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Enhanced photovoltaic panel diagnostics through AI integration with experimental DC to DC Buck Boost converter implementation
Clinical predictive model of new-onset atrial fibrillation in patients with acute myocardial infarction after percutaneous coronary intervention
A systematic review and meta-analysis on light therapy for sleep disorders in shift workers
Post-myocardial infarction ventricular septal defects: incidence and treatment trends during and after the COVID-19 pandemic
Global stability boundary analysis and verification of aviation pressure servo-controlled actuator system
Identification of deposits from modern and ancient large tsunamis by means of environmental DNA
AbstractWe examined the potential of environmental DNA (eDNA) for identifying tsunami deposits in the geological record using lake-bottom sediments in the Tohoku region, Japan. The presence of eDNA from marine organisms in a lacustrine event deposit provides very strong evidence that the deposit was formed by an influx of water from the ocean. The diverse DNA assemblage in the deposit formed by the 2011 Tohoku-oki tsunami included DNA of marine origin indicating that eDNA has potential as an identifying proxy for tsunami deposits. Subsequently, we examined the applicability of eDNA for recognizing paleo-tsunami events using the deposits formed by the 869 CE Jogan tsunami and a prehistoric event (2400–2900 cal year BP). The taxa detected in the tsunami deposits were markedly different from those of the background sediments. Many taxa that were represented in the Jogan tsunami deposit were also detected in the layer immediately above the tsunami deposit. This layer was indistinguishable from the overlying peat by visual observation, but the eDNA results suggest that it is likely to be a muddy tsunami deposit. The results of this study indicate that eDNA has the potential to elucidate the origin of event deposits that have been difficult to identify.
Local-non-local complementary learning network for 3D point cloud analysis
Projected changes in climate extremes over Tanzania
Abstract Understanding projected changes in climate extremes at local and regional scales is critical for reducing society’s vulnerability to such extremes, as it helps to devise informed adaptation strategies and contributes to informed decision-making processes. In this paper, we analysed projected changes in climate extremes across regions in Tanzania using outputs of high-resolution regional climate models from the Coordinated Regional Climate Downscaling Experiment program (CORDEX-Africa). The indices analysed here are those recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI) to characterise climate extremes over different regions. The results revealed that Tanzania would experience an increased number of warm days and nights during the present (2011–2040), mid (2041–2070), and end centuries under the RCP4.5 emission scenarios. Further, projections reveal that in future climate conditions, heavy, very heavy and exceptionally heavy rainfall events would dominate over regions along coast, central regions, northwestern parts and southwestern and northeastern highland.The number of consecutive wet days (CWDs) are likely to increase across large areas of Tanzania and more rapidily over coastal regions than that in other regions for all seasons. However, many regions in Tanzania are likely to experience an unchanged to decreasing number of consecutive dry days (CDDs). Areas along coastal regions would experience increased intensity and frequency of extreme rainfall events in the present, mid, and end centuries under the RCP4.5 emission scenario. These increases in extreme climate events are likely to pose significant damage to property, destruction of infrastructure, and other socioeconomic livelihoods for people in many regions of Tanzania. It is therefore recommended that appropriate policies are put in place to help different sectors and communities at large adapt the impacts of climate change in the future climate under RCP 4.5 scenario.
Non vertical ionization-dissociation model for strong IR induced dissociation dynamics of $${{D}_{2}}O^{2+}$$
Spectroscopic, quantum chemical, and topological calculations of the phenylephrine molecule using density functional theory
Clinical impacts of Artocarpus lakoocha agglutinin-binding glycans for prognosis and treatment of cholangiocarcinoma
Joint suppression method for range-Doppler ambiguity sidelobes in DTMB-based passive bistatic radar
The correlation between cumulative cigarette consumption and infarction-related coronary spasm in patients with ST-segment elevation acute myocardial infarction across different age groups
Cost-effectiveness of systematic chemotherapy for metastatic pancreatic cancer: a retrospective study using Japanese clinical data
A novel deep synthesis-based insider intrusion detection (DS-IID) model for malicious insiders and AI-generated threats
AbstractInsider threats pose a significant challenge to IT security, particularly with the rise of generative AI technologies, which can create convincing fake user profiles and mimic legitimate behaviors. Traditional intrusion detection systems struggle to differentiate between real and AI-generated activities, creating vulnerabilities in detecting malicious insiders. To address this challenge, this paper introduces a novel Deep Synthesis Insider Intrusion Detection (DS-IID) model. The model employs deep feature synthesis to automatically generate detailed user profiles from event data and utilizes binary deep learning for accurate threat identification. The DS-IID model addresses three key issues: it (i) detects malicious insiders using supervised learning, (ii) evaluates the effectiveness of generative algorithms in replicating real user profiles, and (iii) distinguishes between real and synthetic abnormal user profiles. To handle imbalanced data, the model uses on-the-fly weighted random sampling. Tested on the CERT insider threat dataset, the DS-IID achieved 97% accuracy and an AUC of 0.99. Moreover, the model demonstrates strong performance in differentiating real from AI-generated (synthetic) threats, achieving over 99% accuracy on optimally generated data. While primarily evaluated on synthetic datasets, the high accuracy of the DS-IID model suggests its potential as a valuable tool for real-world cybersecurity applications.
Depression among Tibetan residents in the Southeastern region of Qinghai-Tibet plateau: a cross-sectional study
AbstractDepression has emerged as a significant public health concern, with its prevalence fluctuating based on varying environmental and demographic factors. This study categorized participants based on altitude. A convenient sampling approach was used, and the hamilton depression rating scale-24 was used to assess depressed symptoms while gathering demographic information. A total of 600 Tibetan residents from the Dege area of Garze Prefecture, Sichuan, China, participated in the survey. The mean age is 56.81 years, males comprising 52.8% and females 47.2% of the sample. Of the participants, 41.2% resided permanently at elevations exceeding 3500 m. The results found that the weighted prevalence of depression in the area was 24.62%. Regardless of gender, the age group with the highest prevalence was 55–64 years old. Depression increased with age as well as gradually decreased after the age of 60. Logistic regression analysis showed that middle-aged (OR 2.86, 95% CI 1.69–4.82, P < 0.01) and elderly people (OR 2.27, 95% CI 1.30–3.98, P < 0.01), living in ultra-high altitude areas (OR 3.48, 95% CI 1.35–2.91, P < 0.01) and low BMI (OR 4.31, 95% CI 1.33–13.93) are high-risk factors for depression. This study enhances the understanding of the characteristics of depression in high-altitude regions of China, contributing to a more comprehensive view of the psychological well-being of residents in these areas. The findings underscore the need for targeted prevention and treatment strategies tailored to the specific needs of these populations.