Bhanu Pratap Reddy Bhavanam, Prashanth Ragam^*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 425-466, 2025, DOI:10.32604/cmes.2024.052355 - 17 December 2024

Abstract The Internet of Things (IoT) has orchestrated various domains in numerous applications, contributing significantly to the growth of the smart world, even in regions with low literacy rates, boosting socio-economic development. This study provides valuable insights into optimizing wireless communication, paving the way for a more connected and productive future in the mining industry. The IoT revolution is advancing across industries, but harsh geometric environments, including open-pit mines, pose unique challenges for reliable communication. The advent of IoT in the mining industry has significantly improved communication for critical operations through the use of Radio Frequency… More >

Haotang Tan¹, Song Sun^2,*, Tian Cheng³, Xiyuan Shu²

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 661-678, 2024, DOI:10.32604/cmc.2024.052208 - 18 July 2024

Abstract Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and More >

BEHROOZ JOHARI^1,2,#,*, MILAD PARVINZAD LEILAN^1,#, MAHMOUD GHARBAVI³, YOUSEF MORTAZAVI¹, ALI SHARAFI², HAMED REZAEEJAM⁴

Oncology Research, Vol.32, No.2, pp. 309-323, 2024, DOI:10.32604/or.2023.043576 - 28 December 2023

Abstract The Myc gene is the essential oncogene in triple-negative breast cancer (TNBC). This study investigates the synergistic effects of combining Myc decoy oligodeoxynucleotides-encapsulated niosomes-selenium hybrid nanocarriers with X-irradiation exposure on the MDA-MB-468 cell line. Decoy and scramble ODNs for Myc transcription factor were designed and synthesized based on promoter sequences of the Bcl2 gene. The nanocarriers were synthesized by loading Myc ODNs and selenium into chitosan (Chi-Se-DEC), which was then encapsulated in niosome-nanocarriers (NISM@Chi-Se-DEC). FT-IR, DLS, FESEM, and hemolysis tests were applied to confirm its characterization and physicochemical properties. Moreover, cellular uptake, cellular toxicity, apoptosis, cell More > Graphic Abstract

Sándor Csibi¹, Mónika Csibi^2,*, József Bognár¹

International Journal of Mental Health Promotion, Vol.25, No.10, pp. 1081-1088, 2023, DOI:10.32604/ijmhp.2023.029390 - 03 November 2023

Abstract Background: Components of Self, completed with the perceived social support determine the individual differences in the evaluation of a stressor and the behavioral responses toward it, such as health-related anxiety. The study set as a goal the analysis of associations between the components of Self, such as self-evaluation, sense of coherence, perceived social support, and reported health-related anxiety in an adult sample. Methods: 147 adults from the 18–73 age group (mean age 37.5) voluntarily completed the questionnaire through Qualtrics online platform containing the Short Health Anxiety Inventory, Core Self-Evaluation Scale, Social Support Assessing Scale, and… More >

LI TIAN^1,#, YONGYI HUANG^1,#, BAOZHEN ZHANG^2,#, YI SONG^1,#, LIN YANG³, QIANQIAN CHEN¹, ZHENG WANG³, YILING WANG¹, QIHAN HE¹, WENHAN YANG¹, SHUYONG YU⁴, TIANYU LU⁵, ZICHEN LIU¹, KAIPING GAO^1,*, XIUJUN FAN^2,*, JIAN SONG^4,*, RIHONG ZHAI^1,*

Oncology Research, Vol.31, No.4, pp. 463-479, 2023, DOI:10.32604/or.2023.028791 - 25 June 2023

Abstract Accumulating evidence has indicated that long non-coding RNAs (lncRNAs) play critical roles in the development and progression of cancers, including esophageal squamous cell carcinoma (ESCC). However, the mechanisms of lncRNAs in ESCC are still incompletely understood and therapeutic attempts for in vivo targeting cancer-associated lncRNA remain a challenge. By RNA-sequencing analysis, we identified that LLNLR-299G3.1 was a novel ESCC-associated lncRNA. LLNLR-299G3.1 was up-regulated in ESCC tissues and cells and promoted ESCC cell proliferation and invasion. Silencing of LLNLR-299G3.1 with ASO (antisense oligonucleotide) resulted in opposite effects. Mechanistically, LLNLR-299G3.1 bound to cancer-associated RNA binding proteins and regulated the expression… More >

Ingo Paetsch^1,*, Roman Gebauer², Christian Paech², Frank-Thomas Riede², Sabrina Oebel¹, Andreas Bollmann¹, Christian Stehning³, Jouke Smink⁴, Ingo Daehnert², Cosima Jahnke¹

Congenital Heart Disease, Vol.18, No.3, pp. 279-294, 2023, DOI:10.32604/chd.2023.029634 - 09 June 2023

Abstract Background: In congenital heart disease (CHD) patients, detailed three-dimensional anatomy depiction plays a pivotal role for diagnosis and therapeutical decision making. Hence, the present study investigated the applicability of an advanced cardiovascular magnetic resonance (CMR) whole heart imaging approach utilizing nonselective excitation and compressed sensing for anatomical assessment and interventional guidance of CHD patients in comparison to conventional dynamic CMR angiography. Methods: 86 consecutive pediatric patients and adults with congenital heart disease (age, 1 to 74 years; mean, 35 years) underwent CMR imaging including a free-breathing, ECG-triggered 3D nonselective SSFP whole heart acquisition using compressed… More >

P. Ramya^1,*, B. Karthik²

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2379-2391, 2023, DOI:10.32604/iasc.2023.026291 - 05 January 2023

Abstract Word Sense Disambiguation has been a trending topic of research in Natural Language Processing and Machine Learning. Mining core features and performing the text classification still exist as a challenging task. Here the features of the context such as neighboring words like adjective provide the evidence for classification using machine learning approach. This paper presented the text document classification that has wide applications in information retrieval, which uses movie review datasets. Here the document indexing based on controlled vocabulary, adjective, word sense disambiguation, generating hierarchical categorization of web pages, spam detection, topic labeling, web search, More >

Junya Tanehashi¹, Szuchi Chang², Takahiro Hirosei³, Masaki Izawa², Aman Goyal², Ayumi Takahashi⁴, Kazuhito Misaji^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.3, pp. 2287-2306, 2023, DOI:10.32604/cmes.2023.022432 - 23 November 2022

Abstract This research aims to quantify driver ride comfort due to changes in damper characteristics between comfort mode and sport mode, considering the vehicle’s inertial behavior. The comfort of riding in an automobile has been evaluated in recent years on the basis of a subjective sensory evaluation given by the driver. However, reflecting driving sensations in design work to improve ride comfort is abstract in nature and difficult to express theoretically. Therefore, we evaluated the human body’s effects while driving scientifically by quantifying the driver’s behavior while operating the steering wheel and the behavior of the… More > Graphic Abstract

Byeongmin Choi¹, YongHyun Lee¹, Yeunwoong Kyung², Eunchan Kim^3,*

Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 71-82, 2023, DOI:10.32604/iasc.2023.032783 - 29 September 2022

Abstract Recently, pre-trained language representation models such as bidirectional encoder representations from transformers (BERT) have been performing well in commonsense question answering (CSQA). However, there is a problem that the models do not directly use explicit information of knowledge sources existing outside. To augment this, additional methods such as knowledge-aware graph network (KagNet) and multi-hop graph relation network (MHGRN) have been proposed. In this study, we propose to use the latest pre-trained language model a lite bidirectional encoder representations from transformers (ALBERT) with knowledge graph information extraction technique. We also propose to applying the novel method, More >

Passent El-kafrawy^1,2, Maie Aboghazalah^2,*, Abdelmoty M. Ahmed³, Hanaa Torkey⁴, Ayman El-Sayed⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 909-926, 2023, DOI:10.32604/cmes.2022.021713 - 31 August 2022

Abstract Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice. Encryption of medical images is very important to secure patient information. Encrypting these images consumes a lot of time on edge computing; therefore, the use of an auto-encoder for compression before encoding will solve such a problem. In this paper, we use an auto-encoder to compress a medical image before encryption, and an encryption output (vector) is sent out over the network. On the other hand, a decoder was used to reproduce the original image back after the… More >