Additive manufacturing technology and its benefits have a significant impact on different industrial applications. The 3D printing technologies help manufacture lightweight intricate geometrical designs with enhanced strengths. The present study investigates the blended effects of previously recommended parameters of different infill patterns (line, triangle, and concentric) and infill densities (75, 80, and 85%) with varying thicknesses of layers (100, 200, and 300 μm). The test samples were created through Fused Filament Fabrication (FFF) technology using Acrylonitrile Butadiene Styrene (ABS) 3D printing. Mechanical properties were evaluated through tensile and impact strength tests conducted in accordance with ASTM standards. The experimental investigation reveals that the infill pattern greatly affected both tensile and impact strength. The best results were obtained with a concentric infill pattern, along with 80% infill density and 100 μm layer thickness. These conditions resulted in 123% and 115% higher tensile strength and 168% and 80% higher impact strength compared to line and triangle patterns, respectively.

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.

Employing Psidium guajava (P. guajava) extract from leaves, copper oxide nanoparticles (CuO NPs), likewise referred to as cupric oxide and renowned for their sustainable and harmless biogenesis, have the possibility of being useful for the purification of pollutants as well as for medicinal purposes. The current study examined the generated CuO NPs and their physical qualities by using ultraviolet-visible (UV) spectroscopy. The distinctive peak at 265 nm of the CuO NP production was originally seen. Additionally, an X-ray diffraction (XRD) investigation was conducted to identify the crystalline arrangement of the produced CuO NPs, and a Fourier transform infrared (FTIR) spectroscopy examination was performed to validate the functional compounds of the CuO NPs. Additionally, the synthesized nanoparticles' catalytic activities (wastewater treatment) were analyzed in dark and sunlight modes. The catalytic properties of CuO NPs in total darkness resulted in 64.21% discoloration, whereas exposure to sunshine increased the nanomaterials' catalyst performance to 92.31%. By lowering Cr(VI), Ni, Pb, Co, and Cd in sewage by proportions of 91.4, 80.8, 68.26, 73.25, and 72.4% accordingly, the CuO NP demonstrated its effectiveness as a nanosorbent. Total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biological demand for oxygen (BOD), and conductance were all successfully reduced by nanotreatment of tanning effluents, with proportion reductions of 93.24, 88.62, 94.21, 87.5, and 98.3%, correspondingly.

The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.

Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction.

Parkinson’s disease is a progressive neurodegenerative disease that destroys substantia nigra dopaminergic neurons, causing tremors, bradykinesia, rigidity, and postural instability. Current treatment approaches primarily focus on symptom management, employing pharmacological, non-pharmacological, and surgical methods. However, these treatments often result in fluctuating symptoms, side effects, and disease progression. Here, the authors have reviewed the emerging field of nanomedicine as a promising path for Parkinson’s disease treatment, emphasizing its potential to overcome the limitations of traditional therapies. Nanomedicine utilizes nanoparticles for targeted drug delivery, leveraging their small size and high surface area to volume ratio to cross the blood-brain barrier and deliver therapeutic agents directly to affected brain regions. Various nanoparticles, including lipid-based, polymeric, metallic, and carbon-based, have shown potential in Parkinson’s disease treatment. Additionally, nanocarrier systems like liposomes, nanogels, dendrimers, and solid lipid nanoparticles offer controlled and sustained release of therapeutic agents, enhancing their bioavailability and reducing side effects. This review provides insights into the pathophysiology of Parkinson’s disease, highlighting the mechanisms of neurodegeneration, the role of alpha-synuclein, and the disruption of dopaminergic pathways. It further discusses the application of gene therapy in conjunction with nanomedicine for targeted therapeutic interventions.