Adaptive Radio Management (ARM) is a cornerstone of Aruba’s enterprise-grade wireless infrastructure, providing intelligent and automated radio frequency (RF) optimization across distributed network environments. This paper delves into the functional architecture and operational mechanisms of ARM, with a specific focus on its channel and power assignment strategies. Unlike traditional centralized RF management systems, ARM operates in a distributed manner, pushing intelligence to individual Access Points (APs). These APs continuously assess their RF surroundings through both home-channel monitoring and off-channel scanning, allowing for localized, real-time decision-making. A critical component of this process is the integration with Aruba’s Wireless Intrusion Detection System (WIDS), which enables APs to operate in promiscuous mode—capturing all frames, including corrupted ones caused by CRC errors. WIDS classifies these packets and compiles extensive lists of neighboring APs and clients, categorizing them as valid or interfering sources.

This environmental intelligence feeds into ARM’s internal algorithms to calculate metrics for optimal channel selection and transmit power levels[2]. The scan patterns and intervals are adaptive, dynamically adjusting based on client density and traffic activity. The collected over- the-air data also accelerates neighbor discovery and network topology awareness. Our study includes a thorough protocol-level examination of ARM’s decision-making logic, supported by simulated scenarios in high-density deployments. Results show that ARM significantly enhances RF performance, reduces interference, and improves client connectivity by proactively adjusting parameters in response to fluctuating network conditions.

 Ultimately, this paper demonstrates that Aruba ARM is not only a robust RF management tool but also an enabler of scalable, self-healing wireless networks. While highly effective, current limitations such as the latency in inter-AP coordination and challenges in extremely congested environments are acknowledged. Future research directions include enhancing ARM’s predictive analytics capabilities and integrating AI-driven decision models to further increase its responsiveness and efficiency in next-generation wireless deployments.

Gram staining is a cornerstone technique in microbiology for the preliminary identification of bacteria, differentiating them into Gram-positive and Gram-negative groups based on cell wall composition [14]. Traditionally, crystal violet serves as the primary stain; however, concerns regarding its potential toxicity and environmental impact have spurred interest in natural, eco-friendly alternatives [12]. This study investigates the potential of Nephelium lappaceum (rambutan) peel extract, rich in anthocyanins, as a novel primary stain for Gram staining. Through an observational assessment, the study aims to evaluate its staining efficacy, color characteristics, and differentiation capabilities compared to conventional crystal violet. The methods would involve preparing the extract, applying it in a modified Gram staining procedure to representative bacterial cultures, and evaluating the results microscopically. Preliminary observations suggest that rambutan peel extract exhibits promising staining properties, effectively differentiating bacterial cell types. The findings highlight the potential of this natural extract as a sustainable and safer alternative, contributing to greener laboratory practices in bacterial identification.

Most of the glycogen metabolism disorders that affect skeletal muscle involve enzymes of glycogenolysis (myophosphorylase ( PYGM ), glycogen debranching enzyme ( AGL ), phosphorylase b -kinase ( PHKB )) and glycolysis (phosphofructokinase ( PFK ), phosphoglyceromutase ( PGAM 2), aldolase A ( ALDOA ), β -enolase ( ENO 3)); however, 3 of them involve glycogen synthesis (glycogenin-1 ( GYG 1), glycogen synthase ( GSE ), and debranching enzyme ( GBE 1)). Many present with exercise-induced cramps and rhabdomyolysis with more intense exercise (ie, PYGM , PFK , PGAM 2), while others present with muscle wasting and weakness ( GYG 1, AGL , GBE 1). Failure of serum lactate to rise with exercise, with an exaggerated response to ammonia, is a common but not invariant feature. Serum creatine kinase ( CK ) levels are often elevated in myopathic forms and PYGM deficiency , but may be normal and elevated only in rhabdomyolysis ( PGAM 2, PFK , ENO 3). Therapy for glycogen storage diseases that result in exercise-induced symptoms involves lifestyle adaptations and carefully selected exercises. Immediate carbohydrate ingestion before exercise improves symptoms in glycogenolytic defects (i.e., PYGM), but may worsen symptoms in glycolytic defects (i.e., PFK ). Low-dose creatine monohydrate may provide modest improvement in PYGM mutations.

This paper examines the transformative role of Oracle Cloud Maintenance Automation in modernizing preventive maintenance practices across organizations. By automating asset maintenance workflows, minimizing manual interventions, and incorporating predictive technologies, Oracle Cloud facilitates a shift from reactive to proactive maintenance strategies. Key capabilities—including asset tracking, maintenance forecasting, and work order automation—contribute to enhanced asset reliability, operational efficiency, and cost optimization. The study highlights critical configurations and best practices for establishing an effective maintenance program using Oracle Cloud, positioning it as a cornerstone for asset lifecycle management and long-term operational success. Real-time analytics and data-driven decision-making further align maintenance activities with broader organizational objectives, promoting a culture of continuous improvement. Findings indicate that Oracle Cloud Maintenance Automation significantly improves maintenance resource allocation, reduces unplanned downtime, and increases equipment reliability. As cloud-based solutions become central to maintenance strategies, their adoption reflects a broader industry trend toward maximizing availability, minimizing lifecycle costs, and driving strategic alignment between maintenance and business goals. This transition empowers organizations to enhance productivity, ensure high asset performance, and achieve sustainable competitive advantage.

The Increased complexity of Pharmacy Benefit Management (PBM) and the growing focus on lowering administrative expenses have expedited the search for Robotic Process Automation (RPA). This paper provides a comprehensive analysis of implementing RPA in PBM quality, focusing on core challenges such as claims adjudication, prior authorization, and audit preparation. When AI, ML, and RPA technologies work together, they become smarter and more scalable, which makes it easier to make decisions and follow the compliance rules. This paper briefs the benefits, challenges, and outcomes of intelligent automation in PBM Quality through case studies and literature review.

Rolling out MES across many countries is hard. Each site has its own set of rules, tools, and working methods. A global plan must still fit local needs. That is the challenge. Many companies attempt to use a single MES setup across all locations. This often results in delays, confusion, and resistance from users. What works in one plant may not work in another. Language, regulations, and manufacturing workflows vary from country to country, and even within some cases, from plant to plant. For successful implementations and rollouts, it is crucial to establish a bridge between global objectives and local needs. It is necessary to plan, listen, and adjust throughout the process.

Additionally, support after the launch is just as important as the initial rollout. This study explores the factors that influence the success or failure of global Manufacturing Execution System (MES) projects. It is based on a real case from a worldwide manufacturer with strict rules and complex sites. This study examines the rollout of MES in various countries. It covers the steps, problems, and what leads to success.

The goal is to identify what helps people use it effectively and maintain consistency in system operation across sites. The findings support that both schools and companies learn how to scale MES in real-world settings. This can guide future projects in digital manufacturing. This study is based on a real case from a global manufacturing company. The company operates in a highly regulated, rules-based industry. It rolled out MES across many production sites. Each site required robust tracking, process control, and integration with ERP systems. The goal was to study how the rollout worked in practice. It examined problems, deployment time, user buy-in, system health, compliance, and integration. The study employed both numerical data and stories to provide a comprehensive picture. Data came from project documents, talks with IT staff, MES leads, and plant supervisors. Surveys and feedback were also taken from floor workers and rollout teams.

Results from different sites were compared after launch to assess their performance. The study found that global MES rollouts can work and lead to strong user adoption. However, success depends on local changes, good planning, and strong teamwork. After rollout, fine-tuning and addressing regional gaps remain challenging and require a clear focus. The study showed that MES rollouts can be completed in approximately three to nine eighth per site. This proves that a global setup is possible. However, success requires precise planning, strong control, and local adjustments. One plan will not work everywhere.

Good rollouts depend on more than just the tech. Require adequate planning, local support, and adaptable regulations. The study offers clear steps for future MES projects. It emphasizes the importance of post-go-live support, user training, and customized plans tailored to each site.

Fraud detection has become an essential component of financial security systems. Traditional algorithms have long served as the backbone of these systems. The rise of neural networks is revolutionizing the process as it offers new approaches to identifying complex fraud patterns. The paper presents a comparative analysis of neural networks and traditional algorithms. These include decision trees, rule-based systems, and logistic regression in fraud detection. The comparison is based on scalability, accuracy, interpretability, computational efficiency, and adaptability. The findings reveal that neural networks outperform traditional methods in subtle, non-linear fraud patterns but suffer from interpretability and data requirements. A hybrid detection framework that combines neural intelligence with rule-based logic is proposed for real-time, robust fraud management. For instance, a neural ensemble model achieved over 97% accuracy while traditional systems achieved 89-91%. The paper highlights that the hybrid approach offers optimal results in real-world scenarios.

The manufacturing industry in the US is changing quickly because of things like tariffs on both sides, more people wanting to buy things online, and complicated supply chains around the world. The fixed costs of each department go up, which lowers the margins on products and, in the end, the bottom line. The key to making order processing, inventory management, and carrying costs more efficient is automation. ERP systems have created important tools for making important business processes, like order processing and inventory management, more efficient and automated. This article looks into how ERP's use of AI in order processing and inventory management can boost productivity. This, in turn, helps manufacturing companies make more money and keep their customers happy.

The mergers and acquisitions field within investment banking is changing because artificial intelligence tools like natural language processing and machine learning are now part of everyday work. Historically, entry-level analysts performed extensive data-driven analyses crucial for executing transactions, but this role has been significantly influenced by the introduction of AI. This study explored how AI has altered the duties and skill requirements for junior analysts in M&A environments. A qualitative content analysis framework was used to synthesize insights from over 50 reports, journal articles, case studies, and industry surveys published between 2020 and 2025. Data were collected from reputable sources, including global consulting firms, academic journals, and financial services publications. Through an in-depth literature review, new critical skills were identified. These encompassed advanced data analysis, proficient prompt engineering, and thorough validation of AI models. The results highlighted a need for analytical flexibility, technological expertise, and adaptability among analysts entering the industry. As a result, investment banking firms focused on M&A were compelled to update training programs and redefine analyst responsibilities. Adopting these skills helped facilitate a smoother shift into technology-enhanced workflows, ultimately allowing analysts to contribute greater strategic value towards transaction processes. But there are still obstacles to overcome, especially when it comes to relying too much on AI and not having enough human supervision to avoid algorithmic mistakes. Looking outward, we see that analysts are turning into AI-enabled specialists, combining their knowledge with digital abilities to improve deal outcomes.

Multinational corporations face a trend of an even more globalized business environment in which they are obliged to report consolidated financial statements using various accounting regulations, including US GAAP, IFRS and local statutory GAAPs within a few days of quarter-end. This process of financial reporting reconciliation among different regulatory regimes and accounting standards has become more complex and expensive at times often involving thousands of labor hours and has a high probability of introducing a human error. Manual entry of ledger and chart of account and disclosure into different forms is not only a tedious business, but is subject to inaccuracies which may lead to accounting reports and financial misstatement, regulatory fine and loss of stakeholder’s confidence.

Artificial Intelligence (AI) which previously was left to automate simple processes provides a scalable and transformative answer to this multidimensional problem. Enhancements of advanced rule-based mapping engines by machine-learning models allow detecting patterns in financial data, detecting anomalies, and even creating adjusting journal entries automatically. This research article leads to a multifaced structure of AI-enabled multi-GAAP reconciliation, it explores regulatory incentives, taxonomy distinctions, data-model designs, algorithmic strategies, and control demands. The framework also describes the real world opportunities and constraints of these systems providing the opportunity to draw a balanced view as exposed by the analysis of pros and cons and roadmap of implementation. In practice-oriented case studies of a fortune 200 tech giant, a European unicorn, and a Latin American energy conglomerate, the real-world results are shown as cycling-time decreases by as much as 65% and a 40% reduction of audit results. The paper ends in a practical AI governance checklist consistent with the principles of COSO internal controls and NIST AI risk management, as well as new digital-reporting guidelines, published by the IASB.

Modern businesses increasingly rely on internal data products, such as curated datasets or analytical services, to drive innovation and informed decisions. Despite substantial investments in data technologies, including a global Artificial Intelligence market valued at $230 to $280 billion in 2024, large organizations struggle with inconsistent API interfaces. This inconsistency hinders efficient data exchange and robust governance. This paper tackles this challenge by proposing a framework for mandatory OpenAPI Specification (OAS) adoption and automated enforcement for all internal data products. Our approach defines clear organizational standards and implements a twostep compliance checking mechanism. This involves Static Type Analysis (STA) for foundational rule enforcement and an AI agent for nuanced, contextual validation. Integrated within CI/CD pipelines, this automated system ensures continuous adherence to design standards, enhancing data product discoverability, interoperability, and overall data governance. This work provides a practical methodology for establishing standardized control over internal data product APIs, streamlining development, and fostering a resilient data ecosystem.

The use of cloud-based Graphics Processing Units (GPUs) to train and deploy Deep Learning models has grown rapidly in importance, with the demand to learn more about their thermal and acoustic behavior under real-world workloads. A normal cloud cannot make direct telemetry like temperature, fan speed, or acoustic emissions. To overcome such shortcomings, this study quantifies GPU workloads' thermal and acoustic output with a proxy-based model derived from available metrics such as GPU utilization, memory provisioning, power consumption, and empirical Thermal Design Power (TDP) values. They compare the two typical AI tasks, BERT on natural language processing and YOLOv5 on real-time object detection, on Colab-based NVIDIA GPUs (T4, V100, P100). The nvidia-smi was used to gather runtime logs, and the specifications of the GPUs have been obtained in the form of public Kaggle datasets. Proxy statistics, including TDP-per-MHz and thermal load (Power * Duration), were calculated to model heat loss due to workload. To measure the degree of acoustic impact, a threshold of TDP was applied to approximate the level of fan-driven acoustics. The visual analytics, such as boxplot, scatterplot, and bubble plot, demonstrated certain considerable distinctions in the stress patterns of GPUs: the BERT jobs demanded extremely high cumulative thermal load and medium acoustic effect, whereas the YOLOv5 demonstrated bursty power footprint and substantial acoustic imprint on high-TDP GPUs. The findings reveal that proxy estimation is reproducible, interpretable, and a lightweight substitute for determining the GPU thermal and acoustic behavior of a machine used in the cloud setting. Such a solution facilitates making thermal-aware schedules, optimizing the infrastructure, and deploying AI models with reduced energy consumption in multi-tenant GPU environments.

The paper presents a comprehensive comparative analysis of two leading enterprise-grade Platform as a Service (PaaS) solution: Pivotal Cloud Foundry (PCF) and Red Hat OpenShift. It examines their architectures, deployment models, operational characteristics, developer experiences, security features, performance attributes, and ecosystem support. The research highlights key differences between PCF's custom architecture with Warden containers and OpenShift's Kubernetes-native approach. The analysis covers installation procedures, management tools, application deployment workflows, and migration strategies between platforms. Through case studies and literature review, the paper provides organizations with guidance for making informed decisions about which platform best suits their specific requirements and constraints.