bluuebunny/crossref_metadata_2025 huggingface dataset:
First validation of a model-based hepatic percutaneous microwave ablation planning on a clinical dataset
Authors: Frackowiak Bruno , Van den Bosch Vincent , Tokoutsi Zoi , Baragona Marco , de Greef Martijn , Elevelt Aaldert , Isfort Peter
Published: October 2023

A model-based planning tool, integrated in an imaging system, is envisioned for CT-guided percutaneous microwave ablation. This study aims to evaluate the biophysical model performance, by comparing its prediction retrospectively with the actual ablation ground truth from a clinical dataset in liver. The biophysical model uses a simplified formulation of heat deposition on the applicator and a heat sink related to vasculature to solve the bioheat equation. A performance metric is defined to assess how the planned ablation overlaps the actual ground truth. Results demonstrate superiority of this model prediction compared to manufacturer tabulated data and a significant influence of the vasculature cooling effect. Nevertheless, vasculature shortage due to branches occlusion and applicator misalignment due to registration error between scans affects the thermal prediction. With a more accurate vasculature segmentation, occlusion risk can be estimated, whereas branches can be used as liver landmarks to improve the registration accuracy. Overall, this study emphasizes the benefit of a model-based thermal ablation solution in better planning the ablation procedures. Contrast and registration protocols must be adapted to facilitate its integration into the clinical workflow.
The difference in the elderly’s visual impact assessment of pocket park landscape
Authors: Wang Xinyu , Li Guanjun , Pan Jingyin , Shen Jiamin , Han Chenping
Published: October 2023

As an important part of urban public open space, pocket parks have become an important activity place for the elderly in the context of the aging society in China. With the pocket parks in Nanjing, Jiangsu province, China as research object, this paper set six landscape features to be studied, namely, Height of trees, Green color richness, Stratification of green landscapes, Green space ratio, Leisure facilities, and Water landscape. The elderly respondents with different demographic characteristics, such as age, gender, education level and residential type, were subjected to the picture stimulation experiment whose results were then statistically analyzed. The results indicate that gender and residential type exert certain influence on the elderly’s visual impact assessment of pocket park landscape. To be specific, the male elderly prefer the pocket park landscape with 3-6 m high trees, medium green space ratio, and more leisure facilities; the female elderly are in greater favor of pocket park landscapes with 0-3 m high trees, five or more colors, three or more layers; the elderly who live with their families prefer pocket park landscapes with medium green space ratio and more leisure facilities; to the elderly who live alone, pocket park landscapes with trees which are 0–3 m high, five or more colors, and medium leisure facilities are more attractive. This study can provide valuable reference for pocket park design in China.
Reduced mortality associated to cementless total hip arthroplasty in femoral neck fracture
Authors: Pangaud Corentin , Pauly Vanessa , Jacquet Christophe , Orleans Veronica , Boyer Laurent , Khakha Raghbir , Argenson Jean Noël , Ollivier Matthieu
Published: October 2023

Mortality related to femoral neck fractures remains a challenging health issue, with a high mortality rate at 1 year of follow-up. Three modifiable factors appear to be under control of the surgeon: the choice of the implant, the use of cement and the timing before surgery. The aim of this research project was to study the impact on mortality each of these risk factors play during the management of femoral neck fractures. A large retrospective epidemiological study was performed using a national database of the public healthcare system. The inclusion criteria were patients who underwent joint replacement surgery after femoral neck fracture during the years 2015 to 2017. All data points were available for at least 2 years after the fracture. The primary outcome was mortality within 2 years following the surgery. We evaluated the association between mortality and the type of the implant hemiarthroplasty (HA) versus total hip arthroplasty (THA), cemented versus non cemented femoral stem as well as the timing from fracture to surgical procedure. A multivariate analysis was performed including age, gender, comorbidities/autonomy scores, social category, and obesity. We identified 96,184 patients who matched the inclusion criteria between 2015 and 2017. 64,106 (66%) patients underwent HA and 32,078 (33.4%) underwent THA. After multivariate analysis including age and comorbidities, patients who underwent surgery after 72 h intra-hospital had a higher risk of mortality: Hazard Ratio (HR) = 1.119 (1.056–1.185) p = 0.0001 compared to the group who underwent surgery within 24 h. THA was found to be a protective factor HR = 0.762 (0.731–0.795) p < 0.0001. The use of cement was correlated with higher mortality rate: HR = 1.107 (1.067–1.149) p < 0.0001. Three key points are highlighted by our study in the reduction of mortality related to femoral neck fracture: the use of hemiarthroplasty a surgery performed after 48 h and the use of cement for femoral stem fixation adversely affect mortality risk.
Association between breakfast frequency and metabolic syndrome among young adults in South Korea
Authors: Kim Hyo Myoung , Kang Hyun Jung , Lee Dong Hoon , Jeong Su-Min , Joh Hee-Kyung
Published: October 2023

Skipping breakfast is highly prevalent but it is not clear whether breakfast frequency is associated with metabolic syndrome in young adults. We aimed to assess the association between breakfast frequency and metabolic syndrome in Korean young adults. This cross-sectional study was based on health check-up data of university students aged 18–39 years between 2016 and 2018. Participants were stratified into three groups by breakfast frequency (non-skipper, skipper 1–3 days/week, skipper 4–7 days/week). Multivariable-adjusted logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) of metabolic syndrome. Out of 12,302 participants, 56.8% skipped breakfast at least 4 days/week. Metabolic syndrome prevalence was higher in those skipping breakfast for 4 or more days/week compared to non-skipper. (3.1% vs 1.7%) In the age- and sex-adjusted model, individuals skipping breakfast for 4–7 days per week had a higher OR of metabolic syndrome (OR 1.73, 95% CI 1.21–2.49) compared to non-skipper. Although this association became insignificant (OR 1.49, 95% CI 0.99–2.23) after a fully adjusted multivariable model, trends of positive association between frequency of breakfast skipping and metabolic syndrome was significant (P for trend = 0.038). Frequent breakfast skipping was associated with higher odds of metabolic syndrome in young adults. Further longitudinal studies in the long term are needed to understand the association of meal patterns with metabolic syndrome.
Impact of humid climate on rheumatoid arthritis faecal microbiome and metabolites
Authors: Wang Dingnan , Zheng Zhili , Yu Han , Dou Dou , Gao Yining , Xu Shuang , Li Zhiming , Sun Lili , Qiu Xudong , Zhong Xianggen
Published: October 2023

Studies have shown that high humidity is a condition that aggravates the pain of rheumatoid arthritis (RA), but the relevant mechanism is controversial. Currently, there is a lack of experimental animal studies on high humidity as an adverse factor related to the pathogenesis of RA. We used healthy SD rats and collagen-induced arthritis (CIA) rats to investigate the effects of high humidity on arthritis. Integrated metabolomics analyses of faeces and 16S rRNA sequencing of the faecal microbiota were performed to comprehensively assess the diversity of the faecal microbiota and metabolites in healthy and CIA rats. In this study, high humidity aggravated arthritis in CIA rats, which manifested as articular cartilage lesions, increased arthritis scores, and an increase in proinflammatory cytokines. High humidity had a certain effect on the articular cartilage extent, arthritis score and proinflammatory cytokines of healthy rats as well. Furthermore, high humidity caused significant changes in faecal microbes and metabolites in both healthy and CIA rats. 16S rRNA sequencing of faecal samples showed that high humidity increased the amount of inflammation-related bacteria in healthy and CIA rats. Faecal metabolomics results showed that high humidity significantly altered the level of faecal metabolites in healthy rats and CIA rats, and the changes in biological functions were mainly related to the inflammatory response and oxidative stress. Combined analysis showed that there was a strong correlation between the faecal microbiota and faecal metabolites. High humidity is an adverse factor for the onset and development of RA, and its mechanism is related to the inflammatory response and oxidative stress. However, the question of how high humidity impacts RA pathogenesis needs to be further investigated.
Simulation and multi-objective optimization of the dimethyl carbonate production process
Authors: Maleki Ali , Bahadori Fatemeh
Published: October 2023

Greenhouse gases such as CO2 are considered effective materials in global warming due to their high absorptivity. So, lowering atmospheric CO2 is one of the most practical strategies. Utilizing carbon dioxide in chemical processes is an applicable method. In this study, Aspen HYSYS 10 was used to investigate how carbon dioxide can be added to the process of dimethyl carbonate production, and the affective parameters of the process, including temperature, residence time, feed ratio, and recycle ratio, were evaluated. It was observed that the production of DMC grew as temperature rose. The simulation results also revealed that a maximum conversion of roughly 8% was attained in the MeOH/EC. Additionally, boosting the recycle ratio is detrimental, and the impact of temperature and MeOH/EC has been enhanced by increasing the residence time. The interactions of the above parameters have been studied by Design Expert 12. The optimum value of effective parameters for the production of dimethyl carbonate has been obtained as follows: temperature of 164.7° C, recycle ratio of 0.2, residence time of 139.45 min, and feed ratio of 5.9%, leading to the conversion of 70%.
Identifying the potential genes in alpha synuclein driving ferroptosis of Parkinson’s disease
Authors: Wang Min , Li Taole , Gao Rong , Zhang Yu , Han Yanqing
Published: October 2023

Parkinson’s disease (PD) is a common neurodegenerative disease with aggregation of α-synuclein (α-syn) in substantia nigra (SN). The association between the α-syn and ferroptosis in PD remains unclear. GSE49036 was obtained from the Gene Expression Omnibus (GEO) database and intersected with ferroptosis genes. Bioinformatics analysis was used to identify the potential differentially expressed genes (DEGs) included the development of Gene set enrichment analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein–protein interaction (PPI) network. We screened 8 key genes were modulated and crosslinked by 238 miRNAs. Additionally, 5 hub genes were predicted and 38 lncRNAs targeting 3 key miRNAs were revealed. Finally, 3 hub genes (PIK3CA, BRD4, ATM) and the key lncRNA (NEAT1) were verified in neurotoxic PD models. The in vitro experiments showed that PIK3CA and ATM were significantly upregulated or the BRD4 was downregulated in the rotenone treatment and they could be rescued by the specific ferroptosis inhibitor, liproxstatin-1. The expression of the key lncRNA NEAT1 were consistent with the hub genes in same models. This study identified the proposed NEAT1-PIK3CA/ATM ceRNA network may be a specific biomarker in α-syn driving ferroptosis as well as to predict clinical outcomes and therapeutic targets in PD patients.
The relationship between albumin corrected anion gap levels and mortality in patients with asthma in the ICU
Authors: Wang Shidong , Teng Hong , Han Hongyan , Xu Yunjie
Published: October 2023

Although previous studies have suggested that albumin-corrected anion gap (ACAG) may be a predictor of mortality in critically ill patients in intensive care unit (ICU), its utility in the context of asthma has not been definitively established. In this study, baseline data, albumin concentration, anion gap (AG) and 30-d mortality data were retrieved from the Medical Information Mart for Intensive Care IV database (MIMIC-IV) for patients with asthma in the intensive care unit. Receiver operating characteristic (ROC) curves were constructed to analyze the predictive ability of ACAG and AG. The risk of 30-day mortality among patients with ACAG and asthma was analyzed using a restricted cubic spline (RCS) plot. Decision curve analysis (DCA) was used to evaluate the clinical usefulness of ACAG as a prognostic factor for 30-day mortality. Subsequently, subgroup analysis was conducted to explore potential variations in the relationship between ACAG and 30-day mortality based on factors such as sex, age, whether the asthma was acute, and other co-morbidities. Our study reveals that ACAG is a significant independent predictor of 30-day mortality in asthmatic patients receiving intensive care. The area under the AUC curve for ACAG was found to be 0.703, which is higher than that of AG, indicating that ACAG has a better predictive ability for 30-day mortality in this population. Furthermore, higher levels of ACAG were found to be associated with increased risk of 30-day mortality in asthmatic patients. In addition, decision curve analysis (DCA) demonstrated that the net benefit of ACAG was greater than that of AG. These findings suggest that ACAG may be a valuable prognostic factor for predicting 30-day mortality in asthmatic patients in the ICU. Our study provides evidence that ACAG is associated with an increased risk of 30-d mortality and has better predictive value in patients with combined asthma who are admitted to the ICU than AG.
Introduction of Mass Spectrometry-based Microorganism Detection
Authors: Yi Jia , Zhang Dongxue , Qiao Liang
Published: October 2023

Microorganisms are involved in all aspects of human life and are closely related to human disease and health. Mass spectrometry (MS) is a promising technique for microorganism characterization, with high throughput, high sensitivity, and high accuracy. This chapter provides an overview of rapid microorganism detection based on MS. The MS techniques for microorganism determination including matrix-assisted laser desorption/ionization time-of-flight MS, electrospray ionization MS, and ambient ionization MS are summarized. In addition, the applications of MS-based microorganism detection in clinical laboratories, environmental science, and the food industry are described. Finally, this chapter discusses the challenges and prospects of MS-based microorganism detection.
Proteomics Analysis for Identification and Antimicrobial Resistance Analysis of Bacteria
Authors: Zhang Dongxue
Published: October 2023

Proteomics techniques provide comprehensive information about protein molecules and their functions for bacterial identification and antimicrobial resistance (AMR) studies. This chapter summarizes bacterial analysis based on proteomics, including basic proteomics concepts (proteomics strategies, data acquisition modes, quantitative methods, etc.), bacterial identification and classification, bacteria–bacteria/host cell interactions, AMR determination, and AMR mechanistic studies. In addition, proteomics-based diagnosis of bacterial infectious diseases, biomarker screening for AMR and infectious diseases, and vaccine development are also described. Finally, this chapter discusses the challenges of bacterial analysis based on proteomics techniques, and looks forward to the application prospects of proteomics in bacterial analysis.
Mass Spectrometry Imaging in Microbiology
Authors: Zou Yuchen , Li Bin
Published: October 2023

Capable of simultaneously interrogating spatial and chemical information about molecules, mass spectrometry imaging (MSI) has contributed to numerous breakthroughs in various disciplines, including microbiology. Over the last decade, the spatiotemporal perspective provided by MSI has granted microbiologists unique insights into the dynamic molecular processes of microbial metabolism. In this chapter, common microbial MSI techniques are introduced with respect to their instrumentation, sample preparation, and pros and cons. A wide range of reported applications is also described to demonstrate the strengths as well as potential issues of current MSI. With the non-stop progress of this technique, it is believed that MSI will only continue to provide more splendid pictures of microbial worlds in the future.
Mass Spectrometry (MS)-based Bacterial Identification in the Food Industry
Authors: Bi Hongyan
Published: October 2023

Mass spectrometry (MS) has been broadly applied for the analysis of bacteria in various food matrices. This chapter comprehensively summarizes MS-based bacterial identification in different foods. The detection of pathogenic microbes in animal-based foods including poultry, aquatic food products, beef, and porcine meat as well as plant-based foods including fruits, lettuce, nuts, rice seeds, and potatoes is addressed. The detection of bacteria in processed foods, including fruit juice, ready-to-eat foods, baby foods, and fermented food products, as well as probiotics used in (canned) foods, is discussed. Identification of recombinant bacteria or unauthorized genetically modified (GM) bacteria, detection of antibiotic residues and drug resistance of bacteria, and analysis of bacterially produced toxins by MS are also discussed. The contamination and spoilage of foods monitored by MS are summarized. Many other concerns like the factors that affect the reliability of identification of bacteria by MALDI-TOF MS, the accuracy and sensitivity of identification of bacteria by MALDI-TOF MS, the impact of probiotics on fruit juice aroma by GC–MS, the integration of MS techniques with other methods, and MALDI-TOF MS applied to food product quality inspection are also covered. MALDI-TOF MS has been broadly utilized in bacteria-related food safety supervision together with GC–MS, LC–MS, nanoLC–MS, HPLC–MS/MS, LC–ESI/MS, UHPLC–MS/MS, FT–ICR/MS, or other emerging MS techniques. It is envisaged that MS will continue to be applied in the food industry for food safety control.
Fusion data from FT-IR and MALDI-TOF MS result in more accurate classification of specific microbiota
Authors: Gao Wenjing , Han Ying , Chen Liangqiang , Tan Xue , Liu Jieyou , Xie Jinghang , Li Bin , Zhao Huilin , Yu Shaoning , Tu Huabin , Feng Bin , Yang Fan
Published: October 2023

A new microbiota analysis method incorporating MALDI-TOF MS and FT-IR technology for improving classification accuracy has been reported.
Smart sensing flexible sutures for glucose monitoring in house sparrows
Authors: Alsaedi Mossab K. , Riccio Rachel E. , Sharma Atul , Xia Junfei , Owyeung Rachel E. , Romero L. Michael , Sonkusale Sameer
Published: October 2023

This study highlights innovative, minimally-invasive glucose sensing sutures for monitoring glucose levels in house sparrows.
A simple electrochemical strategy for the detection of the cancer marker CA19-9 by signal amplification using copper organic framework nanocomposite
Authors: Zhang Tingting , Ma Yan , Zhang Yuzhong
Published: October 2023

A label-free electrochemical immunosensing of the cancer biomarker CA19-9.
Construction of metallo-helicoids with high antimicrobial activity via intermolecular coordination
Authors: Zhang Zhanpeng , Lu Shuai , Yu Xiujun , Hua Lei , Wang Weiguo , Xue Menglin , Cai Jianfeng , Wang Heng , Li Xiaopeng
Published: October 2023

Metallo-helicoids are constructed by intermolecular coordination interactions between covalent linear polymer and tritopic/hexatopic molecular templates.
Advances in circularly polarized luminescence materials based on chiral macrocycles
Authors: Wang Jia-Qi , Han Xiao-Ni , Han Ying , Chen Chuan-Feng
Published: October 2023

This review comprehensively summarizes circularly polarized luminescence materials from chiral macrocycles, especially chiral macrocyclic hosts and their derivatives.
Stimuli-responsive magnetic materials: impact of spin and electronic modulation
Authors: Kaushik Krishna , Mehta Sakshi , Das Mayurika , Ghosh Sounak , Kamilya Sujit , Mondal Abhishake
Published: October 2023

Stimuli-responsive switchable molecular magnetic materials undergo reversible changes in their physical properties upon application of external stimuli and have great application potential in spintronics, memory devices, switches, and sensors.
Trace Sc3+-electrolyte additive enabling stable Zn metal anodes for aqueous zinc-ion batteries
Authors: Chen Chun , Li Liansheng , Long Zuxin , Liang Qinghua
Published: October 2023

Trace Sc3+-electrolyte additive in the aqueous ZnSO4 electrolyte enables stable Zn metal anodes for zinc-ion batteries due to the homogeneous Zn plating/stripping and improved redox kinetics.
Optoelectronic materials as emerging photocatalysts: opportunities in sustainable organic synthesis
Authors: Zhou Cen , An Bohang , Lan Feng , Zhang Xiao
Published: October 2023

This feature article describes recent examples of the implementation of two types of organic electronic materials including phenothiazine and truxene in heterogeneous photocatalysis for organic synthesis.
Ionizing radiation induces cross-linking of two noncovalently bound collagen mimetic peptide triple helices in the absence of a molecular environment
Authors: Abdelmouleh Marwa , Amin Muhamed , Lalande Mathieu , Schlathölter Thomas , Poully Jean-Christophe
Published: October 2023

We show experimental evidence that ionizing radiation is able to cross-link two collagen mimetic peptide triple helices in the gas phase, challenging the previously proposed water-mediated collagen cross-linking mechanism.
Resonance-enhanced excitation and relaxation dynamics of coherent phonons in Fe1.14Te
Authors: Li Ning , Zhang Chenhui , Liang Weizheng , Jiang Yaohua , Zhang Xi-Xiang , Mi Yang , Luo Sheng-Nian
Published: October 2023

We investigate the relaxation dynamics and underlying mechanism of coherent phonons in Fe1.14Te single crystals by femtosecond transient optical spectroscopy.
Efficient exploration of transition-metal decorated MXene for carbon monoxide sensing using integrated active learning and density functional theory
Authors: Boonpalit Kajjana , Kinchagawat Jiramet , Prommin Chanatkran , Nutanong Sarana , Namuangruk Supawadee
Published: October 2023

Y@Zr3C2O2 and Sc@Zr3C2O2, the transition-metal decorated MXene for improved CO sensing, were identified using active learning and the DFT screening pipeline. They showed excellent sensing performance in terms of recovery time and electrical response.
Carbon dioxide adsorption to UiO-66: theoretical analysis of binding energy and NMR properties
Authors: Atsumi Michiko , Zheng Jia-Jia , Tellgren Erik , Sakaki Shigeyoshi , Helgaker Trygve
Published: October 2023

This theoretical work on gas adsorption to UiO-66 revealed the adsorption structures and adsorption energies of CO2, acetone and MeOH using a CM/PM-combined calculations and changes of NMR parameters by CO2 adsorption using DFT calculations.
K-doped g-C3N4 decorated with Ti3C2 for efficient photocatalytic H2O2 production
Authors: Zhou Suyu , Cheng Shaoli , Han Junhe , Huang Mingju
Published: October 2023

Hydrogen peroxide (H2O2) is an important chemical widely used in the chemical industry.
The molecular mechanisms of diapause and diapause-like reversible arrest
Authors: Easwaran Sreesankar , Montell Denise J.
Published: October 2023

Diapause is a protective mechanism that many organisms deploy to overcome environmental adversities. Diapause extends lifespan and fertility to enhance the reproductive success and survival of the species. Although diapause states have been known and employed for commercial purposes, for example in the silk industry, detailed molecular and cell biological studies are an exciting frontier. Understanding diapause-like protective mechanisms will shed light on pathways that steer organisms through adverse conditions. One hope is that an understanding of the mechanisms that support diapause might be leveraged to extend the lifespan and/or health span of humans as well as species threatened by climate change. In addition, recent findings suggest that cancer cells that persist after treatment mimic diapause-like states, implying that these programs may facilitate cancer cell survival from chemotherapy and cause relapse. Here, we review the molecular mechanisms underlying diapause programs in a variety of organisms, and we discuss pathways supporting diapause-like states in tumor persister cells.
Integrating Aerial Electric Vehicles for Sustainable Agriculture and to Optimize the Overhead Cost of Farming
Authors: V. Vijaya Rama Raju , Varma Sai Nikhila , Vangari Pratheek , Kumar Sumit , Joshi Kapil , Penta Samyuktha
Published: October 2023

India is a country whose roots are buried deep in agriculture. The livelihood of about 58% of the population depends on farming. Yet technological progress in the country’s agricultural practices is lacking. The growing technology and increased demand for agriculture calls for the incorporation of technology into sustainable agriculture. The proposed work, therefore aimed at building a drone to serve this purpose. The rapid growth of the Electric Vehicle (EV) market and India’s aim to shift to the complete use of EVs by the year 2023 demand a paradigm shift in the agriculture sector. The use of aerial electric vehicles would be an impeccable execution of EVs in agriculture, serving a vast set of functions. The proposed work focuses on one specific application: remote spraying of pesticides in an efficient way so that the overhead costs of farming are reduced. The harmful effects of exposure to pesticides are eminent. Farmers who take it upon themselves to spray these pesticides are at risk of contracting a wide range of health adversities and deadly chronic diseases. As a solution to this imminent need, it is proposed to design a drone to spray pesticides according to the requirement, as directed by the farmer. The Agriculture Drone has a hexagonal structure controlled using BLDC motors and Electronic Speed Controllers (ESCs). For flight control, Pixhawk 2.4.6 is used. It is equipped with a GPS module that can track the location of the drone and plan its mission. The drone operates on autopilot and requires no manual control for flight. Specific coordinates are given to the Drone so that it moves along the given coordinates for its flight using the software ‘Mission Planner.’ The designed drone has a removable tank for the storage of pesticides, which is placed under the landing gear. The tank has a spraying system that operates on the instructions of the farmer, which are given to the drone using a Radio transmitter.
Power Quality Improvement and Sustainable operation in A Standalone Micro Grid by Regulating Frequency in A Deregulated Market
Authors: Phani Kumar K.S.V. , Awaar Vinay Kumar , Sonnati Venkateshwarlu , Mahammad Rafi K. , Krishna Gopal
Published: October 2023

Cooperative decision-making has always benefited from achieving objectives subjected to system constraints in a microgrid. This paper considers the power balance between generation and load as a significant power quality issue. Hence, frequency regulation is regarded as a primary challenge in the system. When the system operator works towards profit for the power producer, an integrated operation provides a solution. The sources considered in this system are Solar PhotoVoltaics (SPV), FuelCells (FC), Diesel Generators (DG) and Battery driven Electric Vehicles (BEV), where the BEVs operate in Vehicle microgrid mode. A central controller and local controllers are present to operate the generators at desired levels. A cascaded fuzzy controller is designed that chooses the best suitable BEV to be connected to the microgrid. The system is implemented in a MATLAB Simulation environment, and various scenarios and cases have been considered for evaluating the system response and its sustainability.
Automated Abstractive Text Summarization using Deep Learning
Authors: Karuna G. , Akshith M. , Sai Dinesh Parige , Vishnu Vardhan Bodhan , Singh Bisht Yashwant , Narsaiah M.N.
Published: October 2023

A class of neural networks known as Recurrent Neural Networks (RNNs) are capable of processing sequential input, including time series and plain language. In the shortest possible time to find relevant and useful information, it is for sure very helpful if the information is summarized, but it typically requires a lot of effort, dedication, patience, and attention to detail for humans to go through and summarize the lengthy texts. This can be done using automated abstractive text summarization techniques by using deep learning which is the way of selecting the most significant information in a text, then condensing it while maintaining its underlying meaning. The objective is to construct an abstractive text summarizer using deep learning. It takes large meaning-full text data as input and gives the summary of the data as output. The algorithm which is been used here is the Long Short Term Memory model (LSTM) which is a type of RNN model. And then next the model which is been used is the Sequence to Sequence model (Seq2Seq). Seq2Seq learning is the training model that can modify the sequences of one input into the sequences of another output. The dataset used is News Summary dataset which is taken from Kaggle. Experimental results on the news summary dataset show that the proposed method results are appropriate and match with the other methods.
Automated Caption Generation for Video Call with Language Translation
Authors: Polepaka Sanjeeva , Prashanth Kumar Varikuppala , Umesh Chandra S. , Sri Krishna Hema Nagendra , Thakur Gaurav
Published: October 2023

In the modern era, virtual communication between individuals is common. Many people’s lives have been made simpler in a number of circumstances by providing subtitles, generating automated captions for social media videos, and language translation from a source language to a targeted language. Both are included, which offers face-to-face translated captions during video conversations. React is used for application development. To send the data, socket programming is utilized. Context is understood and translated using Google translate API and speech recognition modules. With OpenAI and Whisper, captions are generated. This paper will directly create the translated user voice rather than translating the text and creating subtitles.
IoT - based Sustained Belt for Visually Impaired People
Authors: Deepthi P. , Abhinav A. , Ganesh Srikar K. , Saketh E. , Singh Devendra
Published: October 2023

This paper is an IoT based Sustained Belt for Visually impaired people, which uses Raspberry Pi for image identification and image processing techniques to help a blind person to identify and rectify people and things in front of them and help them navigate everyday life and have a better lifestyle like any other common man. The key techniques that will be used are real-time images and object recognition as well as image processing. First would be an image, object recognition is to capture the image and then rectify the image, rectifying the image is done through image processing and then the rectified image is converted as audio output. The scope of this paper is to scale up a level where a blind person has a normal lifestyle, just like any other normal person, right without hustles or anything and as well as they can use this as his adjacent person, where this has inbuilt GPS and it is a mini computer as well it can be also useful for general questions, usually blind people when they can’t see things they have a lot of questions and be curious, it will be easier to know about them. What differentiates our paper from usual blind people’s normal stick is that the stick helps them not to crash anything but we are not focused on that, what we focused on is to have a very normal lifestyle like normal person
Automation of Credit Card Customer Churn Analysis using Hybrid Machine Learning Models
Authors: Ram Kumar R.P. , Sahithi B. , Neeharika K. , Shivaleela M. , Singh Digvijay , Rama Krishna Reddy K.
Published: October 2023

Credit Card Customer Churn Analysis (C4A) is a phenomenon where customers stop using a specific business credit card service. Predicting customer churn is crucial for Credit Card (CC) companies because it enables them to spot at-risk customers and take precautions to retain them. The aim of the paper named C4A is to create a model that accurately predicts customers who are most likely to stop using CC. The paper involves gathering and analyzing customer information from Kaggle, including transaction history, demographics and credit card usage patterns for prediction. Machine learning algorithms namely, Logistic Regression, KNN, XGBoost Classifier, Decision Tree and Hybrid Models integrating Logistic Regression and KNN, Logistic Regression and Decision Tree are used to train to find patterns and correlations that point to customer churn. The accuracy of the proposed method is 0.846 with LR, 0.849 with KNN, 0.90 with a hybrid model integrating LR and KNN, 0.928 by integrating LR and DT, 0.91 with DT, and 0.93 with XGBoost.
Automated Detection of Drowsiness using Machine Learning Approach
Authors: Sanjeeva Polepaka , Sriya Vallepu , Saniya Mahjabeen , Lohitha Matta , Ahmad Ishteyaaq , Swapna Rani K.
Published: October 2023

Nowadays, there is a steady rise in the number of traffic accidents. The primary causes of these accidents are impaired driving due to alcohol consumption and driver fatigue. The primary goal is to create a system capable of measuring a driver’s degree of sleepiness. If drowsiness is identified, a warning will be sent out via integration with an alert warning system and text message system. Drowsiness detection is built using OpenCV, Python, and Machine Learning. A significant number of annotated driver images depicting different levels of drowsiness, alongside images of diverse driving scenarios and lighting conditions, were utilized in the research to enhance the dataset. The system for detecting driver drowsiness provides a viable method to avert car accidents resulting from driver tiredness. It examines the driver’s eye and alerts them when necessary. Further improvements could increase the alarm system’s accuracy by minimizing the number of false warnings.
Feasible Skin Lesion Detection using CNN and RNN
Authors: Ram Kumar R.P. , Varun Racha , Sreeshwan Jageer , Arun Kumar Kondroju , Rana Upasana , Rajyalakshmi A.
Published: October 2023

A prevalent form of cancer that affects millions of individuals globally is skin cancer. The visual examination of skin lesions, however, is a challenging and time-consuming procedure that calls for the knowledge of dermatologists. The proposed effort intends to create an accurate, feasible and effective system for detecting skin lesions that can help dermatologists identify and treat a variety of skin conditions. To extract features from skin lesion photos, the method uses a pre-trained Convolutional Neural Network (CNN). These characteristics are then fed into a Recurrent Neural Network (RNN) for temporal modelling. The early diagnosis of numerous skin illnesses depends greatly on the detection of skin lesions. Deep learning models, particularly CNNs, have demonstrated impressive performance in the computer-aided diagnosis of skin lesions in recent years. This work uses the HAM 10000 dataset to suggest a hybrid CNN and RNN model for skin lesion detection.
Feasible Prediction of Multiple Diseases using Machine Learning
Authors: Ramesh Banoth , Srinivas G. , Ram Praneeth Reddy P. , Huraib Rasool M.D. , Rawat Divya , Sundaray Madhulita
Published: October 2023

Automated Multiple Disease Prediction System using Machine Learning is an advanced healthcare application that utilizes machine learning algorithms to accurately predict the likelihood of a patient having multiple diseases based on their medical history and symptoms. The system employs a comprehensive dataset of medical records and symptoms of various diseases, which are then analysed using machine learning techniques such as decision trees, support vector machines, and random forests. The system’s predictions are highly accurate, and it can assist medical professionals in making more informed decisions and providing better treatment plans for patients. Ultimately, the viable Multiple Disease Prediction System using Machine Learning has the potential to improve healthcare outcomes and reduce healthcare costs by predicting and preventing disease early.
Autonomous Driving: Enhancing Mileage, Road Safety with AI
Authors: Gajula Ramesh , Sathwik Rudraraju G.H.S. , Suppala Pavan Teja , Surya Raghav K.N.D.V. , Randwal Babita Rani , Praveen J.
Published: October 2023

The aim of this paper is to solve the problems in driving by introducing autonomous features into vehicles. An autonomous vehicle is a vehicle that can operate and drive itself without any human interference, with the help of sensors, to sense its surroundings and navigate itself. An autonomous vehicle is equipped with an automated driving system that is designed and trained by humans to allow the vehicle to respond to surroundings in a way that a human would act. In this paper, we develop a program with the help of AI/ML to detect roads, traffic signs, traffic conditions, and Surroundings and act according to them by altering the steering, acceleration, and braking. We plan to achieve this by taking input from a series of sensors and training the model to act accordingly. In this paper, we train neural networks with the help of human behavior. So, the model tries to mimic human behavior and human actions in a given situation.
Voice-based e-mail for Information systems: An aid to visually impaired
Authors: Surekha P. , Pavani M. Ganga , Jahanavi B. , Siri B. , Singh Tilottama
Published: October 2023

Artificial intelligence has the potential to revolutionize the way that visually impaired individuals interact with technology. With this in mind, a voice-based email system for visually impaired individuals has been proposed as a solution to provide a convenient and accessible way of managing email. The system leverages advanced Natural language processing (NLP)and Automatic Speech Recognition(ASR) technologies to convert spoken commands into email actions. The system supports voice commands for dictating emails, text-to-speech functionality for reading emails, and speech-to-text for writing emails, navigating the inbox, giving the count of unseen emails, and managing emails. This system is designed to be user-friendly, intuitive, and accessible, ensuring that blind individuals can easily navigate and use it with ease. Implementation of this system has the potential to greatly improve the daily lives of visually impaired individuals by providing a more convenient and accessible way to manage emails. This project presents an automation system for AI-powered voice-based email designed specifically information passing system to help blind individuals.
Automated Voice-to-Image Generation Using Generative Adversarial Networks in Machine Learning
Authors: Yeluri Lakshmi Prasanna , Ramesh G. , Vijayalata Y. , Shareef Khaja , Chamola Shailesh , Gundavarapu Mallikarjuna Rao
Published: October 2023

Creating visuals from words may appear to be a complex process, but it is achievable with today’s technological advancements in Information Systems. Naturally, all the human-centric actions and assumptions may lead to visualization using Artificial Intelligence. In today’s Information Systems technological world, any item or a thing can be best described in pictorial form as a human person. Our paper aims to focus on providing machines with this intelligence. To complete this challenge, we used Natural Language Processing with Deep Learning. Our primary focus is on Generative Adversarial Networks. GANs will generate data based on word labels that are provided. NLP is also important since it helps to translate the provided speech into embedding vectors that the model can use. Our study is on the CUB dataset, which comprises bird photos. In today’s world, there are text-to-image generating models accessible. The authors investigated all of them, extending text-to-image generation to voice-to-image generation.
Decoding the Human Genome: Machine Learning Techniques for DNA Sequencing Analysis
Authors: C. Sravani , P. Pavani , G.Y. Vybhavi , Ramesh G. , Farman Ali , Reddy L. Venkareswara
Published: October 2023

The decoding of the human genome has been a landmark achievement in the field of genomics, generating vast amounts of DNA sequencing data that necessitate sophisticated analysis techniques. In recent years, machine learning has emerged as a powerful tool in unravelling the complexities of genomic data and expediting research discoveries. This article explores the integration of machine learning techniques in DNA sequencing analysis, elucidating their applications in genome assembly, variant calling, personalized medicine, and drug discovery. Additionally, it addresses the ethical considerations surrounding the use of genomic data. By harnessing the potential of machine learning, researchers are unlocking new insights into human genetics and paving the way for transformative advancements in healthcare and scientific understanding.
Sustainable development of Flexible Assertion on Multi-Modal Classification of Brain Tumours using Deep Learning
Authors: Sahiti Yellanki V. , B. Sankara Babu , Srihitha Gunapriya N. , Indupriya B. , Chouhan Sanjay Singh
Published: October 2023

In the field of medical science, classifying brain tumours is vital. In order to get an effective and proper treatment for the disease, accurate and finding type of the brain tumour is much essential in the case of brain tumour treatment. In addition to providing treatment for tumours as early as possible, it also helps in saving a life by allowing medication to be administered in due course. DL has developed into a fantastic tool for medical professionals and researchers to act quickly and decisively with tumour patients. In this paper, we suggest Sustainable development of flexible approach aimed at multi-model organization of brain tumours using the popular deep learning architecture ResNet-50. By leveraging the flexibility of ResNet-50, we aim to achieve improved accuracy and robustness in classifying brain tumours across a diverse range of datasets. Our approach integrates multiple ResNet-50 models, each specialized in identifying specific tumour types, enabling a comprehensive classification framework. Experimental findings show that our strategy is successful and more accurate than other approaches. In this paper we provide an interface that can be used to classify and label the tumours. We used Keras and Tensorflow to create a cutting-edge Convolutional Neural Network (CNN) architecture to categorise 3 kinds of growth or tumours namely - Meningioma, Gliomaand Pituitary using ResNet50 algorithm. It is estimated that this model has a maximum mean accuracy score of 98.88%.
IoT Sensor Based Sustainable Air Quality Monitoring System for Humans and Ecosystems in the World Empowerment
Authors: Gopal Krishna P. , Subbarayudu Yerragudipadu , Sai Keerthana G. , Varsha K. , Vijendar Reddy G. , Ramola Bharti
Published: October 2023

Due to elements that can harm human health, such as industries, urbanisation, population growth, and automobile use, the level of pollution is rising quickly. Using an Internet-connected web server, an IOT-based air pollution monitoring system is employed to track the air quality which sustains environment. When the amount of dangerous chemicals including CO2, smoking, alcohol, benzene, NH3, and NOx is high enough, it will sound an alarm when the air quality drops below a specified threshold. It will display the air quality in PPM on the LCD and on the website, making it very simple to monitor air pollution. The MQ135 and PM 2.5 sensors are used by the system to monitor air quality since they can accurately measure and detect the majority of hazardous gases. In recent years, air pollution has become a severe issue on a global scale and has surpassed advised national limitations. In addition to harming ecosystems and human health, air pollution also has an impact on global climate. The population is expanding, there are more industries, and there is an excessive amount of transportation that uses fuel, which are all contributing factors to the rapid rise in air pollution. To address this danger, the Air Quality Monitoring System was developed.
Ecosense: An IoT System for Detecting Suitable and Sustainable Living Conditions
Authors: Jeevan Nagendra Kumar Y. , Chandan Ragi , Somanini Sri Harsh , Vadtya Suresh , Dangi Deepika
Published: October 2023

The EcoSense project intends to create a sustainable IoT system to detect and track whether living circumstances are suitable for individuals. The system gathers sensor data and measures important aspects including temperature, air quality, and noise level. The degree of sustainable air pollution is determined by air pollution sensors, which monitor several dangerous chemicals including CO and CO2 in the atmosphere. The noise sensor measures the level of noise and pinpoints the source of excessive noise. Environmental temperature and humidity are measured using sensors. These sensors sustainably and continuously track all of these parameters, sending out notifications when specified thresholds are reached or exceeded. In order to assess the habitat’s general compatibility, the sensor data is processed, saved in the cloud, and then examined. Following that, this information is used to give locals immediate feedback so they may decide for themselves how best to enhance their living arrangements as well as the environment and public health. their general wellbeing. This initiative helps to spread the word about living sustainably. The EcoSense initiative has the potential to have a big impact on the environmental sustainability industry. The initiative can assist people and communities in making educated decisions about their environment and taking constructive action to enhance it by offering real-time data on quality of life. The project has the potential to increase public awareness of environmental problems and inspire individuals to take action to safeguard the environment.