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Abstract Background One of the most common emergencies seen in emergency departments is acute gastrointestinal bleeding (GIB). It’s associated with more disease burden and mortality. Increased venous lactate levels are common in critically ill patients, and it has been used as a possible predictor of patients’ outcomes in many critical cases, but not much is known about its predictive role in patients with acute GIB. With increasingly limited health care resources, there has been such an interest in coast saving measures and measuring lactic acid through venous blood gases is such a simple bedside test which can be easily done to patients with acute GIB at emergency department. Objective This study assessed the predictable value of lactate level on hospital admission on resource use, including length of hospital stay and admission to the intensive care unit (ICU), as well as other patient-oriented outcomes, including the need for blood transfusions and endoscopy. Patients and methods A prospective cohort hospital based study that included 300 patients with acute GIB who presented to emergency department (ED) of Assiut University Hospital between September 2019 and June 2022 were subjected to venous blood lactate through venous blood gases. Results The current study enrolled 300 patients with acute GIB. Two hundred of those patients had elevated blood lactate and 100 patients had normal blood lactate. It was found that patients with elevated blood lactate had significantly prolonged hospital stay (7.38 days ± 2.05 days vs. 3.96 days ± 1.74 days; (P

ABSTRACTBackground Pain control after surgery is of highest priority. Erector spinae plane block [ESPB] was reported to be a successful method to reduce pain scores and consumption of postoperative opioids. However, hardly any investigation has looked at the ideal volume of bupivacaine for ESPB. So, this work tried to fill this gap of literature in patients undergoing simple nephrectomy.Methods In this randomized double blinded control study, 100 cases undergoing simple nephrectomy were randomized into 4 groups with 25 patients per group. Group E1, 2, and 3 received ESPB with 2.5, 3.4, and 6.6 ml/segment of 0.25% bupivacaine, respectively, whereas there was no block given to the control group. Intraoperative fentanyl consumption, heart rate [HR] and the blood pressure [BP], Postoperative morphine consumption, time passed to first analgesic request, numerical rating score [NRS] of pain, the complications, HR and BP were documented for 24 hours after the operation.Results The reduction in intraoperative fentanyl consumption, postoperative morphine consumption and NRS were a significantly different between the block groups and control. Group E3 had significant difference from other groups with the longest time to first analgesia request. A lower HR was shown in block groups but within normal range. BP showed insignificant difference between groups. There were no reported complications in all study groups.Conclusions The volume of 6.6 ml/level was associated with the best pain control, without significant hemodynamic changes or adverse events as compared to other groups. This suggests that this volume may be the optimal for ESPB in patients undergoing simple nephrectomy.

Amplify-and-forward (AF) orthogonal frequency division multiplexing (OFDM) transmissions encounter a significant difficulty in the form of in-phase and quadrature phase (IQ) mismatch. Previous reports on this problem have solely been discussed in the context of uncoded transmissions. In addition, in these precedent studies, IQ equalization must be conducted following the estimation stage for accurate detection of data symbols. This research delves into the issue of the IQ mismatch between transmission and reception in AF-OFDM systems in the context of channel coding. We design a magnificent code-aided approach to predict the overall channel impulse responses (CIRs), which encompass the actual CIRs and IQ mismatch originating at the source, relay, and destination. Instead of using a collection of algorithms, the proposed approach can be utilized to estimate nine parameters simultaneously. Due to the impractical nature of the precise maximum-likelihood (ML) strategy to this situation, we instead utilize an expectation-maximization (EM) process as a low complexity strategy to predict the parameters under consideration. The suggested estimation approach uses an iterative process to improve predictions exploiting the a priori knowledge gained from the soft information supplied by the channel decoder. In addition, we demonstrate how to carry out data detection by making use of the estimated parameters. The simulation results verify the effectiveness of the proposed estimator and detector for usage in real-world settings, with superiority over the conventional ones.

The space-time block code (STBC) classification has recently been recognized as a prominent fundamental tool for the development of smart communication systems, which are applied in both the defense and private sectors. The use of multiple antennas at the transmitter introduces a new dimension of difficulty to the signal classification task since it is essential to make judgments about the number and arrangement of broadcast antennas. The reported research has only addressed STBC classification over non-cooperative environments, with no solutions offered for cooperative transmissions. A comprehensive STBC classification algorithm for amplify-and-forward cooperative transmissions is proposed in this investigation. The classification procedure is predicated on analytical formulas established for the time-domain features of the STBC signals. There are two phases involved in implementing the offered algorithm into practice. The first stage includes estimating the correlation functions of the incoming signals across two different time periods using second-order statistics, and a false alarm oriented assessment is utilized in the second stage to produce a judgment. The proposed algorithm’s many benefits involve not necessitating any previous information about the modulation form, channel characteristics, signal-to-noise ratio (SNR), or the start of STBC blocks. Simulation experiments demonstrate that the proposed algorithm can give adequate classification performance in the presence of transmission imperfections even at relatively low SNR levels.

Background and Aim : Drug abusers are presenting a challenge to the anaesthetist because of the added potential risks involved in the administration of anaesthesia to this subset of patients. In this study, we aimed at screening all patients scheduled for elective orthopaedic, general, vascular, and plastic at the Cairo University Teaching Hospital during a set period of time for the most commonly abused drugs in Egypt. Methods : All patients included in the study were consented for taking part in this study. Each patient was asked to answer a form and submit a urine sample in order to be screened for the most commonly abused drugs in Egypt (hashish, tramadol, benzodiazepine, and morphine). Patients were then followed up both intra and post-operatively. Incidence of drug abuse among those patients was set as primary outcome, intraoperative hemodynamic, Richmond Agitation-Sedation Scale (RASS) scores as well as clinical recovery scores (CRS) were set as secondary outcomes. Results : Out of a total of 1106 patients screened during the study period, only 500 met inclusion criteria and consented to be part of the study. Results showed evidence of drug abuse among 14.4% of study patients, with frequencies of drugs abused as follows: hashish (11.4%); tramadol (5%); benzodiazepines (1.6%); and morphine (0.4%). Most patients that showed evidence of drug abuse were among those scheduled for orthopaedic surgeries, and a higher percentage of them were males. Conclusions : Within the study group, cannabinoids were the most frequent substance of abuse, followed by tramadol and then other opioids.

This paper creates an approximate three-dimensional model for normal and cancerous cervical cells using image processing and computer-aided design (CAD) tools. The model is then exposed to low-frequency electric pulses to verify the work with experimental data. The transmembrane potential, pore density, and pore radius evolution are analyzed. This work adds a study of the electrodeformation of cells under an electric field to investigate cytoskeleton integrity. The Maxwell stress tensor is calculated for the dispersive bi-lipid layer plasma membrane. The solid displacement is calculated under electric stress to observe cytoskeleton integrity. After verifying the results with previous experiments, the cells are exposed to a nanosecond pulsed electric field. The nanosecond pulse is applied using a drift-step rectifier diode (DSRD)-based generator circuit. The cells’ transmembrane voltage (TMV), pore density, pore radius evolution, displacement of the membrane under electric stress, and strain energy are calculated. A thermal analysis of the cells under a nanosecond pulse is also carried out to prove that it constitutes a non-thermal process. The results showed differences in normal and cancerous cell responses to electric pulses due to changes in morphology and differences in the cells’ electrical and mechanical properties. This work is a model-driven microdosimetry method that could be used for diagnostic and therapeutic purposes.

In this work, a compact, light-weight, low-cost, and easy install and incorporate mmwave printed square loop antenna with a perturbed ground plane is proposed. The antenna is fabricated on an ultra-thin 0.254 mm Rogers RT/Duroid 5880 substrate. The antenna resonates between 26 GHz and 40 GHz providing the broad bandwidth of 13 GHz ( $\sim 47$ %). First, a single element consisting of three rectangular square loops and a transmission line with total dimensions of 9 mm $\times11$ mm ( $0.84\,\,\lambda _{0} \times 1\,\,\lambda _{0}$ ) is designed. The loop elements are arranged on top of each other and with the further insertion of square slot in ground plane, wideband resonance response has been achieved. The antenna demonstrates gain of more than 3.3 dBi, and radiation and a total efficiency of 98% at 28 GHz. The proposed design enables spatial diversity and minimize the effects of interference between adjacent channels by providing dual-beam within desired frequency band. In addition, a linear array ( $18.5\times22.5$ mm2) of four elements with a traditional feeding network is also designed, simulated, and measured. The gain of the array is 10.1 dBi, while the radiation and the total efficiency is more than 92 % at 28 GHz. A brief literature review and comparison of this work with other published works is also presented. To validate the proposed design and concept, a prototype is fabricated for both, a single element, and an array. It is found that the measured results and the computed results are in good agreement. Therefore, we believe that this system will find its applications within modern mmwave communication cellular devices.

The development of intelligent radios in wireless applications is mainly driven by the growing need for higher data rates, along with constrained spectrum resources. An intelligent radio is one that can autonomously assess the communication environment and automatically update the communication parameters to achieve optimal performance. The problem of determining the type of space-frequency block coding (SFBC) for orthogonal frequency division multiplexing (OFDM) transmissions is one of the main tasks of an intelligent receiver. Previous approaches to this problem are restricted to uncoded communications; nevertheless, existing systems typically utilize error-correcting codes. This study develops a maximum-likelihood (ML) classifier that discriminates among SFBC-OFDM signals using the soft outputs of a channel decoder. The mathematical analysis shows that the maximization of the likelihood function can be carried out by employing an iterative expectation-maximization (EM) procedure. A channel estimator is also included in the proposed classifier as a vital step. The findings show that the classification performance of the proposed algorithm is considerably better than the classical classifiers reported in the literature, at the cost of an acceptable increase in computing complexity.

With the advancement and widespread implementation of multiple-input multiple-output (MIMO) wireless communication systems over the last decade, space-time block coding (STBC) identification has become a critical task for intelligent radios. Previous examinations of STBC identification were focused on single-user transmissions over single-carrier and multi-carrier systems in combination with uncoded broadcasts. Practical systems, on the other hand, contain many users and employ error-correcting codes. For the first time in literature, this work explores the problem of STBC identification for multi-user uplink transmissions in single-carrier frequency division multiple access (SC-FDMA) systems. We take another step closer to real systems by addressing asynchronous transmissions and by conducting multi-user channel estimation. We also exploit the outputs of the channel decoder, which is usually used in many practical systems, to improve the identification and estimation processes. The mathematical analysis demonstrates that the maximum-likelihood (ML) solution of STBC identification, channel estimation, and synchronization can be executed by an iterative approach. The space-alternating generalized expectation-maximization (SAGE) algorithm is used to separate the overlaid signals arriving at the base-station (BS). The parameters under consideration for each user are then updated using an expectation-maximization (EM) processor. Simulation results show that the proposed architecture outperforms other identification methods published in the literature while maintaining a reasonable level of processing time.

In robotic manipulators, feedback control of nonlinear systems with fast finite-time convergence is desirable. However, because of the parametric and model uncertainties, the robust control and tuning of the robotic manipulators pose many challenges related to the trajectory tracking of the robotic system. This research proposes a state-of-the-art control algorithm, which is the combination of fast integral terminal sliding mode control (FIT-SMC), robust exact differentiator (RED) observer, and feedforward neural network (FFNN) based estimator. Firstly, the dynamic model of the robotic manipulator is established for the n-degrees of freedom (DoFs) system by taking into account the dynamic LuGre friction model. Then, a FIT-SMC with friction compensation-based nonlinear control has been proposed for the robotic manipulator. In addition, a RED observer is developed to get the estimates of robotic manipulator joints’ velocities. Since the dynamic friction state of the LuGre friction model is unmeasurable, FFNN is established for training and estimating the friction torque. The Lyapunov method is presented to demonstrate the finite-time sliding mode enforcement and state convergence for a robotic manipulator. The proposed control approach has been simulated in the MATLAB/Simulink environment and compared with the system with no observer to characterize the control performance. Simulation results obtained with the proposed control strategy affirm its effectiveness for a multi-DoF robotic system with model-based friction compensation having an overshoot and a settling time less than 1.5% and 0.2950 seconds, respectively, for all the joints of the robotic manipulator.

This paper introduces a novel two-port ultra-wideband (UWB) multiple-input multiple-output (MIMO) antenna system with enhanced isolation characteristics. The antenna, designed on a thin 0.787 mm RO5880 substrate, achieves a compact form factor of 52 × 26 mm2 and offers a wide bandwidth of 9.2 GHz (2.3 GHz to 11.5 GHz) while meeting the VSWR 2:1 criterion. Notably, the proposed antenna demonstrates an impressive increase in isolation, up to 16 dB, through the integration of a shared radiator with small rectangular slots, effectively reducing interference and improving overall performance. Furthermore, a comprehensive analysis of additional MIMO performance parameters, including the envelope correlation coefficient (ECC) and diversity gain, confirms their satisfactory limits, validating the potential of the proposed UWB-MIMO antenna for various UWB applications. The time domain analysis of the UWB antenna is also analyzed, and results are found to be within satisfactory limits. Simulation and measurement results further support the practicality and effectiveness of the antenna design, highlighting its compact size, wide bandwidth, and enhanced isolation characteristics, positioning it as a promising solution for advanced UWB microwave imaging systems.

The fifth generation (5G) of mobile networks is a significant technological advancement in telecommunications that provides faster data speeds, lower latency, and greater network capacity. One of the key technologies that enables 5G is multiple-input/multiple-output (MIMO) antenna systems, which allow for the transmission and reception of multiple data streams simultaneously, improving network performance and efficiency. MIMO is essential to meeting the demand for higher data rates and improved network performance in 5G networks. This work presents a four-element MIMO antenna system dedicated to the upper 5G millimeter-wave (mmWave) spectrum. The suggested antenna system is designed using an ultra-thin RO5880 substrate having total dimensions of 20 × 20 × 0.254 mm3 with symmetrical geometry. The proposed antenna covers a fractional bandwidth of 46.875% (25–38 GHz), covering potential 5G bands of 26, 28, and 32 GHz, and offers isolation of >18 dB. The proposed MIMO system is fabricated and tested in-house. The antenna showed efficiency >88% at the potential band of interest and a peak gain of 3.5 dBi. The orthogonal arrangement of the resonating elements provides polarization diversity. Also, the MIMO parameters obtained, such as mean effective gain (MEG), envelope correlation coefficient (ECC), diversity gain (DG), channel capacity loss (CCL), and total active reflection coefficient (TARC), are found to have good performance. The measured results obtained are found to be in good agreement with simulations, hence making the proposed MIMO antenna suitable for handheld mmWave 5G devices.

In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks.

A radio is adaptive if it can autonomously analyze the communications environment and instantly modify its settings to achieve the best possible efficiency. In orthogonal frequency division multiplexing (OFDM) transmissions, identifying the space frequency block coding (SFBC) category utilized is one of the most important tasks of an adaptive receiver. Previous approaches to this problem did not take into consideration the fact that real systems typically suffer from transmission defects. This study offers a novel maximum likelihood recognizer capable of distinguishing between SFBC OFDM waveforms in the context of inphase and quadrature phase differences (IQDs). The theoretical findings show that IQDs arising from the transmitter and recipient can be combined with channel paths to generate so-called effective channel paths. The conceptual examination demonstrates that the outlined maximum likelihood strategy of the SFBC recognition and effective channel estimation processes is implemented by an expectation maximization tool utilizing the error control decoders’ soft outputs. The simulations results reveal that the suggested strategy delivers a much greater recognition accuracy than the typical approaches outlined in the comparable literature. At a signal-to-noise ratio (SNR) of 14 dB, for example, the proposed approach achieves a bit error rate (BER) of 0.00002, which is very close to the case of perfect estimation and compensation for IQDs, outperforming the previous reported works which achieved BERs of 0.01 and 0.02.

Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions.

Hesitancy about receiving vaccines has been deemed a global danger to public health by WHO. The sociocultural backgrounds of the people have an impact on vaccine acceptance. The purpose of this study was to examine the effect of sociodemographic factors on COVID-19 vaccination hesitancy as well as to identify the factors that contributed to COVID-19 vaccine hesitancy. Methods: A cross-sectional study was carried out to determine the primary variables causing COVID-19 vaccination hesitancy among residents of Pune. The general population was sampled through simple random sampling. The minimum sample size was determined to be 1246. The questionnaire inquired about the individuals’ sociodemographic information, vaccination status, and reasons for vaccine hesitancy. Results: In total, there were 5381 subjects, 1669 of whom were unvaccinated and 3712 of whom were partially vaccinated. Fear of adverse effects (51.71%), fear of losing a few days of work (43.02%), and inability to secure a vaccine slot online (33.01%) were the most frequently cited reasons. An older population (>60 years, p = 0.004), males (p = 0.032), those who were literate (p = 0.011), those of lower middle socioeconomic status (p = 0.001), and smokers were significantly associated with fear and mistrust of the COVID-19 vaccine, while mistrust of the vaccine was greatest among individuals from the upper and lower middle classes (p = 0.001). Conclusion: Vaccine hesitancy due to concerns about the side effects and long-term complications was prevalent among the elderly, males, those from the lower middle class, and smokers. This study emphasizes the importance of communicating effectively about the vaccine’s efficacy, its distribution, and vaccination sites.

A challenging issue for single carrier frequency division multiple access (SC-FDMA) transmissions is in-phase and quadrature-phase mismatch (IQM). There has been prior reporting on this issue; however, it has always been in the setting of single-hop broadcasts. This study explored the problem of IQM in multiple users upstream SC-FDMA networks within the framework of amplify-and-forward (AF) dual-hop transmissions. We got closer to realistic scenarios by supposing that each node in the network creates its own IQM. Moreover, we profited from the channel decoders that are included in most wireless standards to provide a valuable aid to the offered estimator. A mathematical analysis revealed that IQM originating from all nodes can be merged into channel coefficients to provide so-called effective channel coefficients. This eliminates the requirement for a chain of algorithms to be employed at each node to estimate and adjust for IQM, as well as the algorithms needed to evaluate channel coefficients between the base station and each node in the network. A theoretical investigation showed that the maximum likelihood (ML) solution to the effective channel coefficients estimation is too complicated to be applied in reality. Alternatively, we employed a simple methodology relying on the space-alternating generalized expectation maximization (SAGE) process to determine the ML predictions of the required parameters. The proposed approach incorporates a feedback loop in which the estimator and the channel decoder exchange information to boost one another’s efficiency. Furthermore, we detail how to execute data detection by making use of the predicted effective channel coefficients. The simulation results verify the effectiveness of the proposed approach and show that it outperforms the current methods.

This research work presents a compact design of a Multiple-Input Multiple-Output (MIMO) multiband antenna along with high-isolation characteristics. The presented antenna was designed for 3.50 GHz, 5.50 GHz, and 6.50 GHz frequencies for 5G cellular, 5G WiFi, and WiFi-6, respectively. The fabrication of the aforementioned design was undertaken using FR-4 (1.6 mm thickness) substrate material with a loss tangent and relative permittivity of about 0.025 and 4.30, respectively. The two-element MIMO multiband antenna was miniaturized to 16 × 28 × 1.6 mm3, making it desirable for devices operating in 5G bands. High isolation (>15 dB) was attained with thorough testing without employing a decoupling scheme in the design. Laboratory measurements resulted in a peak gain of 3.49 dBi and an efficiency of around 80% in the entire operating band. The evaluation of the presented MIMO multiband antenna was carried out in terms of the envelope correlation coefficient (ECC), diversity gain (DG), total active reflection coefficient (TARC), and Channel Capacity Loss (CCL). The measured ECC was less than 0.04, and the DG was well above 9.50. The observed TARC was also lower than −10 dB, and the CCL was below 0.4 bits/s/Hz in the entire operating band. The presented MIMO multiband antenna was analyzed and simulated using CST Studio Suite 2020.

Abstract Background One of the most important and dangerous complications of rheumatoid arthritis (RA) is bone loss, which manifested by erosions and juxta-articular or systemic bone loss. Anti-carbamylated protein (anti-CarP) antibodies which are also called anti-homocitrulline antibodies have recently been found in RA. Increase anti-CarP antibody titres may lead to severe disease and increase the progression of bone loss. Osteoprotegrin and receptor activator for nuclear factor kappa B and its ligand (RANKL) are the main players in the pathogenesis of osteoporosis. Thus, we aimed to investigate and detect the presence and prevalence of anti-CarP in rheumatoid arthritis and their association with disease severity and osteoporosis, as well as with OPG/RANKL in 80 Egyptian RA patients to highlight this relationship which could be useful in managing RA patients with osteoporosis. Results Serum anti-CarP levels were significantly increased in the RA group compared with the control group (P< 0.001). We found a negative association between anti-CarP and anti-CCP and disease activity (r=−0.878, −0.534, respectively, P

In this paper, a wideband circularly polarized (CP) textile multiple-input-multiple-output (MIMO) antenna is presented for wearable applications. The designed MIMO antenna consists of two sickle-shaped resonating elements and a common ground plane. Each antenna element is excited by a microstrip line feed, and an inverted L-shaped strip is introduced in the ground plane to support circular polarization. The antenna covers impedance bandwidth ( $\text{S}_{11}\le -10$ dB) of 3.6–13 GHz and 3-dB axial ratio bandwidth (ARBW) of 5.2–7.1 GHz. The proposed textile MIMO antenna exhibits envelope correlation coefficient (ECC) < 0.02, diversity gain (DG) $>9.96$ , total active reflective coefficient (TARC) $ < -10$ dB, channel capacity loss (CCL) < 0.2 b/s/Hz, and mean effective gain (MEG) ratio within ±0.5 dB. The antenna offers dual-sense circular polarization and high isolation (>18) between the resonating elements. Also, the proposed antenna is investigated for different human body situations, and its specific absorption rate (SAR) for human tissues specimen is studied. The overall size of the proposed CP textile MIMO antenna is $32.5 \times 42 \times 1$ mm3. The designed MIMO antenna could be useful for wearable applications due to its textile layers, reasonable on-body performance, and compact size.

The intelligent reflecting surface (IRS) is a cutting-edge technology for cost-effectively achieving future spectrum- and energy-efficient wireless communication. In particular, an IRS comprises many low-cost passive devices that can independently reflect the incident signal with a configurable phase shift to produce three-dimensional (3D) passive beamforming without transmitting Radio-Frequency (RF) chains. Thus, the IRS can be utilized to greatly improve wireless channel conditions and increase the dependability of communication systems. This article proposes a scheme for an IRS-equipped GEO satellite signal with proper channel modeling and system characterization. Gabor filter networks (GFNs) are jointly proposed for the extraction of distinct features and the classification of these features. Hybrid optimal functions are used to solve the estimated classification problem, and a simulation setup was designed along with proper channel modeling. The experimental results show that the proposed IRS-based methodology provides higher classification accuracy than the benchmark without the IRS methodology.

Categorization of space time block code (STBC) signals has become widely acknowledged as a crucial foundational mechanism for creating intelligent wireless transmissions in both the governmental and business sectors. The use of multiple antennas at a broadcaster complicates a signal categorization task because assumptions about the number and transmission matrix of the sent antennas must be made. STBC categorization has only been investigated in the context of non-relaying environments, and no methods for relaying transmissions have been reported. This work proposes a revolutionary strategy for categorizing STBC signals that can be implemented in amplify-and-forward (AF) relaying networks. Time-domain characteristics of the STBC waveforms provide the basis of the mathematical ingredients used in the offered categorization process. The employed STBC waveform is reflected in the spikes observed in the fast Fourier transform of the second-order lag product of the collected waveforms. This creates the foundation for an effective discriminating feature. Advantages of the described strategy include not requiring any prior awareness of the modulation type, channel conditions, signal-to-noise ratio (SNR), or the block timing synchronization of the STBC waveforms. The indicated strategy has been shown through simulation experiments to be capable of providing appropriate categorization accuracy despite the existence of transmission faults, even at relatively low SNR levels.

In this article, we propose a light weight, low profile Multiple Input Multiple Output (MIMO) antenna system for compact 5th Generation (5G) mmwave devices. Using a RO5880 substrate that is incredibly thin, the suggested antenna is made up of circular rings stacked vertically and horizontally on top of one another. The single element antenna board has dimensions of 12 × 12 × 0.254 mm3 while the size of the radiating element is 6 × 2 × 0.254 mm3 (0.56λ0 × 0.19λ0 × 0.02λ0). The proposed antenna showed dual band characteristics. The first resonance showed a bandwidth of 10 GHz with a starting frequency of 23 GHz to an ending frequency point of 33 GHz followed by a second resonance bandwidth of 3.25 GHz ranging from 37.75 to 41 GHz, respectively. The proposed antenna is transformed into a four element Linear array system with size of 48 × 12 × 0.254 mm3 (4.48λ0 × 1.12λ0 × 0.02λ0). The isolation levels at both resonance bands were noted to be >20 dB which shows high levels of isolation among radiating elements. The MIMO parameters such as Envelope Correlation Co-efficient (ECC), Mean Effective Gain (MEG) and Diversity Gain (DG) were derived and were found to be in satisfactory limits. The proposed MIMO system model is fabricated and through validation and testing of the prototype, the results were found to be in good agreement with simulations.

The co-channel interference for mobile users (MUs) of a public safety network (PSN) in the co-existence of heterogeneous networks such as unmanned aerial vehicles (UAVs) and LTE-based railway networks (LRNs) needs a thorough investigation, where UAVs are deployed as mobile base stations (BSs) for cell-edge coverage enhancement. Moreover, the LRN is employed for the train, and its control signal demands high reliability and low latency. It is necessary to provide higher priority to LRN users when allocating resources from shared radio access channels (RACs). By considering both sharing and non-sharing of RACs, co-channel interference was analyzed in the downlink network of the PSN, UAV, and LRN. By offloading more PSN MUs to the LRN or UAVs, the resource utilization of the LRN and UAV BSs was enhanced. In this paper, we aimed to adopt deep-learning (DL)-based enhanced inter-cell interference coordination (eICIC) and further enhanced ICIC (FeICIC) strategies to deal with the interference from the PSN to the LRN and UAVs. Moreover, a DL-based coordinated multipoint (CoMP) for coordinated scheduling technique was utilized along with FeICIC and eICIC to enhance the performance of PSN MUs. In the simulation results, the performance of DL-based interference management was compared with simple eICI, FeICIC, and coordinated scheduling CoMP. The DL-based FeICIC and CoMP for coordinated scheduling performed best with shared RACs.

As the number of road accidents increases, it is critical to avoid making driving mistakes. Driver fatigue detection is a concern that has prompted researchers to develop numerous algorithms to address this issue. The challenge is to identify the sleepy drivers with accurate and speedy alerts. Several datasets were used to develop fatigue detection algorithms such as electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), and electromyogram (EMG) recordings of the driver’s activities e.g., DROZY dataset. This study proposes a fatigue detection system based on Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) with machine learning and deep learning classifiers. The FFT and DWT are used for feature extraction and noise removal tasks. In addition, the classification task is carried out on the combined EEG, EOG, ECG, and EMG signals using machine learning and deep learning algorithms including 1D Convolutional Neural Networks (1D CNNs), Concatenated CNNs (C-CNNs), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), k-Nearest Neighbor (KNN), Quadrature Data Analysis (QDA), Multi-layer Perceptron (MLP), and Logistic Regression (LR). The proposed methods are validated on two scenarios, multi-class and binary-class classification. The simulation results reveal that the proposed models achieved a high performance for fatigue detection from medical signals, with a detection accuracy of 90% and 96% for multiclass and binary-class scenarios, respectively. The works in the literature achieved a maximum accuracy of 95%. Therefore, the proposed methods outperform similar efforts in terms of detection accuracy.

Owing to the rapid development and broad adoption of multiple antenna communication systems over the past few years, space-time block coding (STBC) identification has emerged as a crucial responsibility for smart radios. The majority of previous analysis of STBC identification assumed that the utilized modulation schemes for single-user and uncoded broadcasts were known. This paper investigates the challenge of joint STBC and modulation identification for uplink transmissions with numerous users in single-carrier frequency division multiple access (SC-FDMA) systems using coded transmissions. Multi-user channel estimation brings us one step closer to implementing the proposed design in real-world systems. We additionally employ the channel decoder’s deliverables, which are common in many real-world systems, to enhance the identifying and estimating procedures. Mathematical findings prove that a recursive approach can be utilized to tackle the maximum likelihood (ML) problem of simultaneous STBC and modulation identification with channel estimation. Distinguishing the superimposed signals that originate at the base-station (BS) is accomplished with the use of the space-alternating generalized expectation-maximization (SAGE) algorithm. After that, an expectation-maximization (EM) engine is deployed to make the necessary adjustments to the parameters being considered for each user. The success of the above-mentioned architecture for usage in practical applications is demonstrated by the simulation results obtained under various conditions.

This paper presents the design of an arc-shaped near-isotropic self-complementary antenna for spectrum sensing application. An arc-shaped dipole with horizontal and vertical arms is used to achieve a near isotropic radiation pattern. The radiation pattern improved by adjusting the horizontal and vertical arm lengths. Simulated and experimental results show that the proposed antenna has an impedance bandwidth of 146% (2.4–18.4 GHz) for VSWR ≤ 2 with a good radiation pattern. In order to quantify the antenna performance, antenna gain variation, bandwidth, efficiency, and size have been compared with previously reported designs. It is shown that the proposed arc-shaped antenna can achieve nearly isotropic radiation patterns with a maximum radiation efficiency of 92%. The isotropic performance of the antenna has been characterized by observing the radiation pattern and solid angle. The FR4 substrate is used as a dielectric with relative permittivity 4.4 and loss tangent of 0.02. (εr = 4.4, h = 1.6 mm) The simulated and measured results are in good comparison, and the proposed design is a suitable candidate for spectrum sensing.

This paper presents the design and development of a quad-port smart textile antenna for bio-healthcare applications. The antenna is designed to operate in the ultra-wideband (UWB) spectrum (3.1–12 GHz) with an impedance bandwidth of 8.9 GHz. The size of the unit cell and multiple-input multiple-output (MIMO) antenna are 0.25λ0 × 0.2λ0 × 0.015λ0 and 0.52λ0 × 0.52λ0 × 0.015λ0, respectively. The antenna has a maximum efficiency of 93% and a peak gain of 4.62 dBi. The investigation of diversity metrics is performed and the results obtained are found to be ECC < 0.08 and DG < 9.99 dB. The computed CCL and TARC values are

Dengue is a mosquito-borne viral infection, found in tropical and sub-tropical climates worldwide, mostly in urban and semi-urban areas. Countries like Pakistan receive heavy rains annually resulting in floods in urban cities due to poor drainage systems. Currently, different cities of Pakistan are at high risk of dengue outbreaks, as multiple dengue cases have been reported due to poor flood control and drainage systems. After heavy rain in urban areas, mosquitoes are provided with a favorable environment for their breeding and transmission through stagnant water due to poor maintenance of the drainage system. The history of the dengue virus in Pakistan shows that there is a closed relationship between dengue outbreaks and a rainfall. There is no specific treatment for dengue; however, the outbreak can be controlled through internet of medical things (IoMT). In this paper, we propose a novel privacy-preserved IoMT model to control dengue virus outbreaks by tracking dengue virus-infected patients based on bedding location extracted using call data record analysis (CDRA). Once the bedding location of the patient is identified, then the actual infected spot can be easily located by using geographic information system mapping. Once the targeted spots are identified, then it is very easy to eliminate the dengue by spraying the affected areas with the help of unmanned aerial vehicles (UAVs). The proposed model identifies the targeted spots up to 100%, based on the bedding location of the patient using CDRA.

In this work, a low-cost, deployable, integratable, and easy-to-fabricate multiple-input multiple-output (MIMO) Kirigami antenna is proposed for sub-6 GHz applications. The proposed MIMO antenna is inspired by Kirigami art, which consists of four radiating and parasitic elements. The radiating and parasitic elements are composed of a rectangular stub. These elements are placed in such a way that they can provide polarization diversity. The proposed MIMO antenna is designed and fabricated using a soft printed board material called flexible copper-clad laminate (FCCL). It is observed from the results that the proposed MIMO antenna resonates in the 2.5 GHz frequency band, with a 10 dB reflection coefficient bandwidth of 860 MHz ranging from 2.19 to 3.05 GHz. It is worthwhile to mention that the isolation between adjacent radiating elements is higher than 15 dB. In addition, the peak realized gain of the MIMO antenna is around 11 dBi, and the total efficiency is more than 90% within the band of interest. Moreover, the envelope correlation coefficient (ECC) is noted to be less than 0.003, and the channel capacity is ≥17 bps/Hz. To verify the simulated results, a prototype was fabricated, and excellent agreement between the measured and computed results was observed. By observing the performance attributes of the proposed design, it can be said that there are many applications in which this antenna can be adopted. Because of its low profile, it can be used in 5G small-cell mobile MIMO base stations, autonomous light mobility vehicles, and other applications.

Modulation recognition (MR) has become an essential topic in today’s wireless communications systems. Recently, convolutional neural networks (CNNs) have been employed as a potent tool for MR because of their ability to minimize the feature’s susceptibility to its surroundings and reduce the need for human feature extraction and evaluation. In particular, these investigations rely on the unrealistic assumption that the channel coefficient is typically one. This motivates us to overcome the previous constraint by providing a novel MR suited to fading wireless channels. This paper proposes a novel MR algorithm that is capable of recognizing a broad variety of modulation types, including M-ary QAM and M-ary PSK, without enforcing any restrictions on the modulation size, M. The analysis has shown that each modulation choice has a distinct two-dimensional in-phase quadrature histogram. This property is beneficially utilized to design a convolutional neural-network-based MR algorithm. When compared to the existing techniques, Monte Carlo simulations demonstrated the success of the proposed design.

This work presents a cost-effective multiple-element multiple-input multiple-output (MIMO) antenna system for next-generation smartphones. The proposed antenna system is developed on a 0.8 mm thin FR-4 substrate with a relative permittivity of 4.4, which consists of one main board and two sideboards. The dimensions of the main board and the two side boards are 150 × 75 mm2 and 150 × 6 mm2, respectively. The radiating elements are printed on the sideboards to provide space for other radio frequency (RF) components to be embedded on the main board. The proposed antenna resonates at two distinct allotted 5G bands, i.e., 3.5 GHz and 5.4 GHz, with impedance bandwidths of 200 MHz and 700 MHz, respectively. The isolation between the antenna elements is noted to be >18 dB and >12 dB for the 3.5 GHz and 5.4 GHz frequency bands. In addition, the proposed MIMO antenna provides pattern and spatial diversity characteristics in both bands with good gain and efficiency. Furthermore, the MIMO parameters such as envelope correlation coefficient (ECC), mean effective gain (MEG), and channel capacity (CC) are calculated, and it is observed that the MIMO antenna offers good diversity performance for the bands of interest. A prototype is fabricated and measured to verify the numerical data. The simulated results were discovered to be in excellent agreement with the measured results. It is also observed that the proposed MIMO antenna system holds promising features, and can be utilized for future generations of smartphones.

Automatic modulation recognition (AMR) is an essential component in the design of smart radios that can intelligently communicate with their surroundings in order to make the most efficient use of available resources. Throughout the last few decades, this issue has been subjected to in-depth examination in the published research literature. To the best of the authors’ knowledge, there have only been a few studies that have been specifically devoted to the task of performing AMR across cooperative wireless transmissions. In this contribution, we examine the AMR problem in the context of amplify-and-forward (AAF) two-path consecutive relaying systems (TCRS) for the first time in the literature. We leverage the property of data redundancy associated with AAF-TCRS signals to design a decision feedback iterative modulation recognizer via an expectation-maximization procedure. The proposed recognizer incorporates the soft information produced by the data detection process as a priori knowledge to generate the a posteriori expectations of the information symbols, which are employed as training symbols. The proposed algorithm additionally involves the development of an estimate of the channel coefficients as a secondary activity. The simulation outcomes have validated the feasibility of the proposed design by demonstrating its capacity to achieve an excellent recognition performance under a wide range of running conditions. According to the findings, the suggested technique converges within six rounds, achieving perfect recognition performance at a signal-to-noise ratio of 14 dB. Furthermore, the minimal pilot-to-frame-size ratio necessary to successfully execute the iterative procedure is 0.07. In addition, the proposed method is essentially immune to time offset and performs well throughout a broad range of frequency offset. Lastly, the proposed strategy beats the existing techniques in recognition accuracy while requiring a low level of processing complexity.

Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

Modulation awareness and cooperative transmissions have individually received a significant amount of research in the scholarly literature. However, a limited number of works are principally concerned with the combination of the two topics, and they are restricted to frequency-flat wireless channels. In this study, we propose a new modulation awareness method applicable to dual-hop amplify-and-forward cooperative broadcasts. The suggested method is built on the creation of theoretical representations of cross-correlation functions of the received signals. We conceptually prove that a family of modulation types generates spikes for certain cross-correlation functions, while others do not. We create a numerous layer hypothesis evaluation for the purpose of making judgments centered on this attribute. The suggested method has a number of benefits, such as the ability to operate on both frequency-flat and frequency-selective channels, as well as the absence of the necessity of channel awareness or noise power. Computer simulations analyze the performance of the proposed method, which delivers adequate awareness performance in a variety of operational scenarios.

This study provides an eight-component multiple-input multiple-output (MIMO) antenna architecture for fifth-generation (5G) mobile communication systems. The single antenna element is comprised of an L-shaped radiating component, an L-shaped parasitic element, and a ground plane with a rectangular slot. The main element with a slot-loaded ground plane helps to draw current from a coaxial feed from the other side of the board, while the parasitic element helps to elongate the current path and improve the impedance of the system. This enables the system to radiate at two different frequency ranges: 3.34–3.7 GHz and 4.67–5.08 GHz, with 360 MHz and 410 MHz bandwidths, respectively. For MIMO configuration, the radiating elements are designed on either side of a 0.8 mm thick FR-4 substrate, allowing space to accommodate a battery, radio frequency (RF) systems and subsystems, and camera and sensor modules. The corner and the middle elements are arranged in such a manner so that they can provide spatial and pattern diversity. Furthermore, at least 12 dB of isolation is established between any two radiating elements. Various MIMO performance parameters were evaluated, e.g., mean effective gain (MEG), channel capacity (CC), envelope correlation coefficient (ECC), realized gain, far-field characteristics, and efficiency. Single- and double-hand mode evaluations were performed to further demonstrate the capability of the proposed MIMO antenna. A prototype of the proposed MIMO antenna was manufactured and assessed to verify the simulated data. The measured and simulated results were found to be in good agreement. On the basis of its performance characteristics, the designed MIMO system could be used in 5G communication systems.

Background: Adding intrathecal opioids to intrathecal local anesthetics to decrease their doses and provide hemodynamic stability are major goals during spinal anesthesia in cesarean section. Different opioids were used to select the one with the longest duration of analgesia and the least side effects. In this study, intrathecal nalbuphine was compared with intrathecal fentanyl as an adjuvant to hyperbaric bupivacaine in cesarean section. Patients and methods: Sixty female patients of ASA grades I and II presented for elective cesarean deliveries with spinal anesthesia were randomly allocated to 2 equal groups; Group F: 30 patients received intrathecal injection of 2 ml of 0.5% hyperbaric bupivacaine plus 0.5 ml fentanyl (25 μg); Group N: 30 patients received intrathecal injection of 2 ml of 0.5% hyperbaric bupivacaine plus 0.5 ml nalbuphine (0.8 mg). The onset of sensory and complete motor blockade, time of sensory blockade, duration of analgesia and motor blockade, fetal Apgar score, visual analog scale score, oxygen saturation, adverse effects and hemodynamic parameters were recorded intra-operatively and up to 4 h post-operatively. The effective analgesic time was recorded. Results: The onset of complete motor block was significantly more rapid in fentanyl group than in nalbuphine group. The duration of post-operative analgesia was more prolonged in nalbuphine group but the difference was insignificant. No significant difference was found between both groups as regards the duration of sensory block, motor block, duration of analgesia, fetal Apgar score, visual analog scale score, hemodynamic parameters and oxygen saturation. Adverse effects were less common in nalbuphine group but the difference was insignificant. Conclusion: Either intrathecal nalbuphine 0.8 mg or intrathecal fentanyl 25 μg combined with 10 mg bupivacaine provides good intra-operative and early post-operative analgesia in cesarean section.

Background: This study was aimed to evaluate protective and therapeutic effects of a specific mixture, containing vitamin C, lysine, proline, epigallocatechin gallate and zinc, as well as alpha-1-antitrypsin protein on lung tumorigenesis induced by benzo(a) pyrene [B(a)P] in mice. Materials and Methods: Swiss albino mice were divided into two main experiments, experiment (1) the mice were injected with 100 mg/kg B(a)P and lasted for 28 weeks, while experiment (2) the mice were injected with 8 doses each of 50 mg/kg B(a)P and lasted for 16 weeks. Each experiment (1 and 2) divided into five groups, group (I) received vehicle, group (II) received the protector mixture, group (III) received the carcinogen B(a)P, group (IV) received the protector together with the carcinogen (simultaneously) and group (V) received the carcinogen then the protector (consecutively). Results: Total sialic acid, thiobarbituric acid reactive substances, vascular epithelial growth factor, hydroxyproline levels, as well as elastase and gelatinase activities showed significant elevation in group (III) in the two experiments comparing to control group (P < 0.001). These biochemical alterations were associated with histopathological changes. Administration of the protector in group IV and group V causes significant decrease in such parameters with improvement in histopathological alterations with improvement in histopathological alterations when compared with group III in the two experiments (P < 0.001). Conclusion: The present protector mixture has the ability to suppress neoplastic alteration and restore the biochemical and histopathological parameters towards normal on lung carcinogenesis induced by benzo(a) pyrene in mice. Furthermore, the present mixture have more protective rather than therapeutic action.

Background: Improper management and late detection of complications, especially peri-operative acute kidney injury (AKI), could result in higher morbidity and mortality rates. This work was aimed at confirming the tidal volume challenge (TVC) test's predictability while performing laparoscopic abdominal surgery for early postoperative AKI and intra-operative hypotension. Methods: Our prospective observational study involved 70 cases whose age ranged between 18 and 65 years, American Society of Anesthesiologists (ASA) I and ASA II going through general anesthesia (GA) for laparoscopic abdominal elective surgery. Patients went through a categorization into AKI or Non-AKI according to the worst Kidney Disease: Improving Global Outcomes (KDIGO) classification according to serum creatinine and urine output that are measured at 0, 24 and 48h. Results: TVC exhibited significant correlation with the AKI development with the OR of 2.3, 95% CI, p = 0.052, while the exposure index was deemed to be an AKI development risk factor with the OR of 1.0, 95% CI, p = 0.034. However, baseline neutrophil gelatinase-associated lipocalin in urine (UNGAL) showed insignificant association with the AKI development with the OR 1.04 and p = 0.366. The relationship between TVC and hypotension frequency was estimated using linear regression. The regression showed that there exists a positive significant relation between these two parameters; as hypotension frequency increases, TVC increases as well with a coefficient estimate of 0.5. Conclusions: AKI represents complication after laparoscopic abdominal surgery. To avoid peri-operative renal impairment, the limiting exposure index should be considered. Our work introduces the TVC test for AKI prediction in laparoscopic abdominal surgeries. [ABSTRACT FROM AUTHOR]