bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of antifungal and antioxidant activities of extracts prepared from earthworm (Perionyx excavates) using different solvents
Authors: Dung N. T. , Son Ph. H. , Trinh N.T.N. , Thao V. N.T. , Tu B. L.K. , Luan L. Q. , Thinh B. B. , Hai L. V.
Published: January 2025

This study evaluated the antifungal and antioxidant activities of earthworm (Perionyx excavatus) powder extracts using different solvents including 80% ethanol, 50% ethanol, and distilled water. The extraction efficiencies ranged from 18.5 to 21.2%, while total protein contents ranged from 64.8 to 67.5%. Notably, the aqueous extract exhibited the highest values in both extraction efficiency and total protein content. Thin-layer chromatography analysis revealed the presence of amino acids and peptides with Rf values ranging from 0.42 to 0.65. Fourier-transform infrared spectroscopy spectra displayed characteristic peaks associated with protein structures. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis indicated protein compositions primarily below 50 kDa molecular weight in the extracts, particularly with the 80% ethanol extract predominantly consisting of proteins below 30 kDa. Antifungal tests against Candida albicans demonstrated the highest efficacy with 80% ethanol extract, exhibiting an inhibition zone diameter of 13.3 mm and a minimum inhibitory concentration of 75 mg/mL. Additionally, the extracts showed DPPH and ABTS radical scavenging activities, with 80% ethanol extract displaying the highest antioxidant potential with IC50 values of 231.3 μg/mL for DPPH assay and 208.9 μg/mL for ABTS assay. In conclusion, earthworm powder extracts exhibited significant biological activities, rendering them promising candidates for pharmaceutical and cosmetic applications.
Quantum information and the C-theorem in de Sitter
Authors: Abate Nicolás , Torroba Gonzalo
Published: January 2025

Information-theoretic methods have led to significant advances in nonperturbative quantum field theory in flat space. In this work, we show that these ideas can be generalized to field theories in a fixed de Sitter space. Focusing on 1+1-dimensional field theories, we derive a boosted strong subadditivity inequality in de Sitter, and show that it implies a C-theorem for renormalization group flows. Additionally, using the relative entropy, we establish a Lorentzian bound on the entanglement and thermal entropies for a field theory inside the static patch. Finally, we discuss possible connections with recent developments using unitarity methods.
A rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning
Authors: Zhang Ping , Liu Debo
Published: January 2025

It usually affects the accuracy and reliability of deep learning based intelligent diagnosis methods under the condition of insufficient samples. Existing methods for handling insufficient samples often have problems such as requiring rich expert experience or consuming a lot of time. To solve the above problems, a rolling bearing fault diagnosis method under insufficient samples condition based on multi-scale long-term and short-term memory network (MSLSTM) transfer learning is proposed, which mainly consists of an improved long-term and short-term memory network named as MSLSTM and transfer learning. By introducing multi-scale convolution operation into the traditional LSTM to improve its drawback that only extracts single type of fault feature information, which leads to poor diagnostic performance in noisy environments. Besides, the pooling layer and global average pooling layer in traditional LSTM are replaced with convolution operation to avoid the problem of information loss. Subsequently, the MSLSTM is combined with transfer learning, and a rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning is proposed, which fine tunes the model parameters using a small amount of target domain data. Feasibility of the proposed method is verified through two kinds of experiments. The proposed method has stronger feature extraction ability and training efficiency compared with other models.
Multi-scale optimization design of viscoelastic damping sandwich plate
Authors: Fang Zhanpeng , An Haoping , Xiao Yanqiu
Published: January 2025

Aiming at the optimization design problem of viscoelastic damping sandwich plate under the background of lightweight, a multi-scale optimization design method is proposed to realize the collaborative optimization design of viscoelastic damping sandwich plate in both macro and micro scales. It is assumed that the viscoelastic damping material is microscopically composed of 3D periodic unit cells. The effective constitutive matrix of the unit cell is derived using the homogenization method. The topology optimization model is established, and sensitivity analysis is conducted. The iteration of the design variables is realized by the Optimization Criterion (OC) method. Several numerical examples are presented to demonstrate the effectiveness of the proposed multi-scale optimization method. In-depth discussions are given for the impact of optimization objectives and volume fractions on the design results. The results show that the optimized configuration on both scales are dependent on the objective mode. The macroscopic optimal layout is affected by the microscopic volume fraction, while the change in macroscopic volume fraction has little effect on the microscopic optimal configuration. The dynamic characteristics of the viscoelastic damping sandwich plate are improved significantly through multi-scale optimization design.
Exploring PANoptosis Related Novel Diagnostic Biomarkers and Potential Drugs for Sarcopenia based on Machine Learning and Experimental Validation
Authors: Deng Zhibo , Song Chao , Zhang Rongsheng , Xiu Yu , Yang Linhai , Dai Hanhao , Luo Jun , Xu Jie
Published: January 2025

Background:

Sarcopenia, an aseptic chronic inflammatory disease, is a complex
and debilitating disease characterized by the progressive degeneration of skeletal muscle.
PANoptosis, a novel proinflammatory programmed cell death pathway, has been linked to
various diseases. However, the precise role of PANoptosis-related features in sarcopenia remains
uncertain.

Methods:

According to the intersection of differentially expressed genes (DEGs) in the sarcopenia
dataset GSE167186 and the PANoptosis gene set, we classified patients into
PANoptosis-related subtypes (PANRS) using consensus clustering. The DEGs of PANRS
were intersected with weighted gene co-expression network analysis (WGCNA). Proteinprotein
interaction network and cytoHubba algorithms were employed to further identify potential
genes related to PANoptosis. The most characteristic genes were selected using LASSO
regression and validated by ROC curve analysis, followed by relevant immune infiltration
analysis. Additionally, small-molecule drug screening was performed using Cmap. The
relative expression levels of hub genes in sarcopenia were confirmed by PCR. Finally,
single-cell analysis and GSEA were used to examine the distribution and function of hub
genes.

Results:

Thirty-five candidate genes were identified through WGCNA and PANRS. Machine
learning and ROC curve analysis revealed three core genes: LTBP2, ETS2, and
H3.3B, all of which were up-regulated in patients with sarcopenia (p<0.01). Immune infiltration
analysis indicated that these three diagnostic genes were linked to the activation of NK
cells and macrophages. Single-cell analysis demonstrated that LTBP2 was mainly localized
in fibroblasts, while ETS2 and H3.3B exhibited a uniform distribution. Enrichment analysis
indicated that the three hub genes were predominantly associated with the inhibition of energy
metabolism.

Conclusion:

In this study, the hub genes LTBP2, ETS2, and H3.3B associated with PANoptosis
in sarcopenia were successfully identified through a combination of bioinformatics and
experimental verification methods. This establishes a foundation for new candidate diagnostic
and therapeutic targets for sarcopenia.
LGALS3BP: A Potential Prognostic Biomarker Influencing Antitumor Immunity in Triple-negative Breast Cancer
Authors: Hu Anqi , Pan Shuaikang , He Yuan , Wang XueRu , Qian Dong , Li Xiaoyang
Published: January 2025

Objective:

LGALS3BP exhibits differential expression in various types of tumors.
This study aimed to analyze its potential diagnostic and prognostic value in Triple-
negative Breast Cancer (TNBC).

Methods:

We conducted a comprehensive analysis of LGALS3BP's differential expression
and its association with patient survival outcomes using data from public databases.
To further validate these findings, Immunohistochemistry (IHC) experiments were performed
to confirm the differential expression of LGALS3BP protein in TNBC. Additionally,
we also investigated the relationship among LGALS3BP, tumor immune infiltration,
and drug sensitivity.

Results:

Results indicated LGALS3BP to be significantly upregulated in TNBC, with its
high expression correlating with improved survival outcomes. Furthermore, LGALS3BP
expression correlated with immune cell infiltration. Notably, high LGALS3BP expression
may confer a greater likelihood of benefiting from immunotherapy.

Conclusion:

LGALS3BP may serve as a diagnostic and prognostic biomarker for TNBC.
Synthesis and Anti-Toxoplasma Activity In vitro of Arctigenin Derivatives
Authors: he miaohai , jin chunmei , lu pingzhu , yue xiuyong , yang hongjing , fu zhengfeng , qin xue , yang haoran
Published: January 2025

Background:

At present, the therapeutic drugs for Toxoplasmosis have serious side effects
and limitations in application, so it is urgent to develop low-toxicity and high-efficiency drugs.

Objective:

A series of new derivatives based on arctigenin were designed and synthesized, aiming to
obtain target derivatives with superior anti-Toxoplasma activity.

Methods:

A series of quinoline groups were introduced into the phenolic hydroxyl group of arctigenin
compound, and 29 novel arctigenin derivatives were designed and synthesized. The chemical
structures were confirmed by 1H NMR, 13C NMR, and HRMS spectra. The cytotoxicity of all
compounds to host cells (HeLa) and the half inhibitory concentration of HeLa cells infected with
Toxoplasma gondii were determined by the MTT assay, and the selectivity index (SI) was calculated.

Results:

The selectivity index of compounds B8 and B12 was 1.45, indicating the anti-Toxoplasma
activity of compound B8 and B12 to be higher than that of the lead compound arctigenin (SI= 0.99)
and the positive control drug spiramycin (SI= 0.92).

Conclusion:

Compounds B8 and B12 demonstrated the most potent anti-Toxoplasma activity, with
an SI value of 1.45. This offers valuable guidance for the subsequent screening of more effective
anti-Toxoplasma drugs.
Environmental Metrics of World Cuisine Recipes' Plant-Based Substitutes: Greenhouse Gas Emissions, Water Footprint, and Land Use
Authors: Bayindir-Gümüş Aylin , Yardimci Hülya
Published: January 2025

Introduction/Objective:

Evidence has shown that consuming plant-based foods instead
of animal-based foods has positive effects on human and planet health. The current study aimed to
compare animal-based recipes (ABRs) and plant-based recipes (PBRs) nutritionally and environmentally.

Methods:

Plant-based recipes (adapted from animal-based recipes) were obtained from the “Vegan
Nutrition with Healthy and Delicious Recipes” book for inclusion in this study. Nutrient components,
including energy content, energy density (ED), macro and micronutrient profiles, such as environmental
metrics, which are greenhouse gas emissions (GHGE), water footprint (WF), and
land use (LU), were calculated and compared between animal-based and plant-based recipes.

Results:

While dietary fiber and insoluble fiber were higher, protein, total fat, saturated fatty
acids, monounsaturated fatty acids, vitamin B12, and zinc contents were lower in PBRs, compared
to ABRs. With exceptions (magnolia, muffins, and vegan souffle), medians GHGS, WF, and LU
of PBRs were lower than those of ABRs(). Plant-based hamburgers and meatballs, whose main ingredients
were fresh vegetables, had the lowest environmental impact.

Conclusion:

It is well known that human diet and food choices are among the main components
that contribute to environmental impact. Although the ecological footprints of foods and recipes
are essential in the diet, their nutrient densities should not be overlooked. Enriching the nutritional
values of the foods used in PBRs may improve their dietary values. In addition, it should also be
forgotten that using high amounts of non-nutritious foods, such as sugar, in PBRs due to their low
ecological impact is incorrect.
High-prediction QSAR Modeling Study Based on the Efficacy of a Novel 6-hydroxybenzothiazole-2-carboxamide Targeted Monoamine Oxidase B in the Treatment of Neurodegenerative Diseases
Authors: Xie Dong , Cai Zhibiao , Mao Junxiang , Qu Xiaodong , Cao Li , Zhou Jie
Published: January 2025

Background:

Neurodegenerative diseases are a group of disorders characterized by progressive
neuronal degeneration and death, of which Alzheimer's disease and Parkinson's disease
are the most common. These diseases are closely associated with increased expression of monoamine
oxidase B (MAO-B), an important enzyme that regulates neurotransmitter concentration, and
its overactivity leads to oxidative stress and neurotoxicity, accelerating the progression of neurodegenerative
diseases. Therefore, the development of effective MAO-B inhibitors is important for
the treatment of neurodegenerative diseases.

Objective:

This study aims to improve the prediction of the efficacy of novel 6-hydroxybenzothiazole-
2-carboxamide compounds in inhibiting MAO-B by improving the quantitative constitutive
effect relationship (QSAR) modeling and to provide a theoretical basis for the discovery of
novel neuroprotective drugs.

Methods:

The study first optimized the structures of 36 compounds using the heuristic method
(HM) in CODESSA software to construct linear QSAR models. Subsequently, key descriptors were
screened by using the gene expression programming (GEP) technique to generate nonlinear QSAR
models and validate them.

Results:

The R², F-value, and R²cv of the linear model were 0.5724, 10.3752, and 0.4557, respectively,
whereas the nonlinear model constructed by the GEP algorithm showed higher prediction
accuracies by achieving R² values of 0.89 and 0.82, and mean squared errors (MSE) of 0.0799 and
0.1215 for the training and test sets, respectively. In addition, molecular docking experiments confirmed
that the novel compound 31 was tightly bound to the MAO-B active site with significant
inhibitory activity.

Conclusion:

In this study, we successfully improved the prediction ability of the efficacy of novel
6-hydroxybenzothiazole-2-carboxamide compounds to inhibit MAO-B by improving the QSAR
model. This not only provides new drug candidates for the treatment of neurodegenerative diseases,
but also provides important theoretical guidance for subsequent drug design and development,
which can help accelerate the process of new drug discovery and reduce the disease burden of patients.
Biosynthesis and Characterization of Silver Nanoparticles using Hypochaeris
radicata L. Extract and their Bioactivity
Authors: Tarmoul Houria , Brahmi Fatiha , Issaadi Ouarda , Chennit Boualam , Djermoune Atmane , Elsebai Mahmoud Fahmi , Boulekbache-Makhlouf Lila
Published: January 2025

Background:

The use of plant extracts in the biosynthesis of nanoparticles has garnered attention in recent years due to the rapid, ecological, and economical production protocol.
The current study aimed to biosynthesize silver nanoparticles (AgNPs) of Hypochaeris radicata
L. extract

Methods:

These nanoparticles were characterized by UV-Vis, Fourier Transform Infrared Spectroscopy (FTIR), and X-Ray Diffraction (XRD) to determine their size and composition. The bioactivity was assessed by the determination of their antimicrobial effect using the well diffusion
method and their cytotoxicity by hemolysis method.

Results:

The results showed that AgNPs have been successfully synthesized and exhibit a surface
plasmon resonance spectrum with an absorption maximum at 420 nm. FTIR affirmed the role of
H. radicata L. as a reducing and capping agent of silver ions and XRD revealed their crystalline
nature. Interestingly, the synthesized AgNPs exhibited significant antimicrobial effects particularly against Escherichia coli and Aspergillus niger with inhibition zones of 20.5 and 19 mm, respectively. The cytotoxicity test confirmed the non-toxic characteristics of the synthesized
AgNPs

Conclusion:

The bio-ingredients present in the plant extract were effective for the synthesis of
Ag nanoparticles with significant biological efficacy
Aloperine Alleviates Atherosclerosis by Inhibiting NLRP3 Inflammasome Activation in Macrophages and ApoE-/- Mice
Authors: Wang Zengxu , Wang Yuchuan , Raza Faisal , Zafar Hajra , Guo Chunling , Sui Weihua , Yang Yongchao , Li Ran , Fang Yifen , Li Bao
Published: January 2025

Background and Aims:

Atherosclerosis is a chronic cardiovascular disease which is regarded as one of the most common causes of death in the elderly. Recent evidence
has shown that atherosclerotic patients can benefit by targeting interleukin-1 beta (IL-1β). Aloperine (ALO) is an alkaloid which is mainly isolated
from Sophora alopecuroides L. and has been recognized as an anti-inflammatory disease. Herein, the effect of ALO on atherosclerosis was
investigated.

Methods:

ApoE-/- mice fed with western diet received ALO once daily. Plaques in the aortas were evaluated using oil red O and hematoxylin & eosin (H&E)
staining. Inflammation, lipids and kinases phosphorylation levels were evaluated using ELISA assay and western blot. Pyroptosis was examined by
THP-1 cells treated with oxidized low-density lipoprotein (ox-LDL).

Results:

Plaque development in aortic sinus and en face aortas were reduced after ALO treatment in ApoE-/- miceTreatment with ALO ameliorated
inflammation and profile of blood lipid. Western blot assay showed that ALO treatment substantially inhibited phosphorylation of p38 and Jun Nterminal
kinase (JNK) in aorta of ApoE-/- mice. Meanwhile, ALO significantly inhibited levels of IL-1β and IL-18 in serum and cleaved caspase-1
and IL-1β expression in aorta of ApoE-/- mice. Interestingly, ALO mildly increased pro-caspase-1 expression in ApoE-/- aorta in comparison with
saline group. In a dose dependent fashion, ALO treatment markedly inhibited ox-LDL-induced IL-1β and IL-18 levels in THP-1 cells and reduced
cleaved caspase-1 and IL-1β expression and caspase-1 activity, while ALO had little effect on nod-like receptor protein containing pyrin-3
(NLRP3), apoptosis associated speck-like protein containing a caspase-1 recruitment domain (ASC).

Conclusion:

It is of great practical significance to find the natural product to regulate macrophage pyroptosis, which are key drivers to accelerate the
progression of atherosclerosis. ALO could inhibit NLRP3 inflammasome activation in macrophages during atherogenesis, which may serve as a
potential candidate for the treatment of atherosclerosis.
INFLUENCE OF LORJUK MUSSEL SHELLS AND PEANUT SHELLS AS CARBURIZER MEDIA IN THE PACK CARBURIZING PROCESS OF AISI 1020
Authors: Achmadi Achmadi , Widodo Teguh Dwi , Irawan Yudy Surya
Published: January 2025

AISI 1020 steel is low-carbon steel with a carbon content of 0.20% and good ductility but low hardness. One of the methods to increase the hardness is by infusing carbon. The addition of carbon, called carburizing, was done by heating at a high enough temperature, namely at austenite temperature, in an environment containing active carbon atoms so that the active carbon atoms would diffuse into the steel surface and reach a certain depth. After the diffusion process, a rapid cooling treatment (quenching) is followed to obtain a harder surface, but the center is ductile. The carburizing process requires activation energy to diffuse carbon in the material. Catalysts are one solution to increase activation energy so that the time required during the carburization process is less. The study used carburizer from Lorjuk mussel shells as a catalyst and peanut shell charcoal as a source of activated carbon. The variations used were 0% catalyst/100% charcoal, 10% catalyst/90% charcoal, 20% catalyst/80% charcoal, and 30% catalyst/70%. The results of the study obtained the highest hardness and carbon diffusion values, the smallest corrosion rate obtained in the addition of 30% catalyst, and the lowest hardness in specimens without catalyst.
THE EFFECT OF EXHAUST EMISSIONS L, O2, CO, CO2 AND HYDROCARBON FOR PERFORMANCE OF ALL NEW ERTIGA AUTOMOTIVE
Authors: Batutah Moh Arif , Nguyen Duong Tuan Anh
Published: January 2025

The automotive with various and brands, both from Asia and Europe are all here. How much exhaust emissions are produced if each person uses the motor vehicle itself. The purpose of this study is to compare the quality of fuel used with the exhaust emissions of l, O2, CO, CO2 and HC produced. The test method is conducting sampling tests on All New Ertiga and testing emissions using a Gas Analyzer Tester. The fuel variants used in the emission testing process are pertamax, pertamax + octan booster, pertamax turbo and pertamax turbo + octan booster. With several variants of fuel used, can find effect l, O2, CO, CO2 and HC produced on these automotive.  The best engine performance at 115 Nm of torque with emissions are still below TLV (threshold limit value) for pertamax, while for pertamax turbo the best performance at 120 Nm with emissions below TLV.
MAPPING OF NDVI INDEX BASED MANGROVE AREA AND DENSITY CHECTARENGES USING LANDSAT 8 SATELLITES IMAGES IN NORTHERN COASTAL AREA OF EAST JAVA PROVINCE, INDONESIA
Authors:   Lecturer of the Water Resource Management Study Program, Faculty Fisheries and Marine Research, Zakiyah Umi , Gayatri Alifia ,   Water Resource Management Study Program, Faculty Fisheries and Marine Research, Maharani Patricia Dita ,   Water Resource Management Study Program, Faculty Fisheries and Marine Research, Mulyanto Mulyanto ,   Lecturer of the Water Resource Management Study Program, Faculty Fisheries and Marine Research, Arfiati Diana ,   Lecturer of the Water Resource Management Study Program, Faculty Fisheries and Marine Research, Loka Widi Ayom ,   Water Resource Management Study Program, Faculty Fisheries and Marine Research
Published: January 2025

Mangrove ecosystems are important for maintaining ecological balance in coastal areas. To monitor changes in mangrove area and density, remote sensing technology with Landsat 8 and NDVI Index was used. The research focuses on the North Coast of East Java, from 2019-2024, on the coast of Surabaya, Sidoarjo, Pasuruan, Probolinggo, and Situbondo. The purpose of this study is to analyze changes in mangrove area and density. The survey method was conducted with Landsat 8 images and in situ data in 2024. There are 5 observation station points with the determination of station points using purposive sampling technique. Accuracy test using RMSE. The area of mangroves on the coast of Surabaya, Sidoarjo and Pasuruan from 2019 to 2024 ranged from 1611.88 ha-2270.41 ha. The highest cover area was in 2024 and the lowest in 2019. Mangroves with sparse NDVI category ranged from 46640.61 ha-53284.95 ha. Moderate NDVI ranged from 4313.25 ha-6984.72 ha and dense NDVI ranged from 493.29 ha-4711.5 ha. The area of mangroves on the coast of Probolinggo and Situbondo increased from 2019-2024, which amounted to 1487.05 ha-1885.79 ha. Mangroves with sparse NDVI category have an area between 51367.12 ha-54852.64 ha. Moderate NDVI ranged from 5564.87 ha-54852.64 ha and high NDVI ranged from 668.18 ha-1585.52 ha. The results show that there are changes in the area and density of mangroves on the north coast of East Java every year. These changes need to be monitored annually to address the decline in mangrove area and density quickly and appropriately.
Analisis Kasus Kecanduan Media Sosial Menggunakan Metode Runge-Kutta Orde 15
Authors: Fatahillah Arif , Pradista Vyke Triawilly , Prihandini Rafiantika Megahnia , Setiawani Susi , Monalisa Lioni Anka
Published: January 2025

ecanduan media sosial adalah salah satu kasus yang dapat dianalisis menggunakan matematika. Kasus ini dapat dikemas menjadi model matematika. Model matematika yang digunakan pada penelitian ini adalah SEARQ. Metode runge-kutta orde 15 adalah metode numerik yang digunakan untuk menyelesaikan kasus kecanduan media sosial pada penelitian ini. Penelitian ini menganalisa konvergensi, efektivitas, dan efisiensi metode runge-kutta orde 15 serta melakukan simulasi numerik untuk melihat perilaku penyebaran kasus kecanduan media sosial. Data yang digunakan dalam penelitian ini diperoleh dari hasil kuesioner dan metode dokumentasi. Hasil dari penelitian ini mendapatkan dua formula metode runge-kutta orde 15 yang dinamakan RK15X dan RK15Y. Hasil analisa kasus kecanduan media sosial secara numerik menunjukkan bahwa kasus kecanduan media sosial ini bersifat endemik. Kecanduan media sosial dapat menjadi kasus yang merebak dan menetap dari waktu ke waktu.
Does Self-Expression Really Matter? Investigating Gender Perspective in Global Luxury Fashion Purchase
Authors: Anand Thakur  , Ishita Bansal
Published: January 2025

The fashion industry's business is expanding more than ever with the growing tendency of people towards self-glorification. According to McKinsey (2020), brands like LVMH, Kering, and Richemont defied market expectations with an extreme surge in sales in the second quarter of 2021. Luxury fashion brands enable individuals to express their personality traits in society. Through their symbolic appeals, these brands attempt to resonate with consumers' feelings. Self-expressive products reflect the essence of consumer personality. The expectations of both men and women vary considerably with the symbolic appeals of the brands. The present study evaluates the perceived significance of self-expression through luxury fashion brands in male and female consumers. A cross-sectional survey of 217 university students using the top ten global luxury brands, including Louis Vuitton, Chanel, Hermes, Gucci, Rolex, Cartier, Dior, Saint Laurent / Yves Saint Laurent, Burberry, Prada from Northern India was conducted. An independent sample Z test was used to analyse the perceived significance of self-expression in both genders. Results indicate that males consider ‘the inner self’, ‘social role’, ‘social perception’, and ‘personal connection’ as essential dimensions of self-expression. At the same time, females value the dimensions of ‘personality’, ‘social status’, ‘social image’, and ‘inner self’in global luxury fashion purchases, and overall perceived significance is the same for both genders. Marketers can utilise the study's findings while designing symbolic attributes of luxury fashion products.
Property Estimation of Thin Cortical Bone from Clinical QCT: An Analysis by Synthesis Approach
Authors: Reinholt Stefan Patrick
Published: January 2025

This dissertation addresses the challenge of assessing the thickness and density of thin cortical bone. Due to the limited spatial resolution of Quantitative Computed Tomography (QCT) the Nyquist-Shannon sampling
theorem is violated, making direct reconstruction impossible. To overcome this, a probabilistic Analysis by Synthesis (AbS) framework is proposed that exploits the geometry of the bone and the anisotropy of the
CT point spread function. By modeling uncertainties through a bone and measurement model, the AbS optimization is framed as a maximum a posteriori problem, using a Monte Carlo expectation maximization strategy
to infer the bone parameters. The framework is validated on high-resolution µ-CT scans of real vertebrae, demonstrating its ability to accurately estimate cortical bone thickness and density.
While there are limitations in mapping the spatial distribution of these parameters, the results are promising and further research offers the potential to refine and improve the method.
AI Machine Learning–Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis
Authors: Lee Hocheol , Park Myung-Bae , Won Young-Joo
Published: January 2025

Background

Diabetes is prevalent in older adults, and machine learning algorithms could help predict diabetes in this population.

Objective

This study determined diabetes risk factors among older adults aged ≥60 years using machine learning algorithms and selected an optimized prediction model.

Methods

This cross-sectional study was conducted on 3084 older adults aged ≥60 years in Seoul from January to November 2023. Data were collected using a mobile app (Gosufit) that measured depression, stress, anxiety, basal metabolic rate, oxygen saturation, heart rate, and average daily step count. Health coordinators recorded data on diabetes, hypertension, hyperlipidemia, chronic obstructive pulmonary disease, percent body fat, and percent muscle. The presence of diabetes was the target variable, with various health indicators as predictors. Machine learning algorithms, including random forest, gradient boosting model, light gradient boosting model, extreme gradient boosting model, and k-nearest neighbors, were employed for analysis. The dataset was split into 70% training and 30% testing sets. Model performance was evaluated using accuracy, precision, recall, F1 score, and area under the curve (AUC). Shapley additive explanations (SHAPs) were used for model interpretability.

Results

Significant predictors of diabetes included hypertension (χ²1=197.294; P<.001), hyperlipidemia (χ²1=47.671; P<.001), age (mean: diabetes group 72.66 years vs nondiabetes group 71.81 years), stress (mean: diabetes group 42.68 vs nondiabetes group 41.47; t3082=−2.858; P=.004), and heart rate (mean: diabetes group 75.05 beats/min vs nondiabetes group 73.14 beats/min; t3082=−7.948; P<.001). The extreme gradient boosting model (XGBM) demonstrated the best performance, with an accuracy of 84.88%, precision of 77.92%, recall of 66.91%, F1 score of 72.00, and AUC of 0.7957. The SHAP analysis of the top-performing XGBM revealed key predictors for diabetes: hypertension, age, percent body fat, heart rate, hyperlipidemia, basal metabolic rate, stress, and oxygen saturation. Hypertension strongly increased diabetes risk, while advanced age and elevated stress levels also showed significant associations. Hyperlipidemia and higher heart rates further heightened diabetes probability. These results highlight the importance and directional impact of specific features in predicting diabetes, providing valuable insights for risk stratification and targeted interventions.

Conclusions

This study focused on modifiable risk factors, providing crucial data for establishing a system for the automated collection of health information and lifelog data from older adults using digital devices at service facilities.
Subreddit to Symptomatology: A Lexicon-based Approach to Extract Symptoms of Complex Conditions from Online Discourse (Preprint)
Authors: Hossain Bushra , Preum Sarah M , Ali Mohammed Eunus , Rabbi Md Fazle , Ara Rifat
Published: January 2025

BACKGROUND

Millions of people affected with complex medical conditions with diverse symptoms, often turn to online discourse to share their experiences. While some studies have explored natural language processing (NLP) methods and medical information extraction tools, these typically focus on generic symptoms in clinical notes and struggle to identify disease-specific, subtle symptoms from the informal language used on social media.

OBJECTIVE

We aim to extract disease-specific symptoms from peoples’ lived experiences shared on social media, exploring their characteristics, prevalence, and occurrence patterns.

METHODS

We propose a lexicon-based symptom extraction (LSE) method to identify a comprehensive list of disease symptoms, capturing nuanced mentions in social media posts. We initially use a large language model (LLM) to automate the extraction of symptom-related keyphrases that form the lexicon. We evaluated the effectiveness of lexicon extraction against human annotation using a Jaccard Index score. Then, we perform embeddings such as BERT-base, BioBERT, Phrase-BERT to learn representations of these symptom-related keyphrases and employ clustering techniques such as k-means and HDBSCAN. Finally, we chose the BioBERT-based k-means clustering to characterize the unique symptoms. Additionally, we applied symptom normalization to eliminate duplicate and redundant entries in the comprehensive symptom list. We evaluated the outcome with prevalent baselines and major health guidelines.

RESULTS

In a real-world Polycystic Ovary Syndrome (PCOS) subreddit dataset, we find that LSE significantly outperforms state-of-the-art baselines, achieving at least 41% and 20% higher F1 scores than automatic medical extraction tools and LLMs, respectively. Notably, the comprehensive list of 64 PCOS symptoms generated by LSE ensures extensive coverage of symptoms reported in 7 reputed e-health forums. Analyzing PCOS symptomatology reveals 28 emerging new symptoms including 8 self-reported comorbidities concurring with PCOS.

CONCLUSIONS

The comprehensive patient-reported disease-specific symptom list potentially helps patients and health practitioners resolve the uncertainties surrounding the disease, eliminating the variability of PCOS symptoms prevailing in the community. Analyzing PCOS symptomatology across varied dimensions provides valuable insights for public health research.
The association of follicle stimulating hormone with the level of triglycerides during postmenopausal periods in the prediction of cardiovascular diseases. A correlation meta-analysis (Preprint)
Authors: Kindie Yonatan
Published: January 2025

BACKGROUND

Background: Different studies conducted by different researchers about the metabolism of lipids among postmenopausal women is related with the level of estrogen but recent evidence has provided evidence for extragonadal effects of FSH on lipids. However, the findings are still unclear and inconsistent that necessitated systematic review and metanalysis

OBJECTIVE

Objective: To determine the effect of follicle stimulating hormone on the level of triglycerides among postmenopausal women

METHODS

Method: PubMed, Cochran library, Scopus and Google scholar was used to search literatures systematically and a total of 747 papers published between 1998-2021 were retrieved. Studies with postmenopausal women as study participants and had reported either of the correlation coefficient or beta coefficient of triglycerides were included. The pooled estimated correlation coefficient between triglycerides and follicle stimulating hormone was computed using random effect model. Heterogeneity test was conducted by using forest plot. Publication bias was checked using eggers test. Meta-regression and subgroup analysis was done to find out the responsible modifiers for the overall correlation coefficient.

RESULTS

Result: A total of 11 papers ,8 cross-sectional,2 retrospective cohort and 1 prospective cohort were included in this systematic review and meta-analysis. The pooled correlation coefficient was calculated for triglycerides and follicle stimulating hormone and positive association were observed between triglycerides and follicle stimulating hormone.

CONCLUSIONS

Conclusion
The levels of FSH positively associated with TG and but the relationship was not significant.
The use of wearables with real-time feedback in rhythmic activity among older adults: A qualitative study (Preprint)
Authors: Lu Tien-Ying , Che Jie , Rosato Aurora , Dual Seraina , Kuoppamäki Sanna
Published: January 2025

BACKGROUND

Wearable devices with real-time feedback (WRF) provide increasing opportunities to enhance adherence and improve performance in rehabilitation through collecting and processing health-related data. Real-time feedback from the device is expected to result in a more dynamic, coordinated and synchronous rhythmic activity. However, age-specific characteristics of WRF integrating real-time audio or sound feedback during rhythmic activity have largely remained unexplored.

OBJECTIVE

The study investigates the use of a wearable device with real-time feedback (WRF) for rhythmic activity among older adults aged 65 and above from the perspective of age heterogeneity. The study explores older adults’ previous use of wearables to track their physical activity, adherence with WRF during rhythmic activity, and the interactive features of WRF enhancing the synchronisation during rhythmic activity.

METHODS

The study was conducted as a qualitative interview study with 18 older adults who used a WRF for rhythmic activity during a 3-week period at home in Sweden. The wearable used in the study is a chest-band sensor device that helps users to synchronise their steps with their heartbeat through the provision of real-time audio feedback. The material consists of a pre-interview before using the device, and post-interview after using the device. Material from the semi-structured interviews was analysed with an inductive content analysis method. Device-generated data on the usage of WRF for rhythmic activity were used to contextualise the qualitative interview data.

RESULTS

The study identified three themes in the emergence of age heterogeneity in the use of wearables: (1) Limited or non-use of wearables in physical activity, (2) synchronisation of step and heart rate in device-guided rhythmic activity, and (3) adherence to interaction modalities and audio feedback with a WRF. The contradiction between personal goals and device-related features might hinder the use of wearables, whereas enjoyment, sense of accomplishment and social motivations can drive the uptake of wearables. Device-guided synchronisation required participants to change their natural walking rhythm, which resulted in a sense of disruption during rhythmic activity. Real-time audio feedback was considered convenient in finding the right walking rhythm, which resulted in perceived improvement in synchronisation over time.

CONCLUSIONS

Age heterogeneity in the use of wearables with real-time feedback is pronounced at three interconnected levels: previous use of wearables, efficiency in device-guided synchronisation and adherence to interaction modalities. The study concludes that real-time feedback can enhance the user experience in synchronisation during rhythmic activity, but the heterogeneity in responding to these modalities require adaptive solutions to ensure long-term engagement with wearables.
Self-Logical Consistency Assessment of Large Language Models for Patient Feedback Classification : Algorithm Development and Validation Study (Preprint)
Authors: Loi Zeno , Morquin David , Derzko François-Xavier , Corbier Xavier , Gauthier Sylvie , Taourel Patrice , Prin-Lombardo Emilie , Mercier Grégoire , Yauy Kévin
Published: January 2025

BACKGROUND

Patient satisfaction feedback is crucial for hospital service quality, but manual reviews are time-consuming, and traditional natural language processing methods remain inadequate. Large Language Models (LLMs) show promise but are prone to extrinsic faithfulness hallucinations—fabricated or illogical outputs that limit their reliability in healthcare.

OBJECTIVE

This study aimed to evaluate the Self-Logical Consistency Assessment (SLCA), an original method designed to enhance LLM feedback classification reliability by enforcing a logically-structured chain of thought.

METHODS

SLCA uses two validation steps: self-consistency (identifying the most coherent response) and logical consistency (ensuring alignment with the original statement and expert classifications). We evaluated SLCA using GPT-4 and Llama-3.1 405B on 12,600 classifications from 100 patient feedback samples to assess hallucinations, and tested its performance on a 49,140-classification benchmark derived from 1,170 feedbacks.

RESULTS

SLCA reduced hallucinations among detected categories from 15.80% (168/1063) to 0.51% (4/786) with GPT-4 and from 7.17% (51/711) to 1.67% (10/599) with Llama-3.1, with residual errors confined to the emergency feedback category. On the benchmark, SLCA achieved precision-recall scores of 0.86-0.78 for GPT-4 and 0.84-0.58 for Llama-3.1. These results demonstrate SLCA’s ability to achieve human-level performance across LLMs.

CONCLUSIONS

SLCA offers a scalable, explainable solution for improving LLM classification reliability in healthcare. Its capacity to enhance performance without fine-tuning or additional training data positions it as a valuable tool for analyzing patient feedback and supporting hospital service quality improvement.
Depiction of Social Discrimination in Arvind Adiga’s The White Tiger
Authors: Dr. Priyanka Dr. Priyanka
Published: January 2025

Arvind Adiga has emerged as a strong writer who has emphatically given voice to the social evils of our contemporary postmodern and post-colonial world— both rural and urban . His debut novel The White Tiger is a prominent signature in the realm of Indian writing in English. In the novel he has meticulously depicted the social evils like corruption and poverty in India which are really dangerous and causing real nuisance in society. Aravind Adiga through his novel tries to throw light on the ugly side of social problems to show how such problems have created incorrigible circumstances to the under privileged Indians even in the twenty-first century.
Comparative Analysis of Machine Learning and Deep Learning Approaches in IoT-Enabled Enterprise Resource Planning Systems
Authors: Yadav Kanika
Published: January 2025

The integration of Internet of Things (IoT) technologies into Enterprise Resource Planning (ERP) systems has
revolutionized real-time decision-making, process automation, and operational efficiency. Machine Learning (ML) and Deep
Learning (DL) provide two distinct approaches to leveraging IoT data within ERP systems, each with its own strengths and
limitations. ML is well-suited for structured and semi-structured data, making it effective for tasks where the data format is
consistent and manageable. However, it struggles with large-scale unstructured data typical of IoT environments, such as
images and videos, and often requires manual, domain-specific feature extraction. In contrast, DL excels in processing
unstructured and high-dimensional data streams, such as those from IoT sensors, by automatically extracting relevant features
without human intervention. While ML is advantageous for simpler datasets and straightforward IoT applications, DL offers
superior performance for complex, high-dimensional, and unstructured data, making it particularly beneficial in advanced IoTenabled ERP systems
Applications of AI in Design, Manufacturing and Production
Authors: Choubey Mr. Amit
Published: January 2025

Innovation, Adaptation and Improvement are the major thrust for the manufacturing Industries. AI, an emerging
technology which is bringing a boon in the market industries. With its increasing features and technology now, it is making its
way for manufacturing and product designing. When it comes to AI the base starts with machine learning which has supervised
and unsupervised learning in it. Artificial Intelligence models to some extent has a conceptual adaptation of neural networks
,which works similar to that of human brain .AI analyses all the data and draws conclusions for machine and detect all the
irregularities and make perfect out of it.AI in relation to CAD is a virtual assistant which gives a broad way into the areas of
creativity and ideas which helps in creating multiple variants after recognising the pattern and which in turns helps in making
the products in less time. The application of AI which are based upon Machine learning, Internet of things ,block-chain
technology, cloud computing and cyber physical system have paved the path for significant improvement in the product
designing, quality of product ,improving the manufacturing process and has reduced the time between the development of the
new products and its launching while focusing on sustainability. This scientific paper describes how Artificial intelligence is
applied in designing of products, interfaces, relation to CAD packages and improving the way of manufacturing which is the
need of the hour and demand of society to nurture the satisfaction of customers.
Pengaruh Kemudahan, Kepercayaan, Kualitas Informasi, Dan Risiko Terhadap Keputusan Penggunaan E-Wallet OVO
Authors: Lazuardy Ghefira Mahar , Muslikhati Muslikhati , Millatina Afifah Nur
Published: January 2025

The purpose of this study is to analyze what is the variabel of Easiness, Trust, Information Quality and Risk to the Decision to Use E-wallet OVO. This is quantitative research with Structural Equation Model (SEM) software SmartPLS 4.0. This Source is primary data from questionnaire. The result shows variable Easiness no significant effect to the Decision to Use, while the variabel Trust, Information Quality and Risk significant effect to the Decision to Use. Based on the result of Goodness of Fit in Inner Model independent variable can explain the effect to the Decision to influenced by other variables.
Implementation of Deep Learning Based on Convolution Neural Network for Batik Pattern Recognition
Authors: Sugiarto Edi , Budiman Fikri , Fahmi Amiq
Published: January 2025

Batik as a cultural heritage is one of the heritages that needs to be preserved so that it continues to be recognized from generation to generation. Efforts to preserve batik can be made by using technology that can recognize batik motifs. Pattern recognition is a branch of science related to the identification, classification, and interpretation of patterns. Deep learning is one of the technologies that can be used very well for pattern recognition, especially for syllable and image recognition. Convolutional neural network (CNN) is one of the most popular deep learning methods and the most established algorithm for deep learning models. The main advantage of CNN over the preceding methods is its ability to automatically detect features, making the feature extraction and classification process highly organized. This study aims to apply CNN for batik pattern recognition. The batik patterns used were geometric patterns, divided into 7 batik classes. Experiments were conducted on 3100 data, consisting of 3000 for training set and 100 for testing set. At the preprocessing stage, the batik image was resized to 28x28, and the color was changed to grayscale. Training was carried out on 100, 200, and 300 epochs. The classification results prove that CNN can recognize batik patterns well with an accuracy rate of 95%.