Traditional Hybrid Research Articles

Novel Approach to Optimizing Express-Local Metro Train Timetable Considering Multiple Train Stopping Patterns

A traditional hybrid operational mode of a metro system usually combines the express and local trains. The stopping patterns of express trains are generally assumed to be the same. To provide a diverse service, this paper aims to explore the train timetable optimization method when the express trains have different stopping patterns. A passenger-oriented timetabling model for reducing passenger waiting time is established, which is proved to be a quadratic programming problem. Then, an Interior Point Method is applied to obtain the best timetable. A case study based on the Guangzhou Metro Network is conducted, symbolizing different operation strategies. The results indicate that the proposed model can efficiently reduce the passenger waiting time by 16.8% and the number of stranded passengers by 15.9%. The practical implications of this research are significant, as it introduces a new approach to train timetable optimization that can be applied to real-world metro systems.

A Deep Ensemble Gene Selection and Attention-guided Classification Framework for Robust Cancer Diagnosis from Microarray Data

Microarray technology has enabled unprecedented insight into cancer diagnosis through large-scale gene expression analysis. However, the high dimensionality and complexity of microarray datasets pose significant challenges, as only a small subset of genes is typically informative, with the remainder introducing noise and complicating classification. Traditional gene selection methods, including filter, wrapper, and hybrid techniques, have achieved promising results but often fail to capture complex gene interactions, suffer from computational inefficiencies, or lack interpretability. This study presents DEGS-AGC (Deep Ensemble Gene Selection and Attention-Guided Classification), a novel integrated framework for gene selection and classification. DEGS-AGC is designed to address these limitations through two primary components: Deep Ensemble Gene Selection (DEGS), which leverages ensemble learning with Random Forest, XGBoost, and Deep Neural Networks to select relevant genes while reducing redundancy via sparse autoencoders, and Attention-Guided Classification (AGC), where an attention mechanism dynamically assigns weights to genes to improve interpretability and classification precision. The DEGS-AGC framework was evaluated against traditional methods, using consistent classification models for robust comparisons. Evaluation metrics demonstrated the potential of DEGS-AGC as an effective tool for high-dimensional biomedical data analysis. The results highlighted the ability of DEGS-AGC to offer accurate, interpretable, and computationally feasible solutions for cancer diagnosis, advancing the development of data-driven personalized approaches in healthcare.

A comparative study of traditional machine learning and hybrid fuzzy inference system machine learning models for air quality index forecasting

A comparative study of traditional machine learning and hybrid fuzzy inference system machine learning models for air quality index forecasting

Genomics-assisted stacking of waxy1, opaque2, and crtRB1 genes for enhancing amylopectin in biofortified maize for industrial utilization and nutritional security.

Waxy maize is highly preferred diet in developing countries due to its high amylopectin content. Enriching amylopectin in biofortified maize meets food security and fulfils the demand of rising industrial applications, especially bioethanol. The mutant waxy1 (wx1) gene is responsible for increased amylopectin in maize starch, with a wide range of food and industrial applications. Conventional maize has a modest amount of amylopectin (70-75% of starch), but waxy maize, with favourable wx1 allele, has ~ 95-100% amylopectin. In this study, thewx1 allele was introgressed into the multi-nutrient-rich maize parental inbreds (PMI-PV9 and PMI-PV5) of APQH8, a multi-nutrient rich maize hybrid having high lysine, tryptophan, and provitamin-A nutritionally superior over the traditional hybrids. Gene-specific markers specific for o2 and crtRB1 were employed to select desirable gene segregants from BC1F1, BC2F1, and BC2F2. Background selection was employed with > 90 SSR markers. Selected backcross progenies showed high recovery of recurrent parent genomes (RPG: 94.8-96.8%). The reconstituted waxy hybrids exhibited anincrease in amylopectin (mean: 98.4%) compared to the original hybrid (mean: 72.7%). The reconstituted hybrids also recorded enhanced lysine (mean: 0.382%), tryptophan (mean: 0.092%), and provitamin-A (mean: 10.36ppm), respectively, than normal maize, however similar to the original hybrid with high lysine: 0.330%, tryptophan: 0.079% and provitamin-A: 10.42ppm. Furthermore, MAS-derived genotypes showed similar agro-morphological traits and grain yield. These biofortified waxy maize hybrids, rich in provitamin-A, lysine, and tryptophan with enhanced amylopectin hold great potential in diverse industrial applications and nutritional security.

Displacing the Christian Theodicy of Hell: Yi Kwangsu’s Search for the Willful Individual in Colonial Modernity

This article aims to offer Yi Kwangsu’s The Heartless (Mujŏng, 1917), the first modern Korean novel, as an emblem of hybrid religiosity in colonial modernity that sheds light on an ambivalent alterity in the problem of hell in non-Western cultures. To the extent that the problem of hell in Christianity pertains to the question of why God allows evil to exist eternally, God’s omnipotent authority with justice and fairness beyond the grave is placed at the center of the inquiry into the ultimate standard of moral goodness the religious feasibility of which justifies the existence of sinners suffering eternal damnation in hell. But the co-existence of the omnipotent God and unrepentant sinners is not always questioned in the religiosity of hell in non-Western cultures. The Christian imaginary of hell in non-Western cultures often demarcates the question of God’s sovereignty from the sufferings of sinners in the problem of hell. Based on these observations, this article will investigate Yi’s narratives of hell in The Heartless, which are associated with Christianity but intertwined with his ethical demands for shaping a new individuality beyond the traditional hybrid religiosity of hell. Specifically, first, we will show that Yi’s Christian imaginary of hell is reformulated through the traditional imaginaries of hell in which, regardless of the existence of God’s sovereignty over the created order, the sufferings of sinners in hell function to secure social norms and orders. In doing so, we claim that the Christian imaginary of hell in The Heartless is relegated to a rhetorical means to beget the need for the self-awakening of the inner-self through which individual desires can be freed from the influences of Confucian morality as well as Christian theodicy. Second, in comparison with Lu Xun’s sympathetic relocation of Christian spirituality within the traditional Chinese imaginaries of hell in his longing for modern subjectivity, we explore Yi’s hybrid religiosity within colonial modernity, the vitality of which cannot be confined within the simple dichotomy between Western and non-Western cultures. At this juncture, the upshot of Yi’s hybrid religiosity within colonial modernity is that the theodicy of hell in Christianity can be displaced and thereby disenfranchised from the centrality of the search for a new individuality.

Research on Assembly Robots Based on Flexible Control Technology

Industrial robots play a vital role in modern production automation, especially in improving production efficiency and reducing labor costs. However, traditional robot position control methods often have difficulty coping with complex assembly tasks, especially in changing environments. To solve this problem, flexible control technology has emerged, which can dynamically adjust the state of the robot according to the force applied by the external environment, thereby effectively avoiding rigid collisions and improving the accuracy and safety of the robot. This paper summarizes recent research on force-aware control in scenarios of shaft-hole assembly, including improvements to traditional force-position hybrid control and the introduction of intelligent control principles. This technique has significantly improved the efficiency and success rate of the alignment and loading phases. However, the high cost of force-aware control and data transmission limitations remain challenges. Future research should focus on improving system flexibility, expanding application scope and optimizing the underlying hardware to drive robotics towards intelligence and refinement

Breeding by Design for Functional Rice with Genome Editing Technologies.

The conventional approaches to crop breeding, which rely predominantly on time-consuming and labor-intensive methods such as traditional hybridization and mutation breeding, face challenges in efficiently introducing targeted traits and generating diverse plant populations. Conversely, the emergence of genome editing technologies has ushered in a paradigm shift, enabling the precise and expedited manipulation of plant genomes to intentionally introduce desired characteristics. One of the most widespread editing tools is the CRISPR/Cas system, which has been used by researchers to study important biology-related problems. However, the precise and effective workflow of genome editing has not been well-defined in crop breeding. In this study, we demonstrated the entire process of breeding rice varieties enriched with high levels of resistant starch (RS), a functional trait that plays a crucial role in preventing diseases such as diabetes and obesity. The workflow encompassed several key steps, such as the selection of functional SBEIIb gene, designing the single-guide RNA (sgRNA), selecting an appropriate genome editing vector, determining the vector delivery method, conducting plant tissue culture, genotyping mutation and phenotypic analysis. Additionally, the time frame necessary for each stage of the process has been clearly demonstrated. This protocol not only streamlines the breeding process but also enhances the accuracy and efficiency of trait introduction, thereby accelerating the development of functional rice varieties.

Research Advances on Lithium‐Ion Batteries Calendar Life Prognostic Models

ABSTRACTIn military reserve power supplies, there is an urgent need for superior secondary batteries to replace conventional primary batteries, and lithium‐ion batteries (LIBs) emerge as one of the best choices due to their exceptional performance. The life of LIBs includes cycle life and calendar life, with calendar life spanning from years to decades. Accurate prediction of calendar life is crucial for optimizing the deployment and maintenance of LIBs in military applications. Model‐based prognostics are usually established to estimate calendar life using accelerated aging methods under various storage conditions. This review firstly outlines the general prognostic workflow for calendar life of LIBs, analyzes degradation mechanisms, and summarizes influencing factors; then, we introduce calendar life prognostic models, evolving from simplistic empirical models (EMs) to nonempirical mechanistic models (MMs) based on LIB calendar aging knowledge and then to traditional hybrid empirical‐mechanistic models (trad‐EMMs). Finally, the data‐driven models (DDMs) based on machine learning (ML) are discussed due to the limitation of the traditional methods, evolving from pure data‐driven to knowledge‐integrated models and establishing a comprehensive framework for calendar life assessment. To the best of our knowledge, this paper presents the first comprehensive review in this field, summarizing calendar life prognostic models of LIBs and offering some insights into future model development directions. Model‐based prognostics can facilitate researchers in calendar aging analysis and calendar life prolongation, thereby better serving the application of LIBs in national economic life.

Study on the process of submerged combustion vaporizer flue gas membrane-cryogenic hybrid carbon capture for coupled liquefied natural gas cold energy power generation

ABSTRACT Under the background of “carbon peaking and carbon neutrality goals,” China has put forward the requirements for the low-carbon development of Liquefied Natural Gas (LNG) industrial chain. Carbon capture technology is an effective means to reduce direct carbon emissions and can promote the decarbonization of the LNG industry chain. So far carbon capture is still an energy-intensive process. Based on the traditional membrane-cryogenic hybrid CO2 capture process, a membrane-cryogenic hybrid CO2 capture process coupled with Liquefied Natural Gas cold energy generation was proposed in this paper. In the proposed process, LNG cold energy and high temperature flue gas heat energy were used to drive Organic Rankine Cycle power generation and reduced carbon capture energy consumption. The effects of key parameters (membrane pressure, membrane temperature, cryogenic pressure, and cryogenic temperature) on the performance of the process (CO2 purity, CO2 recovery, and specific energy consumption) were analyzed by single factor analysis. The process parameters were optimized by response surface analysis, the optimized system resulted in an CO2 purity of 98.39%, CO2 recovery of 90.16%, and specific energy consumption of 605 kJ/kg which was much lower than that of the same type of carbon capture process. This process opens up a new technological path for the development of low energy consumption in carbon capture and the decarbonization of the LNG industry chain, and provides a new solution to meet the challenges in the context of “dual carbon.”

Detection and classification of long terminal repeat sequences in plant LTR-retrotransposons and their analysis using explainable machine learning

BackgroundLong terminal repeats (LTRs) represent important parts of LTR retrotransposons and retroviruses found in high copy numbers in a majority of eukaryotic genomes. LTRs contain regulatory sequences essential for the life cycle of the retrotransposon. Previous experimental and sequence studies have provided only limited information about LTR structure and composition, mostly from model systems. To enhance our understanding of these key sequence modules, we focused on the contrasts between LTRs of various retrotransposon families and other genomic regions. Furthermore, this approach can be utilized for the classification and prediction of LTRs.ResultsWe used machine learning methods suitable for DNA sequence classification and applied them to a large dataset of plant LTR retrotransposon sequences. We trained three machine learning models using (i) traditional model ensembles (Gradient Boosting), (ii) hybrid convolutional/long and short memory network models, and (iii) a DNA pre-trained transformer-based model using k-mer sequence representation. All three approaches were successful in classifying and isolating LTRs in this data, as well as providing valuable insights into LTR sequence composition. The best classification (expressed as F1 score) achieved for LTR detection was 0.85 using the hybrid network model. The most accurate classification task was superfamily classification (F1=0.89) while the least accurate was family classification (F1=0.74). The trained models were subjected to explainability analysis. Positional analysis identified a mixture of interesting features, many of which had a preferred absolute position within the LTR and/or were biologically relevant, such as a centrally positioned TATA-box regulatory sequence, and TG..CA nucleotide patterns around both LTR edges.ConclusionsOur results show that the models used here recognized biologically relevant motifs, such as core promoter elements in the LTR detection task, and a development and stress-related subclass of transcription factor binding sites in the family classification task. Explainability analysis also highlighted the importance of 5’- and 3’- edges in LTR identity and revealed need to analyze more than just dinucleotides at these ends. Our work shows the applicability of machine learning models to regulatory sequence analysis and classification, and demonstrates the important role of the identified motifs in LTR detection.

Collision Avoidance Trajectory Planning Based on Dynamic Spatio-Temporal Corridor Search in Curvy Road Scenarios for Intelligent Vehicles

To avoid collisions and ensure driving safety, comfort, and efficiency, in this study, we propose a trajectory planning strategy for intelligent vehicles navigating curvy road scenarios. This strategy is based on a dynamic spatio-temporal corridor search. First, an obstacle space expansion module is constructed using a critical safety distance model to generate a searchable spatio-temporal corridor. Next, a dynamic step expansion is performed to improve the traditional hybrid A* search algorithm by the discretization of front-wheel steering angles and acceleration. The bisection method is applied to iteratively optimize the child nodes at each step, and the child node with the lowest cost is selected as the rough search node. Subsequently, a locally weighted dual-regression fitting algorithm is employed for segment trajectory fitting, and the optimal trajectory is generated. Finally, the performance of the proposed trajectory planning strategy is validated on the Carla simulation platform. The results show the effectiveness and efficiency of our strategy in three typical scenarios.

Enhancing Sensitivity of Point-of-Care Thyroid Diagnosis via Computational Analysis of Lateral Flow Assay Images Using Novel Textural Features and Hybrid-AI Models.

Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research.

Comparative Analysis of Traditional and Hybrid Engines: Efficiency and Emissions

As two dominant types of automotive engines, traditional internal combustion engines and hybrid engines hold significant market shares. This paper focuses on comparing the efficiency and emissions of these two engine types. The traditional engine, characterized by its crankshaft linkage structure, presents both its best feature and its biggest drawback. This structure leads to substantial efficiency losses and increased combustion time, resulting in lower overall efficiency and higher environmental pollution compared to hybrid engines. On the other hand, hybrid engines boast a more intelligent system, offering three distinct modes that can be selected based on the required power. This flexibility enables hybrid engines to conserve energy more effectively, thereby achieving higher efficiency. Regarding emissions, hybrid engines can switch between electric power and internal combustion depending on the situation, resulting in healthier emissions profiles. This paper aims to elucidate the operational differences and environmental impacts of traditional and hybrid engines, encouraging readers to form their own opinions on potential improvements for these technologies.

Performance Analysis of Parallel Free-Space Optical/Radio Frequency Transmissions in Satellite–Aerial–Ground Integrated Network with Power Allocation

Satellite–aerial–ground integrated networks (SAGINs) combined with hybrid free-space optical (FSO) and radio frequency (RF) transmissions have shown great potential in improving service throughput and reliability. The coverage mismatch and rate limitation of traditional hybrid FSO/RF design have restricted its development. In this paper, we investigate the performance of parallel FSO/RF transmissions in SAGIN, taking into account the effect of weather conditions and the quality of service (QoS) of ground users. A three-hop relay system is proposed, where the FSO and RF links jointly provide services to ground users in remote areas. Specifically, considering the limited transmit power of the relay node, we have studied the optimal power allocation between parallel FSO and RF links to further improve system energy efficiency. The performance of the proposed system is evaluated in terms of capacity outage probability, weighted average bit-error rate (BER), and energy efficiency. Moreover, asymptotic capacity outage probability is also derived to obtain more engineering insights. Finally, numerical results show that the energy efficiency of the proposed parallel scheme improves by 30.9% compared to only the FSO scheme at a total transmit power of 15 dBW.

Investigation of the Weldability of 3D-Printed Multi-Material Materials (PLA and PLA Wood) Using Friction Stir Welding.

In the industry sector, it is very common to have different types of dissimilar materials on the same construction rather than products made from a single type of material. Traditional methods (welding, mechanical fastening, and adhesive bonding) and hybrid techniques (friction stir welding, weld bonding, and laser welding) are used in the assembly or joining of these materials. However, while joining similar types of materials is relatively easy, the process becomes more challenging when joining dissimilar materials due to the structure and properties of the materials involved. In recent years, additive manufacturing and 3D printing have revolutionized the manufacturing landscape and have provided great opportunities for the production of polymer-based multi-materials. However, developments in the joining of multi-material parts are limited, and their limits are not yet clear. This study focuses on the joining of 3D-printed products made from PLA-based multiple materials (PLA and PLA Wood) using friction stir welding. Single-material and multi-material parts (with 100% infill ratio and three different combinations of 50% PLA/50% PLA Wood) were welded at a feed rate of 20 mm/min and three different tool rotational speeds (1750, 2000, and 2250 rpm). Tensile and bending tests were conducted on the welded samples, and temperature measurements were taken. The fractured surfaces of the samples were examined to perform a damage analysis. It is determined that the weld strength of multi-materials changes depending on the combination of the material (material design). For multi-materials, a welding efficiency of 74.3% was achieved for tensile strength and 142.68% for bending load.

Application of CRISPR/Cas9 Technology in Rice Germplasm Innovation and Genetic Improvement.

Improving the efficiency of germplasm innovation has always been the aim of rice breeders. Traditional hybrid breeding methods for variety selection rarely meet the practical needs of rice production. The emergence of genome-editing technologies, such as CRISPR/Cas9, provides a new approach to the genetic improvement of crops such as rice. The number of published scientific papers related to "gene editing" and "CRISPR/Cas9" retrievable on websites both from China and other countries exhibited an increasing trend, year by year, from 2014 to 2023. Research related to gene editing in rice accounts for 33.4% and 12.3% of all the literature on gene editing published in China and other countries, respectively, much higher than that on maize and wheat. This article reviews recent research on CRISPR/Cas9 gene-editing technology in rice, especially germplasm innovation and genetic improvement of commercially promoted varieties with improved traits such as disease, insect, and herbicide resistance, salt tolerance, quality, nutrition, and safety. The aim is to provide a reference for the precise and efficient development of new rice cultivars that meet market demand.

Cooling high power electronics using dynamic phase change material

Phase change materials (PCMs) offer effective transient cooling due to their high latent heat of fusion and energy density. Unfortunately, PCMs generally have relatively low thermal conductivity, impeding effective heat dissipation from the heat source and limiting their power density. This work uses dynamic PCM (dynPCM) cooling for thermal management of high power electronics. DynPCM cooling uses pressure-enhanced close-contact melting of a PCM. The applied pressure causes liquid PCM to be pumped away from the heat transfer surface, maintaining a thin melt layer and high heat transfer. Through experimental investigations with a circuit board mounted 2 × 2 array of gallium nitride (GaN) power transistors integrated with heat spreaders of different thicknesses, we evaluate the cooling performance of dynPCM across various device heat dissipation levels (4.4 W/cm² to 46.6 W/cm²) and under both homogeneous and heterogeneous heating conditions. Using paraffin as the PCM, we explore the effects of different pressures (0 Pa, 750 Pa, and 7.5 kPa) on dynPCM cooling effectiveness. DynPCM significantly enhances cooling for electronics operating at high power, achieving over a 50 % reduction in steady-state junction temperature when compared to both traditional air-cooled and hybrid PCM-cooled systems at a 32.4 W/cm² individual GaN device power loss. We developed a reduced-order thermal resistance model to assess heat transfer from the electronic devices through the heat spreader into the dynPCM. The model helps to illustrate the critical role of the heat spreader design and PCM geometry on cooling performance, offering design guidelines for dynPCM thermal management systems. This work highlights the potential of dynPCM as a thermal management strategy for high-power electronic devices, facilitating the advancement of more effective cooling methods for a variety of applications.

Numerical simulation of jet interaction flow field of complex configuration based on hybrid grid method

Abstract The supersonic/hypersonic jet interaction flow field includes many fluid phenomena such as shock waves, separation and reattachment of the boundary layer, expansion wave, Mach disk, shear layer, and so on. The complexity of the jet interaction flow field makes it difficult to simulate accurately, and the structured grid method is generally used in engineering applications. However, the structured grid has many shortcomings when dealing with complex configurations, and the traditional hybrid grid method has the problem of insufficient accuracy. In the paper, a structured/unstructured grid algorithmic coupling numerical simulation method is developed. Firstly, the feasibility and accuracy of this method are verified by numerical simulation of the jet interference flow field of cone-cylinder-skirt shape, along with a comparison with the results of the experiment and structural method. Then, the jet interference flow fields of the rudders and the grid fins configuration are simulated to show the efficiency of the method, and the accuracy is further verified. The results show that the hybrid numerical simulation method can save grid generation time and grid quantity to a certain extent when compared with the structural, showing higher accuracy, which provides an accurate and efficient solution to jet interaction problems with complex configurations.

Static Characteristics of Hybrid Water-Lubricated Herringbone Groove Journal Bearing

Abstract The hydrodynamic herringbone groove journal bearing (HGJB) performs exceptionally well at high speeds but is limited by a low load-carrying capacity, largely due to the lubrication characteristics of water. To address this issue, a hybrid water-lubricated HGJB is proposed in this study. A lubrication model for the high-speed hybrid water-lubricated HGJB is developed, taking into account turbulence, thermal effects, and tilt. A comparative analysis of the static characteristics is conducted between the hybrid HGJB and both the hydrodynamic HGJB and the hybrid plain journal bearing (PJB). The results show that the proposed hybrid water-lubricated HGJB offers significantly greater load-carrying capacity than the conventional hydrodynamic HGJB, particularly during start-up or at low speeds. For example, when the bearing operates at 1000 rpm with an eccentricity ratio of 0.5, the load-carrying capacity of the water-lubricated hybrid HGJB under a supply pressure of 1.6 MPa reaches 650 N, compared to just 261 N for the water-lubricated hydrodynamic HGJB. Additionally, the hybrid water-lubricated HGJB demonstrates a higher flowrate and lower temperature rise than the traditional hybrid PJB, thanks to the improved pumping effect of the herringbone grooves at high speeds.

Hybrid Coffee (<i>Coffea arabica</i> L) Plantlet Production via Indirect Somatic Embryogenesis in Ethiopia: Current Statue and Future Direction

Traditional hybrid coffee propagation methods using seed or vegetative cuttings has tremendous limitations. Seed propagation is associated with hand pollination which is time consuming and need large number of skilled labor. On the other hand, vegetative cuttings ensure uniformity, but; cuttings generate relatively low multiplication rates as they can only be obtained from orthotropic branches. Multiplication by tissue culture techniques could provide a best alternative to these traditional methods of coffee propagation as it allowed the production of relatively uniform plantlets on a large scale in a shorter period in any climatic condition. Among tissue culture techniques, indirect somatic embryogenesis of hybrid coffee plays substantial role in rapid industrial scale multiplication of high valued varities. Research on somatic embryogenesis of <i>C. arabica</i> hybrid has been conducted since the late 70s with the objectives to substitute the conventional vegetative propagation of selected varieties. In Ethiopia, in vitro propagation of hybrid coffee is recent phenomenon and research on indirect somatic embryogenesis is already well underway by different research groups. Here, we discussed the current status of coffee research on somatic embryogenesis in general and specifically, we provide recommendations for future research for the establishment of mass propagation protocol for F1 hybrids varities in Ethiopia that utilized wild endogenous lines.