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Abstract A multipurpose antenna system that can handle a broad area of frequencies is crucial in the effort to build up widespread 5G Internet-of-Things (IoT) networks. For fifth-generation Internet-of-things applications, this research introduces a new multi-band antenna that can operate in the sub-6 GHz band (2–7 GHz), Ku-band (13–17.5 GHz), and millimeter wave band (25–39 GHz). The antenna achieves a remarkable three-band operational bandwidth through cleverly integrated slots and parasitic components. Maximum realized gain of 4.3 dBi in the sub-6 GHz band, 5.5 dBi in the Ku-band, and 9.9 dBi in the millimeter wave (mm-wave) band for 3D MIMO setup is ensured. In addition, the machine learning prediction is used to verify the single element realized gain, and the results demonstrate that it performs admirably with an accuracy of more than 89% using the random forest regression model throughout the entire frequency spectrum. A 6-port, one-of-a-kind MIMO design with strong diversity performance is built from the single-element configuration. This 6-port MIMO system uses a new codesign technique to achieve 360-degree coverage in the elevation and azimuth planes, exceptional isolation (21 dB at sub-6 GHz band, 25 dB at the Ku-band, and 30 dB at mm-wave band), and pattern diversity. This MIMO antenna module is a shining example of the future, with the potential to completely alter the state of affairs in terms of 5G IoT connectivity in settings such as smart homes, offices, cities, vehicle-to-everything communications, and broadcast satellite service (BSS).

Abstract Background Various medicinal plants and their bioactive compounds exhibited promising anticancer activities by inducing apoptosis, inhibiting angiogenesis, and modulating several signaling pathways in cancer cells. This study aims to assess whether two medicinal plant extracts have anticancer properties, Suaeda Palaestina and Zygophyllum album. Methods This study used Ehrlich solid tumor mice as its in vivo model. We divided male mice into five groups (n = 5 per group). Group I was used as a control for Ehrlich ascites carcinoma (EAC). Groups 2 and 3 were given Z. album extract 180 mg/kg and 360 mg/kg body weight intraperitoneally. Groups 4 and 5 were given the same dose of S. palaestina and treated three times a week for 2 weeks, starting on day 10 after EAC implantation. After 3 weeks, we collected blood samples and thigh skeletal muscle, homogenized them, and processed them for analysis. The results showed that Ehrlich solid rats (EST) treated with low-dose dichloromethane extracts from Z. album and S. palaestina had significantly smaller tumor sizes than the control group. Protein expression levels of p53, caspase 3, and Bcl-2 were quantified by western blotting. Results The extracts from both plants induced the hunger mechanism, leading to increased expression of p53 and caspase 3 and decreased expression of Bcl-2 at the protein level in EST mice treated with Z. album and S. palaestina. In addition, the comet assay indicated that these plants have a genotoxic potential for solid tumor cells. The T3 and T4 levels in EST blood samples revealed that both plants had significantly reduced the concentration of T3 and significantly increased T4 compared to the EST mice untreated group. Furthermore, these results showed that Z. album and S. palaestina had antiproliferative effects in EST mice through apoptosis-mediated genotoxicity. Conclusions These findings indicated that S. palaestina and Z. album could be considered potential natural sources of anticancer agents.

The benefits of drug repositioning to the pharmaceutical industry have garnered significant attention in the field of drug development in recent years. Deep learning techniques have significantly improved drug repositioning by studying therapeutic drug profiles, diseases, and proteins. As the number of drugs increases, their targets and interactions generate imbalanced data, which may be undesirable as input to computational prediction model. The approach proposed in this paper uses a hierarchical network embedding technique and a graph autoencoder (GAE) scheme to solve this problem. The approach extracts embedding feature vectors of drugs and targets from a heterogeneous multi-source network to predict unknown drug-target interactions (DTIs). We employ a Meta-Path instance that has extensive drug and target characteristic data. The effectiveness of utilizing Meta-Path instance, the number of attention heads, and Graph Convolutional Network (GCN) and ensemble learning algorithm is analyzed on gold-standard datasets to evaluate the accuracy of the model and validity of the discovered DTI. The results achieved by our model using 10-fold cross-validation testing showed an improvement of 2.52 % in prediction accuracy, 4.2 % in recall, 3.94 % in AUC, and 3.6 % in F-score compared to state-of-the-art methods.

Abstract The concept, performance, and analyses of distinctive, miniaturized metamaterial (MTM) unit cell addressing the forthcoming Sub 6 GHz 5G applications are presented in this paper. Two circular split-ring resonators (CSRR) with two parallel rectangular copper elements in front of the design and a slotted square element in the background make up the suggested metamaterial. It has a line segment with tunable features that is positioned in the center of the little ring copper structure. The suggested design offers a significant operating frequency band of 220 MHz together with a resonance of transmission coefficient S21 at 3.5 GHz. Furthermore, in two (z & x) principal axes of wave propagation, wide-range achievement, single/double-negative (S/DNG) refractive index, negative permittivity, and near-zero permeability properties were demonstrated. Through varying central slotted-strip line length, resonance frequencies can be selectively altered. Moreover, the metamaterial has overall dimensions of 9 × 9 mm2 and is composed on a Rogers 5880 RT substrate. In order to create the suggested MTM's equivalent circuit, which shows similar coefficient of transmission (S21), a proposed design’s numerical simulation is carried out in the CST micro-wave studio. This simulation is after that put to comparison with manufacturing of the design.

Abstract Deep learning algorithms provide visual tracking robustness at an unprecedented level, but realizing an acceptable performance is still challenging because of the natural continuous changes in the features of foreground and background objects over videos. One of the factors that most affects the robustness of tracking algorithms is the choice of network architecture parameters, especially the depth. A robust visual tracking model using a very deep generator (RTDG) was proposed in this study. We constructed our model on an ordinary convolutional neural network (CNN), which consists of feature extraction and binary classifier networks. We integrated a generative adversarial network (GAN) into the CNN to enhance the tracking results through an adversarial learning process performed during the training phase. We used the discriminator as a classifier and the generator as a store that produces unlabeled feature-level data with different appearances by applying masks to the extracted features. In this study, we investigated the role of increasing the number of fully connected (FC) layers in adversarial generative networks and their impact on robustness. We used a very deep FC network with 22 layers as a high-performance generator for the first time. This generator is used via adversarial learning to augment the positive samples to reduce the gap between the hungry deep learning algorithm and the available training data to achieve robust visual tracking. The experiments showed that the proposed framework performed well against state-of-the-art trackers on OTB-100, VOT2019, LaSOT and UAVDT benchmark datasets.

Abstract In recent times, the Internet of Things (IoT) and Deep Learning Models (DLMs) can be utilized for developing smart agriculture to determine the exact location of the diseased part of the leaf on farmland in an efficient manner. There is no exception that convolutional neural networks (CNNs) have achieved the latest accomplishment in many aspects of human life and the farming sector. Semantic image segmentation is considered the main problem in computer vision. Despite tremendous progress in applications, approximately all semantic image segmentation algorithms fail to achieve sufficient hash results because of the absence of details sensitivity, problems in assessing the global similarity of image pixels, or both. Methods of post-processing improvement, as a wonderfully critical means of improving the underlying flaws mentioned above from algorithms, depend almost on Conditional Random Fields (CRFs). Therefore, plant disease prediction plays important role in the premature notification of the disease to alleviate its effects on disease forecast investigation purposes in the smart farming arena. Hence, this work proposes an efficient IoT-based plant disease recognition system using semantic segmentation methods such as FCN-8 s, CED-Net, SegNet, DeepLabv3, and U-Net with the CRF method to allocate disease parts in leaf crops. Evaluation of this network and comparison with other networks of the state art. The experimental results and their comparisons proclaim over F1-score, sensitivity, and intersection over union (IoU). The proposed system with SegNet and CRFs gives high results compared with other methods. The superiority and effectiveness of the mentioned improvement method, as well as its range of implementation, are confirmed through experiments.

Abstract Background Recently, drug repositioning has received considerable attention for its advantage to pharmaceutical industries in drug development. Artificial intelligence techniques have greatly enhanced drug reproduction by discovering therapeutic drug profiles, side effects, and new target proteins. However, as the number of drugs increases, their targets and enormous interactions produce imbalanced data that might not be preferable as an input to a prediction model immediately. Methods This paper proposes a novel scheme for predicting drug–target interactions (DTIs) based on drug chemical structures and protein sequences. The drug Morgan fingerprint, drug constitutional descriptors, protein amino acid composition, and protein dipeptide composition were employed to extract the drugs and protein’s characteristics. Then, the proposed approach for extracting negative samples using a support vector machine one-class classifier was developed to tackle the imbalanced data problem feature sets from the drug–target dataset. Negative and positive samplings were constructed and fed into different prediction algorithms to identify DTIs. A 10-fold CV validation test procedure was applied to assess the predictability of the proposed method, in addition to the study of the effectiveness of the chemical and physical features in the evaluation and discovery of the drug–target interactions. Results Our experimental model outperformed existing techniques concerning the curve for receiver operating characteristic (AUC), accuracy, precision, recall F-score, mean square error, and MCC. The results obtained by the AdaBoost classifier enhanced prediction accuracy by 2.74%, precision by 1.98%, AUC by 1.14%, F-score by 3.53%, and MCC by 4.54% over existing methods.

The success of next-generation Internet of Things (IoT) applications could be boosted with state-of-the-art communication technologies, including the operation of millimeter-wave (mmWave) bands and the implementation of three-dimensional (3D) networks. With some access points (APs) mounted on unmanned aerial vehicles (UAVs), the probability of line-of-sight (LoS) connectivity to IoT nodes could be augmented to address the high path loss at mmWave bands. Nevertheless, system optimization is essential to maintaining reliable communication in 3D IoT networks, particularly in dense urban areas with elevated buildings. This research adopts the implementation of a geometry-based stochastic channel model. The model customizes the standard clustered delay line (CDL) channel profile based on the environmental geometry of the site to obtain realistic performance and optimize system design. Simulation validation is conducted based on the actual maps of highly dense urban areas to demonstrate that the proposed approach is comprehensive. The results reveal that the use of standard channel models in the analysis introduces errors in the channel quality indicator (CQI) that can exceed 50% due to the effect of the environmental geometry on the channel profile. The results also quantify accuracy improvements in the wireless channel and network performance in terms of the CQI and downlink (DL) throughput.

A new compact wideband filter is introduced to address the requirements of recent communication and radar systems. The filter is based on a quarter-wavelength short-circuit coupled stepped impedance resonator (SIR). The analytical solution shows that the suggested SIR resonator provides a compact size and a wide stopband response, which are essential features in many wireless communication systems. The analytical results also reveal that increasing the impedance ratio of the SIR extends the stopband by increasing the first spurious response and reducing its total length. A compact two-coupled short-circuit SIR filter is designed at 1.23 GHz. The design approach is validated using an ideal transmission line modeling analysis and electromagnetic simulation using CST Microwave Studio 2021. The proposed structure is shown to be flexible, allowing the achievement of a relative bandwidth as low as 5% and as high as 50%. A four-resonator filter is designed by cascading two stages of the designed two-coupled short-circuit SIR filter, which are coupled through a quarter-wavelength line. The simulation results illustrate that the suggested structure can be used to design a filter with any number of resonators. The filter is implemented using a high-resolution LPKF laser machine on Rogers RT/duroid 6010.2LM material with a thickness of 0.635 mm. From the measurements, the bandwidth is found to be 390 MHz and centered at 1325 MHz (29.4% relative bandwidth) and the insertion loss is 1.3 dB. The simulation and experimental results verify the proposed approach and indicate the potential of this component in meeting the design requirements of next-generation microwave circuits related to flexibility and size-compactness.

Visual tracking is an open and exciting field of research. The researchers introduced great efforts to be close to the ideal state of stable tracking of objects regardless of different appearances or circumstances. Owing to the attractive advantages of generative adversarial networks (GANs), they have been a promising area of research in many fields. However, GAN network architecture has not been thoroughly investigated in the visual tracking research community. Inspired by visual tracking via adversarial learning (VITAL), we present a novel network to generate randomly initialized masks for building augmented feature maps using multilayer perceptron (MLP) generative models. To obtain more robust tracking these augmented masks can extract robust features that do not change over a long temporal span. Some models such as deep convolutional generative adversarial networks (DCGANs) have been proposed to obtain powerful generator architectures by eliminating or minimizing the use of fully connected layers. This study demonstrates that the use of MLP architecture for the generator is more robust and efficient than the convolution-only architecture. Also, to realize better performance, we used one-sided label smoothing to regularize the discriminator in the training stage and the label smoothing regularization (LSR) method to reduce the overfitting of the classifier in the online tracking stage. The experiments show that the proposed model is more robust than the DCGAN model and offers satisfactory performance compared with the state-of-the-art deep visual trackers on OTB-100, VOT2019 and LaSOT datasets.

The paper presents a Flexible Alternating Current Transmission Systems (FACTS) based dynamic stabilization scheme using a modified series–parallel switched filter compensation (MSPFC). The proposed dynamic scheme is controlled by an Incremental Fuzzy Logic controller (MIFLC) to ensure fast response dynamic voltage stabilization and efficient energy utilization. The MIFLC employs a multi loop dynamic error driven time-descaled regulation to ensure the fast response of the dynamic controller. A second Weighted Modified PID controller with fixed gains is assessed with the proposed MIFLC controller. The modified control schemes are validated under normal and fault operating situations to ensure fast dynamic control, ac power quality, feeder loss reduction, efficient energy utilization and dynamic AC voltage stabilization regulation. The performance of the proposed controllers is assessed using statistical studies and qualitative comparison that prove the high ability of the proposed MIFLC compared with modified PID controller.

This paper addresses the design of a hybrid beamforming system considering the circuit parameter of six-bit millimeter-wave phase shifters based on the process design kit. The phase shifter design adopts 45 nm CMOS silicon on insulator (SOI) technology at 28-GHz. Various circuit topologies are utilized, and in particular, a design is presented based on switched LC components, connected in a cascode manner. The phase shifter configuration is connected in a cascading manner to get the 6-bit phase controls. Six different phase shifters are obtained, which are 180°, 90°, 45°, 22.5°, 11.25°, and 5.6°, with a minimum number of LC components. The circuit parameters of the designed phase shifters are then incorporated in a simulation model of hybrid beamforming for a multiuser MIMO system. The number of OFDM data symbols used in the simulation is ten for eight users, 16 QAM modulation schemes, −25 dB SNR, 120 simulation runs, and around 170 h runtime. Simulation results are obtained considering four and eight users, assuming accurate technology-based models of RFIC components of the phase shifter as well as ideal phase shifter parameters. The results indicate that the performance of the multiuser MIMO system is affected by the accuracy level of the phase shifter RF component models. The outcomes also reveal the performance tradeoff based on user data streams and the number of BS antennas. By optimizing the amount of parallel data streams per user, higher data transmission rates are achieved, while maintaining acceptable error vector magnitude (EVM) values. In addition, stochastic analysis is conducted to investigate the distribution of the RMS EVM. The outcomes show that the best fitting of RMS EVM distribution of the actual and ideal phase shifters agreed with the log-logistic and logistic distributions, respectively. The obtained (mean, variance) values of the actual phase shifters based on accurate library models are (46.997, 481.36), and for ideal components the values are (36.47, 10.44).

A communication system is proposed for the Internet of Things (IoT) applications in desert areas with extended coverage of regional area network requirements. The system implements a developed six-element array that operates at a 2.45 GHz frequency band and is optimized to reduce the size and limit element coupling to less than −20 dB. Analysis of the proposed system involves a multiple-input multiple-output (MIMO) operation to obtain the diversity gain and spectral efficiency. In addition, the radiation efficiency of the proposed antenna is greater than 65% in the operation bandwidth (more than 30 MHz) with a peak of 73% at 2.45 GHz. Moreover, an adaptive beamforming system is presented based on monitoring the direction of arrival (DOA) of various signals using the root MUSIC algorithm and utilizing the DOA data in a minimum variance distortionless response (MVDR) technique beamformer. The developed array is found to have an envelope correlation coefficient (ECC) value of less than 0.013, mean effective gain (MEG) of more than 1 dB, diversity gain of more than 9.9 dB, and channel capacity loss (CCL) of less than 0.4 bits/s/Hz over the operation bandwidth. Adaptive beamforming is used to suppress interference and enhance the signal-to-interference noise ratio (SINR) and is found to achieve a data rate of more than 50 kbps for a coverage distance of up to 100 km with limited power signals.

This paper presents a new, compact, high-coding-capacity resonator suitable for applications in chipless RFID tags. These tags consist of multiple resonators and two cross-polarized ultra-wide band antennas. Each resonator contains a U-shaped coupled microstrip frame with isolated (K-1) legs inside. One of the legs is designed to be connected to the U-frame via a metallic strip in order to adjust its resonance frequency. Using this feature, the frequency of each resonator can be reconfigured to be equal to one of the K-resonance frequencies. Therefore, each resonance presents one of the K-states of the resonator. This allows each resonator to represent more than one bit of information; this arrangement also permits N resonators tags to provide KN codes. The structure can store large volumes of data in a small area and can simplify the detection process by only reading N resonance frequencies for each code. When N resonator tags are used in the proposed structure, the amount of data that can be encoded increases to 2(logK/log2)N compared to 2N in the case of conventional one-bit resonator (2-states) tags. An additional arrangement is also proposed and investigated in order to improve the spectral efficiency by allowing the bandwidth of each single resonator to be shared between two resonators. Several tags for codes with K = 8 are designed and implemented on the RT Duroid 5880 substrate as a proof-of-concept. An 8-state of the proposed resonator can be implemented in an area that is almost the same size as the area of a conventional 2-state resonator operating at 5 GHz. A satisfactory agreement between the empirical and simulated results is then confirmed.

The paper proposes a design of two fuzzy and Proportional-Integral-Derivative (PID) controllers for a position tracking of the 1400 Celestron telescope. The position responses; right ascension and declination in such a way that it minimizes the integral absolute error (ITAE) using a modified particle swarm optimization (MPSO). Based on the structure of the MPSO, the accelerated coefficients of the particle swarm optimization are adapted dynamically by minimizing the system error with the iteration index. The adaptive control tool combines the Fuzzy controller and MPSO to produce a powerful controller in the system (FLC-PSO). The parameters of membership functions and the PID gains are tuned simultaneously based on the MPSO, which is an efficient and simple tool for the multidimensional problem. The simulation results for both controllers are analyzed and compared on a basis of the time response specifications.