bluuebunny/crossref_metadata_2025 huggingface dataset:
The miR-1290/OGN axis in ovarian cancer-associated fibroblasts modulates cancer cell proliferation and invasion
Authors: Jiang Biyao , Xiao Songshu , Zhang Shan , Xiao Fang
Published: February 2024

Despite receiving first-line treatment, ovarian cancer patients continue to experience a high rate of recurrence; nearly all women with ovarian cancer develop chemoresistance and succumb to the disease. In this study, cancer-associated fibroblasts (CAFs) and normal fibroblasts (NFs) were isolated from tumor-containing and normal omenta, respectively, and the downregulation of osteoglycin (OGN) in CAFs was observed. OGN overexpression in CAFs significantly inhibited ovarian cancer cell viability, DNA synthesis, and cell invasion. OGN overexpression also changed epithelial-mesenchymal transition (EMT) markers and promoted mTOR and Akt phosphorylation in ovarian cancer cells. miR-1290 targeted OGN and inhibited OGN expression. miR-1290 overexpression in CAFs significantly promoted ovarian cancer cell viability, DNA synthesis, and cell invasion. Moreover, miR-1290 overexpression in CAFs also changed EMT markers and promoted mTOR and Akt phosphorylation within ovarian carcinoma cells. Finally, when ovarian cancer cells in a conditioned medium derived from CAFs co-transduced with miR-1290 mimics and OGN-OE were cultured, the effects of miR-1290 overexpression were partially reversed by OGN overexpression. In nude mouse xenograft tumor models, OGN overexpression in CAFs suppressed tumor growth, whereas miR-1290 overexpression in CAFs increased tumor growth. In conclusion, a miRNA/mRNA axis in ovarian cancer CAFs modulating the proliferative and invasive abilities of ovarian cancer cells, possibly via the Akt/mTOR pathway, was demonstrated.
Prevalence of extended-spectrum beta-lactamase and molecular detection of blaTEM, blaSHV, and blaCTX-M genotypes among gram-negative Bacilli isolates from hospital acquired infections in pediatrics, one institutional study
Authors: Elsayed Ahmed Gomaa Ahmed , Badr Dina F. , El Kheir Nermene Youssef Abo , Zaki Maysaa El Sayed , Mossad Abdelrahman Eid Mahmoud , Mahmoud Ehab Mohammed Fahmy
Published: February 2024

Background

Gram-negative bacilli represents an important pathogen in hospital-acquired infections (HAIs) worldwide. The emergence of antibiotic resistance in these pathogens warrants attention for the proper management of infections. Extended-spectrum beta-lactamase (ESBL) resistance represents a major therapeutic problem in infections due to Gram-negative bacilli.

The present study aimed to study the extended-spectrum beta-lactamase genes blaTEM, blaSHV, and blaCTX-M by multiplex polymerase reaction in isolated Gram-negative bacilli from HAIs in pediatric patients.

Methods

The study included one hundred-five isolates of Gram-negative bacilli from pediatric patients with different types of HAIs. The isolates were subjected to full microbiological identification, antibiotics susceptibility by disc diffusion method, the phenotypic study of ESBL, and the genetic study of ESBL genes by multiplex PCR.

Results

Fifty isolates of Gram-Negative bacilli showed ESBL activity by a phenotypic study by double disc diffusion method (50/105). All ESBL producers’ isolates were positive by PCR for ESBL genes. The most frequent gene was blaTEM (64%), followed by blaSHV (30%) and CTX-M (22%). Mixed genes were found in 4 isolates (8%) for blaTEM and blaSHV, blaTEM and CTX-M. There was a significant association between PCR for ESBL genes and phenotypic ESBL detection (P = 0.001). There was significant detection of ESBL genes in E. coli (28%), followed by Enterobacter spp. (26%), Klebsiella spp. (24%), Serratia (14%), Pseudomonas spp. (6%) and Proteus (2%), P = 0.01. There Seventy percent of isolates positive for ESBL production had an insignificant association between MDR and PCR for ESBL genes (P = 0.23).

Conclusion

The present study highlights the prevalence of ESBL activity among clinical isolates of Gram-negative bacilli isolated from hospital-acquired infections in pediatric patients. The most common gene responsible for this activity was blaTEM gee followed by blaSHV and blaCTX-M. There was a high prevalence of multiple antibiotic resistance among isolates with ESBL activity. The finding of the present study denotes the importance of screening extended beta-lactamase among Gram-negative bacilli associated with HAIs in pediatric patients.
Acute fatal ventricular arrhythmia induced by severe hyperkalemia in a toddler with decompensated methylmalonic acidemia
Authors: Hakimzadeh Zahra , Gilani Abolfazl , Yousefichaijan Parsa , Sarmadian Roham
Published: February 2024

Background

Methylmalonic acidemia is a very rare genetic metabolic disease. Patients with isolated methylmalonic acidemia typically present with acute alterations of consciousness, failure to thrive, anorexia, vomiting, respiratory distress, and muscular hypotonia. Despite the evidence-based management, affected individuals experience significant morbidity and mortality. Hyperkalemia is one of the unusual complications of methylmalonic acidemia.

Case presentation

In this paper, we describe a 4-year-old Persian boy with methylmalonic acidemia who developed life-threatening arrhythmia following severe hyperkalemia and metabolic acidosis. Emergent management of the condition was successfully carried out, and the rhythm changed to normal sinus rhythm by effectively reducing the serum potassium level. We discuss the possible etiology of this lethal condition and describe its management on the basis of the available evidence.

Conclusion

During metabolic decompensation in methylmalonic acidemia, frequent blood gas and electrolyte testing to prescribe and adjust therapy and annual echocardiogram and electrocardiogram screening are essential.
Primary thyroid lymphoma: a case series
Authors: Sakhri Saida , Zemni Ines , Ayadi Mohamed Ali , Kamoun Salma , Chargui Riadh , Ben Dhiab Tarek
Published: February 2024

Introduction

Primary Thyroid Lymphoma (PTL) is defined as lymphoma involving the thyroid gland alone or the thyroid gland and adjacent neck lymph nodes without contiguous spread or distant metastases at the time of diagnosis. Most thyroid lymphomas are B cell lymphomas, and 98% of all PTL cases are non-Hodgkin’s lymphoma. It is a rare disease accounting for around 5% of the thyroid neoplasms and 2% of extranodal lymphomas. If properly diagnosed and treated, the prognosis is favorable.

Case presentation

Five cases (three men and two women) of PTL were diagnosed and treated in our institute between January 2005 and September 2019. These are 5 cases of Caucasian origin. The mean age was 76.2 (range: 63–95 years); one patient had associated hypothyroid. One patient had a medical history of breast cancer; one was hypothyroid, and four were euthyroid at the diagnosis. In 4 of these patients, PTL started with compressive symptoms. No patients underwent fine needle aspiration cytology (FNAC) or biopsy for the diagnostic only. In sonography, two cases showed bilateral nodules with goiter; in the three cases it showed nodules in the lobe and isthmus. Technetium-99m scintigraphy was performed on only two patients. Bone Marrow Biopsy (BMB) showed normal cellularity in 4 cases and only one case showed tumor cells. LDH levels were increased in all cases. The extension was evaluated in all patients with cervical and thoracic CT scans, Bone Marrow Biopsy (BMB), beta-2 microglobulin, and serum lactate dehydrogenase (LDH) levels. Three cases were staged as IE and two cases as IIE. Three patients underwent total thyroidectomy; two of them underwent cervical lymph node dissection. Two patients underwent lobectomy. All were diagnosed with lymphoma postoperatively and all were diffuse large B cell lymphoma (DLBCL). One patient completed treatment with R-CHOP (Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone), and two cases received adjuvant chemo-radiotherapy (30 Gy). Two patients died immediately after surgery.

Conclusion

PTL is a rare disease whose diagnosis should be considered in cases of rapidly growing goitres. Timely needle biopsy in suspected cases can avoid unnecessary surgery. Systemic treatment is required, depending on the stage of the tumour.
Efficient crop straws biotreatment using the fungus Cerrena Unicolor GC.u01
Authors: Ying Wang , Chunjing Cai , Junhua Lu , Xuan Li , Zhaojiang Wang , Jie Chu
Published: February 2024

Lignin is main composition of agricultural biomass which can be decomposed through enzymatic hydrolysis by fungi. However, there are still needs to identify more efficient and effective fungal stain for biomass valorization. In this study, lignin degrading fungi from birch forest were screened for sustainable degradation of waste agricultural straws. The most effective strain was identified as Cerrena unicolor GC.u01 using 18 S rDNA gene-sequencing technology. Three different crop straws (corn stalk, rice and wheat straws) were used for the biotreatment studies. The activities of lignin degrading enzymes, laccase (Lac), cellulase and xylanase, secreted by C. unicolor were also determined. Scanning electron microscopy (SEM), fourier transform infrared spectroscopy (FTIR) and thermal gravimetric analyzer (TGA) were further used to monitor the effects of the biotreatment process. The results showed that C. unicolor degraded 34.3% rice straw lignin, a percentage which was higher than other isolated strains after 15 d straw liquid fermentation. The highest Lac activity (8.396 U•mL− 1) was observed with corn stalk on the 7 d. Cellulase and xylanase activities, in the same biomass, were higher than those of wheat and rice straws after 15 d. Furthermore, SEM, FTIR and TGA analyses showed that C. unicolor pretreatment process had significant effects on corn stalk, rice and wheat straws’ structures. The newly isolated stain of C. unicolor demonstrated high lignin degradation potential that can provide effective, ecofriendly means of valorizing biomass to industrial useable raw-material.
Distributed transmission and optimization of relay-assisted space-air-ground IoT systems
Authors: Sun Ying
Published: February 2024

This paper investigates the integration of relay-assisted Internet of Things (IoT) systems, focusing on the use of multiple relays to enhance the system performance. The central metric of interest in this study is system outage probability, evaluated in terms of latency. Our research provides a comprehensive analysis of system outage probability, considering different relay selection criteria to optimize the system’s transmission performance. Three relay selection strategies are employed to enhance the system transmission performance. Specifically, the first strategy, optimal relay selection, aims to identify the relay that minimizes the latency and maximizes the data transmission reliability. The second approach, partial relay selection, focuses on selecting a subset of relays strategically to balance the system resources and achieve the latency reduction. The third strategy, random relay selection, explores the potential of opportunistic relay selection without prior knowledge. Through a rigorous investigation, our paper evaluates the impact of these relay selection criteria on the performance of relay-assisted edge computing systems. By assessing the system outage probability in relation to latency, we provide valuable insights into the trade-offs and advantages associated with each selection strategy. Our findings contribute to the design and optimization of reliable and efficient edge computing systems, with implications for various applications, including the IoT and intelligent data processing.
RETRACTED ARTICLE: RFID tag recognition model for Internet of Things for training room management
Authors: Wu Shengqi
Published: February 2024

With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.
Soybean flower-specific R2R3-MYB transcription factor gene GmMYB108 induces anthocyanin production in Arabidopsis thaliana
Authors: Moon Ju Yeon , Lee Saet Buyl , Jeong Yu Jeong , Lim Gah-Hyun , Shin Gilok , Choi Man-Soo , Kim Jeong Ho , Park Ki Hun , Lee Jiyoung , Jeong Jae Cheol , Kim Cha Young
Published: February 2024

R2R3-MYB transcription factors (TFs) are known to play a key role in regulating the expression of structural genes involved in plant flavonoid biosynthesis. However, the regulatory networks and related genes controlling isoflavonoid biosynthesis in soybean are poorly understood. We previously reported that ethephon application increases the production of isoflavonoids in soybean leaves. In this study, we attempted to identify a potential regulatory gene that positively controls isoflavonoid production in response to ethephon treatment in soybean (Glycine max L.). RNA sequencing (RNA-seq) revealed that ethephon application led to the upregulation of 22 genes, including the genes for R2R3-MYB TFs, related to isoflavonoid biosynthesis in soybean plants. Ethephon treatment highly induced the expression of GmMYB108, and its expression was exclusively enriched in flowers as determined using in silico and real-time quantitative PCR analyses. Furthermore, GmMYB108 overexpression resulted in an intense accumulation of anthocyanins as well as total flavonoid production in the leaf tissues of transgenic Arabidopsis plants. In addition, GmMYB108 overexpression increased the transcript levels of several genes involved in the biosynthesis of anthocyanins and their regulatory pathways in Arabidopsis. These results suggest that GmMYB108 is a potential positive regulator of the biosynthesis of flavonoids and anthocyanins in soybean flowers.
Challenges and strategies associated with CAR-T cell therapy in blood malignancies
Authors: Liu Zhaoyun , Lei Wenhui , Wang Hao , Liu Xiaohan , Fu Rong
Published: February 2024

Cellular immunotherapy, particularly CAR-T cells, has shown potential in the improvement of outcomes in patients with refractory and recurrent malignancies of the blood. However, achieving sustainable long-term complete remission for blood cancer remains a challenge, with resistance and relapse being expected outcomes for many patients. Although many studies have attempted to clarify the mechanisms of CAR-T cell therapy failure, the mechanism remains unclear. In this article, we discuss and describe the current state of knowledge regarding these factors, which include elements that influence the CAR-T cell, cancer cells as a whole, and the microenvironment surrounding the tumor. In addition, we propose prospective approaches to overcome these obstacles in an effort to decrease recurrence rates and extend patient survival subsequent to CAR-T cell therapy.
Multi-omics reveal mechanisms of high enteral starch diet mediated colonic dysbiosis via microbiome-host interactions in young ruminant
Authors: Jin Chunjia , Wu Shengru , Liang Ziqi , Zhang Jun , Lei Xinjian , Bai Hanxun , Liang Gaofeng , Su Xiaodong , Chen Xiaodong , Wang Peiyue , Wang Yue , Guan Leluo , Yao Junhu
Published: February 2024

Background

Although rumen development is crucial, hindgut undertakes a significant role in young ruminants’ physiological development. High-starch diet is usually used to accelerate rumen development for young ruminants, but always leading to the enteral starch overload and hindgut dysbiosis. However, the mechanism behind remains unclear. The combination of colonic transcriptome, colonic luminal metabolome, and metagenome together with histological analysis was conducted using a goat model, with the aim to identify the potential molecular mechanisms behind the disrupted hindgut homeostasis by overload starch in young ruminants.

Result

Compared with low enteral starch diet (LES), high enteral starch diet (HES)-fed goats had significantly higher colonic pathology scores, and serum diamine oxidase activity, and meanwhile significantly decreased colonic mucosal Mucin-2 (MUC2) protein expression and fecal scores, evidencing the HES-triggered colonic systemic inflammation. The bacterial taxaPrevotella sp. P4-67,Prevotella sp. PINT, andBacteroides sp. CAG:927, together with fungal taxaFusarium vanettenii,Neocallimastix californiae,Fusarium sp. AF-8,Hypoxylon sp. EC38, andFusarium pseudograminearum, and the involved microbial immune pathways including the “T cell receptor signaling pathway” were higher in the colon of HES goats. The integrated metagenome and host transcriptome analysis revealed that these taxa were associated with enhanced pathogenic ability, antigen processing and presentation, and stimulated T helper 2 cell (TH2)-mediated cytokine secretion functions in the colon of HES goats. Further luminal metabolomics analysis showed increased relative content of chenodeoxycholic acid (CDCA) and deoxycholic acid (DCA), and decreased the relative content of hypoxanthine in colonic digesta of HES goats. These altered metabolites contributed to enhancing the expression of TH2-mediated inflammatory-related cytokine secretion including GATA Binding Protein 3 (GATA3),IL-5, andIL-13. Using the linear mixed effect model, the variation of MUC2 biosynthesis explained by the colonic bacteria, bacterial functions, fungi, fungal functions, and metabolites were 21.92, 20.76, 19.43, 12.08, and 44.22%, respectively. The variation of pathology scores explained by the colonic bacterial functions, fungal functions, and metabolites were 15.35, 17.61, and 57.06%.

Conclusions

Our findings revealed that enteral starch overload can trigger interrupted hindgut host-microbiome homeostasis that led to impaired mucosal, destroyed colonic water absorption, and TH2-mediated inflammatory process. Except for the colonic metabolites mostly contribute to the impaired mucosa, the nonnegligible contribution from fungi deserves more future studies focused on the fungal functions in hindgut dysbiosis of young ruminants.
Measuring regularity of human physical activities with entropy models
Authors: Shi Keqin , Chen Zhen , Sun Weiqiang , Hu Weisheng
Published: February 2024

Regularity is an important aspect of physical activity that can provide valuable insights into how individuals engage in physical activity over time. Accurate measurement of regularity not only advances our understanding of physical activity behavior but also facilitates the development of human activity modeling and forecasting. Furthermore, it can inform the design and implementation of tailored interventions to improve population health outcomes. In this paper, we aim to assess the regularity of physical activities through longitudinal sensor data, which reflects individuals’ all physical activities over an extended period. We explore three entropy models, including entropy rate, approximate entropy, and sample entropy, which can potentially offer a more comprehensive evaluation of physical activity regularity compared to metrics based solely on periodicity or stability. We propose a framework to validate the performance of entropy models on both synthesized and real-world physical activity data. The results indicate entropy rate is able to identify not only the magnitude and amount of noise but also macroscopic variations of physical activities, such as differences on duration and occurrence time. Simultaneously, entropy rate is highly correlated with the predictability of real-world samples, further highlighting its applicability in measuring human physical activity regularity. Leveraging entropy rate, we further investigate the regularity for 686 individuals. We find the composition of physical activities can partially explain the difference in regularity among individuals, and the majority of individuals exhibit temporal stability of regularity.
Optimizing IoT intrusion detection system: feature selection versus feature extraction in machine learning
Authors: Li Jing , Othman Mohd Shahizan , Chen Hewan , Yusuf Lizawati Mi
Published: February 2024

Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT. These often use feature reduction techniques like feature selection or extraction before feeding data to models. This helps make detection efficient for real-time needs. This paper thoroughly compares feature extraction and selection for IoT network intrusion detection in machine learning-based attack classification framework. It looks at performance metrics like accuracy, f1-score, and runtime, etc. on the heterogenous IoT dataset named Network TON-IoT using binary and multiclass classification. Overall, feature extraction gives better detection performance than feature selection as the number of features is small. Moreover, extraction shows less feature reduction compared with that of selection, and is less sensitive to changes in the number of features. However, feature selection achieves less model training and inference time compared with its counterpart. Also, more space to improve the accuracy for selection than extraction when the number of features changes. This holds for both binary and multiclass classification. The study provides guidelines for selecting appropriate intrusion detection methods for particular scenarios. Before, the TON-IoT heterogeneous IoT dataset comparison and recommendations were overlooked. Overall, the research presents a thorough comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks.
Advances in CO2 circulation hydrogen carriers and catalytic processes
Authors: Ono Seo , Santra Dines Chandra , Kanega Ryoichi , Kawanami Hajime
Published: February 2024

Hydrogen serves as a renewable, clean energy carrier, and the critical development of technologies for safer and simpler storage and transportation is imperative for addressing global warming. There is also a growing demand for efforts to capture and utilize CO2 to tackle similar issues. Consequently, considerable attention has been drawn to carriers that chemically store hydrogen. Hydrogen can be stored and released through hydrogenation and dehydrogenation. Notably, the storage and release of hydrogen via CO2 hydrogenation and subsequent dehydrogenation of its hydrogenation product could potentially bolster the future hydrogen economy, rendering it an appealing option as a CO2 circulation hydrogen carrier (CCHC). To leverage CCHC for various applications, a catalytic process enabling the reversible storage and release of hydrogen is essential. This review focuses on CCHC candidates, such as methanol (MeOH), dimethyl ether (DME), and formic acid (FA), summarizing recent catalytic approaches for hydrogen production through pivotal dehydrogenation processes within the CCHC cycle.
Horse Language and Improvised Memorials: Gong Kai's Equine Paintings and Song Loyalism
Authors: Kim Najung
Published: February 2024

The loyalist painter Gong Kai (1222–1307) utilized the historical literary trope of the ji 驥 horse, the fine steed capable of covering one thousand li in a day, as an essential part of his artistic rhetoric in response to the violent Song-Yuan dynastic turn. Drawing literary forebears of Gong's equine art into the field of investigation reveals the mechanics of his utilization of the equine trope and his stance within the broad spectrum of Song loyalism. Standing against passive sympathizers of the loyalist cause, Gong embraced the poetics of Daoxue (Learning of the Way) to uphold moral rigor and masculine heroics, couched metaphorically in the suffering ji horse, which was at once physiognomically ideal and metaphysically meaningful. The polysemy and ambiguity of Gong's art invited his colleagues to interiorize equine imagery, thus helping reconstitute an ideal moral image of themselves. Gong's equine art brought forth interactive responses from its viewers and thus served as an improvised memorial to Song loyalists, both living and deceased.
Demon-Immortal Monkey: Categories of Being in the Cosmos of Journey to the West
Authors: McMahon Keith
Published: February 2024

A little-studied aspect of the Monkey character 孫悟空 (Sun Wukong) in Wu Cheng'en's 吳承恩 Xiyou ji 西遊記 (Journey to the West) is his identity in terms of category of being, that is, how he fits into and rebels against the cosmic system of the ranking and classifying of beings that governs the novel. He is a human-like monkey, who is also an aspiring immortal, who was once a miscreant demon, who moreover never completely sheds his demon-like behavior. Journey to the West pays particular attention to the taxonomy of beings, which therefore needs a more detailed examination than has appeared so far, including a look at the lists of categories that Journey to the West applies. A deep sense of category envy and species rivalry courses through the novel, which underlies both the conflicts that Monkey initiates and the ones he meets head on. Two types of being, immortal (xian 仙) and demon (yao 妖), are in special need of unfolding. Originating in ancient times, they remain as expansive and malleable as ever in Journey to the West, in which Monkey alternates between the two even after his subjugation.
How Should the Dragon King Memorialize the Jade Emperor? Margins of Political Thought in Late Ming China
Authors: Yin Shoufu
Published: February 2024

A vast array of “vernacular” novels from late imperial and modern China feature well-edited official documents such as decrees, edicts, and memorials. The present study argues that these long-overlooked official documents offer unique access to creative political ideas that would otherwise be inaccessible to us. Using Xiyou ji 西遊記 (The Journey to the West) as a case study, this article initiates a critical dialogue between histories of documents, print culture, knowledge dissemination, vernacular novels, and political thought. The study begins with an exposition of the Ming regulations governing the formatting and representation of official documents. Tracing how the civil service examinations familiarized the reading public with documentary formatting, it then details how anonymous producers of various editions of the novel problematized the emperor's two bodies on documentary margins. It concludes by providing examples from Manchu manuscripts and Lu Xun's novels, suggesting that the methods explored here may be applied to further studies beyond Sinitic texts and the late imperial period.
An Elixir for Blockchain Scalability with Channel based Clustered Sharding
Authors: Kumar V. Vinoth , Padmavathi U. , Ranjith C. Prasanna , Balaji J , Kumar C.N.S. Vinoth
Published: February 2024

Blockchain refers to distributed ledger technology which stores records without the help of a central authority. Born with bitcoin, this brainstorming technology finds its applications in healthcare, land registry, education, pharmaceutical industry, digital records, manufacturing companies and so on. The properties of blockchain such as immutability, distributed nature, tamper-resistant made it a disruptive technology in many applications. The highlighting feature of this pioneering technology is the distributed storage of ledger on all the nodes of the network. This helps to achieve decentralization without the trust for third party. The transactions are proposed, executed, validated and are then added as blocks to the blockchain. The problems with all the blockchain framework is scalability with respect to storage space and throughput. Scalability is the most significant factor to be considered in this big data era. This article proposes a solution called Channel Based Clustered Sharding (CBCS) approach for Hyperledger fabric blockchain framework. In this work, a lookup table is maintained which helps in forwarding the transactions to the clustered shards for validation. The CBCS approach helps in parallel transaction processing which in turn improves scalability and throughput of the system. The performance of the proposed work is measured with the help of Hyperledger caliper, a benchmarking tool for the performance analysis of Hyperledger fabric. The results show that the performance of the proposed system is increased from 3000 tps to 30,000 tps.
Feature Extraction and Classification of Gray-Scale Images of Brain Tumor using Deep Learning
Authors: Kondra Pranitha , Vurukonda Naresh
Published: February 2024

Deep Learning using CNN plays a paramount role for the classification methods applied on medical image data. With a crucial role in accurate diagnosis, treatment planning and patient management for medical and healthcare systems, CNNs won accolades in the Deep Learning research. As simple the learning model so precise are the results for decision making. The proposed Sequential model of CNN is built with Parametric ReLU with the values aligned to geometric mean, attains a specific goal of tumor classification. The additional support of ground-truth aid in deciding the shape and severity of tumor in the Grayscale MRI of brain tumor. The simple Sequential model, although a minimal version has proved achieved significant classification goals using the GMP-ReLU. Comparative results with variants of ReLU have been charted in this article standing with the proof of consistent classification model with parametric-ReLU. The proposed design is conducted on images from Kaggle and a model is trained (classifier is built), which can be considered as ideal filter for all the benchmark images. The accuracy of proposed design is considerably improved compared to normal ReLU up to 89.214%.
Methodology for Developing an IoT-based Parking Space Counter System using XNO
Authors: Tiruvayipati Sujanavan , Yellasiri Ramadevi , Narayandas Vikram , Maruthavanan Archana , Meduri Anupama
Published: February 2024

The Blockchain-IoT integration is treated as the future technological value by many adopters despite the cost or complexity involved. But, there are technological advancements brought in by communities that make solutions affordable and simple to the fact that they can be used in applications such as parking space counters. This research portrays use of XNO which is a digital currency in an alternative way to keep track of available parking spaces via IoT nodes installed at entry and exit points of parking lots. The available parking spaces data using this approach can be displayed on LED boards at the entry point of the parking lots and on a website for remote status view. An add-on for this research is the issue of entry tickets with timestamp and unique ID by using the block data during asset transfer. This research can be further enhanced for collection of parking fees by the help of IoT nodes at the exit points of the parking lots.
Application of Genetic Algorithm in Optimization Simulation of Industrial Waste Land Reuse
Authors: Bai Peng , Zhao Yunan , Chang Junjia
Published: February 2024

In order to better understand the application of optimization simulation for industrial waste land reuse, the author proposes an application study based on nonlinear genetic algorithm in the optimization simulation of industrial waste land reuse. The author takes the landscape renovation and reuse of industrial waste sites as the research object, and through research on the current situation of landscape renovation and reuse of industrial waste sites both domestically and internationally, as well as on-site inspections, attempts to use landscape design techniques to deal with this once glorious but destructive industrial landscape that has already declined. Secondly, a genetic algorithm for enhancing the timeliness of industrial waste land reuse is proposed, which is based on random walks, combine users' long-term and short-term preferences to calculate the most suitable Top-N industrial waste land reuse optimization model for the current period. Finally, the two algorithms proposed by the author were experimentally validated on the dataset. In the CiteU Like dataset, the best performance was achieved at a=0.4, while in the JD dataset, the best performance was achieved at a=0.6. When k=6, the hit rate significantly decreases by about 50%. The URT-R genetic algorithm exhibits a high recommendation hit rate in recommendations targeting timeliness. The author analyzed the different characteristics of industrial waste reuse in scenic areas and optimized their essence and transformation methods, further improving the transformation and renewal methods of industrial waste land in the process of urban development in China. I hope to provide useful references for future research on related topics and practices.
Space Layout Simulation of Assembled Nanoarchitecture Based on Improved Particle Swarm Optimization
Authors: Huang Huan
Published: February 2024

In order to solve the problem that the traditional building space configuration model cannot meet the optimization of building space characteristics, the author proposes the optimization of building space utilization based on spatialized particle swarm optimization. First, to solve the problem of optimal allocation of space units, the PSO algorithm is modified to encode the space units by means of character coding.Secondly, the maximum standardization method is used for data processing, and the factors affecting space utilization are summarized, the objective function of optimal allocation of architectural space is given from three aspects: economic benefits, social benefits and ecological benefits; Finally, by analyzing the advantages and disadvantages of master-slave parallel model and point-to-point parallel model, a chained parallel structure is proposed. The experimental results show that: The experimental data is based on the utilization of building space in these three regions in 2015, and the vector map is divided into 30 m × A grid of 30 m in size, and all statistical data and spatial data are projected on each grid cell. The difference between the fitness values of the final convergence of the three parallel models is small, and the main difference is the convergence speed. During the run time test, set the three parallel models to run under the conditions of 8, 16, 32 and 64 nodes respectively. Because of the combination of the advantages of master-slave model and point-to-point model, the running time of chained parallel model is significantly lower than that of the other two parallel models. Conclusion: Through data simulation test, it is verified that the chained parallel model has higher fitness, convergence speed and shorter running time, and its performance is better than the other two, indicating that the optimization algorithm proposed by the author has good performance.
Optimization of Nonlinear Convolutional Neural Networks based on Improved Chameleon Group Algorithm
Authors: Zhang Qingtao
Published: February 2024

In order to solve the most difficult problem of the architectural model established by CNN in solving specific problems, which results in parameter overflow and inefficient training, an optimization algorithm for nonlinear convolutional neural networks based on improved chameleon swarm algorithm is proposed. This article mainly introduces the use of Chameleon Swarm Optimization (PSO) algorithm to research the parameters of CNN architecture, solve them, and achieve the optimization of the optimization model.Although the number of parameters that need to be set up in CNN is very large, this method can find better testing space for Alexnet samples with 5 different images. In order to improve the performance of the improved pruning algorithms, two candidate pruning algorithms are also proposed. The experimental results show that compared with the traditional Alexnet model, the improved pruning method improves the image recognition ability of the Caffe primary parameter set from 1.3% to 5.7%. This method has wide applicability and can be applied to most neural networks which do not require any special functional modules of the Alexnet network model.
A Class Specific Feature Selection Method for Improving the Performance of Text Classification
Authors: V Venkatesh , S B Sharan , S Mahalaxmy , S Monisha , D S Ashick Sanjey , P Ashokkumar
Published: February 2024

Recently, a significant amount of research work has been carried out in the field of feature selection. Although these methods help to increase the accuracy of the machine learning classification, the selected subset of features considers all the classes and may not select recommendable features for a particular class. The main goal of our paper is to propose a new class-specific feature selection algorithm that is capable of selecting an appropriate subset of features for each class. In this regard, we first perform class binarization and then select the best features for each class. During the feature selection process, we deal with class imbalance problems and redundancy elimination. The Weighted Average Voting Ensemble method is used for the final classification. Finally, we carry out experiments to compare our proposed feature selection approach with the existing popular feature selection methods. The results prove that our feature selection method outperforms the existing methods with an accuracy of more than 37%.
Channel Estimation of Urban 5G Communication System based on Improved Particle Swarm Optimization Algorithm
Authors: Xia Xigang , Yang Bo , Liu Zhiyu
Published: February 2024

In order to solve the problem that the channel estimation accuracy of the traditional urban communication system is not high, the author proposes the channel estimation of the urban 5G communication system based on the improved particle optimization algorithm. This method converts the channel estimate into a regression fit and adjusts the fit. Focusing on regression fitting problems, big data models are used to display offline data, study channel nonlinearities, and obtain initial channel prediction models. To solve the adaptive problem, the author collects real-time teaching data in a real online learning mode and integrates blended learning to update the model, to avoid the problem of overspending on offline training. Offline tests show that the performance of the channel estimation model is the best for different channels. As the signal-to-noise ratio increases, the MSE value is stable at around 1200. Conclusion: The channel estimation method can produce different characteristics of channel estimation in different situations and improve the signal recovery function of the communication system.
Multi-source and Multi-level Coordination of Energy Internet under V2G based on Particle Swarm Optimization Algorithm
Authors: Xu Jian , Chang Yunyan , Sun Xiaoming
Published: February 2024

In order to effectively improve the excessive load of microgrid during peak hours of urban electricity consumption, a multi-source and multi-level coordination method of energy Internet under V2G based on particle swarm optimization algorithm was proposed. First, a mathematical model for V2G energy integration in microgrids was developed and a scheduling concept based on a particle-by-particle optimization algorithm was used. Second, an improved PSO algorithm is proposed and experimentally validated, and the experimental results are compared with previous particle swarm optimization algorithms. Experiments have shown that as the number of iterations increases, the value of the objective function decreases and the optimal solution can be obtained until the maximum number of iterations is reached. The iteration speed and power processing cost of the improved PSO algorithm are better than before.The original load curve is the load trough period from 23:00 to 6:00, and two load peaks occur from 12:00 to 14:00 and 19:00 to 22:00. The V2G technology basically realizes the coordinated control of microgrid electric energy and achieves the effect of peaking and valley filling. The improved algorithm has obvious improvement compared with the original power grid state. Conclusion: The application of EV V2G technology can smooth the daily load curve of power grid and coordinate the electric energy of micro-grid to achieve ``peak cutting and valley filling'', and the effect of this algorithm is more outstanding than the previous algorithm. Finally, the future development direction and suggestions of V2G technology are put forward.The power grid with V2G discharge depth limit has the ability to basically reduce and eliminate the daily peak load, so the technology has broad research space and development prospects.
Breast Cancer Image Classification based on Adaptive Interpolation Approach Using Clinical Dataset
Authors: Uddaraju Sushmitha , A Koushlya , I Hemalatha , M Maragatharajan , C Balasubramanian , L Sathish Kumar
Published: February 2024

In the healthcare and bioinformatics disciplines, the categorization of breast cancer has become an emerging paradigm due to the second most common cause of cancer-related mortality in women. A biopsy is a procedure in which tissue has been examined to determine whether or not it is breast cancer through histopathologists that may lead to a mistaken diagnosis. The research is mainly focused on the patient data (884 case reports of patients) is acquired from the American Oncology Institute implemented with preprocessing techniques consists of missing values and those values are recovered with Novel Modified Interpolation (MI) Method. Deep learning networks effectively detect and assess the pattern for annotating histological data based on the labelling, which preserves time, system cost and enhances the system accuracy. This framework addressed feature acquisition and missing analysis strategies based on entropy confidence weight factor. First, the iterative patterns have been treated as a potential diagnostic rule, and the attention-based rule combination formulates the classification issue based on integrating convolutional and recurrent neural networks, and the short-term and long-term spatial correlations between patches. Second, the key part of label construction is carried out with an entropy confidence-weight factor assessment which detects and predict different patterns to construct the rule for classification. Third, optimization of clustering data by assessing missing parameters based on mean square error and the concept of interpolation to reduce data loss by around 20% and enhance the system accuracy. Simulation results show that the proposed system achieves 91.3% accuracy to state-of-art approaches, potentially allied in clinical applications. Modified Interpolation (MI) method recovered missing values with least mean square error and less data loss of 0.0123 and 1.38% respectively. This method also compared with existing Linear Interpolation (LI) method which is able to recover least men square error and data loss of 3.295 and 18.925% respectively. Comparatively the modified interpolation method recovered the missing values with less mean square error and less data loss.
SecureSense: Enhancing Person Verification through Multimodal Biometrics for Robust Authentication
Authors: Juluri Samatha , G Madhavi
Published: February 2024

Biometrics provide enhanced security and convenience compared to conventional methods of individual authentication. A more robust and effective method of individual authentication has emerged due to recent advancements in multimodal biometrics. Unimodal systems offer lower security and lack the robustness found in multimodal biometric systems. The research paper introduces a novel approach, employing multiple biometric modalities, including face, fingerprint, and iris, to authenticate users in a multimodal biometric system. The paper proposes the ”Secure Sense” framework, which combines multiple biometric modalities to improve person verification accuracy. The proposed system utilizes both web-based and real-time datasets. For the web-based dataset, we employed the Chicago Face dataset for facial data, the MMU1 dataset for iris data, and the SOCOfing dataset for fingerprint data. In real-time data collection, facial data is captured using a Zebronics Zeb-Gem webcam, fingerprint data is obtained using the Mantra MFS scanner, and iris data is collected using the Mantra MIS scanner. In the envisioned system, we introduce an innovative approach that employs a decision-level fusion technique across three unique biometric modalities, resulting in an impressive accuracy rate of approximately 93% across all modalities.
Conservation Design of Industrial Heritage based on Nonlinear GA Optimization Algorithm and Three-dimensional Reconstructioneconstruction
Authors: Zhao Yunan , Bai Peng
Published: February 2024

In order to understand the industrial heritage protection design of Iterative reconstruction, the author proposes a research on industrial heritage protection design based on GA optimization algorithm and Iterative reconstruction. Firstly, the author establishes the 3D model of industrial heritage through Iterative reconstruction, and optimizes the model parameters through GA algorithm to achieve the purpose of protecting and utilizing industrial heritage. Secondly, the author proposes a method of Iterative reconstruction of industrial heritage based on GA algorithm, uses this method to conduct Iterative reconstruction of industrial heritage, and imports the reconstructed model into the 3D model management system for management. This method solves the problem of high reconstruction cost caused by low model quality in traditional Iterative reconstruction, and makes industrial heritage protection design more practical. Finally, an experimental analysis was conducted using a factory building in a certain city as an example. The results showed that the model optimized using the GA algorithm had significantly better performance than traditional reconstruction methods, and could more accurately reflect the spatial form and structural characteristics of industrial heritage, this provided new ideas and methods for the subsequent protection and utilization of industrial heritage. The GA algorithm optimized 3D model established by the author can effectively evaluate industrial heritage in historical urban areas, not only revealing the value of industrial heritage better, but also providing a certain reference for similar work in the future.