivation, family environment, and intrinsic motivation indirectly influence entrepreneurial intent through their impact on attitude. In conclusion, this study provides a deeper understanding of the psychosocial mechanisms that drive female entrepreneurship in the Colombian university context and lays the groundwork for the design of educational programs and public policies that enhance the talent and entrepreneurial aspirations of women in higher education.
Research on the Connection Market Trading Issues of Green Certificates and CCER Based on Contribution Degree and Social Welfare
Authors: Li Yan , Gou Lili , Zhang Haiwen , Guo Jiacheng , Wang Mengyu , Yang Shuxia
Published: December 2024

The connection between the green certificate market and the CCER market can further achieve the dual carbon goals, so it is imperative for the green certificate market, CCER market, and the connection market to coexist. it is necessary to convert green certificates into CCER in the connection market to achieve transactions between the two. The research is aimed at exploring the interface between green certificates and CCER, with the main objective of finding trading mode and trading method to address the interface between the two. This paper firstly proposes a concept of contribution degree that assists fair trading in the market, based on the different ways in which contribution degree is introduced in the trading of green certificates, CCER markets, and connecting markets, and establishes basic trading mode, unilateral contribution trading mode (UCM) and bilateral contribution trading mode. Then, the rolling matching trading model with the goal of maximizing total social welfare, the contribution degree model and the effect test model are established to realize the implementation of the above three modes through different combinations of the models. Finally, the quantitative results are obtained by simulating the trading situation of the three modes, and CCER offset ratio and contribution degree indicator weight are discussed. The results show that it is feasible to build a bridging market between green certificate and CCER by using the contribution degree, in which the UCM is the optimal solution to achieve the dual-carbon goal and market development. The setting of CCER offset ratio can be based on the difference of enterprise types, and the weight of indicators affecting the contribution level should be adjusted with the policy. The research in this paper has the following contributions: (1) a new criterion to satisfy fair trade—contribution degree is proposed to provide ideas for mutual recognition of products in the bridging market, and proposed a contribution degree calculation model for the green certificate and CCER markets, as well as the bridging market; (2) from the perspective of the contradiction between supply and demand in the market and fair trade, different schemes to build a bridging market are given in a fixed context and compared and analyzed; (3) from the market level, the quantitative analysis of the indicator settings affecting emission reduction, providing suggestions for the differentiated evaluation of warrants and the formulation of carbon reduction policies.
Utilizing Large Language Models for Hyper Knowledge Graph Construction in Mine Hoist Fault Analysis
Authors: Shu Xiaoling , Dang Xiaochao , Dong Xiaohui , Li Fenfang
Published: December 2024

The rapid development of artificial intelligence technology is driving the intelligentization process across various fields, particularly in knowledge graph construction, where significant achievements have been made. However, research on hyper-relational knowledge graphs in the industrial domain remains relatively weak. Traditional construction methods suffer from low automation, high cost, and poor reproducibility and portability. To address these challenges, this paper proposes an optimized construction process for a hyper-relational knowledge graph for mine hoist faults based on large language models. This process leverages the strengths of large language models and the logical connections of fault knowledge, employing GPT’s powerful reasoning abilities. A combined strategy of template-based and template-free prompts is designed to generate fault entities and relationships. To address potential data incompleteness caused by prompt engineering, link prediction is used to optimize the initial data generated by GPT o1-preview. We integrated the graph’s topological structure with domain-specific logical rules and applied the Variational EM algorithm for alternating optimization while also incorporating text embeddings to comprehensively enhance data optimization. Experimental results show that compared to the unoptimized MHSD, the optimized MHSD achieved a 0.008 improvement in MRR. Additionally, compared to the latest KICGPT, the optimized MHSD showed a 0.002 improvement in MRR. Finally, the optimized data were successfully imported into Neo4j for visualization.
Load Forecasting for Commercial Buildings Using BiLSTM–Transformer Network and Cyber–Physical Cognitive Control Systems
Authors: Xiong Xiong , Huang Zicheng , Chen Yilin , Sun Jian
Published: December 2024

With the widespread adoption of electric vehicles (EVs), their charging and discharging schedules pose new challenges for real-time load forecasting in commercial buildings. This study proposes a prediction model based on the integration of bidirectional long short-term memory (BiLSTM) networks and Transformer architecture, along with the introduction of a cognitive control system and cyber–physical systems (CPS) to address issues such as data loss and excessive computation time during the forecasting process. The BiLSTM–Transformer model significantly improves load-forecasting accuracy and real-time performance by combining time-series modeling with global feature extraction capabilities. Additionally, the cognitive control system includes user-aware cognitive control (UACC) and Microgrid Control Center Cognitive Control (MACC). UACC quantifies information gaps in real time and adaptively adjusts strategies during communication instability, while MACC employs Q-learning algorithms to evaluate the impact of data loss on scheduling and optimize power resource allocation. The synergy between these mechanisms ensures system stability and predictive performance in scenarios involving data loss or communication disruptions. Experimental results demonstrate that the model achieves outstanding predictive accuracy under complete data conditions and significantly reduces errors in scenarios with data loss, validating its superior accuracy and robustness. This provides reliable support for load forecasting in commercial buildings.
Mixed Marginal Rates of Substitution for Analyzing Banking Efficiency Using a Linear Programming Approach
Authors: Masrouri Simin , Amirmohammadi Homeira , Dabiri Arman
Published: December 2024

This paper introduces a Mixed Marginal Rates of Substitution (MMRS) approach for analyzing banking efficiency within a linear programming framework. For this purpose, a linear programming model is proposed that efficiently calculates Marginal Rates, revealing how partial input changes affect multiple outputs simultaneously. Applying this methodology to data from the Taiwanese banking system spanning 2010 to 2020 reveals diverse efficiency patterns across financial institutions. The analysis demonstrates that while increased financial inputs generally improve outputs, individual banks exhibit varying sensitivity to these changes. Some institutions were resilient to input fluctuations, while others showed marked sensitivity, particularly in loans and investments. As a result, the proposed MMRS framework provides a robust tool for analyzing indicator relationships within efficient units, offering valuable insights for tailored management strategies and policy formulation in the banking sector. The findings emphasize the importance of institution-specific approaches to enhancing banking efficiency and managing risk effectively, contributing to a more nuanced understanding of economic outcomes in the financial industry. Additionally, symmetry concepts in input–output relationships are suggested as a potential area for future research, offering a possible framework for identifying balanced efficiency patterns among banks.
Computer Vision Technology for Short Fiber Segmentation and Measurement in Scanning Electron Microscopy Images
Authors: Kurkin Evgenii , Minaev Evgenii , Sedelnikov Andrey , Pioquinto Jose Gabriel Quijada , Chertykovtseva Vladislava , Gavrilov Andrey
Published: December 2024

Computer vision technology for the automatic recognition and geometric characterization of carbon and glass fibers in scanning electron microscopy images is proposed. The proposed pipeline, combining the SAM model and DeepLabV3+, provides the generalizability and accuracy of the foundational SAM model and the ability to quickly train on a small amount of data via the DeepLabV3+ model. The pipeline was trained several times more rapidly with lower requirements for computing resources than fine-tuning the SAM model, with comparable inference time. On the basis of the pipeline, an end-to-end technology for processing images of electron microscopic fibers was developed, the input of which is images with metadata and the output of which is statistics on the distribution of the geometric characteristics of the fibers. This innovation is of great practical importance for modeling the physical characteristics of materials. This paper proposes a few-shot training procedure for the DeepLabV3+/SAM pipeline, combining the training of the DeepLabV3+ model weights and the SAM model parameters. It allows effective training of the pipeline using only 37 real labeled images. The pipeline was then adapted to a new type of fiber and background using 15 additional real labeled images. This article also proposes a method for generating synthetic data for training neural network models, which improves the quality of segmentation by the IoU and PixAcc metrics from 0.943 and 0.949 to 0.953 and 0.959, i.e., by 1% on average. The developed pipeline significantly reduces the time required to evaluate fiber length in scanning electron microscope images.
Deep Neural Networks for Estimating Regularization Parameter in Sparse Time–Frequency Reconstruction
Authors: Jurdana Vedran
Published: December 2024

Time–frequency distributions (TFDs) are crucial for analyzing non-stationary signals. Compressive sensing (CS) in the ambiguity domain offers an approach for TFD reconstruction with high performance, but selecting the optimal regularization parameter for various signals remains challenging. Traditional methods for parameter selection, including manual and experimental approaches, as well as existing optimization procedures, can be imprecise and time-consuming. This study introduces a novel approach using deep neural networks (DNNs) to predict regularization parameters based on Wigner–Ville distributions (WVDs). The proposed DNN is trained on a comprehensive dataset of synthetic signals featuring multiple linear and quadratic frequency-modulated components, with variations in component amplitudes and random positions, ensuring wide applicability and robustness. By utilizing DNNs, end-users need only provide the signal’s WVD, eliminating the need for manual parameter selection and lengthy optimization procedures. Comparisons between the reconstructed TFDs using the proposed DNN-based approach and existing optimization methods highlight significant improvements in both reconstruction performance and execution time. The effectiveness of this methodology is validated on noisy synthetic and real-world signals, emphasizing the potential of DNNs to automate regularization parameter determination for CS-based TFD reconstruction in diverse signal environments.
Evolving Landscape of Sickle Cell Anemia Management in Africa: A Critical Review
Authors: Musuka Hazel W. , Iradukunda Patrick Gad , Mano Oscar , Saramba Eric , Gashema Pierre , Moyo Enos , Dzinamarira Tafadzwa
Published: December 2024

Sickle cell disease (SCD) is a prevalent inherited blood disorder, particularly affecting populations in Africa. This review examined the disease’s burden, its diverse clinical presentations, and the challenges associated with its management in African settings. Africa bears a significant burden of SCD, with prevalence varying across countries and age groups. Newborn screening programs have highlighted the high prevalence of SCD at birth, emphasizing the need for early diagnosis and intervention. The clinical manifestations of SCD in Africa are multifaceted, encompassing acute complications like vaso-occlusive crises, acute chest syndrome, and stroke, as well as chronic complications such as organ damage and leg ulcers. Biological factors, including fetal hemoglobin levels, and demographic factors, like age and sex, influence disease severity and outcomes. The management of SCD in Africa faces numerous challenges. Limited access to resources, including diagnostic tools, medications, and trained healthcare professionals, hinders optimal care. The high cost of advanced therapies further restricts patient access. Cultural stigma and a lack of awareness create additional barriers to effective management. To address these challenges, early diagnosis through newborn screening programs and point-of-care testing is crucial. Comprehensive care models, including hydroxyurea therapy, pain management, and patient education, are essential for improving outcomes. Collaboration with international networks and leveraging local resources can enhance the sustainability of SCD programs. In conclusion, SCD significantly impacts African populations. Overcoming the challenges associated with its management requires addressing resource limitations, affordability issues, and cultural barriers. Early diagnosis, comprehensive care models, and ongoing research focused on affordability and accessibility are crucial for improving the lives of individuals living with SCD in Africa.
A Retrospective Study of Urinary Schistosomiasis in the Eastern Cape Province, South Africa
Authors: Abaver Dominic Targema
Published: December 2024

Schistosomiasis is caused by infection with trematode flukes of the genus Schistosoma. More than 700 million people worldwide are estimated to be susceptible to infection. In sub-Saharan Africa, schistosomiasis is the second most widespread neglected tropical disease after malaria. This retrospective investigation evaluated the incidence and impacts of schistosomiasis on communities across three major districts of the Eastern Cape province in South Africa using a cross-sectional retrospective observational analysis of secondary data from patients with microscopically confirmed schistosomiasis between 2019 and 2020. This study focused upon both rural and semi-urban areas, including Bizana, Butterworth, Centane, Elliotdale, Flagstaff, Idutywa, Lusikisiki, Libode, Mqanduli, Port St. Johns, Willowvale, and Mthatha. Data were obtained from three districts—Alfred Nzo, Amatole, and OR Tambo—covering both rural and semi-urban regions. This study included patients of all ages who submitted urine samples for schistosomiasis testing in the specified districts. A simple random sampling method was used to select 337 clinical records from the National Health Laboratory Service (NHLS) of Mthatha. Hospital records from the NHLS Microbiology Department of Mthatha were analyzed. St Barnabas Laboratory had the highest frequency of cases (34.1%), followed by Greenville Depot (17.8%) and Willowvale Laboratory (11.3%). Most cases were in the 10–19 age group (63.4%), followed by those under 10 years of age (24.9%). Male patients constituted 76.4% of the cases, while female patients accounted for 23.6%. Viable ova were observed in 98.2% of the samples. This study highlights a significant prevalence of schistosomiasis in the Eastern Cape province, with a higher incidence in rural areas and among males aged 10–19. These findings underscore the need for targeted public health interventions and continuous monitoring to control and prevent schistosomiasis in this region.
Premerger Phenomena in Neutron Star Binary Coalescences
Authors: Suvorov Arthur G. , Kuan Hao-Jui , Kokkotas Kostas D.
Published: December 2024

A variety of high-energy events can take place in the seconds leading up to a binary neutron star merger. Mechanisms involving tidal resonances, electrodynamic interactions, or shocks in mass-loaded wakes have been proposed as instigators of these precursors. With a view of gravitational-wave and multimessenger astrophysics, more broadly, premerger observations and theory are reviewed, emphasising how gamma-ray precursors and dynamical tides can constrain the neutron-star equation of state, thermodynamic microphysics, and evolutionary pathways. Connections to post-merger phenomena, notably gamma-ray bursts, are discussed together with how magnetic fields, spin and misalignment, crustal elasticity, and stratification gradients impact observables.
Recognising Tactual Aspects of the Urban Environment Through Walking—A Critical Discussion of Touch-, Tactile-, and Haptic-Based Sensewalk Methodologies
Authors: Parker Murray , Spennemann Dirk H. R. , Bond Jennifer
Published: December 2024

Tactile aspects of the urban environment may be recognised through various capacities of human sensation, including cutaneous, kinaesthetic, and proprioceptive awareness. Haptic attributes often need intentional engagement for ultimate experience and information provision, but it is exactly this imprecision that initiates challenges when dealing with tactility in a space. Whilst tactile components can be experienced when stationary or through walking an identified space (a sensewalk), there is no standard method for tactile sensewalk implementation. We therefore critically discuss common methodologies, outlining benefits, disadvantages, and suitability for each identified sensewalk method. We find that immersive sensory enquiry approaches are well suited to reveal past experience of sensory perceptions, deeper understandings and rediscovery of places; the recording of dimensional measurements is appropriate when determining linkages and correlations between physical and perceptual dimensions; and interviews and post-data collection reflections provide an opportunity to explore sensory characteristics and experience, and for realisation of local heritages. Furthermore, unusual methods such as the visualisation of urban textures and multimodal post-processing techniques are identified and discussed.
Exploring the Impact of Spatial Arrangements on BREEAM Outstanding Projects in London, UK
Authors: Butt Anosh Nadeem , Rigoni Carolina
Published: December 2024

The spatial configuration of urban areas impacts environmental sustainability, social equity, and economic and social resilience. This study examines the intricate relationship between spatial arrangements and the planning and design of BREEAM Outstanding projects in London, UK. It analyses the relationship between urban morphology and the effectiveness of sustainable building practices and contributes to the broader objectives of urban sustainability. This research focuses on London, UK—a city renowned for its complex urban fabric and architectural heterogeneity—using a multi-case study approach to dissect the elements that facilitate the development of BREEAM Outstanding projects. This study analyses key spatial characteristics such as land use diversity, subway network analysis, and street network analysis using betweenness centrality of edges and node degrees. These factors are considered due to their impact on energy performance, carbon emissions, and social sustainability metrics. Furthermore, this research explores how urban design strategies, such as enhanced walkability and mixed-use development, reinforce the success of BREEAM-certified Outstanding-rated projects. The findings of this investigation reveal a correlation between urban environments and the development of BREEAM Outstanding-rated projects in London. By aligning the spatial organisation of urban form with BREEAM principles, urban planners, policymakers, and architects can facilitate the creation of cities that are environmentally sustainable, socially inclusive, and economically prosperous. The research offers substantive insights and actionable recommendations for future urban development, advocating for a comprehensive and interdisciplinary approach to sustainable city planning and design. The spatial arrangement of urban form impacts the planning and design of BREEAM Outstanding projects. Findings from current and future research will be used to investigate the connections between spatial arrangement and various categories in BREEAM and how they can influence future sustainable urban environments to set a benchmark for sustainability for contributing to a more equitable urban future.
Scatter Search Algorithm for a Waste Collection Problem in an Argentine Case Study
Authors: Rossit Diego , González Landín Begoña , Frutos Mariano , Méndez Babey Máximo
Published: December 2024

Increasing urbanization and rising consumption rates are putting pressure on urban systems to efficiently manage Municipal Solid Waste (MSW). Waste collection, in particular, is one of the most challenging aspects of MSW management. Therefore, developing computer-aided tools to support decision-makers is crucial. In this paper, a Scatter Search algorithm is proposed to address the waste collection problem. The literature is relatively scarce in applying this algorithm, which has proven to be efficient in other routing problems, to real waste management problems. Results from real-world instances of an Argentine city demonstrate that the algorithm is competitive, obtaining, in the case of small instances, the same outcomes as those of an exact solver enhanced by valid inequalities, although requiring more computational time (as expected), and significantly improving the results of the latter for the case of larger instances, now requiring much less computational time. Thus, Scatter Search proves to be a competitive algorithm for addressing waste collection problems.
Detection of Hepatitis C Virus Infection from Patient Sera in Cell Culture Using Semi-Automated Image Analysis
Authors: Schäfer Noemi , Rothhaar Paul , Heuss Christian , Neumann-Haefelin Christoph , Thimme Robert , Dietz Julia , Sarrazin Christoph , Schnitzler Paul , Merle Uta , Pérez-del-Pulgar Sofía , Laketa Vibor , Lohmann Volker
Published: December 2024

The study of hepatitis C virus (HCV) replication in cell culture is mainly based on cloned viral isolates requiring adaptation for efficient replication in Huh7 hepatoma cells. The analysis of wild-type (WT) isolates was enabled by the expression of SEC14L2 and by inhibitors targeting deleterious host factors. Here, we aimed to optimize cell culture models to allow infection with HCV from patient sera. We used Huh7-Lunet cells ectopically expressing SEC14L2, CD81, and a GFP reporter with nuclear translocation upon cleavage by the HCV protease to study HCV replication, combined with a drug-based regimen for stimulation of non-modified wild-type isolates. RT-qPCR-based quantification of HCV infections using patient sera suffered from a high background in the daclatasvir-treated controls. We therefore established an automated image analysis pipeline based on imaging of whole wells and iterative training of a machine learning tool, using nuclear GFP localization as a readout for HCV infection. Upon visual validation of hits assigned by the automated image analysis, the method revealed no background in daclatasvir-treated samples. Thereby, infection events were found for 15 of 34 high titer HCV genotype (gt) 1b sera, revealing a significant correlation between serum titer and successful infection. We further show that transfection of viral RNA extracted from sera can be used in this model as well, albeit with so far limited efficiency. Overall, we generated a robust serum infection assay for gt1b isolates using semi-automated image analysis, which was superior to conventional RT-qPCR-based quantification of viral genomes.
RSV Vaccine with Nanoparticle-Based Poly-Sorbitol Transporter (PST) Adjuvant Improves Respiratory Protection Against RSV Through Inducing Both Systemic and Mucosal Humoral Immunity
Authors: Jung Seong-Mook , Kim Soo Ji , Park Young Chae , Seo Eun Sang , Kim Cheol Gyun , Kim Taewoo , Lee Sumin , Cho Eunjin , Chang Jun , Yun Cheol-Heui , Shim Byoung-Shik , Cheon In Su , Son Young Min
Published: December 2024

Background/Objectives: Respiratory syncytial virus (RSV) causes symptoms similar to a mild cold for adults, but in case of infants, it causes bronchitis and/or pneumonia, and in some cases, mortality. Mucosal immunity within the respiratory tract includes tissue-resident memory T (TRM) cells and tissue-resident memory B (BRM) cells, which provides rapid and efficient protection against RSV re-infection. Therefore, vaccine strategies should aim to generate mucosal immune responses. However, the interactions between RSV vaccines and mucosal immune responses within the respiratory tract are poorly understood. We evaluated a mucosal immune system following immunization by RSV vaccine with poly-sorbitol transporter (RSV-PST), a nanoparticle adjuvant. Methods: We intranasally immunized the RSV-PST and identified the systemic and mucosal immune responses. Furthermore, we challenged with RSV A2 strain after immunization and investigated the protective effects. Results: Consequently, antigen-specific CD8+ TRM cells were markedly elevated in the lung parenchyma, yet exhibited impaired cytokine expression. In contrast, humoral immunity, with systemic antibody production from serum, but not in the respiratory tract, was significantly increased by RSV-PST immunization. Interestingly, the production of respiratory mucosal antigen-specific IgG after RSV A2 challenge dramatically increased in the bronchoalveolar lavage fluid (BALF) of the RSV-PST immunized group in the presence of FTY720, and the lung-infected RSV titer was significantly lower in this group. Furthermore, after RSV A2 challenge, CD69+ IgG+ BRM cells were significantly increased in lung tissues in the RSV-PST group. Conclusions: The RSV-PST vaccine has protective effects against RSV infection by promoting both systemic and local humoral immunity rather than cellular immunity.
A Preclinical Immunogenicity Study of the Recombinant Human Papillomavirus Nine-Valent Virus-like Particle Vaccine
Authors: Xu Dan , Li Jia-Dai , An Jiao , Ma Xin-Xing , Wang Xiao-Liang , Zhou Zheng , Liu Hai-Ping , Diao Mei-Jun , Jiang Yuan-Xiang , Zhou Ling-Yun , Tong Xin , Zhou Chen-Liang
Published: December 2024

Background: Cervical cancer is associated with persistent infection of high-risk human papillomaviruses (HPVs). Prophylactic HPV vaccines have been recommended and have significant efficacy in preventing cervical cancer. Multivalent HPV vaccines have a better preventative effect on HPV-related diseases. However, there is currently only one nine-valent HPV vaccine on the market: Gardasil® 9. The development of new HPV vaccines is still urgent in order to achieve the goal of eliminating cervical cancer as proposed by the WHO. Methods: In this study, we developed a nine-valent recombinant HPV virus-like particle (VLP) vaccine (HPV-9 vaccine) containing HPV type 6, 11, 16, 18, 31, 33, 45, 52, and 58 antigens, with an adjuvant of aluminum phosphate (AlPO4). The type-specific L1 proteins were recombinantly expressed using Pichia pastoris, followed by self-assembly into VLPs. Immunogenicity studies of the HPV-9 vaccine were performed using rodents (mice and rats) and non-human primates (macaques) as animal models. Results: Immunogenicity studies showed that the HPV-9 vaccine is able to elicit a robust and long-lasting neutralizing antibody response in rodents (mice and rats) and non-human primates (cynomolgus macaque) models. The HPV-9 vaccine shows immunogenicity comparable to that of Walrinvax® and Gardasil® 9. Conclusions: In summary, this study provides a comprehensive investigation of the immunogenicity of the HPV-9 vaccine, including its immune persistence. These findings, derived from using models of diverse animal species, contribute valuable insights into the potential efficacy of the vaccine candidate in clinical settings.