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Image recognition based on computers is something human beings have been working on for many years. It is one of the most difficult tasks in the field of computer science, and improvements to this system are made when we speak. In this paper, we propose a methodology to automatically propose an appropriate title and add a specific sound to the image. Two models have been extensively trained and combined to achieve this effect. Sounds are recommended based on the image scene and the headings are generated using a combination of natural language processing and state-of-the-art computer vision models. A Top 5 accuracy of 67% and a Top 1 accuracy of 53% have been achieved. It is also worth mentioning that this is also the first model of its kind to make this forecast. [ABSTRACT FROM AUTHOR]

This paper proposes a three-stage buffered Clos-network switch (TSBCS) architecture along with a novel batch scheduling (BS) mechanism. We found that TSBCS/BS can be mapped to a “fat” combined input-crosspoint queued (CICQ) switch. Consequently, the well-studied CICQ scheduling algorithms can be directly applied in TSBCS. Moreover, BS drastically reduces the time complexity of TSBCS scheduling when compared with ordinary CICQ switches of the same number of switch ports, which enables us to build a larger-capacity switch with reasonable scheduling complexity. We further show that TSBCS/BS can achieve 100 percent throughput under any admissible traffic if a stable CICQ scheduling algorithm is used. Direct cell forwarding schemes are proposed to overcome the performance drawback of BS under light traffic loads. With extensive simulations, we show that the performance of TSBCS/BS is comparable to that of output-queued switches and the latter are usually considered as theoretical optimal. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel open-loop autorate failback algorithm (i.e., the AutoRate Faliback for High-Throughput (ARFHT) algorithm) for the emerging highthroughput (HT) IEEE 802.11n wireless networks. ARFHT extends the legacy link adaptation algorithms for single-input-single-output (SISO) wireless networks to be applicable in the context of multiple-input--multiple-output (MIMO)-based 802.11n wireless networks. It adapts the MIMO mode in terms of spatial multiplexing and spatial diversity, which are the two fundamental characteristics of MIMO technology. It also modifies the link estimation and probing behavior of legacy SISO algorithms. The combined adaptation to the appropriate MIMO mode and the modulation coding scheme together achieves more efficient channel utilization. We will present in detail the design guidelines and key ideas for the ARFHT algorithm. A comprehensive simulation study using ns-2 demonstrates that ARFHT achieves excellent throughput and packet error-rate performance in diverse environments and is highly responsive to time-varying link conditions with minimum computational complexity and protocol overhead. [ABSTRACT FROM AUTHOR]

The article discusses several solutions addressing challenges in the memory subsystems within Internet router buffer design. Regarding storage capacity, it is noted that transmission control protocol (TCP) can work well under network congestion through a formula where RTT is a packet's round-trip lifetime and R is the line rate. There is an access requirement that is described by the capabilities of current static random access memory (SRAM) and dynamic RAM (DRAM). To address the queuing scheme, a typical router is noted to have multiple flow queues, making the router buffer even harder to create. It is emphasized that memory-management algorithms (MMAs) must meet the latency gap between the SRAM and DRAM to avoid memory overflow.

This paper addresses the design of high-performance buffers for high-end Internet routers. The buffers are typically implemented using a combination of SRAM and DRAM technologies in order to simultaneously meet the routers' high speed and capacity requirements. The major challenge in designing router buffers is to maintain multiple flow queues in the memory, unlike computer memory buffers (i.e., memory system). The major objective is to minimize the use of expensive but fast SRAM while providing acceptable delay guarantees to packets. In this paper, we first investigate hybrid SRAM/DRAM solutions proposed in the past. We show that one of the architectural limitations of these solutions is that the required SRAM size grows linearly with the number of flows in the system. This prevents the solutions from scaling to support a large number of flows. We then break down this shortcoming by proposing a parallel hybrid SRAM/DRAM (PHSD) architecture. We design a series of memory management algorithms (MMAS) for PHSD, based on tradeoffs between the complexity of the MMAs and the guarantee of in-order delivery of packets (segmentations). We perform a detailed analysis of the proposed algorithms and conduct extensive simulations to show that PHSD can significantly outperform solutions proposed in the past in terms of the SRAM requirements and packet delay. [ABSTRACT FROM AUTHOR]

The design of the medium-access control (MAC) protocol is the most crucial aspect for high-speed and high-performance local and metropolitan area networks, since the decisions made at this level determine the major functional characteristics of these networks. Most of the MAC protocols proposed in the literature are not suitable for multimedia applications, since they have been designed with one generic traffic type in mind. As a result, they perform quite well for the traffic types they have been designed for, but poorly for other traffic streams with different characteristics. In this paper, we propose an integrated MAC protocol called the Multimedia-MAC (M-MAC), which integrates different MAC protocols into a hybrid protocol in a shared-medium network to efficiently accommodate various types of multimedia traffic streams with different characteristics and quality-of-service demands, namely, a constant-bit-rate traffic, bursty traffic (say, variable-bit-rate traffic), and emergency messages (say, control messages). We have developed a mathematical framework for the analysis and performance evaluation of our M-MAC protocol, which involves a queueing system with vacation. We have applied our M-MAC design approach to a wavelength-division multiplexing network, and evaluated its performance under various traffic conditions. [ABSTRACT FROM AUTHOR]

We address the problem of the performance analysis of the stochastic fair sharing (SFS) algorithm for fair link sharing. The SFS scheme has been proposed to carry out a fair link sharing and fair sharing among virtual private networks. Depending upon the current utilization and provisioned capacities of the classes, the SFS admission control algorithm decides which sessions to accept and which to reject. In this letter, we undertake the performance evaluation of the SFS scheme analytically. We explore the tradeoff between fairness and the blocking probability by varying the trunk reservation parameter. The results show that the analytical performance measure agrees well with the simulation results. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of routing and wavelength assignment as well as the problem of the placement of wavelength converters in all optical networks. In particular, we present a general framework, based on the blocking island (BI) paradigm, to illustrate how it can be used to solve these problems in a unified way. We first give a brief introduction about the BI graph network model, and then use this model to derive simple and general algorithms that can be used in various applications in optical networks. We discuss the implementation issues of our algorithms and present simulation results to evaluate and compare our solutions with other heuristic algorithms under both static and dynamic traffic assumptions. [ABSTRACT FROM AUTHOR]

A Video-on-Demand (VOD) system delivers videos on demand over an installed network. Due to the large size of digitized videos, expensive video servers with high I/O capability are needed in order to provide VoD services in metropolitan areas. In addition, there is a great need for efficient networking distribution/interaction schemes so that the video servers can serve as many clients as possible. In particular, because of scalability problems, the classical unicast VoD system is not suitable for large-scale deployments. In this paper, a highly scalable VoD system with a low per-user cost is described and evaluated. We first analyze the performance degradation problems using recently proposed VoD systems, namely batched and centralized-buffer VoD systems that occur during the handling of interactions. Then a new system called the Multi-Batch Buffer (MBB) system, which attempts to solve these problems, is proposed. The proposed system handles a majority of interaction requests by scalable buffering techniques employed in the buffer of the local servers and the set-top boxes (STBs). We have performed extensive simulation for the analysis and performance evaluation of our proposed VoD system. The simulation results will demonstrate that our VoD system is very scalable and outperforms related state-of-the-art VoD systems. [ABSTRACT FROM AUTHOR]

Presents information on a study which analyzed the performance of multiple input-queued asynchronous transfer mode switches that use parallel iterative matching (PIM) for scheduling the transmission of head-of-line cells in the input queues. Analysis of the PIM switch; Numerical results; Conclusions.

Provides an overview of research efforts conducted in the design of scalable high-speed switches capable of providing quality-of-service guarantees to various traffic streams. Information on the queuing strategies; Description on the virtual output queuing/combined input-output queuing switches; Conclusion.

Presents information on a study which surveyed the technologies that enabled the Quality of Service (QoS) support for voice communications in the next-generation Internet. Review of technologies for voice over Internet Protocol (IP)-based networks; Basic mechanisms in the IETF Internet telephony architecture; Conclusions.

Proposes a parallel algorithm for computing the Euclidean distance transform (EDT) for binary images on reconfigurable mesh-connected computers. Details on the reconfigurable mesh-connected computers; Information on the sequential algorithm; Description of the two-dimensional parallel algorithm; Conclusions.

Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual’s health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19. [ABSTRACT FROM AUTHOR]

Broadband integrated services digital networks (BISDN) are designed to offer a variety of services with bit rates ranging from several kb/s (e.g. teleactions) to hundreds of Mb/s (e.g. HDTV), and in some cases approaching Gb/s (e.g. in interconnections of high-speed LANs). A multiplicity of rates and the burstiness of traffic sources lead naturally to systems based on the fast packet switching (or asynchronous transfer mode) concept. The requirements of data buffering and high-speed processing of packet headers have resulted in a plethora of proposals for ATM switching nodes and an equal number of ways for evaluating them. In particular, the class of non-blocking architectures of ATM switches has received the most attention from the research community. This paper reviews this class of architecture with emphasis on contention resolution methods and communication traffic performance. The contention resolution methods are divided into four categories: input buffering, output buffering, shared buffering, and hybrid buffering. The communication traffic characteristics are divided into two categories: uniform traffic and bursty traffic. [ABSTRACT FROM AUTHOR]

Describes an embedding method for optimally embedding hierarchical hypercube networks into the hypercube. Type of transformations the method is based on; Practical importance of the method; Presentation of the hypercube networks to be embedded.

The evolution of 4G telecommunication propagated various resource‐crunched clients to experience rate‐effective resources at ease. However, it extends its underlying centralised architecture, which arouses various challenges correlated with network data availability, network information protection, and operational infrastructure charges. With the recent revolution of telecommunication, 5G networks promised to provide credible schemes like the high quality of service, ultra‐low latency, and much security over the pre‐existing architecture. However, the deployment of end‐to‐end 5G network cutting‐edge systems in the present heterogeneous world limits its core idea of extensive data privacy, native interoperability, risk‐free interference, and radio spectrum sharing. Perhaps, to achieve its true capability, improved versions of blockchain technology could be aligned to strengthen various real‐time complex applications at a flourishing rate. One of the multiplexed real‐time enterprise applications is a keyword search engine where the integrity of user data files and keyword searches are bound to come under cyber hackers. On the one hand, it was found that when a 5G‐based blockchain emulated network gets deployed with intact encryption techniques, the entire system facilitates to give reliable, efficient, and risk‐free keyword search over variegated 5G network data and its complex computational calculations. Consequently, the use of blockchain‐based decentralised cloud orchestration scheme at various levels enabled the architecture to remain incorruptible and protects all the confidential files and keywords in a fully controlled file access environment. The results of the simulation kernel shows that proposed architecture which, when combined with blockchain‐based decentralised cloud orchestration network system, justify all the essential characteristics and effectuates the optimal use of 5G network sharing by each network entity. [ABSTRACT FROM AUTHOR]

Recently, the development of building a smart city has been inclined rapidly with the help of emerging technologies in 5G and internet of things (IoT). In addition with smart city, concepts of smart traffic systems and smart health are also being developed. Combining the above two, smart navigation system for ambulances is being coined. When critical patients are referred by an ambulance, there is a lot of time wastage in facilitation of information and the hospital is completely unaware of the patient's parameters who is arriving beforehand. Therefore, patient monitor and ambulance tracking system is an efficient system used to carry out a quick thirty-second diagnosis using heartbeat, temperature, breath rate sensors to record vital patient parameters required initially by the doctors to start any treatment and remotely transmit these parameters over wireless medium to the hospital even before the ambulance is deployed. The patient also gets an instant way to request an ambulance on touch of a button without having to call up the hospital and also instantly send a short message service (SMS) to an emergency contact giving both the hospital and contact necessary emergency details a lot earlier. This saves a lot of time, each second of which is important to the patient at life risk. The patient can keep a track of the ambulance's location, which gives them an idea of its arrival and can also get instant navigation toward the nearest hospital for themselves if need be. The device can be kept with the patient and also installed inside the ambulance. The device enables the use of IoT sensors and android applications for better user interaction and efficient information transmission. The model dictated here is potentially outperformed with easiness and in a better way toward smart healthcare monitoring services. [ABSTRACT FROM AUTHOR]

The author reflects on the development of the wireless telecommunication technology, WiMAX systems. The technology has shown great potential to provide broad- band transmission services to residential premises as a wireless access network. Furthermore, WiMAX systems can easily be configured as wireless metropolitan area networks.

Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks. [ABSTRACT FROM AUTHOR]

High-speed routers rely on well-designed packet buffers that support multiple queues, provide large capacity and short response times. Some researchers suggested combined SRAM/DRAM hierarchical buffer architectures to meet these challenges. However, these architectures suffer from either large SRAM requirement or high time-complexity in the memory management. In this paper, we present scalable, efficient, and novel distributed packet buffer architecture. Two fundamental issues need to be addressed to make this architecture feasible: 1) how to minimize the overhead of an individual packet buffer; and 2) how to design scalable packet buffers using independent buffer subsystems. We address these issues by first designing an efficient compact buffer that reduces the SRAM size requirement by (k-1)/k. Then, we introduce a feasible way of coordinating multiple subsystems with a load-balancing algorithm that maximizes the overall system performance. Both theoretical analysis and experimental results demonstrate that our load-balancing algorithm and the distributed packet buffer architecture can easily scale to meet the buffering needs of high bandwidth links and satisfy the requirements of scale and support for multiple queues. [ABSTRACT FROM AUTHOR]

Introduces a series of articles which review techniques, architectures and technologies that make possible the provision of differentiated qualities of service in very high capacity switches.

Cervical cancer has become the third most common form of cancer in the in-universe, after the widespread breast cancer. Human papillomavirus risk of infection is linked to the majority of cancer cases. Preventive care, the most expensive way of fighting cancer, can protect about 37% of cancer cases. The Pap smear examination is a standard screening procedure for the initial screening of cervical cancer. However, this manual test procedure generates many false-positive outcomes due to individual errors. Various researchers have extensively investigated machine learning (ML) methods for classifying cervical Pap cells to enhance manual testing. The random forest method is the most popular method for anticipating features from a high-dimensional cancer image dataset. However, the random forest method can get too slow and inefficient for real-time forecasts when too many decision trees are used. This research proposed an efficient feature selection and prediction model for cervical cancer datasets using Boruta analysis and SVM method to deal with this challenge. A Boruta analysis method is used. It is improved from of random forest method and mainly discovers feature subsets from the data source that are significant to assigned classification activity. The proposed model's primary aim is to determine the importance of cervical cancer screening factors for classifying high-risk patients depending on the findings. This research work analyses cervical cancer and various risk factors to help detect cervical cancer. The proposed model Boruta with SVM and various popular ML models are implemented using Python and various performance measuring parameters, i.e., accuracy, precision, F 1 – Score , and recall. However, the proposed Boruta analysis with SVM performs outstanding over existing methods. [ABSTRACT FROM AUTHOR]

Leveraging concurrent transmission is a promising way to improve throughput in wireless networks. Existing media access control (MAC) protocols like carrier sense multiple access always try to minimize the number of concurrent transmissions to avoid collision, although collisions at sender sides are harmless to the overall performance. The reason for such conservative strategy is that those protocols cannot obtain accurate channel status (who is transmitting and receiving) with low cost. They can only avoid potential collisions through rough channel status (idle or busy). To obtain additional information in a cost-efficient way, we propose a novel coding scheme, Attachment Coding, to allow control information to be “attached” on data packet. Nodes then transmit two kinds of signals simultaneously, without degrading the effective throughput of the original data traffic. Based on Attachment Coding, we propose an Attached-RTS MAC (AR-MAC) to exploit exposed terminals for concurrent transmissions. The attached control information provides accurate channel status for nodes in real time. Therefore, nodes can identify exposed terminals and utilize them for concurrent transmission. We theoretically analyze the feasibility of Attachment Coding, and implement it on the GNU Radio testbed to further verify it. We also conduct extensive simulations to evaluate the performance of Attached-RTS. The experimental results show that by leveraging Attachment Coding, AR-MAC achieves up to 180 percent in dense deployed ad hoc networks. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor and actor networks (WSANs) have been extensively deployed to monitor physical environment and facilitate decision making based on data collected. Emerging applications such as road surveillance highlight some interesting research issues in WSANs, including coordination problems in sensor–actor or actor–actor communications. In this paper, the issue of choosing a set of working actors for coordinating data transmission in a road sensor and actor network with minimum communication cost is studied. A theoretical model is introduced to analyze the communication cost of data transmission in WSANs, and the sensor–actor coordination problem is formulated as an optimization problem. It is demonstrated that the problem can be divided into subproblems, and optimal solutions can be obtained by using a dynamic programming algorithm. A novel graph-based algorithm is also proposed with a communication-cost graph used to depict the cost of data transmission and a modified Dijkstra's algorithm to find optimal solutions in reduced time complexity. The efficiency of the proposed algorithms is confirmed using extensive simulations. [ABSTRACT FROM PUBLISHER]

In recent years, a number of sophisticated architectures have been proposed to provide video-on-demand (VoD) service using multicast transmissions. Compared to their unicast counterparts, these multicast VoD systems are highly scalable and can potentially serve millions of concurrent users. Nevertheless, these systems are designed for streaming constant-bit rate (CBR) encoded videos and thus cannot benefit from the improved visual quality obtainable from variable-bit rate (VBR) encoding techniques. To tackle this challenge, this paper presents a turbo-slice-and-patch (TSP) algorithm to support VBR video streaming in a multicast VoD system. Results obtained from trace-driven simulation of 300 VBR videos show that serving VBR videos with the TSP algorithm increases the average latency by only 9% compared to the CBR case with the same average video bit rate. Moreover, in 165 out of the 300 video titles, the TSP algorithm actually butperforms the CBR equivalent by shortening the latency by 0.04%–99%. Given that we can achieve similar visual quality by encoding VBR video at half the average rate of CBR video, this TSP algorithm can potentially serve VBR videos with more consistent visual quality and with less resource compare to CBR-based video streaming systems. [ABSTRACT FROM AUTHOR]

Provides an overview of a scalable computer network quality of service (QoS) approach. Advantage of a proportional QoS model over a conventional relative QoS model; Effectiveness of the proportional service model as a solution for quantitative service differentiation in Internet protocol networks; Details of the implementation strategies for various QoS metrics.

Recently, machine learning algorithms have been proposed to design new security systems for anomalies detection as they exhibit fast processing with real-time predictions. However, one of the major challenges in machine learning-based intrusion detection methods is how to include enough training examples for all the possible classes in the model to avoid the class imbalance problem and accurately detect the intrusions and their types. in this article, we propose a novel weighted classes classification scheme to secure the network against malicious nodes while alleviating the problem of imbalanced data. in the proposed system, we combine a supervised machine learning algorithm with the network node past information and a specific designed best effort iterative algorithm to enhance the accuracy of rarely detectable attacks. The machine learning algorithm is used to generate a classifier that differentiates between the investigated attacks. The system stores these decisions in a private database. Then, we design a new weight optimization algorithm that exploits these decisions to generate a weights vector that includes the best weight for each class. The proposed model enhances the overall detection accuracy and maximizes the number of correctly detectable classes even for the classes with a relatively low number of training entries. The UNSW dataset has been used to evaluate the performance of the proposed model and compare it with state of the art techniques. [ABSTRACT FROM AUTHOR]

In this work, we present a unified framework for the performance analysis of dual-hop underwater wireless optical communication (UWOC) systems with amplify-and-forward fixed gain relays in the presence of air bubbles and temperature gradients. Operating under either heterodyne detection or intensity modulation with direct detection, the UWOC is modeled by the unified mixture Exponential-Generalized Gamma distribution that we have proposed based on an experiment conducted in an indoor laboratory setup and has been shown to provide an excellent fit with the measured data under the considered lab channel scenarios. More specifically, we derive the cumulative distribution function (CDF) and the probability density function of the end-to-end signal-to-noise ratio (SNR) in exact closed-form in terms of the bivariate Fox’s H function. Based on this CDF expression, we present novel results for the fundamental performance metrics such as the outage probability, the average bit-error rate (BER) for various modulation schemes, and the ergodic capacity. Additionally, very tight asymptotic results for the outage probability and the average BER at high SNR are obtained in terms of simple functions. Furthermore, we demonstrate that the dual-hop UWOC system can effectively mitigate the short range and both temperature gradients and air bubbles induced turbulences, as compared to the single UWOC link. All the results are verified via computer-based Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

The holy grail of networking is to create cognitive networks that organize, manage, and drive themselves. Such a vision now seems attainable thanks in large part to the progress in the field of machine learning (ML), which has now already disrupted a number of industries and revolutionized practically all fields of research. But are the ML models foolproof and robust to security attacks to be in charge of managing the network? Unfortunately, many modern ML models are easily misled by simple and easily-crafted adversarial perturbations, which does not bode well for the future of ML-based cognitive networks unless ML vulnerabilities for the cognitive networking environment are identified, addressed, and fixed. The purpose of this article is to highlight the problem of unsecure ML and to sensitize the readers to the danger of adversarial ML by showing how an easily crafted adversarial ML example can compromise the operations of the cognitive self-driving network. In this article, we demonstrate adversarial attacks on two simple yet representative cognitive networking applications (namely, intrusion detection and network traffic classification). We also provide some guidelines to design secure ML models for cognitive networks that are robust to adversarial attacks on the ML pipeline of cognitive networks. [ABSTRACT FROM AUTHOR]

Neurogenic paraosteoarthropathies are ectopic ossifications which develop near the joints. They are a process of neo-ectopic osteogenesis occurring after central or peripheral neurological lesions, in some types of comas (oxygen carbon intoxication, prolonged sedation) and following peripheral traumas including burns. They inolve almost exclusively the large proximal joints of the limbs. Elbow is the second area of involvment. The purpose of our study was to analyze the results of surgical arthrolysis in 37 patients with elbow stiffness due to neurogenic osteoarthropathy of the elbow. We conducted a retrospective study of 35 patients and 37 elbows over a 25-year period. Preoperative assessment included clinical and radiological examination. Since 2003 the patients had undergone systematic elbow arthroscopy. The gold standard surgical treatment was arthrolysis. All patients underwent functional rehabilitation protocol. Outcomes were analyzed after a mean 5-year follow-up period (6 months - 10 years). Neurogenic paraosteoarthropathy was caused by head injury with coma in 58.8% of cases. Preoperative assessment showed bending stiffness in the majority of cases (88%), severe or very severe in 64.7% of cases. Intraoperatively functional elbow range of motion from -30° to 130° was obtained in 61.7% of cases and in 41% of cases in the long term. Ulnar nerve liberation was satisfactory in 92% of cases. No postoperative instability of the elbow was reported. Two patients with definitive neurological lesions had osteoma recurrence. The results were equivalent regardless surgical delay. Surgical arthrolysis is an effective treatment for neurogenic osteomas of the elbow. [ABSTRACT FROM AUTHOR]

A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential–generalized gamma (EGG) distribution. We use the expectation–maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient. [ABSTRACT FROM AUTHOR]

Several surgical procedures, including mosaic arthroplasty, can be used to treat patients with cartilage loss in the femoral condyles. This study aims to assess mid-term clinical and radiological results as well as the main prognostics elements. We conducted a retrospective epidemiological study over a period of 15 years. During the study period we collected data from 35 workable medical records of patients with osteochondritis dissecans of the femoral condyles treated by mosaic arthroplasty, with an average follow-up of 24 months. The level of complaints as well as preoperative knee function were evaluated and compared with the healthy knee according to the International Cartilage Repair Society (ICRS) score, the International Knee Documentation Committee (IKDC) score and visual analogue scale (VAS). It was less than 60% in 27 patients. During the follow-up period, the results were analyzed according to Hughston's functional and radiological criteria. After an average follow-up of 24 months, algoneurodystrophy was reported in 5 cases with a single case of haemarthrosis. A net ICRS score improvement was observed with a mean increasing from 54% to 74% on the follow-up visit. Most of patients were satisfied or very satisfied (82.9%). The elements of good prognosis recognized in our study included: a mean time between symptom onset and surgery of less of 18 months, having deep lesions with a diameter less than 02 cm and having lesions in the internal condyle. The treatment of cartilage loss is necessarily based on the correction of its direct and indirect causes namely the morphotype, the laxity and meniscus capital. No consensus in the decision-making was reached and no one could confirm the superiority of a technique in relation to the other but we can say that cartilage defect which sizes from 2 to 4 cm? may be the best indication for mosaic arthroplasty. [ABSTRACT FROM AUTHOR]

We conducted a retrospective study of 35 patients with subungual exostosis of the hallux, also known as Turrett's exostosis, in the Department of Orthopedics and Traumatology at the Senior Military Hospital of Instruction of Tunis over the period between 1995 and 2015. We here summarize the outcomes of patients treated for this disease. The average age of patients was 29 years, with a sex ratio of 1.7. The median consultation time was six months. This delay in consultation was caused by a diagnostic error due to clinical picture resemblance with ingrown nail. Diagnosis was always confirmed by frontal and profile X-ray of the involved hallux. Treatment was based on total resection of the exostosis either through large ungual window or by latero-ungual approach. Anatomo-pathological examination was performed systematically. It allowed to confirm the benignity of the disease in all cases. All patients recovered and returned to their previous activity, on average, in 2 months. No patient had a recurrence. [ABSTRACT FROM AUTHOR]

• This research emphasizes enhancing data security in real-time IoV communication considering secure communication also presents a trust-driven privacy method using encryption and steganography for IoV, "Efficient Algorithm for Secure Transmission (EAST)," integrating encryption and steganography algorithms. • We develop a data encryption method for IoV communication and verification using distinct symmetric encryption for all three components of vehicle communication. • Development of the standards on multiple systems for secure data transmission in IoV. Comprehensive performance measurement from various angles, including transaction cost, memory overhead, processing time, and energy use. • The proposed EAST method is compared with existing AES [12] , DES [13] , G-DES [14] , and standard LSB [15] using different indicators such as time efficiency, avalanche effect, PSNR, and file cover size. High-speed wireless network technologies play a vital role in autonomous vehicle communication systems (AVS), i.e., IoV (Internet of Vehicles), and enhance the effectiveness and reliability of the communication network. Since the size of IoV communication is rapidly growing, it is now an essential part of our regular routines. Due to its dynamic nature and massive size, it is essential to avoid any miss-communication and protect the data privacy of each vehicle.IoVallowsAVS to link remotely to each other and to various platforms and groups that may be using the same communications system. This quality link between vehicles and the need for information exchanges among vehicles introduces vulnerability and threats that can be exploited by cybercriminals. This research emphasizes on enhancing data security in real-time IoV communication. It presents a trust-driven privacy method using encryption and steganography for IoV. This method uses an Efficient Algorithm for Secure Transmission (EAST) that integrates encryption and steganography algorithms. The proposed EAST is then compared to the various current state of arts, i.e., AES (Advanced Encryption Standard), DES (Data Encryption Standard), G-DES (Generalized DES), and Standard LSB on the basis of various performance measuring parameters, i.e., encryption and decryption time, efficiency, avalanche effect, PSNR, and file cover size. Experimental results are promising for the proposed EAST method, which reported a better time efficiency of 0.86 ms, avalanche effect of 58.81%, and PSNR of 78.58%, compared to the current state arts. [Display omitted] [ABSTRACT FROM AUTHOR]

This paper deals with the mathematical modeling of the second wave of COVID-19 and verifies the current Omicron variant pandemic data in India. We also we discussed such as uniformly bounded of the system, Equilibrium analysis and basic reproduction number R 0 . We calculated the analytic solutions by HPM (homotopy perturbation method) and used Mathematica 12 software for numerical analysis up to 8th order approximation. It checked the error values of the approximation while the system has residual error, absolute error and h curve initial derivation of square error at up to 8th order approximation. The basic reproduction number ranges between 0.8454 and 2.0317 to form numerical simulation, it helps to identify the whole system fluctuations. Finally, our proposed model validated (from real life data) the highly affected five states of COVID-19 and the Omicron variant. The algorithm guidelines are used for international arrivals, with Omicron variant cases updated by the Union Health Ministry in January 2022. Right now, the third wave is underway in India, and we conclude that it may peak by the end of May 2022. [ABSTRACT FROM AUTHOR]

The Industrial Internet of Things (IIoT) has led to the growth and expansion of various new opportunities in the new Industrial Transformation. There have been notable challenges regarding the security of data and challenges related to privacy when collecting real-time and automatic data while observing applications in the industry. This paper proposes an Federated Transfer Learning for Authentication and Privacy Preservation Using Novel Supportive Twin Delayed DDPG (S-TD3) Algorithm for IIoT. In FT-Block (Federated transfer learning blockchain), several blockchains are applied to preserve privacy and security for all types of industrial applications. Additionally, by introducing the authentication mechanism based on transfer learning, blockchains can enhance the preservation and security standards for industrial applications. Specifically, Novel Supportive Twin Delayed DDPG trains the user model to authenticate specific regions. As it is considered one of the most open and scalable interacting platforms of information, it successfully helps in the positive transfer of different kinds of data between devices in more significant and local operations of the industry. It is mainly due to a single authentication factor, and the poor adaptation to regular increases in the number of users and different requirements that make the current authentication mechanism suffer a lot in IIoT. As a result, it has been very clearly observed that the given solutions are very useful. [ABSTRACT FROM AUTHOR]

Femto base stations (FBSs) are cost-effective choices for wireless service providers (WSPs) to provide better indoor coverage and capacity in cellular networks. With the introduction of femtocells, spectrum allocation among macro base stations (MBSs) and numerous FBSs becomes challenging. WSPs will allocate resources among hierarchical infrastructures to mitigate mutual interference and maximize WSPs' revenue from service provisioning. In this paper, we propose a channel allocation and service provision framework to help WSPs allocate resources and increase revenue. The spectrum allocation and revenue maximization problem is formulated as a convex problem, and a centralized method is proposed to efficiently solve it. We also propose a distributed algorithm called LD (based on Lagrangian decomposition) to achieve a near-optimal solution with low computational and communication complexity, which can be used when the system is on a large scale. The simulation results show that our scheme can significantly improve the system's revenue. [ABSTRACT FROM PUBLISHER]

Dynamic spectrum sharing is a promising approach to alleviate the spectrum scarcity problem in wireless communications. Hence, it enhances the flexibility and, as a result, the efficiency of spectrum usage. In this paper, we address the problem of spectrum sharing in the secondary spectrum market involving multiple primary and secondary strategic users. In this scenario, primary users (PUs) are willing to offer part of their spectrum to secondary users (SUs) to earn extra revenue. For PUs, the more spectrum that is sold to SUs, the more revenue will be made from spectrum leasing. However, they will suffer quality-of-service (QoS) degradation for their primary service. SUs have their spectrum demands. They buy spectrum by taking into account the service satisfaction and cost in terms of payment. The profit of PUs and SUs is directly related to the bandwidth proportional allocation and price charging through a spectrum broker. PUs compete with each other to offer spectrum leasing, and SUs compete for spectrum sharing. We model this scenario as a noncooperative game and analyze it by exploring the properties of Nash equilibrium point. We discuss the two cases under complete and incomplete information assumptions. The simulation results demonstrate that our theoretic analysis is sound and accurate. [ABSTRACT FROM PUBLISHER]