American Journal of Engineering , Mechanics and Architecture (2993-2637)

Laser in Dermatological Treatments: A Review of How Laser is used in Treating Skin Issues such as Acne, Scars, and Pigmentation

Thu, 03 Oct 2024 00:00:00 -0400

Laser technology has emerged as a pivotal tool in dermatological treatments, offering a range of therapeutic options for various skin conditions, including acne, scars, and pigmentation disorders. This review explores the mechanisms by which different types of lasers, such as ablative, non-ablative, fractional, and Q-switched lasers, are employed to address these common dermatological concerns. The effectiveness of laser treatments in stimulating collagen production, remodeling scar tissue, and reducing melanin concentration is examined, alongside an analysis of the safety profiles and potential side effects associated with each laser type. Additionally, the review highlights the importance of personalized treatment plans tailored to individual patient characteristics, such as skin type and the severity of the condition, to maximize therapeutic outcomes. Emerging trends in laser technology, including the development of more targeted and less invasive approaches, are also discussed. This comprehensive review aims to provide clinicians and researchers with a deeper understanding of the current state of laser-based dermatological treatments, as well as future directions in the field.

Network Security for Cyber-Physical Systems Using Deep Neural Network-Based Anomaly Detection

Maytham Mohammed Tuaama — Thu, 03 Oct 2024 00:00:00 -0400

In recent years, Cyber-Physical Systems (CPS) have seen explosive growth in popularity thanks to their many practical uses. Network security and user privacy are key concerns while deploying CPS networks because of the high number of internet-connected devices in such an ecosystem, which makes them more susceptible to cyber-attacks. An effective and efficient Intrusion Detection System (IDS) might be a feasible way to defend CPS networks from different threats. This study proposes a new intrusion detection system (IDS) for Cyber-Physical Systems networks that uses deep learning to detect anomalies. In particular, we have introduced a Deep Neural Network (DNN) model for filter-based feature selection that drops features with strong correlations.

In addition, several parameters and hyperparameters are used to fine-tune the model. The UNSW-NB15 dataset, which includes four types of attacks, is used for this. To address class imbalance concerns in the dataset, the suggested model was trained using Generative Adversarial Networks (GANs). It then generated synthetic data of minority assaults and attained a 98% accuracy rate with the balanced class dataset.

Excessive Car Engine Smoking: Investigating the Causes and Remedial Strategies for Safety and Sustainability

Uwem Jackson Edemidiongs — Thu, 03 Oct 2024 00:00:00 -0400

The phenomenon of excessive engine smoking in cars is a serious problem that not only affects the performance of the vehicle but also presents a significant risk to the environment and public safety. It is frequently caused by underlying mechanical failures, such as worn-out piston rings, malfunctioning valve seals, or clogged air filters, which allow fuel, oil, or coolant to burn within the engine. On the other hand, excessive engine smoke can lead to serious engine damage, decreased fuel economy, and increased emissions of dangerous pollutants if ignored. The study aims to identify the main factors causing excessive engine smoke, evaluate the effects on vehicle safety and environmental sustainability, and provide practical solutions. The paper also examines typical engine components and diagnostic techniques to emphasize preventative maintenance strategies, including using cleaner fuels, changing oil on time, and performing routine engine inspections. It also looks at innovative remedies, such switching to low-emission vehicles and sustainable automobile technology that lessen environmental impact. In addition to being essential for road safety, making sure automobile engines are operating properly is a critical first step in developing sustainable transportation.

Abnormal Files Classification using Convolutional Neural Network CNN

Mohammad Musaddak — Mon, 07 Oct 2024 00:00:00 -0400

As of late, turning Windows files into pictures and analyzing them with deep learning and machine learning have been regarded as state-of-the-art methods for identifying and classifying malware. This is mostly because deep learning model performance in image classification has recently experienced a boom in success, and image-based malware detection and classification is platform independent. Convolutional neural network (CNN) deep learning techniques are successfully used for image-based Windows malware classification, according to a review of the literature. Nevertheless, just a small percentage of the entire picture representation had the infection. Finding and identifying these impacted little areas is crucial to achieving a high degree of malware classification accuracy. This study locates and identifies the little contaminated spots in the overall picture by integrating a Data augmentation technique with a CNN. On a dataset of malware images, a thorough examination and analysis of the suggested technique were conducted. The effectiveness of the suggested Data augmentation-based CNN method was evaluated against many non-attention-CNN-based methods using different data splits from the benchmark malware picture training and testing dataset. The proposed CNN approach ensured computational efficiency and outperformed non-attention-based CNN methods in all the data-splits. Most notably, the majority of the techniques demonstrate consistent performance across all training and testing data splits, illuminating CNN's multi-headed attention. model’s generalizability to perform on the diverse datasets.

Efficient Transformer Models for Large Scale NLP: An Empirical Statistical Research

Abubakri O. Murainah, Fadilulahi O. Popoola, Abdulazeez Murainah — Wed, 09 Oct 2024 00:00:00 -0400

The advent of Transformer models has essentially improved performance in Natural Language Processing (NLP) tasks such as machine interpretation and text summarization. However, their resource-intensive nature poses challenges for large-scale applications. This study evaluates the efficiency of four Transformer-based models—BERT, XLNet, DistilBERT, and ALBERT, focusing on significant metrics: accuracy, training time, memory usage, and inference speed. Evaluations were conducted using the Wikipedia dump corpus on an NVIDIA Tesla V100 GPU, employing the PyTorch library with a reliable batch size and a learning pace of 3e-5. The findings shows that XLNet and BERT attain higher accuracy, at 94.8% and 92.8%, respectively, and are resource-intensive as a result of their high parameter counts (340 million for XLNet and 345 million for BERT). DistilBERT, with 91.3% accuracy and only 66 million parameters, efficiently balances performance and resource efficiency, making it a strong contender for conditions demanding lower computational control. ALBERT, known for its memory efficiency, delivers acceptable performance with 90.0% accuracy and just 18 million parameters, thanks to its parameter-sharing techniques. This study highlights the trade-offs between accuracy, computational efficiency, and memory usage in selecting Transformer models for large-scale NLP tasks. The study recommended among others, that BERT and XLNet are ideal for applications where maximum accuracy and resources are available, while DistilBERT and ALBERT provide viable choices for resource-constrained situations, ensuring effective deployment in practical circumstances.

Self-organizing non-linear dynamic object control system based on neuro-fuzzy networks

Isamidin Siddikov, Feruzahon Sodikova, Nashvandov Khumoyunc — Thu, 10 Oct 2024 00:00:00 -0400

The paper explores the creation of a self-organizing regulator utilizing a neuro-fuzzy network, capable of accurately approximating nonlinear functions with precision. Employing neuro-fuzzy networks as self-organizing regulators introduces nonlinear characteristics, extending the object's control range and enhancing adaptability within control systems. To streamline the learning process of the neuro-fuzzy network and ensure overall asymptotic stability, the proposal suggests subdividing the system model into smaller sub-models, effectively reducing dimensionality. This approach is not only beneficial for single-dimensional systems but also proves applicable to multidimensional control systems of nonlinear dynamic objects.

Vertical Vibration Analysis of Conveyor With Compound Screw and Belt Bearing Support

Djurayev Anvar — Mon, 07 Oct 2024 00:00:00 -0400

The article presents the construction scheme and the principle of operation of the conveyor for transporting and cleaning cotton with two screw, strap element supports. Calculation scheme and mathematical model of two-screw and belt-supported shaft vibrations were obtained, and the expression for calculating the vibration amplitude was obtained. Connection graphs are presented for different modes of operation, taking into account the number of strap support singularities and dissipative coefficients. Based on the analysis, the recommended values of the parameters were determined.

Restoration of Cylindrical Parts: Combination of Surface Coating and Turning

Djemilov Denis Igorovich, Tolipov Axrorjon Nazirjon o‘g‘li — Wed, 09 Oct 2024 00:00:00 -0400

This paper investigates the restoration of cylindrical parts through a novel combination of surface coating and turning techniques. As cylindrical components are widely used in various industrial applications, their wear and degradation can lead to significant operational challenges and costs. The research focuses on addressing these issues by integrating advanced surface coating methods, such as thermal spraying and chemical vapor deposition, with precision turning processes.

Architecture of Central Asia in the XII-XIV Centuries

Arif Kadirov, Nozima Kadirova — Tue, 15 Oct 2024 00:00:00 -0400

It is essential to understand the principles of construction and distinctive features of the architectural landmarks of Central Asia and to effectively utilize this knowledge in the present. Professionals in the field should possess the ability to establish the time period of historical architectural monuments. This article explores the architectural landscape of Central Asia during the 13th-14th centuries, shedding light on the emergence of the Khorezm and Bukhara architectural schools, as well as the evolution of urban planning and building architecture during that time.

A Chatbot that uses Artificial Intelligence to Automatically Respond to Whatsapp Messages

R. Regin, S. Suman Rajest — Fri, 18 Oct 2024 00:00:00 -0400

The recent advancement trend of the technology had motivated the need for the study to explore the readiness of the employees in the workplace in accepting the new technology engagement. With this, the current study will focus on investigating the readiness of the employees towards the social interaction with the technology in the workplace in Malaysia. The previous study had suggested that there is indication of the social interaction among employees to have positive relationship against the acceptance of the technology at workplace which provide the indication that the employees are not ready for the shift into the digitalization era of working environment. The quantitative analysis method had been taken into the account for the methodology of the research where the quantitative analysis will focus on the output based on the 150 questionnaires distribution among the Malaysian employees based on the convenience sampling. The outcome of the result through the quantitative analysis had observe the lack of evidence to prove the presence of the significant in the relationship between the social interaction of the employee towards the acceptance of the technology in the workplace. This had been providing the evidence to show that the Malaysian employees had been not affected by the social interaction factor when comes to the transitioning towards the engagement of new technology in the workplace provide the indication that the Malaysian employees are likely to be ready for the shift into the transitioning and shift into the use of technology in the workplace.

Evaluation of Existing Materials Used in Wind Turbine Blade Construction: Assessing their Structural Characteristics, Sustainability and Performance

Uchechukwu Richard Olisedeme, Oluchukwu Richmond Olisedeme — Sat, 19 Oct 2024 00:00:00 -0400

The study investigatedthe existing materials used in wind turbine blade construction: assessing their structural characteristics, sustainability and performance.The study employed Finite Element Analysis (FEA) to assess various materials' performance, considering factors such as deformation, stress, and safety. Materials examined include Aluminum 2024-T4, Carbon Fiber-Reinforced Polymer (CRFP), Glass Fiber-Reinforced Polymer (GRFP), Bamboo, Aramid Fiber, Pine, and Glulam. The analysis covered a range of angular velocities to simulate real-world conditions. Results indicated that material choice significantly impacts blade performance. The study acknowledges limitations such as simplified environmental conditions, limited consideration of moisture effects, and the absence of fatigue and thermal studies. Recommendations include future research in Computational Fluid Dynamics (CFD) analysis, geographical-specific designs, and advanced materials research. Finally, this work underscores the importance of sustainable material choices in wind turbine blade design. The findings offer valuable insights for engineers, researchers, and policymakers seeking to advance wind energy technology with environmental responsibility at its core. In conclusion Wind energy is a pivotal player in the global transition towards sustainable power generation. Wind turbines, specifically their blades, constitute the driving force behind this transformation. The selection of materials for these blades is a crucial endeavor, entailing a delicate balance between sustainability, performance, and cost-effectiveness. In this pursuit, sustainability is a lodestar guiding the way.One of the recommendations made was that tailoring material choices to specific wind energy projects is essential.

SYNTHESIS OF CORROSION INHIBITOR IN A 0.5 M HCl MEDIUM AND ITS THERMODYNAMIC MECHANISMS OF ITS INHIBITION

Misirov Z.X — Thu, 24 Oct 2024 00:00:00 -0400

In this article, the PKA-1 corrosion inhibitor was synthesized based on polyethylenepolyamine and croton aldehyde, its structure was analyzed in YAMR and PMR devices, and its formula was proposed. Also, the inhibition mechanism of this corrosion inhibitor was studied through various thermodynamic parameters. Using the Arrhenius equation, the activation energy (Ea), activation enthalpy (∆H) and activation entropy (∆S) of the steel surface in inhibitory and non-inhibitory environments were determined. Along with this, Langmuir, Frumkin and Temkin adsorption isotherms were studied.

Justification of the use Cases of BIM and Lean in DFMA with Identification of Associated Themes

Anuri Chinatu NJOKU — Fri, 25 Oct 2024 00:00:00 -0400

Considering the fact that the UK construction industry faces a growing demand for sustainable practices, this research is conducted to explore the justification of the use cases of BIM and Lean in DfMA with identification of associated themes. Research by Martinez et al. (2013) and Li et al., (2019) highlights the inefficiencies in industry and the resultant impact on productivity and sustainability, necessitating the need for modern measures to address these challenges. DfMA offers a promising path towards sustainable construction, and this study delves into the combined effects of BIM and Lean within DfMA workflows. BIM's collaborative design capabilities enable early identification and mitigation of sustainability concerns in construction. Lean principles, emphasising continuous improvement and waste reduction, complement DfMA's focus on resource efficiency. The justification, among others was that the integration of BIM and Lean methodologies within the framework of DfMA for sustainable construction is a research area of profound importance. The construction industry, identified as a cornerstone of economic growth, not only directly contributes to a nation's economic prosperity but also exerts indirect influences on the growth trajectories of other industries. Besides the exploration of the integration of BIM and Lean within the DfMA context is paramount for achieving a holistic understanding of their collective contributions to sustainable construction practices in the industry. Interestingly, the research examined how BIM can optimise DfMA components for minimal environmental impact during design and construction; and how Lean practices, integrated within a BIM-DfMA framework, can further enhance resource efficiency. This is achieved by reducing rework through improved planning, optimising on-site logistics, and promoting just-in-time deliveries. By exploring the strategic integration of BIM, Lean, and DfMA, this research can inform industry best practices and guide policy development towards a more sustainable future for the UK construction sector. One of the recommendations was that there is need to develop standardised guidelines and best practices for BIM, Lean, and DfMA integration to benefit all stakeholders.

IMPACT OF MODIFIED POLYVINYL CHLORIDE-BASED SORBENTS ON HEAVY METAL ION REMOVAL FROM INDUSTRIAL WASTEWATER

Kh.Kh.Turaev, L.U.Bozorov, Kh.E.Eshmurodov — Mon, 28 Oct 2024 00:00:00 -0400

This study investigates the synthesis and application of modified polyvinyl chloride (PVC)-based sorbents for the removal of heavy metal ions from industrial wastewater. PVC was chemically modified with various amines, such as diethylamine, monoethanolamine, and diphenylamine, to enhance its sorption capacity. The modified sorbents were tested for their ability to adsorb metal ions, including copper (Cu²⁺), cadmium (Cd²⁺), and zinc (Zn²⁺), under both static and dynamic conditions. The results indicate significant improvements in the adsorption efficiency and thermal stability of the sorbents, demonstrating their potential for industrial applications.

Protection Systems in the Integration of Renewable Energy Sources in City Power Supply

M.V.Melikuziev — Sat, 02 Nov 2024 00:00:00 -0400

The article addresses the issues related to the protection systems of integrated microgrids, which ensure uninterrupted and high quality power supply to all types of electrical consumers connected to the city power supply system. It elaborates on the technical challenges in microgrids, abnormal operating conditions in the power supply, and potential faults. The analysis highlights both internal and external factors that must be considered when applying protection systems for microgrids supplying electrical energy to city areas. Additionally, recommendations have been developed regarding optimal protection systems for city power supply microgrids

SYNTHESIS OF CORROSION INHIBITORS BASED ON (THIO)UREA AND ORTHOPHOSPHORIC ACID

Abror Nomozov — Thu, 31 Oct 2024 00:00:00 -0400

In this article, the optimal conditions for the synthesis of two types of oligomeric corrosion inhibitors, polymethylene diamidophosphate (PDAF-1) and polymethylene thiodiamidophosphate (PTAF-2), based on compounds such as thiourea, urea, and orthophosphoric acid containing nitrogen and phosphorus, as well as their formulas, are determined. At first, (thio)diamidophosphate - PDAF-1 brand corrosion inhibitor was synthesized using urea and orthophosphoric acid in a 2:1 mol ratio at a temperature of 135-140 ºC. The second type of polymethylene thiodiamidophosphate (PTAF-2) corrosion inhibitor was synthesized based on the process of condensation in an aqueous environment at a temperature of 60 ºC, by adding formaldehyde to this obtained compound in a stoichiometric 1:1 mol ratio. The resulting substance is a white solid, non-volatile, the composition of the main component is 98.7%, and other substances - 1.3%. IR-spectra investigated the structure of these two types of corrosion inhibitors. Also, the inhibition efficiency of these corrosion inhibitors was studied by gravimetric and electrochemical methods in corrosive media such as HCl, H₂SO₄ and NaCl=3%. In addition, the factors affecting the inhibition efficiency, such as the pH of the solution, the duration of time, and the concentration of the inhibitor, were also studied. According to the obtained results, the inhibition efficiency of these corrosion inhibitors was between 95.3 and 97.8%. Also, electron microscopy studied and analysed the protection mechanism of corrosion inhibitors on the steel surface.