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Item type:Publication, Cyber security of smart metering infrastructure using median absolute deviation methodology(Hindawi Limited, 2022-01-01)To combat cyber threats in the smart grid, an intrusion detection system can be integrated into the advanced metering infrastructure. Anomaly-based intrusion detection can detect even the tiniest changes in the parameter under investigation, whereas signature-based intrusion detection only recognises known attacks. The growing usage of smart grids necessitates the classification, identification, and implementation of countermeasures to threats. At the absolute least, smart grids must be protected against cyberattacks; thus, the highest level of information security must be offered. As a result of digitisation and the usage of more smart applications, the research looked at a variety of attack types, smart grid assaults, and major cyber threats on the voltage regulation. Machine learning techniques that analyse data in real time and formulate patterns to recognise an attack and scan through huge data for anomalies can be implemented into the advanced metering infrastructure (AMI) for intrusion detection for anomaly-based intrusion detection. The comparative test study done for the research found that the proposed method, median absolute deviation for anomaly identification in smart metering datasets, produced the most accurate and precise differentiations with the highest accuracy and precision. The median absolute deviation (MAD) algorithm model is trained using test data, and raw predictions are made, before true data are used to derive final test result parameters, precision, recall, and F1 scores. The methodology of the entire study is discussed in this paper, as well as how the MAD algorithm is best suited for anomaly-based intrusion detection, as well as result comparisons of other machine learning algorithms. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Supply Chain Optimization in Manufacturing: An Approach Based on Project Management(2023-12-22)A documentary review was carried out on the production and publication of research papers related to the study of Supply Chain, Manufacturing Industry, project management. The purpose of the bibliometric analysis proposed in this document was to know the main characteristics of the volume of publications registered in the Scopus database during the period between 2017 and 2022, achieving the identification of 321 publications. The information provided by this platform was organized through graphs and figures, categorizing the information by Year of Publication, Country of Origin, Area of Knowledge and Type of Publication. Once these characteristics have been described, the position of different authors on the proposed topic is referenced through a qualitative analysis. Among the main findings made through this research, it is found that India with 66 publications with the highest scientific production registered in the name of authors affiliated with institutions in that country. The Area of Knowledge that made the greatest contribution to the construction of bibliographic material related to the study of supply chain optimization in the manufacturing industry based on project management was offered in the intensive care unit was Engineering with 181 published documents, and the most used Publication Type during the period indicated above were Journal Articles with 54% of the total scientific production. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measurement of exclusive <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow> <mml:msup> <mml:mrow> <mml:mi>?</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>+</mml:mo> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> -argon interactions using ProtoDUNE-SP(American Physical Society, 2026-03-26)We present the measurement of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline"> <a:msup> <a:mi>?</a:mi> <a:mo>+</a:mo> </a:msup> </a:math> -argon inelastic cross sections using the ProtoDUNE single-phase liquid argon time projection chamber in the incident <c:math xmlns:c="http://www.w3.org/1998/Math/MathML" display="inline"> <c:msup> <c:mi>?</c:mi> <c:mo>+</c:mo> </c:msup> </c:math> kinetic energy range of 500–800 MeV in multiple exclusive channels (absorption, charge exchange, and the remaining inelastic interactions). The results of this analysis are important inputs to simulations of liquid argon neutrino experiments such as the Deep Underground Neutrino Experiment and the Short Baseline Neutrino program at Fermi National Accelerator Laboratory. They will be employed to improve the modeling of final state interactions within neutrino event generators used by these experiments, as well as the modeling of <e:math xmlns:e="http://www.w3.org/1998/Math/MathML" display="inline"> <e:msup> <e:mi>?</e:mi> <e:mo>+</e:mo> </e:msup> </e:math> -argon secondary interactions within the liquid argon. This is the first measurement of <g:math xmlns:g="http://www.w3.org/1998/Math/MathML" display="inline"> <g:msup> <g:mi>?</g:mi> <g:mo>+</g:mo> </g:msup> </g:math> -argon absorption at this kinetic energy range as well as the first ever measurement of <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline"> <i:msup> <i:mi>?</i:mi> <i:mo>+</i:mo> </i:msup> </i:math> -argon charge exchange.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An AI-enabled comprehensive breast ultrasound diagnostic system for low-resource settings without a sonographer or a radiologist(Nature Portfolio, 2026-12-01)Breast cancer is the most common non-skin related malignancy and the leading cause of cancer death in women. Mammography remains the gold standard for early detection; however, its accessibility is limited in low-resource settings due to cost and technical complexity. Ultrasound (US) is a viable alternative, but its implementation is hindered by the scarcity of trained radiologists and sonographers. Volume sweep imaging (VSI) has addressed the issue of US acquisition by enabling non-specialists to perform standardized scans. However, these still require expert interpretation, limiting their impact. To overcome this barrier, we propose a fully automated Breast VSI (VSI-B) system integrating artificial intelligence (AI) for segmentation and classification of breast lesions, aiming to provide an accessible diagnostic tool for low-resource environments. This study developed an AI-driven diagnostic system for VSI-B, combining a segmentation model (Attention U-Net 3D) with a classification model for lesion detection. A total of 98 patients with palpable breast lumps were included in the study. The dataset consisted of 392 VSI-B US videos and 2,100 classified frames. A new method was implemented to enhance mass identification by selecting key frames for analysis. A majority voting algorithm was used to optimize lesion classification. The system's performance was assessed based on sensitivity, specificity, and accuracy. Following a detection step that achieved 100% sensitivity and 93.6% specificity for cancer and no cancer patients, as well as 95.0% sensitivity and 63.0% specificity for mass and no mass patients, the DenseNet classification model reached 87% accuracy, 100% sensitivity, and 83% specificity. A majority voting algorithm optimized classification, yielding an AUC of 0.91. This study highlights the potential of an AI-enabled VSI-B system as a reliable diagnostic tool in low-resource settings. By integrating multi-modal segmentation and classification, the system automates breast lesion detection and stratification, reducing reliance on radiologists. The results suggest that this approach could enhance early breast cancer diagnosis and guide clinical decision-making, particularly in underserved regions.1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Damage Identification in Concrete Bridges Using Unmanned Aerial Vehicles and Neural Networks(Springer Science and Business Media Deutschland GmbH, 2025)Bridge monitoring systems using cameras and unmanned aerial vehicles (UAV) are increasingly being used worldwide. Additionally, artificial intelligence techniques are being used to improve performance in the structural damage detection and processing stage. This article shows a non-destructive methodology for damage identification using neural networks in a real bridge on the coast of Peru. The 104 m long Villena Rey bridge is the case study inaugurated in 1960 to improve the conditions and vehicular resilience of the Malecon de la Reserva avenue crossing in Lima. As a first step, many images were taken using photogrammetry with a UAV and the noise was filtered for data preparation. The data is then prepared and labeled to train the neural network model in conjunction with flexible training tools and an optimal architecture using one of the most efficient systems known as YOLOv7. The results show an optimal calibration of the system with percentages that exceed 60% in the identification of structural damage in bridges. Finally, this research work has a great contribution since it would be the first time that these modern technologies are used in developing countries such as Peru in South America.1