Experimental Evaluation Research Articles

Microscopic augmented reality calibration with contactless line-structured light registration for surgical navigation.

The use of AR technology in image-guided neurosurgery enables visualization of lesions that are concealed deep within the brain. Accurate AR registration is required to precisely match virtual lesions with anatomical structures displayed under a microscope. The purpose of this work was to develop a real-time augmented surgical navigation system using contactless line-structured light registration, microscope calibration, and visible optical tracking. Contactless discrete sparse line-structured light point cloud is utilized to construct patient-image registration. Microscope calibration optimization with dimensional invariant calibrator is employed to enable real-time tracking of the microscope. The visible optical tracking integrates a 3D medical model with surgical microscope video in real time, generating an augmented microscope stream. The proposed patient-image registration algorithm yielded an average root mean square error (RMSE) of 0.78 ± 0.14mm. The pixel match ratio error (PMRE) of the microscope calibration was found to be 0.646%. The RMSE and PMRE of the system experiments are 0.79 ± 0.10mm and 3.30 ± 1.08%, respectively. Experimental evaluations confirmed the feasibility and efficiency of microscope AR surgical navigation (MASN) registration. By means of registration technology, MASN overlays virtual lesions onto the microscopic view of the real lesions in real time, which can help surgeons to localize lesions hidden deep in tissue.

Experimental evaluation of DC-DC buck converter based on adaptive fuzzy fast terminal synergetic controller

This study suggests an enhanced version of the adaptive fuzzy fast terminal synergetic controller (AF-FTSC) for controlling the uncertain DC/DC buck converter based on the synergetic theory of control (STC) and newly developed terminal attractor technique (TAT). The benefits of the proposed SC algorithm involve the features of finite-time convergence, unaffected by parameter variations, and chattering-free phenomenon. A type-1 fuzzy logic system (T1-FLS) make the considered controller more robust and is utilized to estimate the undefined converter nonlinear dynamics without resorting to the usual linearization and simplifications of the converter model. Taking a switching DC-DC buck converter as a demonstration, the suggested AF-FTSC is thoroughly analyzed and executed on a dSPACE ds1103 controller board. The outcomes of the experiment confirm the competence and applicability of the suggested regulator.

Mechanically Robust Bismuth-Embedded Carbon Microspheres for Ultrafast Charging and Ultrastable Sodium-Ion Batteries.

Advancements in the development of fast-charging and long-lasting microstructured alloying anodes with high volumetric capacities are essential for enhancing the operational efficiency of sodium-ion batteries (SIBs). These anodes, however, face challenges such as declined cyclability and rate capability, primarily due to mechanical degradation reduced by significant volumetric changes (over 252%) and slow kinetics of sodium-ion storage. Herein, we introduce a novel anode design featuring densely packed bismuth (Bi) embedded within highly conductive carbon microspheres to overcome the aforementioned challenges. Remarkably, the high loading Bi anode within carbon microspheres with a high tap density of 2.59 g cm-3 possesses significant mechanical strength exceeding 590 MPa and limits volume swelling of only 10.9% post-sodiation. This anode demonstrates a high volumetric capacity (908.3 mAh cm-3), ultrafast chargeability (200 A g-1, full charge/discharge in just 5.5 s), and outstanding cyclability over 12,000 cycles and maintains exceptional cycling stability even at -30 °C. The full cell paired with a Na3V2(PO4)3 cathode retains over 80% capacity after 600 cycles at 36 C, demonstrating a remarkable rate capability of 126 C (full charge/discharge in 28.6 s). Our comprehensive experimental evaluations and chemo-mechanical simulations shed light on the mechanisms underpinning the anode's superior performance. This development marks a significant advancement in the design of durable and fast-charging anodes for high-performance SIBs.

Surface State Control of Apatite Nanoparticles by pH Adjusters for Highly Biocompatible Coatings.

Apatite nanoparticles are biocompatible nanomaterials, so their film formation on biodevices is expected to provide effective bonding with living organisms. However, the biodevice-apatite interfaces have not yet been elucidated because there is little experimental evaluation and discussion on the nanoscale interactions, as well as the apatite surface reactivities. Our group has demonstrated the biomolecular adsorption properties on a quartz crystal microbalance with dissipation (QCM-D) sensor coated with apatite nanoparticles, demonstrating the applicability of apatite nanoparticle films on devices. Therefore, it is important to clarify the biodevice-apatite nanointerfaces by characterizing their physicochemical properties. This research aims to control the apatite nanoparticle surfaces using different types of pH adjusters as well as to investigate biodevice-apatite interfaces. In this study, tetramethylammonium hydroxide, sodium hydroxide, and potassium hydroxide were used to adjust the pH during the synthesis of apatite nanoparticles. As a result, it was found that the ratio of Ca-deficient hydroxyapatite phase to B-type carbonate ion-substituted hydroxyapatite phase could be controlled by adjusting the OH- concentration and that the formation of B-type carbonate ion substituted hydroxyapatite phase was demonstrated in terms of the charge compensation because hydrogen phosphate ion in the non-apatitic layer would be diffused and substituted inside the apatite core structure by the replacement of carbonate ion. By contrast, the phosphate ions in the core structure were moved and contained in the non-apatitic layer, and the proportion of phosphate ions in the non-apatitic layer increased. The surface changes of the nanoparticles provide an effective biodevice surface coating. It was observed that the thickness of the electrophoretically deposited nanoparticles clearly increased with the proportion of phosphate ions in the non-apatitic layer. Furthermore, the formation of the hydration layer with immersion in biological fluid was evaluated. It was inferred that the water molecules in the hydration layer interacted with the substituted ions and remained as nonfreezing water layer on the top surface of the nanoparticles, while the abundant phosphate ions newly interacted with the water molecules in the non-apatitic layer, thus increasing the proportion of intermediate water. These results indicated that the hydrogen phosphate and phosphate ions were retained in the non-apatitic layer on the top surfaces of apatite nanoparticles, so that the thickness of the electrophoretically deposited film and the weight fraction of the hydrated layer can be controlled by the component ratio of phosphate ions in the non-apatitic layer. It is expected that surface coating technology using apatite nanoparticles will be applied for biodevices.

Research on the construction of intelligent-aided design platform for green buildings

While socio-economic development and technological advancement bring great convenience to our life, energy consumption and environmental pollution have also become the core issues affecting human survival and development. This study focuses on the development of an intelligent design platform for green buildings, aimed at promoting sustainability through low energy consumption and reduced environmental impact. The study of existing national standards and requirements for the intelligent design of green buildings, combined with the unique characteristics of green buildings and user needs, has led to the development of an optimized auxiliary design platform system. By incorporating algorithm optimization and modeling techniques, this platform enhances the design process. Experimental evaluations of the system reveal that the intelligent auxiliary design offers more accurate predictions, thereby improving user service and experience. Additionally, the system contributes to energy efficiency and environmental sustainability by reducing the building’s ecological footprint. This research highlights the potential of intelligent systems to facilitate more efficient and environmentally friendly green building designs.

Gold Nanoparticle Enhanced Polyurethane Elastomer with High-Efficiency Self-healing and Potent Mechanical Property

Thermoplastic polyurethanes (TPUs) are renowned for their superior mechanical properties, including elasticity, durability, and abrasion resistance, which make them indispensable in industries such as automotive, construction, and electronics. However, conventional TPUs often lack self-healing capabilities, limiting their lifespan and increasing maintenance costs. This study addresses this limitation by developing a novel TPU elastomer (PUAN-1) that incorporates gold nanoparticles (AuNPs) and disulfide bonds to enhance both mechanical strength and self-healing efficiency. The AuNPs serve as a reinforcing phase, improving the TPU's mechanical performance by strengthening the hard segments and promoting dynamic disulfide bond reformation through photothermal effects. This innovative approach enables the TPU to achieve rapid self-healing under mild conditions while maintaining excellent mechanical properties. Experimental evaluations demonstrated that PUAN-1 exhibited an ultimate tensile strength of 4.4 MPa, an elongation at break of 1165%, and a toughness of 39.7 MJ/m, significantly outperforming conventional TPUs. Additionally, PUAN-1 displayed a high self-healing efficiency, with over 88% recovery in mechanical properties within two hours of damage. Conversely, a higher concentration of AuNPs, as in PUAN-2, was found to hinder both self-healing and mechanical performance due to restricted chain mobility and suppressed microphase separation. These findings highlight the critical balance required in optimizing AuNP content to maximize both reinforcement and flexibility. The developed TPU elastomer presents a promising candidate for applications demanding durable, self-repairing materials, such as wearable electronics, flexible sensors, and biomedical devices. This study provides valuable insights into the design of advanced TPUs that combine high mechanical robustness with effective self-healing capabilities, offering significant potential for use in dynamic and demanding environments.

Decentralized Federated Learning with Prototype Exchange

As AI applications become increasingly integrated into daily life, protecting user privacy while enabling collaborative model training has become a crucial challenge, especially in decentralized edge computing environments. Traditional federated learning (FL) approaches, which rely on centralized model aggregation, struggle in such settings due to bandwidth limitations, data heterogeneity, and varying device capabilities among edge nodes. To address these issues, we propose PearFL, a decentralized FL framework that enhances collaboration and model generalization by introducing prototype exchange mechanisms. PearFL allows each client to share lightweight prototype information with its neighbors, minimizing communication overhead and improving model consistency across distributed devices. Experimental evaluations on benchmark datasets, including MNIST, CIFAR-10, and CIFAR-100, demonstrate that PearFL achieves superior communication efficiency, convergence speed, and accuracy compared to conventional FL methods. These results confirm PearFL’s efficacy as a scalable solution for decentralized learning in heterogeneous and resource-constrained environments.

Inactivation of S. mutans dental biofilm by using Cold Atmospheric Plasma

Abstract S. mutans cells in oral cavity could adhere to surfaces of the teeth form the bacterial biofilm, strongly resist daily cleaning and brushing. The growth of the bacterial protected by the biofilm may lead to dental plaque formation and tooth decay. Cold Atmospheric Plasma (CAP) generated from a capillary-guided corona discharge is investigated for the inactivation of Streptococcus mutans biofilm associated with dental caries. The plasma device was optimised to generate a 4-mm plasma plume while maintaining a temperature near to ambient. The heat generated was not susceptible to thermal damage for the biofilm, as well as the oral cavity. Through experimental evaluations, the gaseous plasma plume from the CAP with a treatment durations of up to 15 minutes were responsible in reducing the S. mutans biofilm. The highest removal was achieved after 15-minute of CAP treatment. The results shown that a log-reduction of 3.08 of the cells in the S. mutans biofilm was killed during the treatment. The 10-minute treatment demonstrated a high value of log reduction at 1.79 compared to the control well. The efficacy is found to be strongly correlated to treatment duration whereby at the minimum duration of 1 minute treatment resulted a log reduction of 0.23. The outcomes of this research highlight CAP as a promising and safe approach for combating S. mutans biofilm. By offering a non-thermal alternative to traditional antimicrobial methods, CAP presents an opportunity to minimize harm to healthy tissues while effectively targeting biofilm.

Enhancing Precipitation Nowcasting Through Dual-Attention RNN: Integrating Satellite Infrared and Radar VIL Data

Traditional deep learning-based prediction methods predominantly rely on weather radar data to quantify precipitation, often neglecting the integration of the thermal processes involved in the formation and dissipation of precipitation, which leads to reduced prediction accuracy. To address this limitation, we introduce the Dual-Attention Recurrent Neural Network (DA-RNN), a model that combines satellite infrared (IR) data with radar-derived vertically integrated liquid (VIL) content. This model leverages the fundamental physical relationship between temperature and precipitation in a predictive framework that captures thermal and water vapor dynamics, thereby enhancing prediction accuracy. The results of experimental evaluations on the SEVIR dataset demonstrate that the DA-RNN model surpasses traditional methods on the test set. Notably, the DA-TrajGRU model achieves reductions in mean squared error (MSE) and mean absolute error (MAE) of 30 (9.3%) and 89 (6.4%), respectively, compared with those of the conventional TrajGRU model. Furthermore, our DA-RNN exhibits robust false alarm rates (FAR) for various thresholds, with only slight decreases in the critical success index (CSI) and Heidke skill score (HSS) when increasing the threshold. Additionally, we present a visualization of precipitation nowcasting, illustrating that the integration of multiple data sources effectively avoids overestimation of VIL values, further increasing the precision of precipitation forecasts.

Feasibility study of patient-specific four-dimensional in vivo tracking system for high-dose-rate brachytherapy: Experimental evaluation.

High-dose-rate (HDR) brachytherapy using Iridium-192 as a radiation source is widely employed in cancer treatment to deliver concentrated radiation doses while minimizing normal tissue exposure. In this treatment, the precision with which the sealed radioisotope source is delivered significantly impacts clinical outcomes. This study aims to evaluate the feasibility of a new four-dimensional (4D) in vivo source tracking and treatment verification system for HDR brachytherapy using a patient-specific approach. A hardware system was developed for the experiments, featuring a high-resolution compact gamma camera with a redesigned diverging collimator, enhanced detector, and precision control system. The collimator was redesigned to improve spatial resolution by reducing the hole size and increasing the hole array, while reducing the pixel size of the detector and increasing the number of pixels. The performance was evaluated using Monte Carlo simulations, which demonstrated significant improvements in spatial resolution. Experiments were conducted in a controlled setup using a phantom to simulate clinical conditions. The phantom was positioned at various distances from the gamma camera (327.30, 377.30, and 427.30mm) and imaged at multiple angles. The accuracy of the system was tested in four different cases: three with fixed distances and one employing a multi-focusing method. The multi-focusing method allows the gamma camera to adjust its focus based on the anatomical characteristics of individual patients, thereby enhancing source-tracking accuracy. The performance of the system was evaluated under these four different scenarios. The Euclidean distance and three-dimensional gamma analysis were used to evaluate tracking accuracy and dose distribution. The redesigned collimator demonstrated significant improvements (compared to the previous design) in the spatial resolution of the gamma camera, showing 34.21% and 23.46% enhancements in the horizontal and vertical profiles, respectively. These improvements in gamma camera resolution are crucial for enhancing the tracking system's accuracy. The experimental results demonstrated varying degrees of accuracy across different cases, reflecting the performance of the system under different conditions. The average Euclidean distance errors were Case 1 (327.30mm): 1.358mm; Case 2 (377.30mm): 1.731mm; Case 3 (427.30mm): 1.973mm; and Case 4 (multi-focusing): 1.527mm. The gamma pass rates for the four cases were:- Case 1: 86.39%; Case 2: 75.28%; Case 3: 72.22%; and Case 4: 81.67% (1mm/1%). For the 2mm/2% criterion, the gamma pass rates were 97.11, 94.72, 92.38, and 96.78% for Cases 1, 2, 3, and 4, respectively. Case 4 (multi-focusing) showed an improvement over Case 3, with a 22.6% reduction in the average Euclidean distance error and a 13.1% increase in the gamma pass rate (1mm/1%). These results demonstrate that the new 4D in vivo source tracking and treatment verification system for HDR brachytherapy is feasible and has potential clinical benefits.

Apnet: Lightweight network for apricot tree disease and pest detection in real-world complex backgrounds

Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques. Despite this, the academic realm currently lacks extensive, realistic datasets and deep learning strategies specifically crafted for apricot trees. This study introduces ATZD01, a publicly accessible dataset encompassing 11 categories of apricot tree pests and diseases, meticulously compiled under genuine field conditions. Furthermore, we introduce an innovative detection algorithm founded on convolutional neural networks, specifically devised for the management of apricot tree pests and diseases. To enhance the accuracy of detection, we have developed a novel object detection framework, APNet, alongside a dedicated module, the Adaptive Thresholding Algorithm (ATA), tailored for the detection of apricot tree afflictions. Experimental evaluations reveal that our proposed algorithm attains an accuracy rate of 87.1% on ATZD01, surpassing the performance of all other leading algorithms tested, thereby affirming the effectiveness of our dataset and model. The code and dataset will be made available at https://github.com/meanlang/ATZD01.

Datis: data augmentation for trust intensity prediction in incomplete signed networks

Prediction of trust and distrust in nodes in signed network analysis is an important task with diverse applications. However, the presence of imbalanced and incomplete rankings in signed networks makes prediction of node-level trust values using machine learning (ML) methods a very challenging task. To overcome these challenges, we introduce DATIS, an innovative approach employing generative adversarial networks (GANs) for data augmentation in node-level trust prediction tasks in signed networks. DATIS addresses imbalances in both sign and value of rankings, handling missing rankings by training on nodes’ local and global network features without explicit information on edge rankings assigned to nodes. Unlike existing methods, we consider the trust prediction problem as a regression task to imply the strength of trust a node gained in a network. Our experimental evaluation shows that DATIS can significantly improve the accuracy of node-level trust intensity prediction on real-world datasets.

A Case Study of a Solar Oven’s Efficiency: An Experimental Approach

This research presents the design, construction, and experimental evaluation of a novel box-type solar oven optimized for enhanced thermal efficiency and heat retention, developed to address the challenges of sustainable cooking in temperate climates. The solar oven, measuring 120 cm × 60 cm × 45 cm, incorporates strategically designed rock wool insulation and 5 kg of steel plates as thermal mass, along with a double-glazed glass cover tilted at an experimentally optimized angle of 15° relative to the horizontal plane. Extensive experimental testing was conducted in Viseu, Portugal (40° N latitude) under varying meteorological conditions, including solar irradiance levels ranging from 400 to 900 W/m2 and wind speeds of up to 3 m/s. The results demonstrated that the oven consistently achieved internal temperatures exceeding 160 °C, with a peak temperature of 180 °C, maintaining cooking capability even during periods of intermittent cloud cover. Quantitative analysis showed that the thermal efficiency of the oven reached a peak of 38%, representing a 25–30% improvement over conventional designs. The incorporation of thermal mass reduced temperature fluctuations by up to 40%, and the enhanced insulation reduced conductive heat loss by approximately 30%. Cooking tests validated the oven’s practical effectiveness, with the successful preparation of various foods including rice (90 min), cake (120 min), vegetables (60 min), and bread (110 min). This study provides comprehensive performance data under different meteorological conditions, including detailed temperature profiles, heating rates, and thermal efficiency measurements. By addressing key limitations of prior models, particularly the challenge of temperature stability during variable solar conditions, the proposed solar oven offers a cost-effective, efficient solution that can be adapted for use in diverse climates and regions, with particular relevance to areas seeking sustainable alternatives to traditional cooking methods.

Enhanced buck-boost converter performance using a snubber circuit and advanced semiconductor devices

Conventional buck-boost converter designs often suffer from efficiency losses and performance degradation due to switching transients and voltage spikes. To address these challenges, we propose the incorporation of a snubber circuit, which is designed to absorb and mitigate these detrimental effects. The study involves the design, implementation, and experimental evaluation of the proposed enhanced buck-boost converter. The analysis focuses on the impact of the snubber circuit on key performance metrics, including efficiency, thermal behavior, and electromagnetic interference (EMI) reduction. The experimental results indicate that the snubber circuit significantly reduces voltage spikes and switching losses, leading to improved converter efficiency and reliability. These findings provide valuable insights for optimizing converter designs in a variety of applications, from renewable energy systems to portable electronic devices, ultimately contributing to the advancement of power electronics technology. Finally, the performance of the proposed converter is analyzed using advanced semiconductor devices.

Crisscross Moss Growth Optimization: An Enhanced Bio-Inspired Algorithm for Global Production and Optimization.

Global optimization problems, prevalent across scientific and engineering disciplines, necessitate efficient algorithms for navigating complex, high-dimensional search spaces. Drawing inspiration from the resilient and adaptive growth strategies of moss colonies, the moss growth optimization (MGO) algorithm presents a promising biomimetic approach to these challenges. However, the original MGO can experience premature convergence and limited exploration capabilities. This paper introduces an enhanced bio-inspired algorithm, termed crisscross moss growth optimization (CCMGO), which incorporates a crisscross (CC) strategy and a dynamic grouping parameter, further emulating the biological mechanisms of spore dispersal and resource allocation in moss. By mimicking the interwoven growth patterns of moss, the CC strategy facilitates improved information exchange among population members, thereby enhancing offspring diversity and accelerating convergence. The dynamic grouping parameter, analogous to the adaptive resource allocation strategies of moss in response to environmental changes, balances exploration and exploitation for a more efficient search. Key findings from rigorous experimental evaluations using the CEC2017 benchmark suite demonstrate that CCMGO consistently outperforms nine established metaheuristic algorithms across diverse benchmark functions. Furthermore, in a real-world application to a three-channel reservoir production optimization problem, CCMGO achieves a significantly higher net present value (NPV) compared to benchmark algorithms. This successful application highlights CCMGO's potential as a robust and adaptable tool for addressing complex, real-world optimization challenges, particularly those found in resource management and other nature-inspired domains.

Novel deep neural network architecture fusion to simultaneously predict short-term and long-term energy consumption.

Energy is integral to the socio-economic development of every country. This development leads to a rapid increase in the demand for energy consumption. However, due to the constraints and costs associated with energy generation resources, it has become crucial for both energy generation companies and consumers to predict energy consumption well in advance. Forecasting energy needs through accurate predictions enables companies and customers to make informed decisions, enhancing the efficiency of both energy generation and consumption. In this context, energy generation companies and consumers seek a model capable of forecasting energy consumption both in the short term and the long term. Traditional models for energy prediction focus on either short-term or long-term accuracy, often failing to optimize both simultaneously. Therefore, this research proposes a novel hybrid model employing Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional LSTM (Bi-LSTM) to simultaneously predict both short-term and long-term residential energy consumption with enhanced accuracy measures. The proposed model is capable of capturing complex temporal and spatial features to predict short-term and long-term energy consumption. CNNs discover patterns in data, LSTM identifies long-term dependencies and sequential patterns and Bi-LSTM identifies complex temporal relations within the data. Experimental evaluations expressed that the proposed model outperformed with a minimum Mean Square Error (MSE) of 0.00035 and Mean Absolute Error (MAE) of 0.0057. Additionally, the proposed hybrid model is compared with existing state-of-the-art models, demonstrating its superior performance in both short-term and long-term energy consumption predictions.

Adaptive Oversampling via Density Estimation for Online Imbalanced Classification

Online learning is a framework for processing and learning from sequential data in real time, offering benefits such as promptness and low memory usage. However, it faces critical challenges, including concept drift, where data distributions evolve over time, and class imbalance, which significantly hinders the accurate classification of minority classes. Addressing these issues simultaneously remains a challenging research problem. This study introduces a novel algorithm that integrates adaptive weighted kernel density estimation (awKDE) and a conscious biasing mechanism to efficiently manage memory, while enhancing the classification performance. The proposed method dynamically detects the minority class and employs a biasing strategy to prioritize its representation during training. By generating synthetic minority samples using awKDE, the algorithm adaptively balances class distributions, ensuring robustness in evolving environments. Experimental evaluations across synthetic and real-world datasets demonstrated that the proposed method achieved up to a 13.3 times improvement in classification performance over established oversampling methods and up to a 1.66 times better performance over adaptive rebalancing approaches, while requiring significantly less memory. These results underscore the method’s scalability and practicality for real-time online learning applications.

Concrete Block Paving Tested as Articulated Slabs

This paper describes the experimental evaluation of articulated slabs composed of concrete block paving laid and compacted within a steel frame. By testing the paving in isolation from the bedding sand, basecourse and other elements of a complete block pavement it proved feasible to study the load spreading and other structural characteristics of the pavers themselves. This provided insights into the structural behaviour of concrete block paving under load. Factors studied in the project included the effects of paver shape, laying pattern, joint width, spacer nibs and load repetition. Each of these factors was shown to significantly influence the structural capacity of the paving course. By analysing the paver performance as a quasi-elastic slab it was possible to quantify the behaviour of block paving in a form suitable for inclusion in mechanistic pavement design and analysis. Where appropriate these measures were compared with data obtained both from Accelerated Trafficking Tests and Falling Weight Deflection studies of full scale block pavements.

Automated Tomato Defect Detection Using CNN Feature Fusion for Enhanced Classification

Tomatoes are among the most widely cultivated and consumed vegetable crops worldwide. They are usually harvested in large quantities that need to be promptly and accurately classified into healthy and defective categories. Traditional methods for tomato classification are labor-intensive and prone to human error. Therefore, this study proposes an approach that leverages feature fusion from two pre-trained convolutional neural networks (CNNs), VGG16 and ResNet-50, to enhance classification performance. A comprehensive evaluation of multiple individual and hybrid classifiers was conducted on a dataset of 43,843 tomato images, which is heavily imbalanced toward the healthy class. The results showed that the best-performing classifier on fused features achieved an average precision (AP) and accuracy of 0.92 and 0.97, respectively, on the test set. In addition, the experimental evaluation revealed that fused features improved classification performance across multiple metrics, including accuracy, AP, recall, and F1-score, compared to individual features of VGG16 and ResNet-50. Furthermore, the proposed approach was benchmarked against three standalone CNN models, namely MobileNetV2, EfficientNetB0, and DenseNet121, and demonstrated superior performance in all evaluated metrics. These findings highlight the efficacy of deep feature fusion in addressing class imbalance and improving automated tomato defect detection.

The activity regulation of lipase from Aspergillus fumigatus by ligand through allosteric exploration.

Lipase activity from Aspergillus fumigatus (AFL) were modulated by an effector (HMD) that was discovered for an allosteric site on the bioactive macromolecule. Experimental evaluation and computational modeling of allosteric effects revealed alterations in the structure of AFL. It was found that AFL's activity in HMD solution increased by approximately 46 % due to mainly enhanced lid mobility. HMD-AFL interaction was driven by enthalpy and entropy. However, when AFL was coupled to HMD-modified microspheres (PS-HMD-p), its hydrolysis activity decreased by ~14.3 % due to reduced lid mobility. After immobilization, AFL's ester-synthesis activity also decreased, due to changes in the conformational dynamics and the geometric characteristics of active site. Investigating the structural dynamics of allosteric regulation of the lipase not only reveals its structural changes underlying the functional variation but also enhances the understanding of the allosteric property that is underappreciated in exoenzymes.