bluuebunny/crossref_metadata_2025 huggingface dataset:
Monitoring water quality parameters impacted by Indonesia’s weather using internet of things
Authors: Riftiarrasyid Mohammad Faisal , Soewito Benfano
Published: July 2024

Increasing need for food resources, State of Indonesia to strive to maximize the output of food production. Not only in agriculture but also aquaculture results are also trying to be improved. This is also supported by the increase of Indonesia’s national fish consumption rate from 50.69 Kg per capita in 2018 to 55.37 Kg per capita in 2021. Recent aquaculture research only explored topics about monitoring the cultivation environment. But there have been no studies exploring how bad the impact of weather on the process of farming. Hence, this study aims to measure the influence of weather on freshwater aquaculture pond water quality and analyze its impact on fish growth namely Oreochromis Sp., using pH sensors and dissolved oxygen (DO). Then a weather simulation was carried out based on Indonesia’s tropical climate, which majorly consists of sunny and rainy weather. The experimental results indicate the instability of the pH value during the rainy period. DO values tend to decrease at the end of periods of sunny weather. Moreover, fish growth analysis showed that there was a decrease in food conversion ratio (FCR) by 0.956, specific growth rate (SGR) by 2.13% and survival rate (SR) by 5.715% during rainy weather.
Design and analysis of low power sense amplifier for static random access memory
Authors: Yadav Vishal , Tiwari Brij Bihari
Published: July 2024

Today’s era is a digital world where each and every section of the society is experiencing and encountering with semiconductor chips. In very large-scale integration (VLSI) circuits the design of static random-access memory (SRAM) plays a crucial role in ensuring both low-power consumption and high-speed performance. The sense amplifiers (SA) are integral parts for information accessing storage in SRAM IC design. This paper introduces a dual voltage latch sense amplifier (DVLSA) for SRAM integrated circuits (IC). The comparative analyses of various SA are studied and then design a low-power SA through the implementation of energy-efficient technique. Further, we have elucidated the causes of delay and power dissipation in different SA with useful solutions and performance evaluation is conducted by comparing the proposed design with existing SA reported in the literature. The performance parameters such as power 1.604 uw, energy 470.50 fJ, delay 80.04 ps, and current 5.406 are scrutinized to assess the efficiency of the designs. The cell outcomes have been validated with cadence tool on 180 nm technology and operate at 1.8 V. The proposed design, namely, DVLSA demonstrates minimal energy consumption and low power dissipation, making it a promising advancement in SRAM IC technology.
Temporal attention network for CNNE model of variable-length ECG signals in early arrhythmia detection
Authors: Karthikeyan Poomari Durga , Abirami M. S.
Published: July 2024

Cardiac arrhythmia identification and categorization are crucial for prompt treatment and better patient outcomes. Arrhythmia identification is the main focus of this study's temporal attention network (TAN)-based multiclass categorization of varied-length electrocardiogram (ECG) data. The suggested TAN is designed to handle variable-duration ECG signals, making it ideal for real-time monitoring. The TAN uses a dynamic snippet extraction approach to choose meaningful ECG segments to ensure the model captures essential properties despite the constraints of processing such heterogeneous data. Training and assessment use a large dataset of atrial fibrillation, ventricular, and supraventricular arrhythmias. The TAN outperforms current approaches in multiclass early arrhythmia classification and is very accurate. Concatenating EfficientNet with CNN layer helped overcome different data and variable-length signals. High accuracy: 98% of normal, 97.1% of atrial fibrillation (AF), 98% of other, and 98% of noisy using the proposed CEEC model. Early arrhythmia diagnosis has improved due to the TAN's ability to effectively identify varied-length ECG data and give interpretability. It enables quicker interventions, personalised treatment plans, and improved arrhythmia control, which can greatly benefit patient care.
Deep transfer learning classification of apple fruit diseases
Authors: Loutfy Shaimaa Kamal , Rahouma Kamel Hussein
Published: July 2024

This paper applies deep convolution neural networks (DCNN) to apple fruit disease classification. Twelve DCNN methods (SqueezeNet, GoogleNet, InceptionV3, DenseNet201, ReaNet50, ResNet101, Xception, InceptionResnetV2, EfficientnetB0, AlexNet, VGG16, and VGG19) have been used. These methods have been trained to classify apples into four categories: normal, blotch, rot, and scab. A dataset of 5179 images, including 3472 for normal, 171 for blotch, 1166 for rot, and 370 for scab, has been used. A practical test on 120 images (30 for each category) has been applied. Seven of these DCNNs—InceptionV3, DenseNet201, ResNet101, ResNet50, GoogleNet, AlexNet, and VGG16—have the best accuracy. InceptionV3 is the highest. It has achieved an accuracy of 100% for all categories. The used dataset is unbalanced and small. So, it's necessary to use data augmentation to overcome any overfitting that may cause. After applying data augmentation, the dataset is balanced and contains 13888 images (3472 for each category). The seven DCNNs are retrained by the balanced dataset and retested by the same 120 images. All DCNN's accuracy has enhanced except InceptionV3, which has decreased. On the other hand, RasNet101 has achieved an accuracy of 100% for all categories. Therefore, ResNet101 has been recommended for apple fruit disease classification.
Image segmentation of Komering script using bounding box
Authors: Hamanrora Muhammad Dio , Kunang Yesi Novaria , Yadi Ilman Zuhri , Mahmud Mahmud
Published: July 2024

The development of deep learning technology is widely used for various purposes, including recognizing characters in a document. One of the scripts that can benefit from this deep learning technology is the Komering script, which is a local script in the South Sumatra region. However, there are challenges in reading documents written in this script, requiring a method to separate each character in a document. Therefore, there is a need for a technology that can automatically segment images of documents written in the Komering script. This research introduces an innovative technique for segmenting images of characters in documents that contain Komering script characters. The segmentation technique employs bounding box technology to separate each Komering script character, subsequently recognized by a pre-trained deep learning model. The bounding box approach imposes restrictions on the segmented object area. To recognize Komering characters, a deep learning model with a convolutional neural network (CNN) algorithm is employed.
Evaluating the impact of downsampling on 3D MRI images segmentation results based on similarity metrics
Authors: Fajar Aziz , Sarno Riyanarto , Fatichah Chastine
Published: July 2024

Medical imaging plays a crucial role in diagnosing patient conditions, with magnetic resonance imaging (MRI) standing as a significant modality for numerous years. However, leveraging convolutional neural network (CNN) architectures like U-Net and its variations for anatomical segmentation demands considerable memory, particularly when working with full 3D image sets. Therefore, downsampling 3D MRIs proves advantageous in reducing memory consumption. Nevertheless, downsampling leads to a reduction in voxel count, potentially impacting the performance of commonly used segmentation metrics. The jaccard similarity index (JSI), dice similarity coefficient (DSC), and structural similarity index (SSIM) are extensively employed in image segmentation contexts. Hence, this study employs all three metrics to assess downsampled images and evaluate the robustness of the metrics when used to evaluate the downsampled 3D MRI images. The results show that JSI and DSC are more robust than SSIM when handling the downsampled data.
Stock market index prediction based on market trend using LSTM
Authors: Yenireddy Ankireddy , Narayana Marimganti Srinivasa , Bangaru Ganesh Kalla Venkata , Kumar Guvvaladinne Prasanna , Venkateswarlu Madduri
Published: July 2024

The stock market data analysis has received interest as a result of technological advancements and the investigation of new machine learning models, since these models provide a platform for traders and business people to choose gaining stocks. The business price prediction is a challenging and extremely complex process due to the impact of several factors on company prices. The numerous patterns that the stock market goes, they have been the focus of extensive research and analysis by numerous experts. There are several large data sets accessible, an artificial intelligence and machine learning techniques are developing quickly, and because of the machine’s improved computational power, complex stock price prediction algorithms can be developed. This paper presents stock market index prediction based on market trend using long short-term memory (LSTM). Using built-in application programmable interface (API), Yahoo Finance offers a simple method to programmatically retrieve any historical stock prices of an organization using the ticker name. The standard and poor’s 500 index (S&P 500 index) include the firms that have been taken into consideration here. Utilizing the selected input variable, single-layer and multi-layer LSTM models are implemented, and the measurement parameters of mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (R) are used to compare each performance. Nearly all of the real closing price’s curve and the prediction curve’s closing price for test data overlap. A potential stock investor may benefit significantly from such a prediction by using it to make well-informed choices that would increase his earnings.
Predicting customer churn in telecommunication sector using Naïve Bayes algorithm
Authors: Agasti Biswa Ranjan , Satpathy Susanta
Published: July 2024

The telecom sector creates huge amounts of information every day as a result of its large customer base. Business professionals and decision-makers emphasized that maintaining existing clients is less expensive than recruiting new ones. Business analysts and customer relationship management (CRM)need to know the reasons why customers leave and the behavior patterns from earlier churn consumer’s data. Today, there is a problem with customer churn examination and prediction in the telecom industry since it is crucial for the sector to examine customer behavior to identify those who are going to stop their subscriptions. Customer retention could be increased by utilizing detection system to detect consumer behavior. Recent advancements in machine learning(ML)have made churn prediction more precise and practical. It is essential for identifying customers ready to leave using company’s products and services in the early stage. Hence in this work, predicting customers churn in telecommunication sector usingNaïveBayes(NB) model is presented. The performance of presentedNBalgorithmis evaluated using the parameters accuracy, precision, and sensitivity. The NB algorithm will have better performance than pervious approaches.
Urban traffic congestion and its association with gas station density: insights from Google Maps data
Authors: Hasabi Rafif , Kurniawan Robert , Sugiarto Sugiarto , Tri Wahyuni Ribut Nurul , Nurmawati Erna
Published: July 2024

Analyzing air pollution caused by traffic conditions requires appropriate indicators. Currently, air pollution indicators are approximated by the number of vehicles and gas station density. However, this approach cannot provide information at a smaller level. This study aims to identify traffic congestion distribution from Google Maps data as an alternative air pollution indicator at smaller level using map digitization method. In addition, this study examines its relationship with the existing indicator called gas station density. The results show that the digitization method can map the traffic congestion distribution where most areas in West, North, and Central Jakarta are classified as high traffic. In addition, this study found that there is a strong and significant relationship of 0.58277 between traffic congestion distribution and gas station density. Thus, traffic congestion distribution and gas station density data from Google Maps can be used as an indicator of traffic-related air pollution, especially land transportation. Furthermore, this research is expected to serve as a basis for the government in determining mitigation strategies related to traffic congestion and the resulting emissions.
Detection of cyberattacks using bidirectional generative adversarial network
Authors: Vallabhaneni Rohith , Vaddadi Srinivas A. , Somanathan Pillai Sanjaikanth E Vadakkethil , Addula Santosh Reddy , Ananthan Bhuvanesh
Published: July 2024

Due to the progress of communication technologies, diverse information is transmitted in distributed systems via a network model. Concurrently, with the evolution of communication technologies, the attacks have broadened, raising concerns about the security of networks. For dealing with different attacks, the analysis of intrusion detection system (IDS) has been carried out. Conventional IDS rely on signatures and are time-consuming for updation, often lacking coverage for all kinds of attacks. Deep learning (DL), specifically generative methods demonstrate potential in detecting intrusions through network data analysis. This work presents a bidirectional generative adversarial network (BiGAN) for the detection of cyberattacks using the IoT23 database. This BiGAN model efficiently detected different attacks and the accuracy and F-score values achieved were 98.8% and 98.2% respectively.
Investigation of digital rupiah acceptance using UTAUT-3 model
Authors: Mau Bere Alejandro Billyjoe , Putra Richard Win , Wedari Linda Kusumaning
Published: July 2024

The adoption of central bank digital currencies (CBDC) has been popular in many countries, especially Indonesia which currently develops its own CBDC called digital rupiah, due to its potential benefits such as financial inclusion. Despite the potential benefits of digital rupiah, there is a lack of understanding regarding factors that affect digital rupiah user acceptance. This research aims to investigate the potential factor affecting the user acceptance of digital rupiah using the unified theory of acceptance and use of technology (UTAUT-3) model, incorporating awareness and privacy as additional variables. There are 218 respondents to this study from five provinces in Indonesia: Jakarta, West Java, East Java, Central Java, and Yogyakarta. The data were analyzed using the SEM-PLS method. The results of this study found that performance expectancy, effort expectancy, habit, personal innovativeness, and awareness are the significant factors that affect the behavioral intention of digital rupiah meanwhile facilitating condition, habit, personal innovativeness, and behavioral intention are the factors that significantly affect the use behavior of digital rupiah. This study identifies key factors influencing the user acceptance of the digital rupiah, providing valuable insights for stakeholders seeking to promote its adoption and use in Indonesia.
Deep learning-based digitization of Kurdish text handwritten in the e-government system
Authors: Shareef Shareef Maulod , Ali Abbas Mohamad
Published: July 2024

Many government institutions in developing countries such as the Kurdistan Region of Iraq (KRI) keep a variety of paper-based records that are available in printed or handwritten format. The need for technology that turns handwritten writing into digital text is therefore highly demanded. E-government in developed and developing countries is a crucial facilitator for the provision of such services. This paper aims to develop a deep learning model based on the mask region convolutional neural network (mask-RCNN) to effectively digitize kurdish handwritten text recognition (KHTR). In this research, typical datasets, which includes the isolated handwritten Central Kurdish character images, an extensive database of 40,410 images, and 390 native writers have been produced to determine the developed approach’s performance in terms of identification rates. This approach achieves adequate outcomes in terms of training time and accuracy. The proposed model gives higher performance for detection, localization, and recognition when using a dataset containing many challenges, the results were 80%, 96%, and 87.6 for precision, recall, and F-score respectively. The findings revealed that the proposed model obtained better results compared to other similar works. The accuracy of optical character recognition (OCR) is more than 99%.
Analyzing electroencephalogram signals with machine learning to comprehend online learning media
Authors: Venu Vasantha Sandhya , Moorthy Chellapilla V. K. N. S. N. , Patil Preeti S. , Kale Navnath D. , Andhare Chetan Vikram , Tripathi Mukesh Kumar
Published: July 2024

In E-learning, evaluating students' comprehension of lecture video content is significant. The surge in online platform usage due to the pandemic has been remarkable, but the pressing issue is that learning outcomes still need to match the growth. Addressing this, a scientific system that gauges the comprehensibility of lecture videos becomes crucial for the effective design of future courses. This research paper is based on a cognitive approach utilizing EEG signals to determine student's level of comprehension. The study involves the design, evaluation, and comparison of multiple machines learning models, aiming to contribute to developing an efficient learning system. Fifteen distinct machine learning (ML) classifiers were implemented, among them AdaBoost (ADA), gradient boosting (GBC), extreme gradient boosting (XGboost), extra trees (ET), random forest (RF), light gradient boosting machine (light gum), and decision tree (DT) algorithms standouts. The DT exhibited exceptional performance across metrics such as area under the curve (AUC), accuracy, recall, F1 score, Kappa, precision, and matthews correlation coefficient (MCC). It achieved nearly 1.0 in these metrics while taking a short training time of only 1.7 seconds. This reveals its potential as an efficient classifier for electroencephalography (EEG) datasets and highlights its viability for practical implementation.
Improvement of horizontal streak on disparity map thru parameter optimization for stereo vision algorithm
Authors: Yeou Wei Melvin Gan , Hamzah Rostam Affendi , Nik Anwar Nik Syahrim , Herman Adi Irwan , Jamil Alsayaydeh Jamil Abedalrahim
Published: July 2024

In this paper, an improved local based stereo vision disparity map (SVDM) algorithm is proposed. The proposed local based SVDM algorithm include four stages and they are matching cost computation, cost aggregation disparity optimization and disparity refinement. The matching cost computation started by combining pixel to pixel matching techniques, which are absolute difference (AD) and gradient matching (GM) in producing the initial disparity map. Next, the cost aggregation uses minimum spanning tree (MST) segmentation, which equipped with edge preserving properties and noise filtering. Then, disparity optimization uses local approach with winner-take-all (WTA) technique. At the final stage, disparity refinement uses bilateral filter (BF) with weighted median (WM), which can improve the disparity map through noise removing and edges preserving. Then, the research continues to optimize the proposed local based SVDM algorithm through parameters optimization in obtaining the final disparity map. Here, multiple parameters from the proposed SVDM algorithm are manipulated and they are constant values for GM and several constant parameters in BF. By selecting the optimum parameter values, the performance of the proposed SVDM algorithm increased, especially robustness towards the horizontal streaks.
A general framework for metaverse based on parallel computing and HPC
Authors: Al Khaldy Mohammad Ali , Al-Qerem Ahmad , Aldweesh Amjad , Alauthman Mohammad
Published: July 2024

As virtual and actual universes merge inside the creating metaverse, requests have pointedly ascended for continuous, intuitive, and intense encounters. The ability of the metaverse to effectively analyze and render complicated links and information supplied by clients is critical for realizing that goal. These demanding computational demands are starting to be supported by parallel processing, and high-performance computing (HPC) is beyond uncertainty key to this domain. The integrative framework presented in this paper addresses the core challenges of inertness, flexibility, and ease of use while integrating equal registration into the metaverse. The system enables prompt handling of client actions and quick response times by distributing calculations over multiple processors, which is essential for the seamless client experience. It also manages the vast amount of metaverse material and interactions as well as the various data processing needs. The paper looks at intrinsic equal processing difficulties in this unique climate, including creating versatile and energy-effective equal calculations that consider load adjusting and asset designation. It features the need to democratize equal figuring assets to produce metaverse extension while accentuating the significance of information protection and security conventions in multi-client settings. The cooperative energy between metaverse development and equal registering progressions vows to push limits, empowering remarkable degrees of virtual submersion and collaboration.
Secured web application based on CapsuleNet and OWASP in the cloud
Authors: Vallabhaneni Rohith , Somanathan Pillai Sanjaikanth E. Vadakkethil , Vaddadi Srinivas A. , Addula Santosh Reddy , Ananthan Bhuvanesh
Published: July 2024

The tremendous use of sensitive and consequential information in the advanced web application confronts the security issues. To defend the web application while it processing the information must requires the security system. The detection of attacks of web is made by the payload or HTTP request-based detection in association with the scholars. Some of the scholars provide secured attack model detection; however, it fails to achieve the optimal detection accuracy. In concern with these issues, we propose an innovative technique for the attack detection the web applications. The proposed attack detection is based on the novel deep CapsuleNet based technique and the process begins with pre-processing steps known as decoding, generalization, tokenization/standardization and vectorization. After the pre-processing steps the information are passed to deep CapsuleNet for extracting the features for attaining the temporal dependencies from the sequential data. The subtle patterns in the information also detected using the proposed work. Simulation is effectuated to demonstrate the effectiveness of the proposed work and compared with other existing works. Our proposed system provides better accuracy in detecting the attacks than the state-of-art works.
Network load balancing and data categorization in cloud computing
Authors: Komathi Arunachalam , Kishore Somala Rama , Kishore Somala Rama , Velmurugan Athiyoor Kannan , Velmurugan Athiyoor Kannan , Pavithra Maddipetlolu Rajendran , Pavithra Maddipetlolu Rajendran , Selvaraj Yoganand , Selvaraj Yoganand , Begum Akbar Sumaiya , Begum Akbar Sumaiya , Muthukumaran Dhakshnamoorthy , Muthukumaran Dhakshnamoorthy
Published: July 2024

Cloud computing (CC) is rising quickly as a successful model presenting an on-demand structure. In the CC, the present investigation shows that load-balancing methods established on meta-heuristics offer better solutions for appropriate scheduling and allotment of resources. Conversely, several traditional approaches believe in only some quality of service (QoS) metrics and reject several significant components. Network load balancing and data categorization (NBDC) is proposed. This approach aims to enhance load balancing in the cloud field. This approach consists of two phases: the support vector machine (SVM) algorithm-based data categorization and the ant colony optimization (ACO) algorithm for distributing the network load on the virtual machine (VM). The SVM algorithm performs several data formats, such as text, image, audio, and video, resultant data class that offers high categorization accuracy in the cloud. The ACO algorithm reaches an efficient load balancing based on the time of execution (TE), time of throughput (TT), time of overhead (TO), time of optimization, and migration count (MC). Simulation results related to the baseline approach demonstrate an enhanced system function in terms of service level agreement violation, throughput, execution time, energy utilization, and execution time.
Credit card fraud detection with advanced graph based machine learning techniques
Authors: Renganathan Krishna Kumari , Karuppiah Janaki , Pathinathan Mahimairaj , Raghuraman Sudharani
Published: July 2024

In the realm of credit card fraud detection, the landscape is continually evolving, demanding innovative approaches to stay ahead of increasingly sophisticated fraudulent activities. Our research pioneers a groundbreaking methodology that amalgamates the power of bipartite graph visualization with advanced machine learning techniques. This fusion yields a comprehensive framework capable of effectively evaluating the efficacy of a random forest classifier in uncovering fraudulent credit card transactions. Our study showcases the compelling application of this methodology, offering a paradigm shift in how we analyze and understand credit card fraud detection systems. By seamlessly integrating machine learning algorithms with network analysis, we provide a holistic view of the data, unveiling intricate patterns hidden within. At the heart of our approach lies the innovative use of bipartite graphs, which serve as a dynamic visual bridge between model predictions and real-world outcomes. This visual representation not only enhances interpretability but also facilitates a deeper understanding of the classifier’s performance. By visually mapping the relationships between transactions and their respective classifications, our methodology offers actionable insights into both successful detection and potential areas for improvement. Empowering analysts and stakeholders, our approach facilitates informed decision-making by enabling them to fine-tune model parameters and enhance the overall effectiveness of fraud detection systems. Through this synergy between cutting-edge machine learning and network analysis techniques, we provide a powerful tool to combat the critical challenge of credit card fraud prevention. Step into the future of fraud detection with our innovative methodology, where every transaction is scrutinized with precision, and where security is not just a possibility, but a promise fulfilled.
Feature selection technique on convolutional neural network – multilabel classification task
Authors: Hayami Regiolina , Yusoff Nooraini , Daud Kauthar Mohd , Mukhtar Harun , Al Amien Januar
Published: July 2024

Automated text-based recommendation, an artificial intelligence development, finds application in document analysis like job resumes. The classification of job resumes poses challenges due to the ambiguity in categorizing multiple potential jobs in a single application file, termed multi-label classification, deep learning, particularly convolutional neural networks (CNN), offers flexibility in enhancing feature representations. Despite its robust learning capabilities, the black-box design of deep learning lacks interpretability and demands a substantial number of parameters, requiring significant computational resources. The primary challenge in multilabel learning is the ambiguity of labels not fully explained by traditional equivalence relations. To address this, the research employs feature selection techniques, specifically the Chi-square method. The goal is to reduce features in deep learning models while considering label relevance in multi-label text classification, easing computational workload while preserving model performance. Experimental tests, both with and without the Chi-square feature selection technique on the dataset, underscore its substantial impact on the classification model's ability. The conclusion emphasizes the influence of the Chi-square feature selection technique on performance and computational time. In summary, the research underscores the importance of balancing computational efficiency and model interpretability, especially in complex multi-label classification tasks like job applications.
The Influence of Leverage, Liquidity and Profitability toward Tax Avoidance in Property and Real Estate Companies Listed on Indonesia Stock Exchange
Authors: Ciptawan  , Wenny
Published: July 2024

Tax avoidance is an effort to avoid tax which have an impact on tax liabilities that are carried out by still complying in the tax provisions and do not violate the tax provisions predetermined. The objective of this research is to analyze whether Leverage, Liquidity and Profitability have a significant influence toward Tax Avoidance in Property and Real Estate Companies Listed on the Indonesia Stock Exchange from the year 2020-2022. This research method is using quantitative approach and collection of secondary data through Financial Report listed on the Indonesia Stock Exchange. All of the Property and Real Estate Companies listed on the Indonesia Stock Exchange are the population in this research. 15 companies are taken as the number of samples through purposive sampling method and result in 45 companies in total with three year observations. The data analysis method in this research includes: descriptive statistics, normality test, multicollinearity test, heteroscedasticity test, autocorrelation test, multiple linear regression analysis, t-test, f-test, and coefficient of determination. Based on the data analysis, the findings of this research shows that Leverage partially does not have a significant influence toward tax avoidance. Liquidity partially does not have a significant influence toward tax avoidance. Profitability partially has a significant influence toward tax avoidance. Leverage, Liquidity and Profitability simultaneously have a significant influence toward tax avoidance.
Survival Analysis of Lung Cancer Patients at Dr. Saiful Anwar Hospital
Authors: Pratiwi Suryanti Dwi , Setyawan Ungky Agus , Falyani Silvy Amalia , Permatasari Adinda Pramitra , Santosa Andrew , Febriawati Juwita , Prasetyo Kevin Wahyudy , Yokanan
Published: July 2024

Background: Comprising 14% of all cancer, lung cancer is the main cause of cancer-related mortality worldwide. Previous studies showed various findings regarding the survival rate of lung cancer, and factors significantly correlated with survival rate in lung cancer patients.
Aim: This research aims to identify the profile and survival rate of lung cancer patients receiving chemotherapy in Saiful Anwar General Hospital from 2018 to 2020.
Methods: This cohort retrospective study used medical records of patients diagnosed with lung cancer who underwent chemotherapy from 2018 to 2020. Data analysis was done using the Mantel Cox log rank test. Survival data shown as progression free survival (PFS) and overall survival (OS) were shown using Kaplan-Meier curves.
Results: Subjects were largely male (73,2%). Adenocarcinoma is the most often type of lung cancer found (59,8%) with wild type EGFR mutation being the most prevalent (20,6%). Carboplatin – Paclitaxel is the most widely used chemotherapy regimen (54,6%). Survival analysis showed a significant correlation between age, OS, and PFS (p < 0,05)
Conclusion: Various factors such as gender, cancer cell type, EGFR mutation, and chemotherapy regimen were not found to be related to survival rate in lung cancer patients. A significant correlation between age and survival rate showed potential in utilizing age as a predictor of survival rate in lung cancer patients.
Comentário sobre leis dos bens de raiz das ordens religiosas no Brasil
Authors: Malheiro Maria Cecília Stávale
Published: July 2024

The portuguese legislation that was applied to Colonial Brazil, was based upon Philipine Ordinances, code published in 1603. There was a great number of extraordinary laws, completing or opposing the Ordinances. These laws tried to adapt the Code to the real situations, either in Portugal or in Brazil. The Philipine Code remained here for three centuries, from 1603 to 1917, when the Brazilian Civil Code was enforced. The reasons of this long permanency could be in the centralist and absolutist character of this code. One of the aspects of this legislation is examined here, the landed property belonging to the religious orders in Brazil. The text of the Ordinances was ratified and completed by means of extraordinary laws, since 1609, imposing limits upon the anlargement of these landed property of Religious Orders. This process reached at its maximum during Dom Joseph’s reign, in the second half of the eighteenth century. There are possibilities of a survey of the real estate of the religious orders during the Colonial Period, through the examination the official correspondence of the documents requested by the Metropolitan Government.
Admixture Mapping of Chronic Kidney Disease and Risk Factors in Hispanic/Latino Individuals From Central America Country of Origin
Authors: Horimoto Andrea R.V.R. , Sun Quan , Lash James P. , Daviglus Martha L. , Cai Jianwen , Haack Karin , Cole Shelley A. , Thornton Timothy A. , Browning Sharon R. , Franceschini Nora
Published: July 2024

BACKGROUND:

Chronic kidney disease (CKD) is highly prevalent in Central America, and genetic factors may contribute to CKD risk. To understand the influences of genetic admixture on CKD susceptibility, we conducted an admixture mapping screening of CKD traits and risk factors in US Hispanic and Latino individuals from Central America country of origin.

METHODS:

We analyzed 1023 participants of HCHS/SOL (Hispanic Community Health Study/Study of Latinos) who reported 4 grandparents originating from the same Central America country. Ancestry admixture findings were validated on 8191 African Americans from WHI (Women’s Health Initiative), 3141 American Indians from SHS (Strong Heart Study), and over 1.1 million European individuals from a multistudy meta-analysis.

RESULTS:

We identified 3 novel genomic regions for albuminuria (chromosome 14q24.2), CKD (chromosome 6q25.3), and type 2 diabetes (chromosome 3q22.2). The 14q24.2 locus driven by a Native American ancestry had a protective effect on albuminuria and consisted of 2 nearby regions spanning the
RGS6
gene. Variants at this locus were validated in American Indians. The 6q25.3 African ancestry–derived locus, encompassing the
ARID1B
gene, was associated with increased risk for CKD and replicated in African Americans through admixture mapping. The European ancestry type 2 diabetes locus at 3q22.2, encompassing the
EPHB1
and
KY
genes, was validated in European individuals through variant association.

CONCLUSIONS:

US Hispanic/Latino populations are culturally and genetically diverse. This study focusing on Central America grandparent country of origin provides new loci discovery and insights into the ancestry-of-origin influences on CKD and risk factors in US Hispanic and Latino individuals.
GABA levels decline with age: A longitudinal study
Authors: Zuppichini Mark D. , Hamlin Abbey M. , Zhou Quan , Kim Esther , Rajagopal Shreya , Beltz Adriene M. , Polk Thad A.
Published: July 2024

One factor that might contribute to functional deterioration in healthy older adults is a decline in the brain’s major inhibitory neurotransmitter, gamma-aminobutyric acid (GABA). Previous studies have reported mixed results regarding whether GABA declines in healthy aging. These previous studies were cross-sectional and therefore cannot provide insight into GABA changes over time within aging individuals. Furthermore, aging is associated with gray and white matter atrophy that may confound age-related differences in GABA. In the present study, we utilized a repeated-measures, longitudinal design and MR spectroscopy to measure GABA levels in bilateral auditory, sensorimotor, and ventrovisual voxels of interest (VOI) in 30 healthy older adults at two time points a few years apart. Furthermore, we applied two of the most common tissue correction strategies to control for the effects of tissue composition on GABA estimates. Results from mixed-effects models showed that longitudinal change in age is a significant predictor of tissue-corrected longitudinal change in GABA levels: as age increases, GABA declines. In contrast, there was no cross-sectional effect of age on GABA in our sample (e.g., the oldest old did not have lower GABA levels than the youngest old). In conclusion, results from this study provide support for within-person, age-related declines in GABA over time, even after controlling for tissue composition.
The impact of functional correlations on task information coding
Authors: Ito Takuya , Murray John D.
Published: July 2024

State-dependent neural correlations can be understood from a neural coding framework. Noise correlations—trial-to-trial or moment-to-moment covariability—can be interpreted only if the underlying signal correlation—similarity of task selectivity between pairs of neural units—is known. Despite many investigations in local spiking circuits, it remains unclear how this coding framework applies to large-scale brain networks. Here, we investigated relationships between large-scale noise correlations and signal correlations in a multitask human fMRI dataset. We found that task-state noise correlation changes (e.g., functional connectivity) did not typically change in the same direction as their underlying signal correlation (e.g., tuning similarity of two regions). Crucially, noise correlations that changed in the opposite direction as their signal correlation (i.e., anti-aligned correlations) improved information coding of these brain regions. In contrast, noise correlations that changed in the same direction (aligned noise correlations) as their signal correlation did not. Interestingly, these aligned noise correlations were primarily correlation increases, suggesting that most functional correlation increases across fMRI networks actually degrade information coding. These findings illustrate that state-dependent noise correlations shape information coding of functional brain networks, with interpretation of correlation changes requiring knowledge of underlying signal correlations.
Evidence on the origin of Narcissus ×weickertii (Amaryllidaceae), an endemic daffodil of the SW Iberian Peninsula
Authors: DÍAZ LIFANTE ZOILA , DECENA RODRÍGUEZ MARÍA ÁNGELES , ANDRÉS CAMACHO CRISTINA , CABRERA CABALLERO ADORACIÓN , SÁNCHEZ GULLÓN ENRIQUE , SANTOS GALLY ROCÍO , ARROYO JUAN
Published: July 2024

The hypothesis regarding the origin of Narcissus ×weickertii through hybridization of its putative parent species, N. papyraceus and N. gaditanus, is investigated in populations from the southwestern of the Iberian Peninsula. Multivariate analyses of quantitative morphological characters in both vegetative and flower structures, coupled with a descriptive analysis of qualitative traits, reveal that N. ×weickertii displays an intermediate morphology between its putative parents. Several anatomical traits of the leaf and scape provide additional support for its hybrid nature. Karyotype analyses show that N. ×weickertii has a 2n=21 karyotype, which falls between those of the putative parent species, with several chromosomes showing an inability to pair. Fluorescence in situ hybridization procedures, encompassing both FISH (targeting NOR regions) and GISH (using total DNA from the putative parents), yield conclusive evidence regarding the hybrid nature of N. ×weickertii and confirm the hypothesis about the identity of their putative parent species. Furthermore, phylogenetic relationships suggest that N. gaditanus likely served as the maternal parent, while N. papyraceus acted as the paternal species, maintaining the hybrid species the style polymorphism of their parent species. The degree of viability observed in the N. ×weickertii pollen grains might indicate that it could bear some possibilities of sexual reproduction beyond the typical vegetative propagation of many described Narcissus hybrid species.
The chitons of the family Leptochitonidae (Mollusca: Polyplacophora) from the Emperor Seamounts, Pacific Ocean
Authors: SIRENKO BORIS
Published: July 2024

A small number of chitons collected from three Emperor Seamounts was studied. Three species were identified. Of these, one species, Leptochiton milwaukensis n. sp., is new. The new species is distinguished by a unique sculpture of the central area of the intermediate valves, with narrowed pores of micraesthetes on the tegmentum granules and the shape of central teeth of the radula. For the first time three specimens of Belknapchiton belknapi were collected from the bathyal zone near the biogeographic border between the boreal and the tropical realms.
Northern hemisphere land-atmosphere feedback from prescribed plant phenology in CESM
Authors: Li Xiaolu , Ault Toby , Richardson Andrew D. , Frolking Steve , Herrera Dimitris A. , Friedl Mark A. , Carrillo Carlos M. , Evans Colin P.
Published: July 2024

Plant phenology influences both the terrestrial carbon cycle and land-atmosphere interactions, and therefore can potentially modify large-scale circulations in the atmosphere. However, considerable discrepancies are present among models and between model simulations and observations of plant phenology, adding large uncertainties to future climate projections. Here we modified plant phenology in the Northern Hemisphere in the Community Earth System Model and conducted simulations to characterize how differences in plant phenology influence land-atmosphere coupling. Plant phenology changes the land surface and land-atmosphere interactions by directly modulating absorbed solar radiation and evapotranspiration and indirectly modifying cloud feedback and snow-albedo feedback. Over the Northern Hemisphere, the largest effects occur from March to June when seasonal deciduous phenology is modified from satellite-derived values to model simulations, which results in a >3K increase in surface temperature that propagates to 500hPa (~5km height). Phenology-induced changes in canopy evapotranspiration and surface temperature depend on soil moisture availability during the growing season. Surface temperature decreases significantly due to increasing latent heat flux and cloud reflection where soil moisture is abundant, while soil moisture control over evapotranspiration increases and surface temperature remains little-changed or even increases in more arid regions. Characterizing the influence of phenology on biogeophysical processes is critical, as significant impacts are present both at the land surface and in the atmospheric layers above.
Changes of Intense Extratropical Cyclone Deepening Mechanisms in a Warmer Climate in Idealized Simulations
Authors: Chen Ting-Chen , Braun Christoph , Voigt Aiko , Pinto Joaquim G.
Published: July 2024

To understand how extratropical cyclones (ETCs) may change in a warmer climate, we conduct idealized baroclinic life cycle simulations using the Icosahedral Nonhydrostatic (ICON)–NWP model with varied initial conditions. With respect to a present-day climate, two experiments are highlighted: a 4-K uniform warming and a more realistic late twenty-first-century warming pattern projected by a Coupled Model Intercomparison Project, version 6 (CMIP6), model. Different ETC deepening mechanisms, especially by diabatic processes, are quantified via the pressure tendency equation analysis, and the horizontal model resolution dependency is examined by contrasting coarse-grid (80 km) and convection-permitting (2.5 km) simulations. While our simulated ETCs are primarily baroclinically driven, dominated by the horizontal warm-air advection in the air column above the surface low, such an effect only strengthens by 10% in both warming experiments. However, the direct contribution of diabatic heating to surface pressure drop almost doubles, which likely feeds back positively to horizontal warm-air advection. Although their combined response to warming is pronounced, it is largely offset by the strengthened adiabatic cooling (17%) due to enhanced upward motions in warmer and moister ETCs, leading to a marginal ETC deepening at maturity (lowers by ∼1.5–4 hPa). Nevertheless, the near-surface impacts strongly increase, particularly the local extreme precipitation (up to 56%). The convection-permitting and the coarse-grid simulations show qualitatively consistent ETC responses to global warming. We suggest that the systematically weaker ETCs (with higher central pressure) in 2.5-km compared to 80-km simulations might be primarily caused by model uncertainty in representing the convective-diabatic heating over the warm front near the cyclone core.
Exploring the Metaverse from a Legacy Company Perspective: A Capabilities-Based View
Authors: Benassi Mario , Rialti Riccardo
Published: July 2024

Legacy companies are firms that rely on stable business models, established internal processes, and familiar technologies. Existing research acknowledges that legacy companies need to explore new technologies, but scholars largely neglect to analyze how this process unfolds. This knowledge gap is particularly relevant when disruptive technological paradigms are about to modify the business arena, as in the case of the metaverse. This article presents an inductive qualitative approach to understanding why and how legacy companies explore the metaverse. The metaverse exploration process is purpose-driven and largely incremental. Capabilities co-specialization facilitates the use of complementary resources for new purposes, subsequently determining the path toward different strategic fit configurations.
Unexpected regulatory functions of cyprinid Viperin on inflammation and metabolism
Authors: Chaumont Lise , Jouneau Luc , Huetz François , van Muilekom Doret R. , Peruzzi Mathilde , Raffy Claudine , Le Hir Jérôme , Minke Jules , Boudinot Pierre , Collet Bertrand
Published: July 2024

Background

Viperin, also known as radical S-adenosyl-methionine domain containing protein 2 (RSAD2), is an interferon-inducible protein that is involved in the innate immune response against a wide array of viruses. In mammals, Viperin exerts its antiviral function through enzymatic conversion of cytidine triphosphate (CTP) into its antiviral analog ddhCTP as well as through interactions with host proteins involved in innate immune signaling and in metabolic pathways exploited by viruses during their life cycle. However, how Viperin modulates the antiviral response in fish remains largely unknown.

Results

For this purpose, we developed a fathead minnow (Pimephales promelas) clonal cell line in which the unique viperin gene has been knocked out by CRISPR/Cas9 genome-editing. In order to decipher the contribution of fish Viperin to the antiviral response and its regulatory role beyond the scope of the innate immune response, we performed a comparative RNA-seq analysis of viperin−/− and wildtype cell lines upon stimulation with recombinant fathead minnow type I interferon.

Conclusions

Our results revealed that Viperin does not exert positive feedback on the canonical type I IFN but acts as a negative regulator of the inflammatory response by downregulating specific pro-inflammatory genes and upregulating repressors of the NF-κB pathway. It also appeared to play a role in regulating metabolic processes, including one carbon metabolism, bone formation, extracellular matrix organization and cell adhesion.