To achieve rapid post-earthquake repair of self-centering ribbed floor flat beam frame structures, a ductile hybrid joint consisting of dog bone weakened energy dissipating steel bars connected to the upper and lower column sections through high-strength threads based on the damage control design concept is proposed. By moving the ductile energy dissipating zone out to the locally weakened section of the energy dissipating steel bars and the locally unbonded prestressed steel bars in the core area, the residual deformation was limited and the seismic performance was improved. Based on the working principle of hybrid joints, low cycle loading tests were conducted on two joint specimens to analyze the influence of lateral prestress on the seismic performance of the hybrid joints. Numerical modeling methods were used to compare the position of the energy-consuming steel bars in the composite layer and the friction performance of joints. The research results indicated that the hybrid joint had stable load-bearing, deformation, and energy dissipation capabilities, with damage primarily concentrated on the energy-consuming steel bars. Even at an inter-story displacement angle of 5.5%, the upper and lower column segments remained elastic. After unloading, the connection seam at the joint was closed, and the self-centering performance was good. When the inter-story displacement angle reached 5.5%, the lateral prestress increased from 150 kN to 250 kN, the ultimate bearing capacity of the joint increased by 16.3%, and the cumulative energy consumption increased by 30.0%. The influence of the friction coefficient of the joint surface on the structural performance was set at a threshold of 0.7. When it was less than the threshold, the ultimate bearing capacity and initial stiffness of the joint increased with an increase in the friction coefficient. After reaching the threshold, the increase in the ultimate bearing capacity of the joint slowed down, and the rate of stiffness degradation gradually accelerated. This joint had excellent seismic performance and can also achieve post-earthquake repair of structures.

This study investigates the potential of a hybrid nanofluid composed of MoS2 and ZnO nanoparticles dispersed in engine oil, aiming to enhance the properties of lubricant's chemical reaction with the Soret effect on stretching sheet under the influence of applied magnetic field. With the growing demand for efficient lubrication systems in various industrial applications, including automotive engines, the development of novel nanofluid-based lubricants presents a promising avenue for improving engine performance and longevity. However, the synergistic effects of hybrid nanoparticles in engine oil remain relatively unexplored. This research addresses this gap by examining the thermal conductivity, viscosity, and wear resistance of the hybrid nanofluid, shedding light on its potential as an advanced lubrication solution. By analyzing the dispersion stability and interaction mechanisms between nanoparticles and base oil, valuable insights are gained into the fundamental aspects governing the entertainment of hybrid nano lubricants. Overall, the objectives of studying hybrid nano lubricant MoS2+ZnO with engine oil aim to advance the development of more efficient and durable lubrication solutions for automotive engines, contributing to improved reliability, fuel efficiency, and environmental sustainability. Governing non-linear partial differential equations are simplified as ordinary differential equations by utilizing similarity variables. MATLAB Bvp4c technique is used to solve the obtained linear ODE equations. The outputs are presented through graphs and tables for various state variables. The comparative survey in all the graphs is presented for the nanofluid (MoS2/Engine oil) with the hybrid nanofluid (MoS2+ZnO/Engine oil). These graphs show the effect of various parameters M, Q, β, Pr, Ec, Sc, Sr, Kp, Kr and (hybrid nano lubricant parameter). The velocity profile diminished against the values of M, Kp, and β. Temperature profile rising with Ec and Q. reciprocally goes down to Pr. The concentration profile incremented with the value of Sr and decremented with the value of Sc, and Kr. With the increasing value of both f′ (η) and θ(η) increases. But in θ(η) hybrid nanofluid slows down with the higher value of .

Species of the genus Drosophila have served as favorite models in speciation studies; however, genetic factors of interspecific reproductive incompatibility are underinvestigated. Here we performed the analysis of hybrid female sterility by crossing Drosophila melanogaster females and Drosophila simulans males. Using transcriptomic data analysis, molecular, cellular, and genetic approaches, we analyzed differential gene expression, transposable element (TE) activity, piRNA biogenesis, and functional defects of oogenesis in hybrids. Premature germline stem cell loss was the most prominent defect of oogenesis in hybrid ovaries. Due to the differential expression of genes encoding piRNA pathway components, rhino and deadlock, the functional RDCmel complex in hybrid ovaries was not assembled. However, the activity of the RDCsim complex was maintained in hybrids, independent of the genomic origin of piRNA clusters. Despite the identification of a cohort of overexpressed TEs in hybrid ovaries, we found no evidence that their activity can be considered the main cause of hybrid sterility. We revealed a complicated pattern of Vasa protein expression in the hybrid germline, including partial AT-chX piRNA targeting of the vasasim allele and a significant zygotic delay in vasamel expression. We arrived at the conclusion that the hybrid sterility phenotype was caused by intricate multi-locus differences between the species.

Asymmetric lithium salts, such as Lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), have been demonstrated to surpass traditional symmetric lithium salts with improved Li+ conductivity and the capacity to generate stable solid electrolyte interphase (SEI) while maintaining compatibility with aluminum (Al0) current collector. However, the intrinsic reductive mechanism through which LiDFTFSI influences battery performance remains unclear and under debate. Herein, the detailed SEI reactions of LiDFTFSI-based electrolytes were investigated by combining density functional theory and molecular dynamics, aiming to clarify the formation process and atomic structure of SEI. Our results show that asymmetric DFTFSI– weakens the interaction between carbonate solvents and Li+, and substantially alter the solvation structure, exhibiting a well-balanced coordination capacity to bis(trifluoromethanesulfonyl)imide (TFSI–). Nanoseconds hybrid molecular dynamics simulation further reveals that the preferential decomposition of LiDFTFSI produces sufficient LiF and Li2O to facilitate a robust SEI. Moreover, the abundant F– generated from LiDFTFSI decomposition accumulates on the Al surface and subsequently combines with Al3+ from the current collector to form AlF3, potentially inhibiting corrosion of the current collector. Overall, these findings elucidate how LiDFTFSI regulates the solvation sheath and SEI structure, advancing the development of high-performance electrolytes compatible with current collector.

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt digital array structure and digital beamforming technique, and it has limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on hybrid beamforming technique. Firstly, a novel fully connected hybrid array receiver structure is proposed; Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of strong interference by jointly suppression in analog domain and digital domain. After mathematical simulation experiments, it is verified that compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

The results shown in this paper extend our research group's previous work, which presents the theoretically achievable hydrogen engine-out NOx (H2-NOx) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOx trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing the NOx. In a case study it is shown that the MPC is able to satisfy user-defined NOx limits over the course of various driving missions. Moreover, a comparison to the optimal Pareto front highlights the MPC's ability to achieve close-to-optimal fuel-performance for any desired cumulated NOx target on four realistic routes for passenger cars.

The work aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We develop a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving operation, avoiding incompatible driving patterns with energy-saving mode and performance improvement. The protocol is submitted to a simulation process to predict the energy rate lowering and battery performance enhancement. The proposed protocol applies to any hybrid electric vehicle type and route conditions since it uses vehicle mass, drag and rolling coefficients, and road slope as variable parameters to determine the minimum energy consumption rate. We run experimental tests to validate the simulation data and the proposed protocol. Furthermore, the protocol applies to variable starting vehicle speeds, from 10 to 50 km/h, corresponding to the current driving patterns, sport, normal, and eco, set up by car manufacturers. A reduction of 10% in vehicle speed in urban and peripheral routes achieves a minimum energy rate, enhancing battery management. Current vehicle speed shows a deviation from optimum management of 18% while applying the vehicle speed reduction limits the deviation to 0.2%. Experimental results show a good agreement with simulation data with 94% accuracy. We tested the protocol for urban and peripheral routes with maximum vehicle speed limits of 60 and 90 km/h.

This study delves into Personal Narratives (PN), which encompass both oral and written recounting of individual experiences, encompassing facts, events, people, and thoughts. Traditional emotion recognition and sentiment analysis primarily focus on broader categories like utterances or documents. Our research, however, centers on identifying Emotion Carriers (EC), which are specific segments within speech or text that elucidate the narrator's emotional state (e.g.,"losing a parent", "decision-making moments"). Extracting these ECs enriches the representation of a user's emotional state, thereby enhancing natural language understanding and the sophistication of dialogue models. While previous studies have utilized lexical attributes to identify ECs, we argue that incorporating spoken narratives offers a more nuanced view of the context and emotional state. This paper explores the integration of speech and textual embeddings at the word level, alongside both early and late fusion techniques, for improved EC detection in spoken narratives. We employ Residual Neural Networks (ResNet), initially pre-trained on diverse speech emotion datasets and subsequently fine-tuned for EC detection. Our experimental findings demonstrate that late fusion, in particular, significantly enhances EC detection capabilities.

Graphene hybrid-filler polymer composites have emerged as prominent materials that revolutionize heavy industries. This study encapsulates the in-depth analysis of different influential factors such as filler/graphene type, aspect ratios, dispersion methods, filler-matrix compatibility, fiber orientation, synergistic effects, different processing techniques, and post-curing conditions that affect the processing, properties and determine the resultant applications of graphene hybrid polymer composites. Additionally, it discusses the substantial role of graphene collaboration with other fillers such as carbon nanotubes, silica, nano-clays and metal oxides to produce functionalized hybrid polymer composites with synergistically enhanced tailored properties to offer solutions for heavy industries like aerospace, automotive, electronics, and energy harvesting. The review concludes with some suggestions and outlook on the future of these composite materials by emphasizing the need for continued research to fully optimize their potential.

Yeast two-hybrid approaches, which are based on fusion proteins that must co-localise to the nucleus to reconstitute the transcriptional activity of GAL4 have greatly contributed to understand the nitrogen interaction network of cyanobacteria, whose main hubs are the trimeric PII and the monomeric PipX regulators. The bacterial two-hybrid system, based on the reconstitution in the E. coli cytoplasm of the adenylate cyclase of Bordetella pertussis, should provide a relatively faster and presumably more physiological assay for cyanobacterial proteins than the yeast system. Here we use the bacterial two-hybrid system to gain additional insights into the cyanobacterial PipX interaction network while simultaneously assessing the advantages and limitations of the two most popular two-hybrid systems. Comprehensive mutational analysis of PipX and bacterial two-hybrid assays were performed to compare outcomes between yeast and bacterial systems. We detected interactions previously recorded in the yeast two-hybrid system as negative as well as a “false positive”, the self-interaction of PipX, which is rather an indirect interaction dependent on PII homologs from the E. coli host, a result confirmed by Western analysis with relevant PipX variants. This is, to our knowledge, the first report of the molecular basis of a false positive in the bacterial two-hybrid system.

Hybrid Renewable Energy Systems (HRES) integrate renewable sources, storage, and optionally conventional energies, offering an eco-friendly solution to fossil fuels. Microgrids (MGs) bolster HRES integration, enhancing energy management, resilience, and reliability at various levels. This study, emphasizing the need for refined optimization methods, investigates three themes: renewable energy, microgrid, and multiobjective optimization (MOO), through a bibliometric analysis of 470 Scopus documents from 2010-2023, analyzed with SciMAT software. It segments the research into two periods, 2010-2019 and 2020-2023, revealing a surge in MOO focus, especially in the latter period, with a 35% increase in MOO-related research. This indicates a shift towards com-prehensive energy ecosystem management that balances environmental, technical, and economic elements. The initial focus on MOO, genetic algorithms, and energy management systems has expanded to include smart grids and electric power systems, with MOO remaining a primary theme in the second period. The increased application of Artificial Intelligence (AI) in optimizing HMGS within the MOO framework signals a move towards more sustainable, intelligent energy solutions. Despite progress, challenges remain, including high battery costs, the need for reliable MOO data, the intermittency of renewable energy sources, and HMGS network scalability issues, highlighting directions for future research.

One of the industries with the fastest rate of growth is healthcare, and this industry's enormous data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. Increased customer satisfaction with security and privacy preservation is crucial since healthcare big data and its sources are stored in the cloud and contain increasingly private and sensitive information. However, maintaining privacy and security in an untrusted green cloud environment is difficult, thus the data owner should have complete data control. A new work SecPri-BGMPOP(security and privacy of Boost graph Convolutional Network -Pinpointing-Optimization Performance) is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. Initially Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, is first applied to the input dataset to begin the clustering process. Second, enlarge by employing a piece of the magnifying bit string to generate a safe key, pinpointing based encryption avoids amplify leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid fragment horde bland lobo Optimization (HFHBLO). Our proposed method currently kept in a cloud environment, allowing analytics users to utilize it without risking their privacy or security.

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of the Driving Scenario Knowledge Graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable Driving Scenario Knowledge Graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia.

Rice blast and brown planthopper (BPH) are two of the most destructive diseases and pests in rice (Oryza sativa L.) production in China. The development of multi-resistant varieties is widely recognized as the most efficient and environmentally friendly approach to controlling crop diseases and pests. Functional molecular markers (FMMs) have been developed from functional variants in the genic region associated with trait variation, greatly enhancing the efficiency of identifying and pyramiding valuable genes in crop breeding. In this study, two FMMs and a multiplex PCR amplification system were developed for two major broad-spectrum BPH resistance genes, Bph6 and Bph9. By assistance of FMMs in multi-resistant rice restorer line development pipeline, two lines (Huahui7713 and Huahui3006) with blast and BPH resistance were developed by pyramiding three resistance genes Pigm, Bph6 and Bph9. Huahui7713 and Huahui3006 as male lines, two new hybrid rice varieties (WeiLiangYou7713 and Xuanliangyou3006) were certificated and commercial released in China. WeiLiangYou7713 and Xuanliangyou3006 showed improved blast and BPH resistance, and no penalty on yield and grain-quality. Annual promotion area of WeiLiangYou7713 has exceeded 53.3 thousand hectares, indicating its widespread adoption and success in the market. Huahui7713 and Huahui3006 could have great potential for utilization in blast and BPH resistance rice breeding.

Keywords: Energy harvesting, Hybrid, Medical Wearable Sensor Nodes, Health Monitoring, Power Management

Aseptic implant loosening after total joint replacement is partially influenced by material-specific factors when cobalt-chromium alloys are used, including osteolysis induced by wear- and corrosion products, and stress shielding. Here, we aim to characterize a hybrid material consisting of alumina-toughened zirconia ceramics (ATZ) and additively manufactured Ti-35Nb-6Ta (TiNbTa), joined by a glass solder. The structure of the joint, static and fatigue shear strength, the influence of accelerated aging, and the cytotoxicity using an elution assay with human osteoblasts is characterized. Furthermore, the biomechanical properties of functional demonstrators of a femoral component for total knee replacements are evaluated. TiNbTa-ATZ specimens showed a homogenous joint with statistical distributed micro-pores and a slight accumulation of Al-rich compounds at the glass solder-TiNbTa interface. The static shear strength was 26.4 ± 4.2 MPa (Ti-ATZ reference: 38.2 ± 14.4 MPa, p = 0.117), all specimens survived 107 cycles of shear loading to 10 MPa, and aging did not reduce the strength. TiNbTa-ATZ did not impair the proliferation and metabolic activity of human osteoblasts. Functional demonstrators of TiNbTa-ATZ provided a maximum bearable extension-flexion moment of 40.7 ± 2.2 Nm. Biomechanical and biological properties of TiNbTa-ATZ demonstrate potential application for endoprosthetic implants.

: The crystallization conditions from the solution play an important role in determining the mor-phology, phase composition, and photovoltaic properties of perovskite films. Post-processing of the obtained films can have a crucial role in increasing the grain size of perovskite and enhancing its crystallinity. It has been shown that the formation of crystal nuclei can be utilized to accelerate crystallization. In this case, crystallization occurs through the growth of seed crystals created in the solution, enabling the formation of relatively large crystals. For the deposition of CH3NH3PbI3 hybrid halide perovskite films from a solution of the perovskite in dimethylformamide the spin coating technique was employed. Pre-crystallization was achieved by annealing the films at a temperature of 100 or 110 °C. The dissolution process involved adding a drop of dimethylformamide onto the substrate surface and allowing it to partially dissolve the perovskite film. Subsequently, residual solvent was removed through spin coating. The mor-phological analysis of the perovskite film surface after recrystallization at temperatures ranging from 80 to 130 °C was performed. The infrared transmission spectra of the obtained perovskite films were investigated, and their light absorption characteristics were studied through transmission spectra. The perovskite structure in the obtained films was confirmed by the peaks observed in the X-ray diffraction patterns.

Background: The commonly known assumption that combinations of several herbs in one formulation can have better efficacy due to additive or synergistic effects has not been systematically studied despite some evidence supporting the synergy concept. Study aim: The study aimed to reveal the molecular interactions in situ of host cells in response to the intervention of BHP and justify the benefits of implementing BHP in clinical practice. Results: This overview provides the results of recent clinical and network pharmacology studies of botanical herbal preparations (BHP) of Rhodiola with other plants, including Ashwagandha, Green Tea, Eleutherococcus, Schisandra, Eleutherococcus, Leuzea, Caffeine, Cordyceps, Gingko, Black Cohosh, saffron, and L-carnosine. Conclusions: The most important finding from network pharmacology studies of BHP was the evidence supporting the synergistic interaction of BHP ingredients, revealing unexpected new pharmacological activities unique and specific to the new BHP. Some studies show the superior efficacy of BHP compared with mono-drugs. At the same time, some a priori-designed combinations can fail, presumably due to antagonistic interactions and crosstalk between molecular targets within molecular networks involved in the cellular and overall response of organisms on the intervention. Network pharmacology studies help predict the results of studies to discover new indications and unpredicted adverse events.

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of the GRU and LSTM. LSTM networks learn longer dependencies well, but due to the number of gates, it takes longer to train; on the other hand, GRU networks have a weaker performance than LSTM, but their training speed is much higher than GRU, which is The speed is due to its fewer gates. The proposed approach used the combination of speed and accuracy of these two networks. The proposed approach achieved an accuracy of 0.99 in the 4,499,0641 points dataset, which includes eight classes (unlabeled, man-made terrain, natural terrain, high vegetation, low vegetation, buildings, hardscape, scanning artifacts, cars). Meanwhile, the traditional machine learning approaches could achieve a maximum accuracy of 0.9489 in the best case.

Time evolution of the quantum correlation of the hybrid qubit-qutrit system in multilocal and global dephasing under the effect of a classical noises environment is presented. We considered the qutrit in z direction and the qubit x direction. In addition, our system presents two configurations: the different and the common environment and our discussion involves three types of noises: A Static Noise (SN), Random Telegraph Noise (RTN) and Ornstein- Uhlenbeck noise (OU). By using the density matrix approach to determine the evolution of the state of the system, the found that the static noise is fatal for the quantum entanglement, the quantum correlation and for RTN and OU noises the quantum entanglement, the quantum correlation decreases monotonically with a sufficiently long time. Therefore, we can say that, entanglement and the quantum correlation are more robust to the noise precisely those Random telegraph noise and Ornstein-Uhlenbeck noise.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

The insufficient flexibility of the hybrid renewable integrated energy system (HRIES) makes it difficult to accommodate renewable energy with a high proportion. It is thus necessary to excavate its potential flexibility to improve its operational performance and consume renewable energy. This paper unlocks the adjustment potential of flexible buildings and builds a flexible thermal load regulation model according to the dynamic thermal characteristics and thermal comfort elastic interval of the buildings and a regulation model of the flexible electrical load based on its transferability, resectability, and rigidity. An operation optimization model, which incorporates multiple flexible load regulations and the variable load of devices, is then developed. A case study is presented to analyze the regulation and synergy mechanism in different types of flexible loads. Its results show a saturation effect between the flexible thermal and electrical loads in increasing renewable energy consumption and a synergy effect in decreasing the total cost. This synergy can reduce the total cost by 0.73%. Besides, the total cost can be reduced by 15.13%, and the renewable energy curtailment rate can decrease by 12.08% if the flexible electric and thermal loads are integrated into the operation optimization of HRIES.

In this study, we address the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on very-large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

Shape-shifting polymers usually require not only reversible stimuli-responsive ability, but also strong mechanical properties. A novel shape-shifting photochromic hydrogel system was designed and fabricated through embedding hydrophobic spiropyran (SP) into double polymeric network (DN) by micellar copolymerization. Here, sodium alginate (Alg) and poly acrylate-co-methyl acrylate-co-spiropyran (P(SA-co-MA-co-SPMA)) were employed as the first network and the second network respectively to realize high mechanical strength. After soaked in the CaCl2 solution, the carboxyl groups in the system undergo metal complexation with Ca2+ to enhance the hydrogel. Moreover, after the hydrogel is exposed to UV-light, the closed isomer of spiropyran in the hydrogel network can be converted into an open zwitterionic isomer MC, which is considered to interact with Ca2+ ions. Interestingly, Ca2+ and UV-light responsive programmable shape of the copolymer hydrogel can recover to its original form by immersing in pure water. Since its excellent metal ion and UV-light stimuli-responsive and mechanical properties, the hydrogel has potential applications in the field of soft actuators.

The escalating demand for multifunctional and advanced materials for growing industries has turned scientific exploration to graphene-based hybrid filler polymer composites. The introduction of graphene with other fillers into polymer matrices results in astonishing properties and thus has gained significant attention in recent years. This study addresses the significance of graphene-based hybrid filler polymer composites in progressive materials science and engineering. The paper encloses some traditional and current preparation techniques employed in the fabrication of composites and a comprehensive review of successfully prepared graphene hybrid polymer composites with enhanced mechanical, thermal, and electrical properties reported in published research. The role of different factors, e.g., filler type, aspect ratios, filler-matrix compatibility, dispersion effects, homogeneity, filler orientation through polymer matrices, and curing conditions, are summarized. Finally, the applications in heavy industries, e.g., aerospace, electronics, energy storage, and automotive sectors were also analyzed. We hope this study will serve as a practical resource for new researchers and practitioners to understand and harness the full potential of these composites in various scientific and industrial fields.

Hybrid reinforcement in a composite material (CM), in many situations, allows better govern material mechanical properties. Polymer matrix CM plate is reinforced with small particles and macro-fibers. Particles are oil shale ash (OSA) powder, macro-fibers – basalt fiber threads im-pregnated by matrix material. With the goal to find out elastic properties of the matrix- polymer with OSA, samples with its different concentrations were experimentally fabricated and their elastic properties were experimentally measured. Obtained data were used in numerical model-ling. Was observed polymer matrix composite slab, reinforced with OSA and three-dimensional (3D) basalt fiber fabric. Fabric are forming threads impregnated by matrix (polymer resin mixed with OSA particles). Damage accumulation in the stretched slab was modelled. Impregnated thread, in the modelling, is observed as a macro-fiber (MF) with averaged elastic properties. With the load increase MF in the material began to break. At first, appeared isolated single breaks. A probabilistic sequential MF rupture accumulation model was accepted and numerical-ly realized. The geometry of the reinforcing knitted fabric was modeled using the Leaf and Glaskin approach. In the knitted fabric highly, stressed MF cross-sections are observed as po-tential location where thread breakage can occur. If one thread breaks, the cross-sections of the adjacent threads experience the overload caused by this rupture. More overloaded crossection in the adjacent overloaded thread breaks and we obtain two adjacent broken MF designated as a defect with the size j=2. The probabilities of obtaining defect consisting of at least 2, 3, … j adjacent broken MF in the plate were calculated. The calculations continued until the rupture process becomes unstable (j→∞). The overstresses on adjacent threads in the CM were calculat-ed using the FEM three-dimensional numerical approach. Analytical and stochastic methods were used for the load-carrying capacity determination and different size defects accumulation in the hybrid composite material (HCM) plate. Also, was calculated the probability of the plate rupture.

A The aim of this study is to analyze the effect of different endodontic irrigants and adhesive systems on resin bond strength of fiber post cementation. 144 single-rooted, unrestored human teeth were endodontically treated and randomly divided into 12 groups according to 4 endodontic irrigants (distilled water as control; EDTA 17%; NaOCl 5%; clorhexidine 2%) and 3 different adhesive/resin cement systems (etch-and-rinse: ortofosforic acid, Parabond® A+B/Paracore®; self-etch: ParaBond® Non-Rinse Conditioner, Parabond® A+B /Paracore®; Universal: ClearfilTM Universal Bond/ ClearfilTM DC Core Plus). 48 h after post cementation ten teeth from each group were cross sectioned into 3 discs (cervical, middle and apical regions). 30 specimens of each group (n=30) were submitted to push-out test at a crosshead speed of 1 mm/min. The remaining 2 teeth of each group were sectioned in the same manner and the resin-dentin interface was evaluated using scanning electron microscopy (SEM). The results were statistically analyzed with the ANOVA and Tukey’s test (< 0,01). The adhesive protocols and post space region showed no significant effect on bond strength (> 0.01). The combination of NaOCl 5% and ClearfilTM Universal Bond reduced the adhesive strength (< 0.01). The NaOCl 5%, in relation with other irrigants, significantly decreased the push-out bond strength.

The rapid advancements and the merging of hybrid quantum-classical computing, artificial intelligence (AI), machine learning (ML), and deep learning (DL) pose a potentially unseen and significant threat to encryption that may impact Post-Quantum Cryptography (PQC) transition timelines. There is a direct hybrid quantum-classical computing threat on cryptography, and there is also a direct threat AI/ML on cryptography. However, the synergistic combination of these technologies presents known and unknown threats that need attention, focus action, and research. This paper reviews Grover's Adaptive Search (GAS), which combines Grover's Algorithm with adaptive techniques to optimize search further, potentially making it even more efficient for attacking encryption. This work also examines the quantum-accelerated Harrow-Hassidim-Lloyd (HHL) Algorithm, designed to solve systems of linear equations exponentially faster than classical algorithms in certain conditions. The HHL algorithm can solve some lattice-based problems, which have implications for lattice-based encryption. This technological confluence and its potential impact on cryptography and encryption necessitate a proactive and coordinated approach to developing and implementing quantum-resistant AI/ML cryptographic solutions. This paper reviews the technological confluence and its potential implications for classical cryptography and PQC transition timelines and calls for further research.

This investigation mainly focused on the microstructural analysis of Al based hybrid metal matrix composites. Aluminium as base metal and reinforced with 2% B4 C, ZrO2 (2%,4%,6%), SiC (4%,8%,12%). Specimens were manufactured by using conventional stir casting technique. Hybrid metal matrix composites were examined by using Scanning electron microscope. This research explained XRD, EDS and Fractography analysis. This investigation shows the reinforced material has given better results in all aspects than pure Al6061 alloy and homogeneous dispersion of particles inside the matrix. Porosity, delamination, cracks, particle distributions were observed through microstructural analysis. Additionally determined the crystal size of pure Aluminium 6061 alloy and hybrid composites.

Styryl dyes are generally poor fluorescent molecules inherited from their flexible molecular structures. However, their emissive properties can be boosted by restricting their molecular motions. A tight confinement into inorganic molecular sieves is a good strategy to yield highly fluorescent hybrid systems. In this work, two Mg-aluminophosphate zeotypes (AEL framework, a one-dimensional channeled structure with elliptical pores of 6.5 Å x 4.0 Å; and CHA framework, composed by large cavities of 6.7 Å x 10.0 Å connected by 8-ring narrower windows) were selected for the encapsulation of 4-DASPI dye (trans-4-[4-(Dimethylamino)styryl]-1-methylpyridinium iodide). The synthesis and photophysical properties of 4-DASPI styryl dye occluded within these two types of confinements, AEL and CHA, are optimized and analyzed. The resultant hybrid systems display significantly improved photophysical features compared to 4-DASPI in solution. Molecular simulations reveal a tighter confinement of 4-DASPI in the elliptical channels of AEL, whilst a particular orientation is found within the CHA framework, where one 4-DASPI molecule spans along two adjacent cavities. In the latter scenario, each aromatic ring is positioned on these adjacent cavities and the polymethine chain resides within the 8-ring window, being this confinement slightly less tight than for AEL.

As marine traffic is contributing to pollution and mostly have predictable route with repetitive load profiles, to reduce their impact on environment, hybrid system with proton exchange membrane fuel cells (PEMFC-s) and battery pack, is promising replacement. For this purpose, the numerical model of repowered existing vessel that have two diesel engines with 300 kWe is developed. The goal of this paper is to develop numerical model which analyzes and determine equivalent hybrid ship propulsion system for known traveling route. The developed numerical model consists of an interconnected system with PEMFC stack and battery pack as power sources. Numerical model is developed and optimized to meet minimal required power demand for successful route demand, which has variable loads and daily sails six-time same voyage- in total 54 nautical miles. The results have shown that equivalent hybrid power system consists of 300 kWe PEMFC stack and battery pack with 424 kWh battery and state of charge varying between 20 and 87%. To power this new hybrid power system, hydrogen tank of 7200 l holding 284.7 kg at pressure of 700 bar is needed, compared to previous system that spend 1524 kg of diesel and generate 4886 kg of CO2.

Autonomous driving navigation relies on diverse approaches, each with advantages and limitations depending on various factors. For HD maps, modular systems excel, while end-to-end methods dominate mapless scenarios. However, few leverage the strengths of both. This paper innovates by proposing a hybrid architecture that seamlessly integrates modular perception and control modules with data-driven path planning. This innovative design leverages the strengths of both approaches, enabling a clear understanding and debugging of individual components while simultaneously harnessing the learning power of end-to-end approaches. Our proposed architecture achieved 1st and 2nd place in the 2023 CARLA Autonomous Driving Challenge’s SENSORS and MAP tracks, respectively. These results demonstrate the architecture’s effectiveness in both map-based and mapless navigation.

This study conducts a comprehensive review of the integration of Hybrid Electric Turbochargers in mass-produced road vehicles. The primary objective is to inform the readers about the potential advantages and innovative applications of HETs. Employing a SWOT analysis, the research compares the performance of HETs on thermal engines versus traditional turbochargers and HETs on thermal engines versus HETs on hybrid engines based on a synthesis of multiple research findings. The study elucidates the fundamental mechanics of turbochargers, emphasizing their inherent benefits. Additionally, it introduces a novel approach with the integration of the hy-brid-electric turbocharger, demonstrating its capacity, supported by practical examples. The in-corporation of turbochargers with energy recovery mechanisms, including HETs, is shown to significantly contribute to emissions reduction. Moreover, the electric drive facilitates the utili-zation of hybrid turbines, particularly on low-displacement engines, resulting in enhanced effi-ciency, torque, and liter power. In conclusion, the research underscores the potential of HETs to substantially mitigate the environmental impact of the transportation sector by reducing emis-sions and conserving energy. Their ability to optimize the fuel efficiency and power output of internal combustion engines, coupled with the feasibility of downsized engines, positions HETs as an attractive option for both manufacturers and consumers. However, it is acknowledged that the implementation of HETs is in its early stages, necessitating further research to comprehensively understand their environmental impact. Despite this, the promising positive impact on the en-vironment designates HETs as a technology deserving close attention in the forthcoming years.

The dimethylaminophenyl-substituted silver phthalocyanine [dmaphPcAg] can be used as a UV-vis photoinitiator for in situ preparation of a silver/polymer nanocomposite. To verify early steps of the supposed mechanism of radical polymerization, we performed quantum chemical calculations of m[dmaphPcAg]q complexes with charges q = +1 to -2 in the two lowest spin states m, of a free ligand and its dehydrogenated/deprotonated products m[dmaphPcHn]q, n = 2 to 0, q= 0, -1 or -2, in the lowest spin states m. The calculated electronic structures and electron transitions of all the optimized structures in CHCl3 solutions are compared with experimental EPR and UV-vis spectra, respectively. The unstable 3[dmaphPcAg]+ species deduced only from previous EPR spin trap experiments was identified. In addition to 2[dmaphPcAg]0, our results suggest the coexistence of both reaction intermediates 1[dmaphPcAg]- and 3[dmaphPcAg]- in reaction solutions. Silver nanoparticle formation is a weak point of the supposed reaction mechanism from the energetic, stereochemistry, and electronic structure points of view.

Space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high fidelity simulation, the impact of force measurement delay, discrete control cycle, and simulator response delay on stability is analyzed firstly. Then, the dynamic equation and transfer function identification model of hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a switching compensator with variable gain is proposed, the compensation principle diagram of which is build, and its mathematical model including the energy observer and tracking differentiator is also established. Finally, three sets of numerical simulations are conducted to validate the correctness of the stability analysis and effectiveness of proposed compensation method. Simulation results show that all three types of delay can cause SITS to lose stability under critical stable motion state, and the delay in force measurement has the greatest impact, followed by the influence of control cycle. Compared with the force applied to simulated target, velocity and its recovery coefficient of space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.

This report covers the analysis of Intrusion Detection Systems nowadays. Therefore, it focuses on the IDS classification, which includes not only a well-known explanation of Network/Host-Based IDS type but also cloud-based solutions. The paper explores ways of intrusion detection, providing examples of the best detecting tools (OSSEC, Snort or Bro (ZEEK)). Another aspect covered in this report is the juxtaposition of three systems: IDS, IPS and IRS and their response. Finally, there will be presented Intrusion Detection Systems' evasion techniques and challenges, followed by critical conclusions.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited because fixed recipes are usually applied to run the processes. Therefore, a digital twin has been developed to optimize an industrial brownfield DCF plant. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation of selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

This work targets the development of a mobility control system for high-speed series hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of the research encompasses the modeling of the series hybrid powertrain specific to military tracked vehicles, as well as an in-depth analysis of their dynamic behavior. Subsequently, the study conducts a critical review of the mobility control approaches sourced from literature, identifying key techniques relevant to high-inertia vehicular applications. Building on the foundational models, the study puts forward a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system's resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are carried out in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system's capacity to improve vehicular response and highlight its potential impact on the future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

Structural health monitoring (SHM) is critical for ensuring the safety of infrastructures like bridges. This article presents a digital twin solution for SHM of railway bridges using low-cost wireless accelerometers and Machine Learning (ML). The system architecture combines edge on-premises computing and cloud analytics to enable efficient real-time monitoring and complete storage of relevant time-history datasets. After train crossings, accelerometers stream raw vibration data, which is processed in the frequency domain and analyzed via machine learning to detect anomalies indicating potential structural issues. The digital twin approach is demonstrated on an in-service railway bridge, where vibration data was collected over two years under normal operating conditions. By learning allowable ranges for vibration patterns, the digital twin model identifies abnormal spectral peaks, suggesting changes in structural integrity. The long-term pilot proves that this affordable SHM system can provide automated and real-time warning of bridge damage and also supports the use of in-house designed sensors of lower-cost and edge computing capabilities than those used in the demonstration. The successful on-premises-cloud hybrid implementation provides a cost effective and scalable model for expanding monitoring to thousands of railway bridges, democratizing SHM to improve safety by avoiding catastrophic failures.

The paper presents an implementation and numerical study of the nonlinear optimal-based vibration control solutions for the full-scale TLP – NREL 5MW wind turbine tower-nacelle model with an MR-damper-based 10 ton tuned vibration absorber (TVA) located at the nacelle, under excessive wave/wind polyperiodic excitations yielding continual transient vibration states. The MR damper operates simultaneously with an electromagnetic force actuator (forming a hybrid TVA), or independently (a semiactive TVA). The study includes both actuators’ nonlinearities and dynamics, whereby the former are embedded in the Hamilton-principle-based nonlinear control solutions. As a reference, the structure model with a passive TVA, with a modified ground-hook control law, and without any vibration reduction solutions is used. The TVA was tuned either to the NREL 5MW tower-nacelle 1st bending mode frequency (TVA-TN) or to the TLP surge frequency (TVA-TLP). The optimal control task, redeveloped here with regard to the TVA stroke amplitude minimisation, including the implementation of the protected structure’s acceleration (control case III / III-H) and relative displacement terms as well as nonzero velocity term in the quality index, is stated and solved, yielding optimal-based control propositions for both the MR damper current and the actuator force. On the basis of the obtained results, the TVA-TN solution is by far superior to the TVA-TLP one regarding the tower deflection amplitude / root-mean-square (RMS) and TVA stroke amplitude values. All the regarded TVA-TN solutions provide tower deflection safety factor of ca. 2, while a structure without any vibration reduction solutions or with a TVA tuned to the TLP surge frequency (especially passive and hybrid TVA-TLPs) are at risk of tower structural failure. The obtained TVA stroke amplitudes are reduced with regard to the previously developed approaches. Moreover, these reductions are obtained due to the sole control algorithm enhancement; thus, no additional resources are necessary, while this attainment is accompanied with a reduction in the MR damper force that is necessary for the TVA operation. The protected structure’s acceleration term utilisation produce the TVA stroke amplitude reduction more efficiently than the TVA relative displacement term usage, as in the previously developed approach (the latter being ineffective for the semiactive TVA implementation) which, in turn, efficiently reduces TVA stroke RMS values and the required mean actuator power for the hybrid TVA implementation. Concerning the limited TVA stroke, the most favourable vibration control solutions are the newly introduced semiactive control case III, providing favourable cumulative primary structure deflection indexes at the lowest obtained TVA travel distance, and possibly the hybrid control case III-H, providing even more favourable primary structure deflection qualities and reduced nacelle acceleration levels thanks to the utilisation of the force actuator of the relatively low power (ca. 6 kW); however, side effect of the latter is the increased TVA stroke amplitude as for all hybrid solutions tested (comparing to semiactive solutions). The analysed passive TVA systems along with the modified ground-hook hybrid TVA solution, utilising the assumed absorber mass, can hardly be implemented in the nacelle, especially along the demanding side-side direction, without enforcing end-stop collision bumpers, which result in the efficiency deterioration. The ground-hook control, well proven for the primary structure steady-state deflection minimisation, yielded unsatisfactory results, comparing to the newly implemented solutions. The results of the current study may be used during the design of the full-scale vibration reduction system for the real-world NREL 5MW-class floating, TLP-based, wind turbine structure.

Space–air–ground integrated network (SAGIN) is considered as an enabler for the sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seem to be a quite promising solution to provide reliable connectivity everywhere and all the time. Their availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.

Abstract: This review addresses the complexities of type 1 diabetes (T1D)and its associated complications, with a par-ticular focus on diabetic retinopathy (DR). This review outlines the progression from non-proliferative to proliferative diabetic retinopathy and diabetic macular edema, highlighting the role of dysglycemia in the pathogenesis of these conditions. A significant portion of the review is devoted to technological advances in diabetes management, particu-larly the use of Hybrid Closed Loop systems (HCLS) and to the potential of open-source HCLS systems, which could be easily adapted to different patiens’needs using big data analytics and machine learning. Personalized HCLS algorithms that integrate factors such as patient lifestyle, dietary habits, and hormonal variations are highlighted as critical to reducing the incidence of diabetes-related complications and improving patient outcomes.

This work aims to study and analyze a hybrid power system for electric vehicles consisting of a dual low and high-rate lithium battery block and a fuel cell. In this configuration, the high-rate lithium battery powers the electric vehicle in high power demand processes like acceleration mode or on an uphill road; the low-rate battery operates at a low output power range, servicing the auxiliary systems and low power loads and the fuel cell supplies energy at intermediate power demand conditions, normal driving mode, constant velocity, or flat and downhill terrain. The dual power system improves global efficiency since every power unit operates optimally depending on driving conditions. Power sharing optimizes the lithium battery performance and fuel cell capacity, minimizing the size and weight of each energy system and enlarging the driving range. A comparative study between different lithium battery configurations and fuel cell shows an efficiency improvement of 31.4% for the hybrid dual battery block and fuel cell operating in low, high, and intermediate output power ranges, respectively. The study bases on a simulation process recreating current driving conditions for electric cars in urban, peripheral, and intercity routes. An alternative solution consisting of a hybrid system, fuel cell and a high rate lithium battery produces a 29 % power gain.

Many industrial processes, several natural processes involving non-living matter, and all the processes occurring within living organisms take place in solution. This means that the molecules playing active roles in the processes are present within another medium, called solvent. The solute molecules are surrounded by solvent molecules and interact with them. Understanding the nature and strength of these interactions, and the way in which they modify the properties of the solute molecules, is important for a better understanding of the chemical processes occurring in solution, including possible roles of the solvent in those processes. Computational studies can provide a wealth of information on solute-solvent interactions and their effects. Two major models have been developed to this purpose: a model viewing the solvent as a polarizable continuum surrounding the solute molecule, and a model considering a certain number of explicit solvent molecules around a solute molecule. Each of them has its advantages and challenges, and one selects the model that is more suitable for the type of information desired for the specific system under consideration. These studies are important in many areas of chemistry research, from the investigation of the processes occurring within a living organism to drug design and to the design of environmentally benign solvents meant to replace less benign ones in the chemical industry, as envisaged by the green chemistry principles. The paper presents a quick overview of the modelling approaches and an overview of concrete studies, with reference to selected crucial investigation themes.

The coupling of AC and DC power will impact the protective actions on the AC side and pose a threat to the stable operation of the interconnection system. Therefore, a new longitudinal protection method is proposed based on the comprehensive distance similarity of voltage waveforms. Initially, the measured voltage and current data are extracted to calculate the reference voltage, and the voltage waveform fitting is optimized. Subsequently, the Euclidean dynamic time warp (DTW) distance and entropy weight method are utilized to process the voltage waveform, enabling the calculation of its comprehensive distance similarity. This similarity is adopted to determine fault location. A hybrid DC multi-feed AC/DC interconnection system, incorporating line commutated converter-voltage source converter (LCC-VSC) and line commutated converter-modular multilevel converter (LCC-MMC), was established in PSCAD, and fault data was simulated and output. The effectiveness of the protection scheme was validated using MATLAB. Simulation results demonstrate that the proposed method can accurately distinguish between faults inside and outside a region. When compared to existing protection methods, it demonstrates superior performance in resisting transition resistance and noise interference, while also mitigating the impact of data asynchronicity. The speed and reliability of the method are further enhanced.

The meticulously engineered powertrain mounting system of hybrid-electric vehicles plays a critical role in minimizing vehicle vibrations and noise, thereby enhancing the longevity of vital powertrain components. However, developing and designing such a system demands substantial time and financial investments due to intricate analysis and modeling requirements. To tackle this challenge, this study integrates data-mining technology into the design and optimization processes of the powertrain mount system. The research focuses on the powertrain mounting system of a transverse four-cylinder hybrid-electric vehicle, employing the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology to establish a data-mining prediction model for mounting stiffness. This model utilizes three data mining algorithms—Multi-SVR, MRTs, and MLPR—assessing their predictive accuracy concerning mounting system stiffness estimation. A comparative analysis reveals that the MRTs algorithm outperforms others as the most effective prediction model. The proposed predictive model elucidates the quantifiable correlation between vibration isolation performance and installation stiffness, overcoming complexities associated with traditional modeling approaches. Applying this model in powertrain mounting system design showcases the efficacy of the CRISP-DM-based approach, significantly enhancing design efficiency without compromising prediction accuracy.

Agricultural tractors are high fuel-consuming and polluting machines, thus the introduction of new sustainable technologies, as hybridization, can be very impactful for the development of electric hybrid agricultural tractors. These vehicles combine the classic internal combustion engine with an electric machine, to reduce the environmental impact and improve the efficiency. This paper reports the modeling and the simulation conducted, using a simulation software typically used for on-road vehicles, of a two wheel-drive agricultural tractor in three different configurations: the conventional one, the series and the parallel electric hybrid powertrain. The simulated task is the trailing of a big square baler during the process of straw wrapping and baling. The evaluation and the comparison of the fuel consumption, CO2 emissions and the depth of discharge of the different configurations has been carried out to determine if it is possible to downsize the ICE, maintaining the same performances. This study points out that the hybridization of agricultural tractors powertrains is one of the most promising approaches to reduce pollutant emissions and fuel consumption.

Structural hydroxide groups in layered magnesium-aluminum double hydroxides were partially replaced by fluoride ions. Fluorinated and fluorine-free materials were used as hosts for two dyes, carminic acid and hydroxyl naphthol blue, resulting in a hybrid pigment color palette. Additionally, the pigments were protected with a magnesium hydroxide phase to prevent the color from fading over time. The color properties of the pigments were significantly influenced by the host of dye, since the presence of fluorine directly influences the acid-base properties of the layered double hydroxides. Finally, the pigments were successfully applied to prepare colored creams.

Finding the roots of an equation is a fundamental problem in diverse fields. Optimization problem leads to non-linear equations for calculating the roots of the equation efficiently. Numerical iterative techniques are frequently applied when analytic solution is not available. Hybrid techniques to find roots of an equation is new to this fundamental problem. Recently, some attempts emerged to design hybrid algorithms for efficient solutions. A new heuristic algorithm is designed which is an intuitive approach to hybridization. In this paper, we implement two new algorithms: (1) a new BTsection algorithm blending the Bisection and Trisection algorithm, (2) a hybrid algorithm, that promises to be more efficient than the existing hybrid algorithms. The new hybrid algorithm is a blend of BTsection algorithm and Regula Falsi algorithm. The implementation results validate that the new algorithm, Hybrid4, surpasses the efficiency of existing hybrid algorithms Hybrid1, Hybrid2, and Hybrid3 algorithms. This paper contributes an essential hybrid algorithm to the repertoire of hybrid root finding algorithms.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

We study the tight focusing properties of generic bright and dark HyOPS beams in the presence of primary coma. The role of the polarization singularity index and handedness of the polarization of the HyOPS beams on the focused structure has been discussed. Results have been presented for the total intensity, component intensities, and component phase distributions for left-, and right-handed bright and dark star and lemon types singularities. The presence of primary coma distorted the focal plane intensity distributions for both positive and negative index generic C-points. Coma is known to disturb the circular symmetry of the focal plane intensity distribution. Similarly in tight focusing polarization is known to disturb the symmetry. Therefore, a beam with structured and inhomogeneous polarization distribution tightly focused under the influence of coma is a fit case to study. It is found that the presence of primary coma aberration in the focusing system produces a positional shift of the high-intensity peaks and a reduction of the intensity on one side of the center. As the strength of the primary coma increases, the focal plane intensity distributions shift more and more toward the right from the initial position. Unlike the scalar vortex case, in the case of HyOPS beams, the focal plane intensity distribution undergoes rotation, as the helicity of the HyOPS beams is inverted, in addition to shifting. All the component phase distributions are found to be embedded with phase vortices of charge ±1.

The integration of renewable energy and hybrid energy storage system (HESS) into electrified railways to build electric railway smart microgrid system (ERSMS) is beneficial for reducing fossil fuel consumption and minimizing energy waste. However, the fluctuations of renewable energy generation and traction load challenge the energy management effectiveness for such a complex system. In this work, an energy management strategy is proposed which firstly decomposes the renewable energy into low-frequency and high-frequency components by an integrated empirical mode decomposition (IEMD). Then, a two-stage energy distribution approach is utilized to appropriately distribute the energy flow among the ERSMS. Finally, the feasibility and effectiveness of the proposed solution are validated through case study.

Thermoplastic pultrusion is a suitable process for fabricating continuous unidirectional thermoplastics with a uniform cross-section, high mechanical properties due to continuous fiber reinforcement, low cost, and suitability for mass production. In this paper, jute and glass fibers were reinforced with a polypropylene matrix and fabricated using the thermoplastic pultrusion process. The volumetric fraction of the composite was designed by controlling the filling ratio of the reinforcing fiber and matrix. The effects of molding parameters were investigated, such as pulling speed and molding temperature, on the mechanical properties and microstructure of the final rectangular profile composite. The pulling speed and molding temperature were varied from 40 to 140 mm/min and 190 to 220°C, respectively. The results show that an increase in molding temperature initially led to an increase in mechanical properties, up to a certain point. Beyond that point, they started to decrease. The increase in molding temperature also led to an increase in void content. Meanwhile, un-impregnation decreased with the increase in molding temperature, and the jute fiber began to degrade at high temperatures. In the next step, with varying pulling speed, the mechanical properties decreased as the pulling speed increased, with a corresponding increase in void content and un-impregnation. The decrease in mechanical properties was influenced by the increase in void content and un-impregnation, as the jute fiber degraded at higher temperatures.

To address the challenges associated with the difficult electromagnetic (EM) scattering computations of anisotropic dielectric-coated metallic composite targets, we proposed an efficient hybrid algorithm for simulating the EM scattering of complex targets with anisotropic dielectric coatings. This method, based on Impedance Boundary Condition (IBC), utilizes surface impedance vectors to describe the EM properties of the dielectric. It fully leverages the respective advantages of the low-frequency Method of Moments (MoM) and the high-frequency Physical Optics (PO) to achieve high-precision and rapid EM simulation of anisotropic dielectric-coated metallic composite targets. Employing boundary conditions and equivalent principles, we equivalently transform the EM scattering problem of targets with thin dielectric coatings into the radiation problem of equivalent EM current on impedance surfaces. This enables the high-precision and rapid calculation of Radar Cross Sections (RCS) for complex targets with anisotropic dielectric coatings. Using examples such as a square plate structure, a simplified aircraft, and a complex satellite model, the simulation results exhibit a high degree of agreement with full-wave numerical solutions and demonstrate a significant advantage in computational resources. This research convincingly demonstrates the accuracy and effectiveness of the proposed method.

Indigo leaves form various plant species are sources of dyes/pigments, not fully exploited for making sustainable textiles. Blue indigo vat dye extracted from indigo leaves, yields high wash colorfastness but fades slowly with light, and is not easily used for direct printing. Indigo leaves can be used to produce textiles of various color shades, while light resistant Mayan-inspired hybrid pigments have not yet been used for textile coloring. Using blue indigo dyes from 3 plant species, with exhaustion dyeing, intense wash resistant blue colored textiles are produced, and in the case of Indigofera Persicaria tinctoria, textiles have antibacterial activity against S. Epidermis and E. Coli.100% Natural Mayan-inspired blue indigo pigment, made from Sepiolite clay and indigo dye, was used both in powdered and paste forms to perform pigment textile dyeing by pad cure process, and direct screen printing on textiles. A water-based bio-binder (40%) was used efficiently for both padding and printing. Bio-based Na Alginate thickener allowed color fast printing on both polyester and cotton fabrics, while bio-based glycerin allowed good print color fastness on polyester only, yielding prints with excellent color fastness to wash (5/5), to dry and wet rubbing (5/5) and light (7/7).

The strength of laser joined metal and plastic hybrids is based purely on adhesion. Unpolar plastics like polypropylene depend solely on the mechanical adhesion while polar plastics like polyamide 6.6 develop covalent bonds with the metal. In this research the influence of different surface metallizations on the lap shear strength of laser joined hybrids was investigated. Sandblasted AISI 304 was used as base metal and reference. Aluminum and titanium coatings were deposited by the means of Arc-PVD. Polypropylene and polyamide 6.6 were utilized as polar und unpolar plastics. The lap shear strength of all hybrids with polyamide 6.6 was higher than the polypropylene samples. For all investigated variations of the laser power, scanning speed and the cohesive and adhesive failure modes, the samples joined with the sandblasted AISI 304 showed the highest lap shear strength. The reduction in lap shear strength for the coated samples was due to the increased difference in thermal conductivity between the plastics and the coating materials. This lead to increased warping and residual stresses. It can be concluded that the thermal properties of the metallization in laser joined metal and plastic hybrids has a high impact on the lap shear strength.

Among the electrochemical energy storage (EES) technologies, rechargeable batteries (RBs) and supercapacitors (SCs) are the two most desired candidates for powering a range of electrical and electronic devices. The RB operates on Faradaic processes, whereas the underlying mechanisms of the SC vary as non-Faradaic in the electrical double layer capacitors (EDLCs), Faradaic at the surface of the electrodes in the pseudo capacitors (PCs), and a combination of both non-Faradaic and Faradaic in the hybrid capacitors. The EDLCs offer high power density but low energy density. The hybrid capacitors take advantage of the Faradaic process without compromising their capacitive nature. Unlike batteries, supercapacitors provide high power density and numerous charge-discharge cycles; however, their energy density lags that of batteries. Here, we review recently published critically selected articles on supercapatteries, a generic term that refers to EES devices that can combine the merits of EDLCs and RBs. Also discussed are the properties, design strategies, and future perspectives of supercapatteries.

Antimicrobial resistance is an increasing problem for the global public health. One of the strategies to combat this issue is the synthesis of novel antimicrobials through rational drug design based on extensive structure-activity relationship studies. The thiazole nucleus is a prominent feature in the structure of many authorized antimicrobials, being clubbed with different heterocycles. The purpose of this review is to study the structure-activity relationship in antimicrobial thiazoles clubbed with various heterocycles reported in the literature between 2017 and 2022, in order to offer an overview of the last years in terms of antimicrobial research and provide a helpful instrument for future research in the field.

Abstract: The present work evaluates the feasibility of using volcanic fly ash (VFA), generated by the eruption of the Tajogaite volcano on the island of La Palma (Spain) in 2021, as a precursor in the preparation of cementitious materials with different Portland cement (PC) replacement levels (0%, 30%, 70% and 100%), in the absence (Blended Cement, BC) and presence of an alkaline ac-tivator (Hybrid Alkaline Cement, HAC, and Alkaline Cements, AC). Hydration kinetics (isother-mal conduction calorimetry), paste mechanical strengths and reaction products were character-ised by XRD, FTIR, TG/DTG and BSEM/EDX. The results obtained indicate that the strengths de-veloped by the hybrid alkaline cements (HAC) are higher than those of the blended cements (BC), especially at the age of 2 days, where 25 MPa was obtained with the replacement of 70% PC by VFA. Alkaline cements (AC, 100% VFA) prepared with 8M NaOH solution as activator reached 40 MPa after 2 days. It was observed that in all the binders, depending on the initial composition of the binder mixture and the percentage of replacement and/or activator, VFA reacts to form cementitious gels, C-A-S-H and N-A-S-H type, which supports its use as a mineral addition to blended cement or as a precursor in the preparation of alkaline and hybrid alkaline cements.

Crown gall (Agrobacterium tumefaciens), crown/root rot (Phytophthora spp.), root lesion disease (Pratylenchus vulnus), and tree vigor are key traits affecting productivity and quality of walnuts in California. Non-inoculated hybrid rootstocks were analyzed by RNA-seq to examine pre-formed factors affecting these traits. Enrichment analysis of the differentially expressed genes revealed that increased expression of cell wall biogenesis-related genes plays a key role in susceptibility to A. tumefaciens, Phytophthora spp., increased vigor, but not P. vulnus. Analysis of the predicted subcellular loci of the encoded proteins revealed that many gene products associated with vigor and susceptibility were targeted to the plasma membrane and extracellular space, connecting these traits to sustaining barrier function. We observed that RNA processing and splicing, along with predicted nuclear targeting, were associated with resistance to A. tumefaciens, Phytophthora spp., low vigor, but not P. vulnus. Four genes within the J. microcarpa QTL region for resistance to A. tumefaciens and Phytophthora spp. were represented among our transcripts, with two of the genes being differentially expressed in association with resistance to A. tumefaciens and decreased vigor. No differential expression related to Phytophthora spp. or P. vulnus traits was observed in this region. Additionally, the J. microcarpa haplotype expressed more transcripts associated with resistance to A. tumefaciens, Phytophthora spp., and low vigor, but not P. vulnus than the J. regia haplotype. We also report unique and shared hormone and defense responses associated with each trait. This research suggests a link between cell wall biogenesis, vigor, and critical root diseases of walnut.

Hybrid materials derived adsorbents have showed a great applicable efficiency in various fields including industrial uses and environmental remediation. Herein, the zinc oxide nanoparticles anchored carbon (ZnO-C) is fabricated and utilized for wastewater treatment by adsorption of Zn(II), Cd(II), Co(II) and Mn(II). The surface and structural characteristics are examined by TEM, SEM, XRD, FTIR, EDS and BET surface area. The Kinetics and equilibrium investigations are applied to optimize the adsorptive removal of Zn(II), Cd(II), Co(II) and Mn(II) onto ZnO-C. Results indicated formation of ZnO-C in crystalline spherical granules with nano-size between 29.3 and 48.8 nm. In addition, the spherical granules are gathered to form clusters. The FTIR indicated that the ZnO-C surface is rich with OH groups and ZnO. The adsorption capacity is reported as 212, 209, 200 and 230 mg/L for Zn(II), Cd(II), Co(II) and Mn(II) respectively. The optimum condition for maximum adsorption were pH between 5 and 6, contact time of 180 min and adsorbent dose of 0.1 g/L. The adsorptive removal data modeling for uptake of Zn(II), Cd(II), Co(II) and Mn(II) onto ZnO-C showed agreement with the assumption of pseudo 2nd order kinetic model and Freundlich isotherm suggesting fast adsorption rate and multilayer mechanism. The achieved adsorption capacity using the prepared ZnO-C is more effective compared to ZnO, carbon, Fe3O4 and Fe3O4-C. Real wastewater samples are applied including valley water, industrial wastewater and rain wastewater and evaluated for applicable uptake of Zn(II), Cd(II), Co(II) and Mn(II) using ZnO-C and Fe3O4-C with high efficiency.

The first paper of this two-part series paper discussed the development of a novel lightweight 3D wood dowel-reinforced glass epoxy hybrid composite material (3DdrFRP) and its manufacturing procedures. It also experimentally compared the performance of scaled utility poles made from conventional 2D E-glass epoxy and 3DdrFRP materials. In the second part, we developed robust finite element (FE) models in LS-DYNA, calibrated them based on experimental results, and used them to simulate the performance of equivalent 2D and 3D poles, proving the integrity of the numerical model. Additionally, we developed a simplified analytical calculation method for engi-neers to determine the stiffness of 3D-DrFRP poles accurately and quickly.

Nowadays, the increasingly serious environmental pollution and energy problems urgently require internal combustion engine-based transportation vehicles to upgrade or replace, so the new energy transportation vehicles based on hybrid power and fuel cells have gradually stepped on the stage and continue to innovate. The shipping industry is one of the main sources of global greenhouse gas emissions. The development of clean energy ships represented by fuel cells has attracted wide attention. Fuel cell has the advantages of clean, pollution-free and low noise, but it has some disadvantages such as insufficient dynamic response performance and fast performance decay. Limited by the characteristics of a single energy source, a variety of energy sources and bidirectional DC converters are usually mixed together to form a hybrid ship to improve the flexibility, stability and economy of the ship, and enhance its adaptability to complex sea conditions through the reconfiguration of power system energy. Taking fuel cell ferry "FCS Alsterwasser" as the research object, this study proposed an improved equivalent minimum hydrogen consumption energy management strategy based on fuzzy logic control. First, the power system of the modified mother ship was simulated, and a hybrid power system including fuel cell, lithium iron phosphate battery and supercapacitor was proposed. Then, the dynamic system simulation model and double closed loop PI control model are established in MATLAB/Simulink, and the reliability of the model is verified by the simulation analysis of charge and discharge characteristics. Then, the feasibility of the proposed method is demonstrated by designing simulation experiments based on typical working conditions of the mother ship. The simulation results show that under the premise of meeting the load requirements, the control strategy designed in this paper has a better optimization effect than the S-type penalty function in terms of lithium battery power, lithium battery SOC, bus voltage stability and equivalent hydrogen consumption, which improves the stability and economy of the power system, and has certain engineering practical value.

Optical wireless communication is a promising emerging technology that addresses the limitations of radio-frequency-based wireless technologies. This study presents a novel hybrid modulation for optical camera communication (OCC); it integrates two waveforms transmitted from a single transmitter light-emitting diode (LED) and received by two rolling-shutter camera devices. Then, a smart camera with a high-resolution image sensor captures the high-frequency signal, and a low-resolution image sensor from a smartphone camera captures the low-frequency signal. Through this hybrid scheme, two data streams are transmitted from a single LED lamp; this reduces the cost of the indoor OCC device compared to one that transmits two signals from two different LEDs. In proposed scheme, rolling-shutter orthogonal frequency-division multiplexing is used for the high-frequency signals, and M-ary frequency-shift keying is used for the low-frequency signals in the time domain. This proposed scheme is compatible with smart phone camera and USB camera. By controlling the OCC parameters, the hybrid scheme can be implemented with high performance for a communication distance of 10 m.

Recently, more wind turbine systems are being installed in deep waters far from the coast. Several concepts of floating wind turbine systems (FWTSs) are developed, among which the semisubmersible platform, due to its applicability in different water depths, good hydrodynamic performance, and facility in the installation process, constitutes the most explored technology compared to the others. However, a significant obstacle to the industrialization of this technology is a design of a cost-effective FWTS, which can be achieved by optimizing the geometry, size and weight of the floating platform, along with the mooring system. This is only possible by selecting a method capable of accurately analyzing the FWTS coupled hydro-aero-structural dynamics at each design stage. Accordingly, this paper aims at providing a detailed overview of the most common coupled numerical and physical methods, including their basic assumptions, formulations, limitations, and costs, used for analyzing the dynamics of FWTSs, mainly those supported by a semisubmersible, to assist the choice of the most suitable method at each design phase of FWTSs. Finally, the article discusses possible future research to address challenges in modeling FWTSs dynamics that persist to date.

Hybrid organic/inorganic conducting and magnetic composites of core–shell type have been prepared by in-situ coating of nickel microparticles with polypyrrole. Three series of syntheses have been made. In the first, pyrrole was oxidized with ammonium peroxydisulfate in water in the presence of various amounts of nickel and the composites contained up to 83 wt% of this metal. The similar syntheses in 0.1 M sulfuric acid followed. Finally, the composites with polypyrrole nanotubes were prepared in water in the presence of structure-guiding methyl orange dye. FTIR and Raman spectroscopies confirmed the formation of polypyrrole. The resistivity of composite powders of the order of tens to hundreds Ω cm was monitored as a function of pressure up to 10 MPa. The resistivity of composites slightly increased with increasing content of conducting nickel. This apparent paradox is explained by the coating of nickel particles, which prevents their contacts and generation of metallic conducting pathways. Electrical properties were practically independent of the way of composite preparation or nickel fraction and were controlled by polypyrrole phase. On the contrary, magnetic properties were determined exclusively by nickel content. The composites were used as a solid phase to prepare a magnetorheological fluid. The test showed better performance when compared with a different nickel system.

This paper addresses a mode-dependent state-feedback H∞ control for descriptor hybrid systems, considering both the absence and presence of actuator saturation. Firstly, the necessary and sufficient conditions for the stochastic admissibility criterion with H∞ performance ? of the closed-loop system is proposed. Given the proposed non-convex condition, the author reformulates it into linear matrix inequalities (LMIs). Then, to extend the result to the systems with actuator saturation, the actuator saturated control input is expressed as a linear combination of given state-feedback control input and a virtual control input that remains under the saturation level. To verify this expression, the set invariant condition is also suggested by using the singular mode-dependent Lyapunov function candidate. Therefore, the LMI conditions for the existence of the mode-dependent state-feedback H∞ control and the ellipsoidal shape of invariant set are successfully derived. Two numerical examples demonstrate the effectiveness of the proposed approach in optimizing H∞ performance $\gamma$ and identifying the largest invariant set.

Although the potential of natural minerals for purification of liquid radioactive wastes (LRW) from radionuclides has been widely studied, the use of hybrid polymer composites made of zeolite is still rather scarce. This article reports on the preparation of zeolite based hybride polymer composites using the in-situ polymerization technique in the body of mineral matrix and its intercalated with copper ferrocyanide (CuFC) forms. This hybrid polymer composites have shown unique and enhanced properties as compared to the individual mineral. The change in conventional properties of two mixed minerals such as zeolite and bentonite and their intercalated with CuFC forms were probed using technics such as scanning electron microscopy (SEM), X-ray diffraction (XRD), Mossbauer spectroscopy (MS) and FT-IR analysis. The totality of analysis showed a coexistence of intercalated and percolated zeolite phases. The hybrid polymer composites exhibited both adsorption and ion-exchangr properties in removal of radionuclides from LRW.

The goal of this study was to evaluate the feasibility of methods based on the Worldwide Harmonized Light-Duty Vehicles Test Procedure Brake Cycle to measure brake wear particle emissions for currently used electrified vehicles. Our results indicated that reducing brake friction work reduced brake wear particle emissions. Commercially available, plug-in hybrid electric vehicles reduced emissions by 85% for PM₁₀, 78% for PM_2.5, and 87% for particle numbers (PNs) compared to vehicles with internal combustion engines. Brake friction work showed a linear relationship with PM₁₀ and PM_2.5. Nanoparticle PM emissions tended to increase slightly with regenerative braking, but did not contribute significantly to the overall PM percentage. the emission events of high number concentrations of nuclei mode particles (<20 nm in diameter) in electric vehicle brake assemblies designed for regenerative braking use under high-temperature, high-load braking conditions with full-friction brakes. The nuclei mode particles amplified the PN emissions and led to a high variability. In strict regulatory certification tests, where measurement reproducibility and stability are required, it is appropriate to measure PNs under brake conditions appropriate for the actual use of electric vehicles rather than under full-friction brake conditions or to remove particle measurements smaller than 20 nm.

Smart, reliable, and connected multi-modal mobility has been a long-standing goal of transit services. This paper focuses on the smart, seamless, and multi-modal mobility service in the context of “Mobility as a Service” (MaaS). Intelligent mobility is the smarter, greener, and more efficient movement of passengers around the world. Increasingly, mobility is approached as a service. This study first conducts an extensive literature review on mobility behavior and demand pattern of MaaS end-users. It then extends the mechanism of supply chain, MaaS, synergy (i.e., vertical cooperation synergy, horizontal competition synergy), and coopetition to develop the multi-tier closed-loop intelligent mobility service supply chain network. This paper explains the intelligent mobility service supply chain network from following perspectives: (i) mobility service taxonomy of MaaS; (ii) aims of intelligent mobility service supply chain network; (iii) urban rail transit (URT)-centered alternatives for integrated multimodal journey planning, i.e. access + URT + egress, and both access and egress can be served by Mobility-On-Demand (MOD) transport; (iv) node member imperatives. From a synthesis of insights from the ‘during’ journey, this study puts forward the synergetic design of intelligent mobility service supply chain network, including: (i) multi-tier closed-loop structure; (ii) key nodes identification for the physical multimodal transport network in the supply chain; (iii) hybrid synergy mechanisms among the partners, i.e., synergy principle, temporal splitting approach for coopetition synergy; (iv) index systems and evaluation methods for synergy measurement. This study also contributes to the integrated multimodal journey planning. In concluding, the paper highlights the important implications of the proposed intelligent mobility service supply chain network for MaaS bundle design and adverse effects reduction, resulting from 1 + 1 > 2 synergy effects.

Plasmonic nanocomposites have the ability to enhance light-matter interaction due to the plasmonic effects leading to promising potential applications. In this paper, we theoretically study the group index of the silver nanoparticle-gold nanoparticle-quantum dot plasmonic hybrid system in the optical region of the electromagnetic spectrum using the density matrix approach. The quantum dot is considered to be a three-level atomic system of a lambda configuration, interacting with strong control and weak probe fields. We find that the plasmonic hybrid system exhibits at near resonance a strong switching between slow and fast light, which can be controlled by the inclination angle of the gold nanoparticle and the size of the metals nanoparticles, in addition to the strength and detuning of the control field. Thus, the silver nanoparticle-gold nanoparticle-quantum dot could potentially be used in optoelectronic devices.

In space application, hybrid pixel detectors of the Timepix family have been considered mainly for measurement of radiation levels and radiation dosimetry in low earth orbits. By the example of the Space Application of Timepix Radiation Monitor (SATRAM), we demonstrate the unique capabilities of Timepix-based miniaturized radiation detectors for particle separation. Using a novel method for proton spectrum reconstruction, we were able to measure the spectrum of protons trapped in the inner Van-Allen radiation belt, for the first time with a single-layer detector. We assess the measurement stability and the resiliency of the detector to the space environment demonstrating that, even though degradation is observed, data quality has not been affected significantly over more than 10 years. Based on the SATRAM heritage and the capabilities of the latest-generation Timepix-series chips, we discuss their applicability for use in a compact magnetic spectrometer for deep-space mission or in the high radiation environment of the Jupiter radiation belts, and their capability for use as single-layer X- and γ-ray polarimeter. The latter was supported by measurement of the polarization of scattered radiation in a laboratory experiment, where a modulation of 80% was found.

This paper investigates if the polar groups induced by a plasma treatment can increase the lap shear strength of laser joined metal and plastic hybrids. Optimal laser joining parameters for cold-rolled AISI304 – Polyamide 6.6 and sandblasted AISI304 - Polypropylene hybrids are developed. The OWRK-method was utilized to identify the change in surface free energy of a low-pressure plasma treatment. Plasma treated samples were joined and tested. The fractured surfaces were investigated via light microscopy and SEM. The arithmetic means of the plasma treated hybrids lap shear strength with polyamide 6.6 varied slightly but all measured values were within the range of the untreated samples. The lap shear strengths of the polypropylene hybrids were significantly reduced. This was attributed to an overaging and the development of low molecular weight oxidized materials which lead to a weak boundary layer. No correlation between surface free energy and the lap shear strength could be found.

Hybrid electric vehicles are certainly one of the key solutions for improving fuel efficiency and reducing emissions, especially in special vehicle application and with the use of CO2 neutral fuels. Determining the energy management strategy and finding the optimal solution with regard to the aforementioned goals re-mains the one of the main challenges in the design of HEV. This paper presents a new vehicle modeling method, with an emphasis on HEVs, which is based on the frequency analysis of emissions and consumption according to the current specific traction power of the vehicle. The evaluation of the newly introduced model in the RDE, NEDC and WLTP cycle was performed and the results were compared with the standard verified vehicle model that was created in AVL's CruiseM software package. Positive traction energies have positive deviations of between 0.35% and 2.85%. The largest deviation in CO2 emissions was recorded for the HEV model in the RDE cycle and in the non-hybrid model in the WLTP cycle of 3.79% and 4.4% respectively, all other combinations of cycle and vehicles had deviations of up to about 1%. As expected, the largest relative deviations were recorded for NOx emissions from 0.13% to 9.62% for HEV in the WLTP cycle.

This paper aims to understand better how attitudes towards the environment could influence preferences and willingness to pay for the development of sustainable tourism products on the Spanish island of Gran Canaria. A hybrid choice model is estimated to analyse how different latent constructs related to environmental concerns affect individuals’ preferences for a set of sustainable tourism activities. The data used in the analysis is obtained from a discrete choice experiment where different scenarios with nature-based tourism packages are created. A set of measurement indicators allowed us to gain insight into the underlying latent structure regarding the individuals’ attitudes towards the environment. The analysis consists of integrating these attitudes into a choice model, focusing on a market segment primarily comprised of potential customers who are young residents and non-residents. Results reveal significant heterogeneity in preferences and willingness to pay for the various activities under study when attitudinal latent factors are incorporated into the model. Our findings provide valuable insights for researchers, policymakers, and practitioners promoting sustainable tourism products.

Glycosaminoglycans (GAGs) with unique structures from marine animals show intriguing pharmacological activities and negligible biological risks, providing more options for us to explore safer agents. Swim bladder is a tonic food and folk medicine, and its GAGs show good anticoag-ulant activity. In this study, two GAGs SJG-1.0 and GMG-1.0 have been extracted and isolated from the swim bladder of Sphyraena jello and Gadus morhua. The physicochemical properties, pre-cise structural characteristics, and anticoagulant activities of these GAGs were determined. The analysis results of the SJG-1.0 and GMG-1.0 showed that they were chondroitin sulfate (CS)/dermatan sulfate (DS) hybrid chains with molecular weights of 109.32 kDa and 123.13 kDa, respectively. They were mainly composed of the repeating disaccharide unit of -{IdoA-α1,3-GalNAc4S-β1,4-}- (DS-A). The DS-B disaccharide unit of -{IdoA2S-α1,3-GalNAc4S-β1,4-}- also existed in both SJG-1.0 and GMG-1.0. SJG-1.0 had a higher proportion of CS-O disaccharide unit -{-GlcA-β1,3-GalNAc-β1,4-}-, while GMG-1.0 contained higher proportion of CS-E disaccha-ride unit -{-GlcA-β1,3-GalNAc4S6S-β1,4-}-. The disaccharide compositions of the GAGs varied in a species-specific manner. Anticoagulant activity assay revealed that both SJG-1.0 and GMG-1.0 had potent anticoagulant activity, which can significantly prolong activated partial thrombosis time. GMG-1.0 also can prolong the thrombin time. SJG-1.0 showed no intrinsic tenase inhibition ac-tivity, while GMG-1.0 can obviously inhibit intrinsic tenase with EC50 of 58 nM. Their significantly different anticoagulant activities may be due to their different disaccharide structural units and proportions. These findings suggested that the swim bladder CS/DS hybrid chains with well-defined structure may be safer anticoagulants with low bleeding risk.

The open hole tensile and flexural test of composites holds immense importance in aerospace and structural engineering. These tests provide critical insights into how composites perform under real-world conditions, allowing for the development of materials that meet rigorous standards, thereby ensuring the safety and durability of aircraft and other applications. This study investigates the influence of hole size on the open hole tensile (OHT) and flexural properties (OHF) of plane woven aramid/epoxy (AFRP), basalt/epoxy (BFRP), and aramid-basalt/epoxy (ABFRP) sandwiched laminates. Specimens with three different hole sizes viz., 4 mm, 6 mm, 8 mm were prepared and tested according to ASTM D5766 and ASTM D7264 for OHT and OHF performance respectively. Results indicated that the hybridization of aramid-basalt layers in a sandwich lay up significantly improved the mechanical performance when compared to only aramid laminate. However, with the increase in hole size the mechanical properties declined irrespective of the material. Basalt fibres showed higher hole sensitivity under tensile loading due to its brittle fracture mechanism while aramid fibres exhibited higher hole sensitivity under flexural loading which can be attributed to its the poor compressive strength. The effect of hole size on the damage mechanism has also been highlighted through scanning electron microscope (SEM). Major failure modes observed are delamination around the hole edges, ductile and brittle fibre fractures, and delamination.

In the ever-evolving landscape of higher education, the shift to hybrid teaching approaches has introduced novel challenges and opportunities for both educators and students. This qualitative study, grounded in a robust theoretical framework, combines in-depth literature reviews, informal interviews, observations, and thematic analysis to explore the multifaceted dynamics of hybrid language learning within a College of Arts and Sciences. By investigating the experiences of twenty students (referred to as 'S-Part 1' to 'S-Part 20'), the research delves into the core theme of 'Teacher-Student Interaction in Higher Education Institutions' and unpacks nine additional themes: 'Adaptability and Resilience,' 'Flexibility and Autonomy,' 'Connectivity Challenges,' 'Feelings of Isolation,' 'Technology Competence,' 'Personalized Learning,' 'Resource Accessibility,' 'Support Mechanisms,' and 'Positive Learning Experiences.' In this study, these themes encapsulate the diverse experiences and challenges encountered by teachers and college students as they navigate the evolving educational landscape. The insights derived from the study are underpinned by a robust theoretical framework and complemented by existing scholarly literature. As we delve into these themes, it becomes evident that they shed light on not only the intricacies of hybrid language learning but also the broader educational context, offering valuable guidance for educators, institutions, students, and researchers. Based on the ample results, recommendations are presented for school administrators, teachers, students, and future researchers to enhance the quality of hybrid teaching and learning, aligning it with the demands of the post-academic era. Overall, this study contributes to the ongoing dialogue on innovative pedagogical strategies and responsive educational environments, underlining the significance of teacher-student interaction in the new normal of higher education.

This paper presents a) the novel hybrid learning method to train the type-1 non-singleton interval type-3 (IT3) fuzzy logic systems (IT3 NSFLS-1) and b) the novel method named enhanced Wagner-Hagras (EWH) IT3 NSFLS-1 fuzzy systems which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the back propagation (BP) method to train the antecedent parameters. This proposal dynamically changes the parameters of only the level alpha 0 minimizing some criterion function as new information becomes available to each level alpha k. The antecedent sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the EWH IT3 NSFLS-1 (OLS-BP) system. The algorithm was tested in a hot strip mill facility to predict the transfer bar surface temperature showing its superior capability to obtain the industrial pyrometer’s knowledge uncertainty for tuning and its better performance when compared with IT2 SFLS, IT2 NSFLS-1, GT2 SFLS, GT2 NSFLS-1, IT3 SFLS, and IT3 NSFLS-1 trained with the BP-BP algorithm.

Stock market prediction is a challenging task to perform, as we know the fluctuations that take place in the market makes it versatile and hard to predict the prices. In this research, we have explored the power of usage of hybrid ensemble algorithms to improve the predictive accuracy in the stock market forecasting. Our research comprises construction and evaluation of diverse hybrid models using different algorithms. The methodology presented in the paper involves comprehensive data preparation, feature engineering, and model normalization. Evaluating the different hybrid models, one stands out distinctly: LSTM (long short-term memory networks) + GRU (Gated recurrent units) + Conv1D (one-dimensional convolutional layer) hybrid. It illustrated its potential to revolutionize decision-making tools for investors and financial analysts in stock market analytics. The harmonious integration of algorithms not only underscores the effectiveness of hybrid modeling but also beckons for further exploration within the ever-evolving domain of predictive modeling, driven by the pursuit of precision and accuracy. This model shows a remarkable accuracy metrics including a Mean Absolute Error (MAE) of 0.95, a Mean Squared Error (MSE) of 2.1222, a Root Mean Squared Error (RMSE) of 1.52, and a R-squared (R2) score of 0.9982.

Two new compounds, named aspergilins A (1) and B (2), were isolated from the mangrove endophytic fungus Aspergillus sp. A1E3 associated with the fruit of Rhizophora mucronata, together with averufanin (3). The planar structures and absolute configurations of these compounds were unambiguously established by extensive NMR investigations, quantum-chemical electronic circular dichroism (ECD) calculations, and single-crystal X-ray diffraction analysis. Most notably, the absolute configuration of aspergillin A (1) was determined by single-crystal X-ray diffraction analysis of its tri-pivaloyl derivative 4, conducted with Cu Kα radiation, whereas that of averufanin (3) was first clarified by quantum-chemical ECD calculations. Aspergilin A is the first orsellinic acid–ribose–​pyridazinone-Noxide hybrid containing a unique β-oxo-2,3-dihydropyridazine 1-oxide moiety, whereas aspergilin B and averufanin are the sterigmatocystin and anthraquinone derivative, respectively. From the perspective of biosynthesis, aspergilin A could be originated from the combined assembly of three building blocks, viz., orsellinic acid, β-D-ribofuranose, and L-glutamine. It is an unprecedented alkaloid-Noxide involving biosynthetic pathways of polyketide, pentose, and amino acid. Aspergilin B (2) exhibited potent antiproliferative activity against four cancer cell lines. In addition, Aspergilin B (2) dose-dependently induced G2/M phase arrest in HepG2 cells.

The classical joint economic lot-sizing (JELS) policy in a single-vendor single-buyer system generates an equal production quantity in all cycles, where the input parameters remain static indefinitely. In this paper, a new two-echelon supply chain inventory model is developed involving a hybrid production system that simultaneously focuses on green and regular production methods with optimal allocation fraction of green and regular productions. Unlike the classical mathematical formulation, each cycle is independent from the previous one, and consequently, the input parameters can be adjusted to be responsive to the dynamic nature of demand rate and price fluctuation. A rigorous heuristic approach is used to derive a global optimal solution for a joint hybrid production system. The model accounts for carbon emissions from production and storage activities related to green and regular produced items along with transportation activity under a multi-level emission-taxing scheme. The results emphasize the significant impact of green production on emissions. That is, the higher the allocation fraction of green production the lower the total amount of emissions generated by the system, i.e., the system becoming more sustainable. Adopting a hybrid production method not only decreases the greenhouse gas (GHG) emissions dramatically, but also reduces the per unit time total cost when compared with regular production. One of the main findings is that the total system cost generated by the base closed-form formula of the proposed model is considerably lower than that of the existing literature i.e., 33.59% (16.13%) lower in the first cycle (subsequent cycles) when the regular production method is assumed. Moreover, the optimal production rate generated by the proposed model is the one that minimizes the emissions production function. Illustrative examples and special cases that reflect different realistic situations are compared to outline managerial insights.

Developing a biomolecular detection method that minimizes photodamage while maintaining an environment suitable for biological constituents to maintain their physiological state is expected to drive new diagnostic and mechanistic breakthroughs. In addition, ultra-sensitive diagnostic platforms are needed for rapid and point-of-care technologies for various diseases. Considering this, surface-enhanced Raman scattering (SERS) is proposed as a non-destructive and sensitive approach to address the limitations of fluorescence, electrochemical, and other optical detection techniques. However, to advance the applications of SERS, novel approaches to enhance the signal of substrate materials are needed to improve reproducibility and costs associated with manu-facture and scale-up. Due to their physical properties and synthesis, semiconductor-based nanostructures have gained increasing recognition as SERS substrates; however, low signal en-hancements have offset their widespread adoption. To address this limitation and assess the po-tential for use in biological applications, zinc oxide (ZnO) was coated with different concentra-tions (0.01-0.1 M) of gold (Au) precursor. When crystal violet (CV) was used as a model target with the synthesized substrates, the highest enhancement was obtained with ZnO coated with 0.05 M Au precursor. This substrate was subsequently applied to differentiate exosomes derived from three cell types to provide insight into their molecular diversity. We anticipate this work will serve as a platform for colloidal hybrid SERS substrates in future bio-sensing applications.

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, or/and ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discussed various types of EMSs, such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and Mutative Fuzzy Logic Controller (MFLC). However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This research aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as State of Charge (SOC) and FC deterioration.

Low-complexity ultrasound systems are increasingly desired for wearable point-of-care ultra-sound and massive-channel ultrasound for 3-D matrix imaging. However, the system complexity is closely associated with the imaging capabilities, remaining as a challenge. To resolve this limitation, this study revisits the general structures of analog and digital beamformers and in-troduces a hybrid approach to implement efficient ultrasound systems. The suggested hybrid beamforming takes two stages, where the first analog stage partially beamforms M-channel RF signals to N sum-out data (M-to-N beamforming), and the second digital stage beamforms N partial sums to single final beamformed data (N-to-1 beamforming). Our approach was systematically designed and implemented, which was demonstrated with a customized 64-channel 1-D phased array using a tissue mimicking phantom. The demonstrated results indicate that the analog-digital hybrid beamforming can be applied to any kind of arrays for sophisticated 3-D imaging and tiny wearable ultrasound applications.

This study investigates the dynamic characteristics of Natural Rubber (NR)/Polybutadiene Rubber (PBR) based hybrid magnetorheological elastomer (MRE) sandwich composite beams through numerical simulations and finite element analysis, employing Reddy's third-order shear defor-mation theory. Four distinct hybrid MRE sandwich configurations were examined. The validity of finite element simulations was confirmed by comparing them with results from magnetorheo-logical (MR) fluid-based composites. Further, parametric analysis explored the influence of magnetic field intensity, boundary conditions, ply orientation, and core thickness on beam vi-bration responses. Results reveal a notable 10.4% enhancement in natural frequencies in SC4-based beams under a 600mT magnetic field with clamped-free boundary conditions, attributed to in-creased PBR content in MR elastomer cores. However, higher magnetic field intensities result in slight frequency decrements due to filler particle agglomeration. Additionally, augmenting magnetic field intensity and magnetorheological content under clamped-free conditions improves the loss factor by 66% to 136%, presenting promising prospects for advanced applications. This research contributes to a comprehensive understanding of dynamic behavior and performance enhancement in hybrid MRE sandwich composites, holding significant implications for engi-neering applications. Furthermore, this investigation provides valuable insights into the intricate interplay between magnetic field effects, composite architecture, and vibration response.

Epilepsy is a prevalent neurological disorder with considerable risks, including physical impairment and irreversible brain damage from seizures. Given these challenges, the urgency for prompt and accurate seizure detection cannot be overstated. Traditionally, experts have relied on manual EEG signal analyses for seizure detection, which is labor-intensive and prone to hu-man error. Recognizing this limitation, the rise of deep learning methods has been heralded as a promising avenue, offering more refined diagnostic precision. On the other hand, the prevailing challenge in many models is their constrained emphasis on specific domains, potentially diminishing their robustness and precision in complex real-world environments. This paper presents a novel model that seamlessly integrates the salient features from the time-frequency domain alongside pivotal statistical attributes derived from EEG signals. This fusion process involves the integration of essential statistics, including the mean, median, and variance, combined with the rich data from compressed time-frequency (CWT) images processed using auto-encoders. This multidimensional feature set provides a robust foundation for subsequent analytic steps. A long short-term memory (LSTM) network, meticulously optimized for the renowned Bonn Epilepsy dataset, was used to enhance the capability of the proposed model. Preliminary evaluations underscore the prowess of the proposed model: remarkable 100% accuracy in most of the binary classifications, impressive performance exceeding 95% for three-class and four-class challenges, and a commendable rate exceeding 93.5% for the five-class classification.

In the crustacean immune system, leucine-rich repeat (LRR) is one of the major structures for recognizing pathogen-associated molecular patterns (PAMPs). LRR domain-containing proteins belong to the LRR family, which is a large group of proteins with more than 6000 genes in the database. They are involved in very diverse physiological functions mainly by interacting with other proteins. In a previous study, the LvLRRm, a transmembrane protein containing only LRR domain, was identified in the white leg shrimp, Litopenaeus vannamei. Its versatile role in performing multiple immunomodulation activities has been reported. However, there is still a lack of research on its efficient function at the protein level. To investigate its interactions with other proteins, we applied a convenient method called ‘Hybrid LRR technique’ to produce a recombinant LvLRRm. The LvLRRm and hagfish’s variable lymphocyte receptors (VLRs) fragments were fused to the conserved LxxLxLxxN motif while retaining the β-strand. In addition, we established interactions between hybrid-proteins and flagellin of Salmonella by performing surface plasmon resonance (SPR) analysis. The designed hybrid proteins showed potential for universal applications without losing protein functions.

Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging comparatively to other industries. A promising approach is to combine Deep Neural Networks (DNN) with prior knowledge in Hybrid Neural Network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It revealed that HNNs were applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs were mainly applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies combined shallow Feedforward Neural Networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Physics Informed Neural Networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps.

Electro-optic modulator (EOM) is one of the key devices of high-speed optical fiber communication system and ultra-wideband microwave photonic system. Silicon-organic hybrid (SOH) integration platform combines the advantages of silicon photonics and organic materials, providing a high electro-optic effect and compact structure for photonic integrated devices. In this paper we present a SOH integrated EOM with comprehensive investigation of EOM structure design, silicon waveguide fabrication with Slot structure, on-chip poling of organic electro-optic material, and characterization of EO modulation response. The SOH integrated EOM is measured with 3dB-bandwidth of over 50 GHz and half-wave voltage length product of 0.26 V·cm. Furthermore, we demonstrate a microwave photonics phase shifter by using the fabricated SOH integrated dual parallel Mach-Zehnder modulator. The phase shift range of 410 degree is completed from 8 GHz to 26 GHz with a power consumption of less than 38 mW.

The hybridization of conventional thermal power plants by incorporation of renewable energy systems has witnessed widespread adoption in recent years. This trend aims not only to mitigate carbon emissions but also to enhance the overall efficiency and performance of these power generation facilities. However, calculating the performance of such intricate systems using fundamental thermodynamic equations proves to be both laborious and time-intensive. Nevertheless, possessing accurate and real-time insights into their performance is of utmost significance to ensure optimal plant operation, facilitate decision-making, and streamline power production planning. In this paper, we delve into the application of machine learning techniques, particularly hybrid methodologies, to predict the performance of power plants. Specifically, we employ three approaches: the Adaptive Neuro-Fuzzy Inference System (ANFIS), the ANFIS optimized via Particle Swarm Optimization (ANFIS-PSO), and the ANFIS optimized through Genetic Algorithm (ANFIS-GA). These techniques are alternatively applied to a complex hybrid thermal power plant, namely an Integrated Solar Combined Cycle Power Plant (ISCCPP). This ISCCPP comprises a steam turbine section, a gas turbine section, and a concentrated solar power system utilizing troughs as collectors. The energy transfer fluid, in conjunction with exhaust from the gas turbine section, heats water within a vessel or steam generator, producing steam to propel the steam turbine's rotor. Electricity generation is facilitated by both gas and steam turbines, which are coupled to a generator. The findings of this study underscore the accuracy and appropriateness of ANFIS-based models in evaluating and predicting the performance of intricate hybrid power plants. The ANFIS model exhibited an impressive overall coefficient of correlation of 0.9991. Notably, the integration of evolutionary algorithms (PSO and GA) into the ANFIS framework elevated model performance, yielding correlation coefficients of 0.9994 for ANFIS-PSO and 0.9997 for ANFIS-GA. It is noteworthy that ANFIS-GA demonstrated superior predictive capability. The modeling framework developed in this research offers valuable support to designers, energy managers, and decision-makers. It provides a robust and dependable tool to assess the performance of hybrid thermal power plants, which are poised for a global surge in numbers amid the ongoing energy transition.

Although Sn has been intensively studied as one of the most promising anode materials to replace commercialized graphite, its cycling performance and rate performance are still unsatisfactory owing to insufficient control of its large volume change during cycling and poor electrochemical kinetics. Herein, we propose a Sn-TiO2-C ternary composite as a promising anode material to overcome these limitations. The hybrid TiO2-C matrix synthesized via two-step high-energy ball milling effectively regulated the irreversible lithiation/delithiation of the active Sn electrode and facilitated Li-ion diffusion. At the appropriate C concentration, Sn-TiO2-C exhibited significantly enhanced cycling performance and rate capability compared to its counterparts (Sn-TiO2 and Sn-C). Sn-TiO2-C delivers good reversible specific capacities (669 mAh g-1 after 100 cycles at 200 mA g-1 and 651 mAh g-1 after 500 cycles at 500 mA g-1) and rate performance (446 mAh g-1 at 3000 mA g-1). The superiority of Sn-TiO2-C over Sn-TiO2 and Sn-C was corroborated by electrochemical impedance spectroscopy, which revealed faster Li-ion diffusion kinetics in the presence of the hybrid TiO2-C matrix than in the presence of TiO2 or C alone. Therefore, Sn-TiO2-C is a potential anode for next-generation Li-ion batteries.

One of the rapidly developing research areas in the power system is the integration of distributed generation (DG) with the distribution system. The size and location of DG sources had a considerable impact on power system networks. Artificial intelligence (AI) techniques can be used to address multidimensional problems relating to DG size and location in distribution systems. Heuristic optimization offers a reliable and effective method for solving complicated real-world problems. This work focuses on a hybrid approach that combines the two heuristic optimization methods i.e., Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) for the optimal siting and sizing of DG in distribution systems. This hybrid approach combines ideas from GA and PSO and generates individuals in a new generation using both PSO mechanisms and the procedures found in GA. An extensive performance analysis of the IEEE 14 busbar standard test system is conducted to demonstrate the viability of the suggested methodologies. In the designated locations, DG is placed, and the outcomes have been verified. The results indicated that the right placement of DG injection enhances the voltage profile and lowers the distributed system’s power losses. These techniques offer unique methods for determining the location of the DG unit, demonstrating the potential of such a computational techniques to reduce computing time and complexity while simultaneously reducing human errors associated with hit-and-trail methods.

The stress concentrations have become common phenomenon of steel elements when arresting a fracture by implementing the crack stop hole (CSH) technique. Embedding the CSH with Carbon Fibre Reinforced Polymer (CFRP) enhance the fatigue life by delaying the fractures while achieving a stiffness recovery due to superior mechanical characteristics of CFRP material. Hence, low cyclic fatigue (LCF) behaviour of 162 strengthened and non-strengthened CSH specimens were examined in this context. These specimens were subjected to a range of 0 to10,000 fatigue load cycles with the frequency of 5 Hz. At the end of fatigue exposure, the average tensile strength was measured in each case. The average strength reductions in the range between 13% to 25% was noted in steel elements with CSH subjected to fatigue exposure. The application of a CFRP patch on CSH had effectively recovered the strength losses while enhancing the strength in the range between 32% to 45% with respect to the non-strengthened non specimens. The developed numerical model based on cyclic J-integral technique agrees with test results and is capable of predicting characteristics for this novel hybrid technique.

Germanium (Ge) nanostrip is embedded in polymer and studied as a waveguide. Measurement reveals that this new type of semiconductor/polymer heterogeneous waveguide exhibits strong absorption for the TE mode from 1500 nm to 2004 nm, while the propagation loss for the TM mode declines from 20.56 dB/cm at 1500 nm to 4.89 dB/cm at 2004 nm. The transmission characteristics serves as an essential tool to verify the optical parameters (n-κ) of the thin strips, addressing to the ambiguity raised by spectroscopic ellipsometry regarding highly absorbing materials. Furthermore, the observed strong absorption for the TE mode at 2004 nm is well beyond the cut-off wavelength of the crystalline bulk Ge (~1850 nm at room temperature). This redshift is modeled to manifest the narrowing of the Tauc-fitted bandgap due to the grain order effect in the amorphous Ge layer. The accurate measurement of the nanometer-scale light-absorbing strips in a waveguide form is a crucial step toward the accurate design of integrated photonic devices that utilize such components.

Immersive Unit Visualization is an emergent form of visualization that arose from Immersive Analytics where, unlike traditional visualizations, each data point is represented by an individual visual mark in an immersive virtual environment. This practice has focused almost exclusively on virtual reality, excluding augmented reality (AR). This article develops and tests a prototype of an immersive Unit Visualization (Floating Companies II) with two AR devices: head-mounted display (HMD) and hand-held display (HHD). Results from the testing sessions with 20 users were analyzed through qualitative research analysis and thematic coding indicating that, while the HHD enabled a first contact with AR visualization on a familiar device, HMD improved the perception of hybrid space by supporting greater stability of virtual content; wider field of view; improved spatial perception; increased sense of immersion; and more realistic simulation, which impacted on information reading and sense-making. The materialization of abstract quantitative values into concrete reality through its simulation in the real environment and the ludic dimension stand out as important opportunities of this type of visualization. This paper investigates the aspects distinguishing two experiences regarding data visualization in hybrid space; and characterizes ways of seeing information with AR, identifying opportunities to advance information design research.

Nearest-neighbour clustering is a simple yet powerful machine learning algorithm that finds natural application in the decoding of signals in classical optical-fibre communication systems. Quantum k-means clustering promises a speed-up over the classical k-means algorithm; however, it has been shown to currently not provide this speed-up for decoding optical-fibre signals due to the embedding of classical data, which introduces inaccuracies and slowdowns. Although still not achieving an exponential speed-up for NISQ implementations, this work proposes the generalised inverse stereographic projection as an improved embedding into the Bloch sphere for quantum distance estimation in k-nearest-neighbour clustering, which allows us to get closer to the classical performance. We also use the generalised inverse stereographic projection to develop an analogous classical clustering algorithm and benchmark its accuracy, runtime and convergence for decoding real-world experimental optical-fibre communication data. This proposed `quantum-inspired' algorithm provides an improvement in both the accuracy and convergence rate with respect to the k-means algorithm. Hence, this work presents two main contributions. Firstly, we propose the general inverse stereographic projection into the Bloch sphere as a better embedding for quantum machine learning algorithms; here, we use the problem of clustering quadrature amplitude modulated optical-fibre signals as an example. Secondly, as a purely classical contribution inspired by the first contribution, we propose and benchmark the use of the general inverse stereographic projection and spherical centroid for clustering optical-fibre signals, showing that optimizing the radius yields a consistent improvement in accuracy and convergence rate.

Combining wind and solar photovoltaic (PV) generation can provide complementary renewable power production, but depends on correlated resources. This study analyzed 10 years of wind data from Naama, Algeria to evaluate the potential for evening wind generation to offset the loss of solar at sunset. Average wind speeds showed a distinct increase during evening hours, coinciding with the decrease in solar irradiance. Wind turbine simulations using a 1.5 MW turbine and the wind data showed sufficient resources for profitable power production after sunset. Statistical analyses confirmed significantly higher wind speeds and simulated power output in evening vs daylight periods (p&lt;0.05). The Pearson correlation coefficient between evening wind speeds and decreasing solar irradiance was 0.63, supporting a strong positive relationship. These findings indicate Naama has adequate wind resources to deploy economically viable wind power capacity that can complement existing solar infrastructure and provide renewable electricity after dark , .

Taiwan is highly susceptible to global warming, experiencing a 1.4°C increase in air temperatures from 1911-2005, which is twice the average for the Northern Hemisphere. This has led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To improve forecast reliability, statistical post-processing is needed to address systematic errors. In September 2020, NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and Quantile mapping (QQ) approaches to predict daily maximum temperatures and extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias, RMSE, and highest correlation coefficient. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method was more effective in predicting summer daily Tmax and its extremes on an extended-range time scale deterministic and ensemble probabilistic forecasts over Taiwan.