Breast cancer is becoming more dangerous by the day. The death rate in developing countries is rapidly increasing. As a result, early detection of breast cancer is critical, leading to a lower death rate. Several researchers have worked on breast cancer segmentation and classification using various imaging modalities. The ultrasonic imaging modality is one of the most cost-effective imaging techniques, with a higher sensitivity for diagnosis. The proposed study segments ultrasonic breast lesion images using a Dilated Semantic Segmentation Network (Di-CNN) combined with a morphological erosion operation. For feature extraction, we used the deep neural network DenseNet201 with transfer learning. We propose a 24-layer CNN that uses transfer learning-based feature extraction to further validate and ensure the enriched features with target intensity. To classify the nodules, the feature vectors obtained from DenseNet201 and the 24-layer CNN were fused using parallel fusion. The proposed methods were evaluated using a 10-fold cross-validation on various vector combinations. The accuracy of CNN-activated feature vectors and DenseNet201-activated feature vectors combined with the Support Vector Machine (SVM) classifier was 90.11 percent and 98.45 percent, respectively. With 98.9 percent accuracy, the fused version of the feature vector with SVM outperformed other algorithms. When compared to recent algorithms, the proposed algorithm achieves a better breast cancer diagnosis rate.

Thiophene and sulfonamide derivatives serve as biologically active compounds, used for the manufacture of large numbers of new drugs. In this study, 11 selected derivatives of thiophene sulfonamide were computed for their geometric parameters, such as hyperpolarizability, chemical hardness (ƞ), electronic chemical potential (μ), electrophilicity index (ω), ionization potential (I), and electron affinity (A). In addition, FT-IR and UV-Vis spectra were also simulated through theoretical calculations. The geometrical parameters and vibrational frequencies with assignments of the vibrational spectra strongly resemble the experimentally calculated values. Besides, the frontier molecular orbitals were also determined for various intramolecular interactions that are responsible for the stability of the compounds. The isodensity surfaces of the frontier molecular orbitals (FMOs) are the same pattern in most of the compounds, but in some compounds are disturbed due to the presence of highly electronegative hetero-atoms. In this series of compounds, 3 shows the highest HOMO–LUMO energy gap and lowest hyperpolarizability, which leads to the most stable compound and less response to nonlinear optical (NLO), while 7 shows the lowest HOMO–LUMO energy gap and highest hyperpolarizability, which leads to a less stable compound and a high NLO response. All compounds have their extended three-dimensional p-electronic delocalization which plays an important role in studying NLO responses.

Sustainable development is now the focus of researchers and organizations worldwide. Several concepts, such as reverse logistics (RLs) and closed-loop supply chains, have been introduced to encourage sustainability in supply chains. RLs refers to the set of activities needed by consumers to collect the product used for reuse, repair, remanufacturing, recycling, or disposal of the used product. There are various processes involved in RL, and one of them is collection systems. Collection refers to a company obtaining custody of specific items. We review the literature on RLs collection systems. A bibliometric analysis was conducted to provide better insight into the field and establish any trends. Firstly, we present the classification methods used in the field, based on available review papers. Secondly, we evaluate literature from several fields that are related to either the problem setting or the technical features. Different perspectives are presented and classified. This method facilitates the identification of manuscripts related to the reader’s specific interests. Throughout the literature review, trends in measuring the performance of collection systems are identified, and directions for future research are identified and presented.

In the present study, the corrosion inhibition effect of Spilanthes acmella aqueous leaves extract (SA-LE) on mild steel was investigated in 1.0 M HCl solution at different temperature using weight loss, Tafel polarization, linear polarization resistance (LPR), and electrochemical impedance (EIS) measurements. Adsorption of inhibitor on the surface of the mild steel obeyed both Langmuir and Temkin adsorption isotherms. The thermodynamic and kinetic parameters were also calculated to determine the mechanism of corrosion inhibition. The inhibition efficiency was found to increase with an increase in the inhibitor concentration i.e., Spilanthes acmella aqueous leaves extract, however, the inhibition efficiency decreased with an increase in the temperature. The phytochemical constituents with functional groups including electronegative hetero atoms such as N, O, and S in the extract adsorbed on the metal surface are found responsible for the effective performance of the inhibitor, which was confirmed by Fourier-transform infrared spectroscopy (FT-IR) and ultraviolet–visible spectroscopic (UV-Vis) studies. Protective film formation against corrosion was confirmed by scanning electron microscopy (SEM), atomic force microscopy (AFM), and contact angle studies. The result shows that the leaves extract acts as corrosion inhibitor and is able to promote surface protection by blocking active sites on the metal.

This research proposes the dandelion optimizer (DO), a bioinspired stochastic optimization technique, as a solution for achieving maximum power point tracking (MPPT) in photovoltaic (PV) arrays under partial shading (PS) conditions. In such scenarios, the overall power output of the PV array is adversely affected, with shaded cells generating less power and consuming power themselves, resulting in reduced efficiency and local hotspots. While bypass diodes can be employed to mitigate these effects by redirecting current around shaded cells, they may cause multiple peaks, making MPPT challenging. Therefore, metaheuristic algorithms are suggested to effectively optimize power output and handle multiple peaks. The DO algorithm draws inspiration from the long-distance movement of a dandelion seed, which relies on the force of the wind. By utilizing this bioinspired approach, the DO algorithm can successfully capture the maximum power point (MPP) under different partial shading scenarios, where traditional MPPT algorithms often struggle. An essential contribution of this research lies in the examination of the performance of the proposed algorithm through simulation and real-time hardware-in-the-loop (HIL) results. Comparing the DO algorithm with the state-of-the-art algorithms, including particle swarm optimization (PSO) and cuckoo search (CS), the DO algorithm outperforms them in terms of power tracking efficiency, tracking duration, and the maximum power tracked. Based on the real-time HIL results, the DO algorithm achieves the highest average efficiency at 99.60%, surpassing CS at 96.46% and PSO at 94.74%. These findings demonstrate the effectiveness of the DO algorithm in enhancing the performance of MPPT in PV arrays, particularly in challenging partial shading conditions.

Photovoltaic (PV) arrays have a considerably lower output when exposed to partial shadowing (PS). Whilst adding bypass diodes to the output reduces PS’s impact, this adjustment causes many output power peaks. Because of their tendency to converge to local maxima, traditional algorithms like perturb and observe and hill-climbing should not be used to track the optimal peak. The tracking of the optimal peak is achieved by employing a range of artificial intelligence methodologies, such as utilizing an artificial neural network and implementing control based on fuzzy logic principles. These algorithms perform satisfactorily under PS conditions but their training method necessitates a sizable quantity of data which result in placing an unnecessary demand on CPU memory. In order to achieve maximum power point tracking (MPPT) with fast convergence, minimal power fluctuations, and excellent stability, this paper introduces a novel optimization algorithm named PSO-AWDV (particle swarm optimization–adaptive weighted delay velocity). This algorithm employs a stochastic search approach, which involves the random exploration of the search space, to accomplish these goals. The efficacy of the proposed algorithm is demonstrated by conducting experiments on a series-connected configuration of four modules, under different levels of solar radiation. The algorithm successfully gets rid of the problems brought on by current traditional and AI-based methods. The PSO-AWDV algorithm stands out for its simplicity and reduced computational complexity when compared to traditional PSO and its variant PSO-VC, while excelling in locating the maximum power point (MPP) even in intricate shading scenarios, encompassing partial shading conditions and notable insolation fluctuations. Furthermore, its tracking efficiency surpasses that of both conventional PSO and PSO-VC. To further validate our results, we conducted a real-time hardware-in-the-loop (HIL) emulation, which confirmed the superiority of the PSO-AWDV algorithm over traditional and AI-based methods. Overall, the proposed algorithm offers a practical solution to the challenges of MPPT under PS conditions, with promising outcomes for real-world PV applications.