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The Ni-based cathode is central to the performances of solid oxide electrolysis cells (SOECs), yet it suffers from poor oxygen ion conductivity, sluggish electron transport, and inefficient CO2/H2O activation. This study explores a heteroatom-doping strategy to comprehensively address the ionic, electronic, and molecular issues in solid oxide cells. When operated in SOEC mode, the maximum power density of the N-doped Ni/CGO (NiO/CGON) cathode achieved a 29.6% improvement over its undoped Ni/CGO, along with a 27.3% reduction in polarization resistance. Moreover, a 31.3% increase in maximum current density was obtained along with considerable stable operation over 150 h at an industrial-scale current density of 0.5 A/cm2. Combined electrochemical measurements, in situ diffuse reflectance infrared Fourier transform (DRIFT) spectroscopy, and density functional theory (DFT) simulations reveal that N-doped-induced local covalency elevation via the formation of Ce–O/N bonds substantially promotes the oxygen ion and electron conductivity and creates the synergistic Lewis acid–base sites for simultaneous activation of both CO2 and H2O, thereby collectively addressing the ionic, electronic, and molecular issues in SOCs in one simple method.

This study aims to analyze the effect of incentives and job satisfaction on employee performance, with work enthusiasm as an intervening variable at PT. Keaj Beaute Group. The research employs a quantitative approach using descriptive methods. Data were collected through questionnaires distributed to 30 employees and analyzed using multiple linear regression and path analysis. The results show that incentives do not have a significant effect on either work enthusiasm or employee performance. Job satisfaction significantly influences work enthusiasm but has no direct impact on performance. Work enthusiasm has a significant positive effect on employee performance and serves as a mediating variable between job satisfaction and employee performance, but does not mediate the relationship between incentives and performance. These findings highlight the importance of enhancing work enthusiasm through effective management of job satisfaction to achieve optimal employee performance.

Purpose To enhance the efficiency of personnel assignment in layout operations, this study employs 0–1 integer programming optimization theory, integrated with practical layout operations in steel structure construction, to assign manpower under known constraints to find the optimal personnel assignment and minimal cost and to provide management decision-makers with an effective way to control operational costs. Design/methodology/approach The research considers constraints such as personnel availability and part types, constructing a mathematical model with one objective function and 11 constraints. By applying this model to a real-world project for constructing a technology factory and using LINGO 18.0 software, the study demonstrates that optimizing personnel assignment can reduce total costs by 12.82% and manual assignment time by 99%. Findings The findings also elucidate the following: (1) Sensitivity analysis indicates that decreasing the number of working days slightly increases total operational costs, whereas extending the duration to 30–40 days results in cost reductions. Furthermore, reducing high-efficiency and high-salary personnel has a minor impact on overall costs, while cutting low-efficiency and low-salary personnel leads to a substantial cost increase of approximately 50–52%. (2) Efficient and rapid personnel assignment contributes to achieving minimal operational costs, with a cost reduction of 12.82%, while also facilitating the attainment of project goals in the most effective manner. Originality/value The study developed a mathematical model featuring a single objective function and 11 practical constraints, providing practitioners with the flexibility to adapt it as needed to address empirical engineering challenges related to personnel layout assignments.

Two new species of the fungal-inhabiting plant bug genus Peritropis Uhler, 1891 are described from the Indochina Peninsula: P. andrzeji sp. nov. and P. cheroti sp. nov. In addition, P. thailandica is recorded from Vietnam for the first time. Morphological information, including a detailed description of male and female genitalia, is presented. The importance of structural examination in the genus Peritropis is discussed, and a key to the species of Peritropis occurring in the Indochina Peninsula is provided.

The rapid advancements of Artificial Intelligence, particularly deep learning, has enhanced features extraction mainly building footprints. However, models trained on developed countries struggle to perform better while testing on the datasets of high-density residential areas of developing countries like Nepal. This research aims to improve the performance of the models by developing and utilizing a region-specific dataset for Bhaktapur. Initially, models like Unet, PSPNet and LinkNet with different backbone architectures like resnet18, resnet34, resnet50, and vgg19 were trained on the Massachusetts dataset and the performance was poor when tested with the dense residential areas of Bhaktapur datasets. To address this issue, new datasets of Bhaktapur were introduced for high density residential area where houses are closely attached. The datasets were prepared by digitizing each house on the high resolution orthomosaics which was then converted to mask. Subsequently, the orthomosaic was patches into 300 x 300 with the corresponding mask. These patches were split into training, validation and testing datasets. Models with different backbones were trained with custom datasets applying data augmentation techniques, including random clipping of 256 x 256, flipping and rotation to prevent overfitting and make the model more robust to variations in real world data. The models with different backbones were validated and tested, and the best performance was with LinkNet model with vgg19 backbone with an IoU of 94.29% and F1 Score of 98.29%, demonstrating good results for dense residential areas. This study highlights the importance or need of region based custom datasets to improve the accuracy of deep learning segmentation models for building extractions, mainly on unique urban structure which can be useful for urban spatial planning, and disaster risk management and monitoring.

The integration of renewable energy sources, particularly photovoltaic (PV) and wind energy, presents significant challenges due to their inherent variability and nonlinear dynamics. This chapter introduces a novel hybrid energy system that combines PV and wind power, managed by an advanced control strategy that integrates model predictive control (MPC) with particle swarm optimization (PSO). The MPC&PSO approach is designed to optimize power output and ensure the stability of the system under dynamic environmental conditions.

Abstract Oil palm (Elaeis guineensis Jacq.) is a vital global crop, and its genetic improvement benefits from comprehensive genome sequencing. This study presents the whole-genome sequencing and annotation of two key genetic resources: the wild (Eg-DCM) and ancestral (Eg-DBG) Dura accessions. Comparative analysis with the advanced breeding Dura (Eg-D1) provides insights into genome structure, gene content, and evolutionary shifts during domestication. Retroelements dominated both genomes, constituting 46.10% in Eg-DBG and 43.85% in Eg-DCM. Gene prediction found 61,256 and 53,985 genes, refined into high-confidence sets of 39,263 (Eg-DBG) and 35,298 (Eg-DCM). A decline in resistance genes (R-genes) was observed in Eg-D1, suggesting that selection pressures influenced their retention. Codon usage analysis revealed significant shifts, showing increased mutational bias, with a stronger correlation between GC content at the third (GC3) and the first and second (GC12) codon positions in Eg-D1. Moreover, the relationship between GC3 and the effective number of codons (ENC) suggests stronger selection pressure driving codon optimization, a trend commonly observed in domesticated crops. These findings highlight how artificial selection has shaped genome composition, resistance mechanisms, and translational efficiency. The assembled genomes provide essential resources for understanding oil palm evolution and optimizing breeding strategies for enhanced productivity and sustainability.

This study aims to examine the effect of capital intensity and firm size on tax aggressiveness, with profitability as a moderating variable. The research objects are mining companies listed on the Indonesia Stock Exchange (IDX) during the period from 2019 to 2023, with an observation span of five years. A total of 17 companies were selected as samples using purposive sampling. The data were analyzed using multiple linear regression analysis. The results show that, partially, capital intensity has a positive but insignificant effect on tax aggressiveness, and firm size also has a positive but insignificant effect on tax aggressiveness. Simultaneously, capital intensity and firm size have a positive and significant effect on tax aggressiveness. Based on the moderated regression analysis (MRA), profitability is able to strengthen the influence of capital intensity but is unable to strengthen the influence of firm size on tax aggressiveness.

The solid oxide electrolyzer with perovskite as the cathode can electrolyze CO2 to produce a valuable chemical resource, revealing the great potential of direct electrolysis of CO2 at high temperatures. However, its insufficient electrocatalytic activity limits the performance of a solid oxide electrolysis cell. In the present work, we successfully anchored metal nanoparticles, such as nickel and copper, on the perovskite cathode surface to form a composite cathode, thereby creating an active electrochemical interface for CO2 cleavage to improve the electrocatalytic activity for CO2 electrolysis and Faraday efficiency improvement. The perovskite cathode based on metal nanoparticles can increase the conductivity of the composite cathode compared to an undoped perovskite cathode, and it can be considered that the improved CO2 electrolytic performance is attributed to the synergistic effect of the metal nanocatalysts with the La0.8Sr0.2CrO3−δ ceramic electrodes. The electrochemical properties remained stable after 100 h of high-temperature testing, suggesting that the construction of metal–oxide active interfaces can improve the electrocatalytic performance and durability of the materials.