Search Results (166)

This paper presents a novel integration of bioclimatic-passive architectural elements—Trombe walls, pergolas, and deciduous climbers—in the context of residential buildings in Eastern and Central Europe, a combination that remains largely underexplored in the current literature. The innovativeness of the proposed concept is reflected in the combined use of the following building elements: three types of passive Trombe wall (single-glazed, double-glazed, and triple-glazed), pergolas, and four types of deciduous climbers (V. coignetiae, H. lupulus, W. sinensis, and A. macrophylla). By using meteorological data for the towns Kragujevac and Kielce, the influence of location parameters for two dominant European climate zones (moderate continental and continental) is also included in this investigation. The initial single-family building models were created following the Serbian and Polish rulebooks on energy efficiency for new buildings and equipped with the same thermo-technical systems and people occupancy conditions. Based on the conducted simulations (using Google SketchUp 8 and EnergyPlus 7.1) and obtained results on the annual level, the following main conclusions can be drawn: (1) a moderate continental climate is more suitable for implementing the proposed concept; (2) a single-glazed passive Trombe wall is not energy or environmentally justified; (3) the energy, environmental, and economic benefits for both selected locations are greatest in the case of the combined use of pergolas, V. coignetiae, and triple-glazed passive Trombe wall; and (4) before the wider commercial application of the proposed concept in the future, efforts should be made to explore economic opportunities, which, among other things, involve a focus on market stability and accessibility. Full article

Traffic measurement systems are an essential part of intelligent transportation systems (ITS). These are specialized transport infrastructures where traffic data is collected and analyzed in order to optimize the use of road systems, improve transport safety, and implement future transport plans. The rapid development of transportation systems, urbanization, and industrialization have led to a global problem of air pollution. This has raised the topical issue of measuring and monitoring environmental parameters at traffic monitoring stations in ITS. In this paper, we present a wireless environmental monitoring system, which is a subsystem of a traffic monitoring station. Along with measuring traffic parameters, the station also collects useful meteorological information. A novel hybrid, dual-band IoT system based on LoRa and LoRaWAN for environmental parameters monitoring is presented. The hardware realization of a developed hybrid LoRaWAN end device, together with the sensors used for the measurement of air parameters, is described. Initial results from real test monitoring of environmental parameters on the road in urban environments are presented as a proof of concept. The presented wireless environmental monitoring system can also be used for indoor or outdoor air pollution monitoring, serving as a useful complement to intelligent transport systems. Full article

Background/Objectives: A critical part of the maize life cycle takes place during the summer, and due to climate change, its growth and development are increasingly exposed to the irregular and unpredictable effects of drought stress. Developing and using new cultivars with increased drought tolerance for farmers is the easiest and cheapest solution. One of the concepts to screen for drought tolerance is to expose germplasm to various growth scenarios (environments), expecting that random drought will occur in some of them. Methods: In the present study, thirty-two maize hybrids belonging to four FAO maturity groups were tested for grain yield at six locations over two consecutive years. In parallel, data of the basic meteorological elements such as air temperature, relative humidity and precipitation were collected and used to compute two indices, scPDSI (Self-calibrating Palmer Drought Severity Index) and VPD (Vapor Pressure Deficit), that were assessed as indicators of drought (water deficit) severity during the vegetation period. Practical implementation of these indices was carried out indirectly by first analyzing yield data using a factor analytic model to detect latent environmental variables affecting yield and then correlating those latent variables with drought indices. Results: The first latent variable, which explained 47.97% of the total variability, was correlated with VPD (r = −0.58); the second latent variable explained 9.57% of the total variability and was correlated with scPDSI (r = −0.74). Furthermore, latent regression coefficients (i.e., genotypic sensitivities to latent environmental variables) were correlated with genotypic drought tolerance. Conclusions: This could be considered an indication that there were two different acting mechanisms in which drought affected yield. Full article

We present a model evaluation of road weather forecasts in Norway with the METRo model in a quasi-operational setting. The road weather forecasts are initialized with measurements made by road weather stations and driven by mesoscale weather forecast data from the Norwegian Meteorological Institute. One important source of hazardous driving conditions in Norway are freezing road-surface temperatures. We quantify the skill of our model setup to predict such conditions by computing the hit rates and false-alarm rates for incidences of freezing temperatures, relative to the climatological rates of occurrence. The METRo forecasts consistently add skill in wintertime and the crucial transitional seasons of spring and fall. Our study illustrates a successful proof-of-concept for novel, operational road weather forecasts in Norway, that could easily be realized with an open-source prediction model and readily available input data. Full article

The continuous evolution of Internet of Things (IoT) technologies presents significant opportunities and challenges within the domain of engineering education. This paper introduces a novel and comprehensive framework that extends the established Engineering Design Process (EDP) by incorporating a modular Digital Twin (DT) structure specifically tailored to smart building IoT applications in education. Unlike previous approaches, our framework enables real-time system feedback, simulation-based design iteration, and hands-on experimentation—all integrated within a pedagogical flow aligned with engineering curricula. It comprises seven distinct phases, providing a complete methodology that guides learners from fundamental concepts to advanced applications, including data visualization, real-time simulation, and system optimization. To demonstrate the applicability of the proposed framework, we design and experiment with a practical use case related to a meteorological station and data, which incorporate IoT-enabled sensors, actuators, and microcontrollers for real-time monitoring of environmental parameters and energy consumption within a smart building campus facility. Additionally, to support EDP extension, a hybrid pedagogical approach is introduced, which combines traditional engineering hands-on education methodologies with DT activities, to further foster experimental learning, iterative system design, and complex systems thinking development. To this end, our approach aims to bridge the gap between theoretical science and engineering knowledge, along with practical application use cases, contributing to a better preparation of future engineers capable of addressing interdisciplinary challenges associated with smart systems and digital transformation within the Industry 4.0 era. Full article

Multi-source systems are gaining attention as an effective approach to seamlessly incorporate renewable energies within electrical networks. These systems offer greater flexibility and better energy management possibilities. The considered multi-source system is based on a 50 MW wind farm connected to battery energy storage and electrolyzers through modular multi-level DC/DC converters. Wind energy systems interface with the DC-bus via rectifier power electronics that regulate the DC-bus voltage and implement optimal power extraction algorithms for efficient wind turbine operation. However, integrating intermittent renewable energy sources with optimal microgrid management poses significant challenges. It is essential to mention that the studied multi-source system is connected to the DC loads (modular electrolyzers and local load). This work proposes a new regulation method designed specifically to improve the performance of the system. In this strategy, the excess wind farm energy is converted into hydrogen gas and may be stored in the batteries. On the other hand, when the wind speed is low or there is no excess of energy, electrolyzer operations are stopped. The battery energy management depends on the power balance between the DC load (modular electrolyzers and local load) requirements and the energy produced from the wind farm. This control should lead to eliminating the fluctuations in energy production and should have a high dynamic performance. This work presents a nonlinear control method using a backstepping concept to improve the performances of the system operations and to achieve the mentioned goals. To evaluate the developed control strategy, some simulations based on real meteorological wind speed data using Matlab are conducted. The simulation results show that the proposed backstepping control strategy is satisfactory. Indeed, by integrating this control strategy into the multi-source system, we offer a flexible solution for battery and electrolyzer applications, contributing to the transition to a cleaner, more resilient energy system. This methodology offers intelligent and efficient energy management. Full article

The impact of wind direction on comfort and health remains underexplored in the field of natural ventilation. This study adopts the concepts of invigorating wind/deficiency wind from the “nine palaces and eight winds” theoretical framework in the Huangdi Neijing, integrating solar terms and wind direction as temporal-spatial elements into existing environmental factor analysis paradigms. Three key questions were explored, namely, the temporal principles of meteorological cycle division from an annual perspective, the impact of invigorating wind/deficiency wind on climatic stability during solar term cycles, and the spatiotemporal distribution characteristics of invigorating wind/deficiency wind. Multi-scale analyses were conducted using typical meteorological year data and real-time meteorological data from case cities. Results showed that solar term cycle divisions adjusted based on temperature variations better align with regional climatic characteristics. The ratio of invigorating wind/deficiency wind on solar term days may imply climatic stability within solar term cycles. Also, significant differences exist between deficiency wind and invigorating wind during high-disease-incidence solar terms, though their manifestations vary. These findings help to find new wind characteristics to explain the comfort and health effects of natural ventilation and will provide scientific foundations for further exploration of well-being in indoor environments. Full article

Offshore wind farms play a pivotal role in the global transition to clean energy but remain susceptible to diverse meteorological hazards—ranging from highly variable wind speeds and temperature anomalies to severe oceanic disturbances—that can jeopardize both turbine safety and overall power output. Although Hidden Markov Models (HMMs) have a longstanding track record in operational forecasting, this study leverages and extends their capabilities by introducing a dynamic HMM framework tailored specifically for multi-risk offshore wind applications. Building upon historical datasets and expert assessments, the proposed model begins with initial transition and observation probabilities and then refines them adaptively through periodic or event-triggered recalibrations (e.g., Baum–Welch), thus capturing evolving weather patterns in near-real-time. Compared to static Markov chains, naive Bayes classifiers, and RNN (LSTM) baselines, our approach demonstrates notable accuracy gains, with improvements of up to 10% in severe weather conditions across three industrial-scale wind farms. Additionally, the model’s minutes-level computational overhead for parameter updates and state decoding proves feasible for real-time deployment, thereby supporting proactive scheduling and maintenance decisions. While this work focuses on the core dynamic HMM method, future expansions may incorporate hierarchical structures, Bayesian uncertainty quantification, and GAN-based synthetic data to further enhance robustness under high-dimensional measurements and rare, long-tail meteorological events. In sum, the multi-risk forecasting methodology presented here—though built on an established HMM concept—offers a practical, adaptive solution that significantly bolsters safety margins and operational reliability in offshore wind power systems. Full article

Onboard millimeter-wave radar is one way to improve helicopter flight safety at low altitudes in difficult meteorological conditions. Low-altitude flight is associated with the risk of collision with low-visibility obstacles such as power lines. Since power lines have the property of polarization anisotropy, it is necessary to use radar polarimetry methods to increase the radar visibility of such obstacles for detection. This paper investigates the possibility of identifying low-visibility polarization-anisotropic objects, such as power lines, in polarimetric images obtained by onboard helicopter radars to warn the pilot about the danger of a collision with a low-visibility object. Consequently, the use of a polarimetric radar with a polarization modulation of the emitted signal, the two-channel polarization synchronous reception of reflected signals and Eigenvalue signal processing in real time was proposed, aiming to eliminate the dependence of reflected signals on the spatial orientation of power transmission line wires. Additionally, an optimal algorithm for adaptive polarization selection, obtained by the maximum likelihood method, was proposed to detect such objects against the background of the underlying surface. The effectiveness of the proposed algorithm was tested using computer modeling methods. Moreover, a W-band polarimetric radar system was designed for experimental studies of this concept. The developed radar system provides digital real-time signal processing and the reconstruction of composite polarimetric images. It displays information about the polarization characteristics of the objects in the scanned area and the results of their polarization selection. Therefore, the system informs the pilot about dangerous objects along the helicopter’s route. Radar experimental tests in actual environmental conditions have confirmed the proposed concept’s correctness and the proposed method and algorithm’s effectiveness. Full article

The length of the beach season at Lake Balaton (Hungary, Central Europe) in the period 2002–2024 is estimated using daily data of air temperature, water temperature and wind speed. Daily thermal load is approached by calculating operative temperature using operative temperature-air temperature relationships on monthly scale. We introduced the concepts of beach day and bath day in order to estimate when the environmental conditions were suitable for lakeside activities. Data are taken from meteorological station Siófok. The main results are as follows. In the summer months, the median number of beach days and bath days is 72 and 68, respectively. The unbiased standard deviation of beach days and bath days is 4.62 and 6.4 days, respectively. As regards bath days, the best year was 2023, then the number of bath days was 105. The smallest number of bath days was 57; this occurred in the year 2010. As regards beach days, the best year was 2018 with 114 days, the worst year was 2021 with 72 days. Full article

Urban expansion into rural and peri-urban areas increases landslide risks, posing significant threats to infrastructure and public safety. However, most studies focus on surface displacement or meteorological inputs, with less emphasis on subsurface sensor data that could detect early instability precursors. To address these gaps, this study presents a proof-of-concept validation, establishing the feasibility of using subsurface sensor data to predict near-surface slope displacements. A laboratory-scale slope model (300 cm × 50 cm × 50 cm) at a 30° inclination was subjected to simulated rainfall (150 mm/h for 180 s), with displacement measured at depths of 5 cm and 25 cm using PDP-2000 extensometers. The Gradient Boosting Regressor (GBR) effectively captured the nonlinear relationship between subsurface and surface displacements, achieving high predictive accuracy (R² = 0.939, MSE = 0.470, MAE = 0.320, RMSE = 0.686). Results demonstrate that, while subsurface sensors do not detect sudden failure events, they effectively capture progressive deformation, offering valuable inputs for multi-sensor EWS in proactive urban planning. Despite demonstrating feasibility, limitations include the controlled laboratory environment and simplified slope conditions. Future work should focus on field-scale validation and multi-sensor fusion to enhance real-world applicability in diverse geological settings. Full article

This paper introduces a singular measurement infrastructure for real-time monitoring of transmission lines, applied to a 230 kV section of the Brazilian grid. The system aimed to expand the scope of monitoring variables using new concepts of optical sensing. Thus, variables are captured not only in the electrical domain but also in the mechanical, thermal, and environmental domains through optical technologies and meteorological measurement sensors strategically positioned along the transmission line. The system relies on new features, including a high-voltage polymeric insulator instrumentalized with optical fiber sensors to measure line electrical current, conductor temperature, mechanical strain, and an electro-optical signal processing unit fed by a solar system. The correlations between the monitored variables provide more complete information about what happens in the transmission line compared to the analysis of purely electrical quantities. For instance, the Spearman coefficient of 0.9909 highlights the strong correlation between anchoring force and ambient temperature. This new way of monitoring systems opens the doors to a multivariate power system analysis. Full article

To mitigate the prevalence of highway accidents in Southwest China during adverse weather conditions, this study introduces a novel method for identifying accident-prone sections in complex meteorological circumstances. The technique, anchored in data mining’s support index, pioneers the concept of meteorological responsiveness, which includes the elucidation of its mechanisms and the development of computational methodologies. Historical meteorological data and accident records from mountainous highways were meticulously analyzed to quantify the spectrum of adverse weather impacts on driving risks. By integrating road geometry, weather data, and accident site information, meteorological events were identified, categorized, and assigned a meteorological responsiveness score. Outlier sections were processed for preliminary screening, enabling the identification of high-risk segments. The Meteorological Response Ratio Index was instrumental in highlighting and quantifying the influence of adverse weather on traffic safety, facilitating the prioritization of critical sections. The case study of the SC2 highway in Southwest China validated the method’s feasibility, successfully pinpointing eight high-risk sections significantly affected by adverse weather, which constituted approximately 19.05% of the total highway length. Detailed analysis of these sections, especially those impacted by rain, fog, and snow, revealed specific zones prone to accidents. The meteorological responsiveness method’s efficacy was further substantiated by correlating accident mechanisms under adverse weather with the road geometry of key sections. This approach stands to significantly enhance the safety management of operational highways. Full article

To address the urgent challenges of sustainability in our changing world, STEM education must evolve to integrate a stronger focus on socioenvironmental dimensions. This study examines how students in geomatics courses understand the nature of STEM (NoSTEM) in the context of meteorology and fire ecology—fields closely tied to sustainability. Using two validated mixed-method instruments comprising closed- and open-ended items, we assessed students’ comprehension across cognitive–epistemic and socio-institutional dimensions, framed within the family resemblance approach (FRA). Data collected from 44 students in meteorology and 57 in fire ecology were analyzed using descriptive statistics and phenomenographic methods. Our findings indicate that, while students demonstrate a stronger grasp of technical concepts, their understanding of socio-institutional implications is comparatively limited. These results highlight the need to align STEM education with sustainability education, emphasizing real-world applications and the integration of socio-institutional elements into the curriculum. Addressing these gaps is essential for preparing students to engage with complex sustainability challenges, such as those posed by climate change, resource management, and disaster mitigation. Future research should investigate long-term interdisciplinary educational strategies to foster a holistic understanding of NoSTEM and its role in promoting sustainable development. Full article

In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model’s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model. Full article