Digital Commons Network^™

Reusable Integrated Software-Instrument Architecture: Sound Engine + Effects Chain + Drawable Automation + File Recording + Project Save/Load, Shared Across Interchangeable Engines, Bret Bailey

Defensive Publications Series

A self-contained software instrument integrating five subsystems — a pluggable sound engine, a shared effects chain, a drawable automation layer addressing parameters through a shared registry, a direct post-effects file recorder, and a versioned backward-compatible project persistence layer — structured so the effects/automation/recording/persistence shell is written once and reused unchanged across structurally different, interchangeable engines (e.g., a subtractive synth and an arpeggiator).

Dual-Path Audio Recording: Simultaneous Real-Time Monitoring And Offline Deterministic Render, With Automatic Effect-Tail-Length Computation And Post-Trim, Bret Bailey

Defensive Publications Series

From a single schedule, a real-time monitoring path (audible + playhead) runs alongside an offline non-real-time render that produces a deterministic, jitter-free buffer. Effect-tail length is computed automatically (e.g., n = ceil(ln(0.01)/ln(g)) for feedback echo; decay/impulse length for reverb), the offline render is extended by exactly that, then trimmed to elapsed time + tail and encoded to WAV — a clip/dropout-free export while the user still hears live monitoring.

Gci Sentinel‑Av‑1 Alpha Staged Acoustic‑Vortex Concentration And Cavitation‑Plasma Side‑Stream Reactor For Pfas And Microplastic Reduction In Dirty Water, Michael Victor Caldwell Mr.

Defensive Publications Series

Abstract

GCI Sentinel‑AV‑1 Alpha is a staged, modular, open‑hardware water‑treatment architecture intended to improve the real‑world feasibility of PFAS reduction and microplastic concentration in contaminated municipal, industrial, and coastal water streams. The system does not claim that all PFAS can be destroyed in full-flow municipal water in a single pass. Instead, it uses a more defensible and buildable process train: pretreatment and flow conditioning, multi-stage acoustic‑vortex concentration, and cavitation/plasma polishing of a small concentrated side-stream. The design is intentionally modular so that engineers can test each stage independently, measure mass balance and energy use, and scale only after the physics …

Caldwell Universal Open-Hardware Cancer Research Ecosystem (Cucore-1.0): — Integrated Engineering Architecture For Precision Oncology Research, Michael Victor Caldwell Mr.

Defensive Publications Series

Abstract

This disclosure introduces the Caldwell Universal Open-Hardware Cancer Research Ecosystem (CUCORE-1.0), an integrated engineering architecture intended to support future oncology research through open scientific collaboration. Rather than proposing a single therapeutic intervention, CUCORE organizes numerous previously disclosed open-hardware engineering systems into a unified research framework designed to improve laboratory investigation, precision diagnostics, non-invasive therapeutic delivery, extracorporeal blood-conditioning research, computational oncology, biomarker monitoring, immunoengineering, and AI-assisted systems integration.

The architecture incorporates prior Caldwell Technical Disclosure Commons publications involving acoustofluidics, focused ultrasound concepts, vortex fluid dynamics, microbubble engineering, plasma-assisted processing, extracorporeal circulation research, adaptive sensing, AI-guided monitoring, non-invasive drug delivery, biomedical …

Technical Disclosure: Gci Oncology Mrna-Acoustic Vortex-Synergy Protocol (Gci-Oas V1.0), Michael Victor Caldwell Mr.

Defensive Publications Series

Abstract

This disclosure outlines a protocol to enhance the clinical efficacy of mRNA-based cancer therapeutics by integrating targeted acoustic-vortex delivery systems with systemic physiological stabilization. Traditional mRNA therapeutics face barriers in tumor microenvironment (TME) penetration and immune-cell recruitment. By utilizing GCI-developed acoustic-vortex resonance to modify tissue permeability and local interstitial pressure, this protocol provides a non-invasive, hardware-based method to concentrate therapeutic payloads at tumor sites. This is a purely conceptual, builder-ready framework intended to be integrated into existing clinical research pipelines.

Caldwell–Copilot Universal Oncology Research Stack (Uors‑1.0) Unified Multi‑Layer Cancer Research Acceleration Architecture, Michael Victor Caldwell Mr.

Defensive Publications Series

Abstract

UORS‑1.0 is an open‑hardware, multi‑layer oncology research acceleration platform integrating mRNA cancer‑payload research, vortex–acoustic systems, microbubble platforms, extracorporeal conditioning engines, non‑invasive delivery architectures, regenerative neuro‑vascular research tools, and global oncology breakthroughs.

This is not a treatment. It is a research acceleration architecture for modeling tumor mechanisms, exploring microenvironment dynamics, accelerating nanoparticle and mRNA payload research, enhancing biological‑process mapping, and supporting non‑clinical oncology experimentation.

Caldwell Open‑Hardware Oncology Research Platform (C‑Ohrp V1.0) A Modular, Non‑Clinical Testbed For Mrna Cancer Vaccine And Immunotherapy Delivery, Sensing, And Thermal Management, Michael Victor Caldwell Mr.

Defensive Publications Series

Abstract

Recent evidence, including a 2026 review in The Lancet, confirms that mRNA vaccines are robustly effective against infectious diseases and show strong promise for cancer immunotherapy, particularly in melanoma and in combination with immune checkpoint inhibitors. However, critical barriers remain: delivery to the right tissue, control of the treatment environment, and reproducible measurement of immune response and toxicity.

The Caldwell Open‑Hardware Oncology Research Platform (C‑OHRP v1.0) is a modular, non‑clinical research platform that integrates the most relevant elements of the Caldwell open‑hardware portfolio to improve the viability of mRNA cancer vaccine and immunotherapy programs. It does not claim to …

Autonomous Predictive Execution Intelligence Architecture For Agentic Systems, Hp Inc

Defensive Publications Series

This invention introduces a predictive execution intelligence architecture that governs autonomous AI actions prior to production execution within enterprise environments. Unlike existing approaches based on permissions, prompt filtering, sandboxing, or post-execution monitoring, the proposed system evaluates potential enterprise impact before execution occurs.

Autonomous AI agents increasingly perform real-world operations such as workflow orchestration, infrastructure updates and transactional processing. Existing governance systems lack the ability to predict downstream consequences, leading to unintended state changes, cascading workflow failures, compliance risks and operational instability.

The proposed system introduces a synchronized execution twin representing enterprise state and dependencies. Autonomous actions are simulated within this …

Adaptive Multi-Factor Knowledge Prioritization And Temporal Control In Artificial Intelligence, Hp Inc

Defensive Publications Series

Artificial intelligence systems increasingly rely on persistent memory to improve personalization, long-term reasoning, enterprise assistance and autonomous decision making. Existing memory architectures primarily store information as static embeddings or conversational history without mechanisms for structured lifecycle management, gradual forgetting, temporal relevance evaluation, or policy-aware governance. This invention introduces a computational framework for intelligent memory lifecycle management in AI systems. Information generated or ingested by the AI is transformed into structured claim-level semantic units enriched with contextual, temporal and risk metadata. A scoring engine continuously evaluates each claim using relevance, usage frequency, recency, sensitivity and policy signals to dynamically determine retrieval …

The Multi-Static Integrated Sensing For Predictive Mitigation Of Network Beam Blockages, Mohit Chandra

Defensive Publications Series

Systems and methods can provide a cooperative sensing framework where network access nodes can operate in a multi-static configuration using integrated sensing and communication capabilities. In this framework, one node may transmit a signal while multiple, geographically separated nodes capture reflections from an object. This data can be processed at an edge computing layer to model the object's kinematic profile and predict its trajectory. This prediction may be used to trigger a pre-emptive remediation action, such as a make-before-break handover to an alternate path, which could mitigate connectivity loss before the active communication link is degraded and contribute to improved …

Dlp-Gated Ephemeral Credential Minting For Webrtc Realtime Voice Sessions With Tracked-Mode Fallback, Gustavo Matthew Assuncao

Defensive Publications Series

Realtime, low-latency voice assistants are increasingly built on WebRTC: the browser negotiates a peer connection directly with the model provider's realtime endpoint and streams audio over SRTP/DTLS, bypassing the application server entirely for the media path. This delivers sub-second latency but destroys the enforcement point that data-loss-prevention (DLP) and cloud-access-security-broker (CASB) systems rely on. A prompt-DLP proxy inspects HTTP request and response bodies; it can neither decrypt nor even observe a WebRTC audio stream that never traverses it. The result is an enforcement blind spot precisely where the most sensitive channel — a natural-language voice conversation with a persona that …

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Pre-Answer Entitlement Gate For Ai-Answered Phone Lines With Subscription-Shape-Differentiated Fail Policy And Metering-Lag Reinforcement, Gustavo Matthew Assuncao

Defensive Publications Series

This publication discloses, in enabling detail, a pre-answer entitlement gate for telephone lines that are answered by an autonomous AI agent (an AI "receptionist" or voice bot) and billed on a metered subscription. The distinguishing constraint is architectural: the platform runs on top of a Communications-Platform-as-a-Service (CPaaS) provider that emits its usage/charging signal only when the call completes. There is no mid-call credit-control channel available at the CPaaS layer — the platform cannot ask the carrier to reserve, decrement, or terminate a call against a balance while the call is live, the way a telecom Online Charging System (OCS) can. …

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Resource Constraint Evacuation Route Planning A Capacity-Aware Charge-Encoded State-Space Approach, Praveen Borra

Electronic Theses and Dissertations 2020 - Present

Emergency Management Information Systems (EMIS) are defined as a set of tools that assist decision-makers in risk assessment and disaster response for significant multi-hazard threats and disasters. Over the past several decades, EMIS have become increasingly important for understanding, managing, and governing transportation systems during large-scale emergency events. One of the primary objectives of EMIS is to efficiently utilize spatial and network datasets to support evacuation planning, identify critical transportation patterns during emergencies, and allocate resources effectively. However, the increasing complexity and scale of modern transportation systems present significant challenges in developing reliable evacuation planning solutions.

One of the most …

Microwave-Plasma Disinfector (Origreen), Samir M. El-Ghazaly, Amirreza Ghadimi Avval, Sara Ghayouraneh, Saleh A. Alfawaz, Ibrahim Nasser I. Alquaydheb

Patents Granted

A microwave-plasma disinfector (Origreen) with a microwave source with multi-feed points and variable output power. It also has a low-temperature atmospheric-pressure plasma source. This versatile microwave-plasma disinfector has the ability to decontaminate heat-sensitive materials by subjecting them to a microwave-assisted plasma with controlled microwave power. This dual-action, at suitable non-destructive microwave and plasma doses, sterilizes the equipment at a lower temperature with higher effectiveness than either plasma or microwaves alone.

Efficient Edge Implementation Of Midasnet For Real-Time Depth Estimation In Indoor Robotics, Muhammed Yasi̇n Adiyaman, İsmai̇l Fai̇k Başkaya

Turkish Journal of Electrical Engineering and Computer Sciences

Real-time depth estimation is crucial in many vision-related tasks, including autonomous driving, 3D reconstruction, robotics, and simultaneous localization and mapping. In recent years, many methods have been proposed to solve depth maps from images by utilizing different modality setups like monocular vision, binocular vision, or sensor fusion. However, for real-time deployment on edge devices, complex methods are not suitable due to latency constraints and limited computation capacity. For edge implementation, models should be simple, minimal in size, and hardware-friendly. Considering these factors, we implemented MiDaSNet, which works on the simplest setup of monocular vision and utilizes hardware-friendly convolutional neural network-based …

Comparable Outcomes, Divergent Learning Pathways: Comparing Metacognitive Reflection And Practice Problems In Organic Chemistry, Jitendra D. Belani

College of Life Sciences Faculty Papers

Organic Chemistry I presents significant challenges for students, requiring both conceptual understanding and effective study strategies. Two commonly used approaches to support learning are structured practice problems and metacognitive reflection activities; however, these strategies are rarely compared directly within the same course context. In this study, students enrolled in a postbaccalaureate Organic Chemistry I course (n = 31) were randomly assigned to one of two low-stakes interventions: weekly practice problem sets or structured reflection surveys focused on study strategies. Student performance was evaluated using three midterm examinations, a cumulative final exam, and overall course grades, along with measures of participation. …

Unveiling The Diversity Within Salvia Caespitosa Sensu Lato (Lamiaceae): Three New “Hidden” Endemic Species From Türkiye​, Ferhat Celep, Musa Doğan, Gali̇p Akaydin, Ahmet Kahraman, Ersi̇n Karabacak, Ömer Çeçen, Meti̇n Armağan, Chun-Lei Xiang, Bryan T. Drew

Turkish Journal of Botany

Documenting species, particularly those that are threatened or endangered, is fundamental to biodiversity conservation. However, many taxa remain scientifically unrecognized, being either undiscovered or unpublished. While some plants can be easily recognized as new species, other identifications are obscured by morphological, taxonomic, and nomenclatural complexity. Detecting cryptic or “hidden” species is therefore essential to understanding biodiversity and plant evolution. Our revisionary studies on Salvia demonstrate that the morphologically complex Salvia caespitosa is not a single species but instead comprises at least four distinct species, three with narrow distributions. This conclusion is supported by integrative evidence from extensive field studies, herbarium …

Evolutionary Neural Architecture Search: A Survey, Ferda Nur Özçeli̇k, Mehmet Önder Efe

Turkish Journal of Electrical Engineering and Computer Sciences

Deep Neural Networks (DNNs) have achieved remarkable success across diverse machine learning applications, yet designing effective architectures remains a laborious, expert-driven process. Neural Architecture Search (NAS) was introduced to automate this process, with Evolutionary NAS (ENAS) emerging as one of the most effective and widely adopted NAS paradigms. This survey provides a comprehensive and systematic review of 164 ENAS studies published between 2020 and 2024, categorized according to the specific evolutionary algorithm employed as the search strategy. Unlike prior surveys—which either treat evolutionary methods at a high level or focus on general NAS pipelines—this study is, to the best of …

A Holistic Approach For Workforce Scheduling And Routing, Kerem Can Manalp, Ansel Kaplan Erol, Kutluhan Erol, Cem Evrendi̇lek

Turkish Journal of Electrical Engineering and Computer Sciences

The workforce scheduling and routing problem (WSRP) involves assigning tasks across multiple locations while accounting for varying travel times, service durations, time windows, and skill requirements in a wide range of industries, from healthcare to telecommunications. This paper presents a mixed-integer programming model for the WSRP that balances the trade-off between cost and customer satisfaction using a score-generation function and subsequently evaluates the trade-off between solution quality and computation time for several algorithms on well-known datasets. We demonstrate that our model effectively balances cost, service-level agreement satisfaction, and task priorities while providing high-quality solutions in a timely manner. Observing that …

Cnkg: Harnessing Large Language Models For Cognitive Neuroscience Knowledge Graph Construction, Ali Sarabadani, Kheirollah Rahsepar Fard, Hamid Dalvand

Turkish Journal of Electrical Engineering and Computer Sciences

Textual resources are among the most valuable sources of information in cognitive neuroscience (CN) for understanding and investigating brain activity and cognitive processes. Extracting and constructing knowledge graphs (KGs) from these texts can facilitate medical research by providing deeper insights into neurological diseases and brain function. In recent years, the use of large language models (LLMs) in natural language processing (NLP) has become increasingly widespread, significantly enhancing the extraction of meaningful information from large volumes of text. This study proposes a novel approach for constructing and evaluating a specialized knowledge graph, termed the cognitive neuroscience knowledge graph (CNKG), from scientific …

Dual-Stream Bilstm Framework With Histogram-Based Shape Features For Household Load Forecasting, Chang Xu, Wong Jee Keen Raymond, Hazlee Azil Illias, Hazlie Mokhlis

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a dual-stream BiLSTM framework for household load forecasting that integrates time-series dynamics with histogram-based daily shape features. Unlike existing models relying on weather or external data, the proposed method extracts intrinsic load-shape information directly from normalized daily curves. A multihead attention module fuses temporal and shape representations, enabling adaptive weighting of informative dimensions. Experiments on three real-world datasets show consistent improvements over the baseline BiLSTM, with up to 30.12%, 24.27%, and 19.03% reductions in MAE, RMSE, and SMAPE, respectively. The results highlight the framework’s robustness and efficiency for fine-grained load forecasting without external inputs.

Robust Variable-Gain Backstepping Control For Nonlinear Systems With Real-Time Application To Induction Motor, Fadi Alyoussef, İbrahi̇m Kaya, Ahmad Akrad, Rabia Sehab, Cristina Morel

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes a novel variable-gain mechanism with a minimal number of tuning parameters to enhance the performance of conventional backstepping controllers for nonlinear systems while avoiding singularity and peaking phenomena. The proposed approach is simple, computationally efficient, and well suited for real-time implementation without imposing a significant computational burden. Its effectiveness is validated through real-time experiments conducted using a dSPACE DS1104 controller board and a 7.5-kW induction motor (IM). Simulation results demonstrate that the proposed controller outperforms the conventional backstepping controller. Robustness analyses under variations in stator resistance, load inertia, and viscous friction coefficient reveal substantial reductions in the …

Enhancement Of Nested Hexagonal Fractal Antenna Performance For Multiband Wireless Applications, Abdelbasset Azzouz, Rachid Bouhmidi, Mohammed Chetioui, Redouane Berber, Ahmed Jamal Abdullah Al-Gburi

Turkish Journal of Electrical Engineering and Computer Sciences

This work focuses on developing a compact multiband antenna to meet the growing demand for versatile and efficient radiating structures in modern wireless communication systems. A hexagonal fractal antenna is proposed and analyzed for applications such as mobile communications, WLAN, industrial, scientific and medical (ISM) bands, Wi-Fi, satellite links, radar systems, and military communications. By iteratively modifying the antenna geometry with larger hexagonal elements, the design enhances multiband behavior and improves key performance parameters including gain, S11, voltage standing wave ratio (VSWR), and radiation characteristics. The antenna is modeled using high-frequency structure simulator (HFSS)® and fabricated on a low-cost 0.8 …

Parameter Optimization Of Dual-Qsg Based Pll For Real-Time Control Of Grid-Connected Ev Chargers, Gaurav Yadav, Sudhanshu Mittal, Vineet Kumar, Sombir Kundu, Praveen Bansal

Turkish Journal of Electrical Engineering and Computer Sciences

Dual-Quadrature Signal Generator (D-QSG) based Phase lock loop (PLL) has been recently proposed to handle the nonideal grid voltage conditions. However, selecting the parameter for D-QSG based controller has been a great challenge, especially for higher-order systems. Inappropriate parameter selection tends to increase settling time both in terms of amplitude as well as harmonics attenuation. Hence, in the proposed work, the main focus is on parameter selection to achieve a faster response. Here, a fourth-order Quasi-Synchronous Generator has been realized by cascading the two nonidentical second order generalized integrators (NISOGIs). Furthermore, the parameters of both the NISOGIs are selected in …

Erratum To “Sgsc-Kko-Lstm: A Deeplearning Classifier Model For Smart Grid” [Turkish Journal Of Electrical Engineering & Computer Sciences 34 (2) 2026 185-213], Samaniba Imchen, Dushmanta Kumar Das

Turkish Journal of Electrical Engineering and Computer Sciences

The first and second authors were incorrectly ordered in the article PDF due to a typesetting error. To rectify this oversight and ensure the accuracy of the published work, the author order have been corrected as follows: 1. Samaniba Imchen – First Author 2. Dushmanta Kumar Das – Second Author

A link to the original article can be found at: https://doi.org/10.55730/1300-0632.4170

Influence Of A New Nanoensemble Of Graphene Oxide And Indole-3-Acetic Acid On The Morphophysiological, Biochemical, And Ultrastructural Characteristics Of Upland Cotton (Gossypium Hirsutum L.), Nurlan Amrahov, Ulviyya Hasanova, Aliyeva Gunel, Aghazada Goncha, Fuad Rzayev, Eldar Gasimov, Aliyeva Solmaz, Lala Gahramanli, Elvin Ibrahimli, Ruhangiz Mammadova, Zarema Gakhramanova, Cem Bayram, Goncha Eyvazova, Si̇bel Hasanova, Ziyaddin Mammadov

Turkish Journal of Botany

​The exogenous application of phytohormones plays a crucial role in the activation of adaptive mechanisms in plants under both normal and stress conditions. Therefore, the present study investigates the synthesis, characterization, and application of nanoensembles based on indole-3-acetic acid (IAA) and graphene oxide (GO) nanolayers. The surface of GO synthesized by the modified Hummer method was noncovalently functionalized with IAA molecules. The structure and morphological properties of the prepared materials were analyzed by Fourier transform infrared spectroscopy, X-ray diffraction, UV-Vis spectroscopy, and transmission electron microscopy. The IAA and IAA+GO nanoensemble enhanced the morphological parameters, lateral root formation, catalase and peroxidase …

Effects Of Calcium And Magnesium Ions On Wheat (Triticum Aestivum) Chromosome Structure Visualized Using Light And Scanning Elec​Tron Microscopes, Andi Salamah, Fitri Amalia Nurchasanah, Vycka Fikriyani Fairuz, Astari Dwiranti

Turkish Journal of Botany

Divalent cations, particularly calcium (Ca2+) and magnesium (Mg2+), are essential for maintaining chromosomal architecture, yet their roles in plants remain poorly characterized. This study investigated the effects of Ca2+ and Mg2+ on the chromosomal structure of wheat (Triticum aestivum, 2n = 6x = 42), a widely used model in plant cytogenetics. Root-tip chromosomes were isolated enzymatically and treated with BAPTA (a Ca2+-specific chelator), EDTA (a broad cation chelator), and buffer solutions containing either 5 mM Mg2+ (XBE5) or no Mg2+ (XBE). Chromosomes were stained with aceto-orcein for light microscopy and processed for scanning electron microscopy (SEM). Microscopic observations revealed that …

A Revenue-Driven Approach For Enhanced Task Utilization In Vehicular Cloud Computing, Ashish Singh Saluja, Satyabrata Das, Sanjib Kumar Nayak, Sohan Kumar Pande

Turkish Journal of Electrical Engineering and Computer Sciences

Vehicular networks support intelligent transportation through vehicle-to-roadside Units (V2R) and vehicle-to-vehicle (V2V) communication but face challenges from dynamic topologies, limited RSU coverage, and bandwidth scarcity, which impact service delivery and revenue. RDA-ITU addresses these challenges by integrating V2R and V2V paradigms to maximize RSU revenue, enhance service availability, and improve system efficiency. It dynamically allocates services based on real-time network conditions and vehicle mobility, leveraging V2V relays to optimize both RSU-direct and cooperative communication. Through extensive simulations, RDA-ITU significantly outperforms four baselines: RBSM, VVMM-U, VVMM-LW, and VVMM-MA. It achieves 81.1% higher total revenue, 154.8% more completed requests, and 103.6% higher …

Class-Aligned Frequency Augmentation Using Variational Mode Decomposition Forfew-Shot Image Classification, Leila Boussaad

Turkish Journal of Electrical Engineering and Computer Sciences

Few-shot image classification benefits from data augmentation, yet most existing methods operate in pixel space with limited control over spectral semantics. We introduce a lightweight, frequency-guided augmentation strategy based on Variational Mode Decomposition (VMD). Our method constructs an offline, per-class ModeBank by decomposing downsampled luminance patches and retaining midband modes that encode class-specific texture patterns. During episodic training, VMD is never executed online: instead, for each support image, a same-class midband mode is selected and blended using PSNR-targeted scaling with a luminance energy cap, ensuring perceptual consistency. The augmentation is fast, reproducible, class-consistent, and integrates seamlessly into standard metric-based pipelines …

Scalefusion: Hierarchical Feature Aggregation For Unified Multiscale Object Detection, Ayşenur Yaylaci, Berru Kaya, Mehmet Kiliçarslan

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting objects across a wide range of scales, particularly small ones, remains a significant challenge in computer vision. Existing methods often improve small object detection at the cost of performance on larger objects or introduce significant computational overhead through external techniques like image slicing. This paper introduces ScaleFusion, a novel, unified, end-to-end object detection architecture designed to provide robust performance across all scales within a single model. The core of our approach is a hierarchical feature aggregation strategy structured like a tree. ScaleFusion processes an image by running a shared backbone network only on fine-grained patches at the lowest level …