Background: The aim of the study is to initiate a new quantitative mathematical objective tool for evaluation of response to neoadjuvant chemotherapy (NAC) and prediction of residual disease in breast cancer using contrastenhanced spectral mammography (CESM). Forty-two breast cancer patients scheduled for receiving NAC were included. All patients underwent two CESM examinations: pre and post NAC. To assess the response to neoadjuvant chemotherapy, we used a mathematical image analysis software that can calculate the difference in the intensity of enhancement between the pre and post neoadjuvant contrast images (MATLAB and Simulink) (Release 2013b). The proposed technique used the pre and post neoadjuvant contrast images as inputs. The technique consists of three main steps: (1) preprocessing, (2) extracting the region of interest (ROI), and (3) assessment of the response to chemotherapy by measuring the percentage of change in the intensity of enhancement of malignant lesions in the pre and post neoadjuvant CESM studies using a quantitative mathematical technique. This technique depends on the analysis of number of pixels included within the ROI. We compared this technique with the currently used method of evaluation: RECIST 1.1 (response evaluation criteria in solid tumors 1.1) and using another combined response evaluation approach using both RECIST 1.1 in addition to a subjective visual evaluation. Results were then correlated with the postoperative pathology evaluation using Miller-Payne grades. For statistical evaluation, patients were classified into responders and non-responders in all evaluation methods.

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Solar photovoltaic (PV) arrays in remote applications are often related to the rapid changes in the partial shading pattern. Rapid changes of the partial shading pattern make the tracking of maximum power point (MPP) of the global peak through the local ones too difficult. An essential need to make a fast and efficient algorithm to detect the peaks values which always vary as the sun irradiance changes. This paper presents two algorithms based on the improved particle swarm optimization technique one of them with PID controller (IPSO-PID), and the other one with Brain Emotional Learning Based Intelligent Controller (IPSO-BELBIC). These techniques improve the maximum power point (MPP) tracking capabilities for photovoltaic (PV) system under partial shading circumstances. The main aim of these improved algorithms is to accelerate the velocity of IPSO to reach to (MPP) and increase its efficiency. These algorithms also improve the tracking time under complex irradiance conditions. Based on these conditions, the tracking time of these presented techniques improves to 2 msec, with an efficiency of 100%.

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Metamaterial absorbers have been extensively researched due to their potential applications in photonics. This paper presents a highly efficient Broadband Metamaterial Absorber (BMA) based on a Manganese–Silica–Manganese three layer structure with a shaped pattern at the top layer. For maximum absorption efficiency, the geometrical parameters of the proposed absorber have been optimized based on Particle Swarm Optimization (PSO). The optimal structure with a thickness of 190 nm, can achieve more than 94% absorption spanning visible band (400–800) nm with 98.72% average absorption, and more than 90% absorption over the range from 365 to 888 nm. In the range from 447 to 717 nm, the design presented above 99% absorptivity, providing an ultra-wide bandwidth of 270 nm. The physical mechanism of absorption is illustrated through the exploration of the electric and magnetic field distributions. Additionally, the proposed structure maintains 85% absorption stability for wide incident angles up to 70° for both the TE and TM polarizations under oblique incidence. Further, the optimized absorber structure with excellent absorption capabilities makes it suitable for various applications, including optical sensors, thermal emitters, and color imaging applications.

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