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Measuring the small tree trunk variations during the day–night cycle, seasonal cycles, as well as those caused by the plant’s growth and health regime is a very important action in horticulture or forestry because by analyzing the collected data, assessments can be made on the health of the trees, but also on the climatic conditions and changes in a certain region. This can be performed with devices called dendrometers. This paper presents a new type of approach to these measurement types in which the trunk volume changes are highly sensitively converted into the axial stress on sensitive elements made of magnetic materials in wire form in which the giant stress impedance effect occurs. Finally, by electronic processing of the signals provided by the sensitive elements, digital words with a decimal value proportional to the diameter variations are obtained. This paper presents the operating principle, the constructive details and the experimental results obtained by testing the device in the laboratory and in-field. The proposed dendrometer, compared to those available commercially, has the advantage of good resolution and sensitivity, good immunity to temperature variations, the possibility of transmitting the result remotely, robustness and low price. Some metrological parameters obtained from the experimental testing are the following: resolution 1.6 µm, linearity 1.4%, measurement range 0 to 5 mm, temperature coefficient 0.012%/°C.

In the spirit of the recent debate on the 15-minute city, two concepts are central: urban proximity and resilience. They became cornerstones of new urban planning perspectives on sustainability, livability, and inclusiveness in cities and metropolitan areas. Very recently, the notion of ‘proxilience’ has been introduced as an integration of urban planning views on the drivers of citizens’ wellbeing. The present study seeks to conceptualize and operationalize the proxilience concept for the case of metropolitan agglomerations, in which the core is termed here ‘Superbowl Economy’. Consequently, the paper presents a data-driven analytical approach that uses detailed empirical data on spatial density patterns, demographic factors, socioeconomic indicators, environmental quality attributes, infrastructure accessibility, and access to services and amenities. The empirical part of the study is based on a blend of geostatistical and econometric models (correlation and regression analysis, AHP modelling, and Random Forest model). The analysis framework and the underlying propositions on the proxilience impacts on spatial patterns of disparities in wellbeing are applied and tested for the greater Iași Metropolitan Area, which is one of the largest urban poles in Romania. The findings confirm proxilience as a novel, multidimensional tool that advances spatial (urban–regional) livability in a polarized yet fragmented urban system.

Vegetation cover in urban and peri-urban areas is threatened by urban sprawl, through habitat fragmentation, loss of green space, biodiversity reduction, and the urban heat island effect intensifying. The intrusion of cities into natural landscapes reduces vital ecosystem services provided by vegetation. Hence, sustainable and integrated urban planning practices are required. Our study aims to investigate the dynamics of the urban and peri-urban fabric by exploring the relationship between the green fabric distribution and recent trends in urban expansion, focusing specifically on the peri-urban areas of Iași Municipality, Romania. We designed a mixed-method approach combining a multivariate analysis of four critical indicators (vegetation cover, built-up space, land surface temperature, and population density), emerging hot-spots, and space-time cubes in a GIS environment to achieve our research aims. Our results demonstrate that uncontrolled urban expansion has manifested in diverse patterns, impacting territories next to road transport networks and with construction-suitable topography. Concurrently, the development of green spaces prevails in forests and unexpected locations such as brownfields, railway corridors, and old industrial zones, through the growth of urban greenery. This approach provides a comprehensive understanding of how urban sprawl impacts the environment and how different land types are prone to this transformation, creating a path towards sustainability, resilience, and equitable development.

Gas sensors, like any other type of sensors, are affected by external influencing factors among which the most aggressive are the ambient temperature and humidity. If the influence is small, their effect on the global accuracy of the sensor is reduced, and the error caused by these factors is included in the admissible error provided in the datasheet. However, if the influence ais significant, their effect can no longer be neglected and compensation of these errors is necessary based on the known influence characteristics found in the datasheet of the sensor. Unfortunately, these characteristics are not linear and the compensation must be accomplished according to an analytical relationship, if it can be known, or based on look-up tables implemented in the memory of the measuring device. Things get complicated when there are several influence factors. The paper describes a method for compensating the influence of ambient temperature and humidity on an MQ7 metal oxide gas (MOG) sensor, mainly dedicated to measuring carbon monoxide (CO), by mathematically modelling the surfaces of the characteristics given in the sensor’s datasheet and their implementation on a microcontroller platform. Experimental data show that, for a temperature variation between 10 and 50 Celsius degrees (°C) and a relative humidity (RH) variation between 30 and 90%, a reduction of the total amount of error is obtained by compensating the influence quantities resulting in an accuracy improvement of more than 60%.

Human Activity Recognition (HAR) leveraging wearable sensors has emerged as a critical research area, with broad applications spanning healthcare, elderly assistance, sports analytics, and human-computer interaction. While traditional approaches using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have effectively extracted local spatial and sequential temporal features from multi-channel sensor data, recent advancements incorporate Transformer-based architectures featuring attention mechanisms that capture long-range temporal dependencies without recurrence. This paper introduces a novel multivariate Transformer model designed to integrate multiple physiological and kinematic data streams such as: electrocardioagram-ECG, photoplethysmogram-PPG (wrist and finger infrared/red), Galvanic Skin Response (GSR), respiration, body temperature, three-axis acceleration, and gyroscope signals. Distinctively, the designed architecture assigns dedicated encoders to individual streams to effectively handle signal diversity, sampling frequency variations, and latency discrepancies, using multi-head attention and learnable positional encodings. Evaluated across five experimental scenarios (rest, standing, sitting, running, and walking) segmented into uniform 30-seconds windows, the Transformer-based model demonstrated exceptional performance, achieving approximately 99% accuracy, along with near-perfect sensitivity and F1-scores, highlighting its robustness and superior generalization capability.

Purpose – The paper aims to present a device devoted to detect and measure earth displacements produced by landslides. This device is an inclinometer type geotechnical instrument. It is widely known that landslides are categorized among the most destructive disasters that yearly produce huge damages and even human lives losses. Design/methodology/approach – The principle of operation is based on measuring the deformation produced during soil layers sliding to a rod vertically mounted into the ground. The rod deformation is detected by highly sensitive strain gauges developed by authors using the stress impedance effect occurring in non-magnetostrictive magnetic amorphous microwires. The gauges are mounted in bridge configurations along the rod, beside the corresponding analogue and digital signal processing circuitry. Findings – The landslide transducer is able to calculate the displacement of the soil layers at different levels of depth and the direction of the landslide. It has been tested in laboratory in terms of sensitivity and accuracy. A resolution of less than 1 mm has been achieved for displacement detection, whereas orientation may be calculated with about a maximum accuracy of less than 20 degrees. Research limitations/implications – Problems occurred in the manufacturing process of the gauges because of the quite large dispersion of the microwire parameters, as well as with gluing the gauges on the rod, that is compulsory to be well done, otherwise the gauges relaxation occurs with consequence in time stability decay. Originality/value – With respect to other commercial devices, our inclinometer is characterized by high sensitivity and also by possibility of 3D measuring, it being able to gauge in depth the amplitude and orientation of the landslide.

The paper presents a study on the measurement stability of a landslide sensor based on four magnetic amorphous wire strain gauges used in an Wheatstone bridge configuration. The sensor is aimed for in-situ monitoring using a distributed sensor network. The experiment has been performed in the lab using a test stand, while a time step progressive deformation has been applied to the sensor. Results show a good stability of the measurement and a variable sensitivity with respect to the bending point.

The paper presents a device devoted to detect and measure earth displacements produced by landslides. It is part of inclinometer type geotechnical instruments and is based on measuring the deformation produced to a rod vertically mounted into the ground during soil layers sliding. With respect to other commercial devices, our inclinometer is characterized by high sensitivity to very small deformations owing to special strain gauges utilized in its construction, and also by possibility of 3D measuring, it being able to gauge in depth the amplitude and orientation of the soil layers displacement.

This paper analyses the power consumption of an IoT node that captures images and transmits them to a cloud storage. Various implementation versions of the IoT node are proposed, focusing on the utilization of common components to achieve cost-effectiveness and minimize the maintenance requirements. The realised node variants were initially tested in laboratory conditions and then commissioned and deployed outdoors, being located in an orchard where they remained operational during 7 months under different meteorological conditions. The proposed solution utilizes an ESP32 microcontroller and a camera with a resolution of 1.3 Megapixels as a data acquisition node whose energy is assured by a Li-Po battery, charged through a solar panel. Wi-Fi communication was employed to transmit the images to the server. The power consumption of the node was evaluated for different variants of software optimization using the HTTP or the MQTT transmission modes. The instantaneous current consumption of the nodes was measured in laboratory to identify the power consumption for each phase of the program execution.The experiments revealed that the MQTT transmission mode consumed considerably less power than the HTTP mode. Additional experiments have been performed in which the variation of the battery voltage, the solar panel voltage and the number of daily emissions in the examined period, as well as correlations between these quantities have been analyzed.

Practical applications in electrical engineering often deal with analog signals, but there are employed also digital circuits and microcontrollers. Sometimes it is easier or cheaper to improvise DA and AD converters instead of using dedicated ones. This paper is a comparative analysis of how accurate the analog signals can be generated from digital systems, like Field Programmable Gate Arrays (FPGAs) using only additional passive circuits. For this analysis, different FPGAs families were tested. Two alternatives of generating analog signals were considered in this study: PWM and R2R network. The analysis has been performed for a common frequency band, from 100 Hz to 30 kHz, using a virtual instrument for building an automated test system. At 10 kHz the PWM module obtains better THD values, reaching even 0.013% and at 30 kHz the R2R module obtains better THD values, reaching even 0.222%.