Water automation is all about controlling, monitoring and even billing of water usage in different places like hotel, house, irrigation land and industry with less human effort. Water is precious gift to mankind and we need to preserve quality of this natural resource. This paper presents a model showing how efficiently water can be distributed in urban areas through pipelines and how corrosion of pipes driving water to individuals can be prevented along with theft identification system which shows theft of water from a particular pipeline by giving visual presentation. The researchers done water automation based on different purposes using different types of hardware and technologies. This paper develops an automatic domestic well-water supply distribution system and treatment plant which can monitor water tank by measuring the water flow, water level, water temperature, cut ON/OFF water supply and send notifications to the user through mobile messaging. All of the component like water level and flow switch was connected through an android application that is much more efficient and easier to control the whole process.

The rapid evolution of wireless communication systems and high-voltage power equipment monitoring has driven sustained scholarly and industrial interest in compact, wideband, and multifunctional antenna architectures. Microstrip patch antennas and ultra-high-frequency printed antennas have emerged as dominant solutions across these domains due to their structural simplicity, compatibility with planar fabrication techniques, and adaptability to multiband and broadband operation. Simultaneously, the increasing operational complexity of power transformers, switchgears, and wireless terminals has intensified the demand for antennas that can simultaneously satisfy stringent electromagnetic performance criteria, spatial constraints, and application-specific functional requirements. This research article presents an extensive theoretical and analytical investigation into the design philosophies, electromagnetic behaviors, and application-driven performance trade-offs of compact microstrip and UHF antenna structures, drawing exclusively on established scholarly literature.

Grounded in classical antenna theory and extended through contemporary developments such as fractal boundaries, slot-loaded geometries, meandered lines, and bio-inspired topologies, the article synthesizes insights from wireless communication research and partial discharge sensing studies. The investigation situates microstrip patch antennas within the historical trajectory of wireless standards development, including WLAN, WiMAX, and emerging fifth-generation systems, while concurrently analyzing UHF antenna evolution for non-invasive condition monitoring of high-voltage power equipment. Particular attention is given to the theoretical mechanisms enabling multiband behavior, polarization diversity, impedance bandwidth enhancement, and radiation stability under constrained form factors.

Methodologically, the study adopts a qualitative analytical framework rooted in electromagnetic field theory, simulation-driven design reasoning, and comparative literature interpretation. The results are presented as descriptive findings that elucidate how specific geometric modifications and substrate choices influence antenna performance metrics across diverse application contexts. The discussion advances a critical synthesis of competing scholarly viewpoints, addressing unresolved challenges related to miniaturization, environmental robustness, and measurement uncertainty in both wireless communication and partial discharge detection. By integrating these traditionally distinct research streams into a unified theoretical discourse, this article contributes a comprehensive academic foundation for future antenna innovation in converging communication and sensing applications.

This article examines the issue of friction in software development and proposes, as an alternative and complement to top-down initiatives, the bottom-up enhancement of Developer Experience (DevEx). Large-scale productivity losses caused by cognitive overload, tool inefficiencies, and organizational misalignment translate into big bucks when it happens on an industrial scale. That is what makes this study relevant. The objective is to provide theoretical and practical justification for empowering engineers as agents of change within DevEx governance. The study’s novelty lies in integrating three analytical frames—DevEx, Cognitive Load Theory, and the socio-technical congruence model—into a unified, multi-level model of friction, and in proposing practical instruments (a friction log and the DX-champion role) that transmute local engineer-led initiatives into mechanisms for diagnosing and correcting systemic incongruences. The main takeaways speak to the constraints of solely top-down efforts which depend on accumulated measures and choices that disregard the daily worker's perspective. The suggested bottom-up method demonstrates awareness of small-scale mental blocks and establishes ways to leverage nearby worker insights into company progress. An optimal configuration likely combines both approaches. Top-down initiatives provide empirical grounding and organizational commitment. Bottom-up efforts offer concrete means of change and organic growth. The article will be useful to software engineering researchers, technology organization leaders, and practicing engineers interested in systematically elevating the quality of DevEx.

The use of basalt fibers in concrete should be expected to increase its resistance to short-term loads. However, this assumption is not supported by the results of large-scale experimental and theoretical studies. We believe the answer to this question can be found by analyzing the fundamentals of basalt fiber-reinforced concrete strength. This article focuses on the impact of randomly distributed basalt fibers and concrete composition on its strength properties. Results on this issue were obtained by determining the optimal composition and studying its strength.

The article presents an analysis of feeders for cotton feeding, their advantages and disadvantages, as a result of which it is stated that existing feeders have the ability to increase the cleaning efficiency of the equipment by dividing cotton into pieces with a low degree of loosening and increasing the degree of loosening.

Courier-related phishing and impersonation scams have become a persistent threat, exploiting user trust in logistics providers and the rapid growth of e-commerce. This article presents a detailed case study of a delivery scam that impersonated the international courier company CDEK. The investigation reconstructs the full attack chain, beginning with initial social engineering via telephone contact and continuing through the use of a fraudulent web domain designed to harvest sensitive information. Technical artifacts including domain registration details, TLS certificate misuse, web content structure, and interaction flow are analyzed to illustrate how attackers combine psychological manipulation with low-cost technical infrastructure. The study highlights common weaknesses in user awareness, brand protection, and domain abuse monitoring that enable such scams to succeed. Based on the findings, practical detection indicators and mitigation recommendations are proposed for security teams, domain registrars, and end users. The case demonstrates how real-world incident investigations can contribute actionable insights into modern phishing operations and complement existing academic research on social engineering and online fraud (Cloudflare, 2025; Let’s Encrypt, 2021).

The rapid expansion of the Internet of Things (IoT) has resulted in an exponential increase in the volume of data transmitted across networks, posing significant challenges related to bandwidth limitations, energy consumption, latency, and overall network performance. This study analyzes contemporary methods aimed at reducing the volume of data transmitted within IoT systems while maintaining data accuracy, reliability, and system responsiveness. The research examines three main categories of approaches: data compression techniques, data reduction at the sensor level (including sampling optimization and event-driven transmission), and edge or fog computing–based processing. Comparative analysis demonstrates that local preprocessing and intelligent filtering significantly decrease communication overhead, leading to improved energy efficiency in resource-constrained IoT devices. The findings highlight that hybrid approaches—combining compression, adaptive sampling, and distributed processing—offer the most effective solutions for large-scale IoT deployments. The study concludes by emphasizing the importance of developing adaptive, context-aware algorithms to further minimize data traffic without compromising system quality and user experience.

This paper introduces a novel hybrid storage architecture (HyFDS), designed by the author to address the dual challenge of ultra-low latency trading workloads and cost-efficient archival storage in financial markets. Unlike prior works that optimize individual components, HyFDS integrates Apache Kafka, In-Memory Data Grids, lock-free patterns, and Apache Iceberg into a unified framework, validated against the requirements of high-frequency trading. This work proposes a conceptual model of a hybrid data storage architecture (HyFDS) that addresses this problem through the synthesis of heterogeneous technological approaches. The architecture is based on an event-driven model built on Apache Kafka, which serves as a unified bus for all system events. The “hot” tier, implemented on an In-Memory Data Grid (IMDG) and optimized through the LMAX Disruptor pattern and lock-free data structures, enables transaction processing with sub-millisecond latency. The “cold” tier, based on object storage with the Apache Iceberg tabular format, ensures scalable and cost-effective storage. The study analyzes data migration mechanisms, transactional consistency strategies (2PC, Saga), and disaster recovery plans, forming an integrated framework for designing next-generation financial systems.

The increasing popularity of digitization in retail has emphasized application performance as a critical component of customer satisfaction, business continuity, and operational efficiency. Retail applications today operate in hyper-dynamic environments, where performance can be introduced by concerns such as workload peaks, scaling cloud infrastructure performance, spikes in transactions, and real-time data processing. Poor performance erodes user experience, depletes objectives, and lowers competitive benefit. Although many surveys of cloud or performance monitoring exist, they do not incorporate retail contexts that emphasize customer facing KPIs, seasonal demand variability, and other omnichannel complexities. The current paper seeks to address this gap in research by providing a systematic review of 45 peer-reviewed studies (2015–2025) specific to monitoring tools, optimization frameworks, and best practices in retail performance monitoring. The findings in the review synthesize performance monitoring platforms (i.e. Splunk, Datadog, New Relic), log management tools, key performance indicators, performance continuous monitoring, performance visualization, and performance integration into retail systems. The paper also offers comparison evaluations of Amazon and Google Cloud monitoring offerings, as well as limitations for optimization planning, and the effect of intelligent analytics to improve scalability and resiliency. The paper contributes by connecting scholarly viewpoints with practical suggestions, providing a distinctive road map for practitioners and academics to develop and enhance high-performance retail applications in cloud-based corporate settings.

The adoption of artificial intelligence (AI) to critical IT infrastructure offers substantial opportunities for efficiency, resiliency and automation, but also means that you face complex and significant security, governance and operational risks. Traditional project-based implementation approaches are often inadequate for dealing with the long-term, cross-functional and high-risk nature of AI systems in safety-critical and mission-critical environments. This article investigates strategic program management models as an enabling frame for secure and sustainable adoption of Artificial Intelligence (AI) in critical IT infrastructure. It examines the shortcomings associated with ad hoc and siloed AI deployment strategies and makes the case for program level governance structures with integrated security, compliance, risk, and lifecycle oversight. The paper investigates established program management models such as governance-driven, capability based, and hybrid adaptive and assesses their appropriateness for AI initiatives that work in the face of stringent regulatory and cybersecurity constraints. Key enablers such as stakeholder alignment, security-by-design, continuous risk assessment and organizational maturity are discussed in the context of critical infrastructure requirements. The article further suggests a structured program management approach that will bring the AI innovation together with security assurance, regulatory compliance, and operation resilience. By synthesizing the insights from program management theory, cybersecurity governance and from artificial intelligence lifecycle management, this study provides organizations with both a practical framework and strategic foundation to responsibly deploy AI in critical IT environments.

This study explores a closed‑loop aerodynamic tube that recirculates air to create stable, repeatable flow conditions in a laboratory setting. In this configuration, a fan continuously drives air through a closed duct, past a central test section where measurements and experiments are carried out, and then back to the fan inlet. The closed loop reduces the impact of ambient disturbances and helps maintain the desired flow with relatively modest energy use. The test section is designed with a nearly constant cross‑sectional area so that the velocity field is as uniform as possible, which simplifies both instrumentation and data interpretation. Upstream, a contraction accelerates and conditions the flow, while downstream diffusers and curved ducts gently slow and redirect the air, preserving flow quality as it recirculates. Taken together, these features provide a compact and efficient platform for high‑fidelity aerodynamic testing and control‑oriented studies of internal flows.

The article describes the details and results of a test conducted to increase the strength of the connection by expanding the bonding surface in the joints of wooden elements, and to study the deformability of wooden structural joints using the ANSYS Workbench computer program.

The integration of Internet of Things (IoT) technologies into industry and everyday life has become a key driver of digital transformation in the modern world. IoT enables physical devices, sensors, and intelligent systems to collect, exchange, and analyze data in real time, creating interconnected ecosystems that enhance efficiency, safety, and user experience. In industrial sectors, IoT supports automation, predictive maintenance, energy optimization, and smart manufacturing, significantly improving productivity and reducing operational costs. In everyday life, IoT technologies empower smart homes, healthcare monitoring systems, transportation services, and personal devices, contributing to greater comfort, convenience, and accessibility. This study examines the fundamental principles, architecture, and key applications of IoT, analyzes its role in industrial and household contexts, and highlights the challenges associated with security, interoperability, and data management. The findings demonstrate that IoT continues to evolve as a transformative technology, offering vast opportunities for innovation while requiring robust strategies to ensure safety, reliability, and long-term sustainability.

Modern data pipelines demand continuous ingestion capabilities where insights must flow within minutes of data arrival. This article presents a production-validated architecture for automating data ingestion from Amazon S3 to Snowflake using S3 Event Notifications, SQS queuing, External Stages, and Snowpipe. Through controlled experiments across three enterprise deployments processing 847,000+ daily files, we demonstrate 94.3% reduction in mean time to detection (MTTD) for ingestion failures, 89.7% improvement in mean time to resolution (MTTR), and 99.97% data delivery guarantee. The framework incorporates comprehensive audit logging, automated health monitoring achieving sub-5-minute failure detection, self-healing recovery with 96.2% autonomous resolution rate, and systematic file lifecycle management. Quantitative analysis reveals 73% reduction in operational overhead measured in engineering hours, while maintaining sub-10-minute end-to-end latency for 95th percentile file ingestion. These empirically validated improvements address critical enterprise challenges: silent failures, data drift, compliance requirements, and operational visibility gaps that limit production reliability of standard Snowpipe implementations.

The accelerating pace of digital transformation has fundamentally altered how organizations design, execute, and optimize their business processes. Among the most influential developments in this transformation journey is Robotic Process Automation (RPA), which initially emerged as a pragmatic solution for automating repetitive, rule-based tasks. Over time, however, the limitations of traditional RPA—particularly its dependence on structured data and deterministic logic—have prompted both scholars and practitioners to explore more advanced paradigms. This evolution has given rise to Intelligent Process Automation and, more recently, hyperautomation, a holistic approach that integrates RPA with artificial intelligence, machine learning, natural language processing, process mining, and low-code development platforms. This research article provides an extensive, publication-ready examination of the theoretical foundations, technological enablers, organizational implications, and future trajectories of hyperautomation. Drawing strictly from the provided literature, the study synthesizes insights from academic research, industry analyses, and conceptual frameworks to construct a comprehensive narrative of how automation is transforming from task-level efficiency tools into enterprise-wide intelligence systems. The article elaborates on the conceptual transition from RPA to hyperautomation, explores the role of enabling technologies such as generative artificial intelligence and process mining, and analyzes implementation frameworks and challenges in diverse organizational contexts. Methodologically, the study adopts a qualitative integrative review approach, enabling deep theoretical elaboration and cross-comparison of perspectives. The findings highlight that hyperautomation is not merely a technological upgrade but a strategic and organizational paradigm shift that redefines decision-making, workforce roles, governance models, and value creation mechanisms. The discussion critically examines limitations related to ethics, scalability, skills gaps, and governance, while also identifying promising avenues for future research and practice. By offering a deeply elaborated, theoretically grounded, and systematically structured analysis, this article contributes to the growing body of knowledge on intelligent automation and provides a robust foundation for both academic inquiry and managerial decision-making in the era of hyperautomation.