bluuebunny/crossref_metadata_2025 huggingface dataset:
A Preliminary Determination of Mechanical and Suspension Properties of Waste Mulch Film and Cotton Stalk
Authors: Guo Kai , Cao Mingzhu , Yang Hongguang , Luo Weiwen , Qin Mingyang , Wu Feng , Gu Fengwei , Hu Zhichao
Published: August 2023

In order to provide basic data and a theoretical basis for waste mulch film crushing and separation equipment, the mechanical and suspension properties of waste mulch film and cotton stalk were determined in this study. In the mechanical properties test, the tensile properties of waste mulch film and the shear properties of different parts of cotton stalk were investigated. In the suspension properties test, the suspension properties of different qualities of waste mulch film and different parts of cotton stalk were investigated. The results showed that the average maximum tensile forces and fracture displacements of the waste mulch film were 1.70 N (horizontal), 0.80 N (vertical) and 254.75 mm (horizontal), 145.22 mm (vertical). The shear properties of the cotton stem after wrapping the mulch film were greatly improved; the maximum shearing force was 186.89% higher than that of the unwrapped specimens. When the total suspension velocity was from 3.09 to 4.07 m/s, separation of mulch film and cotton stalk could be achieved by wind.
Pneumatic Defoliation Enhances Fruit Skin Color and Anthocyanin Pigments in ‘Picnic’ Apples
Authors: Win Nay Myo , Lee Youngsuk , Kim Seonae , Do Van Giap , Cho Young Sik , Kang In-Kyu , Yang Sangjin , Park Juhyeon
Published: August 2023

Apple skin color is essential for market value. Therefore, this study evaluated the efficiency of pneumatic defoliation (PD) on the enhancement of red skin color and anthocyanin pigments in ‘Picnic’ apples. Three weeks before harvesting, apple trees were treated with PD with low- (0.7 bar) and high- (0.9 bar) air pressure at a tractor driving speed of 2 km/h. Undefoliated trees served as controls. Higher leaf defoliation and leaf damage rates were observed in the high-PD treatment than those in the low-PD treatment. Photosynthetically active radiation inside the canopies was the highest in trees subjected to high-pressure PD than in those that underwent low-pressure PD and the controls. At harvest, the fruit color (a*) value, red-colored area, and anthocyanin content in the fruit skin were the highest in the high-PD treatment, intermediate in the low-PD treatment, and the lowest in the control treatments. Additionally, a higher expression of anthocyanin biosynthetic genes was observed in both defoliation treatments, especially under high PD. However, except for higher flesh firmness under low PD, the L* and b* values and fruit quality indices (fruit weight, starch pattern index, titratable acidity, soluble solids content, and sunburn occurrence) were not significantly affected by either PD treatment. In conclusion, PD can be used to enhance skin coloration and anthocyanin pigments in apples; further PD with high air pressure achieved optimum red skin coloration in ‘Picnic’ apples.
Phenotypic and Genotypic Analysis of Bacterial Pathogens Recovered from Patients Diagnosed with Fever of Unknown Origin in Egypt
Authors: Mostafa Shimaa H. , Saleh Sarra E. , Khaleel Eman F. , Badi Rehab Mustafa , Aboshanab Khaled M. , Hamed Samira M.
Published: August 2023

Fever of unknown origin (FUO) is a medical term describing fever that lasts for at least three weeks without a diagnosis being reached after extensive diagnostic evaluation. Therefore, this study aimed to identify the common pathogens causing FUO in patients admitted to Abbasia Fever Hospital in Egypt from January 2020 to December 2022, their antimicrobial susceptibility profiles, and associated resistance genes. The study also aimed to investigate the burden of multidrug-resistant (MDR) pathogens and the priority pathogens nominated by the World Health Organization (WHO) for posing the greatest threat to human health due to antibiotic resistance. During the study period, about 726 patients were diagnosed with FUO. After extensive investigations, the cause of the FUO was found to be infectious diseases in 479/726 patients (66.0%). Of them, 257 patients had positive bacterial cultures, including 202 Gram-negative isolates that comprised Klebsiella pneumoniae (85/202; 42.1%), Escherichia coli (71/202; 35.1%), Acinetobacter baumannii (26/202; 12.9%), and Pseudomonas aeruginosa (14/202; 6.9%) and 55 Gram-positive isolates, including Staphylococcus aureus (23/55; 41.8%), Streptococcus pneumoniae (7/55; 12.7%), and Enterococcus spp. (25/55; 45.5%). The MDR phenotype was shown by 68.3% and 65.5% of the Gram-negative and Gram-positive isolates, respectively. Carbapenem resistance (CR) was shown by 43.1% of the Gram-negative isolates. Of the 23 S. aureus isolates obtained from research participants, 15 (65.2%) were methicillin-resistant S. aureus (MRSA). A high-level aminoglycoside resistance (HLAR) phenotype was found in 52.0% of the Enterococcus sp. isolates. The PCR screening of resistance genes in the MDR isolates showed that blaOXA−48 was the most prevalent (84%) among the carbapenemase-coding genes, followed by blaVIM (9%) and then blaIMP (12%). The ESBL-coding genes blaTEM, blaCTX-M,aac(6′)-Ib, and blaSHV, were prevalent in 100%, 93.2%, 85,% and 53.4% of the MDR isolates, respectively. This study updates the range of bacteria that cause FUO and emphasizes the burden of multidrug resistance and priority infections in the region. The obtained data is of relevant medical importance for the implementation of evidence-based antimicrobial stewardship programs and tailoring existing empirical treatment guidelines.
BIM-Based Optimization of Drilling Angle and Hole Position of Outside Corner Anchor Rods
Authors: Jiao Zihan , Zhao Xuefeng , Li Danyang , Sun Zhe , Li Wangbing
Published: August 2023

In recent years, safety issues involving foundation pits have attracted extensive attention in the industry. The external angle of the foundation pit bears a greater load and is more prone to collapsing. Anchor support technology is one of the most widely used support technologies in construction engineering, but it has the problem of anchor rod crossing and colliding from the initial design stage, and this reduces anchoring force and eventually leads to many safety issues. Compared with traditional methods, Building Information Modeling (BIM) technology can save a lot of time as well as reduce costs, and applying it to concealed engineering can solve the problems that exist during the design stage and reduce unpredictable construction risks. This study proposes an optimization method based on BIM that can accurately and quickly optimize the drilling of anchor rods in the external angles of foundation pits. The results show that the proposed method can reduce the number of anchor rod collision points at the external angle of the foundation pit, minimize the loss of horizontal force caused by anchor rod collision, and ensure the safety and stability of the foundation pit support system.
Applications of Data Science and Artificial Intelligence
Authors: Costa Carlos J. , Aparicio Manuela
Published: August 2023

A series of waves have marked the history of artificial intelligence (AI) [...]
In Vitro Investigation of the Mechanical Properties of Blended 3D-Printing Resins for Orthodontic Aligners: A Comparison between Commercial Resin and Nickel-Titanium Wire
Authors: Zecca Piero Antonio , Bocchieri Salvatore , Borgese Marina , Dolci Carolina , Campobasso Alessandra , Battista Giovanni , Caprioglio Alberto , Raspanti Mario
Published: August 2023

This scientific article investigates the mechanical properties of a novel three-dimensional-printing resin specifically designed for orthodontic aligners and compares it to other commonly used resins. The resin was made by blending two commercially available and certified resins to produce aligners with improved mechanical properties compared to each separate resin. The study examined the effect of the addition of NextDent Ortho Rigid resin on the mechanical properties of the specimens, more specifically analyzing the relationship between the amount of the added rigid resin and the mechanical properties of the specimens. The mechanical properties of the specimens and the nickel-titanium wires were analyzed using a three-point bending test setup. The results showed no statistically significant differences within the different groups of specimens. The study provides valuable insights into the potential of the resin to meet the mechanical demands of orthodontic treatments. It will contribute to the advancement of personalized orthodontic care through three-dimensional-printing technology. Blending commercially available and certified resins enables orthodontic practitioners to select the most suitable resin for each case, providing better outcomes for patients and increasing the efficiency of the treatment process. Further research and evaluation would be required to determine the suitability of the blended resin for orthodontic treatments.
Swin–MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
Authors: Rong Zifan , Jiang Xuesong , Huang Linfeng , Zhou Hongping
Published: August 2023

Pan-sharpening aims to create high-resolution spectrum images by fusing low-resolution hyperspectral (HS) images with high-resolution panchromatic (PAN) images. Inspired by the Swin transformer used in image classification tasks, this research constructs a three-stream pan-sharpening network based on the Swin transformer and a multi-scale feature extraction module. Unlike the traditional convolutional neural network (CNN) pan-sharpening model, we use the Swin transformer to establish global connections with the image and combine it with a multi-scale feature extraction module to extract local features of different sizes. The model combines the advantages of the Swin transformer and CNN, enabling fused images to maintain good local detail and global linkage by mitigating distortion in hyperspectral images. In order to verify the effectiveness of the method, this paper evaluates fused images with subjective visual and quantitative indicators. Experimental results show that the method proposed in this paper can better preserve the spatial and spectral information of images compared to the classical and latest models.
Modified Extended Complex Kalman Filter for DC Offset and Distortion Rejection in Grid-Tie Transformerless Converters
Authors: El-Nagar Mohammed , Ahmed Khaled , Hamdan Eman , Abdel-Khalik Ayman S. , Hamad Mostafa S. , Ahmed Shehab
Published: August 2023

Proper operation of the grid-tie transformerless converters under unbalanced and distorted conditions entails a precise detection of the frequency and fundamental component of the grid voltage. One of the main problems that could arise during the estimation of grid parameters is the existence of a DC offset generated from measurement and A/D conversion. This undesirable induced DC offset could appear as a part of the reference sinusoidal current of grid-tie converters. Although literature has proposed the use of an extended complex Kalman filter (ECKF) for the estimation of positive and negative sequence voltage components as a promising competitor to phase locked loops, mitigating the effect of possible DC offsets when a Kalman filter is employed remains scarce. This paper proposes a new extended complex Kalman filter to improve the filter stability for estimating the frequency and the fundamental positive and negative symmetrical components of the grid voltages, where DC offset, scaling error, and noise can successfully be rejected. The theoretical findings are experimentally validated.
A New Robust Weak Supervision Deep Learning Approach for Reservoir Properties Prediction in Malaysian Basin Field
Authors: Ahmad Fuad Muhammad Izzuljad , Hermana Maman , Jaya Makky Sandra , Ishak Muhammad Anwar
Published: August 2023

The conventional seismic inversion approach is practical for operational work, as it only uses simple linearized algorithms and assumptions, but may be less applicable when dealing with a complex geological setting, especially in the Malay basin fields, as it may introduce non-linear noises and non-unique solutions. In the Malay basin, we also frequently struggle with a scarcity of reliable well data when performing seismic inversions. This makes finding an accurate prior model for inversion challenging and contributes to high uncertainty in properties’ estimation. Implementation of deep learning for seismic inversion has become routine and has shown increasing capability in addressing nonlinearity in inverse problems. In this work, we develop a robust approach to deep learning-based seismic inversion to predict elastic properties from seismic data. The approach incorporates synthetic well and seismic data generation from a set of rock physics knowledge called the rock physics library, which plays a significant role in dataset input for network training, validation, and testing to improve elastic properties in this field. The deep learning network architecture comprising UNET and RESNET-18 with weak supervision networks has proven to be useful to enhance computational work efficiency and prediction accuracy while handling the non-linearity of the data and the non-uniqueness of the solutions. We successfully validated the proposed method on actual field data from a clastic fluvial-dominated field in the Malay basin. Upon comparative analysis with the conventional method, both inversion results are comparable and capable of identifying the reservoir occurrence and distribution. The conventional method exposed the presence of scattered amplitude noises and prominent seismic imprints masking the reservoir. Meanwhile, the proposed method showed more stable, clearer definition and fewer noise inversion results but with a faster turn-around time and a more efficient workflow. There are substantial improvements of up to 31% in correlation accuracy achieved upon implementing the proposed method for elastic properties prediction compared to the conventional. The result implies that the proposed method can provide a good elastic properties prediction framework while addressing data limitations and sparsity issues in typical deep learning-based inversions.
Research on the Predictive Algorithm of Wear Degree Based on Adaptive Feature Learning
Authors: Wang Zhao , Qiu Ningjia , Wang Peng , Li Meng
Published: August 2023

In the prediction and modeling analysis of wear degree in the field of industrial parts processing, there are problems such as poor prediction ability for long sequence data and low sensitivity of output feedback to changes in input signals. In this paper, a combined prediction model is proposed that integrates dual attention mechanisms and self-regressive correction. Firstly, pre-processing is performed on the collected wear data to eliminate noise and aberrant mutation data. Then, the feature attention mechanism is introduced to analyze the input data sequence, and the weights of each feature under the temporal condition are set based on the contribution of the prediction results, thereby obtaining the LSTM hidden state at the current time. Subsequently, the temporal attention mechanism is introduced to perform a weighted calculation of the hidden state information, analyze the correlation of long-term sequential wear data, and decode and output the analysis results. Finally, the ARIMA model is used to perform linear correction on the predicted results to improve the accuracy of wear degree prediction. The proposed model is compared and analyzed with the models that are highly related in recent research on real-world wear degree datasets. The experimental results show that the improved model has a better ability to improve the corresponding problems and has a significant increase in prediction accuracy.
A Hybrid Approach to Representing Shared Conceptualization in Decentralized AI Systems: Integrating Epistemology, Ontology, and Epistemic Logic
Authors: Adhnouss Fateh Mohamed Ali , El-Asfour Husam M. Ali , McIsaac Kenneth , El-Feghi Idris
Published: August 2023

Artificial Intelligence (AI) systems are increasingly being deployed in decentralized environments where they interact with other AI systems and humans. In these environments, each participant may have different ways of expressing the same semantics, leading to challenges in communication and collaboration. To address these challenges, this paper presents a novel hybrid model for shared conceptualization in decentralized AI systems. This model integrates ontology, epistemology, and epistemic logic, providing a formal framework for representing and reasoning about shared conceptualization. It captures both the intensional and extensional components of the conceptualization structure and incorporates epistemic logic to capture knowledge and belief relationships between agents. The model’s unique contribution lies in its ability to handle different perspectives and beliefs, making it particularly suitable for decentralized environments. To demonstrate the model’s practical application and effectiveness, it is applied to a scenario in the healthcare sector. The results show that the model has the potential to improve AI system performance in a decentralized context by enabling efficient communication and collaboration among agents. This study fills a gap in the literature concerning the representation of shared conceptualization in decentralized environments and provides a foundation for future research in this area.
Atmospheric Oxidation Capacity and Its Impact on the Secondary Inorganic Components of PM2.5 in Recent Years in Beijing: Enlightenment for PM2.5 Pollution Control in the Future
Authors: Chu Wanghui , Li Ling , Li Hong , Zhang Yuzhe , Chen Yizhen , Zhi Guorui , Yang Xin , Ji Yuanyuan , Chai Fahe
Published: August 2023

In recent years, the concentrations of PM2.5 in urban ambient air in China have been declining; however, the strong atmospheric oxidation capacity (AOC) represents challenges to the further reduction of PM2.5 concentration and the continuous improvement of ambient air quality in China in the future, since the overall AOC is still at a high level. For this paper, based on ground observation data recorded in Beijing from 2016 to 2019, the variation in AOC was characterized according to the concentration of odd oxygen (OX = O3 + NO2). The concentrations of the primary and secondary components of PM2.5 were analyzed using empirical formulas, the correlation between AOC and the concentrations of secondary PM2.5 and the secondary inorganic components (SO42−, NO3−, NH4+, and SNA) in Beijing were explored, the impact of atmospheric photochemical reaction activity on the generation of atmospheric secondary particles was evaluated, and the impact of atmospheric oxidation variations on PM2.5 concentrations and SNA in Beijing was investigated. The results revealed that OX concentrations reached their peak in 2016 and reached their lowest point in 2019. The OX concentrations followed a descending seasonal trend of summer, spring, autumn, and winter, along with a spatial descending trend from urban observation stations to suburban stations and background stations. The degree of photochemical activity and the magnitude of the AOC have a large influence on the production of atmospheric secondary particles. When the photochemical reaction was more active and the AOC was stronger, the mass concentrations of the secondary generated PM2.5 fraction were higher and accounted for a higher proportion of the total PM2.5 mass concentrations. In the PM2.5 fraction, SNA accounted for 50.7% to 94.4% of the total mass concentrations of water-soluble inorganic ions in the field observations. Higher concentrations of the atmospheric oxidant OX in ambient air corresponded to a higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), suggesting that the increase in AOC could promote the increase of PM2.5 concentration. Based on a relationship analysis of SOR, NOR, and OX, it was inferred that the relationship between OX and SOR and the relationship between OX and NOR were both nonlinear. Therefore, when establishing PM2.5 control strategies in Beijing in the future, the impact of the AOC on PM2.5 generation should be fully considered, and favorable measures should be taken to properly regulate the AOC, which would be more effective when carrying out further control measures regarding PM2.5 pollution.
Research Progress in Thermal Runaway Vent Gas Characteristics of Li-Ion Battery
Authors: Qiu Mingming , Liu Jianghong , Cong Beihua , Cui Yan
Published: August 2023

The wide application of lithium-ion batteries (LIBs) brings along with it various safety problems, such as fire and explosion accidents. Aiming at the thermal runaway (TR) and fire problems of LIBs, we reviewed the evolution of TR within LIB and the release of TR gases and their hazards, as well as the research progress in recent years in the area of fire separation of LIBs. To begin with, physical, electrical, and thermal abuse are the three main factors leading to TR and the thermal stability of aging batteries significantly deteriorates. Furthermore, the decomposition of the electrolyte and the reaction between the active materials generates CO, CO2, H2, HF, and a variety of hydrocarbons. These TR gases have serious toxic and explosive hazards. In addition, fire separation can effectively delay the occurrence and propagation of TR within LIB modules. As a good heat-absorbing material, phase-change materials are widely used in the thermal management system and have a great prospect of wide applications in the fire separation of LIBs. Finally, the research on the TR gases’ hazards of aging LIB and safer and more effective fire separation are prospected.
Binding of Pentagalloyl Glucose to Aortic Wall Proteins: Insights from Peptide Mapping and Simulated Docking Studies
Authors: Simionescu Dan , Tharayil Nishanth , Leonard Elizabeth , Carlyle Wenda , Schwarz Alex , Ning Kelvin , Carsten Christopher , Garcia Juan Carlos Carrillo , Carter Alexander , Owens Collin , Simionescu Agneta
Published: August 2023

Pentagalloyl glucose (PGG) is currently being investigated as a non-surgical treatment for abdominal aortic aneurysms (AAAs); however, the molecular mechanisms of action of PGG on the AAA matrix components and the intra-luminal thrombus (ILT) still need to be better understood. To assess these interactions, we utilized peptide fingerprinting and molecular docking simulations to predict the binding of PGG to vascular proteins in normal and aneurysmal aorta, including matrix metalloproteinases (MMPs), cytokines, and fibrin. We performed PGG diffusion studies in pure fibrin gels and human ILT samples. PGG was predicted to bind with high affinity to most vascular proteins, the active sites of MMPs, and several cytokines known to be present in AAAs. Finally, despite potential binding to fibrin, PGG was shown to diffuse readily through thrombus at physiologic pressures. In conclusion, PGG can bind to all the normal and aneurysmal aorta protein components with high affinity, potentially protecting the tissue from degradation and exerting anti-inflammatory activities. Diffusion studies showed that thrombus presence in AAAs is not a barrier to endovascular treatment. Together, these results provide a deeper understanding of the clinical potential of PGG as a non-surgical treatment of AAAs.
Mechanisms of Modulation of Mitochondrial Architecture
Authors: Muñoz Juan Pablo , Basei Fernanda Luisa , Rojas María Laura , Galvis David , Zorzano Antonio
Published: August 2023

Mitochondrial network architecture plays a critical role in cellular physiology. Indeed, alterations in the shape of mitochondria upon exposure to cellular stress can cause the dysfunction of these organelles. In this scenario, mitochondrial dynamics proteins and the phospholipid composition of the mitochondrial membrane are key for fine-tuning the modulation of mitochondrial architecture. In addition, several factors including post-translational modifications such as the phosphorylation, acetylation, SUMOylation, and o-GlcNAcylation of mitochondrial dynamics proteins contribute to shaping the plasticity of this architecture. In this regard, several studies have evidenced that, upon metabolic stress, mitochondrial dynamics proteins are post-translationally modified, leading to the alteration of mitochondrial architecture. Interestingly, several proteins that sustain the mitochondrial lipid composition also modulate mitochondrial morphology and organelle communication. In this context, pharmacological studies have revealed that the modulation of mitochondrial shape and function emerges as a potential therapeutic strategy for metabolic diseases. Here, we review the factors that modulate mitochondrial architecture.
Therapeutic Role of Pharmacological Chaperones in Lysosomal Storage Disorders: A Review of the Evidence and Informed Approach to Reclassification
Authors: Keyzor Ian , Shohet Simon , Castelli Jeff , Sitaraman Sheela , Veleva-Rotse Biliana , Weimer Jill M. , Fox Brian , Willer Tobias , Tuske Steve , Crathorne Louise , Belzar Klara J.
Published: August 2023

The treatment landscape for lysosomal storage disorders (LSDs) is rapidly evolving. An increase in the number of preclinical and clinical studies in the last decade has demonstrated that pharmacological chaperones are a feasible alternative to enzyme replacement therapy (ERT) for individuals with LSDs. A systematic search was performed to retrieve and critically assess the evidence from preclinical and clinical applications of pharmacological chaperones in the treatment of LSDs and to elucidate the mechanisms by which they could be effective in clinical practice. Publications were screened according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) reporting guidelines. Fifty-two articles evaluating 12 small molecules for the treatment of seven LSDs are included in this review. Overall, a substantial amount of preclinical and clinical data support the potential of pharmacological chaperones as treatments for Fabry disease, Gaucher disease, and Pompe disease. Most of the available clinical evidence evaluated migalastat for the treatment of Fabry disease. There was a lack of consistency in the terminology used to describe pharmacological chaperones in the literature. Therefore, the new small molecule chaperone (SMC) classification system is proposed to inform a standardized approach for new, emerging small molecule therapies in LSDs.
Genome-Wide Expression Profile in People with Optic Neuritis Associated with Multiple Sclerosis
Authors: Habek Mario , Blazekovic Antonela , Gotovac Jercic Kristina , Pivac Nela , Outero Tiago Fleming , Borovecki Fran , Brinar Vesna
Published: August 2023

The aim of this study was to perform a genome-wide expression analysis of whole-blood samples from people with optic neuritis (ON) and to determine differentially expressed mRNAs compared to healthy control subjects. The study included eight people with acute ON and six healthy control subjects. Gene expression was analyzed using DNA microarrays for whole-human-genome analysis, which contain 54,675 25-base pairs. The additional biostatistical analysis included gene ontology analysis and gene set enrichment analysis (GSEA). Quantitative RT-PCR (qPCR) was used to confirm selected differentially expressed genes. In total, 722 differently expressed genes were identified, with 377 exhibiting increased, and 345 decreased, expression. Gene ontology analysis and GSEA revealed that protein phosphorylation and intracellular compartment, apoptosis inhibition, pathways involved in cell cycles, T and B cell functions, and anti-inflammatory central nervous system (CNS) pathways are implicated in ON pathology. qPCR confirmed the differential expression of eight selected genes, with SLPI, CR3, and ITGA4 exhibiting statistically significant results. In conclusion, whole-blood gene expression analysis showed significant differences in the expression profiles of people with ON compared to healthy control subjects. Additionally, pathways involved in T cell regulation and anti-inflammatory pathways within CNS were identified as important in the early phases of MS.
Effect of Leflunomide–Metal Complexes on ROS, TNF, and Brain Indolamines in Comparison with Anti-Depressants as Adjunct Therapy in Rheumatoid Arthritic Model
Authors: Naeem Almas , Jahan Noor , Khan Moona Mehboob , Abbas Ghulam , Siddiqui Faheema , Khalid Muhammad Usaid , Farooqui Waqas Ahmed
Published: August 2023

Leflunomide is an isoxazole immunomodulating drug used to treat rheumatoid arthritis (RA). It is adopted as a metal-containing molecule to proceed with saturated salts of essential and detected metals; it amends the pharmacokinetic and pharmacodynamics activity of leflunomide to provide [M(Lef)4]X2-type complexes. Earlier it has been reported that after forming complexes with metals, leflunomide anti-arthritic activity was significantly altered in an acute arthritic model. In the present study, we evaluated the possible modification in anti-arthritic activities of leflunomide–metal complexes (Mg+2, Ca+2, Fe+2, Zn+2) with and without an anti-depressant drug, i.e., fluoxetine (10 mg/kg) in a chronic AIA model. Rats (n = 5) were administered with 0.1 mL of CFA into the right hind paw while treated groups received leflunomide and its metal complexes orally (3.2 mg/kg) for 24 days. On the final day of experiment, rats were sacrificed; a specific rat immunoassay ELISA kit was used to assess TNF-α in serum samples and read at 450 nm; a tissue sample of a paw was homogenized in a phosphate buffer using DCFH-DA dye for binding to assess ROS. A rat’s brain sample was homogenized and evaluated for tryptophan, serotonin (5-HT), and HIAA by RP-HPLC with EC detector. The overall TNF production was altered in treated rats. In addition, a decreased ROS was observed in all categories, except lef+Mg+2 group. Moreover, depletion in the brain indolamine levels were found in treated groups; an upraised level of these indolamines was observed when fluoxetine was added. It is concluded that metals affect leflunomide activity on complexation and simultaneous administration of fluoxetine cope up with the depression in arthritic-induced rats.
Determination of Final Strand Slips of Prestressed Precast Hollow-Core Slabs Subjected to Flexural Load Using Machine Learning Algorithms
Authors: Haruna Sadi Ibrahim , Ibrahim Yasser E. , Adamu Musa , Ahmed Omar Shabbir
Published: August 2023

Precast prestressed concrete hollow-core slabs (HCUs) are structural elements with less self-weight, providing improved structural effectiveness in withstanding the straining action and allowing for a long span. This study investigated the additional strand slips and developed machine learning (ML) models for evaluating the final strand slips (Śf) of the precast HCUs. Two groups of HCUs, with nominal widths of 1.2 m and 0.55 m, were subjected to flexural loading conditions. One sample from each group was selected to form composite specimens by casting a concrete topping slab, and the restrain mechanism was attached at the ends of the additional HCU specimens. The experimental datasets used to train the ML models, including the support vector machine (SVM), multi-linear regression (MLR), and improved eliminate particle swamp optimization hybridized artificial neural network (IEPANN) models for the prediction of Śf. The efficacy of the IEPANN model compared to the nonlinear predictive models was evaluated, and the performances of the developed ML models were checked using the evaluation matrices. The results indicated that the prestressing strands with relatively higher initial strand slips may result in larger additional slips during flexural loading. The restraining mechanism and cast-in-place topping slab influenced the additional strand slip rate. The hybridized IEPANN model outperformed other classical models in estimating the additional slips with the R2 values greater than 0.9 in the two modelling stages, indicating the efficacy of the IEPANN compared to the nonlinear predictive modes.
Advances in the Application of Nano-Enzymes in the Electrochemical Detection of Reactive Oxygen Species: A Review
Authors: Gao Rongwei , Bao Shujuan
Published: August 2023

Reactive oxygen species (ROS) play an important role in maintaining human health and are recognized as indicators of oxidative stress linked to various conditions such as neurodegenerative and cardiovascular diseases, as well as cancer. Consequently, detecting ROS levels in biological systems is crucial for biomedical and analytical research. Electrochemical approaches offer promising opportunities for ROS determination due to their exceptional sensitivity, speed, and simplicity of equipment. This review covers studies using advanced electrochemical nanozyme sensors for detecting ROS in biological samples that were published over the last ten years, from 2013 to 2023. Emphasis is placed on the sensor materials and different types of modifiers employed for selective ROS detection. Furthermore, a comprehensive analysis of the sensors’ selectivity was performed.
“Making Data the Drug”: A Pragmatic Pilot Feasibility Randomized Crossover Trial of Data Visualization as an Intervention for Pediatric Chronic Pain
Authors: Boerner Katelynn E. , Desai Unma , Luu Jessica , MacLean Karon E. , Munzner Tamara , Foladare Haley , Shen Jane , Gill Javed , Oberlander Tim F.
Published: August 2023

Data tracking is a common feature of pain e-health applications, however, viewing visualizations of this data has not been investigated for its potential as an intervention itself. We conducted a pilot feasibility parallel randomized cross-over trial, 1:1 allocation ratio. Participants were youth age 12–18 years recruited from a tertiary-level pediatric chronic pain clinic in Western Canada. Participants completed two weeks of Ecological Momentary Assessment (EMA) data collection, one of which also included access to a data visualization platform to view their results. Order of weeks was randomized, participants were not masked to group assignment. Objectives were to establish feasibility related to recruitment, retention, and participant experience. Of 146 youth approached, 48 were eligible and consented to participation, two actively withdrew prior to the EMA. Most participants reported satisfaction with the process and provided feedback on additional variables of interest. Technical issues with the data collection platform impacted participant experience and data analysis, and only 48% viewed the visualizations. Four youth reported adverse events not related to visualizations. Data visualization offers a promising clinical tool, and patient experience feedback is critical to modifying the platform and addressing technical issues to prepare for deployment in a larger trial.
Percutaneous Radiofrequency Ablation of Thyroid Carcinomas Ineligible for Surgery, in the Elderly
Authors: Marcy Pierre Yves , Tassart Marc , Marchand Jean-Guillaume , Thariat Juliette , Bizeau Alain , Ghanassia Edouard
Published: August 2023

Thirty to 50% of differentiated thyroid carcinomas include papillary thyroid microcarcinomas (mPTC). Most of these tumors remain clinically silent, have a bright prognosis and a disease-specific mortality <1%. Surgery has been recommended as first line-treatment by current guidelines, the standard treatment being lobectomy. However, surgery has some drawbacks, including potential recurrent laryngeal nerve paralysis, hypothyroidism, hypoparathyroidism, in -patient basis hospital stay, lifelong medication, scarring of the neck, and general anesthesia related risks. Moreover, elderly patients who present severe comorbidities, could be ineligible for surgery, and others may refuse invasive surgery. Another option supported by the American Thyroid Association is active surveillance. This option can be considered as unattractive and difficult to accept by European patients, as there is a 2–6% risk of disease progression. Percutaneous image-guided thermal ablation has been successfully applied in the treatment of liver and lung tumors in the 1990s and 2000s; and has recently been proposed as an alternative to surgery in patients presenting with thyroid diseases. This minimally invasive treatment has similar efficacy, fewer complications, better quality of life and cosmetic outcomes than surgery. We report herein two cases of radiofrequency ablation of mPTC and T2 PTC in elderly patients who were ineligible for surgery.
Hybrid Majority Voting: Prediction and Classification Model for Obesity
Authors: Solomon Dahlak Daniel , Khan Shakir , Garg Sonia , Gupta Gaurav , Almjally Abrar , Alabduallah Bayan Ibrahimm , Alsagri Hatoon S. , Ibrahim Mandour Mohamed , Abdallah Alsadig Mohammed Adam
Published: August 2023

Because it is associated with most multifactorial inherited diseases like heart disease, hypertension, diabetes, and other serious medical conditions, obesity is a major global health concern. Obesity is caused by hereditary, physiological, and environmental factors, as well as poor nutrition and a lack of exercise. Weight loss can be difficult for various reasons, and it is diagnosed via BMI, which is used to estimate body fat for most people. Muscular athletes, for example, may have a BMI in the obesity range even when they are not obese. Researchers from a variety of backgrounds and institutions devised different hypotheses and models for the prediction and classification of obesity using different approaches and various machine learning techniques. In this study, a majority voting-based hybrid modeling approach using a gradient boosting classifier, extreme gradient boosting, and a multilayer perceptron was developed. Seven distinct machine learning algorithms were used on open datasets from the UCI machine learning repository, and their respective accuracy levels were compared before the combined approaches were chosen. The proposed majority voting-based hybrid model for prediction and classification of obesity that was achieved has an accuracy of 97.16%, which is greater than both the individual models and the other hybrid models that have been developed.
Ozone Therapy in Medicine and Dentistry: A Review of the Literature
Authors: El Meligy Omar A. , Elemam Noha M. , Talaat Iman M.
Published: August 2023

Ozone has been successfully used in medicine for over 100 years due to its microbiological qualities. Its powerful oxidation impact, which results in the production of free radicals, and its ability to cause the direct death of nearly all microorganisms is the basis for its bactericide, virucide, and fungicide properties. Ozone also has a medicinal impact that speeds up blood flow and aids wound healing. Ozone may be applied as a gas or dissolved in water for medical purposes. Despite the benefits of using ozone therapeutically, concerns about its use in dentistry still exist. We aimed to provide a summary of the current uses of ozone in medicine and dentistry. An electronic search was performed for all English scientific papers published between 2012 and 2023 using PubMed, Cochrane, and Google Scholar search engines. Ozone, clinical applications, medicine, and dentistry were the search terms used. Seventy full-text articles describing the use of ozone therapy in medicine and dentistry were included in the present review. Ozone has shown several beneficial effects in the medical field. However, despite the encouraging in vitro evidence, the clinical use of ozone in dentistry has not yet been demonstrated as highly effective.
Budgeted Bandits for Power Allocation and Trajectory Planning in UAV-NOMA Aided Networks
Authors: Hosny Ramez , Hashima Sherief , Mohamed Ehab Mahmoud , Zaki Rokaia M. , ElHalawany Basem M.
Published: August 2023

On one hand combining Unmanned Aerial Vehicles (UAVs) and Non-Orthogonal Multiple Access (NOMA) is a remarkable direction to sustain the exponentially growing traffic requirements of the forthcoming Sixth Generation (6G) networks. In this paper, we investigate effective Power Allocation (PA) and Trajectory Planning Algorithm (TPA) for UAV-aided NOMA systems to assist multiple survivors in a post-disaster scenario, where ground stations are malfunctioned. Here, the UAV maneuvers to collect data from survivors, which are grouped in multiple clusters within the disaster area, to satisfy their traffic demands. On the other hand, while the problem is formulated as Budgeted Multi-Armed Bandits (BMABs) that optimize the UAV trajectory and minimize battery consumption, challenges may arise in real-world scenarios. Herein, the UAV is the bandit player, the disaster area clusters are the bandit arms, the sum rate of each cluster is the payoff, and the UAV energy consumption is the budget. Hence, to tackle these challenges, two Upper Confidence Bound (UCB) BMAB schemes are leveraged to handle this issue, namely BUCB1 and BUCB2. Simulation results confirm the superior performance of the proposed BMAB solution against benchmark solutions for UAV-aided NOMA communication. Notably, the BMAB-NOMA solution exhibits remarkable improvements, achieving 60% enhancement in the total number of assisted survivors, 80% improvement in convergence speed, and a considerable amount of energy saving compared to UAV-OMA.
Semiosis as a Source of Providing Empirical Phenomena with a New Type of Cohesion
Authors: Matsuno Koichiro
Published: August 2023

Embodying the indexical signs is vital to semiosis as a cohesive material agency mediating between consequents and antecedents. One unique factor of biology compared with standard physics and chemistry is the cohesion enabling the biological components, codes and organizations to accommodate themselves with a specific material embodiment. Every individual body is uniquely biological and requires a specific cohesion of material origin for its own sake that could not be found in the non-living material world. The relevant cohesion comes from the exchange interaction of the atomic quantum particles, such as the carbon atoms, which is far greater than the electrons as a common exchange mediator adopted for the spatial cohesion ubiquitous in physics and chemistry. What is specific to the temporal cohesion latent in the atomic exchange is the immutable identity of the individual quantum particle surviving only over a limited time, while being constantly alternated with the new ones of the same kinds in a successive manner. Semiosis is supported by the underlying teleonomic cohesion, such that the preceding temporal cohesion may constantly induce the succeeding similar one ad infinitum.
Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis
Authors: Chen Zhe , Ma Xiaodong , Fu Jielin , Li Yaan
Published: August 2023

Entropy quantification approaches have gained considerable attention in engineering applications. However, certain limitations persist, including the strong dependence on parameter selection, limited discriminating power, and low robustness to noise. To alleviate these issues, this paper introduces two novel algorithms for time series analysis: the ensemble improved permutation entropy (EIPE) and multiscale EIPE (MEIPE). Our approaches employ a new symbolization process that considers both permutation relations and amplitude information. Additionally, the ensemble technique is utilized to reduce the dependence on parameter selection. We performed a comprehensive evaluation of the proposed methods using various synthetic and experimental signals. The results illustrate that EIPE is capable of distinguishing white, pink, and brown noise with a smaller number of samples compared to traditional entropy algorithms. Furthermore, EIPE displays the potential to discriminate between regular and non-regular dynamics. Notably, when compared to permutation entropy, weighted permutation entropy, and dispersion entropy, EIPE exhibits superior robustness against noise. In practical applications, such as RR interval data classification, bearing fault diagnosis, marine vessel identification, and electroencephalographic (EEG) signal classification, the proposed methods demonstrate better discriminating power compared to conventional entropy measures. These promising findings validate the effectiveness and potential of the algorithms proposed in this paper.
Absence of Transgender Identities in Primary Education Teachers’ Training and Its Implications in the Classroom: A Phenomenological Study
Authors: Sánchez Torrejón Begoña , Granero Andújar Alejandro , Esteban Mora Jesús
Published: August 2023

The main objective of this article is to acquire in-depth knowledge of the training primary education teachers receive regarding transgender identities, as well as the resulting consequences in school realities. A phenomenological qualitative research approach was used to accomplish this purpose. The data were collected using a semi-structured interview technique. The participants were 38 primary education teachers from different public schools in the province of Cádiz, Spain. They acted as key informants, allowing us to gain knowledge, understanding, and meaning regarding our object of study. Among the results obtained, the absence of transgender identities in pre-service and in-service teacher training is observed. As a result, a severe lack of knowledge and confusion about the subject, as well as discriminatory values, are perceived in teachers. The need for transgender identities to be present in initial and continuous teacher training is stressed in order to see to the social and educational needs of transgender students in primary education and avoid reproducing the invisibility of transgender identities and the transmission of inegalitarian values.
Low-Voltage Network Modeling and Analysis with Rooftop PV Forecasts: A Realistic Perspective from Queensland, Australia
Authors: Anderson Jake , Agalgaonkar Ashish P.
Published: August 2023

Recent years have seen a rapid uptake in distributed energy resources (DER). Such technologies pose a number of challenges to network operators, which ultimately can limit the amount of rooftop solar photovoltaic (PV) systems that can be connected to a network. The objective of this industry-based research was to determine the potential network effects of forecast levels of customer-owned rooftop solar PV on Energy Queensland’s distribution network and formulate functions that can be used to determine such effects without the requirement for detailed network modeling and analysis. In this research, many of Energy Queensland’s distribution feeders were modeled using DIgSILENT PowerFactory and analyzed with forecast levels of solar PV and customer load. Python scripts were used to automate this process, and quasi-dynamic simulation (QDSL) models were used to represent the dynamic volt–watt and volt–var response of inverters, as mandated by the Australian Standard AS/NZS 4777. In analyzing the results, linear relationships were revealed between the number of PV systems on a feeder and various network characteristics. Regression was used to form trend equations that represent the linear relationships for each scenario analyzed. The trend equations provide a way of approximating network characteristics for other feeders under various levels of customer-owned rooftop solar PV without the need for detailed modeling.