High Level Of Efficiency Research Articles

Ultra-broadband achromaticity of metalens with low-relative phase enabled by wide-band fusion

Metalenses have a high design degree of freedom in controlling the light field and excellent performance in chromatic aberration elimination. However, in designing ultra-broadband achromatic metalenses, integrating multiple types of unit structures is necessary to compensate for phase differences caused by different incident wavelengths. Here, we propose an ultra-broadband achromatic metalens composed only of a single type of square nano-pillar. which controls the entire operating band by utilizing three wide-band fusions, and have characteristics depending on the phase coverage and relative phase. In the operational range of 2–5 μm, the achromatic metalens demonstrates a maximum focal shift of 2.1 μm. The average focal shift is 0.98 %, the average NA value is 0.35, with an average relative phase of 0.77π. The average transmittance and focus efficiency are 94.17 % and 58.7 %, respectively. This broad-spectrum fusion design strategy simplifies manufacturing complexity while maintaining high focusing efficiency and transmittance levels throughout the entire operational bandwidth. This design approach can improve image resolution and quality by minimizing chromatic aberration.

Efficiency analysis in bi-level on fuzzy input and output

To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model’s parameters.

Efficient Elimination of Inorganic/Organic Pollutants by Fe3O4/biochar@sodium Alginate Gel Beads.

The magnetic composite gel bead (Fe3O4-C@SA GB) adsorbent was prepared by sodium alginate (SA) crosslinking with pitaya peel-derived porous carbons (PPDPCs) and magnetic iron oxide nanoparticles (Fe3O4 NPs). The adsorption effects of Fe3O4-C@SA GBs on heavy metal ions (HMIs) and 17 β-estradiol (E2) in water are evaluated by classical kinetic models and isotherm models. The pseudo-second-order kinetic model shows that Fe3O4-C@SA GBs have maximum adsorption capacities of 9.62, 7.50, and 13.61 mg/g for Cu (II), Cd (II), and Pb (II), respectively. Meanwhile, the highest adsorption performance of the synthesized gel beads to E2 is of ca. 276.3 mg/g. In addition, the Fe3O4-C@SA GBs can still maintain a high level of adsorption efficiency after five adsorption cycles, displaying economic efficiency and reusability. Hence, this work provides useful insights into the efficient adsorption elimination of pollutants in sewage and the corresponding adsorption mechanisms.

Internet of Things-Driven Precision in Fish Farming: A Deep Dive into Automated Temperature, Oxygen, and pH Regulation

The research introduces a revolutionary Internet of Things (IoT)-based system for fish farming, designed to significantly enhance efficiency and cost-effectiveness. By integrating the NodeMcu12E ESP8266 microcontroller, this system automates the management of critical water quality parameters such as pH, temperature, and oxygen levels, essential for fostering optimal fish growth conditions and minimizing mortality rates. The core of this innovation lies in its intelligent monitoring and control mechanism, which not only supports accelerated fish development but also ensures the robustness of the farming process through automated adjustments whenever the monitored parameters deviate from desired thresholds. This smart fish farming solution features an Arduino IoT cloud-based framework, offering a user-friendly web interface that enables fish farmers to remotely monitor and manage their operations from any global location. This aspect of the system emphasizes the importance of efficient information management and the transformation of sensor data into actionable insights, thereby reducing the need for constant human oversight and significantly increasing operational reliability. The autonomous functionality of the system is a key highlight, designed to persist in adjusting the environmental conditions within the fish farm until the optimal parameters are restored. This capability greatly diminishes the risks associated with manual monitoring and adjustments, allowing even those with limited expertise in aquaculture to achieve high levels of production efficiency and sustainability. By leveraging data-driven technologies and IoT innovations, this study not only addresses the immediate needs of the fish farming industry but also contributes to solving the broader global challenge of protein production. It presents a scalable and accessible approach to modern aquaculture, empowering stakeholders to maximize output and minimize risks associated with fish farming, thereby paving the way for a more sustainable and efficient future in the global food supply.

A novel ensemble approach with deep transfer learning for accurate identification of foodborne bacteria from hyperspectral microscopy

The detection of foodborne bacteria is critical in ensuring both consumer safety and food safety. If these pathogens are not properly identified, it can lead to dangerous cross-contamination. One of the most common methods for classifying bacteria is through the examination of Hyperspectral microscope imaging (HMI). A widely used technique for measuring microbial growth is microscopic cell counting. HMI is a laborious and expensive process, producing voluminous data and needing specialized equipment, which might not be widely available. Machine learning (ML) methods are now frequently utilized to automatically interpret data from hyperspectral microscopy. The objective of our study is to devise a technique that employs deep transfer learning to address the challenge of limited data and utilizes four base classifiers - InceptionResNetV2, MobileNet, ResNet101V2, and Xception - to create an ensemble-based classification model for distinguishing live and dead bacterial cells of six pathogenic strains. In order to determine the optimal weights for the base classifiers, a Powell's optimization method was utilized in conjunction with a weighted average ensemble (WAVE) technique. We carried out an extensive experimental study to verify the efficiency of our proposed ensemble model on live and dead cell images of six different foodborne bacteria. In order to gain a better understanding of the regions, we performed a Grad-CAM analysis to explain the predictions made by our model. Through a series of experiments, our proposed framework has proven its capacity to effectively and precisely detect numerous bacterial pathogens. Specifically, it achieved a perfect identification rate of 100 % for Escherichia coli (EC), Listeria innocua (LI), and Salmonella Enteritidis (SE), while achieving rates of 96.30 % for Salmonella Typhimurium (ST), 87.13 % for Staphylococcus aureus (SA), and 94.12 % for Salmonella Heidelberg (SH). As a result, it can be considered as an effective tool for the identification of foodborne pathogens, due to its high level of efficiency.

Numerical implementation of electrostatic solver within OpenFOAM for GT intake filtration

Abstract Gas turbines require filtered air to preserve operability, availability, and efficiency. Fouling, corrosion, and erosion severely affect the performance of the axial compressor and it can be in part prevented by filtering the air intake. For this reason, it is possible to use multiple filter stages in series to obtain a high level of capture efficiency. Traditionally, separation is achieved using porous material cartridges, which guarantee a high level of filtration but with an increasing pressure drop over time. Another common type of filter is the inertial one, whose operating principle is based on the inertial force acting on the solid particles to separate them from the mainstream. The capturing efficiency of the inertial filter is not comparable with that of the porous one, so it is used upstream as a pre-filter. In recent years, another technique has been developed to filter air flows thanks to an electrostatic field induced inside the flow. In the present work, numerical investigations are carried out to simulate the effect of the electrostatic force on gas turbine filtering systems. The CFD tool used is OpenFOAM, whose official release does not contain a library able to simulate the electrostatic force acting on the discrete phase. A new library was developed to calculate: the electric field, the electric potential, and the charge density of the continuous phase. Simulation results show how the calculation of these quantities allows us to predict the electrostatic force acting on particle flows in the presence of electrostatic fields

ANALYSIS OF THE REGULATORY AND METHODOLOGICAL APPARATUS FOR CARRYING OUT TESTS OF ARMORED TANK AND VEHICLE EQUIPMENT TO THE INFLUENCE OF EXTERNAL MECHANICAL AND CLIMATE FACTORS

In the work is carried out an analytical study of normative and methodical support of the processes of testing armored and automobile vehicles to the influence of climatic and mechanical factors. Unsolved aspects of the testing are highlighted. The advantages and disadvantages of conducting tests of military equipment and its components on the influence of external mechanical and climatic factors have been identified for national standards of Ukraine and national standards harmonized with international standards in order to form the direction of further improvement of the regulatory and methodical apparatus to increase the accuracy and reliability of equipment evaluation in operating conditions. The need to improve test methods based on increasing the accuracy and reliability of equipment evaluation during laboratory tests is substantiated. The provisions of foreign military standards, which are used in Ukraine and the world as basic – STANAG 4370, MIL-STD-810 were analyzed. Based on the analysis of these standards, which include requirements for conducting tests, methods, procedures and acceptance criteria for equipment in operational conditions, the direction and method of improving test methods is determined. Prospective directions for ensuring standardization in the process of testing are outlined, in which there is a high need to maintain a high level of reliability and efficiency of armored and automobile vehicles in operational conditions. The need to adapt standards to modern challenges and threats has been established by optimizing test methods in conditions that are as close as possible to real ones, as well as using the latest technologies to improve the processes of monitoring the state of equipment.

A numerical analysis of isolated propeller noise using a lattice Boltzmann method approach

Current environmental needs to achieve carbon neutral commercial aviation by 2050 require the development of new technologies and aircraft configurations. Among the different technological developments proposed, the use of open fan or Ultra-High Bypass Ratio configurations is of interest due to their higher propulsive efficiency and performance. To support the development of such technologies, accurate and efficient tools early in the design stages are needed to predict aerodynamic performances and noise levels. One such tool is the lattice Boltzmann method, which can perform high-fidelity aeroacoustic simulations of flows around propellers and engines. The lattice Boltzmann approach can be advantageous over other numerical methods due to its easy integration with complex geometries, its low numerical dissipation properties, and its high level of computational efficiency. This paper presents the aerodynamic and aeroacoustic numerical study of an isolated propeller using the commercial lattice Boltzmann solver ProLB. Large Eddy Simulations were carried out using a compressible hybrid thermal version of the solver. An aeroacoustic analysis using a Ffowcs-Williams and Hawkings acoustic analogy for far-field noise was carried out. The objective is to assess the capabilities of the solver to predict acoustic emissions of an isolated propeller towards the future study of an installed case.

Agricultural sustainability assessment in Fars province of Iran through the lens of the elimination multi-criteria decision-making method

The main objective of the present research was to assess agricultural sustainability through the lens of the Elimination Method. Iranian wheat farmers in Fars province comprised the population of the study. Smapling table of Krejcie and Morgan was applied to estimate the statistically plausible sample size. A stratified sampling approach was used for selecting sampling randomly. The research instrument included a structured questionnaire. A panel of experts approved the face and content validities. Average Variance Extracted (AVE), Composite Reliability (CR), and items correlations were used to confirm the convergent validity of the constructs. The average shared squared variance (ASV) and the maximum shared squared variance (MSV) criteria were applied to examine the divergent validity of constructs. Employing the Elimination Method to assess agricultural sustainability in four counties of Fars revealed that Marvdasht is the most sustainable area for the development of wheat cultivation. Because it was able to fulfill 16 indicators out of 23 indicators of agricultural sustainability. The high level of productivity and efficiency of the wheat crop in this region is one of the main reasons for the relatively high sustainability of wheat production practices. Furthermore, the results demonstrated that Darab was the second most suitable region for developing wheat production. Based on the Elimination Method, the region was able to fulfill six indicators out of 23 agricultural sustainability assessment indicators. According to the results of ranking alternatives using the Elimination Method, the two counties of Kazeroun and Eghlid could not acceptably fulfill indicators of agricultural sustainability. In other words, it seems that these two regions are not suitable for the development of wheat production in Fars province. In the end, it was recommended that the Elimination Method can be a useful approach for urgent decision-making and agricultural managers and decision-makers in Fars province should focus more intensively on the development of wheat production in Marvdasht.

Synthesis, characterization, and evaluation of pyrimidinone-linked thiazoles: DFT analysis, molecular docking, corrosion inhibition, and bioactivity studies

The paper describes the construction of a new series of pyrimidinone-linked thiazole derivatives through bromination of the initial Biginelli reaction product followed by the Hantzsch thiazole synthesis route. Various analytical techniques, including FT-IR, 1H NMR, 13C NMR, and LCMS analysis, were employed to confirm the formation of the products. The synthesized compounds were primarily evaluated for their antibacterial activity, with a specific focus on their IC50 values. Compound 4c demonstrated the most potent efficacy, displaying MIC and MBC values that varied from 0.23 to 0.71 mg/mL and 0.46–0.95 mg/mL, respectively. The anti-inflammatory potential was also observed in analogs 4a and 4c with marked activity in the 33.2–82.9 μM concentration range. Moreover, compounds 4a, and 4c demonstrated strong antioxidant effects, as reflected by their excellent IC50 values of 38.6–43.5 μM respectively. DFT investigation showed that B. cereus was more susceptible, and E. coli was more resistant, with chloro-substituted compounds exhibiting potential reactivity. Some molecules with chloro-substituents showed promising results in density functional theory when compared to other substituents. In addition, the molecules underwent a corrosion study and demonstrated a high level of inhibition efficiency (4c) in comparison to other molecules. Further in silico studies of the synthesized thiazoles confirmed the good interactions with the target.

Development and validation of a droplet digital PCR method for quantifying lentiviral vector infectious titer

Lentiviruses, with their high transduction efficiency and gene expression levels, are widely used as gene delivery vectors in the development of chimeric antigen receptor T cells (CAR-T) and other genetically modified cell therapies. Accurate determination of the lentiviral vector infectious titer is essential to ensure effective transduction and product consistency. In this study, we developed an efficient method for lentiviral vector titration based on digital droplet polymerase chain reaction (ddPCR) technology, enabling absolute quantification of the target gene. Benzonase treatment of non-transduced plasmids substantially shortened the experimental period, reducing cell culture duration from 10-14 days–3 days. The method was rigorously validated by assessing specificity, working range, limit of quantification, precision, accuracy, and robustness. This study demonstrates the feasibility of combining enzymatic digestion with ddPCR to quantify lentiviral vector infectious titer and provides a detailed and readily adaptable methodology for the scientific community.

Enhancing quantitative imaging to study DNA damage response: A guide to automated liquid handling and imaging

Laboratory automation and quantitative high-content imaging are pivotal in advancing diverse scientific fields. These innovative techniques alleviate the burden of manual labour, facilitating large-scale experiments characterized by exceptional reproducibility. Nonetheless, the seamless integration of such systems continues to pose a constant challenge in many laboratories. Here, we present a meticulously designed workflow that automates the immunofluorescence staining process, coupled with quantitative high-content imaging to study DNA damage signalling as an example. This is achieved by using an automatic liquid handling system for sample preparation. Additionally, we also offer practical recommendations aimed at ensuring the reproducibility and scalability of experimental outcomes. We illustrate the high level of efficiency and reproducibility achieved through the implementation of the liquid handling system but also addresses the associated challenges. Furthermore, we extend the discussion into critical aspects such as microscope selection, optimal objective choices, and considerations for high-content image acquisition. Our study streamlines the image analysis process, offering valuable recommendations for efficient computing resources and the integration of cutting-edge deep learning techniques. Emphasizing the paramount importance of robust data management systems aligned with the FAIR data principles, we provide practical insights into suitable storage options and effective data visualization techniques. Together, our work serves as a comprehensive guide for life science laboratories seeking to elevate their high-content quantitative imaging capabilities through the seamless integration of advanced laboratory automation.

Динамика взаимосвязи ментальных структур и эффективности саморегуляции положительных состояний в ходе учебных занятий студентов

This study describes the results of a research that was aimed at studying the dynamics of changes in positive mental states during the educational activities of students at a higher educational institution, their relationship with mental structures and the effectiveness of self-regulation of states. The relationships between mental structures (reflexive, semantic, self-system) and positive mental states were studied and subsequently analyzed during the course of the lesson (the beginning of the lesson, the middle, where the peak of students’ activity is observed, and the end of the lesson). The aim of the study was to analyze the relationship between the structures of consciousness and the effectiveness of self-regulation of positive states in the dynamics of students' educational activities. The study of the characteristics of mental states and the effectiveness of their self-regulation was carried out during the students' performance of a group assignment at a seminar. Separately, outside of educational activities, we diagnosed the severity of mental structures. In our work, we used both well-known questionnaires for diagnosing mental structures and mental states, and specially developed methods for studying the effectiveness of self-regulation and the severity of positive states. As a result, we found that by the middle of the lesson, there is an increase in the activity of experiencing positive states, and by the end, there is a decrease in its intensity. We found that positive states in the classroom are interconnected with the expression of mental structures and a high level of self-regulation efficiency. We found that the effectiveness of self-regulation of states at the beginning of the lesson is associated with semantic structures, in the middle - with value-semantic structures and the self-system, and at the end of the lesson reflexive structures acquire special importance. The results obtained can be used to update and maintain positive states during educational activities and they are novel.

Mechanobiological mechanisms in the remediation of soil lead pollution using two-dimensional carbon materials and Morchella: A molecular-level study

Graphene-based 2D carbon composites and Morchella mushrooms are used in the paper to study the mechanobiological mechanisms of soil lead remediation. Soil is contaminated with lead, which threatens ecosystems and human health, making it even more important to find remedies. To fully comprehend these processes, present-day materials, and organic compounds are needed to improve soil nutrition and eradicate lead. To deal with the challenge of finding effective means of lead removal; limitations associated with traditional soil pollution remediation methods; and merging biology and material sciences. Multifunctional graphene wettability-patterned nanocoated membranes (MGW-PNM) is a novel technique developed to overcome these challenges. Such processes result in membranes with different wettability patterns that take advantage of the properties of graphene. This facilitates better interaction between the membrane itself and the surrounding soil as well as lead contaminants by modifying its hydrophobicity or hydrophilicity characteristics. For effective removal of lead, extensive simulation studies were done using MGW-PNM. In line with this, it can be inferred that MGW-PNM also remediates highly capable soils at high-efficiency levels. This was established when comparing modern techniques to past ones where considerable improvements were made on how much lead is extracted from them. The study suggests new ways of addressing environmental contamination resulting from microbial activities in soils by combining advanced materials with biological substances such as Morchella spp. For this purpose, it investigates various molecular interactions occurring among carbonaceous species called Morchella microbes and environmental pollutants like those including Pb.

Characteristics and Driving Factors of Benthic Animal Communities in Different Water Functional Zones of the Jiangsu Section of the Yangtze River

This study explored the characteristics of benthic animal communities in different water functional areas and the driving factors affecting changes in the community structure of four water functional zones of the Jiangsu section of the Yangtze River: the protection, buffer, reserve, and development and utilization zones. The results showed that the alpha diversity of the benthic animal communities in the protected and reserved zones was significantly higher than that of the buffer and development and utilization zones, and the benthic animal community structure differed significantly across different water functional zones. These zones indirectly affected the community of benthic fauna due to their environmental heterogeneity. Furthermore, the average degree, map density, and average clustering coefficient of the molecular ecological network were highest in the protected zone. The average path length was shorter, and there were more types and numbers of key species in the benthic animal community in the protected zone, indicating high levels of connectivity and efficiency in transferring substances, energy, and information between benthic animals. These results will provide a scientific basis for studying the characteristics and driving factors of benthic animal communities in the Yangtze River and have important significance for assessing and restoring aquatic ecology in the Yangtze River Basin.

MANAGEMENT OF SCALABILITY IN CLOUD-BASED APPLICATIONS

The following is an abstract of the article. The article presents an analysis of the challenges associated with monitoring and managing the scalability of a cloud application. To this end, a module for monitoring and managing the scalability of a cloud application has been developed as part of this study. The development process included the introduction of automatic scaling, and monitoring using Prometheus and Grafana, which allows for a high level of availability and resource efficiency. The study comprised a series of phases, including requirements analysis, system design, development, testing, and evaluation. Consequently, the system's performance, stability, and capacity to scale in response to fluctuating workloads were enhanced. The module exhibits a high degree of adaptability to changes in system requirements and load, which is a crucial attribute for the dynamic development of business applications. This solution assists in optimizing the allocation of resources and reducing infrastructure costs. The project has been found to fully meet the set goals and objectives, as well as the requirements for effective resource management of the Amazon Web Services cloud platform using Terraform, Prometheus, and Grafana. The practical value of the developed module is evidenced by a significant improvement in resource efficiency, service stability and cost optimisation. The module design has been subjected to rigorous testing and has been successfully implemented in a test environment, thereby demonstrating the sustainability and efficiency of the developed solution. The experience gained in the implementation and operation of this solution may prove useful for further expansion and optimization of cloud solutions in other projects and companies specializing in the provision of cloud solutions. The findings of this study were validated in a test environment at an IT company with a specialization in cloud technologies. The objective was to ascertain the functionality and efficiency of the developed module in a real-world context of cloud infrastructure operation. The testing process entailed the configuration of the module on pre-existing cloud infrastructure systems, its integration with Prometheus and Grafana for monitoring purposes, and the execution of a series of stress tests designed to assess the module's scalability. As a result of this testing, a number of critical points were identified that required further optimization. The results of the study and the issues identified during the project testing have enabled the identification of several areas for further improvement and development of the system. First and foremost, the optimization of automatic scaling algorithms represents a crucial avenue for improvement. The development of these algorithms should be oriented towards utilizing historical monitoring data to anticipate potential shifts in system load. Another pivotal area for enhancement is the precision of monitoring systems. The integration of supplementary tools and the expansion of existing monitoring systems' functionality will facilitate the acquisition of more comprehensive insights into the system's condition. This, in turn, will facilitate the expedient identification and eradication of potential issues.

World energy futures market efficiency and its determinants; evidence from white noise test based on block-wise wild bootstrap approach

ABSTRACT The study examines the time-varying efficiency of nine energy futures traded across different exchanges with varied trade settlement methods and pricing currency using the novel white noise test based on block-wsie wild bootstrap approach. The advantage of using this approach is that it relaxes the assumption of serial independence and the martingale difference sequence. Time-varying efficiency is observed for all nine energy futures consistent with the Adoptive Market Hypothesis (AMH). The three most (least) efficient energy futures based on efficiency ratio are Coal, Gasoline and NaturalGas (WTI, Brent and OmanCrude). Quantile regression analysis shows that the higher the level of illiquidity furthers the level of efficiency towards inefficiency (high level of efficiency) in the case of a less (more) efficient market. A mixed impact of volatility is observed across energy futures. No differential impact is found due to exchange, method of settlement and pricing currency. The robustness of the results is investigated, policy implications are discussed, and further research is recommended.

Impacts of impeller blade number on centrifugal pump performance under critical cavitation conditions

The number of impeller blades is a significant geometric parameter that considerably impacts centrifugal pump performance. A transient numerical analysis of a centrifugal pump is conducted to examine the influence of the variable impeller blade number on pump performance under critical cavitation conditions. Six different impellers with 3, 5, 7, 8, 9, and 11 blades are examined numerically at a rotational speed of 2900 r/min when other impeller parameters remain unchanged. Fields of interior flow and properties of centrifugal pumps are studied concerning static pressure, velocity magnitude, and vapor volume fraction using ANSYS Fluent to perform the numerical simulation. The results show that numerical analysis can accurately predict centrifugal pump internal flow. The current results match experimental and numerical data for the NPSH, with a 4.65% discrepancy. Blade numbers affect the flow field and pressure amplitude, especially at the outlet region. As blade numbers increase, pressure increases, and the impeller with 11-blade has the maximum pressure amplitude. The impeller with a seven-blade achieved its highest efficiency level, exhibiting a 0.48% improvement under non-cavitation conditions and a 1.4% improvement under critical cavitation conditions compared to the original model. Furthermore, the cavity size on an individual blade of a model with three-blade is more extensive compared to other models. In addition, the impeller with a nine-blade exhibits the lowest value of the vapor volume percentage. This research analyses the influence of different blade numbers on the performance of centrifugal pumps while operating under critical cavitation conditions. It aims to provide novel insights into the flow characteristics associated with such circumstances.

Optimizing Automated Optical Inspection: An Adaptive Fusion and Semi-Supervised Self-Learning Approach for Elevated Accuracy and Efficiency in Scenarios with Scarce Labeled Data.

In the field of automatic optical inspection (AOI), this study presents innovative strategies to enhance object detection accuracy while minimizing dependence on large annotated datasets. We initially developed a defect detection model using a dataset of 3579 images across 32 categories, created in collaboration with a major Taiwanese panel manufacturer. This model was evaluated using 12,000 ambiguously labeled images, with improvements achieved through data augmentation and annotation refinement. To address the challenges of limited labeled data, we proposed the Adaptive Fused Semi-Supervised Self-Learning (AFSL) method. This approach, designed for anchor-based object detection models, leverages a small set of labeled data alongside a larger pool of unlabeled data to enable continuous model optimization. Key components of AFSL include the Bounding Box Assigner, Adaptive Training Scheduler, and Data Allocator, which together facilitate dynamic threshold adjustments and balanced training, significantly enhancing the model's performance on AOI datasets. The AFSL method improved the mean average precision (mAP) from 43.5% to 57.1% on the COCO dataset and by 2.6% on the AOI dataset, demonstrating its effectiveness in achieving high levels of precision and efficiency in AOI with minimal labeled data.

Sensitivity optimization of monolithic integrated refractive index sensor based on grating LED

In this study, we present a potential ultra-thin refractive index sensor model that utilizes a monolithically integrated gallium nitride-based light-emitting diode platform. This light-emitting diode not only emits light but also detects changes in refractive index. The sensitivity of this sensor is defined as the response of light extraction efficiency to unit changes in refractive index. We have incorporated a one-dimensional grating on the surface of the light-emitting diode to investigate the effects of the grating’s modulation on the light field and the sensor’s sensitivity.Through strategic optimization of the grating structure, we have significantly enhanced the sensitivity of the sensor. Our results indicate that, compared to a conventional flat light-emitting diode, the optimized grating structure increases the light extraction efficiency by approximately 2 to 3 times. Furthermore, the sensitivity of the sensor has achieved a maximal enhancement of up to 41-fold.The device offers a compact design and demonstrates high levels of light extraction efficiency and sensitivity, making it highly suitable for monolithic integration in optical sensing applications. This advancement provides a substantial contribution to the field of optical sensing, indicating promising potential for future research and application.