Numerous Applications Research Articles

The Role of Thermal Analysis in Engine Fin Design: Insights and Perspectives

Objective: The study aims to address the significance of fins in enhancing the heat transfer rate of engine cylinders and highlight their applications and advancements in various fields. Methods: Thermal analysis of fins using ANSYS Workbench is an invaluable tool for engineers and researchers in determining and optimizing the heat transfer characteristics of finned structures. ANSYS Workbench provides a comprehensive platform for evaluating the performance of fins in various applications using simulations of computational fluid dynamics (CFD) and finite element analysis (FEA). With ANSYS Workbench, it is possible to assess the thermal behaviour of fins under a variety of conditions, such as variable boundary conditions and thermal loads. Some significant recent patents on Engine fin are also discussed in this review article. Results: Fins are designed to maximize contact with the surrounding air or coolant, thereby facilitating engine heat transfer to the surrounding environment. Engineers can enhance heat dissipation capabilities by considering parameters such as fin geometry, density, material selection, and manufacturing techniques while minimizing associated drawbacks. Conclusion: The numerous applications of fins highlighted in this review article will encourage researchers to find novel strategies to improve heat transfer by employing fins for use in a variety of sectors.

Computation-Efficient Fault Detection Framework for Partially Known Nonlinear Distributed Parameter Systems.

Fault detection for distributed parameter systems (DPSs) generally requires the complete model information to be known so far. However, for numerous industrial applications, it is common that accurate first-principles physical models are extremely difficult to obtain. Hence, the applicability of traditional model-based methods is being restricted. To pave the way, an adaptive neural network (AdNN) is constructed to simultaneously estimate the state variable and the unknown nonlinearity for a class of partially known nonlinear DPSs. Moreover, considering that full-state measurement is unrealistic in applications, the proposed adaptive neural observer is based on a reduced-order model, which also increases the computation efficiency. Then, the residual generation and evaluation are conducted using the output estimation error of the proposed adaptive neural observer. Bearing the effects of the neglected fast dynamics in mind, a data-driven threshold generation scheme is proposed. Extensive experimental results are presented and analyzed to validate the effectiveness of the proposed method.

Numerical Model Applicability Based on a Hydraulic Characteristic Analysis of an Eco-friendly Double-row Submerged Breakwater

In this study, a submerged breakwater with effective wave control and eco-friendly characteristics is developed and proposed. Hydraulic experiments are conducted to compare the hydraulic performance of a submerged breakwater and an eco-friendly double-row submerged breakwater. The hydraulic characteristics are analyzed based on wave reflections and the transmission-splitting method for each experimental cross-section. This splitting technique utilizes Goda’s two-point method, which employs the spectra of two irregular superposed wave fields. In addition, the reliability of the results obtained from the hydraulic experiments is discussed by comparing the results with empirical formulas. The eco-friendly double-row submerged breakwater features approximately half the width of a typical submerged breakwater. Nevertheless, its transmission coefficient (KT) is approximately 20% more effective, and the difference in the average reflection coefficient (KR) values between the two is approximately 0.17. Moreover, the dissipation coefficient (KD) shows a generally similar trend. Based on these experimental results, the hydraulic performance of the eco-friendly double-row submerged breakwater is more efficient regarding wave control, compared with a typical submerged breakwater. These hydraulic characteristics confirm that the numerical model developed for the eco-friendly double-row submerged breakwater accurately reproduces the KT, KR, and KD values within ±5%.

Sequential Point Clouds: A Survey.

Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.

Cooperative supply game and its revenue allocation method considering location of transportation hub

PurposeThis paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.Design/methodology/approachBased on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.FindingsThrough numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.Originality/valueThis paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

Exploring the Theranostic Applications and Prospects of Nanobubbles.

Anticancer medications as well as additional therapeutic compounds, have poor clinical effectiveness due to their diverse distribution, non-selectivity for malignant cells, and undesirable off-target side effects. As a result, ultrasound-based targeted delivery of therapeutic compounds carried in sophisticated nanocarriers has grown in favor of cancer therapy and control. Nanobubbles are nanoscale bubbles that exhibit unique physiochemical properties in both their inner core and outer shell. Manufacturing nanobubbles primarily aims to enhance therapeutic agents' bioavailability, stability, and targeted delivery. The small size of nanobubbles allows for their extravasation from blood vessels into surrounding tissues and site-specific release through ultrasound targeting. Ultrasound technology is widely utilized for therapy due to its speed, safety, and cost-effectiveness, and micro/nanobubbles, as ultrasound contrast agents, have numerous potential applications in disease treatment. Thus, combining ultrasound applications with NBs has recently demonstrated increased localization of anticancer molecules in tumor tissues with triggered release behavior. Consequently, an effective therapeutic concentration of drugs/genes is achieved in target tumor tissues with ultimately increased therapeutic efficacy and minimal side effects on other non-cancerous tissues. This paper provides a brief overview of the production processes for nanobubbles, along with their key characteristics and potential therapeutic uses.

A variable-node element model and its application to damage analysis of RAC using the 2D base force element method

The existing methods find it difficult to resolve the inconsistency in the nodal displacements of nodes at the interface of dense and sparse element models. Therefore, this paper proposes a new variable-node element model using the base force element method (BFEM) and successfully applies it to study the mechanical properties of recycled aggregate concrete (RAC). First, the variable-node element model with an arbitrary change of node number in the mid-edges was established, and the explicit expressions of the 2 × 2 nodal compliance matrix and nodal displacements were given. Subsequently, the triangular and quadrilateral elements of single-sides and double-sides of hanging element models were developed based on the variable-node element model. In addition, a double-notched model of RAC with different aggregate types but the same aggregate distribution position was established, and numerical simulations of uniaxial tension were performed. The results show that the compliance matrix and nodal displacement are independent of the element characteristic without Gaussian integration. The variable-node hanging element model has high numerical accuracy and applicability, and the nodes between the sparse and dense meshes do not require any construction measures. The variable-node elements ensure the coordination of the nodal displacement at the interfaces. The aggregate shape has a significant effect on the damage mode, maximum principal strain, principal stress distribution, and stress concentration, while has a negligible effect on RAC tensile strength when the aggregate distribution is the same.

Observing System Simulation Experiments (OSSEs) in Support of Next-Generation NOAA Satellite Constellation

Abstract Between 2014 and 2018, the National Oceanic and Atmospheric Administration conducted the NOAA Satellite Observing System Architecture (NSOSA) study to plan for the next generation of operational environmental satellites. The study generated some important questions that could be addressed by observing system simulation experiments (OSSEs). This paper describes a series of OSSEs in which benefits to numerical weather prediction from existing observing systems are combined with enhancements from potential future capabilities. Assessments include the relative value of the quantity of different types of thermodynamic soundings for global numerical weather applications. We compare the relative impact of several sounding configuration scenarios for infrared (IR), microwave (MW), and radio occultation (RO) observing capabilities. The main results are 1) increasing the revisit rate for satellite radiance soundings produces the largest benefits but at a significant cost by requiring an increase in the number of polar-orbiting satellites from 2 to 12; 2) a large positive impact is found when the number of RO soundings per day is increased well beyond current values and other observations are held at current levels of performance; 3) RO can be used as a mitigation strategy for lower MW/IR sounding revisit rates, particularly in the tropics; and 4) smaller benefits result from increasing the horizontal resolution along the track of the satellites of MW/IR satellite radiances. Furthermore, disaggregating IR and MW instruments into six evenly distributed sun-synchronous orbits is slightly more beneficial than when the same instruments are combined and collocated on three separate orbits.

An Overview of Text to Visual Generation Using GAN

Text-to-visual generation was once a cumbersome task until the advent of deep learning networks. With the introduction of deep learning, both images and videos can now be generated from textual descriptions. Deep learning networks have revolutionized various fields, including computer vision and natural language processing, with the emergence of Generative Adversarial Networks (GANs). GANs have played a significant role in advancing these domains. A GAN typically comprises multiple deep networks combined with other machine learning techniques. In the context of text-to-visual generation, GANs have enabled the synthesis of images and videos based on textual input. This work aims to explore different variations of GANs for image and video synthesis and propose a general architecture for text- to-visual generation using GANs. Additionally, this study delves into the challenges associated with this task and discusses ongoing research and future prospects. By leveraging the power of deep learning networks and GANs, the process of generating visual content from text has become more accessible and efficient. This work will contribute to the understanding and advancement of text-to-visual generation, paving the way for numerous applica-tions across various industries.

A Novel Industrial Software Model for Social Manufacturing

Abstract Distinct from the conventional manufacturing paradigm, the functioning of the Social Manufacturing (SocialM) model relies on a vast and dispersed array of social manufacturing resources. SocialM communities/groups emerge autonomously through business interactions, resource self-organization, and production information sharing, all driven by product orders within the SocialM system. This necessitates a more open, distributed, and autonomous software model to facilitate the allocation and operation of social manufacturing resources in support of the SocialM model. Consequently, this paper introduces a novel software development model designed specifically for SocialM. This proposed model employs business interaction software as the system's primary control portal, enabling efficient management of resources and users based on the social manufacturing communities/groups. Additionally, it facilitates the administration and oversight of numerous applications by utilizing an industrial Appstore. Moreover, the paper delves into the technological roadmap for realizing the essential technologies of the software model by integrating multi-agent and Message Queuing Telemetry Transport (MQTT) technologies. A 3D printing task simulation case was employed to demonstrate the model's working principle, which served to confirm the reliability and scalability of the SocialM software, as well as the feasibility of the SocialM theory.

Numerical Simulation and Application Research of Straight-Hole Cut Blasting with Empty Holes in Railway Tunnels

Numerical Simulation and Application Research of Straight-Hole Cut Blasting with Empty Holes in Railway Tunnels

Cubosomes: An Emerging and Promising Drug Delivery System for Enhancing Cancer Therapy.

Cancer and other diseases can be treated with cubosomes, which are lyotropic nonlamellar liquid crystalline nanoparticles (LCNs). These cubosomes can potentially be a highly versatile carrier with theranostic efficacy, as they can be ingested, applied topically, or injected intravenously. Recent years have seen substantial progress in the synthesis, characterization, regulation of drug release patterns, and target selectivity of loaded anticancer bioactive compounds. However, its use in clinical settings has been slow and necessitates additional proof. Recent progress and roadblocks in using cubosomes as a nanotechnological intervention against various cancers are highlighted. In the last few decades, advances in biomedical nanotechnology have allowed for the development of "smart" drug delivery devices that can adapt to external stimuli. By improving therapeutic targeting efficacy and lowering the negative effects of payloads, these well-defined nanoplatforms can potentially promote patient compliance in response to specific stimuli. Liposomes and niosomes, two other well-known vesicular systems, share a lipid basis with cubosomes. Possible applications include a novel medication delivery system for hydrophilic, lipophilic, and amphiphilic drugs. We evaluate the literature on cubosomes, emphasizing their potential use in tumor-targeted drug delivery applications and critiquing existing explanations for cubosome self-assembly, composition, and production. As cubosome dispersion has bioadhesive and compatible features, numerous drug delivery applications, including oral, ocular, and transdermal, are also discussed in this review.

Community Deception in Large Networks: Through the Lens of Laplacian Spectrum

Many complex networks in the real world have community structures. Typical examples include online social networks and ecology networks. While the identification of communities bears numerous practical applications, with the increasing awareness of data security and privacy concerns, the need to protect the community affiliations of individuals from disclosing by attackers emerges. This raises the community deception (CD) problem, that is, the opposite of community detection, which asks for ways to minimally perturb the network structures by rewiring nodes so that the target communities maximally hide themself from community detection algorithms. To this end, we investigate the CD problem through a Laplacian spectrum lens and propose a method named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> to hide a flexible target set of communities, which is more universal than most existing methods that either focus on hiding the entire communities or a single community. The key idea of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> is to first allocate the resources of perturbations fairly and effectively. By proving that hiding communities through intercommunity edge addition and intracommunity edge deletion correspond to maximizing the second smallest eigenvalue <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda_2$</tex-math> </inline-formula> and minimizing the largest eigenvalue <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda_n$</tex-math> </inline-formula> of the graph Laplacian, respectively, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> then incorporates efficient heuristics for approximately solving the problems, thus selecting the appropriate edge to perturb. Experimental results over nine real-world networks and six community detection algorithms not only demonstrate the efficiency of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathtt{ComDeceptor}$</tex-math> </inline-formula> , but also the superior performance on obfuscating community structures over the baselines.

Organocatalyzed Stereoselective Glycosylation: An Overview of the Last Decade

Abstract: Even though there has been an important evolution in the synthesis of oligosaccharides, the efficient and stereoselective study of glycosidic bonds through non-toxic, moderate, and inexpensive techniques is one of the most challenging fields in organic synthesis. Glycosyl reactions play a fundamental role in biological material and structure-activity relationships, having numerous medicinal chemistry applications. For this, interesting strategies have evolved over the years to control the stereoselectivity of glycosidic bonds, including the manipulation of different reaction elements, mainly promoters or catalysts, but also involving the nature of donors and solvents. This review looks at glycosylation methodologies in the last decade resulting in the specific formation of alpha or beta glycosidic bonds.

A Study of YOLO-Based Object Detection for Visually Impaired Individuals

Abstract: An essential area of investigation involves recognizing and tracking objects, given the frequent changes in object motion, scene dimensions, occlusions, appearance, ego-motion, and lighting variations. Effective object tracking heavily relies on feature selection due to its critical role. This process is integral to numerous real-time applications like vehicle detection and video surveillance. To address detection challenges, tracking methods need to be adept at handling object movement and appearance changes. Among these methods, tracking algorithms play a pivotal role in smoothing video sequences and receive significant attention. However, only a few approaches leverage previously gathered data on object attributes such as shape and color. This study delves into a tracking algorithm that encompasses all these object properties. Its aim is to investigate and assess past methodologies for tracking and detecting moving objects across various stages of video sequences. Additionally, it aims to identify existing challenges and propose innovative strategies to enhance object tracking throughout video frames

Performance Evaluation of Forward Osmosis Membranes for Desalination Applications

Forward osmosis (FO) has become a technology with great potential for numerous applications, including water desalination. One of the critical factors in determining the FO performance is the selection of the appropriate membrane material that compatible with draw solution. In this study, commercial cellulose triacetate (CTA) and aquaporin-based membranes, as well as a fabricated PES/PVP membrane, were used, with 1-Butyl-3-methylimidazolium tetrafluoroborate ([Bmim][BF4]) as the thermo-responsive ionic liquid (TRIL) draw solution. The bench scale of FO system was setup upon co-currently flow rate of 60.0 mL/ min at room temperature. The 7 % PVP with 15 % PES had the best performance, with the highest water flux (Jw) (4.93 LMH), lowest reverse solute diffusion (RSD) (0.43 gMH). The fabricated membrane demonstrated a significantly higher performance compared to the commercial aquaporin-based FO membrane, with an improvement of approximately 60%.

Scenarios for the appearance of strange attractors in a model of three interacting microbubble contrast agents

We study nonlinear dynamics in a model of three interacting encapsulated gas bubbles in a liquid. The model is a system of three coupled nonlinear oscillators with an external periodic force. Such bubbles have numerous applications, for instance, they are used as contrast agents in ultrasound visualization. Certain types of bubbles oscillations may be beneficial or undesirable depending on a given application and, hence, the dependence of the regimes of bubbles oscillations on the control parameters is worth studying. We demonstrate that there is a wide variety of types of dynamics in the model by constructing a chart of dynamical regimes in the control parameters space. Here we focus on hyperchaotic attractors characterized by three positive Lyapunov exponents and strange attractors with one or two positive Lyapunov exponents possessing an additional zero Lyapunov exponent, which have not been observed previously in the context of bubbles oscillations. We also believe that we provide a first example of a hyperchaotic attractor with additional zero Lyapunov exponent. Furthermore, the mechanisms of the onset of these types of attractors are still not well studied. We identify two-parametric regions in the control parameter space where these hyperchaotic and chaotic attractors appear and study one-parametric routes leading to them. We associate the appearance of hyperchaotic attractors with three positive Lyapunov exponents with the inclusion of a periodic orbit with a three-dimensional unstable manifold, while the onset of chaotic oscillations with an additional zero Lyapunov exponent is connected to the partial synchronization of bubbles oscillations. We propose several underlying bifurcation mechanisms that explain the emergence of these regimes. We believe that these bifurcation scenarios are universal and can be observed in other systems of coupled oscillators.

DVSAI: Diverse View-Shared Anchors Based Incomplete Multi-View Clustering

In numerous real-world applications, it is quite common that sample information is partially available for some views due to machine breakdown or sensor failure, causing the problem of incomplete multi-view clustering (IMVC). While several IMVC approaches using view-shared anchors have successfully achieved pleasing performance improvement, (1) they generally construct anchors with only one dimension, which could deteriorate the multi-view diversity, bringing about serious information loss; (2) the constructed anchors are typically with a single size, which could not sufficiently characterize the distribution of the whole samples, leading to limited clustering performance. For generating view-shared anchors with multi-dimension and multi-size for IMVC, we design a novel framework called Diverse View-Shared Anchors based Incomplete multi-view clustering (DVSAI). Concretely, we associate each partial view with several potential spaces. In each space, we enable anchors to communicate among views and generate the view-shared anchors with space-specific dimension and size. Consequently, spaces with various scales make the generated view-shared anchors enjoy diverse dimensions and sizes. Subsequently, we devise an integration scheme with linear computational and memory expenditures to integrate the outputted multi-scale unified anchor graphs such that running spectral algorithm generates the spectral embedding. Afterwards, we theoretically demonstrate that DVSAI owns linear time and space costs, thus well-suited for tackling large-size datasets. Finally, comprehensive experiments confirm the effectiveness and advantages of DVSAI.

HyperFast: Instant Classification for Tabular Data

Training deep learning models and performing hyperparameter tuning can be computationally demanding and time-consuming. Meanwhile, traditional machine learning methods like gradient-boosting algorithms remain the preferred choice for most tabular data applications, while neural network alternatives require extensive hyperparameter tuning or work only in toy datasets under limited settings. In this paper, we introduce HyperFast, a meta-trained hypernetwork designed for instant classification of tabular data in a single forward pass. HyperFast generates a task-specific neural network tailored to an unseen dataset that can be directly used for classification inference, removing the need for training a model. We report extensive experiments with OpenML and genomic data, comparing HyperFast to competing tabular data neural networks, traditional ML methods, AutoML systems, and boosting machines. HyperFast shows highly competitive results, while being significantly faster. Additionally, our approach demonstrates robust adaptability across a variety of classification tasks with little to no fine-tuning, positioning HyperFast as a strong solution for numerous applications and rapid model deployment. HyperFast introduces a promising paradigm for fast classification, with the potential to substantially decrease the computational burden of deep learning. Our code, which offers a scikit-learn-like interface, along with the trained HyperFast model, can be found at https://github.com/AI-sandbox/HyperFast.

Abstract 6177: Machine learning enables prediction of ADC targets from whole slide H&amp;E images

Abstract Background: The efficacy of antibody-drug conjugates (ADCs) depends on the expression and specificity of the target antigen by tumor cells. Although H&amp;E-stained slides are routinely collected during cancer care, specialized IHC staining is typically required to ascertain antigen expression. Such stains are not always available or readily deployed. The need to perform separate IHC tests for each candidate ADC may burden clinical labs and can hinder access to care in resource-limited settings. Here we develop an ensemble of machine learning models to accurately predict the expression of 166 distinct ADC targets directly from H&amp;E images. Methods: For each ADC-targeted gene, patients with copy-number amplifications (CNAs) were identified from somatic whole exome sequencing. Genes differentially expressed in patients with CNAs were identified from bulk transcriptomics. For each gene, an expression signature was developed based on the expression levels of differentially upregulated genes. Next, whole-slide, H&amp;E-stained histopathology images were embedded into a lower-dimensional representation via a transformer model trained with self-supervised learning. Neural networks were developed to predict a patient’s probability of having a CNA in an ADC-targeted gene, as indicated by an expression signature exceeding the p90. All evaluation metrics were ascertained by 5-fold cross-validation, with training and evaluation on independent patients. Results: For each of the 166 ADC-targeted genes, a median of 154 patients were found to harbor a CNA, and the expression signature included a median of 180 genes. In all cases, patients with CNAs had significantly higher expression signatures than those without. For predicting likely CNA status directly from H&amp;E histopathology images, the mean AUROC was 0.888 (95% CI, 0.876-0.900) and the mean AUPRC was 0.571 (95% CI, 0.531-0.611). Among the 166 ADC-targeted genes, the AUROC exceeded 0.95 for 31.3%, 0.8 for 80.7%, and 0.725 for 100%. The best-predicted ADC target was SLC7A5 (AUROC: 0.995 [95% CI, 0.994-0.998]; AUPRC: 0.967 [95% CI, 0.963-0.976]). Conclusion: We have developed models that accurately predict the likely expression of ADC targets based solely on H&amp;E images. The ability to accurately discern the presence of ADC antigens from H&amp;E images has numerous potential applications, including cohort refinement, computer-aided diagnosis, and personalized treatment planning. Citation Format: Zachary Ryan McCaw, Anna Shcherbina, Yajas Shah, insitro Research Team, Philip Tagari, Daphne Koller, Christopher Probert. Machine learning enables prediction of ADC targets from whole slide H&amp;E images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6177.