Bi-Directional Input Bridge For Mapping Limited-Input Device Events To Embedded Web Content, Erica Halpern, Matt Joseph

Defensive Publications Series

A bridging system for bi-directional input bridging that maps limited input device events to embedded web content and bridging system user interfaces. The bridging system implements an SDK module injected into an embedded content frame that intercepts input events before other components in the embedded content frame can process them, creates copies of events and forwards them to the bridging system, maintains a coordinated focus state to enable correct event routing between the bridging system and embedded content, and executes synthetic input events including pointer and mouse events in response to bridging system dispatch messages.

Selective Use Of Expanded Region Of Interest For Luma-Based Exposure Determination, Alex William Greer, Ariel Chiati Yin, Hsing Ju Hung, Lin Lu, Sz Han Yu, Yu-Lin Chang, Chung Kai Hsieh, Vincent Alfred

Defensive Publications Series

Devices may be used to capture images and videos.  For example, a device may include one or more cameras (e.g., image sensors) to capture images and videos of people.  When capturing an image or video of a person, exposure parameters for the person’s face may be prioritized by (i) calculating the luma for the facial region and (ii) adjusting the exposure parameters based on an average luma value across the facial region.

Typically, a small region of interest is used to calculate the luma for the facial region.  For example, the region of interest may be an initial facial area …

Optimization Of Gemifloxacin Immediate-Release Tablets Using Quality By Design, Jong Woo Kim, Hyun Bon Kang, Yeun Hee Kim, Kang Min Kim

Journal of Food and Drug Analysis

The objective of this study is to determine the design space (DS) for a dissolution-enhanced gemifloxacin immediate-release (IR) tablet incorporating hydroxypropyl-β-cyclodextrin (HPCD) for improved solubility, utilizing a quality by design (QbD) approach. Based on the preliminary studies, hardness, friability, disintegration time, assay, content uniformity, and dissolution rate were identified as the critical quality attributes (CQAs) for optimization. Through the initial risk assessment, crospovidone, main compression force, and the amount of granulating fluid were identified as factors influencing the CQAs, and a 2³ + 3 full factorial design of experiments (DoE) was conducted for optimization. According to the analysis of variance …

Pharmacological Potential Of Pomelo (Citrus Maxima) Peel: A Bibliometric Review Of Global Research Trends And Future Directions, Indah Zunita Sari, Alma Nur Azizah, Fitriana Nur Hanifah, David Fernando, Agung Endro Nugroho, Abdul Rohman

Journal of Food and Drug Analysis

Pomelo (Citrus maxima) is a widely cultivated tropical fruit with global production reaching approximately 8.55 million tons in 2023. This large-scale production generates a substantial amount of peel waste despite the fact that pomelo peel contains secondary metabolites with promising pharmacological potential. This study aimed to evaluate global research trends related to the pharmacological potential of pomelo peel using a bibliometric and review-based approach, as well as to identify future research directions. Data were collected from the Scopus database and filtered for English-language scientific articles, reviews, and conference proceedings. The analysis was performed using the Biblioshiny and VOSviewer …

Plant By-Products As A Valuble Source Of Natural Antiglycemic Compounds: Economic Aspects And Alternative Raw Material Sources, Tatjana Đorđević, Jelena Vujetić, Isidora Dickov, Diandra Pintać Šarac

Journal of Food and Drug Analysis

Plant by-products, often considered as waste in various agricultural and food processing industries, have emerged as a valuable source of natural antidiabetic or antihyperglycemic compounds. These compounds, derived from plant materials that would otherwise be discarded, have the potential to mitigate the global burden of diabetes and offer sustainable solutions for glycemic control. Prominent groups of natural antidiabetic compounds found in plant by-products are polyphenols, as well as bioactive proteins and peptides. In addition, agro-industrial residues such as wheat bran, oat husks, and fruit peels are excellent sources of carbohydrates that can be incorporated into functional foods to help manage …

Emerging Antifungal Strategies Against Candida Albicans Using Metal–Organic Frameworks And Novel Therapeutics: Opportunities And Challenges, Amanpreet Singh, Sandeep Kumar, Sehrabpreet Singh, Jeewanjot Singh

Journal of Food and Drug Analysis

Candida albicans is an opportunistic fungal pathogen responsible for infections ranging from superficial mucosal disease to life-threatening systemic candidiasis, particularly in immunocompromised hosts. Its pathogenicity is enhanced by morphological plasticity, biofilm formation, and immune evasion, contributing to antifungal resistance. Limitations of existing antifungals have driven interest in emerging therapeutics. This review highlights strategies including metal–organic frameworks (MOFs), host-directed therapies (HDTs), CRISPR-Cas9 gene editing, and phytochemicals. MOFs offer high drug-loading capacity, controlled release, and enhanced penetration into biofilms, showing promise against drug-resistant strains. Selected phytochemicals demonstrate inhibitory effects on virulence traits such as hyphal formation and enzyme secretion. CRISPR-Cas9 …

An Optical Fiber Caffeine Sensor Using A Molecularly Imprinted Polymethacrylic Acid Membrane Coated On Optical Fiber Surface As A Transducer, Josiah W. Miller, Jason D. Mccaslin, Shiquan Tao

Journal of Food and Drug Analysis

Caffeine is a neurotransmitter, important to human health. This compound exists in many food products, has been added to soft drinks, pharmaceutical drugs. For many applications, a field deployable technique for quick detecting caffeine is attractive. In this paper, we report a selective optical fiber caffeine sensor using a molecularly imprinted polymer (MIP) membrane as a transducer. The membrane extracts/concentrate caffeine from sample solutions significantly improved sensor’s sensitivity with a 95 times sensitivity improvement. The sensor achieved a detection limit of 0.20 µg/mL. This sensor was used to directly analyze caffeine in soft drink products without interference from sample matrix.

Ora‑C3 (2026 Reinforced Edition) Oncology Resonant Micro‑Lattice Research Engine, Michael Victor Caldwell Mr.

Defensive Publications Series

Abstract — 2026 Reinforced Edition

ORA‑C3 is a non‑clinical, open‑hardware oncology research engine designed to accelerate mRNA‑payload stability studies, micro‑environmental flow mapping, and resonance‑assisted cellular‑analog modeling. The 2026 Reinforced Edition integrates a gyroid micro‑lattice chamber, multi‑band ultrasound drivers, vortex‑microbubble conditioning, Arachne‑Chip‑2.0 telemetry, and CUDRE‑linked biological‑model correlation ports.

ORA‑C3 provides a builder‑ready, open‑hardware, non‑clinical platform for cancer‑research labs seeking repeatable, tunable, multi‑physics testbeds for mRNA oncology research acceleration.

Method, System, And Computer Program Product For Fairness-Aware Test-Time Prompt Tuning, Kamalaruban Parameswaran, Michael William (Tom) Kempton, Stuart Burrell, David Sutton, Yoann L. Launay

Defensive Publications Series

Methods, systems, and computer program products are provided for fairness-aware, test-time prompt tuning of a vision-language machine learning model. An example method includes receiving text prompts and images, inputting the text prompts and the images to a vision-language machine learning model, generating text embeddings based on the text prompts and image embeddings based on the images, calculating target similarity scores between the target text embeddings and the image embeddings and spurious similarity scores between the spurious text embeddings and the image embeddings, calculating a measure of target entropy loss based on the target similarity scores and a measure of spurious …

Ai Adoption And Organizational Resilience In Turbulent Environments: The Mediating Role Of Employee Digital Literacy And The Moderating Role Of Innovation-Supportive Climate In The Iraqi Hospitality Sector, Hasan Mutashar Gbouri

Arab Economic and Business Journal

This study examines whether adopting artificial intelligence (AI) strengthens organizational resilience when the operating environment is hostile, and identifies the internal conditions an organization must satisfy before an AI investment translates into resilience. Two mechanisms anchor the argument: employee digital literacy, which carries the effect, and an innovation-supportive climate, which conditions it. We surveyed 400 employees across three premium hotels in Baghdad, Iraq, sampled by operational level, and tested the model with structural equation modeling in AMOS 26.0. The results show that AI adoption alone yields only a modest direct gain in resilience. Slightly more than half of its total …

Image Fusion Based On Deep Learning With Different Locations And Sizes Of Objects, Baneen Al-Kalabi, Tawfiq Al-Assadi

Journal of Intelligent Informatics, Networking, and Cybersecurity

Multi-focus image fusion combines partially focused images into a single all-in-focus composite. Existing object-based methods assume precise spatial and scale alignment across source images, an assumption that frequently fails in Misaligned Multi-Focus Dataset scenarios due to camera displacement and focal length variation. This paper proposes a novel training-free, object-aware fusion framework to address this limitation through a five-stage pipeline: YOLOv8x detection, SAM2-L segmentation, LoFTR correspondence matching, a novel Scale-Aware Area Resize Algorithm, and GLCM-guided MSB/LSB bit-level fusion. The framework was evaluated on the EDMF benchmark (20 image pairs, synthetically modified to simulate Misaligned Multi-Focus Dataset shifts) and a Misaligned Multi-Focus …

Lossless Medical Image Compression Using Integer Discrete Wavelet Transform With Adaptive Subband Differencing And Context-Adaptive Entropy Coding, Rasha F. Nadhim, Ibrahim Adel Ibrahim, Ashwaq T. Hashim

Journal of Intelligent Informatics, Networking, and Cybersecurity

From transform-domain decorrelation and adaptive entropy coding, we propose a method for efficient lossless compression of medical images in this work. We implement the Integer Discrete Wavelet Transform (IDWT) to decompose the input image into four subbands of LL, LH, HL, HH, encompassing approximation and directional detail elements, in the initial implementation. It also removes spatial redundancy in image information and decomposes image information into a less correlated and more sparsely distributed set of coefficients. To decrease redundancy further, it proposes a subband-dependent differencing scheme, which decorrelates neighbouring wavelet coefficients with directional prediction. So horizontal differencing is done on LH, …

A Blockchain-Integrated Federated Learning Model And Autoencoder-Based Feature Reduction For Improving Iot Intrusion Detection, Tahseen A. Wotaifi

Journal of Intelligent Informatics, Networking, and Cybersecurity

The rapid growth of Internet of Things (IoT) environments has brought forth a wealth of security challenges in detecting network intrusions in diverse and resource-restricted systems. Privacy, scalability, and single point of failure issues plague traditional centralized intrusion detection solutions. To address these challenges, the study proposes a secure and adaptive intrusion detection model using Federated Learning (FL) and Blockchain, augmented with autoencoder-based feature reduction. The ToN-IoT dataset is pre-processed, and then an unsupervised autoencoder is used to build informative low-dimensional feature representations. The processed data is deployed to various clients to mimic a real federated situation. Every client will …

A Dual- Task Hierarchical Graph Attention Network For Protein-Protein Interaction Sites Prediction, Oras A. Hussein, Eman S. Al-Shamery

Journal of Intelligent Informatics, Networking, and Cybersecurity

In computational structural biology, it is still very hard to accurately find protein-protein interaction (PPI) sites and estimate how strong the interaction would be. In this research, we provide an innovative two-stage deep learning framework that combines residue-level graph representation learning with protein-level regression to achieve a thorough modeling of protein interactions. Protein structures first encoded as residue graphs, with nodes that stand for amino acids and edges that show how close they are to each other in space. To find binding residues, a deep residual Graph Attention Network v2 (GATv2) uses multi-head attention, residual connections, and Jumping Knowledge aggregation …

Candida Albicans Purulent Pericarditis Complicated By Cardiac Tamponade In A Critically Ill Burn Patient: A Case Report, Mutaz Althobaiti, Abdulrahman Alqahtani, Faisal Alanazi, Mohammad S. Alotaibi, Ibrahim Bahabri, Shikah Alsuwaid, Turki Almutairi, Fawaz Almutairi

Journal of the Saudi Heart Association

Candida pericarditis is a rare, life-threatening complication of invasive candidiasis, associated with high mortality.

We report this case of a young woman with 60% body surface area burn, multidrug-resistant polymicrobial sepsis, and multiorgan failure who developed Candida albicans pericarditis complicated by cardiac tamponade. Despite urgent pericardiocentesis and escalation of antifungal therapy, she ultimately progressed to refractory shock and multiorgan failure.

This case illustrates the diagnostic challenge and therapeutic limitations of fungal pericarditis in critically ill host, and underscores the importance of maintaining a high index of suspicion in burn patients with multisite Candida colonization and prolonged antibiotic exposure.

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When Auditors Trust Ai: A Qualitative Study Of Professional Judgment, Data Quality, And Adoption Barriers In Lebanon, Joseph Serghani

Arab Economic and Business Journal

This study explores auditors’ trust in AI in a limited-resource and turbulent context, namely Lebanon. Using semi-structured interviews with 14 junior and senior auditors in Lebanon, this study identifies the factors that make auditors either willing or unwilling to trust in AI-powered audit processes. The results indicate that auditors perceive the potential benefits of AI, such as increased speed, capacity, and ability to detect anomalies. Yet, trust in AI is limited by concerns about implementation costs, the erosion of professional judgment, blockchain complexity, data overload, and reliance on high-quality data. Instead of excluding AI from the equation, auditors perceive it …

Artificial Intelligence Meets The Dermatologist Eye: A New Approach To Melanoma Detection, Sandra Marinceski

Summer Interdisciplinary Research Symposium

Abstract

Objective: Melanoma is the deadliest form of skin cancer, and early detection significantly improves survival. The purpose of this literature review was to evaluate the diagnostic accuracy of artificial intelligence (AI)-assisted dermoscopy compared with clinician assessment for melanoma detection among adults with suspicious pigmented skin lesions. Biopsy-confirmed diagnosis served as the reference standard.

Methods: A comprehensive literature search was conducted using PubMed, EBSCO, Research Rabbit, and Google Scholar. Studies published from 2020 to 2025 involving adults with suspicious pigmented skin lesions were included if they compared AI-assisted dermoscopic analysis with clinician interpretation using biopsy-confirmed diagnosis and reported sensitivity and …