Explore the vast range of titles available.

This paper presents the design and analysis of a broad-band patch antenna using split ring metamaterial. The SRR metamaterial structures are embedded in a unique and novel way in the patch antenna, so that subwavelength modes get introduced in the patch cavity and a broad bandwidth antenna with good performance characteristics is obtained. A rectangular microstrip patch antenna is taken as a reference antenna, which resonates at a frequency of 5.2 GHz and has an impedance bandwidth of 70 MHz. To improve the bandwidth of the patch antenna, firstly the split ring resonator (SRR) is designed according to the reference patch antenna. The optimized SRR metamaterial is placed in between the patch and ground plane of the proposed antenna. The – 10 dB impedance bandwidth of the metamaterial-embedded proposed antenna is 1.63–4.88 GHz and has an average gain of 4.5 dB. The Prototype of the proposed antenna and reference antenna is fabricated and experimental results are obtained. Experimental and simulated results are in good agreement. The presented antenna can be used for LTE, GSM, WiMAX, Bluetooth, and other wireless applications.

This paper presents a low-cost circular patch antenna sensor developed for measurement of moisture content in lentils and rice. The proposed sensor was modeled and simulated in HFSS after that the prototype was fabricated by chemically etching the FR4 material of 1.6 mm thickness. Measurements were conducted using a Vector Network Analyzer. The presented antenna sensor is resonating at 2.45 GHz frequency. Calibration equations were obtained by utilizing moisture content and reflection coefficients values. The moisture content varied between 2.04 and 11.11% based on wet weight. A good agreement between the actual and calculated values of moisture content validates that this proposed antenna sensor could serve as a moisture sensor for rice and lentil grains. For rice, the sensitivity is found to be 0.0071, and the regression coefficient is equal to 0.9976. In case of lentil, the values of the regression coefficient and sensitivity are 0.9981 and 0.0088, respectively. Furthermore, a real-time moisture sensing setup has made using a laptop with LabVIEW software, ESP32 microcontroller, VNA, and an Ethernet connection. The moisture content values can be seamlessly displayed in real time on mobile devices or laptop for convenient monitoring and analysis.

Aberrant alterations in any of the two dimensions of consciousness, namely awareness and arousal, can lead to the emergence of disorders of consciousness (DOC). The development of DOC may arise from more severe or targeted lesions in the brain, resulting in widespread functional abnormalities. However, when it comes to classifying patients with disorders of consciousness, particularly utilizing resting-state electroencephalogram (EEG) signals through machine learning methods, several challenges surface. The non-stationarity and intricacy of EEG data present obstacles in understanding neuronal activities and achieving precise classification. To address these challenges, this study proposes variational mode decomposition (VMD) of EEG before feature extraction along with machine learning models. By decomposing preprocessed EEG signals into specified modes using VMD, features such as sample entropy, spectral entropy, kurtosis, and skewness are extracted across these modes. The study compares the performance of the features extracted from VMD-based approach with the frequency band-based approach and also the approach with features extracted from raw-EEG. The classification process involves binary classification between unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS), as well as multi-class classification (coma vs. UWS vs. MCS). Kruskal-Wallis test was applied to determine the statistical significance of the features and features with a significance of p < 0.05 were chosen for a second round of classification experiments. Results indicate that the VMD-based features outperform the features of other two approaches, with the ensemble bagged tree (EBT) achieving the highest accuracy of 80.5% for multi-class classification (the best in the literature) and 86.7% for binary classification. This approach underscores the potential of integrating advanced signal processing techniques and machine learning in improving the classification of patients with disorders of consciousness, thereby enhancing patient care and facilitating informed treatment decision-making.

Abstract Background and Hypothesis Hallucinations may be driven by an excessive influence of prior expectations on current experience. Initial work has supported that contention and implicated the anterior insula in the weighting of prior beliefs. Study Design Here we induce hallucinated tones by associating tones with the presentation of a visual cue. We find that people with schizophrenia who hear voices are more prone to the effect and using computational modeling we show they overweight their prior beliefs. In the same participants, we also measured glutamate levels in anterior insula, anterior cingulate, dorsolateral prefrontal, and auditory cortices, using magnetic resonance spectroscopy. Study Results We found a negative relationship between prior-overweighting and glutamate levels in the insula that was not present for any of the other voxels or parameters. Conclusions Through computational psychiatry, we bridge a pathophysiological theory of psychosis (glutamate hypofunction) with a cognitive model of hallucinations (prior-overweighting) with implications for the development of new treatments for hallucinations.

Characterizing functional connectivity (FC) in the human brain is crucial for understanding and supporting clinical decision making in disorders of consciousness. This study investigates FC using sliding window correlation (SWC) analysis of electroencephalogram (EEG) applied to three connectivity measures: phase-lag index (PLI) and weighted phase-lag index (wPLI), which quantify phase synchronization, and amplitude envelope correlation (AEC), which captures amplitude-based coactivation patterns between pairs of channels. SWC analysis is performed across the five canonical frequency bands (delta, theta, alpha, beta, gamma) of EEG data from four distinct groups: coma, unresponsive wakefulness syndrome, minimally conscious state, and healthy controls. The extracted SWC metrics, mean, reflecting the stability of connectivity, and standard deviation, indicating variability, are analyzed to discern FC differences at the group level. Multiclass classification is attempted using various models of artificial neural networks that include different multilayer perceptrons (MLP), recurrent neural networks, long-short-term memory networks, gated recurrent units, and a hybrid CNN-LSTM model that combines convolutional neural networks (CNN) and long-short-term memory network to validate the discriminative power of these FC features. The results show that MLP model 2 achieves a classification accuracy of 96.3% using AEC features obtained with a window length of 16s, highlighting the effectiveness of AEC. An evaluation of the model performance for different window sizes (16 to 20 s) shows that MLP model 2 consistently achieves high accuracy, ranging from 95.5% to 96.3%, using AEC features. When AEC and wPLI features are combined, the maximum accuracy increases to 96.9% for MLP model 2 and 96.7% for MLP model 3, with a window size of 17 seconds in both cases.

Injury to the meniscus is common, but few viable strategies exist for its repair or regeneration. To address this, animal models have been developed to translate new treatment strategies toward the clinic. However, there is not yet a regulatory document guiding such studies. The purpose of this study was to carry out a systematic review of the literature on meniscus treatment methods and outcomes to define the state of the field. Public databases were queried by using search terms related to animal models and meniscus injury and/or repair over the years 1980–2015. Identified peer-reviewed manuscripts were screened by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. One of nine reviewers read each manuscript and scored them based on whether the publication described a series of predefined study descriptors and outcome measures. Additional data were extracted to identify common assays used. A total of 128 full-length peer-reviewed manuscripts were identified. The number of publications increased over the time frame analyzed, with 48% focused on augmented repair. Rabbit was, by far, the most prevalent species utilized (46%), with dog (21%) and sheep (20%) being the next most common. Analysis of study descriptors revealed that most studies appropriately documented details of the animal used, the surgical approach, and defect and implant characteristics (e.g., 63% of studies identified clearly the defect size). In terms of outcome parameters, most studies carried out macroscopic (85%), histologic (90%), and healing/integration (83%) analyses of the meniscus. However, many studies did not provide further analysis beyond these fundamental measures, and less than 40% reported on the adjacent cartilage and synovium, as well as joint function. There is intense interest in the field of meniscus repair. However, given the current lack of guidance documentation in this area, preclinical animal models are not performed in a standardized fashion. The development of a “Best Practices” document would increase reproducibility and external validity of experiments, while accelerating advancements in translational research. Advancement is of paramount importance given the high prevalence of meniscal injuries and the paucity of effective repair or regenerative strategies.

The present study was conducted to examine the accessibility and corrosion of web references cited in articles of DESIDOC Journal of Library & Information Technology. A total of 1921 web references cited in 273 articles for five years from 2014-2018 were identified and checked to test their accessibility in terms of decay and persistence. Nearly 23.31% web references disappeared with passage of time. The results revealed that older the age of URL references, higher the percentage of inactive URL references. The .gov domain was found to be most constant with 95.99% accessibility rate. The average half-life for the missing web references during 2014-2018 was 6.55. The permanency of online resources is not assured because of the corrosion of web references but collective efforts are required to preclude the corrosion of web references.

Background: The corrective procedures for meniscal injury are dependent on tear type, severity, and location. Vertical longitudinal tears are common in young and active individuals, but their natural progression and impact on osteoarthritis (OA) development are not known. Root tears are challenging and they often indicate poor outcomes, although the timing and mechanisms of initiation of joint dysfunction are poorly understood, particularly in large-animal and human models. Purpose/Hypothesis: In this study, vertical longitudinal and root tears were made in a large-animal model to determine the progression of joint-wide dysfunction. We hypothesized that OA onset and progression would depend on the extent of injury-based load disruption in the tissue, such that root tears would cause earlier and more severe changes to the joint. Study Design: Controlled laboratory study. Methods: Sham surgeries and procedures to create either vertical longitudinal or root tears were performed in juvenile Yucatan mini pigs through randomized and bilateral arthroscopic procedures. Animals were sacrificed at 1, 3, or 6 months after injury and assessed at the joint and tissue level for evidence of OA. Functional measures of joint load transfer, cartilage indentation mechanics, and meniscal tensile properties were performed, as well as histological evaluation of the cartilage, meniscus, and synovium. Results: Outcomes suggested a progressive and sustained degeneration of the knee joint and meniscus after root tear, as evidenced by histological analysis of the cartilage and meniscus. This occurred in spite of spontaneous reattachment of the root, suggesting that this reattachment did not fully restore the function of the native attachment. In contrast, the vertical longitudinal tear did not cause significant changes to the joint, with only mild differences compared with sham surgery at the 6-month time point. Conclusion: Given that the root tear, which severs circumferential connectivity and load transfer, caused more intense OA compared with the circumferentially stable vertical longitudinal tear, our findings suggest that without timely and mechanically competent fixation, root tears may cause irreversible joint damage. Clinical Relevance: More generally, this new model can serve as a test bed for experimental surgical, scaffold-based, and small molecule–driven interventions after injury to prevent OA progression.

An analysis of 5,703 theses accepted by 54 Indian State Agricultural Universities in 2018 and indexed by Krishikosh, a digital repository of Indian Council of Agricultural Research (ICAR) indicates that male scholars produced more than half of the theses at different levels of education, i.e. M.Sc., M.V. Sc., M.B.A., M. Tech and Ph.D. However, at M. Tech level, female scholars produced more theses than their male counter parts. Highest number of theses was produced by Govind Ballabh Pant University of Agriculture and Technology (Pant Nagar) followed by Punjab Agriculture University (Ludhiana). Agronomy and soil science was the area emphasized by more number of universities. A total of 4218 supervisors supervised 5703 theses. Of these, 80.8% were male supervisors and rest 19.2% were female supervisors. Male and female supervisors produced 1.3 and 1.4 theses per supervisor respectively.