Engineering Commons^™

Photothermal Excitation And Optical Interferometric Readout Of Mos2 Nanomechanical Resonators, Sadia Afrin

Graduate Studies Theses and Dissertations 2026

Two-dimensional (2D) materials have emerged as promising candidates for nanoelectromechanical systems (NEMS) due to their exceptional mechanical, optical, and electrical properties. Among these materials, molybdenum disulfide (MoS2) has attracted considerable interest for nanomechanical resonator applications because of its low mass density, high mechanical strength, and semiconducting nature. This thesis presents the fabrication, theoretical modeling, and experimental characterization of suspended MoS2 drumhead resonators. The devices were fabricated by mechanically exfoliating MoS2 flakes from bulk MoS2 crystals and transferring selected flakes onto pre-patterned substrates using a dry-transfer process. Mechanical resonance was excited through photothermal actuation using a modulated blue laser, while device …

Cfd Analysis Of A Full Heat Exchanger Between Ammonia And Supercritical Co2 In Aviation Application, Mairah Ahmed

Graduate Studies Theses and Dissertations 2026

The aviation industry’s transition toward lower-carbon propulsion systems has accelerated interest in alternative fuels, including ammonia–hydrogen fuel blends. In this work, a supercritical CO2 Brayton cycle is integrated with the exhaust stream of a turbofan engine to recover waste heat and utilize it for ammonia preheating and cracking. The recovered thermal energy raises the ammonia temperature to the level required for catalytic decomposition, enabling onboard hydrogen production. The proposed architecture combines ammonia cracking with a high-bypass, two-shaft turbofan engine representative of the propulsion system employed on the Boeing 737 MAX 8. This approach addresses the challenges associated with onboard hydrogen …

Pseudo-Boiling Of Supercritical Co2 In A Parallel-Flow Microchannel And A Micro-Jets Impingement Device And Single-Phase And Two-Phase Heat Transfer Of Subcritical Co2 Jets, Pranzal Ahmed

Graduate Studies Theses and Dissertations 2026

Carbon dioxide (CO2) is gaining attention as a low-toxicity, zero-ozone-depletion refrigerant with a global warming potential of 1, making it an attractive alternative to synthetic HFCs/HFOs. Near its critical point, CO2's thermophysical properties change sharply with small shifts in temperature and pressure — a behavior that can be exploited to enhance heat transfer in trans-critical power cycles and electronics cooling. This dissertation experimentally investigates heat transfer during the pseudo-boiling of supercritical CO2 in a parallel-flow microchannel and a micro-jet impingement device, alongside CO2 flow boiling and single-phase water heat transfer in micro-jet impingement. Using microfluidic devices instrumented with embedded resistance …

Numerical Investigation Of Reacting Flow In Pmma-Fueled Solid Fuel Scramjet Combustors Under Experimental Test Conditions, Hunter Bassett

Graduate Studies Theses and Dissertations 2026

A simplified steady RANS-based numerical study of reacting flow in solid-fuel scramjet combustors is presented to evaluate the ability of commercial CFD frameworks to reproduce key features observed in experimental test sections. The work focuses on solid PMMA fuel in two supersonic combustor configurations: a cavity-assisted flameholding combustor and a variable-angle diverging combustor. The cavity combustor is analyzed as a baseline case and results are compared directly with experimental data. Three combustor geometry states corresponding to ignition, early-burning, and steady-burning conditions are investigated. Meshes are assessed through a systematic grid refinement method to establish grid independence. A novel variable-angle diverging …

Connecting The Existing Fiber Infrastructure To The Future With Antiresonant Hollow Core Fibers, Timothy Bate

Graduate Studies Theses and Dissertations 2026

Optical fiber systems based on solid-core silica waveguides underpin modern telecommunications, high-power laser delivery, precision sensing, and coherent optical systems. However, nonlinear effects, material absorption, and thermal limitations within silica increasingly constrain further scaling in both optical power and transmission performance. Antiresonant hollow-core fibers provide a promising alternative by guiding light predominantly in air, substantially reducing nonlinear interactions, latency, and optical damage while enabling transmission regimes inaccessible to conventional solid-core fibers. Despite rapid advances in antiresonant hollow-core fiber attenuation and power handling, one of the largest remaining barriers to widespread adoption is reliable integration with the existing solid-core fiber ecosystem. …

Integrated Optical Probes For Confocal Scanning Imaging And Adjustable Coherent-Gated Dynamic Sensing, Yonglin Huang

Graduate Studies Theses and Dissertations 2026

Optics and photonics have been one of the most important sciences and technologies that impact modern human life in a big way. For example, fiber-optics for communications and artificial intelligence. Optical probes are critical components for optical imaging and optical sensing technologies that have been actively researched and developed in the past decades. Advanced fiber-optic sensor probes with smaller size, better performance, lower noise, higher photon efficiency, rapid sensing time, and lower cost are needed in many applications, such as nanoscale material science, chemistry, and biomedical fields, etc.  In this project, new fiber-optic sensor probe technologies and integrated micro-optic devices …

Cognitive Load Classification Using Functional Near-Infrared Spectroscopy, Pratham Shah

Theses and Dissertations (Comprehensive)

This thesis investigates the classification of cognitive load using functional near-infrared spectroscopy (fNIRS) signals recorded during an N-back working memory task. The study introduces a novel short-channel correction layer designed to suppress superficial physiological noise adaptively, addressing limitations of traditional General Linear Model (GLM) based regression. A single participant dataset comprising 69 validated sessions was analyzed using both conventional machine learning and deep learning approaches. Traditional classifiers: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM), Random Forests, and Gradient Boosting were first evaluated using statistical features (mean, variance, peak, and slope). Among these, Gradient Boosting achieved the highest accuracy (55.6%), …

A Novel Federated Llm Framework For Distributed Traffic Modelling In Intelligent Transportation Systems, Seerat Kaur

Theses and Dissertations (Comprehensive)

Intelligent transportation systems (ITS) depend on accurate traffic prediction to support congestion management, infrastructure planning, and real-time operational decisions. Despite substantial progress in data-driven forecasting, several challenges continue to limit practical deployment: traffic data is distributed across independent regional authorities, making centralized aggregation infeasible, standard federated aggregation strategies ignore traffic-specific characteristics that meaningfully affect model quality, and existing models produce only numerical outputs without interpretable reasoning that urban planners can act upon. This thesis addresses these challenges through four contributions that collectively advance privacy-preserving, explainable, and scalable traffic forecasting.

The first contribution provides a systematic review of 129 peer-reviewed publications, …

Taylorkan-Vit: Parameter-Efficient Vision Transformers For Medical Image Classification, Kaniz Fatema, Dr. Emad Mohammed, Dr. Sukhjit Singh Sehra

Theses and Dissertations (Comprehensive)

Effective and interpretable classification of medical images remains a critical challenge in computer-aided diagnosis, particularly in data-scarce and resource-constrained clinical settings where traditional deep learning models prove impractical. This study addresses the fundamental barrier to Vision Transformer adoption in medical imaging—massive parameter counts and data requirements—through a systematic two-phase methodology. Phase 1 evaluates three spline-based Kolmogorov–Arnold Network (KAN) variants to identify the optimal nonlinear approximation function for parameter-efficient medical image classification: SBTAYLOR-KAN (B-splines with Taylor series), SBRBF-KAN (B-splines with Radial Basis Functions), and SBWAVELET-KAN (B-splines with Morlet wavelets). Comprehensive experiments across brain MRI, chest X-rays, and tuberculosis datasets—without any image …

Assessing Dissolved Organic Matter Sources And Dynamics In Urban Stormwater: Implications For Greenhouse Gases, Harper W. Schmalz

Theses and Dissertations (Comprehensive)

Stormwater management ponds (SWMPs) are important aspects of land-use planning and increasingly recognized as active sites of biogeochemical processing that influence carbon cycling; however, little research has investigated the controls on dissolved organic and dissolved inorganic carbon (DOC and DIC) within these systems. This thesis examined the processing and transformations of dissolved carbon between three compartments to support the development of a greenhouse gas (GHG) box-model for urban stormwater ponds, including SWMP sediment, surface water, and vegetation. The objective of this thesis was to assess the biogeochemical processes that govern the rate and transformation of DOC and DIC between these …

Evaluating The Impact Of Cognitive Distraction On Spaceflight-Relevant Task Performance Using Surface Electromyography And Motion Capture, Allyson K. Mitchell

UNF Graduate Theses and Dissertations

Cognitive distraction poses a risk to astronaut performance during complex, multitasking operations in spaceflight environments. This study examined the effects of cognitive load on neuromuscular coordination and task execution using surface electromyography (sEMG) and motion capture. Thirteen participants performed spaceflight-relevant tasks under undistracted and distracted conditions, with distraction induced through verbal questioning. EMG signals from eight upper-extremity muscles were processed using envelope filtering, peak normalization, and time normalization to enable inter-subject comparison, and group-level mean activation with standard deviation was analyzed. While overall muscle activation was similar between conditions, phase-dependent differences were observed, with undistracted trials showing higher activation during …

Mechanical Behavior Of Additively Manufactured Ti-6al-4v Eli Parts: Effects Of Laser Parameter Selection, Samuel Lopez

UNF Graduate Theses and Dissertations

Additive manufacturing (AM) of TI-6AL‑4V Extra‑Low Interstitial (ELI) enables complex geometries for fatigue‑critical medical device applications, yet fatigue performance remains sensitive to process‑induced defects. This work investigates the effect of laser process parameter selection on the microstructure, mechanical properties, and fatigue behavior of TI- 6AL‑4V ELI fabricated via laser powder bed fusion (L‑PBF) using a Renishaw RenAM system. The influence of laser parameters was isolated by holding powder chemistry, build orientation, scan strategy, sub‑transus annealing, and post‑processing constant between a non‑optimized baseline and an optimized parameter set selected based on tensile performance.

Optical microscopy showed the optimized condition exhibited improved …

Structural Batteries For Aerospace Applications, Tariqullah Wardak

Honors Undergraduate Theses

There is increasing pressure on the aviation sector to lower carbon emissions and switch to entirely electric and hybrid propulsion systems. However, the feasibility of standard lithium-ion batteries for long-range aircraft is limited, as they add substantial weight and occupy significant volume. Structural batteries, which combine load-bearing capability with energy storage, offer a potential pathway to lighter and more efficient aerospace systems.

This work investigates a carbon-fiber-based structural battery that utilizes carbon fiber as both a current-collecting, load-bearing electrode and a component of the composite structure. In contrast to lithium-ion chemistries, a zinc-based aqueous electrolyte is chosen for better environmental …

Preventing G/J Tube Dislodgement: A Device And Communication Innovation, Sarah Clark

2026

The Problem
G/J tube dislodgement is a frequent complication in pediatric patients
Leads to:

• Emergency department visits
• Hospital admissions
• Delays in nutrition/medication

Impact on Patients & Families:

• IV placement (traumatic)
• Radiation exposure
• Overnight hospital stays

Impact on Nurses & System:

• Increased workload (admissions, coordination)
• Occupied inpatient beds for stable patients
• Inefficient care processes

Aims/Objectives
Aim: Reduce unplanned G/J tube dislodgements and related hospital utilization.
Objectives: Develop a breakaway connector prototype

Implementation and Evaluation
Setting: Pediatric inpatient & outpatient system
Participants: Nurses (bedside, GI, IR), caregiver, innovation team
Process:
Roundtable discussions → identified workflow gaps
Communication/workflow audit
Developed device …

Using A Study Journal To Support Class Engagement And The Development Of Entrepreneurial Mindset Habits, Otilia Popescu, Dimitrie C. Popescu

Engineering Technology Faculty Publications

Engineering Technology programs were historically introduced to support non-traditional students and offer college pathways for students working and having already a developing career. This is even more true in the current academic environment, with a large percentage of students enrolled in engineering technology programs being either fully or part-time employed, active or retired military, and at different stages in their lives, usually with families to care for. Often, non-traditional students attend classes online, either synchronously or even more often asynchronously, due to their schedule constraints. Course instructors regularly face schedule or time management constraints from the students’ side, and they …

Towards Supporting Real-Time Estimation Of Vehicle Fuel Consumption And Co2 Emissions In Smart City Applications, Abrar Alali, Stephan Olariu

Computer Science Faculty Publications

This paper evaluates a simplified physics-based energy demand model designed to estimate vehicle fuel consumption and CO₂ emissions—a critical tool for sustainable transportation planning and smart city applications. Unlike data-driven regression models that lack generalizability for user-defined conditions or complex physics-based approaches that rely on extensive, often proprietary data, the simplified model is distinguished by its minimal parameter requirements, depending primarily on a single, overarching powertrain efficiency value. A key contribution is the comprehensive empirical evaluation of the simplified model against official Environmental Protection Agency (EPA) test data across multiple driving cycles and vehicle types, providing a rigorous validation previously …

Privacy-Preserving Federated Learning With Optimized Ensemble Weighting And Knowledge Distillation For Covid-19 Detection From Non-Iid Medical Imaging Data, Richard Annan, Hong Qin, Robert Newman, Madhuri Siddula, Letu Qingge

Computer Science Faculty Publications

Medical imaging enables rapid and accurate diagnosis of COVID-19, with CT scans proving especially effective. However, data privacy concerns limit collaborative model development across hospitals. To address this issue, we introduce a novel federated learning framework. It is referred to as Independent Knowledge Distillation with post-Ensemble Federated Learning (IKDEFL). Differential Privacy (DP) is integrated into the framework to improve privacy guarantees. Three DP mechanisms are evaluated. These include Fixed Gaussian, Gaussian Adaptive, and Tree Adaptive. The evaluation has been conducted on heterogeneous and Non-Independent and Identically Distributed (Non-IID) datasets. These datasets reflect real-world hospital scenarios. Results show that IKDEFL significantly …

Biosecure-Llm Framework: Protecting Llms From Cyberbiosecurity Threats And The Case For Independent Ai Safety Governance, Xavier-Lewis Palmer, Lucas Potter, Srdjan Lesaja, Sotirios Karathanasis, Mohammad Ghasemigol

Computer Science Faculty Publications

Large Language Models (LLMs) are becoming critical infrastructure in scientific, healthcare, and governmental contexts. As frontier AI laboratories increasingly partner with government agencies, a fundamental question arises: Who should control the safety and policy-enforcement layers that constrain model behavior? Current safety mechanisms (LLM guardrails) are typically designed for generic "harmlessness" and operate by detecting semantic patterns and refusing requests. However, they are inadequate governance instruments because they cannot implement auditable, domain-specific controls tied to external regulatory policy objects (e.g., control lists or rules governing personally identifying information). Even a perfectly aligned model is not able to express institution-specific policy without …

A Comparative Analysis Of Explainable Ai (Xai) Techniques For Transparent And Reliable Image Classification, Sovon Chakraborty, Shakib Mahmud Dipto, Kevin R. Pilkiewicz, Michael L. Mayo, Pratip Rana

Computer Science Faculty Publications

Evaluating the trustworthiness of black-box machine learning models remains a significant methodological challenge. Their lack of transparency and interpretability limits applicability, because stakeholders often seek transparency before trusting the results of black-box machine learning models. Explainable AI (XAI) methods provide for human-understandable justifications and informed decision-making of these black-box architectures. Therefore, it is imperative to select the proper XAI model tailored to specific tasks. In this research, we focus on examining four XAI techniques: PEEK, LRP, GRAD-CAM, and LIME to understand how they perform against each other for image classification tasks. We evaluate the performance, robustness, generalizability, noise stability, and …

Isotropic Shrinkage Of Patterned Vacancies Enables Three-Dimensional Nanoprecise Metastructures For Visible Light Applications, Quansan Yang, Gaojie Yang, Takahiro Nambara, Hiroyuki Kusaka, Yuichiro Kunai, Alex C. Matlock, Corban Swain, Brett Pryor, Yannick Salamin, Daniel Oran, Hasindu Kariyawasam, Ramith Hettiarachchi, Dushan Wadduwage, Marin Soljačić, Peter T. C. Soflaei, Edward S. Boyden

Computer Science Faculty Publications

Three-dimensional metastructures with nanoscale feature sizes exhibit unique properties compared with structures with larger feature sizes, but are difficult to fabricate. Here we introduce implosion carving (ImpCarv), a method for photopatterning vacancies of complex geometry throughout materials, followed by isotropic shrinkage (>10-fold). ImpCarv works by photoactivating sensitizers to generate reactive oxygen species that cleave a swollen hydrogel at defined points, followed by controlled shrinkage via dehydration. ImpCarv creates three-dimensional metastructures where the refractive index of each point throughout a material can be specified with nanoscale precision via material presence or absence. By leveraging refractive index programmability for precise phase …

Memebuddy: Dialog-Style Audio Representations For Engaging Non-Visual Meme Experiences, Chirag Bhansali, Vikas Ashok, Hae-Na Lee

Computer Science Faculty Publications

Image memes are a pervasive form of online communication, widely used to convey humor, opinions, and cultural references. Prior work has explored making memes accessible to blind users, primarily through auto-generated descriptive captions. While these approaches improve comprehensibility and sometimes incorporate prosodic or emotional cues, they often fail to capture the humor, narrative structure, and contextual nuances that make memes engaging. We present MemeBuddy, a system that models memes as dialog, generating structured, multi-turn audio representations using role-based speakers. MemeBuddy reinterprets a meme as a conversation between two speakers, integrating extracted meme text with contextual knowledge implicitly inferred by a …

Susceptibility To High-Fidelity Misinformation: An Eye-Tracking Analysis, Yasasi Abeysinghe, Gavindya Jayawardena, Enkelejda Kasneci, Sampath Jayarathna

Computer Science Faculty Publications

With the rise of online misinformation and AI-generated text, understanding human perception of news truthfulness is critical. In this study, we examine visual attention and cognitive processing using eye-tracking measures as individuals read fake and real news articles sharing nearly identical structure and imagery, differing only in subtle textual changes. Using the public FakeNewsPerception dataset, we analyze advanced gaze measures, including scanpaths, AOI transitions, and luminance-corrected pupil measures, beyond basic gaze features, in relation to news truthfulness and perceived believability. Results show that, given the high fidelity of the fake news, readers exhibited comparable visual scanning patterns, attention allocation across …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

Untrained Position-Encoded Multilayer Perceptron Network For Structured Illumination Microscopy Reconstruction, Sahil Sharma, Leonidas Zimianitis, Krishnendu Samanta, Balpreet Singh Ahluwalia, Joby Joseph, Dushan N. Wadduwage

Computer Science Faculty Publications

Structured Illumination Microscopy (SIM) enables super-resolution imaging by encoding high-frequency spatial information through patterned light. While traditional Fourier-based reconstruction methods are prone to artifacts under suboptimal conditions, recent deep learning approaches often require large training datasets and lack adaptability across different imaging setups. In this work, we present Position Encoded Multi-Layer Perceptron (PEM) network that leverages implicit neural representations (INRs) and SIM forward-model-driven modeling to reconstruct super-resolved images without any training data. PEM-SIM represents each spatial coordinate as a combination of sinusoidal functions across multiple frequencies, enabling rich encoding of fine spatial detail. A forward model grounded in SIM image …

Adaptive Self-Attention For Enhanced Segmentation Of Adult Gliomas In Multi-Modal Mri, Evan P. Savaria, Jiangwen Sun

Computer Science Faculty Publications

Every year there are an estimated 80,000–90,000 new glioma cases, highlighting the need for reliable imaging-based decision support. Although deep learning has improved tumor sub-region segmentation, many state-of-the-art models fail to fully capture complementary information across T1, T1Gd, T2, and FLAIR MRI modalities and often operate as “black boxes,” limiting physician trust when precise delineation is critical for surgical planning, radiation targeting, and treatment monitoring. To address these limitations, we propose AIMS, an Adaptive Integrated Multi-Modal Segmentation framework that maintains modality-specific feature streams and employs adaptive self-attention within a hierarchical CNN-Transformer architecture to prioritize and fuse multi-modal MRI features. We …

Application Paths Of Semantic Modeling In Financial Fraud Detection And Risk Identification, Victor P. Gauthier, Daniel S. Wu

Computer Science Faculty Publications

Financial fraud and risk pose significant threats to economic stability and individual well-being. Traditional detection methods often struggle to keep pace with increasingly sophisticated fraudulent schemes. Semantic modeling, which focuses on understanding the meaning and relationships within data, offers a promising avenue for enhancing fraud detection and risk identification. This review paper explores the application paths of semantic modeling in this domain. We begin with a historical overview of fraud detection techniques, highlighting the limitations of traditional approaches. Subsequently, we delve into core themes, including knowledge graph-based fraud detection and semantic rule-based inference for risk assessment. We then compare and …

Comparative Life Cycle Assessment Of End-Of-Life Crystal Silicon Photovoltaic Panels: Recovery Methods And Extended Life In Agricultural Application, Patima Chaichana, Vacharaporn Soonsin, Nattapong Tuntiwiwattanapun

Applied Environmental Research

The increasing deployment of crystalline silicon (c-Si) photovoltaic (PV) panels has raised concerns about their waste management. This study evaluated management strategies for discarded c-Si PV panels in Thailand, integrating environmental and economic analyses. Life cycle assessment (LCA) and cost-effectiveness analysis (CEA) were applied. The LCA can be divided into 2 parts: (1) secured landfill vs decentralized recycling by existing facilities vs centralized full recovery and (2) reusing PV panels in agricultural applications. The results revealed that secured landfills were the most environmentally burdensome (34.43 Pt), whereas centralized recycling achieved net benefits (-211.93 Pt) through emission reductions and recovery of …

Land Use And Land Cover Simulation Via Integrated Modelling With Gis Techniques For Sustainable Land Utilization Development In The Northeast Khong Subwatershed Of Thailand, ฺBanchongsak Faksomboon, Thipphaphone Keoviyavong

Applied Environmental Research

Land utilization is an important indicator of socioeconomic and environmental changes caused by both natural and man-made factors. Land use and land cover (LULC) simulation is a critical tool for monitoring and predicting LULC and is essential for sustainable development, land resource management and planning. The cellular automata (CA) Markov model is the basis for the current study’s prediction of LULC changes in the Northeast Khong Sub Watershed (NKSW). Landsat data from 2013 to 2023 were used to investigate LULC classification and determine the spatiotemporal distributions of LULC. In addition, LULC data from 2013 and 2023 were used to generate …

Life Cycle Assessment Of Co2-To-Methanol: Comparative Evaluation Of Direct And Alcohol-Assisted Hydrogenation Routes, Chayet Worathitanon, Naphat Chansonthi, Pawat Pinthong, Kritsana Suwanamad, Viganda Varabuntoonvit

Applied Environmental Research

The increasing severity of global warming, primarily driven by greenhouse gas emissions, underscores the urgent need for CO2 reduction and utilization strategies. Converting CO2 into methanol presents a promising approach, as methanol serves both as a fuel and a feedstock in various industries. This study evaluates the life cycle environmental impacts of three methanol production routes: (1) direct CO2 hydrogenation, (2) ethanol-assisted CO2 hydrogenation, and (3) propanol-assisted CO2 hydrogenation. Two energy scenarios are considered: conventional energy and wind power. Process simulations were performed using Aspen Plus V.14, and inventories were analyzed through Life Cycle Assessment (LCA) using the ReCiPe 2016 …

Reduction Of Cr(Vi) With Infrared Light And Chemical Adsorption Of Cr(Iii) By A God-Crown/Bentonite Composite For Electroplating Waste Remediation, Andre Taufik Kurniawan, Muhammad Djoni Bustan, Sri Haryati

Applied Environmental Research

Heavy metal pollution, particularly chromium (Cr) from electroplating industrial waste, has severely threatened environmental quality and human health. This study aims to develop a composite adsorbent material based on bentonite and god crown biomass capable of removing chromium ions from liquid waste through a combination of reduction and adsorption mechanisms. The god crown/bentonite (GC/Bt) composite was synthesized at a mass ratio of 2:1 and calcined at 900°C. FTIR characterization revealed active functional groups (–OH, C=O, Si–O, and Al–O–Si), whereas BET analysis revealed a mesoporous structure (surface area 31.12 m2 g-1, pore diameter 4.37 nm) suitable for ion diffusion. The reduction …