Convergence Of Modalities Research Articles

The contribution of higher order modes to shock response spectra

Abstract The shock response spectrum is a widely used design tool that engineers use when assessing the severity of transient excitation on a structure. The shock response spectrum is typically defined as the peak response of a series of single degree-of-freedom (DOF) oscillators each with a different natural frequency. For structures representable by a single DOF system, such is the case when only a single mode dominates the response, the shock response spectrum can be used to evaluate this response directly and a brief review of the existing accepted metrics is presented. In general, the response of many structures may comprise contributions from higher order modes leading to inadequate predictions from the shock response spectrum, especially for acceleration responses. In such cases a form of shock response spectrum can be obtained that accounts for all modes of vibration but as a function of shock duration. However, a multi-modal model of the structure is then required and its shock response obtained numerically over a range of shock durations which can be computationally expensive and less amenable to physical interpretation. This paper adopts a previously proposed modal approach to obtaining the shock response of systems with contributions from higher order modes, in which the transient response is calculated for each mode in turn which are then superposed. A key question that arises is the number of modes that need to be retained in the summation. Two numerical case studies are presented which explore modal convergence for displacement, velocity and acceleration shock response spectra. Results suggest that a priori mode selection is not generally possible although inspection of the mode shapes can offer guidance. However, the benefit of the modal approach to interpretability of the results is illustrated.

A Hybrid Approach for Damage Detection and Localization on a Thin Metallic Plate

This paper illustrates a hybrid method for identifying structural damage that includes concepts from Physics-Based approaches within a Data-Driven framework. The lessons learned from the use of damage identification methods based on the analysis of modal curvature allows us to develop effective feature engineering in the development of Machine Learning (ML) algorithms. Moreover, the training of the algorithms exploits data provided from a population of damage cases generated by Finite Element (FE) analysis. The above data-analysis pipeline is applied to the identification of damage in a rectangular metallic plate for which measured modal data are available from a dedicated experimental campaign in CNR-INM laboratory using accelerometers. The damage is modelled as a stiffness reduction over a small area being also representative of material thinning due to corrosion. To have meaningful samples of the training database, the modal convergence of the FE model to the real structure is guaranteed by a structural optimization process. Experimentally identified noise, representative of real-life applications, is then added to the FE results before algorithm training. Damage existence and position are determined by a Novelty Detection approach and a Regression Neural Network, respectively. First, damage is identified on new test cases generated by the same FE procedure used for training. Secondly, the trained algorithms are applied to the experimental dataset. Sensitivity analysis on several parameters (number of samples for training, damage severity and noise levels) is numerically carried out to understand the applicability limits of the present methodology.

Chimeric Antigen Receptor T Cell Therapy for Hepatocellular Carcinoma: Where Do We Stand?

Hepatocellular carcinoma (HCC) remains a global health challenge that urgently calls for innovative therapeutic strategies. Chimeric antigen receptor T cell (CAR T) therapy has emerged as a promising avenue for HCC treatment. However, the therapeutic efficacy of CAR T immunotherapy in HCC patients is significantly compromised by some major issues including the immunosuppressive environment within the tumor, antigen heterogeneity, CAR T cell exhaustion, and the advanced risk for on-target/off-tumor toxicity. To overcome these challenges, many ongoing preclinical and clinical trials are underway focusing on the identification of optimal target antigens and the decryption of the immunosuppressive milieu of HCC. Moreover, limited tumor infiltration constitutes a significant obstacle of CAR T cell therapy that should be addressed. The continuous effort to design molecular targets for CAR cells highlights the importance for a more practical approach for CAR-modified cell manufacturing. This review critically examines the current landscape of CAR T cell therapy for HCC, shedding light on the changes in innate and adaptive immune responses in the context of HCC, identifying potential CAR T cell targets, and exploring approaches to overcome inherent challenges. Ongoing advancements in scientific research and convergence of diverse treatment modalities offer the potential to greatly enhance HCC patients' care in the future.

Multi-Modal Machine Learning in Engineering Design: A Review and Future Directions

Abstract In the rapidly advancing field of multi-modal machine learning (MMML), the convergence of multiple data modalities has the potential to reshape various applications. This paper presents a comprehensive overview of the current state, advancements, and challenges of MMML within the sphere of engineering design. The review begins with a deep dive into five fundamental concepts of MMML: multi-modal information representation, fusion, alignment, translation, and co-learning. Following this, we explore the cutting-edge applications of MMML, placing a particular emphasis on tasks pertinent to engineering design, such as cross-modal synthesis, multi-modal prediction, and cross-modal information retrieval. Through this comprehensive overview, we highlight the inherent challenges in adopting MMML in engineering design, and proffer potential directions for future research. To spur on the continued evolution of MMML in engineering design, we advocate for concentrated efforts to construct extensive multi-modal design datasets, develop effective data-driven MMML techniques tailored to design applications, and enhance the scalability and interpretability of MMML models. MMML models, as the next generation of intelligent design tools, hold a promising future to impact how products are designed.

До питання розвитку транспортних перевезень у регіональному аспекті

Today, globalization trends are becoming evident both in Ukraine and in the world. This approach is aimed at increasing urbanization and the development of agglomerations. Therefore, the issues of development of the transport industry of Ukraine in the regional aspect remain relevant. At the same time, it should be noted that modern multimodal transport networks have their own specific structure. A typical multimodal transport network includes modern components of such elements of the network of such types of transport as road, rail, air, river, sea. The authors identified multimodal transport hubs in the structure of the transport system of Ukraine, analyzed their efficiency in terms of freight and passenger transport. It has been established that the existing distribution of cargo and passenger transportation by modes of transport indicates a certain priority of the use of road transport for all types of communication. At the same time, it should be noted that the automobile mode of transport today in Ukraine and the world is not an example of efficient and economical use of energy resources, has a significant share of the environmental load among other modes of transport, and is not always the objectively optimal option for transporting goods and passengers according to the criteria of reliability, economy and delivery terms. Thus, given that the transport industry of any region, country and at the global level is an important component of the economy, attracts a large number of specialists and affects the pace of development of other sectors of the economy, it is necessary to carefully study the development of this industry at all levels of operation and constantly to direct its development along vectors that constantly change over time. The transport industry at the regional level should also be noted as the basis for building a developed and efficient transport industry in the country. The modern development of the international transport system shows that the gateway or hub can perform the functions of modal and intermodal convergence. Especially if it is a combination of two or more operating modes of different systems. At the same time, the currently operating structures of regional multimodal networks unite the main gateways (hubs), which ensures connection to the international transport system through the sea-land-air interface.

Assessing the EU’s and Turkey’s Triangular Development Cooperation partnership in the Sub-Saharan Africa

This paper investigates whether the Triangular Development Cooperation (TDC) in the Sub-Saharan Africa (SSA) is foreseeable or not between the EU and Turkey. In doing so, we first built a new and original framework based upon five independent variables as follows: (1) foreign policy interests and strategic areas in the recipient country; (2) Convergence of development policy modalities and practices; (3) level of interdependence between DAC-donor and southern provider; (4) friendly relations and share of norms and values between the development cooperation providers and (5) African leaders’ perception about the added value of the TDC. Second, we tested these five variables in our case studies to determine the TDC’s likelihood level between the EU and Turkey in the SSA by using three indicators as follows: positive, negative or partially positive. In this vein, it first profiles Turkey’s and the EU’s position as development aid providers in the SSA to highlight their specificities. Then, it examines the TDC's main features, strengths and weaknesses.

Nonlinear dynamic analysis of galloping of a single iced conductor with four degrees of freedom based on curved beam theory

Based on the nonlinear strain-displacement relationship of spatial curved beam theory, a finite element model for galloping analysis of iced transmission lines is established, which involves three translational degrees of freedom (DOF) and one rotational degree of freedom for each node. Considering the aerodynamic nonlinearity and the geometric nonlinearity of large amplitude motions of the iced transmission line, the nonlinear dynamic finite element equation is presented by using the virtual work principle. The equation is transformed into the sub-space according to the mode superposition method and a time integration algorithm is also performed in the sub-space. Then, the element independence and modal convergence are researched. Finally, the influence of aerodynamic forces on structural frequency is analyzed. The numerical results show that aerodynamic forces will greatly affect the torsional frequency of the transmission line, which accurately reflects the dynamic characteristics of transmission lines. Furthermore, the galloping calculation of the transmission line has reliable accuracy by using 4-DOF (per node) elements for the iced conductor. On the other hand, the model can predict the actual galloping response of the transmission line, which is convenient for the implementation of subsequent control design.

Linking transcriptomes with morphological and functional phenotypes in mammalian retinal ganglion cells.

Retinal ganglion cells (RGCs) are the brain's gateway to the visual world. They can be classified into different types on the basis of their electrophysiological, transcriptomic, or morphological characteristics. Here, we characterize the transcriptomic, morphological, and functional features of 472 high-quality RGCs using Patch sequencing (Patch-seq), providing functional and morphological annotation of many transcriptomic-defined cell types of a previously established RGC atlas. We show a convergence of different modalities in defining the RGC identity and reveal the degree of correspondence for well-characterized cell types across multimodal data. Moreover, we complement some RGC types with detailed morphological and functional properties. We also identify differentially expressed genes among ON, OFF, and ON-OFF RGCs such as Vat1l, Slitrk6, and Lmo7, providing candidate marker genes for functional studies. Our research suggests that the molecularly distinct clusters may also differ in their roles of encoding visual information.

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.

.Significance: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology).Aim: Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption.Approach: We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations.Results: While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow.Conclusions: The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.

A parametric and feasibility study for data sampling of the dynamic mode decomposition: range, resolution, and universal convergence states

Scientific research and engineering practice often require the modeling and decomposition of nonlinear systems. The dynamic mode decomposition (DMD) is a novel Koopman-based technique that effectively dissects high-dimensional nonlinear systems into periodically distinct constituents on reduced-order subspaces. As a novel mathematical hatchling, the DMD bears vast potentials yet an equal degree of unknown. This effort investigates the nuances of DMD sampling with an engineering-oriented emphasis. It aimed at elucidating how sampling range and resolution affect the convergence of DMD modes. We employed the most classical nonlinear system in fluid mechanics as the test subject—the turbulent free-shear flow over a prism—for optimal pertinency. We numerically simulated the flow by the dynamic-stress Large-Eddies Simulation with Near-Wall Resolution. With the large-quantity, high-fidelity data, we parametrized and identified four global convergence states: Initialization, Transition, Stabilization, and Divergence with increasing sampling range. Results showed that Stabilization is the optimal state for modal convergence, in which DMD output becomes independent of the sampling range. The Initialization state also yields sufficient accuracy for most system reconstruction tasks. Moreover, defying popular beliefs, over-sampling causes algorithmic instability: as the temporal dimension, n, approaches and transcends the spatial dimension, m (i.e., m < n), the output diverges and becomes meaningless. Additionally, the convergence of the sampling resolution depends on the mode-specific dynamics, such that the resolution of 15 frames per cycle for target activities is suggested for most engineering implementations. Finally, a bi-parametric study revealed that the convergence of the sampling range and resolution are mutually independent.

Random dynamic analysis on a high-speed train moving over a long-span cable-stayed bridge

ABSTRACT This paper presents random dynamic analysis on a high-speed train moving over a long-span cable-stayed bridge. In the study, the train, slab track, and bridge are regarded as an integrated system, each vehicle is modelled as a four-wheelset mass-spring-damper system with 10 degrees of freedom (DOFs), and the rail and track slabs are modelled as Euler-Bernoulli beams supported by spring-damper elements. The long-span cable-stayed bridge model is established by the ANSYS software, based on which its frequencies and mode shapes are exported and implemented into the dynamics programme executed in the MATLAB platform. The train and substructures are coupled via nonlinear wheel–rail interaction and the large-scale dynamics model is solved by means of a fast explicit integration algorithm. Several numerical examples are performed involving modal convergence study, the effect of train speeds on system dynamic responses and stochastic analysis based on the probability density evolution method.

Fully Coupled Nonlinear Aerothermoelastic Computational Model of a Plate in Hypersonic Flow

A recent structural model of a plate is extended to include a nonuniform temperature differential that varies in time using a modal expansion. The nonlinear structural plate model with first-order piston theory aerodynamics and cavity dynamics is coupled with the heat equation to form a single system of dynamical equations for the fluid-structural-thermal system. The heat flux from the boundary layer to the plate is modeled using the temperature reference method. A computational scheme is formulated by linearizing the heat flux in terms of two variables: the local aerodynamic downwash due to plate motion and the local wall temperature. The proposed linearization produces a local force equivalent for the pointwise heat flux and substantially simplifies the time-marching scheme of the coupled equations. The model is correlated with results from a recent supersonic wind-tunnel experiment conducted at Air Force Research Laboratory (AFRL). It is found that when a nonlinear response is expected in an experiment (for example, flutter that reaches limit cycle oscillation or a large static buckled deformation), the in-plane boundary stiffness should be measured before the wind-tunnel experiment to fully characterize the experimental model. The heat flux linearization error is quantified, and the modal convergence of the temperature differential is investigated.

When Wireless Communications Meet Computer Vision in Beyond 5G

This article articulates the emerging paradigm, sitting at the confluence of computer vision and wireless communication, enabling beyond-5G/6G mission-critical applications (autonomous/ remote-controlled vehicles, visuo-haptic virtual reality, and other cyber-physical applications). First, drawing on recent advances in machine learning and the availability of non-radio-frequency (RF) data, vision-aided wireless networks have been shown to significantly enhance wireless communication reliability without sacrificing spectral efficiency. In particular, we demonstrate how computer vision enables look-ahead prediction in a millimeter-wave channel blockage scenario before the blockage actually occurs. From a computer vision perspective, we highlight how RF-based sensing and imaging are instrumental in robustifying computer vision applications against occlusion and failure. This is corroborated via an RF-based image reconstruction use case, showcasing a receiver-side image failure correction resulting in reduced retransmission and latency. Taken together, this article sheds light on the much needed convergence of RF and non-RF modalities to enable ultra-reliable communication and truly intelligent 6G networks.

Accident risk prediction model based on attention-mechanism LSTM using modality convergence in multimodal

Anytime, anywhere, anyone can collect not only personal information but also surrounding situation information from home or a vehicle on the move without being restricted by time and space. To this end, the need for various devices such as biosensors, wearable devices, and temperature sensors is increasing. In this circumstance, there are many problems with the interaction of multiple devices and data processes, and there is a lack of structured or unstructured emerging contextual data. Accordingly, it is necessary to come up with a data collection method to obtain a variety of information efficiently. Such a method can obtain a diversity of information consistently with the use of data convergence. Since the method makes possible decision making, entity identification, and context prediction, it is applicable to diverse areas, including object detection and context awareness. The data generated through convergence makes it possible to the improve performance of data analysis through machine learning and deep learning. For this reason, a variety of modal types are applied to data analysis. Modal convergence for analysis can improve analysis performance more than unimodal. In this study, propose the accident risk prediction model based on attention-mechanism LSTM using modality convergence in multi-modal. In order to predict the risk of an accident, the proposed method makes the convergence of structured and unstructured modality data on the basis of judgmental fusion and statistical fusion and generates data sets. It is capable of solving the problem of data shortage and obtaining a variety of information consistently. In the proposed method, firstly, preprocessing is performed, and data sets of accident risk information are generated with the uses of the road risk equation and accident risk equation. Next, the correlation coefficient and regression coefficient for each variable is calculated through regression analysis modal and correlation coefficient analysis modal. A correlation coefficient is used to judge which variable needs to be learned intensively in order for the attention-mechanism of the accident risk prediction model. A regression coefficient is used as attention-weight to adjust a data weighting factor in attention-mechanism LSTM. Accordingly, whether there is the risk of an accident is judged. Performance evaluation is conducted in two ways. Firstly, epoch-based loss is evaluated in the comparison between the attention-mechanism LSTM-based accident risk prediction model with convergence modal and the model without it. In short, their RMSE is evaluated and compared. Secondly, the proposed model is compared with conventional models in terms of RMSE.

Multi-Modal Natural Frequency Response of Utility Transmission Tapered Wood Poles Under Various Soil Foundation Conditions

Studied herein is the multi-modal natural frequency response of utility transmission tapered wood poles under various soil foundation conditions. Strong winds and hurricanes in various parts of the world have resulted in collapse of such utility poles and have resulted in the disruption of electrical distribution systems in addition to creating hazardous conditions for the public. To avoid the development of resonance under such dynamic loading, the multi-modal natural vibration of the utility poles first needs to be understood in the presence of practical soil foundation conditions. To capture the soil-structure interaction effects on the multi-modal frequencies, a SAP2000 dynamic finite element model is created in which the foundation soil stiffness is characterized by means of a series of ‘soil springs’ below the ground level. The properties of the soil springs vary with types of foundation soils and depths. Three types of foundation soils are considered, namely sandy, clayey soils and Granite (Rock). The results are compared to a standard fixed base model. It is found that the fundamental natural frequencies decreased by 52%, 37%, and 3% for sandy, clayey soils and granite, respectively, when compared to fixed base model. It was observed that there was an increase in the frequencies of the embedded utility poles in clay and granite, when compared to those with the fixed based after the 1st mode whereas, poles embedded in sandy soils showed increase in modal frequencies after the 3rd mode. The 10th mode appears to be a starting point of modal frequency convergence, while an apparent convergence occurs after the 20th mode. The convergent modal frequency was about 740 Hz for the Class H1 utility pole. However, there was a significant increase in the higher modal frequencies such as nearly 55% at the 20th mode, in all soil types when compared to the fixed base model.

Multi-Modal Natural Frequency Response of Utility Transmission Tapered Wood Poles Under Various Soil Foundation Conditions

Studied herein is the multi-modal natural frequency response of utility transmission tapered wood poles under various soil foundation conditions. Strong winds and hurricanes in various parts of the world have resulted in collapse of such utility poles and have resulted in the disruption of electrical distribution systems in addition to creating hazardous conditions for the public. To avoid the development of resonance under such dynamic loading, the multi-modal natural vibration of the utility poles first needs to be understood in the presence of practical soil foundation conditions. To capture the soil-structure interaction effects on the multi-modal frequencies, a SAP2000 dynamic finite element model is created in which the foundation soil stiffness is characterized by means of a series of ‘soil springs’ below the ground level. The properties of the soil springs vary with types of foundation soils and depths. Three types of foundation soils are considered, namely sandy, clayey soils and Granite (Rock). The results are compared to a standard fixed base model. It is found that the fundamental natural frequencies decreased by 52%, 37%, and 3% for sandy, clayey soils and granite, respectively, when compared to fixed base model. It was observed that there was an increase in the frequencies of the embedded utility poles in clay and granite, when compared to those with the fixed based after the 1st mode whereas, poles embedded in sandy soils showed increase in modal frequencies after the 3rd mode. The 10th mode appears to be a starting point of modal frequency convergence, while an apparent convergence occurs after the 20th mode. The convergent modal frequency was about 740 Hz for the Class H1 utility pole. However, there was a significant increase in the higher modal frequencies such as nearly 55% at the 20th mode, in all soil types when compared to the fixed base model.

Self-Assembled Hybrid Nanocomposites for Multimodal Imaging-Guided Photothermal Therapy of Lymph Node Metastasis.

Multimodal imaging-guided therapy holds great potential for precise theranostics of cancer metastasis. However, imaging agents enabling the convergence of complementary modalities with therapeutic functions to achieve perfect theranostics have been less exploited. This study reports the construction of a multifunctional nanoagent (FIP-99mTc) that comprises Fe3O4 for magnetic resonance imaging, radioactive 99mTc for single-photon-emission computed tomography, and IR-1061 to serve for the second near-infrared fluorescence imaging, photoacoustic imaging, and photothermal therapy treatment of cancer metastasis. The nanoagent possessed superior multimodal imaging capability with high sensitivity and resolution attributing to the complement of all the imaging modalities. Moreover, the nanoagent showed ideal photothermal conversion ability to effectively kill tumor cells at low concentration and power laser irradiation. In the in vivo study, FIP-99mTc confirmed the fast accumulation and clear delineation of metastatic lymph nodes within 1 h after administration. Attributing to the efficient uptake and photothermal conversion, FIP-99mTc could raise the temperature of metastatic lymph nodes to 54 °C within 10 min laser irradiation, so as to facilitate tumor cell ablation. More importantly, FIP-99mTc not only played an active role in suppressing cancer growth in metastatic lymph nodes with high efficiency but also could effectively prevent further lung metastasis after resection of the primary tumor. This study proposes a simple but effective theranostic approach toward lymph node metastasis.

Convergence of carbon emissions at the household level in China: A distribution dynamics approach

Since household carbon emissions (HCEs) have become a new growing source and a significant contributor to global emissions, the reduction of HCEs is crucial. To formulate targeted and effective emission mitigation policies, we need to fully understand the characteristics of the distribution and evolution of per capita HCEs and their urban-rural and regional heterogeneity. This paper explores the transitional dynamics of per capita HCEs in China by employing the distribution dynamics approach with panel data compiled at the household level. We find that the overall per capita HCEs are unimodal distribution in the long run and the distribution dynamics of the per capita HCEs between the urban and rural areas or among the regional subgroups are quite different. However, the speed of convergence has accelerated over time. Moreover, our findings indicate that the per capita HCEs in the urban areas will achieve convergence to an emission level much higher than that of the rural areas. Meantime the per capita HCEs in Northeast China will converge to an emission level much higher than those of the other three regions. These findings provide valuable insights for policymakers in implementing differentiated environmental policies for different regions and between urban and rural households in China. • Distribution dynamics approach is used to investigate per capita carbon emissions at the household level. • Significantly different dynamics can be found for the urban and rural subgroups. • Disparity in carbon emissions is very different among the regions across time. • Single modality convergence can be reached in the long run.

Dynamic response of Euler–Bernoulli beam resting on fractionally damped viscoelastic foundation subjected to a moving point load

The dynamic behaviour of an Euler–Bernoulli beam resting on the fractionally damped viscoelastic foundation subjected to a moving point load is investigated. The fractional-order derivative-based Kelvin–Voigt model describes the rheological properties of the viscoelastic foundation. The Riemann–Liouville fractional derivative model is applied for a fractional derivative order. The modal superposition method and Triangular strip matrix approach are applied to solve the fractional differential equation of motion. The dependence of the modal convergence on the system parameters is studied. The influences of (a) the fractional order of derivative, (b) the speed of the moving point load and (c) the foundation parameters on the dynamic response of the system are studied and conclusions are drawn. The damping of the beam-foundation system increases with increasing the order of derivative, leading to a decrease in the dynamic amplification factor. The results are compared with those using the classical integer-order derivative-based foundation model. The classical foundation model over-predicts the damping and under-predicts the dynamic deflections and stresses. The results of the classical (integer-order) foundation model are verified with literature.

Advanced Optical Imaging of Blood Thrombus

The application of advanced optical imaging technology for platelet biology is in its infancy. In this talk, we shall introduce the potential impact of optical imaging in understanding the roles of platelets in thrombus formation and in hemostasis. We will summarize how techniques in optical imaging has enabled exploration of morphological, molecular and fluidic parameters that determine platelet adhesion, activation and consequent roles in thrombus formation. Finally, we discuss the convergence of multiple imaging modalities towards a complete understanding of platelet roles in thrombus formation.