bluuebunny/crossref_metadata_2025 huggingface dataset:
Early-Life Metabolic Traits and Physical Fitness in Tarahumara, Mennonite, and Mestizo Adolescents from Northern Mexico
Authors: Nájera-Longoria Raúl J. , Leal-Berumen Irene , Rangel-Ledezma Yunuen S. , Licón-Trillo Angel , Moreno-Brito Verónica , González-Rodríguez Everardo , Conchas-Ramírez Miguel , Alcalá-Sánchez Imelda G.
Published: July 2023

The WHO identifies high BMI, high blood pressure, and high fasting plasma glucose as chronic disease risk factors, whereas physical fitness is identified as a protective behavioral factor. This study responds to the rising interest in assessing metabolic factors and physical activity within young populations of Mestizo, Tarahumara, and Mennonite from Chihuahua Mexico, due to its strong relationship with disease development and low well-being. A cross-sectional study was conducted with 201 teenagers from rural towns in Northern Mexico, and relationships between physical fitness and cardio-metabolic risk related to anthropometric, glycolipid, and vascular function factors were assessed. ANOVA-tested differences among ethnic groups using physical fitness as a grouping variable and measures of cardio-metabolic risks were used as dependent variables. A stepwise regression analysis allowed us to identify the best predictors for physical fitness. Clinical risk factors were analyzed by ethnic group and sex. No differences were found among ethnic groups in physical fitness and cardio-metabolic health risks; sex differentiated higher health risks related to behavioral factors, since young women showed lower physical fitness across ethnicities. Clinically, the Mestizo sample showed higher numbers of individuals with one risk factor. Mennonites showed a high frequency of anthropometric and fitness health risks with low glycolipid and vascular risks. Tarahumara had fewer risk factors as compared with both Mestizo and Mennonite. Rural populations are harder to reach, both for health assessment and intervention; health professionals must work close to local community organizations to gain access.
Alpha Lipoic Acid Efficacy in PCOS Treatment: What Is the Truth?
Authors: Guarano Alice , Capozzi Anna , Cristodoro Martina , Di Simone Nicoletta , Lello Stefano
Published: July 2023

Polycystic ovary syndrome (PCOS) is among the most common female endocrinopathies, affecting about 4–25% of women of reproductive age. Women affected by PCOS have an increased risk of developing metabolic syndrome, type 2 diabetes mellitus, cardiovascular diseases, and endometrial cancer. Given the pivotal role of insulin resistance (IR) in the pathogenesis of PCOS, in the last years, many insulin-sensitizing factors have been proposed for PCOS treatment. The first insulin sensitizer recommended by evidence-based guidelines for the assessment and treatment of PCOS was metformin, but the burden of side effects is responsible for treatment discontinuation in many patients. Inositols have insulin-mimetic properties and contribute to decreasing postprandial blood glucose, acting by different pathways. ALA is a natural amphipathic compound with a very strong anti-inflammatory and antioxidant effect and a very noteworthy role in the improvement of insulin metabolic pathway. Given the multiple effects of ALA, a therapeutic strategy based on the synergy between inositols and ALA has been recently proposed by many groups with the aim of improving insulin resistance, reducing androgen levels, and ameliorating reproductive outcomes in PCOS patients. The purpose of this study is to review the existing literature and to evaluate the existing data showing the efficacy and the limitation of a treatment strategy based on this promising molecule. ALA is a valid therapeutic strategy applicable in the treatment of PCOS patients: Its multiple actions, including antinflammatory, antioxidant, and insulin-sensitizing, may be of utmost importance in the treatment of a very complex syndrome. Specifically, the combination of MYO plus ALA creates a synergistic effect that improves insulin resistance in PCOS patients, especially in obese/overweight patients with T2DM familiarity. Moreover, ALA treatment also exerts beneficial effects on endocrine patterns, especially if combined with MYO, improving menstrual regularity and ovulation rhythm. The purpose of our study is to review the existing literature and to evaluate the data showing the efficacy and the limitations of a treatment strategy based on this promising molecule.
Fish Consumption during Pregnancy in Relation to National Guidance in England in a Mixed-Methods Study: The PEAR Study
Authors: Beasant Lucy , Ingram Jenny , Taylor Caroline M.
Published: July 2023

Guidance on foods to limit or avoid in pregnancy is provided on the NHS website for England. Advice on fish consumption is related to exposure to mercury, dioxins and polychlorinated biphenyls, which may have adverse effects on fetal neurodevelopment. Our aim was to provide evidence on the effectiveness of the guidance in minimising exposure to toxins while maximising nutrient intake in a mixed-methods study. An online questionnaire on fish consumption before/during pregnancy was completed by postpartum women (≤12 months) in England (n = 598). A subsample of participants was invited to take part in an interview (n = 14). Women who ate fish before pregnancy reduced their intakes of both oily and white fish during pregnancy, with some avoiding it altogether. Women did not exceed the limit on tinned tuna, but there was evidence of mis-recall on the suggested limit. Overall intakes of fish were below that recommended during pregnancy (36% compliance for pre-pregnancy consumers). Barriers to fish consumption included risk aversion, confusion over specific details of the guidance, cost, availability, family preferences and smell/taste. Clarity and simplicity of the NHS guidance, with an overall message on the number of portions of fish a week advised prominently shown, would help pregnant women to benefit from the nutrients in fish while minimising exposure to toxins. The guidance on the number of cans of tuna advised per week is poorly recalled and needs to be disseminated accurately. The guidance on shark/marlin/swordfish could receive less prominence as it is rarely eaten by pregnant women in England.
Prevalence and Pattern of Birth Defects in Saudi Arabia: A Systematic Review of Observational Studies
Authors: Eltyeb Ebtihal Elameen , Halawi Majidah Hussain Asiri , Tashari Thekra Badr Mohammed , Alharbi Khaled , Alsayari Ohoud Saad , Albarrak Danah Abdullah , Eltayeb Raga Abdelkhalig , Al-Makramani Ali Ali Ahmed , Medani Isameldin Elamin M.
Published: July 2023

Introduction: Birth defects are a significant concern since they can lead to permanent disability and death. This study comprehensively reviews the prevalence and patterns of birth defects in Saudi Arabia. Methods: A systematic analysis of the literature retrieved from three databases (Pub Med, Science Direct, and the Saudi digital library) published between 1989 and 2022 was performed. Observational studies that addressed the prevalence and patterns of birth defects in Saudi Arabia were chosen based on the eligibility criteria, while systematic reviews, review articles, non-relevant articles, and studies that did not fulfill the eligibility criteria were excluded. Quality and risk of bias were evaluated based on the JBI and GRADE tools, respectively. Results: We identified 26 eligible publications of 1277 records that included 297,668 patients from different regions of Saudi Arabia. The highest overall prevalence of birth defects was 46.5 per 1000 live births compared to a lowest rate of 8.6 per 1000 in one study. Several studies have reported positive associations of consanguinity, maternal folic acid supplementation, family history of birth defects or genetic abnormalities, and maternal co-morbidities. The most frequent birth defects include cardiac, genitourinary, craniofacial, and nervous system defects. Conclusion: Robust findings have improved our understanding of the prevalence and pattern of birth defects in Saudi Arabia. Importantly, future studies will likely require multicenter collaboration to arrive at appropriate sample sizes in the context of the effects of risk factors on elevated prevalence. Furthermore, quantitative data require careful evaluation in more complex statistical models.
Gellan Gum/Alginate Microparticles as Drug Delivery Vehicles: DOE Production Optimization and Drug Delivery
Authors: Carrêlo Henrique , Cidade Maria Teresa , Borges João Paulo , Soares Paula
Published: July 2023

Gellan gum is a biocompatible and easily accessible polysaccharide with excellent properties to produce microparticles as drug delivery systems. However, the production methods often fail in reproducibility, compromising the translational potential of such systems. In this work, the production of gellan gum-based microparticles was optimized using the coaxial air flow method, and an inexpensive and reproducible production method. A design of experiments was used to identify the main parameters that affect microparticle production and optimization, focusing on diameter and dispersibility. Airflow was the most significant factor for both parameters. Pump flow affected the diameter, while the gellan gum/alginate ratio affected dispersibility. Microparticles were revealed to be sensitive to pH with swelling, degradation, and encapsulation efficiency affected by pH. Using methylene blue as a model drug, higher encapsulation, and swelling indexes were obtained at pH 7.4, while a more pronounced release occurred at pH 6.5. Within PBs solutions, the microparticles endured up to two months. The microparticle release profiles were studied using well-known models, showing a Fickian-type release, but with no alteration by pH. The developed microparticles showed promising results as drug-delivery vehicles sensitive to pH.
Hydrophilic Copolymers with Hydroxamic Acid Groups as a Protective Biocompatible Coating of Maghemite Nanoparticles: Synthesis, Physico-Chemical Characterization and MRI Biodistribution Study
Authors: Charvátová Hana , Plichta Zdeněk , Hromádková Jiřina , Herynek Vít , Babič Michal
Published: July 2023

Superparamagnetic iron oxide nanoparticles (SPION) with a “non-fouling” surface represent a versatile group of biocompatible nanomaterials valuable for medical diagnostics, including oncology. In our study we present a synthesis of novel maghemite (γ-Fe2O3) nanoparticles with positive and negative overall surface charge and their coating by copolymer P(HPMA-co-HAO) prepared by RAFT (reversible addition–fragmentation chain-transfer) copolymerization of N-(2-hydroxypropyl)methacrylamide (HPMA) with N-[2-(hydroxyamino)-2-oxo-ethyl]-2-methyl-prop-2-enamide (HAO). Coating was realized via hydroxamic acid groups of the HAO comonomer units with a strong affinity to maghemite. Dynamic light scattering (DLS) demonstrated high colloidal stability of the coated particles in a wide pH range, high ionic strength, and the presence of phosphate buffer (PBS) and serum albumin (BSE). Transmission electron microscopy (TEM) images show a narrow size distribution and spheroid shape. Alternative coatings were prepared by copolymerization of HPMA with methyl 2-(2-methylprop-2-enoylamino)acetate (MMA) and further post-polymerization modification with hydroxamic acid groups, carboxylic acid and primary-amino functionalities. Nevertheless, their colloidal stability was worse in comparison with P(HPMA-co-HAO). Additionally, P(HPMA-co-HAO)-coated nanoparticles were subjected to a bio-distribution study in mice. They were cleared from the blood stream by the liver relatively slowly, and their half-life in the liver depended on their charge; nevertheless, both cationic and anionic particles revealed a much shorter metabolic clearance rate than that of commercially available ferucarbotran.
Optimization of Electrical and Mechanical Properties through the Adjustment of Design Parameters in the Wet Spinning Process of Carbon Nanotube/Polyvinylidene Fluoride Fibers Using Response Surface Methodology
Authors: Choi Chan-Woong , Jin Ji-Won , Kang Ki-Weon
Published: July 2023

The optimal process conditions for fabricating carbon nanotube (CNT)/polyvinylidene fluoride (PVDF) fibers with varying properties using a wet spinning process were experimentally determined. A dope solution was prepared using multi-walled nanotubes, PVDF, and dimethylacetamide, and appropriate materials were selected. Design parameters affecting the chemical and physical properties of CNT/PVDF fibers, such as bath concentration, bath temperature, drying temperature, and elongation, were determined using a response surface method. The wet-spinning conditions were analyzed based on the tensile strength and electrical conductivity of the fibers using an analysis of variance and interaction analysis. The optimized process conditions for fabricating CNT/PVDF fibers with different properties were derived and verified through fabrication using the determined design parameters.
Monitoring Individual Tree Phenology in a Multi-Species Forest Using High Resolution UAV Images
Authors: Kleinsmann Jasper , Verbesselt Jan , Kooistra Lammert
Published: July 2023

Monitoring tree phenology is important for understanding ecosystem functioning and for assessing ecosystem responses to climate change. Satellite imagery offers open-access global coverage but is restricted to forest-level analyses, due to its coarse spatial resolution. Unmanned aerial vehicle (UAV) imagery can monitor phenology at the individual tree level by utilizing a centimeter-scale resolution. Two research objectives were identified for this study: (1) to derive phenological metrics at the individual tree level, using various vegetation indices (VIs); and (2) to assess the accuracy of automatic crown delineation in a diverse ecosystem. To achieve this, fourteen multi-spectral UAV flights were performed, and the ability of the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), optimized soil-adjusted vegetation index (OSAVI), and chlorophyll index red-edge (CIre) to model seasonal phenology was assessed. A double logistic model was fitted on the VI observations for each individual tree, to derive the start of season (SOS) and end of season (EOS). Individual tree crowns were delineated automatically using marker-controlled watershed segmentation (MCWS), and the treetops were identified using a local maximum filter (LMF). Overall, the automatic segmentation performed well (F-score: 0.79, IoU: 0.58), with higher accuracies in single-species areas, while it underperformed in complex mixed forest structures. All VIs captured a strong seasonal signal for the deciduous trees and derived SOS and EOS estimates consistent with literature and ground observations. General phenological patterns included an early silver birch SOS, a quick beech budburst, and large within-species phenology variations for oak trees. Seasonal VI variation for coniferous evergreen trees was limited, and the resulting phenology estimates proved unreliable. In conclusion, these findings emphasize the capabilities of UAV imagery for individual tree crown phenology monitoring. However, they also show the difficulty of monitoring evergreen phenology with the commonly-used VIs and stress the need for further investigations.
Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data
Authors: Yu Han , Luo Zengliang , Wang Lunche , Ding Xiangyi , Wang Shaoqiang
Published: July 2023

Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote sensing can improve the accuracy of flood susceptibility modeling needs further study. In this study, a framework was proposed for improving the accuracy of flood susceptibility prediction (FSP) by combining machine learning models and the expanded FID using Sentinel-1A radar images. Five widely used machine learning models were employed to verify the accuracy of the proposed method by taking Wuhan City as a case study, including the random forest (RF), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) models. Sentinel-1A images from time points before, during, and after flood events were used to expand the FID for training the machine learning models. The results showed that the performance of the machine learning models for predicting flood susceptibility was improved greatly by considering the expanded FID, being improved by approximately 1.14–19.74% based on the area under the receiver operating characteristic curve (AUC). Among the used machine learning models, taking into account all the statistical indicators, the ANN showed the best performance, while the SVM showed the best generalization performance in Wuhan City. According to the results of the ANN model, approximately 19% of the area in Wuhan City, mainly distributed near rivers and lakes, is at a high flood susceptibility level. This study provides an essential reference for flood susceptibility analyses in regions with limited flood sampling data.
Conversion of Upper-Limb Inertial Measurement Unit Data to Joint Angles: A Systematic Review
Authors: Fang Zhou , Woodford Sarah , Senanayake Damith , Ackland David
Published: July 2023

Inertial measurement units (IMUs) have become the mainstay in human motion evaluation outside of the laboratory; however, quantification of 3-dimensional upper limb motion using IMUs remains challenging. The objective of this systematic review is twofold. Firstly, to evaluate computational methods used to convert IMU data to joint angles in the upper limb, including for the scapulothoracic, humerothoracic, glenohumeral, and elbow joints; and secondly, to quantify the accuracy of these approaches when compared to optoelectronic motion analysis. Fifty-two studies were included. Maximum joint motion measurement accuracy from IMUs was achieved using Euler angle decomposition and Kalman-based filters. This resulted in differences between IMU and optoelectronic motion analysis of 4° across all degrees of freedom of humerothoracic movement. Higher accuracy has been achieved at the elbow joint with functional joint axis calibration tasks and the use of kinematic constraints on gyroscope data, resulting in RMS errors between IMU and optoelectronic motion for flexion–extension as low as 2°. For the glenohumeral joint, 3D joint motion has been described with RMS errors of 6° and higher. In contrast, scapulothoracic joint motion tracking yielded RMS errors in excess of 10° in the protraction–retraction and anterior-posterior tilt direction. The findings of this study demonstrate high-quality 3D humerothoracic and elbow joint motion measurement capability using IMUs and underscore the challenges of skin motion artifacts in scapulothoracic and glenohumeral joint motion analysis. Future studies ought to implement functional joint axis calibrations, and IMU-based scapula locators to address skin motion artifacts at the scapula, and explore the use of artificial neural networks and data-driven approaches to directly convert IMU data to joint angles.
Geological Hazard Susceptibility Analysis Based on RF, SVM, and NB Models, Using the Puge Section of the Zemu River Valley as an Example
Authors: Li Ming , Li Linlong , Lai Yangqi , He Li , He Zhengwei , Wang Zhifei
Published: July 2023

The purpose of this study was to construct a geological hazard susceptibility evaluation and analysis model using three types of machine learning models, namely, random forest (RF), support vector machine (SVM), and naive Bayes (NB), and to evaluate the susceptibility to landslides, using the Puge section of the Zemu River valley in the Liangshan Yi Autonomous Prefecture as the study area. First, 89 shallow landslide and debris flow locations were recognized through field surveys and remote sensing interpretation. A total of eight hazard-causing factors, namely, slope, aspect, rock group, land cover, distance to road, distance to river, distance to fault, and normalized difference vegetation index (NDVI), were selected to evaluate the spatial relationship with landslide occurrence. As a result of the analysis, the results of the weighting of the hazard-causing factors indicate that the two elements of rock group and distance to river contribute most to the creation of geological hazards. After comparing all the indices of the three models, the random forest model had a higher correct area under the ROC curve (AUC) value of 0.87, root mean squared error (RMSE) of 0.118, and mean absolute error (MAE) of 0.045. The SVM model had the highest sensitivity to geological hazards. The results of geological hazard prediction susceptibility analysis matched the actual situation in the study area, and the prediction effects were good. The results of the hazard susceptibility assessment of the three models are able to provide support and help for the prevention and control of geological hazards in the same type of areas.
Benchmarking Biologically-Inspired Automatic Machine Learning for Economic Tasks
Authors: Lazebnik Teddy , Fleischer Tzach , Yaniv-Rosenfeld Amit
Published: July 2023

Data-driven economic tasks have gained significant attention in economics, allowing researchers and policymakers to make better decisions and design efficient policies. Recently, with the advancement of machine learning (ML) and other artificial intelligence (AI) methods, researchers can now solve complex economic tasks with previously unseen performance and ease. However, to use such methods, one is required to have a non-trivial level of expertise in ML or AI, which currently is not standard knowledge in economics. In order to bridge this gap, automatic machine learning (AutoML) models have been developed, allowing non-experts to efficiently use advanced ML models with their data. Nonetheless, not all AutoML models are created equal in general, particularly for the unique properties associated with economic data. In this paper, we present a benchmarking study of biologically inspired and other AutoML techniques for economic tasks. We evaluate four different AutoML models alongside two baseline methods using a set of 50 diverse economic tasks. Our results show that biologically inspired AutoML models (slightly) outperformed non-biological AutoML in economic tasks, while all AutoML models outperformed the traditional methods. Based on our results, we conclude that biologically inspired AutoML has the potential to improve our economic understanding while shifting a large portion of the analysis burden from the economist to a computer.
Recent Advances in Biomass Pyrolysis Processes for Bioenergy Production: Optimization of Operating Conditions
Authors: Aboelela Dina , Saleh Habibatallah , Attia Attia M. , Elhenawy Yasser , Majozi Thokozani , Bassyouni Mohamed
Published: July 2023

Bioenergy has emerged to be among the primary choices for the short- and medium-term replacement of fossil fuels and the reduction in greenhouse gas (GHG) emissions. The most practical method for transforming biomass into biofuel is thermochemical conversion, which may be broken down into combustion, torrefaction, pyrolysis, hydrothermal liquefaction, and gasification. In this study, producing biofuels using a biomass pyrolysis process was investigated. This study explored the pyrolysis process and operating conditions to optimize the process parameters to maximize the desired product yields and quality. The pyrolysis process produces three main products, which are bio-oil, bio-char, and gas. There are three classifications for the pyrolysis method, with each of them producing a majority of a certain product. First, slow pyrolysis is conducted in the temperature range of 300–950 °C and residence time of 330–550 s. It produces around a 30% oil yield and 35% char yield, and thus, the majority yield of slow pyrolysis is char. Second, fast pyrolysis produces around 50% oil, 20% char, and 30% gas yields with a temperature range of 850–1250 °C and a residence time of 0.5–10 s. The average yield of flash pyrolysis was found to be 75% bio-oil, 12% bio-char, and 15% gas, which is conducted within less than 1 s. It was reported that the pyrolysis of biomass was simulated using ASPEN Plus, where the effects of several parameters, such as the temperature, heating rate, and residence time, on the product yield and composition were investigated. Pyrolysis was performed under different conditions ranging from 400 to 600 °C. The effects of different catalysts on the pyrolysis process were studied. It was found that the addition of a catalyst could increase the yield of bio-oil and improve the quality of the product. The optimal operating condition for the pyrolysis process was determined to be a temperature of 500 °C, which resulted in a higher bio-oil yield. It was found that the biofuel yield was enhanced by selecting appropriate raw materials, such as rice husk, along with the pyrolysis temperature (e.g., 450 °C) and particle size (350–800 µm), and using a low residence time and pressure.
Smart Agricultural–Industrial Crop-Monitoring System Using Unmanned Aerial Vehicle–Internet of Things Classification Techniques
Authors: Vijayalakshmi K. , Al-Otaibi Shaha , Arya Leena , Almaiah Mohammed Amin , Anithaashri T. P. , Karthik S. Sam , Shishakly Rima
Published: July 2023

Unmanned aerial vehicles (UAVs) coupled with machine learning approaches have attracted considerable interest from academicians and industrialists. UAVs provide the advantage of operating and monitoring actions performed in a remote area, making them useful in various applications, particularly the area of smart farming. Even though the expense of controlling UAVs is a key factor in smart farming, this motivates farmers to employ UAVs while farming. This paper proposes a novel crop-monitoring system using a machine learning-based classification with UAVs. This research aims to monitor a crop in a remote area with below-average cultivation and the climatic conditions of the region. First, data are pre-processed via resizing, noise removal, and data cleaning and are then segmented for image enhancement, edge normalization, and smoothing. The segmented image was pre-trained using convolutional neural networks (CNN) to extract features. Through this process, crop abnormalities were detected. When an abnormality in the input data is detected, then these data are classified to predict the crop abnormality stage. Herein, the fast recurrent neural network-based classification technique was used to classify abnormalities in crops. The experiment was conducted by providing the present weather conditions as the input values; namely, the sensor values of temperature, humidity, rain, and moisture. To obtain results, around 32 truth frames were taken into account. Various parameters—namely, accuracy, precision, and specificity—were employed to determine the accuracy of the proposed approach. Aerial images for monitoring climatic conditions were considered for the input data. The data were collected and classified to detect crop abnormalities based on climatic conditions and pre-historic data based on the cultivation of the field. This monitoring system will differentiate between weeds and crops.
Analysis of Spatial and Temporal Evolution of Regional Water Resources Carrying Capacity and Influencing Factors—Anhui Province as an Example
Authors: Jiang Huaiyin , He Gang
Published: July 2023

The study of water resources carrying capacity is a crucial aspect of water resources ecological security research. To account for the interplay between regional natural conditions and economic and social factors, a natural–economic–social–environmental evaluation index system is established within the framework of water resources. The comprehensive index of water resources carrying capacity in Anhui Province is then calculated using the entropy weight method and CRITIC model. Spatial kernel density and Dagum analysis are employed to analyze the results. The findings indicate that (1) the overall water resources carrying capacity of Anhui Province has exhibited an upward trend from 2003 to 2021, with the overall index of water resources carrying capacity increasing from 0.34 to 0.49. (2) The presence of water resources carrying capacity polarization in Anhui Province is evident, although this trend has been diminishing in recent years. (3) The Gini coefficient of water resources carrying capacity in Anhui Province first increases and then decreases, with the Gini coefficient between groups being more pronounced than the Gini coefficient within groups. (4) Geospatial factors play a significant role in shaping the future spatial distribution of water resources carrying capacity in Anhui Province. Additionally, the interaction of water resources carrying capacity among neighboring areas contributes to the reduction of differences between water resources carrying capacity in different regions in the future.
A Hybrid DEA–Fuzzy COPRAS Approach to the Evaluation of Renewable Energy: A Case of Wind Farms in Turkey
Authors: Yilmaz Ibrahim
Published: July 2023

The production of renewable energy is becoming one of the most important issues for communities due to the increasing energy demand. The purpose of this paper is to develop a systematized, sustainability-focused evaluation framework for determining the efficiency of wind farms in Turkey. The environmental impact and long-term viability of wind farms are evaluated using an evaluation framework centered on sustainability. The evaluation of their sustainability involves analyzing their energy production, environmental impacts and economic viability. In this study, DEA–Fuzzy COPRAS aims to evaluate the efficiency of 11 wind power plants located in Turkey in the Marmara Region. As inputs, the number of wind turbines, investment cost and distance from the grid are selected. As output, electricity is produced, and daily production time is considered. The proposed DEA–Fuzzy COPRAS aims to eliminate the disadvantages of the conventional methods and to be able to make better decisions regarding the weight value under uncertain conditions. The main advantages of the proposed DEA–Fuzzy COPRAS include a more accurate evaluation of efficiency and the ability to consider multiple criteria simultaneously. Additionally, the proposed DEA–Fuzzy COPRAS considers uncertainty in the inputs and outputs of wind energy production. The results of the proposed work are validated by comparing them with those obtained from a sensitivity analysis of the criteria. Therefore, decision makers can evaluate the efficiency of wind power plants accurately under an imprecise environment. Wind power plant managers or investors and other renewable energy projects can benefit from the proposed method’s implementation by allowing governments and stakeholders to save money and make better use of resources during the planning phase.
An End-to-End Transfer Learning Framework of Source Recording Device Identification for Audio Sustainable Security
Authors: Wang Zhifeng , Zhan Jian , Zhang Guozhong , Ouyang Daliang , Guo Huaiyong
Published: July 2023

Source recording device identification poses a significant challenge in the field of Audio Sustainable Security (ASS). Most existing studies on end-to-end identification of digital audio sources follow a two-step process: extracting device-specific features and utilizing them in machine learning or deep learning models for decision-making. However, these approaches often rely on empirically set hyperparameters, limiting their generalization capabilities. To address this limitation, this paper leverages the self-learning ability of deep neural networks and the temporal characteristics of audio data. We propose a novel approach that utilizes the Sinc function for audio preprocessing and combine it with a Deep Neural Network (DNN) to establish a comprehensive end-to-end identification model for digital audio sources. By allowing the parameters of the preprocessing and feature extraction processes to be learned through gradient optimization, we enhance the model’s generalization. To overcome practical challenges such as limited timeliness, small sample sizes, and incremental expression, this paper explores the effectiveness of an end-to-end transfer learning model. Experimental verification demonstrates that the proposed end-to-end transfer learning model achieves both timely and accurate results, even with small sample sizes. Moreover, it avoids the need for retraining the model with a large number of samples due to incremental expression. Our experiments showcase the superiority of our method, achieving an impressive 97.7% accuracy when identifying 141 devices. This outperforms four state-of-the-art methods, demonstrating an absolute accuracy improvement of 4.1%. This research contributes to the field of ASS and provides valuable insights for future studies in audio source identification and related applications of information security, digital forensics, and copyright protection.
Increasing Output Power of a Microfluidic Fuel Cell Using Fuzzy Modeling and Jellyfish Search Optimization
Authors: Alhumade Hesham , Moujdin Iqbal Ahmed , Al-Shahrani Saad
Published: July 2023

An efficient electrochemical energy conversion system with little to no environmental impact is the fuel cell (FC). FCs have demonstrated encouraging results in various applications and can even run on biofuel, such as bio-glycerol, a by-product of biodiesel. The most effective ways to operate FCs can significantly enhance their effectiveness. Incorporating fuzzy modeling and metaheuristic methods, this work used artificial intelligence to determine the ideal operating parameters for a microfluidic fuel cell (MFC). The concentrations of the following four variables were considered: bio-glycerol concentration, anode electrocatalyst loading, anode electrolyte concentration, and cathode electrolyte concentration. The output power density of the MFC was used to assess its performance. The output power density of the MFC was modeled using fuzzy logic, taking into account the aforementioned operational parameters. A jellyfish search optimizer (JSO) was then used to find the ideal operating conditions. The results were contrasted with response surface methodology (RSM) and experimental datasets to demonstrate the superiority of the proposed integration between fuzzy modeling and the JSO. In comparison with the measured and RSM approaches, the suggested strategy boosted the power density of the MFC by 9.38% and 8.6%, respectively.
Environmental Sustainability within Attaining Sustainable Development Goals: The Role of Digitalization and the Transport Sector
Authors: Kwilinski Aleksy , Lyulyov Oleksii , Pimonenko Tetyana
Published: July 2023

Accepting sustainable development goals leads to the reorientation of all sectors at all levels. The European Union (EU) actively accepts a vast range of policies to achieve environmental sustainability due to declining carbon dioxide emissions. Within the Green Deal Policy, and in particular the Fit for 55 packages, the EU declared ambitious goals to reduce carbon dioxide emissions by at least 55% from the transport industry by 2030 and 100% by 2035. These goals require introducing appropriate digital technologies into the ecologically friendly functioning of the transport sector to attain sustainable development. This paper aims at analyzing the impact of digitalization on environmental sustainability by providing an effective transport sector that functions with minimum environmental degradation. The object of research is the EU countries for the period 2006–2020. This study applies the panel-corrected standard errors technique to achieve the paper’s aims. The findings allow us to conclude that digitalization is conducive to environmental sustainability. Thus, digital inclusion, the input of the IT sector to GDP, and e-commerce have direct negative and statistically significant linear effects on carbon dioxide emissions. Growth of digital inclusion, input of the IT sector to GDP, and enterprises with web sales by one point allow for decreasing CO2 emissions by 0.136, 2.289, and 0.266, respectively. However, key enablers and digital public services for citizens have a nonlinear, statistically significant impact on carbon dioxide emissions. The findings could be the basis for upgrading incentive policies for reducing carbon dioxide emissions.
Young Saudis’ Evaluations and Perceptions of Privacy in Digital Communities: The Case of WhatsApp and Telegram
Authors: Aldalbahi Sharifah Sharar , Albesher Abdulmohsen Saud
Published: July 2023

Digital communities have gained increasing popularity in the past decade. However, they have put users at security risks, especially when they neglect to pay attention to the privacy statement and privacy settings. Therefore, digital community platforms must provide clear privacy statements and usable privacy settings. This research aims to evaluate the usability of privacy on WhatsApp and Telegram from the perspective of young Saudis. A total of 51 young Saudis participated in remote usability testing, followed by questionnaires and interviews. The results showed some privacy concerns in the two apps. Specifically, there are differences in the youths’ evaluation of the perceived privacy of the participants, with females seeing WhatsApp as more secure than the Telegram App. In the end, some recommendations for improving the privacy policy and settings for each app are discussed to ensure the safety and confidentiality of users’ information.
Comprehensive Evaluation of Island Habitat Quality Based on the Invest Model and Terrain Diversity: A Case Study of Haitan Island, China
Authors: Huang He , Xiao Yanzhi , Ding Guochang , Liao Lingyun , Yan Chen , Liu Qunyue , Gao Yaling , Xie Xiangcai
Published: July 2023

The assessment of habitat quality is instrumental in preserving regional species diversity and ecosystem health, thereby forming the theoretical foundation for sustainable urban development. While the Invest model is a commonly employed tool for habitat quality evaluation, it fails to consider the terrain. This study, centered on Haitan Island, introduces the terrain diversity index to rectify the Invest model’s lack of terrain evaluation. The terrain diversity index, encompassing indices for terrain slope, undulation, and humidity, combined with the Invest model, was applied for a comprehensive assessment of the study area’s habitat quality. Furthermore, the distribution characteristics of habitat quality on Haitan Island, China, were examined using Moran’s I and LISA indices. The research indicates that forest land is the primary land cover type on Haitan Island, with blue-green space comprising forests, farmland, water bodies, and grassland, making up 66.8% of the island’s area, thus implying a positive overall ecological base. Habitat quality distribution within the study area displays spatial heterogeneity, with regions of superior habitat quality primarily found in the northeast areas such as Junshan. Compared to the standalone Invest model, the combined method considering terrain and vegetation cover types yields a more sensitive impact on habitat quality evaluation and improves the precision of identifying superior habitat quality by 56.7%. Spatial autocorrelation analysis revealed that the comprehensive habitat quality index in the study area exhibited clustered distribution. Hotspots were mainly identified in areas like Junshan and the western mangrove wetland, regions with a high concentration of habitat quality values, while low-value clusters were mostly found in the central city and southwestern plains. This study offers a novel methodology for habitat quality evaluation, compensating for the traditional Invest model’s neglect of terrain factors, and enriching the research on island habitat quality. It can provide fresh approaches and references for future habitat-related studies.
Soil Remediation after Sewage Sludge or Sewage Sludge Char Application with Industrial Hemp and Its Potential for Bioenergy Production
Authors: Kniuipytė Inesa , Praspaliauskas Marius , Venclovienė Jonė , Žaltauskaitė Jūratė
Published: July 2023

Sewage sludge reuse in agriculture is increasing and is highly encouraged; however, it may pose environmental risk. Therefore, an integrated approach combining soil phytoremediation and further plant usage for bioenergy production is needed. In this study, we have examined the potential of industrial hemp (Canabis sativa L.) to remediate sewage sludge (SS) and sewage sludge char (SSCh)-amended soil (25–200 Mg ha−1) and improve soil quality. Additionally, hemp’s biomass and probable bioenergy yield was calculated for biomass and methane production. Heavy metal soil content increased with SS and SSCh dose, though hemp cultivation significantly reduced their soil concentrations. The heavy metals’ removal efficiency could be ranked Zn > Cu > Cr > Ni. There was an enrichment of micro- (Ca, Mg, Mn, S) and macro-nutrients (P) in SS and SSCh-amended soils. P and S removal by hemp was highly efficient, whereas other macronutrients did not show a substantial decrease in the soil. Only marginal removal was detected for Ba, Fe, Na, Ti and Al. The study showed that the optimal fertilization with SS or SSCh could be up to 25 Mg ha−1, when the highest efficiency of contaminant removal from the soil and the highest plant biomass production and bioenergy production were observed.
High Levels of Physical Activity Reduce the Esthetic Durability of Botulinum Toxin Type A: A Controlled Single-Blind Clinical Trial
Authors: Morhy Omar Neves , Sisnando Andréa Lisbôa , Câmara-Souza Mariana Barbosa , Carbone Ana Claudia , De la Torre Canales Giancarlo
Published: July 2023

The present study aimed to evaluate the influence of physical activity on the durability of the esthetic effect of botulinum toxin type A (BoNT-A). Sixty female patients were allocated to three groups (n = 20) according to their physical activity level (PA): Low PA, Moderate PA, and High PA. All groups received a single injection of onabotulinumtoxinA, considering standardized doses in the frontalis (12U), corrugator supercilia (7U, each), and procerus muscles (4U). Outcomes were measured using electromyography (EMG), Merz 5-point scales, and Face-Q scales (perceived age and lines between eyebrows). A follow-up occurred after 30, 60, and 90 days. EMG results showed a significant decrease in muscle activity in the Low-PA group at all follow-ups compared with the other groups (p < 0.001). The Merz scale scores showed that the severity of forehead and glabellar lines significantly improved in the Low-PA group throughout this study compared with the other groups (p < 0.001). No significant differences between groups were found in the Face-Q scale for perceived age, while the Face-Q scale for lines between eyebrows showed better results for Low-PA (p < 0.01) and Moderate-PA (p < 0.01) groups compared to the High-PA group at the 30- and 90-day follow-ups. The durability of the esthetic effect of BoNT-A seems to be negatively influenced by the level of physical activity.
A Retrospective Cohort Study of Cobra Envenomation: Clinical Characteristics, Treatments, and Outcomes
Authors: Tansuwannarat Phantakan , Tongpoo Achara , Phongsawad Suraphong , Sriapha Charuwan , Wananukul Winai , Trakulsrichai Satariya
Published: July 2023

This study investigated the clinical characteristics, treatments, and outcomes of envenomation involving cobra species in Thailand (Naja kaouthia, Naja siamensis, and Naja sumatrana). Data of patients who had been bitten by a cobra or inoculated via the eyes/skin in 2018–2021 were obtained from the Ramathibodi Poison Center. There were 1045 patients admitted during the 4-year study period (bite, n = 539; ocular/dermal inoculation, n = 506). Almost all patients with ocular/dermal inoculation had eye involvement and ocular injuries, but none had neurological effects. Most of the patients bitten by a cobra had local effects (69.0%) and neurological signs and symptoms (55.7%). The median interval between the bite and the onset of neurological symptoms was 1 h (range, 10 min to 24 h). Accordingly, patients should be observed closely in hospitals for at least 24 h after a bite. Intubation with ventilator support was required in 45.5% of patients and for a median duration of 1.1 days. Antivenom was administered in 63.5% of cases. There were nine deaths, most of which resulted from severe infection. Neurological effects and intubation were significantly more common after a monocled cobra bite than after a spitting cobra bite. The administration of antivenom with good supportive care, including the appropriate management of complications, especially wound infection, might decrease fatality.
Synergistic Activity of Remdesivir–Nirmatrelvir Combination on a SARS-CoV-2 In Vitro Model and a Case Report
Authors: Gidari Anna , Sabbatini Samuele , Schiaroli Elisabetta , Bastianelli Sabrina , Pierucci Sara , Busti Chiara , Saraca Lavinia Maria , Capogrossi Luca , Pasticci Maria Bruna , Francisci Daniela
Published: July 2023

Background: This study aims to investigate the activity of the remdesivir–nirmatrelvir combination against Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) and to report a case of Coronavirus Disease 2019 (COVID-19) cured with this combination. Methods: A Vero E6 cell-based infection assay was used to investigate the in vitro activity of the remdesivir–nirmatrelvir combination. The SARS-CoV-2 strains tested were 20A.EU1, BA.1 and BA.5. After incubation, a viability assay was performed. The supernatants were collected and used for viral titration. The Highest Single Agent (HSA) reference model was calculated. An HSA score >10 is considered synergic. Results: Remdesivir and nirmatrelvir showed synergistic activity at 48 and 72 h, with an HSA score of 52.8 and 28.6, respectively (p < 0.0001). These data were confirmed by performing supernatant titration and against the omicron variants: the combination reduced the viral titer better than the more active compound alone. An immunocompromised patient with prolonged and critical COVID-19 was successfully treated with remdesivir, nirmatrelvir/ritonavir, tixagevimab/cilgavimab and dexamethasone, with an excellent clinical–radiological response. However, she required further off-label prolonged therapy with nirmatrelvir/ritonavir until she tested negative. Conclusions: Remdesivir–nirmatrelvir combination has synergic activity in vitro. This combination may have a role in immunosuppressed patients with severe COVID-19 and prolonged viral shedding.
Inflammatory Control of Viral Infection
Authors: Chakravarty Sukanya , Chakravarti Ritu , Chattopadhyay Saurabh
Published: July 2023

Inflammatory responses during virus infection differentially impact the host. Managing inflammatory responses is essential in controlling viral infection and related diseases. Recently, we identified a cellular anti-inflammatory mechanism, RIKA (Repression of IRF3-mediated inhibition of NF-κB activity), which controls viral inflammation and pathogenesis. The RIKA function of IRF3 may be explored further in other inflammatory diseases beyond viral infection.