Radiofrequency ablation versus intra-articular mesenchymal stem cell injection for knee osteoarthritis: a systematic review and network meta-analysis.

BACKGROUND: Knee osteoarthritis (OA) is a prevalent degenerative disease and causes disability, pain and imposes a substantial burden on patients. Conventional treatments for knee OA show limited effectiveness. Consequently, innovative treatments, such as radiofrequency ablation (RFA) and intra-articular mesenchymal stem cells (IA MSC), have gained attention for addressing these limitations. OBJECTIVE: We compared the efficacy of RFA and IA MSC for knee OA through a network meta-analysis (NMA). EVIDENCE REVIEW: A literature search was conducted using PubMed, MEDLINE, Embase, Cochrane Library, Web of Science and handsearching. Randomized controlled trials (RCTs) comparing RFA or IA MSC to conventional treatments for knee OA were included. The primary outcomes comprised the pain score and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The clinical outcomes were compared using a frequentist approach, and the treatments were ranked using the surface under the cumulative ranking curve (SUCRA) values. FINDINGS: We included 34 RCTs (n=2371). Our NMA revealed that RFA and IA MSC were significantly more effective than conventional treatments in managing pain at both 3 and 6 months with moderate certainty. Specifically, RFA demonstrated the highest SUCRA values, indicating its superior efficacy. For WOMAC scores, both RFA and MSC showed significant improvements at 3 months, with RFA maintaining its lead at 6 months, although MSC did not display significant superiority at this stage. CONCLUSIONS: This analysis suggests that RFA and MSC are resilient treatment options in knee OA. Despite some study heterogeneity, these treatments consistently outperformed conventional treatments, particularly in the short to mid-term, although with varying levels of certainty in their efficacy. PROSPERO REGISTRATION NUMBER: CRD42023492299.

Tides of Blockchain in IoT Cybersecurity.

This paper surveys the implementation of blockchain technology in cybersecurity in Internet of Things (IoT) networks, presenting a comprehensive framework that integrates blockchain technology with intrusion detection systems (IDS) to enhance IDS performance. This paper reviews articles from various domains, including AI, blockchain, IDS, IoT, and Industrial IoT (IIoT), to identify emerging trends and challenges in this field. An analysis of various approaches incorporating AI and blockchain demonstrates the potentiality of integrating AI and blockchain to transform IDS. This paper's structure establishes the foundation for further investigation and provides a blueprint for the development of IDS that is accessible, scalable, transparent, immutable, and decentralized. A demonstration from case studies integrating AI and blockchain shows the viability of combining the duo to enhance performance. Despite the challenges posed by resource constraints and privacy concerns, it is notable that blockchain is the key to securing IoT networks and that continued innovation in this area is necessary. Further research into lightweight cryptography, efficient consensus mechanisms, and privacy-preserving techniques is needed to realize all of the potential of blockchain-powered cybersecurity in IoT.

Output Current and Efficacy of Pulsed Radiofrequency of the Lumbar Dorsal Root Ganglion in Patients With Lumbar Radiculopathy: A Prospective, Double-blind, Randomized Pilot Study.

BACKGROUND: Lumbar radicular pain (LRP) is a common but challenging clinical symptom. Pulsed radiofrequency (PRF), a neuromodulation technique that uses short pulses of radiofrequency current, is effective in treating various pain disorders. However, few studies have been conducted on the effects of PRF and its modifying parameters. OBJECTIVES: Our study aimed to determine the intraoperative parameters of PRF of the lumbar dorsal root ganglion (DRG) that are related to clinical effects in patients with LRP unresponsive to transforaminal epidural steroid injections (TFESI). STUDY DESIGN: Prospective double-blind randomized controlled trial, pilot study. SETTING: Single medical center in the Republic of Korea. METHODS: Patients were allocated to one of 2 groups, high-voltage (60 V) or standard-voltage (45 V), according to the preset maximum voltage at which the active tip temperature does not exceed 42°C. Intraoperative parameters, such as output current, sensory threshold, and impedance, were measured. The primary outcomes were radicular pain intensity, physical functioning, global improvement and satisfaction with treatment, and adverse events. The assessments were performed up to 3 months postprocedure. RESULTS: The patients in the standard-voltage group showed significant improvements in the Numeric Rating Scale pain score (P = 0.007) and Oswestry Disability Index (ODI) (P = 0.008) scores at 3 months post-PRF; however, no difference was observed in the high-voltage group. Among the intraoperative parameters, the output current showed a significant negative linear relationship with analgesic efficacy. The output current also showed a significant association with pain intensity (P = 0.005, R2 = 0.422) and ODI score (P = 0.004, R2 = 0.427) at 3 months postprocedure in a multiple regression analysis. The optimal cut-off value of the output current to lower pain intensity after 3 months was 163.5 mA with a sensitivity of 87.5%, specificity of 100%, and an area under the receiver operating characteristic curve value of 0.92 (95% CI. 0.76 - 1.00). LIMITATIONS: Limitations of our study include an imbalance of baseline characteristics, small sample sizes, and short follow-up periods. CONCLUSIONS: Lower output currents during PRF application to the lumbar DRG were associated with greater analgesic effects in patients who did not respond to therapeutic TFESI.

ALIEN: Assisted Learning Invasive Encroachment Neutralization for Secured Drone Transportation System.

Priority-based logistics and the polarization of drones in civil aviation will cause an extraordinary disturbance in the ecosystem of future airborne intelligent transportation networks. A dynamic invention needs dynamic sophistication for sustainability and security to prevent abusive use. Trustworthy and dependable designs can provide accurate risk assessment of autonomous aerial vehicles. Using deep neural networks and related technologies, this study proposes an artificial intelligence (AI) collaborative surveillance strategy for identifying, verifying, validating, and responding to malicious use of drones in a drone transportation network. The dataset for simulation consists of 3600 samples of 9 distinct conveyed objects and 7200 samples of the visioDECT dataset obtained from 6 different drone types flown under 3 different climatic circumstances (evening, cloudy, and sunny) at different locations, altitudes, and distance. The ALIEN model clearly demonstrates high rationality across all metrics, with an F1-score of 99.8%, efficiency with the lowest noise/error value of 0.037, throughput of 16.4 Gbps, latency of 0.021, and reliability of 99.9% better than other SOTA models, making it a suitable, proactive, and real-time avionic vehicular technology enabler for sustainable and secured DTS.

Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario.

Industry 4.0 requires high-speed data exchange that includes fast, reliable, low-latency, and cost-effective data transmissions. As visible light communication (VLC) can provide reliable, low-latency, and secure connections that do not penetrate walls and are immune to electromagnetic interference; it can be considered a solution for Industry 4.0. The non-orthogonal multiple access (NOMA) technique can achieve high spectral efficiency using the same frequency and time resources for multiple users. It means that smaller amounts of resources will be used compared with orthogonal multiple access (OMA). Therefore, handling multiple data transmissions with VLC-NOMA can be easier for factory automation than OMA. However, as the transmit power is split, the reliability is reduced. Therefore, this study proposed a deep neural network (DNN)-based power-allocation algorithm (DBPA) to improve the reliability of the system. Further, to schedule multiple nodes in VLC-NOMA system, a priority-based user-pairing (PBUP) scheme is proposed. The proposed techniques in VLC-NOMA system were evaluated in terms of the factory automation scenario and showed that it improves reliability and reduces missed deadlines.

Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities.

Three-dimensional printing, often known as additive manufacturing (AM), is a groundbreaking technique that enables rapid prototyping. Monitoring AM delivers benefits, as monitoring print quality can prevent waste and excess material costs. Machine learning is often applied to automating fault detection processes, especially in AM. This paper explores recent research on machine learning-based mechanical fault monitoring systems in fused deposition modeling (FDM). Specifically, various machine learning-based algorithms are applied to measurements extracted from different parts of a 3D printer to diagnose and identify faults. The studies often use mechanical-based fault analysis from data gathered from sensors that measure attitude, acoustic emission, acceleration, and vibration signals. This survey examines what has been achieved and opens up new opportunities for further research in underexplored areas such as SLM-based mechanical fault monitoring.

A Circular-Based Reference Point Extraction Method for Correcting the Alignment of Round Parts.

For products such as smartphones, the technology gap between companies is gradually narrowing with the advancements in technology. Therefore, product design can be a visible strategy for differentiation. However, it is difficult to apply automated production and defect detection processes to the various designs that are being developed. This study proposes a high-speed circular measurement method for correcting the alignment of round parts, which is difficult in an automated process. For analyzing the performance of the proposed method, its processing speed and accuracy are compared with those of the existing methods. The results of the analysis indicate that the overall performance of the proposed method is better than those of the existing methods.

On the Performance Evaluations of Cooperative Retransmission Scheme for Cell-Edge Users of URLLC in Multi-Carrier Downlink NOMA Systems.

Non-orthogonal multiple access (NOMA) has a key feature that the cell-center user (CCU) has prior information about the messages of the cell-edge user (CEU) in the same user-pair. It means that CCU can be used for retransmission when the CEU requests retransmission. As ultra-reliability and low-latency communication (URLLC) requires high-reliability constraints (e.g., 99.999%), using CCU for retransmission can be useful to satisfy the reliability constraint. In this study, to ensure the reliability of CEU, cooperative retransmission (CR) scheme for downlink NOMA systems is proposed. And the CR scheme is evaluated with Block error rate (BLER) considering reliability and with packet loss rate (PLR) in terms of reliability and latency constraints. And the evaluation results showed that the proposed CR scheme can satisfy the target BLER for URLLC low SNR compared to the conventional retransmission scheme, and showed the improved PLR compared to the conventional retransmission scheme in low SNRs.

Visualizing Inertial Data For Wearable Sensor Based Daily Life Activity Recognition Using Convolutional Neural Network.

Nowadays human activity recognition (HAR) plays an crucial role in the healthcare and wellness domains, for example, HAR contributes to context-aware systems like elder home assistance and care as a core technology. Despite promising performance in terms of recognition accuracy achieved by the advancement of machine learning for classification tasks, most of the existing HAR approaches, which adopt low-level handcrafted features, cannot completely deal with practical activities. Therefore, in this paper, we present an efficient wearable sensor based activity recognition method that allows encoding inertial data into color image data for learning highly discriminative features by convolutional neural networks (CNNs). The proposed data encoding technique converts tri-axial samples to color pixels and then arranges them for image-formed representation. Our method reaches the recognition accuracy of over 95% on two challenging activities datasets and further outperforms other deep learning-based HAR approaches.

A comprehensive availability modeling and analysis of a virtualized servers system using stochastic reward nets.

It is important to assess availability of virtualized systems in IT business infrastructures. Previous work on availability modeling and analysis of the virtualized systems used a simplified configuration and assumption in which only one virtual machine (VM) runs on a virtual machine monitor (VMM) hosted on a physical server. In this paper, we show a comprehensive availability model using stochastic reward nets (SRN). The model takes into account (i) the detailed failures and recovery behaviors of multiple VMs, (ii) various other failure modes and corresponding recovery behaviors (e.g., hardware faults, failure and recovery due to Mandelbugs and aging-related bugs), and (iii) dependency between different subcomponents (e.g., between physical host failure and VMM, etc.) in a virtualized servers system. We also show numerical analysis on steady state availability, downtime in hours per year, transaction loss, and sensitivity analysis. This model provides a new finding on how to increase system availability by combining both software rejuvenations at VM and VMM in a wise manner.