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While the asking data rate demand grows higher, a new perspective with the usage of mmWave technology is proposed by the network system of fifth generation New Radio (5G NR) which offers low latency along with higher bandwidth and thus can fulfill the requirement the current generation cellular communication needs. However, utilizing mmWave frequencies involves more gNodeBs (5G base stations) since high-frequency signals cannot travel far, which means that handovers (HOs) occur more frequently; this presents a challenge to ensure smooth data flow for both stationary and mobile users. An increase in HOs increases the risk of ping-pong events, interruptions of data flow, and in the worst-case scenario, can result in complete radio link failure (RLF). The main purpose of this research is to increase handover efficiency by lowering the quantity of unnecessary and delayed handovers while sustaining high received signal quality. To achieve this objective, every handover-related parameter was examined; through MATLAB simulation, a feasible range of critical parameters was determined, such as handover margin (HOM) and time to trigger (TTT). Then all the related data was fed to a deep hybrid model which is built by combining long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and dense neural network (DNN). The outcome from the hybrid model is promising as it could be able to reduce unnecessary HOs by up to 60%, and handover delay was reduced significantly while maintaining strong signal strength which was reflected in the simulation. As deep learning models heavily depend on the dataset, if the dataset is well obtained this type of approach has the potential to be an appropriate way to make handover efficient.

The cellular users, on high speed railways and highways, travel at a very high speed and follow a nearly straight path, in general. Thus, they typically undergo a maximum frequency of handovers in the cellular environment. This requires a very fast triggering of the handover. In the existing method of handover in 5G cellular communication, for high speed users, neither the decision-making of handover nor the triggering of handover is sufficiently fast. This can lead to poor signal quality and packet losses and in the worst case, radio link failure (RLF) during a handover. This paper proposes a forward handover based method, combined with PN sequence detections, to facilitate a quicker handover for high speed users on railways and highways. The proposed method adds some complexity but can offer a significant improvement in the overall handover delay. A simplistic simulation is used to demonstrate the improvement of the proposed method.

The growing adoption of smart grid systems presents significant advancements in the efficiency of energy distribution, along with enhanced monitoring and control capabilities. However, the interconnected and distributed nature of these systems also introduces critical security vulnerabilities that must be addressed. This study proposes a secure communication protocol specifically designed for smart grid environments, focusing on authentication, secret key establishment, symmetric encryption, and hash-based message authentication to provide confidentiality and integrity for communication in smart grid environments. The proposed protocol employs the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, Elliptic Curve Diffie–Hellman (ECDH) for secure key exchange, and Advanced Encryption Standard 256 (AES-256) encryption to protect data transmissions. The protocol follows a structured sequence: (1) authentication—verifying smart grid devices using digital signatures; (2) key establishment—generating and securely exchanging cryptographic keys; and (3) secure communication—encrypting and transmitting/receiving data. An experimental framework has been established to evaluate the protocol’s performance under realistic operational conditions, assessing metrics such as time, throughput, power, and failure recovery. The experimental results show that the protocol completes one server–client request in 3.469 ms for a desktop client and 41.14 ms for a microcontroller client and achieves a throughput of 288.27 requests/s and 24.30 requests/s, respectively. Furthermore, the average power consumed by the protocol is 37.77 watts. The results also show that the proposed protocol is able to recover from transient network disruptions and sustain secure communication.

Background Once considered primarily a pediatric concern, respiratory syncytial virus (RSV) infection is gaining recognition as a cause of significant morbidity and mortality in adults. A better understanding of RSV epidemiology and disease in adults is needed to guide patient management and to assess the need for prophylaxis, vaccines, and treatments. Methods We conducted a retrospective cohort study of adults admitted to four hospitals in Toronto, Canada, between September 2012 and June 2013 with RSV identified by a qualitative real-time reverse-transcriptase polymerase chain reaction assay in nasopharyngeal swab or bronchoscopy specimens. Main outcomes were hospital length of stay, need for intensive care unit (ICU) or mechanical ventilation, and all-cause mortality. Results Eighty-six patients were identified as requiring hospitalization for RSV infection (56% female). Median age was 74 (range 19–102) years; 29 (34%) were < 65 years. Eighty-three (97%) had underlying chronic medical conditions; 27 (31%) were immunosuppressed, and 10 (12%) known smokers. The most common symptoms and signs were cough in 73 (85%), shortness of breath in 68 (79%), sputum production in 54 (63%), weakness in 43 (50%), fever in 41 (48%), and wheezing in 33 (38%). Lower respiratory tract complications occurred in 45 (52%), cardiovascular complications occurred in 19 (22%), and possible co-pathogens were identified in 11 (13%). Sixty-seven (78%) were treated with antibiotics and 31 (36%) with anti-influenza therapy. Thirteen (15%) required ICU care and 8 (9%) required mechanical ventilation. Five (6%) died during hospitalization. Need for ICU and mechanical ventilation were associated with mortality (P ≤ 0.02). Median hospital length of stay was 6 days (mean 10.8 days). Conclusions RSV infection is associated with the need for extended hospital stay, ICU care and mortality in adults of all ages with chronic underlying conditions. Presenting signs and symptoms are nonspecific, co-infections occur, and patients often receive antibiotics and anti-influenza therapy. There is need for ongoing research and development of RSV prophylaxis, vaccines and treatments for adults.

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted from each subband signal of an EEG frame. The features extracted from individual subbands are concatenated, yielding a high-dimensional feature vector. A discriminative subset of features is selected from the feature vector using a graph eigen decomposition (GED)-based approach. Thus, the reduced number of features obtained is effective for differentiating the underlying characteristics of EEG signals that indicate seizure events and those that indicate nonseizure events. The GED method ranks the features according to their contribution to correct classification. The selected features are used to classify seizure and nonseizure EEG signals using a feedforward neural network (FfNN). The performance of the proposed method is evaluated by conducting various experiments with a standard dataset obtained from the University of Bonn. The experimental results show that the proposed seizure-detection scheme achieves a classification accuracy of 99.55%, which is higher than that of state-of-the-art methods. The efficiency of FfNN is compared with linear discriminant analysis and support vector machine classifiers, which have classification accuracies of 98.72% and 99.39%, respectively. Hence, the proposed method is confirmed as a potential marker for EEG-based seizure detection.

One of the most important factors in radio network design is path loss - a phenomenon that may be measured using a variety of techniques, including deterministic, empirical, machine learning, and deep learning models. Each approach has its own limitations, such as inability to capture non-linear interactions, high computational resource demand, and inability to reflect changes in environmental conditions, among many others. The deep learning model has the capacity to recognize intricate patterns and has been essential in removing those obstacles; therefore, in this study it is used for path loss prediction in 5G communications in the South Asian region. The model makes use of long- and short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN), and dense neural network (DNN) approaches to take advantage of all the benefits that each algorithm provides. The performance of the proposed strategy was validated by testing it against multiple state-of-the-art approaches, while relying on the same dataset. An examination of the relevance of characteristics has also been carried out to gain a better understanding of the influence of path loss. A variety of characteristics that are directly related to path loss were evaluated, followed by an examination of how they affect the decision-making process. The results show a possible solution that can help handle this path loss estimation for mmWave communication, especially for SG networks and beyond.

In present era demand for small and portable electronic devices which supports internet connectivity is increasing all around the world and across all industries. These IoT based devices are gaining popularity because of their ease of use. These devices also provide powerful interoperability and feasibility in almost all situation. We can use Internet of Things (IoT) as a great instrument for achieving the desire of connecting people and devices everywhere all the time and in all situations. IoT builds a network of different types of devices such as small handheld devices, vehicles, home appliances etc. that are connected together. This network is incorporated with small sensor nodes, wireless connectivity and actuators for collecting data from adjacent environment and transfer data seamlessly to the base station without any computer or human interaction. Though Internet of Things (IoT) is a primitive term which has greatly attracted the attentions of researchers and academicians it is now getting limited by a number of factors such as flexibility, interoperability, concurrency, scalability and addressing issues. In this paper we will discuss how to make approach when we will go for IoT based solutions considering several parameters such as cost effectiveness, profit generation, maintenance, security etc. In which projects IoT based solutions will be feasible considering. Its network infrastructure, architecture, processing power and scalability.

Background Marjolin’s ulcer (MU) is a rare malignancy arising from various forms of scars. This potentially fatal complication typically occurs after a certain latency period. This article attempts to reveal the importance of the latency period in the prevention and early treatment of the malignancy. Methods A retrospective review of 17 MU patients who underwent surgical procedures between June of 2005 and December 2011 was conducted. Etiology of injuries, latency period, repeated ulceration, and outcomes were recorded. This observational report reveals characteristics of patients who develop MU. Results An incidence of 0.7% of MU was found amongst patients complaining of existing scars in our study; burns and trauma were the most common etiology of MU. The mean latency period was 29 years (SD = 19) and the mean post-ulceration period was 7 years (SD = 9). Statistical analysis revealed a negative correlation between the age of patients at injury and the length of latency period (r = −0.8 , P <0.01), as well as the lengths of pre-ulceration and post-ulceration periods (r = −0.7, P <0.01). Conclusions Patients experience different lengths of pre- and post-ulceration periods during the latency period. Younger patients tend to have a longer latency period. Skin breakdown on chronic scars and chronic unhealed ulcers are two main sources of MU. MU may be preventable with a close surveillance of the ulcer during the latency period.

Abstract Background Understanding the burden of influenza is necessary to optimize recommendations for influenza vaccination. We describe the epidemiology of severe influenza in 50- to 64-year-old residents of metropolitan Toronto and Peel region, Canada, over 7 influenza seasons. Methods Prospective population-based surveillance for hospitalization associated with laboratory-confirmed influenza was conducted from September 2010 to August 2017. Conditions increasing risk of influenza complications were as defined by Canada's National Advisory Committee on Immunization. Age-specific prevalence of medical conditions was estimated using Ontario health administrative data. Population rates were estimated using Statistics Canada data. Results Over 7 seasons, 1228 hospitalizations occurred in patients aged 50–64 years: 40% due to A(H3N2), 30% A(H1N1), and 22% influenza B. The average annual hospitalization rate was 15.6, 20.9, and 33.2 per 100 000 in patients aged 50–54, 55–59, and 60–64 years, respectively; average annual mortality was 0.9/100 000. Overall, 33% of patients had received current season influenza vaccine; 963 (86%) had ≥1 underlying condition increasing influenza complication risk. The most common underlying medical conditions were chronic lung disease (38%) and diabetes mellitus (31%); 25% of patients were immunocompromised. The average annual hospitalization rate was 6.1/100 000 in those without and 41/100 000 in those with any underlying condition, and highest in those with renal disease or immunocompromise (138 and 281 per 100 000, respectively). The case fatality rate in hospitalized patients was 4.4%; median length of stay was 4 days (interquartile range, 2–8 days). Conclusions The burden of severe influenza in 50- to 64-year-olds remains significant despite our universal publicly funded vaccination program. These data may assist in improving estimates of the cost-effectiveness of new strategies to reduce this burden. Despite a universal influenza vaccination program, average annual rates of influenza-associated hospitalization and mortality in adults aged 50–64 years were 22.4 and 0.9/100 000/year in our population. Incidence increased with age, and to a greater extent, in the presence of chronic comorbidities.

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to select effective EEG channels for motor imagery (MI) classification in brain–computer interface (BCI) systems. The method identifies channels with higher entropy scores, which is an indication of greater information content. It discards redundant or noisy channels leading to reduced computational complexity and improved classification accuracy. High entropy means a more disordered pattern, whereas low entropy means a less disordered pattern with less information. The entropy of each channel for individual trials is calculated. The weight of each channel is represented by the mean entropy of the channel over all the trials. A set of channels with higher mean entropy are selected as effective channels for MI classification. A limited number of sub-band signals are created by decomposing the selected channels. To extract the spatial features, the common spatial pattern (CSP) is applied to each sub-band space of EEG signals. The CSP-based features are used to classify the right-hand and right-foot MI tasks using a support vector machine (SVM). The effectiveness of the proposed approach is validated using two publicly available EEG datasets, known as BCI competition III–IV(A) and BCI competition IV–I. The experimental results demonstrate that the proposed approach surpasses cutting-edge techniques.