Search Results (1,254)

We develop a method to automatically and stably anonymize and de-anonymize face images with encoder-decoder networks and provide a robust and secure solution for identity protection. Our fundamental framework is an NN-based encoder-decoder pair with a dual inferencing mechanism. We denote it as the Secure Dual Network (SDN), which can simultaneously achieve multi-attribute face de-identification and re-identification without any pre-trained/auxiliary model. In more detail, the SDN can take responsibility for successfully anonymizing the face images while generating surrogate faces, satisfying the user-defined specific conditions. Meanwhile, SDN can also execute the de-anonymization procedure and visually indistinguishably reconstruct the original ones if re-identification is required. Designing and implementing the loss functions based on information theory (IT) is one of the essential parts of our work. With the aid of the well-known IT-related quantity, Mutual Information, we successfully explained the physical meaning of our trained models. Extensive experiments justify that with pre-defined multi-attribute identity features, SDN generates user-preferred and diverse appearance anonymized faces for successfully defending against attacks from hackers and, therefore, achieves the goal of privacy protection. Moreover, it can reconstruct the original image nearly perfectly if re-identification is necessary. Full article

The Computing Network is an emerging paradigm that integrates network and computing resources. One of its goals is to satisfy the requirements of delay-sensitive services through network scheduling capabilities. However, traditional TCP/IP networks are deficient in accurately being aware of requirements and performing flexible routing based on service levels. Information-Centric Networking (ICN) addresses these issues through its flexible protocol design and content-based routing mechanism. Additionally, the integration of Software-Defined Networking (SDN) technology further enhances its routing flexibility. Therefore, this paper proposes an ICN-based delay-sensitive service scheduling architecture with an SDN stateful programmable data plane. The network nodes are first layered based on the type of computing clusters they are linked with, and then within each layer, they are divided into several domains according to delay constraints. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, combined with the Best-Worst Method (BWM) weighting method, is adopted to evaluate the candidate clusters, and the corresponding scheduling strategy is executed in the stateful programmable data plane. The simulation results show that compared with other scheduling architectures and traditional TOPSIS with the Entropy Weight Method (EWM), the proposed architecture and algorithm show significant advantages in reducing the overall delay of service requests and improving the scheduling success ratio, as well as the load balance of the computing clusters. Full article

Despite significant improvements in the comprehension of neuro-regeneration, restoring nerve injury in humans continues to pose a substantial therapeutic difficulty. In the peripheral nervous system (PNS), the nerve regeneration process after injury relies on Schwann cells. These cells play a crucial role in regulating and releasing different extracellular matrix proteins, including laminin and fibronectin, which are essential for facilitating nerve regeneration. However, during regeneration, the nerve is required to regenerate for a long distance and, subsequently, loses its capacity to facilitate regeneration during this progression. Meanwhile, it has been noted that nerve regeneration has limited capabilities in the central nervous system (CNS) compared to in the PNS. The CNS contains factors that impede the regeneration of axons following injury to the axons. The presence of glial scar formation results from this unfavourable condition, where glial cells accumulate at the injury site, generating a physical and chemical barrier that hinders the regeneration of neurons. In contrast to humans, several species, such as axolotls, polychaetes, and planarians, possess the ability to regenerate their neural systems following amputation. This ability is based on the vast amount of pluripotent stem cells that have the remarkable capacity to differentiate and develop into any cell within their body. Although humans also possess these cells, their numbers are extremely limited. Examining the molecular pathways exhibited by these organisms has the potential to offer a foundational understanding of the human regeneration process. This review provides a concise overview of the molecular pathways involved in axolotl, polychaete, and planarian neuro-regeneration. It has the potential to offer a new perspective on therapeutic approaches for neuro-regeneration in humans. Full article

Streptococcus pyogenes, or Group A Streptococcus (GAS), is responsible for over 500,000 deaths per year. Approximately 15% of these deaths are caused by necrotizing soft-tissue infections. In 2008, we isolated an M5 GAS, named the LO1 strain, responsible for the nosocomial transmission of necrotizing fasciitis between a baby and a nurse in Belgium. To understand this unusual transmission route, the LO1 strain was sequenced. A comparison of the LO1 genome and transcriptome with the reference M5 Manfredo strain was conducted. We found that the major differences were the presence of an additional DNase and a Tn916-like transposon in the LO1 and other invasive M5 genomes. RNA-seq analysis showed that genes present on the transposon were barely expressed. In contrast, the DNases presented different expression profiles depending on the tested conditions. We generated knock-out mutants in the LO1 background and characterized their virulence phenotype. We also determined their nuclease activity on different substrates. We found that DNases are dispensable for biofilm formation and adhesion to both keratinocytes and pharyngeal cells. Three of these were found to be essential for blood survival; Spd4 and Sdn are implicated in phagocytosis resistance, and Spd1 is responsible for neutrophil extracellular trap (NET) degradation. Full article

The environmental impact of peat extraction in plant nurseries requires urgent attention due to climate change and habitat destruction. Substituting peat moss with compost derived from palm oil waste in oil palm nurseries presents a viable solution. However, the challenges in its implementation must be considered. This research focuses on optimizing composting conditions for palm oil waste and examines the impact of the compost on soil quality, nutrient availability, and seedling growth. Measurements such as the culling rate, plant height, leaf length, and chlorophyll content were taken to assess seedling growth in nurseries. The compost was also tested as a soil amendment for 5-year-old palm trees, with foliar analysis conducted to evaluate the nutrient assimilation. The results show that optimized compost significantly enhanced the seedling growth by 20–50%, evidenced by the increased plant height, longer leaf length, and higher chlorophyll content. Additionally, the foliar analysis demonstrated an improvement of 5–15% in the nutrient assimilation in the 5-year-old palm trees. This research highlights the potential of optimizing oil palm waste composting for sustainable planting media in nurseries, mitigating environmental impacts and promoting productivity in oil palm plantations. Adopting this circular economy model can address waste management challenges while ensuring a resilient and sustainable approach in the palm oil industry. Full article

Among the non-motor symptoms associated with Parkinson’s disease (PD), cognitive impairment is one of the most common and disabling. It can occur either early or late during the disease, and it is heterogeneous in terms of its clinical manifestations, such as Subjective Cognitive Dysfunction (SCD), Mild Cognitive Impairment (MCI), and Parkinson’s Disease Dementia (PDD). The aim of the present review is to delve deeper into the molecular mechanisms underlying cognitive decline in PD. This is extremely important to delineate the guidelines for the differential diagnosis and prognosis of the dysfunction, to identify the molecular and neuronal mechanisms involved, and to plan therapeutic strategies that can halt cognitive impairment progression. Specifically, the present review will discuss the pathogenetic mechanisms involved in the progression of cognitive impairment in PD, with attention to mitochondria and their contribution to synaptic dysfunction and neuronal deterioration in the brain regions responsible for non-motor manifestations of the disease. Full article

Software Defined Wireless Sensor Networks (SDWSN) enable flexibility in Wireless Sensor Network (WSN) environments by defining the controllable functions to WSN nodes by the Software Defined Network (SDN) controller. Due to the rapid evolution of SDWSNs, adverse effects also have occurred in terms of interference, energy consumption, and security issues. Several state-of-the-art works lend their utmost best to the SDWSN environment. However, the complete picture (i.e., relatability and security in SDWSN) poses severe challenges. The state-of-the-art issues is addressed in this research by proposing interference-aware Multi-Tier Scheduling for the SDWSN environment (MTS-SDWSN). First, we perform network construction in which the proposed network is constructed in a 2D hexagonal grid structure to resolve the connectivity issue. Upon constructing the network, the SDWSN nodes are clustered and managed to reduce the energy consumption using the Divide Well To Merge Better (DWTMB) algorithm in which the optimal Cluster Leader (CL) is selected based on adequate constraint. The data from the clustered nodes are sent to the Local Base Station (LBS) via CL in which they are scheduled in multi-tier format to diminish the complexity and interference issues. The first tier involved in scheduling among Cluster Members (CMs) and CL using adequate metrics, whereas the successive tiers (i.e., second and third) involved in scheduling among CLs to LBSs and LBSs to Sink Node (SN) are done using the Non-Cooperative Fuzzy Theory (NCFT) method. Last, the scheduled nodes are routed to appropriate destinations using Secure and Optimal Routing Protocol (SORP). The proposed SORP includes the Alibaba and Forty Thieves (AFT) and Multi Criteria Decision Making (MCDM) algorithms for selecting and ranking the optimal routes. Further, the security of the routes is enabled by adopting trust and Moving Target Defense (MTD) mechanisms. The MTD includes route switching among the SDWSN devices and active switch handling using Cycle Generative Adversarial Networks (CGAN) among the switches. The proposed work is implemented using a NS-3.26 simulation tool, and performance of the proposed model and existing works shows that the proposed work outperforms the existing works. Full article

Hydrogen production is essential in the transition to sustainable energy. This study examines two hydrogen production routes, steam methane reforming (SMR) and chemical looping reforming (CLR), both using raw natural gas as feedstock. SMR, the most commonly used industrial process, involves reacting methane with steam to produce hydrogen, carbon monoxide, and carbon dioxide. In contrast, CLR uses a metal oxide as an oxygen carrier to facilitate hydrogen production without generating additional carbon dioxide. Simulations conducted using Aspen HYSYS analyzed each method’s performance and energy consumption. The results show that SMR achieved 99.98% hydrogen purity, whereas CLR produced 99.97% purity. An energy analysis revealed that CLR requires 31% less energy than SMR, likely due to the absence of low- and high-temperature water–gas shift units. Overall, the findings suggest that CLR offers substantial advantages over SMR, including lower energy consumption and the production of cleaner hydrogen, free from carbon dioxide generated during the water–gas shift process. Full article

Whole-body positron emission tomography (PET)–computed tomography (CT) imaging performed for oncological purposes may provide additional parameters such as the coronary artery calcium (CAC) and epicardial adipose tissue (EAT) volume with cost-effective prognostic information in asymptomatic people beyond traditional cardiovascular risk factors. We evaluated the feasibility of measuring the CAC score and EAT volume in cancer patients without known coronary artery disease (CAD) referred to whole-body ¹⁸F-FDG PET–CT imaging, regardless of the main clinical problem. We also investigated the potential relationships between traditional cardiovascular risk factors and CAC with EAT volume. A total of 109 oncological patients without overt CAD underwent whole-body PET–CT imaging with ¹⁸F-fluorodeoxyglucose (FDG). Unenhanced CT images were retrospectively viewed for CAC and EAT measurements on a dedicated platform. Overall, the mean EAT volume was 99 ± 49 cm³. Patients with a CAC score ≥ 1 were older than those with a CAC = 0 (p < 0.001) and the prevalence of hypertension was higher in patients with detectable CAC as compared to those without (p < 0.005). The EAT volume was higher in patients with CAC than in those without (p < 0.001). For univariable age, body mass index (BMI), hypertension, and CAC were associated with increasing EAT values (all p < 0.005). However, the correlation between the CAC score and EAT volume was weak, and in multivariable analysis only age and BMI were independently associated with increased EAT (both p < 0.001), suggesting that potential prognostic information on CAC and EAT is not redundant. This study demonstrates the feasibility of a cost-effective assessment of CAC scores and EAT volumes in oncological patients undergoing whole-body ¹⁸F-FDG PET–CT imaging, enabling staging cancer disease and atherosclerotic burden by a single test already included in the diagnostic work program, with optimization of the radiation dose and without additional costs. Full article

This review aims to compare the effect of photobiomodulation therapy (PBMT) using visible and near-infrared diode laser wavelengths to that of the dry needling technique (DNT) on the management of orofacial pain in patients with Temporomandibular Disorder Myofascial Pain Syndrome (TMD/MPS) in term of effectiveness, speed of recovery, and lasting of treatment. A systematic search of multiple electronic databases was carried out to identify the relevant clinical trials published between 1 January 2010 and 1 January 2024. The included studies were limited to human subjects who had orofacial pain associated with Axis 1 of TMD/MPS, involving two genders with age >18 years and were treated either with photobiomodulation using diode laser with wavelengths ranging from 600 up to 1200 nanometer (nm), or with the dry needling (DN) technique (superficial SDN or deep DDN), as a non-pharmacological therapies to decrease the intensity of orofacial pain associated with TMD/MPS. The risk of bias for included studies was assessed using the Cochrane RoB tool (for randomized studies). Three distinct meta-analyses were performed to quantify the pooled effects of PBM and DN in the management of TMD/MPS myofascial pain and deactivation of myofascial trigger points (MTPs). The meta-analyses were performed using Review Manager (RevMan) 5.3 from Cochrane. The confidence interval (CI) was established at 95% and p-values of less than 0.05 (p < 0.05) were considered statistically significant. Statistical heterogeneity was assessed using I2. Qualitative data were extracted and summarized in tables for each group study, while quantitative data were reported as Mean and Standard Deviation (SD) values for assessment variables in each sub-group study. The results among groups were systematically evaluated to draw the final conclusion. A rigorous electronic and manual search revealed 4150 possible articles. Following the application of the inclusion and exclusion criteria, twelve eligible studies were included in the analysis. Both PBMT and DNT were found to be effective in controlling the myalgia pain and primary symptoms associated with TMD/MPS, as well as deactivating the MTPs. DNT was statistically superior in terms of recovery time while PBMT was the more effective long-term therapy. Full article

In this paper, we present a novel method to enhance the sum-rate effectiveness in full-duplex unmanned aerial vehicle (UAV)-assisted communication networks. Existing approaches often couple uplink and downlink associations, resulting in suboptimal performance, particularly in dynamic environments where user demands and network conditions are unpredictable. To overcome these limitations, we propose a decoupling of uplink and downlink associations for ground-based users (GBUs), significantly improving network efficiency. We formulate a comprehensive optimization problem that integrates UAV trajectory design and user association, aiming to maximize the overall sum-rate efficiency of the network. Due to the problem’s non-convexity, we reformulate it as a Partially Observable Markov Decision Process (POMDP), enabling UAVs to make real-time decisions based on local observations without requiring complete global information. Our framework employs multi-agent deep reinforcement learning (MADRL), specifically the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, which balances centralized training with distributed execution. This allows UAVs to efficiently learn optimal user associations and trajectory controls while dynamically adapting to local conditions. The proposed solution is particularly suited for critical applications such as disaster response and search and rescue missions, highlighting the practical significance of utilizing UAVs for rapid network deployment in emergencies. By addressing the limitations of existing centralized and distributed solutions, our hybrid model combines the benefits of centralized training with the adaptability of distributed inference, ensuring optimal UAV operations in real-time scenarios. Full article

Managing queuing delays is crucial for maintaining Quality of Service (QoS) in real-time media communications. Customizing traditional routing protocols to meet specific QoS requirements—particularly in terms of minimizing delay and jitter for real-time media—can be both complex and time-intensive. Furthermore, these protocols often encounter challenges when adapted for vendor-specific hardware implementations. To address these issues, this paper leverages the programmable features of Software-Defined Networking (SDN) to simplify the process of achieving user-defined QoS, bypassing the limitations of traditional routing protocols. In this work, we propose a policy-based routing module that integrates with traditional routing protocols to ensure QoS for real-time media flows. QoS is achieved by rerouting the flow along a new low-latency path calculated by the proposed module when the queuing delay exceeds a certain threshold. The experimental results demonstrate that the proposed solution significantly enhances the performance of traditional routing protocols within an SDN framework, effectively reducing the average end-to-end delay by 80% and total packet loss by 73%, while also improving jitter and alleviating network congestion. Full article

The advent of 5G heralds unprecedented connectivity with high throughput and low latency for network users. Software-defined networking (SDN) plays a significant role in fulfilling these requirements. However, it poses substantial security challenges due to its inherent centralized management strategy. Moreover, SDN confronts limitations in handling malicious traffic under 5G’s extensive data flow. To deal with these issues, this paper presents a novel intrusion detection system (IDS) designed for 5G SDN networks, leveraging the advanced capabilities of binarized deep spiking capsule fire hawk neural networks (BSHNN) and blockchain technology, which operates across multiple layers. Initially, the lightweight encryption algorithm (LEA) is used at the data acquisition layer to authenticate mobile users via trusted third parties. Followed by optimal switch selection using the mud-ring algorithm in the switch layer, and the data flow rules are secured by employing blockchain technology incorporating searchable encryption algorithms within the blockchain plane. The domain controller layer utilizes binarized deep spiking capsule fire hawk neural network (BSHNN) for real-time data packet classification, while the smart controller layer uses enhanced adapting hidden attribute-weighted naive bayes (EAWNB) to identify suspicious packets during data transmission. The experimental results show that the proposed technique outperforms the state-of-the-art approaches in terms of accuracy (98.02%), precision (96.40%), detection rate (96.41%), authentication time (16.2 s), throughput, delay, and packet loss ratio. Full article

Hybrid turbine blade protection systems, which combine thermal barrier coatings (TBCs) and cooling mechanisms, are essential for safeguarding turbine blades in advanced gas turbine applications. However, conventional furnace evaluation methods are inadequate for accurately simulating the complex thermal conditions experienced by TBCs in these environments. Initial testing revealed substantial degradation of TBCs when subjected to high temperatures without the necessary cooling support. To address this limitation, the furnace setup was modified to incorporate a cooling air system. This system channeled 400 °C air to the back surface of the TBC while subjecting the front to 1400 °C furnace air, effectively replicating the thermal gradient encountered in hybrid protection systems. The modified furnace setup demonstrated a remarkable improvement in the performance of yttria-stabilized zirconia TBCs. By cooling the back surface of the TBC, the metal substrate temperature decreased, thereby improving the thermal gradient on the coating and its durability. The thermal gradient achieved by the modified furnace was verified to simulate accurately the conditions experienced by TBCs in advanced gas turbines. The conventional furnace setup, lacking a cooling mechanism, overestimated the heat transfer on the TBCs, leading to inaccurate results. The modified furnace, with its integrated cooling system, more accurately simulated the conditions experienced by TBCs in real-world advanced gas turbine applications and more reliably assessed their performance. Full article

To guarantee the reliability of Tactile Internet (TI) applications such as telesurgery, which demand extremely high reliability and are experiencing rapid expansion, we propose a novel smart resilience mechanism for Next-Generation Ethernet Passive Optical Networks (NG-EPONs). Our architecture integrates Artificial Intelligence (AI) and Software-Defined Networking (SDN)-Enabled Broadband Access (SEBA) platform to proactively enhance network reliability and performance. By harnessing the AI’s capabilities, our system automatically detects and localizes fiber faults, establishing backup communication links using Radio Frequency over Glass (RFoG) to prevent service disruptions. This empowers NG-EPONs to maintain uninterrupted, high-quality network service even in the face of unexpected failures, meeting the stringent Quality-of-Service (QoS) requirements of critical TI applications. Our AI model, rigorously validated through 5-fold cross-validation, boasts an average accuracy of 81.49%, with a precision of 84.33%, recall of 78.18%, and F1-score of 81.00%, demonstrating its robust performance in fault detection and prediction. The AI model triggers immediate corrective actions through the SDN controller. Simulation results confirm the efficacy of our proposed mechanism in terms of delay, system throughputs and packet drop rate, and bandwidth waste, ultimately ensuring the delivery of high-quality network services. Full article