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In the medical diagnostics domain, pathology and histology are pivotal for the precise identification of diseases. Digital histopathology, enhanced by automation, facilitates the efficient analysis of massive amount of biopsy images produced on a daily basis, streamlining the evaluation process. This study focuses in Stain Color Normalization (SCN) within a Whole-Slide Image (WSI) cohort, aiming to reduce batch biases. Building on published graphical method, this research demonstrates a mathematical population or data-driven method that optimizes the dependency on the number of reference WSIs and corresponding aggregate sums, thereby increasing SCN process efficiency. This method expedites the analysis of color convergence 50-fold by using stain vector Euclidean distance analysis, slashing the requirement for reference WSIs by more than half. The approach is validated through a tripartite methodology: 1) Stain vector euclidean distances analysis, 2) Distance computation timing, and 3) Qualitative and quantitative assessments of SCN across cancer tumors regions of interest. The results validate the performance of data-driven SCN method, thus potential to enhance the precision and reliability of computational pathology analyses. This advancement is poised to enhance diagnostic processes, therapeutic strategies, and patient prognosis.

Construction companies frequently struggle with poor cash flow management of their projects. Financial terms, including retainage, advance payments, and interest rates, significantly impact the project’s cash flow. This study investigates the financial aspects of projects with close beginnings. In addition, considers how to deal with financial deficits by suggesting a multi-project scheduling optimization model to minimize maximum negative cash flow while maintaining maximum profit. Sequential quadratic programming algorithms (SQP) generate workable schedules that optimally use available resources. Several scenarios have been used to test and examine the model. The outcomes indicate a decrease in negative cash flow for company 1 from (-1,852,096) to (-1,817,485) and for company 2 from (-484,524) to (-459,769) in scenario 1. Furthermore, a decrease in negative cash flow to (-1,661,660) alongside a profit of (776,593) for company 1 and a decrease to (-434,970) alongside a profit of (141,228) for company 2 in scenario 2. On the other hand, a decrease in negative cash flow to (-1,698,992) alongside a profit of (786,243) for company 1 and a decrease to (-370,815) alongside a profit of (209,363) for company 2 in scenario 3.

This study was conducted to develop an improved coffee grinder tailored for small and medium enterprises (SMEs) to address challenges due to limited resources. The development phase included a broad description, sizing, defining main components, developing technical drawings, manufacturing, and conducting functional tests. The machine had an overall dimension of 743 mm in length, 367 mm in width, and 580 mm in height. It was powered by a 1 HP induction motor with a rotation speed of 1400 rotations per minute (RPM) and a shaft diameter of 19 mm. The prototype achieved a grinding capacity of 23.8 kg/h for acceptable coffee grounds while maintaining a constant grind size, essential for achieving the best flavor and aroma. However, the noise level reached 86.5 dB, requiring hearing protection for prolonged usage. Future investigations should focus on exploring alternative materials and developing noise mitigation strategies, as noise reduction efforts can enhance operator physical and mental health in the coffee production process.

Background: Leptospirosis is a potentially life-threatening disease caused by bacteria of the genus Leptospira. The accurate identification and characterization of Leptospira species are critical for disease surveillance, outbreak investigation, and treatment strategies. Molecular techniques, such as Polymerase Chain Reaction (PCR) and Deoxyribonucleic acid (DNA) sequencing, have revolutionized the field of microbiology, providing rapid and accurate identification of Leptospira strains. However, optimizing these molecular techniques for accurate examination of Leptospira species can be challenging due to the genetic diversity and complexity of these bacteria. Purpose: This research aims to identify the most suitable primers for the precise identification of pathogenic Leptospira strains. Method: This research used the PCR method, using LipL32, rrs2, seqY, LipL41, IcdA, and Adk primers. A total of 17 isolates of pathogenic Leptospira bacteria were cultured from Institute of Vector Control and Reservoir Disease (IVRCD) in Salatiga, Indonesia. Result: The results of the research showed that the LipL41 and IcdA primers were found to be effective in distinguishing pathogenic strains, while the seqY, LipL32, Adk, and rrs2 primers required further refinement. The suitable Melting Temperature (TM) or annealing temperature is 58°C with 35 cycles of amplification. DNA concentration and purity had an A260/A280 ratio ranging between 1.8 and 2.8. Conclusion: LipL41 (500 bp) and IcdA (700 bp) are suitable primers for identifying pathogenic Leptospira.

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models. [ABSTRACT FROM AUTHOR]

In this paper we have scrutinized the structural, mechanical, electro-magnetic and thermoelectric properties of CsUO3 perovskite with the help of density functional theory. The ground state stability of the alloy was determined by optimizing their total ground state energies in two different phases which defines that the alloy is stable in ferromagnetic phase. The elastic constants again ensured the stability of the alloy in cubic structure and suggests the ductile nature of the alloy. The electronic profile from GGA and mBJ simulations reflects the half-metallic nature of the alloy. We have studied the thermoelectric response of the material by calculating the different transport parameters. Finally, we have calculated different optical parameters which highlighted the use of this oxide-based perovskite in optoelectronic devices. Considering the above properties suggest the applications of this alloy in solar cells, energy storage devices and various other domains. [ABSTRACT FROM AUTHOR]

To ensure that ecotourism development remains sustainable, the best place for such activities should be chosen based on the ecological potential. This study attempts to identify suitable ecotourism sites by developing a quantitative geographic model using multi-criteria evaluation (MCE), optimized by a non-dominated sorting genetic algorithm (NSGA-II). Three criteria (physical, biological, and socio-economic features), 13 sub-criteria, and 33 indices were first collected from primary and secondary data sources. Then, MCE method was applied to find ecotourism suitable areas, in which two methods of fuzzy overlay and weighted linear combination (WLC) were used to overlay criteria maps. Finally, NSGA-II was used to optimize ecotourism zoning through defining three objectives, including minimizing the distance from the sub-criteria of natural attractions, vegetation, and historical-cultural sites. Results show the WLC method is better than the fuzzy method at combining different layers to determine suitable zones for ecotourism, through which more than 50% of the study area, about 28,000 hectares, was classified as suitable for ecotourism. Matching 85% of suitable areas obtained by NSGA-II with high and very high suitable classes obtained by WLC shows that combining the MCE method with NSGA-II provided a more suitable hybrid method for ecotourism site suitability evaluation. This study creates a valuable tool for those responsible for planning and carrying out ecotourism initiatives, allowing them to further assess and conduct ecotourism projects. [ABSTRACT FROM AUTHOR]

This paper investigated the influence of process pressure variations on the accuracy and performance of ultrasonic flow meters. Process measurement technology provides a tool for optimizing production processes and dosing operations. Accurate measurement is key and primary to profitability in the business of supply and purchase of liquids like petroleum, gas and chemical products. Three 6” size ultrasonic flow meters were mounted on a skid and used to carry out the experiment parallel in connections each other to take flows from a common header, measure and discharge their individual flows into a common discharge header. The three meters were designate 1, 2 and 3 respectively. Meters 1 and 2 being service meters while Meter 3 is the calibrated master meter. The experiment was carried ten times to increase reliability of results. Experimental data were collected and analyzed using computational formulae technique. Results showed that; Meter 1 had an optimum process pressure of 12.38 and 9.43 bar with respect to flow rate and meter factor respectively as performance indicator. While Meter 2 had an optimum process pressure of 12.4 and 12.41 bar with respect to flow rate and meter factor respectively as performance indicator. Findings indicated significant relationship between process pressure, flow rate and meter factor using ultrasonic flow meter. The outcome of this study will be a useful guide to users of ultrasonic flow meters to maintain optimum process pressures of each meter during fluid supply.

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for the multi-label categorization of chest X-ray images, covering a broad spectrum of conditions, including lung opacity, normative pulmonary states, COVID-19, bacterial pneumonia, viral pneumonia, and tuberculosis. An optimized deep learning model based on the modified VGG16 architecture with SE blocks was developed and applied to a large dataset of chest X-ray images. The model was evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, and area under the curve (AUC). Results: The modified VGG16-SE model demonstrated superior performance across all evaluated metrics. The model achieved an accuracy of 98.49%, an F1-score of 98.23%, a precision of 98.41%, a recall of 98.07% and an AUC of 98.86%. Conclusion: This study provides an effective deep learning approach for categorizing chest X-rays. The model’s high performance across various lung conditions suggests its potential for integration into clinical workflows, enhancing the accuracy and speed of pulmonary disease diagnosis.

The increasing world population has increased food supply needs in recent years. Despite the numerous studies in the field of food supply chains, there are few articles on optimizing a sustainable egg supply chain. A fuzzy multi-objective mathematical model is presented to optimize a sustainable egg supply chain in a tactical decision structure. Switchable green packaging and labeling machines in parallel production lines, suppliers with different reliability rates and the provision of raw materials with different qualities have been studied in this article. Despite the significant reduction of the total costs while using switchable machines, the majority of the costs are still related to raw materials so that an increase of 2 to 4 times in the amount of input materials can change the values of objective functions by 23% to 60%. By using the unused capacity of the packaging and labeling machines, the total costs can be reduced significantly. [ABSTRACT FROM AUTHOR]

This paper investigated the influence of process pressure variations on the accuracy and performance of ultrasonic flow meters. Process measurement technology provides a tool for optimizing production processes and dosing operations. Accurate measurement is key and primary to profitability in the business of supply and purchase of liquids like petroleum, gas and chemical products. Three 6" size ultrasonic flow meters were mounted on a skid and used to carry out the experiment parallel in connections each other to take flows from a common header, measure and discharge their individual flows into a common discharge header. The three meters were designate 1, 2 and 3 respectively. Meters 1 and 2 being service meters while Meter 3 is the calibrated master meter. The experiment was carried ten times to increase reliability of results. Experimental data were collected and analyzed using computational formulae technique. Results showed that; Meter 1 had an optimum process pressure of 12.38 and 9.43 bar with respect to flow rate and meter factor respectively as performance indicator. While Meter 2 had an optimum process pressure of 12.4 and 12.41 bar with respect to flow rate and meter factor respectively as performance indicator. Findings indicated significant relationship between process pressure, flow rate and meter factor using ultrasonic flow meter. The outcome of this study will be a useful guide to users of ultrasonic flow meters to maintain optimum process pressures of each meter during fluid supply. [ABSTRACT FROM AUTHOR]

Background: Evaluating chest X-rays is a complex and high-demand task due to the intrinsic challenges associated with diagnosing a wide range of pulmonary conditions. Therefore, advanced methodologies are required to categorize multiple conditions from chest X-ray images accurately. Methods: This study introduces an optimized deep learning approach designed for the multi-label categorization of chest X-ray images, covering a broad spectrum of conditions, including lung opacity, normative pulmonary states, COVID-19, bacterial pneumonia, viral pneumonia, and tuberculosis. An optimized deep learning model based on the modified VGG16 architecture with SE blocks was developed and applied to a large dataset of chest X-ray images. The model was evaluated against state-of-the-art techniques using metrics such as accuracy, F1-score, precision, recall, and area under the curve (AUC). Results: The modified VGG16-SE model demonstrated superior performance across all evaluated metrics. The model achieved an accuracy of 98.49%, an F1-score of 98.23%, a precision of 98.41%, a recall of 98.07% and an AUC of 98.86%. Conclusion: This study provides an effective deep learning approach for categorizing chest X-rays. The model's high performance across various lung conditions suggests its potential for integration into clinical workflows, enhancing the accuracy and speed of pulmonary disease diagnosis. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learning's capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction. [ABSTRACT FROM AUTHOR]

Coating hydrocolloids are appropriate barriers against carbon dioxide, oxygen and lipids, therefore the amount of absorbed oil can be decreased. Salep as a hydrocolloid source cultivated in western and northwestern Iran is known as a food and pharmaceutical substance. In the present research, Salep mucilage (SaM) was studied as a coating agent with the aim of declining oil absorption and increasing the amount of moisture of stuck-pot rice based on potato (SpP) by using a central-composite design. Salep mucilage concentration (0.75, 1 and 1.25% w/w), frying time (3, 4.5, 6 min) and frying temperature (160, 170 and 180°C) were the examined parameters of this research. The effects of frying oil type (Sunflower oil, corn oil, rice bran oil, canola oil, palm olein oil and hydrogenated vegetable oil as the frying media) and blanching (85°C, 3.5 min in hot water) were examined only at the optimum point. According to the results, optimal conditions for coating and frying processes obtained from RSM were 1.24% (w/w) SaM concentration, 3.6 min frying time at 162°C frying temperature. Salep mucilage can be used as a promising agent to coat deep-fat fried potatoes. [ABSTRACT FROM AUTHOR]

L-tryptophan is an essential amino acid widely used in the pharmaceutical and feed industries. Enhancing its production in microorganisms necessitates activating and inactivating specific genes to direct more resources toward its synthesis. In this study, we developed a classification model based on Qualitative Perturbation Analysis and Machine Learning (QPAML). The model uses pFBA to obtain optimal reactions for tryptophan production and FSEOF to introduce perturbations on fluxes of the optima reactions while registering all changes over the iML1515a Genome-Scale Metabolic Network model. The altered reaction fluxes and their relationship with tryptophan and biomass production are translated to qualitative variables classified with GBDT. In the end, groups of enzymatic reactions are predicted to be deleted, overexpressed, or attenuated for tryptophan and 30 other metabolites in E. coli with a 92.34% F1-Score. The QPAML model can integrate diverse data types, promising improved predictions and the discovery of complex patterns in microbial metabolic engineering. It has broad potential applications and offers valuable insights for optimizing microbial production in biotechnology. [ABSTRACT FROM AUTHOR]

This study, conducted at the Lath House of Agriculture College, Al-Qasim Green University during the 2021-2022 growth season, aimed to evaluate the effects of KHARZA nanoparticles (NPs) and organic fertilizer (Nutirgreen) on sour orange seedlings. Different concentrations of KHARZA NPs (0, 1, 2, and 3 g L-1) and organic fertilizer (0, 1.5, 3, and 4.5 ml L-1) were applied. The experimental design followed a randomized complete block design (RCBD) with two factors, each having four treatments, and each treatment replicated thrice. Our findings reveal that spraying KHARZA NPs at a concentration of 3 g.L-1 significantly enhanced various vegetative parameters, such as plant height, stem diameter, leaf number, and leaf area, alongside improvements in chemical properties such as mineral concentration, chlorophyll content, and carbohydrate levels in leaves. Similarly, applying organic fertilizer at a rate of 4.5 ml L-1 led to notable enhancements in both vegetative and chemical characteristics compared to the control group. Notably, combined application of KHARZA NPs at 3 g L-1 and organic fertilizer at 4.5 ml L-1 resulted in synergistic effects, yielding the most favorable outcomes in terms of vegetative growth and chemical properties. These results underscore the potential of utilizing KHARZA nanoparticles and organic fertilizers in enhancing the growth and physiological properties of sour orange seedlings, offering promising avenues for sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity. [ABSTRACT FROM AUTHOR]

DC-DC converters are an important class of power electronics due to their wide use in various applications as sources of efficient power supplies. They step down or step up the applied voltage so that it is always either lower or higher than the supplied voltage. This is crucial in power delivery and portable systems, especially in battery-operated systems. The purpose of the paper is to investigate how the efficiency of Buck-Boost converters improves by using sliding mode control when operating under different conditions. The work aims to develop a control strategy that increases the efficiency and reliability of Buck-Boost converters, employed in a myriad of power electronics applications. The research focuses on a sliding mode control approach to overcome the challenges of nonlinear dynamics and susceptibility to external disturbances. The methodology involves studying the behavior of the converter under different conditions such as changes in loads, input voltage variations, and reference voltage changes. The study uses theoretical modeling and simulation to evaluate the concept of sliding mode in addressing the challenges for improved efficiency. Such investigations show how sliding mode control improves efficiency. SMC approach reduces the response time by 5%, improves efficiency by 3%, and enhances overall stability under fluctuating conditions. The use of sliding mode control enhances the converters against disturbances and provides an efficient voltage regulator. The research is useful to the field as it offers more insights into the control strategy that significantly improves the performance of converters concerning efficiency and stabilization. [ABSTRACT FROM AUTHOR]

Reducing the aerodynamic drag and noise levels of high-speed pantographs is important for promoting environmentally friendly, energy efficient and rapid advances in train technology. Using computational fluid dynamics theory and the K-FWH acoustic equation, a numerical simulation is conducted to investigate the aerodynamic characteristics of high-speed pantographs. A component optimization method is proposed as a possible solution to the problem of aerodynamic drag and noise in high-speed pantographs. The results of the study indicate that the panhead, base and insulator are the main contributors to aerodynamic drag and noise in high-speed pantographs. Therefore, a gradual optimization process is implemented to improve the most significant components that cause aerodynamic drag and noise. By optimizing the cross-sectional shape of the strips and insulators, the drag and noise caused by airflow separation and vortex shedding can be reduced. The aerodynamic drag of insulator with circular cross section and strips with rectangular cross section is the largest. Ellipsifying insulators and optimizing the chamfer angle and height of the windward surface of the strips can improve the aerodynamic performance of the pantograph. In addition, the streamlined fairing attached to the base can eliminate the complex flow and shield the radiated noise. In contrast to the original pantograph design, the improved pantograph shows a 21.1% reduction in aerodynamic drag and a 1.65 dBA reduction in aerodynamic noise. [ABSTRACT FROM AUTHOR]

The advancement technology of artificial intelligence and e-commerce has increased and this has called for new ways to improve last-mile transportation, which is regarded as an essential part of the logistics value chain, especially in smart logistics. This paper addresses the problem of developing effective routes for autonomous drones in last-mile logistics using deep Q-learning. This paper aims to improve the process of delivery by utilizing the flexibility and intelligence of self-driven autonomous drones in smart logistics transportation. The key challenge for the effective provision of last-mile delivery services remains the decision on the routing of many aerial drones in an indoor urban environment, concerning the restrictions of a time window for delivery, energy consumption and traffic. This paper implements a deep Q-learning paradigm that allows drones to relearn their flight paths and delivery strategy during the lifecycle, thereby reducing the cost in the long run while using the costing strategies as part of the reengineering process. The approach has been validated through extensive experimentation and simulations. Results obtained indicate that the delivery drones modified for the study attained the designed requirements of deep Q-learning, including optimal navigation and performance that attained 12.8% shorter delivery time, an increase in energy efficiency by 8.4%, and a route quality improvement of 20.1%. Furthermore, highlights the performance of the system in various situations where deep Q-learning and standard routing approaches are compared. This paper not only aids in the minimization of the last-mile delivery constraint by the use of shipping drones but also emphasizes the capacities of reinforcement learning strategies such as deep Qlearning in tackling the routing problems in smart logistics systems. At last, it advocates carrying on deeper into the application of reinforcement learning in the solving of complex optimization problems in various other fields. [ABSTRACT FROM AUTHOR]

Phishing attacks are crimes committed by sending spoofed Web URLs that appear to come from a legitimate organization in order to obtain another party's sensitive information, such as usernames, passwords, and other confidential data. The stolen information is then used to commit fraud, such as identity theft and financial fraud, and can cause reputational damage to the party that is the victim of the phishing attack. This can cause great harm to the victimized individual or organization. To overcome these problems, this research uses feature selection using ANOVA and Deep Neural Networks (DNN) to detect web phishing attacks. Feature selection is used to optimize the performance of the DNN model to achieve more accurate results. Based on the results of feature selection using ANOVA, there are 52 attributes that have a significant impact on web phishing attack detection. The next step is to implement DNN to build a web phishing attack detection model. The results of testing the web phishing detection model show that in the training phase, the accuracy value increased by 17.51% for the 80:20 dataset and 18.39% for the 70:30 dataset. During the testing phase, the accuracy value increased by 17.8% for the 80:20 dataset and 18.58% for the 70:30 dataset. The resulting recognition model shows consistent and reliable results not only during training, but also during testing in situations closer to real-world conditions. Conclusively, the use of ANOVA proves effective in mitigating less relevant features and contributing to the optimization of web phishing detection models.

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases.

Microgrids driven by distributed energy resources are gaining prominence as decentralized power systems offering advantages in energy sustainability and resilience. However, optimizing microgrid operation faces challenges from the intermittent nature of renewable sources, dynamic energy demand, and varying grid electricity prices. This paper presents an AI-driven day-ahead optimal scheduling approach for a grid-connected AC microgrid with a solar panel and a battery energy storage system. Genetic Algorithm generates demand response strategies and optimizes battery dispatch, while LightGBM forecasts solar power generation and building load consumption. The approach aims to minimize operational costs and ensure microgrid sustainability, using a battery degradation cost function to extend its lifespan. Simulation results conducted in the University of Moratuwa microgrid show a significant 14.22% decrease in electricity costs under Sri Lanka’s current tariff structure, attributed to intelligent energy dispatch scheduling. Proactive demand response management has the potential to minimize costs further. This research contributes to microgrid optimization knowledge, promoting the adoption of intelligent and sustainable energy systems.

Background: Ischemic heart disease (IHD) impacts the quality of life and is the most frequently reported cause of morbidity and mortality globally. Aims: To assess the changes in the exhaled volatile organic compounds (VOCs) in patients with vs. without ischemic heart disease (IHD) confirmed by stress computed tomography myocardial perfusion (CTP) imaging. Objectives: IHD early diagnosis and management remain underestimated due to the poor diagnostic and therapeutic strategies including the primary prevention methods. Materials and Methods: A single center observational study included 80 participants. The participants were aged ≥ 40 years and given an informed written consent to participate in the study and publish any associated figures. Both groups, G1 (n = 31) with and G2 (n = 49) without post stress-induced myocardial perfusion defect, passed cardiologist consultation, anthropometric measurements, blood pressure and pulse rate measurements, echocardiography, real time breathing at rest into PTR-TOF-MS-1000, cardio-ankle vascular index, bicycle ergometry, and immediately after performing bicycle ergometry repeating the breathing analysis into the PTR-TOF-MS-1000, and after three minutes from the end of the second breath, repeat the breath into the PTR-TOF-MS-1000, then performing CTP. LASSO regression with nested cross-validation was used to find the association between the exhaled VOCs and existence of myocardial perfusion defect. Statistical processing performed with R programming language v4.2 and Python v.3.10 [^R], STATISTICA program v.12, and IBM SPSS v.28. Results: The VOCs specificity 77.6% [95% confidence interval (CI); 0.666; 0.889], sensitivity 83.9% [95% CI; 0.692; 0.964], and diagnostic accuracy; area under the curve (AUC) 83.8% [95% CI; 0.73655857; 0.91493173]. Whereas the AUC of the bicycle ergometry 50.7% [95% CI; 0.388; 0.625], specificity 53.1% [95% CI; 0.392; 0.673], and sensitivity 48.4% [95% CI; 0.306; 0.657]. Conclusions: The VOCs analysis appear to discriminate individuals with vs. without IHD using machine learning models. Other: The exhaled breath analysis reflects the myocardiocytes metabolomic signature and related intercellular homeostasis changes and regulation perturbances. Exhaled breath analysis poses a promise result to improve the diagnostic accuracy of the physical stress tests using machine learning models.

This paper has investigated the inventory and financial flows in supply chains. Its purpose is to provide a method to optimize these two flows for chain members, where Return on Capital (ROC) is defined as the dependent variable, and cash conversion cycle (CCC) equation components, which show financial and physical flows, are formulated as independent variables. The data of chain members from six selected industries, including auto & parts, pharmacy, food, petrochemical, metal, and mining, have been extracted. Two scenarios, 1) revision of independent variables without a change in the cash conversion cycle of the entire supply chain, and 2) reducing the days of independent variables along with reducing the cash conversion cycle, have been defined. The problem is simulated using Agent-Based Modeling and NetLego software. Results of the first scenario indicate that if Days Inventory Outstanding (DIO) is reduced in downstream and transferred to upstream of the chain, and Days Payment Outstanding (DPO) in the upstream is shortened, ROC is improved for the entire chain. Also, the results of the second scenario show that, in proportion to the reduction of the cash conversion cycle through productivity under collaboration of chain members, the performance improvement of ROC is remarkable.

Abstract Introduction Quality care delivery is an essential lifesaving interventions for maternal healthcare and reduction in mortality from preventable reproductive conditions. In African countries like Nigeria, numerous perceptions and militating factors present unique challenges in optimizing the utilization of maternal and reproductive healthcare services. As women continuously evolve away from the utilization of healthcare services, achieving universal health coverage for all emerges as a matter of concern. Method A phenomenological and descriptive research design was used. The study participants comprised a total of 38 women selected from primary and tertiary healthcare institutions. They were purposively selected from four healthcare institutions in Nsukka, Enugu State, Nigeria. Result Findings revealed that most rural women at the prenatal stage, utilize maternal healthcare services, but at the postnatal stage, they reject reproductive healthcare services owing to certain perceptions. Concerns about sub-optimal utilization of maternal and reproductive healthcare services were found under enabling, predisposing and need factors. Evidence-based interventions included instituting health insurance policies, improving the healthcare sector, personnel, collaboration among stakeholders, and grass-roots community education. Participants showed little knowledge of social workers’ engagement in healthcare institutions. Conclusion Functional network of care between private and public healthcare system is the key to optimizing maternal and reproductive healthcare utilization. The study recommends stakeholder and community engagement in achieving functional networks of care, strengthening relational linkages between frontline health workers and equip rural women with better knowledge. All these are geared toward achieving optimal utilization of maternal and reproductive healthcare services among women in low-resourced Nigerian settings.

DC-DC converters are an important class of power electronics due to their wide use in various applications as sources of efficient power supplies. They step down or step up the applied voltage so that it is always either lower or higher than the supplied voltage. This is crucial in power delivery and portable systems, especially in battery-operated systems. The purpose of the paper is to investigate how the efficiency of Buck-Boost converters improves by using sliding mode control when operating under different conditions. The work aims to develop a control strategy that increases the efficiency and reliability of Buck-Boost converters, employed in a myriad of power electronics applications. The research focuses on a sliding mode control approach to overcome the challenges of nonlinear dynamics and susceptibility to external disturbances. The methodology involves studying the behavior of the converter under different conditions such as changes in loads, input voltage variations, and reference voltage changes. The study uses theoretical modeling and simulation to evaluate the concept of sliding mode in addressing the challenges for improved efficiency. Such investigations show how sliding mode control improves efficiency. SMC approach reduces the response time by 5%, improves efficiency by 3%, and enhances overall stability under fluctuating conditions. The use of sliding mode control enhances the converters against disturbances and provides an efficient voltage regulator. The research is useful to the field as it offers more insights into the control strategy that significantly improves the performance of converters concerning efficiency and stabilization.

The advancement technology of artificial intelligence and e-commerce has increased and this has called for new ways to improve last-mile transportation, which is regarded as an essential part of the logistics value chain, especially in smart logistics. This paper addresses the problem of developing effective routes for autonomous drones in last-mile logistics using deep Q-learning. This paper aims to improve the process of delivery by utilizing the flexibility and intelligence of self-driven autonomous drones in smart logistics transportation. The key challenge for the effective provision of last-mile delivery services remains the decision on the routing of many aerial drones in an indoor urban environment, concerning the restrictions of a time window for delivery, energy consumption and traffic. This paper implements a deep Q-learning paradigm that allows drones to relearn their flight paths and delivery strategy during the lifecycle, thereby reducing the cost in the long run while using the costing strategies as part of the re-engineering process. The approach has been validated through extensive experimentation and simulations. Results obtained indicate that the delivery drones modified for the study attained the designed requirements of deep Q-learning, including optimal navigation and performance that attained 12.8% shorter delivery time, an increase in energy efficiency by 8.4%, and a route quality improvement of 20.1%. Furthermore, highlights the performance of the system in various situations where deep Q-learning and standard routing approaches are compared. This paper not only aids in the minimization of the last-mile delivery constraint by the use of shipping drones but also emphasizes the capacities of reinforcement learning strategies such as deep Q-learning in tackling the routing problems in smart logistics systems. At last, it advocates carrying on deeper into the application of reinforcement learning in the solving of complex optimization problems in various other fields.

Concrete with fiber reinforcement is stronger and more ductile than concrete without reinforcement. Significant efforts have been made to demonstrate the properties and enhancements of concrete after reinforcement with various types and shapes of fibers. However, the issue of optimization in the reinforcement process is still unanswered. There is no academic study in the literature now available that can pinpoint the ideal fiber type, quantity, and shape and, more crucially, the overall technical viability of the reinforcement. The parametric analysis in this study determines the ideal shape, size, and proportion of fibers. The input and output parameters were separated from the optimization design variables. Input parameters included assessment of samples of fresh and mechanical concrete properties and the influence of type, length, and percentage of fiber on concrete performance. The aim was to establish the most efficient relationship between fiber dose and dimension to optimize the combined responses of workability and splitting tensile, flexural, and compressive strength. The mechanical and fresh properties of concrete reinforced with four different fibers, PFRC‐1, PFRC‐2, SFRC‐1, and SFRC‐2, were tested. The analysis showed that SFRC‐2‐20 mm‐1%, with compressive, split tensile, flexural, and workability values of 44.7 MPa, 3.64 MPa, 5.3 MPa, and 6.5 cm respectively, was the most effective combination among the materials investigated. The optimization technique employed in this study offers new, important insights into how input and output parameters relate to one another. [ABSTRACT FROM AUTHOR]

Chest X-ray imaging plays a vital and indispensable role in the diagnosis of lungs, enabling healthcare professionals to swiftly and accurately identify lung abnormalities. Deep learning (DL) approaches have attained popularity in recent years and have shown promising results in automated medical image analysis, particularly in the field of chest radiology. This paper presents a novel DL framework specifically designed for the multi-class diagnosis of lung diseases, including fibrosis, opacity, tuberculosis, normal, viral pneumonia, and COVID-19 pneumonia, using chest X-ray images, aiming to address the need for efficient and accessible diagnostic tools. The framework employs a convolutional neural network (CNN) architecture with custom blocks to enhance the feature maps designed to learn discriminative features from chest X-ray images. The proposed DL framework is evaluated on a large-scale dataset, demonstrating superior performance in the multi-class diagnosis of the lung. In order to evaluate the effectiveness of the presented approach, thorough experiments are conducted against pre-existing state-of-the-art methods, revealing significant accuracy, sensitivity, and specificity improvements. The findings of the study showcased remarkable accuracy, achieving 98.88%. The performance metrics for precision, recall, F1-score, and Area Under the Curve (AUC) averaged 0.9870, 0.9904, 0.9887, and 0.9939 across the six-class categorization system. This research contributes to the field of medical imaging and provides a foundation for future advancements in DL-based diagnostic systems for lung diseases. [ABSTRACT FROM AUTHOR]

As global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development. [ABSTRACT FROM AUTHOR]

This study aimed to analyze: (1) the causes of spatial interaction between the regencies in West Timor based on horticultural commodities, (2) the existing and optimizing benefits of spatial interactions between the regencies in West Timor based on horticultural commodities. The research was conducted in 5 regencies on Timor Island namely Kupang, TTS, TTU, Belu, and Malaka at wholesale markets which were determined purposive and snowball for traders. Purposive sampling with the criteria of traders who market horticultural products to 4 other districts in West Timor, followed by a snowball to trace traders who provide the intended horticultural products to the destination market. Data analysis uses gravity analysis, and the shortest path multi-object optimization model. The results showed that the variables of cost, population, price, and GDP had a significant effect on spatial interactions; while the transport capacity had no significant effect on spatial interactions. The total benefit of spatial interaction between the regencies in West Timor is IDR 1,055,467,000.-. The benefit of spatial interaction will increase by 65.60% to IDR 1,747,888,918,000.-. Therefore, interactions between regions must pay attention to the number of requests, selling prices, and transportation costs in order to maximize benefits. These findings are useful for policy makers and horticultural traders in determining the number of horticultural commodities marketed at each destination market in 4 districts in West Timor that provide optimum profits. [ABSTRACT FROM AUTHOR]

The purpose of this study is to enhance the reliability of emergency shutdown systems in the electric production industry by addressing spurious activations. Such activations may lead to production losses, stress on affected components and systems, and increase hazards during the restoration process of the system and losing the trust in safety system. This can lead to ignorance of serious detections of dangerous situations. Hence, the optimization and control of spurious activations becomes imperative for ensuring both efficiency and cost-effectiveness of any industrial plant. In the last few decays, several optimization meta-heuristic techniques are developed in literature. Particle swarm optimization is power and robust tool dedicated to solve complex problems. This paper presents a comprehensive review of the application of particle swarm optimization to minimize spurious trip rate by the optimization of performance parameters of emergency shutdown system installed in a combined cycle power plant. The results show that the obtained spurious activations rate is minimum. [ABSTRACT FROM AUTHOR]

This paper has investigated the inventory and financial flows in supply chains. Its purpose is to provide a method to optimize these two flows for chain members, where Return on Capital (ROC) is defined as the dependent variable, and cash conversion cycle (CCC) equation components, which show financial and physical flows, are formulated as independent variables. The data of chain members from six selected industries, including auto & parts, pharmacy, food, petrochemical, metal, and mining, have been extracted. Two scenarios, 1) revision of independent variables without a change in the cash conversion cycle of the entire supply chain, and 2) reducing the days of independent variables along with reducing the cash conversion cycle, have been defined. The problem is simulated using Agent-Based Modeling and NetLego software. Results of the first scenario indicate that if Days Inventory Outstanding (DIO) is reduced in downstream and transferred to upstream of the chain, and Days Payment Outstanding (DPO) in the upstream is shortened, ROC is improved for the entire chain. Also, the results of the second scenario show that, in proportion to the reduction of the cash conversion cycle through productivity under collaboration of chain members, the performance improvement of ROC is remarkable. [ABSTRACT FROM AUTHOR]