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Dried meat is one of the ethnic and aesthetic food products popular among global civilizations and communities. The background of the production is associated with several methods practiced conventionally in the olden days. This review focused on investigating the advantages, challenges, research gaps, and technological intervention in dried meat production in the modern era. Moreover, it presented a gestalt of cutting-edge thermal and non-thermal food processing technologies and their effectiveness in extending shelf life. It delved into the specific characteristics of dried meat, including biochemical, sensory, and microbiological properties and processing techniques, and addressed the contamination sources. The pros and cons of various drying methods like hot-air drying, vacuum pulsed electric field, microwave-assisted techniques, and non-thermal drying processes are comprehended. The impact on meat's structural properties, nutritional value, shelf-life, quality control, and food safety are thoroughly presented. Moreover, the review explored the biochemical dynamics of the drying process and underscored the health risks associated with mycotoxin contamination in dried meat products. Furthermore, the study also presented the avenues of AI-based platforms and non-destructive technology for validating the quality of dried meat products. [ABSTRACT FROM AUTHOR]

The use of unmanned aerial vehicles (UAVs) equipped with multispectral and thermal sensors provides a promising approach to wildlife monitoring, especially in the dynamic environment of Komodo National Park. This study explores the effectiveness of UAVs in tracking Komodo dragons and other wildlife using thermal imaging, which distinguishes animals based on body temperature contrasts with the surrounding environment. Thermal sensors detect wildlife more effectively in the afternoon, as animals like the Komodo dragon exhibit higher body temperatures compared to the cooler surroundings. Challenges, however, arise in the morning when animals body temperatures are closer to the environment, making them harder to detect. Factors such as fog, animal movement, and sensor limitations also impact detection accuracy. The study highlights the advantages of combining UAV thermal imaging with multispectral data to enhance monitoring accuracy. Despite the challenges, this method proves to be an efficient tool for wildlife management and conservation in remote, vast areas like Komodo National Park. [ABSTRACT FROM AUTHOR]

Uncrewed aerial vehicles (UAVs) have transformed remote sensing, offering unparalleled flexibility and spatial resolution across diverse applications. Many of these applications rely on mapping flights using snapshot imaging sensors for creating 3D models of the area or for generating orthomosaics from RGB, multispectral, hyperspectral, or thermal cameras. Based on a literature review, this paper provides comprehensive guidelines and best practices for executing such mapping flights. It addresses critical aspects of flight preparation and flight execution. Key considerations in flight preparation covered include sensor selection, flight height and GSD, flight speed, overlap settings, flight pattern, direction, and viewing angle; considerations in flight execution include on-site preparations (GCPs, camera settings, sensor calibration, and reference targets) as well as on-site conditions (weather conditions, time of the flights) to take into account. In all these steps, high-resolution and high-quality data acquisition needs to be balanced with feasibility constraints such as flight time, data volume, and post-flight processing time. For reflectance and thermal measurements, BRDF issues also influence the correct setting. The formulated guidelines are based on literature consensus. However, the paper also identifies knowledge gaps for mapping flight settings, particularly in viewing angle pattern, flight direction, and thermal imaging in general. The guidelines aim to advance the harmonization of UAV mapping practices, promoting reproducibility and enhanced data quality across diverse applications. [ABSTRACT FROM AUTHOR]

Bimetallic inorganic nanoparticles are vital due to their high biocompatibility, stability and comparatively less toxicity. Nanosized bimetallic oxide materials have been extensively studied worldwide due to their unique properties such as electrical, magnetic and mechanical properties. Present work is reporting the size-controlled synthesis of strontium aluminate and magnesium cobaltate nanoparticles (SrAl2O4 and MgCoO2) by self-propagating combustion reaction using polymer fuel. Size of the prepared SrAl2O4 and MgCoO2 samples distributed on the DLS spectrum was 76 nm and 100 nm respectively. The structural and morphological characterization of the prepared nano oxide samples were carried out by X-ray diffraction (XRD) and scanning electron microscope (SEM) analysis respectively. It shows high crystalline and compact nature of the samples. FT-IR instrumentation was used to study the bonding nature of both the oxide nano samples. Formation of metal oxide and metal–metal bonding can supports the sample formation. EDX analysis carried out to further confirm the presence of Sr, Al, Mg and Co metals and also characteristics absorption peaks of signals demonstrate the final phases of the samples. UV–vis spectroscopic analysis and fluorescence study of the samples was undertaken to know the absorption and emission behaviour respectively. In continuation, the thermal and electrochemical studies of prepared samples were analyzed. Thermal investigation assesses the thermal behaviour of the samples, which shows high thermal stability. The preliminary results of CV study concluding that, the samples are electro active nature and also show possible electrochemical applications. The insights gained from this work contribute to the growing body of knowledge in metal oxide nanomaterials and pave the way for innovations in electrical and thermal-related technologies. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) equipped with multi-sensor remote sensing technologies provide an efficient approach for mapping spatial and temporal variations in vegetation traits, enabling advancements in precision monitoring and modeling. This study's objective was to analyze UAV multiple sensors' performance in monitoring winter wheat chlorophyll content (SPAD), plant nitrogen accumulation (PNA), and N nutrition index (NNI). A two-year field experiment with five N fertilizer treatments was carried out. The color indices (CIs, from RGB sensors), vegetation indices (VIs, from multispectral sensors), and temperature indices (TIs, from thermal sensors) were derived from the collected images. XGBoost (extreme gradient boosting) was applied to develop the models, using 2021 data for training and 2022 data for testing. The excess green minus excess red index, red green ratio index, and hue (from CIs), and green normalized difference vegetation index, normalized difference red-edge index, and normalized difference vegetation index (from VIs), showed high correlations with three N indicators. At the pre-heading stage, the best performing CIs correlated better than the VIs; this was reversed in the post-heading stage. CIs outperformed VIs in SPAD (CIs: R2(coefficient of determination) = 0.66, VIs: R2 = 0.61), PNA (CIs: R2 = 0.68, VIs: R2 = 0.64), and NNI (CIs: R2 = 0.64, VIs: R2 = 0.60) in the pre-heading stage, whereas VI-based models achieved slightly higher accuracies in post-heading and all stages compared to CIs. Models built with CIs + VIs significantly improved the models' performance compared to single-sensor models. Adding TIs to CIs and CIs + VIs further improved the models' performance slightly, especially at the post-heading stage, resulting in the best model performance with three sensors. These findings highlight the effectiveness of UAV systems in estimating wheat N and establish a framework for integrating RGB, multispectral, and thermal sensors to enhance model accuracy in precision vegetation monitoring. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems are characterized by their efficiency and performance decreasing as the operating temperature increases. The maximum available tested efficiency of the PV module decreases by up to 0.8% with each 10-degree temperature increase. To perform better PV, it is necessary to extract the heat, and thus decrease its temperature. In the context of this particular research investigation, a series of empirical investigations were conducted at Al-Rumaitha Technical Institute situated in Iraq (31°42' - 45°12'). These experimental endeavors were undertaken to analyze the performance and characteristics of photovoltaic thermal (P'VT) collectors. It is noteworthy to mention that one of these solar panels was effectively incorporated into a photovoltaic/thermal (PV/T) module, while the other panel was employed independently without any cooling mechanism. It is essential to underscore that the induction and maintenance of airflow were achieved through the utilization of an air intake fan. It is important to highlight that these experimental investigations were undertaken over a diverse array of days throughout the year, thereby ensuring the thorough coverage of various atmospheric conditions and scenarios. The results show the maximum electrical efficiency achieved was found to be a commendable 19%, representing an impressive improvement of 5.1% when compared to the PV panel without porous. Moreover, it is important to highlight that the maximum output power attained was an astounding 330.7W. Also, the thermal characteristics of the system showed that the maximum amount of heat gain reached a notable 707.1 W. It is also worth noting that the thermal efficiency of the system was calculated to be 29.57%. Lastly, it is pertinent to mention that the overall efficiency of the system, encompassing both electrical and thermal aspects, amounted to an impressive 46.8%. [ABSTRACT FROM AUTHOR]

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field. [ABSTRACT FROM AUTHOR]