PAULINA CHMIEL^1,2, ALEKSANDRA SłOWIKOWSKA^1,2, ŁUKASZ BANASZEK^1,2, ANNA SZUMERA-CIEćKIEWICZ³, BARTłOMIEJ SZOSTAKOWSKI¹, MATEUSZ J. SPAłEK^1,4,*, TOMASZ ŚWITAJ¹, PIOTR RUTKOWSKI¹, ANNA M. CZARNECKA¹

Oncology Research, Vol.32, No.7, pp. 1141-1162, 2024, DOI:10.32604/or.2024.050350

Abstract Inflammatory myofibroblastic tumor (IMT) is a rare neoplasm with intermediate malignancy characterized by a propensity for recurrence but a low metastatic rate. Diagnostic challenges arise from the diverse pathological presentation, variable symptomatology, and lack of different imaging features. However, IMT is identified by the fusion of the anaplastic lymphoma kinase (ALK) gene, which is present in approximately 70% of cases, with various fusion partners, including ran-binding protein 2 (RANBP2), which allows confirmation of the diagnosis. While surgery is the preferred approach for localized tumors, the optimal long-term treatment for advanced or metastatic disease is difficult… More >

Mahatir Ahmed Tusher¹, Saket Choudary Kongara¹, Sagar Dhanraj Pande², SeongKi Kim^3,*, Salil Bharany^4,*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4063-4080, 2024, DOI:10.32604/cmc.2024.049007

Abstract The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional… More >

Tengfei Li¹, Wenhui Zhang¹, Ke Mi¹, Qingming Lin¹, Shuangwei Zhao^2,*, Jiayi Song²

Energy Engineering, Vol.121, No.7, pp. 1991-2007, 2024, DOI:10.32604/ee.2024.049460

Abstract Aiming at the problem of energy storage unit failure in the spring operating mechanism of low voltage circuit breakers (LVCBs). A fault diagnosis algorithm based on an improved Sparrow Search Algorithm (ISSA) optimized Backpropagation Neural Network (BPNN) is proposed to improve the operational safety of LVCB. Taking the 1.5kV/4000A/75kA LVCB as an example. According to the current operating characteristics of the energy storage motor, fault characteristics are extracted based on Empirical Wavelet Transform (EWT). Traditional BPNN has problems such as difficulty adjusting network weights and thresholds, being sensitive to initial weights, and quickly falling into More >

Yan Zhang¹, Bowen Du^2,*, Benren Pan¹, Guannan Wang¹, Guoqiang Xie¹, Tong Jiang²

Energy Engineering, Vol.121, No.7, pp. 1903-1920, 2024, DOI:10.32604/ee.2024.048718

Abstract During faults in a distribution network, the output power of a distributed generation (DG) may be uncertain. Moreover, the output currents of distributed power sources are also affected by the output power, resulting in uncertainties in the calculation of the short-circuit current at the time of a fault. Additionally, the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources. Thus, it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network. In this study, an affine arithmetic algorithm… More >

Hua Tao, Yiqin Zhou, Chengyun Yao, Dayong Gu, Wei Chen, Jincheng Lu

Oncology Research, Vol.25, No.8, pp. 1357-1362, 2017, DOI:10.3727/096504017X14889842609577

Abstract The aim of this study was to evaluate the efficacy and toxicity of intensity-modulated radiotherapy concurrent with weekly docetaxel in patients with node-positive esophageal squamous cell carcinoma after radical surgery. Between January 2011 and December 2013, a total of 46 eligible patients were enrolled. All patients received intensity-modulated radiotherapy concurrent with weekly docetaxel (20 mg/m² ). Patients were treated 5 days per week at 2.0 Gy/day. The total dose of external radiotherapy given was 50 Gy in 25 fractions. The primary endpoints included treatment completion and safety. The secondary endpoint was to assess whether the approach… More >

Abhishek Singhal^*, Devendra Kumar Sharma

Computer Systems Science and Engineering, Vol.48, No.3, pp. 609-625, 2024, DOI:10.32604/csse.2023.046730

Abstract This article presents an exhaustive comparative investigation into the accuracy of gender identification across diverse geographical regions, employing a deep learning classification algorithm for speech signal analysis. In this study, speech samples are categorized for both training and testing purposes based on their geographical origin. Category 1 comprises speech samples from speakers outside of India, whereas Category 2 comprises live-recorded speech samples from Indian speakers. Testing speech samples are likewise classified into four distinct sets, taking into consideration both geographical origin and the language spoken by the speakers. Significantly, the results indicate a noticeable difference… More >

Yinkong Wei^1,2, Mucong Wu^1,2,*, Wei Wei³, Paulo R. F. Rocha⁴, Ziyi Cheng^1,2, Weifang Yao⁵

Computer Systems Science and Engineering, Vol.48, No.3, pp. 723-738, 2024, DOI:10.32604/csse.2023.036062

Abstract Ultra-high voltage (UHV) transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment. The ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power grid. Yet, the accurate prediction of the ground total electric field remains a technical challenge. In this work, we collected the total electric field data from the Ningdong-Zhejiang ±800 kV UHVDC transmission project, as of the Ling Shao line, and perform an outlier analysis of the More >

Chao-Lung Yang^1,*, Atinkut Atinafu Yilma^1,2, Bereket Haile Woldegiorgis², Hendrik Tampubolon^3,4, Hendri Sutrisno⁵

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 233-254, 2024, DOI:10.32604/iasc.2024.043091

Abstract This study proposed a new real-time manufacturing process monitoring method to monitor and detect process shifts in manufacturing operations. Since real-time production process monitoring is critical in today’s smart manufacturing. The more robust the monitoring model, the more reliable a process is to be under control. In the past, many researchers have developed real-time monitoring methods to detect process shifts early. However, these methods have limitations in detecting process shifts as quickly as possible and handling various data volumes and varieties. In this paper, a robust monitoring model combining Gated Recurrent Unit (GRU) and Random… More >

Abeer Abdullah Alsadhan¹, Abdullah A. Al-Atawi², Hanen karamti³, Abid Jameel⁴, Islam Zada⁵, Tan N. Nguyen^6,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 135-155, 2024, DOI:10.32604/iasc.2023.041130

Abstract IoT (Internet of Things) devices are being used more and more in a variety of businesses and for a variety of tasks, such as environmental data collection in both civilian and military situations. They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power. In this study, we investigate the possibility of detecting IoT malware using recurrent neural networks (RNNs). RNN is used in the proposed method to investigate the execution operation codes of ARM-based More >

Lifeng Li¹, Zaimin Yang¹, Xiongping Yang¹, Jiaming Li², Qianyufan Zhou^3,*, Ping Yang³

Energy Engineering, Vol.121, No.5, pp. 1329-1346, 2024, DOI:10.32604/ee.2023.046447

Abstract As the global demand for renewable energy grows, solar energy is gaining attention as a clean, sustainable energy source. Accurate assessment of solar energy resources is crucial for the siting and design of photovoltaic power plants. This study proposes an integrated deep learning-based photovoltaic resource assessment method. Ensemble learning and deep learning methods are fused for photovoltaic resource assessment for the first time. The proposed method combines the random forest, gated recurrent unit, and long short-term memory to effectively improve the accuracy and reliability of photovoltaic resource assessment. The proposed method has strong adaptability and More >