The monitoring, diagnosis, and treatment of patients has been completely transformed by the Internet of Things (IoT) in healthcare, especially in underprivileged and distant locations. IoT-enabled remote patient monitoring (RPM) makes it possible to gather and transmit health data like heart rate, glucose levels, and blood pressure in real-time, facilitating proactive and individualized care. IoT-enabled smart wearable devices like smartwatches, ECG patches, and fitness bands track physical activity and health parameters provide real-time data to clinicians via mobile apps or cloud servers and empower patients with self-monitoring tools. IoT-enabled RFID tags to locate critical medical equipment (e.g. ventilators, infusion pumps) enhances operational efficiency and reduces loss or theft and assists in hospital inventory and supply chain management. IoT devices facilitate real-time consultation with physicians using health data from patients and bridges the healthcare gap in rural and underserved areas. With a focus on RPM systems, Smart Wearables and RFID tags this chapter examines the architecture, technology, uses, and advantages of IoT in healthcare. The difficulties with interoperability, system integration, security, and data privacy are also covered. IoT has the revolutionary potential to improve healthcare outcomes, lower readmission rates to hospitals, and improve the quality of life for patients with chronic illnesses, as demonstrated by case studies and recent advances.

This paper explores the architectural foundations and practical implications of screen readers, focusing particularly on NVDA (Non-Visual Desktop Access), in enhancing accessibility to digital resources for visually impaired users. Core architectural components such as APIs, ARIA, Alt Text, and keyboard navigation form the backbone of screen reader technology. APIs, built on SOAP and REST frameworks, facilitate interoperability across systems, enabling seamless data exchange and integration. ARIA (Accessible Rich Internet Applications), part of the WAI initiative, provides semantic structure for dynamic web content, enhancing accessibility where native HTML elements fall short. Alt Text supports non-text content accessibility by providing descriptive alternatives, while keyboard navigation is essential for content interaction and control without a mouse. The NVDA screen reader, an open-source, Windows-compatible tool, demonstrates advanced capabilities including multi-language support, Braille display integration, and customizable settings, making it a vital tool for inclusive education and information access. NVDA's functionality spans document navigation, form interaction, web browsing, and system-level access, with extensive keyboard shortcuts for efficient operation. Additionally, other screen readers like Apple's Voice Over, Linux-based ORCA and BRLTTY, and browser-based Web Anywhere offer alternative solutions, each with unique features and varying levels of affordability. The role of APIs in enabling this ecosystem is emphasized, as they support the sharing and interaction between software, systems, and services including mobile apps, cloud platforms, and IoT devices. Screen readers are not only assistive technologies but also integral tools in democratizing access to digital content.

Context. Modern software is developed under conditions of continuously increasing complexity of computing systems. Today, developers must consider a vast diversity of platforms, ranging from resource-constrained mobile devices to servers with highperformance processors and specialized architectures such as GPUs, FPGAs and even quantum computers. This heterogeneity requires software to operate efficiently across various hardware platforms. However, portability remains one of the most challenging tasks, especially when high performance is required. One of the promising directions to address this problem is the use of adaptive compilers that can automatically optimize code for different architectures. This approach allows developers to focus on the functional part of the software, minimizing the effort spent on optimization and configuration. Genetic algorithms (GAs) play a special role among the methods used to create adaptive compilers. These are powerful evolutionary techniques that allow finding optimal solutions in complex and multidimensional parameter spaces.Objective. The objective of this research is to apply genetic algorithms in the process of adaptive compilation to enable automatic optimization of software across different hardware platforms.Method. The approach is based on genetic algorithms to automate the compilation process. The key stages include: population initialization – creation of an initial set of compilation parameters; fitness function evaluation – assessment of the efficiency of each parameter combination; evolutionary operations – applying crossover, mutation and selection to generate the next generation of parameters; termination criteria – stopping the iterative process upon reaching an optimal result or stabilization of metrics.Results. The developed algorithm was implemented in Python using the numpy, multiprocessing and subprocess libraries.Performance evaluation of the algorithm was carried out using execution time, energy consumption and memory usage metrics.Conclusions. The scientific novelty of the study lies in the development of: an innovative approach to automatic compilation parameter optimization based on genetic algorithms; a method for dynamic selection of optimization strategies based on performance metrics for different architectures; integration of GAs with modern compilers such as LLVM for automatic analysis of intermediate representation and code optimization; and methods for applying adaptive compilers to solve cross-platform optimization problems. The practical significance is determined by the use of genetic algorithms in adaptive compilers, which significantly improves the efficiency of the compilation process by automating the selection of optimal parameters for various architectures. The proposed approach can be successfully applied in the fields of mobile computing, cloud technologies and high-performance systems.

ABSTRACT Data security and intrusion detection in contemporary mobile cloud systems are severely hampered by the sharp rise in Distributed Denial of Service (DDoS) assaults. To overcome these obstacles, this research utilizes the CIC-DDoS2019 dataset to propose a Bidirectional Long Short-Term Memory with Federated Learning (BLSTM-FL) framework for intrusion detection. The suggested method employs a Vertical FL (VFL) mechanism, where clients hold complementary feature subsets of network traffic, ensuring that raw data is never shared across nodes. Local BLSTM models are trained at client sites and securely synchronized with a central aggregation server via SSL/TLS channels. Model updates are integrated using the Federated Averaging (FedAvg) algorithm, which effectively handles data heterogeneity by weighting contributions according to dataset size. This leads to a powerful global intrusion detection approach that protects privacy. The suggested BLSTM-FL model outperforms current techniques, such as LSTM, RNN, and DNN, according to experimental results, achieving 99.13% accuracy, 99.21% precision, 99.01% recall, and an F1-score of 99. Moreover, it reduces training time to 40.65 seconds and achieves an execution time of 15.07 µs, demonstrating both superior accuracy and efficiency in mobile cloud intrusion detection. The results show that cloud-based intrusion detection systems are more secure and efficient when BLSTM and Federated Learning are integrated.

The purpose of this study is to address the persistent security and privacy challenges in cloud-based Electronic Health Record (EHR) sharing by proposing a blockchain-enabled architecture that integrates decentralized storage and smart contracts. Traditional mobile cloud solutions improve data accessibility but rely on centralized control, making them vulnerable to unauthorized access, single points of failure, and limited patient transparency. To overcome these limitations, this research designs a user-centric access control framework that leverages the Ethereum blockchain, smart contracts, and the InterPlanetary File System (IPFS) within a mobile cloud environment. The methodology involves developing and deploying a prototype on Amazon Web Services, supported by an Android-based mobile application that enables healthcare providers and patients to interact with the blockchain network. Experimental evaluation was conducted using wearable sensor data to test the performance, scalability, and resilience of the proposed system. The findings indicate that the framework ensures secure EHR exchange, enforces fine-grained access policies, and achieves reduced latency compared to conventional centralized approaches. Unauthorized requests were reliably detected and blocked through the smart contract mechanism, while authorized users accessed records with minimal delay. The results also confirm the lightweight overhead of the system, making it practical for mobile healthcare environments. The practical implications of this work lie in offering a tamper-resistant, transparent, and patient-centric solution for medical data sharing, thereby improving trust, reducing administrative overhead, and supporting real-time healthcare services in distributed and resource-constrained settings.

<p>Progress in mobile technology, the internet, cloud computing, digital platforms, and social media has substantially facilitated interpersonal connections following the COVID-19 pandemic. As individuals increasingly prioritise health, there is an escalating desire for novel methods to assess health and well-being. This study presents an internet of things (IoT)-based system for remote monitoring utilizing a long range (LoRa), a low-cost and LoRa wireless network for the early identification of health issues in home healthcare environments. The project has three primary components: transmitter, receiver, and alarm systems. The transmission segment captures data via sensors and transmits it to the reception segment, which then uploads it to the cloud. Additionally, machine learning (ML) methods, including convolutional neural networks (CNN), artificial neural networks (ANN), Naïve Bayes (NB), and long short-term memory (LSTM), were utilized on the acquired data to forecast heart rate, blood oxygen levels, body temperature patterns. The forecasting models are trained and evaluated using data from various health parameters from five diverse persons to ascertain the architecture that exhibits optimal performance in modeling and predicting dynamics of different medical parameters. The models' accuracy was assessed using mean absolute error (MAE) and root mean square error (RMSE) measures. Although the models performed similarly, the ANN model outperformed them in all conditions.</p>

Academic interest in understanding the role of financial technology (FinTech) in financial institutions has grown significantly in recent years. FinTech is defined as the use of innovative digital tools and platforms — such as mobile applications, blockchain, artificial intelligence (AI), cloud computing and application programming interfaces — to improve, automate and transform the delivery of financial services and risk management processes. Many studies have explored FinTech adoption, yet few have examined its integration with risk management and regulatory compliance. Therefore, this study sheds light on the literature trends associated with FinTech adoption and operational risk management using a systematic literature review (SLR) and the Theory, Context, Characteristics and Methods (TCCM) framework. The analysis reviews the habits and patterns of FinTech adoption across financial institutions, emphasising the regulatory landscape and cyber security challenges. With the SLR approach, 44 articles published from 2002 to 2023 were analysed. The findings indicate a strong nexus between FinTech adoption, operational efficiency and regulatory compliance, showing an increasing interest among scholars and practitioners. A comprehensive review of dominant theories (eg technology acceptance model and diffusion of innovations), specific contexts (eg banking and financial service providers), characteristics (eg independent, dependent, moderating and mediating variables) and methods (eg research approaches and analytical tools) is provided. This review is the first to critically assess the underexplored intersection between FinTech adoption and regulatory compliance. The findings may help policy makers, banking service providers and academics understand the necessity of balancing FinTech innovation with risk management. The future research agenda outlined in this study will facilitate researchers in exploring new insights within the domain of FinTech adoption and risk mitigation. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.

Agriculture, as a critical sector of the Indian economy, is increasingly benefiting from advancements in mobile and cloud-based technologies. This paper presents Fertiflow, a cross platform agricultural application developed using Flutter and Firebase, designed to modernise the digital infrastructure available to farmers. Fertiflow leverages modern software engineering principles to deliver a modular and scalable solution that connects farmers with essential services, including market pricing, government schemes, agency support, and digital payments. The system architecture comprises key application modules, including Home Screen, Agency Screen, Profile Screen, Login Screen, Payment Screen, and dedicated components for Government MSP integration and Farming Tools access. Built on Firebase’s real-time database and authentication services, the app ensures seamless synchronization, secure user management, and efficient data handling in low-connectivity rural environments. This paper examines the user interface design, component-based architecture, and backend integration strategy of the application, as well as the challenges of delivering reliable performance in resource-constrained settings. Additionally, it discusses the broader implications of Fertiflow in advancing rural digital transformation, highlighting how mobile-first software solutions can bridge the gap between traditional farming practices and emerging digital ecosystems. Fertiflow exemplifies the convergence of mobile computing, cloud services, and agricultural informatics, demonstrating how computer science can directly contribute to socio-economic development in rural sectors.

The relevance of the work stems from the importance of using digital technologies to achieve UN goals and the lack of publications using Scilit platform data filtered for this topic. The aim of the article is to show the possibility of using Scilit, an aggregator of open access metadata of scientific publications, to identify current challenges in the use of digital technologies to accelerate the achievement of the Sustainable Development Goals. Results . The study found that the relevant challenges to achieve the objectives are: integration of different energy and storage sources, demand side management, security solutions, digital twin implementation, 5G industrial mobile communications, edge and cloud computing, group robotics, bi-directional DC-DC converters, and application of fintech and blockchain to finance SMEs. Conclusions . The use of the Scilit data allows the identification of relevant research challenges within the topic under consideration, however, due to the wide range of issues highlighted, further work is recommended to narrow the topic under consideration to engineering challenges.

Purpose Virtual reference services (VRS) are part of the key activities of libraries and information centers that have encounter widespread modifications with the development of new technologies. The study aimed to investigate the challenges, areas and strategies of application new technologies in virtual reference services. Design/methodology/approach To gather data from library science literature, a search was performed using virtual reference terms and their various equivalents, combined with chatbot, blockchain, next-generation 5G mobile technology, metaverse, cloud computing, augmented reality and artificial intelligence keywords across different databases, including WOS, Scopus, PubMed, LISTA, LISA and Google Scholar. A limitation period of 10 years was considered (2014–2024). The inclusion criteria were focused on addressing the study objective, while articles published in languages other than English were excluded. Relevant literature was extracted and content analysis was subsequently applied to identify emerging trends, implementation challenges and strategies. Findings A total of 36 studies related to the subject of reference were extracted. The most frequently analyzed articles were review studies (8), exploratory studies with a qualitative approach (7) and descriptive survey studies (3). Recent trend analysis indicates that reference services are being shaped by various new technologies, including artificial intelligence (AI), chatbots, robots, blockchain, next-generation 5G mobile technology, the metaverse, cloud computing and augmented reality. Some libraries and information centers are adopting VRS based on these technologies. Nevertheless, several significant challenges have been identified, including privacy concerns, budget constraints, technological infrastructure and a deficiency in skills among reference staff regarding AI. Research limitations/implications While searching for information sources, some cases may have been overlooked. Additionally, data analysis was conducted meticulously, but some concepts may exhibit overlapping themes. A more comprehensive understanding of the extensive use of technologies in libraries necessitates further comparative studies. Practical implications Optimal utilization of the aforementioned technologies in the virtual reference environment presents policymakers and managers with limitations related to infrastructural, ethical and legal frameworks. Additionally, the absence of a suitable digital strategic plan, standards and ethical models must be taken into account. Social implications Although the recent technologies have permeated various sectors of different societies, limitations in the development of infrastructural, cultural and ethical dimensions remain evident among diverse groups, including employees, institutions responsible for virtual reference services and educational establishments. Originality/value Modern technologies have enabled library an information centers to fulfill the information needs of their patrons. However, the absence of fundamental infrastructure, financial resources and personnel with relevant expertise and skills across various fields has delayed the effective implementation of technology-based systems. Managers and policymakers in library and information science can facilitate library development by acknowledging new technologies and the challenges of implementation.

Against the backdrop of the rapid development of digital technologies, such as mobile internet, big data, and cloud computing, the geographical agglomeration of industries is gradually shifting toward virtual agglomeration. In this paper, we examine the effect of both geographical and virtual agglomeration of the agricultural industry on farmers’ agricultural income, and we focus on the transmission mechanism of technological innovation in this process. In the empirical section, using the citrus industry as an example, we employed a moderated mediation effect model for verification and derived the following conclusions: (1) Both geographical and virtual agglomeration of the agricultural industry promote an increase in farmers’ agricultural income by enhancing technological innovation, respectively. (2) Virtual agglomeration of the agricultural industry has a negative moderating effect on the relationship between geographical agglomeration and farmers’ agricultural income, that is, virtual agglomeration alleviates the “crowding effect” and to some extent substitutes for geographical agglomeration. (3) In the mechanism where geographical agglomeration in the agricultural industry increases farmers’ agricultural income through technological innovation, virtual agglomeration has a positive moderating effect. This paper is important for enabling farmers to share the benefits of the digital economy and achieve continuous growth in agricultural income. It is also important for the sustainable development goals adopted by the United Nations, such as eliminating poverty (SDG1), eliminating hunger (SDG2), promoting sustainable economic growth and full employment (SDG8), and promoting innovation (SDG9).

In the global frenzy of blockchain technology, experts at home and abroad have conducted in-depth research on blockchain technology, vigorously promoted the digitalization and intelligent construction of blockchain applications, and deeply integrated advanced technologies such as mobile internet, big data, cloud computing, and artificial intelligence to promote innovation with technology. The decentralization of blockchain, reliable database, security, credibility, and quasi-anonymity have opened up new ideas for people, got rid of the shackles of centralized system and put forward new solutions to problems. “Internet plus” has brought great challenges and new development opportunities to academic journals in colleges and universities. Digitalization of paper content, modularization of periodical database, media of academic journals in colleges and universities, construction of new digital platforms, and integration and innovation of academic journals and new media are all active explorations for academic journals in colleges and universities to cope with digital transformation.

Many problems and limitations appeared with the spread of mobile cloud computing (MCC) technology, due to the battery life of the mobile, limited resources, storage space and processors. In addition, the biggest challenge is to make decisions about executing tasks between local devices and cloud servers (edge) to save energy and reduce delay time by reducing the energy consumption (or cost) of the system concerned. In this paper, an optimization method based on the Lion Optimization Algorithm (LOA) is proposed for task- offloading of mobile cloud computing. Task- offloading is the transfer of tasks from mobile devices to servers that handle high-level computational operations, such as cloud servers or Mobile-Edge Computing (MEC) servers, to reduce task execution time and energy consumption. The proposed algorithm has a high performance in reducing the cost for offloading tasks. This method can be used to improve the performance of 5G, 6G and later mobile technologies to implement applications that require a large amount of computing, also, multi-objectives could be applied for more cost reduction. The results showed that the proposed algorithm LOA is better than other traditional algorithms such as the genetic algorithm in terms of calculating the costs to find the best solutions.

Mobile Edge Computing (MEC) is an innovative solution designed to address key challenges in mobile cloud computing, including latency, limited capacity, and resource constraints. The primary objective of MEC is to enable dynamic scheduling and efficient resource allocation with minimal cost. This paper proposes a three-tier system architecture comprising mobile devices, edge computing nodes, and traditional cloud infrastructure. It introduces two methods for task offloading and scheduling. For task allocation on mobile devices, the system leverages the Greedy Auto-Scaling Offloading algorithm, which prioritizes high-energy-consuming tasks to enhance energy efficiency. On the edge computing layer, a dynamic scheduling approach based on fuzzy logic is presented, which ranks and allocates tasks according to two specific criteria. Numerical evaluations demonstrate that, compared to existing alternatives, the proposed method significantly reduces task waiting time, latency, and overall system load, while maintaining system balance with minimal resource consumption. Moreover, the proposed system achieves up to ~ 64% reduction in battery consumption in our simulated environment compared with local execution. The results also indicate that over 93% of tasks are successfully executed within the edge environment.

High-Dimensional and Secure Spatial Keyword Query With Arbitrary Ranges in Mobile Cloud

Natural elements such as vegetation, water bodies, and sky, together with artificial elements including buildings and paved surfaces, constitute the core of urban visual environments. Their perception at the pedestrian level not only influences city image but also contributes to residents’ well-being and spatial experience. This study develops a hybrid 3D visibility assessment framework that integrates a city-scale LOD1 solid model with high-resolution mobile LiDAR point clouds to quantify five visual exposure indicators. The case study area is Yueya Lake Park in Nanjing, where a voxel-based line-of-sight sampling approach simulated eye-level visibility at 1.6 m along the southern lakeside promenade. Sixteen viewpoints were selected at 50 m intervals to capture spatial variations in visual exposure. Comparative analysis between the solid model (excluding vegetation) and the hybrid model (including vegetation) revealed that vegetation significantly reshaped the pedestrian visual field by reducing the dominance of sky and buildings, enhancing near-field greenery, and reframing water views. Artificial elements such as buildings and ground showed decreased exposure in the hybrid model, reflecting vegetation’s masking effect. The calculation efficiency remains a limitation in this study. Overall, the study demonstrates that integrating natural and artificial elements provides a more realistic and nuanced assessment of pedestrian visual perception, offering valuable support for sustainable landscape planning, canopy management, and the equitable design of urban public spaces.

Abstract -The IoT-Based Smart Rain Detection and Alert System is an innovative solution designed to provide real-time rain monitoring and instant alerts using Internet of Things (IoT) technology. This system integrates smart sensors, microcontrollers, and wireless communication modules to detect rainfall and notify users through mobile apps, SMS, or cloud dashboards. It is especially useful in agriculture, smart cities, outdoor event management, and home automation. The core component of the system is a rain sensor that detects water droplets and sends signals to a microcontroller (such as Arduino or ESP32). Once rain is detected, the system processes the data and triggers alerts via Wi-Fi, GSM, or LoRa networks. Users receive notifications on their smartphones or connected devices, allowing them to take timely action—such as covering crops, adjusting irrigation schedules, or postponing outdoor activities. Key Words: The IoT-Based Smart Rain Detection and Alert System provides real-time rainfall monitoring and instant alerts for proactive weather response.

The rapid development of mobile applications and cloud computing has led to the widespread adoption of cloud service platforms for mobile backend services. However, improper use of cloud credentials has frequently resulted in the leakage of application data on cloud servers. Despite security recommendations from cloud service providers, vulnerabilities persist. To assess the effectiveness of these measures, we propose a detection system to identify cloud credential leaks in mobile applications, including hard-coded credentials and those stored on servers. We analyzed 21,724 applications from Google Play and one Chinese market, revealing new attacks triggered by stolen cloud credentials. Our findings indicate that even temporary credentials recommended by cloud providers may pose security risks. We identified 893 applications using cloud credentials from the three major providers, with 945 credentials found. By analyzing these credentials, we uncovered severe vulnerabilities in 356 apps, such as personally identifiable information (PII) leakage, credential forgery, and remote code execution (RCE). These issues threaten user privacy and app security. We also evaluated developer adherence to recommended IAM best practices and provided suggestions for improving cloud credential security, highlighting issues such as improper permissions, insufficient protection, outdated versions, and regional variants.

Cross-sensor adaptive semantic segmentation for mobile laser scanning point clouds based on continuous potential scene surface reconstruction

The scientific article presents a comprehensive study of the problematic issues arising from the application of artificial intelligence technologies in the process of detecting, documenting, analyzing, and evaluating digital traceswithin the framework of criminal proceedings. The relevance of implementing intelligent technologies into the practice of law enforcement agencies and forensic institutions is substantiated, particularly in the context of the digitalization of criminal activity and the growing volume of information in digital format. The article outlines current scientific approaches to interpreting the concept of «digital traces», highlighting their typical characteristics and forensic significance during pre-trial investigation. Special attention is paid to the potential use of artificial intelligence for the automated detection, classification, analysis, and systematization of digital data stored on mobile devices, servers, cloud platforms, social networks, and messaging services. The study describes the key functional capabilities of artificial intelligence in the context of its application in law enforcement activities. At the same time, it identifies a range of challenges and threats associated with the introduction of artificial intelligence into the domain of criminal justice, including the limited transparency and explainability of algorithms, susceptibility to adversarial attacks, the likelihood of erroneous outcomes, and the inability to accurately interpret complex contextual information. The lack of adequate regulatory frameworks governing the use of artificial intelligence in criminal proceedings is critically analyzed. Attention is drawn to the potential risks of violating fundamental rights of the parties involved in the process, particularly the right to defense and the right to a fair trial. The necessity of developing legal, technical, and ethical standards for the application of artificial intelligence in the examination of digital traces is substantiated.The study concludes that the effective and safe integration of artificial intelligence into forensic practice is possible only under the condition of combining innovative technological solutions with clear procedural regulations and strict adherence to the principles of objectivity, scientific validity, and the protection of human rights.