Miracle-dz/UniFAR-FTUs huggingface dataset:
Is There a Protective Effect of Sequential Grafting in Coronary Bypass Surgery Using Bilateral Internal Thoracic Artery?

Objectives: It is unclear whether the additional bypass techniques to supplement bilateral internal artery grafting (BITA) influence the patient outcome in coronary surgery. We analyzed the impact of sequential ITA grafts on late survival after BITA used on the left side. Methods: From 1989 to 2014, 1644 patients underwent BITA surgery without any additional arterial graft. The revascularization of the left side was optimized with a sequential ITA graft in 824 patients. The revascularization of the right side was performed with an associated vein graft in 599 patients. Propensity score was calculated by logistic regression model and patients were matched 1 to 1 leading to two groups of 334 matched patients. The primary end point was overall mortality from any cause. Results: The population was not homogenous: Greater the arterial revascularization, lower the risk profile. The 30-day mortality was 1.2% without influence of the surgical technique performed. The mean postoperative follow-up was 12.4 ± 6.7 years and 95% complete. Late mortality was significantly influenced by age, heart failure, 3-vessel disease, LV ejection fraction, number of arterial anastomoses and sequential ITA. The significant difference in patients’ survival observed at 20-years in favour of sequential ITA in unmatched groups was confirmed in matched groups. In multivariable Cox model analysis, the use of sequential ITA remained predictor of long-term survival in matched groups. Conclusions: These results confirm that higher the number of ITA anastomoses, better the long-term survival. It is a strong support of the extensive use of arterial grafting with multiple ITA bypass.
Evolving fuzzy model identification of nonlinear Wiener-Hammerstein processes

This paper presents a new approach to neuro-fuzzy model identification based on a filtered recursive least squares method combined with an incrementally evolving Gaussian clustering method. The proposed identification algorithm generates the model on the fly and requires few user-defined parameters, which is one of the main advantages compared to other methods. The partitioning of the input-output data space depends on the chosen criteria and thresholds and depends only on the operating point of the model. As an example, the Wiener-Hammerstein type of a dynamic process was identified to show the potential of the proposed method in identifying nonlinear dynamic models. The Wiener-Hammerstein structure was used because a variety of processes can be modeled with this type of structure. Moreover, we tested the same identification concept on a real heat exchanger plant with strong nonlinear behavior. In addition, the limitations of the real sensors and actuators represent a serious challenge to the identification procedure. Both experiments, on a simulated Wiener-Hammerstein model and on a real plant, have shown that the proposed new neuro-fuzzy model identification with the new merging concept is very easy to implement, can perform all necessary calculations online, and can generate meaningful models.
Modeling, Design, and Development of Soft Pneumatic Actuators with Finite Element Method  

This work presents a comprehensive open‐source simulation and design tool for Soft pneumatic actuators (SPAs) using finite element method, compatible and extensible to a diverse range of soft materials and design parameters. Thorough characterization of the hyperelastic and viscoelastic behavior is illustrated using a sample soft material (Ecoflex 00_30), and an appropriate material constitutive law. SPA performance (displacement and blocked‐force) are simulated for two types of SPA and validated with experimental testing. Real‐world case studies are presented in which SPA designs are iteratively optimized through simulation to meet specified performance criteria and geometric constraints.
Investigation of the transient behavior of a cantilever using a point-wise fiber Bragg grating displacement sensor system

This paper presents a fiber Bragg grating (FBG) sensor system setup method to measure the transient response of dynamic displacement for a cantilever subjected to impact loadings. An LPFG filter-based wavelength–optical intensity modulation technique is used in this study. The transient responses of particle motions measured by an FBG displacement sensor are compared with those obtained simultaneously by a laser Doppler vibrometer (LDV). The resonant frequencies of the cantilever are determined by employing a fast Fourier transform on the transient displacement responses obtained from the FBG and the LDV. An optical full-field measurement technique called amplitude-fluctuation electronic speckle pattern interferometry (AF-ESPI) is also used to provide the full-field vibration mode shapes and resonant frequencies of the cantilever. The resonant frequencies obtained from four experimental measurement techniques, i.e., FBG, LDV, AF-ESPI, and shaker, are compared with those predicted from theoretical and FEM numerical calculations. Furthermore, the amplitude of the frequency spectra obtained from the FBG and the LDV are emphasized from the mode shapes and impact locations of the steel ball. The proposed FBG displacement sensor is proved to be able to measure transient point-wise displacement responses which can also be used to determine the resonant frequencies of structures.
Power Management of a Plug-in Hybrid Electric Vehicle Based on Cycle Energy Estimation

Abstract Plug-in Hybrid Electric Vehicles (PHEV) are being investigated in many research and development programs motivated by the urgent need for more fuel-efficient vehicles that produce fewer harmful emissions. There are many potential advantages of hybridization such as the improvement of transient power demand, the ability of regenerative braking and the opportunities for optimization of the vehicle efficiency. The coordination among the various power sources requires a high level of control in the vehicle. In order to solve the power management problem, the controller proposed in this work is divided into two levels: the upper one calculates the power that must be supplied by the engine at each moment taking into account the estimation of the energy that must be supplied by the powertrain until the end of the journey. The lower one manages the torque/speed set points for all the devices. Besides, the operation modes are changed according to some heuristic rules. Several simulation results are presented, showing that the proposed control strategy can provide good performance with low computational load.
A NEW APPROACH TO DESIGNING REPAIR CLAMPS FOR OFFSHORE STRUCTURES

A consideration of the structural assessment procedure for offshore tubular steel jackets is given, which may be followed in the event of damage, potential static overstress or inadequate fatigue life. In the event that repair/strengthening is necessary, state-of-the-art techniques such as clamping, welding and grinding are reviewed in the light of the latest research data and operating experience. Performance characteristics following repair/strengthening are discussed with reference to each of the above techniques. Ways in which the design of a repair may be simplified and optimised are discussed with reference to the degree of offshore effort involved in carrying out each of the repair techniques. As part of this process this paper describes a recent review of test data for grouted and ungrouted stressed clamps. New formulae are presented which demonstrate enhanced capacities.
Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model

State-of-the-art machine learning (ML) interatomic potentials use local representations of atomic environments to ensure linear scaling and size-extensivity. This implies a neglect of long-range interactions, most prominently related to electrostatics. To overcome this limitation, we herein present a ML framework for predicting charge distributions and their interactions termed kernel charge equilibration (kQEq). This model is based on classical charge equilibration (QEq) models expanded with an environment-dependent electronegativity. In contrast to previously reported neural network models with a similar concept, kQEq takes advantage of the linearity of both QEq and Kernel Ridge Regression to obtain a closed-form linear algebra expression for training the models. Furthermore, we avoid the ambiguity of charge partitioning schemes by using dipole moments as reference data. As a first application, we show that kQEq can be used to generate accurate and highly data-efficient models for molecular dipole moments.
Two End-to-End Quantum-Inspired Deep Neural Networks for Text Classification

In linguistics, the uncertainty of context due to polysemy is widespread, which attracts much attention. Quantum-inspired complex word embedding based on Hilbert space plays an important role in natural language processing (NLP), which fully leverages the similarity between quantum states and word tokens. A word containing multiple meanings could correspond to a single quantum particle which may exist in several possible states, and a sentence could be analogous to the quantum system where particles interfere with each other. Motivated by quantum-inspired complex word embedding, interpretable complex-valued word embedding (ICWE) is proposed to design two end-to-end quantum-inspired deep neural networks (ICWE-QNN and CICWE-QNN representing convolutional complex-valued neural network based on ICWE) for binary text classification. They have the proven feasibility and effectiveness in the application of NLP and can solve the problem of text information loss in CE-Mix [1] model caused by neglecting the important linguistic features of text, since linguistic feature extraction is presented in our model with deep learning algorithms, in which gated recurrent unit (GRU) extracts the sequence information of sentences, attention mechanism makes the model focus on important words in sentences and convolutional layer captures the local features of projected matrix. The model ICWE-QNN can avoid random combination of word tokens and CICWE-QNN fully considers textual features of the projected matrix. Experiments conducted on five benchmarking classification datasets demonstrate our proposed models have higher accuracy than the compared traditional models including CaptionRep BOW, DictRep BOW and Paragram-Phrase, and they also have great performance on F1-score. Eespecially, CICWE-QNN model has higher accuracy than the quantum-inspired model CE-Mix as well for four datasets including SST, SUBJ, CR and MPQA. It is a meaningful and effictive exploration to design quantum-inspired deep neural networks to promote the performance of text classification.
An Integrated Model of Ring Pack Performance

This paper describes an integrated model developed for the detailed characterization and simulation of piston ring pack behavior in internal combustion engines and the prediction of ring pack performance. The model includes comprehensive and coupled treatments of (1) ring-liner hydrodynamic and boundary lubrication and friction; (2) ring axial, radial, and (toroidal) twist dynamics; (3) inter-ring gas dynamics and blowby. The physics of each of these highly inter-related phenomena are represented by submodels, which are intimately coupled to form a design-oriented predictive tool aimed at the calculation of ring film thicknesses, ring motions, land pressures, engine friction, and blowby. The paper also describes the results of a series of analytical studies investigating effects of engine speed and load and ring pack design parameters, on ring motions, film thicknesses, and inter-ring pressures, as well as ring friction and blowby.
Color computational holographic display based on laser direct writing lithography technology

Color holographic display has been a research hot in many years, since it has huge applications in the holographic display field. In this paper, we employ a laser direct write lithography system, that has the feature of high-resolution drawing and high-speed drawing, to make the color computational holograms with different pixel numbers and pixel pitches. Based on the method of spatial division, the optical experimental system is built, and the diffractive optical elements (DOEs) are fabricated by laser direct writing lithography technology to realize the color holographic display. The experimental results verify the feasibility of this method.
A Prognostic Three-Axis Coordination Model for Supply Chain Regulation Using Machine Learning Algorithm

In this article, data processing of a supply chain management system has been monitored using the Internet of Space (IoS) which can be able to create possessions for managing the business process. In modern circumstances, many business inventiveness are trading and exporting products on their possession, but in many cases, information on such manufactured products is not monitored in an effective manner. To overcome the abovementioned issue, a precise model of monitoring several distributed products in supply chain management has been introduced with high sustainability error reduction. The framed model in the management process has been integrated with the boosting algorithm, a type of machine learning algorithm where training dataset has been introduced appropriately. This variation in the incorporation of the boosting optimization process not only increases the efficiency of the proposed model but also attempts to prove the success strategy under five different scenarios, where after sequential tests IoS model delivers high improvement in the distribution process for an average percentile of 67% than the existing methods.
Inductance and resistance computations for three-dimensional multiconductor interconnection structures

A computer-aided analysis system has been established to calculate the equivalent inductance and resistance matrices for three-dimensional multiconductor interconnection structures. Based on partial element equivalent circuit theory, the interconnection structures are first decomposed into many straight segments which are of circular or rectangular cross sections but can be in arbitrary orientation. The resistances and partial inductances between all these segments are calculated using analytical integration and quadrature formulae. They are assembled into the desired equivalent impedance matrix by general network theory. Illustrative examples include the analysis for nonuniformly coupled transmission lines and the calculation for skin-effect impedances of transmission lines and three-dimensional structures. The numerical results are in good agreement with the measurement data and results in the literature. >
The Valuation and Analysis of Apple Inc

In order to define a reasonable value of Apple’s stocks, a number of valuation models can be considered, including FCFF model, DDM, VC method, FCFE model and multiples valuation model. In the research, we use WACC and DCF calculation to evaluate the Apple company’s further value, and judging whether it is undervalued or overvalued based on historical data and market direction. As different models and data provide various results, they have different limitations and uses. For DCF, although some of the data used in its calculations are based on people's subjective analysis of the future, its valuation serves as a baseline for the target price, making its results relatively conservative and reasonable. Based on the results generated by valuation models, we think Apple still has a great potential to develop, and we advise customers to buy or hold the stock.
Study and Analysis of Doors of BCNHL Wagons

With the economy of India growing at a very healthy pace, the freight traffic has naturally been growing too. Since the extent of addition to the route kilometrage of Indian Railway is not rapid enough, a quantum jump in the unit wagon capacity is the need of the hour. A case for developing heavy axle load wagons on IR is, therefore, inevitable for our sustenance. In the last decade (since 2000-10) the route kms of IR have increased by just 6.5% whereas the freight traffic (NTKms) has increased by 90.6%. This, despite an increase in wagon lead kms of just 2.4%, has largely been possible because the unit wagon capacity has increased by over 21.5% in the same period.The capacity of wagon in terms of axle load has been increased from 20.32t to22.9t. Railway freight stock (wagons) is used for carrying goods in bagged form or in bulk form, machines, ores etc. from one place to another place. For a strong economy an efficient transportation system is necessary. Major portion of the revenue of railway comes from freight operation. Rail freight uses many types of goods stock. Currently Indian railways use less capacity wagons in comparison to other players in international market. Over the last decade Indian Railways has adopted various measures to improve the operational and commercial performance of its rail freight operations. These include an increase in the permissible axle-loading for major commodities; improvement in wagon utilization by improving wagon turn-round times together with incentives to customers and more focus on availability and reliability by adopting modern techniques of maintainability.By improving reliability and availability it is possible to transport maximum volume of goods (throughput) fromone place to other in time.
3D printed lens on optical fiber tip for imaging applications

Current lens-based optics is limited in dimensions and shapes due to the fabrication process. With the development of 3D lithography in recent years, the polymer-based lens fabricated by direct laser writing based on two-photon lithography shows unique capabilities compared to those fabricated by traditional methods. It shows advantages such as fast writing speed and high resolution. However, for those tiny delicate structures such as resonators and waveguides, the quality will be losing if the speed is too high, besides, it costs time to fabricate large structures. In this paper, we present a promising three-dimensional microfabrication system based on single-photon polymerization using a digital micromirror device with a UV light source at 390 nm. The designed system has a high resolution and it needs shorter time for fabrication. Here, the lenses with different sizes and curvatures are fabricated directly on a single mode fiber tip, for optical fiber imaging system. The paper presents experiment-details of the design of the single-photon polymerization system, fabrication the optical components on optical fiber tip and the results for imaging applications. We demonstrate the optical design and manufacturing using a DMD-based 3D printer for potential applications for optics, fabrication of biosensor and imaging.
Hardware Trojan Detection Technique Using Frequency Characteristic Analysis of Path Delay in Application Specific Integrated Circuits

Abstract— Since the last decade, hardware Trojan (HT) have become a serious problem for hardware security because of outsourcing trends in Integrated Circuit (IC) manufacturing. As the fabrication of IC is becoming very complex and costly, more and more chipmakers outsource their designs or parts of the fabrication process. This trend opens a loophole in hardware security, as an untrusted company could perform malicious modifications to the golden circuit at design or fabrication stages. Therefore, assessing risks and proposing solutions to detect HT are very important tasks. This paper presents a technique for detecting HT using frequency characteristic analysis of path delay. The results show that measuring with the frequency step of 0.016 MHz can detect a HT having the size of 0.2% of the original design. Tóm tắt— Từ thập niên 2010, Trojan phần cứng (HT) đã trở thành một vấn đề nghiêm trọng đối với bảo mật phần cứng, do xu hướng thuê sản xuất mạch tích hợp (Integrated Circuit - IC). Khi quá trình chế tạo IC trở nên phức tạp và tốn kém, ngày càng nhiều nhà sản xuất chip lựa chọn phương án thuê lại một phần hoặc toàn bộ thiết kế IC. Xu hướng này tạo ra lỗ hổng trong bảo mật phần cứng, vì một công ty không đáng tin cậy có thể thực hiện các sửa đổi độc hại vào trong mạch nguyên bản ở giai đoạn thiết kế hoặc chế tạo. Do đó, đánh giá rủi ro và đề xuất giải pháp phát hiện HT là một trong những nhiệm vụ hết sức quan trọng. Bài báo này trình bày một giải pháp phát hiện HT sử dụng phân tích đặc tính tần số của độ trễ đường truyền tín hiệu. Kết quả cho thấy, thực hiện khảo sát với bước tần số 0,016 MHz có thể phát hiện được HT có kích thước 0,2% so với thiết kế ban đầu.
Optimized data-driven pipeline for digital mapping of quantitative and categorical properties of soils in Colombia

Soil maps provide a method for graphically communicating what is known about the spatial distribution of soil properties in nature. We proposed an optimized pipeline, named dino-soil toolbox, programmed in the R software for mapping quantitative and categorical properties of legacy soil data. The pipeline, composed of four main modules (data preprocessing, covariates selection, exploratory data analysis and modeling), was tested across a study area of 14,537 km located between the departments of Cesar and Magdalena, Colombia. We assessed the feasibility of the toolbox to model three soil properties: pH at two depth intervals (0.00-0.30 and 0.30-1.00 m), soil taxonomy (great group) and taxonomic family by particle-size, according to a set of 25 environmental factors derived from auxiliary layers of climate, land cover and terrain. As a result, we successfully deployed the proposed semi-automatic and sequential pipeline, yielding rapid digital soil mapping (DSM) outputs across the study area. By providing multiple outputs such as tables, charts, maps, and geospatial data in four main modules, the pipeline offers considerable robustness to support outcomes and analysis of a DSM project. Future studies might be interesting to expand on further machine learning frameworks for predictive modeling of soil properties such as ensembles and deep learning models, which have shown a high performance for DSM.
Hybrid opto-mechanical gyroscope with injection-interferometer readout

A hybrid micromachined-optoelectronic mechanical system (MOEMS) gyroscope is proposed. It consists of an electrically excited micro-mechanical resonator and an optical readout performed by a laser diode operating as an injection interferometer. An analysis of the system, validated by preliminary experimental results, indicates an attainable performance of 1 deg/h/spl radic/Hz.
Integrated Piezoelectrics for Smart Microsystems - A Teamwork of Substrate and Piezo

Microelectronic substrates like silicon, alumina and LTCC (Low Temperature Cofired Ceramics) allow for high robustness and reliability, 3D packaging (electrical connection, channels, cavities and membranes) as well as integration and application of electronic components whereas piezoceramic materials offer sensor and actuator operations. To combine the advantages of both, integrated solutions are of great interest. This paper deals with two approaches of monolithic integration, (i) screen printing of piezoceramic thick films on microelectronic substrates and subsequent post firing and (ii) integration of pre-fired piezoceramic components into green LTCC multilayer packages and subsequent sintering. Functionality of smart microsystems not only depends on the outer design and construction but to a great part on interaction of substrate and piezoceramic material properties. A thorough choice of materials as well as the understanding and prevention of chemical reactions are necessary to build effective systems.