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Barriers to human remains identification using forensic odontology in resource-constrained settings

Forensic science International: Synergy 2025年 10卷 100575页

作者： Guzman, Ernest Joie T. De Ungria, Maria Corazon A. Philippine Genome Center Program on Biodiversity Ethnicity and Forensics University of the Philippines Philippines Department of Science and Technology Science Education Institute Taguig City Philippines DNA Analysis Laboratory Natural Sciences Research Institute University of the Philippines Diliman Philippines Research Ethics Board University of the Philippines Diliman Philippines

An effective strategy for human remains identification with a high success rate and low cost is vital for low to low-middle-income countries. Prompt identification of human remains is essential for the swift recovery of families and communities affected by large-scale disasters. However, the systematic integration of forensic disciplines into standard practice remains to be implemented in many areas. Even countries like the Philippines – where over 20 typhoons occur annually, leaving numerous, even thousands of persons remain missing so many years after the disaster, institutions continue to struggle in adopting standard protocols for human remains identification (HRI). This paper examines the barriers to implementing such protocols and explores the feasibility of maximizing the use of forensic odontology in the identification process in resource-constrained settings. The discussion highlights the situation in the Philippines resulting from these barriers and provides actionable plans for overcoming these challenges. In doing this, we can maximize the use of available technologies such as forensic odontology when local resources are limited, trained professionals are scarce and a sustainable and efficient operational local framework to handle mass fatality incidents (MFI) is not in place. © 2025 The Authors

关键词： Dentistry Disaster victim identification Forensic odontology Human identification Philippine forensic science Resource-constrained

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Enhancing 6G-Satellite Network Integration with Artificial Intelligence: A Future communication Paradigm

Enhancing 6G-Satellite Network Integration with Artificial I...

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Learning and technology Conference (L&T)

作者： Ibrahim Salah Aziza I. Hussein Mostafa Wageh Lotfy Mohamed Mourad Mabrook Control and Computer Department College of Engineering Almaaqal University Basrah Iraq College of Engineering Energy and Technology Research Center Effat University Saudi Arabia Dept. of Process Control Technology Faculty of Technology and Education Beni-Suef University Beni-Suef Egypt Space Communication Dept. Faculty of Navigation Science & Space Technology Beni-Suef University Beni-Suef Egypt

ISBN: (数字)9798331532215

ISBN: (纸本)9798331532222

The integration of 6G networks and satellite communications is set to revolutionize global connectivity, offering seamless coverage across terrestrial and non-terrestrial environments. Artificial Intelligence (AI) is essential for improving this integration, addressing challenges such as dynamic resource management, latency reduction, and network optimization. AI techniques like machine learning, deep learning, and reinforcement learning offer innovative solutions to handle the complexities of 6G-satellite networks. These advancements promise to significantly improve network efficiency, enhance data transmission reliability, and ensure seamless connectivity across different areas. Potential use cases include smart cities, autonomous vehicles, and Internet of Things (IoT) applications, where AI-driven 6G-satellite integration will be crucial. The proposed AI-enhanced 6G-satellite framework not only addresses current challenges but also lays the groundwork for a resilient, scalable, and globally interconnected communication infrastructure, offering a promising future.

关键词： 6G mobile communication Wireless communication Smart cities Resource management Reliability Internet of Things Stakeholders Artificial intelligence Vehicle dynamics Satellite communications

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Laser-enhanced and UV-driven photocatalysis for the degradation of methylene blue using CeO2 nanostructures;A comparative study

Laser-enhanced and UV-driven photocatalysis for the degradat...

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International Laser technology and Optics Symposium 2024, iLATOS2024

作者： Niaz, Usman Aziz, M.S. Jamil, Ayesha Apsari, Retna Bilal Tahir, M. Hidayat, Nurul Salleh, M.S. Johor Skudai81310 Malaysia Johor Skudai81310 Malaysia Department of Physics Science Division University of Education Lahore54770 Pakistan Department of Engineering Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Jl. Mulyorejo Surabaya60115 Indonesia Building Research Station Communication and Works Department Lahore54770 Pakistan Department of Physics Faculty of Science and Technology Airlangga University Surabaya60115 Indonesia Rahim Yar Khan Pakistan Center of Advanced Materials for Renewable Energy Universitas Negeri Malang Jl. Semarang Malang65145 Indonesia Department of Physics Faculty of Mathematics and Natural Sciences Universitas Negeri Malang Jl. Semarang 5 Malang65145 Indonesia

Water pollution caused by organic dyes, particularly methylene blue (MB), poses a significant environmental challenge. This study investigates the novel application of laser-enhanced photodegradation of MB using cerium oxide (CeO2) nanostructures (NSs) as a semiconductor photocatalyst. CeO2 NSs were synthesized via a precipitation method employing Ce(NO3)3·6H2O and NaOH at room temperature, followed by calcination at 600°C for 2 hours. The structure of the CeO2 NSs was analysed using XRD, FTIR, EDX, SEM and FESEM. To our knowledge, for the first time, photocatalytic activity of CeO2 NSs under laser light irradiation was studied and contrasted with the UV light degradation. These results showed that laser induced photodegradation (12.11% in 1 hour) was much more effective than UV light degradation (4.89% in 1 hour). These improvements are due to the more better Ce3+/Ce4+ redox couple which helps in the production of reactive oxygen species that in turn increases the degradation of MB under laser. This research, therefore, aims at proposing an improved efficient method of removal of organic contaminants from the wastewater with an emphasis on the dye-contaminated industrial effluents. More studies should be conducted to enhance these strategies use in large scale in wastewater treatment. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Wastewater treatment

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Low storage temperature maintains the quality, antioxidant activity, and nutraceutical properties of edible flowers and their anti-inflammatory effects on RAW 264.7 cells

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Journal of Agriculture and Food research 2025年 21卷

作者： Buanong, Mantana Khunmuang, Sudarat Promboon, Juthamard Penchaiya, Pathompong Promyos, Natnicha Thepouyporn, Apanchanid Postharvest Technology Program School of Bioresources and Technology King Mongkut's University Technology Thonburi (Bangkhuntien) Bangkok 10150 Thailand Postharvest Technology Innovation Center Science Research and Innovation Promotion and Utilization Division Office of the Ministry of Higher Education Science Research and Innovation 10400 Thailand Plant Standard and Certificate Division Department of Agriculture Chatuchak Bangkok 10900 Thailand Tropical Nutrition and Food Science Department Faculty of Tropical Medicine Mahidol University Bangkok 10400 Thailand

Edible flowers are increasingly used as functional foods that enhance human health by providing antioxidant and antiaging benefits, scavenging free radicals, and aiding in the prevention and care of cardiovascular diseases and cancer. However, the rapid loss of quality after harvesting, which reduces the aesthetic value, shelf life, and marketability of flowers, is a major research issue compared to their nutraceutical value. Therefore, this study investigated the changes in the quality, phytochemical properties, antioxidant activity, nutraceutical efficacy, and anti-inflammatory effects on RAW 264.7 cells of ten edible flowers after being harvested, packed in waterproof paper trays, covered with a transparent plastic lid, and stored at 10 °C and 21 °C for 6 days. Low storage temperature significantly delayed this deterioration while maintaining the antioxidant capacities and nutraceutical properties of flowers, whereas storage at 21 °C decreased these properties in some flowers. The result revealed that total phenolic content of C. pulcherrima (pink and orange) expressed higher to 1.22 and 1.20 mg/g FW, respectively, than other edible flowers. Also, the antioxidant capacity of C. pulcherrima orange flowers was effective against DPPH which initial value was 1632.89 μmol TE/g, while that of FRAP, ABTS, and ORAC were found in C. indicum showing 2364.20, 1974.87 and 5807.70 μmol TE/g, respectively. Compared with other flower extracts, C. pulcherrima and A. leptopus ‘Alba’ had high acetylcholinesterase activity of approximately 0.01–0.06 mg/mL and strong inhibition level of tyrosinase 0.16–0.22 mg/mL, while ACE inhibition was undetectable in all investigated edible flowers. However, flowers of C. pulcherrima (yellow) showed 100 % decay at 21 °C on day 3 of storage. Moreover, the inflammatory and anti-inflammatory effects of MeOH extracts (0–5 mg/mL, at the zero time) were also explored in RAW 264.7 cells. Almost all flowers modulated inflammation;A. goyazensis effective

关键词： Anti-inflammation Bioactive compounds Edible flower Low temperature NO Postharvest

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Auto Digit Selection for Most Significant Digit First Multiplication

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IEEE Embedded Systems Letters 2025年

作者： Gorgin, Saeid Jaberipur, Ghassem Lee, Jeong-A Shin, Seokjoo Kim, Jungrae Computer Engineering Iranian Research Organization for Science and Technology Tehran Iran Semiconductor Systems Engineering Department College of Information and Communication Engineering SungKyunKwan University Korea Republic of Brain Pool Program for Chosun University Gwangju Korea Republic of Shahid Beheshti University Tehran Iran Chosun University Department of Computer Engineering Gwangju Korea Republic of

Performance in modern embedded systems, particularly those executing computation-intensive signal/image processing and machine learning algorithms, is critically dependent on the efficiency of multiplication operations. Serial multiplication offers key advantages in these resource-constrained environments, including low memory usage and minimal interconnect complexity. The most significant digit first (MSDF) serial multiplication scheme further enhances efficiency by enabling early termination and supporting variable precision. It is ideal for embedded applications where power and performance are critical. This paper proposes an auto-selection scheme for MSDF multiplication, where product digits are generated as a byproduct of residual maintenance, leading to speed, cost, and power efficiency improvements. We demonstrate the implementation of serial-serial and serial-parallel multipliers, where the multiplier operand in the latter is fully available throughout the computation. Simulation and synthesis results, compared to the most relevant previous works, show reductions in delay (16% and 20%), area consumption (11% and 23%), and power dissipation (7% and 20%), making our approach well-suited for embedded systems requiring efficient, low-power multiplication hardware. © 2009-2012 IEEE.

关键词： Left-to-Right Arithmetic Machine Learning Algorithms MSDF Multiplication Online Arithmetic

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Deep Learning-based Mango Leaf Disease Detection using Yolov8

Deep Learning-based Mango Leaf Disease Detection using Yolov...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Gladson Oliver S Prathusha Perugu Jeevitha S Rashima Mahajan Jasmine Gnana Malar A Raghavan Sheeja AhilanAppathurai Aarti Information Technology Government College of Technology Coimbatore India Department of Computer Science Engineering Sri Venkateswara College of Engineering Tirupathi India Department of Computer Science and Information Technology Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Manav Rachna International Institute of Research and Studies Faridabad India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India Artificial Intelligence and Data Science R.M.K Engineering College Gummidipoondi Kavaraipettai Tamil Nadu India Department of Electronics and Communication Engineering PSN College of Engineering and Technology Tirunelveli Tamilnadu India School of Computer Science and Engineering Geeta University Panipat

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The mango holds a significant position in India's fruit crop rotation and plays a major part in the nation's economy. The best strategy to avoid losing money and time is to recognize diseases in crops earlier. Obtaining a comprehensive dataset can be challenging and time-consuming. Additionally, deep learning models may struggle with detecting early-stage symptoms of diseases. In this work, a novel deep learning-based model is proposed for the early detection of mango leaf disease (MLD). An adaptive unsharp mask-guided (AUG) filter is used as a pre-processing step to improve the input pictures from the collected dataset. The enhanced images are fed into the YOLOV8 model for detecting mango leaf disease (MLD) and classifying the different kinds of disease. According to the experimental result, the proposed model attains a 99.29% success rate for the classification of disease in mango leaf. The proposed approach is compared to DL methods such as SSD, Mask RCNN, and YOLO v8. The detection accuracy is improved by the YOLO network over SSD, Mask R-CNN, and Yolo v8 by 7.57%, 4.89%, and 2.36%, respectively. In comparison to SVM, ANN, and CNN-HOG, the proposed approach advances the overall accuracy range by 4.72%, 0.73%, 13.18%, and 5.44%, respectively.

关键词： YOLO Support vector machines Deep learning Adaptation models Accuracy Biological system modeling Neural networks Crops Convolutional neural networks Diseases

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CNN-Based Pneumonia Detection and Severity Analysis in Chest X-Ray Images for Enhanced Diagnostic Support

CNN-Based Pneumonia Detection and Severity Analysis in Chest...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： D. Surendiran Muthukumar Bommineni Sreekar Chowdary D. Senthil Kumar P. Jagadeesan B. Mythili B. Swapna Department of computer science and Engineering Kalasalingam Academy of Research and Education Srivilliputhur Tamilnadu India Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Virudhunagar Tamil Nadu India Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering R.M.D. Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Vel tech Multi tech Dr.Rangarajan Dr. Sakunthala Engineering college Chennai Tamil Nadu India Department of Electronics and communication engineering Dr.M.G.R Educational and Research Institute Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Pneumonia continues to pose a substantial global health challenge, necessitating prompt and precise diagnosis to enhance patient outcomes. In order to detect pneumonia and classify its severity into mild, moderate, and severe phases, the web-based program uses deep learning algorithms to analyse chest X-ray pictures. A Convolutional Neural Network (CNN) model that has been trained on publicly accessible datasets and achieved high accuracy is integrated into the application. Furthermore, it provides personalized health recommendation, downloadable PDF reports, and a local hospital database to facilitate users in locating nearby treatment facilities. The system utilizes Fast API for backend processing and user-friendly interfaces to enhance accessibility and efficiency. This solution underscores the potential of artificial intelligence in supporting early diagnosis and effective pneumonia management.

关键词： Deep learning Pneumonia Accuracy Hospitals Databases Classification algorithms Reliability Convolutional neural networks Artificial intelligence X-ray imaging

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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RefSCAT-2.0: Formal Verification of Large-Scale Optimized Multipliers via Quantum-Inspired Ant Colony Optimization-Based Reference Generation

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Li, Rui Li, Lin Yu, Heng Fujita, Masahiro Jiang, Weixiong Ha, Yajun ShanghaiTech University School of Information Science and Technology Shanghai201210 China Chinese Academy of Sciences Shanghai Institute of Microsystem and Information Technology Shanghai200050 China University of Chinese Academy of Sciences School of Electronic Electrical and Communication Engineering Beijing100049 China University of Nottingham Ningbo China Ningbo315100 China University of Tokyo VLSI Design and Education Center Tokyo113-0032 Japan ShanghaiTech University School of Information Science and Technology Shanghai Engineering Research Center of Energy Efficient and Custom Ai Ic Shanghai201210 China

Formal verification of large-scale optimized integer multipliers remains a critical yet insufficiently addressed challenge in industry and academia. Current methods employ reference multiplier generators to automatically construct structurally similar reference multipliers, which are then used by Satisfiability (SAT)-based techniques to verify equivalence with optimized multipliers. However, these approaches face limitations when generating references for large-scale optimized multipliers within acceptable timeframes. To address these limitations, we introduce the RefSCAT-2.0 framework, designed to rapidly produce high-quality large-scale reference multipliers. Firstly, we generate the macro-architecture to determine the number of adders required for constructing the reference multiplier. We propose a novel Integer Linear programming (ILP)-based macro-architecture generation algorithm that minimizes the number of allocated adders, thereby reducing the overall problem complexity. Secondly, we organize the allocated adders into groups to simplify the subsequent generation process. We present a multi-level scheduler that automatically decomposes adders into groups with minimized interdependencies, ensuring both the quality of generation and a reduction in overall generation complexity. Thirdly, we generate the micro-architecture for each scheduled group, wherein we finalize the connections between adders. We present a graph-based design space representation coupled with a quantum-inspired ant colony optimization (QACO)-based generation algorithm that can efficiently explores the micro-architectures of each scheduled group. Experimental results show that RefSCAT-2.0 successfully verifies all 124 cases in a 256-bit optimized multiplier benchmark suite, outperforming SCA-based tcad22revsca and hybrid RefSCATTCAD24 methods which solve only 24 cases each. © 1982-2012 IEEE.

关键词： Ant colony optimization

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Adaptive Learning Systems: Integrating IoT Sensors with Machine Learning for Dynamic Curriculum Adjustment

Adaptive Learning Systems: Integrating IoT Sensors with Mach...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Mrudula Kilaru S. Surya Mrs. Sindhu Boianapalli S. Vijaya Kumar Dr. Mamta Thakur M. Kalyan Chakravarthi Siddhartha Academy of Higher Education Velagapudi Ramakrishna Siddhartha School of Engineering Sri Ramachandra Faculty of Engineering & Technology (SRET) Sri Ramachandra Institute of Higher Education & Research Porur Chennai Department of CSE CVR College of Engineering Vastunagar Mangalpalli I Brahimpatnam Telangana English B S Abdur Rahman Crescent Institute of Science and Technology Vandalur Chennai India Department of Mathematics Chaitanya Bharathi Institute of Technology Hyderabad Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Al Khuwair Muscat Oman

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

The broad implementation of learning statistics in different curriculum contexts is still difficult, despite encouraging results in universities. Secondary and primary institutions are hesitant to employ these technologies. By providing individualized education encounters catered to each student's requirements, machine learning (ML) based adaptive learning platforms have revolutionized learning by increasing student involvement and results. Physical training can now be communicated through technology via the growth of the Internet of Things (IoT), which links affordable, diverse gadgets and sensors to open, public spaces. This research investigates the relationship between curriculum adjustment, a crucial aspect of sports, and physical activities using a decision tree. This platform gathers vital statistics from IoT-based wearable sensors, and by virtualization of the gadget, it interacts with the statistics in real time. Implementing and promoting physical education and learning curriculum in the real world and building healthier atmosphere platforms to address the health surveillance problems brought on by IoT sensors are the solutions suggested by the empirical evaluation. research results demonstrate the remarkable physical activity surveillance accomplished by the suggested approach in comparison to the traditional technologies. With excellent accuracy ratios (98.5%), estimation ratios (96.6%), communication ratios (94.5%), functioning ratios (95.3%), effectiveness ratios (93.4), F-values (92.3%), and lower error rates (17.6%), the computational evaluation of physical education curriculum can assist learners and enhance the related facets of their physical skills.

关键词： Training Adaptive learning Surveillance Machine learning Sensor systems Real-time systems Internet of Things Virtualization Wearable sensors Sports

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