bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Ground-Penetrating Radar (GPR) to Investigate the Exceptionally Thick Deposits from the Storegga Tsunami in Northeastern Scotland
Authors: Bristow Charlie S. , Buck Lucy K. , Shah Rishi
Published: June 2024

A submarine landslide on the edge of the Norwegian shelf that occurred around 8150 ± 30 cal. years BP triggered a major ocean-wide tsunami, the deposits of which are recorded around the North Atlantic, including Scotland. Ground-penetrating radar (GPR) was used here to investigate tsunami sediments within estuaries on the coast of northeastern Scotland where the tsunami waves were funnelled inland. Around the Dornoch Firth, the tsunami deposits are up to 1.6 m thickness, which is exceptionally thick for tsunami deposits and about twice the thickness of the 2004 IOT or 2011 Tohoku-oki tsunami deposits. The exceptional thickness is attributed to a high sediment supply within the Dornoch Firth. At Ardmore, the tsunami appears to have overtopped a beach ridge with a thick sand layer deposited inland at Dounie and partly infilled a valley. Later, fluvial activity eroded the tsunami sediments locally, removing the sand layer. At Creich, on the north side of the Dornoch Firth, the sand layer varies in thickness; mapping of the sand layer with GPR shows lateral thickness changes of over 1 m attributed to a combination of infilling an underlying topography, differential compaction, and later reworking by tidal inlets. Interpretation of the GPR profiles at Wick suggests that there has been a miscorrelation of Holocene stratigraphy based on boreholes. Changes in the stratigraphy of spits at Ardmore are attributed to the balance between sediment supply and sea-level change with washovers dominating a spit formed during the early Holocene transgression, while spits formed during the subsequent mid-Holocene high-stand are dominated by progradation.
Evaluation of Soybean Drought Tolerance Using Multimodal Data from an Unmanned Aerial Vehicle and Machine Learning
Authors: Liang Heng , Zhou Yonggang , Lu Yuwei , Pei Shuangkang , Xu Dong , Lu Zhen , Yao Wenbo , Liu Qian , Yu Lejun , Li Haiyan
Published: June 2024

Drought stress is a significant factor affecting soybean growth and yield. A lack of suitable high-throughput phenotyping techniques hinders the drought tolerance evaluation of multi-genotype samples. A method for evaluating drought tolerance in soybeans is proposed based on multimodal remote sensing data from an unmanned aerial vehicle (UAV) and machine learning. Hundreds of soybean genotypes were repeatedly planted under well water (WW) and drought stress (DS) in different years and locations (Jiyang and Yazhou, Sanya, China), and UAV multimodal data were obtained in multiple fertility stages. Notably, data from Yazhou were repeatedly obtained during five significant fertility stages, which were selected based on days after sowing. The geometric mean productivity (GMP) index was selected to evaluate the drought tolerance of soybeans. Compared with the results of manual measurement after harvesting, support vector regression (SVR) provided better results (N = 356, R2 = 0.75, RMSE = 29.84 g/m2). The model was also migrated to the Jiyang dataset (N = 427, R2 = 0.68, RMSE = 15.36 g/m2). Soybean varieties were categorized into five Drought Injury Scores (DISs) based on the manually measured GMP. Compared with the results of the manual DIS, the accuracy of the predicted DIS gradually increased with the soybean growth period, reaching a maximum of 77.12% at maturity. This study proposes a UAV-based method for the rapid high-throughput evaluation of drought tolerance in multi-genotype soybean at multiple fertility stages, which provides a new method for the early judgment of drought tolerance in individual varieties, improving the efficiency of soybean breeding, and has the potential to be extended to other crops.
Lunar High Alumina Basalts in Mare Imbrium
Authors: Chen Jingran , Chen Shengbo , Ma Ming , Jiang Yijun
Published: June 2024

High-alumina (HA) mare basalts play a critical role in lunar mantle differentiation. Although remote sensing methods have speculated their potential presence regions based on sample FeO and TiO2 compositions, the location and distribution characteristics of HA basalts have not been provided. In this study, the compositions of exposed rocks in Mare Imbrium were determined using Lunar Reconnaissance Orbiter (LRO) Diviner oxides and Lunar Prospector Gamma-Ray Spectrometer (LP-GRS) Thorium (Th) products. The exposed HA basalts were identified based on laboratory lithology classification criteria and Al2O3 abundance. The HA basalt units were mapped based on lunar topographic data, and their morphological geological characteristics were calculated based on elevation data. The results show that there are 8406 HA basalt pixels and 17 original units formed by volcanic eruptions in Mare Imbrium. The statistics of their morphology characteristics show that the HA basalts are widely distributed in the northern part of Mare Imbrium, and their compositions have a large range of variation. These units have different area and volume, and the layers formed were discontinuous. The characteristic analysis shows that the aluminum-bearing volcanic activities in Mare Imbrium were irregular. The eruptions of four different source regions occurred in three phases, and the scale and extent of the eruptions were different. The results in this study provide reliable evidence for the heterogeneity of the lunar mantle and contribute valuable information to the formation process of early lunar mantle materials.
Visual Strain Sensors Based on Fabry–Perot Structures for Structural Integrity Monitoring
Authors: Chen Qingyuan , Liu Furong , Xu Guofeng , Yin Boshuo , Liu Ming , Xiong Yifei , Wang Feiying
Published: June 2024

Strain sensors that can rapidly and efficiently detect strain distribution and magnitude are crucial for structural health monitoring and human–computer interactions. However, traditional electrical and optical strain sensors make access to structural health information challenging because data conversion is required, and they have intricate, delicate designs. Drawing inspiration from the moisture-responsive coloration of beetle wing sheaths, we propose using Ecoflex as a flexible substrate. This substrate is coated with a Fabry–Perot (F–P) optical structure, comprising a “reflective layer/stretchable interference cavity/reflective layer”, creating a dynamic color-changing visual strain sensor. Upon the application of external stress, the flexible interference chamber of the sensor stretches and contracts, prompting a blue-shift in the structural reflection curve and displaying varying colors that correlate with the applied strain. The innovative flexible sensor can be attached to complex-shaped components, enabling the visual detection of structural integrity. This biomimetic visual strain sensor holds significant promise for real-time structural health monitoring applications.
Real-Time Arabic Sign Language Recognition Using a Hybrid Deep Learning Model
Authors: Noor Talal H. , Noor Ayman , Alharbi Ahmed F. , Faisal Ahmed , Alrashidi Rakan , Alsaedi Ahmed S. , Alharbi Ghada , Alsanoosy Tawfeeq , Alsaeedi Abdullah
Published: June 2024

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired.
Lightweight Ghost Enhanced Feature Attention Network: An Efficient Intelligent Fault Diagnosis Method under Various Working Conditions
Authors: Dong Huaihao , Zheng Kai , Wen Siguo , Zhang Zheng , Li Yuyang , Zhu Bobin
Published: June 2024

Recent advancements in applications of deep neural network for bearing fault diagnosis under variable operating conditions have shown promising outcomes. However, these approaches are limited in practical applications due to the complexity of neural networks, which require substantial computational resources, thereby hindering the advancement of automated diagnostic tools. To overcome these limitations, this study introduces a new fault diagnosis framework that incorporates a tri-channel preprocessing module for multidimensional feature extraction, coupled with an innovative diagnostic architecture known as the Lightweight Ghost Enhanced Feature Attention Network (GEFA-Net). This system is adept at identifying rolling bearing faults across diverse operational conditions. The FFE module utilizes advanced techniques such as Fast Fourier Transform (FFT), Frequency Weighted Energy Operator (FWEO), and Signal Envelope Analysis to refine signal processing in complex environments. Concurrently, GEFA-Net employs the Ghost Module and the Efficient Pyramid Squared Attention (EPSA) mechanism, which enhances feature representation and generates additional feature maps through linear operations, thereby reducing computational demands. This methodology not only significantly lowers the parameter count of the model, promoting a more streamlined architectural framework, but also improves diagnostic speed. Additionally, the model exhibits enhanced diagnostic accuracy in challenging conditions through the effective synthesis of local and global data contexts. Experimental validation using datasets from the University of Ottawa and our dataset confirms that the framework not only achieves superior diagnostic accuracy but also reduces computational complexity and accelerates detection processes. These findings highlight the robustness of the framework for bearing fault diagnosis under varying operational conditions, showcasing its broad applicational potential in industrial settings. The parameter count was decreased by 63.74% compared to MobileVit, and the recorded diagnostic accuracies were 98.53% and 99.98% for the respective datasets.
The Pollution Characteristics and Fate of Microplastics in Typical Wastewater Treatment Systems in Northern China
Authors: Ma Yi , Bao Zhenkang , Cai Shangying , Wang Qiong , Dou Beibei , Niu Xiangyu , Meng Qingzhen , Li Penghao , Guo Xiaoying
Published: June 2024

This study focuses on the occurrence status and removal efficiency of microplastics in wastewater treatment plant processes. Analysis of effluent and sludge samples from the Wulongkou and Shuangqiao wastewater treatment plants in Zhengzhou revealed an overall microplastic removal efficiency of 95.64% and 92.53%, respectively, indicating the effectiveness of wastewater treatment plants in reducing microplastic emissions. Microplastics primarily exist in forms such as fiber, fragment, floc, film, and grain. Fibers are predominant in the effluent of the Wulongkou plant, while fibers and films predominate in the effluent of the Shuangqiao plant. Moreover, microplastics are predominantly sized below 500 μm, with larger microplastics (2–5 mm) exhibiting higher removal efficiencies after secondary treatment. Analysis of microplastic types revealed that PE is the most common type in the effluent of the Wulongkou plant, while the Shuangqiao plant predominantly contains PE and PA66. The abundance of microplastics in sludge samples was found to be 6.4 ± 0.8 items/g and 11.3 ± 2.3 items/g, highlighting sludge as an important sink for microplastics. Surface analysis of microplastics revealed characteristics such as wrinkles and cracks, with energy-dispersive spectroscopy indicating significant adsorption of heavy metal elements such as Zn, Hg, and Pb onto microplastic surfaces in sludge. These findings underscore the importance of microplastic removal in wastewater treatment processes and provide scientific evidence for the control and management of microplastic pollution in the future.
Bioinspired Coastal Barriers: A Preliminary Laboratory Study on the Hydraulic Performances of Shapes Inspired by Marine Organisms
Authors: Perricone Valentina , Contestabile Pasquale , Mele Antonio , Hassanpour Nasrin , Vicinanza Diego , Buono Mario
Published: June 2024

Coastal erosion, extreme climate events, and the loss of biodiversity are important consequences of climate change that directly impact our society. The needs to develop effective engineering practices using nature as model and mentor are now emerging aimed to develop high-performance coastal infrastructures integrating and sustaining local marine ecosystems. In this scenario, the present article describes the concept development and preliminary experimentation of simplified bioinspired models to evaluate their hydraulic performances. This study is part of a future vision to develop submerged barriers, printed with eco-friendly materials, characterized by high hydraulic performances and cable of supporting local biodiversity. Following a top-down approach, the functional features of key organisms were abstracted and transferred to create three bioinspired models. The hydraulic performance of these models is analyzed in terms of wave transmission, reflection, and dissipation under various wave conditions. Under certain circumstances, the bioinspired barriers demonstrate wave attenuation comparable to traditional submerged breakwaters. A method based on skin friction and drag-related processes provides a simple heuristic explanation of how the shape of the models affect the transmission coefficient. The results achieved offer valuable insights for the design of future coastal defense systems that are inspired by, and integrated with, natural ecosystems.
Machine Learning-Based Extraction Method for Marine Load Cycles with Environmentally Sustainable Applications
Authors: Sun Xiaojun , Gao Yingbo , Zhang Qiao , Ding Shunliang
Published: June 2024

The current lack of harmonized standard test conditions for marine shipping hinders the comparison of performance and compliance assessments for different types of ships. This article puts forward a method for extracting ship loading cycles using machine learning algorithms. Time-series data are extracted from real ships in operation, and a segmented linear approximation method and a data normalization technique are adopted. A hierarchical-clustering type of soft dynamic time-warping similarity analysis method is presented to efficiently analyze the similarity of different time-series data, using soft dynamic time warping (Soft-DTW) combined with hierarchical clustering algorithms from the field of machine learning. The problem of data bias caused by spatial and temporal offset characteristics is effectively solved in marine test condition data. The validity and reliability of the proposed method are validated through the analysis of case data. The results demonstrate that the hierarchically clustered soft dynamic time-warping similarity analysis method can be considered reliable for obtaining test cases with different characteristics. Furthermore, it provides input conditions for effectively identifying the operating conditions of different types of ships with high levels of energy consumption and high emissions, thus allowing for the establishment of energy-saving and emissions-reducing sailing strategies.
Data Drive—Charging Behavior of Electric Vehicle Users with Variable Roles
Authors: Wu Weihua , Wei Jieyun , Nam Eun-Young , Zhang Yifan , Chun Dongphil
Published: June 2024

The global proliferation of electric vehicles (EVs) has brought forth new challenges in electric vehicle (EV) charging infrastructure. This paper utilizes operational data from the 5G real-time system of EV and traffic platforms (5gRTS-ET) in China, encompassing 12,597,109 cases and 32,259 EVs. By employing frequency density analysis, a dynamic charging behavior model is devised to address the limitations of static models in accommodating the diverse roles of EV users. Analysis reveals distinct charging behavior preferences among three urban EV operation modes, paving the way for an adaptive model for integrating charging points into networked operations on the platform.
Definition of Food Consumption, Loss, and Waste
Authors: Ramaprasad Arkalgud , Kashyap Shwetmala
Published: June 2024

The global food system has three recognized challenges: (a) increasing the availability of food for consumption; (b) reducing food loss; and (c) reducing food waste. The increasing demand for food for consumption, the increasing quantity of food loss, and the corresponding increase in food waste are resulting in serious health, aesthetic, social, economic, and environmental problems due to a lack of appropriate planning and management. Despite its importance, there is no clear, concise, and comprehensive definition of food consumption, loss, and waste. Generally, food consumption, food loss, and food waste are dealt with separately. This article presents a logically constructed ontological framework of food consumption, loss, and waste. It gives equal importance to all three aspects of global food management. The systemic ontological framework is general, and the analysis can be applied to any country. The framework deconstructs the combinatorial complexity of the problem and explicates the pathways to manage the consumption, loss, and waste. The ontological framework encapsulates 19 × 11 × 7 × 4 × 6 = 35,112 possible components of the challenge. A critical analysis based on available data using the framework will help to develop strategies to deal with the problem. It can help us to discover the gaps and to find ways to bridge the gaps. It is a novel way to conceptualize food consumption, loss, and waste together.
Constrained MPPT Strategy for Sustainable Wave Energy Converters with Magnetic Lead Screw
Authors: Zhong Wei , Zhang Meng , Zhang Jiahui , Liu Jiaqi , Yu Haitao
Published: June 2024

Emerging magnetic lead screws (MLSs) have been proven to be promising in sustainable wave energy conversion areas due to their high efficiency and power density. This study is aimed at developing a constrained maximum power point tracking (MPPT) strategy for MLS-based wave energy converters (WECs). In this paper, the mechanism of the MLS is analyzed and the dynamic model of the MLS-based WEC is established. The variations in hydrodynamic coefficients were analyzed using ANSYS AQWA, based on which the theoretical MPPT requirements were explored. Afterward, two constraints (stroke and translator force constraint) were introduced to ensure the safe operation of the converter. An adaptive constrained genetic algorithm (ACGA) was applied to realize MPPT under constraints. For irregular wave situations, an extended Kalman filter (EKF) was applied to estimate the frequency and amplitude of the wave excitation force with which the constrained GA can be realized. Simulations and experiments were carried out to verify the constrained MPPT. In the two cases (wind speed u = 7 m/s and u = 10 m/s) of the simulation, the proposed ACGA can improve the energy harvest rate by 3.95% and 3.57% compared to the standard constrained genetic algorithm (SCGA), while this rate was improved by 6% in the experimental case.
Faith and Sustainability: Exploring Religiosity’s Impact on Intentions to Reduce Food Waste
Authors: Baran Tamer , Lupu Cristina , Privitera Donatella
Published: June 2024

This study investigates the influence of religiosity on environmental concern and intentions to reduce food waste in Islam and Christianity. The study involves 575 adult participants, predominantly Muslims and Christians, utilizing the Duke University Religion Index (DUREL) religiosity scale, environmental concern scale, and food waste reduction intention scale. The investigation was conducted in Romania, Italy, and Turkey. Utilizing structural equation modeling (SEM) via AMOS software, the research reveals that religiosity significantly affects environmental concern in both religious groups. Furthermore, environmental concern acts as a mediator between religiosity and both Muslim and Christian participants. Notably, the impact of religiosity on the intention to reduce food waste is significant among Muslims, but is not observed among Christians. The study underscores the importance of integrating religiosity into consumer behavior research, especially concerning food waste reduction. It suggests that religiosity and environmental concern are crucial for successful campaigns targeting food waste reduction among Muslim and Christian consumers.
A Rapid Assessment Technique for Identifying Future Water Use and Pesticide Risks Due to Changing Cropping Patterns
Authors: Mullen Jeffrey D. , Rubin Mary Katherine
Published: June 2024

Changing weather patterns have already put pressure on cropping systems around the globe. Projected increases in mean temperatures and variance in precipitation will likely affect the profitability of current cropping patterns, leading to shifts in which crops are grown in a given location. The pressure on water resources in a location, in terms of both water quantity and water quality, will also change with the types of crops grown. While the southeastern United States is projected to become warmer under each of the representative concentration pathways, it is also projected to become somewhat wetter. California’s Central Valley, where much of the fresh produce in the US is grown, will likely continue to suffer significant and extended droughts. The southeastern US is a prime candidate for expanding fresh produce production in response to reduced yields in the west. This paper explores the consequences on water withdrawals and water quality of shifting from row crop to vegetable production in the southeastern US. The water quality consequences are based on changes in pesticide products and application rates. The water quantity consequences are based on crop water needs. The methodology used here can be applied to other production systems around the world. Identifying the water quality and quantity implications of shifting cropping patterns is critical to the long-term sustainability of water resources.
The Elusive Phenomenon: Unveiling Deconsumption in the EU
Authors: Niewiadomski Michał , Niemczyk Agata , Gródek-Szostak Zofia , Surówka Marcin
Published: June 2024

This article analyzes the phenomenon of deconsumption, which is relatively new and insufficiently researched or defined. Based on a review of the literature on the subject, it was found that there was little interest in deconsumption compared with sustainable consumption. Moreover, the number of scientific publications was negligible as the concept of deconsumption was rarely studied as a phenomenon. In addition, it should be noted that deconsumption can play a role in sustainable development and care for the environment and natural resources. Our study on this phenomenon sought to determine whether the phenomenon has spread to a noticeable degree, despite the deconsumption trend, which is important for its effective popularization in societies. Therefore, the aim of this study was to analyze whether the deconsumption phenomenon was reflected in macroeconomic data on consumption in selected European Union countries prior to the COVID-19 pandemic. The analysis of macroeconomic data on per capita consumption in the years 2000–2019 did not reveal a clear phenomenon of deconsumption; however, changes in the consumption structure were identified. In some countries, consumption fluctuated or decreased in certain sectors, suggesting the possible emergence of deconsumption. The computation method used in this study was fuzzy c-means clustering (FCM).
Sustainable Mobility Transition: A SWOT-AHP Analysis of the Case Study of Italy
Authors: Ben Ali Marwa , Rauch Erwin
Published: June 2024

The significance of sustainable mobility transition projects extends beyond borders. Transportation, particularly passenger vehicles, is a crucial sector in achieving sustainability. Therefore, prioritizing sustainable green mobility has led to the inclusion of alternative solutions, with a focus on accelerating the shift towards electric vehicle (EV) technologies and implementing a ban on the sale of new petrol and diesel cars in all European countries by 2035. Italy has been making progress in this area as the country seeks to address environmental concerns, reduce emissions, and promote sustainable transportation. However, compared to other European countries in 2024, Italy still has a long way to go to achieve a sustainable market share. In this regard, this article aims to address several questions related to the promotion and scaling up of the electric mobility transition project in Italy, taking into account the current situation. Specifically, it seeks to identify internal and external factors associated with this technology ecosystem, along with their relative importance. To conduct this study, a strengths, weaknesses, opportunities, and threats (SWOT) analysis was conducted to identify the factors, which was followed by the analytical hierarchy process (AHP) methodology to determine their priority and importance. A total of 8 internal factors and 14 external factors were analyzed, and their overall priority was determined. This study reveals that it is crucial to capitalize on the opportunities and strengths related to technology ecosystems while effectively mitigating the threats and technological limitations in order to scale up technology adoption. In particular, strengths S1 and S3 were given the highest overall priority scores, suggesting that they are the most important factors to leverage for the successful adoption of the technology. These prioritized factors and subfactors are crucial for expediting the transition process and can influence consumers’ decisions. However, without a substantial increase in consumer understanding and knowledge of these technologies, public education campaigns will be necessary. The significance of this study is paramount, and its results can contribute to the continuous enhancement in the formulation of practical plans and regulations to promote sustainable transportation, taking into account the identified factors.
Optimizing Renewable Energy Systems for Water Security: A Comparative Study of Reanalysis Models
Authors: Vargas-Brochero José , Hurtado-Castillo Sebastián , Altamiranda Jesús , de Menezes Filho Frederico Carlos M. , Beluco Alexandre , Canales Fausto A.
Published: June 2024

The current global scenario of unequal access to water and electricity motivates the search for solutions based on available resources, such as renewable energies and desalination. Additionally, adequate sizing of renewables requires extensive and reliable time series, which are usually unavailable. Reanalysis models are an option to consider, but only after evaluating their local accuracy, generally through performance metrics. This study evaluated the performance of the solar radiation, temperature, and wind speed products from MERRA2 and ERA5-Land in comparison to ground data, as well as their influence on the optimal initial configuration of a renewable energy system for desalination in La Guajira, Colombia. HOMER Pro was the software tool employed to establish the best arrangements for the resulting renewable power systems, and the study included a sensitivity analysis considering different annual capacity shortages, operating hours, and energy needs for desalting. ERA5-Land performed better than MERRA2 in matching the time series from the local station. The relative error of the cost of electricity of systems dimensioned from reanalysis was less than 3% compared to systems from ground measurements, with a renewable fraction above 98%. For the study area, ERA5-Land reanalysis represents a reliable alternative to address the scarcity of solar resource records, but both reanalyses failed to reproduce the wind speed regime.
Leveraging Deep Learning Models for Targeted Aboveground Biomass Estimation in Specific Regions of Interest
Authors: Arumai Shiney Selvin Samuel , Geetha Ramachandran , Seetharaman Ramasamy , Shanmugam Madhavan
Published: June 2024

Over the past three decades, a lot of research has been conducted on remote sensing-based techniques for estimating aboveground biomass (AGB) in forest ecosystems. Due to the complexity of satellite images, the conventional image classification methods have been unable to meet the actual application needs. In our proposed work, the estimation of aboveground biomass has been performed on the basis of a Region of Interest (RoI). Initially, this method is employed to measure the green portions of the areas at the local level. The biomass of the subtropical woods in the areas of India, Indonesia, and Thailand is estimated in this work, using data from Deep Globe LIDAR images. Initially, the satellite images are pre-processed. The ROI method is used to select the green portion of the area. The green portion in the satellite images is segmented using the K-means algorithm and binary classification. An empirical formula is used to calculate the carbon weight. The results obtained show 92% accuracy.
Causal Forest Machine Learning Analysis of Parkinson’s Disease in Resting-State Functional Magnetic Resonance Imaging
Authors: Solana-Lavalle Gabriel , Cusimano Michael D. , Steeves Thomas , Rosas-Romero Roberto , Tyrrell Pascal N.
Published: June 2024

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson’s disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson’s Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.
Therapeutic Modulation of Arginase with nor-NOHA Alters Immune Responses in Experimental Mouse Models of Pulmonary Tuberculosis including in the Setting of Human Immunodeficiency Virus (HIV) Co-Infection
Authors: Chauhan Sadhana , Nusbaum Rebecca J. , Huante Matthew B. , Holloway Alex J. , Endsley Mark A. , Gelman Benjamin B. , Lisinicchia Joshua G. , Endsley Janice J.
Published: June 2024

L-arginine metabolism is strongly linked with immunity to mycobacteria, primarily through the antimicrobial activity of nitric oxide (NO). The potential to modulate tuberculosis (TB) outcomes through interventions that target L-arginine pathways are limited by an incomplete understanding of mechanisms and inadequate in vivo modeling. These gaps in knowledge are compounded for HIV and Mtb co-infections, where activation of arginase-1 due to HIV infection may promote survival and replication of both Mtb and HIV. We utilized in vitro and in vivo systems to determine how arginase inhibition using Nω-hydroxy-nor-L-arginine (nor-NOHA) alters L-arginine pathway metabolism relative to immune responses and disease outcomes following Mtb infection. Treatment with nor-NOHA polarized murine macrophages (RAW 264.7) towards M1 phenotype, increased NO, and reduced Mtb in RAW macrophages. In Balb/c mice, nor-NOHA reduced pulmonary arginase and increased the antimicrobial metabolite spermine in association with a trend towards reduced Mtb CFU in lung. In humanized immune system (HIS) mice, HIV infection increased plasma arginase and heightened the pulmonary arginase response to Mtb. Treatment with nor-NOHA increased cytokine responses to Mtb and Mtb/HIV in lung tissue but did not significantly alter bacterial burden or viral load. Our results suggest that L-arginine pathway modulators may have potential as host-directed therapies to augment antibiotics in TB chemotherapy.
Photocatalytic Degradation of Algal Organic Matter Using TiO2/UV and Persulfate/UV
Authors: Leite Luan de Souza , Hoffmann Maria Teresa , dos Santos Danilo Vitorino , Daniel Luiz Antonio
Published: June 2024

Eutrophication in water reservoirs releases algal organic matter (AOM), a key precursor to the formation of disinfection by-products (DBPs) during the disinfection process. Typical drinking water treatment is not efficient for AOM removal, and advanced treatments are necessary for the removal of residual AOM before chlorination. UV-based technology with PS and TiO2 is widely used as a pre-oxidation step in water treatment; however, no publications have focused on them for AOM degradation. In this context, this work investigated the effect of oxidant concentration (0.1 to 0.5 g∙L−1) and pH (6 to 10) on AOM degradation with TiO2/UV and persulfate (PS)/UV using response surface methodology. In general, PS/UV was more effective in removing protein, while TiO2/UV was more effective in carbohydrate degradation. TiO2/UV removals varied from 27 to 57% for protein and from 48 to 86% for carbohydrates. The optimal condition (57% for protein and 86% for carbohydrates) was obtained using 0.5 g∙L−1 TiO2 at pH 10. PS/UV removals varied from 33 to 81% for protein and from 24 to 53% for carbohydrates. The optimal condition (81% for protein and 53% for carbohydrates) was obtained using 0.5 g∙L−1 PS concentration at pH 8. Degradation kinetics showed a good fit to the pseudo-first-order model (R2 > 95%) for both processes. The DBP formation reductions observed with TiO2/UV—trihalomethane (THM) (85 to 86%) and chloral hydrate (CH) (94 to 96%)—were similar to the efficiencies observed for PS/UV—THM (87 to 89%) and CH (83 to 88%). These results show the efficiency of UV-based technology for AOM degradation and the control of DBP formation.
Antibiotics in Wastewater Treatment Plants in Tangshan: Perspectives on Temporal Variation, Residents’ Use and Ecological Risk Assessment
Authors: Dong Zhuo , Hu Jian , Wang Pengjie , Han Gengtao , Jia Zheng
Published: June 2024

In 2023, this study monitored nine types of antibiotics in the influent and effluent of wastewater treatment plants (WWTPs) in the urban and suburban areas of Tangshan. The total antibiotics concentration detected in influent WWTPs was highest in winter, followed by spring, summer, and autumn. The antibiotics concentration in influent and effluent urban WWTPs was higher than that in the suburban WWTPs in spring, summer, and winter, while the trend was reversed in autumn. Roxithromycin and oxytetracycline had a risk quotient (RQ) value of ≥0.1 in the effluent of WWTPs in winter, indicating that they are medium-risk antibiotics that pose a risk to the aquatic ecosystem after discharge. In the study area, the per capita pollution load of antibiotics was highest in spring, summer, and autumn for sulfamethoxazole, while it was highest in winter for ofloxacin. In the urban area, the use of roxithromycin, sulfamethoxazole, sulfamethoxazole, and ofloxacin was highest in spring, summer, autumn, and winter, respectively, while in suburban areas, the use of sulfamethoxazole, norfloxacin, sulfamethoxazole, and ofloxacin was highest during the same period. The use of antibiotics in the urban area was one order of magnitude higher than that in suburban areas, indicating a possible overuse of antibiotics in urban environments.
Effect of Green Infrastructure with Different Woody Plant Root Systems on the Reduction of Runoff Nitrogen
Authors: Zhang Bei , Chen Liang , Gao Taolve
Published: June 2024

Rainfall-runoff nitrogen (N) pollution has emerged as the primary source of water contamination due to rapid urbanization. Green infrastructure (GI), as the representative measure, is widely used in controlling N pollution in runoff. However, there is limited research on the impact of woody plants on N reduction in GIs. Therefore, this study aimed to investigate the influence and relationship of Sophora japonica (with tap root) and Malus baccata (with fibrous root) on N removal in GIs. Utilizing the advanced root analysis software WinRHIZO (version 4.0b), a meticulous examination of the morphological traits of plant roots was conducted. The findings unveiled a striking contrast between the root systems of two species: S. japonica primarily boasts a vertically oriented root configuration, whereas M. baccata’s root system is characterized by an extensively lateral, or horizontal, growth pattern. Specifically, in comparison to S. japonica, the horizontal roots of M. baccata demonstrated a substantial superiority, with their total root length measuring 10.95 times longer, the surface area spanning 6.25 times wider, and the cumulative volume being 3.93 times greater. For comparing the load reduction rates on runoff NH3-N, NO3-N, and TN of the different root morphologies’ GIs, S. japonica GI had the highest purification effect on the three pollutants, and the average load reduction rates of three pollutants reached 67.74%, 33.83%, and 38.96%, respectively, which were 11.42%, 27.46%, and 6.16% higher than those of the control. The variance contribution rate of vertical root and horizontal root characteristics on runoff nitrogen load reduction accounted for 86.47% of the total root contribution rate. The volume of vertical roots emerged as the most crucial characteristic factor affecting the reduction of N load.
Operating Energy Needed for Desalination Systems in Cogeneration Plants
Authors: Zeitoun Obida
Published: June 2024

This study investigated the energy requirement for running desalination units coupled to cogeneration plants. Various cogeneration systems were explored using power- and heat-allocated approaches. The specific work and heat necessary for operating different desalination systems were determined. The investigation revealed that the specific work and heat remain consistent regardless of the desalination daily capacity. It was observed that the energy demand for operating a desalination system mainly relies on power plant efficiency. The investigation revealed that the energy demand for a plain multi-effect desalination system was lower than that for multi-effect desalination with thermal vapor compression. Additionally, the energy requirement for a multi-effect desalination system with preheaters was lower than that for plain multi-effect desalination. Comparisons also indicated that the energy demand of multi-stage flash exceeds that of different multi-effect desalination systems. Based on the primary thermal energy input, a universal performance ratio was used to evaluate the desalination unit performance. Furthermore, a new correlation was proposed to predict the universal performance ratio.
Development of a Lightweight Floating Object Detection Algorithm
Authors: Xian Rundong , Tang Lijun , Liu Shenbo
Published: June 2024

YOLOv5 is currently one of the mainstream algorithms for object detection. In this paper, we propose the FRL-YOLO model specifically for river floating object detection. The algorithm integrates the Fasternet block into the C3 module, conducting convolutions only on a subset of input channels to reduce computational load. Simultaneously, it effectively captures spatial features, incorporates reparameterization techniques into the feature extraction network, and introduces the RepConv design to enhance model training efficiency. To further optimize network performance, the ACON-C activation function is employed. Finally, by employing a structured non-destructive pruning approach, redundant channels in the model are trimmed, significantly reducing the model’s volume. Experimental results indicate that the algorithm achieves an average precision value (mAP) of 79.3%, a 0.4% improvement compared to yolov5s. The detection speed on the NVIDIA GeForce RTX 4070 graphics card reaches 623.5 fps/s, a 22.8% increase over yolov5s. The improved model is compressed to a volume of 2 MB, representing only 14.7% of yolov5s.
Review of cultural and educational tourism objects based on the samurai heritage of Japan
Authors:   Graduate School of Management, People’s Friendship University of Russia named after Patrice Lumumba, Moscow, Catachin A. S. , Zeveke O. Yu. ,   Graduate School of Management, People’s Friendship University of Russia named after Patrice Lumumba, Moscow
Published: June 2024

The article examines the main objects of display of the cultural samurai heritage of Japan: historical monuments, museums and architectural objects of cultural value, as well as festivals of Japan with samurai.
Sustainable Livestock Farming with Oil Seed Crops and Their By-Products
Authors: Ahmed Ibrar , Riaz Roshan , Sızmaz Özge
Published: June 2024

The increasing human population and food shortage are fueling the demand for alternative feed resources for animals not meant for human consumption. Oil seeds and their derivatives are suitable options to meet the escalating global demand for animal feed proteins; camelina is one of them. Camelina sativa (CS), an ancient oilseed crop belonging to the Brassicaceae family, is known for its resistance to drought and cold, as well as its various uses for meal, oil, and other products. However, it also has some anti-nutritional factors (ANF) that can limit its use as animal feed. These ANFs can be reduced by various methods, such as enzyme addition, heat treatment, fermentation, or genetic engineering. CS and its by-products can affect animal metabolism, especially lipid metabolism and hormone levels, and can also improve the fat profile of meat and milk products, making them more suitable for human consumption and health. CS and its by-products achieved weight gain and protected dietary PUFAs, but decreased bio-hydrogenation intermediates. Small ruminants fed CS-supplemented diets produced meat with a suitable fat profile for human consumption. Feeding with CS seeds and derivatives decreased milk fat concentration, yield, and fat-corrected milk. Camelina forage, however, increased the milk fat percentage. The effects of CS and its by-products on milk fatty acid composition were contradictory. CS meals may improve the composition of milk products, making them healthier for humans. Researchers need to determine how CS meals can be used in dairy ewe and goat diets at different life stages.
Klinik Örneklerden İzole Edilen Staphylococcus aureus Suşlarının Antibiyotiklere Direnç Oranlarının Araştırılması
Authors: Varış Karakaş Zehra , Behçet Mustafa
Published: June 2024

Amaç: Hastane ve toplum kökenli enfeksiyonların en önemli etkenleri arasında yer alan Staphylococcus aureus (S. aureus) deri-yumuşak doku enfeksiyonları başta olmak üzere osteomyelit, septik artrit, endokardit, pnömoni ve bakteriyemiye neden olabilmekte ve antibiyotiğe direnç bakımından en inatçı patojenlerden biridir. Bu çalışmada klinik örneklerden S. aureus izolatlarının antibiyotik direnç profillerinin belirlenerek ampirik tedavi ve antibiyotik kullanım politikalarına katkı sağlanması amaçlanmıştır.
Gereç ve Yöntem: S. aureus izolatlarının bakteri tanımlanmasında konvansiyonel yöntemler ve otomatize yöntemler antibiyotik duyarlılık testi için ise yalnızca otomatize yöntemler kullanılmıştır. Antibiyotik duyarlılık testleri EUCAST kriterlerine göre değerlendirilmiştir.
Bulgular: Toplam 513 S. aureus izolatının %34,1’i yara, %29,2’si kan, %15’i balgam ve %21,7’si diğer örneklerden elde edilmiş ve izolatların %25,5’i Metisiline dirençli Staphylococcus aureus (MRSA) olarak saptanmıştır. Metisilin direnci 2020 yılında 2018 yılına göre artmış, 2021 yılında ise 2020 yılına göre azalmıştır. MRSA izolatlarında 2020, 2021 yıllarında ko-trimoksazol ve 2021 yılında levofloksasin direncinin diğer yıllara göre azaldığı görülmüştür. MSSA ve MRSA'da linezolid, vankomisin, teikoplanin, tigesiklin, daptomisine ve ek olarak MSSA'larda gentamisin'e karşı direnç gelişmediği saptanmıştır.
Sonuç: Günümüzde en önemli halk sağlığı sorunlarından biri olan antibiyotik direncini kontrol altına almak için her hastanenin ampirik tedavide ampirik tedavide her hastanenin antibiyotik direnç profillerini belli aralıklarla takip etmesi, uygunsuz antibiyotik kullanımının engellenmesi ve enfeksiyon kontrol önlemlerine uyulması bu bakterilerin direnç oranlarının azalmasına katkı sağlayacaktır.
Assessment of geographical location and population characteristics of Thu Dau Mot city in the past and presente
Authors: Thinh Pham Van
Published: June 2024

Before becoming a first-class urban area, the administrative, economic, cultural, and educational center of Binh Duong province, Thu Dau Mot was considered the "capital" of the Southeast region in many historical periods. This geographical location has brought together many important resources for the socio-economic development of the locality and the region. This article analyzes two development resources of Thu Dau Mot: natural resources (geographical location - the central hub of the Southeast region) and social resources (people in terms of harmonious relationships in the development of a region). From analyzing these two resources, the article discusses issues that need attention in building strategic directions to ensure Thu Dau Mot city continues to uphold its central position in the Southeast region, integrating the city regionally and internationally in terms of economy, society, and culture.