bluuebunny/crossref_metadata_2025 huggingface dataset:
Smart Agriculture Based on Artificial Intelligence and Drones Through a Systematic Review
Authors: Mhaned Ali , Mouatassim Salma , El Haji Mounia , Benhra Jamal
Published: February 2025

The use of drones in agriculture is increasingly important due to challenges like population growth, climate change, and water scarcity. Advances in communication, remote sensing, IoT, wireless sensor networks, artificial intelligence and image processing have expanded research in this field. This study uses the systematic literature review method to provide an overview of current knowledge on drone use in agriculture. It identifies studies by country, studied crops, drones and AI algorithms applications in agricultural sector. Of 246 articles screened, 167 were fully reviewed, and 93 were included in the final analysis. Findings show drones are used for crop mapping, yield estimation, and monitoring plant health, targeting crops like fruits, vegetables, cereals, and greenhouse cultivations. The review also identifies AI algorithms used by researchers, highlighting the potential of drones in agriculture and the need for further research to address industry challenges.
An Enhanced Deep Convolutional Neural Network for Plant Disease Detection and Classification
Authors: Shafik Wasswa , Tufail Ali , De Silva Liyanage Chandratilak , Apong Rosyzie Anna Haji Mohd
Published: February 2025

This research introduces a novel enhanced deep convolutional neural network for plant disease detection and classification, a cutting-edge tool that is set to revolutionize the field. The study enhances the ResNet50 network by replacing the fully connected layer with three layers that improve discrimination and feature extraction, namely the convolution, batch normalization, and Leaky rectified linear unit (RELU) activation layers. Experimental performance assessments were conducted to evaluate the performance of the proposed model in comparison to the original ResNet50, EfficientNet, DesNet201, and Inception Version 3 using popular evaluation criteria such as precision, recall, and F1-Score. The proposed model achieved an accuracy of 99.33% and 93.93% on the Namibia University of Science and Technology Maize Dataset and the Nelson Mandela African Institution of Science and Technology Maize dataset, respectively.
Harnessing Artificial Intelligence and Data Science for Sustainable Water Management and Irrigation
Authors: Medida Lakshmi Haritha , Kumar G. L. N. V. S.
Published: February 2025

This chapter examines how Data Science (DS) and Artificial Intelligence (AI) may advance sustainable irrigation and water management. It draws attention to the problems that exist today, such as data gaps, ineffective irrigation, water scarcity, and financial limitations. Predictive analytics, real-time monitoring, decision support systems, and optimization algorithms are used to analyze AI's function, while data collection, integration, analysis, and visualization are used to assist DS. Case studies from Europe, the US, India, and Australia show the advantages and practical uses. The chapter addresses technological obstacles, data privacy issues, economic limitations, and talent shortages while highlighting the potential of AI and DS to improve resource efficiency, lower prices, raise agricultural yields, and assist environmental conservation. In order to accomplish sustainable water management, future directions include integrating developing technology, supporting policies, and cooperative efforts.
The Wealth Beneath the Ocean Floor
Authors: Ghazali Farahdilah
Published: February 2025

The mining sector is integral to energy security and industrial growth. With growing issues of environmental degradation and scarcity of mineral deposits, many mining companies have started exploring more remote areas that can be technically challenging and less cost effective. While the deep seabed mining is increasingly seen as a potential solution to some of the limitations in traditional land-based mining. The growing interest in this sector is largely driven by the increasing global demand for these minerals, essential for technologies such as electric vehicles, renewable energy systems, and electronic devices . This chapter explores the potential of deep seabed mining from the perspective of the Blue Economy. This chapter also discusses the issues and challenges of this activity regarding the Blue Growth and Blue Justice Concepts.
A Comprehensive Assessment Model for Examining Sustainable Practices and Green Economy Criteria
Authors: Selim Haitham Sadek , Gamal Zeinab , Salem Doaa A. H.
Published: February 2025

The Administrative Capital is emerging as a prominent urban center in the Middle East, prompting the need for significant expansion projects to achieve balanced urban growth. This research examines the urban renaissance using evaluation methodologies that incorporate sustainability criteria, focusing on environmental, social, and green economy aspects. Employing the ETE method, the study established evaluation criteria and their weights through the Delphi method. Experts engaged in two rounds of questionnaires, refining their evaluations based on anonymous feedback from prior responses. The TOPSIS applied to create an integrated evaluation model with particular emphasis on the iconic tower project. Data collection involved extensive site visits, comprehensive project documentation, and expert evaluations through structured questionnaires. aimed to deliver a nuanced understanding of architectural needs concerning sustainability, with findings designed to inform future development initiatives in line with sustainability objectives.
A Bibliometric Analysis of Political and Green Investment
Authors: Hadi Krishno , Roziqin Ali , Rakhma Nilam Adini , Nurjaman Asep , Cahyaningtyas Vandha Eka , Sutanto Slamet Hari
Published: February 2025

The current global situation is facing the issue of climate change and massive environmental damage due to industrialization. In line with the direction of the Sustainable Development Goals, the Green Investment (GI) concept is considered as one of the approaches that can preserve the environment.This chapter aims to mapping the research trends related to political and green investment. This research approach uses bibliometric analysis that refers to the Scopus index database with data analysis through the VOSviewer and Biblioshiny R Studio. The results show that research on political in green investment programs began to become an international debate in 2010. Advanced countries are the main contributors to the discourse on the topic of political and green investment with the United States and China as leading countries. Another finding is the clustering of research that is currently developing related to political and green investment is environmental policy, political stability-green economy, social responsibility, and climate change.
Strength Optimized Weight Balancing for Traffic Management in Vehicular Ad-hoc Networks
Authors: Rath Mamata , Chatterjee Jyotir Moy
Published: February 2025

Due to rapid population growth and industrialization, residents of large cities often face severe traffic congestion during their commutes. This leads to unexpected delays, increased accident risks, fuel wastage, and a decline in public health, particularly in urban areas where pollution exacerbates unsanitary conditions. In response, many smart cities are implementing traffic control systems based on traffic automation principles to mitigate these issues. A key challenge lies in using real-time analytics and online traffic data to efficiently manage traffic flow. To address this, the current research proposes an advanced monitoring system leveraging highly flexible mobile agent technology for intelligent data analytics. In the context of a Vehicular Ad-hoc Network (VANET), the mobile agent incorporates additional features such as crime reduction, accident prevention, enhanced driver flexibility, and improved security. These features are combined with a congestion control algorithm to optimize traffic flow and prevent congestion at the entry points of smart traffic zones. Simulation results using the Ns2 simulator demonstrate significant improvements in reducing delays and preventing accidents caused by heavy traffic.
The Correlation Between Global Longitudinal Strain by Speckle Tracking Echocardiography and Coronary Angiogram Finding in Patients with Suspected Chronic Stable Angina Pectoris
Authors: Jeyakanth T. , Mayurathan G. , Kularatne A. , Jeevaraj T. , Prashanthi J. , Abeysingha T.
Published: February 2025

The objective of this study was to evaluate and forecast the significance of Global Longitudinal Strain (GLS) measured at rest and its correlation with the presence and severity of coronary artery disease (CAD) in individuals with suspected stable angina pectoris in the Sri Lankan populationPatients diagnosed with chronic stable angina and normal left ventricular function (EF ≥ 50%) were included in the study and underwent coronary angiography, followed by speckle-tracking echocardiography. The study measured the global longitudinal strain and analyzed its correlation with the coronary angiography findings for each patient.A total of 78 participants were included in this study, 44(56.5%) were diagnosed with coronary artery disease. Significant differences between CAD and non-CAD groups were observed in terms of age (P - 0.003), diabetes mellitus (P - 0.02), hypertension (P - 0.004) and family history of premature CAD (P - 0.01). There was a notable decrease in GLS values among CAD patients (-17.97 ±0.85) compared to non-CAD patients (-20.26 ±1.03), with a significant decrease observed as the count of affected vessels rose (P = 0.0001). A GLS cutoff value of -19 demonstrated promising sensitivity (72.7%) and specificity (70.6%) in predicting significant CAD, supported by an AUC of 0.717 ( P = 0.001). In regression analysis, only diabetes showed a significant association with reduced GLS. GLS demonstrated a significant p-value of less than 0.001 (β -2.292 CI [-3.6594, -0.9246]) establishing it as an independent and significant predictor of the presence of significant coronary artery disease.Assessing global longitudinal strain at rest demonstrates excellent diagnostic accuracy for detecting coronary artery disease in patients with chronic stable angina and normal systolic function.
Predictive value of Frank’s sign for obstructive coronary artery disease confirmed by coronary computed tomography angiography
Authors: Phi Hung Truong , Minh Kha Nguyen , Tuong Vi Dang , Trong Nhan Tran , Van Sy Hoang
Published: February 2025

Numerous studies have suggested a link between Frank’s sign and coronary artery disease (CAD); however, there is limited data available from Southeast Asia, and no previous studies have been conducted in Vietnam to evaluate this association. This cross-sectional study examined 124 consecutive Vietnamese individuals who underwent coronary computed tomography angiography at our center. Differences in patient characteristics and the association between Frank’s sign and CAD were analyzed using Chi-square and t-tests. Multivariate regression was performed to adjust for confounding variables, and receiver operating characteristic curve analysis was utilized to evaluate the predictive accuracy of Frank’s sign for CAD. In this study, 124 patients with a mean age of 58.5±12.4 years, predominantly male, were included. Frank’s sign was present in 84% of the participants. Among these, males and older individuals had significantly higher rates compared to those without the sign. Obstructive CAD was observed in 48 patients (38.7%). The prevalence of obstructive CAD was significantly higher in those with Frank’s sign (52.5%) compared to those without it (13.6%), with p<0.001. Multivariate analysis identified Frank’s sign [odds ratio (OR) 3.79; 95% confidence interval (CI) 1.33-10.82] and age (OR 1.06; 95% CI 1.01-1.10) as independent risk factors for CAD. Frank’s sign demonstrated an area under the curve of 0.69, with 88% sensitivity and 50% specificity in predicting CAD. This study highlights a significant association between Frank’s sign and CAD, independent of established risk factors, in a Vietnamese population.
A CFD simulation method for nozzle droplet deposition characteristics and corresponding experimental validation
Authors: Yan Jie , Wang Jianyu , Lv Chi , Wang Songlin
Published: February 2025

Spray booms can often tilt during operation due to factors such as uneven ground, tires deformation and crop canopy structure and height, adversely affecting droplet deposition. Therefore, it is crucial to study and understand how spray height and nozzle tilt angle affect droplet deposition to enhance the effectiveness of Plant Protection Products (PPPs) application. The TeeJet®XR8002 nozzle was selected as the research object, and simulations and spray tests were conducted at three spray heights (0.5, 0.6, and 0.8 m) and 10 tilt angles (1°~10°). The film length and nozzle tilt angle were used to determine the relative position of the virtual origin and the center coordinates to determine the tilt angle of the spray model. Depositional characteristics at different spray heights were analyzed using the ratio of deposition and changes in the spray height. The dense spraying effect was observed when the nozzle was tilted at spray heights of 0.5, 0.6, and 0.8 m. For spray heights of 0.8, 0.6, and 0.5 m, the maximum allowable tilt angles were 4°, 3°, and 4°, respectively, to ensure that the effect of changes in tilt angle on droplets deposition is minimized. The maximum relative errors with respect to the experimental tests for the accurate deposition ratio and deposition ratio were 3.09% and 4.64%, respectively, thus validating the reliability of the simulation results.
Pre- and Post-Surgical Cutaneous Reflexes and Perceived Instability During Gait in an Individual with Chronic Ankle Instability
Authors: Friedman Annalee M. H. , Madsen Leif P.
Published: February 2025

Context: Recent studies exploring chronic ankle instability (CAI) have found alterations in cutaneous reflexes of musculature surrounding the ankle which may contribute to perceived instability and recurrent LAS seen in this population. CAI is considered a multifaceted condition, making it difficult to determine the underlying cause of these altered reflexes.

Objective: To observe how mechanical laxity of the ankle affects lower limb cutaneous reflexes and perceived instability during gait and how surgical intervention to correct laxity affects these measures.

Design: Clinical Case Report

Setting: Research Laboratory

Patient: A physically active 25-year-old female (64in;130lbs) with 7 previous lateral ankle sprains (LAS) and met CAI diagnostic criteria based on CAI questionnaire scores. The patient underwent a Broström reconstruction of the CFL via allograft and partial synovectomy.

Main Outcome Measures: CAI questionnaire scores, middle latency lower limb cutaneous reflexes, and perceived instability following sural nerve stimulation during gait.

Results: Post-surgery, the patient’s CAIT and FAAM questionnaire scores aligned with those of a healthy individual. PL reflexes were diminished or inhibitory during the stance phases of gait. Pronounced variability of PL reflexes may have contributed to this lack of facilitation. BF facilitation at midstance was absent during both testing sessions while BF and RF facilitation was generally reduced post-surgical intervention. The patient’s average perceived instability following sural stimulation was markedly reduced from the pre-surgical (6.5±0.48) to post-surgical (1.9±0.24) session.

Conclusions: Mechanical instability likely contributed to the reflex variations seen in this patient pre- surgically, while the enhanced static stability provided by the surgical procedure may have reduced the need for dynamic stability via lower limb cutaneous reflexes observed in the follow-up session. Identifying the specific limitations experienced by an individual with CAI will allow for a more effective monitoring and treatment and provide improved long-term health-related quality of life outcomes.
The cross-sectional study of assessment of cognitive status in patients with poor glycemic control and good glycemic control of diabetes mellitus
Authors: Panati Dinesh , Madarapu Yethiraju , Sibbala Bharathi , Puthalapattu Swetha , Gandla Sujana Devi
Published: February 2025

Context:

Microvascular and macrovascular complications of diabetes are well-known but the cognitive dysfunction is least known which can show major impact on the quality of life of diabetic patients.

Aims:

This study aims to assess the cognitive status among type 2 diabetes mellitus patients.

Methodology:

This is a cross-sectional study conducted from November 2023 to July 2024 after obtaining ethical committee approval. The sample of the study comprised of 90 persons, 30 persons were controls, 30 cases with good glycemic control, and 30 cases with poor glycemic control based on glycosylated hemoglobin (HbA1c) done within 3 months of the study. The Mini-Mental State Examination (MMSE), Trail Making Test-A (TMT-A), and Brief Cognitive Rating Scale (BCRS) were used to assess the cognitive impairment.

Results:

The prevalence of cognitive impairment among type 2 diabetes mellitus patients was 50%, 53.3%, and 56.6% according to TMT-A, BCRS, and MMSE, respectively. All the assessment methods have shown a significant difference in levels of cognition among diabetic cases than controls, and the impairment is more in cases with poor glycemic index. The cognitive decline was found to have a significant association with duration of illness (P < 0.0001), HbA1c (P < 0.0001), and number of medications (P < 0.0001) used by the diabetic patients.

Conclusions:

This study concludes that half of the proportion of diabetic patients are having impairment in various cognitive domains. Global functioning and self-care of the diabetic patients are affected more than the persons without diabetes. Hence, type 2 diabetes mellitus patients with HbA1c > 7, increased duration of illness, and polyoral hypoglycemic medication need to be screened for cognitive impairment.
Bimodal Approach of Phytochemical Profiling of Panchakol Churna: A Polyherbal Formulation
Authors: Kumar Khushal , Joshi Sumedh , Rai Shalini , More Anand B.
Published: February 2025

Introduction:

Phytochemicals from medicinal plants are crucial in traditional systems like Ayurveda and are increasingly important in modern healthcare. Panchakol Churna, a polyherbal Ayurvedic formulation, is known for various therapeutic properties. This study aims to profile the bioactive compounds in Panchakol Churna using a bimodal approach that combines the Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database with high-resolution liquid chromatography–mass spectrometry (HRLCMS).

Materials and Methods:

Panchakol Churna was prepared according to the Ayurvedic Formulary of India, with ingredients authenticated by the Regional Raw Drug Repository at AIIA, New Delhi. Phytochemical identification involved the IMPPAT 2.0 database and HRLCMS analysis using an Agilent G6550A MS Q-TOF system in both positive and negative ionization modes. Data analysis with Venny 2.1 identified common phytoconstituents across both ionization modes.

Results and Discussion:

HRLCMS identified 200 compounds, 100 in each mode, with 45 and 81 major compounds confirmed. Venny 2.1 revealed 127 common compounds between the two modes, highlighting the common constituents of Panchakol Churna. No overlap was observed between the phytoconstituents identified by IMPPAT and HRLCMS.

Conclusion:

The lack of overlap between the IMPPAT and HRLCMS results underscores the formulation’s complexity and suggests further exploration of its therapeutic potential. The findings support the development of a chemical library for medicinal plants, facilitating future pharmacological research and the authentication of medicinal plants.
Role of Ayurveda in the Management of Mixed Connective Tissue Disease - A Single-case Study
Authors: Patel Sarjita Tusharbhai , Gopalakrishna G. , Sajjanar Neelakanta J.
Published: February 2025

Mixed connective tissue disease (MCTD) is a multisystem autoimmune disorder caused by unknown factors. It is considered as a distinct clinical presentation because of the presence of high anti-U1 ribonucleoprotein (anti-U1 RNP) antibodies. The prevalence rate is four times less than that of systemic lupus erythematosus (SLE), indicating an overall prevalence of 10/100,000.It is characterized by a combination of clinical features of SLE, polymyositis, systemic sclerosis, and rheumatoid arthritis. A 36-year-old homemaker diagnosed with MCTD came to the outpatient department, which was managed by the treatment principles of vatarakta (connective tissue disorder in Ayurveda) and kushtha (disease of skin in Ayurveda). Virechana (Therapeutic laxative procedure in Ayurveda) along with oral medications were given for 7 days. The quality of life has been improved within 7 days of treatment. Inflammatory markers erythrocyte sedimentation rate and C-reactive protein and Visual Analog Scale for itching showed significant reduction.
Primaquine-induced Methemoglobinemia with Normal G6PD Levels
Authors: Sharma Ankur , Bhalekar Prashant , Pakale Ashutosh , Jain Sudeep
Published: February 2025

Methemoglobinemia is a rare but serious condition characterized by elevated methemoglobin levels, which impairs oxygen delivery to tissues. It can be induced by various factors, including medications. This report presents a case of primaquine-induced methemoglobinemia in a 26-year-old female with normal G6PD levels. We highlight the importance of clinical awareness, prompt diagnosis, and appropriate management to prevent potentially life-threatening complications.
A Hospital-based Perspective Observational Study on Medication-related Problems in Clinical Practice
Authors: Dave Dhvani Rupeshkumar , Patel Jhanvi Anandrao , Suthar Jalpa V.
Published: February 2025

Background:

One of the most significant problems seen during a patient’s hospital stay is medication-related problems (MRPs). MRPs are linked to longer hospital stays, higher healthcare costs, and higher morbidity and mortality rates. There are numerous domains and sub-domains that make up MRPS. Identification and resolution of such MRPs are largely the responsibility of a clinical pharmacist. To determine the prevalence of MRPs, to categorize them into various domains and identify the risk factors linked with them.

Materials and Methods:

An observational longitudinal study was conducted on 195 hospitalized patients. The socio-demographic details and medication chart of all patients were observed for 3 days. Each medication chart was evaluated for MRP (if any) and was classified into its domains and sub-domains.

Results:

MRP prevalence was found to be 24.61%. It was observed that, prescribing error (42, 89.3%) and unnecessary drug therapy (35, 81.3%) were the major MRP among the other. Statistical Analysis and Network Graph showed strong correlation between medication error and chronic disease and polypharmacy.

Conclusion:

There were many MRPs and a high prevalence rate for MRPs. Medication error and indication type had the greatest observed MRP. There was a significant association between medication error and chronic disease, medication error and polypharmacy, inappropriate drug therapy and geriatric patients, and medication error with polypharmacy.
Prevalence and Determinants of Cognitive Impairment: A Study from Punjab, North India
Authors: Yadav Neha , Chaudhary Vineet , Saraswathy Kallur Nava , Devi Naorem Kiranmala
Published: February 2025

Background:

Due to the rapid aging of the global population, the prevalence of cognitive impairment (CI) has increased several-fold over the years. Studies have revealed that sociodemographic and lifestyle factors play a crucial role in the etiology of CI. While most studies have focused on urban populations, there is limited knowledge about the prevalence and determinants of CI in rural areas.

Objectives:

This study was carried out to estimate the prevalence and determinants of CI among rural adults in Mansa, Punjab, North India.

Methods:

A total of 956 rural adults of either sex were screened for CI using the Mini-Mental State Examination (MMSE). The prevalence of CI was calculated using the standard cutoff (MMSE score <24) as well as cutoffs derived from age and education-stratified norms. Mean, standard deviation, median, and percentiles of MMSE score were used to determine age and education-stratified norms for CI. The association of CI with sociodemographic and lifestyle variables was examined by adjusted binary logistic regression analysis.

Results:

The prevalence (95% confidence interval) of CI using age and education-stratified norms was found to be 8.8% (7.1%–10.8%). Adjusted logistic regression models revealed older age and illiteracy to be associated with an increased risk for CI. Additionally, unemployment and smoking were also identified as risk factors for CI.

Conclusion:

The present study suggests a substantial prevalence rate of CI in the studied population. Older age, illiteracy, smoking, and unemployment were independently associated with CI risk.
Machine Learning: A Novel Approach for Predicting Visual Outcomes and Factors Affecting it in Patients with Pituitary Adenomas
Authors: Shah Shreykumar Pravinchandra , Ranjith G , Narendran Meghana , Patel Biren Khimji , Gowda Arun , George Tobin , Easwer H V , Nair Prakash
Published: February 2025

Objective:

To use machine learning tool to predict visual outcomes.

Methods:

A retrospective cohort of 284 consecutive pituitary adenoma patients with preoperative visual deficit was used. Patient variables were collected. Preprocessing and classification was done in the open source ML tool box Weka (Ver 3.8.4). Four algorithms were used for classification—the J48 trees, LMT algorithm, the REP tree algorithm, and the Random Forest.

Results:

The study included a total of 284 patients. The mean duration of visual symptoms was 10.09 ± 4.3 months. There were 24 patients with uniocular involvement and 260 patients with binocular visual impairment: Vision improved in 89.78% patients, remained same in 9.86% and deteriorated in 0.3% patients and were same in 9.86% patients and deteriorated in 0.3% patient. Factors like extent of resection, preoperative visual acuity, tumor volume, and duration of symptoms were found to have statistically significant effect on postoperative visual outcome. The model predicting visual improvement had an AUC of 0.846; 88.94% accuracy, 78.4% specificity, and 93.7% sensitivity; 90.5% and 85.04% positive and negative predictive value, respectively; an F1 score of 0.921; and a Brier score of 0.291.

Conclusions:

Postoperative visual outcomes of pituitary adenoma surgery and factors affecting it can be predicted with 88.94% accuracy using a machine learning approach. Based on this preliminary evaluation, ML appears promising in predicting visual outcomes after endoscopic resection of pituitary adenomas, thus improving patient-tailored care and counselling.
Whole Exome Sequencing Study of a Multizygotic Quadruplet Discordant for Autism Spectrum Disorder Reveals Novel Autism Candidate Genes
Authors: Anitha Ayyappan , Banerjee Moinak , Thanseem Ismail , Melempatt Nisha , Prakash Anil , Iype Mary , Thomas Sanjeev V.
Published: February 2025

Autism spectrum disorder (ASD) is a childhood-onset complex neurodevelopmental disorder. We carried out a comprehensive genetic study of a quadruplet discordant for ASD to identify the candidate genes of ASD. Whole exome sequencing (WES) was done for the quadruplet and their parents. We identified 218 proband-specific de novo single nucleotide variants (SNVs) and 100 indels, none of which were deleterious. Among these, nine SNVs and six indels are reported in autism databases. A homozygous recessive non-synonymous SNV in TRAM2, and a pair of compound heterozygous non-synonymous SNVs in DGKD, all of which were proband-specific, were predicted to be deleterious. These are novel candidate genes for ASD. Genes harboring proband-specific de novo and inherited variants were enriched in the biological processes related to synaptic transmission and neurodevelopment. This is the first genetic study of a quadruplet in ASD.
Prevalence of Risk Factors for Breast Cancer among Women Aged More Than 30 Years in Rural Delhi, India
Authors: Lepcha Sunom M. , Khokhar Anita
Published: February 2025

Background:

The breast is the most common site for cancer in women in India. Its nature is multifactorial. In accordance with this, the current study was intended to find the prevalence of breast cancer risk factors among rural women in Delhi.

Materials and Methods:

A cross-sectional study was conducted among 244 women aged more than 30 years who resided in the five villages under a primary health centre in rural Delhi. Data were collected using a pre-tested, semi-structured questionnaire and analysed using the SPSS software licensed version 26.0.

Results:

Most 243 (99.6%) participants had at least one or more high-risk factors. Only 1 (0.4%) participant had none of the risk factors for breast cancer, while 148 (60.7%) had more than equal to three risk factors.

Conclusion:

The prevalence of high-risk factors for breast cancer is high as at least one or more risk factors for breast cancer were present in 243 (99.6%) study participants.
Surgical treatment of uveal melanoma: Exoresection versus endoresection
Authors: Lee Seung Min , Kim Min
Published: February 2025

Uveal melanoma, the most common primary intraocular malignancy in adults, presents significant therapeutic challenges. With the advancement of surgical instruments and techniques, exoresection (ab-externo) and endoresection (ab-interno) have emerged as viable treatment methods. These surgical interventions can be implemented particularly for tumors exceeding traditional size criteria for plaque brachytherapy and for tumors that are too large, which pose an elevated risk of radiation-related complications. Moreover, surgical interventions not only enhance the potential for functional vision preservation and globe retention but also provide effective control of intraocular tumors and metastatic disease when combined with adjunctive radiation therapy. Exoresection, an external approach, involves dissection and removal of the tumor through a scleral flap, while endoresection utilizes vitreoretinal surgical techniques for internal tumor excision. Both surgical techniques for uveal melanoma removal are highly challenging, carry significant risks of complications, and should be performed by experienced vitreoretinal surgeons specializing in ocular oncology at specialized centers. They aim to preserve the eye and potentially maintain useful vision while saving patients’ lives, a crucial consideration in ocular oncology. These surgical modalities have become increasingly relevant as the field progresses towards more conservative, function-preserving approaches in cancer treatment. The aim of this article is to conduct a comparative analysis of exoresection and endoresection in the context of uveal melanoma management. By critically evaluating these surgical modalities, this review seeks to provide clinicians with valuable insights to guide treatment decisions for patients with large uveal melanomas.
A Critical Evaluation of Laboratory Test Methods Used to Assess Coke Formation Tendency in Synthetic Lubricants
Authors: Jeyaseelan Thangaraja , S Shanmugasundaram , Bansal Lalit , Negi Ashish , Koka Tirumala Rao , Das Arnab
Published: February 2025

Lubricant oil in combustion engines undergoes thermal degradation under high temperatures and forms solid deposits. These deposits, called coke, are insidious, black, and carbonaceous solids. To mitigate the problems associated with oil coking, an effective testing methodology must be developed to characterize the coke formation qualitatively and quantitatively. Previously, testing methodologies have been developed to measure coking tendency however some of the international standards such as the SAE ARP 6166 use visual inspection methods to assess coke. Such methods are unsuitable for advanced research as they are prone to error in human judgment. This paper intends to bridge this gap and discusses test methodologies that can measure Coke quantitatively and qualitatively. Coke formation has been studied using different laboratory methods such as static immersion, thin film oxidation, and dynamic spray tests to replicate the various conditions. In a static immersion test, a metal sample is submerged in oil and heated to form coke. In the thin film oxidation test, an oil drop is deposited and heated over the metal sample. In the case of dynamic test measurement, the oil is sprayed over a heated metal sample. The lubricant coke has been characterized using its thickness, mass, and chemical composition. One of the key challenges associated with accurate coke measurement is the brittle nature of coke. It has been concluded that the thin film oxidation test method is the fastest method to assess the coke deposit, followed by the static immersion test and the dynamic test method.
A Hybrid Algorithm for Optimizing Machining and Spraying Parameters in MQL-Turning of Inconel 800H
Authors: Kannan Venkatesan , Mokshajna Kotha
Published: February 2025

Inconel 800H superalloy is a difficult-to-turn material. This study aims to achieve optimal machining results, including reduced cutting force, improved surface roughness, and minimized residual stress, by optimizing input machining parameters like cutting speed, feed rate, spraying angle, and nozzle distance on Inconel 800H. The Taguchi L27 method is utilized for experimentation, while the Harris hawks optimizer (HHO) is applied in a multi-objective optimization model. Additionally, the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) is used to identify the optimal input parameters. Five distinct weight schemes were employed, including the Analytic Hierarchy Process (AHP), the Entropy weight method, Criteria Importance through Inter-Criteria Correlation (CRITIC), Grey relational analysis (GRA), and Principal Component Analysis (PCA) to determine response weights. The analysis revealed that the primary factor affecting all measured weights is the feed rate, with the nozzle angle closely followed, as determined by ANOVA, based on a comprehensive evaluation of all output responses. Notable enhancement in MQL turning when contrasted with dry turning, reflected in the output responses of roughness, force, and residual stress at 72.62%, 8.08%, and 19.32%, respectively, using AHP-TOPSIS compared to AHP-HHO.
NVH Optimization of Mounts for Reduction of Structure Borne Motor and Road Noise in Electric Vehicle
Authors: S Nataraja Moorthy , Rao Manchi Venkateswara , Raghavendran Prasath , Selvam Ebinezer
Published: February 2025

Due to stringent emission norms, all OEMs are shifting focus from Internal combustion engine (ICE) to Electric vehicle (EV). NVH refinement of EVs is challenging due to less background noise in EVs in comparison with ICE vehicles.

Motor whine noise is perceived inside cabin till the speed of 20 kmph. Vehicle is powered by electric powertrain (EPT). Electric powertrain is connected to the subframe with the help of three powertrain mounts. Subframe is connected to the body with the help of four mounts. With the help of Transfer Path Analysis (TPA), it is identified that the noise is structure borne and the dominant path is identified. By optimizing the stiffness of the EPT mounts, the structure borne noise levels are reduced. But reducing the stiffness of EPT mount deteriorated the road noise levels. The reason behind deterioration of road noise is investigated. The performance of double isolation of EPT is compared with single isolation of EPT with respect to both road and motor noise. Modal criteria which needs to be considered for EPT and subframe mount development in design stage with respect to road noise and structure borne motor noise is also discussed.
Investigating Mechanical Properties of Jute Fiber and Alumina-Reinforced Epoxy Composites: Application of Taguchi and Grey Analysis for Automotive Components
Authors: Somsole Lakshmi Narayana , Natarajan Manikandan , Pasupuleti Thejasree , Katta Lakshmi Narasimhamu , Vivekananda Soma
Published: February 2025

Natural fiber composites (NFC’s) have considerable promise for a wide range of technological applications due to their exceptional features, which include notable weight reduction, high strength, and affordability. The aforementioned materials are also biodegradable and sustainable, which makes them appealing for use in sustainable engineering methods. This research focuses on evaluating the mechanical features of jute fiber and Al₂O₃ particle fortified polymer composites, exploring their potential for advanced engineering uses. The Taguchi technique is used with a L9 orthogonal array, integrating three-level, three-parameter approach, to systematically examine potential combinations of process variables in the manufacturing of these polymer composites. The primary goal is to optimize the mechanical attributes of the composites, which include tensile modulus, tensile stress, and weight percentage increase. Detailed investigations are conducted to interpret the effects of these process factors on the performance metrics. The research employs ANOVA and regression evaluation to assess the pertinence of process factors on the individual output variables. Furthermore, interaction assessment is carried out to discover the implications of the impact of interactions among the process factors on the intended performance measures. To examine the multi-performance index, Grey analysis is used to establish the Grey Relational Coefficient (GRC) values, which provide a comprehensive performance assessment. The resulting composite materials demonstrate considerable potential for application in interior automotive components, such as dashboards and luggage compartments, due to their enhanced mechanical properties and environmental benefits.
Preparation and Evaluation of Lubricating Properties of Neem Seed Oil under the Influence of an Anti-Wear Additive as Sustainable Alternative to Commercial Engine Oil
Authors: Menon Krishnaprasad S , R Ambigai
Published: February 2025

The search for environmentally friendly and sustainable lubricants for automotive and industrial applications has led to extensive research on bio lubricants as a viable alternative to conventional engine oils and mineral oils. The biodegradable and ecofriendly nature of vegetable oil, makes it an excellent replacement for the depleting mineral oils. Still, a good number of modifications must be brought in, to overcome the drawbacks of vegetable oils. In this work, the preparation and evaluation of lubricating properties like tribological, rheological, thermal etc. of Neem seed oil (NSO) with and without additives were carried out and effectively compared with the lubricating properties of synthetic oil, Polyalphaolefin 6 (PAO 6) and with a commercial engine oil, SAE20W40. The copper oxide nanoparticles were dispersed in neem seed oil as additive in various proportions (0.1, 0.2, 0.3 and 0.4 wt.%) to enhance the tribological properties. The tribological analysis were carried out to evaluate the friction and wear performance of neem seed oil using the four-ball tester. The neem seed oil with 0.2wt% copper oxide (CuO) nanoparticles has attained an improvement of 13.8% in its friction coefficient and a reduction in wear scar diameter of base oil by 20% and seemed to be better than synthetic oil and comparable to SAE20W40. The viscosity was evaluated using redwood viscometer and seemed better than synthetic oil. The properties like flash point, fire point and pour point were also investigated in this study. These results showed that the addition of copper oxide nanoparticles significantly improved the lubricating properties of neem seed oil and the formulated lubricant is comparable to the properties of SAE20W40 and can be considered as an alternative in automotive applications with proper modifications.
Optimization of Wire Electrical Discharge Machining Parameters for Invar 36 Material Using Regression Modeling
Authors: Pasupuleti Thejasree , Natarajan Manikandan , Raju Dhanasekar , Krishnamachary PC , Silambarasan R
Published: February 2025

Wire Electrical Discharge Machining (WEDM) is a sophisticated machining technique that offers significant advantages for processing materials with elevated hardness and complex geometries. Invar 36, a nickel-iron alloy characterized by a reduced coefficient of thermal expansion, is extensively used in the aerospace, automotive, and electronic sectors due to its superior dimensional stability across a wide temperature range. The primary goals are to improve machining settings and develop regression models that can precisely predict critical performance metrics. Experimental experiments were conducted using a WEDM system to mill Invar 36 under diverse machining parameters, including pulse-on time, pulse-off time, and current setting percentage (%). The machining performance was assessed by quantifying the material removal rate (MRR) and surface roughness (Ra). The design of experiments (DOE) methodology was used to systematically explore the parameter space and identify the optimal machining settings. Regression models were developed using statistical methods to validate the link between independent variables and output metrics, allowing precise predictions of machining performance. This work improves the understanding of WEDM for Invar 36 material and provides significant insights into the influence of machining settings on process outcomes. The empirical connection presented serves as a valuable tool for optimizing WEDM variables, enhancing the machining process's performance, and maintaining the desired surface quality in Invar 36 components. This study advocates for the implementation of WEDM as an effective manufacturing technique for Invar 36-based applications, hence advancing precision engineering and materials processing.
Designing Next-Generation Piston Geometry for LPG Powered Spark Ignition Engines: An Integrated CFD and Experimental Approach
Authors: Sagaya Raj Gnana , R L Krupakaran , Pasupuleti Thejasree , Natarajan Manikandan
Published: February 2025

The efficiency of combustion has a major impact on the performance and emission characteristics of a spark-ignited LPG (Liquified Petroleum Gas) engine. The shape of the combustion chamber determines the homogeneous charge intake velocity, which is crucial for the turbulent motion that encourages flame propagation and quickens combustion. It need the right amount of compression ratio, charge squish velocity and turbulent kinetic energy to sustain combustion and propel laminar flames. There are a number of names for the motion of the charge within the cylinder: swirl, squish, tumble and turbulence. All of these terms affect how air and fuel are mixed and burned. Piston shape affects in-cylinder motion, which in turn reduces fuel consumption and improves combustion characteristics. The shape of the piston quench zone has a substantial impact on the charge velocity inside the combustion chamber. The impact on charge motion was analyzed using computer modeling using STAR-CD on pentroof central bowl quenched pistons with different quench areas (10, 20, 30, and 40%). The results were validated by conducting experiments on pistons that had a compression ratio of 10:1 and a greater quench area than is currently utilized in the industry. Results showed that performance, combustion characteristics, and emissions could all be enhanced in a lean-burn SI engine running on LPG with a 30% increase in piston quench area.
An Integrated Material Science Approach to Evaluate the Viability of Ethoxy Ethane and Rubber Seed (Heavea Brasiliensis) Biodiesel as Sustainable Fuel for Compression Ignition Engines
Authors: Sagaya Raj Gnana , Natarajan Manikandan , Pasupuleti Thejasree
Published: February 2025

The primary issues in using pure vegetable oils for internal combustion engines are their high soot output and reduced thermal efficiency. Therefore in the present investigation, a Heavea Brasiliensis biodiesel (HBB) is used as a carbon source of fuel and ethoxy ethane as a combustion accelerator on a compression ignition (CI) engine. In this investigation, an only one cylinder, four-stroke, air-cooled DI diesel engine with a rated output of 4.4 kW at 1500 rpm was utilized. Whereas heavea brasiliensis biodiesel was delivered straightly into the cylinder at almost close to the end of compression stroke and ethoxy ethane was sprayed instantly in the intake manifold in the event of intake stroke. At various loads, the parameter of ethoxy ethane volume rate were optimised. To minimise exhaust emissions, an air plasma spray technology was employed to cover the engine combustion chamber with a thermal barrier coating. Because of its adaptability for high-temperature applications, YSZ (Yttria-stabilized zirconia) was chosen as the coating material. The brake thermal efficiency for a neat HBB is 29.8% and it reached a peak value of 30.5% for ethoxy ethane injection of volume flow rate as 13 cc/min. Also there is a reduction in emissions at all loads except oxides of nitrogen. Smoke emission decreases after ethoxy ethane injection from 6.4 to 5.2 BSN. For HBB, HBB plus ethoxy ethane, and diesel, the HC emission at maximum load is 51 ppm, 47 ppm, and 44 ppm respectively.
A Machine Learning Approach for the Prediction of Surface Roughness Using the Tool Vibration Data in Turning Operation
Authors: S S Safeer , Sadique Anwar , D Navaneeth
Published: February 2025

Surface roughness is a key factor in different machining processes and plays an important role in ergonomics, assembly process, wear and fatigue life of components. Other factors like functionality, performance and durability of parts are also affected by surface roughness. Although maintaining an optimum surface roughness is a major challenge in many manufacturing industries. Surface roughness during machining depends upon machining parameters such as tool geometry, feed rate, depth of cut, rotational speed, lubrication, tool wear, etc. Tool vibrations during machining also have significant influence in surface roughness. In this work an attempt is made to predict the surface roughness of machined components made by the turning process by using machine learning of tool vibration signals. By varying different machining parameters and keeping other tooling and material properties same, a range of surface roughness values can be obtained. For each condition, corresponding tool vibration signals were recorded. Our experimental setup involves a vibration data collector which is used for recording vibration signals generated during the turning operation. The collected data preprocessed and categorized into training and test sets. Various machine learning regression techniques including Linear Regression, Ridge Regression, Support Vector Regression (SVR), Decision Tree Regression, Random Forest Regression, Gradient Boosting Regression, K-Nearest Neighbors Regression (KNN), and Neural Network Regression were used to predict the surface roughness. The study highlights the importance of feature extraction and model selection in achieving accurate and reliable surface roughness predictions, ultimately contributing to enhanced machining process control and product quality.