A Narrative Review on Multisensory Strategies for Enhancing Well-being of Pediatric Cancer Patients within Therapeutic Environments

Pediatric cancer is a complex and challenging illness that not only affects physical health but also profoundly impacts the psychological and emotional well-being of young patients and their families. This research aims to understand the mental conditions arising from physical illnesses of pediatric cancer patients and address their holistic needs. This narrative literature review examines the potential of a multisensory approach to enhance the well-being of pediatric cancer patients. By synthesizing existing research and evidence, this study explores various sensory elements such as visual, auditory, and tactile stimulation, and their role in improving patient outcomes. Furthermore, the review investigates the effectiveness of multisensory interventions to enhance the quality of living in hospitals, promote mental wellness, and reduce stress among pediatric cancer patients. Ultimately, the findings of this narrative literature review seek to inform healthcare professionals, policymakers, and researchers about the potential benefits of incorporating multisensory therapeutic approaches into pediatric cancer care

Assessment of the Performance of the Pharmaceutical Supply Chain of Public Health Facilities: A Case of Tabora Region, Tanzania

Performance of the Pharmaceutical Supply Chain is a cornerstone to achieving universal access to Essential Medicines and Health Supplies (EMHS), however it is influenced by several factors. Several indicators have been developed to measure performance of the Pharmaceutical Supply Chain, making possible to identify areas of improvement for continuous transformation. This study aimed at assessing the performance of the Pharmaceutical Supply Chain in Tabora Region, Tanzania. Quantitative research approach was employed in a cross sectional descriptive study. A sample of 58 health facilities was selected using stratified sampling technique. Pharmaceutical supply Chain Performance was summarized using frequency Tables using weighted average score from quality, cost/financial, responsiveness and productivity indicators scores. The performance was classified using average score range that is <50% equals ‘low’ performance, 50% - 79% equals ’moderate’ performance, and >79% equals ‘high’ performance [49]. Logistics Information System Tools available at the facility was used to gather the information for indicator calculation. The public health facilities pharmaceutical supply chain performance with regard to all indicators was found to be Highly Performing at 82.5% score. The notable low performance in this assessment was order fill rate from MSD which was about 38.6% while efficiency in utilization of space, timely entry of received orders into registers, heath commodities availability rate, adherence to forecast and proper arrangement of inventories performed moderately. Furthermore, only 10% of surveyed facilities had adequate pharmaceutical personnel and 14% of surveyed facilities had adequate number of Laboratory personnel performing PSC activities. Since majority of parameters are moderately performing with the exception of low order fill rate from MSD and few pharmaceutical personnel supervising supply chain activities. Therefore managers supervising the Pharmaceutical Supply Chain should pay particular attention in improving Prime Vendor System to fill the gap of MSD and improve the skills of task shifted non-pharmaceutical personnel supervising supply chain of Health commodities in public health facilities

Outsourcing Logistics Functions to Third-party Specialized Companies: Advantages and Risks

The article examines the outsourcing of logistics functions to third-party specialized organizations, focusing on its advantages and associated risks. Modern companies face the need to optimize logistics processes, making outsourcing a crucial tool for supply chain management. However, delegating these tasks to external entities not only reduces costs and provides access to advanced technologies but also introduces risks such as loss of control and potential data breaches. There is no unified stance in academic literature regarding the extent of outsourcing’s impact on business performance. Some researchers confirm its positive effects on financial indicators and operational flexibility, while others highlight potential challenges and the rise of hidden costs. This study aims to systematize the advantages and risks of logistics outsourcing and identify key factors that determine its successful implementation, forming the basis for strategic recommendations. The author\u27s contribution lies in a comprehensive analysis of the topic, considering strategic, economic, and technological aspects. The findings can be applied in both academic research and practical management decision-making. The presented material is valuable for researchers in logistics and risk management, professionals involved in supply chain organization, and business leaders considering the outsourcing of logistics functions

Faster Time-To-Market: Cloud’s Role in Accelerating Software Deployment

Cloud computing has revolutionized software deployment by enabling rapid provisioning, scalability, and automation, significantly reducing time-to-market. This study examines the impact of cloud technologies on deployment cycles, focusing on the advantages of on-demand provisioning, Continuous Integration/Continuous Deployment (CI/CD) pipelines, and elasticity. Case studies from startups, enterprises, and multinational organizations illustrate how cloud adoption has streamlined workflows, improved resource utilization, and enhanced global scalability. The findings highlight a stark contrast between traditional IT environments and cloud-based solutions, emphasizing the role of cloud computing in driving competitive advantage. Despite challenges such as vendor lock-in and compliance complexities, strategic approaches and robust governance, can help organizations fully leverage the benefits of the cloud. This research provides actionable insights for businesses seeking to optimize deployment efficiency and maintain agility in rapidly evolving markets

Yield Analysis of Boost vs Non-Boost Base Trader Joe Liquidity Pools

This comprehensive study presents an extensive quantitative analysis of the impact of Trader Joe’s Boost Incentive Program on Trader Joe’s liquidity pools. The Boost Incentive Program is a liquidity initiative designed to revitalize a specific DeFi ecosystem by enhancing user engagement and competitiveness. Following the success of a previous program from mid-2021 to early 2022, this new initiative aims to reignite growth and innovation by increasing Total Value Locked (TVL), attracting new protocols, and regaining market share within the DeFi space. The ongoing program focuses on supporting both new and existing DeFi protocols through liquidity mining incentives, direct liquidity deployment, and backing for new assets and products. The strategic use of incentives is designed to maximize impact by concentrating on core primitives and top native protocols, thereby driving substantial growth in TVL. By allocating incentives to specific strategies and liquidity pools, Trader Joe aims to offer higher yields to liquidity providers, thereby attracting more participants and increasing TVL on its platform. This approach aligns with the overarching goal of the Boost program to support innovation and new protocol growth. In the below analysis, I examine how these incentives affect yields will provide insights into the effectiveness of such programs in attracting liquidity and enhancing protocol performance. By integrating detailed data from incentive_analysis.xlsx and traderjoe_base_metrics.csv, we examine how incentive allocations, fee structures, and liquidity provider participation influence liquidity provision, trading volume, fees, and yields. The analysis incorporates statistical insights and trends within the dataset, covering rewards allocation, fee structures, liquidity provider participation, and average USD values across various token pairs. The aim is to offer deep insights into the effectiveness of incentive programs in enhancing protocol performance and user engagement within the decentralized finance (DeFi) ecosystem

Typical Patterns of Interaction between a React Frontend and a WordPress Backend

This article reviews current practices of using React frontend with WordPress backend in a headless setup and typifies main data-transfer patterns, rendering strategies, and auth/reactivity mechanisms. Massive growth in the headless-CMS market, a leading position for WordPress, and the widespread use of React justifies this study’s relevance. The novelty of this work lies in building a three-dimensional model that integrates the data channel (REST vs GraphQL vs RPC) with rendering strategy (CSR, SSR, SSG/ISR) and authorization/update approach (Cookie + Nonce, JWT, Webhooks/Subscriptions), allowing the typical interaction patterns — over ten of them — to be classified and assessed. The significant findings indicate that REST-SPA has a minimal entry threshold due to the built-in WP-REST API but needs more caching to completely get rid of the “N+1” problem and reduce network latency; GraphQL-SPA solves aggregated request problems and also has strict typing but it adds much complexity to schema and access-control design; Next.js Solutions with SSR/ISR have both Static Generation and Incremental Updates via Webhooks or GraphQL Subscriptions. They are high performing, SEO friendly, and offer content consistency; in private scenarios, JWT authorization or request proxying is used; for headless e-commerce, CoCart is chosen; microservice REST-RPC endpoints extend platform capabilities without forking the core. This article will be helpful for architects, developers, and technical leaders choosing an optimal headless infrastructure based on React and WordPress

Time, a three-directional Dimension I

This paper revisits the concept of multidimensional time, extending discussions to incorporate orthogonal time dimensions ? and ? alongside the familiar linear t-axis, in resemblance to space and its intimate intertwinement with time. The presented framework explores how time\u27s multidimensional nature might interact with spatial dimensions and quantum phenomena such as superposition and entanglement. Rather than redefining the arrow of time, this work underscores the need to broaden our perspective on time as multidirectional. By questioning certain conventional views about time’s nature and representation, this model advocates for a forward-moving perspective that aligns with quantum mechanics. Though experimental validation remains a challenge, the model lays a foundation for further theoretical exploration and discussion

Machine Learning-Based Detection of Fake Product Reviews and News Articles

With the proliferation of online platforms, detecting fake content such as fake reviews and fake news has become a critical challenge for ensuring the authenticity and reliability of digital information. This paper presents a comprehensive survey of machine learning (ML) techniques and models applied to fake review and fake news detection. By leveraging advanced Natural Language Processing (NLP) methods and hybrid machine learning approaches, the paper evaluates various algorithms including Support Vector Machines (SVM), Random Forests, Long Short-Term Memory (LSTM) networks, and ensemble models for their performance in detecting deceptive content. Key metrics such as accuracy, precision, recall, and F1-Score are analyzed across multiple datasets to determine the effectiveness and robustness of these approaches. Additionally, this study explores domain-specific challenges, including the handling of imbalanced datasets, linguistic nuances, and real-time detection requirements. The paper concludes by outlining future directions, emphasizing the need for enhanced models capable of addressing evolving deception techniques and integrating contextual factors for more accurate predictions

Cybersecurity in Autonomous Vehicles: Safeguarding Connected Transportation Systems

The increasing integration of autonomous vehicles (AVs) has revolutionized the transport sector, with improved safety, efficiency, and convenience. However, as AVs become more interconnected and integrated into advanced transport systems, the interconnectivity-driven cybersecurity threats present a serious challenge. Current security solutions tend to treat individual systems without taking into account the complexity emanating from interconnected networks, real-time data exchange, and advanced AI-based decision-making systems characteristic of autonomous vehicles. This research tries to fill the crucial gap in autonomous vehicle system cybersecurity frameworks, emphasizing the adoption of a holistic, multi-level approach to secure the vehicle and communication networks. The study explores significant vulnerabilities in AVs, such as vulnerability to remote hacking, data integrity issues, and the risks of system crashes that can jeopardize the vehicle occupants and external stakeholders. It evaluates the effectiveness of current cybersecurity and identifies the loopholes in safeguarding the complex infrastructure behind connected transportation systems. The study also identifies the increasing importance of artificial intelligence and machine learning in identifying and preventing cybersecurity threats in real-time, offering a new direction for proactive threat management. Through an interdisciplinary methodology, the paper proposes a framework for securing AVs and networked transportation infrastructure that uses high-level encryption, AI-assisted anomaly detection, and robust incident response plans. By bridging the cybersecurity gap to the specific autonomous system challenges, this study aims to make it possible to build secure, resilient transportation technology that can scale safely in an increasingly interconnected world. The findings aim to educate policymakers, manufacturers, and researchers on the best practices for securing the autonomous transportation system of the future

Impact of Human Activities on Earthquake Occurrence- a Global Seismological Review

Human activities, such as large-scale fluid injection and withdrawal, reservoir impoundment, underground mining, geothermal energy extraction, and hydrocarbon extraction, affect the Earth’s crust. This is due to the increasing activities that are capable of perturbing the ambient stress field and triggering earthquakes, a term called induced seismicity. The global extent and relative impact of these anthropogenic drivers remain poorly quantified. This study synthesizes worldwide seismic occurrences to evaluate the impact of human activities and interventions on earthquake occurrence. Evidence shows that anthropogenic seismicity accounts for an estimated 6 ± 2 % of global shallow seismicity and dominates Mw 3–5 event rates in several intraplate regions, notably the central United States, western Canada, and parts of China. Fluid-pressure perturbations associated with wastewater disposal and geothermal operations account for 68% of the cataloged anthropogenic events, while reservoir impoundment and mining represent 21% and 11%, respectively. Evidence reveals that policy-driven reductions in injection volumes have decreased the occurrence of seismicity within three to five years. Conversely, emerging energy technologies (e.g., carbon capture and underground hydrogen storage) pose growing seismogenic risks if unmanaged. It is concluded that human activities contribute to earthquake occurrence, yet effective intervention and policies are mitigating the probable occurrence of earthquake