This paper describes two front-end architectures developed in a 28 nm CMOS process for the readout of pixel detectors in future high-energy physics (HEP) colliders and advanced X-ray imaging instrumentation. The front-end channels have been developed in the framework of the PiHEX project, funded by the Italian Ministry of University and Research. PiHEX aims to improve the state of the art of pixel readout chip technology in high-luminosity colliders and X-ray imagers in the next generation of free electron lasers (FELs) by developing, in 28 nm CMOS technology, the fundamental microelectronic building blocks for pixel readout chips. Such blocks, also implementing innovative circuit ideas, will enable, in future applications, the integration of large-scale readout chips, meeting a set of challenging requirements, such as high spatial resolution, high signal-to-noise ratio, very wide dynamic range and the capability to withstand unprecedented radiation levels. Two different front-end channels were designed, integrated into two prototype chips, and tested. One architecture, featuring a pixel size of 25 µm × 100 µm, was optimized for tracking applications in high-energy physics experiments, like the ones that take place at CERN in the high-luminosity upgrade of the Large Hadron Collider (LHC), while the second one, featuring a pixel size of 110 µm × 55 µm, was devised for X-ray imaging applications in FELs.

The SALSA chip is a future versatile MPGD readout ASIC for the tracker of the ePIC experiment at the EIC currently under construction. The ePIC specifications state that SALSA should acquire charges up to 250 fC with a noise below 0.5 fC. SALSA1 prototype is dedicated to the analog front-end and ADC validation. Two different front-end architectures were tested and compared to help select the best one for the next iteration. Measurements of the gain, noise, resilience to saturation and to temperature variations have been conducted on several ASICs. SALSA1 implements four dynamic ranges and eight peaking times. Typical measured gains for ePIC configurations and with a peaking time of 250 ns are 2.6 mV/fC or 3 mV/fC, depending on the channel architecture. The measured noise stays below the 0.5 fC specification for peaking times down to 150 ns and so fulfills the ePIC requirements. Measurements also confirmed the expected sensitivity of the first pole zero compensation architecture considered to temperature variation from 5 to 65°C.

Abstract: Modern web applications demand high performance, scalability, and long-term sustainability to meet the expectations of increasingly global and resource-constrained environments. Next-generation frontend architectures have evolved beyond traditional monolithic designs to incorporate modular, component-driven, and distributed approaches that enhance development efficiency and user experience. This paper explores emerging frontend architectural paradigms—such as micro-frontends, server-side rendering, static site generation, and edge-based delivery—that enable faster load times, improved scalability, and optimized resource utilization. Additionally, it examines the integration of modern frameworks, performance optimization strategies, and sustainable design principles that reduce computational overhead and energy consumption. By analyzing architectural patterns, tooling ecosystems, and deployment strategies, this study highlights how next-generation frontend systems can balance speed, maintainability, and environmental considerations. The findings provide insights for developers and organizations seeking to design resilient, high-performance web interfaces capable of supporting large-scale applications while promoting sustainable computing practices. Keywords: Frontend Development, Monolithic Architecture, Micro-Frontend Architecture, Continuous Integration and Continuous Deployment (CI/CD), Microservices, Server-Side Rendering (SSR), Content Delivery Network (CDN), Page Load Time (PLT), First Contentful Pain (FCP), Time to Interactive (TTI), RESTful API, GraphQL, React, Angular, Vue JS

BackgroundSemantic interoperability remains a significant barrier in healthcare, particularly when integrating patient-reported, clinical, and genomic data to enable personalized care. Existing models rarely focus on patient-centered, ontology-driven front-end architectures based on widely adopted standardized medical ontologies and terminologies. Within broader Personal Health Data Space (PHDS) initiatives, such integration increasingly depends on front-end frameworks that enable semantic consistency and patient-centered usability across heterogeneous clinical domains and systems.ObjectiveThis analysis presents a review-informed framework to support semantic integration, data governance, user experience, and patient engagement. The objective is to present a front-end, standards-aligned, ontology-driven model grounded in established healthcare standards.MethodsBased on our previously published systematic review and thematic synthesis, this paper presents a review-informed conceptual framework. It outlines a modular front-end architecture for semantic healthcare data integration. The framework was developed through a reproducible synthesis-to-design process, consistent with design science principles of treatment design, thereby ensuring conceptual rigor and alignment with evidence. Using a knowledge-based modeling approach, we designed a six-layer architecture comprising User Experience, Security and Compliance, Data Management, Interoperability and Integration, Advanced Analytics, and Support and Scalability. Each layer is aligned with established standards including Health Level Seven—Fast Healthcare Interoperability Resources (HL7 FHIR), Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT), and Logical Observation Identifiers Names and Codes (LOINC), compliance with privacy and security regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA).ResultsThe framework illustrates how ontologies and health IT standards can be conceptually incorporated within front-end system design to unify structured and unstructured data, providing a foundation for secure sharing and standards-aligned integration with existing health information systems.ConclusionsThis review-informed analysis introduces the Self Data Atlas Front-End Framework (SDA-FEF), an ontology-driven, standards-aligned Electronic Health Record (EHR) front-end architecture designed to support patient-centered care. By promoting semantic interoperability, structured data integration, and user-centered design, the framework conceptually advances the development of healthcare systems that may enhance continuity of care and overall quality of life.

As VLSI designs grow increasingly complex and transition to smaller process nodes, accurate and efficient library characterization has become essential for modern design workflows. Existing open-source tools are often constrained by limited functionality, efficiency, and accuracy, making them insufficient for today’s design challenges. This article reviews the shortcomings of current open-source tools and introduces ZlibBoost, a novel open-source framework designed to provide both flexibility and high performance. Its modular, front-end and back-end separated architecture, along with user-friendly interfaces, enables seamless customization, integration of machine learning models, and expanded simulator compatibility. A variety of key features are introduced to significantly enhance both accuracy and efficiency of library characterization. Experimental results demonstrate ZlibBoost’s capability to meet the demands of both academic research and practical applications, establishing it as a robust solution for advancing semiconductor design.

The Circular Electron Positron Collider (CEPC) is a proposed next-generation large-scale collider for precision studies of the Higgs boson and other fundamental particles. The baseline design of the Muon Detector in the CEPC experiment employs plastic scintillator bars, wavelength-shifting fibers, and silicon photomultipliers. This detector has more than 43,000 channels, imposing stringent requirements on the front-end readout architecture. This paper presents the design and evaluation of an ASIC-based readout prototype using the 32-channel MPT2321 chip. Each channel of this chip integrates a preamplifier, a pulse shaper, a discriminator, an ADC, and a TDC. The prototype comprises a custom wire-bonding board and an FPGA evaluation board with a customized mezzanine card for configuration and data acquisition via TCP protocol. Linearity, gain uniformity, and a high signal-to-noise ratio have been demonstrated through charge-injection and SiPM coupling tests, supporting the MPT2321-based design as a promising candidate for the readout of the CEPC Muon Detector.

Web application is the pivotal component of web development. With the growth of web applications, frontend architectures have shifted from monolithic model to micro-frontend. While micro-frontends offer independent deployment, flexibility, adaptability, extensibility, they introduce major challenges in Search Engine Optimization. Micro-frontends work on client side rendering, it uses java script frameworks and decentralized evolved teams which can negatively impact indexing, SERP ranking and page performance. In this review paper analyze the existing micro-frontend approaches and reviews best technique for improving SEO in micro frontend systems. In this study we focus on techniques such as Single- SPA integration, Server Side Rendering, Web components, I- Frames, Shared event bus, Varnish Cache and Black box react components. Existing research on micro-frontends focus on the comparatively analysis and practical implications in web development.

Enterprise cloud migrations frequently modernize back-end infrastructure while underestimating front-end architecture, delivery, and operational concerns. This paper synthesizes practitioner evidence from multi-domain enterprise web systems and prior research on micro-frontends and migration methodologies to propose a cloud-ready UI architecture blueprint. We focus on front-end considerations that are commonly missed: (i) UI-domain decomposition and contract boundaries, (ii) Backends-for-Frontends (BFF) and API gateway placement for latency and blast-radius control, (iii) independent CI/CD and release governance to prevent UI–service coupling, (iv) cross-browser and accessibility compliance as migration risk multipliers, and (v) runtime observability for distributed UIs. The methodology is expressed as reference architectures and checklists to reduce integration risk, improve deploy ability, and preserve user experience continuity during incremental migration.

This paper presents a low-power, ISFET-integrated frontend architecture that directly merges the sensing element with a Gm-C based continuous-time delta sigma modulator (CT-ΔΣM). In the proposed design, the ISFET simultaneously functions as both the biochemical sensor and the input common-source stage of the integrator, eliminating the need for intermediate driver and thereby improving energy efficiency. A passive low-pass filter DAC (LPF-DAC) is introduced to attenuate the input-feedback residue and provide hybrid noise shaping. The input Gm combines source degeneration and gm-boost to mitigate the nonlinearities introduced by input-feedback residue and common-mode variation. Fabricated in 180 nm CMOS, the proposed CT-ΔΣM achieves a measured peak SNDR of 84.2 dB and a dynamic range of 90.4 dB over a 10 kHz bandwidth under a 900 mVpp input, with a power consumption of only 41.5 µW. These results present a Schreier FoM of 168 dB, representing a 8 dB improvement over prior ISFET frontends. The frontend demonstrates an averaged sensitivity of 29.04 mV/pH, along with a resolution of 6.8 m-pH, validating its applicability for lowpower, high-accuracy biochemical sensing.

Health care technology is advancing rapidly, transforming diagnostics through compact, non-invasive, and user-friendly biomedical devices. For instance, diabetes diagnosis that once required significant blood volume can now be achieved in seconds using a nano-pinch sample. Recent developments in portable and wearable systems have minimized the need for invasive procedures, enabling continuous, real-time monitoring, especially among aging populations. This growing demand underscores the need for compact, energy-efficient, and affordable biomedical systems seamlessly integrated into daily life. This tutorial brief provides an in-depth analytical perspective on low-noise, low-cost, and low-power circuit-level design strategies for next-generation biomedical devices. Challenges and escape-out methods for low-noise design implementations are broadly discussed in this work. The study consolidates noise-reduction techniques, such as correlated double sampling and multi-stage amplifier configurations, and highlights their impact on signal integrity, power efficiency, and scalability. It also addresses design trade-offs and cost considerations, offering a practical framework for researchers developing efficient analog front-end architectures for wearable and implantable biomedical applications. Finally, the results presented in this work were carried out in Cadence Virtuoso using 65nm CMOS TSMC technology node.

he Clutch and Coach project is a modern full-stack web application meticulously designed using the MERN stack (MongoDB, Express.js, React.js, Node.js) to redefine how driving schools manage courses, instructors, and learners. Built as a modular single-repository system, it integrates a dynamic React 18 frontend and a powerful Node.js backend, enabling seamless, real-time communication through RESTful APIs. The frontend architecture leverages React Hooks, React Router, Styled Components, and Framer Motion to deliver a visually engaging, responsive, and interactive user experience. The backend, powered by Express.js, ensures efficient API routing, robust validation with express-validator, and optimized database operations through Mongoose ORM. MongoDB Atlas provides scalable, cloud-hosted data persistence for all entities including users, courses, and payments..

Visual SLAM systems face significant performance degradation under dynamic lighting conditions, where traditional feature extraction methods suffer from reduced keypoint detection and unstable matching. This paper presents LDFE-SLAM, a novel visual SLAM framework that addresses illumination challenges through a Light-Aware Deep Front-End (LDFE) architecture. Our key insight is that low-light degradation in SLAM is fundamentally a geometric feature distribution problem rather than merely a visibility issue. The proposed system integrates three synergistic components: (1) an illumination-adaptive enhancement module based on EnlightenGAN with geometric consistency loss that restores gradient structures for downstream feature extraction, (2) SuperPoint-based deep feature detection that provides illumination-invariant keypoints, and (3) LightGlue attention-based matching that filters enhancement-induced noise while maintaining geometric consistency. Through systematic evaluation of five method configurations (M1–M5), we demonstrate that enhancement, deep features, and learned matching must be co-designed rather than independently optimized. Experiments on EuRoC and TUM sequences under synthetic illumination degradation show that LDFE-SLAM maintains stable localization accuracy (∼1.2 m ATE) across all brightness levels, while baseline methods degrade significantly (up to 3.7 m). Our method operates normally down to severe lighting conditions (30% ambient brightness and 20–50 lux—equivalent to underground parking or night-time streetlight illumination), representing a 4–6× lower illumination threshold compared to ORB-SLAM3 (200–300 lux minimum). Under severe (25% brightness) conditions, our method achieves a 62% tracking success rate, compared to 12% for ORB-SLAM3, with keypoint detection remaining above the critical 100-point threshold, even under extreme degradation.

Enterprise e-commerce platforms face unprecedented challenges in delivering seamless user experiences while managing complex technical infrastructures. The evolution of frontend architectures has become critical as businesses scale their digital operations to accommodate millions of concurrent users and vast product catalogs. Component modularization represents a fundamental shift in how modern e-commerce systems are architected, enabling teams to develop, test, and deploy features independently while maintaining system coherence. Single Page Applications (SPAs) and micro-frontend architectures have emerged as dominant paradigms, offering distinct advantages in terms of development velocity and user experience optimization. Testing strategies have evolved beyond traditional quality assurance practices to encompass comprehensive end-to-end validation, visual regression testing, and performance monitoring under realistic load conditions. Server-Side Rendering (SSR) and Client-Side Rendering (CSR) approaches present different trade-offs regarding initial load performance, Search Engine Optimization (SEO) effectiveness, and runtime interactivity. This review examines current state-of-the-art practices in scalable frontend development for enterprise e-commerce, analyzing component design patterns, testing methodologies, performance optimization techniques, and architectural decisions that enable platforms to serve global user bases effectively. The synthesis of recent research reveals that successful implementations leverage modular component libraries, automated testing pipelines integrated with Continuous Integration/Continuous Deployment (CI/CD) systems, and intelligent caching strategies across Content Delivery Networks (CDNs) to achieve sub-second page load times while maintaining development team productivity.

Modern e-commerce platforms demand sophisticated front-end architectures capable of delivering real-time promotional experiences across diverse customer touchpoints. Legacy monolithic front-end systems create deployment bottlenecks, team coordination overhead, and limited scalability during high-traffic promotional events. Customers expect instant price updates, personalized offer displays, and consistent promotional interfaces whether browsing web applications, mobile platforms, or in-store kiosks. Traditional single-page application architectures embed promotional UI logic within tightly coupled component hierarchies, constraining innovation velocity and independent feature deployment. Micro-frontend architectures address these limitations by decomposing promotional user interfaces into independently deployable modules aligned with business capabilities. Module federation enables runtime composition of promotional components developed by autonomous teams using diverse technology stacks. React-based component architectures provide declarative patterns for complex promotional state management and dynamic UI rendering. Event-driven front-end patterns facilitate real-time promotional updates through WebSocket connections and server-sent events, maintaining UI consistency across distributed system boundaries. Legacy UI modernization strategies employing strangler patterns enable gradual migration from monolithic front-end applications to distributed micro-frontend ecosystems. Performance optimization techniques including code splitting, lazy loading, and intelligent bundling strategies ensure promotional interfaces maintain responsiveness under heavy concurrent usage. Future advancement in front-end promotional systems will progressively integrate edge computing for personalized rendering and machine learning models for predictive UI optimization based on user behavioral patterns.

Digital merchandising interfaces have undergone a fundamental transformation from server-rendered templates into component-driven architectures powered by modern JavaScript frameworks. Contemporary e-commerce platforms require responsive user experiences managing dynamic product displays, real-time inventory updates, and personalized content delivery across diverse devices and network conditions. Legacy front-end systems create substantial barriers through monolithic codebases, tightly coupled dependencies, and inefficient rendering patterns. Monolithic JavaScript applications bundle entire feature sets into single deployment artifacts preventing independent team releases. State management through global variables and DOM manipulation introduces unpredictable behavior and debugging challenges. Build processes consuming extended time periods slow development velocity. UI modernization addresses these constraints through React-based component architectures decomposing interfaces into reusable modules with explicit boundaries. Micro-frontend patterns enable autonomous team development through runtime integration strategies including Module Federation. Typed interfaces across federated modules provide compile-time safety despite dynamic loading. State management architectures coordinate concurrent updates across distributed applications. Performance optimization employs code splitting, lazy loading, and virtual scrolling to handle extensive product catalogs within strict rendering budgets. Server-side and static rendering strategies balance initial load performance with interactive responsiveness. Component libraries and design systems establish visual consistency while enabling parallel development across distributed teams. Strangler fig migration patterns enable incremental replacement of legacy interfaces minimizing disruption. Usability validation ensures modernized experiences maintain or improve user engagement metrics.

This paper delves into the design and implementation details of a monitoring-education collaborative framework, addressing the integration challenges of real-time device monitoring and personalized skill development. Focusing on the robust backend architecture, the system employs MyBatis-Plus for efficient data persistence, Spring Boot for application orchestration, and Oracle 11g for data management. Key implementation aspects include a meticulously designed data access layer with optimized transaction management and connection pool configuration, comprehensive database performance monitoring and SQL optimization strategies, a Vue.js-based frontend architecture for role-specific interfaces, and a multi-layered security control mechanism integrating RBAC and ABAC. The implemented framework provides a stable, efficient, and secure technical foundation, enabling the practical realization of the monitoring-education collaboration concept and offering concrete solutions for building enterprise-level operation and maintenance support systems.

Regulated web systems require traceable, rollback-safe UI delivery, yet conventional static deployments and Boolean flagging struggle to provide per-user versioning, deterministic fallbacks, and audit-grade observability. The objective of this research is to develop and validate a runtime frontend architecture that enables per-session component versioning with deterministic fallbacks and audit-grade traceability for regulated systems. We present a dynamic frontend architecture that integrates typed GraphQL flag schemas, runtime module federation, and structured observability to enable per-session and per-route component versioning with deterministic fallbacks. We formalize a version-resolution function v = f(u, r, t) and implement a production system that achieved a 96% reduction in MTTR, a P90 fallback rate below 0.7%, and over 280 k session-level logs across 45 days. Compared to static delivery and standard flag evaluators, our approach adds schema-driven targeting, component-level isolation, and audit-ready render traces suitable for compliance. Limitations include cold-start overhead and governance complexity; we provide mitigation strategies and discuss portability beyond fintech.

This article presents the design and implementation of a novel time-division duplex (TDD) front-end architecture, namely, the Indirectly nonreciprocal load modulated balanced amplifier (INR-LMBA), targeted at emerging massive MIMO communication systems. Unlike conventional solutions that place a circulator at the power amplifier (PA) output to mitigate load mismatches, the proposed architecture relocates the circulator to the low-power control amplifier (CA) path. This significantly reduces power stress on the circulator by nearly an order of magnitude, thus paving the way for compact, nonmagnetic implementations. Combined with a transmit/receive (T/R) switch, INR-LMBA enables standard TDD functionality while offering quasi-isolation during transmission and low loss during reception. Under matched-load conditions, the architecture operates similar to a conventional pseudo-Doherty load modulated balanced amplifier (PD-LMBA). Under mismatched conditions, however, the circulator redirects reflected power to its isolation port, minimizing mismatch effects on the PA. As a proof-of-concept demonstration for this architecture, a 2–2.5-GHz INR-LMBA prototype is implemented, which achieves an efficiency of 62%–73% at peak output power and 53%–61% at 10-dB output back-off (OBO) over the in-band operation at 50-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $</tex-math> </inline-formula> load. In modulated evaluation with a 20-MHz OFDM signal, the measured average PA efficiencies are around 52% for a matched load with ACPR greater than 34 dB across the band of operation for matched load. The PA also exhibits strong mismatch resilience with higher than 43% average efficiency and >35-dB ACPR across the band for a mismatched load at 2:1 voltage standing wave ratio (VSWR).

The Topmetal CMOS pixel detectors employ metal electrodes for direct charge collection and have been widely adopted in gas pixel detectors (GPDs) for X-ray polarization measurements. The performance of these detectors, particularly in terms of dynamic range, event rate capability, and energy measurement accuracy, is critical to achieving high detection efficiency. This work presents a detailed analysis of the pixel front-end circuit architecture of Topmetal detectors and introduces a series of optimizations to enhance their performance. To validate the proposed improvements, a prototype pixel front-end ASIC was designed and fabricated using the GSMC 130 nm CMOS process. Each pixel primarily consists of a charge sensitive amplifier (CSA), a peak detect and hold (PDH) circuit, and a two-stage buffer. Simulation results demonstrate that the optimized pixel achieves an equivalent noise charge (ENC) of 33.25 e- + 0.42 e-/fF, a charge-voltage conversion gain of 69 μ V/e-, and a dynamic range of 26 k e-. Compared to previous Topmetal generations, the optimized pixel exhibits an extended dynamic range, improved linearity, enhanced energy measurement accuracy, and increased event rate capability (supporting up to 10 kcps), demonstrating its potential for large-scale integration in next-generation pixel detectors for X-ray polarization detection missions.

Peripheral artery disease (PAD) is a progressive vascular condition affecting >237 million individuals worldwide. Accurate diagnosis and patient-specific treatment planning are critical but are often hindered by limited access to advanced imaging tools and real-time analytical support. This study presents DECODE, an open-source, cloud-based platform that integrates artificial intelligence, interactive 3D visualization, and computational modeling to improve the noninvasive management of PAD. The DECODE platform was designed as a modular backend (Django) and frontend (React) architecture that combines deep learning-based segmentation, real-time volume rendering, and finite element simulations. Peripheral artery and intima-media thickness segmentation were implemented via convolutional neural networks, including extended U-Net and nnU-Net architectures. Centreline extraction algorithms provide quantitative vascular geometry analysis. Balloon angioplasty simulations were conducted via nonlinear finite element models calibrated with experimental data. Usability was evaluated via the System Usability Scale (SUS), and user acceptance was assessed via the Technology Acceptance Model (TAM). Peripheral artery segmentation achieved an average Dice coefficient of 0.91 and a 95th percentile Hausdorff distance 1.0 mm across 22 computed tomography dataset. Intima-media segmentation evaluated on 300 intravascular optical coherence tomography images demonstrated Dice scores 0.992 for the lumen boundaries and 0.980 for the intima boundaries, with corresponding Hausdorff distances of 0.056 mm and 0.101 mm, respectively. Finite element simulations successfully reproduced the mechanical interactions between balloon and artery models in both idealized and subject-specific geometries, identifying pressure and stress distributions relevant to treatment outcomes. The platform received an average SUS score 87.5, indicating excellent usability, and an overall TAM score 4.21 out of 5, reflecting high user acceptance. DECODE provides an automated, cloud-integrated solution for PAD diagnosis and intervention planning, combining deep learning, computational modeling, and high-fidelity visualization. The platform enables precise vascular analysis, real-time procedural simulation, and interactive clinical decision support. By streamlining image processing, enhancing segmentation accuracy, and enabling in-silico trials, DECODE offers a scalable infrastructure for personalized vascular care and sets a new benchmark in digital health technologies for PAD.