Open Source Technologies

Assessing technology maturity in open source hardware ecosystems presents specific challenges connected to the openness of the technologies involved. Traditional Technology Readiness Levels (TRLs) and other complementary frameworks cannot fully address the readiness assessment of open source technologies, as they rely on intellectual property-based mechanisms, assume well-defined contexts for implementation, and lack a comprehensive evaluation of the adoption challenges in open source systems. This article presents the Open Source Technology Readiness Levels (OSTRLs), a new readiness assessment framework for evaluating the openness maturity of technologies to be integrated into open source ecosystems, considering critical aspects such as intellectual property freedom and accessible documentation. Based on TRLs and open design principles, the OSTRLs classify the openness readiness of technology across nine levels, from potential to full openness, considering product and process openness, i.e., transparency, replicability, reusability, and accessibility. The use of OSTRLs is contextualized with other complementary assessment tools to show their potential use as part of a comprehensive evaluation of the aspects connected to the implementation of open source technology. The use of the OSTRLs is then validated by assessing open source technologies in the distributed recycling for additive manufacturing (DRAM) ecosystem, a model that combines localized plastic recycling and 3D printing. The OSTRLs provide a structured way to assess open source technology ecosystems, such as DRAM, identifying not only mature solutions or areas for improvement in openness, but also existing trends, opportunities, risks, and gaps in adoption. This framework can support researchers, practitioners, institutions, and policymakers in strategic decision-making and guide future research toward sustainable and circular innovation systems.

Hypernasality is a common characteristic of several speech disorders and can significantly affect perceived speech intelligibility and quality. Nasometry quantifies nasalance by calculating the proportion of acoustic energy emitted from the nasal cavity relative to the combined nasal and oral acoustic output during speech production and is commonly used in clinical assessment and research. However, commercially available nasometers are costly and limited in portability, restricting their use in resource-limited or remote settings. The primary purpose of this study was to design and build a low-cost, open-source mobile nasometer prototype (“mNasometer”) by leveraging advances in 3D printing, off-the-shelf electronic components, and a custom open-source mobile application. A secondary aim was to compare the electroacoustic and subjective performance of mNasometer with that of a gold-standard commercial nasometer. Electroacoustic analyses focused on comparing long-term averaged spectra and the oral/nasal acoustic isolation between the gold-standard commercial nasometer and the proposed mNasometer, which incorporates a 3D-printed nasal separation plate. In addition, nasalance scores were collected from ten healthy young adult participants using both systems during structured speech production tasks (i.e., reading standard passages or nasal sentences). Agreement between devices was evaluated using correlational analyses and comparative statistical procedures. Long-term averaged spectra exhibited similar profiles between the commercial nasometer and the mNasometer across different test stimuli, indicating comparable capture of stimulus energy distributions. Although the mNasometer demonstrated reduced oral–nasal acoustic isolation relative to the commercial system, objective nasalance scores followed similar overall trends between devices, with statistically significant stimulus-dependent differences observed. Frame-wise correlational analyses revealed significant correlations between nasalance measures obtained from the commercial nasometer and the mNasometer across most of the speech production tasks, suggesting that the reduced isolation did not critically compromise measurement correspondence. In summary, the low-cost, open-source mNasometer prototype provides nasalance measurements that show promising agreement with those of a gold-standard commercial device. Its reduced cost and increased portability suggest potential for expanded research and field-based applications in the objective assessment of nasalance.

HAIA-CAIPR (Cross AI Platform Review) is the governance protocol for human orchestration of parallel multi-AI execution within the HAIA framework. Positioned between HAIA-RECCLIN and Checkpoint-Based Governance, CAIPR specifies how a human governor dispatches identical prompts to multiple independent AI platforms, collects their structured outputs, compares convergence and divergence, detects hallucinations through cross-validation, governs the AI synthesizing multiple platform outputs, and maintains constitutional source-authority discrimination throughout.

The specification emerged from eight months of daily operational practice across eleven AI platforms, applied to two editions of a full-length book, a Congressional policy package, two academic submissions, long-form white papers, and a blog archive exceeding one thousand posts. Four findings were predicted by theory; twenty-nine were discovered through practice. That ratio of discovered to predicted knowledge is itself a finding.

This revised edition integrates edits from a seven-platform parallel review round conducted in April 2026. Section 3.6 cross-references the GOPEL Discretionary Audit Policy. The Executive Summary ratio sentence is bounded to the current operational record. Section 4.4 rewrites the generalist paragraph using a general contractor analogy to make the generalist-plus-specialist structure explicit.

Section 3.9 documents Prompt-Type Governance as a cross-cutting dispatch variable: structured prompts with fixed parameters produce source-level diversity across platforms, while open prompts without fixed parameters produce perspective-level diversity. This distinction converts prompt type from a style choice into a governance variable calibrated to whether a session requires evidence breadth or framing breadth.

The specification presents eight core operations, three-tier source-authority discrimination (Tier 0 human arbiter, Tier 1 raw platform output, Tier 2 synthesizer output), eight mandatory protocol requirements, fifteen operational failure modes with documented resolutions, and a four-level adoption ladder (Factics, RECCLIN, CAIPR, GOPEL). Four open questions are named explicitly, including convergent error detection where platforms agree on shared training-data errors, architectural independence under industry consolidation, and the partial structural answer provided by the Verified AI Inference Standards Act.

The rapid expansion of digital communication technologies has fundamentally transformed the nature of evidence used in criminal investigations and trials. Social media platforms have become a major source of communication where individuals exchange messages, images, videos, and personal opinions. These platforms have increasingly become relevant in criminal investigations as they often contain valuable information related to criminal activities, motives, and interactions between individuals involved in a crime.

VoXLinux is a Linux-based system enhancement built on Arch Linux that combines autonomous background selfhealing, user-controlled intent execution and a safe conversational assistance module to improve system reliability and usability.
The system continuously monitors system services, running processes, boot integrity, and package states through a dedicated
self- healing daemon that which is designed and implemented using a deterministic state-driven architecture.

Scientific progress is held back by the high cost of proprietary equipment and the restrictive nature of patents, which slow innovation and limit scientific novelty. Free and open-source hardware offers a proven alternative, reducing costs generally by more than 90% for equivalent or lesser proprietary hardware while accelerating technological development through collaborative design and distributed digital manufacturing as well as commercial pathways. Despite these benefits, funding for scientific hardware development predominantly follows the antiquated proprietary model, creating a gap between scientists’ ability to purchase proprietary equipment and their inability to finance lower-cost open source alternatives. This article analyzes four financial models for open hardware development: (1) philanthropy model, where funders (non-profits or governments) shoulder all design risks; (2) standard investor model, where investors assume risk for design and sales in order to earn a return on investment (ROI); (3) crowd-sourced model, where the scientific community funds development and shares risk; and (4) a new decoupled risk investor model, which separates open hardware design risk from risk of an ROI by introducing a guarantor. A case study demonstrates that the decoupled risk investor model provides success for conventional science funders at marginally higher cost while enabling global access to low-cost designs and healthy ROIs with lower risk for investors. Comparative analysis highlights advantages and limitations of each approach, providing actionable recommendations for science funders. This work aims to derisk open hardware design financing, expand adoption, and democratize access to scientific tools globally while fostering innovation and cost savings across research disciplines.

Through years of research through public workshopologies on the project Open Source Estrogen, biohacking methodologies have proven to serve far more than spreading didactic knowledge. These protocols that produce an existential knowing in our bodies and environments inevitably lead to a form of collective worlding and knowledging, strategies that may help us out of ecological ruins. Combining biohacking with performance in a new dramaturgical workshop, Performing the Sublime Sea of Co-Mattering, participants embody the very agency of toxic molecules and their ongoing process of worlding, emerging on the other side with a radical breakage from the past.

Digital transformation in the food service industry has created a need for intelligent, scalable, and secure online food ordering systems. This research proposes a MERN-based platform (MongoDB, Express.js, React.js, Node.js) designed to handle high user concurrency, remote accessibility, responsive UI, secure transactions, and modular food management. The system includes user authentication, cart functionality, checkout workflow, payment integration, and an administrator dashboard for menu and order management. Performance evaluations were conducted to analyze system throughput, API latency, data consistency, and load behavior. Results show that MERN-based systems deliver excellent scalability for small to medium enterprises due to their non-blocking I/O, NoSQL schema flexibility, and frontend reactivity. The platform thus offers a robust foundation for modern food delivery ecosystems and can be extended to enterprise-level applications with microservices and real-time capabilities.

Khmer Natural Language Processing (NLP) has emerged as a vital field for enabling digital tools and services in the Khmer language, serving millions of native speakers in Cambodia and abroad. This review paper investigates the development and current state of five key Khmer NLP technologies: text recognition, speech recognition, text summarization and generation, machine translation, and optical character recognition (OCR). The paper reviews existing tools and frameworks, the purpose behind their development, the algorithms and technologies used, and the institutions or individuals behind each project. Furthermore, it highlights the technical and linguistic challenges faced in processing the Khmer language, such as lack of training data, script complexity, and underrepresentation in international NLP communities. Finally, we explore the societal impact of these technologies and suggest future directions that can foster inclusive digital innovation for the Khmer-speaking population.

Expense and the logistical difficulties with deploying scientific monitoring equipment are the biggest limitations to undertaking large scale monitoring of aquatic environments. The Smart Environmental Monitoring and Assessment Technologies (SEMAT) project is aimed at addressing this problem by creating an open standard for low-cost, near real-time, remote aquatic environmental monitoring systems. This paper presents the latest refinement of the SEMAT system in-line with the evolution of existing technologies, inexpensive sensors and environmental monitoring expectations. We provide a systems analysis and design of the SEMAT remote monitoring units and the back-end data management system. The system’s value is augmented through a unique e-waste recycling and repurposing model which engages/educates the community in the production of the SEMAT units using social enterprise. SEMAT serves as an open standard for the community to innovate around to further the state of play with low-cos...

Membangun dan Mengelola Validator Node Solana dengan Ubuntu Server adalah panduan lengkap bagi sysadmin pemula hingga SRE profesional untuk launch validator profitabel dari Rp100 juta modal menjadi Rp121 juta ROI tahun ketiga, mencakup Ubuntu setup, Solana CLI, vote account, Grafana monitoring, HA scaling, SOL deposit/withdraw, troubleshooting skip rate <0.5%, delegator acquisition via Discord Indo, hingga DCF model Bappebti-compliant—dari 14 hari live validator hingga top 100 trajectory dengan Hetzner AX162 Rp5jt/bln menghasilkan Rp22jt rewards bulanan.

Peat-based planting cups, widely used in horticulture, contribute to significant environmental degradation due to peatland destruction. This study introduces an open-source, low-cost method for converting polypropylene (PP) cups into planting containers using a modified 3D printer equipped with a hot knife for automated hole punching. The system enables precise, repeatable modifications that support plant growth through improved drainage and aeration. Experimental validation showed that modified PP cups support comparable crop yields to peat cups, with only a slight reduction offset by substantial economic and environmental benefits. Peat cups, which must be replaced after every growing cycle, impose higher long-term economic costs. For relatively lowvalue kale, despite higher experimental yields the net profit advantage of peat cups was only 2.2 % over the agrivoltaic agrotunnel system lifetime. In contrast, relatively high-value spring mix experimentally cultivated in PP cups generated higher profit from the first year onward for a total of $5.3 million in the same system. Life cycle assessment revealed that, per use, peat cups have approximately 4.5 times higher carbon footprint and 35 % greater energy consumption than single-use PP cups. When reused over their lifetime of 250 cycles, PP cups reduce total material demand by ~98 % and lower lifetime energy and carbon impacts by up to 59 % and 77 %, respectively. Even under unfavorable dishwashing scenarios, environmental impacts of reusable PP cups remain 44-53 % lower than peat. This approach offers a scalable, sustainable alternative for peat cups in vertical farming of controlled environment agriculture.

Digital transformation in the food service industry has created a need for intelligent, scalable, and secure online food ordering systems. This research proposes a MERN-based platform (MongoDB, Express.js, React.js, Node.js) designed to handle high user concurrency, remote accessibility, responsive UI, secure transactions, and modular food management. The system includes user authentication, cart functionality, checkout workflow, payment integration, and an administrator dashboard for menu and order management. Performance evaluations were conducted to analyze system throughput, API latency, data consistency, and load behavior. Results show that MERN-based systems deliver excellent scalability for small to medium enterprises due to their non-blocking I/O, NoSQL schema flexibility, and frontend reactivity. The platform thus offers a robust foundation for modern food delivery ecosystems and can be extended to enterprise-level applications with microservices and real-time capabilities.

El present document detalla, desglossant la informació en diferents capítols, les tasques realitzades per a la construcció d'un Sistema d'Informació Geogràfica (SIG) de carreteres utilitzant el producte GEOMEDIA PROFESSIONAL 5.2 de INTERGRAPH. S'inicia el document detallant la planificació de tasques i activitats que s'ha seguit, per a continuació explicar què és un SIG, per a que serveix, quins usos pot tenir i què el diferencia d'altres productes similars com ara un programari de disseny assistit per ordinador (CAD) o un sistema de teledetecció. Com que un SIG està fortament lligat a la cartografia es continua amb un capítol on s'expliquen nocions de cartografia amb les quals l'autor d'aquest treball ha hagut de familiaritzar-se i aprendre, essent també fortament recomanables, i pràcticament imprescindibles, per a aquell lector que no conegui gaire de cartografia, doncs permetran comprendre tant les explicacions i argumentacions com el resultat d'aquest projecte de final de carrera (PFC). Més endavant s'estudien els formats d'intercanvi d'informació geogràfica, que com es veurà poden ser classificats en dos tipus en funció de com s'emmagatzema la informació: a partir d'un mostreig de la imatge (conegut com a format raster) o contenint dades poligonals (conegut com a format vectorial). Un cop el lector tingui les nocions de cartografia i conegui els diferents formats en què es poden trobar les dades es fa una petita introducció a GEOMEDIA PROFESSIONAL 5.2, en la qual s'expliquen els conceptes bàsics d'aquest producte. Amb aquests conceptes explicats es passa a detallar el model de dades del SIG de carreteres. A continuació es parla de com emmagatzema GEOMEDIA PROFESSIONAL 5.2 la informació. S'introdueixen els diversos sistemes gestors de base de dades (SGBD) amb els quals pot treballar. També es comparen els models de metadades generats a ORACLE i a ACCESS i es justifica la tria d'aquest últim per dur a terme el PFC. Tenint clar el SGBD a utilitzar i el model de dades del SIG s'explica el procés seguit per a la importació de les dades que conté el SIG de carreteres i els problemes trobats tant en aquest procés com en la recerca d'informació. Abans de passar a l'apartat de proves del sistema i conclusions, el document detalla el disseny de la interfície d'usuari, part força important en projectes aquest tipus, doncs degut a la gran quantitat d'informació que un SIG pot arribar a mostrar alhora és imprescindible una interfície clara que permeti a l'usuari entendre ràpidament el que està veient. Finalment es comenten les conclusions a les que s'ha arribat, destacant la validesa de GEOMEDIA PROFESSIONAL 5.2 per a la realització del prototipus. ENGINYERIA EN INFORMÀTICA Creació d'un Sistema d'Informació Geogràfica de Carreteres amb GEOMEDIA PROFESSIONAL 5.2 CARLOS VILLA FERRER Curs 2004-05 (Septembre) Pàgina 4 de 80 El presente documento detalla, desglosando la información en diferentes capítulos, las tareas desarrolladas para la construcción de un Sistema de Información Geográfica (SIG) de carreteras utilizando para ello el producto GEOMEDIA PROFESSIONAL 5.2 de INTERGRAPH. El documento comienza detallando la planificación de tareas y actividades que se ha seguido, explicando a continuación que es un SIG, para que sirve, que usos puede tener y que lo diferencia de otros productos similares, como podría ser un programa de diseño asistido por ordenador (CAD) o un sistema de teledetecció. Como un SIG esta fuertemente relacionado con la cartografía se continua con un capitulo donde se explican las nociones de cartografía con las que el autor del presente proyecto ha tenido que familiarizarse y aprender, siendo también altamente recomendables, y prácticamente imprescindibles para aquel lector que no conozca demasiado de cartografía, pues permitirán la compresión tanto de las explicaciones y argumentaciones como del resultado de este PFC. Más adelante se estudian los formatos de intercambio de información geográfica, que como se verá pueden ser clasificados en dos tipos en función de como se almacene la información: a partir de un muestreo de la imagen (conocido como formato raster) o conteniendo datos poligonales (conocido como formato vectorial). Una vez el lector posea las nociones de cartografía y conozca los diferentes formatos en los que se pueden encontrar los datos se hace una pequeña introducción a GEOMEDIA PROFESSIONAL 5.2, explicando los conceptos básicos que permiten dar una idea general de lo que permite hacer y a través de que elementos se construye un SIG. Con estos conceptos explicados se pasa a detallar el modelo de datos del SIG de carreteras. A continuación se habla del modelo de meta datos de GEOMEDIA PROFESSIONAL 5.2, sobre como almacena los datos y de las bases de datos a las que da soporte, done se explican las diferencias existentes entre escoger un sistema gestor de bases de datos (SGBD) u otro, destacando la comparación del modelo de meta datos de ORACLE versus el de ACCESS y el porqué de la elección del SGBD ACCESS para el SIG objeto del presente PFC. Quedando claro el SGBD a utilizar y el modelo de datos del SIG, se explica el proceso seguido para la importación de los datos que contiene el SIG de carreteras y los problemas encontrados al buscar e importar dichos datos. Por último, y antes de pasar al apartado de pruebas del sistema i conclusiones, el documento detalla el diseño de la interfaz de usuario, parte muy importante en proyectos de este tipo, pues debido a la gran cantidad de información que puede llegar a mostrar simultáneamente un SIG es imprescindible una interfaz clara que permita al usuario entender rápidamente lo que esta viendo. ENGINYERIA EN INFORMÀTICA Creació d'un Sistema d'Informació Geogràfica de Carreteres amb GEOMEDIA PROFESSIONAL 5.2 CARLOS VILLA FERRER Curs 2004-05 (Septembre) Pàgina 5 de 80 This document details, detachhing the information in different chapters, the tasks developed for the construction of a Geographic Information System (GIS or SIG in catalonian) of highways and roadways using for it an INTERGRAPH's product: GEOMEDIA PROFESSIONAL 5.2. Document begins detailing the planning followed for doing tasks and activities, and continues explaining what a SIG is, what a SIG is used for and explains the differences it has with other similar products, as it could be Computer Aided Dessign software (CAD) or a SCADA system (Supervisory Control And Data Acquisition). As a SIG this strongly related to cartography, document continues with a chapter that explains cartographic concepts which this project's author has had to become familiar and has had to assimilate. In order to allow compression of the explanations and argumentations on this project, that chapter is highly recomendable, and essential for the reader who doesn't know much about cartography. Formats of geographic information exchange are explained after the introduction of cartographic concepts, it's important to say here that these formats can be classified in two types based on how information is stored: from a image sampling (well-known like raster format) or containing polygonal data (well-known like vectorial format). Once reader has cartography slight knowledge and knows the different formats in which data can be found, this document makes a small introduction to GEOMEDIA PROFESSIONAL 5.2, explaining its basic concepts that allow to give a general idea of how it allows to do operations and how a GIS is constructed with it. With these explained concepts one goes to detail the data modeling of the SIG of highways. Next, the metadata of GEOMEDIA PROFESSIONAL 5.2 is explained, detailing how it stores data and explaining which data bases are suported by it and differences between choosing a database manager or another one. The author emphasizes the comparison of the ORACLE metadata model versus the ACCESS one and why ACCESS database has been chosed for this SIG project. Document continues explaining the data import to the GIS and problems found when looking for data and to manipulate and puting into the GIS these data. Finally, and before system testing and conclusion chapters, user interface design is detailed, this is a very important part in projects of this type, because due to the great amount of information that a GIS can show simultaneously is essential to have a clear interface which allows the user to understand quickly what's seeing on display. ENGINYERIA EN INFORMÀTICA Creació d'un Sistema d'Informació Geogràfica de Carreteres amb GEOMEDIA PROFESSIONAL 5.2 CARLOS VILLA FERRER Curs 2004-05 (Septembre) Pàgina 6 de 80 Índex Índex de continguts Dedicatòria i agraïments .

A l g o r i t h m i c T r a d i n g , M E R N F r a m e w o r k , F i n a n c i a l A n a l y t i c s P a p e r t r a d i n g i s a n e s s e n t i a l c o m p o n e n t o f m o d e r n f i n a n c i a l l e a r n i n g , e n a b l i n g t r a d e r s , s t u d e n t s , a n d r e s e a r c h e r s t o p r a c t i c e m a r k e t o p e r a t i o n s w i t h o u t r i s k i n g r e a l c a p i t a l. H o w e v e r , m o s t e x i s t i n g p l a t f o r m s f a i l t o p r o v i d e r e a l i s t i c s i m u l a t i o n e n v i r o n m e n t s , d a t ad r i v e n s t r a t e g y e v a l u a t i o n , a n d p e r s o n a l i z e d p o r t f o l i o i n s i g h t s. T o a d d r e s s t h e s e l i m i t a t i o n s , t h i s s t u d y p r o p o s e s P a p e r T r a d e A I , a m u l t i m o d a l f r a m e w o r k b u i l t u s i n g M E R N a n d P y t h o n. T h e s y s t e m i n t e g r a t e s h i s t o r i c a l m a r k e t d a t a , s i m u l a t e d l i v e f e e d s , a n d u s e r-d e f i n e d t r a d i n g s t r a t e g i e s t o e n h a n c e s i m u l a t i o n a c c u r a c y. T h e a r c h i t e c t u r e e m p l o y s a N o d e. j s b a c k e n d f o r o r d e r v a l i d a t i o n , a P y t h o n-b a s e d s i m u l a t i o n e n g i n e t h a t m o d e l s s l i p p a g e , l i q u i d i t y , a n d v o l a t i l i t y , a n d M o n g o D B f o r p o r t f o l i o a n d t r a n s a c t i o n s t o r a g e. F u r t h e r m o r e , t h e f r a m e w o r k i n c o r p o r a t e s A Id r i v e n r i s k a n a l y t i c s a n d s t r a t e g y p e r f o r m a n c e e v a l u a t i o n t o p r o v i d e p e r s o n a l i z e d i n s i g h t s f o r t r a d e r d e c i s i o n s u p p o r t .

In the rapidly evolving digital era, the exponential growth of web-based applications and online services has made cybersecurity a critical concern for organizations and individuals alike. Modern cyber adversaries continuously exploit serverside vulnerabilities, often launching sophisticated, stealthy, and multi-vector attacks that can bypass traditional perimeter defenses. The complexity and volume of these threats necessitate an adaptive and intelligent approach to intrusion detectionone capable of analyzing real-time data, learning from patterns, and autonomously responding to anomalies. To address this challenge, the present study introduces a Web Log-Based Intrusion Detection System (IDS), a comprehensive framework engineered to detect, analyze, and mitigate security threats by leveraging the potential of automated log analytics and intelligent data-driven mechanisms. The proposed IDS system operates on the principle that web server log files encapsulate vital behavioral signatures of both legitimate and malicious activities. By employing pattern recognition, anomaly detection, and rule-based classification algorithms, the system effectively identifies abnormal behaviors such as unauthorized access attempts, brute-force login trials, SQL injection (SQLi) patterns, cross-site scripting (XSS) payloads, directory traversal attempts, and the injection of malicious scripts or malware. A significant advantage of this approach lies in its continuous, real-time monitoring capability, which enables proactive threat detection and rapid incident response without the need for constant manual supervision. The system's architecture is composed of multiple integrated modules, including log parsing, data preprocessing, signature extraction, threat categorization, visualization, and automated response. The log parsing component filters and structures raw data into meaningful attributes, while the anomaly detection engine utilizes hybrid methodologies-combining heuristic rules with statistical and machine learning models-to enhance detection precision and minimize false positives. Upon identifying a potential threat, the system not only generates immediate alerts but also provides contextual recommendations for remediation, such as blocking malicious IPs, adjusting access permissions, or strengthening input validation mechanisms. An interactive graphical dashboard interface further augments system usability by visualizing detected threats based on severity levels, timestamps, geographic origin, and frequency. The dashboard presents intuitive charts and analytical reports that assist cybersecurity professionals in understanding evolving attack patterns and improving defense strategies. In addition, the system's modular and scalable design allows for seamless integration into existing network infrastructures, making it suitable for deployment across organizations of varying sizes. By incorporating automated detection, intelligent decision-making, and self-learning feedback loops, the proposed IDS system transcends conventional rule-based monitoring approaches. It significantly reduces the time and effort required for manual log inspection, enabling security teams to focus on strategic defense enhancement. The experimental evaluation and simulated attack scenarios demonstrate that the Web Log-Based IDS effectively identifies critical security events with high accuracy and low latency, ensuring enhanced reliability, resilience, and adaptability in complex cyber environments. In conclusion, this research underscores the importance of data-driven, autonomous intrusion detection frameworks in fortifying modern web infrastructures against evolving cyber threats. The proposed system not only strengthens the detection and response mechanisms of existing security operations but also contributes to the broader vision of developing intelligent, selfadaptive cybersecurity solutions that can evolve in tandem with the continuously transforming digital threat landscape.

The 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.

Environmental and energy production monitoring systems not only provide data acquisition (DAQ) but now supervisory control and data acquisition (SCADA) for both meteorological and solar photovoltaic (PV) research. DIY systems are often not robust enough for research and proprietary systems are often economically prohibitive. The Jericho Open Resistive Data Logger (RDL) platform bridges this gap between low-cost DIY devices and high-cost proprietary DAQs. It integrates a custom RDL, Arduino Nano, modular I2C expansion, and a Raspberry Pi for edge processing into a robust, open-source platform. Supporting multiple sensor protocols (analog, digital, resistive, I2C, SDI-12, and USB) and long-distance wired transmission, the system enables reproducible, research-grade data collection at less than half of the cost of proprietary stations. Statistical comparison of irradiance, relative humidity and temperature and wind speed were bench marked against a proprietary system and found to be well within acceptable differences for validation although wind speed was found to have the highest deviation. Two independent open-source units confirm excellent inter-device repeatability across all measured variables. By combining environmental and PV monitoring within a unified platform, Jericho Open RDL provides an accessible and adaptable solution for distributed renewable energy and environmental research.

Educational technology (edtech) products are ubiquitous in schools, yet there is a dearth of research examining their use and efficacy. This leaves schools and districts without evidence to inform important decisions about edtech budgeting, instruction, impact, and implementation. We report results from a study that uncovered startling trends in edtech usage across multiple paid products and dozens of schools. Notably, 36.6% of purchased student licenses were never used. An additional 28.2% of the licenses were used negligibly, failing to meet a quarter of the fidelity goal set by the product companies or districts. Further, anecdotal evidence suggests schooland district-level leaders are unaware of these realities. This suggests a vast amount of resources are being unknowingly squandered or misallocated. Combined with analysis of how product usage impacts student achievement, these results demonstrate how schools and districts can utilize data to understand and manage their edtech ...

This paper presents the design and implementation of a laboratory module utilizing Arduino-based environmental sensors to enhance students' understanding of environmental measurement and digital instrumentation. The module includes hands-on experiments with temperature sensors, humidity sensors, light sensors, rain detection modules, air pressure sensors, gas and smoke sensors, and wind measurement devices. Designed for 10 structured sessions, the laboratory provides practical experience in sensor calibration, data acquisition, and real-time monitoring. Results show that students gained improved technical skills and a deeper understanding of environmental dynamics through digital sensing technologies.

This paper presents the technological innovations introduced to the Archaeological Map of Ancona (Italy) in 2024, when the platform was first published online. The new IT structure is based on the use of two open-source software: Directus for database management and Geonode for the open access distribution of archaeological datasets supported by relevant metadata.

Severe neonatal hyperbilirubinemia (SNH) (jaundice) is responsible for over 114,000 preventable neonatal deaths annually, as the technology that can treat the condition is cost-prohibitive for low-and middle-income countries. In this study an open-source neonatal phototherapy device (NPTD) to treat SNH was designed, built, and validated against the phototherapy technical specifications set by the American Academy of Pediatrics and UNICEF. The opensource device can be built for a tenth of the cost of the least expensive proprietary one on the market, with treatment metrics equivalent to or exceeding commercial devices available in developed nations. This device, whose material costs are USD 93.00, was shown to deliver an irradiance up to 80 µW/cm 2 /nm, within the acceptable wavelength range of 420-500 nm. It was further demonstrated that the unit could deliver a uniform distribution of irradiance (34.5 ± 4.3 µW/cm 2 /nm) over a surface area exceeding 3200 cm 2. These findings show that the open-source NPTD is capable of delivering accurate, consistent, and reliable irradiances for the management of SNH. By releasing full documentation in an open-source manner, the device may be broadly used to ensure affordable and consistent low-cost means of improving the quality of care for SNH.

Argus presents a modular and scalable framework designed to efficiently manage a broad spectrum of computer vision tasks through specialized expert models. At the core, the system employs Multimodal Large Language Models (MLLMs) as instruction-driven routers that intelligently delegate tasks such as image generation, object detection, video analysis, and 3D transformation. Unlike conventional monolithic approaches that struggle with task diversity[1], Argus enables flexible and optimized task handling by adopting a controlled, modular architecture. The framework is trained using supervised learning for vision-language tasks and further refined through reinforcement learning[2], improving routing strategies and overall execution. Task-specific routing tokens are incorporated to support multi-step workflows, allowing sequential task execution from a single user instruction. Key machine learning methods, including the Adam optimizer for efficient convergence and cross-entropy loss for accurate task-specific optimization, form the foundation of the training process. The architecture also allows seamless integration of new expert models, ensuring adaptability, scalability, and computational efficiency. With its modular design, Argus provides a robust and future-ready solution that can evolve alongside advancements in the computer vision domain.

Preparation for interviews is an important step for success in academics and careers. Traditional false interviews require human participation, making them expensive, slow and hard scales. This article presents an AI operated mockery interview program that uses deep learning, Bhavna AI and web technologies to offer real-time automatic response. This system looks at facial expressions, feelings and reactions from the candidates during the practice of practice. This creates a personal performance report. Manufactured with a React frontier, Fastpi Backndar and Mongodb database, the frame dims for emotion recognition and Deeplus for Tensorflow. Experimental analysis suggests that the system can identify emotional stages such as happiness, sadness, anger, surprise, fear and neutrality. The results indicate a remarkable increase in confidence and selfassessment of the candidate than the methods of traditional preparation. This system can help educational institutions, career centers, corporate training programs and individual users. Future updates may include Gameifications to improve voice analysis, NLP-based response assessment and engagement.

Educational crowdfunding has emerged as a transformative mechanism to address financial barriers to learning, enabling students to fund tuition, books, and specialized programs. However, widespread donor mistrust, stemming from unverified campaigns, fraudulent activities, and opaque fund allocation, limits its effectiveness on general platforms like GoFundMe and Kickstarter. This paper introduces a secure, full-stack web application specifically designed for merit-based educational crowdfunding, offering a tailored solution to these challenges. The platform leverages a modern technology stack, including React and Chakra UI for an intuitive frontend, Node.js with Express.js and JSON Web Tokens (JWT) for a secure backend, MongoDB for scalable data storage, and integrations with Stripe for payment processing, Cloudinary for media uploads, and SendGrid for automated notifications. The methodology employs a novel merit verification module combining machine learning (logistic regression) with manual admin review, targeting 95% accuracy to authenticate student credentials, alongside a comprehensive testing strategy encompassing unit, integration, functional, performance, security, usability, and regression testing to ensure system reliability. Results from rigorous testing reveal a 98% success rate in payment processing, sub-200 ms latency with up to 100 concurrent users, and an 88% user satisfaction rate, demonstrating the platform's scalability, security, and user-friendliness. The system effectively reduces fraud through transparent transactions and real-time funding updates, fostering donor confidence and enabling equitable access to education for meritorious students, particularly from underprivileged backgrounds. As of August 25, 2025, this work contributes significantly to educational equity and social mobility, with potential applications in global contexts. Future enhancements could include blockchain-based credential storage for enhanced security, natural language processing for automated verification, and a mobile application to broaden accessibility. This platform stands as a model for technology-driven innovation in addressing educational funding gaps.

Chatbots in the last few years have been the subject of growing interest as smart conversational agents for humancomputer interaction in various applications. Most current systems, however, have limitations like insufficient flexible voice control, limited document support, and minimal user-friendly capabilities. To overcome these drawbacks, this paper introduces a web-based chatbot system designed using Flask as the backend and JavaScript as the frontend. The system incorporates cuttingedge features like dark mode for user ease, text-to-speech with voice toggle control, selective text reading, microphone-based speech input, and document upload (PDF, DOCX, TXT) for content-based question answering. The backend utilizes the OpenRouter API to provide accurate responses, while the frontend offers an interactive and accessible interface. Experimental evaluation and user feedback emphasize that the suggested system provides greater usability, flexibility, and engagement over traditional chatbots. This research shows the possibility of integrating multi-modal capabilities with lightweight web technologies to enhance user experience in conversational AI systems.

This paper introduces the design and development of a web-based Real Estate Management System (REMS) that facilitates hassle-free interactions between buyers and sellers of property via an efficient and secure platform. Conventional real estate transactions, which are usually based on offline procedures or isolated online postings, are marred by issues like deficiency in transparency, inefficiency, and limited access. The system proposed herein solves these problems by offering a central, database-based property registration, listing management, and buyer-seller communication solution. The backend is assisted with a Database Management System (DBMS) for the secure storage, efficient retrieval, and scalable management of property information and user accounts. The frontend employs an easy-to-use interface, facilitating the registration of sellers and updating of property information as well as the searching, filtering, and browsing of properties by location, budget, and size preference for buyers. The system also has additional features such as authentication techniques to secure sensitive user information and direct contact capabilities for ease of communication. Through the incorporation of database security, responsive layouts, and real-time access to listings, the system increases transparency, efficiency, and trust in property dealings. This project is helping achieve the digitalization of the real estate industry through offering an affordable and scalable platform for contemporary real estate management requirements.

Cellular ceramic structures were fabricated via 3D printing of thermoplastic polyurethane (TPU) followed by impregnation with polysilazane, and pyrolysis. The 3D printing was performed using fused filament fabrication (FFF), while the ceramic was obtained through the polymer derived ceramic (PDC) process starting from a commercially available polysilazane, Durazane 1800. We investigated the role of ester-and ether-based TPUs with two different Shore hardness (90A vs 80A) on the impregnation of polysilazane. Regardless of the TPU type and Shore hardness, impregnation of the TPU 3D structure was successful and resulted in dense, non-hollow ceramic struts after pyrolysis. All polyester-and polyether-based TPUs showed a similar mass and volume increase after impregnation with high deviation. The mass loss during pyrolysis was also very similar for all the TPUs. The behavior of these TPUs was then compared with one commercial TPU filament (Ninjaflex with a Shore hardness of 85A). While the Ninjaflex 3D-printed structures showed a greater increase in mass and volume after impregnation, the pyrolysis outcome was almost identical to that of the samples fabricated with both ester-and ether-based TPUs, resulting in dense, non-hollow ceramic struts.

Transcription bottlenecks", created by a shortage of effective human transcribers are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yoloxóchitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation.

About the Book

This book serves as a structured and in-depth resource for engineering students, professionals, and researchers seeking to build expertise in Artificial Intelligence. Starting with core concepts in mathematics and statistics, the book gradually advances through Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Generative AI, ultimately leading to an in-depth exploration of Agentic AI and its potential impact on the future.

Key Features

Balanced Approach: Integrates theory with hands-on practice, including code snippets, exercises, and full project implementations.
Step-by-Step Learning Journey: Starts with foundational math and statistics, moving through ML, DL, NLP, Generative AI, and culminating in Agentic AI.
Future-Focused Insights: Explores emerging AI trends like Large Language Models (LLMs) and Agentic AI, emphasizing adaptability in a fast-evolving field.
From Basics to Cutting Edge: Covers content from grassroots fundamentals to advanced AI, making it a complete guide from ML to Agentic AI.

High-quality lip synchronization is essential for creating realistic talking face videos in applications such as virtual interviews, online education, film dubbing, and digital avatars. Traditional lip-sync methods often struggle with maintaining high visual fidelity, especially in high-resolution outputs. To address this challenge, Wav2Lip-HQ introduces an advanced Generative Adversarial Network (GAN)[1]-based solution capable of generating photorealistic, high-resolution lip-synced videos with accurate mouth movements synchronized to any given speech audio. In this research, we evaluate the performance of Wav2Lip-HQ, leveraging its core components, including the lipsync_gan.pth[1] model trained on the LRS2 dataset[1] for precise audiovisual synchronization, the face_segmentation.pth[2] model trained on CelebAMask-HQ for accurate facial region parsing, and the esrgan_max.pth[3] enhancer utilizing DIV2K[3] and CelebA[2] datasets to upscale and refine facial details post-synchronization. We conducted extensive experiments using diverse video-audio pairs to assess the improvement in lip-sync accuracy and overall video quality. Our analysis demonstrates that Wav2Lip-HQ significantly outperforms traditional methods and the original Wav2Lip model by delivering sharper, more coherent, and highly realistic talking face videos. The findings of this study confirm that Wav2Lip-HQ is an effective solution for high-resolution, photorealistic lip synchronization, making it highly applicable for real-world use cases requiring professional-grade video quality. Future work will focus on enhancing emotional expressions and optimizing performance for real-time applications.

This paper proposes a simplified structure of microcontroller based mechanical ventilator integrated with a Bag-Valve-Musk (BVM) ventilation mechanism. Here, an Ambu bag is operated with computer-aided manufacturing (CAM) arm that is commanded via a microcontroller and manual switches by sending a control signal to the mechanical system and according to this control signal, the mechanical computer-aided manufacturing (CAM) arm simultaneously compresses and decompresses the Ambu bag. It is a selfinflating bag and like a one-way valve around its inlet and outlet corner. By compressing the Ambu bag it delivers air and by relaxing, it takes air from the environment through a mechanical scavenger. The control signals are designed with three modes named adult mode, pediatric mode, and child mode based on the respiratory rate. The device is in assist controlled mode by dint of fixing the tidal volume for all unique control signals. The control signal is visualized by a platform known as the BIOPAC student's lab system. The proposed device is portable, compact, low weight, and efficient performable. It can be supplied around the rural area hospitals for immediate medication with cost efficiency and risk avoidance. Anyone can operate it as no need to study or training of ventilation rules like ICU ventilator. The proposed system is safe, riskless, and repairable. The angle, volume, and respiratory measurement have found 95%, 92%, and 90% accuracy respectively. By applying this portable ventilator system immediate attention can be taken up in rural or general hospitals and in ambulances.

According to recent studies, feed-forward Deep neural networks (DNNs) perform better than text-to-speech (TTS) systems that use decision-tree clustered context-dependent hidden Markov models (HMMs). The feed-forward aspect of DNNbased models makes it difficult to incorporate the long-span contextual influence into spoken utterances. Another typical strategy in HMM-based TTS for establishing a continuous speech trajectory is using the dynamic characteristics to constrain the production of speech parameters. Parametric time-to-speech synthesis is used in this study by capturing the co-occurrence or correlation data between any two points in a spoken phrase using time aware memory network cells. Based on our experiments, a combination of DNN and BLSTM-RNN is the best system to use. Upper hidden layers of this system use a bidirectional RNN structure of LSTM, the low layers use a simple, one way structure followed by additional layers. On objective and subjective metrics, it surpasses both the traditional decision-based tree HMM's and the DNN-TTS system. Dynamic limitations are superfluous since the BLSTM-RNN TTS produces very smooth speech trajectories.

This project implements an intelligent voice-driven form filling assistant using Streamlit, speech recognition, and natural language processing. The system enables users to interact with online forms through voice commands, dynamically extracting form fields from any URL and guiding users through a conversational interface to complete them.

Processors have been among the greatest inventions of the past century, facilitating substantial progress in every aspect of life. In a processor's design, the instruction set architecture (ISA) specifies the operations a processor can perform, its memory addressing modes, and its register set. It also defines how software compilers convert high-level language code into executable machine code for the processor. The x86-64 and ARM, two proprietary ISAs, have dominated the field for decades, but recently, RISC-V, a free and open standard ISA, has been gaining popularity in the chip industry. Interest in RISC-V stems from its modularity and extensibility, enabling cheap, simple, and power-efficient designs. RISC-V's support for the 32/64/128-bit address spaces also makes it well-suited for building processors for the entire computing spectrum, ranging from tiny microcontrollers to supercomputers. Furthermore, its openness is helping many nations build indigenous processors and achieve chip self-sufficiency. This is critical, given the increasing demand for chips driven by the pervasive deployment of artificial intelligence (AI) and the Internet of Things (IoT), global chip shortages, and rising geopolitical tensions. This chapter aims to provide a comprehensive overview of RISC-V, covering its motivations, technical features, implementations, key adopters and initiatives, and challenges faced. Moreover, the chapter provides a detailed case study on the use of RISC-V in datacenter applications, including processing AI workloads at the edge.

The development of scientific hardware has followed a trend since the industrial revolution, which focused on centralized manufacturing of proprietary products to benefit from economies of scale. Unfortunately, scientific hardware, which favors custom, highly sophisticated, and often small specialized markets are not a particularly good fit for this model. The results of using the proprietary model of scientific hardware development are a series of challenges including the following: (1) slow innovation and limited novelty, (2) black box syndrome, (3) reduced technology transfer, (4) vendor lock in, (5) no lateral scaling, and (6) high economic costs. This work evaluates the potential for the free and open-source hardware development model to overcome these challenges and finds the open hardware approach contributes to (1) faster innovation, (2) increased transparency, (3) rapid and widespread technology transfer, 4) enhanced competition, (5) peer production and scalability, and (6) lower economic costs. Although there are some limitations to the open hardware approach, their impact is small compared to the benefits of avoiding the standard proprietary model. Funding the development of open hardware for scientific research is a clear way to enhance impact while garnering a high return on investment for funders.

The term CBIR refers to the process of retrieving similar images from a large collection of image database. The image retrieval is done on the basis of similarity matching between query image and database images. Different feature extraction techniques are being used to extract features of an image. The important image features are Color, Texture, Shape, Spatial location, edges etc. The CBIR (Content Based Image Retrieval) systems have achieved wide growth in past few years but still there are many limitations in CBIR systems yet to overcome. In this paper, we have evaluated feature extraction techniques based on color, texture and shape information and find that not a single feature vector is good for the retrieval parameters like precision and recall. There is the need of combining multiple feature vectors for increasing the effectiveness and efficiency of CBIR Systems.

With new protocols emerging to regulate the field of open-source investigation, this article critiques their widespread deference to the requirements of legal processes that exclude alternative horizons of justice and accountability. We argue that OSI may still compile evidence to support advocacy against systemic and structural violence, but in professionalising OSI, legally centred frameworks give little allowance to speculative or creative forms of advocacy which seek to address forms of violence not yet accepted in law. We therefore propose tactics to counter such epistemic injustice aimed at fostering pluralistic, decentralised, and solidarity-based OSI practices beyond legal constraints.

Introduction: Artificial intelligence has been transformed by deep learning, which makes it possible to recognize patterns and do intricate calculations. Deep network training is computationally demanding, though. GPU acceleration is used for performance in a number of open-source deep learning frameworks, such as Tensor Flow, Py Torch, and CNTK. The capabilities, effectiveness, and applicability of these frameworks for various machine learning applications are compared and assessed in this study. Research signification: For researchers and developers, it is essential to comprehend the advantages and disadvantages of different deep learning frameworks. The best hardware-software combination for AI applications is chosen with the aid of this study. Additionally, it emphasizes how deep learning contributes to technological improvements in domains including image processing, medical diagnostics, and e-learning

Patent-based intellectual property (IP) has come under progressively substantiative attack in the peer-reviewed literature as many studies have shown it retards innovation. In addition, the monopoly period no longer fits the innovation cycle. Although the vast majority of patents are not useful, patent proponents argue monopoly-based economic incentives are specifically necessary to fund medical technologies. Rather than use simple economics, quantifying human deaths has also been proposed as a means to guide public policies. Such an approach can be applied to patents by investigating the lives saved by patents as well as those lost in the current IP systems. This study is the first to provide such a theoretical approach to quantifying human mortality costs of patent-based IP systems. To illustrate the mechanism by which patents are responsible for premature deaths, a case study of the 100-year-old innovation of insulin is provided. The U.S. and Canada were selected to compare because the approach to drug costs in the two countries allows for a fraction of the additional costs of IP to be quantified. By comparing the different death rates of diabetics in U.S. and Canada, it was found that insulin-related patents result in over 94,000 American premature deaths annually (in 2021). The results also make it clear that many human deaths are related to price increases and lack of accessibility to needed medications due to the current monopolistic IP system. These findings require patent proponents to defend the continued existence of patents in the medical innovation space.