• Energy Research
• 2016-2025close
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Melanoma is defined as a disease that has been incurable in advanced stages, which shows the vital importance of timely diagnosis and treatment. To diagnose this type of cancer early, various methods and equipment have been used, almost all of which required a visit to the doctor and were not available to the public. In this study, an automated and accurate process to differentiate between benign skin pigmented lesions and malignant melanoma is presented, so that it can be used by the general public, and it does not require special equipment and special conditions in imaging. In this study, after preprocessing of the input images, the region of interest is segmented based on the Otsu method. Then, a new feature extraction is implemented on the segmented image to mine the beneficial characteristics. The process is then finalized by using an optimized Deep Believe Network (DBN) for categorization into 2 classes of normal and melanoma cases. The optimization process in DBN has been performed by a developed version of the newly introduced Thermal Exchange Optimization (dTEO) algorithm to obtain higher efficacy in different terms. To show the method’s superiority, its performance is compared with 7 different techniques from the literature.

There are many factors that affect short‐term load forecasting performance, such as weather and holidays. However, most of the existing load forecasting models lack more detailed considerations for some special days. In this paper, the applicability of the bagged regression trees (BRT) model combined with eight variables is investigated to forecast short‐term load in Qingdao. The comparative experiments show that the accuracy and speed of forecasting have some improvements using the BRT than the artificial neural network (ANN). Then, an indicator variable is newly proposed to capture the abnormal information during special days, which include national statutory holidays, bridging days, and proximity days. The BRT model combined with this indicator variable is tested on the load series measured in 2018. Experiments demonstrate that the improved model generates more accurate predictive results than BRT model combined with previously variables on special days.

The rapid development of science and technology, i.e., integrated modules that are actuators and sensors, promotes the comprehensive popularization of intelligent products in people’s life. More particularly, with the advent of the hybrid of the Internet of things and artificial intelligence, more and more activities preferably linked to the human beings have been automated and developed. Among those fields, intelligent face recognition has also become a basic technology in work and life. This technology has been widely used in various products and is well known by people. However, the intelligent face recognition system developed at present lacks universal design concept, and the designed system cannot be applied to various products. During the use of users, there are some problems, such as difficult operation and unfriendly interface. In order to improve the satisfaction of users’ physical examination and the accuracy of intelligent face recognition, this study develops an intelligent face recognition system based on the universal design concept. First, the universal design concept is briefly described, and the calculation process of face detection algorithm and face detection algorithm based on the optical flow method is introduced in detail. Then, when designing a face recognition system, this algorithm is used to build a complete system framework. The main functional modules in this system are face detection module, face recognition module, and face training module. The functions of each module are described in detail. Finally, the face feature extraction results of the face detection algorithm based on the optical flow method are verified on the Yale face database and PIE face database. The results show that the algorithm has the highest detection and recognition rate. At the same time, the ORL face database is used to compare and analyze the system performance. The face image recognition rate of this algorithm is 92, which is the highest compared with other algorithms.

In a series of seminal behavioral and electrophysiological experiments, Knudsen and Konishi studied the mechanisms of hearing. Their 1979 article showed how the barn owl utilizes unique anatomical features for creating a systematic internal representation of auditory space. This established the barn owl as a prime model for studying sensory systems.

Harris Hawks optimization (HHO) is a swarm optimization approach capable of handling a broad range of optimization problems. HHO, on the other hand, is commonly plagued by inadequate exploitation and a sluggish rate of convergence for certain numerical optimization. This study combines the fireworks algorithm's explosion search mechanism into HHO and proposes a framework for fireworks explosion-based HHo to address this issue (FWHHO). More specifically, the proposed FWHHO structure is comprised of two search phases: harris hawk search and fireworks explosion search. A search for fireworks explosion is done to identify locations where superior hawk solutions may be developed. On the CEC2014 benchmark functions, the FWHHO approach outperforms the most advanced algorithms currently available. Moreover, the new FWHHO framework is compared to four existing HHO and fireworks algorithms, and the experimental results suggest that FWHHO significantly outperforms existing HHO and fireworks algorithms. Finally, the proposed FWHHO is employed to evolve a kernel extreme learning machine for diagnosing COVID-19 utilizing biochemical indices. The statistical results suggest that the proposed FWHHO can discriminate and classify the severity of COVID-19, implying that it may be a computer-aided approach capable of providing adequate early warning for COVID-19 therapy and diagnosis.

Short sleep duration and evening chronotype are independently associated with negative health outcomes. However, it is unclear how adolescent sleep duration and chronotype are longitudinally associated with health outcomes during early adulthood.Participants from the NEXT Generation Health Study (n = 2,783; 54.5% female) completed measures of sleep duration (scheduled day and unscheduled day) and chronotype in high school. Sleep duration, chronotype, general health, depressive symptoms, and psychosomatic symptoms were also assessed 4 years after high school. Latent variables estimated high school scheduled-day sleep duration, unscheduled-day sleep duration, and chronotype using the during high school measures. Two path analyses tested the prospective associations between high school sleep duration (separate models for scheduled and unscheduled days) and chronotype with 4 years after high school health outcomes as mediated by concurrent sleep duration and chronotype.In the scheduled-day model, longer high school sleep duration and later chronotype were associated with longer duration and later chronotype in early adulthood. Longer high school sleep duration was directly associated with fewer psychosomatic symptoms and indirectly associated with fewer depressive and psychosomatic symptoms through longer sleep duration in early adulthood. Later chronotype in high school was indirectly associated with poorer general health, greater depressive symptoms, and greater psychosomatic symptoms in early adulthood through later chronotype.Findings highlight the roles of scheduled-day sleep duration and evening chronotype in shaping health outcomes and suggest the importance of chronotype and optimal sleep habits among adolescents.Maultsby KD, Temmen CD, Lewin D, et al. Longitudinal associations between high school sleep characteristics and young adult health outcomes. J Clin Sleep Med. 2022;18(11):2527-2536.

Abstract The classification of various power quality events during the integration of renewable energy sources were proposed in this article. This approach provides a data mining tool linear discrimination analysis for the feature extraction in the expert system where an analytical hierarchy process is used in the knowledge base with the various levels of power quality events. Based on the hierarchy levels the data were compared using the group data handling method and thus the power quality events were classified. To validate the results a real time sample data were used.

To describe the findings of focal high signal on T2 weighted (T2W) images of the bone marrow in the axial skeleton as assessed by whole-body MRI in healthy and asymptomatic children and adolescents.We assessed the bone marrow of the mandible, shoulder girdle, thorax, spine, and pelvis on water-only Dixon T2W sequences as part of a whole-body MRI protocol in 196 healthy and asymptomatic children aged 5-19 years. Intensity (0-2 scale) and extension (1-4 scale) of focal high signal areas in the bone marrow were scored and divided into minor or major findings, based on intensity and extension to identify the potentially conspicuous lesions in a clinical setting.We registered 415 areas of increased signal in the axial skeleton whereof 75 (38.3%) were major findings. Fifty-eight (29.6%) individuals had at least one major finding, mainly located in the pelvis (54, 72%). We found no differences according to gender. The number of minor findings increased with age (p = 0.020), but there were no significant differences in the number of major findings. The most conspicuous findings were in the pelvis, spine and sternum.Non-specific bone marrow T2W hyperintensities in the axial skeleton are frequently detected on whole-body MRI in healthy, asymptomatic children. Awareness of this is important as some findings may resemble clinically silent lesions in children with suspected multifocal skeletal disease.