Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Interaction between devices, people, and the Internet has given birth to a new digital communication model, the internet of things (IoT). The integration of smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch attacks to compromise the devices using malware proliferation techniques. Therefore, malware detection is a lifeline for securing IoT devices against cyberattacks. This study addresses the challenge of malware detection in IoT devices by proposing a new CNN-based IoT malware detection architecture (iMDA). The proposed iMDA is modular in design that incorporates multiple feature learning schemes in blocks including (1) edge exploration and smoothing, (2) multi-path dilated convolutional operations, and (3) channel squeezing and boosting in CNN to learn a diverse set of features. The local structural variations within malware classes are learned by Edge and smoothing operations implemented in the split-transform-merge (STM) block. The multi-path dilated convolutional operation is used to recognize the global structure of malware patterns. At the same time, channel squeezing and merging helped to regulate complexity and get diverse feature maps. The performance of the proposed iMDA is evaluated on a benchmark IoT dataset and compared with several state-of-the CNN architectures. The proposed iMDA shows promising malware detection capacity by achieving accuracy: 97.93%, F1-Score: 0.9394, precision: 0.9864, MCC: 0. 8796, recall: 0.8873, AUC-PR: 0.9689 and AUC-ROC: 0.9938. The strong discrimination capacity suggests that iMDA may be extended for the android-based malware detection and IoT Elf files compositely in the future.

Additive manufacturing (AM) has arisen as a transformative technology for manufacturing complex geometries with enhanced mechanical properties, particularly in the realm of continuous fiber-reinforced polymer composites (CFRPCs). Among various AM techniques, fused deposition modeling (FDM) stands out as a promising method for the fabrication of CFRPCs due to its versatility, ease of use, flexibility, and cost-effectiveness. Several research papers on the AM of CFRPs via FDM were summarized and therefore this review paper provides a critical examination of the process-printing parameters influencing the AM process, with a focus on their impact on mechanical properties. This review covers details of factors such as fiber orientation, layer thickness, nozzle diameter, fiber volume fraction, printing temperature, and infill design, extracted from the existing literature. Through a visual representation of the process parameters (printing and material) and properties (mechanical, physical, and thermal), this paper aims to separate out the optimal processing parameters that have been inferred from various research studies. Furthermore, this analysis critically evaluates the current state-of-the-art research, highlighting advancements, applications, filament production methods, challenges, and opportunities for further development in this field. In comparison to short fibers, continuous fiber filaments can render better strength; however, delamination issues persist. Various parameters affect the printing process differently, resulting in several limitations that need to be addressed. Signifying the relationship between printing parameters and mechanical properties is vital for optimizing CFRPC fabrication via FDM, enabling the realization of lightweight, high-strength components for various industrial applications.

Transcranial direct and alternating current stimulation (tDCS and tACS, respectively) can modulate human brain dynamics and cognition. However, these modalities have not been compared using multiple imaging techniques concurrently. In this study, 15 participants participated in an experiment involving two sessions with a gap of 10 days. In the first and second sessions, tACS and tDCS were administered to the participants. The anode for tDCS was positioned at point FpZ, and four cathodes were positioned over the left and right prefrontal cortices (PFCs) to target the frontal regions simultaneously. tDCS was administered with 1 mA current. tACS was supplied with a current of 1 mA (zero-to-peak value) at 10 Hz frequency. Stimulation was applied concomitantly with functional near-infrared spectroscopy and electroencephalography acquisitions in the resting-state. The statistical test showed significant alteration ( p < 0.001) in the mean hemodynamic responses during and after tDCS and tACS periods. Between-group comparison revealed a significantly less ( p < 0.001) change in the mean hemodynamic response caused by tACS compared with tDCS. As hypothesized, we successfully increased the hemodynamics in both left and right PFCs using tDCS and tACS. Moreover, a significant increase in alpha-band power ( p < 0.01) and low beta band power ( p < 0.05) due to tACS was observed after the stimulation period. Although tDCS is not frequency-specific, it increased but not significantly ( p > 0.05) the powers of most bands including delta, theta, alpha, low beta, high beta, and gamma. These findings suggest that both hemispheres can be targeted and that both tACS and tDCS are equally effective in high-definition configurations, which may be of clinical relevance.

Due to the depletion of fossil fuels, cost-effective and efficient alternate energy resources are a major topic of research nowadays. Solar energy is one of the renewable energy sources which is under research. The main emphasis is on developing new technologies for harnessing the sun’s energy efficiently. Solar photovoltaic (PV) cells are one such technology that works on the principle of the photovoltaic effect, with the electric output generated by it being directly influenced by the amount of light reaching the surface of the cell. Identification of those parameters, which act as an obstacle between light and the solar cell surface and decrease the efficiency of the cell drastically, is necessary. This research study focuses on the environmental parameters (dust and humidity) that directly influence PV cell performance. Here, experiments were conducted by using different types of dusts (sand, soil, ash) of varying quantities and then finding their effect on PV cell output. The results clearly indicate an adverse effect of quantity of dust on performance of the solar PV cell. Moreover, the relative humidity effect on the PV cell performance was also checked, which shows significant change in efficiency for high relative humidity.

The availability of fresh drinkable water and water security is becoming a global challenge for sustainable development. In this regard, solar stills, due to their ease in operation, installation, and utilization of direct sunlight (as thermal energy), promise a better and sustainable future technology for water security in urban and remote areas. The major issue is its low distillate productivity, which limits its widespread commercialization. In this study, the effect of silicon (Si) particles is examined to improve the absorber surface temperature of the solar still absorber plate, which is the major component for increased distillate yield. Various weight percentages of Si particles were introduced in paint and coated on the aluminum absorber surface. Extensive indoor (using a self-made halogen light-based solar simulator) and outdoor testing were conducted to optimize the concentration. The coatings with 15 wt % Si in the paint exhibited the highest increase in temperature, namely, 98.5 °C under indoor controlled conditions at 1000 W/m² irradiation, which is 65.81% higher than a bare aluminum plate and 37.09% higher compared to a black paint-coated aluminum plate. On the other hand, coatings with 10 wt % Si reached up to 73.2 °C under uncontrolled outdoor conditions compared to 68.8 °C for the black paint-coated aluminum plate. A further increase in concentration did not improve the surface temperature, which was due to an excessive increase in thermal conductivity and high convective heat losses.