Dynamic Array Research Articles

An Innovative Active Terahertz Modulator Based on Dynamic Graphene-Silicon Arrays

An Innovative Active Terahertz Modulator Based on Dynamic Graphene-Silicon Arrays

A Study on the Application of Meta-Cosmos Technology in Risk Management of Commercial Banks

In recent years, the meteoric rise of Meta-Cosmos technology has established it as a pivotal component of the digital economy. This amalgamation of cutting-edge technologies—including virtual reality (VR), augmented reality (AR), blockchain, and artificial intelligence—has fundamentally reshaped perceptions of the digital realm. As the cornerstone of the financial system, commercial banks have consistently confronted a dynamic array of challenges in risk management. With the acceleration of digitalization, traditional risk management methods have increasingly revealed their inadequacies, particularly in navigating the complexities of fluctuating market conditions and the escalating threats to cybersecurity. The advent of Meta-Cosmos technology offers commercial banks a novel perspective and innovative tools for risk management. By constructing virtual economic environments, banks can conduct more precise risk assessments and controls, thereby enhancing the scientific rigor and timeliness of their decision-making processes. Nevertheless, the application of Meta-Cosmos technology within the banking sector remains in its exploratory phase, facing challenges related to technological maturity, security, and regulatory compliance. This study aims to examine how Meta-Cosmos technology can be integrated into the risk management frameworks of commercial banks, thereby providing theoretical support and practical guidance for the future development of financial technology.

Scaffolding Fifth Graders’ Learning with a Digital Multi-Representation Applet: Design Research on Focusing Multiplicative Structures with Dynamic Dot Arrays

AbstractDigital multi-representation applets offer promising opportunities for promoting students’ conceptual understanding by dynamically converting graphical and symbolic representations and for promoting discoveries and justifications of mathematical properties. However, learning effects of open-ended multi-representation applets seem to depend on targeted scaffolds for students’ processes of conversions, treatments, and conversions of treatments. While these design challenges have come to be well-understood for dynamic geometry and functions, more design research is needed for multi-representation applets for multiplicative reasoning. We report on a design research study in Grade 5 in which the multi-representation applet of a dynamic dot array was integrated into a digital teaching–learning environment with sequenced tasks and more targeted scaffolds. The qualitative analysis of 30 students’ processes revealed multiple potentials of the dynamic array to (a) connect static representations by articulating the underlying multiplicative unit structures (conversions), (b) describe systematic variations (coordinated treatments), (c) explain systematic variations in representations by articulating unit structures (conversions of variations), (d) discover multiplicative commutativity (treatments), and (e) go towards justifying multiplicative commutativity by restructuring the array and its unit structures (conversions of treatments). Design experiment Cycle 1 revealed that all potentials were partially addressed by some students. To enable students to exploit more of these multiple potentials, attention scaffolds and language scaffolds were integrated. In Cycle 2, more potentials were exploited while still leaving room for improvements. In total, the study shows how design research can contribute to embed multi-representation applets into teaching–learning environments with well-sequenced tasks and targeted scaffolds.

Experimental and Numerical Investigation of Dynamic Vibration Absorber Array for Vibration Mitigation of Integral Blisk

Experimental and Numerical Investigation of Dynamic Vibration Absorber Array for Vibration Mitigation of Integral Blisk

Bridging the Gap Between Oncologists and Payers: Impact of an EHR-Embedded CDS Tool in Oncology Clinical Pathway (OCP) Adherence

As value-based oncology care gains traction, health practices and providers on the frontlines of care delivery increasingly rely on technological so­lutions that enable and support favorable performance across a dynamic array of alternative payment models. Successful participation requires a values-based transformation, a key component of which includes practice alignment with ev­idence-based pathways, which prioritizes the highest-quality treatment options over lower-value, clinically equivalent regimens. In this retrospective, observa­tional, real-world study of treatment selection patterns across American On­cology Network medical oncologists, we analyze pathway adherence rates be­fore and after a pilot project where Evolent level 1 pathways were integrated into Flatiron Assist, an electronic health record-embedded clinical decision sup­port tool in OncoEMR. Results suggest that integrating evidence-based path­ways into ordering workflows may improve adherence by providing clinicians with just-in-time clinical and financial insights for optimal treatment decisions.

Phenotype and gene signature of testicular tumors in 129.MOLF-Chr19 mice resemble human teratomas.

Testicular germ cell tumor (TGCT) is the most common type of tumor in young men. Type II germ cell tumors including postpubertal-type teratomas are derived from the germ cell neoplasia in situ (GCNIS), whereas prepubertal-type teratomas arise independently of the GCNIS. The consomic mouse strain 129.MOLF-Chr19 (M19) is a suitable murine model of such tumors, but its characterization remains incomplete. Here, we interrogated the suitability of testicular tumors in M19 mice as a model of human TGCT by analyzing their histological features and gene expression signature. Testes collected from M19 mice of different ages were categorized by macroscopic appearance based on size and the degree of suspected tumorigenesis. Histological sections from selected tumors were stained with Hematoxylin and Eosin, and expression of genes associated with tumorigenesis was determined in frozen tissue samples from a large range of tumors of different subclasses using RT-qPCR and Fluidigm Dynamic Arrays. Macroscopically, testicular specimens appeared very heterogeneous concerning size and signs indicating the presence of a tumor. Histological analysis confirmed the development of teratomas with areas of cells corresponding to all three germ cell layers. Gene expression analyses indicated upregulation of markers related to proliferation, vascular invasive potential and pluripotency, and revealed a strong correlation of gene expression with tumor size and a significant intercorrelation of individual genes. TGCT in M19 mice is reminiscent of human testicular teratomas presenting with areas of cells derived from all germ layers and showing a typical gene signature. We thus confirm that these mice can serve as a suitable murine model of pure teratomas for preclinical research.

Combination of an Optically Induced Dielectrophoresis (ODEP) Mechanism and a Laminar Flow Pattern in a Microfluidic System for the Continuous Size-Based Sorting and Separation of Microparticles.

Optically induced dielectrophoresis (ODEP)-based microparticle sorting and separation is regarded as promising. However, current methods normally lack the downstream process for the transportation and collection of separated microparticles, which could limit its applications. To address this issue, an ODEP microfluidic chip encompassing three microchannels that join only at the central part of the microchannels (i.e., the working zone) was designed. During operation, three laminar flows were generated in the zone, where two dynamic light bar arrays were designed to sort and separate PS (polystyrene) microbeads of different sizes in a continuous manner. The separated PS microbeads were then continuously transported in laminar flows in a partition manner for the final collection. The results revealed that the method was capable of sorting and separating PS microbeads in a high-purity manner (e.g., the microbead purity values were 89.9 ± 3.7, 88.0 ± 2.5, and 92.8 ± 6.5% for the 5.8, 10.8, and 15.8 μm microbeads harvested, respectively). Overall, this study demonstrated the use of laminar flow and ODEP to achieve size-based sorting, separation, and collection of microparticles in a continuous and high-performance manner. Apart from the demonstration, this method can also be utilized for size-based sorting and the separation of other biological or nonbiological microparticles.

Development of high-speed algorithm for binomial arithmetic addition

The object of research is the method and algorithm of arithmetic addition of binomial numbers generated by binary binomial counting systems. The lack of binomial arithmetic, in particular the operation of adding binary binomial numbers, in a certain way prevents their introduction into information systems and the construction of information and communication technologies based on them for combinatorial optimization, generation of combinatorial objects, data compression and encryption. In the framework of the proposed approach, instead of operating with binomial coefficients, only operations with their upper and lower parameters are carried out. At the same time, the weighting coefficients of binary binomial numbers, which are added to each other, are represented in the form of two-component tuples. Taking this into account, this paper presents an algorithm for binomial arithmetic addition using dynamic arrays. The main idea, which is included in the structure of the algorithm of binomial arithmetic addition based on dynamic arrays, is that the transition from a two-dimensional model of summation to a one-dimensional one is carried out. At the same time, only available, existing binomial coefficients are placed in the dynamic array. Accordingly, the search for binomial coefficients equal to or greater than the quantitative equivalent takes place in much smaller areas. In comparison with the algorithm based on matrix models, this quite significantly reduces the amount of time spent when performing the summation operation, and also reduces the requirements for the amount of memory required for placing two-component tuples of the assembly array. In the course of the research, a several-fold decrease in the number of machine cycles required to search for the necessary elements in the dynamic array was practically confirmed. This leads to an increase in the performance of the presented algorithm of binomial arithmetic addition based on dynamic arrays. In turn, this leads to the acceleration of solving information tasks of combinatorial optimization, generation of combinatorial objects, data compression and encryption, for the solution of which the operation of adding binary binomial numbers is used.

Increased threat of urban arboviral diseases from Aedes aegypti mosquitoes in Colombia

ObjectivesOur study targets the potential of the local urban mosquito Aedes aegypti to experimentally transmit chikungunya virus (CHIKV), dengue virus (DENV), yellow fever virus (YFV), and Zika virus (ZIKV). MethodsWe collected eggs and adults of Ae. aegypti in Medellín, Colombia (from February to March 2020) for mosquito experimental infections with DENV, CHIKV, YFV and ZIKV and viral detection using the BioMark Dynamic arrays system. ResultsWe show that Ae. aegypti from Medellín was more prone to become infected, to disseminate and transmit CHIKV and ZIKV than DENV and YFV. ConclusionsThus, in Colombia, chikungunya is the most serious threat to public health based on our vector competence data.

A Review on Searchable Encryption Functionality and the Evaluation of Homomorphic Encryption

Cloud Service Providers, exemplified by industry leaders like Google Cloud Platform, Microsoft Azure, and Amazon Web Services, deliver a dynamic array of cloud services in an ever-evolving landscape. This sector is witnessing substantial growth, with enterprises such as Netflix and PayPal heavily relying on cloud infrastructure for various needs such as data storage, computational resources, and various other services. The adoption of cloud solutions by businesses not only facilitates cost reduction but also fosters flexibility and supports scalability. Despite the undeniable advantages, concerns surrounding security and privacy persist in the realm of Cloud Computing. Given that Cloud services are accessible via the internet, there is a potential vulnerability to unauthorized access by hackers or malicious entities from anywhere in the world. A crucial aspect of addressing this challenge is the implementation of robust security measures, particularly focusing on data protection. To safeguard data in the Cloud, a fundamental recommendation is the encryption of data prior to uploading. Encryption should be maintained consistently, both during storage and in transit. While encryption enhances security, it introduces a potential challenge for data owners who may need to perform various operations on their encrypted data, such as accessing, modifying, updating, deleting, reading, searching, or sharing them with others. One viable solution to balance the need for data security and operational functionality is the adoption of Searchable Encryption (SE). SE operates on encrypted data, allowing authorized users to perform certain operations without compromising the security of sensitive information. The effectiveness of SE has notably advanced since its inception, and ongoing research endeavors aim to further enhance its capabilities. This paper provides a comprehensive review of the functionality of Searchable Encryption, with a primary focus on its applications in Cloud services during the period spanning 2019 to 2023. Additionally, the study evaluates one of its prominent schemes, namely Fully Homomorphic Encryption (FHE). The analysis indicates an overall positive trajectory in SE research, showcasing increased efficiency as multiple functionalities are aggregated and rigorously tested.

Nigella sativa, Anthemis hyaline and Citrus sinensis extracts reduce SARS-CoV-2 replication by fluctuating Rho GTPase, PI3K-AKT, and MAPK/ERK pathways in HeLa-CEACAM1a cells

Traditional remedies have long utilized Anthemis hyaline, Nigella sativa, and Citrus sinensis peel extracts as treatments for microbial infections. This study aimed to investigate the influence of Anthemis hyaline, Nigella sativa, and Citrus sinensis extracts on coronavirus replication and apoptosis-related pathways. HeLa-CEACAM1a cells were exposed to mouse hepatitis virus–A59. After viral inoculation, the mRNA levels of 36 genes were quantified using a Fluidigm Dynamic Array nanofluidic chip. IL-8 level and intracellular Ca2+ concentration was measured, and viral titer was assessed by the TCID50/ml assay to detect the extent of infection. Treatment with Nigella sativa extract surged the inflammatory cytokine IL-8 level at both 24 and 48-hour. Changes in gene expression were notable for RHOA, VAV3, ROCK2, CFL1, RASA1, and MPRIP genes following treatment with any of the extracts. The addition of Anthemis hyaline, Nigella sativa, or Citrus sinensis extracts to coronavirus-infected cells reduced viral presence, with Anthemis hyaline extract leading to a virtually undetectable viral load at 6- and 8-hours after infection. While all treatments influenced IL-8 production and viral levels, Anthemis hyaline extract displayed the most pronounced reduction in viral load. Consequently, Anthemis hyaline extract emerges as the most promising agent, harboring potential therapeutic compounds.

Novel drag and Nusselt number models based on direct numerical simulations of a bidisperse gas–solid system

AbstractFlow and heat transfer in a bidisperse gas–solid system with freely moving spheres are simulated by particle‐resolved direct numerical simulation (PR‐DNS). Gas–solid coupling is enforced by the direct‐forcing immersed boundary method. Compard with the DNS database, it is found that the existing polydisperse drag correction model developed from static systems combined with various monodisperse drag models underestimates the drag force on dynamic arrays of particles. The existing Nusselt number correction model developed from static systems combined with various monodisperse models overestimates the Nusselt number of dynamic arrays of particles. Sensitivity analysis indicates that the effects of the granular temperature on the drag force and Nusselt number are negligible. Novel polydisperse drag and Nusselt number models are derived based on the database. The advantages of the derived polydisperse drag and Nusselt number models are demonstrated and confirmed by comparing the results of the computational fluid dynamics–discrete element method using various drag and Nusselt number models with experimental or additional PR‐DNS data.

Ground-induced suppression of chaos in the self-excited flow behind a plunging airfoil

We numerically investigate the forced synchronization of the self-excited flow behind a plunging airfoil in ground effect at a Reynolds number of Re = 1000. On varying the plunging amplitude and frequency, we find a rich array of nonlinear dynamics, such as a period-1 limit cycle due to natural vortex shedding as well as two-frequency quasiperiodicity on a torus attractor (T2). For certain non-resonant plunging frequencies without a ground surface, we find that low-dimensional chaos emerges via the Ruelle–Takens–Newhouse route. However, we find that the chaos can be suppressed by introducing a ground surface, inducing a direct transition from T2 quasiperiodicity to 1:1 phase locking as the plunging amplitude rises over the boundaries of the Arnold tongue. Apart from suppressing chaos, the ground surface also causes the lift and drag coefficients to become less sensitive to the plunging motion itself. Knowledge of the critical plunging conditions required for forced synchronization and chaos could be useful in various engineering applications, such as the design of pico air vehicles.

High-throughput screening of transient receptor potential (TRP) channels in pterygium.

Pterygium is a hyaline degenerative disease of the conjunctiva characterized by the progression of fibrovascular connective tissue from the bulbar conjunctiva to the cornea. The mechanism of pterygium formation is still not fully understood. Transient receptor potential (TRP) channels are a group of ion channels with distinct characteristics. Recent indications suggest TRP channels may play a significant regulatory role in pterygium development, but previous studies have mainly focused on in silico analysis. Accordingly, in the present study, we aimed to decipher the expression signatures and role of TRP channels in pterygium development. The study encompassed a cohort of 45 patients matched for age and gender distribution, comprising 30 individuals with primary pterygium (PP) and 15 individuals with recurrent pterygium(RP). The control group consisted of unaffected conjunctival tissue obtained from the same set of patients. High-throughput screening of differentially expressed TRP channels in pterygium tissues was achieved with the help of Fluidigm 96.96 Dynamic Array Expression Chip and reactions were held in BioMark™ HD System Real-Time PCR platform. Statistically significant increases were found in the expression of 21 genes, mainly TRPA1 (p = 0.021), TRPC2 (p = 0.001), and TRPM8 (p = 0.003), in patients with PP, and in TRPC5 (p = 0.05), TRPM2 (p = 0.029), TRPM4 (p = 0.03), TRPM6 (p = 0.045), TRPM8 (p = 0.038), TRPV1 (p = 0.01) and TRPV4 (p = 0.025) genes in RP tissues. Collectively, TRP channel proteins appear to play pivotal roles in both the development and progression of pterygium, making them promising candidates for future therapeutic interventions in patients afflicted by this condition.

Harnessing the power of generations through participative strategy: a study on Malaysia Small Family Firms

Small family firms (SFFs) in Malaysia, exemplars of economic development, skillfully balance family legacy preservation with entrepreneurial spirit to navigate generational challenges. In a context marked by paternalism, conservatism, and collectivism, these firms exhibit diverse array of familial dynamics and firm performance (FP) outcome. This study, employing hierarchical regression analysis and interactive plot diagrams on a sample of 112 Malaysian SFFs, thoroughly investigated the impact of entrepreneurial orientation (EO) and participative strategy (PS) on FP, with a special focus on the moderating role of generational involvement (GI). The findings reveal that GI positively affects both EO and FP, particularly when PS is integrated, effectively mitigating conflicts and paternalistic tendencies in multi-generational settings. The study underscores the importance of PS in leveraging intergenerational knowledge (i.e. familiness), converting innovative opportunities (i.e. EO) into significant performance enhancements. This insight is invaluable for SFFs in similar cultural contexts, advocating for the strategic use of PS and GI to optimize entrepreneurial success. The research contributes significantly to understanding the intricate interplay between EO, PS, and GI in SFFs, particularly in culturally distinct environments like Malaysia.

Faults detection and diagnosis of PV systems based on machine learning approach using random forest classifier

Accurate and reliable fault detection procedures are crucial for optimizing photovoltaic (PV) system performance. Establishing a trustworthy PV array model is the primary step and a vital tool for monitoring and diagnosing PV systems. This paper outlines a two-step approach for creating a reliable PV array model and implementing a fault detection procedure using Random Forest Classifiers (RFCs).Firstly, we extracted the five unknown parameters of the one-diode model (ODM) by combining the current–voltage translation method to predict the reference curve and employing the modified grey wolf optimization (MGWO) algorithm. In the second step, we simulated the PV array to obtain maximum power point (MPP) coordinates and construct operational databases through co-simulations in PSIM/MATLAB. We developed two RFCs: one for fault detection (a binary classifier) and another for fault diagnosis (a multiclass classifier).Our results confirmed the accuracy of the PV array modeling approach. We achieved a root mean square error (RMSE) value of 0.0122 for the ODM parameter extraction and RMSEs lower than 0.3 in dynamic PV array output current simulations under cloudy conditions. Regarding the fault detection procedure, our results demonstrate exceptional classification accuracy rates of 99.4% for both fault detection and diagnosis, surpassing other tested models like Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Neural Networks (MLP Classifier), Decision Trees (DT), and Stochastic Gradient Descent (SGDC).

Application of 3D Printing Technology in Customized Garment Production

Abstract In this paper, human body images are acquired by DSLR camera shooting, and the photos are grayscaled based on the HIS model. Then, the different operations weaken the similar parts between the images, and the other parts of the photos are highlighted. The image is separated from the background by image binarization. After the contour detection of the image, the pixel coordinates of the contour points in the image are deposited into a dynamic array by the size judgment of the pixel values. The subsequent size calculation is called to make a pad. According to the human body, clothing size automation measurement has been derived from the human body characteristics of the size, the use of three-dimensional printing technology to produce clothing to meet consumer demand, and three-dimensional printing technology to support the production of customized clothing under the experimental analysis. It is verified that the data difference between the actual size of the subject’s ready-made clothes and the size of the clothes produced based on 3D printing technology is kept within ±0.48cm. The virtual fitting pressure is less than the comfort threshold of women’s upper body pressure (2.75kpa), which indicates that the clothes produced by this research will not cause physiological discomfort to the human body and, at the same time, meet the consumer’s personalized clothing customization needs.

Dynamic memristor array with multiple reservoir states for training efficient neuromorphic computing

Exploration of efficient neuromorphic computing using Pt/Al/TiOy/TiOx/Al2O3/Pt array memristors implemented a reservoir with 16 states, demonstrating the training process of synaptic array images.

CT-navigated C2 Pedicle Screw insertion using dynamic reference array on C2 spinous process: A Novel Technique

ntroduction: Spine surgeons have endeavoured to improve the accuracy of pedicle screw insertion. Computed tomography (CT) guided navigation has been helpful in precisely passing pedicle screws even further accurately but, there were shortcomings noted with the technique due to movement between reference frame and anatomical landmarks, especially in the cervical spine which led to injury of vital neurovascular structures. Thus, there was a need for developing a technique which would eliminate the inherent error due to movement in the vertebral body while passing screws. Research Question: Can using C2 spinous process as an attachment for the reference array improve accuracy of C2 pedicle screws. Material and Methods: We prospectively studied data of 10 patients (20 screws) in whom Computed tomography navigated C2 pedicle screws insertion was carried out using the reference array on C2 spinous process. Accuracy of pedicle screws were confirmed by post instrumentation Computed tomography scan immediately intra operatively. Results: With the presented technique overall breach rate was 5% with one screw having a medial breach of grade 1 with no incidence of neurovascular injury or need for further revision. Discussion and Conclusion: Results from this study shows better accuracy than other techniques described to pass C2 pedicle screws eliminating the error in movement from the cervical vertebrae and the reference frame being nearest to the C2 pedicle. Although there is a learning curve with the adoption of the navigation system, with experience, the challenges associated with Cervical pedicle screws (CPS) insertion can be overcome.

Integrating ecosystem and contaminant models to predict the effects of ecosystem fluxes on contaminant dynamics

AbstractEnvironmental contamination is one of the major drivers of ecosystem change in the Anthropocene. Toxic chemicals are not constrained to their source of origin as they cross ecosystem boundaries via biotic (e.g., animal migration) and abiotic (e.g., water flow) vectors. Meta‐ecology has led to important insights on how spatial flows or subsidies of matter across ecosystem boundaries can have broad impacts on local and regional ecosystem dynamics but has not yet addressed the dynamics of pollutants in recipient ecosystems. Incorporating meta‐ecosystem processes (i.e., flux of materials across ecosystem boundaries) into contaminant dynamics can elucidate how contaminants may reverberate among local food chains. Here, we derive a modeling framework to predict how spatial ecosystem fluxes can influence contaminant dynamics and how this influence is dependent on the type of ecosystem flux (e.g., herbivore movement vs. abiotic chemical flows). We mix an analytical and numerical approach to analyze our integrative model which couples two subcomponents that have previously been studied independently of each other—an ecosystem model and a contaminant model. We observe an array of dynamics for how chemical concentrations change with increasing nutrient input and loss rate across trophic levels. When we tailor our range of chemical parameter values (e.g., environmental uptake of contaminant and assimilation efficiency of the contaminant) to specific organic chemicals, our results demonstrate that increasing nutrient input rates can lead to trophic dilution in pollutants such as polychlorinated biphenyls across trophic levels. However, increasing nutrient loss rate causes an increase in the concentrations of chemicals across all trophic levels. A sensitivity analysis demonstrates that nutrient recycling is an important ecosystem process impacting contaminant concentrations, generating predictions to be addressed by future empirical studies. Importantly, our model demonstrates the utility of our framework for identifying drivers of contaminant dynamics in connected ecosystems including the importance that (1) ecosystem processes and (2) movement, especially movement of lower trophic levels, have on contaminant concentrations.