International Journal of Advanced Engineering Research and Science
Development of artificial aggregates by using sodium silicate activated copper tailings and slag
This study investigated the development of artificial aggregates using copper tailings activated by sodium silicate via granulation. Five different mixes were prepared by incorporating varying amounts of ground granulated blast furnace slag (GGBS) and sodium silicate. The granulation process was performed in a rotating disk granulator, with water sprayed onto the dry mixture to facilitate binding. The resulting aggregates, ranging from 5 to 25 mm in diameter, were cured in a humid environment for 3, 7, and 28 d. The aggregates were then tested for size distribution, oven-dried density, water absorption, crushing strength, and individual-granule strength. Microstructural analysis was conducted using scanning electron microscopy (SEM) and X-ray computed tomography (XCT). The results showed that the addition of GGBS significantly improved the properties of the artificial aggregates, resulting in a higher density, lower water absorption, and increased strength.XCT images revealed a more refined pore structure with smaller and more evenly distributed pores in the GGBS-containing samples. The crushing strength and individual granule strength followed a Weibull distribution, with the GGBS samples exhibiting higher strength values. SEM analysis confirmed the formation of N-A-S-H gel in the GGBS samples, which acted as a binding phase and improved the microstructure. These findings demonstrate the potential of using copper tailings and GGBS to produce sustainable artificial aggregates with enhanced properties for construction applications.
Intelligent Cat Recognition and Feeding System Based on Raspberry Pi and OpenCV Vision Technology
To address the issue of feeding outdoor cats, this study designed an intelligent cat recognition and feeding system. In terms of hardware, it integrates a Raspberry Pi 4B with OpenCV to combine ultrasonic sensor cameras, servos, and pressure sensors, forming a dynamic monitoring, characteristic identification, and precise feeding operation process. In software, OpenCV is used for cat face recognition, and Python scripts are employed to coordinate the work of sensors and actuators. The response time of the system can be controlled to be below 4 seconds. In the accurate recognition and feeding management of stray cats, the effect is quite significant. The adoption of low-cost hardware and lightweight control strategies has endowed an operational approach that is both engineering innovative and socially meaningful, enhancing the efficiency of outdoor stray cat management and facilitating the search for lost pets.
Mentorship and Engineering Excellence: Fostering A Culture of Innovation
In the contemporary engineering landscape, mentorship has emerged as a pivotal enabler of innovation, technical excellence, and leadership development. This article develops a theoretical lens for understanding the dynamic interplay between structured mentorship and innovation outcomes in engineering contexts. Building on multidisciplinary theories from organizational behavior, social learning, and systems thinking, it proposes the Mentorship-Driven Innovation Framework (MDIF). The framework conceptualizes mentorship as a system of inputs, mediating mechanisms, and feedback loops moderated by organizational culture and context. Through historical analysis, empirical synthesis, and conceptual modeling, the paper highlights how mentorship facilitates knowledge transfer, identity formation, psychological safety, and collaborative creativity. Despite its promise, limitations such as measurement complexity, cultural variability, and underrepresentation of informal mentorship are acknowledged. Future research directions are proposed to refine theory and expand its empirical base. This study offers critical insights for educators, engineering managers, and policymakers aiming to cultivate sustainable innovation cultures in technical organizations.
Effects of Burning on Chemical Attributes And Soil Organic Matter In Cerrado Areas
The practice of burning for soil management is common due to its low cost and simplicity, allowing immediate soil reuse. Despite its benefits, such as stimulating pasture regrowth and pest control, there are negative effects, such as soil degradation and reduced productivity in the long term. The technique is controversial, balancing tradition and sustainability, requiring effective alternatives without compromising environmental health. Research indicates both improvements in fertility and damage to soil properties, indicating the need for responsible use. This study evaluated the immediate effect of fire on the chemical attributes of pasture soil, comparing it with a soil of adjacent native vegetation. Conducted at Chácara Vitória, in Monte do Carmo - TO, the experiment Cerrado. analyzed two areas: one of virgin cerrado and another of pasture that had been burned annually for over twenty years. Laboratory analysis showed an increase in K, S, Mn, Zn, P-meh, Mg, Fe, Cu and Ca, and a decrease in Al, B, H+Al. The reviewed literature presents diverse results due to several variables. It is concluded that burning significantly impacts the chemical composition of the soil and should be carefully evaluated considering its long-term implications.
Norms and Associated Health Hazard in Himalayan Water System: A Review
Water is the most important element in our environment. Radionuclides such as Uranium and Radon are soluble in water and can pose a significant health hazard. This review provides a comprehensive analysis of the source, occurrence, impact on inhabitants and affecting factors in Uttarakhand state. As Uttarakhand is a water-rich region known for its abundant rivers, springs and groundwater, monitoring of radionuclide contamination is very crucial from a public health and environmental point of view. Districts Bageshwar and Pauri show the maximum uranium content, and Tehri and Rudraprayag show the maximum concentration of radon in water. Mainly, Geology is the main factor in Uttarakhand for uranium and radon anomalies. The result depicts that there is no significant health risk in Uttarakhand state. All dose rates from the uranium and radon are less than the recommended limit prescribed by UNSCEAR and WHO.
Diffraction Model on the Breakwater Gap Based on Velocity Potential Equation
In this research, the governing equation for wave diffraction at a breakwater gap is formulated. The equation is derived by substituting the three-dimensional velocity potential—obtained as the solution to the Laplace equation—into the Laplace equation, under the condition that there is a variation or differential in the wave constants. The resulting equation is a second-order partial differential equation with respect to one of the wave constants. The governing equation is then solved numerically using the Finite Difference Method, employing the Successive Over Relaxation (SOR) technique for the iterative calculation.
A Comparative Analysis of Logistic Regression and Random Forest for Individual Fairness in Machine Learning
In high-stakes domains such as finance, healthcare, and criminal justice, machine learning (ML) systems must balance predictive performance with fairness and transparency. This paper presents a comparative analysis of two widely used ML models, logistic regression and random forest, evaluated through the lens of individual fairness. Using the UCI Adult Income and COMPAS datasets, we assess performance in terms of accuracy, F1 score, individual consistency, and disparate treatment. Our findings indicate that while random forests offer marginally higher accuracy (by approximately 1%), logistic regression improves individual consistency by up to 4%, suggesting it is preferable in fairness-sensitive applications. This study emphasizes model selection’s role in achieving ethically responsible AI.
Credit Risk Assessment of Listed Companies Based on Long Short-Term Memory Neural Networks
Listed companies are vital to capital markets, but issues like information opacity and poor governance elevate credit risks, impacting economic stability. This study proposes a Long Short-Term Memory (LSTM) neural network model to assess credit risk by analyzing time-series financial indicators. Factor analysis reduces dimensionality of indicators, followed by LSTM training on sequential data to predict risk levels. Using CSI 300 firms’ data, the model achieves 87.5% accuracy, outperforming traditional methods like logistic regression (80.2%). The approach captures temporal dependencies, offering dynamic risk forecasts. Limitations include data quality reliance and computational complexity. Results support regulators and investors in enhancing risk management.
Mathematical Modeling and Simulation of an Alkaline Electrolyzer for Hydrogen Production
In the context of energy transition and the growing interest in green hydrogen as a sustainable fuel, this study presents a comprehensive mathematical modeling of an alkaline water electrolyzer. The paper investigates the fundamental physical and electrochemical processes involved in hydrogen generation, covering electric, thermodynamic, and electrochemical models. Key parameters such as electrolyte concentration, conductivity, membrane resistance, and gas bubble effects are incorporated. Simulation results using MATLAB/Simulink are provided to illustrate the behavior of the system under varying operational conditions. The findings contribute to the optimization of hydrogen production systems through accurate model-based design.
DreamTalk-DMT: A Lightweight Sparse Mechanism Model with Dynamic Thresholds
Aiming at the shortcomings of the DreamTalk 2D digital human synthesis model in computational efficiency and expression generation fineness, this paper proposes an optimization method combining adaptive sparsity and cross-modal feature enhancement. By introducing a dynamic threshold sparsity mechanism into the diffusion model, the sparsity ratio was dynamically adjusted based on the learnable threshold and Exponential Moving Average (EMA), and the Mutual information Constraint (MI Constraint) was combined to minimize the information loss, which reduced the calculation amount of the model while retaining key features. The model architecture is improved, and the decoupled decoder is designed to decompose the facial expression into the upper and lower regions for independent processing. The dynamic linear layer is combined to realize parameter adaptation under the style condition, and the detail expression of expression generation is improved. In addition, Tacotron speech features and Wav2Vec acoustic features are fused to enhance the synchronization of speech and expression, and skip connections are used to optimize the information transmission efficiency.
Mutual information constrained optimization
Culture of Innovation: The Impact on the Day-to-Day of an Organization
This study analyzes stakeholders' perceptions of the culture of innovation in an organization in the Greater ABC region linked to an Innovation Hub. The research adopts an exploratory approach, using qualitative and quantitative methods, with data collection carried out through employee questionnaires and interviews with managers. The results show that, although innovation is recognized as essential, there are challenges in integrating this culture, in training employees, and in strategic alignment with the Hub. The study concludes that it is necessary to strengthen support mechanisms and promote greater internal cohesion to ensure that innovation becomes a consistent practice within the organization.
Determination of the Rigor Mortis Index for the species Eugerres brasilianus (Cuvier, 1830), Captured in Itaipava, Espírito Santo - Brasil
Fish has increasingly become an essential element in the search for sustainable protein sources in human nutrition. The diverse species available along the 8,000 km of our country's coastline provide a rich and varied source of food. To consolidate the fishing market, ensuring the quality of the food presented to the consumer is crucial. Among the existing ways to ensure the freshness of fish, the rigor mortis index reflects the biochemical aspects of the flesh and the stages it undergoes until it becomes unfit for human consumption. This study aimed to determine the rigor mortis index and its stages in the Carapeba species (E. brasilianus). The samples were captured on the beach of Itaipava, Espírito Santo, Brazil, and the experiment was conducted at IFES – Piúma Campus over a period of 30 hours. The measurements taken indicate that Carapeba exhibits pronounced and long-lasting rigor mortis indices, suggesting great potential in terms of quality and freshness. Specimens P2 and P3 reached the rigor mortis stage first, both at around 4.3 hours, while P1 took 5.3 hours. The full rigor mortis stage lasted 40 minutes for P1, whereas P2 and P3 remained in full rigor for 18.7 and 16.7 hours, respectively. The importance of this study for the species is notable, as even under stressful slaughter methods, the rigor mortis response time is significantly high, indicating an extremely attractive preservation potential.
Energy recovery and Analysis of performance criteria of the energy potential of biomass for the production of electricity
This study focuses on the field of energy. Electricity production is an industrial sector that supplies consumers with electrical energy adapted to their needs. With current ecological and environmental requirements in the world, which wants to be more and more sustainable and non-polluting. The electricity production sector is seeking to exploit new sources of clean energy to align with today's sustainable context. The SDG . 7 for example is a leitmotif of the neo-producer of electricity in the 21st century who wants to guarantee access to reliable, sustainable and modern energy services for all at an affordable cost. So we made the choice of biomass in this work, as a source of primary, unlimited, non-polluting energy to produce, following its biochemical decomposition and fermentation, biogas which will be the raw material which will help us in the production of a electricity at a good price and accessible to large masses of the world population, two methods of analyzing energy performance criteria are developed in this work for a good selection of biomass, namely Gaussian Mixture Model (GMM) and DEEPLEARNING.
This research investigates the morphological response of coastlines to the construction of groins, focusing on both single groins and paired groins. The evolution of the coastline is analyzed with the aim of identifying a new stable coastline geometry. The approach involves modeling the stable coastline using polynomial functions, where the polynomial coefficients are determined through the application of point-specific and line-specific characteristics, as well as the principle of mass conservation. The resulting stable coastline configuration typically forms a crenulate-shaped bay an equilibrium shoreline geometry occurring between two headlands. The derived geometry, whether partial or complete, exhibits features consistent with the known characteristics and dimensions of a crenulate-shaped bay.The modeled stable coastline can be calibrated against empirical crenulate bay profiles, confirming its validity as a representation of an actual stable coastal form.
Cross-Environment Deployment Strategies for Power Platform Solutions – Investigating best practices for managing multi-environment deployments, from development to production, using managed environments and DevOps
The cross-environment deployment strategies for Power Platform solutions which include moving Power Platform solutions from development to production using managed environments and DevOps practices. The paper takes advantage of ten key academic and industry sources to evaluate frameworks, automation tools and governing models to streamline deployment and enhance system reliability. The measurable benefits of such case studies are reduced deployment time and improved accuracy. With this DevOps deployed throughout cloud and hybrid platform and agile methodology, it facilitates scalable and secure deployments. The insights provided from the findings contribute to organizations wanting to improve performance, maintain consistency, and direct development to meet operational goals in dynamic contexts of enterprise.
Hyperautomation with Power Platform: Merging AI, RPA, and Low-Code for Business Efficiency – Exploring how AI Builder, Power Automate, and Dataverse can drive end-to-end enterprise automation
Hyperautomation is studied as to how it can improve business efficiency using the Microsoft Power Platform tools. It further breaks down potential savings in costs caused by AI driven automation, unification of data and workflow orchestration and shows how productivity can be increased along with a raised accuracy. Using literature review and then quantitative evidence, the research gives insights for enterprise digital transformation that are actionable.
Simultaneous determination of Copper, Zinc and Nikel in Electroplating Waste water by UV-VIS Spectroscopy Combined with advanced Machine Learning and Deep Learning Models
Monitoring to evaluate wastewater quality during the production process requires simple measurements and Realtime analysis as well. Among common methods for heavy metal analysis, the UV-VIS absorption spectroscopy is considered a potential analytical method due to its low cost and simple operation, direct online integration with treatment tanks. However, it faces limitations in simultaneously analyzing multiple metals due to overlapping absorption spectra. This study applied machine learning (ML) algorithms (Decision Tree (DT), Random Forest(RF) and deep learning (DL) models (Multilayer Perceptron - MLP, and 1D Convolutional Neural Network - 1D-CNN) to improve the accuracy of simultaneous quantitative analysis of three metals—Cu, Zn, and Ni in electroplating wastewater—based on VIS absorption spectra data of their colored complexes in aqueous solution with the PAN reagent in the presence of a surfactant. Large datasets were collected from UV-VIS spectra of 500 wastewater spiked samples in the range of 620-500 nm with a 1 nm interval, resulting in a dataset of size 500x121, followed by the application of ML and DL models using the Python programming language. Model performance was evaluated based on the correlation coefficient (R²) and root mean square error (RMSE). Preprocessing methods such as first-order derivatives and Principal Component Analysis (PCA) were applied to reduce noise in the dataset before training with machine learning algorithms. Results showed that the 1D-CNN model outperformed the others, achieving R² > 0.88 and RMSE < 0.036 for all three analytes. It is supposed by its ability to directly extract nonlinear features from raw data without the need for dimensionality reduction. In contrast, the DT, RF, and even MLP models, which utilized principal component analysis (PCA) for dimensionality reduction, demonstrated significantly lower accuracy due to information loss during the reduction process. The proposed model was successfully applied for rapid and simple metal concentration determination in practical samples using a test kit with reagent, a compact spectrophotometer, and an automated PC-based data reading application. These findings demonstrate that combining UV-VIS spectroscopy with machine learning and deep learning algorithms is an effective and feasible approach for the simultaneous detection of multiple heavy metals in specific matrix wastewater samples.
Systematic review on the relationship between HIV, Antiretroviral Therapy (ART), hyperglycemia and hypercholesteremia
HIV (Human Immunodeficiency Virus) infection continues to be a global public health concern, with millions of people living with the virus around the world, largely due to advances in Antiretroviral Therapy (ART) associated with ostensible prevention campaigns. However, if on the one hand a significant increase in the survival of patients with HIV is observed with the use of ART, on the other hand several studies warn of the adverse effects of prolonged use of ART on the metabolic system, particularly in the induction of a persistent hyperglycemic and hypercholesterolemic state, disorders that contribute to the overall reduction in survival due to the emergence of complications related to prolonged hyperglycemia and dyslipidemia, which motivated the present systematic review on the topic.
Paulo Henrique Martins Santos Da Fonseca
High prevalence of cognitive impairment in elderly with diabetes mellitus and associated factors
Cognitive impairment in people with diabetes mellitus (DM) is multifactorial, with evidence that their poor metabolic control is related to impaired cognitive function. Background/Objectives: Identify the prevalence of diabetic older with cognitive impairment and characterize the factors associated with this finding. Methods: Cross-sectional, quantitative, descriptive and analytical research carried out with 246 diabetic older adults followed up in a specialized center in northeastern Brazil. Sociodemographic and clinical variables related to DM2 and evidence of cognitive decline were assessed using the Mini Mental State Examination (MMSE). Results: There was a prevalence of cognitive impairment in 74.4% of the studied sample with an association between cognitive impairment and the variables advanced age (p = 0.003) and female gender (p = 0.025). There was statistical significance between the presence of self-reported cognitive problems and cognitive impairment at evaluation (p < 0.001) and a 1.4 times higher prevalence ratio of cognitive impairment in diabetic underweight than in obese elderly (p = 0.020). Conclusions: The research reveals the high prevalence of cognitive impairment in older adults with diabetes, especially in women and advanced age. It also corroborates the association already described in the international literature between subjective complaints of memory and nutritional status and cognitive decline.
Food and Beverage Establishment in Post-Pandemic: Business Design and Customer Satisfaction
This study discovers the business strategies food and beverage businesses use in Nueva Ecija to satisfy customers post-pandemic. With the profound changes in consumer behavior and expectations, while experiencing the pandemic, several establishments would need to adapt their operation and marketing strategies to address shifting customer necessities. The study found that the essential approach was to introduce upgraded health and safety protocols and expand capacity in service delivery. Providing a safe and clean location has become one of the most critical aspects of customer visits. This will instill more trust in the customers' minds, considering responsibly checking everything daily regarding regular sanitization and general cleanliness, as well as compliance with other health protocols and an extremely high standard of hygiene. This, along with good queue and wait time management and contactless methods of payment, will also improve the experience. Business strategies are also changing in response to changes in customer behavior, and digitalization marketing is evolving. Additionally, targeted promotions, discounts, and loyalty programs well-suited for a post-pandemic world enhanced customer retention and satisfaction. In addition, relationships between customers and establishments were improved through personalized marketing campaigns, more transparent communication, and security measures. Improving service delivery has also been critical to customer satisfaction. Those who retrained their staff to operate in the post-pandemic period improved service quality significantly.
Numerical modeling of artificial egg incubator efficiency
In summary, this work presented the essential points enabling the process of operating the artificial incubator, having sought to establish the link between theory and reality, the experimental phase was tackled. This involved manipulating the prototype artificial incubator materialized in the mechanical workshop of the Institut Supérieur de Techniques Appliquées de Kinshasa. The Matlab software was used to run simulations which produced results showing the variation in egg hatching rate as a function of temperature, the variation in internal temperature as a function of the incubator's external temperature, and the variation in humidity as a function of time (days). The results found were discussed with those found by other researchers.
Exploring the Potential of Recycled Aggregates in Modern Construction Challenges and Innovations
The reliance of the construction industry reliance on natural aggregates has led to significant environmental concerns, including resource depletion and habitat destruction. This paper explores the potential of recycled aggregates as sustainable alternatives to natural materials, emphasizing their environmental and economic benefits. It provides a comprehensive review of various types of recycled aggregates, their extraction processes, associated challenges, and recent innovations to improve their applicability. Key findings highlight the effectiveness of advanced treatment techniques and material modifications in enhancing the performance of recycled aggregates. The paper concludes with recommendations for future research and standardization efforts, aiming to integrate recycled aggregates into mainstream construction and contribute to a more sustainable built environment.
Territorial management plan of Quilombo Água Fria in Calha Norte as a tool for dialogue in public policies
The Territorial Management Plan (PGT) of Quilombo Água Fria, located in Calha Norte, is a fundamental tool for strengthening the autonomy and territorial preservation of the quilombola community. This study investigated the implementation of the PGT as a means of dialogue in public policies, with the aim of integrating local demands into government actions. The research revealed that, although the PGT has contributed significantly to the recognition and strengthening of the community, it faces challenges, such as the lack of financial resources, inadequate infrastructure and lack of coordination between the different levels of government. The research found that the PGT is fundamental to promoting sustainability and cultural resistance, but that its effectiveness depends on active community participation and inter-institutional partnerships. The main recommendations include strengthening local governance, training leaders, access to financial resources and the integration of public policies at the local, state and federal levels. In terms of results, the implementation of the plan has brought benefits to the community in areas such as sustainable agriculture and environmental preservation, but it has also faced difficulties in implementing some actions due to the lack of financial support and institutional disarticulation. This paper proposes that the creation of monitoring committees and the strengthening of ongoing dialogue between the community and public managers are key elements in ensuring the long-term success of the PGT, making it an effective territorial management model, both for Quilombo Água Fria and for other quilombola communities in Brazil.
The Determination of Phytochemical Properties and Antimicrobial Effect of Salvia verticillata L. subsp. amasiaca
People are constantly interested in tasting, smelling and consuming flavors and aromas obtained from natural sources. At the same time, their health benefits in addition to their flavors and smells increase this interest even more. Salvia verticillata L. is one of these sources known for centuries. Salvia verticillata L. is a species belonging to the Lamiaceae family, Salvia (Lamiaceae) genus. It is widely used in the Mediterranean region not only as an ornamental plant but also in traditional medicine. In this study, the antioxidant activity, essential oil composition and antimicrobial activity of Salvia verticillata L. subsp. amasiaca were determined. While the phenolic substance concentration equivalent to gallic acid was found to be 431.70 mg, the phenolic substance concentration equivalent to ascorbic acid was found to be 351.97 mg. Copper reducing capacity was determined as 112 mmol/g and Carotene-linoleic acid emulsion as 16.35 percent, the major volatile oil ratio was found to be trans-trans-caryophyllene with 35%. It was found to be significantly effective against E. fecalis, S. aureus, P. aeruginosa among the studied microorganisms, but not against E. Coli.
Applications of Design of Experiments (DOES) in Engineering – State of the Art
Design of Experiments (DOE) is an essential statistical methodology for engineering, allowing the systematic analysis of processes and the optimization of products. Its use allows the controlled manipulation of variables to maximize efficiency and quality in several areas, such as chemical, mechanical and materials engineering. However, despite its effectiveness, there are still challenges in the practical implementation of DOE, especially due to the methodological complexity and the lack of training in some industries. The study includes a review of practical cases between 2009 and 2024, selected from the academic database ScienceDirect, which illustrate the applications of DOE in industrial and scientific contexts, with emphasis on the factorial and Taguchi methods. Through DOE, it is possible to identify the most influential variables in a process and determine the ideal conditions to maximize system performance. Methodologies such as the Taguchi method, analysis of variance (ANOVA) and response surfaces allow engineers to explore the simultaneous impact of multiple factors, enabling a robust approach to decision-making in a controlled and efficient manner, with significantly lower costs and efforts. The research results demonstrate applications of DOE in areas such as optimization of machining processes in mechanical engineering, improvement of the thermal efficiency of heat exchangers in chemical engineering, and the development of high-strength metal alloys in materials engineering. In addition, there was an increase in the adoption of DOE in emerging sectors, such as genetic engineering and optimization of advanced materials. It is concluded that DOE is an indispensable tool for innovation and industrial competitiveness, promoting greater efficiency and reliability in processes. However, there is still room for its evolution, mainly in the development of more accessible approaches and in professional training for its effective implementation. This study contributes to the dissemination of knowledge about DOE, encouraging its wider adoption in engineering and driving technological advances in the most diverse areas.
The impact and psychological consequences of post covid on healthcare professionals
The COVID-19 pandemic has thrown the entire world into economic and mental turmoil, changed our lifestyles and changed the way we interact in care in what is a very challenging and rapidly evolving circumstance. The methodology: this is a bibliographical research of a documentary, descriptive and qualitative nature, the information was obtained from the analysis of scientific data published on online platforms. Results and Discussion: Healthcare professionals face additional pandemic stressors. They are known to include an increased risk of infection, of becoming ill or dying, of inadvertently transmitting the disease to others, and overwhelming and serious cases of illness. These professionals will also likely face a large number of deaths at the same time, will be frustrated with themselves for not being able to save many lives, and will be subjected to threats and attacks by people seeking dependent care when resources are limited. Conclusion: Anxiety and depression are the most common mental disorders among healthcare professionals, especially among those working on the front lines against COVID-19. Improvement strategies must be implemented that focus not only on the physical health of employees, but especially on mental health, as it is often overlooked and dismissed in its importance.
Synthesis optimal guidance law for missile in cruise phases with terminal additional requires
The article analyzes and proposes the synthesis of an optimal guidance method for missiles during the cruise phase in a combined remoteautonomous control system The objective of the problem to be solved is to guide the missile to a nonfixed point in space according to a predefined rule meeting specific requirements and criteria In combined remote control systems at the moment of switching to guidance the missile must satisfy several stringent requirements such as ensuring the target is within the seekers field of view determining the distance to the target meeting the initial slip value h at the moment of autonomous guidance and considering the missiles maneuverability among others The article focuses on researching a synthesized optimal guidance law for the missile during the remote control phase while taking into account these requirements at the transition point to autonomous control at the end of the remote control phase
Understanding Legislative Activities in Brazil: Analysis of Open Data from the Chamber of Deputies
The growing evolution of technological means as gears of modern life become responsible for the constant generation of data, which in turn imply the most diverse aspects of society. In this sense, data analysis is a powerful device when it comes to predicting and describing information, in order to improve decision-making. Thus, the insertion of open data in the space of public management enables greater transparency with the citizen, making social control and the consolidation of democracy possible. Based on this bias, the present study aims to implement Data Mining techniques on the open data of the Chamber of Deputies, aiming to broaden the understanding of legislative activities and present the knowledge discovered from the relationships and patterns found in the datasets. Therefore, this is an exploratory study, based on the CRISP-DM methodology, covering the steps of understanding the business, understanding and preparing data, modeling, evaluation and implementation, corresponding to post-processing.
Analyzing the Quality of Motorcycle Taxi Services in Agricultural Product Transportation: A Structural Equation Modeling (SEM) Approach
Pinrang Regency is one of the largest rice producers in South Sulawesi Province, Indonesia, where the main occupation of the population is farming. The need for transportation of agricultural products is very much needed and what is used by the farming community is a modified vehicle, namely a motorcycle taxi. This research aims to determine the level of service quality based on characteristics. The research method uses descriptive quantitative based on a questionnaire survey of 130 respondents with the Structural Equation Model analysis model assisted by Analysis Moment of Structure software version 23. The results of the research analysis show that the Service Quality variable has a positive and significant effect on Customer Satisfaction, Service Quality has a positive and significant effect on Customer Loyalty, Price Fairness has a positive and significant effect on Customer Loyalty, Customer Satisfaction has a positive and significant effect on Customer Loyalty, because the Critical Ratio value shows value >1.960 and P-value <0.005 the hypothesis in this research can be accepted, while the Price Fairness variable has no significant effect on Customer Satisfaction because the P-value is 0.612.
XKFHnet: Xception Kronecker Forward Fractional Net for Intrusion Detection in Cloud
In this new era of on-demand cloud computing, security is crucial. To find breaches in the cloud computing environment, researchers have surveyed a number of intrusion detection methods. The majority of them discuss conventional abuse and anomaly detection methods. By positioning the analysing component outside the virtual machine, usually at the hypervisor, Virtual Machine Introspection techniques are highly useful in identifying various stealth attacks that target user-level and kernel-level processes operating in VMs. Techniques such as Hypervisor Introspection protect the hypervisor and stop a compromised hypervisor from attacking virtual machines that run on it. Through the use of hardware-assisted used in virtualization-enabled technologies, introspection approaches examine the hypervisor. Our paper's primary goal is to present a thorough literature review of the many intrusion detection methods that have been suggested for cloud environments, along with an evaluation of their capacity to detect attacks. To clarify the vulnerabilities in the cloud, we offer a threat model and attack taxonomy. Our taxonomy of IDS techniques offers a thorough analysis of approaches together with their distinguishing characteristics, and it represents the state of the art in classification. In the survey, we have given a thorough understanding of methods based on Virtual Machine Introspection and Hypervisor Introspection. With the help of our study, researchers should be able to start investigating intrusion detection techniques in cloud environments.