Comprehensive Performance Research Articles

A comparative study of early stage Alzheimer’s disease classification using various transfer learning CNN frameworks

ABSTRACT The current research explores the improvements in predictive performance and computational efficiency that machine learning and deep learning methods have made over time. Specifically, the application of transfer learning concepts within Convolutional Neural Networks (CNNs) has proved useful for diagnosing and classifying the various stages of Alzheimer’s disease. Using base architectures such as Xception, InceptionResNetV2, DenseNet201, InceptionV3, ResNet50, and MobileNetV2, this study extends these models by adding batch normalization (BN), dropout, and dense layers. These enhancements improve the model’s effectiveness and precision in addressing the specified medical issue. The proposed model is rigorously validated and evaluated using publicly available Kaggle MRI Alzheimer’s data consisting of 1280 testing images and 5120 patient training images. For comprehensive performance evaluation, precision, recall, F1-score, and accuracy metrics are utilized. The findings indicate that the Xception method is the most promising of those considered. Without employing five K-fold techniques, this model obtains a 99% accuracy and 0.135 loss score. In addition, integrating five K-fold methods enhances the accuracy to 99.68% while decreasing the loss score to 0.120. The research further included the evaluation of the Receiver Operating Characteristic Area Under the Curve (ROC-AUC) for various classes and models. As a result, our model may detect and diagnose Alzheimer’s disease quickly and accurately.

Closed-loop recyclable wood-plastic composite as a sustainable alternative to wood: A solution for utilizing wood waste and promoting carbon sequestration

Utilizing wood waste to construct high-performance materials and partially replace natural wood applications is a feasible solution to address the overconsumption of forest resources and enhance carbon sequestration. A closed-loop recyclable wood-plastic composite (WPC) with high wood waste flour (WF) content (70 wt%) was developed using poly(p-dioxanone) (PPDO) as the polymer component. The two components in WF/PPDO WPC form an interesting symbiotic relationship owing to their strong interactions. PPDO serves as “binder” for WF and integrates them as a whole, whilst WF protects PPDO from hydrolysis, therefore substantially prolonging service life. The comprehensive performance of the WF/PPDO WPC (such as WF content, mechanical properties, hardness, and water absorption) is comparable or better than natural wood or commercial WPC. Moreover, WF/PPDO can be closed-loop recycled almost 100 % to its initial raw materials. The relatively low carbon footprint of WF/PPDO (1.06 kg CO2e/kg of materials) was confirmed by life-cycle assessment (LCA). In addition, the facile preparation of WF/PPDO is scalable and widely applicable to other cellulose-containing waste.

Quantitative evaluation and optimization of synergistic regulation performance considering wear in short-term response of hybrid pumped storage systems

Amid the extensive integration of renewable energy sources and rapid development of variable-speed pumped storage systems (VPSSs), demanding regulation tasks introduce risks to hydraulic-mechanical systems, revealing a growing trade-off between wear and regulation. From the economic and safe operation perspective, this paper aims to accurately quantify and enhance comprehensive regulation performance of hybrid pumped hydro storage systems (HPHSSs) with VPSS. First, a generic framework is established to quantitatively evaluate synergistic regulation performance (SRP), combining regulation intensity and frequency suppression effect of each unit type. Next, a frequency-division coordinated control method is proposed based on the amplitude-frequency characteristics, designed to leverage the rapid response capabilities of VPSS, and the coupling mechanism between this method and the conventional speed limiter module in affecting system dynamics is elucidated. Finally, comprehensive evaluations of HPHSS under typical disturbance scenarios are conducted, with frequency-division parameter further optimized for different system configurations. The results indicate that HPHSS exhibits excellent global stability with the coordinated control, reducing magnitude of average frequency deviation from 10−3 to 10−4, greatly enhancing the SRP. Additionally, an optimal cut-off frequency is identified within 0.05–0.5 Hz, showing an increasing trend as VPSS proportion decreases. These findings provide a novel sight for the safe and optimized operation of HPHSS.

Self-Polycondensation Flux Synthesis of Ultrastable Olefin-Linked Covalent Organic Frameworks for Electrocatalysis.

Creating new functional materials that efficiently support noble metal catalysts is important and in high demand. Herein, we develop a self-polycondensation flux synthesis strategy that can produce olefin-linked covalent organic framework (COF) platforms with high crystallinity and porosity as the supports of Pd nanoparticles for electrocatalytic nitrogen reduction reaction (ENRR). A series of "two in one" monomers integrating aldehyde and methyl reactive groups are rationally designed to afford COFs with square-shaped pores and ultrahigh chemical stability (e.g., strong acid or alkali environments for >1 month). Functionalizing Fluorine significantly boosts the hydrophobicity of fluoro-functionalized COFs, which can inhibit the competing hydrogen evolution reaction (HER) and enhance ENRR performances. The COFs loading Pd nanoparticles show high NH3 production yields up to 90.0 ± 2.6 μg·h-1·mgcat.-1 and the faradaic efficiency of 44% at -0.2 V versus reversible hydrogen electrode, the best comprehensive performance among all reported COFs. Meanwhile, the catalysts are easy to recover and recycle, as demonstrated by their use for 15 cycles and 17 hours, with good performance retention. This work not only provides a new synthesis strategy for olefin-linked COFs, but also paves a new avenue for the design of highly efficient ENRR catalysts.

Experimental study of subcooled flow boiling in microchannel heat sinks integrated with different embedded pin fin arrays microstructures

The optimized microchannel heat sinks could enhance flow boiling for effectively tackling the electronics cooling. The flow boiling experiment for three microchannel heat sinks integrated with different layouts of entrenched pin fins is conducted at flow rate of 273.6–456 kg/(m2.s) and inlet subcooling of 35∼50 K. The overall/local heat transfer features, pressure drop and boiling mechanism are studied. The hybrid pattern presents earliest initial boiling and lower superheat than the microchannel heat sink with uniform pin fins arrangement at moderate and large flow rates. The trend of overall and local HTC (heat transfer coefficient) is similar, which occurs peak at onset nucleation boiling, and then decreasing with increasing heat flux. At the largest flow rate, the hybrid pattern exhibits 2.7–3.5 times peak HTC promotion than other patterns. As for lowest flow rate, the hybrid pattern does not manifest remarkably superior performance due to downstream vapor cores clogging effect. The hybrid pattern shows largest pressure drop, and the smaller inlet subcooling manifests inferior heat transfer and resistance performance. The comprehensive performance factor (CPF) is proposed, and the pattern with uniform small-sized pin fins shows optimal CPF especially for low flow rate, which is considerable compared with the reference heat sink structures until high heat flux. This study may provide some insight into the design of microchannel for flow boiling heat dissipation.

Multi-objective optimization and influence degree analysis of the thermally integrated HP-ORC carnot battery based on the orthogonal design method and grey relational analysis

Thermally integrated heat pump (HP)-organic Rankine cycle (ORC) Carnot battery is a promising solution for efficient energy storage and waste heat utilization. Firstly, in order to research the influence degree of the operating parameters on the system performance, single-objective optimization is conducted by the orthogonal design method. Then, multi-objective optimization is conducted by combining the grey relational analysis (GRA) and orthogonal design method to assess the comprehensive performance of the system. The importance order and contribution rates of the operating parameters and the optimal operating combinations are determined. Finally, in order to explore the influence mechanism of the parameters on the system performance, the influence of the parameters on the subsystems and the subsystems on the whole system is quantitatively analyzed. The results show that thermal storage temperature difference, waste heat temperature difference and cold source temperature are the most important parameters affecting the comprehensive performance of the whole system. Under the optimal operating conditions, round-trip efficiency, exergy efficiency, energy storage capacity (ESC), energy storage density (ESD) and levelized cost of storage (LCOS) are 47.05 %, 22.66 %, 1943 kW, 2.90 kWh/m3 and 0.38$/kWh, respectively. Compared to the ORC subsystem, the HP subsystem is more important to round-trip efficiency and exergy efficiency.

Piano performance evaluation dataset with multilevel perceptual features

This study aims to build a comprehensive dataset that enables the automatic evaluation of piano performances. In real-world piano performance, especially within the realm of classical piano music, we encounter a vast spectrum of performance variations. The challenge lies in how to effectively evaluate these performances. We must consider three critical aspects: (1) It is essential to gauge how performers express the music and how listeners perceive the performance, rather than focusing on the compositional characteristics of the musical piece. (2) Beyond fundamental elements like pitch and duration, we must also embrace higher-level features such as interpretation. (3) Such evaluation should be done by experts to discern the nuanced performances. Regrettably, there exists no dataset that addresses these challenging evaluation tasks. Therefore, we introduce a pioneering dataset PercePiano, annotated by music experts, with more extensive features capable of representing these nuanced aspects effectively. It encapsulates piano performance with a wide range of perceptual features that are recognized by musicians. Our evaluation benchmark includes a novel metric designed to accommodate the inherent subjectivity of perception. Furthermore, we propose an enhanced baseline framework that grounds performance on score data, aligning model predictions with human perception. Harnessing the aligned features enhances the baseline performance and proves to be adaptable to various model structures. In conclusion, our research opens new possibilities for comprehensive piano performance evaluation.

Fluorine-free biomimetic membranes with leaf vein-like and lotus leaf dual structure for high-performance waterproof and breathable textiles

Intelligent microporous membranes with excellent water resistance and moisture permeability have a huge market demand owing to their wide applications in personal protection, outdoor equipment and wearable electronics. However, the construction of such high-performance waterproof and moisture-permeable membranes (WBMs) based on a simple preparation process without post-coating treatment presents significant challenges. Herein, we present a simple strategy for constructing a fluorine-free polyethylene terephthalate (PET)-based WBM with a novel composite structure by combining a leaf vein-like nanofibrous substrate membrane and lotus leaf-inspired biomimetic nanofibrous skin using multi-needle electrospinning and heat treatment techniques. Surface hydrophobicity, along with the stable crosslinked structure of PET-based membranes, was achieved by in-suit doping of fluorine-free two-component reactive silicone rubber (PDMS). In addition, the small pore size and high porosity of the composite membranes were regulated by co-assembling PET/PDMS fibers and microspheres with different morphologies and structures. The resulting composite membranes based on substrate membrane-1:3 (BNFM-1:3) achieved excellent waterproof performance of 90.3 kPa, good moisture permeability of 5116 g m−2 d−1, and air permeability of 1.82 mm s−1. The WBMs based on novel composite structures are expected to be applied in various fields because of their ideal comprehensive performance.

Improving the Kepler optimization algorithm with chaotic maps: comprehensive performance evaluation and engineering applications

The Kepler Optimisation Algorithm (KOA) is a recently proposed algorithm that is inspired by Kepler’s laws to predict the positions and velocities of planets at a given time. However, although promising, KOA can encounter challenges such as convergence to sub-optimal solutions or slow convergence speed. This paper proposes an improvement to KOA by integrating chaotic maps to solve complex engineering problems. The improved algorithm, named Chaotic Kepler Optimization Algorithm (CKOA), is characterized by a better ability to avoid local minima and to reach globally optimal solutions thanks to a dynamic diversification strategy based on chaotic maps. To confirm the effectiveness of the suggested approach, in-depth statistical analyses were carried out using the CEC2020 and CEC2022 benchmarks. These analyses included mean and standard deviation of fitness, convergence curves, Wilcoxon tests, as well as population diversity assessments. The experimental results, which compare CKOA not only to the original KOA but also to eight other recent optimizers, show that the proposed algorithm performs better in terms of convergence speed and solution quality. In addition, CKOA has been successfully tested on three complex engineering problems, confirming its robustness and practical effectiveness. These results make CKOA a powerful optimisation tool in a variety of complex real-world contexts. After final acceptance, the source code will be uploaded to the Github account: nawal.elghouate@usmba.ac.ma.

Enhancing trajectory tracking accuracy in three-wheeled mobile robots using backstepping fuzzy sliding mode control

The rise in robotics technology has increased interest in ThreeWheeled Mobile Robots (TWMRs) due to their agility and adaptability across various applications. However, effectively controlling TWMRs presents a significant challenge owing to their inherent nonholonomic constraints, which restrict independent movement in all directions. Factors like sensor noise, nonlinear system dynamics, and uncertain system parameters also add to the complexity of controlling TWMRs. This research endeavors to enhance the precision of trajectory tracking in TWMRs. Specifically, it employs Backstepping Fuzzy Sliding Mode Control (BFSMC) with parameters optimized through Particle Swarm Optimization (PSO), coupled with the Extended Kalman Filter (EKF) for state estimation. The study conducts a comprehensive performance comparison between Backstepping Sliding Mode Control (BSMC) and Backstepping Fuzzy Sliding Mode Control(BFSMC) across various trajectory patterns, revealing substantial improvements in trajectory tracking accuracy with BFSMC. BFSMC demonstrates improvements in performance across various trajectory types when considering the integral time absolute error (IAE). Specifically, it achieves a 51.97% improvement for circular trajectories, an 82.09% improvement for infinity trajectories, and an 84.073% improvement for spiral trajectories. Moreover, BFSMC demonstrates superior robustness in the presence of disturbances, noise, parameter variations, and unmodeled dynamics compared to BSMC. Integrating the Extended Kalman Filter further improves accuracy, particularly in noisy conditions.

Leveraging Contrastive Language–Image Pre-Training and Bidirectional Cross-attention for Multimodal Keyword Spotting

In resource-limited keyword spotting scenarios, the scarcity of annotated corpora hinders deep learning’s ability to develop robust models for representing acoustic features. Recent studies focus on contrastive learning, using paired examples for self-supervision, indicating a growing research interest in keyword spotting. Typically, models are trained on segmented spoken words. However, the absence of distinct word boundaries in lengthy audio poses significant challenges. This study presents a novel approach that integrates Contrastive Language–Image Pre-Training with Cross-Attention for Self-Supervised Alignment in the field of Multimodal Keyword Spotting. The proposed method introduces a cross-modal process for matching word pairs, enhancing collaborative effectiveness between audio and text embeddings, achieving word-level congruence across different modalities. Employing a self-supervised learning approach, we use a restricted number of annotated audio–text pairs to discern semantic congruities and divergences, resulting in improved multimodal feature representation. Using this strategy, we input into subsequent keyword detection tasks, adopting a Bidirectional Cross-Attention block for detailed coordination across modalities, bridging the semantic gap between diverse audio and text representations, consequently improving keyword localization precision. Comprehensive experiments conducted on the Aishell-2 and Librispeech datasets demonstrate that the proposed method markedly surpasses existing techniques, especially regarding the comprehensive performance metric F1 for keyword spotting. It attains state-of-the-art outcomes for both pristine and noisy speech, showing respective enhancements of 5.2% and 5.4% on Aishell-2, as well as 5.4% and 5.6% on Librispeech.

Thermal performance of a novel hybrid heat pipe with composite heat transfer characteristics

Considering the capillary threshold of oscillating heat pipes (OHP), a novel hybrid heat pipe (HHP) was designed with channels with hydraulic diameters that alternately exceed and fall below this threshold, parallelly arrayed within a unified copper substrate. This technology attains a comprehensive heat transfer performance within the HHP, seamlessly transitioning from pure phase change heat transfer to hybrid pulsating flow heat transfer to effectively accommodate the escalating demands of heat load. Performance and visualization are used to analyze the operational characteristics of HHP. The results indicate that the HHP mainly transfers heat through the phase change in the large channels in lower heat input, while the working fluid in the small channels remains calm. Once the heat input is sufficient, the geyser boiling in the large channels and the liquid slug pulsating in the small channels jointly carry out heat transfer. Furthermore, the liquid content in the large and small channels can be adaptively adjusted to each other. The high-speed liquid in the small channels can be squeezed into the large channels to ensure the sufficient pulsating space and achieve efficient heat transfer under high heat input. In addition, the operation of the HHP can be significantly impacted by the filling ratio (FR). In the phase change stage, the FR of 47.6 % can be considered as the turning point. After transitioning to the hybrid pulsating flow stage, the HHP with low FR can get a better performance. This new type of heat pipe with gradually changing operation characteristics may be critical to solve thermal problems in variable heat load domain.

Raw rubber network of acid-free natural rubber and its application with solution-polymerized styrene butadiene rubber in green tires

Natural raw rubber was prepared by flash evaporation process (VNR), its raw rubber network was compared with that of acid-coagulated rubber (STR20), and the effects of the ratios of VNR and STR20 to SSBR on the comprehensive performance of green tire composites were investigated. The results showed that compared with 80SSBR composites, the tensile strength and tensile product coefficient of 80VNR composites were improved by 23 % and 94 %, and the Payne effect, compression heat generation, and rolling resistance were reduced by 39 %, 9 %, and 5 %, respectively. Compared with 40STR20/40SSBR composites, the tensile product coefficient, wear resistance, and wet skid resistance of 40VNR/40SSBR composites are increased by 11 %, 5 %, and 5 %, respectively, and the Payne effect, compression heat generation, and rolling resistance were reduced by 46 %, 11 %, and 5 %, respectively. This study increased the amount of natural rubber in the green tire formulations, improving the performance of composites while achieving sustainability.

Forest point cloud denoising method based on curvature and density

ABSTRACT When LiDAR scans woodlands, the three-dimensional (3D) point cloud data of woodlands directly collected usually contain a large number of noise points, which is caused by equipment performance, environmental interference, or other factors. The poor data quality of the point cloud data affects the accuracy of the extracted individual tree feature parameters and forest 3D reconstruction. To improve the quality of forest 3D point cloud data, a curvature- and density-based denoising method has been proposed in this study. First, the surface curvature features were calculated for the noise-dense trunk area of the forest for primary denoising, and the initial denoising results were used to construct the K-dimensional-Tree (KD-Tree) space index to search the spherical neighbourhood. The denoising was completed according to the proposed density calculation method (SN-NN, Spherical Neighbourhood and Nearest Neighbour). To evaluate the comprehensive performance of the proposed denoising method, a new denoising evaluation index has been designed by integrating the denoising precision and effective data retention degree of the forest point cloud data. The proposed method was validated based on forest point cloud data collected using Terrestrial LiDAR. The results show that the denoising precision and effective data retention degree of the proposed method are both preferable, which can effectively improve the quality of forest point cloud data.

Effect of functionalized halloysite nanotubes on fire resistance and water tolerance of intumescent fire-retardant coatings for steel structures

To improve the comprehensive performance of intumescent fire-retardant coatings, the functionalized halloysite nanotubes (HNTs-D, HNTs@Fe3O4, and HNTs-D@Fe3O4) were synthesized as synergistic flame retardants and incorporated into intumescent fire-retardant coatings (IFRC) for steel structures, demonstrating significant flame retardant properties. Among these modified halloysite nanosheets, HNTs-D@Fe3O4 showed the best flame retardancy and water resistance. Adding the optimal addition amount (3 wt%) of HNTs-D@Fe3O4 can increase the residual weight of coating from 28.10 % to 38.40 % and reduce the backside temperature of the coated steel plate from 288 °C to 177 °C, indicating that HNTs-D@Fe3O4 can effectively improve the thermal stability and insulation performances of intumescent fire-retardant coatings. Cone calorimetric analysis showed HNTs-D@Fe3O4 can effectively suppress the heat and smoke release of the intumescent fire-retardant coatings. Compared with the unmodified coating sample, the total heat release (THR) and total smoke release (TSR) of the HNTs-D@Fe3O4-containing coating sample were reduced by 66.04 % and 11.36 %. HNTs-D@Fe3O4 facilitates the formation of dense char layers confirmed by the micro morphology characterization. Further mechanism analysis shows that HNTs-D@Fe3O4 has obvious catalytic carbonization effect and is helpful to construct the dense expanded char layer with higher graphitization degree. The uniformly dispersed HNTs-D@Fe3O4 improved the water resistance of intumescent fire-retardant coatings, which is mainly due to the hydrophobicity and barrier effect, thus effectively preventing the loss of water-soluble components.

Effect of pulse current on the creep ageing behavior of pre-strained 2195 Al-Li alloy

Pre-strained aluminum lithium (Al-Li) alloy plates have ultra-high strength and comprehensive performance after aging treatments, and are widely used in the aerospace industry. The initial increase in dislocation density caused by these pre-strains will significantly affect the evolution of deformation, microstructure, and mechanical properties during the pulse current assisted creep ageing forming process. Therefore, the effect of pulse current on the creep ageing behavior of 2195 Al-Li alloy with different pre-strains (0%, 4%, and 8%) was investigated. As the pulse current increases within the first 1h creep aging process of the various pre-strain samples, the 1h creep strain significantly increases and the subsequent steady-state creep rate slightly reduce. Moreover, the larger the pre-strain, the higher the total creep strain. Pulse current promotes the movement and recovery of initial dislocations, and promotes the fine formation of T1 phase and suppresses the nucleation of θ' and phase, thereby improving mechanical properties. Compared with the conventional creep ageing process, the pulse current acting on pre-strained 2195 Al-Li alloy significantly increased the peak aged strengthen and shorten the peak aging time. Meanwhile, the peak time platform period has also been significantly extended. It is evident that the application of pulse current assisted creep ageing forming process to pre-strained 2195 Al-Li alloy plates can significantly improve formability and mechanical properties, enlarges the forming process window and realize collaborative manufacturing of Al-Li alloy component shape and performance.

Refinement of microstructure and improvement of mechanical properties of directed energy deposited titanium alloys by adding Fe

Elemental alloying is a validated strategy for enhancing the mechanical properties of directed energy deposited titanium alloys. However, it is still a challenge to obtain titanium alloys with both high strength and high ductility. In this work, to balance strength and ductility, the alloying element Fe was in-situ added to the TC4 melt pool during the directed energy deposited process and the effects of its content on the microstructure and mechanical performance of the TC4 alloy deposits were investigated. The results showed that the Fe additive ranging from 0.5 to 2.0wt.% were completely solid-solved in the retained β phase of TC4 alloys. Moreover, the primary β columnar grains were transformed into fully equiaxed grains. Meanwhile, the presence of Fe refined the α-laths, increased the proportion of the retained β phase, and the continuous grain boundary α (GB-α) became discontinuous. The tensile strength of the deposited alloys was continually improved with increasing Fe content, while the elongation had a peak value. When the Fe content was 1.0wt.%, the deposited alloy exhibited the best comprehensive performance, with an ultimate tensile strength of 1071MPa and an elongation of 9.3%. The enhancement of tensile strength was attributed to the solid solution strengthening and the refinement of α-laths and primary β grains. The increased proportion of retained β phase and the formation of discontinuous GB-α were beneficial for the alloy ductility. However, increased α/β interfaces inhibited dislocation movements, resulting in a decrease in ductility. The development of this alloy is important for aerospace and other fields that require high strength, high ductility and lightweight.

PAM material that instantly gives ordinary fabrics excellent flame retardant and thermal insulation properties for fire rescue

To effectively reduce the damage caused by flame burns or heat transfer to the human body during fire, we used PAM aqueous solution as the matrix, XG as the thickener, HPMC as the water-retaining agent to form the basic material system, and added different functional particles (APP, PTW, HCB) to prepare a fire-proof and heat-insulating PAM flame-retardant material for fire emergency rescue. Ordinary cotton fabrics were impregnated into PAM flame-retardant materials using a simple impregnation process. After the impregnation, the test was performed in a non-dropping state (simulating the thermal protection effect of PAM flame retardant materials directly acting on the outside of the human body at the fire scene). The results show that the PAM flame retardant material prepared by adding 4 wt% HCB has the best comprehensive performance. TPP test shows that spraying PAM flame retardant material on the outside of the fabric can instantly give the fabric a higher thermal protection performance. Under the total heat flux of 84 kW/m2, the thermal performance protection value of the fabric is 2641.8 kW·s/m2, and the second-degree burn time can reach 31.45 s. PAM flame retardant material does not damage the fabric. After soaping, the air permeability of the fabric decreases slightly, the moisture permeability and wettability are improved, and the breaking strength is almost unchanged. The CCT test showed that the thermal radiation flux was 50 KW/m2, the PHRR value of PAM flame retardant material was 10.64552 KW/m2, the THR was 6.9 MJ/m2, and the flame retardant performance was excellent. The PAM flame retardant material prepared in this project can be applied to the fire scene and directly sprayed on the outside of the clothing of rescuers and recipients, giving the fabric a better thermal protection effect. It can also be used to extinguish fires in the external environment. This material offers a novel solution for enhancing fire rescuers' and victims' safety protection levels.

Communication During Exertion: Language Production And Comprehension Performance During Exercise At The Ventilatory Threshold.

Communication During Exertion: Language Production And Comprehension Performance During Exercise At The Ventilatory Threshold.

Efficient security interface for high-performance Ceph storage systems

Ceph portrays a resilient clustered storage solution with supporting object, block, and file storage capabilities with no single point of failure. Despite these qualifications, data confidentiality defines a concern in the system, as authentication and access control are the only data protection security services in Ceph. CephArmor was proposed as a third-party security interface to protect data confidentiality by adding an extra protection layer to data at rest. Despite the added layer, the initial design of the API needed to be more efficient in addressing security and performance simultaneously. In this study, we propose a new architectural design to address the associated issues with the preliminary prototype. Comprehensive performance and security analysis verify the improvement of the proposed method compared to the initial approach. The benchmark result has indicated a 37% improvement on average in IOPS, elapsed time, and bandwidth for the write benchmark compared to the initial model.