bluuebunny/crossref_metadata_2025 huggingface dataset:
Study on the Design Method of High-Resolution Volume-Phase Holographic Gratings
Authors: Wang Shuo , Dai Lei , Lin Chao , Wang Long , Ji Zhenhua , Fu Yang , Gao Quyouyang , Zheng Yuquan
Published: October 2024

Volume-phase holographic gratings are suitable for use in greenhouse gas detection imaging spectrometers, enabling the detection instruments to achieve high spectral resolution, high signal-to-noise ratios, and high operational efficiency. However, when utilized in the infrared wavelength band with high dispersion requirements, gratings struggle to meet the demands for low polarization sensitivity due to changes in diffraction performance caused by phase delays in the incidence of light waves with distinct polarization states, and current methods for designing bulk-phase holographic gratings require a large number of calculations that complicate the balance of diffraction properties. To overcome this problem, a design method for transmissive bulk-phase holographic gratings is proposed in this study. The proposed method combines two diffraction theories (namely, Kogelnik coupled-wave theory and rigorous coupled-wave theory) and establishes a parameter optimization sequence based on the influence of design parameters on diffraction characteristics. Kogelnik coupled-wave theory is employed to establish the initial Bragg angle range, ensuring that the diffraction efficiency and phase delay of the grating thickness curve meet the requirements for incident light waves in various polarization states. Utilizing rigorous coupled-wave theory, we optimize grating settings based on criteria such as a center wavelength diffraction efficiency greater than 95%, polarization sensitivity less than 10%, maximum bandwidth, and spectral diffraction efficiency exceeding 80%. The ideal grating parameters are ultimately determined, and the manufacturing tolerances for various grating parameters are analyzed. The design results show that the grating stripe frequency is 1067 lines per millimeter, and the diffraction efficiencies of TE and TM waves are 96% and 99.89%, respectively. The diffraction efficiency of unpolarized light is more than 88% over the whole spectral range with an average efficiency of 94.49%, an effective bandwidth of 32 nm, and a polarization sensitivity of less than 7%. These characteristics meet the performance requirements for dispersive elements based on greenhouse gas detection, the spectral resolution of the detection instrument is up to 0.1 nm, and the signal-to-noise ratio and working efficiency are improved by increasing the transmittance of the instrument.
Systematic Assessment of Practical Challenges in Rural Domestic Sewage Treatment in China: Examining Treatment Models, Ecological Risks, and Management Dilemmas
Authors: Gao Yi , Wei Luoyu , Ge Yihong , Xiong Xia , Liu Ming , Jiang Xiaomei , Pan Ke , Guo Xiaobo , Lei Yunhui , Yang Nuan
Published: October 2024

The treatment and purification of rural domestic sewage (RDS) is a pivotal focus in enhancing the living environment in rural areas. Since 2008, special funds for comprehensive rural environmental improvement have been established by the Chinese government. Numerous projects have been implemented to treat RDS, resulting in a significant enhancement of China’s rural sewage treatment ratio. However, current discussions often focus on technical investigations, process selection, and operation modes pertaining to urban sewage while overlooking the unique challenges posed by decentralized sewage treatment facilities in rural areas. This work aims to provide technical support for addressing rural sewage treatment and purification in China through an analysis of limitations associated with prevailing mainstream sewage treatment and separation technologies, ecological risks arising from new pollutants present in domestic wastewater, and subsequent management difficulties.
Natural Gas Consumption Forecasting Based on Homoheterogeneous Stacking Ensemble Learning
Authors: Wang Qingqing , Luo Zhengshan , Li Pengfei
Published: October 2024

Natural gas consumption is an important indicator of energy utilization and demand, and its scientific and high-accuracy prediction plays a key role in energy policy formulation. With the development of deep neural networks and ensemble learning, a homoheterogeneous stacking ensemble learning method is proposed for natural gas consumption forecasting. Firstly, to obtain the potential data characteristics, a nonlinear concave and convex transformation-based data dimension enhancement method is designed. Then, with the aid of a stacking ensemble learning framework, the multiscale autoregressive integrated moving average (ARIMA) and high-order fuzzy cognitive map (HFCM) methods are chosen as the base learner models, while the meta learner model is constructed via a well-designed deep neural network with long short-term memory (LSTM) cells. Finally, with the natural gas energy consumption data of national and 30 provinces (where the data of Xizang are unavailable) of China from 2000 to 2019, the numerical results show the proposed algorithm has a better forecasting performance in accuracy, robustness to noise, and sensitivity to data variations than the seven compared traditional and ensemble methods, and the corresponding model applicability rate could achieve more than 90%.
Global Climate Change, Mental Health, and Socio-Economic Stressors: Toward Sustainable Interventions across Regions
Authors: Nie Peng , Zhao Ke , Ma Dawei , Liu Huo , Amin Saqib , Yasin Iftikhar
Published: October 2024

Global climate change’s pervasive impacts extend beyond the environment, significantly affecting mental health across diverse regions. This study offers a comprehensive multi-regional analysis spanning Asia, Africa, Oceania, Europe, and the Americas, addressing three critical gaps in existing research: (i) the necessity of a global scope given climate change’s widespread impact, (ii) the under-researched mental health dimension compared to general health effects, and (iii) the integration of climate and mental health data. Using data from 1970 to 2020, we found a strong correlation between climate change and rising mental disorders globally. Regional patterns emerged, with Asia, Africa, and Oceania showing broader associations with various mental health issues, while Europe and the Americas saw increases in anxiety and depression. This study contributes to a more comprehensive understanding of the interconnectedness between climate change, mental health, and sustainability. By addressing the mental health impacts of climate change, we can identify sustainable solutions that promote both environmental well-being and human well-being. Our findings highlight the urgent need for global action to mitigate climate change’s mental health effects and provide insights for tailored interventions and public health strategies. Additionally, socio-economic factors like unemployment, urbanisation, GDP growth, and globalisation are incorporated to explore the intricate interplay between climate change, mental health, and societal contexts, offering a clearer understanding of the mechanisms at play.
Urban Flooding Disaster Risk Assessment Utilizing the MaxEnt Model and Game Theory: A Case Study of Changchun, China
Authors: Huang Fanfan , Zhu Dan , Zhang Yichen , Zhang Jiquan , Wang Ning , Dong Zhennan
Published: October 2024

This research employs the maximum entropy (MaxEnt) model alongside game theory, integrated with an extensive framework of natural disaster risk management theory, to conduct a thorough analysis of the indicator factors related to urban flooding. This study conducts an assessment of the risks associated with urban flooding disasters using Changchun city as a case study. The validation outcomes pertaining to urban flooding hotspots reveal that 88.66% of the identified flooding sites are situated within areas classified as high-risk and very high-risk. This finding is considered to be more reliable and justifiable when contrasted with the 77.73% assessment results derived from the MaxEnt model. Utilizing the methodology of exploratory spatial data analysis (ESDA), this study applies both global and local spatial autocorrelation to investigate the disparities in the spatial patterns of flood risk within Changchun. This study concludes that urban flooding occurs primarily in the city center of Changchun and shows a significant agglomeration effect. The region is economically developed, with a high concentration of buildings and a high percentage of impervious surfaces. The Receiver Operating Characteristic (ROC) curve demonstrates that the MaxEnt model achieves an accuracy of 90.3%. On this basis, the contribution of each indicator is analyzed and ranked using the MaxEnt model. The primary determinants affecting urban flooding in Changchun are identified as impervious surfaces, population density, drainage density, maximum daily precipitation, and the Normalized Difference Vegetation Index (NDVI), with respective contributions of 20.6%, 18.1%, 13.1%, 9.6%, and 8.5%. This research offers a scientific basis for solving the urban flooding problem in Changchun city, as well as a theoretical reference for early warnings for urban disaster, and is conducive to the realization of sustainable urban development.
The Impact of Rapid Urbanization on the Efficiency of Industrial Green Water Use in Urban Agglomerations around Poyang Lake
Authors: Li Huirong , Zhao Xiaoke , Ding Xuhui , Zhang Runze
Published: October 2024

The construction of urban agglomerations around Poyang Lake is an important starting point of the strategy for the improvement of central China, but the spatial agglomeration of industry and population brings great pressure to the ecological environment. It is of great practical value to explore the impact of rapid urbanization on the water use efficiency of important ecological functional areas. Considering the undesired output of industrial production, this paper adopts the SE-SBM model to measure industrial green water use efficiency, comprehensively considers different aspects of urbanization of the urban agglomeration around Poyang Lake, empirically tests its inhibiting or boosting effect on industrial green water use and explores its spatial spillover effect with the help of a spatial metrology model. The results show that (1) the industrial green water use efficiency of urban agglomerations shows an overall upward trend, and the efficiency value of central cities is significantly higher than that of non-central cities and continues to show a state of diffusion; (2) social urbanization, environmental urbanization, and balanced urbanization can significantly improve industrial green water use efficiency, while industrial urbanization or industrialization inhibits the improvement in water use efficiency; (3) considering the spatial spillover factor, there are significant positive local effects between population urbanization and balanced urbanization, and significant positive spatial spillover effects between industrial urbanization and environmental urbanization; (4) the original model can pass the significance test by replacing the output-oriented water use efficiency with the input-oriented or non-oriented water use efficiency; the study area is extended to Jiangxi Province, and the impact of urbanization on industrial water use efficiency is basically consistent. We should adhere to the new type of urbanization that improves well-being and is friendly to the environment, rationally plan the industrial spatial pattern of urban agglomerations, adhere to the ecological and environmental threshold on undertaking industrial transfer, and promote the flow and sharing of green production factors.
Empowering Communities to Act for a Change: A Review of the Community Empowerment Programs towards Sustainability and Resilience
Authors: Dushkova Diana , Ivlieva Olga
Published: October 2024

At the global level, significant efforts have been made to address societal challenges and improve the lives of people and restore the planet’s ecosystems through sustainability and resilience programs. These programs, however, tend to be driven by governments, private sectors, and financial institutions, and therefore often lack a process of empowerment to ensure that the local communities can participate actively in co-designing and implementing these programs. More knowledge is needed on how to develop such programs and how the process of empowerment can be organized so that it supports in the long run sustainability transformation. Against this background, the paper explores the role of community empowerment programs as a critical tool for sustainability management strategies and practices. A semi-systematic review of 21 community empowerment programs for sustainability and resilience is conducted. The analysis reveals that the programs mostly aimed to address challenges such as the lack of education and capacity, limited access to basic services and resources, and poor governance and management. The programs initiators involve a diverse set of actors, especially through established partnerships and networks. Most of the programs address the specific needs of vulnerable or marginalized groups or communities. The structure of the programs typically follows a phased methodological approach, beginning with awareness-raising and problem identification, followed by capacity building that allows for making decisions collaboratively and for co-creating innovative solutions based on local knowledge and values. Also, monitoring and evaluation of transformative impact are mentioned as important structural elements. Specifically, the analysis highlights four main focus areas of empowerment: (1) capacity building, (2) self-reliance, control, ownership, responsibility, and independence, (3) participation, engagement, and collective action, and (4) integration of local knowledge and values. However, there is no one-size-fits-all approach to such programs. Instead, successful empowerment programs towards sustainability depend on a deep understanding of local contexts and the ability to tailor strategies to meet specific community needs. The review also identified knowledge gaps that require further investigation to enhance the effectiveness of empowerment programs for both people and nature.
Impact of Enterprise Supply Chain Digitalization on Cost of Debt: A Four-Flows Perspective Analysis Using Explainable Machine Learning Methodology
Authors: Tang Hongqin , Zhu Jianping , Li Nan , Wu Weipeng
Published: October 2024

Rising costs, complex supply chain management, and stringent regulations have created significant financial burdens on business sustainability, calling for new and rapid strategies to help enterprises transform. Supply chain digitalization (SCD) has emerged as a promising approach in the context of digitalization and globalization, with the potential to reduce an enterprise’s debt costs. Developing a strategic framework for SCD that effectively reduces the cost of debt (CoD) has become a key academic challenge, critical for ensuring business sustainability. To this end, under the perspective of four flows, SCD is deconstructed into four distinct features: logistics flow digitalization (LFD), product flow digitalization (PFD), information flow digitalization (IFD), and capital flow digitalization (CFD). To precisely measure the four SCD features and the dependent variable, COD, publicly available data from Chinese listed manufacturing enterprises such as annual report texts and financial statement data are collected, and various data mining technologies are also used to conduct data measurement and data processing. To comprehensively investigate the impact pattern of SCD on CoD, we employed the explainable machine learning methodology for data analysis. This methodology involved in-depth data discussions, cross-validation utilizing a series of machine learning models, and the utilization of Shapley additive explanations (SHAP) to explain the results generated by the models. To conduct sensitivity analysis, permutation feature importance (PFI) and partial dependence plots (PDPs) were also incorporated as supplementary explanatory methods, providing additional insights into the model’s explainability. Through the aforementioned research processes, the following findings are obtained: SCD can play a role in reducing CoD, but the effects of different SCD features are not exactly the same. Among the four SCD features, LFD, PFD, and IFD have the potential to significantly reduce CoD, with PFD having the most substantial impact, followed by LFD and IFD. In contrast, CFD has a relatively weak impact, and its role is challenging to discern. These findings provide significant guidance for enterprises in furthering their digitalization and supply chain development, helping them optimize SCD strategies more accurately to reduce CoD.
Geotechnical Characterization of the Magdalena River Subsoil in Magangué, Colombia: A Study Using CPTu and SPT Tests
Authors: Noriega Rafael Florian , Calabokis Oriana Palma , Baldovino Jair Arrieta
Published: October 2024

This study employs Cone Penetration Tests (CPTu) and Standard Penetration Tests (SPT) to analyze the geotechnical properties of the Magdalena River’s riverbed and banks. While these methods are standard in soil characterization, this research innovatively combines CPTu’s continuous profiling with SPT’s localized sampling to develop a nuanced stratigraphic model of the subsurface. This integrated approach provides a comprehensive view of the soil conditions, which is crucial for understanding sediment variability and stability along the riverbanks. The findings from this methodological integration enhance our ability to predict soil behavior under dynamic riverine conditions, offering valuable insights for erosion control and sustainable river management. The study underscores the practical benefits of synergizing traditional testing methods to address geotechnical challenges in river environments.
Analysis of Industrial Water Use Efficiency Based on SFA–Tobit Panel Model in China
Authors: Liu Han , Liu Heng , Geng Leihua
Published: October 2024

Over the past two decades, the industrial sector of China has experienced rapid development, which has correspondingly led to a significant increase in water resource consumption. To better understand the dynamics of industrial water use, and formulate appropriate water resource conservation and management policies, it is necessary to evaluate the evolution of industrial water use efficiency and its influencing factors in China. Given the high sensitivity and accuracy of the stochastic frontier analysis (SFA) model for efficiency assessment, the Tobit model is more suitable for regression analyses of truncated data. This study employed the SFA–Tobit panel model to evaluate the industrial water use efficiency of provinces in China from 2003 to 2021. The results indicate that national industrial water use efficiency improved from 0.41 to 0.65 during the study period. All provinces showed significant improvements, with developed provinces exhibiting higher industrial water use efficiency than undeveloped provinces. Regionally, the eastern areas demonstrated superior industrial water use efficiency compared to the western regions, with the central regions having the lowest overall water use efficiency. Moreover, the efficiency gap between regions has been narrowing. The national industrial water-saving potential is estimated at 31.306 billion cubic meters, with Jiangsu province having the highest saving potential at 3.709 billion cubic meters. In comparison, Beijing has the lowest at just 32,000 cubic meters. The Tobit regression results reveal that economic development and technological progress positively contribute to increased industrial water use efficiency. In contrast, water use intensity, openness, and urbanization levels negatively impacted the improvement of industrial water use efficiency. Therefore, it is necessary to increase investment in technological innovation, strictly control industrial water intensity, appropriately balance import and export trade with urbanization levels, and promote sustainable economic development. This study can provide effective support for the subsequent green transformation of China’s industry.
Applicability of a Modified Gash Model for Artificial Forests in the Transitional Zone between the Loess Hilly Region and the Mu Us Sandy Land, China
Authors: Wang Xin , Yang Zhenqi , Guo Jianying , Qin Fucang , Wang Yabo , Ning Jiajun
Published: October 2024

Afforestation in the transitional zone between the loess hilly area and the Mu Us Sandy Land of China has reshaped the landscape and greatly affected eco-hydrological processes. Plantations are crucial for regulating local net rainfall inputs, thus making it necessary to quantify the closure loss of plantation species in drought and semi-arid areas. To quantify and model the canopy interception of these plantations, we conducted rainfall redistribution measurement experiments. Based on this, we used the modified Gash model to simulate their interception losses, and the model applicability across varying rainfall types was further compared and verified. Herein, Caragana korshinskii, Salix psammophila, and Pinus sylvestris plantations in the Kuye River mountain tract were chosen to measure the precipitation distribution from May to October (growing season). The applicability of a modified Gash model for different stands was then evaluated using the assessed data. The results showed that the canopy interception characteristics of each typical plantation were throughfall, interception, and stemflow. The relative error of canopy interception of C. korshinskii simulated by the modified Gash model was 8.79%. The relative error of simulated canopy interception of S. psammophila was 4.19%. The relative error of canopy interception simulation of P. sylvestris was 13.28%, and the modified Gash model had good applicability in the Kuye River Basin. The modified Gash model has the greatest sensitivity to rainfall intensity among the parameters of the C. korshinskii and S. psammophila forest. The sensitivity of P. sylvestris in the modified Gash model is that the canopy cover has the greatest influence, followed by the mean rainfall intensity. Our results provide a scientific basis for the rational use of water resources and vegetation restoration in the transitional zone between the loess hilly region and the Mu Us Sandy Land. This study is of import for the restoration and sustainability of fragile ecosystems in the region.
Dynamic Programming-Based ANFIS Energy Management System for Fuel Cell Hybrid Electric Vehicles
Authors: Gómez-Barroso Álvaro , Alonso Tejeda Asier , Vicente Makazaga Iban , Zulueta Guerrero Ekaitz , Lopez-Guede Jose Manuel
Published: October 2024

Reducing reliance on fossil fuels has driven the development of innovative technologies in recent years due to the increasing levels of greenhouse gases in the atmosphere. Since the automotive industry is one of the main contributors of high CO2 emissions, the introduction of more sustainable solutions in this sector is fundamental. This paper presents a novel energy management system for fuel cell hybrid electric vehicles based on dynamic programming and adaptive neuro fuzzy inference system methodologies to optimize energy distribution between battery and fuel cell, therefore enhancing powertrain efficiency and reducing hydrogen consumption. Three different approaches have been considered for performance assessment through a simulation platform developed in MATLAB/Simulink 2023a. Further validation has been conducted via a rapid control prototyping device, showcasing significant improvements in hydrogen usage and operational efficiency across different drive cycles. Results manifest that the developed controllers successfully replicate the optimal control trajectory, providing a robust and computationally feasible solution for real-world applications. This research highlights the potential of combining advanced control strategies to meet performance and environmental demands of modern heavy-duty vehicles.
Addressing the Difficulties and Opportunities to Bridge the Integration Gaps of Bio-Based Insulation Materials in the European Construction Sector: A Systematic Literature Review
Authors: Zerari Salima , Franchino Rossella , Pisacane Nicola , Llatas Carmen , Soust-Verdaguer Bernardette
Published: October 2024

Bio-based insulation materials (BbIMs) represent a potential alternative to conventional insulations, with their characteristics that favor a negative-carbon built environment. However, their use may face challenges that could prevent them from being used on a large scale in certain countries. The current study aims to provide focused insights into the practical difficulties and market opportunities for the application of BbIMs in Europe through a systematic literature review (SLR). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were used as the basis for the conduct and reporting of this review. A keyword search was performed in Web of Science, Scopus, and ScienceDirect databases to select peer-reviewed English-language articles. HubMeta web tool was used to organize the selection process. The quantitative visualization of the literature was made by the Bibliometrix R package V4.1.4. Data were manually extracted and clustered in an Excel sheet. The review included 28 studies that have revealed interrelated insights. Difficulties range from regulatory and policy limitations and variability in performance, such as microbial growth and inconsistency in the behavior of materials under different conditions, to cost barriers. However, there are promising opportunities, including policy incentives and material performance benefits such as improved energy efficiency and indoor air quality. This research contributes to the literature by providing focused insights into the practical difficulties and market opportunities for the application of BbIMs in Europe. Research gaps and future perspectives point to the need for more field validation experiments, exploration of alternative production processes, and expanding life cycle assessment scopes to optimize their integration and performance. Stakeholder perceptions were conducted with a small sample in some countries, so insights from stakeholders are needed to confirm or correct current findings.
Cross-Sectoral and Multilevel Dimensions of Risk and Resilience Management in Urban Areas Enabled by Geospatial Data Processing
Authors: Menoni Scira , Galderisi Adriana , Carrion Daniela , Gerosa Chiara
Published: October 2024

The growing complexity of cities and the unprecedented pace of urbanisation create exposure and vulnerabilities to extreme events and crises that are difficult to manage and plan for as widely acknowledged by the existing literature. In this paper, three main challenges to be tackled are identified based on the selected literature according to the interpretation of the authors based on extended research in the field. Those challenges relate to the multi-risk environment characterising many contemporary cities, the need to overcome sectoral approaches towards increased alignment of emergency and spatial planning at different scales, and the opportunities that derive from integrated risk and resilience management. Such challenges are evidenced in the Pozzuoli case study, a densely inhabited municipality of the metropolitan city of Naples, placed into a volcanic caldera, that has been analysed in the light of the above challenges for an extended period of time of about fifty years. The in-depth assessment of the quality of urban development has been enabled by geospatial data management. Advanced geospatial information systems are not only instrumental in depicting the history of urban development in the period of consideration but also as an enabler to tackle the above-mentioned challenges. In fact, such systems permit a much more dynamic and updatable assessment of multirisk conditions and provide the basis for shared knowledge among the large number of stakeholders that are responsible for different sectoral and comprehensive urban and risk-related plans.
Optimising the Particulate Emission Characteristics of a Dual-Fuel Spark Ignition Engine by Changing the Gasoline Direct Injection Strategy
Authors: Li Xiang , Liu Siyue , Li Wanzhong , Pei Yiqiang , Zhang Xuewen , Ni Peiyong , Peng Zhijun , Wang Chenxi
Published: October 2024

In the current global scenario, it is essential to find more effective and practical solutions to mitigate the problem of particulate emissions from vehicles. In this research, particulate emission characteristics with changing GDI pressure or applying a split GDI strategy with different second injection timings were initially explored in a Dual-Fuel Spark Ignition (DFSI) engine, which employs Ethanol Port Injection (EPI) plus Gasoline Direct Injection (GDI). The experimental results show that by increasing GDI pressure (PGDI) from 5.5 MPa to 18 MPa, ignition delay (θF) shows a small decrease of 0.68 degrees. The parameters, such as maximum in-cylinder temperature (TMI) and exhaust gas temperature (TEG), each increase by 53.75 K and 13.84 K. An apparent reduction of 59.5% and 36.26% was achieved for the concentrations of particulate number (NP) and particulate mass (MP), respectively. Particulate emissions are effectively reduced by a split GDI strategy with an appropriate range of second injection timing (tGDI2). Under tGDI2 = −260 °CA, NP and MP concentrations exhibit a relatively lower level. However, by delaying tGDI2 from −260 °CA to −140 °CA, there is an increase of more than 60% in NP concentration. The research findings help offer new and valuable insights into optimising particulate number and mass emissions from DFSI engines. Moreover, the findings could contribute novel and valuable insights into the optimisation of particulate emission characteristics in DFSI engines.
Sustainable Entrepreneurial Process in the Deep-Tech Industry
Authors: Nguyen Ngoc Thu Hang , Kowalski Arkadiusz Michał , Dzienis Anna Maria
Published: October 2024

To date, deep-tech entrepreneurship and sustainable entrepreneurship are both attracting the interest of researchers. Indeed, deep-tech is being employed to address future sustainable challenges in the present. Nevertheless, the process of deep-tech startups is filled with distinct obstacles. These types of startups may necessitate a more targeted sustainable entrepreneurial process and specialised knowledge compared to conventional technological startups or general sustainable entrepreneurial processes. Currently, most publications discuss either a sustainable entrepreneurial process or conventional technical startups. Therefore, this article aims to investigate the process of sustainable entrepreneurs as they transition from startup ventures to scaleup enterprises within the context of the deep-tech industry. Based on previous sustainable entrepreneurial processes, a multiple case study was conducted within six deep-tech startups, using a narrative progress research strategy, to find the sustainable process pattern. A conceptual model of a sustainable entrepreneurial process in deep-tech industry is established, including five phases with six activities: (i) Sustainable idea definition; (ii) Sustainable opportunity recognition and evaluation; (iii) Venture launch; (iv.i) Sustainable products/services development; (iv.ii) New sustainable enterprise financing; and (v) Sustainable enterprise scaleup. This process enables sustainable deep-tech entrepreneurs, policymakers, and financial investors to necessarily have an overview of the key entrepreneurial stages to navigate from the startup to the scaleup process.
Analysis and Suppression of Oscillations in Doubly Fed Variable Speed Pumped Storage Hydropower Plants Considering the Water Conveyance System
Authors: Chen Yuzhe , Wu Feng , Shi Linjun , Li Yang , Guo Xu , Qi Peng
Published: October 2024

The doubly fed variable speed pumped storage (DFVSPS) system is a hydraulically, mechanically, and electrically coupled system, and the characteristics of the components from the water conveyance system to the transmission line need to be fully considered in the oscillation analysis. Hence, the model of the water conveyance system is included to investigate the oscillation characteristics of the DFVSPS connecting to the grid via a series-compensated line. A small-signal state-space model of the DFVSPS system in the generation mode is first established. The oscillation characteristics of the DFVSPS are studied, and the dominant state variables for each oscillation mode are identified. The impact of system parameters on oscillations is further studied, and simulations are carried out to validate the accuracy of the model. The results indicate the oscillation mode of the DFVSPS comprises the electrical sub-synchronous oscillation (SSO) mode and the hydraulically, mechanically coupled low-frequency mechanical oscillation modes. When the series compensation level is high, the SSO becomes divergent, and the system is more likely to be unstable. Optimizing the rotor-side control parameters and the governor control parameters, sub-synchronous and low-frequency oscillations could be effectively suppressed, respectively. This study provides reference suggestions for the development and use of the future DFVSPS system.
Internalizing External Accident Costs in Safety Investment Evaluation Using Cost–Benefit Analysis
Authors: Nikolova Christina
Published: October 2024

The primary objectives of transportation system development in European countries include improving transport efficiency, enhancing national economic competitiveness, and promoting sustainable mobility. A key aspect of achieving these goals is the evaluation of transport externalities, particularly external accident costs, which is critical for ensuring the economic and social sustainability of road transport development. This study comprehensively evaluates external road accident costs and proposes an innovative approach to internalizing these costs through a cost–benefit analysis (CBA). By integrating these external costs into CBA, policymakers can better understand the societal impact of road safety investments, allowing for more informed decisions regarding measures to reduce road accidents and fatalities. This paper also explores the role of specific safety investments, such as installing vehicle restrain systems (VRS), buffers, and terminals on the national road network, as well as installing speed cameras, in reducing accidents and mitigating external costs. The findings highlight road safety measures’ importance in enhancing economic efficiency and social well-being, providing a solid foundation for the sustainable and efficient development of transportation systems.
Towards Sustainable Biomass Conversion Technologies: A Review of Mathematical Modeling Approaches
Authors: Polesek-Karczewska Sylwia , Hercel Paulina , Adibimanesh Behrouz , Wardach-Świȩcicka Izabela
Published: October 2024

The sustainable utilization of biomass, particularly troublesome waste biomass, has become one of the pathways to meet the urgent demand for providing energy safety and environmental protection. The variety of biomass hinders the design of energy devices and systems, which must be highly efficient and reliable. Along with the technological developments in this field, broad works have been carried out on the mathematical modeling of the processes to support design and optimization for decreasing the environmental impact of energy systems. This paper aims to provide an extensive review of the various approaches proposed in the field of the mathematical modeling of the thermochemical conversion of biomass. The general focus is on pyrolysis and gasification, which are considered among the most beneficial methods for waste biomass utilization. The thermal and flow issues accompanying fuel conversion, with the basic governing equations and closing relationships, are presented with regard to the micro- (single particle) and macro-scale (multi-particle) problems, including different approaches (Eulerian, Lagrangian, and mixed). The data-driven techniques utilizing artificial neural networks and machine learning, gaining increasing interest as complementary to the traditional models, are also presented. The impact of the complexity of the physicochemical processes and the upscaling problem on the variations in the modeling approaches are discussed. The advantages and limitations of the proposed models are indicated. Potential options for further development in this area are outlined. The study shows that efforts towards obtaining reliable predictions of process characteristics while preserving reasonable computational efficiency result in a variety of modeling methods. These contribute to advancing environmentally conscious energy solutions in line with the global sustainability goals.
Development of Air Pollution Forecasting Models Applying Artificial Neural Networks in the Greater Area of Beijing City, China
Authors: Fazakis Panagiotis , Moustris Konstantinos , Spyropoulos Georgios
Published: October 2024

The ever-increasing industrialization of certain areas of the planet combined with the simultaneous degradation of the natural environment are alarming phenomena, especially in the field of human health. The concentration of particulate matter with an aerodynamic diameter of 2.5 μm (PM2.5) and 10 μm (PM10), nitrogen oxides (NOx), carbon monoxide (CO), sulfur dioxide (SO2), and ozone (O3) needs constant monitoring, as they consist of the main cause for many diseases. Based on the existence of statutory limits from the World Health Organization (WHO) for the concentration of each of the aforementioned air pollutants, it is considered necessary to develop forecasting systems that have the ability to correlate the current meteorological data with the concentrations of the above pollutants. In this work, the attempt to predict air pollutant concentrations in the wider area of Beijing, China, is successfully carried out using artificial neural network (ANN) models. In the frame of a specific work, a significant number of ANNs are developed. For this purpose, an open-access meteorological and air pollution database was used. Finally, a statistical evaluation of the developed prognostic models was carried out. The results showed that ANNs present a remarkable prognostic ability in order to forecast air pollution levels in an urban environment.
Methodology and Database for the Quantification of the Technical Recyclability of Electrical and Electronical Equipment Demonstrated on a Smartphone Case Study
Authors: Hämmer Matthias , Wambach Karsten
Published: October 2024

Assessing a given product’s design and its recyclability using mass flow analysis based on the material separation and recovery rates of individual recycling processes under realistic conditions can support design decisions promoting better recyclability. EN 45555 defines the calculation of the technical recyclability of electrical and electronic equipment (EEE). However, the lack of specific recycling rates for material or processes often leads to either too small or too high recyclability values. Herein, an extensive database of such recycling rates is presented. Moreover, the quality of recycling is considered. The typical classification into “recycled” and “lost” is expanded into four categories, namely “circular”, “recycled”, “alternate material recovery” and “lost”. The recycling rate database includes yields for all four categories and covers 30 materials for 14 recycling processes relevant in waste EEE (WEEE) treatment. These data enable a detailed calculation of the recyclability of various EEE for multiple recycling scenarios covering the entire WEEE recycling chain. Fraunhofer IZM performed an internal critical review of the data. The recycling rates database can act as a solid foundation for comparing the recyclability of various electronics in different scenarios and recyclability indices. For example, the recyclability of typical smartphones is investigated comparing different dismantling and recycling scenarios highlighting the potential of both database and methodology.
A Three-Dimensional Model of a Spherically Symmetric, Compressible Micropolar Fluid Flow with a Real Gas Equation of State
Authors: Bašić-Šiško Angela , Simčić Loredana , Dražić Ivan
Published: October 2024

In this work, we analyze a spherically symmetric 3D flow of a micropolar, viscous, polytropic, and heat-conducting real gas. In particular, we take as a domain the subset of R3 bounded by two concentric spheres that present solid thermoinsulated walls. Also, here, we consider the generalized equation of state for the pressure in the sense that the pressure depends, as a power function, on the mass density. The model is based on the conservation laws for mass, momentum, momentum moment, and energy, as well as the equation of state for a real gas, and it is derived first in the Eulerian and then in the Lagrangian description. Through the application of the Faedo–Galerkin method, a numerical solution to a corresponding problem is obtained, and numerical simulations are performed to demonstrate the behavior of the solutions under various parameters and initial conditions in order to validate the method. The results of the simulations are discussed in detail.
Modeling and Simulation of an Integrated Synchronous Generator Connected to an Infinite Bus through a Transmission Line in Bond Graph
Authors: Gonzalez-Avalos Gilberto , Ayala-Jaimes Gerardo , Gallegos Noe Barrera , Garcia Aaron Padilla
Published: October 2024

Most electrical energy generation systems are based on synchronous generators; as a result, their analysis always provides interesting findings, especially if an approach different to those traditionally studied is used. Therefore, an approach involving the modeling and simulation of a synchronous generator connected to an infinite bus through a transmission line in a bond graph is proposed. The behavior of the synchronous generator is analyzed in four case studies of the transmission line: (1) a symmetrical transmission line, where the resistance and inductance of the three phases (a,b,c) are equal, which determine resistances and inductances in coordinates (d,q,0) as individual decoupled elements; (2) a symmetrical transmission line for the resistances and for non-symmetrical inductances in coordinates (a,b,c) that result in resistances that are individual decoupled elements and in a field of inductances in coordinates (d,q,0); (3) a non-symmetrical transmission line for resistances and for symmetrical inductances in coordinates (a,b,c) that produce a field of resistances and inductances as individual elements decoupled in coordinates (d,q,0); and (4) a non-symmetrical transmission line for resistances and inductances in coordinates (a,b,c) that determine resistances and inductance fields in coordinates (d,q,0). A junction structure based on a bond graph model that allows for obtaining the mathematical model of this electrical system is proposed. Due to the characteristics of a bond graph, model reduction can be carried out directly and easily. Therefore, reduced bond graph models for the four transmission line case studies are proposed, where the transmission line is seen as if it were inside the synchronous generator. In order to demonstrate that the models obtained are correct, simulation results using the 20-Sim software are shown. The simulation results determine that for a symmetrical transmission line, currents in the generator in the d and q axes are −25.87 A and 0.1168 A, while in the case of a non-symmetrical transmission line, these currents are −26.14 A and 0.0211 A, showing that for these current magnitudes, the generator is little affected due to the parameters of the generator and the line. However, for a high degree of non-symmetry of the resistances in phases a, b and c, it causes the generator to reach an unstable condition, which is shown in the last simulation of the paper.
Non-Migraine Head Pain and Botulinum Toxin
Authors: Farham Fatemeh , Onan Dilara , Martelletti Paolo
Published: October 2024

Botulinum toxin A (BT-A), a potential neurotoxin produced by the bacterium Clostridium botulinum, is known for its ability to prevent the release of acetylcholine at the neuromuscular synapse, leading to temporary muscle paralysis. BT-A is used for a wide range of therapeutic applications. Several studies have shown mechanisms beyond the inhibition of acetylcholine release for pain control. BT-A inhibits the release of neurotransmitters associated with pain and inflammation, such as glutamate, CGRP, and substance P. Additionally, it would be effective in nerve entrapment leading to neuronal hypersensitivity, which is known as a new pathogenesis of painful conditions. BT-A has been applied to the treatment of a wide variety of neurological disorders. Since 2010, BT-A application has been approved and widely used as a chronic migraine prophylaxis. Moreover, due to its effects on pain through sensory modulation, it may also be effective for other headaches. Several studies using BT-A, at different doses and administration sites for headaches, have shown beneficial effects on frequency and severity. In this review, we provide an overview of using BT-A to treat primary and secondary headache disorders.
SARS-CoV-2 Antibody Seroprevalence in Gabon: Findings from a Nationwide Household Serosurvey in a Sub-Saharan Africa Country
Authors: Zoa-Assoumou Samira , Essone-Ndong Paulin , Adamou Rafiou , Oyegue-Liabagui Sandrine Lydie , Mveang Nzoghe Amandine , Roméo Adegbite Bayodé , Mintsa Ndong Armel , Mboyis-Kandem Herve , Magossou Mbadinga Marien Juliet Verraldy , Ndjoyi-Mbiguino Angelique , Amalet Akagha Armel Brice , Ngou-Milama Krystina Mengue Me , Bagayoko Magaran Monzon , Aboubacar Inoua , Lekana-Douki Jean-Bernard , Djoba Siawaya Joel Fleury , Adegnika Ayola Akim , Ngoungou Edgard-Brice ,   COVID-19 Gabonese Group
Published: October 2024

Seroconversion surveys of anti-SARS-CoV-2 antibodies provide accurate estimates of the prevalence of SARS-CoV-2 infections. This nationwide population-based cross-sectional serosurvey aimed to evaluate the prevalence of SARS-CoV-2 antibodies among residents in Gabon and compare the estimated cumulative number of COVID-19 cases with the officially registered number of laboratory-confirmed cases up to December 2021. Households in each province were randomly selected. Twenty-eight localities, including sixteen urban and twelve rural, were randomly selected for the study. Whole blood samples were collected in dry tubes from all study participants nationwide within 15 days. Serum samples were used to measure SARS-CoV-2-specific ELISA titers. Overall, data from 1672 households were analyzed. Out of the 3659 participants, 3175 were found to be positive for SARS-CoV-2 antibodies, resulting in a crude seroprevalence of 86.77%. Stratification of study participants by age group showed the highest seroprevalences in the 20–29 and 40–49 age groups with 91.78% (95% CI: 89.5–93.6) and 91.42% (95% CI: 88.7–93.5), respectively. Nyanga province had the lowest prevalence (72.8%), and Estuaire and Ogooué-Lolo provinces had the highest prevalence (90 and 92%). Our results suggest a high transmission rate in the Gabonese population 21 months after the first SARS-CoV-2 case in the country. This high seroprevalence estimate could indicate that the population may not have adequately implemented or appropriately adhered to the applied infection control measures.