bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of a Finite-Discrete Element Method Code for Modelling Rock Spalling in Tunnels: The Case of the Lyon-Turin Base Tunnel
Authors: Martinelli Daniele , Insana Alessandra
Published: January 2024

Brittle failure, or spalling, occurs around openings excavated in hard rock masses with high in situ stresses. It takes place due to the nucleation and growth of cracks around the excavation boundary, induced by the redistribution of stresses following the excavation. Modelling this failure process is a tough challenge. The hybrid finite-discrete element method (FDEM) can overcome the boundary between continuum and discontinuum, capturing emergent discontinuities associated with brittle fracturing processes. In this study, FDEM is applied using a commercial code to show its applicability to model brittle behaviour around deep underground excavations in the case of the Torino-Lyon Base tunnel in three different stress conditions. Except for the hydrostatic condition, cracking is triggered immediately after the excavation. Spalling occurring around the tunnel is quite extended; therefore, an accurately designed support must be installed to prevent blocks from falling from the tunnel boundary. The obtained results are aligned with previous results existing in the literature. However, in this case, a deeper spalling is caused by the shape change due to the gradual stress redistribution. Such a phenomenon underlines the importance of using a code able to identify crack propagation, opening, and the formation of loose blocks that progressively modify the tunnel contour.
Population Dynamics in Genetic Programming for Dynamic Symbolic Regression
Authors: Fleck Philipp , Werth Bernhard , Affenzeller Michael
Published: January 2024

This paper investigates the application of genetic programming (GP) for dynamic symbolic regression (SR), addressing the challenge of adapting machine learning models to evolving data in practical applications. Benchmark instances with changing underlying functions over time are defined to assess the performance of a genetic algorithm (GA) as a traditional evolutionary algorithm and an age-layered population structure (ALPS) as an open-ended evolutionary algorithm for dynamic symbolic regression. This study analyzes population dynamics by examining variable frequencies and impact changes over time in response to dynamic shifts in the training data. The results demonstrate the effectiveness of both the GA and ALPS in handling changing data, showcasing their ability to recover and evolve improved solutions after an initial drop in population quality following data changes. Population dynamics reveal that variable impacts respond rapidly to data changes, while variable frequencies shift gradually across generations, aligning with the indirect measure of fitness represented by variable impacts. Notably, the GA shows a strong dependence on mutation to avoid variables becoming permanently extinct, contrasting with the ALPS’s unexpected insensitivity to mutation rates owing to its reseeding mechanism for effective variable reintroduction.
A Study on the Effect of Gear Hobbing Process Parameters on the Residual Stress of the Tooth Root
Authors: Wang Yazhou , Xu Huike , Shen Hao , Wang Gang , Wang Zhen
Published: January 2024

The root residual stress during gear machining has a significant impact on the bending fatigue performance of the gear. The process parameters of gear hobbing (hob speed, axial feed speed, and radial cutting depth) directly affect the residual stress of the tooth root. To investigate the relationship between the process parameters of hobbing and the residual stress of the tooth root respectively, an analysis of an orthogonal and single factor was conducted in the hobbing experiment, taking into account the interactions among factors, which revealed the influence rule and primary–secondary relationship of the process parameters on the residual stress of the tooth root. The importance coefficients of the process parameters on the residual stress of the tooth root were calculated using the Least Absolute Shrinkage and Selection Operator (LASSO) method. The results indicate that the residual tensile stress at the tooth root increases with an increase in the hob speed and axial feed speed within the selected range but decreases with an increase in the radial cutting depth. The influence of the process parameters on the residual stress of the tooth root can be ranked as follows: hob speed (importance coefficient 0.460), axial feed speed (importance coefficient 0.278), and radial cutting depth (importance coefficient 0.262). This research provides a basis for improving the residual stress of the tooth root and enhancing the anti-fatigue manufacturing of gears, thus holding significant research value.
Chlorella vulgaris Harvesting: Chemical Flocculation with Chitosan, Aluminum Sulfate, and Ferric Sulfate
Authors: Machado Cláudia A. , Esteves Ana F. , Pires José C. M.
Published: January 2024

Chlorella vulgaris, a widely cultivated microalgae with diverse commercial applications, faces challenges in economic viability and scalability during the crucial biomass harvesting step. This study explores chemical flocculation followed by sedimentation as a cost-effective solution. Optimization was performed for three flocculants (chitosan, aluminum sulfate, and ferric sulfate), with experiments determining optimal pH and dosage ranges (10–200 mg·L−1). A 24-full factorial design optimized flocculant dosage, settling time, rapid mixing time, and slow mixing time, analyzing their effects on harvesting efficiency through empirical models. The optimal dosage ranges were 50–200 mg·L−1 for aluminum sulfate and 150–200 mg·L−1 for ferric sulfate at pH 9, and 10–50 mg·L−1 for chitosan at pH 5. Empirical models exhibited high fitting performance (R2 > 95%) and predictive capability (predicted R2 > 96%). All flocculants demonstrated high efficiencies (98.4–99.5%), with inorganic types requiring fast and slow mixing phases, while chitosan achieved optimal results without the need for both mixing phases, suggesting potential industrial advantages in time and energy efficiency for microalgae harvesting.
Epileptic Seizure Classification Based on Random Neural Networks Using Discrete Wavelet Transform for Electroencephalogram Signal Decomposition
Authors: Shah Syed Yaseen , Larijani Hadi , Gibson Ryan M. , Liarokapis Dimitrios
Published: January 2024

An epileptic seizure is a brief episode of symptoms and signs caused by excessive electrical activity in the brain. One of the major chronic neurological diseases, epilepsy, affects millions of individuals worldwide. Effective detection of seizure events is critical in the diagnosis and treatment of patients with epilepsy. Neurologists monitor the electrical activity in the brains of patients to identify epileptic seizures by employing advanced sensing techniques, including electroencephalograms and electromyography. Machine learning-based classification of the EEG signal can help differentiate between normal signals and the patterns associated with epileptic seizures. This work presents a novel approach for the classification of epileptic seizures using random neural network (RNN). The proposed model has been trained and tested using two publicly available datasets: CHB-MIT and BONN, provided by Children’s Hospital Boston-Massachusetts Institute of Technology and the University of Bonn, respectively. The results obtained from multiple experiments highlight that the proposed scheme outperformed traditional classification schemes such as artificial neural network and support vector machine. The proposed RNN-based model achieved accuracies of 93.27% and 99.84% on the CHB-MIT and BONN datasets, respectively.
An Interpretable Deep Learning Approach for Detecting Marine Heatwaves Patterns
Authors: He Qi , Zhu Zihang , Zhao Danfeng , Song Wei , Huang Dongmei
Published: January 2024

Marine heatwaves (MHWs) refer to a phenomenon where the sea surface temperature is significantly higher than the historical average for that region over a period, which is typically a result of the combined effects of climate change and local meteorological conditions, thereby potentially leading to alterations in marine ecosystems and an increased incidence of extreme weather events. MHWs have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent years, global warming has intensified MHWs, and research on MHWs has rapidly developed into an important research frontier. With the development of deep learning models, they have demonstrated remarkable performance in predicting sea surface temperature, which is instrumental in identifying and anticipating marine heatwaves (MHWs). However, the complexity of deep learning models makes it difficult for users to understand how the models make predictions, posing a challenge for scientists and decision-makers who rely on interpretable results to manage the risks associated with MHWs. In this study, we propose an interpretable model for discovering MHWs. We first input variables that are relevant to the occurrence of MHWs into an LSTM model and use a posteriori explanation method called Expected Gradients to represent the degree to which different variables affect the prediction results. Additionally, we decompose the LSTM model to examine the information flow within the model. Our method can be used to understand which features the deep learning model focuses on and how these features affect the model’s predictions. From the experimental results, this study provides a new perspective for understanding the causes of MHWs and demonstrates the prospect of future artificial intelligence-assisted scientific discovery.
Real-Time Protozoa Detection from Microscopic Imaging Using YOLOv4 Algorithm
Authors: Kahraman İdris , Karaş İsmail Rakıp , Turan Muhammed Kamil
Published: January 2024

Protozoa detection and classification from freshwaters and microscopic imaging are critical components in environmental monitoring, parasitology, science, biological processes, and scientific research. Bacterial and parasitic contamination of water plays an important role in society health. Conventional methods often rely on manual identification, resulting in time-consuming analyses and limited scalability. In this study, we propose a real-time protozoa detection framework using the YOLOv4 algorithm, a state-of-the-art deep learning model known for its exceptional speed and accuracy. Our dataset consists of objects of the protozoa species, such as Bdelloid Rotifera, Stylonychia Pustulata, Paramecium, Hypotrich Ciliate, Colpoda, Lepocinclis Acus, and Clathrulina Elegans, which are in freshwaters and have different shapes, sizes, and movements. One of the major properties of our work is to create a dataset by forming different cultures from various water sources like rainwater and puddles. Our network architecture is carefully tailored to optimize the detection of protozoa, ensuring precise localization and classification of individual organisms. To validate our approach, extensive experiments are conducted using real-world microscopic image datasets. The results demonstrate that the YOLOv4-based model achieves outstanding detection accuracy and significantly outperforms traditional methods in terms of speed and precision. The real-time capabilities of our framework enable rapid analysis of large-scale datasets, making it highly suitable for dynamic environments and time-sensitive applications. Furthermore, we introduce a user-friendly interface that allows researchers and environmental professionals to effortlessly deploy our YOLOv4-based protozoa detection tool. We conducted f1-score 0.95, precision 0.92, sensitivity 0.98, and mAP 0.9752 as evaluating metrics. The proposed model achieved 97% accuracy. After reaching high efficiency, a desktop application was developed to provide testing of the model. The proposed framework’s speed and accuracy have significant implications for various fields, ranging from a support tool for paramesiology/parasitology studies to water quality assessments, offering a powerful tool to enhance our understanding and preservation of ecosystems.
Interaction Parameters for the Formation of Mixed Micelles and Partitioning of Solutes in Them: A Review
Authors: Suzuki Nozomu
Published: January 2024

When two or more surfactants are mixed, the critical micelle concentration and solubilization capability are changed, and a careful selection of the combination promotes the micelle formation and enhances the solubilizing capability. Thus, understanding the mechanism behind the phenomena is essential for controlling the physical properties of the mixed micelle. The interaction parameters β and B that describe the formation of mixed micelles and their partitioning of solutes, respectively, were proposed by Treiner four decades ago. In this work, data on the formation and partitioning in binary surfactant systems were collected. Although the data on the parameters β and B for polar solutes and theoretical development are still insufficient, the directions of research to acquire an in-depth understanding of the formation and partitioning of the mixed micelle are proposed.
A Co-Infection Model for Onchocerciasis and Lassa Fever with Optimal Control Analysis
Authors: Adeyemo Kabiru Michael , Oshinubi Kayode , Adam Umar Muhammad , Adeniji Adejimi
Published: January 2024

A co-infection model for onchocerciasis and Lassa fever (OLF) with periodic variational vectors and optimal control is studied and analyzed to assess the impact of controls against incidence infections. The model is qualitatively examined in order to evaluate its asymptotic behavior in relation to the equilibria. Employing a Lyapunov function, we demonstrated that the disease-free equilibrium (DFE) is globally asymptotically stable; that is, the related basic reproduction number is less than unity. When it is bigger than one, we use a suitable nonlinear Lyapunov function to demonstrate the existence of a globally asymptotically stable endemic equilibrium (EE). Furthermore, the necessary conditions for the presence of optimum control and the optimality system for the co-infection model are established using Pontryagin’s maximum principle. The model is quantitatively analyzed by studying how sensitive the basic reproduction number is to the model parameters and the model simulation using Runge–Kutta technique of order 4 is also presented to study the effects of the treatments. We deduced from the quantitative analysis that, if there is an effective treatment and diagnosis of those exposed to and infected with the disease, the spread of the viral disease can be effectively managed. The results presented in this work will be useful for the proper mitigation of the disease.
Teleconnections of Atmospheric Circulations to Meteorological Drought in the Lancang-Mekong River Basin
Authors: Fan Lei , Wang Yi , Cao Chenglin , Chen Wen
Published: January 2024

The Lancang-Mekong River Basin (LMRB) is one of the major transboundary basins globally, facing ongoing challenges due to flood and drought disasters. Particularly in the past two decades, the basin has experienced an increased frequency of meteorological drought events, posing serious threats to the local socio-economic structures and ecological systems. Thus, this study aimed to analyze the meteorological drought characteristics in the LMRB and identify the impact and correlation of atmospheric circulation on the meteorological drought in the basin. Specifically, the different levels of meteorological drought events were defined using the Run Theory based on the seasonal and annual SPEI from 1980 to 2018. The time lag correlation between meteorological drought events and the EI Nino-Southern Oscillation (ENSO), Arctic Oscillation (AO), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO), were analyzed in the LMRB. Our results indicated that, from a temporal perspective, the period from November to April of the following year was particularly prone to meteorological droughts in the basin. In terms of spatial distribution, the primary agricultural regions within the basin, including Thailand, Eastern Cambodia, and Vietnam, were highly susceptible to meteorological droughts. Further analysis revealed a teleconnection between drought events in the LMRB and atmospheric circulation factors. The sensitivity of the basin’s drought timing to its response decreased in the order of the ENSO > AO > NAO > PDO. In general, the ENSO had the most substantial influence on drought events in the basin, with the strongest response relationship, while the upper reaches of the basin displayed the most significant response to the AO; the occurrence and progression of meteorological droughts in this area synchronized with the AO. These findings enhance our understanding of drought-prone areas in the LMRB, including the meteorological factors and driving mechanisms involved. This information is valuable for effectively mitigating and managing drought risks in the region.
Exploring PLGA-OH-CATH30 Microspheres for Oral Therapy of Escherichia coli-Induced Enteritis
Authors: Jiao Xiaoqian , Liu Bin , Dong Xufeng , Wang Shubai , Cai Xiulei , Zhang Hongliang , Qin Zhihua
Published: January 2024

Antibiotic therapy effectively addresses Escherichia coli-induced enteric diseases, but its excessive utilization results in microbial imbalance and heightened resistance. This study evaluates the therapeutic efficacy of orally administered poly (lactic-co-glycolic acid) (PLGA)-loaded antimicrobial peptide OH-CATH30 microspheres in murine bacterial enteritis. Mice were categorized into the healthy control group (CG), untreated model group (MG), OH-CATH30 treatment group (OC), PLGA-OH-CATH30 treatment group (POC), and gentamicin sulfate treatment group (GS). Except for the control group, all other experimental groups underwent Escherichia coli-induced enteritis, followed by a 5-day treatment period. The evaluation encompassed clinical symptoms, intestinal morphology, blood parameters, inflammatory response, and gut microbiota. PLGA-OH-CATH30 microspheres significantly alleviated weight loss and intestinal damage while also reducing the infection-induced increase in spleen index. Furthermore, these microspheres normalized white blood cell count and neutrophil ratio, suppressed inflammatory factors (IL-1β, IL-6, and TNF-α), and elevated the anti-inflammatory factor IL-10. Analysis of 16S rRNA sequencing results demonstrated that microsphere treatment increased the abundance of beneficial bacteria, including Phocaeicola vulgatus, in the intestinal tract while concurrently decreasing the abundance of pathogenic bacteria, such as Escherichia. In conclusion, PLGA-OH-CATH30 microspheres have the potential to ameliorate intestinal damage and modulate the intestinal microbiota, making them a promising alternative to antibiotics for treating enteric diseases induced by Escherichia coli.
Editorial for This Special Issue “Synaptic Transmission: From Molecular to Neural Network Levels”
Authors: Mapelli Lisa , Tritto Simona
Published: January 2024

We invited contributions reporting new evidence of synaptic mechanisms and their network-level impacts for this Special Issue [...]
Pathophysiology to Risk Factor and Therapeutics to Treatment Strategies on Epilepsy
Authors: Boleti Ana Paula de Araújo , Cardoso Pedro Henrique de Oliveira , Frihling Breno Emanuel Farias , de Moraes Luiz Filipe Ramalho Nunes , Nunes Ellynes Amancio Correia , Mukoyama Lincoln Takashi Hota , Nunes Ellydberto Amancio Correia , Carvalho Cristiano Marcelo Espinola , Macedo Maria Lígia Rodrigues , Migliolo Ludovico
Published: January 2024

Epilepsy represents a condition in which abnormal neuronal discharges or the hyperexcitability of neurons occur with synchronicity, presenting a significant public health challenge. Prognostic factors, such as etiology, electroencephalogram (EEG) abnormalities, the type and number of seizures before treatment, as well as the initial unsatisfactory effects of medications, are important considerations. Although there are several third-generation antiepileptic drugs currently available, their multiple side effects can negatively affect patient quality of life. The inheritance and etiology of epilepsy are complex, involving multiple underlying genetic and epigenetic mechanisms. Different neurotransmitters play crucial roles in maintaining the normal physiology of different neurons. Dysregulations in neurotransmission, due to abnormal transmitter levels or changes in their receptors, can result in seizures. In this review, we address the roles played by various neurotransmitters and their receptors in the pathophysiology of epilepsy. Furthermore, we extensively explore the neurological mechanisms involved in the development and progression of epilepsy, along with its risk factors. Furthermore, we highlight the new therapeutic targets, along with pharmacological and non-pharmacological strategies currently employed in the treatment of epileptic syndromes, including drug interventions employed in clinical trials related to epilepsy.
Being in Virtual Reality and Its Influence on Brain Health—An Overview of Benefits, Limitations and Prospects
Authors: Sokołowska Beata
Published: January 2024

Background: Dynamic technological development and its enormous impact on modern societies are posing new challenges for 21st-century neuroscience. A special place is occupied by technologies based on virtual reality (VR). VR tools have already played a significant role in both basic and clinical neuroscience due to their high accuracy, sensitivity and specificity and, above all, high ecological value. Objective: Being in a digital world affects the functioning of the body as a whole and its individual systems. The data obtained so far, both from experimental and modeling studies, as well as (clinical) observations, indicate their great and promising potential, but apart from the benefits, there are also losses and negative consequences for users. Methods: This review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework across electronic databases (such as Web of Science Core Collection; PubMed; and Scopus, Taylor & Francis Online and Wiley Online Library) to identify beneficial effects and applications, as well as adverse impacts, especially on brain health in human neuroscience. Results: More than half of these articles were published within the last five years and represent state-of-the-art approaches and results (e.g., 54.7% in Web of Sciences and 63.4% in PubMed), with review papers accounting for approximately 16%. The results show that in addition to proposed novel devices and systems, various methods or procedures for testing, validation and standardization are presented (about 1% of articles). Also included are virtual developers and experts, (bio)(neuro)informatics specialists, neuroscientists and medical professionals. Conclusions: VR environments allow for expanding the field of research on perception and cognitive and motor imagery, both in healthy and patient populations. In this context, research on neuroplasticity phenomena, including mirror neuron networks and the effects of applied virtual (mirror) tasks and training, is of interest in virtual prevention and neurogeriatrics, especially in neurotherapy and neurorehabilitation in basic/clinical and digital neuroscience.
Artificial Intelligence’s Transformative Role in Illuminating Brain Function in Long COVID Patients Using PET/FDG
Authors: Rudroff Thorsten
Published: January 2024

Cutting-edge brain imaging techniques, particularly positron emission tomography with Fluorodeoxyglucose (PET/FDG), are being used in conjunction with Artificial Intelligence (AI) to shed light on the neurological symptoms associated with Long COVID. AI, particularly deep learning algorithms such as convolutional neural networks (CNN) and generative adversarial networks (GAN), plays a transformative role in analyzing PET scans, identifying subtle metabolic changes, and offering a more comprehensive understanding of Long COVID’s impact on the brain. It aids in early detection of abnormal brain metabolism patterns, enabling personalized treatment plans. Moreover, AI assists in predicting the progression of neurological symptoms, refining patient care, and accelerating Long COVID research. It can uncover new insights, identify biomarkers, and streamline drug discovery. Additionally, the application of AI extends to non-invasive brain stimulation techniques, such as transcranial direct current stimulation (tDCS), which have shown promise in alleviating Long COVID symptoms. AI can optimize treatment protocols by analyzing neuroimaging data, predicting individual responses, and automating adjustments in real time. While the potential benefits are vast, ethical considerations and data privacy must be rigorously addressed. The synergy of AI and PET scans in Long COVID research offers hope in understanding and mitigating the complexities of this condition.
Fracture Disaster Assessment of Model Concrete Piles in Loess Slope Engineering under Non-Uniform Lateral Loading
Authors: Bai Weishi , Li Rongjian , Lin Guoqiang , Li Rongjin , Jiang Hao , Wang Laizhu , Bai Chaoneng
Published: January 2024

Existing model tests for reinforcing loess slopes with stabilizing piles are often challenged by simulation inaccuracies in lateral loading modes and scaling. Addressing these concerns, this study conducts model tests and numerical simulations to scrutinize the damage characteristics of concrete piles in two varying loess slope conditions under non-uniform lateral loading. The tests were designed to strictly maintain the similarity ratio of the concrete piles. The results reveal a no table 20% reduction in lateral bearing capacity due to the penetration of a potential sliding surface, exacerbating the stress on the piles. Furthermore, compared to uniform loess slopes, the presence of a sliding surface leads to a 38.4% increase in the height of the stress concentration point, resulting in earlier crack formation in the piles. These findings offer substantial theoretical and practical insights, highlighting the critical need for accurate model simulation in slope stabilization research and providing a basis for improving engineering practices.
Continuous Reactive-Roll-to-Roll Growth of Carbon Nanotubes for Fog Water Harvesting Applications
Authors: Meunier Jean-Luc , Ouellet Jeanne , Basu Kaustubh , Aufoujal Alessio , Boudreault Richard , Tavares Jason Robert
Published: January 2024

A simple method is presented for the continuous generation of carbon nanotube forests stably anchored on stainless-steel surfaces using a reactive-roll-to-roll (RR2R) configuration. No addition of catalyst nanoparticles is required for the CNT-forest generation; the stainless-steel substrate itself is tuned to generate the catalytic growth sites. The process enables very large surfaces covered with CNT forests to have individual CNT roots anchored to the metallic ground through primary bonds. Fog water harvesting is demonstrated and tested as one potential application using long CNT-covered wires. The RR2R is performed in the gas phase; no solution processing of CNT suspensions is used, contrary to usual R2R CNT-based technologies. Full or partial CNT-forest coverage provides tuning of the ratio and shape of hydrophobic and hydrophilic zones on the surface. This enables the optimization of fog water harvesters for droplet capture through the hydrophobic CNT forest and water removal from the hydrophilic SS surface. Water recovery tests using small harp-type harvesters with CNT-forest generate water capture of up to 2.2 g/cm2·h under ultrasound-generated fog flow. The strong CNT root anchoring on the stainless-steel surfaces provides opportunities for (i) robustness and easy transport of the composite structure and (ii) chemical functionalization and/or nanoparticle decoration of the structures, and it opens the road for a series of applications on large-scale surfaces, including fog harvesting.
Survival Comparison of Different Operation Types for Middle Bile Duct Cancer: Bile Duct Resection versus Pancreaticoduodenectomy Considering Complications and Adjuvant Treatment Effects
Authors: Lim Soo Yeun , Alramadhan Hani Jassim , Jeong HyeJeong , Chae Hochang , Kim Hyeong Seok , Yoon So Jeong , Shin Sang Hyun , Han In Woong , Heo Jin Seok , Kim Hongbeom
Published: January 2024

Background: Margin status is one of the most significant prognostic factors after curative surgery for middle bile duct (MBD) cancer. Bile duct resection (BDR) is commonly converted to pancreaticoduodenectomy (PD) to achieve R0 resection. Additionally, adjuvant treatment is actively performed after surgery to improve survival. However, the wider the range of surgery, the higher the chance of complications; this, in turn, makes adjuvant treatment impossible. Nevertheless, no definitive surgical strategy considers the possible complication rates and subsequent adjuvant treatment. We aimed to investigate the appropriate surgical type considering the margin status, complications, and adjuvant treatment in MBD cancer. Materials and Methods: From 2008 to 2017, 520 patients diagnosed with MBD cancer at the Samsung Medical Center were analyzed retrospectively according to the operation type, margin status, complications, and adjuvant treatment. The R1 group was defined as having a carcinoma margin. Results: The 5-year survival rate for patients who underwent R0 and R1 resection was 54.4% and 33.3%, respectively (p = 0.131). Prognostic factors affecting the overall survival were the age, preoperative CA19-9 level, T stage, and N stage, but not the operation type, margin status, complications, or adjuvant treatment. The complication rates were 11.5% and 29.8% in the BDR and PD groups, respectively (p < 0.001). We observed no significant difference in the adjuvant treatment ratio according to complications (p = 0.675). Patients with PD who underwent R0 resection and could not undergo chemotherapy because of complications reported better survival rates than those with BDR who underwent R1 resection after adjuvant treatment (p = 0.003). Conclusion: The survival outcome of patients with R1 margins who underwent BDR did not match those with R0 margins after PD, even after adjuvant treatment. Due to improvements in surgical techniques and the ability to resolve complications, surgical complications exert a marginal effect on survival. Therefore, surgeons should secure R0 margins to achieve the best survival outcomes.
Antiandrogens as Therapies for COVID-19: A Systematic Review
Authors: Cani Massimiliano , Epistolio Samantha , Dazio Giulia , Modesti Mikol , Salfi Giuseppe , Pedrani Martino , Isella Luca , Gillessen Silke , Vogl Ursula Maria , Tortola Luigi , Treglia Giorgio , Buttigliero Consuelo , Frattini Milo , Pereira Mestre Ricardo
Published: January 2024

Background: In 2019, the breakthrough of the coronavirus 2 disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), represented one of the major issues of our recent history. Different drugs have been tested to rapidly find effective anti-viral treatments and, among these, antiandrogens have been suggested to play a role in mediating SARS-CoV-2 infection. Considering the high heterogeneity of studies on this topic, we decided to review the current literature. Methods: We performed a systematic review according to PRISMA guidelines. A search strategy was conducted on PUBMED and Medline. Only original articles published from March 2020 to 31 August 2023 investigating the possible protective role of antiandrogens were included. In vitro or preclinical studies and reports not in the English language were excluded. The main objective was to investigate how antiandrogens may interfere with COVID-19 outcomes. Results: Among 1755 records, we selected 31 studies, the majority of which consisted of retrospective clinical data collections and of randomized clinical trials during the first and second wave of the COVID-19 pandemic. Conclusions: In conclusion, we can state that antiandrogens do not seem to protect individuals from SARS-CoV-2 infection and COVID-19 severity and, thus, their use should not be encouraged in this field.
Density Functional Theory Study of Mechanism of Reduction of N2O by CO over Fe-ZSM-5 Zeolites
Authors: Yuan Ning , Gao Congru , Sun Xiuliang , Li Jianwei
Published: January 2024

Nitrous oxide (N2O) is an industrial waste gas (e.g., from the production of adipic acid), which damages the ozone layer and causes the greenhouse effect. Density functional theory calculations were employed to investigate the mechanism of direct catalytic decomposition of N2O and selective catalytic reduction (SCR) of N2O by CO over Fe-ZSM-5 zeolites. Two stable Fe-active sites with six-membered ring structures on Fe-ZSM-5 were considered. The calculations indicate that the decomposition of N2O is affected by the coordination environment around Fe and can occur through two reaction pathways. However, there is invariably a more considerable energy hurdle for the initiation of the second stage of N2O decomposition. When CO participated in the reaction, it showed good reactivity and stability, the reaction energy barriers of the rate-limiting step were reduced by roughly 20.57 kcal/mol compared to the direct catalytic decomposition of N2O. CO exhibited a superior electron-donating ability and orbital hybridization performance during the reaction, which enhanced the cyclicity of the N2O reduction catalytic process. Our calculations confirmed the significant role of CO in N2O reduction over Fe-ZSM-5 observed in previous studies. This study provides a valuable theoretical reference for exploring CO-SCR methods for N2O reduction over Fe-based zeolite catalysts.
The Matrix Receptor CD44 Is Present in Astrocytes throughout the Human Central Nervous System and Accumulates in Hypoxia and Seizures
Authors: Al-Dalahmah Osama , Sosunov Alexander A. , Sun Yu , Liu Yang , Madden Nacoya , Connolly E. Sander , Troy Carol M. , McKhann Guy M. , Goldman James E.
Published: January 2024

In the mammalian isocortex, CD44, a cell surface receptor for extracellular matrix molecules, is present in pial-based and fibrous astrocytes of white matter but not in protoplasmic astrocytes. In the hominid isocortex, CD44+ astrocytes comprise the subpial “interlaminar” astrocytes, sending long processes into the cortex. The hippocampus also contains similar astrocytes. We have examined all levels of the human central nervous system and found CD44+ astrocytes in every region. Astrocytes in white matter and astrocytes that interact with large blood vessels but not with capillaries in gray matter are CD44+, the latter extending long processes into the parenchyma. Motor neurons in the brainstem and spinal cord, such as oculomotor, facial, hypoglossal, and in the anterior horn of the spinal cord, are surrounded by CD44+ processes, contrasting with neurons in the cortex, basal ganglia, and thalamus. We found CD44+ processes that intercalate between ependymal cells to reach the ventricle. We also found CD44+ astrocytes in the molecular layer of the cerebellar cortex. Protoplasmic astrocytes, which do not normally contain CD44, acquire it in pathologies like hypoxia and seizures. The pervasive and inducible expression of CD44 in astrocytes is a novel finding that lays the foundations for functional studies into the significance of CD44 in health and disease.
Sleep Efficiency and Sleep Onset Latency in One Saskatchewan First Nation
Authors: Karunanayake Chandima P. , Pahwa Punam , Kirychuk Shelley , Fenton Mark , Ramsden Vivian R. , Seeseequasis Jeremy , Seesequasis Warren , Skomro Robert , Rennie Donna C. , McMullin Kathleen , Russell Brooke P. , Koehncke Niels , Abonyi Sylvia , King Malcolm , Dosman James A.
Published: January 2024

Background: Sleep efficiency and sleep onset latency are two measures that can be used to assess sleep quality. Factors that are related to sleep quality include age, sex, sociodemographic factors, and physical and mental health status. This study examines factors related to sleep efficiency and sleep onset latency in one First Nation in Saskatchewan, Canada. Methods: A baseline survey of the First Nations Sleep Health project was completed between 2018 and 2019 in collaboration with two Cree First Nations. One-night actigraphy evaluations were completed within one of the two First Nations. Objective actigraphy evaluations included sleep efficiency and sleep onset latency. A total of 167 individuals participated, and of these, 156 observations were available for analysis. Statistical analysis was conducted using logistic and linear regression models. Results: More females (61%) than males participated in the actigraphy study, with the mean age being higher for females (39.6 years) than males (35.0 years). The mean sleep efficiency was 83.38%, and the mean sleep onset latency was 20.74 (SD = 27.25) minutes. Age, chronic pain, ever having high blood pressure, and smoking inside the house were associated with an increased risk of poor sleep efficiency in the multiple logistic regression model. Age, chronic pain, ever having anxiety, heart-related illness, and smoking inside the house were associated with longer sleep onset latency in the multiple linear regression model. Conclusions: Sleep efficiency and sleep onset latency were associated with physical and environmental factors in this First Nation.
Three-Dimensional Indoor Positioning Scheme for Drone with Fingerprint-Based Deep-Learning Classifier
Authors: Liu Shuzhi , Lu Houjin , Hwang Seung-Hoon
Published: January 2024

Unmanned aerial vehicles (UAVs) hold significant potential for various indoor applications, such as mapping, surveillance, navigation, and search and rescue operations. However, indoor positioning is a significant challenge for UAVs, owing to the lack of GPS signals and the complexity of indoor environments. Therefore, this study was aimed at developing a Wi-Fi-based three-dimensional (3D) indoor positioning scheme tailored to time-varying environments, involving human movement and uncertainties in the states of wireless devices. Specifically, we established an innovative 3D indoor positioning system to meet the localisation demands of UAVs in indoor environments. A 3D indoor positioning database was developed using a deep-learning classifier, enabling 3D indoor positioning through Wi-Fi technology. Additionally, through a pioneering integration of fingerprint recognition into wireless positioning technology, we enhanced the precision and reliability of indoor positioning through a detailed analysis and learning process of Wi-Fi signal features. Two test cases (Cases 1 and 2) were designed with positioning height intervals of 0.5 m and 0.8 m, respectively, corresponding to the height of the test scene for positioning simulation and testing. With an error margin of 4 m, the simulation accuracies for the (X, Y) dimension reached 94.08% (Case 1) and 94.95% (Case 2). When the error margin was 0 m, the highest simulation accuracies for the H dimension were 91.84% (Case 1) and 93.61% (Case 2). Moreover, 40 real-time positioning experiments were conducted in the (X, Y, H) dimension. In Case 1, the average positioning success rates were 50.8% (Margin-0), 72.9% (Margin-1), and 81.4% (Margin-2), and the corresponding values for Case 2 were 52.4%, 74.5%, and 82.8%, respectively. The results demonstrated that the proposed method can facilitate 3D indoor positioning based only on Wi-Fi technologies.
Dead-Time Effect in Inverters on Wireless Power Transfer
Authors: Rajs Vladimir , Herceg Dejana , Despotović Živadin , Bogdanović Miroslav , Šiljegović Mirjana , Popadić Bane , Kiraly Zoltan , Vizvari Zoltan , Sari Zoltan , Klincsik Mihaly , Felde Imre , Odry Peter , Tadic Vladimir
Published: January 2024

This paper presents a comprehensive analysis of the dead-time effects in wireless power transfer systems based on LCC-S topology. In these systems operating at high frequencies, the ratio of dead-time versus the operating period becomes critical, and the dead-time issue can cause certain problems regarding power quality, efficiency, and output voltage ripple. The impact of input quantities such as voltage and switching frequency on the efficiency and output power of the LCC-S-tuned WPT system was also investigated. The optimal combination of these parameters used to achieve the maximum efficiency for a target output power and to set the appropriate value of the dead time were determined by running multiple simulations using the MATLAB R2023b software platform. It was also shown that the output voltage remained unchanged with and without a load and up to 1200 ns of dead-time, which provides a simple implementation of the corresponding mathematical model. In the recommended interval of 600–1500 ns, the influence of the dead-time on the value of the output voltage amplitude is less than 10%. The validity of the proposed method was confirmed through the implementation of the experimental prototype, a 5 kW wireless power transmission system, and the obtained results were in accordance with the simulation results.
Exploring Explainable Artificial Intelligence Techniques for Interpretable Neural Networks in Traffic Sign Recognition Systems
Authors: Khan Muneeb A. , Park Heemin
Published: January 2024

Traffic Sign Recognition (TSR) plays a vital role in intelligent transportation systems (ITS) to improve road safety and optimize traffic management. While existing TSR models perform well in challenging scenarios, their lack of transparency and interpretability hinders reliability, trustworthiness, validation, and bias identification. To address this issue, we propose a Convolutional Neural Network (CNN)-based model for TSR and evaluate its performance on three benchmark datasets: German Traffic Sign Recognition Benchmark (GTSRB), Indian Traffic Sign Dataset (ITSD), and Belgian Traffic Sign Dataset (BTSD). The proposed model achieves an accuracy of 98.85% on GTSRB, 94.73% on ITSD, and 92.69% on BTSD, outperforming several state-of-the-art frameworks, such as VGG19, VGG16, ResNet50V2, MobileNetV2, DenseNet121, DenseNet201, NASNetMobile, and EfficientNet, while also providing faster training and response times. We further enhance our model by incorporating explainable AI (XAI) techniques, specifically, Local Interpretable Model-Agnostic Explanations (LIME) and Gradient-weighted Class Activation Mapping (Grad-CAM), providing clear insights of the proposed model decision-making process. This integration allows the extension of our TSR model to various engineering domains, including autonomous vehicles, advanced driver assistance systems (ADAS), and smart traffic control systems. The practical implementation of our model ensures real-time, accurate recognition of traffic signs, thus optimizing traffic flow and minimizing accident risks.
Area-Power-Delay-Efficient Multi-Modulus Multiplier Based on Area-Saving Hard Multiple Generator Using Radix-8 Booth-Encoding Scheme on Field Programmable Gate Array
Authors: Kuo Chao-Tsung , Wu Yao-Cheng
Published: January 2024

A multi-modulus architecture based on the radix-8 Booth encoding of a modulo (2n − 1) multiplier, a modulo (2n) multiplier, and a modulo (2n + 1) multiplier is proposed in this paper. It uses the original single circuit and shares many common circuit characteristics with a small extra circuit to carry out multi-modulus operations. Compared with a previous radix-4 study, the radix-8 architecture can increase the modulation multiplication encoding selection from three codes to four codes. This reduces the use of partial products from ⌊n/2⌋ to ⌊n/3⌋ + 1, but it increases the operation complexity for multiplication by three circuits. A hard multiple generator (HMG) is used to address this problem. Two judgment signals in the multi-modulus circuit can be used to perform three operations of the modulo (2n − 1) multiplier, modulo (2n) multiplier, and modulo (2n + 1) multiplier at the same time. The weighted representation is used to reduce the number of partial products. Compared with previously reported methods in the literature, the proposed approach can achieve better performance by being more area-efficient, being faster, consuming low power, and having a lower area-delay product (ADP) and power-delay product (PDP). With the multi-modulus HMG, the proposed modified architecture can save 34.48–55.23% of hardware area. Compared with previous studies on the multi-modulus multiplier, the proposed architecture can save 22.78–35.46%, 4.12–11.15%, 12.59–24.73%, 27.88–38.88%, and 20.49–27.85% of hardware area, delay time, dissipation power, ADP, and PDP, respectively. Xilinx field programmable gate array (FPGA) Vivado 2019.2 tools and the Verilog hardware description language are used for synthesis and implementation. The Xilinx Artix-7 XC7A35T-CSG324-1 chipset is adopted to evaluate the performance.
Biotechnological Valorization of Waste Glycerol into Gaseous Biofuels—A Review
Authors: Kazimierowicz Joanna , Dębowski Marcin , Zieliński Marcin , Kasiński Sławomir , Cruz Sanchez Jordi
Published: January 2024

The supply of waste glycerol is rising steadily, partially due to the increased global production of biodiesel. Global biodiesel production totals about 47.1 billion liters and is a process that involves the co-production of waste glycerol, which accounts for over 12% of total esters produced. Waste glycerol is also generated during bioethanol production and is estimated to account for 10% of the total sugar consumed on average. Therefore, there is a real need to seek new technologies for reusing and neutralizing glycerol waste, as well as refining the existing ones. Biotechnological means of valorizing waste glycerol include converting it into gas biofuels via anaerobic fermentation processes. Glycerol-to-bioenergy conversion can be improved through the implementation of new technologies, the use of carefully selected or genetically modified microbial strains, the improvement of their metabolic efficiency, and the synthesis of new enzymes. The present study aimed to describe the mechanisms of microbial and anaerobic glycerol-to-biogas valorization processes (including methane, hydrogen, and biohythane) and assess their efficiency, as well as examine the progress of research and implementation work on the subject and present future avenues of research.
Numerical Investigations of the Cooling Performance of an R410A Closed-Loop Spray Cooling System
Authors: Yang Wenbo , Sang Xuehao , Chen Bin , Li Dong
Published: January 2024

To investigate the spray cooling characteristics and the impact of spray parameters such as chamber pressure, spray height, and spray tilt angle on heat transfer efficiency, a mathematical model based on the Eulerian–Lagrangian frame was established for an R410A closed-loop spray cooling system. The results revealed that the spray pattern is conical, with the center velocity significantly higher than the edge velocity. The temperature distribution of the cooling surface and liquid film height both exhibit a “W” shape, and the surface temperature is lower where the liquid film is thin. There is an optimal liquid film height of approximately 5 μm, at which the cooling surface temperature is the lowest. The surface temperature increases with an increase in the spray chamber pressure. Considering average cooling surface temperature, the optimal tilt angle is 40° with an average surface temperature of 330.1 K. When considering wall temperature and wall heat transfer coefficient uniformity, however, the optimal tilt angle is 10°, leading to the average surface temperature of 332.6 K. When increasing the optimal spray height to 70 mm, the average surface temperature is 313.4 K.