CBAM VGG16: An efficient driver distraction classification using CBAM embedded VGG16 architecture.

Driver monitoring systems (DMS) are crucial in autonomous driving systems (ADS) when users are concerned about driver/vehicle safety. In DMS, the significant influencing factor of driver/vehicle safety is the classification of driver distractions or activities. The driver's distractions or activities convey meaningful information to the ADS, enhancing the driver/ vehicle safety in real-time vehicle driving. The classification of driver distraction or activity is challenging due to the unpredictable nature of human driving. This paper proposes a convolutional block attention module embedded in Visual Geometry Group (CBAM VGG16) deep learning architecture to improve the classification performance of driver distractions. The proposed CBAM VGG16 architecture is the hybrid network of the CBAM layer with conventional VGG16 network layers. Adding a CBAM layer into a traditional VGG16 architecture enhances the model's feature extraction capacity and improves the driver distraction classification results. To validate the significant performance of our proposed CBAM VGG16 architecture, we tested our model on the American University in Cairo (AUC) distracted driver dataset version 2 (AUCD2) for cameras 1 and 2 images. Our experiment results show that the proposed CBAM VGG16 architecture achieved 98.65% classification accuracy for camera 1 and 97.85% for camera 2 AUCD2 datasets. The CBAM VGG16 architecture also compared the driver distraction classification performance with DenseNet121, Xception, MoblieNetV2, InceptionV3, and VGG16 architectures based on the proposed model's accuracy, loss, precision, F1 score, recall, and confusion matrix. The drivers' distraction classification results indicate that the proposed CBAM VGG16 has 3.7% classification improvements for AUCD2 camera 1 images and 5% for camera 2 images compared to the conventional VGG16 deep learning classification model. We also tested our proposed architecture with different hyperparameter values and estimated the optimal values for best driver distraction classification. The significance of data augmentation techniques for the data diversity performance of the CBAM VGG16 model is also validated in terms of overfitting scenarios. The Grad-CAM visualization of our proposed CBAM VGG16 architecture is also considered in our study, and the results show that VGG16 architecture without CBAM layers is less attentive to the essential parts of the driver distraction images. Furthermore, we tested the effective classification performance of our proposed CBAM VGG16 architecture with the number of model parameters, model size, various input image resolutions, cross-validation, Bayesian search optimization and different CBAM layers. The results indicate that CBAM layers in our proposed architecture enhance the classification performance of conventional VGG16 architecture and outperform the state-of-the-art deep learning architectures.

Identification of common genetic factors and immune-related pathways associating more than two autoimmune disorders: implications on risk, diagnosis, and treatment.

Autoimmune disorders (ADs) are chronic conditions resulting from failure or breakdown of immunological tolerance, resulting in the host immune system attacking its cells or tissues. Recent studies report shared effects, mechanisms, and evolutionary origins among ADs; however, the possible factors connecting them are unknown. This study attempts to identify gene signatures commonly shared between different autoimmune disorders and elucidate their molecular pathways linking the pathogenesis of these ADs using an integrated gene expression approach. We employed differential gene expression analysis across 19 datasets of whole blood/peripheral blood cell samples with five different autoimmune disorders (rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, Crohn's disease, and type 1 diabetes) to get nine key genes-EGR1, RUNX3, SMAD7, NAMPT, S100A9, S100A8, CYBB, GATA2, and MCEMP1 that were primarily involved in cell and leukocyte activation, leukocyte mediated immunity, IL-17, AGE-RAGE signaling in diabetic complications, prion disease, and NOD-like receptor signaling confirming its role in immune-related pathways. Combined with biological interpretations such as gene ontology (GO), pathway enrichment, and protein-protein interaction (PPI) network, our current study sheds light on the in-depth research on early detection, diagnosis, and prognosis of different ADs.

Extended phase space thermodynamics of regular-AdS black hole.

After obtaining an exact regular-AdS black hole resulting from the coupling of general relativity with nonlinear electrodynamics (NED), we explore the thermodynamics of the extended phase space, treating the cosmological constant ( Λ ) as the pressure (P) of the black holes and its conjugate as thermodynamic volume (V). Considering the NED parameter (g), we investigate the Hawking temperature, entropy, Gibb's free energy and specific heat at the horizon radius. Due to the presence of NED charge, the black hole exhibits van der Waals-like phase transition instead of Hawking-Page phase transition, which could be observed through the G - T plots, which display a swallowtail pattern below the critical pressure, and it gives rise to second-order phase transitions when pressure attains its critical value. The first-order phase transition shares similarities with the liquid-gas phase transition. We determine the exact critical points and explore the influence of NED on P - V criticality, revealing that the isotherms undergo a liquid-gas-like phase transition for temperatures below its critical value T C , especially at lower T C . The identical critical exponent to that of the van der Waals fluid suggests that the NED does not alter the critical exponents, as observed in other arbitrary AdS black holes.

Antinuclear antibody (ANA) positivity pattern by line immunoassay in a hospital from eastern India: Update from a laboratory perspective.

Context: The existence of more than one antibody in systemic autoimmune rheumatic diseases (SARDs) or connective tissue disease (CTD) along with features of more than one autoimmune disease (AD) in an individual is suggestive of overlap syndrome (OS). Line immunoassay (LIA) can target many autoantibodies in a single approach, thus making the identification of OS feasible. Aims and Objectives: This study aimed to identify the pattern of distribution of antinuclear antibodies by LIA prevalent in a hospital population in eastern India and identify common forms of SARD in this belt based on laboratory findings. Material and Methods: A total of 1660 samples received for ANA profile testing by LIA were analysed. Statistical Analysis: Factor analysis was performed with factor loading scores used in the k-means algorithm to identify clustering of various autoantibodies. Results: U1-snRNP positivity was the highest at 16.69%, and the least frequent autoantibody noted was anti-Jo-1 at 0.71% positivity. Based on the outcome of factor analysis, three clusters were determined. Cluster 1 showed a predominance of anti-PM/Scl antibodies, cluster 2 showed a predominance of anti-dsDNA, anti-histone, anti-SmD1, anti-nucleosomes, anti-PCNA, anti-Po, anti-SSA/Ro52, anti-SSA-Ro60, anti-SSB/La, anti-Scl-70, anti-Mi-2, anti-Ku and anti-AMA-M2, and cluster 3 showed a predominance of anti-U1-snRNP. Conclusions: Mixed connective tissue disease (MCTD) and overlap syndrome (OS) are prevalent more than pure form of an AD in our study population. OS may be missed out by monospecific immunoassays and hence adds to diagnostic challenges. LIA may be more useful in identifying specific autoantibodies by a single approach rather than monospecific immunoassays in populations after a positive screen by indirect immunofluorescence (IIF).

Adversarial Attacks against Deep-Learning-Based Automatic Dependent Surveillance-Broadcast Unsupervised Anomaly Detection Models in the Context of Air Traffic Management.

Deep learning has shown significant advantages in Automatic Dependent Surveillance-Broadcast (ADS-B) anomaly detection, but it is known for its susceptibility to adversarial examples which make anomaly detection models non-robust. In this study, we propose Time Neighborhood Accumulation Iteration Fast Gradient Sign Method (TNAI-FGSM) adversarial attacks which fully take into account the temporal correlation of an ADS-B time series, stabilize the update directions of adversarial samples, and escape from poor local optimum during the process of iterating. The experimental results show that TNAI-FGSM adversarial attacks can successfully attack ADS-B anomaly detection models and improve the transferability of ADS-B adversarial examples. Moreover, the TNAI-FGSM is superior to two well-known adversarial attacks called the Fast Gradient Sign Method (FGSM) and Basic Iterative Method (BIM). To the best of our understanding, we demonstrate, for the first time, the vulnerability of deep-learning-based ADS-B time series unsupervised anomaly detection models to adversarial examples, which is a crucial step in safety-critical and cost-critical Air Traffic Management (ATM).

Combining Fleetwide AviTeam Aviation Emission Modeling with LCA Perspectives for an Alternative Fuel Impact Assessment.

Reducing aviation emissions is important as they contribute to air pollution and climate change. Several alternative aviation fuels that may reduce life cycle emissions have been proposed. Comparative life cycle assessments (LCAs) of fuels are useful for inspecting individual fuels, but systemwide analysis remains difficult. Thus, systematic properties like fleet composition, performance, or emissions and changes to them under alternative fuels can only be partially addressed in LCAs. By integrating the geospatial fuel and emission model, AviTeam, with LCA, we can assess the mitigation potential of a fleetwide use of alternative aviation fuels on 210 000 shorter haul flights. In an optimistic case, liquid hydrogen (LH2) and power-to-liquid fuels, when produced with renewable electricity, may reduce emissions by about 950 GgCO2eq when assessed with the GWP100 metric and including non-CO2 impacts for all flights considered. Mitigation potentials range from 44% on shorter flights to 56% on longer flights. Alternative aviation fuels' mitigation potential is limited because of short-lived climate forcings and additional fuel demand to accommodate LH2 fuel. Our results highlight the importance of integrating system models into LCAs and are of value to researchers and decision-makers engaged in climate change mitigation in the aviation and transport sectors.

Effects of supplementation of different selenium sources on lipid profile, selenium, and vitamin E concentration of yolk.

Egg preference as a source of protein also provides beneficial fatty acids, vital for human consumption. However, rich in lipid products are prone to oxidative damage. The study aims to determine the effect of supplementing biogenic selenium (Se) from Stenotrophomonas maltophilia, ADS18 (ADS18) in laying hens' diet on yolk lipid oxidation status (MDA), beta-carotene (ß-carotene) content, cholesterol, fatty acids, Se, and vitamin E (VE) level. A total of one hundred and twenty (120) laying hens of Lohmann Brown strains aged 50 weeks, weighing 1500 to 2000 g were reared individually in A-shape two-tier stainless-steel cages sized 30 cm x 50 cm x 40 cm (width, depth height). The hens were randomly allotted into four treatments with six replications in a complete randomised design for the period of 12 weeks. The basal diet contains 100 mg/kg VE. Treatment diets consist of basal diet as control, SS containing 0.3 mg/kg sodium selenite, Se-yeast containing 0.3 mg/kg selenised yeast, and VADS18 containing 0.3 mg/kg of ADS18. Forty-eight eggs were collected and freeze-dried biweekly for analysis. The results of the present study showed that hens supplemented ADS18 had significantly (P < 0.05) lower MDA and cholesterol levels while their egg yolks had higher levels of Se and mono-unsaturated fatty acids (MUFA). The control group had significantly (P < 0.05) higher saturated fatty acid (SFA) contents than the VE and dietary Se-supplemented groups, while the ADS18 group had the lowest SFA contents. Conversely, in comparison to the inorganic and control groups, the VE content of the egg yolk was significantly (P < 0.05) higher in organic Se-supplemented (Se-yeast and VADS18) groups. Hens with SS supplementation had significantly (P < 0.05) higher egg yolk ß-carotene content. When compared to other treatment groups, the control group had higher (P < 0.05) polyunsaturated fatty acids (PUFA) content. The ADS18 is therefore deemed comparable to other Se sources. To prevent Se toxicity, however, a better understanding of the levels of ADS18 incorporation in poultry diets is required.

Alcohol Dependence Syndrome With Bipolar Affective Disorder and Hypomanic Current Episode: A Case Report.

Studies have revealed that individuals with bipolar I and bipolar II have a past of substance abuse. The co-occurrence of bipolar disorder and alcoholism is frequent. Although various arguments have been put forward to explain the relationship between these disorders, it is still not fully understood. Since substance abuse is prevalent among bipolar patients, it would be beneficial to investigate the impact of substance abuse on clinical characteristics, as well as the progression of the illness. Thus, this study was carried out to investigate a case of alcohol dependence with bipolar disorder. A 49-year-old male visited the psychiatry outpatient department and then was admitted. The patient's chief complaints were alcohol consumption, cigarette smoking, daily drinking for 35 years, irritability/aggressiveness, boastful talk, overspending, and decreased need for sleep from the last 20 days. According to the literature, self-medicating with alcohol is not an effective treatment for alcoholism, unless it is being used to alleviate the psychological and neurochemical effects caused by alcohol. However, there has been limited research on how to treat individuals who have both alcoholism and another medical condition. A few studies have looked at the impact of medications like valproate, lithium, and naltrexone, as well as psychosocial interventions, in treating patients with bipolar disorder and alcoholism. However, more research is necessary to fully understand the best approach.

Psychometric validation of the 15-item Patient Health Questionnaire - Anxiety and Depression Scale (PHQ-ADS) to assess psychological distress in breast cancer survivors.

OBJECTIVE: Psychological distress persists amongst breast cancer survivors, so reliable assessment of symptoms is essential. The Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) is a composite measure of depression and anxiety and has been used to measure distress. This study aimed to evaluate the psychometric properties of the PHQ-ADS within breast cancer survivors. METHOD: Breast cancer survivors (N = 280) were recruited online and followed up at 12-months. Depression (PHQ-8) and anxiety (GAD-7) items formed the composite PHQ-ADS score. Additional measures included: distress thermometer (convergent validity), fear of cancer recurrence and COVID distress (discriminant validity), and self-compassion (predictive validity). Confirmatory factor analysis (CFA) using weighted least squares mean and variance adjusted estimation was undertaken. RESULTS: One, two, and bifactor models underlying the PHQ-ADS were evaluated. The bifactor model had the most appropriate model fit overall. Omega hierarchical for the general distress factor was 0.914, accounting for 82% of explained variance. This suggests the PHQ-ADS is sufficiently unidimensional to warrant use of a total composite score. The PHQ-ADS demonstrated strong convergent and moderate discriminant validity. Self-compassion was an independent predictor of distress at 12-months. CONCLUSIONS: The PHQ-ADS is a valid measure for psychological distress in breast cancer survivors prescribed hormone therapy.

H*ads dynamics engineering via bimetallic Pd-Cu@MXene catalyst for enhanced electrocatalytic hydrodechlorination.

Electrocatalytic hydrodechlorination (EHDC) is a promising approach to safely remove halogenated emerging contaminants (HECs) pollutants. However, sluggish production dynamics of adsorbed atomic H (H*ads) limit the applicability of this green process. In this study, bimetallic Pd-Cu@MXene catalysts were synthesized to achieve highly efficient removal of HECs. The alloy electrode (Pd-Cu@MX/CC) exhibited better EHDC performance in comparison to Pd@MX/CC electrode, resulting in diclofenac degradation efficiency of 93.3 ± 0.1%. The characterization analysis revealed that the Pd0/PdII ratio decreased by forming bimetallic Pd-Cu alloy. Density functional theory calculations further demonstrated the electronic configuration modulation of the Pd-Cu@MXene catalysts, optimizing binging energies for H* and thereby facilitating H*ads production and tuning the reduction capability of H*ads. Noteably, the amounts and reduction potential of H*ads for Pd-Cu@MXene catalysts were 1.5 times higher and 0.37 eV lower than those observed for the mono Pd electrode. Hence, the introduction of Cu into the Pd catalyst optimized the dynamics of H*ads production, thereby conferring significant advantages to EHDC reactions. This augmentation was underscored by the successful application of the alloy catalysts supported by MXene in EHDC experiments involving other HECs, which represented a new paradigm for EHDC for efficient recalcitrant pollutant removal by H*ads.

A survey on 3D object detection in real time for autonomous driving.

This survey reviews advances in 3D object detection approaches for autonomous driving. A brief introduction to 2D object detection is first discussed and drawbacks of the existing methodologies are identified for highly dynamic environments. Subsequently, this paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings. Based on depth inference basis, learning schemes, and internal representation, this work presents a method taxonomy of three classes: model-based and geometrically constrained approaches, end-to-end learning methodologies, and hybrid methods. There is highlighted segment for current trend of multi-view detectors as end-to-end methods due to their boosted robustness. Detectors from the last two kinds were specially selected to exploit the autonomous driving context in terms of geometry, scene content and instances distribution. To prove the effectiveness of each method, 3D object detection datasets for autonomous vehicles are described with their unique features, e. g., varying weather conditions, multi-modality, multi camera perspective and their respective metrics associated to different difficulty categories. In addition, we included multi-modal visual datasets, i. e., V2X that may tackle the problems of single-view occlusion. Finally, the current research trends in object detection are summarized, followed by a discussion on possible scope for future research in this domain.

Intra-Beam Interference Mitigation for the Downlink Transmission of the RIS-Assisted Hybrid Millimeter Wave System.

Millimeter-wave (mmWave) communication systems leverage the directional beamforming capabilities of antenna arrays equipped at the base stations (BS) to counteract the inherent high propagation path loss characteristic of mmWave channels. In downlink mmWave transmissions, i.e., from the BS to users, distinguishing users within the same beam direction poses a significant challenge. Additionally, digital baseband precoding techniques are limited in their ability to mitigate inter-user interference within identical beam directions, representing a fundamental constraint in mmWave downlink transmissions. This study introduces an innovative analog beamforming-based interference mitigation strategy for downlink transmissions in reconfigurable intelligent surface (RIS)-assisted hybrid analog-digital (HAD) mmWave systems. This is achieved through the joint design of analog beamformers and the corresponding coefficients at both the RIS and the BS. We first present derived closed-form approximation expressions for the achievable rate performance in the proposed scenario and establish a stringent upper bound on this performance in a large number of RIS elements regimes. The exclusive use of analog beamforming in the downlink phase allows our proposed transmission algorithm to function efficiently when equipped with low-resolution analog-to-digital/digital-to-analog converters (A/Ds) at the BS. The energy efficiency of the downlink transmission is evaluated through the deployment of six-bit A/Ds and six-bit pulse-amplitude modulation (PAM) signals across varying numbers of activated RIS elements. Numerical simulation results validate the effectiveness of our proposed algorithms in comparison to various benchmark schemes.

The Effects of Speed and Delays on Test-Time Performance of End-to-End Self-Driving.

This study investigates the effects of speed variations and computational delays on the performance of end-to-end autonomous driving systems (ADS). Utilizing 1:10 scale mini-cars with limited computational resources, we demonstrate that different driving speeds significantly alter the task of the driving model, challenging the generalization capabilities of systems trained at a singular speed profile. Our findings reveal that models trained to drive at high speeds struggle with slower speeds and vice versa. Consequently, testing an ADS at an inappropriate speed can lead to misjudgments about its competence. Additionally, we explore the impact of computational delays, common in real-world deployments, on driving performance. We present a novel approach to counteract the effects of delays by adjusting the target labels in the training data, demonstrating improved resilience in models to handle computational delays effectively. This method, crucially, addresses the effects of delays rather than their causes and complements traditional delay minimization strategies. These insights are valuable for developing robust autonomous driving systems capable of adapting to varying speeds and delays in real-world scenarios.

Prolonged Heat Stress during Winter Diapause Alters the Expression of Stress-Response Genes in Ostrinia nubilalis (Hbn.).

During diapause, a state of temporarily arrested development, insects require low winter temperatures to suppress their metabolism, conserve energy stores and acquire cold hardiness. A warmer winter could, thus, reduce diapause incidence and duration in many species, prematurely deplete their energy reserves and compromise post-diapause fitness. In this study, we investigated the combined effects of thermal stress and the diapause program on the expression of selected genes involved in antioxidant defense and heat shock response in the European corn borer Ostrinia nubilalis. By using qRT-PCR, it has been shown that response to chronic heat stress is characterized by raised mRNA levels of grx and trx, two important genes of the antioxidant defense system, as well as of hsp70 and, somewhat, of hsp90, two major heat shock response proteins. On the other hand, the expression of hsc70, hsp20.4 and hsp20.1 was discontinuous in the latter part of diapause, or was strongly controlled by the diapause program and refractory to heat stress, as was the case for mtn and fer, genes encoding two metal storage proteins crucial for metal ion homeostasis. This is the first time that the effects of high winter temperatures have been assessed on cold-hardy diapausing larvae and pupae of this important corn pest.

The Effect of Explicit Suicide Language in Engagement With a Suicide Prevention Search Page Help-Seeking Prompt: Nonrandomized Trial.

BACKGROUND: Given that signage, messaging, and advertisements (ads) are the gateway to many interventions in suicide prevention, it is important that we understand what type of messaging works best for whom. OBJECTIVE: We investigated whether explicitly mentioning suicide increases engagement using internet ads by investigating engagement with campaigns with different categories of keywords searched, which may reflect different cognitive states. METHODS: We ran a 2-arm study Australia-wide, with or without ads featuring explicit suicide wording. We analyzed whether there were differences in engagement for campaigns with explicit and nonexplicit ads for low-risk (distressed but not explicitly suicidal), high-risk (explicitly suicidal), and help-seeking for suicide keywords. RESULTS: Our analyses revealed that having explicit wording has opposite effects, depending on the search terms used: explicit wording reduced the engagement rate for individuals searching for low-risk keywords but increased engagement for those using high-risk keywords. CONCLUSIONS: The findings suggest that individuals who are aware of their suicidality respond better to campaigns that explicitly use the word "suicide." We found that individuals who search for low-risk keywords also respond to explicit ads, suggesting that some individuals who are experiencing suicidality search for low-risk keywords.

Towards the establishment of national diagnostic reference levels for abdomen, KUB, and lumbar spine x-ray examinations in Sri Lanka: a multi-centric study.

Diagnostic reference levels (DRLs) and achievable doses (ADs) provide guidance to optimise radiation doses for patients undergoing medical imaging procedures. This multi-centre study aimed to compare institutional DRLs (IDRLs) across hospitals, propose ADs and multi-centric DRLs (MCDRLs) for four common x-ray examinations in Sri Lanka, and assess the potential for dose reduction. A prospective cross-sectional study of 894 adult patients referred for abdomen anteroposterior (AP), kidney-ureter-bladder (KUB) AP, lumbar spine AP, and lumbar spine lateral (LAT) x-ray examinations was conducted. Patient demographic information (age, sex, weight, BMI) and exposure parameters (tube voltage, tube current-exposure time product) were collected. Patient dose indicators were measured in terms of kerma-area product (PKA) using a PKAmeter. IDRLs, ADs, and MCDRLs were calculated following the International Commission on Radiological Protection guidelines, with ADs and MCDRLs defined as the 50th and 75th percentiles of the median PKAdistributions, respectively. IDRL ranges varied considerably across hospitals: 1.42-2.42 Gy cm2for abdomen AP, 1.51-2.86 Gy cm2for KUB AP, 0.83-1.65 Gy cm2for lumbar spine AP, and 1.76-4.10 Gy cm2for lumbar spine LAT. The proposed ADs were 1.82 Gy cm2(abdomen AP), 2.03 Gy cm2(KUB AP), 1.27 Gy cm2(lumbar spine AP), and 2.21 Gy cm2(lumbar spine LAT). MCDRLs were 2.24 Gy cm2(abdomen AP), 2.40 Gy cm2(KUB AP), 1.43 Gy cm2(lumbar spine AP), and 2.38 Gy cm2(lumbar spine LAT). Substantial intra- and inter-hospital variations in PKAwere observed for all four examinations. Although ADs and MCDRLs in Sri Lanka were comparable to those in the existing literature, the identified intra- and inter-hospital variations underscore the need for dose reduction without compromising diagnostic information. Hospitals with high IDRLs are recommended to review and optimise their practices. These MCDRLs serve as preliminary national DRLs, guiding dose optimisation efforts by medical professionals and policymakers.

Assisted Robots in Therapies for Children with Autism in Early Childhood.

Children with autism spectrum disorder (ASD) have deficits that affect their social relationships, communication, and flexibility in reasoning. There are different types of treatment (pharmacological, educational, psychological, and rehabilitative). Currently, one way to address this problem is by using robotic systems to address the abilities that are altered in these children. The aim of this review will be to analyse the effectiveness of the incorporation of the different robotic systems currently existing in the treatment of children up to 10 years of age diagnosed with autism. A systematic review has been carried out in the PubMed, Scopus, Web of Science, and Dialnet databases, with the following descriptors: child, autism, and robot. The search yielded 578 papers, and nine were selected after the application of the PRISMA guideline. The quality of the studies was analysed with the PEDRo scale, and only those with a score between four and six were selected. From this study, the conclusion is that the use of robots, in general, improves children's behaviour in the short term, but longer-term experiences are necessary to achieve more conclusive results.

Electrochemical Synthesis of Functionalized Graphene/Polyaniline Composite Using Two Electrode Configuration for Supercapacitors.

An effective approach for the large-scale fabrication of conducting polyaniline (PANI) using in situ anodic electrochemical polymerization on nickel foam which had been coated in aryl diazonium salt (ADS)-modified graphene (ADS-G). In the present work, ADS-G was used as a high surface-area support material for the electrochemical polymerization of PANI. The electrochemical performances of the ADS-G/PANI composites exhibited better suitability as supercapacitor electrode materials than those of the PANI. The ADS-G/PANI composites achieved a specific capacitance of 528 F g-1, which was higher than that of PANI (266 F g-1) due to excellent electrode-electrolyte interaction and the synergistic effect of electrical conductivity between ADS-G and PANI in the composites. These findings suggest that the ADS-G/PANI composites are a suitable composite for potential supercapacitor applications.

From Black Holes Entropy to Consciousness: The Dimensions of the Brain Connectome.

It has been shown that the theory of relativity can be applied physically to the functioning brain, so that the brain connectome should be considered as a four-dimensional spacetime entity curved by brain activity, just as gravity curves the four-dimensional spacetime of the physical world. Following the most recent developments in modern theoretical physics (black hole entropy, holographic principle, AdS/CFT duality), we conjecture that consciousness can naturally emerge from this four-dimensional brain connectome when a fifth dimension is considered, in the same way that gravity emerges from a 'flat' four-dimensional quantum world, without gravitation, present at the boundaries of a five-dimensional spacetime. This vision makes it possible to envisage quantitative signatures of consciousness based on the entropy of the connectome and the curvature of spacetime estimated from data obtained by fMRI in the resting state (nodal activity and functional connectivity) and constrained by the anatomical connectivity derived from diffusion tensor imaging.

Machine learning-based technique for resonance and directivity prediction of UMTS LTE band quasi Yagi antenna.

In this study, we have presented our findings on the deployment of a machine learning (ML) technique to enhance the performance of LTE applications employing quasi-Yagi-Uda antennas at 2100 MHz UMTS band. A number of techniques, including simulation, measurement, and a model of an RLC-equivalent circuit, are discussed in this article as ways to assess an antenna's suitability for the intended applications. The CST simulation gives the suggested antenna a reflection coefficient of -38.40 dB at 2.1 GHz and a bandwidth of 357 MHz (1.95 GHz-2.31 GHz) at a -10 dB level. With a dimension of 0.535λ0×0.714λ0, it is not only compact but also features a maximum gain of 6.9 dB, a maximum directivity of 7.67, VSWR of 1.001 at center frequency and a maximum efficiency of 89.9%. The antenna is made of a low-cost substrate, FR4. The RLC circuit, sometimes referred to as the lumped element model, exhibits characteristics that are sufficiently similar to those of the proposed Yagi antenna. We use yet another supervised regression machine learning (ML) technique to create an exact forecast of the antenna's frequency and directivity. The performance of machine learning (ML) models can be evaluated using a variety of metrics, including the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE). Out of the seven ML models, the linear regression (LR) model has the lowest error and maximum accuracy when predicting directivity, whereas the ridge regression (RR) model performs the best when predicting frequency. The proposed antenna is a strong candidate for the intended UMTS LTE applications, as shown by the modeling results from CST and ADS, as well as the measured and forecasted outcomes from machine learning techniques.