bluuebunny/crossref_metadata_2025 huggingface dataset:
Morocco’s Distinctive Islam at a Crossroads: The State’s Support for Sufism
Authors: Faitour Mouad
Published: October 2024

In the aftermath of the 2003 Casablanca bombings, the Moroccan state emphasized, through official public discourse, the components that constitute “official Moroccan Islam” to combat extremist ideologies. These religious elements include Mālikism in jurisprudence, Ashʿarism in theology, and the Sufism of Imam Al-Junayd (d. 298/910), all balanced by the pledge of allegiance to King Mohammed VI (a descendant of the Sharifian lineage), the constitutionally designated Commander of the Faithful and sole religious leader. Since the reform policy initiated in 2004, the Moroccan state has constructed a narrative on the distinctiveness of Moroccan Islam—moderate and tolerant—and promoted it among its own citizens and beyond its borders. However, while the Moroccan state claims to have a unique form of Islam, controversial arguments have been raised questioning the nature of the state’s purported Islam. Other criticisms include investigating the state’s endorsement of Sufism and its broader policy of institutionalization. Yet, this article argues that the state supports any form of Islam, not necessarily Sufism, that aligns with its religious and political leadership. Like other Arab and Muslim states, Morocco’s religious policy is impacted by the global context, where Salafism is now perceived as a threat to established worldviews. In addition, this article argues that Morocco’s support for Sufi Islam is based not merely on its perceived political passivity, but because it complements the state’s policies and gains advantages from this support. It concludes that the official narrative of Moroccan Islam, which emphasizes a Sufi-oriented approach to counter extremism, is open to question, particularly given that Salafism was the state’s preferred form of Islam in post-colonial Morocco. This highlights the complex and often conflicting relationship between political actors and religious leaders in shaping Morocco’s religious discourse.
The Role of Entrepreneur’s Face Disclosure on Crowdfunding Success
Authors: Mamaro Lenny Phulong , Sibindi Athenia Bongani
Published: October 2024

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the project’s creativity, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data were collected from multiple crowdfunding platforms for projects in Africa. That is, cross-country data from 54 African countries, to overcome data limitations from a single country. An econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success by 3%. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges to the success of online crowdfunding.
Estimation of Urban Tree Chlorophyll Content and Leaf Area Index Using Sentinel-2 Images and 3D Radiative Transfer Model Inversion
Authors: Le Saint Théo , Nabucet Jean , Hubert-Moy Laurence , Adeline Karine
Published: October 2024

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species.
Observation of Boundary-Layer Jets in the Northern South China Sea by a Research Vessel
Authors: Zhang Xiyun , Luo Yuhan , Du Yu
Published: October 2024

Boundary-layer jets (BLJs) in the South China Sea play an important role in heavy rainfall in South China, yet observations in maritime locations are still limited. This study examines the vertical structures and temporal evolutions of BLJs in the northern South China Sea using intensive radiosonde observations from a research vessel from 15 to 18 June 2022 and evaluates the performance of various reanalysis datasets in capturing these features. Observations identified BLJs with jet cores at altitudes of approximately 500–700 m. Wind speeds slightly decreased from 15 to 16 June and then significantly increased after 17 June, showing double peaks on 17 June below 1 km at altitudes of 250 and 700 m. Among the reanalysis datasets, ERA5 exhibited more accurate results on average, followed by MERRA2, both of which outperformed JRA55 and FNL. ERA5 and MERRA2 had mixed performances in depicting BLJ characteristics. ERA5 accurately captured the initial decrease in wind speeds and their subsequent enhancement, while MERRA2 initially faltered but improved later. On the diurnal scale, neither MERRA2 nor ERA5 accurately represented the wind speed peaks observed at 2300 and 1100 LST, whereas ERA5 roughly reflected the nocturnal acceleration of the BLJs. During the observation period, the intensification of BLJs in the northern SCS, influenced by an eastward-moving high-pressure system and a southward-moving low-pressure vortex, led to enhanced precipitation in South China that gradually moved northward from the coastline to inland regions. This study provides new insights into the detailed characteristics of marine BLJs based on direct observations.
Research on Through-Flame Imaging Using Mid-Wave Infrared Camera Based on Flame Filter
Authors: Zheng Fengxun , Sun Guodong , Suo Yanpeng , Ma Hao , Feng Tengxiao
Published: October 2024

High-temperature furnaces and coal-fired boilers are widely employed in the petrochemical and metal-smelting sectors. Over time, the deterioration, corrosion, and wear of pipelines can lead to equipment malfunctions and safety incidents. Nevertheless, effective real-time monitoring of equipment conditions remains insufficient, primarily due to the interference caused by flames generated from fuel combustion. To address this issue, in this study, a through-flame infrared imager is developed based on the mid-wave infrared (MWIR) radiation characteristics of the flame. The imager incorporates a narrowband filter that operates within the wavelength range of 3.80 μm to 4.05 μm, which is integrated into conventional thermal imagers to perform flame filtering. This configuration enables the radiation from the background to pass through the flame and reach the detector, thereby allowing the infrared imager to visualize objects obscured by the flame and measure their temperatures directly. Our experimental findings indicate that the imager is capable of through-flame imaging; specifically, when the temperature of the target exceeds 50 °C, the imager can effectively penetrate the outer flame of an alcohol lamp and distinctly capture the target’s outline. Importantly, as the temperature of the target increases, the clarity of the target’s contour in the images improves. The MWIR through-flame imager presents considerable potential for the real-time monitoring and preventive maintenance of high-temperature furnaces and similar equipment, such as detecting the degradation of refractory materials and damage to pipelines.
Design and Testing of a Seedling Pick-Up Device for a Facility Tomato Automatic Transplanting Machine
Authors: Liu Zhicheng , Shi Lu , Liu Zhiyuan , Xing Jianfei , Hu Can , Wang Xufeng , Wang Long
Published: October 2024

At present, tomato transplanting in facility agriculture is mainly manual operation. In an attempt to resolve the problems of high labor intensity and low efficiency of manual operation, this paper designs a clip stem automatic transplanting and seedling picking device based on the yolov5 algorithm. First of all, through the study of the characteristics of tomato seedlings of different seedling ages, the age of tomato seedlings suitable for transplanting was obtained. Secondly, the improved yolov5 algorithm was used to determine the position and shape of tomato seedlings. By adding a lightweight upsampling operator (CARAFE) and an improved loss function, the feature extraction ability and detection speed of tomato seedling stems were improved. The accuracy of the improved yolov5 algorithm reached 92.6%, and mAP_0.5 reached 95.4%. Finally, the seedling verification test was carried out with tomato seedlings of about 40 days old. The test results show that the damage rate of the device is 7.2%, and the success rate is not less than 90.3%. This study can provide a reference for research into automatic transplanting machines.
Can Environmental Protection Tax Promote Urban Green Transformation? Experimental Evidence from China
Authors: Qi Zhankun , Long Feng , Bi Fenfen , Tian Xue , Qian Ziwei , Duan Xianming , Ge Chazhong
Published: October 2024

As one of China’s important environmental and economic policies, the environmental protection tax (EPT) is important in promoting economic and social green transformation. In this study, the green total factor productivity (GTFP) of 283 prefecture-level cities in China from 2013 to 2022 was calculated using a Super Slack-Based Model (Super-SBM) and the Malmquist-Luenberger (ML) index, which includes undesirable outputs. Moreover, the implementation effect of environmental tax on promoting urban green transformation is identified through the difference-in-differences (DID) model. This study revealed that (1) an EPT can significantly increase the GTFP of a city and promote its green transformation. (2) Industrial structure optimization and technological innovation are important mechanisms through which EPT drives urban green transformation. (3) The implementation effect of EPT in promoting urban green transformation presents significant policy differences across geographic locations, whether cities are key environmental protection cities or types of resource-based cities. EPT can significantly promote the green transformation of local cities, which in turn can positively affect the green transformation of neighboring cities. Based on this study’s conclusions, suggestions are put forward to improve the EPT system to promote urban green transformation.
Analysis of the Water Indicators in the UI GreenMetric Applied to Environmental Performance in a University in Brazil
Authors: Domingos Janaina Melo Franco , Marques Diego Gouveia , Campos Valquíria , Nolasco Marcelo Antunes
Published: October 2024

Universities, as hubs of economic, technological, and social knowledge development, have increasingly adopted metric-based strategies to guide resource management and monitor their growth. The Sustainable University World Ranking, UI GreenMetric, is widely applied for this purpose, measuring performance across six categories aligned with the United Nations Agenda 2030—Sustainable Development Goals (SDGs). This study focused on assessing information concerning the water category of this ranking, or the five water management indicators, at the School of Arts, Sciences, and Humanities of the University of São Paulo, to estimate its classification. The methodology involves assessing the current situation of the university in terms of each indicator, and classifying it according to the ranking guidelines. The information obtained is treated as evidence for posterior validation with the ranking. The findings indicate satisfactory performance in the indicators of water 1, 3, and 5. Notably, the implementation of rainwater collection and storage systems has been successful, alongside maintaining potable water parameters for consumption within the campus, as well as the use of efficient water-saving devices. Indicators 2 and 4, related to effluent treatment and water reuse, are expected to achieve higher classifications with the reactivation of the wastewater treatment system’s operation. Over the period from May 2023 to June 2024, the average daily water consumption was measured at 52.89 ± 25.23 m3 day−1, with a per capita consumption rate of 10.28 L consumer agent−1 day−1. An anticipated 20% reduction in water consumption is expected upon the incorporation of water reuse initiatives. The use of the UI GreenMetric framework has been found strategic and useful as a diagnostic tool, facilitating the identification of areas requiring improvement and guiding efforts toward enhancing the sustainability of the institution.
Identification of Factors Influencing Episodes of High PM10 Concentrations in the Air in Krakow (Poland) Using Random Forest Method
Authors: Gorzelnik Tomasz , Bogacki Marek , Oleniacz Robert
Published: October 2024

The episodes of elevated concentrations of different gaseous pollutants and particulate matter (PM) are of major concern worldwide, especially in city agglomerations. Krakow is an example of an urban–industrial agglomeration with constantly occurring PM10 air limit value exceedances. In recent years, a number of legislative actions have been undertaken to improve air quality in this area. The multitude of factors affecting the emergence of cases of very high air pollutant concentrations makes it difficult to analyze them using simple statistical methods. Machine learning (ML) methods can be an adequate option, especially when proper amounts of credible data are available. The main aim of this paper was to examine the influence of various factors (including main gaseous pollutant concentrations and some meteorological factors) on the effect of high PM10 concentration episodes in the ambient air in Krakow (Poland) using the random forest algorithm. The original methodology based on the PM10 limit and binary classification of cases with and without the occurrence of high concentration episodes was developed. The data used were derived from routine public air quality monitoring and a local meteorological station. A range of random forest classification models with various predictor sets and for different subsets of the observations coupled with variable importance analysis were performed. The performance of the algorithm was assessed using confusion matrices. The variable importance rankings revealed, among other things, the dominant impact of the mixing layer height on elevated PM10 concentration episode formation. This research work showed the usefulness of the random forest algorithm in identifying factors contributing to poor air quality, even in the absence of reliable emission data.
Optimization of Energy Management Strategy of a PHEV Based on Improved PSO Algorithm and Energy Flow Analysis
Authors: Liu Yong , Ni Jimin , Huang Rong , Shi Xiuyong , Xu Zheng , Wang Yanjun , Lu Yuan
Published: October 2024

Single-gear-ratio plug-in hybrid vehicles (SRPHEVs) are favored by major manufacturers due to their excellent energy-saving potential, simple structure, ease of maintenance and control, great cost-saving potential, and the benefits of vehicle lightweighting. Implementing an energy management strategy (EMS) is the key to realizing the energy-saving potential of PHEVs. In this paper, based on a newly developed coaxial configuration, P1-P3 SRPHEV, with the purpose of reducing PHEV fuel consumption, the advantages of various methods were synthesized. An improved intelligent optimization algorithm, the Particle Swarm Optimization (PSO) algorithm, was used to find the optimal rule-based strategy parameters. The PSO algorithm could be easily adjusted to the parameters and obtains the desired results quickly. Different long-distance speed profiles tested under real-world driving cycle (RDC) conditions were used to validate the fuel savings. And an energy flow analysis was conducted to further investigate the reasons for the algorithm optimization. The results show that the optimization plans of the PSO algorithm in different cycle conditions can improve the equivalent fuel consumption of vehicles in different long-distance conditions. Considering the optimization effect of the equivalent fuel consumption and actual fuel consumption, the best case of the equivalent fuel consumption is improved by 2.98%, and the actual fuel consumption is improved by 2.37%. Through the energy flow analysis, it is found that the energy-saving effect of the optimization plan lies in the following principle: lowering the parallel mode switching threshold to increase the parallel mode usage time and to reduce the fuel–mechanical–electrical transmission path loss, resulting in increasing the energy utilization of the whole vehicle.
Testing the Process of Coworker Incivility to Work Withdrawal Behavior: The Moderated Mediation Effects of Employee Resilience in Achieving Organizational Sustainability
Authors: Zhu Lingfeng , Cai Han , Jin Xiu
Published: October 2024

Drawing on the implantation of organizational sustainable strategies, enterprises can effectively manage and recycle resources, reduce resource waste, improve market competitiveness, better respond to changes in the market and environment, and create long-term economic benefits. Although a large number of prior studies have emphasized the importance of improving sustainability and proposed various strategies and practical approaches, relatively few studies have explored the inhibitors of sustainability. Against such a research background, this study differs from previous research that has simply focused on ways to increase sustainability; we instead explore negative variables that reduce sustainability. We assess the variable that reduces organizational sustainability, that is, work withdrawal behavior that reduces employees’ enthusiasm for their jobs, hinders employee performance, causes financial losses, and limits organizational growth and sustainability. This directly affects the organization’s long-term growth and sustainability goals and damages the organization’s overall sustainability image. Therefore, understanding the reasons behind this behavior is important. This study examines how coworker incivility leads to work withdrawal behavior and validates relevant research models. Data from 294 Chinese SME employees show that coworker incivility positively affects workplace loneliness and work withdrawal behavior. Workplace loneliness mediates the impact of coworker incivility on work withdrawal behavior, while resilience negatively moderates these relationships. Based on these findings, this study offers recommendations for reducing work withdrawal behavior and improving workplace environments and employee mental health.
Differences in Anthropogenic Impacts of Typical Mid- to High-Latitude Wetlands in the Heilongjiang Basin
Authors: Liu Jinlong , Dou Xinyu , Zhang Lu , Liang Dong , Zhu Qi , Lv Zhuoran , Liu Yiming , Du Xiaobing
Published: October 2024

Wetlands represent one of the three principal ecosystems and serve a vital function in the protection of water resources and the regulation of climate. However, wetlands are currently experiencing significant challenges, particularly in the agriculturally productive wetlands of the Heilongjiang River Basin, which have been considerably impacted by human activities. This study focuses on three representative wetlands situated within the Heilongjiang River Basin. This study analyses changes in wetland area and landscape patterns from 2002 to 2022, as well as the impact of agriculture and impervious surface expansion on the wetlands. The findings indicate that agricultural expansion is the primary driver of wetland area loss. The wetland area affected by agriculture demonstrates the most significant change, with the largest observed shift reaching 47.2%. The expansion of impervious surfaces was found to have a significant impact on wetland fragmentation, resulting in a notable decrease in wetland connectivity. This was evidenced by a reduction in the average patch size, which decreased by 14.68 ha over the decade from 2007 to 2017, a period during which impervious surfaces expanded. This paper identifies the distinctions in the influence of diverse human activities on wetland landscape patterns in the Heilongjiang Basin and employs natural samples for control, thereby attenuating the impact of the natural environment. This study offers a novel perspective on the processes of wetland change and the maintenance of wetland health, which is crucial for the realisation of clean water and sanitation (Sustainable Development Goal 6.6).
The Shift Towards Plant-Based Lifestyles: Factors Driving Young Consumers’ Decisions to Choose Plant-Based Food Products
Authors: Venter de Villiers Marike , Cheng Joy , Truter Lorna
Published: October 2024

The shift in consumer dietary patterns from meat-based to plant-based food products has become a prominent trend worldwide. This shift is driven by various factors, including concerns about personal health and environmental awareness. Despite the global growth of the plant-based food industry, developing nations tend to be slow with adopting non-meat-based diets. This is mainly due to high levels of food insecurity and meat being the main source of protein, especially in countries like South Africa where food consumption is needs-based, and consumers are unaware of the environmental footprint of meat production. This paper is part of a two-phase study and reports on the quantitative results, which were obtained during the second phase. The main aim of this study was to investigate the factors that influence consumers’ decisions to choose plant-based food products. The factors that were selected were informed by the thematic results from phase 1, which involved focus groups that explored consumers’ opinions and behaviours towards plant-based lifestyles. By means of survey distribution, 426 online questionnaires were distributed among young consumers in South Africa. A conceptual model with six hypotheses was tested and the data were analysed using SmartPLS 4.1.0.8. The findings indicated the product taste and product knowledge are the most important factors that drive young consumers’ decisions to choose plant-based food products. The study further found that product quality, affordability, social influence, and product packaging play a role but to a lesser degree. Food marketers can utilise these findings and implement marketing strategies that can assist with persuading consumers to choose plant-based food products and adopt a healthier, more sustainable lifestyle.
Optimization of Productivity of Fodder Crops with Green Conveyor System in the Context of Climate Instability in the North Kazakhstan Region
Authors: Shayakhmetova Altyn , Bakirov Aldiyar , Savenkova Inna , Nasiyev Beybit , Akhmetov Murat , Useinov Azamat , Temirbulatova Akerke , Zhanatalapov Nurbolat , Bekkaliyev Askhat , Mukanova Fariza , Auzhanova Mariya
Published: October 2024

One of the main challenges in modern animal husbandry in North Kazakhstan is ensuring an uninterrupted supply of sufficient fodder crops. This research, conducted from 2019 to 2023, aimed to develop strategies for cultivating environmentally sustainable fodder crops capable of providing a stable fodder crop base under the changing climatic conditions of the North Kazakhstan region. The studies included analysis of air temperature and precipitation data as well as monitoring of fodder grass mixtures within a green fodder conveyor system. Different sowing dates for fodder crops and mixtures were selected for the development of the conveyor system. The range of experimental variants included fodder crops and their mixtures from various botanical families. The experiment involved both perennial (alfalfa and festulolium) and annual (corn, pea, sunflower, Sudan grass, oats, and rapeseed) crops. The highest green mass yields were achieved by the following variants: fodder crops of corn + pea—74.40 c/ha; mixtures of annual legume–grass crops in the pea + oats variant of the first sowing date—43.64 c/ha; Sudan grass + pea—45.72 c/ha; mixtures of perennial grasses in the second utilization term of alfalfa + festulolium—64.9 c/ha; and rapeseed sown at the first sowing date—46.61 c/ha. In terms of crude and digestible protein content, the best among the annual grass variants was the mixture of Sudan grass and pea (crude protein—33.59 g/kg, digestible protein—24.5 g/kg), and the best among the perennials was the variant of the first utilization term (crude protein—50.42 g/kg, digestible protein—38.2 g/kg). Regarding metabolizable energy content, the annual crop variant of corn + pea had a yield of 1.92 MJ/kg, and in the perennial variant, the mixture of alfalfa and festulolium in the first utilization term had a yield of 2.68 MJ/kg. Such an approach to creating green fodder conveyors can be crucial for developing effective strategies for adapting agriculture to climate change, including the selection of promising fodder crops and optimization of their placement. The results obtained can contribute to enhancing the productivity and sustainability of agricultural production in the North Kazakhstan region.
Cultural Industry Agglomeration and Carbon Emission Performance: Empirical Analysis Based on 276 Cities in China
Authors: Hao Tinglei , Ren Jiajie , Sun Chuanming , Chen Lu , Liu Tao
Published: October 2024

This study investigated the influence of cultural industry agglomeration on the energy carbon emission performance (CEP). Based on panel data from 276 cities in China, we used the Super-SBM model to measure the CEP. We then used the Tobit regression model to calculate the influence coefficient of cultural industry agglomeration and eight control variables on the CEP and analyzed the complex effects of cultural industry agglomeration on the CEP. The results showed that there is the phenomenon of “diseconomies of agglomeration” in cultural industry agglomeration, which cannot improve the CEP. For each unit of cultural industry agglomeration increase, the CEP decreases by 0.055; however, this phenomenon is not linear. Further research showed that the effects of cultural industry agglomeration showed a trend from good to inferior in the order of east, central, and west and did not improve with time. Finally, we used the panel quantile regression model and found that as the CEP levels rise, the negative impact of cultural industry agglomeration improves. Our research results show that strengthening the technical level to promote the upgrading of the cultural industry is the best way to achieve sustainable development. Governments at all levels should pay attention to the emission reduction potential of cultural industry agglomeration under high CEP levels and strengthen the benign agglomeration of the cultural industry.
Out-of-Plane Equilibrium Points in the Photogravitational Hill Three-Body Problem
Authors: Vincent Aguda E. , Tsirogiannis George A. , Perdiou Angela E. , Kalantonis Vassilis S.
Published: October 2024

This paper investigates the movement of a negligible mass body (third body) in the vicinity of the out-of-plane equilibrium points of the Hill three-body problem under the effect of radiation pressure of the primaries. We study the effect of the radiation parameters through the factors qi,i=1,2 on the existence, position, zero-velocity curves and stability of the out-of-plane equilibrium points. These equilibrium positions are derived analytically under the action of radiation pressure exerted by the radiating primary bodies. We determined that these points emerge in symmetrical pairs, and based on the values of the radiation parameters, there may be two along the Oz axis and either none or two on the Oxz plane (outside the axes). A thorough numerical investigation found that both radiation factors have a strong influence on the position of the out-of-plane equilibrium points. Our results also reveal that the parameters have impact on the geometry of the zero-velocity curves. Furthermore, the stability of these points is examined in the linear sense. To do so, the spatial distribution of the eigenvalues on the complex plane of the linearized system is visualized for a wide range of radiation parameter combinations. By a numerical investigation, it is found that all equilibrium points are unstable in general.
An Improved PINN Algorithm for Shallow Water Equations Driven by Deep Learning
Authors: Li Yanling , Sun Qianxing , Wei Junfang , Huang Chunyan
Published: October 2024

Solving shallow water equations is crucial in science and engineering for understanding and predicting natural phenomena. To address the limitations of Physics-Informed Neural Network (PINN) in solving shallow water equations, we propose an improved PINN algorithm integrated with a deep learning framework. This algorithm introduces a regularization term as a penalty in the loss function, based on the PINN and Long Short-Term Memory (LSTM) models, and incorporates an attention mechanism to solve the original equation across the entire domain. Simulation experiments were conducted on one-dimensional and two-dimensional shallow water equations. The results indicate that, compared to the classical PINN algorithm, the improved algorithm shows significant advantages in handling discontinuities, such as sparse waves, in one-dimensional problems. It accurately captures sparse waves and avoids smoothing effects. In two-dimensional problems, the improved algorithm demonstrates good symmetry and effectively reduces non-physical oscillations. It also shows significant advantages in capturing details and handling complex phenomena, offering higher reliability and accuracy. The improved PINNs algorithm, which combines neural networks with physical mechanisms, can provide robust solutions and effectively avoid some of the shortcomings of classical PINNs methods. It also possesses high resolution and strong generalization capabilities, enabling accurate predictions at any given moment.
Classification of Petrov Homogeneous Spaces
Authors: Obukhov V. V.
Published: October 2024

In this paper, the final stage of the Petrov classification is carried out. As it is known, the Killing vector fields specify infinitesimal transformations of the group of motions of space V4. In the case where the group of motions G3 acts in a simply transitive way in the homogeneous space V4, the geometry of the non-isotropic hypersurface is determined by the geometry of the transitivity space V3 of the group G3. In this case, the metric tensor of the space V3 can be given by a nonholonomic reper consisting of three independent vectors ℓ(a)α, which define the generators of the group G3 of finite transformations in the space V3. The representation of the metric tensor of V4 spaces by means of vector fields ℓ(a)α has a great physical meaning and makes it possible to substantially simplify the equations of mathematical physics in such spaces. Therefore, the Petrov classification should be complemented by the classification of vector fields ℓ(a)α connected to Killing vector fields. For homogeneous spaces, this problem has been largely solved. A complete solution of this problem is presented in the present paper, where I refine the Petrov classification for homogeneous spaces in which the group G3, which belongs to type VIII according to the Petrov classification, acts simply transitively. In addition, this paper provides the complete classification of vector fields ℓ(a)α for space V4 in which the group G3 acts simply transitivity on isotropic hypersurfaces.
Deep Learning Evidence for Global Optimality of Gerver’s Sofa
Authors: Leng Kuangdai , Bi Jia , Cha Jaehoon , Pinilla Samuel , Thiyagalingam Jeyan
Published: October 2024

The moving sofa problem, introduced by Leo Moser in 1966, seeks to determine the maximal area of a 2D shape that can navigate an L-shaped corridor of unit width. Joseph Gerver’s 1992 solution, providing a lower bound of approximately 2.2195, is the best known, though its global optimality remains unproven. This paper leverages neural networks’ approximation power and recent advances in invexity optimization to explore global optimality. We propose two approaches supporting Gerver’s conjecture that his sofa is the unique global maximum. The first approach uses continuous function learning, discarding assumptions about the monotonicity, symmetry, and differentiability of sofa movements. The sofa area is computed as a differentiable function using our “waterfall” algorithm, with the loss function incorporating both differential terms and initial conditions based on physics-informed machine learning. Extensive training with diverse network initialization consistently converges to Gerver’s solution. The second approach applies discrete optimization to the Kallus–Romik upper bound, improving it from 2.37 to 2.3337 for five rotation angles. As the number of angles increases, our model asymptotically converges to Gerver’s area from above, indicating that no larger sofa exists.
SWL-LSE: A Dataset of Health-Related Signs in Spanish Sign Language with an ISLR Baseline Method
Authors: Vázquez-Enríquez Manuel , Alba-Castro José Luis , Docío-Fernández Laura , Rodríguez-Banga Eduardo
Published: October 2024

Progress in automatic sign language recognition and translation has been hindered by the scarcity of datasets available for the training of machine learning algorithms, a challenge that is even more acute for languages with smaller signing communities, such as Spanish. In this paper, we introduce a dataset of 300 isolated signs in Spanish Sign Language, collected online via a web application with contributions from 124 participants, resulting in a total of 8000 instances. This dataset, which is openly available, includes keypoints extracted using MediaPipe Holistic. The goal of this paper is to describe the construction and characteristics of the dataset and to provide a baseline classification method using a spatial–temporal graph convolutional network (ST-GCN) model, encouraging the scientific community to improve upon it. The experimental section offers a comparative analysis of the method’s performance on the new dataset, as well as on two other well-known datasets. The dataset, code, and web app used for data collection are freely available, and the web app can also be used to test classifier performance on-line in real-time.
A Recent Advance in the Diagnosis, Treatment, and Vaccine Development for Human Schistosomiasis
Authors: Chatterji Tanushri , Khanna Namrata , Alghamdi Saad , Bhagat Tanya , Gupta Nishant , Alkurbi Mohammad Othman , Sen Manodeep , Alghamdi Saeed Mardy , Bamagous Ghazi A. , Sahoo Dipak Kumar , Patel Ashish , Kumar Pankaj , Yadav Virendra Kumar
Published: October 2024

Schistosomiasis, which affects a large number of people worldwide, is among the most overlooked parasitic diseases. The disease is mainly prevalent in sub-Saharan Africa, southeast Asian countries, and South America due to the lack of adequate sanitation. The disease is mainly associated with poor hygiene, sanitation, and contaminated water, so it is also known as a disease of poverty. Three Schistosoma species (S. mansoni, S. japonicum, and S. haematobium) cause significant human infections. Co-infections with Schistosoma and other parasites are widely common. All these parasites may cause intestinal or urogenital schistosomiasis, where the disease may be categorized into the acute, sensitized, and chronic phases. The disease is more prevalent among school children, which may cause anemia and reduce development. Chronic infections frequently cause significant liver, intestinal, and bladder damage. Women exposed to contaminated water while performing normal duties like washing clothes might acquire urogenital schistosomiasis (UGS), which can cause tissue damage and raise the risk of blood-borne disease transmission, including human immunodeficiency virus (HIV) transmission. Praziquantel (PZQ) is the World Health Organization (WHO)-prescribed treatment for individuals who are known to be infected, but it does not prevent further re-infections with larval worms. Vaccine development and new molecular-based diagnosis techniques have promised to be a reliable approach to the diagnosis and prevention of schistosomiasis. The current review emphasizes the recent advancement in the diagnosis of schistosomiasis by molecular techniques and the treatment of schistosomiasis by combined and alternative regimes of drugs. Moreover, this review has also focused on the recent outbreak of schistosomiasis, the development of vaccines, and their clinical trials.
Introduction to Bell’s Inequality in Quantum Mechanics
Authors: Guimaraes Marcelo Santos , Roditi Itzhak , Sorella Silvio Paolo
Published: October 2024

A pedagogical introduction to Bell’s inequality in Quantum Mechanics is presented. Several examples, ranging from spin 1/2 to coherent and squeezed states are worked out. The generalization to Mermin’s inequalities and to GHZ states is also outlined.
Incorporating Boundary Nonlinearity into Structural Vibration Problems
Authors: Elliott Alex J. , Cammarano Andrea
Published: October 2024

This paper presents a methodology for accurately incorporating the nonlinearity of boundary conditions (BCs) into the mode shapes, natural frequencies, and dynamic behaviour of analytical beam models. Such models have received renewed interest in recent years as a result of their successful implementation in state-of-the-art multiphysics problems. To address the need for this boundary nonlinearity to be more completely captured in the equations of motion, a nonlinear algebra expansion of the classical linear approach for developing solvability conditions for natural frequencies and mode shapes is presented. The method is applicable to any BC that can be accurately represented in polynomial form, either explicitly or through the application of a Taylor expansion; this is the only assumption made in removing the need for the use of analytical approximations of the dynamics themselves. By reducing the BCs of the beam to a system of polynomials, it is possible to utilise the tensor resultant to develop these solvability conditions analogous to the conditions placed on the matrix determinant in linear, classical cases. The approach is first derived for a general set of nonlinear BCs before being applied to two example systems to investigate the importance of including nonlinear tip behaviour in the BCs to accurately predict the system response. In the first, a theoretical, symmetric system, in which a beam is supported by nonlinear springs, is used to explore both the applicability of the methodology and the improvements it can make to the accuracy of the model. Then, the more practical example of a cantilever beam with repulsive magnetic interaction at the tip is used to more explicitly assess the importance of properly incorporating boundary nonlinearity into multiphysics problems.
Assessment of Future Water Security under Climate Change: Practical Water Allocation Scenarios in a Drought-Prone Watershed in South Korea
Authors: Kim Wonjin , Choi Sijung , Kang Seongkyu , Woo Soyoung
Published: October 2024

Seomijn River Basin has numerous hydraulic structures designed to satisfy water demands and mitigate future droughts. However, the increasing water demand and export to neighboring areas cause water deficits and conflicts between water users. Therefore, practical strategies to mitigate the potential damage from climate change are essential. In this study, we aimed to propose practical strategies under climate change by examining the future water security of the Seomjin River Basin under five different water allocation scenarios referenced from the practical policies of various countries. Future climate models determined based on extreme precipitation indices of the ETCCDI were used to investigate their impact on water security, which was evaluated using unmet demand; demand coverage; reliability, resilience, and vulnerability; and aggregation index metrics. We found that prioritizing domestic and industrial water use is the optimal water security strategy, and unconditional allocation of instream flow can cause a significant water deficit for other water uses. However, prioritizing all water uses equally also proved effective under some conditions. Thus, our study highlights the importance of adaptive management and suggests that the optimal water allocation strategy lies in its flexibility in response to varying circumstances.
Study on the Evolution Mechanism of Temporal Variability of Chloride Ions in Typical Districts of Ordos City
Authors: Si Letian , Zhang Bing , Zhou Ruiqing , Jiang Ruirui , Dong Wanggang , Ma Rong , Liu Sihang
Published: October 2024

The study of the temporal evolution of chloride ions in groundwater is important for identifying whether their sources are due to anthropogenic pollution or natural factors. Groundwater in the northern part of Dalat Banner, Ordos City, has high chloride ion content and exhibits strong temporal variability. To identify the source of chloride ions and reveal their evolution mechanisms, the fast Fourier transform (FFT) was used to determine the trend and cycle of chloride ion evolution, and the groundwater dynamics field combined with multivariate statistical analysis was used to identify the source of chloride ion pollution. Calculations show that the background value of chloride ions in groundwater in the study area is 195.17 mg/L, reaching a maximum of 459 mg/L under the influence of rainfall. The fluctuation of chloride ion concentration is mainly related to the total rainfall in the study area over 165 days, and a single rainfall of more than 15 mm affects the concentration of chloride ions in groundwater. The results of this study show that the background values of chloride ions are mainly influenced by the groundwater dynamical field, and the temporal volatility is mainly influenced by atmospheric rainfall.
Assessment of Emergency Water Sources Using Electrical Resistivity Tomography: A Case Study in the Longmen Shan Fault Zone
Authors: Liu Pan , Liu Shengbo , Chen Changjing , Hong Tao , Xiao Yifei , He Siyuan
Published: October 2024

The Longmen Shan fold and thrust belt, situated on the eastern margin of the Qinghai–Tibet Plateau, is prone to disasters like earthquakes and debris flows. Thus, applying rapid assessment methods for emergency water sources in disaster-affected areas is crucial for local populations and ensuring an effective response to disasters. In this study, we employed electrical resistivity tomography (ERT) to investigate groundwater resources in post-disaster regions. By integrating the results from ERT profiles with geological and borehole information, we determined the lithology and depth of aquifers. Additionally, we analyzed groundwater recharge and discharge patterns relative to surface water during various precipitation periods and generated a hydrogeological profile for the region. Borehole information confirmed our inferred lithology and aquifer depth, thereby ensuring a reliable water supply during emergencies. This study demonstrates the feasibility of ERT for rapidly identifying water resources in geologically complex environments, providing a scientific foundation for water resource management in disaster-prone regions.
Pathogen Contamination of Groundwater Affecting Drinking Water Quality with Potential Health Effects in Pavlodar Region, Kazakhstan
Authors: Azat Seitkhan , Kuldeyev Erzhan , Khalkhabay Bostandyk , Baikadamova Ainur , Sultakhan Shynggyskhan , Berndtsson Ronny
Published: October 2024

Groundwater is becoming increasingly important as surface water is decreasing and becoming more and more polluted. In particular, rural areas in the arid region of Central Asia face problems with both water quantity and quality. In view of this, we investigated the drinking water quality in the Maysky district in the Pavlodar region, Kazakhstan. The organoleptic properties, together with microbiological indicators, as well as organic and inorganic substances of drinking water before and after treatment, and tap water were studied and compared to recommended levels. The bacteriological indicators of the drinking water, especially, showed that the water represents health risks since the presence of bacteria of the genus Pseudomonas aeruginosa was confirmed. Water treatment reduced the total microbial count (TMC) indicator by 3.6 times. However, TMC still exceeded permissible levels in the tap water, indicating that the drinking water is sanitary and epidemiologically not acceptable. Pathogenic contamination of drinking water can severely affect weaker individuals and children. It has been estimated that the infant mortality rate in Kazakhstan is six times higher as compared to the EU and less than 30% of Kazakhstan’s population have access to safe water. Also, 50% of the population drink water that does not comply with the international standards, e.g., bacteriological levels. Thus, it is important to continuously monitor the groundwater quality to minimize health risks and work towards access to safe drinking water, in line with the UN SDGs.
Kernel-Based Versus Tree-Based Data-Driven Models: On Applying Suspended Sediment Load Estimation
Authors: Sattari Mohammad Taghi , Apaydin Halit , Milweski Adam
Published: October 2024

River sediment load estimation poses a critical challenge for water engineers due to its complex and nonlinear hydrological processes. This study assessed the amount of suspended sediment at the Bagh-e-Kalayeh hydrometric station on the Alamut River in the Qazvin province of Iran using two hydrological and meteorological variables, including discharge and rainfall, by considering three scenarios (discharge, discharge + monthly rainfall, and discharge + monthly rainfall + daily rainfall). For modeling, kernel-based data-driven methods, including Gaussian process regression (GPR) and support vector regression (SVR), and tree models, including the M5 tree, random forest (RF), random tree (RT), extra trees, reduced error pruning tree (REPT), and multi-search methods, were used. The results showed that the best performance was achieved by the SVR, with r = 0.948, Wilmot index = 0.965, and RMSE = 0.011 in the first scenario (only discharge). Discharge had the most significant impact on sediment estimation compared to rainfall. It was determined that the suspended sediment load in the Alamut River can be successfully estimated by the SVR method, where only the discharge was used as the input parameter. Additionally, the results indicated that given its characteristics and inherent features, the multi-search method can be used as a complementary approach in sediment modeling, especially in situations where the data volume is not extensive.