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This study introduces novel phenothiazine-based organic dyes, 2-LBH-100, 2-LBH-44, and 2-Ryu-4, specifically designed for quasi-solid-state dye-sensitized solar cells (QsDSSCs). Employing a donor-π-acceptor architecture, these dyes incorporate varying electron-donating moieties, including bis(3-(hexyloxy)phenyl)amine and diphenylamino, coupled with a cyanoacrylic acid acceptor. Alkoxy substitutions in 2-LBH-100 and 2-LBH-44 enhanced solubility and dye loading on TiO2, leading to improved performance in QsDSSCs. 2-LBH-100 exhibited a power conversion efficiency (PCE) exceeding 5% with excellent stability, while 2-LBH-44 demonstrated a PCE of over 3%, increasing to 4% over time. 2-Ryu-4, with its diphenylamino donor, achieved an initial PCE of over 6%. This research highlights the crucial role of donor–acceptor interactions in optimizing organic dye design for high-performance QsDSSCs, paving the way for efficient and stable next-generation solar energy technologies.

With the tendency of shifting from Knowledge-Based Education into Competence-Based Education, assessment is not only of learning but also for and as learning. In Competence-Based Education, Competence-Based Assessment (CBA) is a process of collecting evidence and making judgments on whether competence has been achieved and regulating teaching and learning activities. Integrating CBA into instruction provides students with opportunities to self-construct their competences by applying academic knowledge to solve assessment tasks well related to real-world contexts. This paper referred to international discussions on the term of CBA, the meaning of integrating CBA into instruction and proposing a process of integrating CBA into instruction Pedagogical subjects for developing core competences of students. This paper also analyzed the development of Problem-Solving Competence and Presentation Competence of 33 Food Technology Education students and 22 Garment Technology Education students after dealing with the assessment tasks integrated into instruction General Psychology and Vocational Education subjects and presenting the results at Ho Chi Minh City University of Technology and Education. Finally, some minor suggestions for developing core competences of Technical and Vocational Education students through integrating CBA into Instruction Pedagogical subjects by carefully considering the consistency of course learning outcomes, teaching-learning activities and assessment were discussed.

Charcoal has a large share in the Brazilian market. The production is carried out by pyrolysis of biomass at different temperatures, between 300 and 500 °C. In this study, the corn cob pyrolysis was investigated using thermogravimetric analysis (TGA) and Fourier Transform Infrared Spectroscopy (FTIR). Samples after pyrolysis were compared with raw biomass to evaluate changes in fuel characteristics. In DTG curves a reduction in the number of degradation peaks in the carbonized material was observed. The FTIR spectra allowed to identify the aromatic ring of the lignin in the charcoals structure, indicating the presence of this compound even in charcoals produced with a temperature of 500 °C. It can be concluded that the temperature of 400 °C was enough to completely degrade the hemicellulose and cellulose of the biomass, resulting the final product (charcoal) less reactive or thermally more resistant than the in natura corn cob.

To mitigate the temperature increase in urban environments and reduce its impact on energy consumption and the quality of the environment, urban retrofitting technologies have been developed and applied worldwide. High albedo in urban surfaces and additional vegetation are the most efficient strategies to accomplish these goals. The objective of this study is to estimate the weight of these strategies, both individually and integrated, on the cooling potential of two Latin American cities. To do this, 36 low and high urban density scenarios were simulated with the ENVI-Met software. The simulation models were calibrated using air temperature curves which were monitored during the summer periods from 2010 to 2013. A Principal Components Analysis was carried out to establish possible associations between the proposed mitigation strategies and then the weight of anthropogenic heat was evaluated according to the configuration. The results show that the integrated mitigation strategies in urban areas -i. e. increase vegetation and albedo on horizontal surfaces- has a great potential to mitigate urban warming, showing a more significant impact on low-density urban configuration. The contribution of anthropogenic heat mainly produced by motorized transport and air conditioning systems, is a crucial input data for the urban microclimate simulations. Its impact on the urban densification processes may cancel out the benefits derived by the application of the mitigation strategies considered.

Load forecasting is very important to manage the electrical power systems. Load forecasting can be analyzed in three different ways as short-term, medium-term and long-term. Long-term load forecasting (LTLF) is in need to plan and carry on future energy demand and investment such as size of energy plant. LTLF is affected by energy consumption, national incoming per year, rates of civilization, increasing population rates and moreover economical parameters. Some of the forecasting models use mathematical formulas and statistical models such as correlation and regression analysis. In this study, a new effective expert-supported dynamic programming algorithm (ESDP) has been improved. Additionally, adaptive neuro-fuzzy inference system (ANFIS) and mathematical modeling (MM) are used to forecast long term energy demand. ANFIS is one of the famous artificial intelligence and has widely used to solve forecasting problems in literature. In addition to numerical inputs, ANFIS has linguistics inputs. The results obtained from ESDP, ANFIS and MM are compared to show availability. In order to show error levels mean absolute percentage error (MAPE) and (MAE) are used. The obtained results show that the proposed algorithms are available.

AbstractThe ethylene glycol oxidation reaction on nickel and ruthenium modified palladium nanocatalysts was investigated with electrochemical, spectroelectrochemical, and chromatographic methods. These carbon‐supported materials, prepared by a revisited polyol approach, exhibited high activity towards the ethylene glycol electrooxidation in alkaline medium. Electrolysis coupled with high performance liquid chromatography/mass spectrometry (HPLC‐MS) and in situ Fourier transform infrared spectroscopy (FTIRS) measurements allowed us to determine the different compounds electrogenerated in the oxidative conversion of this two‐carbon molecule. High value‐added products such as oxalate, glyoxylate, and glycolate were identified in all electrolytic solutions, whereas glyoxylate was selectively formed at the Ru45@Pd55/C electrode surface. In situ FTIRS results also showed a decrease in the pH value in the thin layer near the electrode as a consequence of OH− consumption during the spectroelectrochemical experiments.

AbstractService unavailability of transmission lines, due to the direct and indirect lightning strikes, is evaluated as a challenging issue within electric companies. The lightning event can produce dangerous overvoltage, equipment failures, and power supply interruptions. Here, externally gapped line arresters (EGLAs) performances installed on a 63 kV transmission line are modelled using EMTP‐ATP to investigate their capability to withstand current and energy discharged by lightning strokes during back flashover phenomena. Frequency behaviour of the grounding system impedance, first and subsequent lightning current parameters effects on insulators back flashover and lightning flashover failure rate have been considered by Monte‐Carlo method. Then, extensive analysis has been carried out to study the absorbed energy of the EGLA and its expected life. The results show the dependency of failure rate and the EGLA expected life on the soil resistivity and lightning strike parameters. The selected EGLA must be provided sufficient lightning protection to the transmission line based on its absorbed energy capability, the soil and lightning parameters and be complied with the discharge energy capability requirement specified by the utility company.

The paper presents an experimental study on the formation process of burden surface texture on the blast furnace throat and its influence on the radial distribution of gas flow. The study was performed with the application of blast furnaces equipped with a bell-type charging device using radio-isotope means for the control of burden surface texture (profile) and burden surface level, i.e., gamma locators for burden surface texture. The study was carried out under the conditions of an operating blast furnace in an iron and steel plant using a unique GEOTAPS system for automated control of geometric and temperature parameters of burden material surface on the blast furnace throat. The influence of the surface texture on the gas flow distribution was also investigated. The possibility of a self-stabilization effect for burden surface texture and gas flow in an operating blast furnace under suitable conditions was experimentally proven. As a result of the experimental study performed, four ways of energy-saving technology implementation were determined for the control of blast furnace melting based on the data on the burden surface texture and previously unknown regularities of surface layer formation of burden material on the throat of an operating blast furnace with a bell-type charging device. The main idea of the paper is the development of automated control for the radial distribution of burden material and gas flow using actual or predicted surface texture parameters as important intermediate factors that both describe the process and have a significant simultaneous influence on it.

The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.