bluuebunny/crossref_metadata_2025 huggingface dataset:
Precision Location Keyword Detection Using Offline Speech Recognition Technique
Authors: Imam Mohsin , Gupta Gaurav
Published: November 2023

This study introduces an original comprehensive system centered on identifying specific terms that indicate a user's position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.
Integration of Plasmonics Structures in Photonics Waveguides: Enabling Novel Electromagnetic Functionalities in Photonic Circuits
Authors: Magno Giovanni , Yam Vy , Dagens Béatrice
Published: November 2023

The development of integrated, compact, and multifunctional photonic circuits is crucial to increase the capacity of all-optical signal processing for communications, data management or microsystems. Plasmonics brings compactness to numerous photonic functions, but its integration into circuits in not straightforward due to insertion losses and poor modes matching. The purpose of this article is to detail the integration strategies of plasmonic structures on dielectric waveguides, and to show through some examples the variety and the application prospect of integrated plasmonic functions.
Network Inference from Cancer and Epithelial Cell Co-Culture Reveal Microenvironmental Configurations That Drives Population Dynamics
Authors: Queiroga Alexandre , Morais Mauro Cesar Cafundó , Stransky Beatriz
Published: November 2023

Human keratinocytes and melanoma can stick together to form clusters after eight days in co-culture. As in dynamic system concepts, one can consider cluster formation as the system attractor, cell seeding as the initial condition, and density change over time as a path within the basin of attraction. Herein, Cellular Automata, which is a class of Agent-Based Models, and Boolean Networks are discrete modeling methods used in population dynamics such that running the dynamics of cellular automata backward reveals an underlying network in terms of basin of attraction, also known as global dynamics. Thus, we hypothesize that one can estimate the local dynamic of an agent-based model from the basin of attraction of a boolean network. Here, we propose an approach to estimating these agent-based model rules, which consists of comparing the density states within each state transition and reaching a consensus among state transitions belonging to a basin of attraction of a boolean network. The objectives of this study are: (1) to infer a boolean network from the co-culture growth curve; (2) to estimate the rules of agent-based models; and (3) to implement spatial dynamics simulations. The binarization of the growth curve shows high population density after four days; the estimated agent-based model rules were compatible with cell proliferation and migration in agreement with literature, so we had to propose individual rules for survival and death. Spatial dynamics shows that (1) keratinocytes exhibit a higher density in neighborhoods where melanoma is present; (2) the chance of keratinocyte migration increases until the fourth day, then decreases, and the probability of survival increases substantially after four days; and (3) cells with low proliferation capacity die and free space for those with high ones. Our approach suggests that the attractor state of cell co-culture is induced mainly by an increase in keratinocyte migration and survival. Our approach has the potential to offer valuable clues about microenvironmental interactions or configurations that drive population dynamics.
Organizational Measures to Protect the Mental Health of Healthcare and Social Services Staff during COVID-19: What Worked and What Didn’t according to Human Resources Advisors?
Authors: Pelletier Mariève , Nicolakakis Nektaria , Biron Caroline , Jauvin Nathalie , Letellier Marie-Claude , Vivion Maryline , Beaupré Roxanne , Audy Marie-Ève , Vézina Michel
Published: November 2023

: Healthcare workers are affected by mental health issues, burnout and turnover, and this burden is even greater during epidemics and pandemics, such as COVID-19. The purpose of this research is to provide an overview of the measures introduced or supported in the institutions of Quebec’s health and social services network during the COVID-19 pandemic with the aim of protecting healthcare workers’ mental health. An online questionnaire survey was administered in 2021 among human resources department personnel in health and social services network institutions involved in workplace mental health in Quebec. A total of 223 key informants representing 31 of the 34 public health and social services institutions in the province of Quebec in Canada responded to the questionnaire. Measures that focus on the needs of staff, involve all levels of authority and the ones that provide flexibility, support and recognition at work were more successful, according to the advisors surveyed. Future research into whether the same measures are considered effective or ineffective outside of a pandemic context is needed.
Almost Automorphic Solutions to Nonlinear Difference Equations
Authors: Kostić Marko , Koyuncuoglu Halis Can , Fedorov Vladimir E.
Published: November 2023

In this study, we focus on an abstract form of summation equations, which can be regarded
as a particular class of nonlinear difference equations. We investigate sufficient conditions for
the existence of discrete almost automorphic solutions by appealing to the fixed point theory and
establish a linkage between the existence of discrete almost automorphic solutions and the existence
of bounded solutions for the nonlinear difference equations in the spotlight. Consequentially, we
propose a discrete counterpart of the Bohr-Neugebauer theorem for the qualitative theory of difference
equations.
An Optimised and Scalable Blockchain-Based Distributed Learning Platform for Consumer IoT
Authors: Wang Zhaocheng , Liu Xueying , Shao Xinming , Alghamdi Abdullah , Alrizq And Mesfer , Munir Md Shirajum , Biswas Sujit
Published: November 2023

Consumer IoT (CIoT) manufacturers seek customer feedback to enhance their products and services, creating a smart ecosystem like a smart home. Due to security and privacy concerns, Blockchain-based federated learning (BCFL) ecosystems can let CIoT manufacturers update their Machine Learning (ML) model using end-user data. FL uses privacy-preserving ML techniques to forecast customers' needs and consumption habits, and blockchain replaces the centralised aggregator to safeguard the ecosystem. However, Blockchain technology (BCT) struggles with scalability and quick ledger expansion. In BCFL, local model generation and secure aggregation are other issues. This research contributes a novel architecture emphasising Gateway Peer (GWP) in blockchain network to resolve scalability, ledger optimisation and secure model transmission issues. In the architecture we replace the centralised aggregator by the blockchain network, while GWP restricts the number of local transactions to execute in BCN. Considering the security and privacy of FL processes, we have added differential privacy and advanced normalisation techniques to ML processes. The approaches strengthen end-users' cyber security and encourage the adoption of technological innovation standards by service providers. The proposed approach has been tested extensively using a well-respected Stanford Cars dataset. We experimentally demonstrate that the proposed architecture makes the network scalable and optimises the ledger significantly. In addition, the normalisation technique outperforms batch normalisation when features are under DP protection.
Unveiling Racial and Ethnic Disparities in MPOX Virus Vaccine Distribution and Demographic Patterns in the United States
Authors: Chan Alex Siu Wing
Published: November 2023

Background: This study delves into the examination of racial and ethnic disparities in the distribution of the Monkeypox (MPOX) virus vaccine within the United States, offering a critical healthcare perspective on an emerging public health concern. It particularly focuses on the demographic patterns associated with MPOX transmission and the allocation of vaccines. Methods: A comprehensive analysis was conducted, drawing on a wide array of data sources to understand the vaccine distribution and demographic trends in the United States. The study scrutinized three main aspects: MPOX cases by race and ethnicity, vaccine administration across different racial and gender categories, and the potential disparities therein. Results: The findings reveal significant disparities in the distribution of vaccine doses, with males, particularly gay, bisexual, and other Men who have sex with men (MSM), receiving the majority of doses. Moreover, substantial variations in MPOX cases are observed among different racial and ethnic groups, emphasizing the need for culturally sensitive public health strategies. Conclusions: The study underscores the urgent need for targeted public health interventions to address disparities and safeguard the well-being of those at higher risk, particularly MSM. It highlights the importance of equity in vaccine distribution and culturally sensitive approaches, and serves as a foundation for shaping effective public health policies and strategies to mitigate the spread of MPOX. This research advances our understanding of healthcare disparities in the context of an emerging viral infection, providing valuable insights for future public health initiatives.
Reproductive Coercion and Abuse in Intimate Relationships: Understanding Perpetrator Motivations and Overlaps with Coercive Control
Authors: Tarzia Laura , McKenzie Mandy
Published: November 2023

Reproductive coercion and abuse is a hidden and poorly recognised form of violence against women. It refers broadly to behaviours that interfere with or undermine a person’s reproductive autonomy, specifically to promote or prevent pregnancy. Reproductive coercion and abuse can involve physical, sexual, financial or psychological abuse in order to achieve these aims, and is overwhelmingly perpetrated by men against women. As an emerging field of scholarship, conceptual understanding of reproductive coercion and abuse is still in its infancy; however, it is often described as being linked to coercive control. In this article, we seek to highlight the complexity of this relationship through qualitative analysis of in-depth interviews with 30 victim/survivors in Australia recruited from the community, focusing on their perceptions of the perpetrator’s motivations. Our analysis suggests that perpetrator motivations can range from entitlement and self-interest to a deep desire for domination and entrapment. Pregnancy preventing behaviour was more likely to be linked with entitlement and self-interest, whereas pregnancy promoting behaviour tended to be described by participants in relationships where there was a broader pattern of ongoing control and entrapment. Thus, we suggest that coercive control is a motivating factor for some, but not all men who perpetrate reproductive coercion and abuse. A greater understanding perpetrator motivations may be important for practitioners, particularly those working in sexual and reproductive health services, since it could be relevant to women’s level of risk for coercive controlling behaviour or more extreme forms of physical or sexual violence.
AICyberSec: A Cyber Security Tool
Authors: Pasamonik Dawid , Smolarski Bartłomiej , Strynkowska Weronika , Lubicka Patrycja , Eksman Marlena , Perliński Piotr , Krzywanski Jarosław , Povstenko Yuriy , Sosnowski Marcin , Szcześniak Ireneusz , Woźna-Szcześniak Bożena
Published: November 2023

Chatbots are increasingly used as tools for disseminating knowledge and information across various sectors of business, without the need for human intervention. With the continuous advancement of technology and the internet, the field of cybersecurity is also expanding, leading to the development of newer and more sophisticated systems to counter emerging threats. However, a fundamental question arises: how can one determine what to look for, where to acquire the necessary knowledge, and how to initiate this process? Recognizing the need for more effective tools in the market to educate individuals about online threats and self-protection, this paper aims to bridge the gap in the existing literature. The Artificial Intelligence-based Cybersecurity Chatbot (AICyberSec) has been developed to provide a versatile cybersecurity knowledge resource. This initiative is in response to the absence of a comprehensive solution in the market that effectively informs individuals about online threats and offers guidance on how to safeguard themselves. Furthermore, this chatbot serves as a valuable application for identifying potential hazards in cyberspace and can also function as an educational tool to facilitate the learning process.
On a Generalized Gagliardo-Nirenberg Inequality with Radial Symmetry and Decaying Potentials
Authors: Tarulli Mirko , Venkov George
Published: November 2023

We establish a new Gagliardo-Nirenberg inequality characterized by radial symmetry and involving potentials exhibiting pure power polynomial behaviour. As an application of our result, we investigate the existence of extremals for this inequality, which also correspond to stationary solutions for the nonlinear Schrödinger equation with inhomogeneous nonlinearity, competing with Hs-subcritical nonlinearities, either of local or non-local nature.
Mussel-Inspired Calcium Alginate/Polyacrylamide Dual Network Hydrogel: A Physical Barrier to Prevent Postoperative Intrauterine Re-adhesion
Authors: Su Zekun , Xue Beibei , Xu Chang , Dong Xufeng
Published: November 2023

Intrauterine adhesions (IUA) has become one of the main causes of female infertility, how to effectively prevent postoperative re-adhesion has been a clinical challenge. In this study, a mussel-inspired dual-network hydrogel was proposed for postoperative anti-adhesion of IUA. First, calcium alginate/polyacrylamide (CA-PAM) hydrogel was prepared by covalent and Ca2+ cross-linking. Benefiting from abundant phenolic hydroxyl groups, polydopamine (PDA) was introduced to further enhance the adhesion ability and biocompatibility. This CA-PAM hydrogel immersed in 10 mg/mL dopamine solution possessed remarkable mechanical strength (elastic modulus > 5 kPa) and super stretchability (with a breaking elongation of 720%). At the same time, it shown excellent adhesion more than 6 kPa. Surprisingly, the coagulation index of the hydrogel was 27.27 ± 4.91, demonstrating attractive coagulation performance in vitro and the potential for rapid hemostasis after surgery.
Knowledge, Misconceptions, and Myths of Master’s Degree Students about Attention Deficit Hyperactivity Disorder (ADHD)
Authors: HERRERAS Esperanza BAUSELA
Published: November 2023

IIt is important that Master’s Degree students in psychology, regardless of their future specialisa-tion, know about Attention Deficit Hyperactivity Disorder (ADHD). The aim is evaluate and an-alyse the ADHD-related knowledge held by students of Early Childhood Education and Primary Education. The findings that were obtained in this study (on the chronicity of the disorder, changes in manifestations throughout the life cycle, and the importance of early and multidisci-plinary intervention) have clear implications in relation to the specific training in ADHD for fu-ture teachers. It is essential to broaden the specific training in relation to ADHD in studies for the Master’s Degrees in Early Childhood Education and Primary Education and through contin-uous training.
Overcoming Variants Resistance in Monoclonal Antibody for Antiviral Therapy
Authors: Mustafa Mujahed , Alzebair Awad , Mohamednoor Hanan , Mohammed Ahmed
Published: November 2023

Monoclonal antibody (mAb) therapy has revolutionized the treatment of various diseases, including cancer and autoimmune disorders. However, the emergence of SARS-CoV-2 variants, as well as other pathogenic variants, poses challenges in maintaining the therapeutic efficacy of mAbs. In this mini review article, we aim to explore the strategies to overcome variants resistance in mAb therapy against viral infections. Firstly, we discuss the mechanisms through which variants can evade the neutralizing effects of mAbs. Understanding these mechanisms is crucial in developing targeted approaches to combat resistance. Next, we delve into the strategies being pursued to address variants resistance. These include developing new mAbs or antibody cocktails that target multiple epitopes, engineering mAbs with improved binding affinities or enhanced neutralizing capabilities, and exploring alternative therapeutic modalities such as bispecific antibodies or antibody-drug conjugates. Furthermore, we highlight the role of computational modeling and artificial intelligence in predicting variant escape mutations and aiding in the design of more effective mAbs. Additionally, we examine the importance of continuous surveillance and monitoring of emerging variants to inform treatment strategies. This article emphasizes the urgent need for proactive approaches to tackle variants resistance in mAb therapy. By combining innovative design strategies, computational modeling, and vigilant surveillance, we can maintain the therapeutic effectiveness of mAbs and stay ahead in the battle against evolving pathogens and viral infections.
Regenerative Medicine and Tissue Engineering: Advancements and Challenges in Cell-Based Therapies
Authors: Tören Elçin
Published: November 2023

Regenerative Medicine and Tissue Engineering are interdisciplinary fields that combine cell biology, material science, and bioengineering to develop therapies for tissue repair and regeneration. This field has made significant advances in recent years, particularly in the use of stem cells and biomaterials. Advancements in cell-based therapies have involved the use of cells to repair or replace damaged tissues and organs. Tissue engineering involves using scaffolds and growth factors to promote tissue regeneration. However, a major challenge in this field is the formation of adhesions, which can cause complications, such as intestinal obstruction and chronic pain. Adhesion barriers are used to prevent adhesion of tissues and organs during recovery, thereby reducing the risk of complications. Anti-adhesion materials, such as polysaccharides, proteins, and synthetic polymers, are used to create a physical barrier between tissues and prevent adhesion. These materials can be used alone or in combination, but their effectiveness varies.
Effects of Waterlogging at Flowering Stage on the Grain Yield and Starch Quality of Waxy Maize
Authors: Yang Huan , Cai Xuemei , Lu Dalei
Published: November 2023

ABSTRACT:
Waterlogging is a common abiotic stress in global maize production. Maize flowering stage (from tasseling to silking) is more fragile to environmental stresses among different growth stages. The effects of waterlogging at flowering stage on the grain yield and starch quality of waxy maize were studied using Suyunuo5 (SYN5) and Yunuo7 (YN7). The grain yield and starch concentration of both hybrids were reduced by waterlogging. The size of starch granules and proportion of small-molecule amylopectin increased in SYN5 and decreased in YN7 when plants suffered waterlogging at flowering, in contrast to the change trend of relative crystallinity. The pasting viscosities and gelatinization temperatures were reduced, whereas the gelatinization enthalpy was unaffected by waterlogging in both hybrids. Our study indicated that waterlogging at flowering reduced the grain yield, restricted starch deposition, and deteriorated the pasting property of waxy maize. Between the two hybrids, YN7 and high pasting viscosities and low retrogradation percentage than SYN5, indicated its advantages on produce starch for viscous and less retrograde food. Results provide information for utilization of waxy maize grain in food production.
Estimating the Inertia Tensor Components of an Asymmetrical Spacecraft When Removing It from the Operational Orbit at the End of Its Active Life
Authors: Sedelnikov Andry , Orlov Denis , Bratkova Maria , Khnyryova Ekaterina
Published: November 2023

The paper presents a method for estimating the inertia tensor components of a spacecraft that has expired its active life using measurement data of the Earth's magnetic field induction vector components. The implementation of this estimation method is supposed to be carried out when cleaning up space debris in the form of a clapped-out spacecraft with the help of a space tug. It is assumed that a three-component magnetometer and a transmitting device are attached on space debris. With the help of this measuring system, the parameters for the rotational motion of space debris are estimated. Then the known controlled action from the space tug is transferred to the space debris. Next, measurements for the rotational motion parameters are carried out once again. Based on the available measurement data and parameters of the controlled action, the space debris inertia tensor components are estimated. It is assumed that the measurements of the Earth's magnetic field induction vector components are made in a coordinate system which axes are parallel to the corresponding axes of the main body axis system. Such an estimation makes it possible to effectively solve the problem of cleaning up space debris, to calculate the costs of the space tug working body and the parameters of the space debris removal orbit. Examples of numerical simulation using the measurement data of the Earth's magnetic field induction vector components on the Aist-2D small spacecraft are given. The results of the work can be used in the development and implementation of missions to clean up space debris in the form of clapped-out spacecraft.
Influence of Aspect Ratio of Nylon Fibers on the Mechanical Properties of Concrete
Authors: Khan Suliman , Ferretti Elena , Ashraf Shahzad
Published: November 2023

The initiation of micro-cracks in concrete is primarily due to its brittleness, which causes severe impacts on the overall behaviour of the concrete. However, incorporating fibers in concrete can increase its strength and performance. The nylon fibers used in this study were of two types: Ny-lon Fiber Threads (NFT) and Nylon Fiber Strands (NFS). This research aims to examine the effect of nylon fiber on the mechanical properties of plain cement concrete (PCC). The potential strength of nylon-fiber reinforced concrete (NFRC) was determined versus that of PCC, at fiber content of 2.5 kg/m3. The compressive strength of concrete 30 MPa with a water-cement ratio of 0.4 was produced to investigate the workability and density tests of conventional and fiber-reinforced concrete (FRC). The concrete beams and cylinder samples were tested for compressive, splitting, and flexural strength using ASTM standards (ASTM C-39, C-496, and D-790). It was found that NFT-reinforced samples achieved higher hardened properties of concrete (13.05%) compared to that NFS-reinforced samples. Test results showed that the incorporation of fiber in the concrete decreased the compressive strength of the concrete of cylinder and cube by 0–10% and 5–15%, respectively, however, it increased the splitting tensile and flexural strengths by 0-15%, and 5-15% respectively. Furthermore, the incorporation of nylon fibers (NF) in PCC significantly enhanced the ductility and delayed the cracks initiation in concrete.
Sparse Mix-Attention Transformer for Multispectral Image and Hyperspectral Image Fusion
Authors: Yu Shihai , Zhang Xu , Song Huihui
Published: November 2023

Multispectral image (MSI) and hyperspectral image (HSI) fusion (MHIF) aims to address the challenge of acquiring high-resolution (HR) HSI images. This field combines a low-resolution (LR) HSI with an HR-MSI to reconstruct HR-HSI. Existing methods directly utilize transformers to perform feature extraction and fusion. Despite the demonstrated success, there exist two limitations: 1) Employing the entire transformer model for feature extraction and fusion fails to fully harness the transformer’s potential in integrating the spectral of the HSI and spatial information of the MSI. 2) HSI has a strong spectral correlation and exhibits sparsity in the spatial domain. Existing transformer-based models do not optimize this physical property, which makes their methods prone to spectral distortion. To accomplish these issues, this paper introduces a novel framework for MHIF called Sparse Mix-Attention Transformer (SAMformer). Specifically, to fully harness the advantages of the Transformer architecture, we propose a Spectral Mix Attention Block (SMAB), which concatenates the keys and values extracted from LR-HSI and HR-MSI to create a new multi-head attention module. This design facilitates the extraction of detailed long-range information across spatial and spectral dimensions. Besides, to address the spatial sparsity inherent in HSI, we incorporated a sparse mechanism within the core of SMAB called Sparse Spectral Mix Attention Block (SSMAB). In the SSMAB, we compute attention maps from queries and keys and select the K highly correlated values as the sparse attention map. This approach enables us to achieve a sparse representation of spatial information while eliminating spatially disruptive noise. Extensive experiments conducted on three benchmark datasets, namely Cave, Harvard, and Pavia Center, demonstrate the SMAformer method outperforms state-of-the-art methods.
An Efficient Computational Analysis and Modelling of Transferred Aerodynamic Loading on Direct-Drive System of 5MW Wind Turbine and Results Driven Optimisation for a Sustainable Generator Structure
Authors: Szatkowski Szymon , Jaen-Sola Pablo , Oterkus Erkan
Published: November 2023

The study presents an efficient computational investigation on the behaviour of the direct-drive system integrated into the offshore 5MW NREL wind turbine model under demanding aerodynamic loading conditions with the aim of optimising and developing more sustainable key structural components. The research was based on computational simulation packages to verify the use of real-world wind data, the loading conditions on the blade structures through aero-elastic simulation studies, as well as analyse the behaviour of the drive system. Through the application of validated aerodynamic loading conditions, resulting normal forces on the blades structure were obtained and applied to a dedicated simplified model that was also previously validated, to estimate the transferred loads into the powertrain. The adopted methodology allowed for the identification of shaft misalignment induced air-gap eccentricity. The impact of shaft deflections on resulting magnetomotive force was considered by making use of the Maxwell stress distribution expression. By taking into account the resulting loading cases on the generator structure, as well as the inherent typical loads generated by the electrical machine, a procedure including structural parametric and topology optimisation was developed and performed, achieving a rotor mass reduction between 8.5 and 9.6% if compared with the original model.
Dual Impact of Dietary Lipids on Inflammatory Bowel Disease: A Two-Edged Sword
Authors: Kwon Soon Jae , Khan Muhammad Sohaib , Kim Sang Geon
Published: November 2023

Inflammatory bowel diseases (IBD), including Crohn's disease and ulcerative colitis, is a disease of chronic inflammatory conditions of the intestinal tract due to disturbance of the inflammation and immune system. Symptoms of IBD include abdominal pain, diarrhea, bleeding, reduced weight, and fatigue. In IBD, the immune system attacks the intestinal tract's inner wall, causing chronic inflammation and tissue damage. In particular, IL-6 and IL-17 act on immune cells, including T cells and macrophages, to amplify the immune responses so that tissue damage and morphological changes occur. Of note, excessive calorie intake and obesity also affect the immune system due to inflammation caused by lipotoxicity and changes in lipids supply. Similarly, individuals with IBD have alterations in liver function after sustained high-fat diet feeding. In addition, excess dietary fat intake, along with alterations in primary and secondary bile acids in the colon, can affect the onset and progression of IBD because inflammatory cytokines contribute to insulin resistance; the factors include the release of inflammatory cytokines, oxidative stress, and changes in intestinal microflora, which may also contribute to the disease progression. However, interfering with de novo fatty acid synthase by deleting the enzyme acetyl-CoA-carboxylase 1 in intestinal epithelial cells leads to the deficiency of epithelial crypt structures and tissue regeneration, which seems to be due to Lgr5+ intestinal stem cell function. Thus, conflicting reports exist regarding high-fat diet effects on IBD animal models. This review will focus on the pathological basis of the link between dietary lipids intake and IBD and cover currently available pharmacological approaches.
Sex-specific Relationships Between HDL-Cholesterol Levels and 10-year Mortality in Individuals with Atherosclerotic Cardiovascular Disease: A Nationwide Cohort Study of South Koreans
Authors: Yang Hyun Suk , Jeong Ho Jin , Kim Hyeongsu , Lee Seungho , Hur Mina
Published: November 2023

Large epidemiological studies show U-shaped relationships between high-density lipoprotein cholesterol (HDL-C) levels and all-cause mortality in individuals without atherosclerotic cardiovascular diseases (ASCVD). Association in those with ASCVD by sex is unclear. We examined the association between HDL-C levels and 10-year all-cause mortality in subjects (≥40 years of age) with ASCVD using the 2010 National Health Insurance Service and the National Death Registry of Korea. We categorized HDL-C levels into three groups (low: <40 mg/dL for males, <50 mg/dL for females; high: 40-90 mg/dL for males, 50-90 mg/dL for females; extremely high: >90 mg/dL) and 10 mg/dL intervals. We conducted a sex-stratified and adjusted Cox proportional hazards analysis. Out of 1,711,548 individuals (54% female, mean age 61.4 years), 10-year mortality was observed in 218,252 (12.8%). Males had a higher mortality rate than females (16.2% vs 9.8%; P<0.001). When adjusting for age, body mass index, LDL-cholesterol, triglycerides, hypertension, diabetes, smoking, and alcohol consumption, the low and extremely high HDL-C groups had significantly higher hazard ratios for 10-year mortality compared to the high HDL-C group in males [1.183 (1.166-1.199), 1.359 (1.288-1.434)] and in females [1.153 (1.138-1.169), 1.095 (1.029-1.167)]. The frequency distribution bars for the 10-year mortality rate showed sex-specific nadirs of 50-59 mg/dL in males and 70-79 mg/dL in females. In this ASCVD cohort, the extremely high HDL-C (>90 mg/dL) group had 35.9% and 9.5% higher 10-year mortality risk than the high HDL-C group for males and females, respectively. There was a slightly U-shaped relationship between baseline HDL-C levels and 10-year mortality rate, with earlier inflection in males than in females.
Fructooligosaccharides Supplementation: A Good Choice for Prevention and Treatment of Non-alcoholic Fatty Liver Disease?
Authors: Bezan Priscila Nogueira , Holland Heric , Vercesi Bárbara Ferreira , Ovídio Paula Payão , Simões Livia Maria Cordeiro , Jordão Alceu Afonso
Published: November 2023

Carbohydrates such as fructooligosaccharides (FOS) are associated with improved gastrointestinal health and prevention of excess body fat. We evaluated the long-term effects of high amounts of FOS on metabolic parameters, non-alcoholic fatty liver disease (NAFLD) and short-chain fatty acids (SCFA). Sixty C57BL/6 mice received diets for four months: control (C), normolipid rich in fiber (F), normolipid supplemented with FOS (FOS), high fat (HL), high fat with high fiber (HLF) and high fat with FOS (HLFOS). We analyzed: animal weight; body composition; food intake; fasting blood glucose; serum and liver lipid profile; liver and intestinal histologies; malondialdehyde (MDA), hepatic retinol and α-tocopherol; SCFA in the feces. Supplementation with FOS in a high-fat diet promoted less body weight gain and reduced liver and retroperitoneal adipose tissue weights compared to HL and HF. FOS prevented NASH and decreased alanine aminotransferase and serum cholesterol levels in experimental animal models of obesity and metabolic syndrome (MS). There was found statistical differences in the dosages of the three main SCFAs in feces (acetic, isobutyric and isovaleric acids). Long-term supplementation with high doses of FOS was effective in reducing weight, adiposity, NAFLD and serum cholesterol in C57BL mice with obesity and MS induced by a high-fat diet.
Micro- and Nano-Roughness Separation Based on Fractal Analysis
Authors: Czifra Árpád , Ancza Erzsébet
Published: November 2023

When describing the tribological behaviour of technical surfaces, the need for full length scale microtopographic characterization often arises. The self-affine of surfaces and the characterization of self-affine by a fractal dimension and its implantation into tribological models are commonly used. The goal of our present work was to determine the frequency range of fractal behaviour of surfaces by analysing the microtopographic measurements of a brake plunger. We also wanted to know if the bifractal and multifractal behaviour can be detected in real machine parts. As a result, we developed a new methodology for determining the fractal range boundaries to separate the nano- and micro-roughness. In order to reach our goals, we used atomic force microscope (AFM) and stylus instrument to provide measurements in wide frequency range (19 nm - 3 mm). As a result of the power spectral density (PSD) based fractal evaluation, it was found that the examined surface could not be characterized by a single fractal dimension and that we developed a methodology for stopping the margin of validity of each fractal dimension, and we also developed a methodology for determining the limits of validity of certain fractal dimensions.
A Novel Balanced Arithmetic Optimization Algorithm Optimized Controller for Enhanced Voltage Regulation
Authors: Ekinci Serdar , Çetin Haluk , Izci Davut , Köse Ercan
Published: November 2023

This work introduces an innovative approach that unites a PIDND2N2 controller and the balanced arithmetic optimization algorithm (b-AOA) to enhance the stability of an automatic voltage regulator (AVR) system. The PIDND2N2 controller, tailored for precision, stability, and responsiveness, mitigates the limitations of conventional methods. The b-AOA optimizer is obtained through integration of pattern search and elite opposition-based learning strategies into the arithmetic optimization algorithm. This integration optimizes the controller parameters and the AVR system's response, harmonizing exploration and exploitation. Extensive assessments, including evaluations on 23 classical benchmark functions, demonstrate the efficacy of the b-AOA. It consistently achieves accurate solutions, exhibits robustness in addressing a wide range of optimization problems, and stands out as a promising choice for various applications. In terms of AVR system, comparative analyses highlight the superiority of the proposed approach in transient response characteristics, with the shortest rise and settling times and zero overshoot. Additionally, the b-AOA approach excels in frequency response, ensuring robust stability and a broader bandwidth. Furthermore, the proposed approach is compared with various state-of-the-art control methods for the AVR system, showcasing impressive performance. These important results underscore the significance of this work, setting a new benchmark for AVR control by advancing stability, responsiveness, and reliability in power systems.
From Creative, Reflective, Practice to Affective Reflexivity as an Art Research Methodology
Authors: Michaels Debbie
Published: November 2023

A capacity to reflect on experience and learn through it (reflective and reflexive practice) is deemed an essential part of ethical practice and research. Challenging traditional research approaches this arts-based, inter-disciplinary, Ph.D. project explores artistic practice as a research methodology, and how learning through experiences of ‘making’ things may enhance and amplify our understanding of the more affective aspects of human situations and experience. Grounded in the author’s artistic practice and learning through experiences of ‘making’, the author assembles and adapts frames from psychoanalysis, art (psycho)therapy, and the arts with/through which to observe and document their experience of an organisational situation. Describing the development of their method in three phases, analysis takes place through returning to revisit and rework artistic material produced, and engaging in conversation with the emergent material and others in response. Results take the form of artworks and artistic projects, including documentation of process. Emergent threads draw attention to the speculative, entangled, and affective nature of the research process, the reflexivity generated through moving and handling material, and the reflexive work of undergoing, foregrounding an ethics of responsibility, attention, and care for/of the body. As a space for imaginative encounter and performative enactment, and a site for reflexivity through which one may be pressed to notice and feel more acutely, the author argues that the research value resides in the capacity of this method to embrace complex relationalities, and engage our affective, ethical sensibilities in ways that may not emerge through more traditional approaches to reflective/reflexive practice(s). This has implications for both art therapy practice and research, amplifying the learning opportunities afforded by moving, modifying, and assembling things differently, the embodied ‘work’ of art as a method of enquiry, the cultural sensitivity of documentary fragments captured in/through various media and voices, and the value of collaborative endeavours where meaning is co-constructed in conversation.
Experimental Research on Avoidance Obstacle Control for Mobile Robots Using Q-Learning (QL) and Deep Q-Learning (DQL) Algorithms in Dynamic Environments
Authors: Thuong Than Thi , Ha Vo Thanh
Published: November 2023

This paper presents experimental results of static and dynamic obstacle avoidance methods for a two-wheel mobile robot with independent control using a deep Q-learning (DQL) reinforcement learning algorithm. This is an alternative method that combines the Q-learning (QL) algorithm with a neural network. Neural networks in the DQL algorithm act as Q matrix table approximators for each pair (State - Action). The effectiveness of the proposed solution was verified through simulations, programming, and experimentation. This DQL algorithm is compared to the QL algorithm. First, the mobile robot communicated with the control script using the robot operating system (ROS). The mobile robot is code programmed using a Python in the ROS operating system combined with the DQL controller in Gazebo software. The mobile robot then performed experiments in a workshop with many different experimental scenarios. This DQL controller is improved in term of the computation time, convergence time, planning trajectory accuracy, and obstacles avoidance. Therefore, the DQL controller solved the path optimization problems for mobile robots better than the Q-learning controller.
Amino Acid Residues of the Metal Transporter OsNRAMP5 Responsible for Cadmium Absorption in Rice
Authors: Qu Zhengtong , Nakanishi Hiromi
Published: November 2023

OsNRAMP5—a member of the same family as the iron, manganese (Mn), and cadmium (Cd) transporter OsNRAMP1—is responsible for the transport of Mn and Cd from soil in rice. Knockout of OsNRAMP5 markedly reduces both Cd and Mn absorption; therefore, in low -Mn environments, such plants would exhibit Mn deficiency and suppressed growth. Amino acid mutations in IRT1 alter its metal selectivity. We generated random mutations in OsNRAMP5 via error-prone PCR, and used yeast to screen for the retention of Mn absorption and inhibition of Cd absorption. The results showed ththat alanine 512th is the most important amino acid residue for Cd absorption, and that its substitution resulted in the absorption of Mn but not Cd.
Symbolic Analysis of Classical Neural Networks for Deep Learning
Authors: Lutovac-Banduka Maja , Franc Igor , Milićević Vladimir , Zdravković Nemanja , Dimitrijević Nikola
Published: November 2023

Deep learning is based on matrix computing with a large amount of hidden parameters that is not visible outside the computing module. Deep learning is a non-linear system and a linear approach is not possible. It is natural for people to visualize the algorithm and to follow some hidden parameters. In this paper, we propose a simple graphical programming of a nonlinear system based on drawing the simplest unit, such as a single neuron model. A more complex scheme of a classical neural network is obtained by commands copy-move-place of one neuron. The number of neurons and layers can be chosen arbitrarily. Once the scheme is complete, the implementation code is evaluated with symbolic parameters and nonlinear activation functions. This cannot be done manually. With the symbolic expression of outputs in terms of inputs and symbolic parameters, including symbolic activation pure functions, some other properties can be derived in closed form. This unique original approach can help scientists and developers and design numerical algorithms for machine learning and to understand how deep learning algorithms work.
Lower Caesarean Section and Higher Spontaneous Vaginal Birth Rates Are Connected with Yoga Practice in Pregnancy: A Randomized Controlled Clinical Trial
Authors: Kuder Lucija , Dinevski Dejan , Gašparović Vesna Elveđi , Vesenjak Dinevski Izidora , Takač Iztok , Mujezinović Faris
Published: November 2023

The increasing number of caesarean section (CS) is a major challenge in modern obstetrics. This rise is not supported by better neonatal and maternal outcomes. The prevention of unnecessary primary CS is a global priority. This study focuses on effectiveness and safety of yoga practice during pregnancy and it’s connection to perinatal outcome. We conducted a single-blinded, randomized controlled clinical trial, with healthy singleton primiparas, randomized at first trimester of pregnancy. A total of 214 participants were randomized, 106 in yoga and 108 in controls. The study group performed yoga exercise while the controls received standard antenatal care. The yoga exercises were performed once a week, lasted 90 minutes, under the supervision of a certified yoga teacher. All pregnant women from study group completed 12 yoga exercises. The results showed that the yoga group had a lower rate of urgent intrapartum CS (p-0.004) and more frequent spontaneous vaginal delivery (p-0.009), while the control group had a higher rate of induced labour (p-0.032). Conclusion: Yoga practice in pregnancy is a promising intervention that has a potential to decrease intrapartum urgent CS rates and assures natural and spontaneous vaginal birth; it’s highly recommendable to healthy pregnant women.
Ten Quick Tips for Accurate Reconstruction of Prokaryotic and Eukaryotic Genome-Scale Metabolic Models
Authors: Canto-Encalada Gabriela , Armstrong Jenna , Focil-Espinosa4 Carlos , Adekunle-Fiyin Ademikanra , Peeler Ila , Gebbie William R. , Nunez-Garcia Julio , Saldivar Alexis , Martinez Diego , Zuniga Cristal
Published: November 2023

Constraint-based metabolic modeling approaches have enhanced our knowledge and understanding of the metabolism of prokaryotes and eukaryotes. This approach highly depends on the reconstruction process of genome-scale metabolic models (Mmodels). M-models can guide effective experimental design and yield new insights into the function and control of biological systems. Despite the recent advances in the automated generation of draft metabolic network reconstructions, the manual curation of these networks remains a labor-intensive and challenging task. Thus, these ten quick tips for the manual curation process are essential for optimizing high-quality metabolic model generation in less time. This collection of tips describes in great detail the resources and methods to ensure successful reconstruction. Furthermore, it increases the scope of other protocols of metabolic modeling by including resources to reconstruct eukaryotic organisms. Thus, all tips are applicable to a wide range of eukaryotic organisms. We believe this manuscript will interest a broad audience and researchers from different disciplines, spanning from microbiology and systems biology to biotechnology.
Preterm Birth and Its Association with Maternal Diet, and Placental and Neonatal Telomere Length
Authors: Lis Nikoletta , Lamnisos Demetris , Bograkou - Tzanetakou Aikaterini , Hadjimbei Elena , Tzanetakou Irene P
Published: November 2023

Preterm birth (PTB), a multi-causal syndrome, is one of the global epidemics. Maternal nutrition, but also neonatal and placental telomere length (TL), are among the factors affecting PTB risk. However, the exact relationship between these factors and the PTB outcome, remains obscure. The aim of this review, was to investigate the association between PTB, maternal nutrition and placental-infant TL. Observational studies were sought, with the keywords: maternal nutrition, placenta TL, newborn, TL, and PTB. No study was found that included all keywords simultaneously, and thus, the keywords, were searched in dyads, to reach assumptive conclusions. The findings show that maternal nutrition affects PTB risk, through its influence on maternal TL. On the other hand, maternal TL independently affects PTB risk, and at the same time PTB is a major determinant of offspring TL regulation. The strength of the associations and the extent of the influence from covariates, remains to be elucidated in future research. Furthermore, the question of whether maternal TL, is simply a biomarker of maternal nutritional status and PTB risk, or a causative factor of PTB, to date, remains to be answered.