Data System Research Articles

The Implementation of Basic Education Data System at Muhammadiyah 9 Elementary School, Palembang

This research aims to examine the implementation of the DAPODIK program at Muhammadiyah 9 Elementary School in Palembang, including its supporting and inhibiting factors. It utilizes a qualitative descriptive approach with informants comprising the School Principal, Administrative Staff, and Operators. Data collection techniques include observation, interviews, and documentation, while data analysis involves data reduction, data presentation, and drawing conclusions. Data validity was ensured through technique and source triangulation. Findings indicate that the implementation of the DAPODIK program at SD Muhammadiyah 9 Palembang is fairly effective, focusing on directives, coordination, communication, and motivation. Factors supporting this include adequate facilities, competent human resources, and reliable internet connectivity. Conversely, challenges include data synchronization limitations and server downtime during DAPODIK updates. In conclusion, the DAPODIK program at SD Muhammadiyah 9 Palembang is generally effective, facilitated by effective guidance, motivation, and efficient coordination and communication.

DNA barcode library of Portuguese water mites, with the descriptions of two new species (Acari, Hydrachnidia)

This study presents the first results from the analysis of water mites collected in Portugal as part of the Biodiversity Genomics Europe project. 307 COI DNA barcodes clustered into 75 BINs are provided, with 38 BINs being unique and deposited for the first time in the Barcode of Life Data Systems (BOLD). 65 species have been identified, of which 36 are new to the water mite fauna of Portugal. Two species, Torrenticola soniae Pešić, sp. nov. and T. elisabethae Pešić, sp. nov. (Torrenticolidae), are described as new to science. 47% of the water mite species currently known from Portugal now have reference barcodes in BOLD. High intraspecific distances were recorded for some species, suggesting the presence of cryptic diversity and species complexes that needs further study. Our results improve the DNA barcode reference database for Portuguese water mites, enhancing species identification accuracy and stimulating future investigation.

Diagnostic significance of peritumoral enhancement in distinguishing between muscle-invasive and non-muscle-invasive bladder cancer.

The aim of this study was to assess the prevalence of peritumoral enhancement (PTE) in patients with muscle-invasive bladder cancer (MIBC) and non-MIBC (NMIBC) and to propose a modified diagnostic criterion for Vesical Imaging Reporting and Data System (VI-RADS) that incorporates PTE. This retrospective study included 95 patients with bladder cancer (age, 72 ± 11; 77 men; 36 MIBCs and 59 Non-MIBCs) who underwent multiparametric MRI in our referral center between 2011 and 2023. The images were interpreted by four radiologists. The readers classified the possibility of muscle layer invasion into categories 1-5, based on the VI-RADS categorical diagnostic criterion. PTE was defined as a linear contrast enhancement observed at the edge of tumor invasion which is convex outward from the normal bladder wall and contrasts more than the normal muscle layer and tumor. A modified VI-RADS that upgrades the final VI-RADS category to 4 if PTE is present when the original VI-RADS category is 3 or less was proposed. The frequency of PTE in the MIBC and NMIBC groups was compared using the Fisher's exact test. Sensitivity and specificity for the diagnosis of MIBC were compared with the original VI-RADS using McNemar test. Pathologic diagnosis was used as the reference standard. PTE was present in 70-81% (25/36-29/36) of MIBC and absent in 92-98% (54/59-58/59) of non-MIBC. For all readers, the PTE was significantly more frequent (p < 0.001) in the MIBC group than the NMIBC group. The sensitivities of modified VI-RADS (75.0-86.1%) were significantly higher than those of original VI-RADS (41.7-55.6%) (p = 0.002-0.008). The specificity of modified VI-RADS (91.5-98.3%) were not statistically different from original VI-RADS (98.3-100%). In conclusion, PTE is a highly specific finding for MIBC. modified VI-RADS incorporating PTE increases sensitivity for MIBC.

Correlation of Sarcopenia with Coronary Artery Disease Severity and Pericoronary Adipose Tissue Attenuation: A Coronary CT Study

Objective: To investigate the association between sarcopenia, as appraised with CT-derived muscle metrics, and cardiovascular status, as assessed via coronary CT angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS) and with pericoronary adipose tissue (pCAT) metrics. Methods: A retrospective observational study conducted on patients who underwent CCTA. The cross-sectional area (CSA) and attenuation values of the paravertebral muscles at the T8 level and the pectoralis major muscles at the T6 level were measured. The patient height was employed for the normalization of the skeletal muscle CSA. The pCAT attenuation around the coronary arteries was assessed, and the CAD severity was graded using the CAD-RADS reporting system. Regression analyses were performed to assess the impact of demographics, clinical factors, and CT variables on the CAD-RADS and pCAT. Results: A total of 220 patients were included (132 males, median age 65 years). Regression analyses showed the associations of CAD with age and sex (p &lt; 0.001). Familiarity with CAD was related to the left anterior descending artery pCAT (p = 0.002) and circumflex artery pCAT (p = 0.018), whereas age was related to the left anterior descending artery pCAT (p = 0.032). Weak positive correlations were found between the lower muscle density and lower pCAT attenuation (ρ = 0.144–0.240, p &lt; 0.039). Conclusions: This study demonstrated weak associations between the sarcopenia indicators and the cardiovascular risk, as assessed by the CAD severity and pCAT inflammation. However, these correlations were not strong predictors of CAD severity, as age and traditional cardiovascular risk factors overshadowed the impact of sarcopenia in the cardiovascular risk assessment.

Probing clarity: AI-generated simplified breast imaging reports for enhanced patient comprehension powered by ChatGPT-4o

BackgroundTo assess the reliability and comprehensibility of breast radiology reports simplified by artificial intelligence using the large language model (LLM) ChatGPT-4o.MethodsA radiologist with 20 years’ experience selected 21 anonymized breast radiology reports, 7 mammography, 7 breast ultrasound, and 7 breast magnetic resonance imaging (MRI), categorized according to breast imaging reporting and data system (BI-RADS). These reports underwent simplification by prompting ChatGPT-4o with “Explain this medical report to a patient using simple language”. Five breast radiologists assessed the quality of these simplified reports for factual accuracy, completeness, and potential harm with a 5-point Likert scale from 1 (strongly agree) to 5 (strongly disagree). Another breast radiologist evaluated the text comprehension of five non-healthcare personnel readers using a 5-point Likert scale from 1 (excellent) to 5 (poor). Descriptive statistics, Cronbach’s α, and the Kruskal–Wallis test were used.ResultsMammography, ultrasound, and MRI showed high factual accuracy (median 2) and completeness (median 2) across radiologists, with low potential harm scores (median 5); no significant group differences (p ≥ 0.780), and high internal consistency (α > 0.80) were observed. Non-healthcare readers showed high comprehension (median 2 for mammography and MRI and 1 for ultrasound); no significant group differences across modalities (p = 0.368), and high internal consistency (α > 0.85) were observed. BI-RADS 0, 1, and 2 reports were accurately explained, while BI-RADS 3–6 reports were challenging.ConclusionThe model demonstrated reliability and clarity, offering promise for patients with diverse backgrounds. LLMs like ChatGPT-4o could simplify breast radiology reports, aid in communication, and enhance patient care.Relevance statementSimplified breast radiology reports generated by ChatGPT-4o show potential in enhancing communication with patients, improving comprehension across varying educational backgrounds, and contributing to patient-centered care in radiology practice.Key PointsAI simplifies complex breast imaging reports, enhancing patient understanding.Simplified reports from AI maintain accuracy, improving patient comprehension significantly.Implementing AI reports enhances patient engagement and communication in breast imaging.Graphical

Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.

Question answering (QA) systems for patient-related data can assist both clinicians and patients. They can, for example, assist clinicians in decision-making and enable patients to have a better understanding of their medical history. Substantial amounts of patient data are stored in electronic health records (EHRs), making EHR QA an important research area. Because of the differences in data format and modality, this differs greatly from other medical QA tasks that use medical websites or scientific papers to retrieve answers, making it critical to research EHR QA. This study aims to provide a methodological review of existing works on QA for EHRs. The objectives of this study were to identify the existing EHR QA datasets and analyze them, study the state-of-the-art methodologies used in this task, compare the different evaluation metrics used by these state-of-the-art models, and finally elicit the various challenges and the ongoing issues in EHR QA. We searched for articles from January 1, 2005, to September 30, 2023, in 4 digital sources, including Google Scholar, ACL Anthology, ACM Digital Library, and PubMed, to collect relevant publications on EHR QA. Our systematic screening process followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A total of 4111 papers were identified for our study, and after screening based on our inclusion criteria, we obtained 47 papers for further study. The selected studies were then classified into 2 non-mutually exclusive categories depending on their scope: "EHR QA datasets" and "EHR QA models." A systematic screening process obtained 47 papers on EHR QA for final review. Out of the 47 papers, 53% (n=25) were about EHR QA datasets, and 79% (n=37) papers were about EHR QA models. It was observed that QA on EHRs is relatively new and unexplored. Most of the works are fairly recent. In addition, it was observed that emrQA is by far the most popular EHR QA dataset, both in terms of citations and usage in other papers. We have classified the EHR QA datasets based on their modality, and we have inferred that Medical Information Mart for Intensive Care (MIMIC-III) and the National Natural Language Processing Clinical Challenges datasets (ie, n2c2 datasets) are the most popular EHR databases and corpuses used in EHR QA. Furthermore, we identified the different models used in EHR QA along with the evaluation metrics used for these models. EHR QA research faces multiple challenges, such as the limited availability of clinical annotations, concept normalization in EHR QA, and challenges faced in generating realistic EHR QA datasets. There are still many gaps in research that motivate further work. This study will assist future researchers in focusing on areas of EHR QA that have possible future research directions.

Smart noise pollution monitoring system

Increasing pollution poses serious health risks, necessitating urgent control measures for a healthier future for all. Among various pollution types, sound pollution significantly impacts quality of life and well-being, especially in densely populated urban areas. This proposal describes the creation and deployment of an Internet of Things (IoT)-based noise pollution monitoring system intended to track sound levels in certain regions in real time. The core of this system comprises advanced sensors strategically placed to measure sound intensity accurately. These sensors can detect even minor variations in noise levels, providing precise and reliable data. After being gathered, the data is sent to a microcontroller, which effectively processes it. This microcontroller ensures the data is accurately captured and sent over the internet to an online server. Once the data reaches the online server, it becomes accessible for analysis and monitoring by authorities. The system's real-time data capability is crucial for timely intervention. Authorities can continuously monitor noise levels and identify trends or spikes in sound pollution, allowing them to address issues promptly. This feature is particularly beneficial for sensitive areas such as schools, hospitals, and residential zones, areas with heavy traffic, library, Where excessive noise can have severe health effects, including stress, sleep disturbances, and hearing loss. The system includes an alert mechanism that notifies relevant authorities when noise levels exceed predefined thresholds. These alerts enable quick responses to mitigate noise pollution. For instance, in a school, reducing noise levels can enhance the learning atmosphere and improve students' concentration. In hospitals, maintaining a quiet environment is crucial for patient recovery. The integration of IoT technology in this noise pollution monitoring system not only enhances efficiency but also provides a scalable solution adaptable to various urban and rural settings. This scalability ensures the system can be expanded to cover larger areas or multiple locations as needed.

AI-driven business analytics and decision making

The rapid advancement of Artificial Intelligence (AI) and Machine Language (ML) has revolutionized business analytics, transforming the way organizations make decisions. This paper explores the integration of AI-driven technologies into business analytics to enhance decision-making across various industries. By leveraging predictive and prescriptive analytics, AI enables organizations to not only analyse historical data but also forecast future trends, allowing for more informed, proactive strategies. Machine learning plays a pivotal role in automating data-driven decisions, offering real-time insights that help businesses respond quickly to changing market dynamics. This automation significantly reduces manual intervention, increases efficiency, and enhances the accuracy of predictions. The paper further discusses the integration of AI with Business Intelligence (BI) tools to deliver deeper insights from complex datasets in real time. These insights empower companies to optimize enterprise resources, improve supply chain management, and drive operational excellence. Case studies from AI-driven analytics within Systems, Applications, and Products in Data Processing (SAP) environments highlight the practical applications of AI in real-world business contexts, demonstrating its impact on decision-making and overall performance. The paper concludes with best practices for implementing AI in business analytics, focusing on data quality, system integration, and workforce readiness to embrace AI-enabled decision-making frameworks. The findings underscore the potential of AI as a game-changer in modern business landscapes, fostering smarter, faster, and more effective decision-making processes.

Advanced Detection Techniques to neutralize Supply Chain Cyber-attacks on small and medium businesses in the United States: The Machine Learning and AI Approach.

Cyber-attacks targeting supply chains represent a significant and increasing risk for small and medium-sized businesses (SMBs) in the United States. This paper examines the role of machine learning (ML) and artificial intelligence (AI) in detecting and mitigating these cybersecurity threats. Key challenges faced by SMBs include high implementation costs, a shortage of skilled personnel, inadequate data quality, and the complexity of integrating advanced technologies into existing systems. Despite these hurdles, the paper identifies strategic approaches that can empower SMBs to effectively leverage ML and AI, including affordable solutions, targeted training, and improved data management practices. It emphasizes the importance of addressing privacy and usability concerns to facilitate successful technology adoption. Recommendations include pursuing cost-effective, subscription-based AI and ML models, utilizing government incentives, and enhancing workforce skills through training and partnerships. Additionally, collaboration with larger organizations can improve data management and system efficacy. By implementing robust protective measures and increasing awareness through educational initiatives, SMBs can strengthen their cybersecurity posture and better navigate the evolving landscape of cyber threats.

INOVASI E-COMMERCE UNTUK OPTIMALISASI PROSES KONSUMEN, PRODUKSI, DAN PEMASARAN BAGI PELAKU UMKM

E-commerce technology enables efficient online transactions, providing customers with a convenient and efficient purchasing experience. In terms of production, e-commerce information systems can be integrated with production management, enabling monitoring of the production process, optimizing production efficiency and reducing the risk of stock shortages. E-commerce technology also opens up opportunities for more sophisticated marketing strategies, as MSMEs can utilize consumer data analysis, increasing customer appeal and ordering and sales. E-commerce technology facilitates direct interaction between consumers, fostering closer relationships and improving customer experience. The development of e-commerce technology has significantly increased the effectiveness and efficiency of MSMEs specifically in the fried chicken sector, enabling them to compete in the digital business era. The purpose of this study is to develop an E-Commerce information system in the "Maju Makmur" Kalasan fried chicken industry center that manages MSMEs fried chicken. The research method is a prototype model. This model starts from data collection, system design and testing. The stages of the prototyping development model consist of: communication, rapid planning, rapid modeling, construction, and delivery. The results of this study are a web-based system to optimize consumer, production, and marketing processes in order to increase operational efficiency and market access through an online platform.

Evaluation of Pharmacist-led Telehealth Medicare Annual Wellness Visits to Satisfy Quality Metrics and USPSTF Recommendations After Implementation of a Post-Visit Follow-Up Protocol.

Background: A Medicare Annual Wellness Visit (MAWV) serves Medicare patients by identifying and addressing gaps in preventive services and health screenings, often aligning with outpatient practice quality metrics. Objective: Evaluate an existing pharmacist-led MAWV telehealth service, determine the baseline quality metric satisfaction rate of telehealth MAWVs, and assess for improvement after implementing a post-MAWV follow-up protocol at a suburban, lower-income primary care clinic. Methods: This IRB-exempt, single-center retrospective chart review utilized the electronic health record at Christ Health Center, Birmingham, AL. From August 2020 through May 2022, 288 charts were assessed between 2 retrospective chart reviews that included patients 18 years or older with Medicare insurance and the ability to conduct a telehealth MAWV. The first chart review assessed metric and recommendation satisfaction within 12months of the visit. The second chart review was performed after follow-up protocol implementation to assess for additional improvement within 3 months of the visit. Results: The percentage of MAWV recommendations completed groups after implementing a follow-up protocol. For the first chart review, 57.1% of the assessed Health Resources and Services Administration (HRSA), Uniform Data System (UDS) quality metrics, and Centers for Medicare and Medicaid Services (CMS)-required MAWV components were satisfied from the first chart review compared to 53.3% of satisfied quality metrics post-protocol implementation in spite of a substantially shorter follow-up timeframe. Conclusion: Telehealth MAWVs improve preventive care and quality metric satisfaction for Medicare patients. Post-visit follow-up protocols enhance satisfaction rates. Pharmacist-led MAWVs foster interprofessional collaboration and comprehensive patient care.

Ovarian–Adnexal Reporting and Data System (O-RADS) MRI Scoring: Diagnostic Accuracy, Interobserver Agreement, and Applicability to Machine Learning

Abstract Objectives To evaluate the interobserver agreement and diagnostic accuracy of ovarian–adnexal reporting and data system magnetic resonance imaging (O-RADS MRI) and applicability to machine learning. Material and methods Dynamic contrast-enhanced pelvic MRI examinations 471 lesions were retrospectively analyzed and assessed by three radiologists according to O-RADS MRI criteria. Radiomic data were extracted from T2, and post-contrast fat-suppressed T1-weighted images. Using these data, an artificial neural network (ANN), support vector machine, random forest, and naive Bayes models were constructed. Results Among all readers, the lowest agreement was found for the O-RADS 4 group (kappa: 0.669 (95% confidence interval [CI] 0.634–0.733)), followed by the O-RADS 5 group (kappa: 0.709 (95% CI 0.678–0.754)). O-RADS 4 predicted a malignancy with an area under the curve (AUC) value of 74.3% (95% CI 0.701–0.782), and O-RADS 5 with an AUC of 95.5% (95% CI 0.932–0.972),(p &amp;lt; 0.001). Among the machine learning models, ANN achieved the highest success, distinguishing O-RADS groups with an AUC of 0.948, a precision of 0.861, and a recall of 0.824. Conclusion The interobserver agreement and diagnostic sensitivity of the O-RADS MRI in assigning O-RADS 4–5 were not perfect, indicating a need for structural improvement. Integrating artificial intelligence into MRI protocols may enhance their performance. Advances in knowledge Machine learning can achieve high accuracy in the correct classification of O-RADS MRI. Malignancy prediction rates were 74% for O-RADS 4 and 95% for O-RADS 5.

Inter-Reader Agreement for Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System Major Features and Final Categorization: A Subanalysis From a Prospective Multicenter Study.

Contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) is used to definitively diagnose hepatocellular carcinoma (HCC) in patients at risk. However, the user variability associated with CEUS LI-RADS has not been validated in North American and European patients. This study aims to evaluate the inter-reader agreements of CEUS LI-RADS features for, and final categorization of, HCC in patients at risk. This retrospective multicenter clinical study used the database of a previous prospective multinational study, evaluating the accuracy of CEUS LI-RADS for HCC diagnosis in patients at risk. All cases were first evaluated by a site physician performing/supervising the CEUS examination. Randomly selected cases were re-evaluated by a blinded central reader. Final diagnosis was confirmed with the reference standard, which was a composite of imaging tests and histology. Cohen's kappa test was used to evaluate inter-reader agreement. This study included 150 liver nodules and 58.0% (87/150) were confirmed as HCC, 4.7% (7/150) non-HCC malignancies, 22.7% (34/150) had no confirmed final diagnosis, and 14.7% (22/150) were nonmalignant. Inter-reader agreements were substantial for CEUS LI-RADS categorization (κ = 0.61; 95% confidence interval [CI]: 0.51-0.71) and major features assessment (ranged κ = 0.64-0.78), LR-5 (κ = 0.65; 95% CI: 0.52-0.77), and LR-M (κ = 0.67; 95% CI: 0.44-0.90), while for LR-1 and LR-2 categorization was almost perfect (κ = 0.85; 95% CI: 0.65-1.00). Our study reported a substantial inter-reader agreement for overall CEUS LI-RADS categorization, especially for LR-5 and LR-M, and major imaging features of HCC, further confirming CEUS LI-RADS as a valuable and reliable tool for diagnosis of HCC.

Role of Image-Guided Biopsy in Nonpalpable Breast Lesions: A Study in the Sub-Himalayan Region of North India

Background Nonpalpable breast lesions pose a challenge in their early diagnosis. Image-guided biopsy is preferred in these cases so that a pathological diagnosis of breast carcinoma is reached timely for a better prognosis as the disease has an increased chance of successful outcome with early identification and treatment. Objective The study aims at evaluating the role of stereotactic core needle biopsy (CNB) and percutaneous ultrasound-guided core needle biopsy (US-CNB) in diagnosing suspicious nonpalpable breast lesions. Methods Our study included 35 patients with nonpalpable breast lesions and having a Breast Imaging Reporting and Data System (BI-RADS) risk assessment category IV or V on mammography or sonography. These 35 lesions were subjected to percutaneous image-guided (stereotactic or US) biopsy for histopathological analysis. Results Out of a total of 35 cases, 17 were pathologically malignant (48.6%), with the most common subtype being invasive ductal carcinoma (82.3%). Twenty-nine cases underwent US-CNB, 16 (55.1%) of which were malignant and 13 (44.8%) were benign on histopathological evaluation (HPE). The remaining six cases, which on mammography showed no mass but suspicious malignant calcification only, were subjected to stereotactic CNB, out of which one (16.6%) was malignant and five (83.3%) were benign on HPE. Hence, the lesions visible on sonography were more likely to be malignant. Conclusion Sonography and mammography play a complimentary role in detecting breast carcinoma. Percutaneous biopsy under image guidance can be used as an accurate diagnostic alternative to open surgical excisional biopsy to avoid diagnostic delay.

Exploring the Security Challenges of an E-Voting System (EVS): A Case Study in the Bolgatanga Senior High School

The integration of electronic voting systems (EVS) into electoral processes promises increased efficiency, speed, and transparency. However, this technology also introduces a variety of security challenges that must be addressed to ensure the integrity of the voting process. This study explores the security vulnerabilities of an EVS, focusing on its implementation at Bolgatanga Senior High School (BIGBOSS). Through a case study approach, this research examines the potential threats to the administration of EVS, including issues related to data privacy, system integrity, authentication protocols, and susceptibility to cyber-attacks. A mixed-methods strategy was employed, utilizing surveys, interviews, and system tests to assess both technical vulnerabilities and user experiences. Key findings revealed Potential Voter Manipulation where unauthorized access could allow individuals to cast votes fraudulently; Insider Threats where trusted users exploiting their access could manipulate election outcomes; Weak Authentication; Lack of Robust Verification; Potential Physical Attack where individuals could vandalize machines and Vulnerabilities in Encryption which may expose votes, compromising ballot secrecy. The study recommends the implementation a blockchain-based technology , such as cryptographic techniques, consensus mechanisms, use of biometric verification to ensure the identity of voters as well as the use of digital signatures to verify the authenticity of electronic ballots and ensure that they haven't been altered during transmission, implementation of real-time monitoring of the e-voting system to detect and respond to security threats promptly” and also provide ongoing cybersecurity education and training to election officials, staff, and voters to raise awareness and reduce the risk of human error. These findings contribute to the ongoing discourse on EVS security, providing insights into practical measures for improving the reliability and safety of electronic voting systems in educational institutions and beyond.

Added Value of a Previous Exam Comparison in the Final Breast Imaging and Reporting Data System (BI-RADS®) Assessment in Screening Mammography

Added Value of a Previous Exam Comparison in the Final Breast Imaging and Reporting Data System (BI-RADS®) Assessment in Screening Mammography

A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.

Breast cancer remains a major global health concern. This study aimed to develop a deep-learning-based artificial intelligence (AI) model that predicts the malignancy of mammographic lesions and reduces unnecessary biopsies in patients with breast cancer. In this retrospective study, we used deep-learning-based AI to predict whether lesions in mammographic images are malignant. The AI model learned the malignancy as well as margins and shapes of mass lesions through multi-label training, similar to the diagnostic process of a radiologist. We used the Curated Breast Imaging Subset of Digital Database for Screening Mammography. This dataset includes annotations for mass lesions, and we developed an algorithm to determine the exact location of the lesions for accurate classification. A multi-label classification approach enabled the model to recognize malignancy and lesion attributes. Our multi-label classification model, trained on both lesion shape and margin, demonstrated superior performance compared with models trained solely on malignancy. Gradient-weighted class activation mapping analysis revealed that by considering the margin and shape, the model assigned higher importance to border areas and analyzed pixels more uniformly when classifying malignant lesions. This approach improved diagnostic accuracy, particularly in challenging cases, such as American College of Radiology Breast Imaging-Reporting and Data System categories 3 and 4, where the breast density exceeded 50%. This study highlights the potential of AI in improving the diagnosis of breast cancer. By integrating advanced techniques and modern neural network designs, we developed an AI model with enhanced accuracy for mammographic image analysis.

The association between mammographic breast density, epicardial adipose tissue volume and coronary artery disease

Abstract Background Dysfunctional adipose tissue is strongly linked to cardiometabolic disease. Epicardial adipose tissue (EAT) which is measurable on cardiac computed tomography (CT) is well correlated with cardiovascular disease (CVD), however it is not reported clinically. Breast mammography mandates reporting of breast density (BD), which is also a marker of breast adipose tissue quantity with less dense breasts indicating greater adipose tissue volume. Given the widespread use of screening mammography, this presents an opportunity to employ BD as a possible surrogate for CVD risk. Purpose To evaluate the correlation between BD, EAT and coronary artery disease (CAD). Methods Single-centre, retrospective, cross-sectional study, including women who had both clinically indicated coronary computed tomography angiogram (CCTA) and mammography. Epicardial adipose tissue volume (EATv) was quantified using semi-automated software (QFAT 2.0). BD was visually assessed by standard 4-level Breast Imaging-Reporting and Data System (BI-RADS) grade with grade A-B representing low density, and C-D representing high density. CAD was categorised as presence/absence of any coronary artery plaque, and CAD severity was quantified using the Coronary Artery Disease Reporting and Data Systems (CAD-RADS) score, with CAD-RADS 1-2 representing mild disease, CAD-RADS 3 moderate disease and CAD-RADS 4 severe disease. CAD analyses were adjusted for modifiable (hypertension, dyslipidaemia, diabetes, body mass index) and non-modifiable (age, past smoking, family history, breast cancer) risk factors. Logistic regression was performed with results presented as Odds Ratio (OR) and [95% Confidence Intervals]. Results Among 153 patients (mean age 62±10), 103 (67.3%) had low BD (high breast adiposity). Compared with high BD, low BD patients were older, had greater rates of hypertension, had significantly higher mean EAT (106.6 ± 43.0 vs 81.0 ± 31.6ml, p&amp;lt;0.001) (Figure 1), and body mass index (30.5 ± 6.3 vs 26.4 ± 5.4kg/m², p&amp;lt;0.001). Adjusted for age and hypertension, EATv remained independently predictive of low BD (OR:1.02 [1.01-1.03], p=0.006). Low BD made up a higher proportion of mild (76.5%), moderate (73.9%) and severe (80.0%) disease. Low BD was strongly associated with presence of CAD (prevalence 75% vs 48%, OR 3.21 [1.58-6.53], p=0.001) independent of EATv (p=0.25), and independent of modifiable (OR: 2.69 [1.24-5.92], p=0.012) and non-modifiable (OR: 2.42 [1.04-5.85], p=0.047) risk factors (Figure 2). Conclusions Low breast density is associated with higher EATv and independently associated with CAD presence beyond EATv and other risk factors. Consequently, mammographic breast density may act as an early risk identification tool for CAD in women.

Perancangan Aplikasi Sistem Absensi Guru Dan Karyawan Berbasis Web Sekolah Menengah Kejuruan

The development of information technology is quite extensive and global and has had a changing impact on all aspects ofhuman life. In the field of education, information technology is very necessary in order to support the performance ofeducation so that it can be useful in building an intelligent and competitive generation, one of which is at Global PersadaVocational School. Based on observations, Global Persada Vocational School does not yet have a teacher and employeeattendance system for processing teacher absence data. The obstacles that are often encountered in manual processing ofabsentee data are that it is very slow, ineffective, and makes it difficult for school principals to manage and check data onteachers who are present, and is even prone to losing absenteeism recap data. The aim of this research is to design andbuild a Web-based attendance system for processing teacher and employee attendance data for Global PersadaVocational School to make it easier to process teacher and employee attendance data. In building the teacher andemployee attendance system, the author used the SDLC method with the Waterfall model in designing the system andproduced a system with a simple interface that can help present accurate and relevant value data processing information.

Optimal PMU placement in the Southern Cameroon interconnected grid considering the effect of a group of ZIBs for complete observability and dynamic stability

In this paper, a solution for optimal PMU placement in the southern Cameroon interconnected grid (SIG) is proposed to the issue of dynamic stability within the network. The power system under consideration relies on SCADA parameter monitoring, which offers unsynchronized measurements and inconsistent state estimation of the grid. Moreover, SCADA system's data scan rate hinders its efficiency in capturing the dynamic and transient behaviours in the power grid. The Approach in this research is based on the integer linear programming (ILP) considering the effect of a group of zero-injection buses (ZIBs). Subsequently, a comparative analysis is conducted to evaluate the performance of the ILP method in terms of the number of PMUs, the number of buses observable from one or more nodes, and the sum of redundancy index (SORI) in comparison to other optimization algorithms. The results obtained using MATLAB 2020a demonstrate that the ILP method, considering the effect of a group of ZIBs and the SORI, provides the most optimal solution. Furthermore, a PMU-based dynamic stability assessment is performed, validating the improvement of the visibility of voltage signals.