This work demonstrates that simple calibration procedures can reduce the errors in determining relative humidity with a psychrometer by up to 75 times. The psychrometer uses two temperature transducers to measure relative humidity. The calibration consists of comparing both transducers before assembling the psychrometer. A psychrometer assembly is presented, built from readily available parts, which provides temperature data for comparison with a theoretical model. The results are compared against a reference psychrometer to assess errors with and without calibration. A twenty-two-fold decrease in error was observed for a relative humidity of 62%.

The Internet of Things (IoT) is increasingly deployed at the edge under resource and environmental constraints, which limits the practicality of traditional intrusion detection systems (IDSs) on IoT hardware. This paper presents two IDS configurations. First, we develop a baseline IDS with fixed hyperparameters, achieving 99.20% accuracy and ~0.002 ms/sample inference latency on a desktop machine; this configuration is suitable for high-performance platforms but is not intended for constrained IoT deployment. Second, we propose a lightweight, edge-oriented IDS that applies ANOVA-based filter feature selection and uses a genetic algorithm (GA) for the bounded hyperparameter tuning of the classifier under stratified cross-validation, enabling efficient execution on Raspberry Pi-class devices. The lightweight IDS achieves 98.95% accuracy with ~4.3 ms/sample end-to-end inference latency on Raspberry Pi while detecting both low-volume and high-volume (DoS/DDoS) attacks. Experiments are conducted in a Raspberry Pi-based real lab using an up-to-date mixed-modal dataset combining system/network telemetry and heterogeneous physical sensors. Overall, the proposed framework demonstrates a practical, hardware-aware, and reproducible way to balance detection performance and edge-level latency using established techniques for real-world IoT IDS deployment.

Banana pseudostem is an abundant lignocellulosic residue with potential for value-added applications. This study evaluated five banana varieties to determine their suitability for bioplastic production, with Williams showing the highest cellulose yield (26.99% ± 0.23). Cellulose extracted from this variety was combined with corn-starch (1:1 w/w) to synthesize a bioplastic through gelatinization and lyophilization. FTIR confirmed effective removal of lignin and hemicellulose from the pseudostem and evidenced new hydrogen-bond interactions between cellulose and starch through O–H band shifts (3335 → 3282 cm−1). SEM revealed a porous laminar morphology with cellulose particles (40–52 µm) embedded within the starch matrix. DSC analysis showed that the bioplastic exhibits an intermediate thermal profile between its components, while mechanical compression increased the endothermic transition temperature (from 69 °C to 85 °C) and reduced molecular mobility. Tensile testing demonstrated that compression markedly improved mechanical performance, increasing tensile strength from 0.094 MPa to 0.69 MPa and density from 110 to 638.7 kg/m3. These findings indicate that cellulose–starch bioplastics derived from banana pseudostem possess favorable structural, thermal, and mechanical characteristics for short-use applications. The approach also contributes to the valorization of agricultural waste through biodegradable material development.

This study discusses the role of the equivalence factor and penalty function in improving the performance of energy consumption minimization strategies in Range Extended Electric Vehicles (REEVs). In conventional ECMS, equivalence factors are typically derived from constant efficiency assumptions for simplicity or adaptively adjusted according to driving conditions in adaptive ECMS. In REEVs, however, the battery efficiency exhibits nonlinear behavior in the low SOC range, which directly leads to variability in the equivalence factor within conventional ECMS. This study investigates the influence of the variable equivalence factor on the overall fuel economy. The equivalence factors are usually considered constant or vary adaptively depending on driving cycles. However, the variation in battery efficiency is often neglected. The present study compares the results obtained for both constant and variable battery efficiencies in deriving the equivalence factors. The simulation results show that an improvement of approximately 3% in fuel economy was obtained for UDDS, NEDC, and WLTC driving cycles as a result of applying the variable equivalence factor. Additionally, through an analysis of various penalty function designs, the study highlights their crucial role in optimizing fuel consumption across different driving cycles.

The occurrence patterns of important words found in six texts (one historical pamphlet and five renowned academic books) are analyzed using both univariate and multivariate Hawkes processes. By treating the occurrence patterns as binary time-series data along the texts, we investigate how effectively univariate and multivariate Hawkes processes capture the characteristics of these word occurrence signals. Through maximum likelihood estimation and subsequent simulations, we found that the multivariate Hawkes process clearly outperforms the univariate Hawkes process in modeling word occurrence signals. Moreover, we found that the multivariate Hawkes process can provide a Hawkes graph, which serves as an intuitive representation of the relationships between concepts appearing in the analyzed text. Furthermore, our study demonstrates that the importance of concepts within a given text can be quantitatively estimated based on the optimized parameter values of the multivariate Hawkes process.

In recent decades, simulation has emerged as a pivotal educational tool, bolstering scientific knowledge and honing decision-making skills across diverse disciplines. Surgery and flight simulators are well-known tools used to practice and train safely in surgeries and piloting. Meanwhile, the development of simulation games advances in other scientific fields, such as economics, management, engineering, and mathematics. These simulations offer learners a risk-free virtual platform to apply and refine their knowledge, leveraging animations, graphics, and interactive environments to enrich the learning experience. In engineering, while simulation is widely utilized as a powerful training tool for heavy equipment and process handling, the creation of strategy games for educational purposes is less frequent. This gap primarily stems from the challenge of converting complex engineering concepts and theories into a user-friendly yet comprehensive setup that preserves the more difficult aspects. This study adopts a design-based research approach to develop and evaluate an educational simulation game aimed at enhancing probabilistic and spatial reasoning in mineral exploration. The application generates random scenarios, within which users deploy strategies based on their knowledge, while accommodating the randomness of physical phenomena. The simulation game is adopted as an educational tool in the course “Introduction to Mineral Exploration” in the School of Mining and Metallurgical Engineering of the National Technical University of Athens. Additionally, we present the outcomes of game analytics and a qualitative evaluation derived from three workshops at higher education institutions in Greece.

Polymers are essential materials in the fabrication of partial and complete dentures, where their mechanical properties directly impact durability, comfort, and clinical performance. This study examines the influence of different manufacturing temperatures on the surface hardness of polymeric materials used in dental applications. A total of 60 experimental samples with a rectangular shape of Vertex ThermoSens polymer (Vertex Dental, 3D Systems, Soesterberg, The Netherlands) were fabricated through injection molding at 280 °C and 300 °C and analyzed over time to assess changes in their properties. Hardness measurements, conducted using the EQUOTIP Shore D hardness tester (Proceq SA, Schwerzenbach, Canton of Zürich, Switzerland), indicated increased hardness over time, with higher values observed in samples fabricated at 300 °C. A two-way ANOVA was performed to evaluate the statistical significance of temperature and time on hardness, revealing a significant effect (F = 14.73, p = 0.0185). These findings suggest that processing polymers at elevated temperatures improves surface hardness, significant for denture longevity and patient comfort. Increased hardness contributes to greater wear resistance. Optimizing polymer manufacturing conditions can thus lead to improved clinical outcomes, ensuring more durable and biocompatible dental prostheses.

The main goal of this study is to investigate the possibility of using residual materials (biomass derived from used cooking oils and lignocellulosic biomass from plant waste) on a large scale for producing renewable fuels and, in particular, the best way to collect them. The methodology of Geographic Information Systems (GIS) as well as spatial analysis (SA) techniques were used to investigate the Greek case for this. The data recorded in the geographic database were quantities of waste cooking and household oils as well as quantities of lignocellulosic biomass. The most common global and local indices of spatial autocorrelation were used. Concerning the biomass derived from used cooking oils, it was found that their quantities were important (163.17 million L/year), and these can be used to produce green diesel in the context of the circular economy. Although the dispersion of the used cooking oils was wide, there is no doubt that their concentration in large cities and tourist areas is higher. This finding suggests a collection process that could be carried out mainly in these areas through the development of small autonomous collection units in each neighborhood and central processing plants in small regional units. The investigation of the geographical–spatial distribution of residual lignocellulosic biomass showed the geographical fragmentation and heterogeneity of the distributions. The quantities recorded were significant (4.5 million tons/year) but widely dispersed, such that the cost of collecting and transporting the biomass to central processing plants could be prohibitive. The “geography” of the problem itself suggests solutions of small mobile collection units in every part of the country. The lignocellulosic biomass would be collected and converted in situ into bio-oil by rapid pyrolysis carried out in a tanker vehicle. This would transport the produced bio-oil to the nearest oil refineries for the conversion of bio-oil into biofuels through deoxygenation processes.

Corn (Zea mays L.) productivity is often compromised by phytosanitary challenges, with fungal disease like Curvularia leaf spot being particularly significant. While synthetic fungicides are commonly used, there is growing interest in exploring alternative compounds that are effective against pathogens, ensure food safety, and have low toxicity to non-target organisms. In this study, we examined the biochemical changes in corn plants treated with Lippia sidoides essential oil and its major compound, thymol. Both treatments serve as preventive measures for inoculated plants and induced resistance. We tested five concentrations of each product in in vivo experiments. After evaluating the area under the disease progress curve, we analyzed leaf samples for enzymatic activities, including superoxide dismutase, catalase, ascorbate peroxidase, and chitinase. Phytoalexin induction was assessed using soybean cotyledons and sorghum mesocotyls. Cytotoxicity tests revealed lower toxicity at concentrations below 50 µL/mL. Both essential oil and thymol stimulated the production of reactive oxygen species, with thymol primarily activating catalase and L. sidoides oil increasing ascorbate peroxidase levels. Both thymol and L. sidoides were also key activators of chitinase. These findings suggest that L. sidoides essential oil and thymol are promising candidates for developing biological control products to enhance plant defense against pathogens.

The reduction of noise and vibration is possible with passive, semi-active and active control strategies. Especially where self-adaptive control is required, it is necessary to evaluate the noise reduction potential before the control approach is applied to the real-world problem. This evaluation can be based on a virtual model that contains all relevant sub-systems, transfer paths and coupling effects on the one hand. On the other hand, the complexity of such a model has to be limited to focus on principal findings such as convergence speed, power consumption, and noise reduction potential. The present paper proposes a fully coupled electro-vibro-acoustic model for the evaluation of self-adaptive control strategies. This model consists of discrete electrical and mechanical networks that are applied to model the electro-acoustic behavior of noise and anti-noise sources. The acoustic field inside a duct, terminated by these electro-acoustic sources, is described by finite elements. The resulting multi-physical model is capable of describing all relevant coupling effects and enables an efficient evaluation of different control strategies such as the local control of sound pressure or active control of acoustic absorption. It is designed as a benchmark model for the benefit of the scientific community.