The development of human society and the production and operation activities of enterprises have brought about global warming, resulting in frequent natural disasters. It has become the consensus of all countries in the world to develop a green and low-carbon economy. Under this background, enterprises, as the main body of economic activities, especially energy industry enterprises, should optimize and upgrade the traditional production and operation mode with high pollution, high consumption, and low output to a high-efficiency and low-pollution mode, and pay attention to the co-ordinated development of economic benefits, social benefits, and ecological benefits. Financial performance evaluation indicators have become the main basis for senior leaders of energy industry enterprises to make decisions and evaluate the low-carbon economic benefits of enterprises. This paper constructs a set of financial evaluation index systems of energy industry enterprises under the background of a low-carbon economy from four dimensions: profitability, asset quality, debt risk, and business growth. The analytic hierarchy process (AHP) is used to measure the comprehensive contribution of financial indicators of low-carbon production and operation. The purpose of this study is to provide scientific financial management decisions for energy enterprises to reduce costs and increase the efficiency and low-carbon operation under the background of a low-carbon economy. The research results show that the comprehensive evaluation index system after the traditional financial evaluation index of energy industry enterprises is integrated with the evaluation index of a low-carbon economy can help enterprises make more correct and effective financial decisions in the process of survival, development, and growth, and, at the same time, the financial evaluation index of a low-carbon economy should pay more attention to the indicators with a higher comprehensive contribution, so as to effectively promote the low-carbon operation efficiency of enterprise production, management, and sales. Compared with other research results, this paper innovatively constructs a financial management decision-making index system for measuring the low-carbon operation of energy enterprises in theory, which has important value in guiding energy enterprises to reduce costs and increase the efficiency and low-carbon operation in practice. Full article

Among the approaches to facilitating negative CO₂ emissions is biochar production. Biochar is generated in the pyrolysis of certain biomasses. In the pyrolysis process, carbon in the biomass is turned into a solid, porous, carbon-rich, and stable material that can be captured from the soil after a period of from a few decades to several centuries. In addition to this long-term carbon sequestration role, biochar is also beneficial for soil performance as it helps to restore soil fertility and improves the retention and diffusion of water and nutrients. This work presents a Life Cycle Assessment of different pyrolysis approaches for biochar production. Biomass pyrolysis is performed in a fixed-bed reactor, which operates at a mild temperature (550 °C). Biochar is obtained as solid product of the pyrolysis, but there are also liquid (bio-oil) and gaseous products (syngas). The pyrolysis gas is partly used to fulfil the energy demand of the pyrolysis process, which is highly endothermic. In the conventional approach, CO₂ is produced during the combustion of syngas and emitted to the atmosphere. Another approach to facilitate CO₂ capture and thus obtain more negative CO₂ emissions in the pyrolysis process is burning syngas and bio-oil in a Chemical Looping Combustion unit. Life Cycle Assessment was performed of these approaches toward biomass pyrolysis to evaluate their environmental impact. The Chemical Looping Combustion approach significantly reduced the values of 7 of the 16 environmental impact indicators studied, along with the Global Warming Potential among them, it slightly increased the value of one indicator related to the use of fossil resources, and it maintained the values of the remaining 8 indicators. Environmental impact reduction occurs due to the avoidance of CO₂ and NO_x emissions with Chemical Looping Combustion. The CO₂ balances of the different pyrolysis approaches with Chemical Looping Combustion configurations were compared with a base case, which constituted the direct combustion of wheat straw to obtain thermal energy. Direct biomass combustion for the production of 17.1 MJ of thermal energy had CO₂ positive emissions of 0.165 kg. If the gaseous fraction was burned by Chemical Looping Combustion, CO₂ was captured and the emissions became increasingly negative, until a value of −3.30 kg/17.1 MJ was generated. If bio-oil was also burned by this technology, the negative trend of CO₂ emissions continued, until they reached a value of −3.66 kg. Full article

Energy consumption plays an important role in contemporary economies. Its significance extends beyond utilitarian value, impacting economic robustness, environmental protection, and residents’ well-being. The escalating global energy requisites necessitate efficient energy utilization and a shift towards renewable sources to address climate change and strengthen energy independence. Developing accurate predictive models to forecast long-term energy costs and savings remains a complex problem. This paper aims to provide a methodology to identify the influence of building energy performance on real estate market efficiency, focusing on property maintenance costs. Real estate plays a crucial role in human life, serving both as a fundamental need and as a vehicle for achieving personal aspirations and secure financial investments, particularly during times of economic and social instability. Through interdisciplinary methodological architecture, this study addresses three key issues: the impact of rising energy costs on market efficiency, the responsiveness of the real estate market to energy price fluctuations, and the significance of property maintenance costs on market value. The research approach includes creating and applying AI algorithms capable of evaluating extensive datasets pertaining to real estate features. Utilizing machine learning methods, the algorithm determines the importance of energy efficiency measures as well as various other inherent and external attributes of properties. The suggested methodology provides a novel approach to improve the effectiveness of market efficiency analysis. Full article

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction. Full article

The change in the corrosion activities of SS304 and the carbon steel A36 were studied during their exposure for 30 days to hybrid pumice-Portland cement extract (CE), to simulate the concrete–pore environment. The ionic composition and the initial pH (12.99) of the CE were influenced by the reduction of Portland cement (PC) content, volcanic pumice oxides and alkaline activators. Because of the air CO₂ dissolution, the pH decreased and maintained a constant value ≈ 9.10 (established dynamic ionic equilibrium). The CE promoted the passivation of both steels and their free corrosion potential (OCP) reached positive values. On the surfaces, Fe and Cr oxides were formed, according to the nature of the steel. Over the time of exposure, the presence of chloride ions in the pumice caused a localized pitting attack, and for carbon steel, this fact may indicate an intermediate risk of corrosion. The chloride effect was retarded by the accumulation of SO₄²⁻ ions at the steel surfaces. Based on electrochemical impedance (EIS), the polarization resistance (Rp) and the thickness of the passive layers were calculated. Their values were compared with those previously reported for the steels exposed to CEs of Portland and supersulfated cements, and the hybrid cement was considered as a PC “green” alternative. Full article

Purines are the building blocks of DNA/RNA, energy substrates, and cofactors. Purine metabolites, including ATP, GTP, NADH, and coenzyme A, are essential molecules in diverse biological processes such as energy metabolism, signal transduction, and enzyme activity. When purine levels increase, excess purines are either recycled to synthesize purine metabolites or catabolized to the end product uric acid. Purine catabolism increases during states of low oxygen tension (hypoxia and ischemia), but this metabolic pathway is incompletely understood in the context of histotoxic hypoxia (i.e., inhibition of oxygen utilization despite normal oxygen tension). In rabbits exposed to cyanide—a classical histotoxic hypoxia agent—we demonstrated significant increases in several concordant metabolites in the purine catabolic pathway (including plasma levels of uric acid, xanthosine, xanthine, hypoxanthine, and inosine) via mass spectrometry-based metabolite profiling. Pharmacological inhibition of the purine catabolic pathway with oxypurinol mitigated the deleterious effects of cyanide on skeletal muscle cytochrome c oxidase redox state, measured by non-invasive diffuse optical spectroscopy. Finally, plasma uric acid levels correlated strongly with those of lactic acid, an established clinical biomarker of cyanide exposure, in addition to a tissue biomarker of cyanide exposure (skeletal muscle cytochrome c oxidase redox state). Cumulatively, these findings not only shed light on the in vivo role(s) of cyanide but also have implications in the field of medical countermeasure (MCM) development. Full article

Objectives: This study aims to develop and validate the Serious Educational Game in Nursing Appraisal Scale (SEGiNAS), a tool designed to evaluate the implementation of serious games within nurse education contexts of quantity of process, quality of process, and learning outcomes. Methods and Materials: This methodological and psychometric study aimed to develop and validate a scale. The item generation phase was based on the cognitive theory of multimedia learning, resulting in a 20-item scale. The validation phase involved evaluating the psychometric scale by surveying 160 Portuguese nurses. Results: A factor analysis revealed a three-factor structure corresponding to the scale’s designed dimensions, explaining a total variance of 64.5%. The scale demonstrated high internal consistency for all factors, including engagement and teaching effectiveness (0.925), learning impact and practical application (0.883), and content relevance and clarity (0.848). The dimensions were engagement and teaching effectiveness, learning impact and practical application, and content relevance and clarity. Conclusions: The SEGiNAS scale represents a valid and reliable tool for evaluating serious games in nursing education. Its development fills an existing gap in assessing the teaching–learning process with serious games. This study was not registered. Full article

In cation–anion co-doping, rare earth elements excel at regulating the electronic structure of perovskites, leading to their improved photocatalytic performance. In this regard, the impact of co-doping rare earth elements at the Ba and Ti sites in BaTiO₃ on its electronic and photocatalytic properties was thoroughly investigated based on 2 × 2 × 2 supercell structures of BaTiO₃ with different La concentrations of 12.5% and 25% using DFT calculations. The band structure, density of states, charge density difference, optical properties, and the redox band edge of the co-doped models mentioned above were analyzed. The results indicated that the BaTiO₃ structure co-doped with 25% La at the Ti site exhibited higher absorption in the visible range and displayed a remarkable photocatalytic water-splitting performance. The introduced La dopant at the Ti site effectively reduced the energy required for electronic transitions by introducing intermediate energy levels within the bandgap. Our calculations and findings of this study provide theoretical support and reliable predictions for the exploration of BaTiO₃ perovskites with superior photocatalytic performances. Full article

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution. Full article

This paper deals with the possibility of smelting quasi-high-entropy alloys (QHEAs) with the partial use of ferroalloys in the charge instead of pure metals. The Cantor alloy (CoCrFeMnNi) was used as the base alloy and the comparison sample, into which niobium was introduced in the amount of 14 to 18% by weight. The structure, hardness, strength, and tribological properties of prototypes were studied. The results obtained showed, on the one hand, the possibility of using ferroalloys as charge components in the smelting of QHEAs and, on the other hand, the positive effect of niobium in the amount of 14–17% on the strength and wear resistance of the alloy. Increasing the niobium content above 18% leads to its uneven distribution in the structure, consequently decreasing the strength and wear resistance of the alloy. The structure of the studied alloys is represented by a solid solution of FCC, which includes all metals, and the niobium content varies widely. In addition, the structure is represented by the phases of implementation: niobium carbide NbC 0.76–1.0, manganese carbide Mn₇C₃, and a CrNi intermetallic compound with a cubic lattice. Full article

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy. Full article

Modern UAVs (unmanned aerial vehicles) equipped with video cameras can provide large-scale high-resolution video data. This poses significant challenges for structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms, as most of them are developed for relatively small-scale and low-resolution scenes. In this paper, we present a video-based SfM method specifically designed for high-resolution large-size UAV videos. Despite the wide range of applications for SfM, performing mainstream SfM methods on such videos poses challenges due to their high computational cost. Our method consists of three main steps. Firstly, we employ a visual SLAM (VSLAM) system to efficiently extract keyframes, keypoints, initial camera poses, and sparse structures from downsampled videos. Next, we propose a novel two-step keypoint adjustment method. Instead of matching new points in the original videos, our method effectively and efficiently adjusts the existing keypoints at the original scale. Finally, we refine the poses and structures using a rotation-averaging constrained global bundle adjustment (BA) technique, incorporating the adjusted keypoints. To enrich the resources available for SLAM or SfM studies, we provide a large-size (3840 × 2160) outdoor video dataset with millimeter-level-accuracy ground control points, which supplements the current relatively low-resolution video datasets. Experiments demonstrate that, compared with other SLAM or SfM methods, our method achieves an average efficiency improvement of 100% on our collected dataset and 45% on the EuRoc dataset. Our method also demonstrates superior localization accuracy when compared with state-of-the-art SLAM or SfM methods. Full article

Understanding the effects of various forest management systems, including logging practices, on soil properties is essential for implementing sustainable management strategies. In Malaysia, two types of forest management systems were commonly used: Malayan Uniform System (MUS) and Selective Management System (SMS) practices. However, their effects on soil quality remained elusive, especially after decades of recovery. To address this need, we selected three plots for the MUS and SMS in Johor, Malaysia, to assess soil properties in logged-over forest plots. All the plots were natural forest reserves. Soil properties analyzed include soil acidity, electrical conductivity, cation exchange capacity, selected nutrient contents, and soil compaction. Generally, the results of the study indicate that forests logged using the SMS exhibit superior soil quality compared to those logged using the MUS according to several key soil properties. Specifically, significantly higher cation exchange capacity, potassium content, calcium content, and magnesium content with lower soil compaction was observed in the SMS when compared to MUS plots. In short, the SMS enhances soil quality more effectively than the MUS, even with a shorter logging cycle. This is because the SMS does not harvest all trees and distributes the impact of harvesting more evenly over time, rather than concentrating it at a single time point. Ultimately, this highlights that the SMS can play a significant role in promoting sustainable forest management practices by preserving soil quality. Full article

Darier disease (DD) is an autosomal dominant disorder due to pathogenic variants of the ATP2A2 gene that causes an isolated skin manifestation based on keratinocyte disconnection and apoptosis. Systemic manifestations of DD have not been demonstrated so far, although a high incidence of neuropsychiatric syndromes suggests an involvement of the central nervous system. We report that the pathogenic ATP2A2 gene variant c.118G>A may cause cardiac involvement in patients with DD, consisting of keratinocyte and cardiomyocyte disconnection. Their common pathologic pathway, still unreported, was documented by both skin and left ventricular endomyocardial biopsies because cardiac dilatation and dysfunction appeared several decades after skin manifestations. Keratinocyte disconnection was paralleled by cardiomyocyte separation at the lateral junction. Cardiomyocyte separation was associated with cell disarray, sarcoplasmic reticulum dilatation, and increased myocyte apoptosis. Clinically, hyperkeratotic skin papules are associated with chest pain, severe muscle exhaustion, and ventricular arrhythmias that improved following administration of aminophylline, a phosphodiesterase inhibitor enhancing SERCA2 protein phosphorylation. Cardiac pathologic changes are similar to those documented in the skin, including cardiomyocyte disconnection that promotes precordial pain and cardiac arrhythmias. Phosphodiesterase inhibitors that enhance SERCA2 protein phosphorylation may substantially attenuate the symptoms. Full article

The building sector contributes significantly to global final energy consumption and energy-related CO${}_2$ emissions. The demand for sustainable and energy-efficient passive buildings with a minimal ecological footprint has increased due to the global energy crisis, climate change, and environmental concerns. This need can be met by constructing passive buildings. However, to develop a building that is truly passive, it is required to meet many passive house conditions, negligible for typical buildings, which increase the project complexity and pose challenges and risks threatening its successful completion. The aim of this work is to present the findings from a quantitative risk analysis in passive construction based on the results of expert surveys that were carried out using a Computer-Assisted Web Interview. Feedback from expert surveys covering the experience of 748 passive buildings projects from seven countries (Poland, Germany, Great Britain, the United States, Australia, Spain, and Austria) allowed us to access the frequency of occurrence, severity, detectability, and Risk Priority Numbers of the 32 risk factors identified in passive buildings projects. Those risk factors were identified based on literature research, risk interviews, scenario analysis, brainstorm sessions with passive buildings specialists, and our own observations of passive buildings projects. This study revealed that incorrect costing was the most frequent issue; complicated, non-compact building shapes with an unfavorable area-to-volume ratio had the highest severity of effects; the wrong interpretation of correctly prepared drawings and details obtained from the designer had the lowest detectability; and incorrect costing had the highest Risk Priority Number. In addition, this study allowed us to identify a narrow group of critical risk factors that are the most significant (have the highest RPN) and to which special attention should be paid in the risk-management process. Full article

This study investigates the association between the compensation of Remuneration Committee Chairpersons (RCCs) and their characteristics. Utilizing data from firms listed on the UK FTSE350 index between 2010 and 2020, the research unveils that RCC remuneration is influenced by factors such as observable efforts, time commitment, and accumulated experience. Notably, the analysis reveals a substantial gender gap in RCCs' pay. The results suggest that the contractual pricing of individual director-level attributes plays a role in explaining disparities in compensation for roles with similar responsibilities. Furthermore, the study sheds light on the intricate process of determining compensation within the directorial hierarchy. It delves into how differences in pay among individuals occupying similar positions across various companies can be elucidated by the distinct attributes and qualifications of each individual. Ultimately, the findings advocate for a nuanced examination of directorial roles, highlighting the necessity of distinguishing between different director roles rather than treating them as a homogeneous entity. Full article

The agriculture industry in the Western world is increasingly using biofertilizers, considering the environmental aspects and organic food. Sustainability in agriculture is the primary priority of the government of the Sultanate of Oman. In order to improve and develop the agricultural sector for community development, the government of Oman is paying special attention to its Vision 2040 in line with sustainable development goals. Hence, the aim of the research is to analyze the behavioral aspects of farmers and farmholders towards utilizing biofertilizers for saving the environment as well as providing organic food and bringing sustainability to the agriculture sector of the country. In order to meet the objectives of the study mixed method research has been used. An interview guide has been developed, a questionnaire has also been developed, and the instruments have been approved by the experts. The interview data were analyzed, and afterward, primary data were collected. To test the hypothesis and the framework, Smart PLS 3 has been used. The findings identified that farmers in Oman are reluctant to use biofertilizers because of a lack of awareness, but yet they are using it up to some extent and the proposed model has proven to be significant. The findings are useful not only for the policymakers but also for the practitioners who can obtain guidance about the benefits they can gain from the use of biofertilizers. Full article

Power shift tractors have been widely used in agricultural tractors in recent years because of their advantages of uninterrupted power during shifting, high transmission efficiency and high stability. As one of the indispensable driving states of the power shift tractor, the starting process requires a small impact and a starting speed that meets the driver’s requirements. In this paper, aiming at such contradictory requirements, the starting control strategy of a power shift tractor is formulated with the goal of starting quality and the driver’s intention. Firstly, the identification characteristics of the driver under three starting intentions are obtained by a real vehicle test. An extreme learning machine with fast identification speed and short training time is used to establish the basic driver’s intention identification model. For the instability of the identification results of the Extreme Learning Machine (ELM), the particle swarm optimization algorithm (PSO) is used to optimize the ELM. The optimized extreme learning machine model has an accuracy of 96.891% for driver’s intention identification. The wet clutch is an important part of the power shift gearbox. In this paper, the starting control strategy knowledge base of the starting clutch is established by a combination of bench tests and simulation tests. Through the fuzzy algorithm, the driver’s intention is combined with the starting control strategy. Different drivers’ intentions will affect the comprehensive evaluation model of the clutch (the single evaluation index of the clutch is: the maximum sliding power, the sliding power, the speed stability time, the impact degree), thus affecting the final choice of the starting clutch control strategy considering the driver’s intention. On this basis, this paper studies and establishes the MPC starting controller for the power shift gearbox. Compared with the linear control strategy, the PSO-ELM-fuzzy weight starting strategy proposed in this paper can reduce the maximum sliding friction power by 45%, the sliding friction power by 69.45%, and the speed stabilization time by 0.11 s. The effectiveness of the starting control strategy considering the driver’s intention proposed in this paper to improve the starting quality of the power shift tractor is verified. Full article

This work presents results on laser-induced surface structuring of AlN ceramic and its application in Surface-Enhanced Raman Spectroscopy (SERS). The laser processing is performed by nanosecond pulses in air and vacuum. Depending on the processing conditions, different surface morphology can be obtained. The ablation process is realized by ceramic decomposition as the formation of an aluminium layer is detected. The efficiency of the fabricated structures as active substrates in SERS is estimated by the ability of the detection of ammonium nitrate (NH₄NO₃). It is conducted for Raman spectrometer systems that operate at wavelengths of 514 and 785 nm where the most common commercial systems work. The obtained structures contribute to enhancement of the Raman signal at both wavelengths, as the efficiency is higher for excitation at 514 nm. The limit of detection (LOD) of ammonium nitrate is estimated to be below the maximum allowed value in drinking water. The analysis of the obtained results was based on the calculations of the near field enhancement at different conditions based on Finite Difference Time Domain simulation and the extinction spectra calculations based on Generalized Mie scattering theory. The structures considered in these simulations were taken from the SEM images of the real samples. The oxidation issue of the ablated surface was studied by X-ray photoelectron spectroscopy. The presented results indicated that laser structuring of AlN ceramics is a way for fabrication of Al structures with specific near-field properties that can be used for the detection of substances with high social impact. Full article

The antioxidant properties of butterfly pea flower (BF), which is rich in natural anthocyanins, have garnered significant attention. The impact of digestion and metabolism on BF extracts and evaluate their subsequent antioxidant activities in vivo were explored in the present study. After in vitro digestion, 42.03 ± 2.74% of total anthocyanins from BF extracts remained, indicating a negative influence of the digestion process on the bioaccessibility of phenolic compounds derived from BF. Furthermore, UPLC-LTQ-Orbitrap-MS² analysis identified a total of four prototypes and twenty-seven metabolites in rat plasma or urine samples following the intake of BF extracts. The kinetics of key metabolites including delphinidin 3-glucoside (D3G), cyanidin-3-glucoside (C3G), and 4-hydroxybenzoic acid were subsequently determined in blood, and the Cmax values were 69.034 ± 8.05 nM and 51.65 ± 3.205 nM. These key metabolites derived from BF anthocyanins, including C3G and D3G, and flavonoid quercetin exhibited main antioxidant attributes that improved the plasmic and hepatic activities of various antioxidant enzymes and the total antioxidant capacity (T-AOC) and malondialdehyde (MDA) in a D-galactose-induced rat model. These findings provide insights into the bioaccessibility and bioavailability of bioactive constitutes derived from BF extracts, which are crucial for determining the actual efficacy of BF as well as developing functional foods based on BF. Full article

There is a nationwide effort to increase the representation and engagement of minoritized students in computer science education. Discourse about the barriers to diversity among computer science majors is often characterized by student pathologies and does not consider the impacts of classroom culture and instructor pedagogies. Amid the push for strategies to recruit and retain minoritized students in computer science, little has been done to transform curriculum and analyze faculty perspectives on curriculum and pedagogy as methods to increase students’ access to the computer science major. This paper presents an example of curriculum redesign for an undergraduate introductory computer science course (ICS) that sought to address issues of inequitable representation by centering student identities and redistributing power in favor of students. The authors draw upon critical sociocultural and the 4Es heuristic for disciplinary literacy to reimagine the ICS course as a space that centers on the important roles of identity and power in solving for diversity in computer science education. We highlight for researchers and practitioners how our work may be used to disrupt meritocratic practices that alienate minoritized and economically disadvantaged students and to expand definitions of mastery and expertise in computer science education. Full article

This paper presents the design of a two-port dual-band dual-circularly-polarized dielectric resonator antenna (DRA). The proposed DRA is formed by stacking two dielectric resonators (DRs) of different shapes, including a hexagonal DR on top and a cross-shaped DR on the bottom. It is designed to resonate at two near-degenerate orthogonal modes of TE₁₁₁ and TE₁₁₃, and an aperture-coupled feeding through a cross-like slot is used to achieve dual-band impedance matching simultaneously for right- and left-handed circular polarizations. Tests were conducted on a prototype working in C-band to verify the design concept. The experiment results demonstrate that the proposed DRA has exceptional performance with measured −10 dB reflection bandwidths of 24.4% and 17.4%, 3 dB axial ratio bandwidths of 21.2% and 16.3%, and maximum gains of 5.64 and 8.13 dBic for the lower and upper bands, respectively. Moreover, the measured channel isolation is more than 15.8 dB. The results obtained from the experiments show good agreement with the simulation, and hence, it can be concluded that the proposed DRA is a promising solution that can be used for various wireless communication applications. Full article