Diacerein mitigates endocrine and cardio-metabolic disruptions in experimental PCOS mice model by modulating AdipoR1/ PON 1.

BACKGROUND: This study aimed to explore the impact of Diacerein (DIC) on endocrine and cardio-metabolic changes in polycystic ovarian syndrome (PCOS) mouse model. METHODS: A total of 18 adult female mice (Parkes strain), aged 4-5 weeks, were randomly assigned to three groups, each comprising 6 animals, as follows: Group I (control), received normal diet and normal saline as vehicle for 51 days; Group II received Letrozole (LET; 6 mg/kg bw) for 21 days to induce PCOS; Group III received LET, followed by daily oral gavage administration of DIC (35 mg/kg bw) for 30 days. RESULTS: This study indicates that treatment with LET resulted in PCOS with characteristics such as polycystic ovaries, elevated testosterone, weight gain, visceral adiposity, high levels of insulin as well as fasting blood glucose in addition to insulin resistance, improper handling of ovarian lipids, atherogenic dyslipidemia, impaired Na + /K + -ATPase activity and serum, cardiac, and ovarian oxidative stress. Serum/ovarian adiponectin levels were lowered in LET-treated mice. In mice treated with LET, we also discovered a reduction in cardiac and serum paraoxonase 1 (PON1). Interestingly, DIC restored ovarian andcardio-metabolic abnormalities in LET-induced PCOS mice. DIC prevented the endocrine and cardio-metabolic changes brought on by letrozole-induced PCOS in mice. CONCLUSION: The ameliorative effects of DIC on letrozole-induced PCOS with concurrent oxidative stress, abdominal fat deposition, cardiac and ovarian substrate mishandling, glucometabolic dysfunction, and adiponectin/PON1 activation support the idea that DIC perhaps, restore compromised endocrine and cardio-metabolic regulators in PCOS.

Enhancing construction safety: predicting worker sleep deprivation using machine learning algorithms.

Sleep deprivation is a critical issue that affects workers in numerous industries, including construction. It adversely affects workers and can lead to significant concerns regarding their health, safety, and overall job performance. Several studies have investigated the effects of sleep deprivation on safety and productivity. Although the impact of sleep deprivation on safety and productivity through cognitive impairment has been investigated, research on the association of sleep deprivation and contributing factors that lead to workplace hazards and injuries remains limited. To fill this gap in the literature, this study utilized machine learning algorithms to predict hazardous situations. Furthermore, this study demonstrates the applicability of machine learning algorithms, including support vector machine and random forest, by predicting sleep deprivation in construction workers based on responses from 240 construction workers, identifying seven primary indices as predictive factors. The findings indicate that the support vector machine algorithm produced superior sleep deprivation prediction outcomes during the validation process. The study findings offer significant benefits to stakeholders in the construction industry, particularly project and safety managers. By enabling the implementation of targeted interventions, these insights can help reduce accidents and improve workplace safety through the timely and accurate prediction of sleep deprivation.

A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.

The goal of this research is to create an ensemble deep learning model for Internet of Things (IoT) applications that specifically target remote patient monitoring (RPM) by integrating long short-term memory (LSTM) networks and convolutional neural networks (CNN). The work tackles important RPM concerns such early health issue diagnosis and accurate real-time physiological data collection and analysis using wearable IoT devices. By assessing important health factors like heart rate, blood pressure, pulse, temperature, activity level, weight management, respiration rate, medication adherence, sleep patterns, and oxygen levels, the suggested Remote Patient Monitor Model (RPMM) attains a noteworthy accuracy of 97.23%. The model's capacity to identify spatial and temporal relationships in health data is improved by novel techniques such as the use of CNN for spatial analysis and feature extraction and LSTM for temporal sequence modeling. Early intervention is made easier by this synergistic approach, which enhances trend identification and anomaly detection in vital signs. A variety of datasets are used to validate the model's robustness, highlighting its efficacy in remote patient care. This study shows how using ensemble models' advantages might improve health monitoring's precision and promptness, which would eventually benefit patients and ease the burden on healthcare systems.

Covering assisted intuitionistic fuzzy bi-selection technique for data reduction and its applications.

The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Impact of low-oxalate diet on hyperoxaluria among patients suffering from nephrolithiasis.

Low-oxalate diets are useful for treating hyperoxaluria in nephrolithiasis patients. This study was unique in examining how a low-oxalate diet in addition to a standard diet affected hyperoxaluria and renal function tests in nephrolithiasis patients. The effects of a low-oxalate diet were analyzed by different biochemical tests, that is, anthropometric measurements, blood oxalate test, renal function test, electrolyte profile test, and 24 h urine analysis. For this purpose, 112 patients were divided into 2 groups: Group T1 (Conventional diet) and Group T2 (Low-Oxalate diet) for 8 weeks. Each group was tested at the initiation and end of the study. Using SPSS, the obtained data from each parameter were statistically analyzed. The results showed that a low-oxalate diet had a positive effect on patients suffering from nephrolithiasis. Furthermore, after treatment, anthropometric measurement weight (kg) among the control group (T1) was 100.45 ± 5.65 and the treatment group (T2) was 79.71 ± 9.48 kg. The effect of low-oxalate diet on renal function test: creatinine (g/d) among T1 was 2.08 ± 0.86 and T2 was 1.17 ± 0.13, uric acid(mg/d) among T1 was 437.04 ± 24.20 and T2 was 364.61 ± 35.99, urinary oxalate (mg/d) among T1 was 76.84 ± 10.33 and T2 was 39.24 ± 1.51, respectively. Sodium (mEq/d) among T1 was 156.72 ± 6.37 and T2 was 159.84 ± 6.31, potassium (mEq/d) among T1 was 69.91 ± 15.37 and T2 was 89.21 ± 6.31, phosphorus (g/d) among T1 was 0.96 ± 0.07 and T2 was 0.34 ± 0.27, respectively. This study demonstrated that nephrolithiasis patients with hyperoxaluria benefit from low-oxalate diets. Hyperoxaluria patients should eat a low-oxalate diet to use oxalate without affecting metabolism and eliminate it from the kidney without stones.

Garden cress seeds: a review on nutritional composition, therapeutic potential, and industrial utilization.

The growing preference for natural remedies has resulted in increased use of medicinal plants. One of the most significant and varied plants is garden cress (Lepidium sativum), which has large concentrations of proteins, fatty acids, minerals, and vitamins. It also contains a wide range of bioactive components, including kaempferol glucuronide, gallic acid, protocatechuic acid, coumaric acid, caffeic acid, terpenes, glucosinolates, and many more. These substances, which include antioxidant, thermogenic, depurative, ophthalmic, antiscorbutic, antianemic, diuretic, tonic, laxative, galactogogue, aphrodisiac, rubefacient, and emmengogue qualities, add to the medicinal and functional potential of garden cress. An extensive summary of the phytochemical profile and biological activity of garden cress seeds is the main goal of this review. Research showed that garden cress is one of the world's most underutilized crops, even with its nutritional and functional profile. Consequently, the goal of this review is to highlight the chemical and nutritional makeup of Lepidium sativum while paying particular attention to its bioactive profile, various health claims, therapeutic benefits, and industrial applications.

Robust brain tumor classification by fusion of deep learning and channel-wise attention mode approach.

Diagnosing brain tumors is a complex and time-consuming process that relies heavily on radiologists' expertise and interpretive skills. However, the advent of deep learning methodologies has revolutionized the field, offering more accurate and efficient assessments. Attention-based models have emerged as promising tools, focusing on salient features within complex medical imaging data. However, the precise impact of different attention mechanisms, such as channel-wise, spatial, or combined attention within the Channel-wise Attention Mode (CWAM), for brain tumor classification remains relatively unexplored. This study aims to address this gap by leveraging the power of ResNet101 coupled with CWAM (ResNet101-CWAM) for brain tumor classification. The results show that ResNet101-CWAM surpassed conventional deep learning classification methods like ConvNet, achieving exceptional performance metrics of 99.83% accuracy, 99.21% recall, 99.01% precision, 99.27% F1-score and 99.16% AUC on the same dataset. This enhanced capability holds significant implications for clinical decision-making, as accurate and efficient brain tumor classification is crucial for guiding treatment strategies and improving patient outcomes. Integrating ResNet101-CWAM into existing brain classification software platforms is a crucial step towards enhancing diagnostic accuracy and streamlining clinical workflows for physicians.

Detection of Parkinson disease using multiclass machine learning approach.

Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson's Disease.

A robust self-supervised approach for fine-grained crack detection in concrete structures.

This work addresses a critical issue: the deterioration of concrete structures due to fine-grained cracks, which compromises their strength and longevity. To tackle this problem, experts have turned to computer vision (CV) based automated strategies, incorporating object detection and image segmentation techniques. Recent efforts have integrated complex techniques such as deep convolutional neural networks (DCNNs) and transformers for this task. However, these techniques encounter challenges in localizing fine-grained cracks. This paper presents a self-supervised 'you only look once' (SS-YOLO) approach that utilizes a YOLOv8 model. The novel methodology amalgamates different attention approaches and pseudo-labeling techniques, effectively addressing challenges in fine-grained crack detection and segmentation in concrete structures. It utilizes convolution block attention (CBAM) and Gaussian adaptive weight distribution multi-head self-attention (GAWD-MHSA) modules to accurately identify and segment fine-grained cracks in concrete buildings. Additionally, the assimilation of curriculum learning-based self-supervised pseudo-labeling (CL-SSPL) enhances the model's ability when applied to limited-size data. The efficacy and viability of the proposed approach are demonstrated through experimentation, results, and ablation analysis. Experimental results indicate a mean average precision (mAP) of at least 90.01%, an F1 score of 87%, and an intersection over union threshold greater than 85%. It is evident from the results that the proposed method yielded at least 2.62% and 4.40% improvement in mAP and F1 values, respectively, when tested on three diverse datasets. Moreover, the inference time taken per image is 2 ms less than that of the compared methods.

Piperine-coated zinc oxide nanoparticles target biofilms and induce oral cancer apoptosis via BCl-2/BAX/P53 pathway.

BACKGROUND: Dental pathogens play a crucial role in oral health issues, including tooth decay, gum disease, and oral infections, and recent research suggests a link between these pathogens and oral cancer initiation and progression. Innovative therapeutic approaches are needed due to antibiotic resistance concerns and treatment limitations. METHODS: We synthesized and analyzed piperine-coated zinc oxide nanoparticles (ZnO-PIP NPs) using UV spectroscopy, SEM, XRD, FTIR, and EDAX. Antioxidant and antimicrobial effectiveness were evaluated through DPPH, ABTS, and MIC assays, while the anticancer properties were assessed on KB oral squamous carcinoma cells. RESULTS: ZnO-PIP NPs exhibited significant antioxidant activity and a MIC of 50 µg/mL against dental pathogens, indicating strong antimicrobial properties. Interaction analysis revealed high binding affinity with dental pathogens. ZnO-PIP NPs showed dose-dependent anticancer activity on KB cells, upregulating apoptotic genes BCL2, BAX, and P53. CONCLUSIONS: This approach offers a multifaceted solution to combatting both oral infections and cancer, showcasing their potential for significant advancement in oral healthcare. It is essential to acknowledge potential limitations and challenges associated with the use of ZnO NPs in clinical applications. These may include concerns regarding nanoparticle toxicity, biocompatibility, and long-term safety. Further research and rigorous testing are warranted to address these issues and ensure the safe and effective translation of ZnO-PIP NPs into clinical practice.

Effect of TiO2 Nanofillers on the Mechanical and Abrasive Wear Properties of Epoxy Reinforced with Carbon Fabric Hybrid Composites.

Recent studies show that nanofillers greatly contribute to the increase in the mechanical and abrasive behaviors of the polymer composite. In the current study, epoxy composites were made by hand lay-up with the reinforcement of carbon fabric and titanium dioxide (TiO2) nanoparticles as secondary reinforcement in weight percentages of 0.5, 1.0, and 2.0. Hardness, tensile, and abrasive wear tests have been carried out for the fabricated composites. The obtained results confirm that as the percentage of filler addition increases, hardness of the carbon epoxy (CE) composite increases, and significant enhancement of 10.25% hardness is confirmed in 2 wt % nano TiO2-added CE composite. The CE composite filled with 2 wt % of TiO2 nanofiller shows 15.77 and 9.15% improvement of tensile strength and modulus, respectively, compared to unfilled CE composites. The abrasive wear volume exhibits a nearly linear increasing trend as the abrading distance increases. In addition, it is discovered that the abrasive wear volume is greater for higher applied loads. The inclusion of nano TiO2 reduced the wear loss in the CE composite for all abrading distances, regardless of the load, low or high. The scanning electron microscopy analysis of worn surfaces was carried out to analyze the contribution of the filler to improve the wear resistance.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models.

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

Novel design of cryptographic architecture of nanorouter using quantum-dot cellular automata nanotechnology.

The article introduces a revolutionary Nanorouter structure, which is a crucial component in the Nano communication regime. To complete the connection, many key properties of Nanorouters are investigated and merged. QCA circuits with better speed and reduced power dissipation aid in meeting internet standards. Cryptography based on QCA design methodologies is a novel concept in digital circuit design. Data security in nano-communication is crucial in data transmission and reception; hence, cryptographic approaches are necessary. The data entering the input line is encrypted by an encoder, and then sent to the designated output line, where it is decoded and transferred. The Nanorouter is offered as a data path selector, and the proposed study analyses the cell count of QCA and the circuit delay. In this manuscript, novel designs of (4:1)) Mux and (1:4) Demux designs are utilized to implement the proposed nanorouter design. The proposed (4:1) Mux design requires 3-5% fewer cell counts and 20-25% fewer area, and the propsoed (1:4) Demux designs require 75-80% fewer cell counts and 90-95% fewer area compared to their latest counterparts. The QCAPro utility is used to analyse the power consumption of several components that make up the router. QCADesigner 2.0.3 is used to validate the simulation results and output validity.

Xanthogranulomatous Orchitis With Testicular Atrophy in a Case of Chronic Pyocele: A Rare Case.

Xanthogranulomatous orchitis (XGO) is a benign non-inflammatory condition predominantly affecting the testicular tissue and characterized by the infiltration of lipid-laden macrophages leading to substantial tissue damage. We present the case of an 80-year-old gentleman with chronic pyocele and concurrent testicular atrophy secondary to XGO, a seldom-reported manifestation in the testicular milieu. The patient presented with a protracted history of left-sided scrotal swelling and underwent left orchidectomy subsequent to preoperative diagnosis via ultrasonography. Intraoperative exploration revealed the presence of purulent fluid, and histopathological analysis confirmed characteristic features of XGO, including seminiferous tubule destruction and infiltration of fibroconnective tissue by histiocytes and dilated blood vessels. Differential diagnosis with testicular neoplasms posed a challenge, accentuating the pivotal role of histopathological scrutiny in achieving precise diagnosis. Orchidectomy remains the cornerstone of treatment for XGO. This case underscores the imperative of considering XGO in the diagnostic spectrum of testicular masses and the indispensable role of histopathology in confirming the diagnosis and guiding optimal therapeutic interventions.

Optimizing fault tolerance of RAM cell through MUX based modeling and design using symmetries of QCA cells.

Extensive research is now being conducted on the design and construction of logic circuits utilizing quantum-dot cellular automata (QCA) technology. This area of study is of great interest due to the inherent advantages it offers, such as its compact size, high speed, low power dissipation, and enhanced switching frequency in the nanoscale domain. This work presents a design of a highly efficient RAM cell in QCA, utilizing a combination of a 3-input and 5-input Majority Voter (MV) gate, together with a 2 × 1 Multiplexer (MUX). The proposed design is also investigated for various faults such as single cell deletion, single cell addition and single cell displacement or misalignment defects. The circuit under consideration has a high degree of fault tolerance. The functionality of the suggested design is showcased and verified through the utilization of the QCADesigner tool. Based on the observed performance correlation, it is evident that the proposed design demonstrates effectiveness in terms of cell count, area, and latency. Furthermore, it achieves a notable improvement of up to 76.72% compared to the present configuration in terms of quantum cost. The analysis of energy dissipation, conducted using the QCAPro tool, is also shown for various scenarios. It is seen that this design exhibits the lowest energy dispersion, hence enabling the development of ultra-low power designs for diverse microprocessors and microcontrollers.

Genetic diversity, population structure and marker-trait associations in Indian kale (Brassica oleracea L. gp. acephala) using cross-species microsatellite markers.

Kale is known for its exceptional nourishing and functional benefits to human body. However, it is an understudied species from genomic as well as agronomic aspects. It is important to characterize niche kale germplasms around the world to systematically conserve and utilize its genetic variability, especially for commercial traits in the interest of growers, consumers and industry. With this view, genomic and phenotypic characterizations of 62 Kashmiri kale accessions including popular landraces were done to estimate and partition genetic diversity, understand trait relationships, develop population structure and divulge marker-trait associations of economic significance. Sixty-six cross species microsatellite (SSR) markers within Brassica genus amplified 269 alleles in the germplasm. Their polymorphic information content (PIC) ranged from 0.00078 to 0.953 with an average of 0.407. The population structure analysis and neighbour joining tree clustering categorized the germplasm into three sub-populations. AMOVA revealed more within-population variance (67.73 %) than among-populations (32.27 %) variance. The principal component analysis (PCA) involving 24 agronomical traits revealed seven PCs (PC1 to PC7) having Eigen values more than 1, which explained a cumulative variation of 69.21 %. Association mapping with respect to these 24 agronomical traits using mixed linear model and general linear model revealed six overlapping significant marker-trait relationships with five being significant at probability value of 0.001/0.0001. The highly significant associations of two SSRs with economically important traits (siliqua length and seed weight) significantly correlated/related with leaf yield and seed yield were revealed for their possible utilization in marker assisted breeding for higher leaf and seed yields.

Trained neural networking framework based skin cancer diagnosis and categorization using grey wolf optimization.

Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy.

Exploring fetal brain tumor glioblastoma symptom verification with self organizing maps and vulnerability data analysis.

Brain tumor glioblastoma is a disease that is caused for a child who has abnormal cells in the brain, which is found using MRI "Magnetic Resonance Imaging" brain image using a powerful magnetic field, radio waves, and a computer to produce detailed images of the body's internal structures it is a standard diagnostic tool for a wide range of medical conditions, from detecting brain and spinal cord injuries to identifying tumors and also in evaluating joint problems. This is treatable, and by enabling the factor for happening, the factor for dissolving the dead tissues. If the brain tumor glioblastoma is untreated, the child will go to death; to avoid this, the child has to treat the brain problem using the scan of MRI images. Using the neural network, brain-related difficulties have to be resolved. It is identified to make the diagnosis of glioblastoma. This research deals with the techniques of max rationalizing and min rationalizing images, and the method of boosted division time attribute extraction has been involved in diagnosing glioblastoma. The process of maximum and min rationalization is used to recognize the Brain tumor glioblastoma in the brain images for treatment efficiency. The image segment is created for image recognition. The method of boosted division time attribute extraction is used in image recognition with the help of MRI for image extraction. The proposed boosted division time attribute extraction method helps to recognize the fetal images and find Brain tumor glioblastoma with feasible accuracy using image rationalization against the brain tumor glioblastoma diagnosis. In addition, 45% of adults are affected by the tumor, 40% of children and 5% are in death situations. To reduce this ratio, in this study, the Brain tumor glioblastoma is identified and segmented to recognize the fetal images and find the Brain tumor glioblastoma diagnosis. Then the tumor grades were analyzed using the efficient method for the imaging MRI with the diagnosis result of partially high. The accuracy of the proposed TAE-PIS system is 98.12% which is higher when compared to other methods like Genetic algorithm, Convolution neural network, fuzzy-based minimum and maximum neural network and kernel-based support vector machine respectively. Experimental results show that the proposed method archives rate of 98.12% accuracy with low response time and compared with the Genetic algorithm (GA), Convolutional Neural Network (CNN), fuzzy-based minimum and maximum neural network (Fuzzy min-max NN), and kernel-based support vector machine. Specifically, the proposed method achieves a substantial improvement of 80.82%, 82.13%, 85.61%, and 87.03% compared to GA, CNN, Fuzzy min-max NN, and kernel-based support vector machine, respectively.

Royal Poinciana Biodiesel Blends with 1-Butanol as a Potential Alternative Fuel for Unmodified Research Engines.

This research work investigates the experimental work of a single-cylinder diesel engine operated with royal poinciana biodiesel blends with various proportions of 10, 20, and 30% volume with 1-butanol as an effective ignition-improving additive. The test blends were indicated as D90RP7B3 (90% diesel + 7% royal poinciana biodiesel + 3% butanol), D80RP14B6 (80% diesel + 14% royal poinciana biodiesel + 6% butanol), D70RP21B9 (70% diesel + 21% royal poinciana biodiesel + 9% butanol), and pure royal poinciana biodiesel (RP100) and diesel. The significant findings or results obtained during the experimentation are that BTE is suitable for blend D90RP7B3, and the least BSFC is found for blend D90RP7B3 in the 0.24 kg/kWh range. The inline cylinder pressures are found to be suitable for the blend D90RP7B3 in the range of 7 MPa; HRR is ideal for both the blends D90RP7B3 and D80RP14B6 in the range of 90 and 88 kJ; D90RP7B3 possesses adequate ignition delay at full load conditions 16° in crank angle advance; maximum A/F ratios are well suitable for the blend D90RP7B3 in the ratio 11:1 at higher loads. Volumetric efficiency is achieved well for all the blends and diesel; the emissions released from the royal poinciana blends, such as CO, CO2, HC, and NOX, were reduced by 14.12, 8.33, 11.1, and 18.8% compared to standard diesel. Hence, royal poinciana blends with 1-butanol can be considered the best fuels in the automobile sector.