This review discusses the significant progress made in the development of CNT/GO-based biosensors for disease biomarker detection. It highlights the specific applications of CNT/GO-based biosensors in the detection of various disease biomarkers, including cancer, cardiovascular diseases, infectious diseases, and neurodegenerative disorders. The superior performance of these biosensors, such as their high sensitivity, low detection limits, and real-time monitoring capabilities, makes them highly promising for early disease diagnosis. Moreover, the challenges and future directions in the field of CNT/GO-based biosensors are discussed, focusing on the need for standardization, scalability, and commercialization of these biosensing platforms. In conclusion, CNT/GO-based biosensors have demonstrated immense potential in the field of disease biomarker detection, offering a promising approach towards early diagnosis. Continued research and development in this area hold great promise for advancing personalized medicine and improving patient outcomes.

Breast cancer was a prevalent form of cancer worldwide. Thermography, a method for diagnosing breast cancer, involves recording the thermal patterns of the breast. This article explores the use of a convolutional neural network (CNN) algorithm to extract features from a dataset of thermographic images. Initially, the CNN network was used to extract a feature vector from the images. Subsequently, machine learning techniques can be used for image classification. This study utilizes four classification methods, namely Fully connected neural network (FCnet), support vector machine (SVM), classification linear model (CLINEAR), and KNN, to classify breast cancer from thermographic images. The accuracy rates achieved by the FCnet, SVM, CLINEAR, and k-nearest neighbors (KNN) algorithms were 94.2%, 95.0%, 95.0%, and 94.1%, respectively. Furthermore, the reliability parameters for these classifiers were computed as 92.1%, 97.5%, 96.5%, and 91.2%, while their respective sensitivities were calculated as 95.5%, 94.1%, 90.4%, and 93.2%. These findings can assist experts in developing an expert system for breast cancer diagnosis.

Recently, carbon nanocomposites have garnered a lot of curiosity because of their distinctive characteristics and extensive variety of possible possibilities. Among all of these applications, the development of sensors with electrochemical properties based on carbon nanocomposites for use in biomedicine has shown as an area with potential. These sensors are suitable for an assortment of biomedical applications, such as prescribing medications, disease diagnostics, and biomarker detection. They have many benefits, including outstanding sensitivity, selectivity, and low limitations on detection. This comprehensive review aims to provide an in-depth analysis of the recent advancements in carbon nanocomposites-based electrochemical sensors for biomedical applications. The different types of carbon nanomaterials used in sensor fabrication, their synthesis methods, and the functionalization techniques employed to enhance their sensing properties have been discussed. Furthermore, we enumerate the numerous biological and biomedical uses of electrochemical sensors based on carbon nanocomposites, among them their employment in illness diagnosis, physiological parameter monitoring, and biomolecule detection. The challenges and prospects of these sensors in biomedical applications are also discussed. Overall, this review highlights the tremendous potential of carbon nanomaterial-based electrochemical sensors in revolutionizing biomedical research and clinical diagnostics.

The detection of urban expansion through digital processing of satellite images provides valuable information for understanding the dynamics of land use change and its spatial relationship with environmental factors. In order to apply or generate effective land-use planning policies, it is essential to have a historical record of the regional distribution of human settlements, an element that is practically non-existent in our country. For this reason, this text aims to determine the urban growth rate during the period 2000–2014 in the state of Hidalgo, Mexico, and to identify potential expansion zones from Landsat images. Six Landsat scenes were used for the spatial analysis of the state urban coverage and their relationship with the road influence area was evaluated. Two maps were obtained as cartographic products: one of urban coverage distribution and another of the municipalities with the greatest expansion, whose areas are located in the Valle del Mezquital region. However, Mineral de la Reforma, Tetepango, Tizayuca and Pachuca de Soto stand out for their growth rates during the study period: 183.44%, 102%, 94% and 68.5%, respectively. In total, the state urban area in-creased 72.3 km² from 2000 to 2014 with an average growth rate of 1.8% per year. Such growth was associated with the areas of influence of important road infrastructure, such as the Libramiento Arco Norte in Hidalgo. Therefore, the Mezquital Valley and the Mexico Basin are considered as potential regions for urban expansion in the state.

Proper understanding of LULC changes is considered an indispensable element for modeling. It is also central for planning and management activities as well as understanding the earth as a system. This study examined LULC changes in the region of the proposed Pwalugu hydropower project using remote sensing (RS) and geographic information systems (GIS) techniques. Data from the United States Geological Survey's Landsat satellite, specifically the Landsat Thematic Mapper (TM), the Enhanced Thematic Mapper (ETM), and the Operational Land Imager (OLI), were used. The Landsat 5 thematic mapper (TM) sensor data was processed for the year 1990; the Landsat 7 SLC data was processed for the year 2000; and the 2020 data was collected from Operation Land Image (OLI). Landsat images were extracted based on the years 1990, 2000, and 2020, which were used to develop three land cover maps. The region of the proposed Pwalugu hydropower project was divided into the following five primary LULC classes: settlements and barren lands; croplands; water bodies; grassland; and other areas. Within the three periods (1990–2000, 2000–2020, and 1990–2020), grassland has increased from 9%, 20%, and 40%, respectively. On the other hand, the change in the remaining four (4) classes varied. The findings suggest that population growth, changes in climate, and deforestation during this thirty-year period have been responsible for the variations in the LULC classes. The variations in the LULC changes could have a significant influence on the hydrological processes in the form of evapotranspiration, interception, and infiltration. This study will therefore assist in establishing patterns and will enable Ghana's resource managers to forecast realistic change scenarios that would be helpful for the management of the proposed Pwalugu hydropower project.

A Detailed geophysical investigation was conducted on Knossos territory of Crete Island. Main scope was the detection of underground archaeological settlements. Geophysical prospecting applied by an experienced geophysical team. According to area dimensions in relation to geological and structural conditions, the team designed specific geophysical techniques, by adopted non-catastrophic methods. Three different types of geophysical techniques performed gradually. Geophysical investigation consisted of the application of geoelectric mapping and geomagnetic prospecting. Electric mapping focusses on recording soil resistance distribution. Geomagnetic survey was performed by using two different types of magnetometers. Firstly, recorded distribution of geomagnetic intensity and secondly alteration of vertical gradient. Measured stations laid along the south-north axis with intervals equal to one meter. Both magnetometers were adjusted on a quiet magnetic station. Values were stored in files readable by geophysical interpretation software in XYZ format. Oasis Montaj was adopted for interpretation of measured physical properties distribution. Interpretation results were illustrated as color scale maps. Further processing applied on magnetic measurements. Results are confirmed by overlaying results from three different techniques. Geoelectric mapping contributed to detection of a few archaeological targets. Most of them were recorded by geomagnetic technique. Total intensity aimed to report the existence of magnetized bodies. Vertical gradient detected subsurface targets with clearly geometrical characteristics.