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Terahertz (THz) electromagnetically induced transparency-like (EIT-like) metasurfaces have been extensively explored and frequently used for sensing, switching, slow light, and enhanced nonlinear effects. Reducing radiation and non-radiation losses in EIT-like systems contributes to increased electromagnetic (EM) field confinement, higher transmission peak magnitude, and Q-factor. This can be accomplished by the use of proper dielectric properties and engineering novel designs. Therefore, we fabricated a THz EIT-like metasurface based on asymmetric metallic resonators on an ultra-thin and flexible dielectric substrate. Because the quadruple mode is stimulated in a closed loop, an anti-parallel surface current forms, producing a transparency window with a transmission peak magnitude of 0.8 at 1.96 THz. To control the growing trend of EIT-like resonance, the structure was designed with four asymmetry levels. The effect of the substrate on the resonance response was also explored, and we demonstrated experimentally how the ultra-thin substrate and the metasurface asymmetric novel pattern contributed to higher transmission and lower loss.

This paper presents an experimental investigations and analyses of ultra-wideband antenna diversity techniques and their effect on the on-body radio propagation channels. Various diversity-combining techniques are applied to highlight; how the overall system performance may be enhanced. Diversity gain is calculated for five different on-body channels and the impact of variation in the spacing between diversity branch antennas is discussed, with an emphasis on mutual coupling, correlation and power imbalance. Results demonstrate the repeatability and reliability of the analysis with error variations as low as 0.8 dB. The study highlights the significance of diversity techniques for non-line-of-sight propagation scenarios in body-centric wireless communications. [ABSTRACT FROM AUTHOR]

The present research investigations were carried out on monitoring the population of bark beetle, Hypocryphalus mangiferae(Stebbing) on mango trees through installation of sticky color traps at different heights of mango tree. The results revealed that maximum monthly mean number of adult beetles (507.91) was recorded on green plastic traps followed by 261.75 beetles on black traps, 215.75 beetles on white traps, 109.83 beetles on red traps, 83.75 beetles on blue traps, and 77.75 beetles on yellow traps. With regards to number of bark beetles on color traps at different heights, the maximum monthly mean number of the beetles (429.83) was recorded at height of 1 above ground level followed by 416.00 beetles at 3'level, 334.08 beetles at 6'and 76.83 beetles at 9'level. The results further revealed that the bark beetle population remained higher from February 2006 to June 2006. The mean temperature for that period ranged between 23.12 to 33.46°C with 48.57 to 76.00% RH. [ABSTRACT FROM AUTHOR]

Mercury (Hg) is a very toxic decomposition-resistant metal that can cause plant toxicity through bioaccumulation and oxidative damage. Biochar, derived from organic waste and agricultural garbage, is an on-site modification technique that can improve soil health in heavy metals-polluted regions. The present experiment was designed to explore the role of apple biochar in the management of mercury toxicity in corn (Zea mays cv. 'PL535'). Different levels of biochar derived from apple wood (0%, 2.5%, 5.0%, and 7.5% w/w) along with different Hg concentrations (0, 20, 40, and 60 mg/L) were used in the experiment that was based on a completely randomized design. Based on the results, HgCl 2 at all rates reduced root and shoot dry weight and length, tolerance index, chlorophyll a and b content, the Hill reaction, and dissolved proteins and increased shoot and root Hg content (up to 72.57 and 717.56 times, respectively), cell death (up to 58.36%), MDA level (up to 47.82%), H 2 O 2 (up to 66.33%), dissolved sugars, and proline. The results regarding enzymatic and non-enzymatic antioxidants revealed increases in total phenol and flavonoids content (up to 71.27% and 86.71%, respectively), DPPH free radical scavenging percentage, and catalase (CAT) and ascorbate peroxidase (APX) activity (up to 185.93% and 176.87%, respectively), in corn leaves with the increase in the Hg rate applied to the culture medium. The application of biochar to the substrate of the Hg-treated corns reduced Hg bioavailability, thereby reducing Hg accumulation in the roots (up to 76.88%) and shoots (up to 71.79%). It also reduced the adverse effect of Hg on the plants by increasing their shoot and root dry weight, photosynthesizing pigments, Hill reaction, and APX activity and reducing cell death, H 2 O 2 content, and MDA content. The results reflected the capability of apple wood biochar at all rates in reducing Hg bioavailability and increasing Hg fixation in Hg-polluted soils. However, it was most effective at the rate of 7.5%., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

This paper presents a trainable hybrid approach involving a shallow autoencoder (AE) and a conventional classifier for epileptic seizure detection. The signal segments of a channel of electroencephalogram (EEG) (EEG epochs) are classified as epileptic and non-epileptic by employing its encoded AE representation as a feature vector. Analysis on a single channel-basis and the low computational complexity of the algorithm allow its use in body sensor networks and wearable devices using one or few EEG channels for wearing comfort. This enables the extended diagnosis and monitoring of epileptic patients at home. The encoded representation of EEG signal segments is obtained based on training the shallow AE to minimize the signal reconstruction error. Extensive experimentation with classifiers has led us to propose two versions of our hybrid method: (a) one yielding the best classification performance compared to the reported methods using the k-nearest neighbor (kNN) classifier and (b) the second with a hardware-friendly architecture and yet with the best classification performance compared to other reported methods in this category using a support-vector machine (SVM) classifier. The algorithm is evaluated on the Children's Hospital Boston, Massachusetts Institute of Technology (CHB-MIT), and University of Bonn EEG datasets. The proposed method achieves 98.85% accuracy, 99.29% sensitivity, and 98.86% specificity on the CHB-MIT dataset using the kNN classifier. The best figures using the SVM classifier for accuracy, sensitivity, and specificity are 99.19%, 96.10%, and 99.19%, respectively. Our experiments establish the superiority of using an AE approach with a shallow architecture to generate a low-dimensionality yet effective EEG signal representation capable of high-performance abnormal seizure activity detection at a single-channel EEG level and with a fine granularity of 1 s EEG epochs.

Radar systems are increasingly being employed in healthcare applications for human activity recognition due to their advantages in terms of privacy, contactless sensing, and insensitivity to lighting conditions. The proposed classification algorithms are however often complex, focusing on a single domain of radar, and requiring significant computational resources that prevent their deployment in embedded platforms which often have limited memory and computational resources. To address this issue, we present an adaptive magnitude thresholding approach for highlighting the region of interest in the multi-domain micro-Doppler signatures. The region of interest is beneficial to extract salient features, meanwhile it ensures the simplicity of calculations with less computational cost. The results for the proposed approach show an accuracy of up to 93.1% for six activities, outperforming state-of-the-art deep learning methods on the same dataset with an over tenfold reduction in both training time and memory footprint, and a twofold reduction in inference time compared to a series of deep learning implementations. These results can help bridge the gap toward embedded platform deployment., (© 2023. The Author(s).)

An ultrawide bandwidth (UWB) antenna for ground-penetrating radar (GPR) applications is designed to check soil moisture and provide good-quality images of metallic targets hidden in the soil. GPR is a promising technology for detecting and identifying buried objects, such as landmines, and investigating soil in terms of moisture content and contamination. A paddle-shaped microstrip antenna is created by cutting a rectangular patch at one of its diametrical edges fed by the coplanar waveguide technique. The antenna is loaded by stubs, shorting pins, and a split-ring resonator (SRR) metamaterial structure to increase the antenna's gain and enhance the bandwidth (BW) towards both the lower and higher end of the working BW. The antenna's performance in soil inspection is studied in terms of the operating frequency range, different types of soil, different distances (e.g., 50 cm) between the antenna arrays and soil, S-parameters, and gain. Following this, the antenna's ability to find a metallic target in the soil is tested, considering different array numbers, multi-targets, and locations. The antenna is designed on a thin layer of economic polytetrafluoroethylene (PTFE) substrate with dimensions 50 × 39 × 0.508 mm 3 and works in the frequency range 1.9-9.2 GHz. In addition, two more resonances at 0.9 and 1.8 GHz are also achieved; hence, the antenna works for more than two application bands, such as the ISM- and L-bands. The measurement results validated excellent agreement with the simulated results. Furthermore, the recommended antenna offering a high gain of about 10.8 dBi and maximum efficiency above 97% proved able to discriminate between hidden objects and even recognize their shapes. Moreover, the reconstructed images show that the antenna can detect an object in the soil at any location.

Introduction: Pseudo-Bartter syndrome (PBS) is a rare manifestation of Cystic fibrosis (CF) and can often be the initial presentation in these patients, however, due to significantly overlapping symptoms it is often misdiagnosed as simple dehydration or Bartter syndrome. The objective of our study was to highlight the key features of PBS and electrolyte imbalance in CF patients helping in early and prompt diagnosis., Method: We performed a retrospective study from January 2015 to December 2019 at the Aga Khan University Hospital (AKUH), Pakistan. CF patients aged from 1-18 years, admitted at AKUH were enrolled and their laboratory data and individual charts were reviewed. Patients were categorized into three groups based on their serum electrolyte profile and their clinical findings were compared., Result: We enrolled 72 CF patients, out of which 42 (58%) were categorized into the Normal Electrolyte (NE) group, 19 (26%) into the Electrolyte Imbalance (EI) group and 11 (15%) in the PBS group. Out of 11 cases, 6 (54.54%) patients in PBS group presented with features consistent with PBS leading to CF diagnosis labeled as "early presenters". Mean age of patients in the PBS group was 3.81± 0.86 years and their age at diagnosis were significantly lower as compared to other groups. Gastrointestinal disturbances including diarrhea, vomiting and constipation were more common in the EI and PBS groups. Polyuria was most common in the PBS (72%) group. Length of hospital stay showed no significant difference., Conclusion: Pseudo-Bartter syndrome can be a presenting feature of cystic fibrosis. Electrolyte imbalance should be anticipated in hospitalized CF children and adolescent., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of 0.05° against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication.

Global navigation satellite systems have been used for reliable location-based services in outdoor environments. However, satellite-based systems are not suitable for indoor positioning due to low signal power inside buildings and low accuracy of 5 m. Future smart homes demand low-cost, high-accuracy and low-power indoor positioning systems that can provide accuracy of less than 5 m and enable battery operation for mobility and long-term use. We propose and implement an intelligent, highly accurate and low-power indoor positioning system for smart homes leveraging Gaussian Process Regression (GPR) model using information-theoretic gain based on reduction in differential entropy. The system is based on Time Difference of Arrival (TDOA) and uses ultra-low-power radio transceivers working at 434 MHz. The system has been deployed and tested using indoor measurements for two-dimensional (2D) positioning. In addition, the proposed system provides dual functionality with the same wireless links used for receiving telemetry data, with configurable data rates of up to 600 Kbauds. The implemented system integrates the time difference pulses obtained from the differential circuitry to determine the radio frequency (RF) transmitter node positions. The implemented system provides a high positioning accuracy of 0.68 m and 1.08 m for outdoor and indoor localization, respectively, when using GPR machine learning models, and provides telemetry data reception of 250 Kbauds. The system enables low-power battery operation with consumption of <200 mW power with ultra-low-power CC1101 radio transceivers and additional circuits with a differential amplifier. The proposed system provides low-cost, low-power and high-accuracy indoor localization and is an essential element of public well-being in future smart homes.

The Forensic Algorithm Project (FAP) was born of the need for a holistic approach in the treatment of the inmate with schizophrenia. Schizophrenia was chosen as the first entity to be addressed by the algorithm because of its refractory nature and high rate of recidivism in the correctional setting. Schizophrenia is regarded as a spectrum disorder, with symptom clusters and behaviors ranging from positive to negative symptoms to neurocognitive dysfunction and affective instability. Furthermore, the clinical picture is clouded by Axis II symptomatology (particularly prominent in the inmate population), comorbid Axis I disorders, and organicity. Four subgroups of schizophrenia were created to coincide with common clinical presentations in the forensic inpatient facility and also to parallel 4 tracks of intervention, consisting of pharmacologic management and programming recommendations. The algorithm begins with any antipsychotic medication and proceeds to atypical neuroleptic usage, augmentation with other psychotropic agents, and, finally, the use of clozapine as the common pathway for refractory schizophrenia. Outcome measurement of pharmacologic intervention is assessed every 6 weeks through the use of a 4-item subscale, specific for each forensic subgroup. A "floating threshold" of 40% symptom severity reduction on Positive and Negative Syndrome Scale and Brief Psychiatric Rating Scale items over a 6-week period is considered an indication for neuroleptic continuation. The forensic algorithm differs from other clinical practice guidelines in that specific programming in certain prison environments is stipulated. Finally, a social commentary on the importance of state-of-the-art psychiatric treatment for all members of society is woven into the clinical tapestry of this article.

We collected daily urine volumes (DUV) from 24 nonpolydipsic psychiatric patients and eight polydipsic schizophrenic patients. At 7 AM and 4 PM, we collected spot urine samples and measured urine creatinine concentration (UCr). Using morning weight and the UCr measurements, we compared actual DUV with estimated DUV using 3 methods to estimate DUV currently available in the literature. Each method of estimating DUV was superior among the polydipsic patients compared with the nonpolydipsic patients. We discuss the strengths and weaknesses of trying to estimate DUV in psychiatric patients. One method was simple but less accurate. The two remaining methods gave similar results but one method was substantially easier to use than the other.

The Wellcolex Colour Salmonella and Shigella tests are rapid latex agglutination procedures for grouping Salmonella and Shigella using an antibody attached to multicolored latex particles. The tests correctly grouped 46 (100%) of 46 Salmonella clinical isolates in groups A through E and G, 42 (100%) of 42 Shigella clinical isolates, and seven (88%) of eight Shigella stock cultures. The stock culture missed had been passed through many transfers and may have lost some of its antigenicity. The Wellcolex Colour Salmonella test was also found useful in detecting and grouping Salmonella spp. directly from blood culture bottles.