The article examines how generative artificial intelligence is reshaping Web UI development, primarily by reconfiguring the infrastructure layer, interface component libraries, and design systems, thereby transforming not the speed of layout implementation but the economics of reusable artefacts. The aim of the study is to provide a conceptual explanation of why, under the mass adoption of LLM-based assistants and intelligent interface methods, legacy monetization models based on selling ready-made themes, templates, screen collections, and even paid access to components begin to lose stability. The relevance of the work stems from the fact that contemporary web development functions as a dependency network of package ecosystems, where component libraries act as channels for scaling practices, while AI drastically reduces the cost of obtaining a first working version and reproducing standard solutions, simultaneously increasing the variability of results and the risk of divergence between teams. The novelty of the article lies in shifting the focus from the technological effect of generation to the shift in the unit of value: it is shown that in conditions of commoditization of form, market power moves toward what cannot be generated in a single pass, toward the manageability of the design system, verifiable contracts (tokens, standards, rules of composition), design–code integrations, automated quality control, and guarantees of compatibility and predictable updates. The main conclusion is that the winners are not vendors of yet another button, but platforms that sell trust, disciplined change, and reproducible UI evolution. The article is intended for developers, design leads, product managers, and creators of design systems who are choosing strategies for sustainable monetization in the era of generative AI.

Biofouling prevention and control technology for marine engineering equipment: from traditional methods to intelligent interface engineering.

ABSTRACT Programmable liquid handling platforms based on droplet steering have significant applications in various fields. Despite great progress in this field, achieving programmable and precise control over liquid droplet sliding trajectories still remains a challenge. Herein, a strategy for programmable droplet steering using an electro‐adaptive V‐array elastomer track (EAVET) is proposed, which can control the droplet steering behavior, namely, slip state and slip trajectory. By adjusting electric field to alter the V‐structure spacing, the water‐oil‐solid interface can be adjusted correspondingly, and an asymmetric gradient wettability interface is created, thus enabling regulation of the droplet's dynamic motion behavior, including pinned/sliding states and sliding direction. As a result, through the synergistic effect of electric field and V‐array track, real‐time control over the droplet's lateral slip direction and distance can be achieved, enabling adaptive droplet steering. Finally, micro‐reaction detection experiments were conducted to prove that this work provides a solution for programmable fluid manipulation platforms, which can be utilized in devices requiring high operational precision. Thus, this work provides a solution for programmable droplet steering, with potential applications in intelligent interface materials, microfluidic technology, micro‐reaction systems, and so on.

The Smart Footstep Power Generator is an innovative system designed to harvest energy from human footsteps using piezoelectric sensors. As individuals walk over the embedded tiles, mechanical pressure is converted into electrical energy, which is then stored in a battery for later use. This system is especially useful in crowded areas such as railway stations, airports, or shopping malls where foot traffic is high, ensuring continuous energy generation. The generated energy can power small devices like LED lights or sensors, contributing to sustainable and green energy initiatives. This project not only promotes renewable energy but also encourages awareness about energy conservation through practical implementation this project also integrates a microcontroller-based system to efficiently manage power storage and distribution. The Arduino microcontroller monitors voltage levels and ensures optimal charging of the battery, while an LCD display provides real-time updates on voltage output and energy generation. This intelligent interface adds educational and practical value to the system, making it suitable for implementation in smart city infrastructure. Furthermore, the design is scalable, low-cost, and easy to maintain, making it ideal for both urban and rural settings. By transforming everyday movement into a valuable energy source, the Smart Footstep Power Generator exemplifies the potential of combining technology and sustainability. An Arduino microcontroller manages the entire process, controlling the flow of energy and sometimes displaying the power generation through an LED. This setup can be used in areas with heavy foot traffic, such as walk ways or smart buildings, to power small devices like lights or sensors.

The article presents an approach to synthesizing artificial intelligence agents (AI agents), in particular, control and decision support systems for process operators in various industries. Such a system contains an identifier in the feedback loop that generates digital predictive associative search models of the Just-in-Time Learning (JITL) type. It is demonstrated that the system can simultaneously solve (outside the control loop) two additional tasks: online operator pre-training and mutual adaptation of the operator and the system based on real-world production data. Solving the latter task is crucial for teaching the operator and the system collaborative handling of abnormal situations. AI agents improve control efficiency through self-learning, personalized operator support, and intelligent interface. Stabilization of process variables and minimization of deviations from optimal conditions make it possible to operate process plants close to constraints with sustainable product qualities. Along with higher yield of target product(s), this reduces equipment wear and tear, utilities consumption and associated harmful emissions. This is the key merit of Model Predictive Control (MPC) systems, which justify their application. JITL-type models proposed in the article are more precise than conventional ones used in MPC; therefore, they enable the operation even closer to process constraints. Altogether, this further improves the reliability of production systems and contributes to their sustainable development.

Abstract - With the rapid advancement of computing technology, users increasingly expect systems to be intelligent, responsive, and easy to use. Despite this progress, desktop computing environments still rely heavily on traditional input methods such as keyboards and mouse devices. These methods can be inefficient, especially when performing repetitive tasks like opening applications, searching information, managing files, or controlling system settings. Voice-based assistants have gained popularity in mobile devices and smart home environments; however, their presence in desktop systems remains limited. Existing solutions often depend on cloud services, lack customization, or provide minimal control over local system resources. To address these challenges, this project introduces VANI, an AI-driven personal desktop voice assistant that enables natural and intuitive interaction through both voice and text commands. VANI is designed to improve productivity, accessibility, and user experience by combining intelligent automation with a graphical user interface. The assistant acts as a digital companion capable of understanding user intent, executing tasks, and providing meaningful responses in real time. Key Words: Artificial Intelligence (AI), Personal Desktop Voice Assistant, Voice-Based Interaction, Natural Language Processing (NLP), Speech Recognition, Text-to-Speech (TTS), System Automation, Intent Recognition, Human–Computer Interaction (HCI), Intelligent User Interface.

We introduce a novel approach to enhancing healthcare accessibility and patient interaction through the implementation of a Medical Assistant Chatbot powered by Natural Language Processing (NLP) technology. The chatbot serves as an intelligent interface for users to discuss their medical concerns, symptoms, and queries. Leveraging advanced NLP algorithms, the chatbot comprehensively understands user inputs, providing accurate disease prediction, symptom analysis, and preliminary diagnoses. Additionally, the chatbot serves as a valuable health companion by sending medication reminders via notifications, ensuring users adhere to their prescribed treatment schedules. Furthermore, this application integrates a mapping feature to display the nearest specialized hospitals based on the predicted disease. The integration of real-time maps enhances the user experience, ensuring timely access to appropriate medical facilities. This innovative fusion of NLP and mapping technology not only empowers users with immediate medical insights but also facilitates seamless navigation to specialized healthcare centers, ultimately improving healthcare outcomes and user satisfaction

This project presents the development of a centralized, web-based platform titled Global Terrorism Database, aimed at compiling and providing comprehensive information on known terrorists across the world. The website serves as a unified intelligence interface where verified data—including names, photographs, affiliations, criminal history, and bounty details—are organized and made accessible to both security agencies and the general public. A key feature of the platform is its public reporting system, which allows individuals to submit tips, sightings, or relevant information regarding the listed terrorists. These reports are securely forwarded to concerned law enforcement or intelligence agencies. In cases where a reward is officially declared for information on a particular terrorist, the platform enables a transparent system for informers to claim such incentives, creating a mutual benefit model for both agencies and civilians. By reducing duplication of effort across nations and encouraging crowd-sourced intelligence, the website seeks to support global counter-terrorism initiatives through better coordination, public participation, and rapid information exchange. This project integrates technology with security efforts, offering an innovative tool in the fight against terrorism.

The changes currently occurring in the life and activities of each person and society as a whole are primarily associated with the widespread use of digital technologies. In the modern information flow, the leading positions are occupied by the problems of artificial intelligence (AI). The specifics and consequences of the use of neural networks are analyzed not only in natural science, but also in social and humanitarian literature. For relevant classification of new documents, it is necessary to make appropriate changes and additions to the Medium tables of the Library and Bibliographic Classification (LBC). The purpose of the article is to determine the directions and algorithm for updating the LBC Medium tables in accordance with the trends in the development of modern knowledge. A retrospective analysis of the process of forming the LBC division “16 Computer science and information technology” and modernizing the main tables of the natural science cycle made it possible to summarize the unique experience of specialists of the Research Center for LBC Development of the Russian State Library (RC LBC RSL) in improving the national library classification. Based on the end-to-end analysis of the LBC Medium tables, current monitoring of publications on the use of digital technologies, as well as the ongoing consulting work of the staff of RC LBC RSL, an algorithm was formed for preparing proposals to modify the existing and successfully used in library practice holistic classification system. In two subdivisions “16.3 Information systems and databases” and “16.6 Artificial intelligence”, the symbolic and neural network directions of AI research found their place. In 2024, the subdivision “16.333.3 Intelligent interface” appeared. For literature on virtual reality, the index “16.7 Virtual reality systems” is provided. The widespread use of digital technologies has exacerbated the problem of information security. In this regard, the index “16.8 Information security” was developed. The wording of the division, intended for a uniform reflection of literature on issues related to automation and computerization in any field of activity, has been changed. Now the general standard division (GSD) has the wording “Industry informatics. Information technology”. The wording of the division has been replaced by “Software”. A new GSD “Information systems and databases” has appeared. These changes have made it possible to systematize the literature on industry information technologies in full. In 2024, a new GSD “Artificial intelligence” was added. The presented algorithm can be used in the process of working with any cycle, department and/or section of LBC. Suggestions for reworking some indexes of the GSD LBC table will help to systematize the literature on industry digital technologies in full.

Quantum computing has emerged as a radical paradigm of computationally tasks which cannot be achieved through classical systems. Quantum Computer-Assisted Machine Learning (Quantum Machine Learning) The use of quantum mechanics and principles of artificial intelligence, such that the quantum mechanics principles of superposition, entanglement and tunnelling are used to improve pattern recognition. Big data dimensions, exponential growth feature space, optimization bottlenecks will not be effectively handled using currently available machine learning algorithms. It is anticipated that QML will deliver factors-of-four to exponential training and inference gains, especially on high-performance pattern recognition tasks in on-the-edge applications, including image processing, natural language processing, and cybersecurity. This is a review article of quantum machine learning (high-performance pattern recognition). It studies some of the underlying paradigms of quantum support vector machine (QSVMs), quantum k-means clustering, variational quantum circuits (VQCs) and quantum-classical deep learning systems. The benefits of QML are addressed in relation to scalability, generalization, and resilience of high dimensional data space. Such use cases include biomedical imaging, business fraud, and material discovery. The QML does have several opportunities, but is restricted by noisy intermediate-scale quantum (NISQ) machines and error correction costs, and cannot be as easily co-located with classical pipes. Another objective of this paper is to discover the significance of the hybrid quantum-classical models that are the most realistic line of attack today, to the application of quantum techniques. The next line of research is hardware-efficient algorithms, quantum feature maps, and practical benchmarking of QML. The quantum computing-artificial intelligence interface can be the new frontier to highly-performing, scaled-out, and efficient pattern recognition systems in data-focused industries, QML may become the future of computation.

Optimizing enterprise software interfaces requires a synergistic integration of Artificial Intelligence (AI) and human-centered design to enhance usability, efficiency, and security. This paper presents a framework that leverages AI- driven techniques for intelligent interface optimization, informed by user-centric design principles. Drawing inspiration from machine learning applications in fraud detection, such as Logistic Regression, Random Forest, XGBoost, Decision Tree, and AdaBoost models applied to imbalanced datasets with SMOTE re-sampling, the proposed methodology ensures accurate and reliable system performance. Further, the study incorporates insights from geospatial AI, IoT, and cybersecurity domains, including climate resilience, next-generation drug delivery systems, and real-time environmental monitoring, demonstrating the applicability of AI across diverse enterprise contexts. By combining predictive analytics, secure data management, and intuitive design, the framework facilitates improved decision-making, enhances user engagement, and ensures robust cyber-secured operations. The proposed approach provides a foundation for future research in developing intelligent, human-centered, and secure enterprise systems adaptable to dynamic organizational needs.

This paper introduces a gesture-controlled conversational interface driven by a local AI model, aimed at improving accessibility and facilitating hands-free interaction within digital environments. The technology utilizes real-time hand gesture recognition via a typical laptop camera and connects with a local AI engine to produce customized learning materials. Users can peruse educational documents, obtain topic summaries, and generate automated quizzes with intuitive gestures, including lateral finger movements, a two-finger gesture, or an open palm, without the need for conventional input devices. Upon selection of a file, the AI model analyzes its whole content, producing a structured summary and a multiple-choice assessment, both of which are immediately saved for subsequent inspection. A unified set of gestures facilitates seamless navigating within the user interface and the opened documents. The system underwent testing with university students and faculty (n = 31), utilizing assessment measures such as gesture detection accuracy, command-response latency, and user satisfaction. The findings demonstrate that the system offers a seamless, hands-free user experience with significant potential for usage in accessibility, human–computer interaction, and intelligent interface design. This work advances the creation of multimodal AI-driven educational aids, providing a pragmatic framework for gesture-based document navigation and intelligent content enhancement.

With the advancement of wearable and mobile devices, demand for the real‐time, low‐power processing of physiological and environmental signals is growing rapidly. To achieve this, neuromorphic systems that employ artificial synapses for analog signal processing and parallel computing represent a promising strategy. In this study, a synaptic sensor is developed that simultaneously responds to human respiration and ambient ultraviolet (UV) light, enabling multimodal analog data processing. The proposed device is fabricated using the organic semiconductor 5,5′‐Di(4‐biphenylyl)‐2,2′‐bithiophene, which has distinct bulk and channel phases. Human respiration‐induced airflow is converted into a synaptic current via charge trapping triggered by the interaction between molecules of water and the bulk phase, leading to real‐time detection of the respiratory rate. The inherent photosensitivity of the device also allows for simultaneous UV detection, thus capturing the environmental exposure conditions. Using these multimodal sensing and processing capabilities, a real‐time feedback system is implemented that supports exercise monitoring by integrating physiological and environmental information. This work demonstrates the potential use of synaptic sensors as front‐end components in wearable neuromorphic platforms, offering a compact, energy‐efficient, and intelligent interface for healthcare and personalized information services.

Research Type: Level 5 - Case report, Expert opinion, Personal observation Introduction/Purpose: ChatGPT has shown an ability to provide treatment recommendations for common orthopaedic conditions in accordance with AAOS clinical practice guidelines, including pathology specific to the foot and ankle. Previously, ChatGPT has shown great potential as a clinical support tool when triaging patients with various causes of knee pain. However, no study has looked at ChatGPT’s ability to act as a central scheduling tool that triages patients to a provider. Thus, this study explored ChatGPT’s ability to synthesize differential diagnoses and triage patients into the proper healthcare settings (Primary Care, Specialists, and Emergent Care). ChatGPT-4’s ability to identify primary diagnoses and generate treatment plans was also analyzed. Methods: 24 foot and ankle complaints warranting triage and expanded clinical scenarios were input into ChatGPT-4, with memory cleared prior to each new input to mitigate bias. In each conversation, role prompting (addressing the Artificial Intelligence (AI) interface as “Dr. AI, a fellow foot and ankle trained orthopaedic surgeon”) was employed to maximize the quality of content provided. For the 12 triage complaints, ChatGPT-4 was asked to generate a differential diagnosis and provide a decision on central scheduling (Primary Care Physician (PCP), Podiatrist/Ortho Surgeon, or Emergency Department/Urgent Care). These responses were graded for accuracy and suitability in comparison to a differential created by 2 orthopaedic foot and ankle fellowship-trained orthopedic surgeons. For the 12 clinical scenarios, ChatGPT-4 was prompted to provide treatment guidance for the patient, which was again graded. To test the higher-order capabilities of ChatGPT-4, further inquiry into these specific management recommendations was performed and graded. Results: All ChatGPT-4 diagnoses were deemed appropriate within the spectrum of potential pathologies on a differential. The top diagnosis on the differential was identical between surgeons and ChatGPT-4 for 9 (75%) of scenarios, and the top diagnosis provided by the surgeon appeared as either the first or second diagnosis in 10 (83.3%) of scenarios. Overall, 26 of 36 diagnoses (72.2%) in the differential were identical. ChatGPT’s decision for healthcare setting agreed with foot and ankle fellowship-trained orthopaedic surgeons in 6 cases (50%). When provided with expanded vignettes, the accuracy of ChatGPT-4 was maintained (75%), with the suitability of management graded as appropriate in 11 cases (91.7%). Specific information pertaining to conservative management, surgical approaches, and related treatments was appropriate and accurate in 11 cases (91.7%). Conclusion: ChatGPT-4 provided clinically reasonable differential diagnoses to triage patient complaints of foot and ankle pain, which were generally consistent with the foot and ankle physicians’ differentials. Diagnostic performance was maintained when providing additional clinical context. ChatGPT-4 shows clinically important error rates for diagnosis depending on prompting strategy and information provided; therefore, further refinements are necessary prior to full integration into clinical workflows. However, ChatGPT-4 may serve as an augment to appropriately direct patient care, potentially assisting in a system's effort to streamline scheduling.

Abstract Alkaline water electrolysis (AWE) offers a promising route for scalable renewable hydrogen production but is constrained by significant multiscale mass‐transport challenges that limit its efficiency and durability. Recent advances in hierarchical membrane structures, gradient porous electrodes, and optimized flow‐field designs have enhanced ionic conductivity, gas separation, and electrolyte distribution. Concurrently, innovative bubble‐management strategies, including surface modifications and external‐field assistance, effectively mitigate gas‐induced transport bottlenecks. Looking forward, emerging intelligent interface platforms that integrate adaptive materials, embedded sensors, and AI‐driven digital twins promise real‐time mass transport control and predictive system optimization. This review synthesizes critical progress and outlines future pathways, emphasizing that integrated materials‐to‐system approaches are essential for advancing robust, efficient, and economically viable hydrogen production.

The rapid advancement of generative artificial intelligence is reshaping the field of web development, opening new possibilities for automated code generation and intelligent interface design. This study employs the PRISMA methodology to conduct a systematic review, analyzing AI-assisted web development research's current state and future directions. Based on an analysis of 46 peer-reviewed studies published between 2020 and 2025, the findings reveal a remarkable growth trend, with an average annual increase of approximately 80%. The research primarily focuses on five core domains: (i) automated code generation, (ii) AI-assisted UI/UX design, (iii) intelligent web optimization, (iv) natural language interfaces, and (v) AI-supported testing. Key challenges identified include the lack of standardized evaluation frameworks, concerns over security and privacy, and limitations in scalability. Accordingly, the study proposes a structured research agenda emphasizing three strategic priorities: developing standardized evaluation metrics for AI-generated interfaces; establishing security protocols integrated into CI/CD pipelines; and advancing distributed inference architectures for efficient deployment on edge computing platforms. Keywords: Artificial intelligence; web development; code generation; large language models; generative AI

Radiology reports are an integral part of patient medical records; however, these reports often contain complex medical terminology that are difficult for patients to comprehend, potentially leading to anxiety, misunderstanding, and misinterpretation. The development of user-friendly instruments to improve understanding is thus critically important to enhance health literacy and empower patients. In this study, we introduce a novel artificial intelligence (AI) interface, the Rads-Lit Tool, which can simplify radiology reports for patients using natural language processing (NLP) techniques. This manuscript presents the development process, methodology, and results of the Rads-Lit Tool, demonstrating its potential to simplify radiology reports across various examination types and complexity levels. Our findings highlight that patient-facing AI-driven tools can enhance patient health literacy and foster improved patient-provider communication in radiology.

The problems of managing modern complex organizational and manufacturing systems, such as international production corporations, regional economies, sectoral ministries, etc., in conditions of fierce competition are primarily related to the need to consider the activity of organizational and manufacturing objects that make up a multi-level manufacturing system, that is, the ability to efficiently solve the problem of coordinating interests. This problem cannot be solved efficiently without the use of modern scientific achievements and appropriate software. As an example, we can cite the active systems theory pioneered by Prof. V. M. Burkov and his students, which successfully claims to be a constructive implementation of the idea of coordinated planning. This paper proposes new models and methods of coordinated planning of two-level organizational and manufacturing systems. Our models and methods use original compromise criteria and the corresponding constructive algorithms. The original aggregated volume-time models are used as models of organizational and manufacturing objects. We present a well-founded software structure for the proposed methods of coordinated planning. It contains an intelligent interface for using the presented results in solving applied problems.

Transportation intelligent interface requires Vehicular Ad Hoc Networks that enable management of traffic, provision of road safety and traffic optimization in cities. Dynamic urban landscapes have challenged current routing protocols including AODV, DSR and OLSR with the speed of node movement and fluctuating traffic density and connectivity asymmetry. These restrictions may result in routing lengthiness, additional end-to-end latency, increased control wastage and axiomatic packet transfer. As a way of eliminating these obstacles, the following paper introduces a simplified VANET routing model combining smarter node prioritization, smart path selection and smart lossless routing to guarantee the forwarding of packets. The most dynamic nodes in the model occur relative to the throughput, connectivity and the likelihood of the loss of packets and other related matters and achieves the best possible paths with few hops, latency and controlling traffic and maximum reliability. The strategy exploits the strengths of the high throughput nodes as relays in the backbone and avoids the low throughput nodes to enhance easier distribution of traffic and low bottlenecks. Performance is assessed on the simulation of an urban VANET on a snapshot basis and finally, measurements of performance are the path length, end-to-end delay, throughput, routing overhead, and the loss of packets. Visualizations such as network graphs, routing paths, and intensity heatmaps of coverage as well as the level of throughput of individual nodes all reveal the general behaviour of the network and individual nodes. The results have revealed that the model is superior to the conventional reactive and proactive model in that it offers shorter route, latency and larger throughput and lessened overhead and augmented reliability. The proposed routing model is a robust and adaptable solution to dynamic urban VANET settings that may have desirable values to both useful and scaled motives to next-generation vehicle to vehicle communication networks.

The issues of trust in decisions made (formed) by intelligent systems are becoming more and more relevant. A systematic review of Explicable Artificial Intelligence (XAI) methods and tools aimed at bridging the gap between the complexity of neural networks and the need for interpretability of results for end users is presented. A theoretical analysis of the differences between explainability and interpretability in the context of artificial intelligence as well as their role in ensuring the security of decisions made by intelligent systems is carried out. It is shown that explainability implies the ability of a system to generate justifications understandable to humans, whereas interpretability focuses on the passive clarity of internal mechanisms. A classification of XAI methods is proposed based on their approach (preliminary/subsequent analysis: ante hoc/post hoc) and the scale of explanations (local/global). Popular tools, such as Local Interpretable Model Agnostic Explanations, Shapley Values, and integrated gradients, are considered, with an assessment of their strengths and limitations of applicability. Practical recommendations are given on the choice of methods for various fields and scenarios. The architecture of an intelligent system based on the V.K. Finn model and adapted to modern requirements for ensuring “transparency” of solutions, where the key components are the information environment, the problem solver and the intelligent interface, are discussed. The problem of a compromise between the accuracy of models and their explainability is considered: transparent models (“glass boxes”, for example, decision trees) are inferior in performance to deep neural networks, but provide greater certainty of decision-making. Examples of methods and software packages for explaining and interpreting machine learning data and models are provided. It is shown that the development of XAI is associated with the integration of neuro-symbolic approaches combining deep learning capabilities with logical interpretability.