Processing Capabilities Research Articles

DESIGNING A MICROSERVICES BASED ENTERPRISE ARCHITECTURE USING TOGAF 10: A CASE STUDY APPROACH

PT Umbara Karya Sejati, operating in the home living and industrial product sectors, faced significant digital transformation challenges, marked by inefficiencies due to system integration issues and vendor dependencies that incurred cost penalties of 10-15% each month. Addressing these challenges, this research developed an Enterprise Architecture using TOGAF 10's Microservices Architecture (MSA) from the Preliminary Phase to Phase F: Migration Planning. This approach aimed to enhance system integration and operational efficiency, thereby improving modularity and scalability while reducing reliance on external vendors. A pivotal component of this architecture, an API gateway, provided robust monitoring capabilities for integration processes, enabling quick identification and prioritization of critical issues, which will assist the internal IT team's workflow. Furthermore, the research planned the establishment of a DevOps team, incorporating Agile methodologies, and scheduled IT governance and data security training to prepare for future policy development and strengthen internal controls. This strategic design equips the organization to navigate the volatility, uncertainty, complexity, and ambiguity (VUCA) of market demands with agile and effective responses, projecting a 10% increase in both operational and cost efficiency for PT Umbara Karya Sejati.

Evaluating both usability and accessibility: the case of access@tour by action – a digital solution for accessible tourism

Aim: Information systems can help improve accessible tourism conditions. During the development of accessible technology, a crucial phase is testing. Notwithstanding, usability testing methodologies often dismiss accessibility. To overcome this gap, this work contributes with an innovative mixed-method testing methodology that integrates accessibility and usability components in technology evaluation. The capabilities of this methodological process are demonstrated through testing the prototype of a mobile solution for accessible tourism – access@tour by action. Methods: Nine experts evaluated the platform and helped correct accessibility failures. Afterward, 78 end-users tested the prototype. The procedure includes a set of tasks to be performed in the prototype and a final questionnaire. This questionnaire was specifically created for accessibility markets and is itself a significant contribution. Results: Testing with experts helped improve the prototype’s accessibility before it reached end-users. The results with end-users are provided in two distinct ways. First, the task performance measures are presented, which showed overall good completion rates. Second, the results obtained from the questionnaires are disclosed. Overall, the platform was considered relevant, accessible, and easy to use. Still, some minor flaws were identified regarding interface connectivity, symbology, and reading sequence. Thus, improvements are suggested based on obtained feedback. Conclusion: The testing procedure was essential to gather input from a user point of view, ensuring the final version of the access@tour by action is accessible and, therefore, “usable”. This allowed for validating the methodological method, significantly contributing to future research on evaluating technology accessibility.

COMPARATIVE ANALYSIS OF MULTILINGUAL QA MODELS AND THEIR ADAPTATION TO THE KAZAKH LANGUAGE

This paper presents a comparative analysis of large pretrained multilingual models for question-answering (QA) systems, with a specific focus on their adaptation to the Kazakh language. The study evaluates models including mBERT, XLM-R, mT5, AYA, and GPT, which were tested on QA tasks using the Kazakh sKQuAD dataset. To enhance model performance, fine-tuning strategies such as adapter modules, data augmentation techniques (back-translation, paraphrasing), and hyperparameter optimization were applied. Specific adjustments to learning rates, batch sizes, and training epochs were made to boost accuracy and stability. Among the models tested, mT5 achieved the highest F1 score of 75.72%, showcasing robust generalization across diverse QA tasks. GPT-4-turbo closely followed with an F1 score of 73.33%, effectively managing complex Kazakh QA scenarios. In contrast, native Kazakh models like Kaz-RoBERTa showed improvements through fine-tuning but continued to lag behind larger multilingual models, underlining the need for additional Kazakh-specific training data and further architectural enhancements. Kazakh’s agglutinative morphology and the scarcity of high-quality training data present significant challenges for model adaptation. Adapter modules helped mitigate computational costs, allowing efficient fine-tuning in resource-constrained environments without significant performance loss. Data augmentation techniques, such as back-translation and paraphrasing, were instrumental in enriching the dataset, thereby enhancing model adaptability and robustness. This study underscores the importance of advanced fine-tuning and data augmentation strategies for QA systems tailored to low-resource languages like Kazakh. By addressing these challenges, this research aims to make AI technologies more inclusive and accessible, offering practical insights for improving natural language processing (NLP) capabilities in underrepresented languages. Ultimately, these findings contribute to bridging the gap between high-resource and low-resource language models, fostering a more equitable distribution of AI solutions across diverse linguistic contexts.

Evaluating the impact of risk management and risk-based internal audit on fraud detection in local governments

One of the leading indicators for improving fraud detection capability is the evaluation of the implementation of risk management and risk-based internal audit and information technology systems. This study aims to evaluate how risk management and risk-based internal audits influence fraud detection capabilities in local government when integrated with information technology systems. SPSS 26 was utilized for data analysis. The paper uses a quantitative approach, collecting primary data with questionnaires distributed among 200 auditors and 70 supervisors across four districts in West Java. A purposive sampling approach based on self-selection was used. The findings show that the ability to detect fraud is significantly and positively influenced by the implementation of a risk-based internal audit (sig 0.000 < 0.05) and risk management process (sig 0.000 < 0.05). On the other hand, the risk management framework (sig 0.107 > 0.05) has a negative and insignificant effect on improving fraud detection capability. In addition, the relationships between the risk management process (sig 0.006 < 0.05) and fraud detection capability were found to be moderated by information technology systems. However, information technology systems are unable to moderate the relationship between risk-based internal audit (sig 0.563 > 0.05) and risk management framework (sig 0.115 > 0.05) on fraud detection capability. Therefore, risk management and risk-based internal audits are able to detect fraud, and information technology systems can strengthen the risk management process with the ability to detect fraud. AcknowledgmentThe authors would like to thank the University of North Sumatra, especially the Research Institute, for its support and the Ministry of Education and Research through the Directorate of Research, Technology, and Community Service program for providing intellectual assistance and funding for this project in the PMDSU grant (number: 91/UN5.4.10.S/PPM/KP-DRTPM/2024).

S2VSNet: Single stage V-shaped network for image deraining & dehazing

Producing high-quality, noise-free images from noisy or hazy inputs relies on essential tasks such as single image deraining and dehazing. In many advanced multi-stage networks, there is often an imbalance in contextual information, leading to increased complexity. To address these challenges, we propose a simplified method inspired by a U-Net structure, resulting in the “Single-Stage V-Shaped Network” (S2VSNet), capable of handling both deraining and dehazing tasks. A key innovation in our approach is the introduction of a Feature Fusion Module (FFM), which facilitates the sharing of information across multiple scales and hierarchical layers within the encoder-decoder structure. As the network progresses towards deeper layers, the FFM gradually integrates insights from higher levels, ensuring that spatial details are preserved while contextual feature maps are balanced. This integration enhances the image processing capability, producing noise-free, high-quality outputs. To maintain efficiency and reduce system complexity, we replaced or removed several non-essential non-linear activation functions, opting instead for simple multiplication operations. Additionally, we introduced a “Multi-Head Attention Integrated Module” (MHAIM) as an intermediary layer between encoder-decoder levels. This module addresses the limited receptive fields of traditional Convolutional Neural Networks (CNNs), allowing for the capture of more comprehensive feature-map information. Our focus on deraining and dehazing led to extensive experiments on a wide range of synthetic and real-world datasets. To further validate the robustness of our network, we implemented S2VSNet on a low-end edge device, achieving deraining in 2.46 seconds.

Application of Statistical Process Control in Automotive Manufacturing

This research looks at how “statistical process control”, or SPC, is used in the automotive sector and highlights how it can improve production efficiency, lower defect rates, and improve process quality. SPC is crucial to the monitoring and control of the complex assembly processes and strict quality standards that define the automobile sector. The efficiency of several SPC methods, including control charts and process capability analysis (Cp and Cpk), in preserving process stability and fulfilling quality criteria is assessed in this study. The study shows how these technologies may detect process variations and direct appropriate adjustments to optimize output using simulated data analysis. The results highlight how crucial SPC is to attaining consistent product quality and reliability, which raises performance and safety requirements in the automotive sector.

Sentiment Analysis on Twitter Social Media Regarding Covid-19 Vaccination with Naive Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT)

Abstract. The Covid-19 vaccine is an important tool to stop the Covid-19 pandemic, however, there are pros and cons from the public regarding this Covid-19 vaccine. Purpose: These responses were conveyed by the public in many ways, one of which is through social media such as Twitter. Responses given by the public regarding the Covid-19 vaccination can be analyzed and categorized into responses with positive, neutral or negative sentiments. Methods: In this study, sentiment analysis was carried out regarding Covid-19 vaccination originating from Twitter using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. The data used in this study is public tweet data regarding the Covid-19 vaccination with a total of 29,447 tweet data in English. Result: Sentiment analysis begins with data preprocessing on the dataset used for data normalization and data cleaning before classification. Then word vectorization was performed with TF-IDF and data classification was performed using the Naïve Bayes Classifier (NBC) and Bidirectional Encoder Representations from Transformers (BERT) algorithms. From the classification results, an accuracy value of 73% was obtained for the Naïve Bayes Classifier (NBC) algorithm and 83% for the Bidirectional Encoder Representations from Transformers (BERT) algorithm. Novelty: A direct comparison between classical models such as NBC and modern deep learning models such as BERT offers new insights into the advantages and disadvantages of both approaches in processing Twitter data. Additionally, this study proposes temporal sentiment analysis, which allows evaluating changes in public sentiment regarding vaccination over time. Another innovation is the implementation of a hybrid approach to data cleansing that combines traditional methods with the natural language processing capabilities of BERT, which more effectively addresses typical Twitter data issues such as slang and spelling errors. Finally, this research also expands sentiment classification to be multi-label, identifying more specific sentiment categories such as trust, fear, or doubt, which provides a deeper understanding of public opinion.

Large-Scale High-Accuracy and High-Efficiency Phase Plate Machining.

In this paper, multifunctional, multilevel phase plates of quartz substrate were efficiently prepared by using a newly developed polygon scanner-based femtosecond laser photolithography system combined with inductively coupled discharge plasma reactive-ion etching (ICP-RIE) technology. The femtosecond laser photolithography system can achieve a scanning speed of 5 m/s and a preparation efficiency of 15 cm2/h while ensuring an overlay alignment accuracy of less than 100 nm and a writing resolution of 500 nm. The ICP-RIE technology can control the etching depth error within ±5 nm and the mask-to-mask edge error is less than 1 μm. An 8-level Fresnel lens phase plate with a focal length of 20 mm and an 8-level Fresnel axicon phase plate with a cone angle of 5° were demonstrated. The diffraction efficiency was greater than 93%, and their performance was tested for focusing and glass cutting, respectively. Combined with the high-speed femtosecond laser photolithography system's infinite field-of-view (IFOV) processing capability, the one-time direct writing preparation of phase plate masks of different sizes was realized on a 6-inch wafer. This is expected to reduce the production cost of quartz substrate diffractive optical elements and promote their customized mass production.

Soil texture analysis using controlled image processing

Soil texture analysis is crucial for crop selection, fertilizer recommendation, and production. Traditional soil testing in the lab using chemicals is highly time-consuming, expensive, and risky harmful chemicals; proper equipment and trained professionals are required to get the readings and to conduct the analysis. These issues can be resolved using image processing. In this study, we proposed a Blackbox prototype machine to take images in a controlled environment under the fixed intensity of light, distance, and standard dry conditions to analyze soil texture. This innovative machine, with its efficient and precise image processing capabilities, has the potential to revolutionize soil texture analysis. Also, we marked the center points of each type of soil texture as defined in the USDA texture triangle. Hundreds of soil samples were prepared for each type according to the center point's sand, silt, and clay ratio. The image processing-based model is trained for texture analysis. This research aims to reduce the soil texture analysis time and provide a system that can do extensive analyses automatically and with accuracy. The proposed Blackbox prototype machine has proven effective in providing a controlled environment for taking images. Also, the proposed model detects soil texture with a maximum accuracy of 99.5 %. A proposed model trained on the soil samples of different texture classes available in the USDA texture triangle accurately performed texture analysis. The results benefit the recommendation of appropriate crops and fertilizers based on a given soil sample in a very short time and cost-effectively.

Core-shell architecture and channel suppression: unleashing the potential of SC_RCS_DGJLFET

In this investigation, a suppressed channel-rectangular core–shell double gate junctionless field effect transistor (SC_RCS_DGJLFET) is simulated to enhance the junctionless device’s performance. This study leverages a core–shell architecture and channel suppression technique to improve the gate controllability over the channel region which helps in substantial depletion of the shells in the OFF state of the device. When compared to conventional double gate JLFETs (C_DGJLFET) and rectangular core–shell double gate JLFETs (RCS_DGJLFET), the performance of the SC_RCS_DGJLFET is superior in terms of IOFF, ION/IOFF ratio, DIBL and subthreshold slope (SS). The SC_RCS_DGJLFET achieves an ultra-low IOFF of 7.033 × 10−16 A, indicating a low leakage current with an impressive ION/IOFF = 5.092 × 1011 . Other performance parameters such as subthreshold slope and DIBL has also been improved for the SC_RCS_DGJLFET device. Subthreshold slope has been decresesd by 4.76% whereas the DIBL decreased by 33.82% when compared to existing RCS_DGJLFET. Additionally, to analyze the effect of doping on the device performance, the core doping in SC_RCS_DGJLFET is varied for fixed shell doping. The study found that fixing core doping to an appropriate value is a crucial parameter to achieve good device performance. The impact of variation of oxide extension towards the source and drain LextS/LextD in SC_RCS_DGJLFET is also studied for the first time in the core–shell architecture which has further improved the device’s performance. Finally, a CMOS inverter is designed using the proposed device that provides valuable insights into its suitability for digital circuit applications and verifies its performance benefits compared to existing transistor technologies. The SC_RCS_DGJLFET based CMOS inverter shows a sharp transition in voltage transfer characteristics (VTC), indicating fast switching speed and precise signal processing capabilities when compared to a CMOS inverter based on a conventional double gate junctionless field effect transistor (C_DGJLFET). Moreover, the transient characteristics of the SC_RCS_DGJLFET based CMOS inverter exhibit an improved output voltage swing, suggesting enhanced dynamic behaviour and stability during logic state transitions.

A Novel Hypersonic Target Trajectory Estimation Method Based on Long Short-Term Memory and a Multi-Head Attention Mechanism.

To address the complex maneuvering characteristics of hypersonic targets in adjacent space, this paper proposes an LSTM trajectory estimation method combined with the attention mechanism and optimizes the model from the information-theoretic perspective. The method captures the target dynamics by using the temporal processing capability of LSTM, and at the same time improves the efficiency of information utilization through the attention mechanism to achieve accurate prediction. First, a target dynamics model is constructed to clarify the motion behavior parameters. Subsequently, an LSTM model incorporating the attention mechanism is designed, which enables the model to automatically focus on key information fragments in the historical trajectory. In model training, information redundancy is reduced, and information validity is improved through feature selection and data preprocessing. Eventually, the model achieves accurate prediction of hypersonic target trajectories with limited computational resources. The experimental results show that the method performs well in complex dynamic environments with improved prediction accuracy and robustness, reflecting the potential of information theory principles in optimizing the trajectory prediction model.

Utilizing ChatGPT for On-the-Fly Decision-Making in Autonomous Systems

In the realm of autonomous systems, rapid and efficient decision-making is critical to ensure optimal performance and adaptability in dynamic environments. This paper explores the application of ChatGPT, a large-scale language model, in on-the-fly decision-making within autonomous systems. By leveraging ChatGPT's capabilities in natural language processing, contextual understanding, and real-time response generation, the study demonstrates how it can support autonomous agents in interpreting situations, generating actionable insights, and making informed decisions. The integration of ChatGPT into autonomous systems provides a flexible and scalable approach to improving decision accuracy and response times in unpredictable scenarios. This research investigates the benefits, challenges, and potential use cases, highlighting the model's contributions to enhancing autonomy in various fields such as robotics, self-driving vehicles, and intelligent drones.

Design and fabrication of a sub-3 dB grating coupler on an X-cut thin-film lithium niobate platform.

Thin-film lithium niobate (TFLN) based integrated photonic devices have been intensively investigated due to their promising properties, enabling various on-chip applications. Grating couplers (GCs) are wildly used for their flexibility and high alignment tolerance for fiber-to-chip coupling. However, achieving high coupling efficiency (CE) in TFLN GCs often requires the use of reflectors, hybrid materials, or extremely narrow linewidths of the grating arrays, which significantly increases the fabrication difficulty. Therefore, there is a demand for high-CE GCs on TFLN with simple structure and easy fabrication processes. In this paper, combining process capabilities, we demonstrate a highly efficient apodized GC by linearly optimizing the period length and the fill factor on a 600-nm-thick TFLN platform. Without any reflector or hybrid material, we achieve a remarkable coupling loss of -2.97 dB at 1555 nm on the 600-nm-thick X-cut TFLN platform with only a single lithography and etching step. Our work sets a new benchmark for CE among GCs on the 600-nm-thick TFLN platform.

Comparative analysis of tracking and behavioral patterns between wild-type and genetically modified fruit flies using computer vision and statistical methods

Collective animal behavior occurs in groups and swarms at almost every biological scale, from single-celled organisms to the largest animals on Earth. The intriguing mysteries behind these group behaviors have attracted many scholars, and while it is known that models can reproduce qualitative features of such complex behaviors, this requires data from real animals to demonstrate, and obtaining data on the exact features of these groups is tricky. In this paper, we propose the Hidden Markov Unscented Tracker (HMUT), which combines the state prediction capability of HMM and the high-precision nonlinear processing capability of UKF. This prediction-driven tracking mechanism enables HMUT to quickly adjust tracking strategies when facing sudden changes in target motion direction or rapid changes in speed, reducing the risk of tracking loss. Videos of fruit fly swarm movement in an enclosed environment are captured using stereo cameras. For the captured fruit fly images, the thresholded AKAZE algorithm is first used to detect the positions of individual fruit flies in the images, and the motion of the fruit flies is modeled using a multidimensional hidden Markov model (HMM). Tracking is then performed using the Unscented Kalman Filter algorithm to obtain the flight trajectories of the fruit flies in two camera views. Finally, 3D reconstruction of the trajectories in both views is achieved through polar coordinate constraints, resulting in 3D motion data of the fruit flies. Additionally, the efficiency and accuracy of the proposed algorithm are evaluated by simulating fruit fly swarm movement using the Boids algorithm. Finally, based on the tracked fruit fly flight data, behavioral characteristics of the fruit flies are analyzed from two perspectives. The first is a statistical analysis of the differences between the two behaviors. The second dimension involves clustering trajectory similarity using the DTW method based on fruit fly flight trajectories, further analyzing the similarity within clusters and differences between clusters.

Your Eyes on Speed: Using Pupil Dilation to Adaptively Select Speed-Reading Parameters in Virtual Reality

Rapid Serial Visual Presentation (RSVP) improves the reading speed for optimizing the user's information processing capabilities on Virtual Reality (VR) devices. Yet, the user's RSVP reading performance changes over time while the reading speed remains static. In this paper, we evaluate pupil dilation as a physiological metric to assess the mental workload of readers in real-time. We assess mental workload under different background lighting and RSVP presentation speeds to estimate the optimal color that discriminates the pupil diameter varying RSVP presentation speeds. We discovered that a gray background provides the best contrast for reading at various presentation speeds. Then, we conducted a second study to evaluate the classification accuracy of mental workload for different presentation speeds. We find that pupil dilation relates to mental workload when reading with RSVP. We discuss how pupil dilation can be used to adapt the RSVP speed in future VR applications to optimize information intake.

The Development of an Automated Approach for Designing Quantum Algorithms Using Circuits Generated By Generative Adversarial Networks (Gans)

The advent of quantum computing has inaugurated a novel epoch of computational prowess, offering the potential to tackle intricate problems with unparalleled speed (Zoufal et al., 2019). Quantum circuits, which are essential components of quantum computation, serve as representations of sequences of quantum gates designed for specific quantum processes (Zoufal et al., 2019). Nevertheless, the task of creating efficient quantum circuits continues to be a formidable and labor-intensive undertaking. This research proposal presents a unique methodology that utilizes Generative Adversarial Networks (GANs) to automate the process of generating quantum circuits that are specifically designed for particular quantum gates and operations (Zoufal et al., 2019). The main goal of this study is to create a model based on Generative Adversarial Networks (GANs) that can generate quantum circuits by leveraging the collaborative efforts of the generator and discriminator networks (Zoufal et al., 2019). The GAN model will be trained using a carefully selected dataset that includes established quantum circuits and their corresponding required quantum operations (Zoufal et al., 2019). This dataset will form the basis for the training process. Following this, the quantum circuits that are produced will be thoroughly assessed in terms of fidelity, efficiency, and resource allocation (Zoufal et al., 2019). Additionally, the objective of this work is to refine and optimize the circuits that are formed by employing reinforcement learning and gradient-based techniques (Zoufal et al., 2019). In addition to investigating circuit production, this research will delve into the practical implications and consequences of quantum circuits formed by Generative Adversarial Networks (GANs) on the development of quantum algorithms (Zoufal et al., 2019). The study's value is in its capacity to accelerate the creation of quantum algorithms through the automation of circuit design (Zoufal et al., 2019). This research makes a valuable contribution to the field of quantum computing by improving the efficiency and resource utilization of quantum circuits (Zoufal et al., 2019). These developments are crucial for the development of practical quantum computing applications and will play a significant role in the evolution of quantum algorithms and processing capabilities in the future (Zoufal et al., 2019).

Applying Artificial Intelligence for Emotional Cognition in Game Testing for Quality Assurance

Artificial intelligence (AI) is a branch of computer science that tries to develop computational tools and systems that can carry out tasks comparable to human decision-making and learning. The subject of AI is expanding quickly, and AI technology is becoming more and more significant in a variety of IT specialties. When more automated and intelligent solutions are employed instead of outdated techniques, the quantity of manpower and resources needed for game testing will be greatly decreased. The aim of this study is to find new models that will make game testing easier by utilising state-of-the-art AI techniques. In an attempt to determine the model base and algorithmic foundation for the new approach, the examination and comparison of existing theories, models, and algorithms is used to infer and identify the viability of certain current mainstream AI models and algorithms in game testing sessions. Standard software testing is not the same as game testing. Further consideration should be given to the whole gaming experience and entertainment value in addition to functional testing. User questionnaires and in-game testing are used in the current standard experience testing methodology. Artificial intelligence eliminates human aspects in emotional cognition. It may produce more objective test results while spending less money for both human and material resources by using massive data sets and its own learning and processing capabilities. This paper examines the state of AI in software testing, game creation, and emotion perception using expertise in using and applying AI approaches in game testing for quality assurance. It also demonstrates how well AI methods work for predicting player emotions during game testing. The AI testing reduces testing expenses related to labour and material resources while guaranteeing total game confidentiality before release. It also increases test accuracy and reduces the impact of subjectivity.

Energy-Efficient Industrial Internet of Things in Green 6G Networks

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

Barrier Polarity Reversal Based on Interfacial Modification of Au Nanoparticles for Nonvolatile Multilevel Memory and Optoelectronic Synapses.

Optoelectronic synaptic devices, integrating light sensing and information processing capabilities, have emerged as advantageous tools for the implementation of visual neuromorphic computing. However, the transient light-triggered response characteristic typically results in unstable memory retention times and restricted current response ranges, posing significant challenges to the development and practical application of neural network systems. In response to these limitations, this study developed a nonvolatile optoelectronic memory based on the indium tin oxide (ITO)/Au nanoparticles (NPs)/amorphous Ga2O3 (a-Ga2O3)/Pt structure. Unlike conventional optoelectronic memories, this device features a modification with Au NPs that markedly enhances the Schottky barrier height at the interface. Au NPs function as a charge-trapping layer for sensitive and large-scale modulation of the barrier by the light field, thereby enabling the nonvolatile reversal of the device's barrier polarity. This innovative approach enables controllable multilevel data storage with an ultra large on/off ratio (∼104) and excellent retention capability exceeding 12,000 s. Additionally, the device emulates essential synaptic functions and demonstrates potential application values in visual weak signal perception and image memory. This study introduces a mechanism for Schottky barrier polarity control and presents a promising strategy for the development of future high-performance integrated devices and optoelectronic synaptic elements.

A Methodological Framework for New Product Development in Fuzzy Environments

New product development (NPD) is crucial for helping companies to maintain competitive advantages. In this study, a methodological framework is presented combining a novel Kano model and fuzzy axiomatic design (FAD) for improving the product development capability in the whole NPD process. In the Kano model, a novel mixed-class classification method is presented to classify each evaluation indicator agreed on by the majority, and to calculate the affiliation value based on category strength (CS) to display the degree to which the indicator belongs to a certain attribute. A new importance ratio is also proposed to adjust the importance of each indicator attribute. This helps to achieve higher customer satisfaction and improve the attractiveness of the product or service. FAD is then used to measure the gap between customer satisfaction and the company’s expected levels of satisfaction in terms of product functions. This enables the company to obtain more comprehensive information for decision-making. A case study is provided to verify the practicability of the proposed method. Sensitivity analysis proves the robustness of the results based on the number of respondents. Finally, comparative analysis with existing approaches demonstrates the strengths of the proposed method.