bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Adherence to the Mediterranean Diet and Level of Physical Activity with Liver Steatosis in People Aged > 50 Years and with a BMI > 25 kg/m2: Association with Biochemical Markers
Authors: Cano-Lallave Laura , Ruiz-Tovar Jaime , Martin-de-Bernardo Lucia , Martinez-Oribe Malena , Rodriguez-Obispo Cristina , Carrascosa-Corrochano Sara , Martín-Nieto Ana , Baeza Isabel , Gonzalez-Ramos Marta , Benito Marta , Olazabal Isabel
Published: June 2024

Background: The main objective of this study is to determine the accuracy of different biochemical markers of hepatic steatosis and to correlate liver steatosis with adherence to the Mediterranean diet and level of physical activity. Methods: A cross-sectional study was carried out, including subjects over 50 years of age, with a BMI > 25 kg/m2, but excluding any patient with documented hepatic pathology other than hepatic steatosis. Participants were divided into two groups: patients with hepatic steatosis diagnosed by ultrasound (SG) and a control group of individuals without hepatic steatosis (CG). The level of physical activity was recorded by the IPAQ-SF questionnaire and the adherence to the Mediterranean diet was recorded using the PREDIMED questionnaire. Biochemical markers analyzed included the Hepatic steatosis index (HSI), AST-to-Platelet ratio (APRI) and Fibrosis-4 (FIB-4). Results: A total of 116 patients were included, 71 belonging to the SG and 45 to the CG. A total of 58.6% of the patients showed low adherence to the Mediterranean diet, 35.4% moderate adherence and 6% high adherence. The median estimated physical activity was 495 METS, with most participants reporting light activity. In the SG, significantly higher HSI values were observed (p < 0.001). A cut-off point of a HSI of 40 was established, with a sensitivity of 73.2% and a specificity of 65.8%. Significantly higher FIB-4 values (p = 0.039) were also observed in the SG. A cut-off point of FIB-4 was set at 0.27, with a sensitivity of 69% and a specificity of 57.9%. Patients in the SG showed lower scores in the PREDIMED. Patients in the SG tended to show lower METS scores. However, the higher number of patients with intense activity in the CG group stands out (p = 0.008). Conclusions: The HSI and FIB-4 showed a significant correlation with liver steatosis. Hepatic steatosis is associated with low adherence to the Mediterranean diet and patients with hepatic steatosis tended to have lower METS scores.
The Study of Myo-Inositol’s Anxiolytic Activity on Zebrafish (Danio rerio)
Authors: Derkaczew Maria , Kędziora Bartosz , Potoczna Małgorzata , Podlasz Piotr , Wąsowicz Krzysztof , Jóźwik Marcin , Wojtkiewicz Joanna
Published: June 2024

Introduction: Myo-inositol (MI) is the most abundant inositol found in nature. To date MI supplementation is reported to be effective in the treatment of polycystic ovary syndrome, it is also suggested to alleviate the symptoms of diabetes and neurodegenerative disorders, but to date no statistically significant effects of inositol on depressive and anxiety symptoms were proven. In the study of anxiolytic effects in zebrafish, we often use the thigmotaxis index measuring the ratio of the amount of time the animal spends near the walls compared to the entire arena. Aim: The objective of this paper was to examine the effect of MI on zebrafish embryos’ locomotor activity, as well as its potential anxiolytic activity in zebrafish larvae. Material and methods: In the first part of the experiment, the embryos were incubated with 5, 10, 20, and 40 mg/mL MI. 1-day post fertilization, embryo mobility was evaluated and burst activity was calculated. In the next part of the study, the behavior of 5-day-old larvae was tested. Results: Tests on embryo movement showed an increase in burst activity in the MI group at concentrations of 40 mg/mL (p < 0.0001) and a slight decrease in the group at concentrations of 10 mg/mL (p < 0.05). MI in the light/dark challenge had no impact on the thigmotaxis index. Conclusions: MI was shown to not affect stress reduction in zebrafish larvae. Further research on the potential of MI and other stereoisomers is needed.
IL-1β-Induced CXCL10 Expression in THP-1 Monocytic Cells Involves the JNK/c-Jun and NF-κB-Mediated Signaling
Authors: Kochumon Shihab , Al-Sayyar Amnah , Jacob Texy , Arefanian Hossein , Bahman Fatemah , Almansour Nourah , Alzaid Fawaz , Al-Mulla Fahd , Sindhu Sardar , Ahmad Rasheed
Published: June 2024

CXCL10 (IP-10) plays a key role in leukocyte homing to the inflamed tissues and its increased levels are associated with the pathophysiology of various inflammatory diseases including obesity and type 2 diabetes. IL-1β is a key proinflammatory cytokine that is found upregulated in meta-inflammatory conditions and acts as a potent activator, inducing the expression of cytokines/chemokines by immune cells. However, it is unclear whether IL-1β induces the expression of CXCL10 in monocytic cells. We, therefore, determined the CXCL10 induction using IL-1β in THP1 monocytic cells and investigated the mechanisms involved. Monocytes (human monocytic THP-1 cells) were stimulated with IL-1β. CXCL10 gene expression was determined with real-time RT-PCR. CXCL10 protein was determined using ELISA. Signaling pathways were identified by using Western blotting, inhibitors, siRNA transfections, and kinase assay. Our data show that IL-1β induced the CXCL10 expression at both mRNA and protein levels in monocytic cells (p = 0.0001). Notably, only the JNK inhibitor (SP600125) significantly suppressed the IL-1β-induced CXCL10 expression, while the inhibitors of MEK1/2 (U0126), ERK1/2 (PD98059), and p38 MAPK (SB203580) had no significant effect. Furthermore, IL-1β-induced CXCL10 expression was decreased in monocytic cells deficient in JNK/c-Jun. Accordingly, inhibiting the JNK kinase activity markedly reduced the IL-1β-induced JNK/c-Jun phosphorylation in monocytic cells. NF-κB inhibition by Bay-117085 and resveratrol also suppressed the CXCL10 expression. Our findings provide preliminary evidence that IL-1β stimulation induces the expression of CXCL10 in monocytic cells which requires signaling via the JNK/c-Jun/NF-κB axis.
Endometriosis: Molecular Pathophysiology and Recent Treatment Strategies—Comprehensive Literature Review
Authors: Sadłocha Marcin , Toczek Jakub , Major Katarzyna , Staniczek Jakub , Stojko Rafał
Published: June 2024

Endometriosis is an enigmatic disease, with no specific cause or trigger yet discovered. Major factors that may contribute to endometriosis in the pelvic region include environmental, epigenetic, and inflammatory factors. Most experts believe that the primary mechanism behind the formation of endometrial lesions is associated with Sampson’s theory of “retrograde menstruation”. This theory suggests that endometrial cells flow backward into the peritoneal cavity, leading to the development of endometrial lesions. Since this specific mechanism is also observed in healthy women, additional factors may be associated with the formation of endometrial lesions. Current treatment options primarily consist of medical or surgical therapies. To date, none of the available medical therapies have proven effective in curing the disorder, and symptoms tend to recur once medications are discontinued. Therefore, there is a need to explore and develop novel biomedical targets aimed at the cellular and molecular mechanisms responsible for endometriosis growth. This article discusses a recent molecular pathophysiology associated with the formation and progression of endometriosis. Furthermore, the article summarizes the most current medications and surgical strategies currently under investigation for the treatment of endometriosis.
Assessment of Infant Exposure to Antidepressants through Breastfeeding: A Literature Review of Currently Available Approaches
Authors: Arbitman Leah , Chen Shirley , Kim Brian , Lee Melinda , Zou Peng , Doughty Bennett , Li Yanyan , Zhang Tao
Published: June 2024

Despite the prevalence of depression in lactating mothers, there is a lack of knowledge about the excretion of antidepressants into breast milk and its potential adverse effects on infants. This creates concern, making depressed lactating mothers more likely to avoid pharmacological treatment. Clinical lactation studies are the most accurate and direct method to predict and demonstrate the excretion of antidepressants into human breast milk, and results from clinical studies can be included in drug labels to help physicians and patients make decisions on antidepressant use during lactation. However, there are limited clinical trials and studies on the pharmacokinetics of antidepressants in lactating women because of a lack of enrollment and ethical and confounding factors, creating a lack of knowledge in this area. To bridge this gap in knowledge, alternative methods should be sought to help estimate the antidepressant concentration in breast milk, which is used to assess the safety and transfer of antidepressants into breast milk. We provide a comprehensive review of the usage of these cost-effective, time-efficient, and ethically feasible methods that serve to provide a valuable estimation of the safety and transfer of antidepressants into breast milk before conducting clinical studies.
Fabrication Tolerances’ Impact on an ODAC-Based PAM-4 Transmitter
Authors: Abejide Adebayo E. , Santos João , Chattopadhyay Tanay , Rodrigues Francisco , Lima Mario , Teixeira António
Published: June 2024

Photonic integrated circuits (PIC) devices are impacted by fabrication tolerances and therefore, prior knowledge of such variations could improve the PIC fabrication process and overall yield. This paper presents a method for predicting the fabrication impacts on a telecommunication optical digital to analog converter (oDAC)-based pulse amplitude modulator level four (PAM-4) transmitter as a case study where the certainty of this passive device is subjected to random variation. Our findings allow us to estimate the production yield in a fabrication scenario using the symbol error rate (SER) benchmark and this contributes to the study of the viability of oDAC PAM-4 transmitters to replace conventional electrical digital to analog converter (eDAC) PAM-4 transmitters.
Host Status and Response Differences of Flat-Leaf and Curly-Leaf Parsley to Meloidogyne hapla, M. chitwoodi, M. fallax, and M. incognita Infestation
Authors: Noskov Ilya , Blum Hanna , Komnik Hansjörg , Hallmann Johannes
Published: June 2024

Leaf parsley growth and productivity are often affected by pathogen infection. Root-knot nematodes of the genus Meloiogyne are common pathogens reported on leaf parsley. The response of leaf parsley to Meloidogyne species in tropical and subtropical regions is quite known, while in temperate regions, comparable information is still scarce. In this study, we evaluated the host status and response of three flat-leaf (Laica, Laura, Gigante d’Italia) and three curly-leaf (Grüne Perle, Orfeo, Sombre) parsley cultivars to Meloidogyne species from temperate regions, i.e., M. hapla, M. chitwoodi, and M. fallax, as well as to the southern root-knot nematode M. incognita. Evaluation was based on measuring plant biomass and nematode reproduction nine weeks after nematode inoculation. Our results showed that all four Meloidogyne species did not cause the reduction in leaf parsley growth under the given experimental conditions. Regarding the host status of leaf parsley cultivars for Meloidogyne, results were variable. All six parsley cultivars were found to be good hosts for M. hapla. Regarding M. chitwoodi, the host status could not be clarified properly; however, each cultivar allowed nematode reproduction at least in one experiment. For M. fallax, flat-leaf parsley turned out to be less susceptible than curly-leaf parsley; and for M. incognita, Orfeo, Laura, and Laica were classified as good hosts, Grüne Perle and Sombre as poor hosts, and Gigante d’Italia as a non-host. Amongst all tested cultivars, Gigante d’Italia was found to be the least susceptible cultivar due to its poor host status for M. chitwoodi and non-host status for M. fallax and M. incognita. Infection with M. hapla, M. chitwoodi, and M. incognita, but not with M. fallax, resulted in distinct gall formation on the roots of all six leaf parsley cultivars.
Exploring the Future of Polyhydroxyalkanoate Composites with Organic Fillers: A Review of Challenges and Opportunities
Authors: Thakur Abhishek , Musioł Marta , Duale Khadar , Kowalczuk Marek
Published: June 2024

Biopolymers from renewable materials are promising alternatives to the traditional petroleum-based plastics used today, although they face limitations in terms of performance and processability. Natural fillers have been identified as a strategic route to create sustainable composites, and natural fillers in the form of waste by-products have received particular attention. Consequently, the primary focus of this article is to offer a broad overview of recent breakthroughs in environmentally friendly Polhydroxyalkanoate (PHA) polymers and their composites. PHAs are aliphatic polyesters obtained by bacterial fermentation of sugars and fatty acids and are considered to play a key role in addressing sustainability challenges to replace traditional plastics in various industrial sectors. Moreover, the article examines the potential of biodegradable polymers and polymer composites, with a specific emphasis on natural composite materials, current trends, and future market prospects. Increased environmental concerns are driving discussions on the importance of integrating biodegradable materials with natural fillers in our daily use, emphasizing the need for clear frameworks and economic incentives to support the use of these materials. Finally, it highlights the indispensable need for ongoing research and development efforts to address environmental challenges in the polymer sector, reflecting a growing interest in sustainable materials across all industries.
Polypy: A Framework to Interpret Polymer Properties from Mass Spectrometry Data
Authors: Vlnieska Vitor , Khanda Ankita , Gilshtein Evgeniia , Beltrán Jorge , Heier Jakob , Kunka Danays
Published: June 2024

Mass spectroscopy (MS) is a robust technique for polymer characterization, and it can provide the chemical fingerprint of a complete sample regarding polymer distribution chains. Nevertheless, polymer chemical properties such as polydispersity (Pd), average molecular mass (Mn), weight average molecular mass (Mw) and others are not determined by MS, as they are commonly characterized by gel permeation chromatography (GPC). In order to calculate polymer properties from MS, a Python script was developed to interpret polymer properties from spectroscopic raw data. Polypy script can be considered a peak detection and area distribution method, and represents the result of combining the MS raw data filtered using Root Mean Square (RMS) calculation with molecular classification based on theoretical molar masses. Polypy filters out areas corresponding to repetitive units. This approach facilitates the identification of the polymer chains and calculates their properties. The script also integrates visualization graphic tools for data analysis. In this work, aryl resin (poly(2,2-bis(4-oxy-(2-(methyloxirane)phenyl)propan) was the study case polymer molecule, and is composed of oligomer chains distributed mainly in the range of dimers to tetramers, in some cases presenting traces of pentamers and hexamers in the distribution profile of the oligomeric chains. Epoxy resin has Mn = 607 Da, Mw = 631 Da, and polydispersity (Pd) of 1.015 (data given by GPC). With Polypy script, calculations resulted in Mn = 584.42 Da, Mw = 649.29 Da, and Pd = 1.11, which are consistent results if compared with GPC characterization. Additional information, such as the percentage of oligomer distribution, was also calculated and for this polymer matrix it was not possible to retrieve it from the GPC method. Polypy is an approach to characterizing major polymer chemical properties using only MS raw spectra, and it can be utilized with any MS raw data for any polymer matrix.
Evaluation of PLA-Based Composite Films Filled with Cu2(OH)3NO3 Nanoparticles as an Active Material for the Food Industry: Biocidal Properties and Environmental Sustainability
Authors: Santos Xiomara , Domínguez Gabriela , Rodríguez Juana , Pozuelo Javier , Hernández Manuel , Martín Olga , Fajardo Carmen
Published: June 2024

The globalization of markets has diversified the food supply, but it has also made the distribution chain more difficult, increasing the risk of microbial contamination. One strategy to obtain safer food and extend its shelf life is to develop active packaging with antimicrobial properties that prevent the growth of pathogenic microorganisms or spoilage in food products. In this context, and in line with the growing social awareness about the environmental impact generated by plastic waste, this work evaluated the effectiveness of polylactic acid (PLA) films loaded with different concentrations of copper (II) hydroxynitrate nanoparticles (CuHS) against the microbiota of fresh foods (chicken, fish and cheese). The results showed that the developed films containing 1, 3 and 5% w/w of CuHS in the polymeric matrix caused a decrease in the microbial abundance equal to or higher than 3 logarithmic units in all foods tested. Moreover, the mechanical and thermal properties of the formulated composites showed that the added CuHS concentrations did not substantially modify these properties compared to the PLA films. Taking into account the results obtained for antimicrobial activity, Cu (II) migration levels and the cytotoxicity of the films formulated, the PLA composite loaded with 1% CuHS (w/w) was the most suitable for its potential use as food packaging material. In addition, the biodegradation of this composite film was studied under conditions simulating intensive aerobic composting, demonstrating that almost 100% disintegration after 14 days of testing was achieved. Therefore, the innovative PLA-based films developed represent a promising strategy for the fabrication of packaging and active surfaces to increase food shelf life while maintaining food safety. Moreover, their biodegradable character will contribute to efficient waste management, turning plastic residues into a valuable resource.
Identification Method of Stuck Pipe Based on Data Augmentation and ATT-LSTM
Authors: Zhang Xiaocheng , Dong Pinghua , Yang Yanlong , Zhang Qilong , Sun Yuan , Song Xianzhi , Zhu Zhaopeng
Published: June 2024

Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement.
Behavior of a Mixture of Metals for Competiting Adsorption Sites of Untreated and Alkali-Treated Rice Husk
Authors: Flores-Trujillo Ana Karen Ivanna , Morales-Mendoza Asunción Guadalupe , Rodríguez-Vázquez Refugio
Published: June 2024

Elements are released into water bodies, affecting the environment and human health. To address this problem, the adsorption-desorption capacity of untreated rice husk (URH) and rice husk treated with alkali (RHTA) for Cu(II), Pb(II), Fe(II), Cd(II), and Zn(II) was investigated. Analyses during the process were performed by X-ray diffraction (XRD), scanning electron microscopy, and elemental analysis by energy dispersive X-ray spectroscopy (SEM-EDS), as well as Fourier transform infrared spectroscopy (FTIR). Adsorption-desorption kinetics and isotherms were carried out. The FTIR analysis of RHTA revealed a lowering of intensity of the bonding signals (OH, C-O, CH2, CH3, SiO2, Si-OH) and loss of some signals due to the adsorbent-elements interaction. The adsorption on RHTA presented higher adsorption of Fe, followed by Pb, Zn, Cu, and Cd. Meanwhile, in URH, the adsorption was Fe > Pb > Cu > Zn and Cd. On the other hand, the desorption values for RHTA were Zn > Cd > Pb > Cu > and Fe and Zn > Cd > Cu > Pb and Fe for URH. The adsorption kinetics showed that data fit (R2 ≥ 0.89) to pseudo-second-order kinetics and Freundlich isotherms (R2 ≥ 0.84) for both ATRH and URH, indicating that the process occurs in the multilayer form and is controlled by chemisorption, where the adsorption depends on the active sites of the adsorbent surface.
Influence Factors of Pressure Swing Adsorption for Oxygen Production by the Orthogonal Method and the Response Surface Method
Authors: Jiang Dongsheng , Huang Fengzhen
Published: June 2024

Adsorption pressure is one of the important factors affecting oxygen production in the process of pressure swing adsorption oxygen production. Three important factors, namely, the adsorption period, pressure equalisation time, and outlet flow rate, determine the variation in the adsorption pressure. In this study, the effects of the adsorption period, pressure equalisation time, and outlet flow rate on oxygen concentration were investigated through orthogonal experiments and response surface analysis. The experiments verified that three factors including the adsorption period, pressure equalisation time, and outlet flow rate have optimal values in the oxygen production process. Response surface analysis showed that the adsorption period had the greatest effect on the oxygen concentration, followed by the equalisation time, and the outlet flow rate had the least effect. The optimum process conditions are an adsorption time of 7.88 s, a pressure equalisation time of 0.9 s, an outlet flow rate of 2.31 L/min, and an oxygen concentration of 96.7%.
Zero-Net Liquid Flow Simulation Experiment and Flow Law in Casing Annulus Gas-Venting Wells
Authors: Yu Jifei , Du Xiaoyou , Cao Yanfeng , Zhu Weitao , Han Guoqing , Wu Qingxia , Yang Dingding
Published: June 2024

Under casing annulus gas venting, the annulus of the well is in a special state of zero-net liquid flow (ZNLF), leading to gas production without liquid at the wellhead, resulting in significant holdup issues. Therefore, conventional two-phase flow models cannot be used for calculation. To study the flow characteristics of ZNLF in the annulus of the well, this study established a visual experimental device with a total height of 5.4 m, an outer pipe inner diameter of 140 mm, and an inner pipe outer diameter of 72 mm. The flow characteristics of ZNLF were studied by controlling the casing pressure, initial liquid level, and bottom gas injection rate. The experimental results showed that the flow patterns of ZNLF are mainly bubbly flow and churn flow. Bubbly flow occurred at lower gas rates, while churn flow occurred at higher gas rates. In addition, the experiment found that when the gas injection rate and initial liquid column height were controlled to be the same, the liquid holdup decreased as the casing pressure increased. Analysis of the data patterns indicated that the slip velocity is related to the casing pressure. Based on the experimental results of ZNLF in the annulus, this study established standards for flow pattern transitions, holdup, and a pressure drop calculation model. The model results showed good agreement with the experimental results, with errors not exceeding ±5%.
Oscillation Times in Water Hammer Signatures: New Insights for the Evaluation of Diversion Effectiveness in Field Cases
Authors: Liu Bingxiao , Yue Wenhan , Wang Yajing , Gu Zhibin , Wen Ran , Qiu Yang , Yi Pukang , Hu Xiaodong
Published: June 2024

Diversion is a crucial technique for effectively improving shale reservoir production by creating more complex fracture networks. Evaluating diversion effectiveness is necessary to optimize the parameters in hydraulic fracturing. Water hammer diagnostics, an emerging fracturing diagnosis technique, evaluate diversion effectiveness by analyzing water hammer signals. The water hammer attenuation, as indicated by the oscillation time, correlates with the complexity of fracture networks. However, it remains unclear whether the oscillation time is associated with diversion effectiveness. This paper elucidates the relationship between the water hammer oscillation time and diversion effectiveness by taking the probability of diversion and the treating pressure response as the evaluation criteria. Initially, a high-frequency pressure sensor was installed at the wellhead to sample the water hammer signals. Next, the oscillation times were determined using the feature extraction method. Simultaneously, the probability of diversion and the treating pressure response were calculated using the cepstrum error function and treating pressure curve, respectively. Then, the relationship between the oscillation time and diversion effectiveness was analyzed. Finally, a rapid judgment method for evaluating diversion effectiveness based on the water hammer oscillation time was proposed. The results indicated a negative correlation between the probability of diversion and the oscillation time, with higher probabilities resulting in lower oscillation times. The oscillation times exhibited a negative correlation with the treating pressure response, including the treating pressure increases and diversion pressure spikes, wherein a greater pressure differential led to lower oscillation times. Drawing from the statistics of a shale gas horizontal well in Sichuan, a better diversion effectiveness is associated with fewer oscillations, demonstrating a negative correlation between the diversion effectiveness and the oscillation time in water hammer signatures. Finally, a rapid judgment method for evaluating diversion effectiveness was proposed, utilizing the 95% confidence interval of the mean oscillation time. This paper offers useful insights into evaluating diversion performance in field cases.
Preparation of Bioaerogel from Iron-Rich Microalgae for the Removal of Water Pollutants
Authors: Niu Xinqi , Si Junhui , Chen Binyi , Wang Qianting , Zeng Sen , Cui Zhixiang
Published: June 2024

Microalgae-based materials have gained significant attention considering their rich resources, cost-effectiveness, and environmental friendliness. Herein, iron-rich microalgae (Chlorella pyrenoidosa, CP) were treated by hydrothermal reaction under alkaline conditions to remove the protoplast and obtain a hollow shell with an FexOy core inside. Then, the iron-rich microalgae-based aerogel (Fe-CP aerogel) was fabricated through a freeze-drying process. The as-prepared Fe-CP aerogel exhibited superior adsorption performance, and the maximum adsorption quantity for Cu2+ could reach 208.3 mg/g due to the synergistic adsorption of the hollow shell of CP cells and FexOy core. The Fe-CP aerogel also possessed super-hydrophilicity and displayed high separation efficiency (over 99%) when used for separating different oil/water emulsions. Moreover, the existence of FexOy endowed the Fe-CP aerogel with photo-Fenton activity, thus exhibiting excellent antifouling performance. The prepared Fe-CP aerogel could be considered an ideal adsorbent, with green, efficient, low-cost, and regenerative properties, and has the potential for the practical treatment of wastewater.
What Is the Role of Religious Commitment between an Extrovert Personality and Moral Disengagement through Prosocial/Antisocial Behaviours and Moral Identity? An Investigation on Student-Athletes
Authors: Yildiz Cakir Lale , Goksel Ali Gurel , Togo Osman Tolga , Senel Ender , Yaras Abdurrahman , Kizilet Tuba , Yildiz Mevlut
Published: June 2024

Moral disengagement in sports refers to the psychological process through which individuals distance themselves from the ethical implications of their actions within the sporting context. This study aims to examine the role of religious commitment between personality traits and moral disengagement and investigate the role of prosocial and antisocial behaviours and moral identity. Student-athletes were recruited from individual and team sports, including football, basketball, handball, volleyball, wrestling, combat sports, swimming, track and field, cycling, badminton, and futsal. We hypothesised that extrovert personality traits directly predict moral identity, religious commitment, and prosocial/antisocial behaviours. Extraversion predicted moral disengagement with the mediation roles of internalisation, symbolisation, religious commitment, and pro- and antisocial behaviours. Our findings provide insights into the complex relationships between personality traits, internalisation, symbolisation, religious commitment, social behaviours, and their impact on moral disengagement. They highlight the importance of considering internalisation, symbolisation, and religious commitment mediating roles in understanding the relationships between extraversion, social behaviours, and moral disengagement. Understanding the complex relationship among these factors can inform the development of tailored interventions that address individual athletes’ needs and characteristics. Our study contributes to the growing body of literature on the psychological underpinnings of ethical behaviour in sports. By elucidating the roles of extraversion, moral identity, religious commitment, and social behaviours in moral disengagement, this research provides a more nuanced understanding of the factors influencing ethical decision-making and behaviour in student-athletes.
Temporal Progression of Four Older Adults through Technology Acceptance Phases for a Mobile Telepresence Robot in Domestic Environments
Authors: Baggett Rune , Simecek Martin , Tsui Katherine M. , Fraune Marlena R.
Published: June 2024

Loneliness is increasingly common, especially among older adults. Technology like mobile telepresence robots can help people feel less lonely. However, such technology has challenges, and even if people use it in the short term, they may not accept it in the long term. Prior work shows that it can take up to six months for people to fully accept technology. This study focuses on exploring the nuances and fluidity of acceptance phases. This paper reports a case study of four older adult participants living with a mobile telepresence robot for seven months. In monthly interviews, we explore their progress through the acceptance phases. Results reveal the complexity and fluidity of the acceptance phases. We discuss what this means for technology acceptance. In this paper, we also make coding guidelines for interviews on acceptance phases more concrete. We take early steps in moving toward a more standard interview and coding method to improve our understanding of acceptance phases and how to help potential users progress through them.
Monitoring CH4 Fluxes in Sewage Sludge Treatment Centres: Challenging Emission Underreporting
Authors: Abeywickrama Hiniduma Gamage Kavindi , Bajón-Fernández Yadira , Srinamasivayam Bharanitharan , Turner Duncan , Rivas Casado Mónica
Published: June 2024

In this manuscript, CH4 emissions from sludge treatment centres are quantified using an unmanned aerial vehicle (UAV) framework, with particular focus on anaerobic digesters and digestate storage tanks. The outcomes are compared to those obtained using the carbon accounting workbook (CAW), which is the most commonly used industry tool by UK and Irish water companies to estimate the annual greenhouse gas emissions from their process operations. Path integrated concentrations are monitored with the use of an open-path tuneable diode laser absorption spectroscopy sensor embedded on a UAV. Measurements are interpolated using geostatistics (Kriging) and coupled with the mass balance approach to estimate emissions. The findings show that the CAW seems to underestimate emissions from digestate storage tanks by up to an order of magnitude. The results also show that CH4 emissions are linked with the residence time in the tank and temperature of the digestate. This study highlights the limitations of assumptions made using current reporting methods based on the carbon accounting workbook. This study proves that the UAV framework, together with the mass balance approach, provides high spatial resolution data; it captures the dynamic nature of emissions compared to the CAW and can be a cost-effective solution to estimate CH4 fluxes compared to other sensor-based systems.
Assessing Many Image Processing Products Retrieved from Sentinel-2 Data to Monitor Shallow Landslides in Agricultural Environments
Authors: Cavalli Rosa Maria , Pisano Luca , Fiorucci Federica , Ardizzone Francesca
Published: June 2024

Remote images are useful tools for detecting and monitoring landslides, including shallow landslides in agricultural environments. However, the use of non-commercial satellite images to detect the latter is limited because their spatial resolution is often comparable to or greater than landslide sizes, and the spectral characteristics of the pixels within the landslide body (LPs) are often comparable to those of the surrounding pixels (SPs). The buried archaeological remains are also often characterized by sizes that are comparable to image spatial resolutions and the spectral characteristics of the pixels overlying them (OBARPs) are often comparable to those of the pixels surrounding them (SBARPs). Despite these limitations, satellite images have been used successfully to detect many buried archaeological remains since the late 19th century. In this research context, some methodologies, which examined the values of OBARPs and SBARPs, were developed to rank images according to their capability to detect them. Based on these previous works, this paper presents an updated methodology to detect shallow landslides in agricultural environments. Sentinel-2 and Google Earth (GE) images were utilized to test and validate the methodology. The landslides were mapped using GE images acquired simultaneously or nearly simultaneously with the Sentinel-2 data. A total of 52 reference data were identified by monitoring 14 landslides over time. Since remote sensing indices are widely used to detect landslides, 20 indices were retrieved from Sentinel-2 images to evaluate their capability to detect shallow landslides. The frequency distributions of LPs and SPs were examined, and their differences were evaluated. The results demonstrated that each index could detect shallow landslides with sizes comparable to or smaller than the spatial resolution of Sentinel-2 data. However, the overall accuracy values of the indices varied from 1 to 0.56 and two indices (SAVI and RDVI) achieved overall accuracy values equal to 1. Therefore, to effectively distinguish areas where shallow landslides are present from those where they are absent, it is recommended to apply the methodology to many image processing products. In conclusion, given the significant impact of these landslides on agricultural activity and surrounding infrastructures, this methodology provides a valuable tool for detecting and monitoring landslide presence in such environments.
Spatiotemporal Variabilities in Evapotranspiration of Alfalfa: A Case Study Using Remote Sensing METRIC and SSEBop Models and Eddy Covariance
Authors: Tawalbeh Zada M. , Bawazir A. Salim , Fernald Alexander , Sabie Robert
Published: June 2024

Prolonged drought exacerbated by climate change in the Mesilla Valley, one of the major agricultural areas of New Mexico, USA, is causing a shortage of surface water from the Rio Grande for irrigation. Farmers in the Valley are using groundwater for irrigation and complementing it with limited surface water from the river (Rio Grande). Managing irrigation water better is vital to sustaining agriculture in the Valley. Remote sensing (RS)-based crop evapotranspiration (ETa) models offer significant advantages over traditional methods. The ET maps generated by these RS models provide valuable information that can be used to manage irrigation water and crops in water-scarce areas. This study used METRIC and SSEBop RS models to map the ET of alfalfa on a private farm that is managed as commonly practiced in the Valley. The integrated ET values of the two models are compared to those of the ETa measured using the eddy covariance method. The comparison showed that 91.55% of the variability in SSEBop ETa estimates can be explained by the variability in the METRIC ETa estimates, and the variability in eddy covariance ETa can explain 93.07% of the variability in METRIC ETa and 86.01% in the SSEBop Eta estimates. Both METRIC and SSEBop reflected the ETa of alfalfa during full growth and harvesting periods. However, the absolute percent mean relative difference (MRD) of ET was higher for two out of three cuttings by SSEBop (>32%) compared to those for METRIC and eddy covariance. The spatiotemporal variabilities in crop ET estimates using METRIC and SSEBop showed a need to improve on-farm irrigation conveyance and on-the-field irrigation efficiency. Overall, RS models can provide spatiotemporal maps of ET that can be used for decision-making to manage irrigation water better and improve crop yield on a field, farm, and regional scale.
Fast Algorithm of Passive Bistatic Radar Detection Based on Batches Processing of Sparse Representation and Recovery
Authors: Cui Kai , Wang Changlong , Zhou Feng , Liu Chunheng , Gao Yongchan , Feng Weike
Published: June 2024

In the passive bistatic radar (PBR) system, methods exist to address the issue of detecting weak targets without being influenced by non-ideal factors from adjacent strong targets. These methods utilize the sparsity in the delay-Doppler domain of the cross ambiguity function (CAF) to detect weak targets. However, the modeling and solving of this method involve substantial memory consumption and computational complexity. To address these challenges, this paper establishes a target detection model for PBR based on batch processing of sparse representation and recovery. This model partitions the CAF into blocks, identifies blocks requiring processing based on the presence of targets, and improves the construction and utilization of the measurement matrix. This results in a reduction in the computational complexity and memory resource requirements for sparse representation and recovery, and provides favorable conditions for parallel execution of the algorithm. Experimental results indicate that the proposed approach increases the number of blocks by a factor of four, and reduces the number of real multiplications by approximately an order of magnitude. Hence, compared with the traditional approach, the proposed approach enables fast and stable detection of weak targets.
Inversion of Farmland Soil Moisture Based on Multi-Band Synthetic Aperture Radar Data and Optical Data
Authors: Xu Chongbin , Liu Qingli , Wang Yinglin , Chen Qian , Sun Xiaomin , Zhao He , Zhao Jianhui , Li Ning
Published: June 2024

Surface soil moisture (SSM) plays an important role in agricultural and environmental systems. With the continuous improvement in the availability of remote sensing data, satellite technology has experienced widespread development in the monitoring of large-scale SSM. Synthetic Aperture Radar (SAR) and optical remote sensing data have been extensively utilized due to their complementary advantages in this field. However, the limited information from single-band SARs or single optical remote sensing data has restricted the accuracy of SSM retrieval, posing challenges for precise SSM monitoring. In contrast, multi-source and multi-band remote sensing data contain richer and more comprehensive surface information. Therefore, a method of combining multi-band SAR data and employing machine learning models for SSM inversion was proposed. C-band Sentinel-1 SAR data, X-band TerraSAR data, and Sentinel-2 optical data were used in this study. Six commonly used feature parameters were extracted from these data. Three machine learning methods suitable for small-sample training, including Genetic Algorithms Back Propagation (GA-BP), support vector regression (SVR), and Random Forest (RF), were employed to construct the SSM inversion models. The differences in SSM retrieval accuracy were compared when two different bands of SAR data were combined with optical data separately and when three types of data were used together. The results show that the best inversion performance was achieved when all three types of remote sensing data were used simultaneously. Additionally, compared to the C-band SAR data, the X-band SAR data exhibited superior performance. Ultimately, the RF model achieved the best accuracy, with a determinable coefficient of 0.9186, a root mean square error of 0.0153 cm3/cm3, and a mean absolute error of 0.0122 cm3/cm3. The results indicate that utilizing multi-band remote sensing data for SSM inversion offers significant advantages, providing a new perspective for the precise monitoring of SSM.
Performance Evaluation of Sinusoidal Power Supplies for Ozone Generation in Water Purification Applications
Authors: Shibl Mostafa M. , Shouman Omar , Mahmoud Ahmed , Massoud Ahmed M.
Published: June 2024

Ozone generation is a water disinfection method, superior to chlorine in terms of fewer byproducts and no residual taste. However, its high production cost limits its widespread adoption. This paper designs an ozone generation sinusoidal power supply for water treatment. Ozone generation requires a high-frequency and high-voltage power supply to produce ozone from oxygen molecules. The study evaluates two power supply topologies, one with a parallel LC filter and the other with an LCL filter, assessing their feasibility, effectiveness, and reliability. Theoretically, the LCL filter achieves higher gains than the parallel LC filter. The larger inductance in the parallel LC filter reduces gain, while the larger inductance in the LCL filter increases gain. Simulation and practical results validate these findings, achieving gains of 40 for the parallel LC filter and 150 for the LCL filter.
Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques
Authors: Liu Jiefei , Bailey Derek W. , Cao Huiping , Son Tran Cao , Tobin Colin T.
Published: June 2024

Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.
A Compact Linear Microstrip Patch Beamformer Antenna Array for Millimeter-Wave Future Communication
Authors: Saeed Muhammad Asfar , Obi Emenike Raymond , Nwajana Augustine O.
Published: June 2024

5/6G is anticipated to address challenges such as low data speed and high latency in current cellular networks, particularly as the number of users overwhelms 4G and LTE capabilities. This paper proposes a microstrip patch antenna array comprising six radiating patches and utilizing a microstrip line feeding technique to facilitate the compact design crucial for 5G implementation. ROGER 3003, chosen for its advanced and environmentally friendly features, serves as the dielectric material, ensuring suitability for 5G and B5G applications. The designed antenna, evaluated at a resonating frequency of 28.8 GHz with a −10 dB impedance bandwidth of 1 GHz, offers a high gain of 9.19 dBi. Its compact array, cost-effectiveness, and broad impedance and radiation coverage position it as a viable candidate for 5G and future communication applications.
Intelligent Fault Diagnosis Method Based on Cross-Device Secondary Transfer Learning of Efficient Gated Recurrent Unit Network
Authors: Mo Chaoquan , Huang Ke
Published: June 2024

In response to the issues of low model recognition accuracy and weak generalization in mechanical equipment fault diagnosis due to scarce data, this paper proposes an innovative solution, a cross-device secondary transfer-learning method based on EGRUN (efficient gated recurrent unit network). This method utilizes continuous wavelet transform (CWT) to transform source domain data into images. The EGRUN model is initially trained, and shallow layer weights are frozen. Subsequently, random overlapping sampling is applied to the target domain data to enhance data and perform secondary transfer learning. The experimental results demonstrate that this method not only significantly improves the model’s ability to learn fault features but also enhances its classification accuracy and generalization performance. Compared to current state-of-the-art algorithms, the model proposed in this study shows faster convergence speed, higher diagnostic accuracy, and superior robustness and generalization, providing an effective approach to address the challenges arising from scarce data and varying operating conditions in practical engineering scenarios.