bluuebunny/crossref_metadata_2025 huggingface dataset:
Disease Burden of Non-COVID-19 Lower Respiratory Infections, Risk Factors, and Aetiologies in China 1990-2021: insights from the Global Burden of Disease Study 2021
Authors: Guo Baojun , Chen Guowen , Chen Miaolian , Huang Ruixia , Mo Jinliang , Lu Zhongman , Ji Qingling , Liao Xiangqing , Liao Jianjun
Published: November 2024

Background
Lower respiratory infections (LRI) are the highest mortality diseases among infectious diseases globally, with China ranking second in the incidence and mortality of lower respiratory infections in 2021. This study, based on GBD 2021, investigates the burden of lower respiratory infections, risk factors, and etiologies in China from 1990 to 2021.
Methods
This study analyzes the trends in the disease burden of lower respiratory infections in China from 1990 to 2021, categorized by gender, age, and year, and explores the attributable mortality and disability-adjusted life years (DALYs) rates related to the risk factors and aetiologies associated with lower respiratory infections.
Results
Compared to 1990, the age-standardized incidence rate (ASIR), age-standardized mortality rate (ASMR), and age-standardized DALY rate (ASDAR) of lower respiratory infections in China decreased by 48%, 77%, and 89% in 2021. Ambient particulate matter pollution surpassed household air pollution from solid fuels to become the leading risk factor. Despite the significant decline, Streptococcus pneumoniae remains the primary pathogen associated with lower respiratory infections.
Conclusion
The disease burden of lower respiratory infections in China has significantly decreased over the past 32 years, but there is still a need to strengthen air pollution control, enhance tobacco regulation, and focus on the health of the elderly population. In response to the changes in the pathogen spectrum, it is necessary to improve detection capabilities and develop new antimicrobial drugs and vaccines.
Assessing the Macroeconomic Impact of COVID-19 Pandemic on the Nigerian Economy through the Lens of SIR-Macro Model
Authors: Nwosu Chinedu , Marcus Samuel
Published: November 2024

This study focused on assessing the impact of COVID-19 pandemic on Nigerian economy through the lens of SIR-macro model. We implemented the SIR-macro model developed by Eichenbaum et al. (2021) to study the interaction between agents’ economic decisions and COVID-19 epidemic. We calibrated the parameters of the models based on the characteristics of Nigeria’s economic structure and analyzed the implications of containment policies on the severity of economic recession as well as the dynamics of the epidemics. We found that government containment policies were effective in reducing the severity of the epidemic in terms of the spread of the disease and number of deaths. The containment policies on the other hand exacerbated the severity of the economic recession. We concluded that the predictions of the model are qualitatively sufficient in explaining the macroeconomic outcomes in Nigeria during this period. We suggest that government should consider socio-demographic characteristics of the country in the selection of policies in an epidemic condition. The government should strengthen the health care system with adequate infrastructure to prevent large case fatalities.
JEL Code:C51, C63,E71,I12, I18
Catalysis in Motion: Large-Scale Domain Alternation Enables Co(III)-C Bond Homolysis in Lysine 5,6-Aminomutase
Authors: Tian Shiliang , Pham Khai , Voss Peter , Poore Andrew , Liu Mingxin , Zuercher Eli , Birthright Caitlin , Toba David , Das Natasha , Yachuw Stephen , Denault Christian , Kim Maggie , Klose Thomas , Uyeda Christopher
Published: November 2024

Lysine 5,6-aminomutase (LAM) is a radical-based enzyme that catalyzes the reversible migration of an amino group between the C5 and C6 positions of lysine. This reaction is mediated by the coenzymes adenosylcobalamin (AdoCbl) and pyridoxal 5’-phosphate (PLP). Our study investigated the activation mechanism of AdoCbl in LAM. Using cryo-electron microscopy (cryo-EM), we resolved structures of LAM in both its ‘Open’ and ‘Closed’ states, revealing a substantial 64.1-degree rotation of the Rossmann domain upon substrate binding, which shifts the AdoCbl cofactor 25.5 Å towards PLP. This large conformational shift enables Co(III)-C homolysis and adenosine radical (Ado•) formation. Following domain alternation, adenosine is stabilized by a key hydrogen bond network composed of Lys371α, Asp299α, and Glu412α, while PLP is anchored by hydrogen bonds and electrostatic interactions with two Tyr and two Arg residues, securing the substrate lysine in place. The enzyme pocket is precisely designed to ensure the proper orientation and positioning of Ado• and PLP-substrate complex, facilitating efficient hydrogen abstraction from the substrate after the formation of the highly reactive Ado• radical, which is crucial for maintaining catalytic efficiency. This study provided the first structural information supporting the long-existing hypothesis of Rossmann domain rotation as part of the LAM activation process, shedding light on the enzymes' conformational dynamics and illustrating how nature employs domain movements to activate metallocofactors and control radical chemistry.
An Interpretable Framework for Gastric Cancer Classification Using Multi-Channel Attention Mechanisms and Transfer Learning Approach on Histopathology Images
Authors: Zubair Muhammad , Owais Muhammad , Hassan Taimur , Bendechache Malika , Hussain Muzammil , Hussain Irfan , Werghi Naoufel
Published: November 2024

The importance of gastric cancer and the role of deep learning techniques in categorizing gastric cancer histopathology images have recently increased. Identifying the drawbacks of traditional deep learning models, including lack of interpretability, inability to capture complex patterns, lack of adaptability, and sensitivity to noise. We suggest a multi-channel attention mechanism-based framework that can overcome the limitations of conventional deep learning models by dynamically focusing on relevant features, enhancing extraction, and capturing complex relationships in medical data. The proposed framework uses three different attention mechanism channels and convolutional neural networks to extract multichannel features during the classification process. The proposed framework’s strong performance is confirmed by comparative experiments conducted on a publicly available Gastric Histopathology Sub-size Image Database, which yielded remarkable classification accuracies of 99.07% and 98.48% on the validation and testing sets, respectively. Additionally, on the HCRF dataset, the framework achieved high classification accuracy of 99.84% and 99.65% on the validation and testing sets, respectively. The effectiveness and interchangeability of the three channels are further confirmed by ablation and interchangeability experiments, highlighting the remarkable performance of the framework in gastric cancer histopathological image classification tasks. This offers an advanced and pragmatic artificial intelligence solution that addresses challenges posed by unique medical image characteristics for intricate image analysis. The proposed approach in artificial intelligence medical engineering demonstrates significant potential for enhancing diagnostic precision by achieving high classification accuracy and treatment outcomes.
New technology to utilise and suppress weeds for sustainable maize production
Authors: Wu Kaixian , Zhou Shiyong , Zeng Guang , Yang Hongli , Wu Bozhi
Published: November 2024

Across the agricultural chronicle, mankind has relentlessly pursued myriad strategies to vanquish weeds and avert yield diminishment. Yet, a pragmatic technology adept at leveraging weeds as a beneficial biological resource, without compromising crop yields, has remained elusive. Our study unveils an innovative Integrated Technology to Utilise and Suppress Weeds (ITUSW) for sustainable maize cultivation. ITUSW ingeniously harnesses inter-row weed biomass and curtails intra-row weeds, attaining sustainable maize yields without sacrifice, outshining conventional global weed management practices. This technology amalgamates a spectrum of non-chemical weed suppression tactics into a practical system, harmonizing with globally recognized sustainable agricultural principles. By championing ITUSW, this research propels agriculture towards enhanced sustainability, redefining weeds from foes to allies, safeguarding yields and the environment.
Association between high-sensitivity C-reactive protein and diabetes mellitus
Authors: Yuan Jianxu , Zhou Dalin , Yu Shengjie
Published: November 2024

Background: The objective of this research endeavor was to delve into the interplay between high-sensitivity C-reactive protein (hs-CRP) levels and the risk of diabetes mellitus (DM).
Methods: This study examined the correlation between hs-CRP levels and DM risk by analyzing a sample of 7,614 adults from the United States, drawn from the National Health and Nutrition Examination Survey (NHANES) between 2017 and 2020. Employing multivariable logistic regression models, the research delved into the intricate relationship. Furthermore, to gain insights into potential variations across diverse populations, we incorporated subgroup and interaction analyses. Finally, Mendelian randomization (MR) was used for validation.
Results: In this study, 1,214 people were diagnosed with DM, and 6,400 people did not have DM. Utilizing a weighted multivariate logistic regression model that accounted for various covariates, the findings revealed that hs-CRP levels emerged as a significant risk factor for DM, with an odds ratio (OR) of 1.020 and a 95% confidence interval (CI) ranging from 1.005 to 1.034 (P < 0.05). Subgroup analyses consistently demonstrated a robust positive association across diverse categories, underscoring the universality of this relationship. Interaction tests further validated the consistency of this positive link, remaining unaffected by factors such as gender, age, race, education levels, body mass index (BMI), or diet behavior (DB), as evidenced by all interaction P-values exceeding 0.05. However, as the study observed that individual characteristics like triglyceride (TG) levels mediated the strength of this connection, with interaction P-values falling below 0.01, indicating its potential role in modulating the risk posed by hs-CRP on DM. The results of MR also indicated a positive correlation.
Conclusion: Our research showed that elevated hs-CRP levels increased the risk of DM, and improving its levels might help prevent and treat DM.
3D Printing of High-strength, Degradable and Antibacterial PLA/TPU/PMF composites
Authors: Chen Yinpeng , Niku Guge , He Meiyi , Fan Xiaoxi , Wang Shuang , Zheng Wen Jiang
Published: November 2024

This study investigates the 3D printing strategy for preparing polylactic acid/polyurethane elastomer/phosphorus-magnesium fiber (PLA/TPU/PMF) composites with biocompatibility, mechanical strength and antibacterial properties. The magnesium phosphate whisker PMF significantly enhances the antibacterial performance of the composite and remains stable against environmental oxidation, even after 24 hours in a 20% H2O2 solution at 37°C. Compared to hot-pressing, the 3D printed composite exhibits better mechanical properties, including higher Young's modulus (500 Mpa) and tensile strength (8 Mpa), as well as reduced stress relaxation. The compatibility of 3D printing with composites allows for versatile shapes and structures, such as curved nails, round rings, and more complex shapes like Christmas trees. In addition, the interactions between PLA, TPU and PMF accelerate degradation, allowing complete degradation within 15 days in an oxidative environment at 37°C with 20% H2O2.
Experimental research on the behavior of circular SFRC columns reinforced longitudinally by GFRP rebars
Authors: Saffarian Iman , Atefatdoost Gholam Reza
Published: November 2024

This research presents the experimental and theoretical evaluations on circular steel-fiber-reinforced-concrete (SFRC) columns reinforced by glass-fiber-reinforced-polymer (GFRP) rebar under the axial compressive loading. Test programs were designed to investigate and compare the effect of different parameters on the structural behavior of columns by performing tests. Theses variables included conventional concrete (CC), fiber concrete (FC), steel/GFRP longitudinal rebars, and transversal rebars configurations. A total of 16 specimens were constructed and categorized into four groups in terms of different rebarconcrete configurations, including GFRP-rebar-reinforced-CC columns (GRCC), GFRP-rebar-reinforced-FC columns (GRFC), steel-rebar-reinforced-CC columns (SRCC) and steel-rebar- reinforced-FC columns (SRFC). Experimental observations displayed that failure modes and cracking patterns of four groups of columns were similar, especially in pre-peak branches of load-deflection curves. Although the average ultimate axial load of columns with longitudinal GFRP rebars was obtained by 17.9% less than the average ultimate axial load of columns with longitudinal steel rebars, the average axial ductility index (DI) of them was gained by 10.2% higher than their counterpart columns. Adding steel fibers (SFs) into concrete led to the increases of 7.7% and 6.7% of the axial peak load and the DI of columns than their counterpart columns with CC. The volumetric ratio had greater efficiency on peak loads and DIs of columns than the type of transversal reinforcement. A simple analytical equation was proposed to predict the axial compressive capacity of columns by considering the axial involvement of longitudinal GFRP rebars, volumetric ratio, and steel spiral/hoop rebar. There was a good correlation between test results and predictions of the proposed equation.
Study of medical students' knowledge about the effect of energy drink and nutritional supplement consumption on pulse and blood pressure, a comparative study between males and females.
Authors: Almshhad Falak Mouhamad , alkhoja Rouaa , Hammoud Taghreed
Published: November 2024

Background
Energy drink consumption has increased dramatically in the past two decades, especially among young adolescents. Energy drinks are heavily marketed with the claim that these products provide a boost of energy to improve physical and cognitive performance, however, studies supporting these claims are limited. Dietary supplements (minerals/vitamins) also play a pivotal role in regulating blood pressure because they help in the function of macronutrients and also improve the functions of some blood pressure-lowering factors. Our aim was to study the knowledge of medical students about the effect of consuming energy drinks and dietary supplements on pulse and blood pressure.
Methods
A cross-sectional descriptive study was conducted at the Syrian Private University (Daraa Governorate) during the period 23/9/2024 to 4/10/2024. The responses of participating medical students were studied through a questionnaire distributed in paper and electronic form on the university campus.
Results
Our study included 300 students, with a percentage of males (51%) and females (49%). The largest percentage of participants were fourth-year students (43.7%). The most participating age group was “22 − 18” (79.7%). More than half of the students (60.7%) were aware of the common ingredients found in energy drinks. The majority of students (97.7%) responded that excessive consumption of energy drinks has a clear effect on pulse and blood pressure. The frequency of their consumption of energy drinks was as follows: never (40%), rarely (less than once a month) (29%), daily 16 (5.3%).
Conclusion
Energy drinks and nutritional supplements may have positive beneficial effects on academic performance or exercise and in various other activities, however, potential harmful health problems have been documented, especially among young people and adolescents. We found that the participating students had an acceptable level of awareness about the consumption of energy drinks and nutritional supplements and their potential effect on pulse and blood pressure. We also found that a small percentage of them had consumed these drinks and experienced some of their effects.
Experiment-driven Simplification of Johnson-Champoux-Allard-Lafarge Model for Fibrous Materials
Authors: Yang Tao , Eser Martin , Xiong Xiaoman , Groby Jean-Philippe , Maeder Marcus , Chang Yu-Hao , Marburg Steffen
Published: November 2024

Widely used empirical and semi-phenomenological models lack practicality due to unstable predictions or reliance on numerous parameters that are not directly measurable. This paper presents an experiment-driven method to simplify the Johnson–Champoux–Allard–Lafarge (JCAL) model by utilizing and relying on a two-microphone impedance tube only. Inverse characterization is applied to determine the non-acoustical parameters (i.e., tortuosity, airflow resistivity, viscous and thermal characteristic lengths, and static thermal permeability), and the regression method is then used to correlate porosity with these non-acoustical parameters. The simplified JCAL model is validated against the original model and experimental results for acrylic, silk, and wool fibers. The models perform well for porosities between 92% - 98% but underestimated sound absorption at 99% due to frame vibration effects. This experiment-driven simplification method reduces the JCAL model to a single parameter, porosity, enhancing efficiency, enabling faster predictions, and making the model more applicable to optimization strategies and broader applications.
Drug Evaluation Based on Multiple Criteria for Dry Eye Disease: A QSPR-Enhanced VIKOR and TOPSIS Approach Utilizing Degree-Based Topological indices
Authors: Shanmukha M C , Kirana B , Usha A , Shilpa K C
Published: November 2024

A healthy human tear film constitutes proteins, electrolytes, water, lipids, mucins, and vitamins. The lack of production of tear in the eye leads to dry eye disease(DED) which is also termed as “Sjögren’s syndrome”. Utilizing degree-based topological indices on molecular graphs reveal insights into the physicochemical properties of these drugs, such as polar surface area, polarizability, boiling point, enthalpy, molar refraction, molar volume, molecular weight and complexity are crucial in predicting their efficacy in the treatment. Various studies have been carried out in QSPR/QSAR analysis using linear regression and curvilinear regression. In this article, multiple linear regression is applied to find the correlation between seven physicochemical properties and 11 topological indices for dry eye disease drugs. For the considered properties of drugs under the study, Polarizability has shown significant results with high correlation and least RMSE (R=0.996 & RMSE=1.419) using various significant topological indices. The study is extended to evaluate and rank 22 dry eye disease drugs using Multi-Criteria Decision-Making (MCDM) techniques such as TOPSIS and VIKOR. The analysis revealed that Tacrolimus and Cyclosporine being ranked number 1 and number 22 respectively as identified by TOPSIS and VIKOR.
Anmerkungen zum Problem der Geschossgewichte für Ballisten aus Vitruvs De architectura 10,11,3
Authors: Schellenberg Hans Michael
Published: November 2024

Der Beitrag behandelt eine Reihe von Problemen in Vitruvs De architectura 10,10-16, mit besonderem Schwerpunkt auf der Übersicht über die Geschossgewichte römischer Ballisten in 10,11,3. Es wird die These vertreten, dass Vitruv einen veröffentlichten griechischen Text und nicht eine Vorlesungsmitschrift aus seiner Architektenausbildung verwendet hat. Bei diesem Text handelt es sich entweder um ein unbekanntes Werk über den Geschützbau („Belopoiika“) oder um das verlorene Werk eines Agesistratos. Bei der Übersetzung der Angaben in 10,11,3 ist Vitruv ein gravierender Umrechnungsfehler unterlaufen, indem er die Geschossgewichte von griechischen Minen in römische Pfund mit dem Faktor 1:2 statt 1:1,33 Periode übersetzt hat. Dieser Fehler wurde in der bisherigen Forschung erstmals 1975 von OTTO LENDLE erkannt, sein Beitrag aber nicht zur Kenntnis genommen.
Interaksi Sosial dalam Hubungan Muslim-Kristian di Kota Kinabalu dan Keningau, Sabah
Authors:   Centre for the Promotion of Knowledge and Language Learning, Universiti Malaysia Sabah, Sabah, Malaysia, Sintang Suraya , Kamu Assis ,   Faculty of Science and Natural Resources, Universiti Malaysia Sabah, Sabah, Malaysia, Mohd Khalli Mohd Nazmi ,   School of Humanities, Universiti Sains Malaysia, Pulau Pinang, Malaysia
Published: November 2024

The people of Sabah are known for their exceptional tolerance and ability to foster peaceful coexistence. Although socioreligious relations are deteriorating due to various factors, the situation in Sabah is quite different. This study explains how social interaction between Muslims and Christians in Sabah turns cultural understanding through family ties and neighbours/friends of other religions. A total of 234 respondents were involved in this quantitative case study in Kota Kinabalu and Keningau. Data were analysed using descriptive statistics, cross-tabulations and a chi-square test to describe the distribution of responses for variables of religions between Muslim and Christian respondents. The findings revealed that social interaction between Muslims and Christians in the studied areas was positive. Muslims and Christians practised cultural understanding during festivals and everyday activities. It might be argued that tolerance and mutual understanding across different religions are nurtured through the intertwining of contacts. The family bonding and local ties allow an unlimited connection, which helps to lessen religious gaps in understanding. This study concludes that religion should not be considered solely as a source of conflict but rather serves as a means of fostering a spirit of coexistence.
Bethe vectors and recurrence relations for twisted Yangian based models
Authors: Regelskis Vidas
Published: November 2024

We study Olshanski twisted Yangian based models, known as one-dimensional “soliton non-preserving” open spin chains, by means of algebraic Bethe Ansatz. The even case, when the bulk symmetry is \mathfrak{gl}_{2n}𝔤𝔩2n and the boundary symmetry is \mathfrak{sp}_{2n}𝔰𝔭2n or \mathfrak{so}_{2n}𝔰𝔬2n, was studied in [Ann. Henri Poincaré 20, 339 (2018)]. In the present work, we focus on the odd case, when the bulk symmetry is \mathfrak{gl}_{2n+1}𝔤𝔩2n+1 and the boundary symmetry is \mathfrak{so}_{2n+1}𝔰𝔬2n+1. We explicitly construct Bethe vectors and present a more symmetric form of the trace formula. We use the composite model approach and Y(\mathfrak{gl}_n)Y(𝔤𝔩n)-type recurrence relations to obtain recurrence relations for twisted Yangian based Bethe vectors, for both even and odd cases.
Gestão de acidentes de trabalho com robótica colaborativa
Authors: Reis Agatha Cristye Pires dos , Miranda Fernanda Martins Lopes , Batista Caio Rasini de Lima , Gonçalves Luis Felipe Pereira , Xavier Milton Domingos
Published: November 2024

Este estudo tem como objetivo analisar o impacto dos robôs colaborativos (cobots) na otimização dos processos de montagem em linhas de produção, com foco na segurança dos trabalhadores. A pesquisa avalia técnicas de gestão de cobots em linhas de produção e compara custos operacionais com e sem cobots. A metodologia adotada foi estudo de caso e pesquisa de campo. Os resultados indicam uma melhoria significativa na eficiência produtiva e na segurança dos trabalhadores, com uma redução nas lesões e nos custos operacionais. Conclui-se que a adoção de cobots é uma estratégia viável e vantajosa, com potencial de transformar a indústria de manufatura. As principais contribuições do estudo são a análise dos benefícios e desafios dos cobots e as recomendações para sua implementação eficaz.
Parity-Driven Disparities in Placenta Previa: A Comprehensive Analysis of Obstetric and Perinatal Factors
Authors: Karanfil Yaman Fikriye , Dogru Sukran , Ezveci Huriye , Akkus Fatih , Acar Ali
Published: November 2024

OBJECTIVE: This retrospective cohort study aimed to investigate the incidence of known risk factors for placenta previa in primiparous patients and assess the maternal and fetal outcomes associated with this condition in a tertiary hospital setting.
STUDY DESIGN: Data were collected from electronic medical records of pregnant women with confirmed placenta previa who were monitored at our clinic and delivered between January 2016 and June 2024. Demographic characteristics, obstetric history, and perinatal outcomes were analyzed. Statistical analysis was conducted using SPSS version 22, and comparisons between multiparous and nulliparous women were made using appropriate tests.
RESULTS: A total of 674 pregnant women were included in the study, comprising 542 multiparous and 132 nulliparous women. Significant differences were observed between the two groups in terms of anesthesia type, transfusion rates, operation duration, and hemoglobin levels. Neonatal outcomes, including APGAR scores, showed variations between multiparous and nulliparous women. However, certain outcomes such as birth weight did not differ significantly between the groups.
CONCLUSION: This study provides valuable information on maternal and neonatal outcomes associated with placenta previa in multiparous and nulliparous women. The findings highlight the complex interplay between maternal parity, anesthesia choice, and clinical outcomes and underscore the importance of tailored management approaches in this population.
Resolving the conflict between the need for electricity generation upstream and the water supply for economic development downstream in the Ca River Basin, Vietnam
Authors: Nguyen Thien Dung , Luong Chung Ngoc
Published: November 2024

Vietnam has vigorously developed hydroelectric reservoirs in main river basins with hydroelectricity reserves to produce electricity and exploit and use water resources efficiently and minimally – risks to meet socio-economic development requirements – in the downstream area. The study has shown evidence that the water level's daily mean value of the Ca River is increasingly reduced. However, the number of occurrences of the daily minimum flow was more than that of the period when it was not hydropower reservoirs and was affected by climate change. The study calculated and built five scenarios for water use in the downstream areas. These scenarios are calculated based on the water demand for domestic, industry, irrigation, and water sea level. A simulation model was developed for optimal hydropower generation of reservoir system upstream and forced to discharge downstream with five scenarios. This study's results demonstrated that four upstream reservoirs could operate with scenario 3 corresponding HNam Dan = 0.7 m. The results suggested options for the operation of hydroelectric reservoirs and simultaneously developed scenarios for taking water for agricultural irrigation downstream of the Ca River Basin with water level for irrigation drains and domestic water factories. The benefit of electricity in four reservoirs in scenario 3 reached 1,009 billion Vietnamese Dong (VND).
IPHAN E O PATRIMÔNIO NATURAL NO RIO GRANDE DO SUL
Authors: Monica Marlise Wiggers
Published: November 2024

O Instituto do Patrimônio Histórico e Artístico Nacional atua no território sul-rio-grandense desde a sua criação. No entanto, com relação às ações voltadas ao Patrimônio Natural, verificou-se uma lacuna de estudos para o contexto gaúcho. Assim, o presente artigo teve como objetivo analisar os processos de tombamento abertos pelo órgão sobre bens naturais localizados no estado do Rio Grande do Sul, buscando a identificação dos atores envolvidos nos processos, quais as justificativas para a abertura ou indeferimento destes, os valores atribuídos aos bens pelo órgão e a distribuição espaço-temporal desta atuação. Aponta-se que, até o presente momento, nenhum dos processos abertos envolvendo esta tipologia de patrimônio resultou em tombamento, 3 deles estando ainda em instrução. As solicitações de abertura se deram, predominantemente, por órgão públicos, com justificativas diversas, assim como foram diversos os valores atribuídos pelo Instituto em seus pareceres técnicos presentes nos processos. Aponta-se, ainda, lacunas espaço-temporais nas ações realizadas.
Anthocyanins as Nutraceutical for Gastric Disorders
Authors: Narwal Sangeeta , Kumari Beena , Kumar Sunil , Kumar Manish
Published: November 2024

Vegetables are a crucial part of the human diet and a significant source of
physiologically active nutraceuticals. Due to their safety, effectiveness, possible
nutritional value, and therapeutic effects, they have attracted a lot of attention in the
diet world. Customers are expressing a desire for wholesome food items made with
natural, health-improving components, which is primarily fostering the expansion of
companies involved in the anthocyanin market. Anthocyanin helps to prevent skin
damage and aging, creating potential for suppliers in the nutraceutical and cosmetics
industries. Common phytonutrients include lycopene from tomatoes and carotenoids
from carrots, among others. There are numerous metabolic and degenerative diseases,
most of which are caused by nutritional deficiencies, now increasingly affecting the
population. Nutraceuticals are any ingredient used as food or as a component of food
that has normal nutritional value and offers health advantages, such as illness
prevention or health promotion.The practice of relieving gastric discomfort with particular foods has long recognized
the impact of nutrition on gut health, and more recently, a link between certain diets
and decreased incidences of a number of gastrointestinal disorders has been
established.For the purpose of human disease prevention, the focus of this analysis is on grape seed
nutraceuticals' most recent developments and prospective applications in the future.
Advance Technology and its Impact: A Sustainable Development Goal in the Maritime Sector
Authors: Jegadeeswari G. , Kirubadurai B.
Published: November 2024

Nowadays, shipping firms have discovered faster connectivity for their
ships, not only for the captains but also for the shipping industries. Most of the ships
have developed floating offices in their ships that provide crews and captains with
email, secure Internet connections, route planners, virtual networks, and a variety of
other systems. However, this is the correct time for shipping firms to invest in
emerging smart technology that can increase the ship's performance and streamline and
even reduce the running costs of the ships. This is possible only with emerging
technologies like artificial intelligence and machine learning. Smart computers can
store enormous volumes of data and perform even more quickly than humans. The
growth of smart technologies like machine learning, artificial intelligence, big data, and
data science in collaboration with the shipping industry gives a significant benefit to
the ship owners. The greater usage of AI, big data, and ML gives a greater investment.
Machine learning algorithms can accommodate data that covers almost the entire
history of a vessel's life. The aim of this paper is to provide a technological overview of
smart technologies in the shipping industry.
Enhancing Cyber Security: A Comparative Study of Artificial Neural Networks (ANN) and Machine Learning for Improved Network Vulnerability Detection
Authors: Tiwari Sadhana , Kumar Nitendra , Joshi Kapil , Kumar Santosh
Published: November 2024

As we rely more on the internet in our daily lives, network attacks pose a
severe threat to the safety of computer systems and networks. Cybercriminals utilize a
variety of methods to access sensitive data without authorization by taking advantage
of network flaws. Firewalls and intrusion detection systems, which are common
security measures, have not been found to be effective in preventing network attacks.
The connection between network vulnerability detection and realizing sustainable
development goals lies in the broader impact of cybersecurity on the economic, social,
and environmental aspects of sustainable development. Deep learning, a kind of machine learning that makes use of neural networks with
numerous layers to understand complicated patterns in data, has attracted the attention
of researchers and practitioners in an effort to combat the sophistication of cyberattacks
that are becoming more sophisticated. Deep learning has demonstrated potential in
identifying network attacks due to its ability to automatically extract features from
unprocessed data, enhancing its ability to identify previously unknown assaults.These patterns may include unusually high or low levels of network traffic, adjustments
to communication patterns, and other aberrant behavior that could be a sign of an
impending attack. The first step in using a deep learning algorithm to detect network
attacks is to collect and pre-process the data. In this analysis, we used the NSL-KDD
dataset, a freely accessible dataset that includes information on both regular and attack
traffic. We can start training our deep learning model once we get the data.Large amounts of data are fed into the algorithm during training, and the neural
network's parameters are changed to reduce the discrepancy between expected and
actual results. Different deep learning architectures, such as convolutional neural
networks (CNNs), are available to us. We can use the model to categorize fresh instances of network traffic as either benign
or harmful after it has been trained. The model's performance can then be assessed
using measures like recall, accuracy, and precision. Our test findings demonstrate that
the suggested deep learning technique works better at identifying network assaults than
conventional machine learning algorithms. Deep learning algorithms are better
equipped to manage the complexity of network traffic data and extract useful
information from it.The security of computer systems and networks is seriously threatened by network
attacks, and conventional security measures have not been successful in thwarting
them. Our paper offers a thorough manual on how to detect network assaults using
deep learning, which has shown potential in this area.
Ml and AI Approach to the Global Healthcare Ecosystem
Authors: Kumar Pandey Gaurav , Sumit Srivastava
Published: November 2024

The global healthcare ecosystem is being changed thanks to substantial
advancements in the fields of machine learning (ML) and artificial intelligence (AI).
The potential of ML and AI to improve patient care, increase diagnostic accuracy,
optimize treatment regimens, and reduce administrative procedures is covered in this
chapter as we investigate the many methods and uses of ML and AI within the
healthcare industry. ML and AI have the potential to change healthcare delivery,
resource allocation, and disease prevention through the use of large-scale data analysis,
predictive modeling, and intelligent decision-making systems. This chapter presents a
thorough review of the existing ML and AI methods used in healthcare, emphasizing
their advantages, difficulties, and potential future applications. The global healthcare
ecosystem might be changed by the introduction of ML and AI, resulting in improved
patient outcomes. ML and AI can help expand access to healthcare by enabling remote
diagnosis and telemedicine, especially in underserved areas. This aligns with the goal
of ensuring healthy lives and promoting well-being for all ages.
A Critical Investigation into the Impact of Big Data in the Food Supply Chain for Realizing Sustainable Development Goals in Emerging Economies
Authors: Elkady Ghada , Sayed Ahmed , Mukherjee Rupam , Lavanya D. , Banerjee Dyuti , Aarif Mohd
Published: November 2024

In light of the present circumstances, corporate executives, government
officials, and academics may now place a higher priority on the collection and analysis
of crucial data as a potent instrument for solving the issues of managing the
contemporary food supply chain. As food and beverage (F&B) companies place a
greater emphasis on collecting, processing, and analyzing relevant data from a variety
of sources throughout their respective food systems, data management has become an
invaluable resource in modern food supply chains (FSCs). This is because modern
FSCs are designed to be more efficient than traditional supply chains. In this context,
the phrase “big data” (BD) has only very recently begun to be used to refer to huge
quantities of heterogeneous and geographically dispersed data assets that have fast rates
of change, a wide variety of sizes, and high volumes of information. Recent research
has stated that implementing BD in FSCs might result in a yearly gain in value that
ranges from USD 120 billion to USD 150 billion. The current study is focused on
analyzing the impact of big data in the food supply chain for realizing sustainable
development goals in emerging economies. The researcher intends to collect data from
primary and secondary sources. This paper focuses on understanding the conceptual
framework that incorporates the relationship between FSC performance and BD
applications.
Fintech Revolution: Role in Achieving the Sustainable Development Goals
Authors: Jain Swati
Published: November 2024

Every nation in the world is striving to achieve the Sustainable Development
Goals (SDGs). In keeping with this, a sound global financial system is now required to
fulfill its mandate to encourage the mobilization of private capital for the achievement
of sustainable development and consistent economic growth. The fintech revolution has
significantly altered the financial industry and improved financial inclusion in the
nation by offering digital financial services. Fintech has contributed positively to the
SDGs by providing increased access to funds and financial services, which, in turn, has
resulted in improvised saving opportunities for a large group of people. A wide range
of application developments, comprising blockchain, IoT (the Internet of Things), AI
(artificial intelligence), big data, and mobile platforms, have recently been part of
digital transformation and technological advancement, particularly in the finance
sector. These application developments promised to improve performance in the
financial sector. It becomes clear that financial applications for digital technology can
overcome significant funding challenges for inclusive and sustainable growth. In this
scenario, the chapter provides an overview and in-depth examination of the most recent
advancements in financial technologies (Fintech) that support the SDGs. It also reviews
the significance and effectiveness of fintech in achieving sustainable development
goals like education, health, equality, etc.
Atomic Force Microscopy and its Applications in Secondary Batteries
Authors: Yue Jili , Xie Chaoxiang , Jia Xingze , Li Yixiao , Qian Mengmeng , Song Tinglu , Li Chunli
Published: November 2024

Atomic Force microscope (AFM) not only reveals the surface morphology
of substances at the nanoscale and molecular level but also enables the measurement of
extremely weak forces, which allows for the study of weak interactions between
molecules. Moreover, the AFM possesses certain spatial resolution ability. As a
valuable tool in secondary battery research, AFM could reveal the surface microscopic
morphology of the electrode in real time through the interaction between atoms from
the tip and the electrode surface. It offers nanoscale surface information of the
electrode from both chemical and physical perspectives, thus establishing essential
guidance for further modification of electrode materials and electrolytes. This chapter
reviews the state-of-art application progress of AFM in the study of secondary batteries,
including cathode materials, anode materials and solid electrolyte interface.
Neutron Diffraction and its Applications in Secondary Batteries
Authors: Li Chunli , Li Hao , Yang Guang , Li Huiyu , Feng Yongjun
Published: November 2024

As a well-established method, neutron diffraction has been widely adopted
to characterize the atomic structure of materials. One of the most distinct advantages of
neutron diffraction refers to its high sensitivity to most light elements (e.g., lithium),
which could distinguish isotopes and identify adjacent elements. Moreover, magnetic
structure may also be probed. More importantly, the penetration depth of neutrons is
thick enough for battery detection with centimeter scales. For secondary batteries,
neutron diffraction has shown advantages in determining phase structure and ion
diffusion in active materials, which are critical issues in battery studies due to the
dominant charge and energy transport functions of active ions. Moreover, neutron
diffraction also exhibits superior performance in revealing lithium coordinates in lattice
and related occupations, as well as visualizing the diffusion path of lithium atoms,
which may not be completed via other characterizations. This chapter aims to
comprehensively review the working mechanism and main research progress of
neutron diffraction in secondary batteries. The essential background, working principle
and practical applications of neutron diffraction on the cathode, anode, solid-state
electrolytes and full batteries, as well as its data analysis, are introduced and discussed.
An Ayurveda approach for managing Geriatric Sleep Disturbances: A Comprehensive Review
Authors: Pooja Lekhak  , Ram Kishor Joshi
Published: November 2024

Background: Ageing is defined as the progressive deterioration in structure and function of the body organs. In Ayurveda literature ageing (Jara) has been given importance since Vedic period as the natural, progressive phenomenon. In Ayurveda, Sleep (Nidra) is one of the Upasthambha (Sub-Pillar) which is essential for overall health. Ageing causes multiple changes in sleep patterns, and various diseases can further disrupt sleep. Ayurveda describes the physiological increase of Vata Dosha in old age which causes Vata Prakopa in body leading to degeneration of Dhatu (Dhatukshaya) which alter the sleep patterns. Aim: The paper aims to review common sleep disturbances seen in ageing and their prevention and management through Ayurveda. The study discusses various patterns of sleep during ageing and their possible management through Ayurveda. Primary sleep disorders common in the geriatric population such as; insomnia, obstructive sleep apnea, restless leg syndrome, circadian rhythm sleep-wake disorder was discussed and their management through Ayurveda was reviewed.  The selection of Ayurvedic treatment and procedures which nourishes Dhatu and pacify Vata Dosha are effective in managing sleep disturbances. Result: The study finds that Vatashamak Chikitsa, regulation of Agni, Dincharya, Ritucharya, Vegdharan, use of Rasayana, Panchakarma, Yogasana are beneficial for the management of sleep disturbances. Conclusion: Sleep disturbances are common in ageing and can be managed efficiently through Ayurveda.
Development of therapeutic antibody enhancers through glycan cleavage in refractory cancers
Authors: Nakamura Masao
Published: November 2024

Pancreatic cancer has a poor prognosis and the number of deaths from this disease is increasing dramatically. There are currently no effective treatments for pancreatic cancer, and often the anticancer drugs that are used cannot be continued over the long term due to their serious side
effects. Accordingly, a new treatment approach is required. In this context, Dr Masao Nakamura is developing new drugs targeting glycans that play roles in the pathogenesis of cancers. In particular, he is focusing on chondroitin sulfate (CS), a glycosaminoglycan involved in the mechanisms
of cancer invasion and metastasis. Nakamura has sought to develop drugs that control CS subtypes secreted by inflammatory cells, with the goal of developing enhancers that can increase the efficacy of anticancer drugs and therapeutic antibodies by modifying cell surface glycans that increase
during cancer progression. Currently, with the exception of approximately 1% of cases with specific fusion genes, there are no therapeutic antibodies considered to be effective against most pancreatic cancers. By developing antibody enhancers that can overcome the cell resistance that prevents
therapeutic antibodies from accessing their target molecules, Nakamura is striving to maximize the high specificity and affinity of these antibodies. In addition, he and his team are developing analytical methods using new approaches that will facilitate a comprehensive analysis of the changes
in glycans and proteins necessary for the development of therapeutic antibody enhancers.
Mechanisms of inducing cancer extracellular vesicles secretion and transfer for cancer malignancy
Authors: Nishida-Aoki Nao
Published: November 2024

Assistant Professor Nao Nishida-Aoki, Waseda Institute for Advanced Study (WIAS), is investigating the role of extracellular vesicles (EVs) in physiological tumour tissue environments in order to improve cancer treatments. Communication tools like EVs are used by cancer cells to educate
the surrounding cells and make them supportive to cancer cell growth and metastasis. Nishida-Aoki’s research focuses on understanding EV-mediated communication within the complex tissue environment of patient tumours, where they are secreted in densely packed cells and non-cellular components
forming a 3D structure. She has developed ex vivo brain and lung metastatic models using organotypic tissue slice culture (TSC). This enables her to observe EV transfer within tissue architecture in real time, identify target cells affected by cancer EVs and determine how they are distributed.
Nishida-Aoki is also exploring the mechanisms of inducible secretion of cancer extracellular vesicles upon cell recognition and contribution to cancer malignancy. Her research is investigating the molecular mechanics by which cancer cells regulate and the extent of interactions with neighbouring
cells using EVs at metastatic initiation. She has hypothesised that cancer cells should increase communication frequency using EVs when they meet other types of cells in a new environment to benefit themselves. By elucidating the molecular basis of cancer-normal cell interactions mediated
by EVs, Nishida-Aoki aims to develop novel cancer therapeutic strategies.
Research examining microcirculatory dynamics in diseases that cause oedema using skin blood flow and skin reperfusion pressure
Authors: Kogo Haruki
Published: November 2024

Oedema is a build-up of fluid in the tissues in the body. It can develop due to diseases including heart failure and kidney disease. It usually occurs in the limbs, where it can cause discomfort, joint stiffness or pain. However, fluid can also accumulate in the brain, which is called
cerebral oedema or in the blood vessels serving the lungs, called pulmonary oedema. A type of oedema called pitting oedema is more prevalent than the other type, non-pitting oedema. In order to treat the condition, the underlying cause must be resolved. Treatment can involve diuretics to help
the release of fluids, positioning to encourage draining or compression to force fluids away. Researchers at the Faculty of Rehabilitation at Reiwa Health Sciences University led by Dr Haruki Kogo are examining the microcirculatory dynamics of diseases that cause oedema using skin blood flow
and skin reperfusion pressure. They have developed a device that can diagnose oedema and confirmed the reproducibility and validity of the device. They have registered the trademark and plan to commercialise the device for use in clinical and research settings. The device is able to measure
the depth of the surface imprint in pitting oedema patients, simplifying the evaluation of pitting oedema, which will be extremely useful in clinical settings. The researchers tested the device across two hospitals, comparing the results with results obtained from the popular circumference
measurement method and ultrasound imaging measurement. The team found that there was a strong correlation between measurements from the device and the tissue thickness measurement derived from the ultrasound. This confirmed that the device is a reliable gauge for the measurement of oedema.