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IoT integrated CNN framework for automated detection and quantification of rice and potato crop diseases
No full textIn modern precision agriculture, early and accurate identification of crop diseases is crucial for reducing yield loss and minimizing pesticide overuse. This study proposes an IoT-enabled framework that integrates convolutional neural networks (CNNs) with image processing techniques for automated classification and quantification of diseases in rice and potato crops. A custom-curated dataset was developed, comprising over 1,800 images acquired through smartphone cameras and foldscope devices under natural lighting conditions. The proposed CNN model achieved a classification accuracy of over 95%, with a disease quantification accuracy of 90.5%, calculated using pixel-level segmentation of infected regions. Experimental results revealed infection percentages ranging from 0.68% in early-stage cases to 13.98% in severely affected samples, enabling precise disease severity analysis. The framework includes a MATLAB-based graphical user interface (GUI) for real-time visualization of classification results and severity scores. Training convergence was demonstrated with a mini-batch loss reduction from 1.0879 to 0.0094 over 200 iterations, and classification confidence scores exceeding 90% for most disease categories. In addition to software implementation, the model was synthesized for hardware deployment using FPGA, demonstrating less than 5% LUT and 1% register usage for 512 × 512 images, ensuring resource-efficient performance in IoT environments. This work introduces a scalable, field-deployable tool for crop health monitoring, with potential to enhance sustainable farming practices through timely disease management
Family Factors and Internet Gaming Disorder among Adolescents: A Systematic Review.
Get PDF©2025, Sage Publications. This is an author produced version of a paper published in International Journal of Developmental Science, uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it
Development of High-Strength Aerogel Concrete
No full textAerogel is a synthetic porous ultralight material with very low thermal conductivity, and it is mainly used in buildings for external insulation in the form of blankets. In this study, the development of high-strength concrete with a partial replacement of sand with aerogel powder and aerogel beads is presented. Compressive strength, thermal conductivity, and shrinkage measurements have been conducted, and the results indicate that a replacement of sand with 30% aerogel beads leads to a high compressive strength (70 MPa) and relatively low thermal conductivity (1 W/mK) concrete
DNA barcodes narrow down the possible sources of introductions of an invasive banana skipper, <i>Erionota torus</i> Evans (Lepidoptera, Hesperiidae)
No full textThe banana skipper, Erionota torus Evans (Lepidoptera, Hesperiidae, Hesperiinae, Erionotini) is a South-east Asian pest of banana that, in the last 60 years, has spread to the southern Philippines, Taiwan, Japan, India, Sri Lanka, Mauritius and La Réunion, and potentially threatens Africa and Tropical America. A partial library of DNA barcodes from the indigenous and introduced ranges was built. Based on our analysis, the indigenous populations can be divided into an ‘East’ group, in China and Vietnam, and a ‘West’ group in India, Nepal, Myanmar and west Malaysia. Further, within the ‘West’ group, there is a coherent ‘Malaysia’ subgroup from west Malaysia. Introduced populations in south India, La Réunion and Taiwan showed almost no variation in barcodes, suggesting they are each based on a single homogenous introduction. We conclude that the introduced populations in Taiwan and Japan match the ‘East’ group, the introduced populations in Mauritius and La Réunion match the ‘Malaysia’ subgroup and the introduced population in south India matches the ‘West’ group. These results are discussed in the context of existing ideas regarding the source of each introduction, and the implications in terms of pathways of entry
From radiomics to transformers in pancreatic cancer detection and prognosis
Get PDFIntroductionPancreatic ductal adenocarcinoma (PDAC) remains one of the deadliest malignancies, primarily due to late diagnosis and poor therapeutic response. Advances in artificial intelligence (AI), particularly in medical imaging and multi-modal data integration, have created new opportunities for improving early detection and personalized prognostication.MethodsThis systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. The protocol was prospectively registered with the Open Science Framework, covering studies published between 2015 and 2025.ResultsDistinct from prior surveys that focus narrowly on specific algorithms or data types, this work introduces a generational taxonomy of AI approaches-ranging from classical radiomics-based machine learning to deep learning and contemporary transformer-based models-and maps their application to core clinical tasks such as detection, segmentation, classification, and outcome prediction. A key contribution is the integration of diverse datasets across imaging, pathology, and molecular sources; we further assess trends in availability, usage, and sample scale.DiscussionWe critically evaluate limitations in generalizability, external validation, model calibration, and translational readiness, and outline recommendations for multi-center validation, standardized reporting, domain adaptation, and clinician-centered interpretability.Systematic review registrationhttps://doi.org/10.17605/OSF.IO/2DVHJ
BERT-Based Myers-Briggs Type Indicator Personality Qualification from Social Media Texts
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A syndemic approach to the study of Covid-19-related death: a cohort study using UK Biobank data
No full textBackground The Covid-19 pandemic showed higher infection, severity and death rates among those living in poorer socioeconomic conditions. We use syndemic theory to guide the analyses to investigate the impact of social adversity and multiple long-term conditions (MLTC) on Covid-19 mortality. Methods The study sample comprised 154 725 UK Biobank participants. Structural equation modeling was used to investigate pathways between traumatic events, economic deprivation, unhealthy behaviors, MLTC, for Covid-19 mortality. Cox regression analysis was used to investigate MLTC and Covid-19 mortality. We also tested effect modification by traumatic events, economic deprivation and unhealthy behaviors. Results Covid-19 mortality (n = 186) was directly explained by overall level of MLTC. Economic deprivation and unhealthy behaviors contributed to Covid-19 death indirectly via their negative impact on MLTC. The risk for Covid-19 mortality grew exponentially for every quintile of predicted scores of MLTC. The presence of traumatic events, economic deprivation or unhealthy behaviors did not modify the impact of MLTC on Covid-19 mortality. Conclusions Results suggest a serially causal pathway between economic deprivation and unhealthy behaviors leading to MLTC, which increased the risk of Covid-19 mortality. Policies to tackle the social determinants of health and to mitigate the negative impact of multimorbidity are needed
The Role of Diet, Glycaemic Index and Glucose Control in Polycystic Ovary Syndrome (PCOS) Management and Mechanisms of Progression
Get PDFPurpose of Review: Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder with several causal pathways including impaired glucose tolerance, insulin resistance (IR), compensatory hyperinsulinemia and excess androgens (hyperandrogenism). This heterogeneous condition causes a range of reproductive, metabolic and psychological implications, the severity of which can differ between individuals depending on factors such as age, diet, ethnicity, genetics, medication, contraceptive use, adiposity, and Body Mass Index (BMI). Recent Findings: Dietary interventions that focus on a low glycaemic index and glucose control are an efficient first-line dietary solution for the management of impaired glucose tolerance and IR, which subsequently improves weight management, quality of life and PCOS-related symptoms in individuals with this condition. Summary: This review aims to explore the relevance of nutrition and more specifically, the association of glycaemic index and glycaemic load with PCOS, as well as to assess the potential benefits of manipulating those indexes in the dietary approach for this syndrome