The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

Blockchain technology has a wide range of applicability in the fields of transportation infrastructure construction and maintenance, transportation big data analysis and application, expressway toll collection, and logistics. The core technology lies in the distributed, decentralized, immutable, and programmable features brought about by consensus. This paper studies the dynamic analytical modeling of Proof-Based Consensus (PBC) strategies in blockchain systems, focusing on basic strategies, including Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), and Proof of Luck (PoL), which can be extended to other PBC models. We focus on modeling these typical strategies and discuss their solution characteristics in terms of algorithmic mechanisms and principles. The relevant results can be used for quantitative analysis and evaluation of distributed consensus based on the model.

With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors’ data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data.

In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

Microalgae have emerged as sustainable biofactories producing diverse bioactive compounds with significant applications in nutrition and cosmetics. Their high metabolic versatility makes them promising alternatives to conventional resources for addressing global challenges such as malnutrition, food insecurity, and environmental degradation. This review provides an integrated perspective on microalgal bioactives, highlighting their role in functional foods, dietary supplements, and maternal and infant nutrition, as well as their incorporation into cosmetic formulations for anti-aging, photoprotection, hydration, and microbiome support. Mechanistic insights reveal antioxidant, anti-inflammatory, and extracellular matrix-preserving effects, alongside UV absorption and barrier reinforcement. The review also discusses their biochemical diversity, mechanisms of action, safety, regulatory considerations, and emerging technologies for formulation and delivery. AI-driven and machine-learning approaches using microalgae for cosmetic and nutritional applications have also been discussed. Overall, microalgae serve as a cornerstone for next-generation nutraceuticals and cosmeceuticals, aligning with sustainability and circular-economy principles.

The potential of discrete memristors to improve chaotic systems for safe communication has been demonstrated by recent advancements. This paper presents a novel four-dimensional (4D) memristive hyperchaotic map that combines a sine map with a two-dimensional logistic-Gaussian hyperchaotic (2D LGH) map through a sinusoidal discrete memristor. The proposed system exhibits high-dimensional chaotic behavior, including hyperchaos and strong randomness, as illustrated through bifurcation diagrams, Lyapunov exponent (LE) analysis, and complexity metrics. We present a high-security image encryption approach based on these features, incorporating the proposed 4D memristive hyperchaotic system with a non-associative LA-group structure. This method generates algebraic-chaotic sequences by mixing non-associative algebraic operations with chaotic sequences, significantly broadening the key space. The encryption framework provides great efficiency, low computing cost, and resilience to cryptographic attacks. Comprehensive security assessments demonstrate the scheme’s efficacy, such as key sensitivity analysis, entropy evaluation, correlation testing, and differential attack resistance. The simulation results show that the suggested technique provides better encryption quality, high unpredictability, and resistance to statistical and differential attacks, making it an appealing choice for secure image transmission and data security in current communication systems.