Processing Framework Research Articles

Validation of dementia care-related scales among informal caregivers of Latinos with dementia or mild cognitive impairment.

To test the psychometric properties of several dementia care-related scales among Latinos in the US. We leveraged secondary baseline data from a one-arm mHealth trial on dementia caregiver support. We included 100 responses for caregiver-focused scales and 88 responses for care recipient-focused scales. Scales included the Neuropsychiatric Inventory Questionnaire Severity and Distress scales, six-item Zarit Burden Inventory, Ten-item Center for Epidemiologic Studies Depression Scale, Geriatric Depression Inventory, Quality of Life in Alzheimer's Disease, and Single-item Satisfaction With Life Scale. We calculated concurrent validity using Pearson and Spearman correlations and expected correlations amongst all variables in line with the Stress Process Framework. We calculated internal consistency reliability using Cronbach's alpha. All concurrent validity correlations followed the expected directionality, with 19/21 inter-scale correlations in the total sample reaching statistical significance (p<0.05), and 17/21 reaching at least a low correlation (0.3). Cronbach's alpha ranged from 0.832 to 0.879 in all scales in the total sample. The English and Spanish caregiver-administered scales tested in this manuscript have good psychometric properties. The dementia care-related scales are now appropriately available for use among US Latinos in research and clinical contexts.

Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design

Advancing mmWave Altimetry for Unmanned Aerial Systems: A Signal Processing Framework for Optimized Waveform Design

Framework for scaling-up extraction processes in nutraceutical beverages: A simulation, techno-economic, and environmental analysis approach

The nutraceutical beverages market has increased in recent years, motivated by the increasing trend of consumers choosing food and beverages beneficial to health, mostly after the COVID-19 pandemic. Several researchers have proposed different formulations, where the combination of plants has been tested at the laboratory and pilot scales to maximize the desirable features of the beverages, including antioxidant capacity, anticarcinogens, and anti-inflammatory properties. Developing these products requires scaling-up from these scales to the industry one and, hence, identifying the criteria and/or parameters affecting process yield due to the transport phenomena associated with the scale increment. This work proposes a framework for scaling up solid-liquid extraction in a nutraceutical beverage process using available pilot plant data, combining brute-force and empirical scaling approaches. This framework provides an alternative for industries that have acquired equipment without considering the principles of similarity between the larger scale and the laboratory stage. Operating conditions are tuned to reach the product quality at the pilot level and the maximum beverage's antioxidant capacity. A techno-economic analysis of the production process and an environmental evaluation were performed, providing the basis for an effective scaling-up to the industry level. The scaling-up proved to be feasible, as the net present value of the process is $2018,000 with a payback time of 4.83 years; the major source of solid waste is the raw materials with a carbon footprint less than 0.205 MT eCO2 due this process operates with temperatures lower than 100 °C. The circular economy indicators in this project were circular material usage rate and Waste Stream Recycling Rate. The Circular Material Usage Rate ranged from 16.7 % to 66.7 % depending on the composition of the cocoa husk in the raw material, and the Waste Stream Recycling Rate (%) ranged from 4.4 % to 5 % destined for composting development. The framework is designed to be applicable to other food production processes that encounter equipment constraints. It facilitates the evaluation of process yield and enables the simulation and analysis of economic profitability and environmental impact using circular economy indicators at an industrial/commercial scale.

Three-dimensional computer holography based on two hybrid constraint iterative angular spectrum algorithms

A method for three-dimensional holographic image calculation based on angular spectrum layering is presented, which significantly reduces the noise resulting from image overlap during hologram reconstruction. In order to enhance the reconstruction quality, we proposed a two-hybrid constraint algorithm, which employs two parallel processing frameworks to allocate multiple plane images effectively. Additionally, by applying two different hybrid constraint factors, spatial resource allocation is utilized efficiently to mitigate the impact of axial inter-layer crosstalk noise. The numerical simulations and optical experiment results demonstrated that our method has successfully improved the reconstruction quality of holograms and proved its effectiveness and feasibility.

Efficient detection of intra/inter-frame video copy-move forgery: A hierarchical coarse-to-fine method

With a simple forgery technique but a realistic result, video copy-move forgery has currently become one of the most popular tampering manners. In the last couple of years, various new techniques deriving from machine intelligence and pattern recognition have been widely proposed for image forensics. However, it still faces a very challenging task in the field of video copy-move forgery for four reasons: i) Low F1 score and high false-alarm; ii) Lack of a synthesis processing framework; iii) Weak detection robustness and accuracy; iv) Low efficiency. A novel Hierarchical Coarse-to-Fine framework for effective video copy-move forgery detection is proposed to overcome these challenges: i) In the coarse forgery frame-pair matching, the coarse copy-move frame-pairs matching algorithm with the newly proposed two-pass filters can locate real forgery frame-pairs (FFP) and also reduce false-alarm. ii) Through further analysis of the actual FFP, the detection of intra-frame and inter-frame copy-move forgeries can be accurately and simultaneously determined. iii) In the fine keypoint-pairs matching, our newly designed two-hierarchical keypoint-pair filtering can accurately localize the forgery region at pixel level under various adverse conditions. iv) The novel Hierarchical Coarse-to-Fine framework (together with the newly designed algorithms above) considers only the real FFP and true keypoint-pairs for computation, resulting in higher efficiency and accuracy. Finally, Delaunay Triangulation-based region filling is employed to indicate the forgery regions. Compared to the latest methods, our algorithm has been tested extensively and found to be the best at detecting forgeries, with a top score of F1=0.77 and no false-alarms, even under different types of attacks, as validated by the well-known GRIP dataset.

Machine Learning for Real-Time Building Outdoor Wind Environment Prediction Framework in Preliminary Design: Taking Xinjiekou Area of Nanjing, China as the Case

The incorporation of physical environmental performance as a primary consideration in building design can facilitate the harmonization of the built environment with the surrounding site and climate, enhance the building’s environmental adaptability and environmental friendliness, and contribute to the achievement of energy-saving and emission-reduction objectives through the integration of natural lighting and ventilation. General computational fluid dynamics (CFD) can help architects make accurate predictions and effectively control the building’s wind environment. However, CFD integration into the design workflow in the preliminary stages is frequently challenging due to program uncertainty, intricate parameter settings, and substantial computational expenses. This study offers a methodology and framework based on machine learning to overcome the complexity and computational cost barriers in simulating outdoor wind environments of buildings. In this framework, the machine learning model is trained using an automated CFD simulation system based on Butterfly and implemented within the Rhino and Grasshopper environment. This framework provides real-time simulation feedback within the design software and exhibits promising accuracy, with a Structural Similarity Index Measure (SSIM) ranging from 90–97% on a training dataset of 1200 unique urban geometries in Xinjiekou Area of Nanjing, China. Furthermore, we programmatically integrate various parts of the simulation and computation process to automate multiscenario CFD simulations and computations. This automation saves a significant amount of time in producing machine-learning training sets. Finally, we demonstrate the effectiveness and accuracy of the proposed working framework in the design process through a case study. Although our approach cannot replace CFD simulation computation in the later design stages, it can support architects in making design decisions in the preliminary stages with minimal effort and immediate performance feedback.

Numerical modelling and simulation of methane enrichment: a systematic review on pressure swing adsorption technology

ABSTRACT Enriching methane from sources such as natural gas, biogas, and coalbed methane is a valuable gas separation endeavor, given methane as a cleaner and more sustainable fuel resource. Pressure swing adsorption (PSA) is widely regarded as a mature technology for this purpose. The essence of the mathematical model is to capture the fundamental aspects of the problem, enabling an accurate description of the system. This review summarizes recent trends in the mathematical modelling of PSA for methane enrichment. It provides a comprehensive compilation of numerical modelling and simulation studies, with a critical emphasis on methane enrichment applications. A rigorous mathematical model describing the adsorption phenomenology in a packed bed column is presented, incorporating mass, momentum, and energy balances, which serve as the fundamental framework for the cyclic adsorption process. Key analyses and performance metrics of PSA systems are detailed through examples from the literature. A case study based on a single column is presented to exemplify the mathematical modelling pathway and serve as a basis for model validation. The paper concludes by highlighting instructive avenues for future research directions and development in this area.

Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks

Internet of Things (IoT) has the potential to significantly impact various domains e.g. health, transportation, automation, and emergency response to both man-made and natural disasters, particularly in scenarios where human decision is challenging. In this research a Role-Based Access Control (RBAC) Enabled Secure and Efficient Data Processing Framework for IoT Networks has been proposed. This framework ensures robust security and optimized data handling through granular access control mechanisms based on predefined roles. By leveraging RBAC, it mitigates unauthorized access risks, thereby safeguarding sensitive IoT data during transmission and storage. Our approach emphasizes efficiency by streamlining data processing workflows, reducing latency, and optimizing resource utilization. The framework is designed to scale with IoT network expansions and adapt to evolving security needs, promising enhanced reliability and trustworthiness in data operations for contemporary IoT environments. According to this research work current security effectiveness is 99 percent.Home area network (HAN) can be used for smart connectivity of different home appliances using IoT and automatic start and stop feature may be possible.Access control server (ACS) is used to control access and provide permission for different operations.

Factors affecting financial engineering and product development in Islamic Financial Institutions

Purpose This study aims to analyze Islamic financial institutions’ (IFIs) current financial engineering and product development procedures. Design/methodology/approach The paper is quantitative in nature and the survey questionnaire were collected from managers and IF experts working for Islamic Banks, Takaful and other IFIs in Turkey, Malaysia and UAE. Two-stage structural equation modeling was used to test the hypothesis. Findings The findings highlighted that the Shari’ah Supervisory Board, Strategy and Planning of IFIs, Legal and Regulatory framework, pricing of a new product and financial performance positively impact the new product development (NPD) process. At the same time, Islamic values have no significant positive impact. Research limitations/implications When generalizing the research results, data collection from the right departments was the main limitation of the current study. Future research may opt to collect data only from Product Development Departments. Practical implications The findings of this study will allow IFIs to reflect on their present methods, procedures and Shari’ah compliance framework for the NPD process. Originality/value Factors affecting the product development and financial engineering process are discussed in the literature. The findings of this study can be regarded as building blocks for future academic research on product development and financial engineering in Islamic finance.

Post-wildfire boreal forest vegetation cover change mapping via information fusion for secondary disaster risk assessments

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

Optimizing AoI in IoT Networks: UAV-Assisted Data Processing Framework Integrating Cloud–Edge Computing

Optimizing AoI in IoT Networks: UAV-Assisted Data Processing Framework Integrating Cloud–Edge Computing

Detection and Segmentation of Mouth Region in Stereo Stream Using YOLOv6 and DeepLab v3+ Models for Computer-Aided Speech Diagnosis in Children

This paper describes a multistage framework for face image analysis in computer-aided speech diagnosis and therapy. Multimodal data processing frameworks have become a significant factor in supporting speech disorders’ treatment. Synchronous and asynchronous remote speech therapy approaches can use audio and video analysis of articulation to deliver robust indicators of disordered speech. Accurate segmentation of articulators in video frames is a vital step in this agenda. We use a dedicated data acquisition system to capture the stereovision stream during speech therapy examination in children. Our goal is to detect and accurately segment four objects in the mouth area (lips, teeth, tongue, and whole mouth) during relaxed speech and speech therapy exercises. Our database contains 17,913 frames from 76 preschool children. We apply a sequence of procedures employing artificial intelligence. For detection, we train the YOLOv6 (you only look once) model to catch each of the three objects under consideration. Then, we prepare the DeepLab v3+ segmentation model in a semi-supervised training mode. As preparation of reliable expert annotations is exhausting in video labeling, we first train the network using weak labels produced by initial segmentation based on the distance-regularized level set evolution over fuzzified images. Next, we fine-tune the model using a portion of manual ground-truth delineations. Each stage is thoroughly assessed using the independent test subset. The lips are detected almost perfectly (average precision and F1 score of 0.999), whereas the segmentation Dice index exceeds 0.83 in each articulator, with a top result of 0.95 in the whole mouth.

Irregular surface temperature monitoring in liver procurement via time-vertex signal processing

The liver viability monitoring during its procurement is critical to guarantee the safe liver transportation. Traditionally, the viability is assessed by taking invasive biopsy on the liver surface. Recently, the noninvasive thermal images of liver surface have been used as an alternative assessment way. Researchers have proposed monitoring and classification approaches based on the thermal images. Existing works have demonstrated the importance of the temporal variation or the spatial variation of the thermal images to the viability monitoring. However, there is no prior work to leverage the graph structure of the spatio-temporal processes for the viability monitoring. In this paper, we propose a time-vertex signal processing framework for the irregular thermal data of the pure liver region monitoring. In particular, we extract features of irregular thermal data based on the joint Fourier transform (JFT), which is an integration of graph Fourier transform (GFT) and discrete Fourier transform (DFT). The extracted JFT features can accurately reconstruct the thermal images with a limited number of features. Then, we use a non-parametric online change-point estimation method, based on online scan B statistics, to monitor the JFT features without a parametric distribution. Our proposed framework is applied to both simulation and the liver procurement data, and achieves good monitoring performance.

Unveiling the assessment process behind an integrated flood risk management plan

In the context of the European Floods Directive, flood risk assessment is a critical component for the definition of an integrated management plan that operates within a multidimensional landscape shaped by intricate interactions. This study explores this complex interplay using a comprehensive framework, aimed at enlightening the non-linear pathways that flood risk assessments can traverse. It adopts the Gioia Methodology within the Grounded Theory approaches, enabling a nuanced exploration of flood risk assessment dynamics. Utilizing data from an Italian case study in the Po River District, this study unveils the flood risk assessment process framework by identifying 13 first-order codes, 6 s-order themes and 3 aggregate dimensions. It introduces a qualitative self-assessment tool to facilitate integration across dimensions and enhance Directive alignment, offering valuable insights for future flood risk assessment implementations.

An enzymatic hydrolysis-based platform technology for the efficient high-yield production of cellulose nanospheres

This study evaluates the feasibility of using enzymatic technology to produce novel nanostructures of cellulose nanomaterials, specifically cellulose nanospheres (CNS), through enzymatic hydrolysis with endoglucanase and xylanase of pre-treated cellulose fibers. A statistical experimental design facilitated a comprehensive understanding of the process parameters, which enabled high yields of up to 82.7 %, while maintaining a uniform diameter of 54 nm and slightly improved crystallinity and thermal stability. Atomic force microscopy analyses revealed a distinct CNS formation mechanism, where initial fragmentation of rod-like nanoparticles and subsequent self-assembly of shorter rod-shaped nanoparticles led to CNS formation. Additionally, adjustments in process parameters allowed precise control over the CNS diameter, ranging from 20 to 100 nm, highlighting the potential for customization in high-performance applications. Furthermore, this study demonstrates how the process framework, originally developed for cellulose nanocrystals (CNC) production, was successfully adapted and optimized for CNS production, ensuring scalability and efficiency. In conclusion, this study emphasizes the versatility and efficiency of the enzyme-based platform for producing high-quality CNS, providing valuable insights into energy consumption for large-scale economic and environmental assessments.

Automated Microstructural Analysis of Anodic Catalyst Layers in Proton Exchange Membrane Water Electrolyzers (PEMWEs) Using Python-Based Image Processing Framework

Adverse climatic conditions caused by greenhouse gas (GHG) emissions from non-renewables have led to increased focus on utilization of clean alternative fuels such as hydrogen (H2). Clean H2 can be obtained via electrochemical reaction of water splitting through proton exchange membrane water electrolyzers (PEMWEs). However, challenges to its widespread commercialization lie with the high H2 production cost, a large portion of which comes from use of expensive anodic catalyst materials. To improve anodic catalyst utilization to attain better performance at reduced cost, a thorough understanding of anodic catalyst layer microstructure is needed. In-depth characterization through scanning transmission electron microscopy with energy dispersive spectroscopy (STEM/EDS) can provide a comprehensive dataset, which can be processed further to extract structural parameters and correlate to electrochemical performance. However, extracting relevant information from these datasets can be very challenging and time-consuming.In this study, an automatic python-based image processing framework is implemented to quantify compositional and microstructural parameters for different IrO2 based catalyst inks and their corresponding catalyst layers. The code can provide areal density of catalyst materials, pore/agglomerate size distribution, connectivity of phases, coverage of catalyst particles by ionomer, and its content percentage and distribution. Quantified parameters are then correlated with the electrochemical performance of the PEMWEs. The results provide adequate pointers to not only help capturing microstructural details of catalyst layers but also provide a basis to predict electrochemical behavior and performance of the inks and catalyst layers being studied.

Data driven latent variable adaptive control for nonlinear multivariable processes

This article aims at a new partial least squares (PLS) control design and analysis in a data-driven framework for nonlinear multivariable processes whose mechanistic models are completely unknown. First, a general nonlinear autoregressive moving average with the exogenous input (NARMAX) model is used as the dynamic nonlinear PLS model. Then, by introducing a dynamic linearisation approach in each latent variable (LV) space, the unknown NARMAX-based PLS model is transformed to a linear dynamic PLS data model (dPLSDM), which can be improved in real time by estimating its unknown parameter using the latent input and output (I/O) data. Next, a data-driven latent variable adaptive control (DDLVAC) is proposed in each LV loop. By virtue of the dPLSDM, the multivariable nonlinear process is decoupled into multiple single-loop systems and the high dimensions of the process data are reduced such that the corresponding DDLVAC is simplified. Further, the DDLVAC only depends on the I/O data without requiring any model information of the original process. Theoretical analysis confirms the validity of the DDLVAC. The simulation study demonstrates the advantages of the DDLVAC such as less storage space, smaller computation burden, less control cost, as well as more robustness against uncertainties.

Effects of processing tasks on children's learning of second language words

This study investigated the impact of different semantic tasks (pleasantness-rating and translation reading-aloud) and structural tasks (letter-counting and word-copying) on Chinese-speaking children's learning of English words. Participants' memory of first language (L1) counterparts for second language (L2) words and learning of L2 word phonology and phonology-meaning mapping were respectively assessed via the L1 free recall, L2 free recall, and picture-cued L2 recall tests. Logistic mixed-effects regression modeling yielded the following findings. First, pleasantness-rating and letter-counting had minimal impact on the acquisition of L2 word knowledge, except that letter-counting significantly facilitated the recall of L2 word phonology. Second, translation reading-aloud and word-copying positively influenced L2 word acquisition, although the former did not significantly affect phonology learning. Third, translation reading-aloud outperformed pleasantness-rating when it came to recalling L1 counterparts and learning L2 word phonology and phonology-meaning mapping, and word-copying was more effective than letter-counting in the learning of L2 word phonology and phonology-meaning mapping. These findings partially support the transfer appropriate processing (TAP) framework and confirm the predictions of the processing-resource allocation (TOPRA) model, although some divergent results—most likely attributable to methodological difference—were observed. Additionally, these results offer valuable instructional implications.

Joint Optimization of Relay Communication Rates in Clustered Drones under Interference Conditions

To address the issues of communication failure and inefficiency in clustered drone relay communication due to external malicious interference, this paper proposes a joint optimization method for relay communication rates under interference conditions for clustered drones. This method employs the following two-step processing framework: Firstly, the Discrete Soft Actor-Critic (DSAC) algorithm is used to train the relay drones for dynamic channel selection, effectively avoiding various types of interference. Simultaneously, the Bayesian optimization algorithm is applied to optimize the hyperparameters of the DSAC algorithm, further enhancing its performance. Subsequently, the modulation order, transmission power, trajectory of the relay drones, and power allocation factors of the clustered drones are jointly optimized. This complex problem is transformed into a convex subproblem for determining a solution, aiming to maximize the communication rate of the clustered drones. The simulation’s results demonstrate that the proposed algorithm exhibits excellent performances in terms of anti-interference capability, solution convergence, and stability. It effectively improves the mission efficiency of clustered drones under interference conditions and enhances their adaptability to dynamic environments.

Desegregation of neuronal predictive processing.

Neural circuits construct internal 'world-models' to guide behavior. The predictive processing framework posits that neural activity signaling sensory predictions and concurrently computing prediction-errors is a signature of those internal models. Here, to understand how the brain generates predictions for complex sensorimotor signals, we investigate the emergence of high-dimensional, multi-modal predictive representations in recurrent networks. We find that robust predictive processing arises in a network with loose excitatory/inhibitory balance. Contrary to previous proposals of functionally specialized cell-types, the network exhibits desegregation of stimulus and prediction-error representations. We confirmed these model predictions by experimentally probing predictive-coding circuits using a rich stimulus-set to violate learned expectations. When constrained by data, our model further reveals and makes concrete testable experimental predictions for the distinct functional roles of excitatory and inhibitory neurons, and of neurons in different layers along a laminar hierarchy, in computing multi-modal predictions. These results together imply that in natural conditions, neural representations of internal models are highly distributed, yet structured to allow flexible readout of behaviorally-relevant information. The generality of our model advances the understanding of computation of internal models across species, by incorporating different types of predictive computations into a unified framework.