bluuebunny/crossref_metadata_2025 huggingface dataset:
Dispersive optical activity for spectro-polarimetric imaging
Authors: Cao Zhijie , Sun Siwei , Wei Jingxuan , Liu Yong
Published: February 2025

A spectro-polarimetric imaging approach leverages optical rotatory dispersion in natural crystals to encode spectral information into polarization states. The system demonstrates effectiveness in laboratory and outdoor field experiments, showing potential for biological microscopy, machine vision, and remote sensing applications.
Risk prediction score and equation for progression of arterial stiffness using Japanese longitudinal health examination data
Authors: Inadome Naoko , Kawasoe Shin , Miyata Masaaki , Kubozono Takuro , Ojima Satoko , Mori Ryuko , Miyahara Hironori , Tokushige Koichi , Ohishi Mitsuru
Published: February 2025

The brachial-ankle pulse wave velocity (baPWV) is useful for evaluating arterial stiffness. No longitudinal studies have examined the association between multiple arterial stiffness risk factors and increased baPWV. We sought to identify factors associated with baPWV ≥1400 cm/s within 5 years and create an equation and simple risk score to predict its occurrence, using data from a large-scale Japanese health examination database. Of 10,284 participants aged 30–69 years for whom follow-up data were available over a 5-year period, 3394 men and 2710 women with baseline baPWV<1400 cm/s were analyzed. We used age, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), heart rate (HR), fasting blood sugar (FBS), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), estimated glomerular filtration rate (eGFR), habitual exercise, habitual drinking, and smoking history as variables. In the multivariate logistic regression analysis, baPWV≥1400 cm/s was associated significantly with age, BMI, SBP, DBP, HR, FBS, and TG in men and age, SBP, DBP, HR, and smoking history in women. A prediction score based on these factors yielded an area under the curve (AUC) for the 5-year incidence of baPWV≥1400 cm/s of 0.68 for men and 0.71 for women. Furthermore, a risk prediction equation for the 5-year incidence of baPWV≥1400 cm/s showed an AUC = 0.71 for men and 0.77 for women. The prediction equation and a simple prediction score are easy to implement clinically. The predictive ability of these scores and equations for arterial stiffness should be validated in prospective studies.
Long-term risk of cardiovascular mortality according to age group and blood pressure categories of the latest guideline
Authors: Satoh Michihiro , Ohkubo Takayoshi , Miura Katsuyuki , Harada Akiko , Tsutsui Anna , Hozawa Atsushi , Shimizu Yuji , Ishikawa Shizukiyo , Kokubo Yoshihiro , Okamura Tomonori , Murakami Yoshitaka ,   on behalf of the Evidence for Cardiovascular Prevention from Observational Cohorts in Japan (EPOCH–JAPAN) Research Group
Published: February 2025

This study examined the association between the latest blood pressure (BP) classification and cardiovascular disease (CVD) mortality risk, using data from 70,570 individuals across 10 Japanese cohorts. Participants were stratified by age (40–64 and 65–89 years) and antihypertensive treatment use. BP was classified according to the 2019 Japanese Society of Hypertension Guidelines. During a follow-up period of approximately 10 years, 2304 CVD deaths occurred. Cox models demonstrated that CVD mortality risk increased stepwise with the BP category, with this association being especially pronounced in patients aged 40–64 years, where the Grade I hypertension group showed the highest population-attributable fraction (PAF). When the treated participants were included in the hypertension group, the overall PAF for CVD mortality was 41.1%. Similar patterns were observed for CVD subtype mortality risk, with hypertension showing particularly high PAFs for intracerebral hemorrhage. These findings highlight the importance of early-stage prevention and management of hypertension.
Chimpanzees adapt their exploration to key properties of the environment
Authors: Haux Lou M. , Engelmann Jan M. , Herrmann Esther , Hertwig Ralph
Published: February 2025

Exploration is an important strategy for reducing the uncertainty that pervades daily life. Yet the evolutionary roots of adaptive exploration are poorly understood. We harness and adapt the human decisions-from-experience paradigm to investigate exploration under uncertainty in chimpanzees. In our study, chimpanzees (N = 15; eight females) are simultaneously confronted with an uncertain option (with outcome variance) and a safe option (without outcome variance) and tested in both stable and changing environments. Results reveal that, as in human exploration, how and how much chimpanzees explore depends on the environment. One key environmental property is change: Chimpanzees explore more across trials in changing than in stable conditions. Consistent with the assumption of classic economic models that variance indicates risk, chimpanzees also explore more when they experience variance in the options’ outcomes. Individual risk and uncertainty preferences did not have a statistically significant effect on exploratory efforts. These findings suggest that chimpanzees and humans share key similarities in the way they respond to risk and uncertainty.
STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics
Authors: Su Haohao , Wu Yuesong , Chen Bin , Cui Yuehua
Published: February 2025

One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE’s potential in spatial transcriptomics analysis.
Amphoteric chalcogen-bonding and halogen-bonding rotaxanes for anion or cation recognition
Authors: Tse Yuen Cheong , Docker Andrew , Marques Igor , Félix Vítor , Beer Paul D.
Published: February 2025

The ever-increasing demand in the development of host molecules for the recognition of charged species is stimulated by their fundamental roles in numerous biological and environmental processes. Here, capitalizing on the inherent amphoteric nature of anisotropically polarized tellurium or iodine atoms, we demonstrate a proof of concept in charged guest recognition, where the same neutral host structure binds both cations or anions solely through its chalcogen or halogen donor atoms. Through extensive 1H nuclear magnetic resonance titration experiments and computational density functional theory studies, a library of chalcogen-bonding (ChB) and halogen-bonding (XB) mechanically interlocked [2]rotaxane molecules, including seminal examples of all-ChB and mixed ChB/XB [2]rotaxanes, are shown to function as either Lewis-acidic or Lewis-basic multidentate hosts for selective halide anion and metal cation binding. Notably, the exploitation of the inherent amphoteric character of an atom for the strategic purpose of either cation or anion recognition constitutes the inception of a previously unexplored area of supramolecular host–guest chemistry.
Full-field Brillouin microscopy based on an imaging Fourier-transform spectrometer
Authors: Bevilacqua Carlo , Prevedel Robert
Published: February 2025

Brillouin microscopy is an emerging optical elastography technique that can be used to assess mechanical properties of biological samples in a three-dimensional, all-optical and hence non-contact fashion. However, the low cross-section of spontaneous Brillouin scattering produces weak signals that often necessitate prolonged exposure times or illumination dosages that are potentially harmful for biological samples. Here we present a new approach for highly multiplexed and therefore rapid spectral acquisition of the Brillouin-scattered light. Specifically, by exploiting a custom-built Fourier-transform imaging spectrometer and the symmetric properties of the Brillouin spectrum, we experimentally demonstrate full-field 2D spectral Brillouin imaging of phantoms as well as biological samples, at a throughput of up to 40,000 spectra per second, with a precision of ~70 MHz and an effective 2D image acquisition speed of 0.1 Hz over a ~300 × 300 µm2 field of view. This represents an approximately three-orders-of-magnitude improvement in speed and throughput compared with standard confocal methods, while retaining high spatial resolution and the capability to acquire three-dimensional images of photosensitive samples in biology and medicine.
Using a units ontology to annotate pre-existing metadata
Authors: Porter John H. , O’Brien Margaret , Frants Marina , Earl Stevan , Martin Mary , Laney Christine M.
Published: February 2025

Automated processing of environmental data is hindered by the wide array of unit representations provided in the metadata of digital datasets. For example, gm/m2, g/m2, gm-2, g/m^2, g.m-2 and gramPerMeterSquared are all representations of a single complex unit that might be human-readable but are not machine-interpretable. Connecting ad hoc units to a single unit concept in an ontology permits the identification of datasets sharing units and provides additional information regarding labels, definitions, dimensions and transformations provided in the ontology. Here we use successive string transformations to link ad hoc unit representations to units in the QUDT ontology (e.g., unit: GM-PER-M2). Although only 896 of 7,110 distinct units in a corpus of ecological metadata from DataONE, the Environmental Data Initiative and the U.S. National Ecological Observatory Network were matched, 324,811 unit uses (instances) out of 355,057 of total unit uses were successfully mapped to QUDT units (91%). The resulting lookup table was used to enable a web service and R functions for adding annotation elements to Ecological Metadata Language documents.
MRI-based analysis of thigh intramuscular fat and its associations with age, sex, and BMI using data from the osteoarthritis initiative data
Authors: Joseph Gabby B. , Akkaya Zehra , Sims Wynton M. , McCulloch Charles E. , Nevitt Michael C. , Lynch John A. , Lane Nancy E. , Link Thomas M.
Published: February 2025

The degree of thigh intramuscular fat in individuals without OA is fundamental for distinguishing natural variations in intramuscular fat from pathological changes. The goals of this study were to estimate the degree of thigh intramuscular fat in individuals without radiographic OA or frequent pain and assess the associations of age, sex, and BMI with the degree of intramuscular fat. Individuals without knee or hip radiographic OA, without total knee/hip arthroplasty, and without frequent knee/hip pain were selected from the OAI database (n = 710). Goutallier Grades (GGs) of the quadriceps and hamstring muscles were assessed based on 3 T MR images on a scale from 0 (normal muscle) to 4 (more fat than muscle). The associations between demographic variables and GG outcomes were evaluated using mixed effects models. The most prevalent GGs among the muscles were Grades 1 and 2; Grade 4 was infrequent (< 1%). Greater BMI (p < 0.001) and age (p < 0.001) were each associated with greater GG. Women had greater GG than men (greatest difference in the vastus medialis: coeff. = 0.214, p < 0.001). At lower BMI, women had greater intramuscular fat than men; at higher BMI, men had greater intramuscular fat than women (p = 0.029 for BMI-sex interaction). While individuals without radiographic OA or frequent pain generally had low thigh intramuscular fat, higher BMI and age were associated with greater intramuscular fat, and GGs were greater in women than men. The relationship between BMI and intramuscular fat was sex-dependent. Thus, demographic variables must be considered when evaluating intramuscular fat.
An extensive experimental analysis for heart disease prediction using artificial intelligence techniques
Authors: Rohan D. , Reddy G. Pradeep , Kumar Y. V. Pavan , Prakash K. Purna , Reddy Ch. Pradeep
Published: February 2025

The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive measures and personalized healthcare recommendations. Artificial intelligence is emerging as a powerful tool for healthcare applications, particularly in predicting heart diseases. Researchers are actively working on this, but challenges remain in achieving accurate heart disease prediction. Therefore, experimenting with various models to identify the most effective one for heart disease prediction is crucial. In this view, this paper addresses this need by conducting an extensive investigation of various models. The proposed research considered 11 feature selection techniques and 21 classifiers for the experiment. The feature selection techniques considered for the research are Information Gain, Chi-Square Test, Fisher Discriminant Analysis (FDA), Variance Threshold, Mean Absolute Difference (MAD), Dispersion Ratio, Relief, LASSO, Random Forest Importance, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA). The classifiers considered for the research are Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), Gradient Boosting Classifier, Extra Tree Classifier, CatBoost, LightGBM, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Convolutional Neural Network (CNN), and Hybrid Model (CNN, RNN, LSTM, GRU, BiLSTM, BiGRU). Among all the extensive experiments, XGBoost outperformed all others, achieving an accuracy of 0.97, precision of 0.97, sensitivity of 0.98, specificity of 0.98, F1 score of 0.98, and AUC of 0.98.
Development of a battery free, solar powered, and energy aware fixed wing unmanned aerial vehicle
Authors: Liller Jackson , Goel Rishabh , Aziz Abdul , Hester Josiah , Nguyen Phuc
Published: February 2025

Unmanned Aerial Vehicles (UAVs) hold immense potential across various fields, including precision agriculture, rescue missions, delivery services, weather monitoring, and many more. Despite this promise, the limited flight duration of the current UAVs stands as a significant obstacle to their broadscale deployment. Attempting to extend flight time by solar panel charging during midflight is not viable due to battery limitations and the eventual need for replacement. This paper details our investigation of a battery-free fixed-wing UAV, built from cost-effective off-the-shelf components, that takes off, remains airborne, and lands safely using only solar energy. In particular, we perform a comprehensive analysis and design space exploration in the contemporary solar harvesting context and provide a detailed accounting of the prototype’s mechanical and electrical capabilities. We also derive the Greedy Energy-Aware Control (GEAC) and Predictive Energy-Aware Control (PEAC) solar control algorithm that overcomes power system brownouts and total-loss-of-thrust events, enabling the prototype to perform maneuvers without a battery. Next, we evaluate the developed prototype in a bench-top setting using artificial light to demonstrate the feasibility of batteryless flight, followed by testing in an outdoor setting using natural light. Finally, we analyze the potential for scaling up the evaluation of batteryless UAVs across multiple locations and report our findings.
The beneficial effects of a probiotic mix on bone and lean mass are dependent on the diet in female mice
Authors: Ohlsson Claes , Lawenius Lina , Jiang Yiwen , Horkeby Karin , Wu Jianyao , Nilsson Karin H. , Koskela Antti , Tuukkanen Juha , Movérare-Skrtic Sofia , Henning Petra , Sjögren Klara
Published: February 2025

Bone mass and lean mass decrease with age and these changes are associated with increased fracture risk and sarcopenia. Previous studies demonstrated that a probiotic mixture of Lacticaseibacillus paracasei DSM13434, Lactiplantibacillus plantarum DSM 15312 and DSM 15313 (L. Mix) prevents bone loss in ovariectomized (ovx) female mice. The purpose of the present study is to test if the beneficial effect of L. Mix is modified by the diet. Female mice were fed either a high-fat (HFD, 60% kcal from fat) or a low-fat (LFD, 10% kcal from fat) diet and subjected to either sham or ovx surgery and treated with L. Mix for 12 weeks. L. Mix treatment increased total body bone mineral density (p ≤ 0.01), by increasing cortical bone area, and total body lean mass (p = 0.035) in mice on LFD but not in mice on HFD. Metagenome sequencing of cecal content showed that L. Mix treatment increased the relative abundance of Lacticaseibacillus paracasei and, Lactiplantibacillus plantarum, demonstrating successful treatment. In addition, the probiotic treatment affected the overall gut microbiota composition and functionality. These findings demonstrate that the L. Mix in combination with a healthy diet is beneficial for musculoskeletal health in female mice.
Polytypism phenomenon in GaN nanocrystals grown on van der Waals surface
Authors: Maidebura Yan , Mansurov Vladimir , Malin Timur , Aleksandrov Ivan , Zhuravlev Konstantin , Pécz Béla
Published: February 2025

The polytypism phenomenon in the growth of nanostructures reveals new properties and opportunities for bandgap engineering by enabling controlled formation of different phases of the same material with distinct crystalline...
Chemistry of lignin and condensed tannins as aromatic biopolymers
Authors: Seidi Farzad , Liu Yuqian , Huang Yang , Xiao Huining , Crespy Daniel
Published: February 2025

Lignin and tannins can be functionalized or depolymerized to yield large libraries of organic molecules.
Fluorinated-oligomeric Ionic Liquids for High-performance Wide-temperature Solid Zinc Batteries
Authors: Chen Ze , Liu Tong , Wei Zhiquan , Wang Yiqiao , Chen Ao , Huang Zhaodong , Cao Duanyun , LI Nan , Zhi Chunyi
Published: February 2025

Zn-based solid polymer electrolytes (SPEs) hold immense potential for developing high-performance and safe zinc ion batteries (ZIBs) that can operate effectively even at high temperatures. However, typical plasticizers like ionic...
Cr(iii)-incorporated Fe(iii) hydroxides for enhanced redox conversion of As(iii) and Cr(vi) in acidic solution
Authors: Liu Juanjuan , Gao Xubo , Dai Chong , Zhang Suona , Kong Shuqiong , Wang Lin , Hu Yandi
Published: February 2025

Cr-incorporation decreased the particle size of Fe hydroxides and promoted their intermediate formation, thus As/Cr removal was increased by promoting the adsorption and catalytic efficiency of Fe hydroxides.
Thiol–ene click reaction: a new pathway to hydrophilic metal–organic frameworks for water purification
Authors: Fang Mingyuan , Muñoz-Olivas Riansares , Montoro Carmen , Semsarilar Mona
Published: February 2025

Post-synthetic modification (PSM) is a useful strategy to introduce new functional groups to metal-organic framework (MOF) structures in order to tune their properties for different applications.
Computational design of Bax-inhibiting peptides
Authors: Vlaar Tom , Mayer Bernadette M. , van der Heide Lars P. , Ilie Ioana M
Published: February 2025

The proteins of the Bcl-2 family play crucial roles in regulating apoptosis. It is divided into pro-survival and pro-apoptotic proteins that determine cellular fate. In particular, Bax is a crucial...
Nanoporous platinum microelectrode arrays for neuroscience applications
Authors: Winter-Hjelm Nicolai , Isdal Leik , Köllensperger Peter A. , Sandvig Axel , Sandvig Ioanna , Sikorski Pawel
Published: February 2025

Microelectrode arrays are invaluable tools for investigating the electrophysiological behaviour of neuronal networks with high spatiotemporal precision.
A novel approach on reverse electroplating to remove diamond particles and recover stainless steel wire from waste diamond coated wire
Authors: Murugesan Bharathwaj , Pichan Karuppasamy , Perumalsamy Ramasamy
Published: February 2025

A novel method recovers waste diamond-coated wire (DCW) using reverse electroplating with optimized parameters to extract diamond particles and stainless-steel wire, addressing environmental and economic concerns.
Modulating the spin–flip rates and emission energies through ligand design in chromium(iii) molecular rubies
Authors: Ye Yating , Poncet Maxime , Yaltseva Polina , Salcedo-Abraira Pablo , Rodríguez-Diéguez Antonio , Martín Javier Heredia , Cuevas-Contreras Laura , Cruz Carlos M. , Doistau Benjamin , Piguet Claude , Wenger Oliver S. , Herrera Juan Manuel , Jiménez Juan-Ramón
Published: February 2025

Three homoleptic spin–flip (SF) emitters, namely [Cr(Mebipzp)2]3+ (1), [Cr(IMebipzp)2]3+ (2) and [Cr(bip*)2]3+ (3), have been successfully synthesized and characterized.
Latest advances in research methods for high-yield cellulase production
Authors: Xiang Xiyuan , Zhang Youhong , Wei Wei , Zhou Guohong
Published: February 2025

Lignocellulosic biomass, due to its accessibility, abundance, and environmental friendliness, has become a promising renewable resource.
Transport of partially active polymers in chemical gradients
Authors: Ravichandir Shashank , Valecha Bhavesh , Muzzeddu Pietro Luigi , Sommer Jens-Uwe , Sharma Abhinav
Published: February 2025

Connecting active and passive monomers to form partially active polymers can lead to directed transport towards regions of high or low chemical concentration.
Correction: Photothermal-responsive Prussian blue nanocages loaded with thrombin for tumor starvation therapy and photothermal therapy
Authors: Liu Yang , Yue Bingjie , Wang Ranran , Cong Hailin , Hu Hao , Yu Bing , Shen Youqing
Published: February 2025

Correction for ‘Photothermal-responsive Prussian blue nanocages loaded with thrombin for tumor starvation therapy and photothermal therapy’ by Yang Liu et al., Biomater. Sci., 2023, 11, 4938–4947, https://doi.org/10.1039/D3BM00526G.
Vinasse photoreforming for hydrogen production using Pt/TiO2 as catalyst under UV irradiation
Authors: Silvaino Patrícia Ferreira , Ferreira João Coutinho , Carminati Saulo Amaral , Vaz Jorge Moreira , Spinacé Estevam Vitorio
Published: February 2025

Schematic representation of the vinasse photoreforming for hydrogen production using Pt/TiO2 as catalyst under UV irradiation.
Unusual phenomena of ethylene homopolymerization catalyzed by α-diimine nickel emerging in a microflow platform
Authors: Zhang Zeen , Li Tianjiao , Dong Yong , Lu Yangcheng
Published: February 2025

This study focuses on ethylene homopolymerization catalyzed by α-diimine catalysts within a designed microfluidic system.
Correction: Additive manufacturing of highly conductive carbon nanotube architectures towards 3D-printed carbon-based flexible thermoelectric generators
Authors: Mytafides Christos K. , Wright William J. , Gustinvil Raden , Tzounis Lazaros , Karalis George , Paipetis Alkiviadis S. , Celik Emrah
Published: February 2025

Correction for ‘Additive manufacturing of highly conductive carbon nanotube architectures towards 3D-printed carbon-based flexible thermoelectric generators’ by Christos K. Mytafides et al., Energy Adv., 2024, 3, 1642–1652, https://doi.org/10.1039/D4YA00182F.
The research in Scotland event in Edinburgh: reflections and key issues
Authors: Terry Cassandra , French Fiona , Morrison Neil
Published: February 2025

To celebrate World Parkinson’s day, part of World Parkinson’s Awareness Month, a day of talks, panel discussions and networking was organised at the Royal College of Physicians of Edinburgh in 2024 to bring everyone with an interest in Parkinson’s disease (researchers, clinicians, people with Parkinson’s and their loved ones) together. The aim of the event was to share knowledge, express concerns and frustrations, hear about new advances in the field and build collaborations. Several important points were raised: the requirement for better awareness and better support for those living with Parkinson’s (including specialist care) and the need to invest in research and work together including the importance of Patient and Public Involvement to find better treatments and, hopefully, a cure.
A risk uncertainty–based flexible dispatch method for pumped storage under extreme weather
Authors: Feng Miaoyong , Huang Zhanhong , Yu Tao , Pan Zhenning , Liu Qianjin , Wang Ziyao , Liu Shuangquan
Published: February 2025

The large‐scale integration of renewable energy into new power systems presents significant challenges in terms of controllability and predictability due to its inherent randomness and volatility. The uncontrollability is further compounded by the sudden impacts of extreme weather events, making multi‐scale power balance increasingly difficult. This paper proposes a two‐stage flexible dispatch method (TFDM) for measuring uncertainty under extreme weather. The model constructs probabilistic disaster impact assessments for wind farms and transmission lines using fuzzy sets and captures the associated uncertainties through chance constraints and risk costs. It provides more flexible disaster risk reduction methods by integrating uncertainty prediction errors and unit commitment plans into the interaction between two‐stage dispatch. An accelerated algorithm that combines historical scene learning with improved K‐nearest neighbours (KNN) dispatch is proposed. It is employed to obtain initial solutions for binary unit commitment variables, accelerating the resolution of frequent unit combination switching problems. Tests on the IEEE 39‐bus and IEEE 118‐bus systems show that the proposed method can effectively utilize the regulating capacity of pumped storage units to significantly improve the resilience of the system to cope with extreme weather. The improved KNN algorithm has stronger convergence and efficiency for large‐scale power grid scenarios.
Chronic Kidney Disease Prediction Based On Machine Learning Algorithms
Authors: Kethineni Likitha. , Nithinchandra  , Kumar Narendra , Sk Sajida Sultana.
Published: February 2025

Chronic kidney disease is a progressively bad disease. In most cases, there are serious complications with the kidneys if early identified. Early prediction and diagnosis of CKD are quite important to improve the management and effectiveness of the treatment. This paper analyses the suitability of several machine learning algorithms for CKD prediction in consideration of health-related parameters like age, blood pressure, and blood glucose levels. An integrated dataset of 400 patients has been used for this study, which comprises 24 health- It was performed by employing a variety of classifiers including DT, KNN, RF, SVM ,CNN and Hybrid. For the performance of each model, the accuracy, precision, recall, and F1-score metrics were employed to check its diagnostic ability. It was found that the maximum accuracy was achieved by the Random Forest model that justified its applicability as an effective tool for the early diagnosis of CKD. This study is the perfect example of the use of machine learning techniques in the field of medical diagnoses, with their contribution to the development of future health care. Several recommendation methods were used: KNN Basic, Nonnegative Matrix Factorization (NMF), Co-Clustering, and Singular Value Decomposition (SVD). KNN Basic model obtained a root mean square error of 0.5007 with an accuracy of 44.50. The NMF model gave a better result than the first one with RMSE 0.4999 and accuracy 51.50. Co-Clustering resulted in an RMSE of 0.5000 and an accuracy of 50.50. Most notably, the last SVD model greatly Identify applicable funding agency here. If none, delete this. performed compared to the rest, achieving an RMSE of 0.2261 and an outstanding accuracy of 90.40, highlighting the an incredibly powerful tool for a more personalized type of care. Providers would then have the ability to identify ahead of time high-risk people and monitor their care more carefully. It, however, opens up the possibility of unsupervised learning in health analytics and shows how this can be applied to the role of machine learning for early detection and targeted treatment, thereby contributing to better patient outcomes and proactivity in managing heart disease risks. necessity of more sophisticated methods in recommendation systems. These results not only show how different recommendation algorithms work but also point out the significant influence of hyper parameter tuning on improved predictive precision. This work provides essential contributions to the knowledge on the effectiveness of customized recommendations, which suggests insight for future developments in e-commerce strategies.
Deep Dive into Bone Tumor Segmentation and Classification: Methodological Review and Challenges with Deep Learning Approaches
Authors: Singh Rathla Roop , D Vasumathi
Published: February 2025

This comprehensive review delves into the advancements made in utilizing Deep Learning (DL) procedures for bone tumor separation and classification. Bone tumors present a complex challenge in medical imaging due to their diverse morphological characteristics and potential for malignant behaviour. Traditional methods for tumor analysis often require extensive manual intervention and lack the efficiency needed for clinical applications. Deep learning approaches, with the accessibility of large-scale medical imaging datasets and sophisticated computer resources, have emerged as intriguing alternatives to solve these constraints. In this connection an attempt is made to review synthesizes recent developments in deep learning architectures, tailored specifically for bone tumor segmentation and classification tasks. Additionally, it examines the challenges associated with data acquisition, preprocessing, and annotation, along with strategies to mitigate them. Furthermore, it discusses the integration of multimodal imaging modalities, to improve efficiency and reliability of tumor characterization. The review also surveys benchmark dataset sand various strategies commonly employed in this domain. As a result, propose future directions for advancing the field of bone tumor analysis using deep learning methodologies.
Enhanced Attendance Management of Face Recognition Using Machine Learning
Authors: Ravipati Sowmya. , Modem Lasya. , Yellinedi Sahith. , Namburi Tejeswara Rao. , Sk Sajida Sultana.
Published: February 2025

Conventional attendance tracking has been very timeconsuming, error-prone, and often requires a certain amount of human input and verification. Automating such solutions by using face recognition technology has thus become a viable way to deal with these problems. Our approach does not require pre-registered datasets since it automatically captures and identifies faces from a live camera stream using machine learning to automate attendance. In the alternative, real-time training takes place on location with on-location photos, thereby allowing the system to adapt to specific conditions including lighting variations, subtle facial planes, and even expressions. This results in excellent accuracy and consistency for use in all kinds of scenarios, such as offices, learning institutions, or events.
MCQ Generation using NLP Techniques
Authors: Priyadarsini N. Indira , Yenumula Srividya , Reddy K. Venkat
Published: February 2025

Automated MCQ generation is a well-liked field of study. It is important for students to expertise in their field of study. Our project consists of 2 phases (Video Summarization, MCQ Generation). In the fastgrowing technology today, we can see that the students are being given video lectures in many institutions. Even though it is effective in learning the concept from scratch using a video,it is not very helpful as in the end it takes more time for revision. In our attempt to Video Summarization, we look to create a certain degree of synopsis that clarifies the video's most educational sections. The most important thing in learning is assessment and the question is crucial for assessment. Online tests are becoming more common at universities, colleges and other educational institutions. The assessment pattern is largely evolving towards objective assessment, i.e., MCQ based. Even though MCQ’s have many advantages like electronic evaluation and less testing time, exam question preparation by hand is a difficult undertaking for educators, when the time is limited. An automated test question generator is provided in this study to address this problem in the creation of multiple-choice exam questions. Distractors are created in order to generate options for queries.
Development of an efficient mixed mode heart disease detection model using machine learning and deep learning techniques
Authors: Reddy C. Ravi Shankar , A Niranjan , Kumari P.V. Gopi , Rajendra Babui Y.
Published: February 2025

Heart disease is one of the major causes for increasing mortality worldwide. This is probing the researchers for development of accurate and efficient diagnostic systems. Hence, a huge research is being carried for detection of heart disease using AI model. Here we propose a mixed mode heart disease detection model that employs advantages of classical machine learning model and deep learning techniques. This model combines feature selection techniques with a blend of supervised learning methods, such as k-nearest neighbour algorithm, Support Vector Machine (SVM), and Gradient Boosting, to exploit their complementary strengths. This mixed mode approach demonstrated superior performance in comparison to other models that are taken for comparison purpose. Assessment of the model is done by estimating various metrics like, accuracy, sensitivity, and specificity. The findings suggest that this mixed mode method attain accuracy of 96% while other techniques taken for comparison purpose limit their accuracies to a maximum 89%.
Log Analyzer To Detect Malicious Attacks Based On Pattern Matching
Authors: Murthy N. N. A. G. Sadasiva , Rajasekhar M. Taraka , Reddy G. Praneeth , Shirisha K.
Published: February 2025

The Log Analyzer scan through the system logs and search for patterns found in particular cyber threat and come to conclusion whether a system is infiltrated and data is being breached, given if the patterns of cyber threat match with the system logs and further the tool automates the process of detecting the malicious activities. The tool has the capability to detect and analyze the potential threats such as malware, unauthorized access, DOS attack and other few attacks through the given built-in patterns. Given that the tool might lack the ability to detect sophisticated intrusions only with pattern matching regular expressions, the machine learning algorithm Isolation Forest has been implemented for detecting anomalous behaviors. Tool can take custom patterns from end user to detect new kinds of vulnerabilities. The Log analyzer also has the functionality of creating an analyzed report from given logs in form of chart diagrams using data visualization techniques. The analysis report classifies each attack on basis of severity levels and also state at which hour of a day the system dealt with a kind of cyber threat. Based on the kind of system log provided the tool categories the log under suspicious and unsuspicious groups.
Secure Tweet: Blockchain-Based Social Media Using Web 3.0
Authors: Chaitanya A. Krishna , Nitish K. , Srinivas M. , Padmini K.
Published: February 2025

Blockchain is a technology that offers novel solutions to the distributed environment. The proposed project develops an IPFS data storage system through blockchain technology to decrease social media’s reliance on central systems Smart contracts like Ganache, MetaMask, Truffle, and Ethereum are used to store metadata of files in blockchain and facilitate transaction on Goerli test network and any peer-to-peer distributed network. This decentralized approach was further powered by Pinata which is data processingoriented, data storage, in addition to insuring a secure chain. The system would offer essential features for users who could tweet and view content and delete information in addition to enhancing security measures and enhancing privacy protocols and entity control capabilities to address traditional system failures.
An area-delay efficient Radix-8 12x12 Booth multiplier in CMOS for ML accelerator
Authors: Choubey Abhishek , Choubey Shruti Bhargava
Published: February 2025

The multiplier is a significant module of graphics processing units (GPUs) and digital signal processing (DSP). These applications need low power consumption. This paper proposes a low-power radix-8 12-by-12 Booth multiplier. The proposed radix-8 Booth multiplier is implemented using an optimized Binary to 2-’s complement (B2C), convertor, and optimized multiplexer at each stage of the Booth multiplier architecture. The proposed architecture uses 23% less power and 12% less delay compared to existing architecture. To validate the results, all designs are synthesized using Cadence CMOS technology 45nm.
Real-Time Road Surface Analysis for Pothole Detection
Authors: Kumar Ganjikunta Ganesh , Khan Sibghatullah Inyatullah , Basha Mohammed Mahaboob , Mudda Mallikarjun , Yarrapathni Leela Prasad
Published: February 2025

This paper presents a potentially low cost approach to detecting potholes using segmentation, by pairing a low cost computing platform (the Raspberry Pi) with a camera module and OpenCV image processing software. By getting real-time images processed with computational resources of Raspberry Pi and OpenCV image analysis functions, our system makes the search in real-time for potholes. The small form factor and flexibility of the Raspberry Pi makes it among the perfect platforms to interface with camera modules to capture high-resolution images of road surfaces. OpenCV provides an elaborate tool kit to enable image preprocessing, feature extraction, classification and precise crack detection. According to testing, this system has shown the ability to accurately detect potholes in a variety of environmental conditions and on different road types, making this solution cost-effective and scalable for transportation departments and cities looking to improve road safety and speed up pothole repairs.
Predicting restaurant ratings using regression analysis approach
Authors: Sk Sajida Sultana. , Kumar G. Joseph Anand , Sai V. Leela Venkata Mani , Sai N. Bala , Lakshmi E. Sai Naga
Published: February 2025

Restaurant Builder solves the challenge of building restaurants in a highly competitive market by providing a framework for accurately predicting restaurant prices, a key tool for attracting customers and measuring success. This study identifies and identifies key factors that influence evaluation, allowing restaurant owners to make informed decisions, reduce risk, and save time before starting a business. The study uses seven regression models to compare performance indicators and identify the most reliable predictive models, ultimately providing valuable resources to support informed decision making and increase the likelihood of success for new restaurant ventures.
Personalized product recommendation system for e-commerce platforms
Authors: Sameena Shaik , Javali Guntupalli , Srilakshmi Nelavelli , Jhansi Mandadapu , Sk Sajida Sultana
Published: February 2025

Due to rapid growth in e-commerce, the interest for customized product recommendation systems has grown a lot with high demands for effective models. The attempt is made to explore the development and evaluation of a personalized product recommendation model using the H&M data set. The research highlights the building up of an interaction matrix between a user and items, generation of recommendations suited to the tastes of a particular user, and the hyperparameter tuning of the model for better performance. Different techniques have been utilized, including KNNBasic, Non-negative Matrix Factorization (NMF), CoClustering, and Singular Value Decomposition (SVD). The KNNBasic model had a root mean square error (RMSE) of 0.5022 with an accuracy of 42.00%, the NMF model showed better results with an RMSE of 0.4999 and accuracy of 51.50%. Co-Clustering showed the result of RMSE as 0.5000 and accuracy was 50.50%. Notably, the final SVD model ranked very well compared to the others with an RMSE 0.2261 and a great accuracy of 90.40% in this experiment, emphasizing the importance of advanced techniques in recommendation systems. In these experiments, not only is the relative efficacy of different recommendation algorithms evident but also that optimization of hyperparameters genuinely contributes to increasing predictive precision
Enabling clinical use of linear energy transfer in proton therapy for head and neck cancer – A review of implications for treatment planning and adverse events study
Authors: Chen Jingyuan , Yang Yunze , Feng Hongying , Liu Chenbin , Zhang Lian , Holmes Jason M. , Liu Zhengliang , Lin Haibo , Liu Tianming , Simone Charles B. , Lee Nancy Y. , Frank Steven J. , Ma Daniel J. , Patel Samir H. , Liu Wei
Published: February 2025

Proton therapy offers significant advantages due to its unique physical and biological properties, particularly the Bragg peak, enabling precise dose delivery to tumors while sparing healthy tissues. However, the clinical implementation is challenged by the oversimplification of the relative biological effectiveness (RBE) as a fixed value of 1.1, which does not account for the complex interplay between dose, linear energy transfer (LET), and biological endpoints. Lack of heterogeneity control or the understanding of the complex interplay may result in unexpected adverse events and suboptimal patient outcomes. On the other hand, expanding our knowledge of variable tumor RBE and LET optimization may provide a better management strategy for radioresistant tumors. This review examines recent advancements in LET calculation methods, including analytical models and Monte Carlo simulations. The integration of LET into plan evaluation is assessed to enhance plan quality control. LET-guided robust optimization demonstrates promise in minimizing high-LET exposure to organs at risk, thereby reducing the risk of adverse events. Dosimetric seed spot analysis is discussed to show its importance in revealing the true LET-related effect upon the adverse event initialization by finding the lesion origins and eliminating the confounding factors from the biological processes. Dose-LET volume histograms (DLVH) are discussed as effective tools for correlating physical dose and LET with clinical outcomes, enabling the derivation of clinically relevant dose-LET volume constraints without reliance on uncertain RBE models. Based on DLVH, the dose-LET volume constraints (DLVC)-guided robust optimization is introduced to upgrade conventional dose-volume constraints-based robust optimization, which optimizes the joint distribution of dose and LET simultaneously. In conclusion, translating the advances in LET-related research into clinical practice necessitates a better understanding of the LET-related biological mechanisms and the development of clinically relevant LET-related volume constraints directly derived from the clinical outcomes. Future research is needed to refine these models and conduct prospective trials to assess the clinical benefits of LET-guided optimization on patient outcomes.
FHIR Granular Sensitive Data Segmentation
Authors: Lee Preston , Mendoza Daniel , Kaiser Martha , Lott Eric , Singh Gagandeep , Grando Adela
Published: February 2025

Background Due to fear of stigma, patients want more control over the sharing of sensitive medical records. The Substance Abuse and Mental Health Administration (SAMHSA) and the Office of the National Coordinator (ONC) supported the development of standards-compliant, consent-respecting medical record exchange technology using metadata labeling (e.g., substance use information). Existing technologies must be updated with newer standards and support more than binary-sensitive categorizations to better align with how physicians categorize sensitive medical records.

Objectives Our goal was to deploy, pilot test, and share open-source Fast Healthcare Interoperability Resources (FHIR)-based data segmentation technologies. We pilot-tested the technologies using real-world patient electronic health record data in the context of substance use information. We involved physicians in designing a novel decision engine that supports various confidence levels.

Results We deployed a web-based Patient Portal and Clinical Decision Support (CDS) granular data segmentation Engine to allow patients to make consent-based granular data choices (e.g., not sharing substance use medical records). Compared with previous solutions, the Engine innovates by using the latest Health Level 7 (HL7) standards to support data sensitivity labeling and redaction: FHIR R5 and its Consent resource type and CDS Hooks. It also supports configurable floating point confidence threshold cutoffs as opposed to binary medical record categorizations. Multiple engineering choices were made to simplify software development and maintenance and to improve technology adaptability, reusability, and scalability.

Conclusion The resulting data segmentation technologies update SAMHSA and ONC software with the newest HL7 standards and better mimic how physicians categorize sensitive medical information with various confidence levels. To support reusability, we shared the resulting open-source code through the HL7 FHIR Foundry.
9.10.5 Thiophenes and Thiophene 1,1-Dioxides (Update 2024)
Authors: Harris P. A.
Published: February 2025

Methods for the synthesis of thiophenes and the less-common thiophene 1,1-dioxides are reviewed in this chapter, covering the period from 2011 to 2022. This is a supplement to both the original Science of Synthesis review (Section 9.10) that covered the literature up to 2000, and the subsequent update covering from 2001 to 2010 (Section 9.10.4). Approximately half the cyclizations to form thiophenes reported during this period involved formation of one C—S and one C—C bond, with the formation of both S—C2 and C3—C4 bonds being the most common.