Search Results (564)

In this paper, we introduce an efficient and effective algorithm for Graph-based Semi-Supervised Learning (GSSL). Unlike other GSSL methods, our proposed algorithm achieves efficiency by constructing a bipartite graph, which connects a small number of representative points to a large volume of raw data by capturing their underlying manifold structures. This bipartite graph, with a sparse and anti-diagonal affinity matrix which is symmetrical, serves as a low-rank approximation of the original graph. Consequently, our algorithm accelerates both the graph construction and label propagation steps. In particular, on the one hand, our algorithm computes the label propagation in closed-form, reducing its computational complexity from cubic to approximately linear with respect to the number of data points; on the other hand, our algorithm calculates the soft label matrix for unlabeled data using a closed-form solution, thereby gaining additional acceleration. Comprehensive experiments performed on six real-world datasets demonstrate the efficiency and effectiveness of our algorithm in comparison to five state-of-the-art algorithms. Full article

In the last few decades, diffusion processes have been widely used to solve visual re-ranking problems. The key point of these approaches is that, by diffusing the baseline similarities in the context of other samples, more reliable similarities or dissimilarities can be learned. This was later found to be achieved by solving the optimization problem underlying the framework of the regularized diffusion process. In this paper, the proposed model differs from previous approaches in two aspects. Firstly, by taking the high-order information of the graph into account, a novel smoothness constraint, named the triple-cosine smoothness constraint, is proposed. The triple-cosine smoothness constraint is generated using the cosine of the angle between the vectors in the coordinate system, which is created based on a group of three elements: the queries treated as a whole and two other data points. A hybrid fitting constraint is also introduced into the proposed model. It consists of two types of predefined values, which are, respectively, used to construct two types of terms: the squared $L_2$ norm and the $L_1$ norm. Both the closed-form solution and the iterative solution of the proposed model are provided. Secondly, in the proposed model, the learned contextual dissimilarities can be used to describe “one-to-many” relationships, making it applicable to problems with multiple queries, which cannot be solved by previous methods that only handle “one-to-one” relationships. By taking advantage of these “one-to-many” contextual dissimilarities, an iterative re-ranking process based on the proposed model is further provided. Finally, the proposed algorithms are validated on various databases, and comprehensive experiments demonstrate that retrieval results can be effectively improved using our methods. Full article

Background and Objectives: Intra/postpartum hemorrhage stands as a significant obstetric emergency, ranking among the top five leading causes of maternal mortality. The aim of this study was to assess the predictive performance of four machine learning algorithms for the prediction of postpartum and intrapartum hemorrhage. Materials and Methods: A prospective multicenter study was conducted, involving 203 patients with or without intra/postpartum hemorrhage within the initial 24 h postpartum. The participants were categorized into two groups: those with intra/postpartum hemorrhage (PPH) and those without PPH (control group). The PPH group was further stratified into four classes following the Advanced Trauma Life Support guidelines. Clinical data collected from these patients was included in four machine learning-based algorithms whose predictive performance was assessed. Results: The Naïve Bayes (NB) algorithm exhibited the highest accuracy in predicting PPH, boasting a sensitivity of 96.3% and an accuracy of 98.6%, with a false negative rate of 3.7%. Following closely were the Decision Tree (DT) and Random Forest (RF) algorithms, each achieving sensitivities exceeding 94% with a false negative rate of 5.9%. Regarding severity classification I, the NB and Support Vector Machine (SVM) algorithms demonstrated superior predictive capabilities, achieving a sensitivity of 96.4%, an accuracy of 92.1%, and a false negative rate of 3.6%. The most severe manifestations of HPP were most accurately predicted by the NB algorithm, with a sensitivity of 89.3%, an accuracy of 82.4%, and a false negative rate of 10.7%. Conclusions: The NB algorithm demonstrated the highest accuracy in predicting PPH. A notable discrepancy in algorithm performance was observed between mild and severe forms, with the NB and SVM algorithms displaying superior sensitivity and lower rates of false negatives, particularly for mild forms. Full article

The use of conformal arrays offers a significant advancement in Multiple-Input–Multiple-Output (MIMO) radar, enabling the placement of antennas on irregular surfaces. For joint Direction-of-Departure (DOD) and Direction-of-Arrival (DOA) estimation in conformal-array MIMO radar, the current spectrum-searching methods are computationally too expensive, while the existing rotation-invariant method may suffer from phase ambiguity caused by the non-Nyquist spacing of the sensors. In this paper, an improved rotationally invariant technique is proposed. The core function of the proposed algorithm is to estimate the phase differences between the adjacent sensors; then, it eliminates phase ambiguity via the previous estimated standard phase difference. Thereafter, DODs and DOAs are obtained via Least Squares (LS) fitting. The proposed method provides closed-form estimates for joint DOD and DOA estimation, which is much more efficient than the existing spectrum-searching techniques. Numerical simulations show that the proposed algorithm can accurately determine 2D DODs and DOAs of targets, only requiring approximately 1% of the running time required by existing spectrum-searching approaches. Full article

The aim of this study is to provide a solution for the plastic limit pressure and stress intensity factor of the elbows containing a part-through axial semi-elliptical crack by considering various crack sizes. The supporting system and loading conditions of the pipeline are described. The critical part of the observed pipeline was isolated for analysis and subjected to various sizes of semi-elliptical cracks. By performing numerical analysis, results were obtained for crack dimension ratios of c/a, and depth/thickness ratios of a/t. The obtained results include plastic limit pressure and stress intensity factor. The results were analyzed with a symbolic regression algorithm, and closed-form solutions for the limit pressure and stress intensity factor were proposed. To validate pipeline integrity, the Structural Integrity Assessment Procedure (SINTAP) was applied, and the FAD (Failure Assessment Diagram) was generated for cracks below the FAD function. The failure pressure was calculated by determining the points where the loading paths intersect the FAD function. Full article

Shallow groundwater is an important resource, especially in low- and middle-income countries; however, shallow groundwater is particularly vulnerable to point sources of pollution such as latrines or unlined waste disposal ponds. The objective of this paper is to derive a quantitative criterion for siting an extraction well and an upgradient point source of pollution to ensure that they are hydraulically disconnected, i.e., that no water flows from the point source to the well. To achieve this objective, we modeled the flow of shallow groundwater considering uniform regional flow, a single point source of pollution, and a single extraction well. For any set of flow rates and upgradient point source distance, we sought the minimum “off-center distance” y_min (i.e., the distance in the direction perpendicular to regional flow) that ensures the well and the point source are hydraulically disconnected. For constituencies with access to computing resources and coding expertise, we used a computer-based method for determining y_min that is exact to within the accuracy of a root-finding algorithm; this approach is recommended when computer access is available. For constituencies lacking these resources, we determined a simple, closed-form, approximate solution for y_min that has an average error of less than 3% for the conditions we tested. For a subset of scenarios in which the point source is sufficiently far upgradient of the well (n = 77), the root mean square relative error of the approximate solution is only 0.52%. We found that y_min depends on a length parameter (Q_w + Q_ps)/Q_R, where Q_w is the extraction rate of the well, Q_ps is the injection rate of the point source, and Q_R is the regional groundwater flow rate per unit of perpendicular length. Either the exact solution or the closed-form approximation can help to site wells near point sources of pollution, or to site point sources near wells, in a manner that protects the health of the well user. The approximate solution is valuable because many constituencies that rely on shallow wells for water supply and latrines for sanitation also lack access to the computer resources necessary to apply the exact solution. Full article

The dynamic stiffness method is developed to analyze the natural vibration characteristics of functionally graded beams, where material properties change continuously across the beam thickness following a symmetric law distribution. The governing equations of motion and associated natural boundary conditions for free vibration analysis are derived using Hamilton’s principle and closed-form exact solutions are obtained for both Euler–Bernoulli and Timoshenko beam models. The dynamic stiffness matrix, which governs the relationship between force and displacements at the beam ends, is determined. Using the Wittrick–Williams algorithm, the dynamic stiffness matrix is employed to compute natural frequencies and mode shapes. The proposed procedure is validated by comparing the obtained frequencies with those given by approximated well-known formulas. Finally, a parametric investigation is conducted by varying the geometry of the structure and the characteristic mechanical parameters of the functionally graded material. Full article

By anchoring the lower rollers and dynamically adjusting the upper roller’s downfeed of a pyramid roll bender, one can achieve the precise bending of a workpiece into a desired planar form characterized by variable curvature. To ensure the seamless processing of individual cross sections without impinging upon adjacent areas, critical roller spacings are identified through theoretical mechanics analyses. The reaction force exerted on the top roller is calculated by integrating the desired curvature into the elastoplastic constitutive equation and subsequently deriving a dynamic adjustment of the downfeed from quasi-static finite element simulations. This preliminary downfeed protocol undergoes refinement to mitigate discrepancies between the targeted and the actual curvatures. Numerical instances demonstrate that the application of roller configurations, as outlined herein, yields a product profile that closely mirrors the intended curve. This congruence can be further improved with an additional iteration; however, subsequent iterations are seen to yield negligible improvements, indicating a rapid convergence of this algorithm. Full article

Unmanned aerial vehicle (UAV) communication using non-orthogonal multiple access-based coordinated direct and relay transmission (NOMA-CDRT) supports both massive connectivity and wide-area coverage, becoming a key technology for future emergency rescue communications. However, relay forwarding and high-quality line-of-sight links may subject UAV-aided NOMA-CDRT to multiple eavesdropping attempts by saboteurs. Therefore, we propose a multi-node joint jamming scheme using artificial noise (AN) for the UAV-assisted NOMA-CDRT to improve the system’s physical layer security. In the proposed scheme, the base station directly serves a nearby user while using a UAV relay to serve a disaster-affected user, and both the users and the UAV relay utilize AN to jointly interfere with eavesdroppers around the users. To accurately characterize and maximize the ergodic secrecy sum rate (ESSR) of the proposed scheme, we derive the corresponding closed-form expressions and design a joint power allocation and interference control (JPAIC) algorithm using particle swarm optimization. Simulations verify the correctness of the theoretical analysis, the ESSR advantage of the proposed scheme compared with the conventional NOMA-CDRT, and the effectiveness of the proposed JPAIC. Full article

After the 2007 financial crisis, many central banks adopted policies to lower their interest rates; the dynamics of these rates cannot be captured using classical models. Recently, Meucci and Loregian proposed an approach to estimate nonnegative interest rates using the inverse-call transformation. Despite the fact that their work is distinguished from others in the literature by their consideration of practical aspects, some technical difficulties still remain, such as the lack of analytic expression for the zero-coupon bond (ZCB) price. In this work, we propose novel approximate closed-form solutions for the ZCB price in the zero lower bound (ZLB) framework, when the underlying shadow rate is assumed to follow the classical one-factor Vasicek model. Then, a filtering procedure is performed using the Unscented Kalman Filter (UKF) to estimate the unobservable state variable (the shadow rate), and the model calibration is proceeded by estimating the model parameters using the Particle Swarm Optimization (PSO) algorithm. Further, empirical illustrations are given and discussed using (as input data) the interest rates of the AAA-rated bonds compiled by the European Central Bank ranging from 6 September 2004 to 21 June 2012 (a period that concerns the ZLB framework). Our approximate closed-form solution is able to show a good match between the actual and estimated yield-rate values for short and medium time-to-maturity values, whereas, for long time-to-maturity values, it is able to estimate the trend of the yield rates. Full article

The training phase of a deep learning neural network (DLNN) is a computationally demanding process, particularly for models comprising multiple layers of intermediate neurons.This paper presents a novel approach to accelerating DLNN training using the particle swarm optimisation (PSO) algorithm, which exploits the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimisation method whose objective is to iteratively enhance the solution to a (usually complex) problem by approximating a given objective. The expensive fitness evaluation and updating of particle positions can be supported more effectively by parallel processing. Nevertheless, the parallelisation of an efficient PSO is not a simple process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In this study, two forms of parallelisation have been developed for the PSO algorithm, both of which are designed for execution in a distributed execution environment. The synchronous parallel PSO implementation guarantees consistency but may result in idle time due to global synchronisation. In contrast, the asynchronous parallel PSO approach reduces the necessity for global synchronization, thereby enhancing execution time and making it more appropriate for large datasets and distributed environments such as Apache Spark. The two variants of PSO have been implemented with the objective of distributing the computational load supported by the algorithm across the different executor nodes of the Spark cluster to effectively achieve coarse-grained parallelism. The result is a significant performance improvement over current sequential variants of PSO. Full article

With the rapid development of the internet of things (IoT) era, IoT devices may face limitations in battery capacity and computational capability. Simultaneous wireless information and power transfer (SWIPT) and mobile edge computing (MEC) have emerged as promising technologies to address these challenges. Due to wireless channel fading and susceptibility to obstacles, this paper introduces intelligent reflecting surfaces (IRS) to enhance the spectral and energy efficiency of wireless networks. We propose a system model for IRS-assisted uplink offloading computation, downlink offloading computation results, and simultaneous energy transfer. Considering constraints such as IRS phase shifts, latency, energy harvesting, and offloading transmit power, we jointly optimize the CPU frequency of IoT devices, offloading transmit power, local computation workload, power splitting (PS) ratio, and IRS phase shifts. This establishes a multi-variate coupled nonlinear problem aimed at minimizing IoT devices energy consumption. We design an effective alternating optimization (AO) iterative algorithm based on block coordinate descent, and utilize closed-form solutions, Dinkelbach-based Lagrange dual method, and semidefinite relaxation (SDR) method to minimize IoT devices energy consumption. Simulation results demonstrate that the proposed scheme achieves lower energy consumption compared to other resource allocation strategies. Full article

This paper considers a terahertz (THz) dual-function multi-input multi-output (MIMO) radar and communication system with the assistance of a reconfigurable intelligent surface (RIS) and jointly designs the constant modulus (CM) waveform and RIS phase shifts. A weighted optimization scheme is presented, to minimize the weighted sum of three objectives, including communication multi-user interference (MUI) energy, the negative of multi-target illumination power and the MIMO radar waveform similarity error under a CM constraint. For the formulated non-convex problem, a novel alternating coordinate descent (ACD) algorithm is introduced, to transform it into two subproblems for waveform and phase shift design. Unlike the existing optimization algorithms that solve each subproblem by iteratively approximating the optimal solution with iteration stepsize selection, the ACD algorithm can alternately solve each subproblem by dividing it into multiple simpler problems, to achieve closed-form solutions. Our numerical simulations demonstrate the superiorities of the ACD algorithm over the existing methods. In addition, the impacts of the weighting coefficients, RIS and channel conditions on the radar communication performance of the THz system are analyzed. Full article

Portal frames with built-up cold-formed cross-sections hold significant potential; however, there is a notable gap in the analysis of cross-section types and connections. In this study, an optimization algorithm was developed for the closed cross-sections of portal frame members. An optimization algorithm was tested against optimized open cold-formed cross-sections. The results indicated a portal frame volume up to 38% lower where members were assembled of optimal closed cross-sections when compared to frames with optimal open cross-sections. Parametric analysis was carried out, where two types of cross-sections were examined: Type A, with four web stiffeners bent inwards, and Type B, with four web stiffeners bent outwards. The optimization was conducted using a Genetic Algorithm in MATLAB R2022b. Portal frames with optimal Type B cross-sections had a volume that was up to 22% lower when compared to frames with optimal Type A cross-sections. Significant differences were noted between the optimal beam and column cross-sections, with the optimal column cross-section thickness being on average 74% greater, but the optimal beam cross-section height being on average 81% greater than those of the respective counterparts. In this article, a practical assembly solution for the connection of the frame members was proposed for the optimized novel closed cross-section types in portal frames. However, the strength and stiffness of these connections were not analyzed in this research. Full article

The Parallel Factor Analysis 2 (PARAFAC2) is a multimodal factor analysis model suitable for analyzing multi-way data when one of the modes has incomparable observation units, for example, because of differences in signal sampling or batch sizes. A fully probabilistic treatment of the PARAFAC2 is desirable to improve robustness to noise and provide a principled approach for determining the number of factors, but challenging because direct model fitting requires that factor loadings be decomposed into a shared matrix specifying how the components are consistently co-expressed across samples and sample-specific orthogonality-constrained component profiles. We develop two probabilistic formulations of the PARAFAC2 model along with variational Bayesian procedures for inference: In the first approach, the mean values of the factor loadings are orthogonal leading to closed form variational updates, and in the second, the factor loadings themselves are orthogonal using a matrix Von Mises–Fisher distribution. We contrast our probabilistic formulations to the conventional direct fitting algorithm based on maximum likelihood on synthetic data and real fluorescence spectroscopy and gas chromatography–mass spectrometry data showing that the probabilistic formulations are more robust to noise and model order misspecification. The probabilistic PARAFAC2, thus, forms a promising framework for modeling multi-way data accounting for uncertainty. Full article