Single Core Research Articles

Steady-State Temperature Prediction for Cluster-Laid Tunnel Cables Based on Self-Modeling in Natural Convection

Accurate temperature prediction of the operating tunnel cable is crucial for its safe and efficient function. To achieve a rapid and accurate prediction of the steady-state temperature of the tunnel cable, the self-modeling pattern in natural convection on the cable surface in the rectangular tunnel is investigated, and the self-modeling method for the convective heat transfer coefficient calculation is proposed. A thermal circuit model for single cables is further established to predict the cable core temperature, and the model is extended to predict the cluster-laid cable core temperature based on the combined method. The results show that when the tunnel size is neglected, the maximum relative deviation of the convective heat transfer coefficient between the self-modeling method and the finite element simulation is only 1.78% in the studied cases, indicating that the natural convection on the cable surface approximately satisfies the self-modeling method. Additionally, applying the self-modeling method to the thermal circuit can accurately predict the temperature of the single cable core. Furthermore, for the three-phase four-circuit cable, the maximum deviation between the temperature prediction results and the finite element results is within 2 K in the studied cases, which verifies the predictive accuracy of the combined method for the cluster-laid tunnel cable.

Highly sensitive liquid level and temperature sensors based on all-fiber intermodal interferometers

Abstract Fiber optic-based precise measurements of liquid level and temperature are crucial for remote controlling in pharmaceutics, chemistry, and bromatology. Thus, it is essential to monitor liquid level and temperature simultaneously. In this paper, we propose all-fiber intermodal interferometers for highly sensitive liquid level and temperature measurements. We fabricated two different structures of single mode fiber-no core fiber-single mode fiber (SMF-NCF-SMF). One of them was used to measure the liquid level when the liquid soaked the NCF, and the other one was further coated with PDMS thermal-optic material and utilized to detect temperature. The measurement sensitivity of liquid level was 580 pm/mm within the range of 0-20 mm, while that of temperature was 1 nm/°C within the range of 28-51°C. A matrix was finally obtained by demodulating the measurements of liquid level and temperature. The proposed liquid level and temperature sensors, exhibiting the merits of high sensitivity, easy fabrication, and low cost, could be a candidate for precise and remote monitoring of liquid levels.

Research on High Power Density Flyback Converter With a New Passive Lossless Snubber

ABSTRACTThis paper proposes a flyback converter with a new passive lossless snubber. It eliminates the power dissipation of the traditional RCD snubber, while providing protection against MOS breakdown from energy stored in the power‐transformer leakage. This energy is used to power the power chip, which simplifies the design of the transformer. Meanwhile, an optimal design method for high power density planar transformers is presented. Applying the principle of flux cancellation, a matrix transformer that originally needed an independent magnetic core was integrated into a single magnetic core to reduce the volume of the core. This method effectively reduces the printed circuit board (PCB) layer number of the planar transformer and increases the possibility of the application of the planar transformer in the flyback converter. The detailed operation principle and design considerations are presented. Finally, a multi‐output flyback converter with a frequency of 100 kHz is designed to verify the practical performance of the structure.

Reflective High-Sensitivity Humidity Sensor Based on Single Mode-No Core-Seven Core Fiber Structure

Reflective High-Sensitivity Humidity Sensor Based on Single Mode-No Core-Seven Core Fiber Structure

The ostracod distribution in the Sea of Galilee (Levant): species distribution and post-mortem dispersal of valves and carapaces

The Sea of Galilee is a unique large freshwater or slightly oligohaline natural lake in the Levant. Therefore, it represents an important aquatic habitat in the region that also provides invaluable ecosystem services for the local communities. To improve our knowledge of the lake’s ecosystem and the use of disarticulated ostracod valves and preserved carapaces, micro-crustacean remains commonly used in palaeolimnology and palaeoceanography, as proxies for palaeoenvironmental reconstructions, and to examine the post-mortem dispersal of ostracod remains, 68 surface-sediment samples were collected from the lake bottom in 2012 and analysed for the ostracod assemblages. Both, the noded and smooth, forms of Cyprideis torosa dominate in the Sea of Galilee, with the former more abundant than the latter. Relatively abundant and found at half of the 68 sampling locations or more, are also Ilyocypris hartmanni, I. cf. nitida, Darwinula stevensoni and Neglecandona angulata. In addition, ten less abundant ostracod taxa were recorded in the lake. Of all 15 taxa recorded in our study, ten were also recorded in a study of the Sea of Galilee’s ostracod fauna conducted already in the 1960s. The newly recorded five taxa are relatively rare, and they were mostly found in the region of the Jordan River delta or near the southeastern shore of the lake which were not included in the survey of the 1960s. Thus, there is no evidence for a significant change in the ostracod fauna of the lake over the last half-century. In comparison to the ostracod assemblage from a late Pleistocene archaeological excavation site at the southwestern margin of the lake, the assemblage from the recent survey is slightly less diverse, probably because of the long duration of ca. 5000 years integrated by the sedimentary section of the archaeological site and also due to nearby freshwater inflows from which valves and carapaces were probably washed to the site’s location. Our study also shows that ostracod valves and carapaces are typically relatively abundant in most of the surface-sediment samples collected from locations at 18 m or shallower. In contrast, very few valves and carapaces were recorded at depths greater than 18 m, which is a zone affected by seasonal anoxia in the Sea of Galilee. These few ostracod remains were apparently transported by currents and waves to the central, deeper part of the lake, but their low number shows that such post-mortem dispersal of ostracod remains is insignificant in the deeper part of the lake. Thus, our study provides support for palaeoenvironmental and palaeoclimate reconstructions based on ostracod records from single sediment cores obtained from depths unaffected by post-mortem transport and seasonal or permanent anoxia.

Shape sensing endoscope fiber

Minimally invasive and robotic surgeries are growing areas that benefit patients through reduced recovery time. Medical fiber optics play an important role in these procedures by enabling instrument navigation, imaging, sensing, power delivery, and diagnostics in a small form factor. One route to further miniaturization is to combine these functions, or a subset of these functions, into a single strand of optical fiber. In this work, we present a fiber and fan-in device that enables shape sensing, imaging, power delivery, and potentially additional sensing capabilities, such as temperature and/or pressure, in the same waveguide. The refractive index profile of the multimode waveguide in our fiber is similar to step index fibers used in laser delivery and is suitable for imaging applications; however, it also contains seven single mode cores twisted in a helix and with quasi-continuous Bragg gratings along their entire length, such as are used in fiber shape sensing. We first calibrate the transmission matrix of the multimode waveguide to enable the formation of a focused spot at the distal end of the fiber with a spatial light modulator. A second calibration allows us to reconstruct the shape of the fiber using optical frequency domain reflectometry in the twisted shape sensing cores. We show that these multiple functions can be performed simultaneously with our device and that changes in the curvature of the fiber correlate with the quality of the distal spot produced through the fiber, which is an important step towards maintaining the imaging calibration as the fiber is manipulated.

A system readiness approach to support the packaging and scaling of innovation bundles for farming systems transformation

CONTEXTA newly established ‘systemic approach to technology packaging’ for improved scaling has emerged in the literature. This approach suggests that core transformative innovations can be scaled more effectively through scaling readiness, which helps identify complementary innovations that support and facilitate the scalability of the core innovation. Since these packaging approaches start with single core innovations, we propose that they can benefit from the broader literature on sustainable agricultural systems transformation and readiness. OBJECTIVEThe objective of this paper is to advocate for a more comprehensive packaging of innovations for sustainable agricultural system transformations by better understanding gaps in the agricultural system – and its capacity for change. This approach provides a stronger rationale for the selection of relevant innovations to be packaged together. We introduce the concept of agricultural ‘system readiness’ as a possible framework for guiding such bundles. METHODSThe paper begins with a comprehensive literature review that identifies the current gaps in tools and methods to guide system transformation. It also focuses on the specific literature on innovation packaging for scaling, and builds on the gaps of existing approaches as a rationale for introducing the concept of system readiness (mostly used in infrastructure engineering), and adapting it to agricultural science. We also use illustrative hypothetical examples from mixed crop-livestock systems in the drylands to show how two approaches – ‘packaging for scaling’ and ‘packaging for transformation’ – can lead to different innovation packages. RESULTS AND CONCLUSIONSThe concept of system readiness is introduced, defined and advocated. We show that it can help identify performance gaps in agricultural systems and guide better planning for system strengthening and integration. We conclude with possible complementarities between the two approaches of scaling and system readiness. SIGNIFICANCEThe agricultural system readiness approach, as outlined in this paper, would be particularly beneficial for agricultural research-for-development, as well as other agricultural investment programs that seek to identify strategies for fostering sustainable transition in farming systems. Such programs would typically entail complexity-aware theories of change that focus on stimulating sustainable transformation pathways related to key target areas, such as agroecology, sustainable intensification, and system integration (e.g., crop-livestock integration and integrated pest management). The system readiness approach can support such programs through the identification, prioritization, targeting, and empowerment of the most significant (and transformative) system components by identifying respective innovations to be packaged.

Learning to Move Like Professional Counter‐Strike Players

AbstractIn multiplayer, first‐person shooter games like Counter‐Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high‐level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand‐crafted movement policies for every scenario. We show that it is possible to take a data‐driven approach to creating human‐like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer‐based movement model that generates human‐like team movement for all players in a “Retakes” round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially‐available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of “human‐like”). Using experiments involving in‐game bot vs. bot self‐play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.

Genetically-encoded markers for confocal visualization of single dense core vesicles.

Neuronal dense core vesicles (DCVs) store and release a diverse array of neuromodulators, trophic factors and bioamines. The analysis of single DCVs has largely been possible only using electron microscopy, which makes understanding cargo segregation and DCV heterogeneity difficult. To address these limitations, we developed genetically-encoded markers for DCVs that can be used in combination with standard immunohistochemistry and expansion microscopy, to enable single-vesicle resolution with confocal microscopy.

Matching Tabular Data to Knowledge Graph with Effective Core Column Set Discovery.

Matching tabular data to a knowledge graph (KG) is critical for understanding the semantic column types, column relationships, and entities of a table. Existing matching approaches rely heavily on core columns that represent primary subject entities on which other columns in the table depend. However, discovering these core columns before understanding the table’s semantics is challenging. Most prior works use heuristic rules, such as the leftmost column, to discover a single core column, while an insightful discovery of the core column set that accurately captures the dependencies between columns is often overlooked. To address these challenges, we introduce Dependency-aware Core Column Set Discovery ( DaCo ), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and the core column set. Additionally, DaCo can be seamlessly integrated with pre-trained language models, as proposed in the optimization module. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for real-world scenarios. In addition, it can identify multiple core columns within a table, which is common in real-world tables. We conduct experiments on six datasets, including five datasets with single core columns and one dataset with multiple core columns. Our experimental results show that DaCo outperforms existing core column set detection methods, further improving the effectiveness of table understanding tasks.

Analysis and Design of High Power Density Flyback Converter

ABSTRACTThis paper presents a flyback converter with high efficiency and high power density. An optimal design method for high power density planar transformers is presented. Applying the principle of flux cancellation, a matrix transformer that originally needed an independent magnetic core was integrated into a single magnetic core to reduce the volume of the core. This method effectively reduces the printed circuit board (PCB) layer number of the planar transformer and increases the possibility of the application of the planar transformer in the flyback converter. An optimized design method of the winding structure is given according to that model to further reduce the winding loss of the transformer. Meanwhile, the proposed structure is verified by finite element analysis software. Finally, a multi‐output flyback converter with a frequency of 100 kHz is designed to verify the practical performance of the proposed transformer.

Solvent-Regulated Formation of Metal/Metal-Oxo Nodes in Two Indium Metal-Organic Frameworks: Syntheses, Structures, Selective Gas Adsorption and Fluorescence Sensor Properties.

Two water-stable indium metal-organic frameworks, (NH2Me2)3[In3(BTB)4] ⋅ 12DMA ⋅ 4.5H2O (In-MOF-1) and (NH2Me2)9[In9O6(BTB)8(H2O)4(DMSO)4] ⋅ 27DMSO ⋅ 21H2O (In-MOF-2) (BTB=4,4',4''-benzene-1,3,5-tribenzoate) with 3D interpenetrated structure has been constructed by regulating solvents. Structure analysis revealed that In-MOF-1 has a three-dimensional (3D) structure with a single metal core, while In-MOF-2 features an octahedron cage constructed by three kinds of metal clusters to further form a 3D structure. The fluorescence investigations showed that In-MOF-1 and In-MOF-2 are potential MOF-based fluorescent sensors to detect acetone and Fe3+ ions in EtOH or water with high sensitivity, excellent selectivity, recyclability and a low limit of detection. Moreover, the fluorescence mechanisms of In-MOF-1 and In-MOF-2 toward acetone and Fe3+ ions were further explained. In addition, In-MOF-2 has higher thermal and framework stability than In-MOF-1. The activated In-MOF-2 presents a high BET surface area of 998.82 m2g-1 and a pore size distribution of 8 to 16 Å. At the same time, In-MOF-2 exhibits high selective CO2 adsorption for CO2/CH4 and CO2/N2, respectively. Furthermore, the adsorption sites and adsorption isotherms were predicted using grand canonical Monte Carlo (GCMC) simulations, and the adsorption energy of the lowest-energy adsorption configuration was calculated using molecular dynamics (MD) simulations.

Membandingkan Kekuatan Pengelasan Single Dan Double Kawat Inti Elektroda (Filler Rod) Menggunakan Las Smaw

Advances in welding technology are very helpful in making, repairing or repairing chassis jobs that have a high level of difficulty and requirements. Especially in the chassis, many welding elements are involved because welded joints have an important role to support the body, engine, and vehicle load. In practical work at CV. Pringgandani Welding Analysis Comparing the Strength of Welding Results of Single Electrode Core Wire and Double Electrode Core Wire (Filler Rod) Using SMAW welding which is carried out for chassis welding which is claimed by mechanics in welding with a depth and width of more than 5 mm, it is better to use double electrode core wire which can speed up and strengthen the weld. Then a test is carried out to prove the results of the Single Wire Electrode Core and Double Electrode Core Wire (Filler Rod) welds which are stronger and faster in the process. By testing the weld results on an iron plate with dimensions of 250 mm x 50 mm x 10 mm, the welding process uses the SMAW (Shielded Metal Arc Welding) welding method. Using an E6013 type electrode with a diameter of 2 mm with a current of 90 A, polarity DCEP (Direct Current Electrode Positive) The welding position used is the 1G position or horizontal position with the welding process using a single v-groove bevel butt joint and an angle of 60o with a weld width of 10 mm and a weld depth of 8 mm. The plate samples used were two samples. In sample 1 using a single wire core electrode, and sample 2 using a double wire core electrode. All samples were made of tensile test specimens with the ASME-IX standard, a tensile test was carried out to determine and prove the respective strengths of the welding results of Single Wire Core Electrode and Double Wire Core Electrode (Filler Rod).

Analysis of Sandwich Composite Subjected to Impact Loading for Enhanced Energy Absorption

Abstract Composite materials are engineered to achieve the balance of properties for a wide spectrum of applications. In the current work, PU foam is used as a core, and Epoxy E-Glass (UD) is used as a face sheet. Impact of sandwich composite with single core and double core are considered. The effect of energy absorption, contact force, residual velocity, and deformation on impact velocity are discussed. The phenomenon of perforation, penetration and rebounding was observed in 150 m/s, 130 m/s and 100 m/s respectively. The FE tool ANSYS is used for analysis. It was found double core sandwich composite absorbs more energy and even stiffer than single core sandwich composite.

Unjust enrichment in law and equity

In Moses v Macferlan, Lord Mansfield used money had and received, a common law money count, to provide relief in a case where an action’s outcome failed to align with the actor’s intention. In the First Restatement of Restitution, Warren Seavey and Austin Scott gathered together all cases, quasi-contractual and equitable, under the single principle that ‘a person who has been unjustly enriched at the expense of another is required to make restitution to the other.’ These two influential acts of fusion between common law and equity have caused a great deal of confusion in the scholarship and jurisprudence on unjust enrichment. With the fundamental differences between quasi-contract and equitable unjust enrichment obscured, scholars and judges have struggled to find the single principle or core case that unifies liability in what is now called the law of unjust enrichment. I argue that we can resolve the puzzles of unjust enrichment by rejecting the fusionist claims that started them all – that is, by distinguishing cases of quasi-contract (the common law money counts) from cases of equitable unjust enrichment – and by recognizing that each has a distinctive normative foundation. Quasi-contract, like other common law doctrines, is grounded in respect for the freedom and equality of agents conceived as beings with the capacity for free choice. Quasi-contract is concerned with the objective significance of external acts like requests and agreements on terms; it is not concerned with the frustration of the plaintiff’s particular purpose in acting. Equitable unjust enrichment, like other equitable doctrines that attend to mistakes, expectations, and intentions, is grounded in concern for individual autonomy. It recognizes that a court, as a public institution attuned to law’s self-imposability, cannot enforce an alienation of property with indifference to the way in which it may fail as an expression of the individual’s purposes and reasons for action.

Neural decoding and feature selection methods for closed-loop control of avoidance behavior

Objective.Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.Approach.We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.Main results.Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring<310 ms for training,<0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.Significance.Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Lossless Approximate Pattern Matching: Automated Design of Efficient Search Schemes.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern. Together, these searches ensure the identification of all approximate occurrences of a search pattern within a predefined limit of k errors. While existing literature offers designed schemes for up to k = 4 errors, designing search schemes for larger k values incurs escalating computational costs. Our method integrates a greedy algorithm and a novel Integer Linear Programming (ILP) formulation to design efficient search schemes for up to k = 7 errors. Comparative analyses demonstrate the superiority of our ILP-optimal schemes over alternative strategies in both theoretical and practical contexts. Additionally, we propose a dynamic scheme selection technique tailored to specific search patterns, further enhancing efficiency. Combined, this yields runtime reductions of up to 53% for higher k values. To facilitate search scheme generation, we present Hato, an open-source software tool (AGPL-3.0 license) employing the greedy algorithm and utilizing CPLEX for ILP solving. Furthermore, we introduce Columba 1.2, an open-source lossless read-mapper (AGPL-3.0 license) implemented in C++. Columba surpasses existing state-of-the-art tools by identifying all approximate occurrences of 100,000 Illumina reads (150 bp) in the human reference genome within 24 seconds (maximum edit distance of 4) and 75 seconds (maximum edit distance of 6) using a single CPU core. Notably, our study showcases Columba's capability to align 100,000 reads of length 50, with high error rates and up to an edit distance of 7, in a mere 2 hours and 15 minutes. This achievement is unmatched by other lossless aligners, which require over 3 hours for edit distance 5 alignments. Moreover, Columba exhibits a mapping rate four times higher than that of a lossy tool for this dataset.

Novel genomic and phenotypic traits of polyhydroxyalkanoate-producing bacterium ZZQ-149, the type strain of Halomonas qinghailakensis

BackgroundPolyhydroxyalkanoates (PHAs) are optimal potential materials for industrial and medical uses, characterized by exceptional sustainability, biodegradability, and biocompatibility. These are primarily from various bacteria and archaea. Bacterial strains with effective PHA formation capabilities and minimal production cost form the foundation for PHA production. Detailed genomic analysis of these PHA-generating bacteria is vital to understand their PHA production pathways and enhance their synthesis capability.ResultsZZQ-149, a halophilic, PHA-producing bacterium, was isolated from the sediment of China’s Qinghai Lake. Here, we decoded the full genome of ZZQ-149 using Single Molecule Real Time (SMRT) technology based on PacBio RS II platform, coupled with Illumina sequencing platforms. Physiological, chemotaxonomic traits, and phylogenetic analysis based on 16 S rRNA gene and single copy core genes of ninety-nine Halomonas type strains identified ZZQ-149 as the type strain of Halomonas qinghailakensis. Furthermore, a low average nucleotide identity (ANI, < 95%) delineated the genetic differences between ZZQ-149 and other Halomonas species. The ZZQ-149 genome, with a DNA G + C content of 52%, comprises a chromosome (3, 798, 069 bps) and a plasmid (6, 107 bps). The latter encodes the toxin-antitoxin system, BrnT/BrnA. Through comprehensive genome sequencing and analysis, we identified multiple PHA-synthesizing enzymes and an unprecedented combination of eight PHA-synthesizing pathways in ZZQ-149.ConclusionsBeing a halophilic, PHA-producing bacterium, ZZQ-149 exhibits potential as a high PHA producer for engineered bacteria via genome editing while ensuring low-cost PHA production through continuous, unsterilized fermentation.

RTG-GNN: A novel rock topology-guided approach for permeability prediction using graph neural networks

Digital core permeability is a crucial factor in rock engineering, reservoir simulation, and underground applications, serving as the foundation for evaluating fluid flow underground. However, predicting digital core permeability using artificial intelligence presents challenges such as high model calculation complexity, strong resource dependence, and low efficiency. To overcome these challenges, this paper presents a novel hybrid enhanced rock topology-guided graph neural network (RTG-GNN) to accurately characterize the pore structure of digital cores and predict permeability. This model takes advantage of the physical attributes of pores and throats, utilizing them as features for nodes and edges. It improves information exchange through a gating mechanism and incorporates an attention mechanism for weighted aggregation of neighboring data, culminating in accurate permeability predictions. Furthermore, a scheme for constructing a comprehensive permeability distribution dataset (CPDD) is proposed for digital cores. This scheme effectively mitigates challenges like inadequate training data, subjective sampling bias, and data drift. Multiple experimental results demonstrate that RTG-GNN surpasses most methods in various performance indicators. Additionally, the RTG-GNN model boasts a lightweight design, occupying only 17% to 44% of the size of mainstream models and requiring just 15% to 38% of GPU resources for training. Remarkably, while maintaining high accuracy and low error rates, the computational load is reduced by a factor ranging from 19.06 to 636.41, the training speed is enhanced by 12.12 to 86.47 times, and the inference speed is increased by 6.76 to 16.14 times. The experiment further confirms that both single core and mixed rock datasets, acquired through the CPDD scheme, exhibit excellent generalization and reliability.

Mechanical characteristics of kenaf-glass fiber reinforced hybrid composites by varying the stacking sequences

Fiber composite materials are preferred for their lightweight, low-cost, and commercial uses. As part of this study, laminate materials consisting of two different fiber materials as their reinforcement materials are produced using the hand layup method. This study investigates the mechanical properties of hybrid composite laminates fabricated using kenaf and glass fibers. Six stacking arrangements of the fibers are examined, alongside two reference laminates with individual reinforcements. Epoxy resins HY951 and LY556 serve as matrix materials. ASTM standards guide the mechanical testing of the composites. Results indicate varied tensile strengths based on stacking sequence, with laminate L2 (KKGKK) featuring a single glass fiber core at 75 MPa, and increasing strengths in laminates with additional glass layers: L1 (GKKKG) at 123 MPa, L5 (KGKGK) at 110 MPa, L3 (GKGKG) at 150 MPa, L6 (KGGGK) at 118 MPa, and L4 (GGKGG) at 159 MPa, the highest among all. It was observed that adding one layer of glass fiber with kenaf fiber boosts strength and modulus by 9.52% and 12.19% respectively, compared to pure kenaf fiber composites. Morphological analysis via Scanning Electron Microscopy (SEM) confirms failure due to initial crack propagation in the matrix and fibers. This study offers insights into hybrid composite laminate behavior, pertinent for various industrial applications.