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Go to Editorial ManagerThis study establishes a risk aware, multi objective methodology to select diesel plastic fuel α-terpineol blends that deliver robust engine performance across operating loads in a common rail direct injection diesel engine. Heterogeneous responses brake thermal efficiency, brake specific fuel consumption, oxides of nitrogen, carbon monoxide, hydrocarbons, smoke, and exhaust gas temperature are mapped to a unitless desirability scale and aggregated by load. Conditional Value at Risk across loads then prioritizes worst case behavior. By Conditional Value at Risk at level 0.20, the ranking is: diesel 0.242, diesel plastic fuel with 15% α-terpineol 0.197, 10% α-terpineol is 0.177, 5% α-terpineol is 0.157, diesel plastic fuel is 0.141, waste plastic fuel is 0.081. Relative to neat diesel plastic fuel, worst-case performance improves by 11% at 5% α-terpineol, 26% at 10% α-terpineol, and 40% at 15% α-terpineol, with the 100% load consistently governing the tail. Average brake specific fuel consumption decreases from 0.4553 to 0.4217 kg/kWh at 15% α-terpineol, average exhaust gas temperature falls from 321.4 to 293.0°C. Duty map mean oxides of nitrogen reduce from 512 to 474 ppm, hydrocarbons fall from 42.67 to 35.73 ppm. At full load, carbon monoxide declines from 0.52 to 0.46 %vol., and smoke opacity decreases from 52.28 to 49.71%. Within the tested window, 15% α-terpineol in diesel plastic fuel is the risk aware first choice, with 10% α-terpineol a near optimal alternative. The methodology remains stable under risk aversion and weighting changes at high-load calibration.
Primary healthcare institutions are highly important socio-technical systems, where infrastructure, digital technologies, and service delivery processes collaborate to establish service resilience and performance. Digital systems and sustainable infrastructure may not be integrated as well in a limited environment with resources, which may lead to reduced system robustness and efficiency. This paper is a systematic digital systems and infrastructure evaluation of the process of providing primary healthcare in Paschim Bardhaman District, India, through an engineering-based, multi-criteria analysis framework._x000D_ Cross-sectional engineering analysis was carried out on 25 Primary Health Centers (PHCs) chosen based on the rural and semi-urban settings. They have developed a composite system performance index by incorporating five domains of engineering, namely infrastructure robustness, operational workflow efficiency, outcome performance, digital systems readiness (ICT), and environmental sustainability systems. Information was gathered based on formal auditing of facilities, check of readiness of ICT equipment and software, field observation of operations and review of documents. Domain-specific scores were also summed up to create a Composite Quality and Resilience Index (CQRI). Statistical analyses were made to provide measures of description, correlation analysis and comparative testing between rural and semi-urban facilities._x000D_ Findings showed that there was moderate system performance (mean CQRI = 1.21 ± 0.24) and there was high inter-facility difference. The positive correlation of infrastructure robustness and the efficiency of operational workflow were found to be strong with composite system performance (r = 0.67 and r = 0.74, respectively). The readiness to digital systems, and environmental sustainability scored relatively low, which points out to the underutilization of ICT infrastructure, lack of telemedicine adoption, and the integration of renewable energy sources. The composite performance of semi-urban PHCs was much better than that of rural facilities (p < 0.05), which highlights the importance of infrastructure accessibility and digital connectivity to system resilience._x000D_ The results show that integration of digital systems and infrastructure design that is sustainable are important engineering determinants of resilient primary healthcare delivery. The suggested composite assessment framework can be used as a scalable engineering-based tool to estimate and optimize the performance of low-resource healthcare infrastructure in supporting the resilience of the system and its long-term sustainability and efficiency in delivering the services.
Identifying fish species in natural aquatic environments remains challenging due to changing light conditions, turbid water, and complex underwater scenes. Most current deep-learning models rely on controlled datasets, which limits their use in real-world settings. This study presents Auto Fish, a mobile deep-learning system for real-time, offline fish species identification on Android devices. The system uses the MobileNetV2 architecture, optimized with TensorFlow Lite for processing on the device. This approach ensures high accuracy while keeping computational costs low. We trained and evaluated the model on a balanced dataset of 8,000 annotated images, including nine marine species: Sea bass, Red sea bream, Horse mackerel, Gilt-head bream, Shrimp, Black sea sprat, Trout, Red mullet, and Striped red mullet. Extensive preprocessing, image enhancement, and stratified sampling helped the model perform well despite variations in lighting and background conditions. The experimental results showed a validation accuracy of 99.2%, with both macro and micro Precision, Recall, and F1-scores around 99.3%, and an average False Positive Rate (FPR) of 0.09%. The system supports offline recognition, cloud syncing via Firebase, and delivers real-time results within 4.2 seconds per image on mid-range smartphones. These findings show that Auto Fish can effectively classify fish species in the field while remaining efficient and easy to use. This work offers a practical AI-based solution that connects research with ecological monitoring, empowering citizen scientists and conservationists to document biodiversity using mobile technology.
In precision agriculture, crop disease detection can be a highly valuable undertaking in which scalable and correct solutions may save considerable amounts of money and loss of yield. This paper introduces a comparative analysis of state-of-the-art deep learning models with special attention to EfficientNetB3 hybrids, which are trained on a balanced subsample of the PlantVillage dataset with 33 classes based on nine crops. To overcome the shortcomings of the previous studies, which used unbalanced sample, a leakage-free balancing approach was used, resulting in 13,200 training and 3,300 validation samples. Custom head transfer learning was used where it was tested using two strategies; FreezeUnfreeze fine-tuning, and Singlephase training. MobileNetV2, InceptionV3, DenseNet121, GhostNet, in addition to other baseline CNNs, were compared to baseline Convolutional Neural Networks (CNNs). The findings indicate that EfficientNetB3 hybrids are superior with an accuracy of ≥99.5% and 99.9% Area Under the Curve (AUC) and specificity than the previous CNN-based systems. The paper logically defines a performance ladder between model options and real-life deployment demands, such as lightweight mobile applications to precision agriculture systems, and points out future trends in the field-based validation.
The standardization and preprocessing decisions determine the performance, reproducibility and generalizability of Artificial Intelligence (AI) models in cervical spine Magnetic Resonance Imaging (MRI). In this research, the author suggests a six-step standardized preprocessing pipeline that can help with the reliable analysis of heterogeneous MRI data using AI. It is obtained by the pipeline based on recurrent methodological patterns found in the literature, focusing on the operations reported to stabilize image intensity distribution and reduce unwanted sources of variability. A systematic literature review was screened on 2,882 records, and 43 articles were included per the inclusion criteria. Due to the large variability of imaging procedures, preprocessing plans, and reported outcomes metrics, no quantitative meta-analysis was performed and a qualitative synthesis was done. Basic methods such as segmentation, denoising, intensity normalization, augmentation, bias-field correction were consistently reported to improve AI performance. Studies evaluating tasks such as normalization and automated segmentation reported accuracies of 94% to 99.95%. Moreover, less-specific harmonization methods like Combatting Batch Effects (ComBat), z-score normalization and histogram matching, and Generative Adversarial Network (GAN)-based ones were also often attributed with lower scanner- and site-based variations. Regardless of these improvements, the AI workflow remains unstable because of the irregularity of predefined parameters, unequal standards of acquisition, and lack of methodological reporting. In efforts to deal with such challenges, the current work proposes a methodical six-stage preprocessing model that consists of quality control, noise removal, intensity normalization, anatomical segmentation, harmonization and validation. The suggested workflow model offers a practical and transparent basis of clinically translatable AI usage in cervical spine MRI based on conventional mathematical equations and clear documentation.
The introduction of Artificial Intelligence (AI) in medical care is becoming an acknowledged challenge in the framework of engineering systems, in which the level of computational readiness, the integration of the infrastructure, the level of user competence, and the limitations of the ethical aspect play a role. The paper analyzes the most important engineering predictors of a readiness to use AI in Indian hospitals, which are assessed using correlation and multiple linear regression frameworks._x000D_ There was a structured survey on 120 healthcare workers and 20 deep interviews with experts to evaluate system-level perceptions and institutional preparedness. The results of regression showed that the model is well fitted (R 2 = 0.61, p < 0.001) with Perceived Usefulness (= 0.43), Infrastructure Availability (= 0.38) and AI Training Exposure ( = 0.26) identified as significant and positive predictors of AI adoption readiness. The Ethical Concern Scores (β = -0.32) had a significant negative impact, highlighting the importance of governance as an important constraint of the system._x000D_ These findings were reinforced by qualitative thematic analysis, which identified interoperability challenges, lack of computational training, data governance oversights, and infrastructure vulnerability as significant engineering limitations. The respondents positively indicated the conditional acceptance of AI on the basis of the explainable model transparency, as well as the system of institutional AI training._x000D_ The research adds a new regression-tested engineering preparedness pathway to AI implementation in Low- and Middle-Income Country (LMIC) hospital setting, suggesting system interface, capacity formation, and ethical algorithm control as the main keystones on the sustainable AI implementation in Indian healthcare.
This research investigates the influence of single and hybrid mineral nanofillers on the morphological, thermal, and electrical properties of epoxy-based glass fiber composites. Nanocomposites were fabricated using hand lay-up technique, incorporating nano-calcium carbonate (CaCO₃) and nano-talc with particle size of 50 to 100 nm into an epoxy matrix with 5-ply E-glass/S-glass fiber. The study evaluates composites with 2-8 wt% of nano-CaCO₃, and hybrid systems containing nano-CaCO₃ (2-8 wt%) and nano-talc (1-4 wt%). Morphological analysis via Scanning Electron Microscopy (SEM) assessed nanofiller dispersion, while thermal properties were analyzed using Differential Scanning Calorimetry (DSC) for glass transition temperature (Tg). Electrical performance was evaluated through dielectric breakdown voltage (ASTM D149) and surface resistivity (ASTM D257). SEM analysis showed filler dispersion depended on concentration, with optimal distribution at 4-6 wt% before significant agglomeration at higher concentrations. DSC results revealed increased Tg with nanofillers, peaking for the composite with 6 wt% CaCO₃ and 3 wt% talc, indicating enhanced thermal stability. The dielectric breakdown voltage peaked at 17.6 kV for 2 wt% CaCO₃ and 1 wt% talc composite, while surface resistivity was highest for 4 wt% CaCO₃ and 2 wt% talc (73.74×1012Ω). All formulations maintained UL 94 V-1 flammability rating. The study concludes that the synergistic interaction between nano-CaCO₃ and nano-talc enables significant tailoring of thermal and electrical properties for advanced applications.
The process of increasing the heat transfer coefficient, resulting in enhancing system efficiency, is known as heat transfer enhancement. Enhancing heat transport is both economically beneficial and a considerable energy conservation problem. To improve heat transfer, many passive components are utilized within tubes, including wire plugs, enhanced surfaces, rough edges, twisted tape inserts, and liquid additives. This study evaluated twisted tape inserts, which are highly effective passive devices. Considering its numerous advantages, such as effortless maintenance, uncomplicated operation, and straightforward production. The twisted tape inserts within the tube generated a vortex and swirling flow. The interior convective heat transfer process is significantly improved. A summary of various twisting tape additives that can boost performance.
Women’s safety remains an urgent challenge, particularly in moments when conventional panic button devices fail due to a victim’s inability to act or poor network coverage. To overcome these shortcomings, TRIAD-Lite is introduced as an IoT-enabled wearable framework that unites multimodal physiological sensing with lightweight deep learning for proactive distress identification. The system captures heart rate, blood pressure, galvanic skin response, and motion patterns, while incorporating a triple-tap gesture to confirm user intent, all processed locally on a Raspberry Pi for real-time inference. Unlike reactive mechanisms, this design anticipates danger by analyzing variations in physiological signals that often precede visible distress. Communication reliability is reinforced through a hybrid strategy: alerts are transmitted via GSM or Wi-Fi under normal conditions, but in the event of limited connectivity, a LoRa-based backup ensures long-range transmission. Experimental analysis using simulated datasets yielded an AUC of 1.000 with flawless precision and recall, highlighting the model’s reliability and calibration. Further field evaluation demonstrated that LoRa maintained connectivity across 5.7 kilometers with complete packet delivery, proving effective for both rural and urban environments. By combining predictive analytics, gesture-based confirmation, and dual communication layers, TRIAD-Lite offers a scalable, privacy-conscious, and highly resilient framework that strengthens women’s safety and extends protective technology into regions where conventional systems often fail.
Türkiye possesses abundant geothermal resources. It is ranked seventh globally for this particular energy resources and grade among the first 5 in utilizing geothermal and thermal springs for various purposes such as electricity generation, residential cooling and heating, greenhouse operations, desiccating processes, thermal recreation, therapeutic applications, mining, agricultural uses, and aquaculture. The government's endorsement from renewable power sources is fueling growing interest on this particular energy sector. This article provides a comprehensive analysis of geothermal energy in select locations of Türkiye, including an assessment of its potential and various applications. The study seeks to provide a valuable involvement to the future advancements of a geothermal technology on Türkiye.
In Republic of Iraq, ready-mix concrete production plants have been adversely affected by the lack of modern and advanced technology to assess their performance in line with technological advancements. Current evaluation methods rely on traditional approaches and financial measures, yielding unrealistic performance results. To address this problem, there is a need to utilize modern models and methods for performance evaluation. The study's main objective This was achieved by employing a literature survey methodology and utilizing digital databases such as the Iraqi Scientific Journals website, virtual libraries, and scientific platforms like ScienceDirect, Springer, Google Scholar, and Gate Research between 2015 and 2023. The research study provided a comprehensive overview of performance evaluation, including its definitions, importance, and an introduction to modern models and evaluation methods. The study found that no previous studies have been conducted in Iraq to evaluate ready-mix concrete production plants. However, four studies were found in Egypt, Sudan, and India. The previous similar relevant studies discussed various topics and related studies. Firstly, they discussed the classification, advantages, and disadvantages of concrete mixing plants. Additionally, the previous studies analyzed the factors that most influence the performance of concrete production plants, including laboratory manager efficiency, work team efficiency, communication and relationships within work teams, plant operator, material transportation method, and time and courses. Furthermore, the previous research studies present a comprehensive analysis of all variable data simultaneously using the statistical package for Social Science (SPSS) input stage. The evaluation also extends to the evaluation of laboratories, encompassing plant arrangement, internal quality control systems, and final product quality. The overall evaluation results of previous studies. Indicate that 75% of the concrete production plants failed to meet the required criteria, while only 25% demonstrated satisfactory performance. The study proposed improvements to enhance the performance rate of ready-mix concrete production plants by leveraging the most influential variables, which will be considered in the study.
Natural convection air heat transfer and fluid movement currents around a hot circular cylinder inside an inclined triangular enclosure has been analyzed experimentally. Three different sizes of an enclosure with a long side of 20, 25, and 30 cm, the thickness of 1 mm, and depth of 50 cm were used in the present work to give three radius ratios. The effect of Rayleigh number, radius ratio, the rotation angle of triangle enclosure, and the inclination angle of the apparatus with horizontal axis ? on the heat transfer process was investigated. The ranges of these parameters were: Rayleigh number from 5×106 to 2.5×108, radius ratio (0.345, 0.455, and 0.618), rotation angle (0o, 45o, and 90o), and inclination angle (0o, 45o and 90o). The results show that the heat transfer rates increase with increase in Rayleigh number and as the rotation angle of enclosure is changed from 0o to 90o. Moreover, the heat transfer rate increases linearly with Rayleigh number at higher radius at rotation angle 0o, 90o only. While, it increases slightly with Rayleigh number at rotation angle 45o. Additionally, the higher heat transfer rates occur at vertical position of enclosure inclination angle 90o and rotation angle 0o (the base of triangle at the bottom) and it decreases as inclination angle deviates from 90o to 0o. This behavior is reverse completely at higher radius ratio 0.618. Empirical correlations for the average Nusselt number has been found to depend on Rayleigh number., radius ratio, rotation angle and inclination angle.