Documentos donde el Autor es "Gehlot, Anita"
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Artículo
Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés In recent years, nanotechnology and materials science have evolved and matured, making it increasingly easier to design and fabricate next-generation 3D microelectronics. The process has changed drastically from traditional 2D microelectronics, resulting in improved performance, higher integration density, and new functionalities. As applications become more complex and power-intensive, this technology can address the demands of high-performance computing, advanced sensors, and cutting-edge communication systems via wearable, flexible devices, etc. To manufacture higher-density microelectronics, recent advances in the fabrication of such 3D devices are discussed. Furthermore, the paper stresses the importance of novel materials and architectures, such as monolithic 3D integration and heterogeneous integration, in overcoming these challenges. We emphasize the importance of addressing complex issues to achieve better performance and higher integration density, which will play an important role in shaping the next generation of microelectronic devices. The multifaceted challenges involved in developing next-generation 3D microelectronic devices are also highlighted. metadata Singh, Niharika; Srivastava, Kingshuk; Kumar, Ajay; Yadav, Neha; Yadav, Ashish; Dubey, Santosh; Singh, Rajesh; Gehlot, Anita; Verma, Ajay Singh; Gupta, Neha; Kumar, Tanuj; Wu, Yongling; Hongyu, Zheng; Mondal, Aniruddha; Pandey, Kailash; Brajpuriya, Ranjeet; Kumar, Shalendra y Gupta, Rajeev mail SIN ESPECIFICAR (2024) Challenges and opportunities in engineering next-generation 3D microelectronic devices: improved performance and higher integration density. Nanoscale Advances, 6 (24). pp. 6044-6060. ISSN 2516-0230
Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Cerrado Inglés Silicon photonics is rapidly evolving as an advanced chip framework for implementing quantum technologies. With the help of silicon photonics, general-purpose programmable networks with hundreds of discrete components have been developed. These networks can compute quantum states generated on-chip as well as more extraordinary functions like quantum transmission and random number generation. In particular, the interfacing of silicon photonics with complementary metal oxide semiconductor (CMOS) microelectronics enables us to build miniaturized quantum devices for next-generation sensing, communication, and generating randomness for assembling quantum computers. In this review, we assess the significance of silicon photonics and its interfacing with microelectronics for achieving the technology milestones in the next generation of quantum computers and quantum communication. To this end, especially, we have provided an overview of the mechanism of a homodyne detector and the latest state-of-the-art of measuring squeezed light along with its integration on a photonic chip. Finally, we present an outlook on future studies that are considered beneficial for the wide implementation of silicon photonics for distinct data-driven applications with maximum throughput. metadata Gupta, Rajeev; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Yadav, Neha; Brajpuriya, Ranjeet; Yadav, Ashish; Wu, Yongling; Zheng, Hongyu; Biswas, Abhijit; Suhir, Ephraim; Yadav, Vikram Singh; Kumar, Tanuj y Verma, Ajay Singh mail SIN ESPECIFICAR (2023) Silicon photonics interfaced with microelectronics for integrated photonic quantum technologies: a new era in advanced quantum computers and quantum communications? Nanoscale. ISSN 2040-3364
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Abierto
Inglés
Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world. In general, it is difficult for a person to know if they are under stress. According to previous research, temperature, heart rate variability (HRV), humidity, and blood pressure are used to assess stress levels with the use of instruments. With the development of sensor technology and wireless connectivity, people around the world are adopting and using smart devices. In this study, a bio signal detection device with Internet of Things (IoT) capability with a galvanic skin reaction (GSR) sensor is proposed and built for real-time stress monitoring. The proposed device is based on an Arduino controller and Bluetooth communication. To evaluate the performance of the system, physical stress is created on 10 different participants with three distinct tasks namely reading, visualizing the timer clock, and watching videos. MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e., relaxed for <1.75 volts; Normal: between 1.75 and 1.44 volts and stressed: >1.44 volts. In addition, LabVIEW is used as a data acquisition system, and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.
metadata
Singh, Rajesh; Gehlot, Anita; Saxena, Ritika; Alsubhi, Khalid; Anand, Divya; Delgado Noya, Irene; Vaseem Akram, Shaik y Choudhury, Sushabhan
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR
(2023)
Stress Detector Supported Galvanic Skin Response System with IoT and LabVIEW GUI.
Computers, Materials & Continua, 74 (1).
pp. 1217-1233.
ISSN 1546-2226
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto
Inglés
Currently, sustainability is a vital aspect for every nation and organization to accomplish Sustainable Development Goals (SDGs) by 2030. Environmental, social, and governance (ESG) metrics are used to evaluate the sustainability level of an organization. According to the statistics, 53% of respondents in the BlackRock survey are concerned about the availability of low ESG data, which is critical for determining the organization’s sustainability level. This obstacle can be overcome by implementing Industry 4.0 technologies, which enable real-time data, data authentication, prediction, transparency, authentication, and structured data. Based on the review of previous studies, it was determined that only a few studies discussed the implementation of Industry 4.0 technologies for ESG data and evaluation. The objective of the study is to discuss the significance of ESG data and report, which is used for the evaluation of the sustainability of an organization. In this regard, the assimilation of Industry 4.0 technologies (Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data for obtaining ESG data by an organization is detailed presented to study the progress of advancement of these technologies for ESG. On the basis of analysis, this study concludes that consumers are concerned about the ESG data, as most organizations develop inaccurate ESG data and suggest that these digital technologies have a crucial role in framing an accurate ESG report. After analysis a few vital conclusions are drawn such as ESG investment has benefited from AI capabilities, which previously relied on self-disclosed, annualized company information that was susceptible to inherent data issues and biases. Finally, the article discusses the vital recommendations that can be implemented for future work
metadata
Saxena, Archana; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Twala, Bhekisipho; Singh, Aman; Caro Montero, Elisabeth y Priyadarshi, Neeraj
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, elizabeth.caro@uneatlantico.es, SIN ESPECIFICAR
(2022)
Technologies Empowered Environmental, Social, and Governance (ESG): An Industry 4.0 Landscape.
Sustainability, 15 (1).
p. 309.
ISSN 2071-1050
Artículo
Materias > Ciencias Sociales
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto
Inglés
Financial management is a critical aspect of firms, and entails the strategic planning, direction, and control of financial endeavors. Risk assessment, fraud detection, wealth management, online transactions, customized bond scheme, customer retention, virtual assistant and so on, are a few of the critical areas where Industry 4.0 technologies intervention are highly required for managing firms' finance. It has been identified from the previous studies that they are limited studies that have addressed the significance and application of integrating of Industry 4.0 technologies such as Internet of Things (IoT), cloud computing, big data, robotic process automation (RPA), artificial intelligence (AI), Blockchain, Digital twin, and Metaverse. With the motivation from the above aspects, this study aims to discuss the role of these technologies in the area of financial management of a firm. Based up on the analysis, it has been concluded that these technologies assist to credit risk management based on real-time data; financial data analytics of risk assessment, digital finance, digital auditing, fraud detection, and AI- and IoT- based virtual assistants. This study recommended that digital technologies be deeply integrated into the financial sector to improve service quality and accessibility, as well as the creation of innovative rules that allow for healthy competition among market participants.
metadata
Bisht, Deepa; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Singh, Aman; Caro Montero, Elisabeth; Priyadarshi, Neeraj y Twala, Bhekisipho
mail
SIN ESPECIFICAR
(2022)
Imperative Role of Integrating Digitalization in the Firms Finance: A Technological Perspective.
Electronics, 11 (19).
p. 3252.
ISSN 2079-9292
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Abierto
Inglés
SIN ESPECIFICAR
metadata
Kimothi, Sanjeev; Thapliyal, Asha; Akram, Shaik Vaseem; Singh, Rajesh; Gehlot, Anita; Mohamed, Heba G.; Anand, Divya; Ibrahim, Muhammad y Delgado Noya, Irene
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es
(2022)
Big Data Analysis Framework for Water Quality Indicators with Assimilation of IoT and ML.
Electronics, 11 (13).
p. 1927.
ISSN 2079-9292
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Abierto
Inglés
Large-scale distributed systems have the advantages of high processing speeds and large communication bandwidths over the network. The processing of huge real-world data within a time constraint becomes tricky, due to the complexity of data parallel task scheduling in a time constrained environment. This paper proposes data parallel task scheduling in cloud to address the minimization of cost and time constraints. By running concurrent executions of tasks on multi-core cloud resources, the number of parallel executions could be increased correspondingly, thereby, finishing the task within the deadline is possible. A mathematical model is developed here to minimize the operational cost of data parallel tasks by feasibly assigning a load to each virtual machine in the cloud data center. This work experiments with a machine learning model that is replicated on the multi-core cloud heterogeneous resources to execute different input data concurrently to accomplish distributive learning. The outcome of concurrent execution of data-intensive tasks on different parts of the input dataset gives better solutions in terms of processing the task by the deadline at optimized cost.
metadata
Rajalakshmi, N. R.; Dumka, Ankur; Kumar, Manoj; Singh, Rajesh; Gehlot, Anita; Akram, Shaik Vaseem; Anand, Divya; Elkamchouchi, Dalia H. y Delgado Noya, Irene
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, divya.anand@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es
(2022)
A Cost-Optimized Data Parallel Task Scheduling with Deadline Constraints in Cloud.
Electronics, 11 (13).
p. 2022.
ISSN 2079-9292
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto
Inglés
Food and agriculture are significant aspects that can meet the food demand estimated by the Food Agriculture Organization (FAO) by 2050. In addition to this, the United Nations sustainable development goals recommended implementing sustainable practices to meet food demand to achieve sustainability. Currently, aquaponics is one of the sustainable practices that require less land and water and has a low environmental impact. Aquaponics is a closed-loop and soil-less method of farming, where it requires intensive monitoring, control, and management. The advancement of wireless sensors and communication protocols empowered to implementation of an Internet of Things- (IoT-) based system for real-time monitoring, control, and management in aquaponics. This study presents a review of the wireless technology implementation and progress in aquaponics. Based on the review, the study discusses the significant water and environmental parameters of aquaponics. Followed by this, the study presents the implementation of remote, IoT, and ML-based monitoring of aquaponics. Finally, the review presents the recommendations such as edge and fog-based vision nodes, machine learning models for prediction, LoRa-based sensor nodes, and gateway-based architecture that are beneficial for the enhancement of wireless aquaponics and also for real-time prediction in the future.
metadata
Gayam, Kiran Kumari; Jain, Anuj; Gehlot, Anita; Singh, Rajesh; Akram, Shaik Vaseem; Singh, Aman; Anand, Divya; Delgado Noya, Irene y Ahmad, Shafiq
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@unic.co.ao, divya.anand@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR
(2022)
Imperative Role of Automation and Wireless Technologies in Aquaponics Farming.
Wireless Communications and Mobile Computing, 2022.
pp. 1-13.
ISSN 1530-8669
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Abierto
Inglés
Wearable monitoring devices are in demand in recent times for monitoring daily activities including exercise. Moreover, it is widely utilizing for preventing injuries of athletes during a practice session and in few cases, it leads to muscle fatigue. At present, emerging technology like the internet of things (IoT) and sensors is empowering to monitor and visualize the physical data from any remote location through internet connectivity. In this study, an IoT-enabled wearable device is proposing for monitoring and identifying the muscle fatigue condition using a surface electromyogram (sEMG) sensor. Normally, the EMG signal is utilized to display muscle activity. Arduino controller, Wi-Fi module, and EMG sensor are utilized in developing the wearable device. The Time-frequency domain spectrum technique is employed for classifying the three muscle fatigue conditions including mean RMS, mean frequency, etc. A real-time experiment is realized on six different individuals with developed wearable devices and the average RMS value assists to determine the average threshold of recorded data. The threshold level is analyzed by calculating the mean RMS value and concluded three fatigue conditions as >2 V: Extensive); 1–2 V: Moderate, and <1 V: relaxed. The warning alarm system was designed in LabVIEW with three color LEDs to indicate the different states of muscle fatigue. Moreover, the device is interfaced with the cloud through the internet provided with a Wi-Fi module embedded in wearable devices. The data available in the cloud server can be utilized for forecasting the frequency of an individual to muscle fatigue.
metadata
Gehlot, Anita; Singh, Rajesh; Siwach, Sweety; Vaseem Akram, Shaik; Alsubhi, Khalid; Singh, Aman; Delgado Noya, Irene y Choudhury, Sushabhan
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, irene.delgado@uneatlantico.es, SIN ESPECIFICAR
(2022)
Real Time Monitoring of Muscle Fatigue with IoT and Wearable Devices.
Computers, Materials & Continua, 72 (1).
pp. 999-1015.
ISSN 1546-2226
Artículo Materias > Ingeniería Universidad Internacional Iberoamericana México > Investigación > Artículos y libros Abierto Inglés Unfired admixed soil blocks are made up of soil plus stabilizers such as binders, fibers, or a combination of both. Soil is abundant on Earth, and it has been used to provide shelter to millions of people. The manufacturing and usage of cement and cement blocks raise several environmental and economic challenges. Due to disposal issues, agricultural and industrial waste is currently the biggest hazard to the environment and humanity in the world. Consequently, environmental degradation brought on by agricultural waste harms the ecology. As a result, researchers are attempting to develop an alternative to cement blocks, and various tests on unfired admixed soil blocks have been done. This investigation uses agricultural waste (i.e., paddy straw fiber and sugarcane bagasse ash) and industrial waste (i.e., marble dust) in manufacturing unfired admixed soil blocks. Under this investigation, the applicability of unfired soil blocks admixed with marble dust, paddy straw fiber, and bagasse ash was studied. The marble dust level ranged from 25% to 35%, bagasse ash content ranged from 7.5% to 12.5%, and the content of paddy straw fiber ranged from 0.8% to 1.2% by soil dry weight. Various tests were conducted on the 81 mix designs of the prepared unfired admixed soil blocks to find out the physical properties of the block followed by modeling and optimization. The findings demonstrate that the suggested method is a superior alternative to burned bricks for improving the physical properties of admixed soil blocks without firing metadata Sharma, Tarun; Singh, Sandeep; Sharma, Shubham; Sharma, Prashant; Gehlot, Anita; Shukla, Anand Kumar y Eldin, Sayed M. mail SIN ESPECIFICAR (2022) The Use of Marble Dust, Bagasse Ash, and Paddy Straw to Improve the Water Absorption and Linear Shrinkage of Unfired Soil Block for Structure Applications. Materials, 15 (21). p. 7786. ISSN 1996-1944
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto
Inglés
At this time, efforts are being made on a worldwide scale to accomplish sustainable development objectives. It has, thus, now become essential to investigate the part of technology in the accomplishment of these Sustainable Development Goals (SDGs), as this will enable us to circumvent any potential conflicts that may arise. The importance of wastewater management in the accomplishment of these goals has been highlighted in the study. The research focuses on the role of fourth industrial revolution in meeting the Sustainable Goals for 2030. Given that water is the most important resource on the planet and since 11 of the 17 Sustainable Goals are directly related to having access to clean water, effective water management is the most fundamental need for achieving these goals. The age of Industry 4.0 has ushered in a variety of new solutions in many industrial sectors, including manufacturing, water, energy, healthcare, and electronics. This paper examines the present creative solutions in water treatment from an Industry-4.0 viewpoint, focusing on big data, the Internet of Things, artificial intelligence, and several other technologies. The study has correlated the various concepts of Industry 4.0 along with water and wastewater management and also discusses the prior work carried out in this field with help of different technologies. In addition to proposing a way for explaining the operation of I4.0 in water treatment through a systematic diagram, the paper makes suggestions for further research as well.
metadata
Pandey, Shivam; Twala, Bhekisipho; Singh, Rajesh; Gehlot, Anita; Singh, Aman; Caro Montero, Elisabeth y Priyadarshi, Neeraj
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, elizabeth.caro@uneatlantico.es, SIN ESPECIFICAR
(2022)
Wastewater Treatment with Technical Intervention Inclination towards Smart Cities.
Sustainability, 14 (18).
p. 11563.
ISSN 2071-1050
Artículo
Materias > Ingeniería
Universidad Europea del Atlántico > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Artículos y libros
Universidad Internacional do Cuanza > Investigación > Producción Científica
Abierto
Inglés
Currently, two-wheelers are the most popular mode of transportation, driven by the majority the people. Research by the World Health Organization (WHO) identifies that most two-wheeler deaths are caused due to not wearing a helmet. However, the advancement in sensors and wireless communication technology empowers one to monitor physical things such as helmets through wireless technology. Motivated by these aspects, this article proposes a wireless personal network and an Internet of Things assisted system for automating the ignition of two-wheelers with authorization and authentication through the helmet. The authentication and authorization are realized with the assistance of a helmet node and a two-wheeler node based on 2.4 GHz RF communication. The helmet node is embedded with three flex sensors utilized to experiment with different age groups and under different temperature conditions. The statistical data collected during the experiment are utilized to identify the appropriate threshold value through a t-test hypothesis for igniting the two-wheelers. The threshold value obtained after the t-test is logged in the helmet node for initiating the communication with the two-wheeler node. The pairing of the helmet node along with the RFID key is achieved through 2.4 GHZ RF communication. During real-time implementation, the helmet node updates the status to the server and LABVIEW data logger, after wearing the helmet. Along with the customization of hardware, a LABVIEW data logger is designed to visualize the data on the server side.
metadata
Gehlot, Anita; Singh, Rajesh; Kuchhal, Piyush; Kumar, Adesh; Singh, Aman; Alsubhi, Khalid; Ibrahim, Muhammad; Gracia Villar, Santos y Breñosa, Jose
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, santos.gracia@uneatlantico.es, josemanuel.brenosa@uneatlantico.es
(2021)
WPAN and IoT Enabled Automation to Authenticate Ignition of Vehicle in Perspective of Smart Cities.
Sensors, 21 (21).
p. 7031.
ISSN 1424-8220
<a href="/28573/1/1-s2.0-S0033350626001848-main.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Objectives To describe long-term trends in mortality attributed to community-acquired pneumonia (CAP) in Chile from 1990 to 2021, stratified by age group, and to evaluate associations with selected socioeconomic and demographic indicators. Study design Ecological, observational, longitudinal study using national secondary data. Methods CAP mortality rates were analyzed for the total population and by age group. Associations with the Human Development Index (HDI), poverty rate, aging index, and life expectancy at birth were examined using a hierarchical analytical approach. This included Spearman's rank correlation for initial exploration, multivariable linear regression to assess adjusted associations, and Prais–Winsten generalized least squares regression to account for first-order autocorrelation and shared temporal trends. Stationarity was evaluated using augmented Dickey–Fuller tests, with supplementary analyses using first-differenced variables. Missing data were imputed using time-based regression or interpolation, with sensitivity analyses performed. Results CAP mortality declined substantially across all age groups over the study period. Strong bivariate correlations were observed between mortality and all socioeconomic indicators; however, these associations were attenuated after adjustment for confounding and temporal autocorrelation. In multivariable and time-series models, HDI and the aging index remained significantly associated with CAP mortality in children (0–9 years) and older adults (≥65 years), whereas associations in intermediate age groups were not robust after accounting for shared secular trends. Poverty and life expectancy did not demonstrate independent associations in adjusted models. Conclusions CAP mortality in Chile has decreased markedly over the past three decades. Associations with socioeconomic indicators are strongest at the extremes of age and persist after accounting for temporal structure, although the ecological design precludes causal inference. These findings highlight the importance of considering demographic and socioeconomic context in population-level analyses of infectious disease outcomes.
Italo Salvador López Muñoz mail italo.lopez@doctorado.unini.edu.mx, Maria Loreto Romero Ladrón de Guevara mail , Christian R. Mejia mail , Shyla Del-Aguila-Arcentales mail , Aldo Alvarez-Risco mail , Neal M. Davies mail , Jaime A. Yáñez mail ,
López Muñoz
<a class="ep_document_link" href="/28577/1/PIIS0002944026001367.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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An Integrated Machine Learning and Genomic Framework for Precise Detection of Gastric Cancer
This study presents a novel integrative approach for the analysis of high-dimensional gene expression data, leveraging the complementary strengths of unsupervised clustering and supervised classification. Using K-means clustering, the dataset is stratified into three distinct clusters, revealing intrinsic biological patterns and relationships. The resulting cluster assignments are subsequently employed as pseudo-labels to train machine learning models, including support vector machines, random forest, and a stacking ensemble classifier. To validate and enhance the robustness of clustering, complementary methodologies such as hierarchical clustering and DBSCAN are employed, with results visualized through PCA-driven dimensionality reduction. The high predictive accuracy achieved by the classifiers underscores the separability and reliability of the identified clusters. Furthermore, feature importance analysis highlighted key genetic determinants within each cluster, offering actionable insights into potential biomarkers and critical genomic features. This framework bridges the gap between exploratory unsupervised learning and predictive supervised modeling, providing a scalable and interpretable methodology for analyzing complex genomic datasets. Its applicability extends to biomarker discovery, patient stratification, and other precision medicine applications, emphasizing its utility in advancing genomic research and clinical practice.
Eshmal Iman mail , Sohail Jabbar mail , Shabana Ramzan mail , Ali Raza mail , Farwa Raoof mail , Stefanía Carvajal-Altamiranda mail stefania.carvajal@uneatlantico.es, Vivian Lipari mail vivian.lipari@uneatlantico.es, Imran Ashraf mail ,
Iman
<a class="ep_document_link" href="/28319/1/s41598-026-45575-1_reference.pdf"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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A novel approach for disease and pests detection in potato production system based on deep learning
Vulnerability of potato crops to diseases and pest infestation can affect its quality and lead to significant yield losses. Timely detection of such diseases can help take effective decisions. For this purpose, a deep learning-based object detection framework is designed in this study to identify and classify major potato diseases and pests under real-world field conditions. A total of 2,688 field images were collected from two research farms in Punjab, Pakistan, across multiple growth stages in various seasonal conditions. Excluding 285 symptoms-free images from the earliest collection led to 2,403 images which were annotated into four biotic-stress classes: blight disease (n = 630), leaf spot disease (n = 370), leafroll virus (viral symptom complex; n = 888), and Colorado potato beetle (larvae/adults; n = 515), indicating class imbalance. Several state-of-the-art models were used including YOLOv8 variants (n/s/m), YOLOv7, YOLOv5, and Faster R-CNN, and the results are discussed in relation to recent potato disease classification studies involving cropped leaf images. Stratified splitting (70% training, 20% validation, 10% testing) was applied to preserve class distribution across all subsets. YOLOv8-medium achieve the best performance with mean average precision (mAP)@0.5 of 98% on the held-out test images. Results for stable 5-fold cross-validation show a mean mAP@0.5 of 97.8%, which offers a balance between accuracy and inference time. Model robustness was evaluated using 5-fold cross-validation and repeated training with different random seeds, showing a low variance of ±0.4% mAP. Results demonstrate promising outcomes under the real-world field conditions, while, broader cross-region and cross-season validation is intended for the future.
Ahmed Abbas mail , Saif Ur Rehman mail , Khalid Mahmood mail , Santos Gracia Villar mail santos.gracia@uneatlantico.es, Luis Alonso Dzul López mail luis.dzul@uneatlantico.es, Aseel Smerat mail , Imran Ashraf mail ,
Abbas
<a href="/28320/1/1-s2.0-S1876034126000912-main.pdf" class="ep_document_link"><img class="ep_doc_icon" alt="[img]" src="/style/images/fileicons/text.png" border="0"/></a>
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Concern for mpox infection in Latin America
Background Mpox arrived in Latin America and quickly began to replicate, so it is important to measure the concern it generates among residents. The study aims to assess whether country or other factors are associated with concern about mpox infection in Latin America. Methods The study uses a cross-sectional, multicenter design. Sampling was conducted using non-random snowball sampling. From August to September 2022, concern about being infected with mpox was assessed using a previously validated questionnaire (Cronbach's Alpha: 0.85); it was divided into nine countries and other social variables. Results From 1404 respondents, the majority of respondents were female (60.3%) and young (median age 25 years); also, a few reported that it was a significant problem (6% almost all the time and 11% often) and were concerned (6% almost all the time and 11% often) about the possibility of mpox infection. In multivariate analysis, men (aPR: 0.85; 95% CI: 0.73–0.99; p-value=0.046), younger (aPR: 0.98; 95% CI: 0.97–0.99; p-value<0.001), single (aPR: 0.78; 95% CI: 0.62–0.99; p-value=0.042) and, compared to Peru, those living in Colombia (aPR: 0.75; 95% CI. 0.58–0.97; p-value=0.027) and Costa Rica (aPR: 0.65; 95% CI: 0.44–0.96; p-value=0.032) reported the lowest concern; also, Bolivia (aPR: 1.16; 95% CI: 0.94–1.43; p-value=0.176) and Honduras (aPR: 1.01; 95% CI: 0.80–1.27; p-value=0.943) reported that their concerns tend to be higher. Conclusions There were evident differences across respondents' countries; these baseline results show that the first report was made in many countries that were also significantly affected by mpox and now face a new epidemic threatening public health.
Christian R. Mejia mail , Aldo Alvarez-Risco mail , Luciana Daniela Garlisi-Torales mail , Telmo Raúl Aveiro mail , Jamil Cedillo-Balcázar mail , Néstor Valentin Rocha-Saravia mail , Andrea Retana-González mail , Medally C. Paucar mail , Beatriz Mejia Raudales mail , Jose Armada mail , Shyla Del-Aguila-Arcentales mail , Neal M. Davies mail , Jaime A. Yáñez mail jaime.yanez@unini.edu.mx,
Mejia
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Fish consumption and brain structure: a comprehensive systematic review of observational studies
Background Age-related structural changes in the human brain, including cortical atrophy, reductions in grey and white matter volumes, and the accumulation of small vessel–related lesions such as white matter hyperintensities (WMH) and cerebral microbleeds, represent critical biological substrates underlying cognitive decline and dementia. Fish consumption has been associated with slower cognitive decline and reduced risk of dementia, but a comprehensive evaluation of its relation with brain structures is lacking. Aims The aim of this study was to systematically review current scientific literature providing evidence of relation between fish intake and brain structures in human studies. Methods Studies indexed in two major electronic databases have been screened based on a combination of keywords and MeSH terms. Studies were eligible whether they assessed fish consumption in relation to brain structures in the adult populations. Results A total of 24 studies conducted predominantly on older adults met inclusion criteria. Most brain volume measures were obtained via magnetic resonance imaging (MRI) procedures. Higher fish consumption was associated with reduced severity of white matter hyperintensities (a biomarker of cerebral small vessel disease and white matter damage) and cerebral micro-bleed, preservation of certain brain areas volumes (i.e., hippocampus, temporal lobe and periventricle white matter) and cortical thickness of specific areas (i.e., precuneus, parietal, and cingulate grey matter), among others, compared to lower intake. Some analyses found no association and isolated findings suggested possible adverse associations that were not consistently replicated. Studies reporting null findings may underline the possible relevance of the overall diet (i.e., adherence to the Mediterranean diet). Conclusions Inclusion of fish in a healthy and balanced diet is associated with better white matter grades on MRI and slower progression of white matter hyperintensities and reduction of vascular-related lesions of the aging brain, suggesting a potential role in preventing neurocognitive deterioration. Heterogeneity across studies underscores the need for additional studies.
Justyna Godos mail , Giuseppe Caruso mail , Agnieszka Micek mail , Alberto Dolci mail , Zoltan Ungvari mail , Andrea Lehoczki mail , Lisandra León Brizuela mail , Evelyn Frias-Toral mail , Andrea Di Mauro mail , Mario Siervo mail , Michelino Di Rosa mail , Giuseppe Grosso mail ,
Godos
