Volume 7.
Issue 03.

This article addresses the challenges associated with subwoofer installation in vehicles with limited interior space, a pressing issue in car audio systems. Modern vehicles, especially compact models, often feature constrained dimensions, complicating the selection and installation of acoustic equipment. This study aims to analyze the issues related to subwoofer installation in vehicles with restricted space. Recommendations to improve the acoustic performance of such systems are proposed. The methodology includes a theoretical examination of existing methods for subwoofer installation in confined spaces.

The findings indicate that acoustic performance is influenced not only by the choice of subwoofer type and enclosure but also by the proper placement of the device within the vehicle. Installing a subwoofer in the trunk compartment, with appropriately adjusted enclosure parameters and amplifier settings, enhances the efficiency of low-frequency sound transmission. For vehicles with limited space, such as compact crossovers or hatchbacks, the placement of the subwoofer significantly impacts the overall cabin acoustics, along with the effects of noise and vibration. The choice of enclosure material plays a crucial role in determining resonance characteristics.

The information presented is valuable for engineers working with automotive audio systems and car enthusiasts interested in improving the sound quality of their vehicles. The results obtained can serve as a foundation for the development of new subwoofer models designed for use in restricted spaces.

This study investigates the influence of mold thickness on the cooling rate and hardness of gray cast iron in two distinct mold types: metal and sand molds. The experiment is conducted by casting gray cast iron in molds of varying thickness and measuring the cooling rate and hardness at different intervals during solidification. The results indicate that both mold type and thickness significantly affect the cooling rate and the hardness properties of the cast iron. Metal molds lead to faster cooling and higher hardness, while sand molds show slower cooling rates and lower hardness. This study provides insight into how mold design and thickness can optimize casting quality and material properties for industrial applications.

The aim of this study is to investigate the factors constituting the adoption of Fibre Reinforced Polymers (FRPs) in construction projects, performance characteristics, monetary effectiveness, sustainability, environmental plays, and an obstacle to adoption. Of all the various materials used, FRPs are noteworthy as being capable of uniquely high strength-to-weight ratio, corrosion resistance, and long-term durability, making them an excellent replacement for a traditional steel or concrete. While the advantages of using FRPs mentioned above should drive their adoption in construction, FRPs have not been widely adopted in construction because of high initial costs, shortage of skilled labour and lack of long-term performance data. Quantitative such as survey and statistical analysis are used in this research to determine the association as well as the effect to the adoption of adopting FRPs in construction.

Results indicate that the performance characteristics of FRPs, such as mechanical strength and corrosion resistance, are crucial in determining whether and how they are applied essentially because of their advantages of using in harsh environments. Furthermore, although less of such adoption is made for sustainability and environmental benefits, the adoption is even positively affected. In addition, while cost effectiveness was a well-established cost-saving associated with FRP; it did not have a strong bearing on adoption in this investigation. In the broader adoption though, there was a significant obstacle on the way, mainly the barriers to adoption, including the lack of skilled labour and regulatory constraints.

The study suggests a way to fight these previously mentioned barriers, through creating some standard guidelines, with government incentive, more trained people and display of FRP case studies of the long-term benefits. If the challenges are tackled and the structural construction projects developed in a more sustainable and a less cost manner, the full benefits of FRPs can be captured by the construction industry.

The article analyzes the development of an automated system designed to inform about discounts offered by competitors on the clothing e-commerce platform. The main goal was to replace manual data collection and integration processes with an automated approach that improves the accuracy of company pricing steering strategy and reduces operational overhead. The system model is based on Lagrange equations, which ensures the integration of price information into strategic management.

The implementation methodology includes web scraping through Selenium, scrappy tools, and data processing using machine learning methods. The approach to analyzing text materials allows you to effectively extract meaningful information from advertising content. The architectural solution is based on a microservice model, which increases the adaptability of the system and simplifies scaling. Existing scientific research, studies, and developments, as well as the author's practical experience working on a commercial e-commerce fashion platform, were used as sources, allowing for a comprehensive exploration of the topic.

The results demonstrate cost reduction and improved accuracy of processes related to pricing. The developed system finds applications in e-commerce, marketing, data processing, and software development, where automated solutions for business process management are in demand.

The study presents a method for collecting and analyzing data on competitors' price offers. The developed system uses big data processing algorithms to monitor changes in pricing policy. This allows you to quickly adapt pricing strategies, as well as make adjustments to marketing decisions.

The formulated conclusions confirm the achievement of the stated goals. The introduction of an automated approach has made it possible to optimize tasks related to monitoring and analyzing competitive offers as well as ensuring pricing steering accuracy, i.e. meeting certain business targets on total sales discount rates.

Following the COVID-19 pandemic, U.S. corporations encountered unprecedented financial and operational challenges. This research examines the early warning signals of financial distress for firms from a period of 2020 to 2025 and their strategic responses to such challenges. We employed a mixed-method approach, analyzing company success indicators while incorporating qualitative insights from academic literature and industry sources. Critical findings indicate that in the post-COVID economy, particular operational red flags such as declining revenues, insufficient liquidity, escalating debt burdens, and operational inefficiencies frequently led to significant financial difficulties for enterprises. The research evaluates the effectiveness of the strategic measures employed by firms, including significant cost reductions, restructuring, adaptive pivots, and stakeholder support initiatives. In conclusion, the research indicates that a company's resilience in the post-pandemic years (2020–2025) was contingent upon the early identification of warning indicators and timely strategic actions. Scholars, professionals, and politicians may utilize this information to enhance preparedness for future economic disruptions and establish early warning systems.

The security threats against healthcare IT systems create multiple significant hazards to patient data purity together with compliance requirements and ongoing organizational operations. These days growing healthcare digitization has caused cyberattacks like ransomware and data breaches and phishing attacks to increase sharply while creating financial damage and reputation loss for healthcare facilities. The research will examine how business risk management combines with data privacy strategies to safeguard healthcare cybersecurity structures through analysis of risk mitigation plans and regulatory adherence and technological security development. The research incorporates published works alongside current scientific investigations which expose the security weaknesses and new threats affecting medical IT systems. Statistical data about cybersecurity breaches and financial losses and regulatory compliance failures undergo quantitative analysis for the purpose of delivering applicable findings. Organizations within the healthcare sector fight to properly execute security standards created by regulatory requirements including HIPAA and GDPR and NIST because of financial and operating limitations. Security improvements stem from using AI threat detection together with blockchain secure data exchange protocols and the implementation of Zero Trust Architecture (ZTA). The research identifies the need for healthcare organizations to build cybersecurity defenses through dedicated protective measures which unify regulatory compliance with innovative technology deployments and precautionary security risk approaches. This study brings value to the cybersecurity domain by developing a business-oriented framework which guides healthcare organizations to handle risks and fulfill international data protection mandates.

The challenge of enhancing IT service delivery lies mainly in incorporating real-time user feedback to adapt solutions. Research investigates how AI sentiment analysis helps IT service management by supplying data-driven information for enhancement. The system uses modern natural language processing (NLP) models especially Bidirectional Encoder Representations from Transformers (BERT) to extract and categorize user sentiment from feedback obtained from multiple sources that include service tickets and customer surveys. Research findings demonstrate that negative customer sentiments create service delays which resulted in predictive systems that handle cases more efficiently and reorder service tasks according to importance. When teams employed sentiment-based methods they cut ticket resolution duration down by 35% and user satisfaction strengthened by 22%. The study provides scholars with a flexible system that combines AI-based sentiment evaluation with IT service management processes. The system shows its ability to adapt through automated responses which interact with changing expectational needs and emerging feedback patterns. Any implementation of AI requires focused attention on ethical elements such as how users' privacy will be maintained and the processes by which consent is secured. Sentiment analysis presents a valuable tool which helps providers maintain user need anticipation abilities alongside their capability to prevent bottlenecks and regulate performance statistics. Researchers should study how the integration of sentiment data with behavioral information might create service personalization models of higher quality. The paper provides applicable guidance to IT managers and policymakers which features sentiment analysis as an essential element that drives adaptable user-oriented service enhancement approaches.

This article addresses the issue of improving energy efficiency in the testing of system-on-chip (SoC) semiconductor systems, including heterogeneous computing cores and AI accelerators. An analysis of SoC architecture and existing Design for Testability (DFT) methodologies is presented, considering energy-saving techniques such as clock gating, power gating, and dynamic voltage and frequency scaling (DVFS). A literature review highlights the insufficient development of a comprehensive approach to reducing power consumption specifically during testing procedures. Practical examples, including Qualcomm Snapdragon, Apple A-Series, and Tesla FSD, demonstrate that integrating low-power techniques into DFT can significantly reduce energy consumption (by an average of 20–35%) without compromising test coverage quality. The proposed analysis confirms the effectiveness of combining traditional scan chains, built-in self-test (BIST), and boundary scan with power management mechanisms, contributing to reduced thermal loads and increased reliability of modern SoCs in mass production. The findings presented in this article will be of interest to leading researchers and practicing engineers in the fields of microelectronics, materials science, and energy optimization, aiming to integrate advanced testing methodologies with innovative energy-saving solutions to develop reliable, high-performance, and environmentally sustainable semiconductor systems.

The article explores the potential applications of artificial intelligence (AI) in maritime fleet management, focusing on improving operational efficiency and reducing costs. An analysis of key technological solutions is presented, including predictive maintenance, intelligent routing systems, crew performance monitoring tools, and energy consumption optimization. It is demonstrated that machine learning algorithms processing vast datasets, such as Automatic Identification System (AIS) data, weather information, and vessel sensor readings, can predict emergency situations and schedule maintenance based on actual equipment wear.

The study examines case studies from Maersk, Shell, Wärtsilä, and other companies, highlighting fuel savings of up to 15%, reductions in unplanned maintenance events, and improvements in environmental sustainability. Special attention is given to decision-support systems that integrate diverse data sources into a unified information platform, enabling comprehensive analysis and timely decision-making.

The implementation of AI technologies can enhance not only safety levels but also the profitability of maritime transport by optimizing cargo flows and reducing fuel and maintenance costs. The article concludes with practical recommendations for shipping operators transitioning to a "digital" fleet and outlines promising directions for further research. The information presented will be of interest to professionals and researchers in maritime logistics, digital transformation, and operational management who aim to integrate advanced AI-driven models with systems analysis to develop innovative strategies for improving efficiency and reducing costs in maritime fleet management amid global industry dynamics.

New developments in Artificial Intelligence (AI) in IT services have drastically altered how companies use customer insights to supply personalized and scalable responses to a wide variety of client necessities. The focus of this study consists in the use of AI tools and algorithms in customer data analysis, but also in the sense that they are useful for providing targeted and efficient IT service solutions. The findings are robust because a mixed-methods approach was employed, using qualitative analysis of case studies and quantitative evaluations of service outcomes. The results show that adding AI features into workflows of IT services can significantly improve satisfaction metrics for customer, operating efficiency, and the scalability of the service overall. Additionally, the paper organizes frameworks and different strategies for utilizing AI devices and investigating issues, for example, data secrecy, calculation predisposition, and extendibility. This research also helps bridge a few of the existing gaps in the existing body of knowledge about potential AI applications in customer–centric IT service and provides actionable insights for practitioners and policymakers. The main takeaways indicate how much organizations need to start seeing AI as a business growth strategy and not as a technological advancement. Related to this, future research needed to understand the ethical considerations of artificial intelligence in customer insights, and the overall implications of artificial intelligence, in the context of media distributors and different cultural and regulatory environments.

High-load software systems are pivotal in today’s digital landscape, where organizations must handle ever-growing user volumes, data transactions, and real-time interactions. This article explores the core challenges and corresponding solutions in designing, deploying, and maintaining high-load software applications. Emphasis is placed on architectural scalability through microservices, optimal database management (including sharding and replication), and effective use of caching and load balancing techniques. In addition, the study outlines asynchronous processing methods that enhance system responsiveness by offloading resource-intensive tasks to background queues. A dedicated focus is also given to monitoring, logging, and fault tolerance approaches, showcasing how a combination of redundancy, automated failover, and chaos testing procedures can ensure uninterrupted service delivery. The conclusions are drawn from both the author’s previously published concepts and recent academic insights. By integrating these proven practices—from containerized deployment to distributed tracing—software engineers can more effectively address performance bottlenecks, guarantee high availability, and support real-time scalability. The primary contribution of this article is a consolidated framework, illustrating how modern load handling strategies and robust monitoring pipelines can optimize throughput, lower latency, and reduce operational risks in high-load environments. The solutions proposed are adaptable to diverse technology stacks, with special attention to NoSQL options, microservices orchestration, and automated testing/verification protocols. This consolidated perspective underscores the necessity of proactive design choices, continuous testing, and rigorous observability practices to achieve resilient, scalable software systems in the face of volatile market demands.

The article explores approaches to integrating chatbots into customer interaction management systems, as well as other digital platforms such as educational, financial, and marketing environments. The aim is to study architectural solutions and algorithms that ensure the productive operation of chatbots in terms of performance, scalability, and flexibility in various user interaction scenarios.

The methodology is based on comparing centralized and decentralized integration models. It examines data transfer protocols such as REST API, GraphQL, and WebSocket. Special attention is paid to natural language processing algorithms, including transformers like BERT and GPT, which can interpret queries, maintain context, and quickly adapt to changes in communication scenarios.

The article also discusses hybrid models combining automation with human operators for handling non-standard situations. Approaches focused on active learning are examined, which improve chatbot performance in real-time.

The results demonstrate that the use of chatbots in customer interaction management systems and e-commerce improves query processing, speeds up responses, and enhances personalization. The application of data analytics opens opportunities for predicting customer behavior and generating proposals tailored to user needs. Issues of data security, encryption, authentication, and access control, which are critical for regulatory compliance, are also considered.

The conclusions highlight the necessity of a comprehensive approach to chatbot design, selecting flexible architectural solutions, adapting to business processes, and implementing machine learning algorithms. The proposed methods are expected to benefit software developers, analysts, marketers, and managers engaged in digital transformation.

The research focuses on how Multi-Agent Systems (MAS) coupled with Machine Learning (ML) can help manage the challenges and risks associated with new-generation supply chains networks. The proposed MAS-ML framework improves flexibility, adaptability, and predictiveness in essential roles in supply chain management (SCM), including demand forecasting, inventory management, production planning, and SCM logistics. The framework is based on decentralised decision-making where each agent is responsible for a particular supply chain activity but employs real-time data foresight from the ML model to streamline the activities. This decentralisation enables resilience in supply chains, which can experience events such as demand variability and transportation disruptions. MAS-ML is presented in this paper as the solution capable of enhancing supply chain performance, reliability, and cost optimisation in situations characterised by risk and uncertainty, such as the current global pandemic. In addition, this paper presents potential research areas, such as the integration of more enhanced deep learning algorithms, the extension of proposing MAS-ML into other sectors, and the addressing of ethical and transparency concerns associated with AI-based decision-making systems. The proposed MAS-ML framework improves the adaptability and resiliency of supply chains, providing a flexible solution for modern supply chain problems.

The article discusses the results of research dedicated to the thermomechanical processing of products made from chromium-molybdenum steel, similar to grade 35ХМЛ, but modified with vanadium as a modifier. The experiments were conducted under serial production conditions with the aim of improving the technological process. Within the framework of the study, a method was implemented where the forging process was combined with subsequent heat treatment, performed immediately after forging at a specialized station. This approach eliminates the need for re-heating of products, which significantly reduces energy consumption and enhances production efficiency.

During the cooling process of the products, it is necessary to maintain the optimal temperature regime to ensure a controlled exothermic phase transformation of austenite into pearlite. This allows for the formation of a balanced ferrite-pearlite structure, which provides the necessary mechanical properties, including the required hardness range.

The test results confirmed that the correct selection of the isothermal annealing temperature regime contributes to achieving stable operational characteristics of the products. The implementation of this technology in industrial production will significantly reduce energy consumption—by more than 80% compared to traditional heat treatment methods. In addition, eliminating re-heating reduces the overall manufacturing time, which contributes to increased productivity and a decrease in production costs.

Thus, the proposed technological method not only enhances the energy efficiency of production but also ensures the production of products with predictable mechanical properties. Its application in industry could play a key role in optimizing the processing of chromium-molybdenum steels, which is particularly important in the context of the drive to reduce costs and rational use of resources.

The article examines methods to predict cargo arrival times through Apache Spark and Scala. The necessity for such methods arises due to external factors such as unpredictable road conditions, weather phenomena, and specific logistical operations. Information processing employs methods such as regression, decision trees, and neural networks, which analyze data from sensors, GPS devices, and other sources to build forecasts that consider all factors directly or indirectly affecting calculation accuracy.

The methodology is based on studying the functionality of the Apache Spark platform integrated with the Scala programming language, enabling the processing of large datasets with high operational speed and solution scalability.

The use of Apache Spark combined with Scala accounts for streaming data, which improves prediction accuracy. This method optimizes logistics processes by reducing delays and allowing timely responses to changes in external conditions.

The information presented in the article will be useful for data processing professionals, logisticians, and developers.

The automotive industry is undergoing a profound transformation driven by electrification, connectivity, advanced driver assistance systems, and the gradual transition toward automated and autonomous driving. At the center of this transformation lies functional safety, particularly as codified in the ISO 26262 standard, which provides a structured framework for managing risks arising from systematic and random hardware faults in road vehicle electrical and electronic systems. While ISO 26262 was originally conceived for relatively deterministic systems, contemporary vehicles increasingly incorporate software-intensive architectures, machine learning components, and adaptive functionalities that challenge traditional safety assurance paradigms. This research article presents an extensive, theory-driven examination of ISO 26262 compliance in the context of modern automotive development, integrating insights from hardware safety design, safety lifecycle management, hazard analysis and risk assessment, ASIL decomposition, formal verification, FMEDA-driven verification, and emerging AI-centric methodologies. Drawing strictly from the provided literature, the article synthesizes established practices and recent advancements to identify persistent gaps between normative safety requirements and real-world system complexity. A descriptive methodological approach is employed to analyze how model-based certification, process-driven compliance, and machine learning–specific lifecycle extensions can enhance the robustness, traceability, and credibility of safety cases. The results highlight that while ISO 26262 remains a foundational pillar of automotive functional safety, its effective application increasingly depends on complementary methods such as formal verification, AI-aware safety processes, and holistic safety design frameworks. The discussion critically interprets these findings, addressing limitations related to explainability, tool qualification, and organizational readiness, and outlines future research directions necessary to sustain safety assurance in highly automated mobility ecosystems. The article concludes that integrated, model-driven, and AI-conscious safety assurance strategies are essential for maintaining public trust and regulatory confidence in next-generation vehicles.

This study explores the use of Large Language Models (LLMs) for automating investment strategies through sentiment analysis of financial news, social media, and market data. By fine-tuning models like GPT-3 on financial datasets, sentiment indicators are extracted and integrated with traditional machine learning algorithms to predict stock price movements. A comparative analysis of various models, including LLM-based, traditional machine learning models, and hybrid approaches, was conducted. The results reveal that the hybrid model, combining LLM-generated sentiment with machine learning algorithms, outperforms other models in terms of both prediction accuracy and financial performance. The hybrid approach achieved an accuracy of 77.4%, cumulative returns of 17.2%, and a Sharpe ratio of 1.20, demonstrating its potential for real-world trading applications. These findings highlight the importance of sentiment data in enhancing market predictions and provide a promising framework for automating investment strategies. However, challenges such as ambiguity in sentiment classification and the need for model adaptation to changing market conditions remain. Future research should focus on improving sentiment analysis accuracy and incorporating reinforcement learning for real-time trading.

In this study, we propose a predictive cybersecurity framework for the banking sector by integrating ensemble-based machine learning models. Our approach leverages heterogeneous datasets—including internal firewall and intrusion detection system logs, banking transaction records, user behavior data, and external threat intelligence—to capture a comprehensive view of the cyber threat landscape. Following rigorous data preprocessing, feature selection, and feature engineering, we evaluated multiple models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Deep Neural Networks. Comparative analysis revealed that while advanced individual models demonstrated strong predictive capabilities, the Ensemble Model consistently outperformed all others, achieving an accuracy of 92% and a ROC-AUC of 94%. These results underscore the model’s superior ability to minimize false negatives, which is critical for safeguarding financial assets. Our findings advocate for the adoption of ensemble techniques in real-world banking cybersecurity applications, providing a robust, scalable solution that adapts to evolving threat patterns while significantly enhancing detection performance.

This study presents a novel malware detection framework for cloud infrastructures that harnesses the power of Convolutional Neural Networks (CNNs) to achieve real-time threat identification with superior accuracy and speed. Our approach begins with the collection and meticulous preprocessing of heterogeneous cloud log data, followed by advanced feature engineering to extract meaningful patterns indicative of malicious activity. The CNN model automatically learns hierarchical representations from this high-dimensional data, resulting in a detection system that achieves an accuracy of 98.2%, a precision of 97.5%, a recall of 98.0%, and an F1-score of 97.8%. In addition, the model operates with a low latency of 12 ms, a critical factor for timely threat mitigation in dynamic cloud environments. Comparative analysis against Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest classifiers reveals that the CNN not only outperforms these models in key performance metrics but also maintains a significant advantage in processing speed. These findings highlight the potential of CNN-based approaches to enhance cybersecurity defenses, offering a scalable and efficient solution for detecting evolving malware threats in cloud infrastructures.

Revenue Cycle Management (RCM) stands as an essential healthcare financial element since it manages efficient claim handling combined with payment receipt processes that optimize organizational profits. The conventional RCM operational model suffers from multiple difficulties including inefficiencies, administrative burdens and regular billing mistakes that eventually generate revenue loss and operational delays. This paper investigates the potential of IT and AI solutions to transform RCM operations by streamlining procedures and boosting financial projection quality as well as improving claim verification. The research bases its analysis on real-life implementations of artificial intelligence-based billing automation in addition to robotic process automation (RPA) and predictive analytics solutions in healthcare finance domain. An AI-driven automated system decreases denials processing and speeds up payment times while improving financial performance which enhances healthcare service efficiency. The implementation of blockchain technology as an information technology solution improves both security and interoperability within healthcare financial systems. The ongoing challenges for healthcare organizations include the cost of implementation along with workforce transitioning issues and privacy-related difficulties with data. The research findings demonstrate why healthcare organizations need to implement strategic AI and IT solutions for improving their Revenue Cycle Management systems. Research should focus on how AI systems connect with value-based healthcare approaches for maximum financial performance improvement.

In recent years, Human AI Collaboration has become an exciting new approach to IT systems design that is designed to balance automation and human expertise. Specifically, this paper investigates a broad framework of smart scenario co-creation with IT systems in general, where human and AI work together in dynamically sharing IT tasks, AI provides decision tools for augmentation, and mutual performance is optimized by dynamically adjusting learning parameters. The research employs a mixed method, and the case studies together with the surveys and the quantitative data analysis are used to assess the existing collaboration models. We find that hybrid teams, consisting of both AI agents and human experts, increase productivity by up to 40% when executing iterative design processes. In addition, the study provides important insights regarding the critical success factors such as adaptive system interfaces, trust building mechanisms and the skill augmentation strategies. This information presents a path for overcoming ubiquitous challenge in utilizing collaborative frameworks, such as technological misalignment and user resistance. The proposed framework is intended to enable replication of such integration in the real time IT environment offering flexibility, scalability and long-term efficiency. Second, this research adds to the expanding repository of knowledge in terms of human centered AI development and offers IT leaders practical approaches to take advantage of human AI synergy for innovation and competitiveness.

This study provides a comprehensive analysis of the implementation of modern technologies and hybrid solutions in the manicure industry. Innovative trends are examined, including AI-powered nail printers, augmented reality applications, 3D printing, robotic manicure systems, and the unique characteristics of hybrid manicure techniques that combine the advantages of traditional and contemporary coatings. The research is based on an analysis of existing studies as well as publicly available data from online sources. The applied methodological approaches allow for an assessment of the potential of digital innovations in improving the quality, durability, and safety of manicure procedures. The findings demonstrate that the integration of innovative technologies and hybrid techniques can not only fundamentally transform the aesthetic aspects of nail application but also optimize technological processes in the beauty industry. This study presents an interdisciplinary approach that merges cosmetology with advanced technological solutions, fostering the development of new business models, educational programs, and technological breakthroughs in nail application. The insights provided in this article will be valuable for professional nail technicians, beauty salon owners, researchers, entrepreneurs, technology developers, and educational institutions, as modern technologies and hybrid solutions contribute to workflow optimization, service quality enhancement, and the advancement of innovation in the beauty industry.

This study explores the fundamentals of creating targeted advertising campaigns in social media, considering product types, geographical distinctions, and cultural characteristics. The relevance of the topic is driven by the expanding influence of digital platforms, where advertisers require an in-depth understanding of local specifics and consumer expectations. The novelty of this research lies in its systematic examination of the interconnection between product parameters, regional specifics, and cultural preferences.

The study analyzes the mechanisms for configuring advertising campaigns, proposing a methodology for audience segmentation based on interests, approaches to selecting advertisement formats, and planning ad placements depending on social media usage intensity. Particular attention is given to localization issues, the proper consideration of linguistic, religious, and ethical norms, as well as visual solutions that enhance brand trust. The research aims to develop well-founded recommendations that optimize advertising budgets and increase target audience engagement.

To achieve this objective, comparative and analytical approaches were employed, along with an examination of relevant academic publications and statistical data. The study compares information from works by M. Bonomo, A. La Placa, S.E. Rombo, R. Hochreiter, C. Waldhauser, H. Kefi, S. Indra, and T. Abdessalem, as well as research from various authors addressing different aspects of digital advertising. The conclusions highlight the dependence of advertising effectiveness on the strategic combination of local tools and global trends.

This publication will be beneficial for marketing professionals, entrepreneurs, students, and researchers seeking to enhance their skills in promoting products and services across diverse markets.

The aim of this study is to advance multi-label delivery delay predictions in supply chains using machine learning and deep learning models. The work used Decision Trees, Random Forests, CNN, and FNN on a real-life logistics dataset consisting of customers and products features. EDA and feature selection were examined and performed as a part of the data preprocessing process at the pre-processing step of the models. According to current model results, Random Forest model reached maximum accuracy of 66.5% along with Decision Trees and FNN. CNN, although, worked well in some instances was not up to par in some areas because it overfitted. The results also reveal how Random Forest is a particularly useful algorithm for predicting delivery delays accurately. The conclusion suggests enhancing the deep learning models performance and combining approaches. Further work should also incorporate other variables in order to improve the predictive capability in real-life requirements of supply chain environments including conditions and stocks.

In the article, a planetary gearbox is adopted to ensure the efficiency and compactness of the vas deferent drive. The number of teeth of the sun gear Z₁=12, satellite Z₂=12 and main gear Z₃=36 with a module m=3 mm is checked for compliance with the condition of assembly and proximity to the number of satellites equal to K=4. Verification calculations of the sun gear teeth and the satellite axis for bending are carried out, in which the strength condition is met with a reserve of s_F=24.93 MPa£[s_F]= 465 MPa – 18.5 times and s_u=20.26 MPa£[s_u]= 60 MPa – 2.96 times. In addition, taking into account the strength calculations, the dimensional values of the planetary gearbox units of the vas deferent tube are determined.