bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards Understanding the Functional Connectivity Patterns in Visual Brain Network
Authors: Bhattacharya Debanjali , Sinha Neelam 
Published: July 2024

Abstract

Recent advances in neuroimaging have enabled studies in functional connectivity (FC) of human brain, alongside investigation of the neuronal basis of cognition. One important FC study is the representation of vision in human brain. The release of publicly available dataset ”BOLD5000” has made it possible to study the brain dynamics during visual tasks in greater detail. In this paper, a comprehensive analysis of fMRI time series (TS) has been performed to explore different types of visual brain networks (VBN). The novelty of this work lies in (1) constructing VBN with consistently significant direct connectivity using both marginal and partial correlation, which is further analyzed using graph theoretic measures, (2) classification of VBNs as formed by image complexity-specific TS, using graphical features. In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than that using negative correlation. This result not only reflects the distinguishing graphical characteristics of each image complexity-specific VBN, but also highlights the importance of studying both positively correlated and negatively correlated VBN to understand the how differently brain functions while viewing different complexities of real-world images.
Novel optimized coupled rainfall model simulation based on stepwise decomposition technique
Authors: Zheng Zhiwen , Yao Yuan , Zhang Xianqi , Zhao Yue , Qi Yu 
Published: July 2024

ABSTRACT

Precipitation forecasting plays a pivotal role in guiding the effective management of regional water resources and providing crucial warnings for regional droughts and floods. Finding a monthly precipitation simulation model with robust fitting performance is a significant research endeavor in practical precipitation prediction. This paper introduces two modified African vulture optimization algorithms (MAVOA1 and MAVOA2). It provides hyperparameter optimization techniques for the least squares support vector machine (LSSVM), long short-term memory neural network (LSTM), and random forest (RF) models. These techniques are used to construct a monthly precipitation simulation model based on algorithmic optimization coupled with variational mode decomposition for full decomposition. The test results at five typical stations in the North China Plain reveal the following: (1) the LSSVM model demonstrates significantly better performance than the LSTM and RF models. (2) the MAVOA2-LSSVM model has the best-integrated effect: the average test fitting error is RMSE = 17.50 mm/month, MRE = 117.25%, NSE = 0.90, which shows its superiority in practical application and can significantly improve the accuracy of precipitation prediction; MAVOA2 is more suitable for machine learning models with more hyperparameters of its own, which provides a reference for hyperparameter optimization algorithms in the other fields.
A Systematic Review of Economic Evidence of Cardiovascular Interventions in India
Authors: Abidi Saba , Nair Anandita , Ahuja Rakhi , Dwivedi Shridhar , Talegaonkar Sushama 
Published: July 2024

Background:
Cardiovascular diseases (CVDs) continue to be the primary cause of
mortality globally and invariably in India as well. The rapid upsurge in the prevalence of CVDs
in India has created a pressing need to promote contemporary, sustainable, and cost-effective interventions
to tackle the CVD burden. This systematic review integrates the research-based evidence
of the cost-effectiveness of various interventions that can be adapted to control CVDs in
India.

Methods:
Databases, namely, PubMed, Cochrane Library, Embase, and Google Scholar, were
searched for data on the economic evaluation of interventions targeting CVD based on the Indian
population for a period of 30 years (1991-2021). Two reviewers assessed the articles for eligibility,
and data were extracted from the shortlisted articles as per a predefined template, including
the quantification of methodological aspects.

Results:
In total, 1249 studies were examined, out of which 23 completely met the inclusion criteria
for full-text review. A total of 16 studies were based solely on the Indian population, while
the rest (7) included South Asia/Asia for the intervention, of which India was a participant nation.
Most of the economic evaluations targeted treatment-based or pharmacological interventions
(14) for CVDs. The evaluations were based on Decision-based models (10), Randomized
controlled Trials (RCTs) (9), and Observational studies (4). The cost-effectiveness ratio for the
included studies exhibited a diverse range due to variations in methodological approaches, such
as differences in study settings, populations, and inconsistencies in study design. The mean
ICER (Incremental Cost-effectiveness ratio) for primordial and primary preventions was found
to be 3073.8 (US $2022) and 17489.9 (US $2022), respectively. Moreover, the combined mean
value for secondary and tertiary prevention was 2029.6 (US$2022).

Conclusion:
The economic evidence of public health interventions are expanding, but their focus
is restricted towards pharmacological interventions. There is an urgency to emphasize primordial
and primary prevention for better outcomes in health economics decision-making. Technology-
based avenues for intervention need more exploration in order to cater to a large population
like India.
Impact of Luteinizing Hormone on IVF/ICSI Assisted Reproduction on the Initiation Day of Gonadotropin-releasing Hormone Antagonist Protocol
Authors: Zhang Li-Jia , Liu Dun , Xu Li-Qing , Wei Jin-Yan , Fan Lin , Zhang Xi-Qian , Liu Feng-Hua 
Published: July 2024

Objective:
The aim of the study was to explore the optimal timing of gonadotropin initiation
and the reasonable interval of luteinizing hormone (LH) level in the gonadotropin-releasing
hormone antagonist (GnRH-A) protocol.

Methods:
A retrospective cohort study was conducted to analyze the LH level in patients with different
ovarian response treated in GnRH-A protocol in 1361 IVF/ICSI cycles. Ovarian response
(including AMH, AFC) in these patients were divided into the poor ovarian response group (an antral
follicle count (AFC) ≤ 6, n = 394), the normal ovarian response group (an AFC > 6 and < 15,
n = 570), and the high ovarian response group (an AFC ≥ 15, n = 397), according to the AFC. The
patients were sub-grouped according to LH levels on the protocol initiation day, and the clinical
outcomes (including dose of Gn initiation, Gn administration days, GnRH-ant administration
days, P levels on the HCG day, E2 levels on the HCG day, LH levels on the HCG day, number of
embryos transferred, total fertilization rate, embryo implantation rate (%), proportion of 2PN, proportion
of good-quality embryos, endometrial thickness on the hCG injection day (mm), moderate
to severe OHSS, AFC on the initiation day, proportion of type A endometrium on the hCG injection
day, clinical pregnancy rate, biochemical pregnancy rate, early abortion rate, ectopic pregnancy
rate) were compared.

Results:
On the GnRH-A protocol initiation day, among all patients with different ovarian responses,
the body mass index (BMI) in those with an LH ≥ 5 IU/L was lower. The difference of pregnancy
outcome between the LH < 5 IU/L group and the LH ≥ 5 IU/L group were not statistically
significant among the different ovarian response groups, but the LH < 5 IU/L group had a higher
proportion of good-quality embryos (80.3 ± 24.9 vs. 74.8 ± 26.9, P =0.035) than the LH≥5IU/Lgroup
in those with poor ovarian response. The total fertilization rate (82.2 ± 18.1 vs 85.4 ± 15.1,
P =0.021) and proportion of two pronuclei (2PN) (69.0 ± 20.9 vs 72.7 ± 19.9, P =0.035) were higher
in the LH ≥ 5 IU/L group than the LH<5 IU/L group for those with normal ovarian response.
The embryo implantation rate (41.4 ± 41.3 vs 52.6 ± 43.4, P =0.012) was higher in the LH ≥ 5
IU/L group than in the LH<5 IU/L group in those with high ovarian response. The results of the
multivariate logistic analysis showed that the age of the female partner, number of embryos transferred,
proportion of good-quality embryos, endometrial thickness on the hCG injection day, and
moderate-to-severe ovarian hyperstimulation syndrome (OHSS) were independent factors correlated
with the outcome of live births (P < 0.05).

Conclusion:
The LH levels on the gonadotropins (Gn) initiation day in GnRH-A protocol will not
affect pregnancy outcomes.
Investigating the State of Self-Harm and its Relationship with Suicidal Ideation in College Students: Evidence from a Cross-sectional Study in Southern Iran
Authors: Faryabi Reza , Bordbar Shima , Bahmaei Jamshid , Barfar Eshagh , Yusefi Ali Reza 
Published: July 2024

Introduction
Self-harm behaviors and suicidal ideation as two important psychological challenges can have irreparable consequences for students. Therefore, this study sought to investigate the state of self-harm and its relationship with suicidal ideation in students.

Methods
This cross-sectional study was conducted on 290 students of different fields at Jiroft University of Medical Sciences in the south of Iran between June and September 2023. Data were collected using two standard questionnaires of the Beck scale for suicidal Ideation (BSSI) and self-harm behaviors. Descriptive statistics methods were used for the scores of self-harm behaviors and suicidal ideation. Pearson's correlation coefficient was also used to check the correlation between these two main variables, and statistical tests such as t-test and one-way analysis of variance (ANOVA) were used to compare averages between groups. Statistical analyses were performed using SPSS version 23.

Results
Self-harm behaviors were at low, average, high, and very high levels in 24%, 67%, and 9% of students, respectively. Also, Suicidal ideation was in low, moderate, and high (readiness to commit suicide) levels in 5.6%, 3.4%, and 1.05% of students, respectively. Furthermore, 89.95% of students also had no suicidal thoughts. There was a statistically significant correlation between self-harm behaviors and suicidal ideation of students (p<0.001, r=0.667). Also, the mean score of self-harm behaviors, including dormitory residents (p=0.02), males (p=0.04), singles (p=0.001), undergraduates (p=0.003), and unemployed (p=0.01) was significantly higher than others. In addition, the average score of suicidal ideation was significantly higher in students living in the dormitories (p=0.03), including singles (p=0.005), undergraduates (p=0.02), and unemployed (p=0.04) than in others.

Conclusion
It is suggested that the officials of student, counseling, and cultural affairs of universities improve the living conditions of students in dormitories, pay attention to the needs of students, and try to solve their mental and psychological problems, as well as provide employment and marriage of students to reduce their self-harm behaviors and suicidal ideation. Additionally, government policy interventions, such as routine mental health care screening programs for students and timely interventions to solve mental health problems, are suggested.
SARS-CoV-2 Antibody Level after Homologous and Heterologous Booster Vaccines: an 18-month Longitudinal Observational Cohort Study in Indonesia
Authors: Ardyanto Tonang Dwi , Khariri   , Agus Telly Purnamasari , Handayani Sarwo , Soebandrio Amin 
Published: July 2024

Background
Recently, there have been reports of the rise of COVID-19 cases in several sites. The effectiveness of the COVID-19 vaccine was reported elsewhere. There are still questions on how the kinetics of antibody response during relatively long periods, the need for additional doses, and the effect of homologous and heterologous boosters. The study was conducted to analyze the kinetics of antibody response after the primary dose and the third dose of the ChAdOx1 vaccine in individuals previously receiving two doses of the ChAdOx1 [homologous] and CoronaVac [heterologous] COVID-19 vaccines.

Methods
The study population comprised 52 men and 98 women, divided into CoronaVac Recipients and ChAdOx1 Recipients for the first two doses according to the recommended schedule by the Ministry of Health of Indonesia [MoH].Six months after the second dose, the third dose of ChAdOx1 was administered as a homologous and heterologous booster. COVID-19 antibody levels were measured by the CMIA method before the first dose [time-point or TP1], two weeks after the first dose [TP2], before the second dose [TP3], 1 month after the second dose [TP4], 12 months after the second dose [TP5], and 18 months [TP6] after the second dose administration. Six months after the second dose, the third dose of ChAdOx1 was administered as a homologous and heterologous booster. Along with these, several epidemiological data were collected from subjects on TP1.

Results
A total of 153 serum samples were collected from subjects who had received the third dose, and the antibody response was measured. On TP1, COVID-19 antibody reactivity [the level was >50 AU/mL] was detected on 100 [66,67%] of subjects, indicating a possible previous exposure to SARS-CoV-2. On TP2, the sharp increase in antibody level was documented in the ChAdOx1 group. However, in the following data during the cohort, the gap was narrowing, and on the TP6, the antibody levels showed no significant difference between groups [p>0.05]. Likewise, no significant differences were shown between groups with or without a history of COVID-19 antibody reactivity on TP1 [p>0.05]. Considering epidemiological characteristics, no significant differences were documented based on sex, age groups, and BMI level.

Conclusion
This study provides a deeper understanding of the kinetics of antibody levels longitudinally among those with and without previous history of SARS CoV-2 infection, among the recipients of different vaccines, and the recipients of homologous and heterologous boosters. It is necessary to elucidate further in the next study how the level of antibody reflects the neutralizing antibody level as an indicator of protection against the infection risk.
Intelligent Data Processing for Alzheimer's Disease Using Deep Learning
Authors: garg Nidhi , Chutani Gautam , Bohra Himanshu , Chaudhary shagun , Sharma Preeti 
Published: July 2024

Background:
With the advancement in Alzheimer's disease (AD) brain, cells start to
deteriorate, which eventually creates physical dependency and mental instability that interferes
with daily living. Presently, this disease is immedicable. Therefore, the only suitable treatment is
early detection and prevention.
Although many studies have investigated the usefulness of deep learning in AD detection, relatively
few have focused on the necessary image preprocessing processes, which are essential to
any computer-aided diagnostic system. Furthermore, an optimal classification strategy that takes
into account a diverse handful of prominent features is required.

Method:
This paper focuses on improving MRI-based AD detection by incorporating image enhancement
approaches and deep hybrid learning into a fused framework to harness the power of
multiple Deep Learning (DL) architectures and Machine Learning (ML) classifiers. The deep features
extracted from three heterogeneous CNN architectures, namely, VGG16, DensetNet169, and
MobileNetV1, are fused to produce a more informative and discriminative hybrid feature. Furthermore,
the mRMR approach was used to optimise the acquired features, followed by classification
via a stack of multiple ML classifiers to predict the target class.

Results:
The proposed architecture based on feature fusion strategy and ensemble learning resulted
in 99.53%(Accuracy), 99.73%(Precision),99.70%(Recall), and 99.72% (F1 score). The presented
model outperformed individual deep CNN architectures.

Conclusion:
Lastly, we present a sobol-based sensitivity analysis that illustrates the concentration
of the presented technique upon significant regions of the image and can assist medical professionals
in decoding the decisions. The presented technique exeplifies the potency and constancy of
categorizing Alzheimer's disease.
Acceptability and Engagement of a Smartphone-Delivered Interpretation Bias Intervention in a Sample of Black and Latinx Adults: Open Trial
Authors: Ferguson IreLee , George Grace , Narine Kevin O , Turner Amari , McGhee Zelda , Bajwa Harris , Hart Frances G , Carter Sierra , Beard Courtney 
Published: July 2024

Background
Access to evidence-based interventions is urgently required, especially for individuals of minoritized identities who experience unique barriers to mental health care. Digital mental health interventions have the potential to increase accessibility. Previous pilot studies testing HabitWorks, a smartphone app providing an interpretation bias intervention, have found strong engagement and adherence for HabitWorks; however, previous trials’ samples consisted of predominantly non-Hispanic, White individuals.

Objective
This study conducted an open trial of HabitWorks in a community sample of adults who identified as Black, Hispanic or Latinx, or both. This study aims to test safety, acceptability, and engagement with the HabitWorks app for Black and Latinx adults.

Methods
Black, Hispanic or Latinx adults (mean age 32.83, SD 11.06 y; 22/31, 71% women) who endorsed symptoms of anxiety or depression were asked to complete interpretation modification exercises via HabitWorks 3 times per week for 1 month. Interpretation bias and anxiety and depression symptoms were assessed at baseline and posttreatment assessments. Participants completed qualitative interviews to assess overall perceptions of HabitWorks.

Results
Of the 31 participants that downloaded the app, 27 (87%) used HabitWorks all 4 weeks. On average, participants completed 15.74 (SD 7.43) exercises out of the 12 prescribed, demonstrating high engagement. Acceptability ratings met all a priori benchmarks except for relevancy. Qualitative interviews also demonstrated high acceptability and few negative experiences. Significant improvements were found in interpretation style (t30=2.29; P<.001), with a large effect size (Cohen d=1.53); anxiety symptoms (t30=2.29; P=.03), with a small effect size (Cohen d=0.41); and depression symptoms (t30=3.065; P=.005), with a medium effect size (Cohen d=0.55).

Conclusions
This study adds to the literature evaluating digital mental health interventions in Black and Latinx adults. Preliminary results further support a future controlled trial testing the effectiveness of HabitWorks as an intervention.
Konflik Sosial dalam Novel Mā Ṭabaqa Lakum Karya Ghassan Kanafani: Analisis Sosiologi Sastra
Authors: Armanto Arif 
Published: July 2024

Penelitian ini menganalisis konflik sosial dalam novel Mā Ṭabaqa Lakum karya Ghassan Kanafani menggunakan pendekatan sosiologi sastra. Tujuan dari penelitian ini adalah untuk mengeksplorasi bagaimana elemen-elemen naratif seperti karakter, plot, dan setting digunakan untuk menggambarkan dinamika konflik sosial dalam konteks kejadian nyata di Palestina. Dengan menggunakan metode kualitatif deskriptif, penelitian ini berusaha untuk mendeskripsikan berbagai konflik sosial yang digambarkan dalam novel Mā Ṭabaqa Lakum dan dikuatkan dengan sumber data pustaka lainnya sebagai data pendukung. Dalam kaitannya dengan analisis data, penelitian ini mengintegrasikan teori konflik sosial Pruitt dan Rubin serta pendekatan sosiologi sastra. Adapun hasil dari penelitian ini menemukan bahwa konflik sosial dalam novel mencerminkan ketegangan antara kekuasaan kolonial dan perlawanan rakyat Palestina. Karakter-karakter dalam novel berjuang dengan identitas dan tekanan eksternal yang sekaligus merepresentasikan kondisi sosial dan politik di dunia nyata. Selain itu, novel ini menunjukkan adanya ketidaksetaraan ekonomi, perbedaan budaya, dan pertentangan politik memperburuk ketegangan sosial. Implikasi dari penelitian ini menunjukkan bahwa sastra dapat berfungsi sebagai alat untuk mengkritik ketidakadilan sosial dan menginspirasi perubahan sosial. Saran untuk penelitian mendatang mencakup eksplorasi lebih lanjut tentang konflik sosial dalam berbagai karya sastra Timur Tengah dan penggunaan pendekatan multi-disipliner untuk analisis yang lebih komprehensif.
The Comparative Analysis Using Epanechnikov and Uniform Regression on Cayenne Chili Production in Central Sulawesi in 2020
Authors: Ni Luh Emiliani , Astuti Dewi Ariani Ni Luh Putu , Afifa Afifa , Sora Kedoh Agatha , Handayani Lilies 
Published: July 2024

Cayenne pepper (Capsicum frutencens L.) is an annual horticultural crop that has high economic value. The production of cayenne pepper in general in Central Sulawesi in 2016 reached 30.18.40 tons with a harvested area of ​​75 ha with a productivity of 56.74 kw ha-1 . In 2017 production decreased to 21,299.50 tons with a harvested area of ​​2,940 ha, and productivity of 7,244 kw ha-1 (BPS Central Sulawesi, 2018). The decrease in chili production is due to a decrease in harvested area that occurs every year. The harvest area is getting smaller because a lot of land has been built into residential areas. So that farmers must be able to take advantage of all existing land even though the land is land that has a low fertility level. To obtain an increase in production and optimal growth of cayenne pepper, adequate plantation land is needed. Based on the description above, it is deemed necessary to conduct comparative analysis research using gaussian kernel regression and epanechnikov kernel regression on cayenne pepper production in Central Sulawesi in 2020.
Remote Water Quality Monitoring System In Shrimp Ponds With Photovoltaic (PV)-Based Energy Source
Authors: Sherly Dwi Novianti M  , Rizana Fauzi  , Nurhani Amin  
Published: July 2024

Introduction: Shrimp has great potential to be used as a business field in Indonesia. Especially in Parigi Regency itself, there are now many shrimp ponds that can be found because of the benefits obtained, so people are interested in making shrimp farming a livelihood. One of the main problems of shrimp ponds is pond water quality. There are several factors that affect pond water quality, namely water temperature, water pH, and water salinity, good pond water quality management can maintain quality standards and can increase shrimp yield and productivity Method: To facilitate the shrimp farmers, innovations need to be made in order to help the shrimp farmers manage their shrimp ponds, therefore using a remote water quality monitoring was system in shrimp ponds with photovoltaic (PV) based energy sources is expected to be an innovation in managing shrimp ponds, especially in monitoring water quality in shrimp ponds. The monitoring system designed using the NRF24L01 module as a remote communication module, at the research location the distance from the shrimp pond to the house is approximately 100 meters, so the tools that have been designed can facilitate shrimp farmers to monitor the shrimp pond Results and Disscussion: The results of testing the DS18B20 temperature sensor compared with a digital thermometer measuring instrument get an average error of 0.014%. The test results of the pH sensor compared to the pH meter get an average error of 0.026%. TDS sensor test results compared with the TDS meter get an average error of 0.04%. Conclusion: The temperature, pH, and salinity monitoring system uses transmitter and receiver modules, where the transmitter module reads and sends data to the receiver module wirelessly with NRF24L01. Using DS18B20 temperature, pH, and TDS sensors, as well as energy from photovoltaics, this system helps shrimp farmers monitor pond water quality remotely.