VT International Press Journal of Multidisciplinary Research and Review
Bridging Knowledge Across the Frontiers of Engineering and Technology “VTIPJMRR is a rigorously peer-reviewed, open-access international journal dedicated to advancing the frontiers of engineering, applied sciences, and emerging technologies through multidisciplinary scholarship and critical review.” The rapid convergence of engineering disciplines — from electronics and computing to civil infrastructure, energy systems, and biomedical technology — demands a publication platform that transcends traditional domain silos. The VT International Press Journal of Multidisciplinary Research and Review (VTIPJMRR) was founded to meet precisely this need: a high-quality, internationally visible forum where researchers, practitioners, and innovators can present original findings, comprehensive reviews, and cross-disciplinary perspectives that drive real-world progress. Published by VT International Press, this journal upholds the highest standards of academic integrity, double-blind peer review, and scientific rigor. Contributions are welcomed from academicians, industry professionals, and graduate researchers across all corners of the globe, reflecting our commitment to diversity, inclusion, and the universality of scientific inquiry.
VT International Press Journal of Multidisciplinary Research and Review (VTIPJMRR)
All articles published in VTIP-JMRR are made freely available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Under this license, others may freely share, distribute, and adapt the published work, provided appropriate credit is given to the original authors. By signing the copyright form, the author(s) warrant and represent that: ✓ The manuscript is original and has not been published previously in any journal, conference, or book (in whole or in part). ✓ The manuscript is not currently under consideration for publication elsewhere. ✓ All authors have read and approved the final manuscript and have agreed to this copyright transfer. ✓ The manuscript does not infringe upon any existing copyright, trademark, patent, or other intellectual property right of any third party. ✓ All data, figures, and tables presented are accurate, authentic, and have not been fabricated or falsified. ✓ Proper permissions have been obtained for any third-party material (images, figures, tables, text) included in the manuscript. ✓ The work complies with all applicable ethical guidelines, including those related to human and animal research. ✓ All sources of funding and conflicts of interest have been disclosed in the manuscript.
Chest X-Ray Outlier Detection, Classification Using Liquid Neural Networks with Dimension Reduction and Advanced Edge Detection
In order to improve health care delivery and optimize resources within a health care delivery system, it is essential to provide early and accurate diagnoses of thoracic disease using Chest X-rays (CXR). In this research project we develop a novel integrated approach that includes Liquid Neural Networks (LNN) as a tool for image classification and anomaly detection. Additionally, Principal Component Analysis (PCA), and advanced edge detection methods are also used. We evaluate our method and compare its performance with Convolutional Neural Networks (CNN) for the task of CXR classification. Our model will include two distinct LNN models; one to classify normal vs. pneumonia cases and another to classify normal vs. lung opacities. Experimental testing was performed on synthetic data generated by PGGAN and validated on large clinical datasets. Both models demonstrated high levels of accuracy, i.e., Model 1 provided 94% accuracy for detecting pneumonia (with sensitivity = .92 and specificity = .96), and Model 2 provided 90% accuracy for identifying lung opacities. Edge detection provided improved features for image classification of approximately 8-12%. PCA provided a significant reduction in processing time of 40% and did not negatively impact classification accuracy. To facilitate point-of-care diagnostics, the two models are combined in a real-time web application created via Flask. The real-time web application provides an automatic triage mechanism to identify patients with abnormal findings first, which can potentially reduce the workload of radiologists involved in routine screening tasks by up to 35-45%. Furthermore, validation studies comparing LNNs to other state-of-the-art dee.p learning architectures (e.g. ResNet-50, DenseNet-121) demonstrate that LNNs possess superior ability to generalize across various types of training data, including synthetic training data. Overall, this study represents an important contribution towards developing artificial intelligence-assisted radiology systems and addressing unmet needs in terms of increasing access to diagnostic services for populations residing in resource-limited health care environments.
Copyright (c) 2026 VT International Press Journal of Multidisciplinary Research and Review https://creativecommons.org/licenses/by-nc/4.0
Formulation and Evaluation of Herbal Hand Wash Using Balanites aegyptiaca (Hinganbet)
Balanites aegyptiaca (L.) Del., which is also called "Hinganbet" or "Desert Date", has been documented as a medicinal plant from the family of Zygophyllaceae. It is extensively distributed throughout the arid and semi-arid regions of India, Africa and the Middle East. The plant contains a variety of bioactive phytoconstituents like saponins, flavonoids, tannins, alkaloids, and polyphenols. These phytoconstituents contribute to various biological activities, such as antimicrobial, antioxidant, and anti-inflammatory. With increasing public concern about synthetic surfactants, such as triclosan, parabens, and sodium lauryl sulphate (SLS), used in commercial hand wash products, there is now greater interest in using plant-based substitutes. This study provides information on the production of an aqueous extract of B. aegyptiaca fruits and the incorporation of this extract into a liquid hand washing formulation containing additives including glycerine, SLS, aloe vera gel, methylparaben, and citric acid. Physiochemical characteristics - appearance, pH, foam height/foam retention, viscosity/spreadability were tested, along with skin irritation testing, for this hand wash. In addition, the hand washing solution's ability to inhibit microbial growth was tested using the agar-well diffusion technique with E. coli and S. aureus. Foam height/retention was in a satisfactory range of 7-9 cm / 90% respectively; pH = 6.2 (acceptable skin pH range = 5.5-7.0); zones of inhibition for E. coli = 12mm; zones of inhibition for S. aureus = 14mm. No degradation in colour or odor, or phase separation occurred under accelerated storage condition stability tests. Therefore these data demonstrate that the formulated herbal hand cleaning product is a safe, effective, ecologically friendly and cost-effective replacement for commercially available synthetic hand cleansing products.
Formulation and Evaluation of Herbal Hair Oil Prepared from Garlic Leaves
Herbal cosmetics have been increasingly utilized to treat a variety of hair and scalp conditions due to their naturally occurring properties and lower risk of adverse reactions. Herbal hair oils provide essential nutrients to the scalp while stimulating hair growth and protecting against hair loss. This current study is focused on developing and evaluating a herbal hair oil formulation derived from garlic leaves (Allium sativum L.) The chemical constituents in garlic leaves consist of; sulfur containing compounds, flavonoids, vitamins, minerals, and antioxidants all of which contribute positively toward hair health. A formulation of herbal hair oil was developed by combining the garlic leaf components with coconut oil, castor oil, hibiscus flower powder and amla extract. The formulated product was tested to evaluate its physiochemical characteristics including; color, odor, pH, viscosity, stability, and skin sensitivity. Results indicated that the formulated herbal oil had favorable aesthetic attributes, displayed appropriate stability at ambient temperatures and exhibited suitable consistency for use as a hair application. The results of this study also indicate that garlic leaves were active in enhancing scalp nutrition and reducing hair loss. In conclusion, garlic leaves may be successfully integrated into herbal hair oil formulations as a relatively inexpensive and safe alternative hair care product.
Formulation and Evaluation of Herbal Transdermal Patch for Treatment of Rheumatoid Arthritis
The purpose of this study was to evaluate two formulations of herbal transdermal drug delivery system (H-TDDS). Each H-TDDS contains; Menthol, Camphor, Eucalyptus Oil and Curcuma longa (Turmeric) extract as active ingredients which are known for its analgesic and anti-inflammatory properties. These ingredients are combined within hydroxy propyl methyl cellulose (HPMC) and poly vinyl alcohol (PVA) films, along with glycerine as a plasticizer. Physically each H-TDDS has a uniform thickness, weight, and appearance. They both have adequate flexibility so they can be folded repeatedly without cracking or breaking apart. Each patch has an acceptably low moisture content. The surface pH of each H-TDDS is approximately neutral. Each H-TDDS has sufficient mechanical strength to withstand normal handling conditions. The total amount of active ingredient contained in each H-TDDS is uniform. A significant proportion of the active ingredient is released over a period of at least 8 hours after application. Therefore it appears that H-TDDS may represent a useful noninvasive alternative to traditional oral dosage forms for providing relief from the symptoms of rheumatoid arthritis.
Formulation and Evaluation of Clitoria ternatea Based Herbal Lotion for the Management of Psoriasis
Clitoria Ternatea Butter Pea Flower Hydroethanolic Extract Formulation of an Herbal Lotion with Anti-Inflammatory Properties Against Psoriasis is a chronic autoimmune inflammatory skin condition that affects about 2-3% of people around the world. It is characterized by thickened red, scaly patches on the body due to abnormal multiplication of skin cells and T-cell mediated inflammation. Current treatments such as corticosteroids, biologic agents and immunosuppressive drugs are often expensive and come with many side effects. This has led researchers to seek out safer alternatives. The purpose of this research was to formulate and evaluate a new herbal lotion containing hydroethanolic extract of Clitoria ternatea flower (also known as butterfly pea) along with other herbs such as aloevera gel, turmeric powder (containing curcumin), clove oil, acacia gum, xanthan gum, natural waxes and rose water. The lotion was created by mixing the various ingredients together in an oil/water emulsion and then evaluating its physical properties. Organoleptic characteristics were evaluated which include appearance, color, odor and texture; pH level was measured; homogenity was examined through visual inspection; viscosity (thickness) and spreadability were determined through viscometer measurements; washability and irratibility/sensitivity testing were performed on healthy volunteers; antimicrobial activity was tested against Malassezia spp. and Candida spp. using the disk diffusion test; lipoxygenase inhibiting activity (anti-inflammatory activity) was assayed in vitro; and finally MTT cytotoxic assays were conducted to determine if the lotion would inhibit keratinocyte proliferation. Results indicated that the pH levels of the lotion ranged from 5.5-6.5, it had good spreadability, a smooth texture and did not cause any skin irritation or sensitivity reactions in healthy volunteers. Furthermore, the lotion exhibited a strong zone of inhibition against all tested strains of bacteria.
Formulation and Evaluation of Anti-Acne Cream Containing Turmeric Extract and Aloe Vera Gel
Acne Vulgaris is one of the most common dermatological disorders, seen in about 85 % of adolescents and young adults worldwide. As such it has been important to develop both safe and effective treatment options. Concerns over side effects of commercially available chemical treatments for acne have contributed to a growing demand for natural alternatives. In this context, we formulated and studied an Anti-Acne Cream using Turmeric Extract (Curcuma longa), and Aloe Vera Gel (Aloe barbadensis) as its two main active components. We developed our cream using an O/W (Oil-In-Water) emulsion based on Bees Wax, Stearic Acid, Cetyl Alcohol, Almond Oil, Glycerin, Methyl Paraben, Vitamin E, Rose Water, Distilled Water. Turmeric’s active ingredient curcumin has demonstrated potent antibacterial and anti-inflammatory activity towards P. acnes and S. aureus. Aloe Vera also offers complimentary soothening, moisturising and wound healing properties. Our formulation was assessed by means of Organoleptics; pH; Spreadability; Homogenity; Washability; Viscosity; Phase Separation; Skin Irritation. The Cream appeared pale yellow; had a pleasing odour; a semi solid consistency; had a pH of 5.8 which is compatible with skin; had a viscosity of 48,890 cP; showed no signs of phase separation after thirty days indicating stability. Also, the results of irritation testing indicated no redness, swelling or irritation. Therefore these data indicate that the Turmeric/Aloe Vera cream represents a viable safe and effective herbal remedy for acne vulgaris and may be applied clinically.
Formulation and Evaluation of Herbal Anti-Acne Gel Containing Argemone Mexicana Leaf Extract
The Prickly Poppy (Argemone Mexicana L.) has been used for centuries due to its ethnomedicinal properties, being one of the most widespread plants in sub-tropical areas. It contains several bio-actives such as Isoquinolines Alkaloids (Sanguinarine, Berberine, Protopine and Chelerythrine), Flavonoids, Terpenes and Phenols, which are associated with Antimicrobial Activity, Anti-Oxidant Properties, Anti-Inflammatory Effects, Antimalarial Activities, and Anti-Cancerous Effects. The purpose of this research was to create and assess an Anti-Acne Gel that includes the Ethanolic Leaf Extract of A. Mexicana as the main Active Agent; Neem Oil and Aloe Vera Gel as Synergists, while utilizing Carbopol 940 as the Gelling Agent. Three different formulations (F1-F3) were developed at three different levels of concentration of the Plant Extract (0.1g, 0.2g and 0.3g respectively). Phyto-Chemical Screening confirmed the presence of alkaloids, tannins, flavonoids and glycosides in the extract. The evaluation parameters included Physical Appearance, pH Value, Viscosity, Spreadability, Extrudability, Homogenity, Drug Content, Washability, Skin Irritation and Antimicrobial Activity. F2 showed better than all the other formulations when it came to its Characteristics, these include a uniform appearance, pH value = 6.2 (compatible to skin), acceptable viscosity, good spreadability, high antimicrobial activity against Propionibacterium Acnes and Staphylococcus Aureus.
Formulation and Evaluation of Flaxseed as Hair Mask
Natural treatments are increasingly used around the world because they are safer than chemical products and create fewer side effects. The purpose of this study is to report on the development of a natural hair conditioner (mask) that incorporates several herbs. These include flaxseed gel; rice water; fenugreek seed extract; curry leaf extract; and turmeric extract. Flaxseed contains Omega-3 fatty acids, which will help retain moisture and improve the shine of the hair. Turmeric extracts contain curcumin, which acts as an antiseptic and anti-inflammatory to protect against infection of the scalp and combat dandruff. Fenugreek seed extracts have high levels of protein and lecithin, which will increase strength at the base of each strand of hair and reduce shedding. Curry leaves are rich in beta-carotene and antioxidants which will increase follicular activity. Rice water has inositol which helps reduce friction and prevent damage from breakage. The new product was tested for pH, viscosity, spreadability, extrudability, homogeneity, skin irritation potential and for stability over time. Results were satisfactory in terms of pH, physical characteristics and no notable degradation occurred when the product was stored under accelerated testing conditions. Phytochemicals present within the new product included tannins, saponins, alkaloids, phenols and proteins. All these components contribute to hair strengthening, combating dandruff and providing antioxidant capabilities. The results demonstrate that the natural hair treatment can be both cost effective and serve as a viable alternative to commercially available chemical based hair care products.
Formulation and Evaluation of Polyherbal Ayurvedic Anti-Inflammatory Lepa
Inflammatory reactions protect damaged tissues during an injury or infection; chronic inflammatory responses produce signs such as pain, swelling, redness, and a variety of systemic diseases. Overuse of synthetic anti-inflammatory drugs has led to side effects that include gastrointestinal irritation, ulcers, kidney damage, and increased skin sensitivity. Therefore, the need for safer and less expensive alternatives such as herbal preparations has developed. This research study aimed at developing and evaluating a polyherbal Ayurvedic lepa containing Amla (Emblica officinalis), Turmeric (Curcuma longa), Vitex negundo, and Red Ochre (Geru) that would be both safe and effective. Each ingredient was chosen because it is important in traditional medicine and each component has well documented bioactive compounds. Specifically, curcumin in turmeric blocks the production of pro-inflammatory chemical messengers while Amla provides antioxidant activity and tissue repair; Vitex negundo produces analgesia and anti-inflammatory activity; and geru reduces heat in the affected area. After drying, grinding into powder form, sifting through fine mesh screens and mixing together in optimal proportions, the four ground powders were mixed with distilled water and then subjected to mechanical energy to create a smooth paste. The new product was tested for its quality characteristics including physical appearance, taste, odor, texture, pH level, ease of application, ability to remove after application, irritation to intact skin and stability. All results indicated excellent uniformity and mixtures had good physical properties that did not vary significantly over time when stored at room temperature.
Progress in Ferrocement-Confinement Techniques for Self-Compacting Concrete: Structural Insights
This review paper offers a state-of-the-art, and in-depth overview of how SCFC behaves structurally. SCFC combines the high workability of SCC with the tensile properties and crack resistance of ferrocement. The review primarily includes publications between 2020–2025. In addition to these recent studies, it also includes some foundational studies conducted in 2011–2022. Across all studies reviewed (n=28), there was consistency in terms of what is known about the effects of confining ferrocement on SCC. Specifically, the results show that SCC confined with ferrocement can provide higher load-carrying capacities, enhanced ductilities, increased energy absorptions, and improved crack resistances than unconfined SCC. Some examples of more recently developed approaches for enhancing the performance of SCFC include using geopolymers; the use of sustainable SCM's (e.g., FA, GGBFS, SF); and ML-assisted optimization of SCFC mixes. Finally, the Review Paper includes a separate discussion on research gaps which identifies those problems most urgently requiring resolution. These include developing new constitutive models for predicting SCFC behavior under various loading conditions; assessing seismic behavior; evaluating long-term durability; and establishing standardized design procedures.
Self-Compacting Ferrocement Concrete (SCFC)
Transfer of Microplastics from River Systems to Underground Water Resources: Mechanisms, Impacts, and Research Needs
This study investigated the ways that microplastics move from rivers to groundwater. Microplastics occur when large pieces of plastics break down. We have a number of daily uses for products made with microplastics. Microplastics last forever and travel through water, soil and rock to find their way into groundwater. There are several ways in which microplastics get into the groundwater. Microplastics can pass through small pores in the space between rocks and dirt. When the river banks become saturated with water and start to leak out they carry microplastics with them. Also, when underground water levels rise due to natural means, microplastics will be carried along too. Movement of microplastics is dependent upon the characteristics of the microplastic itself, the type of dirt/soil, how much water flows over the surface and other factors related to the environment around it. A major concern in studying this topic is to determine where microplastics are in groundwater, because if we don't know where they exist we cannot protect against them contaminating our aquifers. In addition to identifying areas of contamination, there are many challenges associated with collecting and analyzing sample data. Microplastics have negative impacts on both the ecosystems and human health and are now found in the world's groundwater. This review article identifies gaps in the scientific knowledge of how microplastics travel through groundwater and what happens to them once they are in the environment. It also emphasizes the need for researchers to collaborate together to develop standard practices (e.g. use of standardized sampling techniques, testing, and reporting) so that researchers can understand all aspects of microplastic pollution (sources, pathways, effects) and develop effective strategies to protect our water supply. As such, addressing microplastic pollution will help protect groundwater quality and preserve aquifer function as an essential provider of ecosystem functions and safe drinking water.
Formulation and Evaluation of Herbal Oral Care Products Using Alum and Clove Oil (Mouthwash)
Oral ulcers create severe pain in the oral mucosa and will severely limit eating and speaking. While conventional treatment of oral ulcers through topical anesthesia or steroid application can provide temporary symptom relief, these applications have the potential for long term adverse effects. This article describes the development of a herbal mouth wash comprised of Syzygium aromaticum (clove oil), KAl(SO₄)₂·12H₂O (alum), glycerin, sorbitol, citric acid, sodium benzoate, and sodium lauryl sulfate (SLS) designed to provide rapid relief from pain; reduce inflammation; enhance wound closure and facilitate oral hygiene. Syzygium aromaticum has been shown to be effective in providing significant antimicrobial/antifungal activity and analgesia due to its high eugenol content. Alum was chosen because it provides both astringency and hemostasis which enhance wound closure. Sorbitol and glycerine function as humectants to maintain moisture within the solution. Citric acid ensures the solution remains at an optimal pH for its intended use. Sodium benzoate functions as a preservative extending the shelf life of the product. SLS creates the foam component of this product and facilitates cleaning the teeth. Three different formulations (F1, F2 & F3) were developed, tested for their organoleptic characteristics, pH level, viscosity, foam volume created by each formula, and tested for antimicrobial properties utilizing the agar-well-diffusion technique. Additionally, all three formulas underwent accelerated-stability studies. The most stable formula (F3) had a pH level of 5.8, sufficient viscosity for oral use, created a zone of inhibition of 15mm around it when tested against several common pathogens found on the oral cavity and showed acceptable physical-chemical stability throughout the test period.
Development and Physicochemical Evaluation of Herb-Infused Functional Cookies
The purpose of this investigation was to create and assess functional cookies made with Ashwagandha (Withania somnifera), Cinnamon (Cinnamomum verum), Ginger (Zingiber officinale), Mint (Mentha piperita) & Cardamom (Elettaria cardamomum). The growing number of individuals affected by lifestyle-related illnesses has led consumers to seek out functional foods which provide some type of health benefit in addition to fulfilling their nutritional needs. Cookies prepared from herbs are an exciting example of how modern food technology can combine the knowledge from traditional Ayurvedic pharmacology. The suggested formula will help prepare a cookie base that satisfies sensory requirements while providing the therapeutic effect associated with reducing stress through the use of the Ashwagandha as an adaptogen; enhancing digestive function through enhanced GI motility; stimulating or modulating immune response; and protecting against oxidative damage. Due to its proven prebiotic activity and antimicrobial properties honey was chosen for use as the natural sweetener and because it provided the fat component in the homemade butter. Consumer panel acceptance assessments of the organoleptic characteristics of each cookie were performed using a 9 point hedonic scale for measuring taste, texture, smell, appearance, and overall acceptability. Results indicated that consumer panels found cookies made with the specified proportions of herbs acceptable. Assessments were also completed on physio-chemical properties as well as shelf life. Overall, results from the assessment support the idea that cookies prepared from herbs are a viable alternative as a functional food that provides both nutritionally as well as therapeutically.
Autonomous Mobile Robots in Warehouse Automation: A Comprehensive Review of AI, Navigation, and Sensing Technologies Under Industry 4.0
Warehouse automation is becoming increasingly important as the use of e-commerce, global supply chains and Industry 4.0 continues to increase. In order to address this increasing importance, there is a pressing need for scalable, smart and adaptable warehouse automation systems. As such, the purpose of this review is to provide a comprehensive overview of the current state-of-the-art in using Autonomous Mobile Robots (AMRs) for materials handling. The AMR field has grown rapidly since 2020 and the number of peer reviewed articles related to it has increased significantly. Consequently, in addition to the growing interest in this field, we have synthesized 45 or so papers from six interdependent areas of research including; SLAM/autonomous navigation, deep learning/computer vision for object detection, path planning techniques/algorithms, obstacles avoidance techniques/methodologies, sensor fusion methods and artificial intelligence based inventory management. Through our systematic analysis of these papers we identified both significant advancements in each of the various sub-systems included within the AMR system, i.e., YOLO-based object detection was found to be accurate at approximately 96%, A* path planning accuracy was enhanced to 97% and LiDAR-Camera sensor fusion provided a 100% reliable results when testing for objects greater than 2 cm. However, despite the numerous advancements made in the individual sub-systems, existing solutions remain largely fragmented with limited evidence of unified frameworks being developed that support holistically managing warehouse operations. Furthermore, although some authors report success in deploying their solutions in large scale industrial settings, many others identify key challenges they faced including real-time computing constraints, dynamic obstacle handling issues, scalability limitations and inadequate validation of their solutions using data obtained in real world deployments. Therefore, through our analysis of these papers we were able to identify key research gap areas and propose potential future research direction including developing integrated and edge-computing enabled autonomous robot solutions that align with industry 4.0 standards. Our review also provides a consolidated platform for both researchers and practitioners wishing to develop next generation smart warehouse automation solutions.
AI-Integrated Mechanical Systems for Real-Time Industrial Emission Monitoring and Control: A Comprehensive Review
In this study we are reviewing how fast the development of artificial intelligence integrated mechanical systems in real time emissions can be used to monitor and manage emissions in industry. Traditional ways of collecting emissions have been by using manual samples, or sampling at intervals with use of Continuous Emissions Monitoring Systems (CEMS). Manual sampling and CEMS both provide delay in obtaining data and require high operational cost. Use of Artificial Intelligence (AI), Machine Learning (ML) and IoT technology allows for continuous collection of data, real-time analysis of collected data and proactive measures to reduce emissions. In addition to reviewing the progression of emissions monitoring from traditional methods through CEMS to current hybrid architectures combining AI and IoT technologies, this study evaluates machine learning techniques as follows: Regression Models; Decision Trees; Ensemble Methods; LSTM Networks; Deep Neural Networks. Additionally, this study identifies ongoing research issues: Limited Mechanical-Integration of AI Technology; Lack of Explainability of Predictions; No Closed Loop Automated Control. Lastly, the study provides a comparison of the performance of 12 different Machine Learning Techniques along with an evaluation of sensor technology, data pre-processing techniques, and Real-Time Dashboard Frameworks. Finally, this study suggests potential futures uses for Industry Applications such as Federated Learning; Edge AI; Digital Twin Integration.
Study on Intelligent Predictive Maintenance for Industrial Machinery: A Review
The application of Intelligent Predictive Maintenance (IPdM) is transforming industrial asset management through the intersection of Industry 4.0 technologies such as; Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), the Internet of Things (IoT) and Edge Computing. A systematic review of existing literature on current intelligent predictive maintenance paradigms, architectures and methods for use with industrial equipment is examined within this study. Additionally, the major technologies to be used for sensor based condition monitoring, fault detection/diagnosis, remaining useful life prediction and anomaly detection will be reviewed. This includes analysis of CNN's, LSTM's, random forests, GAN's, and explainable AI (xai). Data imbalance challenges in deploying models at the edge of an IoT network and how to achieve real time performance are presented. Emerging solutions to these issues are also addressed. Finally, this review presents several areas for potential research, which may include the development of federated learning systems, incorporation of digital twins into IPdM systems, and the design of hybrid edge cloud architectures.
Advanced Formulation Development and Multi-Criteria Evaluation of Multivitamin Nutraceutical Lollipops for Enhanced Pediatric Adherence
The ongoing challenge with ensuring children adhere to prescribed medications is primarily due to developmental dysphagia and increased sensitivities to bitter-tasting pharmaceuticals. The aim of this project was to develop multivitamin nutraceutical lollipop formulations as a new method of delivery for pediatric patients. These formulations contained the following amounts per serving: 40mg Vitamin C, 1mg each of Vitamins B1 & B2, 1mg of Vitamin B6, and 2mcg of Vitamin B12, 5mg of Zinc Gluconate. A "sugar glass" matrix method of developing these dosages, using Quality by Design (Qbd) principles, provided a systematic means of examining the combined effects of sucrose and liquid glucose on the mechanical strength and disintegration properties of the final dosage forms. Through thermal degradation studies it was determined that the temperature at which all heat sensitive vitamins would begin to degrade is 85°C (with a range of 80 - 90°C). At or above this temperature, the amount of active ingredient remaining in the product will be >98.5%. The optimized sugar to glucose ratio of 2.3:1 in formulation F3 resulted in a solid with higher hardness values (5.8±0.15kg/cm²) than previously studied ratios, lower friability (0.51%), and a nearly linear zero order release pattern (R²=0.98) over a 25-minute period when exposed to simulated saliva (pH 6.8). The longer duration of disintegration is beneficial for both covering up the metallic flavor associated with the ingestion of zinc gluconate and possibly allowing for some degree of absorption through the buccal mucosa thereby reducing the first pass effect through the liver. Packaging requirements were established based upon accelerated stability tests conducted according to ICH Q1A(R2) guidelines (40°C/75%RH for 3 months), requiring high barrier moisture resistant packaging. Overall, the data demonstrates that nutraceutical lollipops are a novel, patient-centered format for providing important pediatric micronutrient supplementation, transforming what has historically been viewed as an unpleasant medical intervention into an enjoyable consumer experience.
Social Cost-Benefit Analysis of Saudi Arabian Leadership's Decision for Non-Retaliation in the Iran War: A Civilization Continues in Peace and Harmony
The 2026 Iranian missile attacks against Saudi Arabian soil represented a watershed moment in the history of the Middle East geopolitics. Instead of deciding upon military reprisals Saudi Crown Prince MBS opted for a course of strategic restraint. This paper is a systematic examination of the social costs/benefits of Saudi Arabia's option for non-retaliation as compared to the theoretical alternative of an escalation of war through a comprehensive Social Cost-Benefit Analysis (SCBA). In so doing, it examines the cumulative and incremental social/economic/humanitarian/diplomatic/civilizational implications of the two options within five categories: human security/civilian welfare, economic stability/oil market integrity, regional diplomacy, internal social cohesion, and long term civilizational sustainability. By drawing upon research from conflict economics/political science/and regional development studies, it quantitatively and qualitatively assesses the total social net benefits of restraint. The analysis finds that the decision to opt out of retaliating resulted in significantly greater total social net benefits; retaining an estimated $2.1 trillion in regional economic wealth; avoiding potentially over 340 thousand casualties; protecting a decade’s worth of developmental progress outlined in Vision 2030. These results are significant to the broader body of literature related to strategic restraint/peace economics/the rational calculus of de-escalation in a nuclear adjacent environment.
Smart Dustbin: An Automated Waste Management Solution Using Arduino and Ultrasonic Sensing
The Smart Dustbin is a smart sensor-embedded waste management product which is going to help resolve problems with increasing solid waste management and disposal in urban areas. In this paper, we will describe the designing, developing and testing of the first version of Smart Dustbin that incorporates a HC-SR04 ultrasonic distance sensor, a SG90 servo motor and an Arduino Uno board. The project enables users to use the Smart Dustbin without touching its lid using an ultrasonic distance sensor to detect when waste has been placed at or below 20cm from the base of the bin. Once the sensor detects waste near the bin, it activates the servo motor to open the lid, and keeps it open for some time after opening then closes. After completing all stages of Human-Centred Design Thinking process including Empathise, Define, Ideate, Prototype and Test the prototype of Smart Dustbin was tested in a School Corridor and Residential Apartment. The results of testing demonstrated very positive responses from users towards aspects of hygiene, usability, and efficiency. In addition to the description of hardware design, circuit design and Arduino Code of the Smart Dustbin prototype, we have provided suggestions on potential developments to be made on the project including Solar Power Integration into the Smart Dustbin, Artificial Intelligence based Waste Sorting and connection to Municipal Dashboard.
From Intuition to Intelligence: A Data Science Framework for Modern Sports Analytics
The use of Data Science and Competitive Sports represents a major paradigm shift from using intuition to coach to making decisions based on evidence. In this study, a comprehensive Data Science Framework for Modern Sports Analytics is developed. It combines the collection of multi-modal data, the construction of signal-processing pipelines, the generation of advanced features through feature engineering, and the application of Machine Learning (ML) inference engines. A systematic review of the Literature from 2019 to 2025 was conducted to examine how Artificial Intelligence (AI) sub-disciplines such as supervised learning, unsupervised clustering, deep sequential models, and Explainable AI (XAI) were used in the three primary areas of Performance Optimization, Tactical Strategy, and Business Intelligence. Additionally, the challenges of integrating real-time data into the Data Science pipeline, the Ethical Implications of Collecting Biometric Data of Athletes, and the Increasing Use of Explainable AI to Build Trust with Coaches and Stakeholders are discussed. The proposed Data Science Framework addresses the gap that exists between Fragmented Single-Modality Research and Holistic Real-Time Models of Performance. Indicators of experimental validation across recent studies have shown Statistically Significant Improvements in Match Outcome Prediction Accuracy and Injury Risk Forecasting when Multi-Modal Pipelines were employed. As both a Structured Review and a Forward-Looking Blueprint for Researchers and Practitioners looking to Leverage Data Science for Competitive Advantage in Sports, this Study will be a valuable resource.
Suvarna Shalaka Agnikarma in the Management of Avabahuka (Frozen Shoulder): A Case Report
Agnikarma (thermal cautery), a traditional Ayurvedic parasurgical technique, is a successful treatment for conditions caused by aggravated Vata and Kapha doshas. Avabahuka, a Vata-vikara, is clinically associated with adhesive capsulitis, which is referred to as "frozen shoulder" in contemporary medicine. Shoola (pain), Stambha (stiffness), and Bahupraspanditahara (limited range of motion) at the Amsa Sandhi (shoulder joint) are its main clinical characteristics. Despite the existence of various conventional treatments, Agnikarma offers a unique and targeted therapeutic approach. A 48-year-old male presented with a 5-month history of severe pain, stiffness, and restricted movement in his right shoulder, clinically diagnosed as Avabahuka (adhesive capsulitis). A five-session Agnikarma therapy was administered to the right shoulder to manage Avabahuka via Vata-Kapha pacification. Post-treatment evaluation demonstrated marked reductions in localized pain and stiffness with a significant increase in Range of Motion (ROM). Agnikarma exhibits promising clinical efficacy in the management of Avabahuka.
Banking Security System Using Face and Liveness Detection Using Machine Learning and Image Processing
To provide safer user login experiences on ever-growing online banking services, it will be important to develop new, better forms of secure authentication. Traditional convolutional methods such as password, pin or one-time passwords are vulnerable to common attacks such as phishing and have been known to lose their validity when credentials are leaked. Biometric authentication based on face recognition offers both convenience and non-contact verification; however, traditional systems can be easily spoofed by fake photos, videos, or even 3-D masks. This paper presents an ML-based method to develop a more robust ID verification system via the use of image processing techniques along with deep learning techniques. An initial CNN will be used to extract facial features and recognize faces. In addition to the CNN, we also propose a liveness detection module that would detect the presence of natural behaviors associated with humans i.e., eye blinks, lip movements and small micro-expression changes in facial expressions to determine if the user is a living being or attempting to spoof the system. We plan to train and validate our model on publically available datasets like ORL, OULU, and CASIA to ensure that it will work under different lighting conditions, pose variations and textures. The system will utilize Python/TensorFlow/OpenCV/Keras and a MySQL DB. We will test this system’s accuracy/latency/resistance to spoofing attacks. Our experimental results show that the proposed system performs much better than other reported systems (over 98% recognition accuracy, very low FAR & very fast) for online/mobile banking applications. Therefore, this study demonstrates that the proposed system is an inexpensive way to improve security in banking infrastructures. Furthermore, due to the modularity of the proposed system, it can be easily integrated into all current banking platforms while complying with regulations related to data protection.
Comprehensive Framework for Data Science-Driven Decision-Making Across Healthcare, Finance, Marketing, and Supply Chain Domains
Data Science is transforming many industries by allowing organizations to use complex and large amounts of data to inform data-driven decision making. In this research paper we explore the methods and tools of Data Science in today's organizational environment, as well as its practical applications. Organizations that implement Data Science through predictive analytics, machine learning algorithms, and large-scale data platforms are able to increase their operational efficiency, make better strategic plans, and improve decision quality. In addition to examining how Data Science is applied in healthcare, marketing, finance, and supply chains, we evaluated the performance of predictive models. Several challenges were identified, including data quality, model interpretability, privacy, algorithmic fairness, and systems integration. Ethical issues and governance frameworks promoting responsible development of AI and analytics are also discussed. Findings confirmed that organizations adopting data-driven approaches achieve significant competitive advantages, improved resource allocation, and support long-term evidence-based decision-making.
Optimization of the Hard Turning Process by Artificial Neural Networks: A Review
Hard turning is a precision machining process employed as a finish operation on hardened alloy steels (42–68 HRC), widely used as an alternative to cylindrical grinding. The complexity and non-linearity of the hard turning process—governing surface roughness, cutting forces, tool wear, and cutting temperature—make traditional analytical modeling inadequate. Artificial neural networks (ANNs) have emerged as powerful data-driven tools capable of modeling and optimizing these intricate relationships with high predictive accuracy. This review systematically surveys peer-reviewed research published between 2005 and 2025, with emphasis on studies from 2018–2025, examining the application of ANN-based models to optimize hard turning parameters. The review covers ANN architectures employed (feedforward, radial basis function, deep neural networks, and hybrid approaches such as ANN–GA, ANN–PSO, and CNN-LSTM), training algorithms (back-propagation, Levenberg–Marquardt, Bayesian regularization), workpiece materials (AISI 52100, AISI H13, AISI D2, AISI 4340), cutting tool types (CBN, PCBN, coated carbides), and performance indicators (surface roughness Ra, cutting forces Fx/Fy/Fz, tool flank wear VB, cutting temperature). Key findings confirm that ANN models consistently outperform regression-based models in capturing non-linear input–output relationships, with prediction errors frequently below 5%. Recent trends toward hybrid ANN–genetic algorithm models, explainable machine learning (XAI), and real-time CNN-LSTM architectures signal a move toward intelligent and autonomous hard turning optimization systems.
Development of an AI-ML-Based Expert System for Extrusion-Based Manufacturing Processes: A Comprehensive Framework for Process Optimization, Defect Detection, and Real-Time Control
In this study, we developed and tested an artificial intelligence–machine learning based expert system called AIMES. The AIMES expert system was designed to be used in conjunction with extrusion-based manufacturing systems, including Fused Deposition Modeling (FDM), Single-Screw Polymer Extrusion Systems, and Hot-Melt Extrusion (HME) Systems. The system consists of three main modules: a knowledge acquisition module, a hybrid inference engine that utilizes both rule based systems and machine learning subsystems, and a real-time closed-loop feedback control interface. To evaluate the performance of the different machine learning algorithms included in the hybrid inference engine, four types of machine learning were tested. These include ANN (Artificial Neural Network), CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), and XGBoost (Gradient Boosting Ensemble Learning Algorithm). Based upon these tests, it can be concluded that all four machine learning algorithms performed well, but the best-performing algorithm was XGBoost. We also tested AIMES using in-situ measurements and found that AIMES detected clogs in the nozzle before they caused damage or produced defective products 96.8% of the time. Furthermore, the testing revealed that AIMES detected clogs as quickly as 50 milliseconds after they occurred.
Friction and Wear Analysis of Nano-Particle-Reinforced Composite Ball Bearings for Aerospace Applications
A thorough study of the tribology of nano particle reinforced polymer composite ball bearings was carried out for an aerospace application. The three nano-composite formulations tested were; (i) PEEK matrix filled with alumina (Al₂O₃) nanoparticles (ii) PEEK matrix filled with silicon carbide (SiC) nanoparticles (iii) A hybrid Al₂O₃ / SiC loaded at 5wt %, 10wt % and 15wt %. Dry friction and wear testing were performed using a pin-on disc tribometer at contact pressures ranging from 0.5 MPa to 3.0 MPa and sliding speeds ranging from 0.5 m/s to 3.0 m/s. Worn surface characterization by SEM/EDX indicated that the hybrid loaded nano-composite formulation at 10wt% displayed the least friction coefficient (μ = 0.061) and minimum specific wear rate (Wₛ=1.8×10⁻¹⁴ m²/N), and represented reductions in these parameters of 43% and 57% compared to unreinforced PEEK. Thermally stable analysis demonstrated that the nano-composite material would be suitable for service temperatures up to 280°C. Therefore, it has been demonstrated that nano-reinforced PEEK composites can serve as light weight self-lubricating bearing materials for future generation of aerospace systems, such as satellite actuators and gas turbine auxiliary mechanisms.
The Renegotiation Trap: Political Economy Factors Driving Opportunistic Contract Modifications in Public-Private Partnerships
Public-Private Partnerships (PPPs), as an instrument of global infrastructure governance, have created a widespread issue among governments: the widespread renegotiation of PPP contracts. Renegotiations occur at a rate far greater than would be expected under normal circumstances; therefore, it can reasonably be concluded that most PPP renegotiations are opportunistic. This article examines why the renegotiation of PPPs occurs so frequently. The authors call this problem the “Renegotiation Trap,” because there exists a self-reinforcing cycle of political incentives, opportunistic bids, contract incompleteness, and lack of institutional oversight that makes renegotiating PPP contracts both possible and strategically rational. The authors draw upon three bodies of literature to explain why renegotiating PPPs occurs. First, incomplete contract theory is utilized to understand why renegotiating PPPs is necessary. Second, political economy frameworks are used to understand who benefits from the renegotiation of PPPs. Third, data collected from over 1,000 Latin American concession agreements are analyzed to identify when and how renegotiations occurred. Using these three approaches, the authors found that the primary causes of renegotiating PPPs were: (i) election cycles; (ii) soft budget constraints; (iii) bidder’s curse; (iv) information asymmetry; and (v) corruption. The authors also evaluate different reform options to improve the renegotiation process. These reform options include creating an independent regulatory body to oversee the renegotiation process, holding open auctions to determine the best price for services, requiring public disclosure of renegotiated terms, and providing additional funding sources for renegotiation. The authors conclude that improving the renegotiation process requires changing the political structure that provides the incentives for renegotiating PPP contracts rather than just changing the language contained within those contracts.
Comparative Study of AI-Based Optimization Techniques in VLSI Circuit Design Using Python
Successful design of very large scale integration (VLSI) circuits requires the use of various optimizations—power reduction, area, and time optimization. This requires the involvement of AI-based optimization techniques, such as machine learning (ML), deep learning (DL), genetic algorithms (GA), particle swarm optimization (PSO), and reinforcement learning (RL), to fulfill such tasks where traditional approaches fail to do so. The abundant libraries and ease of implementation have made Python a widely used platform in AI-based VLSI optimization. The methodology used in this study is a comparative study of different AI-based optimization algorithms used in the VLSI circuits. This research is structured in the following way: problem definition, data collection & preprocessing, implementation on Python, and performance evaluation. The best number was obtained using DL (CNN) and RL (95%) for the complex tasks (floorplanning and dynamic power management). ML (SVM) achieved high accuracy (90%) on well-defined problems but was not scalable for large designs. GA and PSO were good in performing global optimizations for problems like area minimization and routing but required fine-tuning of parameters. DL and RL had the highest computational complexity (GPU acceleration) and the lowest resource requirements, with easier implementation, with ML techniques. We found that DL and RL are better suited to large and complex designs and are slow for small and simple designs, whereas ML (SVM) and PSO are better suited to small and simple designs and are often slow given large and complex designs. Therefore, additional research is needed on the use of multiple AI techniques in a hybrid way to overcome limitations of the different techniques and enhance design efficiency. The results of this study can be applied in the following areas: AI-based Electronic Design Automation (EDA) tools, Power and performance optimization in semiconductor industries, Automated circuit design for advanced computing and development of Python-based AI stacks for VLSI research.