For mobile robots operating in indoor environments, this paper proposes a novel 2D positioning method. This method combines RSSI and magnetometer measurements within a fingerprinting framework. Based on both magnetometer data and RSSI values between the mobile unit and anchor nodes, this method employs multilayer perceptron (MLP) feedforward neural networks to ascertain the 2D location. The mobile node gauges magnetic field strength, yielding data on disturbance levels at the specified location. The proposed technique's efficacy is confirmed using data collected from two realistic indoor scenarios, exhibiting a multitude of stationary objects. Three different methodologies are employed in examining the magnetic field measurements: the first involves the collective examination of all three sensor axis readings, the second utilizes just the magnitude of the field, and the third combines the Z-axis measurements with the magnitude of the field in the X-Y plane. Analysis of the obtained results reveals significant enhancement when combining the two data types, particularly in scenarios where the magnetic field displays high variance. Results demonstrate an enhancement exceeding 35% compared to those derived from RSSI or magnetic sensor data alone.Intelligent systems and technologies are a key element in the popularization of the smart city concept within the urbanization process of major metropolitan areas, designed to accommodate the rising human population. At Thammasat University's Rangsit Campus in Thailand, this work developed an automatic light adjustment system, primarily aimed at boosting energy efficiency while ensuring adequate illumination across the campus. The development is structured around two principal elements: device control and the prediction model. Incorporating a user interface for control, the device control functionalities were constructed to enable operation of the smart street light devices, aided by the application programming interface (API) for light-adjusting commands. The prediction model, developed with the help of an AI-driven data analytic platform, yielded predicted illuminance values. Subsequently, the model proposed light-dimming adjustments based on the prevailing environmental situation. Predictions were generated from a nine-month environmental dataset, employing four distinct machine-learning models. The test dataset results definitively show the optimal performance achieved using a three-day window size with the XGBoost model, indicated by a correlation coefficient of 0.922. This showcases a linear association between the predicted and actual illuminance values. By linking the prediction retrieval API to the device control API, an automated system was set up, cycling every 20 minutes. Smart street lighting devices were automatically adjusted in real-time, thanks to the real-time feedback provided by the purpose-built data analytics features.Soil bulk density is a key parameter for evaluating soil's overall condition. In cases where direct laboratory measurement of bulk density is unavailable, alternative prediction methods, including pedotransfer functions (PTFs), are utilized. Current Performance Test Frameworks have thus far failed to incorporate data on soil structure, even though soil structure is a fundamental determinant of soil bulk density. We therefore focused on creating new predictive tools for soil bulk density, utilizing soil macrostructure images for data acquisition. To ascertain the soil's characteristics, laboratory measurements of soil bulk density (BD), texture, and total organic carbon were performed. Soil macroporosity, as determined via image analysis, was utilized to evaluate bulk density (BDim) through image analysis, and macropore cross-sections with a 5 mm diameter were counted and classified (MP5). Finally, PTFs were created that incorporated soil structural parameters, appearing as BD~BDim + MP5 or BD~BDim. We additionally analyzed the proposed PTFs alongside a set of selected existing PTFs. Prediction coefficients of determination for the proposed PTFs showed values between 0.035 and 0.041, coupled with modelling efficiencies from 0.017 to 0.039, and mean prediction errors varying from 0 to -0.002 Mg m-3. The inclusion of the MP5 in the proposed PTFs led to improved prediction of boundary BDs; however, the intermediate BD values exhibited greater dispersion relative to the existing PTFs. The observed interrelationships underscore the value of image analysis data for evaluating soil bulk density, which subsequently contributed to the design of new prediction functions for soil. With only images of the soil structure as input, the proposed models yield the bulk density, requiring no additional data.Land surface models can be uniquely calibrated by satellite remote sensing, given its direct measurement of multiple hydrological factors and expansive spatial and temporal coverage. cyclosporina inhibitor To calibrate a land surface model using multi-objective evolutionary algorithms, this study integrates terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and soil moisture data acquired from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). To refine the model's parameters, the non-dominated sorting genetic algorithm (NSGA) is employed. For the calibration period of 2003 and 2010 in Australia, the process was carried out, and the impact was further tracked and monitored throughout 2011 (forecasting period). To improve model parameters for reliable and consistent forecasting, a new objective function has been developed, leveraging the uncertainties inherent in observations. According to the evaluation of the results, when compared to independent measurements, the calibrated model parameters deliver improved simulations in both the calibration and forecasting stages.This paper proposes a real-time robust optimization strategy for perishable supply chains in complex scenarios, built upon the foundational concept of digital supply-chain twins. Firstly, by analyzing the numerical relationship between production and sales of single-chain series supply chain items, a state-space equation encapsulating the supply chain's logical, structural, and quantitative elements was formulated. Leveraging twin data, this equation was employed to construct the digital twins of the supply chains. From a state-space equation perspective, the perishable supply-chain system within complex environments was determined to be an uncertain closed-loop system. A robust H controller design strategy was consequently proposed, utilizing supply-chain digital twins for real-time adjustments and corrections to the relevant parameters. The result was real-time robust optimization using these digital twins. In conclusion, a simulation experiment was undertaken, focusing on the production of a cake supply chain. Real-time parameter updates within digital twins empower enterprise managers to develop effective management strategies, preventing shortages in the cake supply-chain system and drastically reducing maximum inventory movement standard deviation by 1265%, 650%, and 1487% across different links and the maximum production movement standard deviation by 7021%, 5684%, and 4519% respectively.For seniors, early identification of physical frailty and infectious diseases is imperative to prevent fatal outcomes and allow for prompt healthcare provision. The presence of viral infections is often signified by an elevation of body temperature. To train various You Only Look Once (YOLO) object detection models (YOLOv56 and YOLOv7) for eye detection, this study developed and implemented a multi-age thermal face dataset. For the most accurate temperature reading, eye detection technology precisely identifies and assesses the temperature of the inner canthus of the face. An elderly thermal image dataset is the basis for the creation of a specialized eye detection algorithm dedicated to elderly individuals. The transfer of learning capabilities from a multi-age YOLOv7 model is applied to an elderly YOLOv7 model architecture. Assessing speed, accuracy, and size across trained models, YOLOv7 demonstrated superior performance, achieving a mean average precision at intersection over union of 0.5 (mAP@.5). A Frames per Second (FPS) rate of 150 is represented by the value 0996. A scan of the eye's bounding box, in search of the highest temperature, produces a normalized error distance of 0.003. A deep learning-based model for temperature detection, created using a non-contact infrared camera, is presented in this work, demonstrating speed and reliability.The growing availability of low-power wireless communication solutions has made Wireless Sensor Network deployment more common, notably in the context of Cyber-Physical System development. Within critical environments, these latter components are adaptable for structural health monitoring procedures. Wireless sensors, designed for continuous operation without relying on batteries, are equipped with ambient energy harvesting or wireless power transfer capabilities to guarantee sustained energy autonomy. Due to the high importance of the applications and the constrained resources of the nodes, security often takes a secondary position, resulting in vulnerabilities throughout the system. Using a network of battery-free nodes, this paper details a security analysis of a communicating reinforced concrete structure, employing an example implementation. Examining the inherent security aspects, major vulnerabilities, and usual attack methods of the adopted wireless communication protocols is the focus of this section. Finally, the targeted implementation's security is evaluated, particularly through defining the central attack theory and its effects. Ultimately, the security solutions aimed at protecting both the network and the data are put side by side. From a global perspective, the project's security analysis must begin at the project definition stage and continue without interruption throughout the deployment process, enabling solutions that are adjustable, upgradeable, and updatable.Landfill surface monitoring is absolutely essential, not simply during their active period of use but also for the purpose of future land restoration and development efforts.

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