bluuebunny/crossref_metadata_2025 huggingface dataset:
The short-term and long-term prognosis of discharged COVID-19 patients in Guangdong during the first wave of pandemic
Authors: Li Pei-hong , Xu Hui , Xie Cheng-yuan , Ji Zhong-liang , Hu Bei , Deng Yi-yu , Jiang Wen-qiang , Li Xin , Fang Ming
Published: September 2023

BACKGROUD: People are increasingly concerned about the rehabilitation and sustained sequelae of Coronavirus Disease 2019 (COVID-19) infection. Our study aimed to investigate the sequelae of patients’ psychological and physical condition and its related risk factors in the early and late stages.
METHODS: This longitudinal study was conducted on 281 COVID-19 patients discharged from the first wave of pandemic. Patients were followed up for 12 months with constantly evaluation of psychological and physical condition, the follow-up was divided into early and late stage to observe the development of psychological and physical condition, data were collected and analyzed to find out its risk factors.
RESULTS: COVID-19 survivors had psychological and physical sequelae in the early and late stages, such as depression, anxiety, Post-Traumatic Stress Disorder (PTSD), sleep disorder, and functional damage in heart, liver, kidney and lung. The incidence of sequelae in the late stage decreased comparing to the early stage, the proportion of depression decreased by 10.0%, anxiety by 3.6%, PTSD by 2.9%, liver abnormality by 13.6%, cardiac by 2.2%, renal by 9.6%, and pulmonary by 27.4%. In two stages, gender, age, severity of COVID-19, hospitalization time and various comorbidities were significantly associated with psychological or physical sequelae.
CONCLUSION: We noticed that psychological and physical sequelae occurred to COVID-19 survivors in short and long stages, and these would gradually decrease as time went on. Male gender, age >50 years old, severe clinical condition, longer hospitalization time and comorbidity history were risk factors that significantly affected the rehabilitation of COVID-19 patients.
Comparison of two protocols for the management of post-partum hemorrhage. Retrospective study in two Belgian centers.
Authors: Cordivani Jonathan , Lamy Clotilde , Bouvain Michel , Riera Catherine
Published: September 2023

Post-partum hemorrhage complicates 3 to 5% of deliveries and is the leading cause of morbidity and mortality in the world. Management is often inadequate despite the existence of protocols.
Objective: To compare the results of the management of women with post-partum hemorrhage due to uterine atony in two Belgian centers, one using Sulprostone and the other Misoprostol and Carboprost, in terms of use of 4th level techniques and total blood loss.
Method: This is a retrospective study including patients with post-partum hemorrhage due to uterine atony after vaginal delivery requiring second-line uterotonic administration. The study period was from 1 January 2017 to 31 December 2022. Characteristics of the two populations, total losses, hemoglobin delta and management steps were collected.
Results: In both institutions, patients had similar characteristics. The use of oxytocin, revision of the genital tract, administration of tranexamic acid, and use of fourth-line techniques were similar in both centers. Total losses and hemoglobin delta did not show any significant difference between the two protocols.
Conclusion: The results of the two centers for post-partum hemorrhage due to uterine atony are similar despite the use of two different prostaglandins.
Effect of dexamethasone implant between vitrectomized and non-vitrectomized eyes with diabetic macular edema
Authors: Chen I-Hsien , Tu Hung-Ping , Chen Yi-Hsuan , Lin Hsien-Chung
Published: September 2023

Background
To compare the 3-month foveal thickness, sub-foveal choroidal thickness (SFCT) change and best-corrected visual acuity (BCVA) LogMAR response after a sustained release dexamethasone (DEX) intravitreal implant (Ozurdex®) between non-vitrectomized and vitrectomized eyes with diabetic macular edema (DME).
Methods
Retrospective, comparative, and consecutive review of the medical records of 66 patients with decreased visual acuity, due to refractory DME, who underwent a single and the first injection of Ozurdex® between May 2016 and December 2020 at the teaching hospital of Kaohsiung Medical University. Then, patients were divided into 2 groups: 41 eyes who had undergone standard pars plana vitrectomy (vitrectomized group), and 25 eyes that were not operated on (non-vitrectomized group). Measured outcomes were the change of BCVA LogMAR, foveal thickness, and SFCT from baseline to months 1, 2 and 3.
Results
Forty-one of 66 eyes had prior vitrectomy (62%). Statistically significant improvement in foveal thickness and SFCT was seen at 1, 2 and 3 months with respect to baseline data, after treatment with a DEX implant in either group (P < 0.0001). There was statistically significant difference in SFCT between non-vitrectomized and vitrectomized eyes at month 3 and the trend from baseline to month 3 (P < 0.0001).
Conclusion
In this study, the clinical findings were similar between non-vitrectomized and vitrectomized eyes. Intravitreal treatment with an Ozurdex® implant safely reduced DME both in foveal and SFCT in both groups. However, there was statistically significant difference in the trend of SFCT between non-vitrectomized and vitrectomized eyes from baseline to month 3. A DEX implant could be cleared more rapidly in vitrectomized eyes by early detection from SFCT change.
Changes in anal cancer treatment patterns and the construction of a prognostic nomogram
Authors: Chen Junhao , Chi Pan
Published: September 2023

Purpose
The authors sought to quantify changes in treatment patterns for patients with anal cancer in the United States over the past decade, to compare the impact of treatment patterns on the prognosis of different histological subtypes, and to develop a predictive nomogram.
METHODS
Using the SEER database, 15,566 anal cancer patients treated from 2007 to 2018 were analyzed. The paper compares changes in treatment patterns of squamous and adenocarcinomas and survival rates of patients with local tumor stages in different treatment modalities for three time periods: 2007–2010, 2011–2014, and 2015–2018. Univariate and multivariate Cox proportional risk regression analyses were used to identify prognostic factors affecting anal cancer, developed a new predictive model
RESULTS
Adenocarcinoma and squamous carcinoma treatment patterns differed significantly, with an average of 62.3% of patients with anal squamous carcinoma treated with chemothrapy or /and radiotherapy alone, which increased significantly from 59.9% from 2007 to 2010 to 65.5% from 2015 to 2018 P < 0.001. Surgery alone decreased from 13% from 2007 to 2010 to 10.3% from 2015 to 2018 (P < 0.001). The average percentage of anal adenocarcinoma patients treated with Chemotherapy or /and radiotherapy alone was 30.5%., increased significantly from 25.5% from 2007 to 2010 to 40.1% from 2015 to 2018 P < 0.001. Age, gender, marital status, tumour size, stage, grading, histological subtype and treatment modality were significant independent risk factors in patients with anal cancer (p < 0.05). The C-index of the prediction model is 0.735.
CONCLUSIONS
In anal adenocarcinoma, the 5-year survival rate for Chemotherapy or /and radiotherapy alone is much lower than that of patients undergoing surgery, but there has been a further rise in the treatment modality of radiotherapy alone, which may lead to an increase in the mortality rate of anal cancer. Nomograms can help clinicians predict patient prognosis.
The homing behavior of red deer: A GPS assisted translocation experiment
Authors: Silovský Václav , Landler Lukas , Faltusová Monika , Börger Luca , Burda Hynek , Holton Mark , Lagner Ondřej , Malkemper E. Pascal , Olejarz Astrid , Spießberger Magdalena , Váchal Adam , Ježek Miloš
Published: September 2023

BackgroundMany animals move back to their home area (i.e., ‘homing’) after translocation to sites further away. Such translocations have traditionally been used in behavioral ecology to understand orientation and migration behavior of animals. The movement itself can then be followed by marking and recapturing of animals or by tracking, e.g., using GPS systems. Most detailed studies investigating this behavior were conducted with smaller vertebrates (e.g., birds, amphibians and mice), whereas information on larger mammals, such as red deer, is sparse.MethodsWe conducted GPS assisted translocation experiments with red deer at two sites in the Czech Republic. Individuals were translocated over a distance of about 11 km and their home journey was tracked. We used circular statistics to test for significant homeward orientation at 100, 500, 1000 and 5000 m distance to the release site. In addition, we applied a Lavielle trajectory segmentation, identifying different phases of the homing behavior.ResultsThirty-one out of 35 translocations resulted in successful homing with a median time of 4.75 days (range: 1.23 to 100 days). Animals significantly oriented towards home immediately after release and again when they came closer to home; however, they did not show significant orientation at the distances in between. We were able to identify three homing phases, an initial ‘exploratory phase’, followed by a ‘homing phase’ which sometimes was again followed by an ‘arrival phase’. The ‘homing phase’ was characterized by the straightest paths and fastest movements. However, variation between translocation events was considerable.ConclusionsWe show good homing abilities of red deer after translocation. Our results show the feasibility to conduct experiments with environmental manipulations (e.g., to impede the use of sensory cues) close to the release site. The homing behavior of red deer is comparable to other species and might represent general homing behavior patterns in animals. Follow up studies should further dissect and investigate the drivers of the individual variations we observed, and try to identify the sensory cues used during homing.
On algorithmic time and daily contingencies in the gig economy: The lived work of food delivery service
Authors: Xiao Luyao , Fitzgerald Richard , Sandel Todd , Kim Younhee , Abi-Samara Raquel , Moutinho Ricardo
Published: September 2023

This study takes a praxiological perspective (drawing on ethnomethodology, conversation analysis and membership categorization analysis) to examine the working practices of food delivery service workers in China. The analysis explicates how delivery drivers deal with daily algorithm-generated information and contingencies through the production and mobilization of tacitly assumed conventions to maintain their work-flow. Three intertwined phenomena are identified: (1) coordinating pick up and deliveries involves a high degree of practical interactional work; (2) the job is practice oriented around routine contingencies of time, travel, and waiting, and (3), the job is collaborative and organized through a moral order that involves the mobilization of resources which operate alongside, but separate from the technology. The study shows how a detailed analysis of the lived work of food delivery drivers provides a powerful tool to highlight and examine what is often hidden (and lost) in studies of the gig economy.
Role of structural and diffusion MRI in differentiating between low- and high-grade meningiomas according to the 2021 WHO classification
Authors: PELLETIER Jean-Baptiste , DAL-COL Pierre , GRANGE Sylvain , FOREST Fabien , VASSAL François
Published: September 2023

Background: To determine predictive factors for high-grade (WHO grades 2–3) meningiomas (HGMs), derivable from preoperative MRI.
Methods: We retrospectively reviewed 204 meningiomas (58 HGMs) diagnosed according to the 2021 WHO classification, and assessed a total of 14 variables: location, extradural involvement, shape/sphericity, disruption of the arachnoid layer/tumor-brain interface (TBI), calcifications, heterogeneity on gadolinium-enhanced T1-weighted images (T1Gd), capsular enhancement on T1Gd, necrosis, signal intensity on T2-weighted images, cyst formation, tumor volume (Vt), edema volume (Ve), edema/tumor volume ratio (Vr), and apparent diffusion coefficient (ADC) value.
Results: Convexity location (p = 0.008), irregular shape (p = 0.014), unclear TBI (p = 0.008), necrosis (p < 0.001), larger Vt (p = 0.001) and Ve (p = 0.003), and lower ADC value (p < 0.001) were associated with HGMs. Vt > 21.6 cm3 (p = 0.001), Ve > 7.28 cm3 (p = 0.003), ADC mean value < 0.81 × 10-3 cm2/s (p = 0.001) and normalized ADC value < 1.05 (p = 0.001) were determined as the as the optimal cut-offs for the prediction of HGMs. Multivariate logistic regression analysis confirmed the presence of necrosis (p = 0.024) and larger Ve (p = 0.025) as independent predictors for HGMs. In ROC analyses, the combination of necrosis, edema volume, ADC mean cut-off analysis improved the AUC (0.847).
Conclusion: We found several qualitative and quantitative MRI markers associated with the risk of HGMs, which combination can help in treatment decision-making.
Trial registration number
N° IRB00011687; IRB #1: 2022/04 retrospectively registered
MSTCNN: A Transformer with multi-scale convolutional neural network for multi-focus image fusion
Authors: Zhai Hao , Chen Yun , Zeng Zhi , Luo Nannan , Li Qingyu
Published: September 2023

In the field of multi-focus image fusion, how to efficiently and accurately extract the focus region in the source image is a challenging problem. The existing multi-focus image fusion methods have some problems, such as color distortion, artifacts at the boundary and high computational cost. Therefore, a Multi-Scale Convolu-tional Neural Network Transformer (MSTMCNN) for multi-focus image fusion is proposed in this paper. MSTMCNN is mainly composed of three parts: feature extraction, feature fusion and feature reconstruction. Firstly, down-sampling is used in the feature extraction stage to increase the training speed of the model. feature extraction mainly consists of structure 1 and structure 2. In structure 1, we have designed three different scales of convolution kernels to extract the features of source images. Meanwhile, in order to further improve the performance of the network, the initial module is introduced, which is of great help to the reconstruction of the focus map. Since CNN can extract local information and Transformer can obtain global features, the combination of the two enhances feature extraction capabilities and retains more effective information. In structure 2, we have adopted a network structure called CTR that combines CNN and Transformer. CTR solves the problem of image size limitation and calculation in multi-cluster image fusion with traditional Vision Transformer (Vit). In the feature reconstruction stage, we perform up-sampling for image recovery. Finally, the experimental results of the method are compared with 15 advanced fusion algorithms on Lytro, MFFW2 and MFI-WHU respectively, and the method achieves better fusion performance.
Breaking the Cycle of Stunted Growth: Identifying Key Factors in Indonesian Children Using Indonesia Family Survey (IFLS) 2007 and 2014 Data
Authors: Siramaneerat Issara , Astutik Erni , Agushybana Farid , Bhumkittipich Pimnapat , Lamprom Wanjai
Published: September 2023

Background
Malnutrition is a significant global health issue, with stunting being a primary cause of death in children under the age of 5 worldwide. Despite efforts to address malnutrition and hunger, many people in underdeveloped countries continue to face challenges related to food security. The direct cause of stunting is inadequate food intake, but underlying causes include lack of education, poverty, and social and cultural reasons.
Methods
This study aimed to analyze the factors associated with stunting among children aged 24–60 months in Indonesia using cross-sectional data from the Indonesian Family Life Survey (IFLS) 2 and 5. Child factors, maternal factors, father factors, household factors, and community factors were analyzed. The stunting variable was measured using the Z-Score calculation of the child's height and age based on the child's gender. Descriptive and inferential analyses were used to analyze the data.
Results
The study found that child age, low birth weight, incomplete antenatal care, poor health sanitation, provision of complementary food, and absence of the Mother and Child Health Book or Health Card were significantly associated with stunting. The findings suggest that targeted interventions are needed to improve maternal nutrition, promote exclusive breastfeeding, enhance access to healthcare and sanitation, and increase household expenditure on animal-source and non-grain foods to reduce the prevalence of stunting among Indonesian children.
Conclusions
The study highlights the importance of further research to identify additional factors contributing to stunting and develop effective interventions to address this crucial public health issue. It also emphasizes the need for more comprehensive solutions to address food-related problems in underdeveloped countries, including improved access to nutritious foods, better healthcare, and increased education on healthy eating habits. Addressing malnutrition and stunting in children is crucial for improving child health outcomes and reducing the risk of non-communicable diseases later in life.
On the tree models of mildly dissipative maps
Authors: Correa Javier , Flores Elizabeth
Published: September 2023

We study the tree models of mildly dissipative diffeomorphisms on the disk D. This models, are one dimensional dynamical systems with the ergodic aperiodic data as well as some properties of the original dynamics. Our focus in this work is their quality as dynamical invariant in both the topological and the ergodic sense.
Mathematics Subject Classification (2020) MSC 37C15 · MSC 37D25 · MSC 37D45 · MSC 37E25
Facilitators and Barriers to Vaccination Uptake in Pregnancy: A Qualitative Systematic Review
Authors: Razai Mohammad S , Mansour Rania , Ravindran Kavi , Freeman Samuel , Mason-Apps Charlotte , Morris Joan , Majeed Azeem , Ussher Michael , Hargreaves Sally , Oakeshott Pippa
Published: September 2023

Introduction
Vaccination during pregnancy protects both the mother and the foetus from vaccine-preventable diseases. However, uptake of the recommended vaccines (influenza, pertussis, COVID-19) by pregnant women remains low in Europe and USA. Understanding the reasons for this is crucial to inform strategies to increase vaccination rates in pregnant women. This qualitative systematic review aimed to identify the barriers and facilitators to vaccination against influenza, pertussis and COVID-19 during pregnancy and identify strategies to increase vaccination during pregnancy.
Methods
We conducted a comprehensive search of electronic databases, including Medline, PsycINFO, CINAHL, Web of Science, WHO database, Embase and grey literature to identify qualitative studies that explored barriers and facilitators to vaccine uptake among pregnant women (PROSPERO CRD42023399488). The search was limited to studies published between 2012 and 2022 conducted in high-income countries with established vaccination programmes during pregnancy. Studies were thematically analysed and underwent quality assessment using the Joanna Briggs Institute validated critical appraisal tool for qualitative research.
Results
Out of 2681 articles screened, 28 studies (n = 1431 participants) were eligible for inclusion. Five overarching themes emerged relating to personal, provider and systemic factors. Barriers to vaccine uptake included concerns about vaccine safety efficacy, lack of information about the vaccine, fear of adverse effects on the foetus and low perception of disease severity. Facilitators included recommendations from trusted healthcare providers, easy access to vaccination, clear and consistent communication on the benefits of vaccination, and positive social influences from family and friends. Strategies for increasing vaccination uptake included strong and proactive vaccine recommendations by trusted healthcare professionals, provision of vaccines during routine antenatal care, clear and consistent communication about vaccines and addressing pregnant women’s concerns.
Conclusion
This review highlights the need for interventions that address the identified barriers to vaccine uptake among pregnant women. Provider recommendation can play a significant role in promoting vaccine uptake, as can clear and consistent risk and benefit communication and convenient access to vaccination. Addressing concerns about vaccine safety and providing accurate information about vaccines is also important.
Cultivar and Fungicide Evaluations for Cercospora Leaf Spot Control in Organic and Conventional Table Beet Production
Authors: Dixon Liam , Goldman Irwin L.
Published: September 2023

Cercospora leaf spot (CLS), caused by the fungal pathogen Cercospora beticola, is the most destructive foliar disease of table beet (Beta vulgaris ssp. vulgaris) in Wisconsin, USA, and globally. Under conducive conditions, symptomatic lesions on the leaf expand and coalesce forming large necrotic areas that can ultimately lead to complete defoliation. This damage reduces productivity and threatens the ability to mechanically harvest. CLS damage also detracts from the visual appeal of fresh market bunched beets to such an extent that growers risk buyer rejection if CLS severity is observed to be greater than 5%. Fungicide use for CLS control is threatened by the emergence of resistant C. beticola strains, and the application of host resistance is constrained by limited knowledge of cultivar reaction to CLS in table beet. This study aimed to address the knowledge gaps of fungicide efficacy and cultivar reaction by conducting replicated field trials in multiple table beet growing environments across Wisconsin. Broad variation for resistance to CLS was observed among the 10 included cultivars. The mean area under disease progress curve (AUDPC) across environments for the most susceptible cultivar was 267% greater than the most resistant cultivar. Spearman correlations between environments for mean cultivar AUDPC value ranged from 0.71 to 0.99, revealing consistent cultivar CLS reactions across environments. Although susceptible cultivars surpassed 5% severity in all environments, the resistant cultivars remained below this threshold in six of the 10 environments. By comparison with resistant sugar beet (Beta vulgaris ssp. vulgaris) cultivars, however, all tested table beets appeared susceptible to CLS, highlighting the potential for a CLS breeding effort in table beet. Neither of the evaluated Organic Materials Review Institute–listed treatments were effective at limiting CLS disease progress, whereas both tested conventional fungicides significantly reduced disease severity over the nontreated plots. These findings may provide helpful guidance to table beet growers affected by CLS in Wisconsin and beyond.
The World of Cave Microbiomes: Biodiversity, Ecological Interactions, Chemistry, and the Multi-Omics Integration
Authors: Kato Natália , Arini Gabriel , Silva Ricardo Roberto , Bichuette Maria Elina , Bitencourt José Augusto , Lopes Norberto
Published: September 2023

Caves are ecosystems and natural heritage sites with magnificent biological diversity, from microorganisms to animals, despite their selective environment for development and survival. Until now, studies on cave microbiomes have included taxonomic classification through metagenomics analysis, demonstrating microbiological heterogeneity mainly composed of prokaryotic organisms or bacteria species. The generally oligotrophic environment, with limited energy input and dark zone, is the main feature differentiating the cave microorganisms from the other microbiomes and resulting in a unique habitat, which has unexplored regard to biology and chemical diversity. Multi-omics integration is a key strategy for scanning and integrating the macro and micromolecular universe, providing a better comprehension of processes and answers of the microbiome in natural cavities. Here, we highlight the strategies employed to study the cave microbiome, from the microbial genomes to their ability to secondary metabolites production, and also the multi-omics integration to explore the chemical and biological diversity in caves.
Biochar and mycorrhiza enhance soil carbon storage and reduce CO2 emissions in wastewater-irrigated turf
Authors: Sahin Ustun , Cakmakci Talip , Yerli Caner
Published: September 2023

Irrigation with recycled wastewater can reduce freshwater demand and improve soil fertility, but it can also increase CO2 emissions from soil and contribute to global warming. This study investigated whether biochar and mycorrhiza can reduce CO2 emissions and enhance soil quality in wastewater-irrigated turf. A factorial experiment was conducted with four levels of biochar (0, 0.5, 1, and 1.5%), two mycorrhiza (with and without), and two types of irrigation water (freshwater and recycled wastewater). Soil CO2 and H2O emissions, moisture and temperature, and chemical and physical properties were measured for 3 months. Biochar and mycorrhiza treatments significantly reduced CO2 emissions by 19.4–45.0% compared to the control treatment. The combination of biochar at a 1.5% level with mycorrhiza had the highest emission-reducing effect. Biochar and mycorrhiza treatments also reduced H2O emissions by 8.1–14.6%, increased soil organic matter, carbon, and total nitrogen, regulated soil EC and pH, and improved soil porosity and aggregate stability. The results suggest that biochar and mycorrhiza can be effective strategies to mitigate CO2 emissions and improve soil quality in wastewater irrigation. The combination of biochar with mycorrhiza can have synergistic benefits for soil carbon storage and conservation.
Synthesis and performance of ultrafiltration membranes incorporated with different oxide nanomaterials: experiments and modeling
Authors: Bin Darwish Nawaf , AlAlawi Abdulrahman , AlRomaih Hamad , Alotaibi Nasser , AlEid Musaad
Published: September 2023

In membrane filtration technology, membrane fouling is the primary obstacle to optimizing efficiency and results in a short membrane lifetime and high operating costs. By incorporating nanomaterials into the membrane synthesis process, a mixed-matrix membrane with significantly enhanced characteristics and performance may be obtained. Graphene oxide (GO), aluminum oxide (Al2O3), tin oxide (SnO2), and titanium oxide (TiO2) were incorporated into a polyethersulfone (PESU) membrane. The water permeability of the modified membranes showed improvements when compared with the pure membrane. It increased from 65 L/m2 h bar for the pristine membrane (PES-1) to 143.6, 83.68, 92.12, 75.43 L/m2 h bar for Al2O3 (PES-2), TiO2 (PES-3), SnO2 (PES-4), and GO (PES-5) membranes, respectively. It was discovered that the membrane's surface hydrophilicity was significantly and directly affected by the incorporation of nanoparticles. Fouling parameters include Rr (Reversible fouling ratio), Rir (irreversible fouling ratio), Rt (total fouling ratio), and Frr (flux recovery ratio) and were measured to determine the membrane's fouling tendency. The results showed that the membrane's propensity for fouling could be reduced when nanoparticles were incorporated into it. The experimental results are best explained by the cake layer and both standard and intermediate blocking mechanism models, as determined by the traditional single fouling models.
Energia nuclear: uma opinião da sua relação com a sustentabilidade
Authors: Immich Vinícius , Hart Weber Fernanda , Estêvão Classen Bárbara , Sippert Lanzanova Luciane , Enivar Lanzanova Mastrângello
Published: September 2023

Com o objetivo de analisar, através de revisão bibliográfica, os aspectos ambientais da utilização da energia nuclear como matriz energética e apresentar a opinião do autor em base destes, o trabalho foi elaborado contendo o conceito de energia nuclear e o funcionamento de uma usina, bem como aspectos ambientais, segurança e monitoramento, licenciamento ambiental, rejeito e um comparativo com outras unidades geradoras de energia. Em vista do desafio de diminuir as emissões atmosféricas globais e ao mesmo tempo assegurar o atendimento ao crescimento da demanda energética mundial, a energia nuclear apresenta potencial para diversificar a matriz energética global. A utilização isolada de energias tradicionais já desenvolvidas, como petróleo, carvão, gás natural, hidrelétrica, biomassa e energia eólica impacta o planeta pela emissão de gases de efeito estufa, contaminantes, ocupação de grandes áreas e demais impactos ambientais originados pelo seu funcionamento. Como o desafio global é atender ao objetivo de desenvolvimento sustentável n° 7, estabelecido pela Organização das Nações Unidas (ONU), que busca reduzir as emissões atmosféricas e garantir acesso à energia renovável e sustentável, a energia nuclear apresenta-se como uma ferramenta limpa de diversificação da matriz energética global. Porém, não deve ser vista como a única solução para os problemas energéticos de um país, devendo fazer parte de um programa diverso de produção energética junto com a produção e utilização de outras fontes renováveis ​​de energia, como a eólica e a biomassa.
Comparison of Deep Learning Sentiment Analysis Methods, Including LSTM and Machine Learning
Authors: Habib Jean Max T. , Poguda A. A.
Published: September 2023

Purpose of research. The purpose of the study is to evaluate certain machine learning models in data processing based on speed and efficiency related to the analysis of sentiment or consumer opinions in business intelligence. To highlight the existing developments, an overview of modern methods and models of sentiment analysis is given, demonstrating their advantages and disadvantages.

Materials and methods. In order to improve the semester analysis process, organized using existing methods and models, it is necessary to adjust it in accordance with the growing changes in information flows today. In this case, it is crucial for researchers to explore the possibilities of updating certain tools, either to combine them or to develop them to adapt them to modern tasks in order to provide a clearer understanding of the results of their treatment. We present a comparison of several deep learning models, including convolutional neural networks, recurrent neural networks, and long-term and shortterm bidirectional memory, evaluated using different approaches to word integration, including Bidirectional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was conducted using a simple data augmentation approach. This project uses natural language processing (NLP), deep learning, and models such as LSTM, CNN, SVM TF-IDF, Adaboost, Naive Bayes, and then combinations of models.

The results of the study allowed us to obtain and verify model results with user reviews and compare model accuracy to see which model had the highest accuracy results from the models and their combination of CNN with LSTM model, but SVM with TF-IDF vectoring was most effective for this unbalanced data set. In the constructed model, the result was the following indexes: ROC AUC - 0.82, precision - 0.92, F1 - 0.82, Precision - 0.82, and Recall - 0.82. More research and model implementation can be done to find a better model.

Conclusion. Natural language text analysis has advanced quite a bit in recent years, and it is possible that such problems will be completely solved in the near future. Several different models in ML and CNN with the LSTM model, but SVM with the TF-IDF vectorizer proved most effective for this unbalanced data set. In general, both deep classification algorithm. A combination of both approaches can also learning and feature-based selection methods can be used to solve be used to further improve the efficiency of the algorithm. some of the most pressing problems. Deep learning is useful when the most relevant features are not known in advance, while feature-based
Synthesis, Computational Study of β-lactam Derivatives Bearing Quinazoline Schiff Bases as Anthelmintics
Authors: Singh Gurdeep , Patel Ritesh , Thakur Alok Singh , Singh Mukesh Kumar
Published: September 2023

Background:

Infections caused by parasites continue to pose a risk to both human and animal
health. The most popular drug classes for treating these infections include thiabendazole, imidazothiazoles,
levamisole, and avermectins (obtained from the fermentation products of Streptomyces
avermitilis). The majority of bacteria that we come across in any form of infection are successfully
combatted by β- lactam medicines. Similar to the majority of treatments, problems with treatment
resistance to these drugs have evolved over time, necessitating the development of new anthelmintic
medications. This study aims to investigate a series of quinazoline-bearing β-lactam rings as potent anthelmintic
agents.

Methods:

The new series of quinazoline bearing β-lactam was synthesized via reaction of 5-bromo
anthranilic acid with acetic anhydride to produce 6-bromo-2-methyl-4H-benzo (1,3) oxazin-4-one which
was then converted into 3-amino-6-bromo-2-methylquinazolin-4(3H)-one by the reaction of hydrazine
hydrate in the presence of anhydrous pyridine. The resultant intermediate then goes through a Schiff
reaction with various aromatic aldehydes, followed by reflux with triethylamine and chloroacetyl
chloride.

Results:

Structural assignments of these compounds have been made by elemental analysis,
FTIR,1HNMR and Mass spectral data and the purity of the compounds was determined by TLC. Molecular
docking studies showed the effective binding of synthesized derivatives with tubulin in comparison to
the orientation of standard drugs. The anthelmintic activities of all the synthesized compounds were evaluated
separately for their possible using common Indian earthworm Pheritima posthuma.

Conclusion:

A total of ten derivatives of quinazoline bearing β-lactam ring were designed, synthesized
and evaluated against the Indian earthworm Pheritima posthuma. Out of ten derivatives, SQD2 and
SQD6, showed promising anthelmintic activity when compared with standard drugs albendazole and
piperazine citrate.
Dendrimers: Patents for Alzheimer’s disease
Authors: Maheshwari Shubhrat , Singh Aditya
Published: September 2023

Cells and nervous system connections that are crucial for movement, coordination, strength, sensation, and thought are gradually damaged in neurodegenerative illnesses. Amyloid beta (Aβ)-accumulating macromolecules in the brain are the primary cause of the disease's chronic symptoms, according to analysis carried out during the last 20 years. Plaques and clumps of amyloid- build up in the brain, obstructing neuronal signals and destroying neural connections. Tau, a protein that results in the formation of "neurofibrillary tangles" in the brain, another hallmark of neuronal death, has been the focus of a lot of research. Dendrimers Delivery (DDs) is one of the most promising advancements in nanotechnology for biomedical applications, particularly drug delivery. Some of the main categories of dendrimers employed in the successful management of neurodegenerative illnesses are polyamidoamine dendrimers (PAMAM) dendrimers, polypropylenimine dendrimers (PPI), Poly-l-lysine dendrimers (PLL), and carbosilane dendrimers. The tight blood-brain barrier (BBB), which limits the entry of medications or therapeutic agents, makes it difficult to treat central nervous system disorders. Dendrimers have attracted the attention of scientists more than other non-invasive methods of drug delivery across the BBB and improve the uptake of medicines in the brain's target tissues. The major benefits of dendrimers include their adaptability, biocompatibility, ability to load pharmaceuticals into the core and surface, and nanosize. This review has updated the status of the patent and clinical trials literature pertaining to dendrimer use in AD.
Task Offloading of Intelligent Building Based on Dependency-Aware in
Edge Computing
Authors: Lingzhi Yi , Jianxiong Huang , Yahui Wang , Jiao Long , Bote Luo , Jiangyong Liu
Published: September 2023

Background:

With the rapid development of artificial intelligence, the traditional cloud computing model has serious bandwidth and energy consumption problems when storing and processing massive amounts of raw data. To address this problem, recent patents have investigated methods for intelligent task offloading and allocation in mobile edge computing.

Objective:

A Directed Acyclic Graph (DAG) task unloading model is established to reduce the problem of task delay and energy consumption in edge networks. At the same time, the Modified Tuna Swarm Optimization (MTSO) is used to improve the execution efficiency.

Methods:

Firstly, this paper integrates (i) inter-task dependencies; (ii) heterogeneity of computing resources in the edge network; and (iii) interference of wireless channels in the edge network. A DAG task offloading model is developed to reduce latency and energy consumption. The end users are guided to offload their tasks/sub-tasks to the most appropriate servers in the edge network, thus minimizing the end-to-end latency of all tasks in the edge network. Secondly, the MTSO algorithm is used to coordinate the dependencies and priorities of subtasks to improve execution efficiency.

Results:

The experimental results show that when the number of users including subtasks is 10, the final edge server utilization rate is as high as 92%. A more fine-grained segmentation scheme can reduce the average delay of tasks and improve the utilization rate of edge servers.

Conclusion:

The approach proposed in this paper reduces the end-to-end latency and improves resource utilization in complex applications under the premise of ensuring task dependency. It well relieves the pressure on the cloud and has certain engineering application value
A Review on Various Voltage Boosting Topology in DC-DC Converter
Authors: Singh Mayank , Yadav Mukh Raj , Dhaked Dheeraj Kumar
Published: September 2023

Step-up DC-DC converters are employed to raise the output voltage level from the input
voltage level. Although the basic boost dc-dc converter has advantages like simplicity of implementation,
it also has drawbacks like low boost ability and low power density. The literature has reported
various topologies which have switched inductor/voltage lift, switched capacitor, voltage multiplier,
magnetic coupling, and multistage types. Each converter topology possesses its own advantages
and disadvantages with a focus on power density, cost, efficiency, reliability, and complexity
depending upon the applications. Demands of such applications are being fulfilled by using new
power conversion topologies. Various combinations of such boosting topologies with additional
components are complex. This paper provides a simple glance to the basic law and context for the
development of future DC/DC converters. This paper has surveyed and classified various topologies
according to the voltage-boosting topology and characteristics. The banes and boons of these topologies
are also discussed in the paper with the applications of each boosting topology.
Protective Effect of Quercetin and p-Coumaric Acid (p-CA) Against
Cardiotoxicity: An In Silico Study
Authors: Bhadana Renu , Rani Vibha
Published: September 2023

Background:

Hydroxychloroquine (HCQ) is a common antimalarial drug that has
been used effectively in the treatment of various rheumatic and auto-immunity diseases. The major
side effects and drawbacks associated with HCQ are cardiotoxicity, retinopathy, gastrointestinal
upset, and neuromyopathy however, cardiotoxicity is an increasing concern and it is critical
to avoid heart dysfunction induced by HCQ. The present work is focused on receptor and signaling
molecules associated with pathways attributing to drug-induced cardiotoxicity. We analyzed
the therapeutic efficacy of selected natural products in HCQ-induced cardiotoxicity through insilico.
We selected Syzygium cumini polyphenols, quercetin, and p-coumaric acid. The motivation
behind selecting quercetin, and p-coumaric acid is their wide applicability as an antioxidative,
anti-inflammatory, antiapoptotic, and cardioprotective.

Methods:

For predicting quercetin, p-coumaric acid, and HCQ toxicity and physicochemical
properties, in silico studies were performed using ProTox II and Swiss ADME. We further performed
molecular docking using Autodock Vina and Discovery Studio visualizer to find the affinity
of selected polyphenols against signaling molecules and receptors. Then we performed
network pharmacological studies of selected signaling molecules.

Results:

We analyzed that the computational method indicated quercetin (Δ G -9.3 kcal/mol) has
greater binding affinity than p-Coumaric acid for prevention and restoration of the disease while
hydroxychloroquine was taken as a control.

Conclusion:

It can be concluded that Syzygium cumini, polyphenols may aid in the future therapeutic
potential against HCQ-induced cardiotoxicity.
The Role of Mathematics in Data Science: Methods, Algorithms, and Computer Programs
Authors: Singh Rashmi , Bhardwaj Neha , Islam (Naz) Sardar M. N.
Published: September 2023

The field of data science relies heavily on mathematical analysis. A solid
foundation in certain branches of mathematics is essential for every data scientist
already working in the field or planning to enter it in the future. In whatever area we
focus on, data science, machine learning engineering, business intelligence
development, data architecture, or another area of expertise, it is important to examine
the several kinds of mathematical prerequisites and insights and how they're applied in
the field of data science. Machine learning algorithms, data analysis and analyzing
require mathematics. Mathematics is not the only qualification for a data science
education and profession but is often the most significant. Identifying and translating
business difficulties into mathematical ones are a crucial phase in a data scientist's
workflow. In this study, we describe the different areas of mathematics utilized in data
science to understand mathematics and data science together.
A Machine Learning Application to Predict Customer Churn: A Case in Indonesian Telecommunication Company
Authors: Tri Wibowo Agus , Utami Putri Andi Chaerunisa , Reza Tribosnia Muhammad , Putawara Revalda , Mujiya Ulkhaq M.
Published: September 2023

This study aims to develop a churn prediction model which can assist
telecommunication companies in predicting customers who are most likely subject to
churn. The model is developed by employing machine learning techniques on big data
platforms. Customer churn is one of the most critical issues, especially in high
investment telecommunication companies. Accordingly, the companies are looking for
ways to predict potential customers to churn and take necessary actions to reduce the
churn. To accomplish the objective of the study, it first compares eight machine
learning techniques, i.e., ridge classifier, gradient booster, adaptive boosting, bagging
classifier, k-nearest neighbour (kNN), decision tree, logistic regression, and random
forest. By using five evaluation performance metrics (i.e., accuracy, AUC score,
precision score, recall score, and the F score), kNN is selected since it outperforms
other techniques. Second, the selected technique is used to predict the likelihood of
customers churning.
Unravelling the Role of Vegetables, Pulses and Spices as Therapeutic and Chemopreventive Agents
Authors: Garg Vandana , Anjali  , Kangra Kiran , Dutt Rohit
Published: September 2023

Worldwide, cancer has become the most life-threatening disease. The current remedial treatment of cancer includes chemotherapy, surgery, immunotherapy, stem cell transplant and hormone therapy. Plants produce secondary metabolites in abundance having medicinal properties used for treating various diseases, such as AIDS, diabetes, cancer, inflammation, fever, diarrhoea and bacterial and fungal infections. Naturally derived components are largely considered by scientists and researchers due to their low toxicity and lesser side effects. Functional foods are the food or food components that provide health benefits beyond basic nutrition. Functional foods simply provide nutrients that help to maintain health, thereby reducing the risk of disease. Various vegetables like Asparagus racemosus, Cocos nucifera, Brassica oleracea var. Botrytis, Zingiber officinale, Atrocarpus heterophyllus, etc., pulses, i.e., Cicer arietinum, Phaseolus vulgaris, Vigna radiate, Vigna mungo, etc., and Spices viz., Ferula asafoetida, Piper nigrum, Elettaria cardamomum, Coriandrum sativum, Nigella sativa and Curcuma longa, are explored for their potential role to fight many diseases and anticancer activity. This review aims to highlight the protective and synergistic role of functional foods in cancer prevention.
Allium Species: A Remarkable Repertoire of Nutraceuticals with Anti-cancer Properties
Authors: Shri Richa , Singh Varinder
Published: September 2023

Cancer - the name evokes fear and anxiety. Researchers are working tirelessly to bring hope to countless patients by developing prevention and treatment strategies. One approach is dietary modulation - which is documented to reduce the risk of cancer and increase the benefit of anti-cancer therapy. Allium species are a part of the daily diet in most parts of the world. Important members of this genus - chives, garlic, onions, and shallots add flavour and nutrition to food. These are prized for their organosulphur compounds and flavonoid content which are responsible for their diverse pharmacological activities. Traditional and scientific literature shows that dietary intake of Allium species prevents and aids the treatment of different cancers. In this review, based on an extensive search of available databases, the role of Allium species as nutraceuticals for cancer management was examined to ascertain the truth in the popular claims. Preclinical and clinical investigations show that consumption of the Allium members as a part of the diet and also the functional components (e.g., allicin, diallyl disulphide, diallyl trisulphide, ajoene, S-allyl cysteine, S-allyl mercaptocysteine, tuberoside M, onionin A, fisetin, quercetin, etc.) reduce risk of cancer and have significant antitumor activities. These act by varied mechanisms, including inhibition of gene expression, promotion of apoptosis of cancer cells, antiproliferative activity, and anti-oxidant and anti-inflammatory effects. It is emphasised that standardization of Allium products, their efficacy, dosage, safety profiles and interactions should be ascertained to corroborate their use. This article highlights the importance of Allium species for their prophylactic, therapeutic and immune-boosting ability in cancer management.