Search Results (77)

The aim of this study is to provide a comprehensive analysis of the outcome of two separate techno-economic studies that were conducted for the scaled-up and industrially relevant processes of a) synthetic natural gas (SNG) production from captured (cement-based) CO₂ and green-H₂ (via renewable-assisted electrolysis) and b) combined electricity and crude biofuel production through the integration of biomass pyrolysis, gasification, and solid oxide fuel cells. As was found, the SNG production process seems more feasible from an economic perspective as it can be comparable to current market values. Full article

This paper introduces a novel recursive algorithm for inverting matrix polynomials, developed as a generalized extension of the classical Leverrier–Faddeev scheme. The approach is motivated by the need for scalable and efficient inversion techniques in applications such as system analysis, control, and FEM-based structural modeling, where matrix polynomials naturally arise. The proposed algorithm is fully numerical, recursive, and division free, making it suitable for large-scale computation. Validation is performed through a finite element simulation of the transverse vibration of a fighter aircraft wing. Results confirm the method’s accuracy, robustness, and computational efficiency in computing characteristic polynomials and adjugate-related forms, supporting its potential for broader application in control, structural analysis, and future extensions to structured or nonlinear matrix systems. Full article

This paper introduces a robust neural adaptive MIMO control strategy to improve the stability and adaptability of bipedal locomotion amid uncertainties and external disturbances. The control combines nonlinear dynamic inversion, finite-time convergence, and radial basis function (RBF) neural networks for fast, accurate trajectory tracking. The main novelty of the presented control strategy lies in unifying instantaneous feedback, real-time learning, and dynamic adaptation within a multivariable feedback framework, delivering superior robustness, precision, and real-time performance under extreme conditions. The control scheme is implemented on a 5-DOF underactuated $\mathrm{R}\mathrm{A}\mathrm{B}\mathrm{B}\mathrm{I}\mathrm{T}$ robot using a $\mathrm{d}\mathrm{S}\mathrm{P}\mathrm{A}\mathrm{C}\mathrm{E}\mathrm{D}\mathrm{S}1103$ platform with a sampling rate of $∆t=1.5 \mathrm{m}\mathrm{s} (667 \mathrm{H}\mathrm{z})$. The experimental results show excellent performance with the following: The robot achieved stable cyclic gaits while keeping the tracking error within $e=\pm 0.04 \mathrm{r}\mathrm{a}\mathrm{d}$ under nominal conditions. Under severe uncertainties of trunk mass variations ${∆m}_{\mathrm{t}\mathrm{r}\mathrm{u}\mathrm{n}\mathrm{k}}=+100\%$, limb inertia changes ${∆I}_{\mathrm{l}\mathrm{i}\mathrm{m}\mathrm{b}}=\pm 30\%$, and actuator torque saturation at $\tau =\pm 150 \mathrm{N}\mathrm{m}$, the robot maintains stable limit cycles with smooth control. The performance of the proposed controller is compared with classical nonlinear decoupling, non-adaptive finite-time, neural-fuzzy learning, and deep learning controls. The results demonstrate that the proposed method outperforms the four benchmark strategies, achieving the lowest errors and fastest convergence with the following: $\mathrm{I}\mathrm{A}\mathrm{E}=1.36$, $\mathrm{I}\mathrm{T}\mathrm{A}\mathrm{E}=2.43$, $\mathrm{I}\mathrm{S}\mathrm{E}=0.68$, $t_{ss}=1.24 \mathrm{s}$, and $M_p=2.21\%$. These results demonstrate evidence of high stability, rapid convergence, and robustness to disturbances and foot-slip. Full article

Reliable source term prediction for hazardous pollutant puffs in urban microenvironments is challenging, especially for risk management under strict time constraints. Puff movement is highly stochastic due to atmospheric turbulence, intensified by complex urban canopies. This complexity, combined with time limitations, makes advanced computational modeling impractical. A more efficient approach is leveraging past and present data using Machine Learning (ML) techniques. This study proposes an ML-based method, enriched with simplified physical modeling, for source term estimation of unforeseen hazardous air releases in monitored urban areas. The Random Forest Regression, commonly used in meteorology and air quality studies, has been selected. A novel variable selection method is introduced, including the following: (a) a model-derived Exposure Burden Index (EBI) reflecting plume–morphology interactions; (b) a plume travel time indicator; (c) the standard deviation of input variables capturing stochastic behavior; and (d) the total dosage-to-mass released ratio at sensor locations as the target variable. The case study examines JU2003 field experiments involving SF₆ puffs released at street level in Oklahoma City’s urban core, a challenging scenario due to the limited number of sensors and historical data. Results demonstrate the approach’s effectiveness, offering a promising, realistic alternative to traditional computationally intensive methods. Full article

Microalgae have evolved a diverse carotenoid profile, enabling efficient light harvesting and photoprotection. Previous studies have demonstrated the feasibility of genome editing in the green algal model species Chlamydomonas reinhardtii, leading to significant modifications in carotenoid accumulation. By overexpressing a fully redesigned β-carotene ketolase (bkt), the metabolic pathway of C. reinhardtii was successfully redirected toward astaxanthin biosynthesis, a high-value ketocarotenoid with exceptional antioxidant properties, naturally found in only a few microalgal species. In this study, a tailor-made double knockout targeting lycopene ε-cyclase (LCYE) and zeaxanthin epoxidase (ZEP) was introduced as a background for bkt expression to ensure higher substrate availability for bkt enzyme. The increased zeaxanthin availability resulted in a 2-fold increase in ketocarotenoid accumulation compared to the previously engineered bkt1 or bkt5 strain in the UVM4 background. Specifically, the best Δzl-bkt-expressing lines reached 2.84 mg/L under low light and 2.58 mg/L under high light, compared to 1.74 mg/L and 1.26 mg/L, respectively, in UVM4-bkt strains. These findings highlight the potential of rationally designed microalgal host strains, developed through genome editing, for biotechnological applications and high-value compound production. Full article

The increasing restriction of lead in industrial alloys, particularly in copper–zinc-based materials such as CuZn40Pb2, necessitates the development of environmentally safer alternatives. ZnAl15Cu1Mg (ZEP1510), a zinc-based wrought alloy composed of 15% aluminum, 1% copper, 0.03% magnesium, with the remainder being zinc, has emerged as a promising candidate for lead-free applications due to its favorable forming characteristics and corrosion resistance. This study investigates the performance of ZEP1510 compared to conventional leaded copper alloys and galvanized steel. Corrosion behavior was evaluated using neutral salt spray testing, cyclic climate chamber exposure, and electrochemical potential analysis in chloride- and sulfate-containing environments. ZEP1510 exhibited corrosion resistance comparable to brass and significantly better performance than galvanized steel in neutral and humid atmospheres. Combined with its low processing temperature and high recyclability, ZEP1510 presents itself as a viable and sustainable alternative to brass with lead for applications in sanitary, automotive, and electrical engineering industries. Full article

In recent decades, missile guidance and control have advanced significantly, with methods like pure pursuit (PP), command to line-of-sight (CLOS), and proportional navigation (PN) enabling accurate target interception in uncertain environments through line-of-sight (LOS) tracking. In this work, we propose a novel 3D sliding pure pursuit guidance (3DSPP) law for controlling a surface-to-air missile against a maneuvering target. The algorithm is compared with established guidance laws such as zero-effort miss distance “ZEM-PN” and “3D-PP”, with performance metrics including the miss distance $M_d$ and time of closest approach $t_{cap}$. The results demonstrate that the 3DSPP outperforms the conventional methods by achieving the lowest $M_d=$ 0.1497 m and the fastest $t_{cap}=$ 7.3853 s, ensuring more precise and rapid interception. The algorithm also exhibits superior robustness to noise and efficient energy management, making it a promising solution for real-world missile guidance systems. Full article

Solubility plays a crucial role in drug bioavailability and therapeutic efficacy. Febuxostat (FEB), a BCS Class II drug used to treat hyperuricemia and gout, has low solubility, limiting its effectiveness. Cocrystallization offers a strategy to enhance solubility without modifying the drug’s chemical structure. While FEB exhibits multiple polymorphic forms, no prior studies have explored cocrystal formation from its commercially available hemihydrate. This study examines whether FEB’s initial form—hemihydrate or anhydrous—affects cocrystal formation. We investigated cocrystals with aromatic amides (nicotinamide, isonicotinamide, and picolinamide) and explored new FEB cocrystals with aliphatic amides (diacetamide, malonamide, and D,L-lactamide) to assess solubility enhancement. Our results show that anhydrous FEB cocrystals reliably form with both aromatic and aliphatic amides, regardless of the starting material. However, the aliphatic coformers lead to thermally unstable cocrystals. Nevertheless, the new cocrystals significantly improved FEB’s solubility, with FEBH-LAC (13.9 mg/L) being the most soluble, but thermally unstable. FEBH-DIA showed the best balance, with 12.2 mg/L solubility and the fastest dissolution rate. These findings highlight cocrystallization with aliphatic amides as a promising approach for enhancing FEB’s solubility and therapeutic potential; however, they may pose problems with stability and reproducibility. Full article

Photovoltaic park (PV) and power generator monitoring is a crucial activity that calls for effective coverage path planning. Artificial intelligence and particularly swarm robotics have brought new methods to tasks such as coverage path planning by having multiple robots work together to cover a specific area. Nonetheless, enhancing energy efficiency in these systems continues to be a crucial obstacle, particularly with the growing focus on sustainability. This research investigates techniques to enhance energy efficiency in swarm robotics, focusing on coverage path planning assignments. The proposed approach merges advanced swarm robotics algorithms with energy-efficient methods to reduce power consumption while still ensuring effective coverage. Thorough simulations in simulated environments of Western Macedonia assess the efficiency of the proposed approach. Even though the proposed approach has a longer convergence time compared to a generic ACO approach, the findings of the simulations indicate that the MOACO approach has substantial enhancements up to 22% in path travel time, in terms of solution quality and energy consumption metrics. The findings of the present work offer valuable insights into the design of sustainable robotic systems and underscore the potential of swarm robotics in achieving efficient coverage path planning. This study adds to the overall objective of creating eco-friendly technologies in robotics, leading to upcoming advancements in the industry. Full article

(1) Background: Hippophae gyantsensis, a drought-tolerant plant native to the Tibetan Plateau, plays a crucial ecological and economic role. While its drought tolerance mechanisms have been extensively studied, the role of alternative splicing (AS) in drought resistance remains insufficiently explored. This study aims to elucidate how AS events regulate gene expression to enhance drought tolerance in H. gyantsensis under water-deficit conditions. (2) Methods: H. gyantsensis plants were subjected to progressive drought stress followed by rehydration. Physiological responses, transcriptomic data, and hormonal profiles were analyzed to investigate the plant’s adaptive mechanisms to drought stress, with a particular focus on abscisic acid (ABA) signaling-related genes. (3) Results: The results showed that H. gyantsensis maintained high leaf water content even under severe drought stress, emphasizing its strong drought resistance. A transcriptomic analysis revealed 11,962 differentially expressed genes, primarily enriched in hormone signaling and metabolic pathways. Notably, the accumulation of ABA was closely associated with AS events in ABA-related genes, such as ZEPs, ABCG, and PP2C. These genes produced multiple splice variants, indicating their role in modulating the ABA signaling pathway and enhancing drought tolerance. (4) Conclusions: This study highlights the pivotal role of AS in ABA signaling and drought tolerance in H. gyantsensis. It provides new insights into how AS contributes to plant adaptation to drought stress, bridging the knowledge gap in drought resistance mechanisms and emphasizing the importance of AS in plant stress responses. Full article

We generated and characterized knockout mutant lines of the OsPHS3 gene using the CRISPR/Cas9 system. The knockout lines of the OsPHS3 gene showed that 1 bp and 7 bp deletion, early termination codons were used for protein production. Agronomic characteristics of knock-out lines were reduced in plant height, culm diameter, panicle length, seed size and weight, except for the number of tillers. In addition, we analyzed the expression levels of carotenoid biosynthesis genes by qRT-PCR. Among the genes encoding carotenoid metabolic pathway enzymes, the level of transcripts of PSY1, PSY2, PSY3, PDS and ZDS were higher in the KO lines than in the WT line. In contrast, transcription of the ε-LCY, β-LCY and ZEP1 genes were downregulated in the KO lines compared to the WT line. Also, the KO lines decreased carotenoid content and ABA amount compared to WT, while preharvest sprouts increased. These results suggested that they would certainly help explain the molecular mechanisms of PHS in other crops, such as wheat and barley, which are susceptible to PHS. Full article

Torreya grandis is a widely cultivated fruit species in China that is valued for its significant economic and agricultural importance. The molecular mechanisms underlying pigment formation and photosynthetic performance in Torreya leaf color mutants remain to be fully elucidated. In this study, we performed transcriptome sequencing and measured photosynthetic performance indicators to compare mutant and normal green leaves. The research results indicate that the identified Torreya mutant differs from previously reported mutants, exhibiting a weakened photoprotection mechanism and a significant reduction in carotenoid content of approximately 33%. Photosynthetic indicators, including the potential maximum photosynthetic capacity (F_v/F_m) and electron transport efficiency (Ψ_o, φ_Eo), decreased significantly by 32%, 52%, and 49%, respectively. While the quantum yield for energy dissipation (φ_Do) increased by 31%, this increase was not statistically significant, which may further reduce PSII activity. A transcriptome analysis revealed that the up-regulation of chlorophyll degradation-related genes—HCAR and NOL—accelerates chlorophyll breakdown in the Torreya mutant. The down-regulation of carotenoid biosynthesis genes, such as LCY1 and ZEP, is strongly associated with compromised photoprotective mechanisms and the reduced stability of Photosystem II. Additionally, the reduced expression of the photoprotective gene psbS weakened the mutant’s tolerance to photoinhibition, increasing its susceptibility to photodamage. These changes in gene expression accelerate chlorophyll degradation and reduce carotenoid synthesis, which may be the primary cause of the yellowing in Torreya. Meanwhile, the weakening of photoprotective mechanisms further impairs photosynthetic efficiency, limiting the growth and adaptability of the mutants. This study emphasizes the crucial roles of photosynthetic pigments and photosystem structures in regulating the yellowing phenotype and the environmental adaptability of Torreya. It also provides important insights into the genetic regulation of leaf color in relation to photosynthesis and breeding. Full article

Federated learning (FL) is creating a paradigm shift in machine learning by directing the focus of model training to where the data actually exist. Instead of drawing all data into a central location, which raises concerns about privacy, costs, and delays, FL allows learning to take place directly on the device, keeping the data safe and minimizing the need for transfer. This approach is especially important in areas like healthcare, where protecting patient privacy is critical, and in industrial IoT settings, where moving large numbers of data is not practical. What makes FL even more compelling is its ability to reduce the bias that can occur when all data are centralized, leading to fairer and more inclusive machine learning outcomes. However, it is not without its challenges—particularly with regard to keeping the models secure from attacks. Nonetheless, the potential benefits are clear: FL can lower the costs associated with data storage and processing, while also helping organizations to meet strict privacy regulations like GDPR. As edge computing continues to grow, FL’s decentralized approach could play a key role in shaping how we handle data in the future, moving toward a more privacy-conscious world. This study identifies ongoing challenges in ensuring model security against adversarial attacks, pointing to the need for further research in this area. Full article

The increasing integration of brain–computer interfaces (BCIs) with augmented reality (AR) presents new possibilities for immersive and interactive environments, particularly through the use of head-mounted displays (HMDs). Despite the growing interest, a comprehensive understanding of BCI-AR systems is still emerging. This systematic review aims to synthesize existing research on the use of BCIs for controlling AR environments via HMDs, highlighting the technological advancements and challenges in this domain. An extensive search across electronic databases, including IEEEXplore, PubMed, and Scopus, was conducted following the PRISMA guidelines, resulting in 41 studies eligible for analysis. This review identifies key areas for future research, potential limitations, and offers insights into the evolving trends in BCI-AR systems, contributing to the development of more robust and user-friendly applications. Full article

Carotenoids are vital organic pigments that determine the color of flowers, roots, and fruits in plants, imparting them yellow, orange, and red hues. This study comprehensively analyzes carotenoid accumulation in different tissues of the Brassica rapa mutant “YB1”, which exhibits altered flower and root colors. Integrating physiological and biochemical assessments, transcriptome profiling, and quantitative metabolomics, we examined carotenoid accumulation in the flowers, roots, stems, and seeds of YB1 throughout its growth and development. The results indicated that carotenoids continued to accumulate in the roots and stems of YBI, especially in its cortex, throughout plant growth and development; however, the carotenoid levels in the petals decreased with progression of the flowering stage. In total, 54 carotenoid compounds were identified across tissues, with 30 being unique metabolites. Their levels correlated with the expression pattern of 22 differentially expressed genes related to carotenoid biosynthesis and degradation. Tissue-specific genes, including CCD8 and NCED in flowers and ZEP in the roots and stems, were identified as key regulators of color variations in different plant parts. Additionally, we identified genes in the seeds that regulated the conversion of carotenoids to abscisic acid. In conclusion, this study offers valuable insights into the regulation of carotenoid metabolism in B. rapa, which can guide the selection and breeding of carotenoid-rich varieties. Full article