Search Results (914)

User location has emerged as a pivotal factor in human-centered environments, driving applications like tracking, navigation, healthcare, and emergency response that align with Sustainable Development Goals (SDGs). However, accurate indoor localization remains challenging due to the limitations of GPS in indoor settings, where signal interference and reflections disrupt satellite connections. While Received Signal Strength Indicator (RSSI) methods are commonly employed, they are affected by environmental noise, multipath fading, and signal interference. Round-Trip Time (RTT)-based localization techniques provide a more resilient alternative but are not universally supported across access points due to infrastructure limitations. To address these challenges, we introduce DistilLoc: a cross-knowledge distillation framework that transfers knowledge from an RTT-based teacher model to an RSSI-based student model. By applying a teacher–student architecture, where the RTT model (teacher) trains the RSSI model (student), DistilLoc enhances RSSI-based localization with the accuracy and robustness of RTT without requiring RTT data during deployment. At the core of DistilLoc, the FNet architecture is employed for its computational efficiency and capacity to capture complex relationships among RSSI signals from multiple access points. This enables the student model to learn a robust mapping from RSSI measurements to precise location estimates, reducing computational demands while improving scalability. Evaluation in two cluttered indoor environments of varying sizes using Android devices and Google WiFi access points, DistilLoc achieved sub-meter localization accuracy, with median errors of 0.42 m and 0.32 m, respectively, demonstrating improvements of 267% over conventional RSSI methods and 496% over multilateration-based approaches. These results validate DistilLoc as a scalable, accurate solution for indoor localization, enabling intelligent, resource-efficient urban environments that contribute to SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities). Full article

The Internet of Things (IoT) is set to play a significant role in the future development of smart cities, which are designed to be environmentally friendly. However, the proliferation of these devices, along with their frequent replacements and the energy required to power them, contributes to a significant environmental footprint. In this paper we provide scientific evidences on the advantages of using an energy design pattern named ‘Batch Operations’ (BO) to optimize energy consumption on mobile devices. Big ICT companies like Google already batch multiple API calls instead of putting the device into an active state many times. This is supposed to save tail energy consumption in communication peripherals. To confirm this, we set up an experiment where we compare energy consumption and carbon emission when BO is applied to two communication peripherals on Android mobile device: 4G and GPS. Results show that (1) BO can save up to 40% energy when sending HTTP requests, resulting in an equivalent reduction in $C{O}_2$ emissions. (2) no advantages for the GPS interface. Full article

The article presents an innovative approach for secure authentication in internet banking transactions, utilizing an Out-of-Band visual two-factor authorization protocol. With the increasing rise of cyber attacks and fraud, new security models are needed that ensure the integrity, authenticity, and confidentiality of financial transactions. The identified gap lies in the inability of traditional authentication methods, such as TANs and tokens, to provide security in untrusted terminals. The proposed solution is the Dynamic Authorization Protocol (DAP), which uses mobile devices to validate transactions through visual codes, such as QR codes. Each transaction is assigned a unique associated code, and the challenge must be responded to within 120 s. The customer initiates the transaction on a computer and independently validates it on their mobile device using an out-of-band channel to prevent attacks such as phishing and man-in-the-middle. The methodology involves implementing a prototype in Java ME for Android devices and a Java application server, creating a practical, low-computational-cost system, accessible for use across different operating systems and devices. The protocol was tested in real-world scenarios, focusing on ensuring transaction integrity and authenticity. The results show a successful implementation at Banco do Brasil, with 3.6 million active users, demonstrating the efficiency of the model over 12 years of use without significant vulnerabilities. The DAP protocol provides a robust and effective solution for securing banking transactions and can be extended to other authentication environments, such as payment terminals and point of sale devices. Full article

Cassava production in Africa is constrained by number of biotic factors, including cassava mosaic disease (CMD). In response to this challenge, the PlantVillage Nuru application, which employs artificial intelligence for CMD diagnosis, provides farmers with the ability to independently detect the disease. This study examines the factors influencing the adoption of the innovative Nuru application by farmers in Benin. Data were randomly collected from 305 farmers in three Agricultural Development Poles (PDAs 5, 6 and 7). A binary logit model was used to analyze the determinants of adoption. The results show that, despite the potential of the Nuru application, the adoption rate remained relatively low at 14.1%. The key drivers of adoption were found to be participation in CMD training, disease knowledge, ownership of an Android smartphone, education level and practice of crop association. These findings emphasize the necessity of intensifying farmers’ training and raising awareness about CMD. Effective strategies to reach and train a significant number of farmers are crucial. Enhancing Nuru adoption can lead to more effective CMD management and improved cassava production, which will have a positive impact on food security in Africa and strengthen the resilience of farming communities against biotic challenges. Full article

Background: With the improvement of healthcare, the number of elderly individuals, including those with obesity, is increasing. The accumulation of various ventilation problems associated with the use of face masks in both these patient groups can pose a challenge even for an experienced anesthesiologist. The main aim of this study was to evaluate the ventilation of elderly patients with obesity using face masks, uncuffed or cuffed, and compare it with values obtained among patients with obesity who are not elderly. The secondary aim of the study was to demonstrate which of the two masks tested is better for elderly patients with android and gynoid obesity. Methods: This study was conducted at University Clinical Hospital No. 1 in Lodz among 108 patients with obesity, 50 elderly and 58 non-elderly. Patients’ BMIs ranged from 35.0 to 59.0. For the study, the uncuffed Intersurgical Eco Mask II and cuffed Ambu Ultra Seal face masks were used. Expiratory tidal volume and leakage obtained during the use of both types of masks were examined. The obtained data were analyzed using the Kolmogorov–Smirnov test and supplemented with Wilcoxon test values. Results: In elderly patients with obesity, especially those with gynoid obesity, the use of the Intersurgical Eco Mask II is associated with better ventilation parameters than the Ambu Ultra Seal mask. Only in the case of elderly patients with android obesity did the use of the Ambu Ultra Seal mask yield similar results to the Intersurgical Eco Mask II. Conclusions: Uncuffed face masks provide better ventilation parameters during manual bag ventilation in elderly patients with obesity. Full article

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources. Full article

Background/Objectives: The healthcare sector is rapidly growing, aiming to promote health, provide treatment, and enhance well-being. This paper focuses on the organ donation and transplantation system, a vital aspect of healthcare. It offers a comprehensive review of challenges in global organ donation and transplantation, highlighting issues of fairness and transparency, and compares centralized architecture-based models and blockchain-based decentralized models. Methods: This work reviews 370 publications from 2016 to 2023 on organ donation management systems. Out of these, 85 publications met the inclusion criteria, including 67 journal articles, 2 doctoral theses, and 16 conference papers. About 50.6% of these publications focus on global challenges in the system. Additionally, 12.9% of the publications examine centralized architecture-based models, and 36.5% of the publications explore blockchain-based decentralized models. Results: Concerns about organ trafficking, illicit trade, system distrust, and unethical allocation are highlighted, with a lack of transparency as the primary catalyst in organ donation and transplantation. It has been observed that centralized architecture-based models use technologies such as Python, Java, SQL, and Android Technology but face data storage issues. In contrast, blockchain-based decentralized models, mainly using Ethereum and a subset on Hyperledger Fabric, benefit from decentralized data storage, ensure transparency, and address these concerns efficiently. Conclusions: It has been observed that blockchain technology-based models are the better option for organ donation management systems. Further, suggestions for future directions for researchers in the field of organ donation management systems have been presented. Full article

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability. Full article

Subconcussive blast exposure has been shown to alter neurological functioning. However, the extent to which neurological dysfunction persists after blast exposure is unknown. This longitudinal study examined the potential short- and long-term effects of repeated subconcussive blast exposure on neuromotor performance from heavy weapons training in military personnel. A total of 214 participants were assessed; 137 were exposed to repeated subconcussive blasts and 77 were not exposed to blasts (controls). Participants completed a short stepping-in-place task while an Android smartphone app placed on their thigh recorded movement kinematics. We showed acute suppression of neuromotor variability 6 h after subconcussive blast exposure, followed by a rebound to levels not different from baseline at the 72 h, 2-week, and 3-month post-tests. It is postulated that this suppression of neuromotor variability results from a reduction in the functional degrees of freedom from the subconcussive neurological insult. It is important to note that this change in behavior is short-lived, with a return to pre-blast exposure movement kinematics within 72 h. Full article

As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile and analyze an extensive range of reported vulnerabilities, introducing a novel categorization framework to facilitate a deeper understanding of these risks, classified by affected hardware components, vulnerability type, and the potential impact on system security. The paper addresses key areas such as memory management flaws, side-channel attacks, insecure system-on-chip (SoC) resource allocation, and cryptographic vulnerabilities. In addition, it examines feasible countermeasures, including hardware-backed encryption, secure boot mechanisms, and trusted execution environments (TEEs), to mitigate the risks posed by these hardware threats. By contextualizing hardware vulnerabilities within the broader security architecture of Android devices, this review emphasizes the importance of hardware security in ensuring system integrity and resilience. The findings serve as a valuable resource for both researchers and security professionals, offering insights into the development of more robust defenses against the emerging hardware-based threats faced by Android devices. Full article

From mobile health and wearables to implantable medical devices and neuroprosthetics, the integration of machines into human biology and cognition is expanding. This paper explores the technological advancements that are pushing the human–machine boundaries further, raising profound questions about identity and existence in digital realms. The development of robots, androids, and AI–human hybrids promises to augment human capabilities beyond current limits. However, alongside these advancements, significant limitations arise: biological, technical, ethical, and legal. This paper further discusses the existential implications of these technological strides. It addresses the philosophical dimensions of mortality, forgiveness, and the significance of death in a world where technological immortality may be within reach. By addressing these questions, the paper seeks to provide a comprehensive analysis of the potential for these advancements to reshape our understanding of existence and the quest for immortality. Full article

Mobile devices face significant security challenges due to the increasing proliferation of Android malware. This study introduces an innovative approach to Android malware detection, combining Support Vector Regression (SVR) and dynamic feature analysis to address escalating mobile security challenges. Our research aimed to develop a more accurate and reliable malware detection system capable of identifying both known and novel malware variants. We implemented a comprehensive methodology encompassing dynamic feature extraction from Android applications, feature preprocessing and normalization, and the application of SVR with a Radial Basis Function (RBF) kernel for malware classification. Our results demonstrate the SVR-based model’s superior performance, achieving 95.74% accuracy, 94.76% precision, 98.06% recall, and a 96.38% F1-score, outperforming benchmark algorithms including SVM, Random Forest, and CNN. The model exhibited excellent discriminative ability with an Area Under the Curve (AUC) of 0.98 in ROC analysis. The proposed model’s capacity to capture complex, non-linear relationships in the feature space significantly enhanced its effectiveness in distinguishing between benign and malicious applications. This research provides a robust foundation for advancing Android malware detection systems, offering valuable insights for researchers and security practitioners in addressing evolving malware challenges. Full article

This study introduces a novel framework to apply the artifact subspace reconstruction (ASR) algorithm on single-channel electroencephalogram (EEG) data. ASR is known for its ability to remove artifacts like eye-blinks and movement but traditionally relies on multiple channels. Embedded ASR (E-ASR) addresses this by incorporating a dynamical embedding approach. In this method, an embedded matrix is created from single-channel EEG data using delay vectors, followed by ASR application and reconstruction of the cleaned signal. Data from four subjects with eyes open were collected using Fp1 and Fp2 electrodes via the CameraEEG android app. The E-ASR algorithm was evaluated using metrics like relative root mean square error (RRMSE), correlation coefficient (CC), and average power ratio. The number of eye-blinks with and without the E-ASR approach was also estimated. E-ASR achieved an RRMSE of 43.87% and had a CC of 0.91 on semi-simulated data and effectively reduced artifacts in real EEG data, with eye-blink counts validated against ground truth video data. This framework shows potential for smartphone-based EEG applications in natural environments with minimal electrodes. Full article

Self-healing systems have become essential in modern computing for ensuring continuous and secure operations while minimising downtime and maintenance costs. These systems autonomously detect, diagnose, and correct anomalies, with effective self-healing relying on accurate interpretation of system logs generated by operating systems (OSs). Manual analysis of these logs in complex environments is often cumbersome, time-consuming, and error-prone, highlighting the need for automated, reliable log analysis methods. Our research introduces an intelligent methodology for creating self-healing systems for multiple OSs, focusing on log classification using CountVectorizer and the Multinomial Naive Bayes algorithm. This approach involves preprocessing OS logs to ensure quality, converting them into a numerical format with CountVectorizer, and then classifying them using the Naive Bayes algorithm. The system classifies multiple OS logs into distinct categories, identifying errors and warnings. We tested our model on logs from four major OSs; Mac, Android, Linux, and Windows; sourced from Zenodo to simulate real-world scenarios. The model’s accuracy, precision, and reliability were evaluated, demonstrating its potential for deployment in practical self-healing systems. Full article