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Accurate and appropriate prostate cancer detection can significantly reduce the death rate. In this research, the Henry Firefly Gas Solubility Optimization (HFGSO)-based Deep Residual Network (DRN) is established for the autonomous detection of prostate cancer. The pre-processing is done by Cuckoo Search-Based (T2FCS) filter and Type 2 Fuzzy. Subsequently, segmentation is exhibited by devised multi-objective SegNet scheme. The multi-objective SegNet method is newly designed by updating the objective function of SegNet with loss function. The multi-objective SegNet is trained by HFGSO. Then, data augmentation is done with cropping and rotation, which improves the performance of detection. At last, cancer identification is executed with DRN, and it is trained by HFGSO. The developed optimized multi-objective SegNet with DRN technique also achieved increased performance for the detection of cancer, with a sensitivity, specificity, and accuracy 0.9367, 0.9130, and 0.9263. [ABSTRACT FROM AUTHOR]

Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use. Explaining models helps to address the safety and ethical concerns and is essential for accountability. Interpretability serves to provide these explanations in terms that are understandable to humans. Additionally, post-hoc methods provide explanations after a model is learned and are generally model-agnostic. This survey provides a categorization of how recent post-hoc interpretabilitymethods communicate explanations to humans, it discusses each method in-depth, and how they are validated, as the latter is often a common concern. [ABSTRACT FROM AUTHOR]

Background: Lumbar Disc Herniation (LDH) is the most frequent entity affecting the spine resulting in low back pain and sciatica. Many minimally invasive procedures have been proposed for the treatment of LDH. Percutaneous Endoscopic Discectomy (PELD) is gaining popularity in recent years for the treatment of LDH and its supremacy over the well-established gold standard Microsurgical Lumbar Discectomy (MLD) is yet to be proven. Aim and Objectives: To compare pain relief using Visual Analogue Scale (VAS) score, Oswestry Disability Index (ODI), duration of surgery and incision length between MLD and PELD; To evaluate systemic cytokine response (CPK-CK, hs-CRP, IL-6) between MLD and PELD. Material and Methods: This was a prospective comparative analysis between MLD and PELD in patients undergoing surgery for single-level lumbar disc prolapse from January 2022 to October 2022 at a single institute. Results: The study included 64 patients, divided equally into two groups: 32 patients underwent conventional MLD, and 32 patients underwent PELD. Groups had similar demographic profiles and preoperative clinical features. Postoperatively, both groups showed significant improvements in pain (VAS) scores and functional disability (ODI), with no significant difference between the two groups. However, there were differences in certain biochemical markers: CPK and IL-6 levels were lower in the PELD group compared to the MLD group, and hs-CRP rd levels were also lower in the PELD group, though statistically significant only on the 3rd postoperative day. The surgery duration and incision length were shorter in the PELD group. Complication rates were low, with two complications in the MLD group and one in the PELD group. Conclusion: Both MLD and PELD demonstrated positive outcomes in terms of pain and functional disability reduction, but PELD showed advantages in terms of shorter surgery duration, smaller incision length, and lower levels of certain inflammatory markers. No significant difference was noted in clinical outcomes between MLD and PELD. Postoperative systemic cytokine response is significantly lower in the PELD group than in the MLD group. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid algorithm using a Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm to determine the optimal placement of SSSC devices. A multi-objective function is used to increase voltage stability, voltage profile and minimize overall power losses of the transmission system. At first, PSO algorithm is used to find the optimal SSSC location and further more to reduce the power losses and voltage profile enhancement to overcome the sub-optimal operation of existing algorithms. The HFPSO algorithm is used to determine the optimal placement of SSSC series FACTS device and verification of the proposed algorithm was achieved on standard IEEE 14-bus and IEEE 30-bus transmission systems in the MATLAB environment. Comprehensive simulation results of two different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms to enhance voltage stability and reducing active and reactive power losses in electrical power transmission systems. [ABSTRACT FROM AUTHOR]

Bismuth nanoplatelets are efficient electrocatalysts for reducing carbon dioxide to formatea promising renewable energy storage media. Yet, while various promising catalyst supports are acid-sensitive, deposition of Bi on these supports is hindered by the Bi3+ ions' solubility, which is limited to low pH values (<2). Here, we used monometallic and bimetallic Bi thiolate precursors to electrodeposit metallic Bi nanoplatelets from a pH-neutral solution. Analysis of the electrochemical deposition mechanism revealed an intricate kinetic balance between the electron transfer and precursor dissociation steps and a stronger tendency toward instantaneous nucleation and growth when the bimetallic precursor was used. In continuance, the nanoplatelets electrodeposited from the bimetallic precursor were thinner than the nanoplatelets electrodeposited from the monometallic precursor. They exhibited an ∼120 mV lower overpotential at 5 mA/cm2 for reduction of CO2 to formate. Both electrocatalysts yielded high faradaic efficiency (∼95%) for formate generation when deposited on a flat support. Finally, a highly conformal coating of graphene foam with Bi nanoplatelets was obtained, which yielded a low onset potential (∼350 mV vs RHE) for CO2 reduction to formate. This work establishes a facile route for coating acid-sensitive supports with functional coatings using thiolate-based precursors for the first time. It also lays out the reaction mechanism and correlation between the precursor structure–electrodeposit structure and functionality. [ABSTRACT FROM AUTHOR]

The effective dissipation of heat from electronic devices is essential to enable their long-term operation and their further miniaturization. Graphene foams (GF) and carbon nanotube (CNT) forests are promising materials for thermal applications, including heat dissipation, due to their excellent thermal conduction and low thermal interface resistance. Here, we study the heat transfer characteristics of these two materials under forced convection. We applied controlled airflow to heated samples of GF and CNT forests while recording their temperature using infrared micro-thermography. Then, we analyzed the samples using finite-element simulations in conjunction with a genetic optimization algorithm, and we extracted their heat fluxes in both the horizontal and vertical directions. We found that boundary layers have a profound impact on the heat transfer characteristics of our samples, as they reduce the heat transfer in the horizontal direction. The heat transfer in the vertical direction, on the other hand, is dominated by the material conduction and is much higher than the horizontal heat transfer. Accordingly, we uncover the fundamental thermal behavior of GF and CNT forests, paving the way toward their successful integration into thermal applications, including cooling devices. [ABSTRACT FROM AUTHOR]