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In this paper, the physical layer security aspects of a wireless framework over α − κ − μ shadowed (AKMS) fading channel are examined by acquiring closed-form novel expressions of average secrecy capacity, secure outage probability (SOP), and strictly positive secrecy capacity. The lower bound of SOP is derived along with the asymptotic expression of SOP at the high signal-to-noise ratio regime in order to achieve secrecy diversity gain. Capitalizing on these expressions, the consequences due to the simultaneous occurrence of fading and shadowing are quantified. Finally, Monte-Carlo simulations are demonstrated to assess the correctness of the expressions.

For a given graph $G$, the metric and edge metric dimensions of $G$, $\dim(G)$ and ${\rm edim}(G)$, are the cardinalities of the smallest possible subsets of vertices in $V(G)$ such that they uniquely identify the vertices and the edges of $G$, respectively, by means of distances. It is already known that metric and edge metric dimensions are not in general comparable. Infinite families of graphs with pendant vertices in which the edge metric dimension is smaller than the metric dimension are already known. In this article, we construct a 2-connected graph $G$ such that $\dim(G)=a$ and ${\rm edim}(G)=b$ for every pair of integers $a,b$, where $4\le b, Comment: 12 pages

We present a new branch-and-bound algorithm for the Quadratic Multiple Knapsack Problem. A key component of our algorithm is a new upper bound that divides the pairwise item values among individual items, estimates the maximum potential value contributed by each individual item, and calculates the upper bound via a transportation model. A local search is used to adjust the division of pairwise item values in order to improve the upper bound. Reduced costs from the solution of the transportation problem are used to forbid some item-to-knapsack assignments. Information from the upper bound is also used in selecting the item to branch on next. Computational experiments are carried out on three sets of benchmark instances from the literature, two sets of smaller instances with 20–35 items and 3–10 knapsacks per instance, and one set of larger instances with 40–60 items and 3–10 knapsacks per instance. Our algorithm finds and verifies optimal solutions for all small benchmark instances in less than one hour of CPU time apiece and outperforms the best previously proposed algorithms for the problem in terms of both the average computation time and the number of instances for which optimality is verified. For the larger benchmark instances, our algorithm obtains smaller average gaps than the best previously proposed methods.

This paper investigates an event triggered control problem of connected vehicles under multiple cyber-attacks, including denial-of-service attacks (DoS) and deception attacks, which occur aperiodically. Then, a novel event triggered control scheme is proposed in the presence of multiple cyber-attacks. The upper bound of attack frequency, attack length rate of DoS attacks and the conditions of controller gains are obtained. It is proven that the proposed distributed control scheme can realize the asymptotic tracking performance of connected vehicles while avoiding Zeno behavior. A string stability analysis under multiple cyber-attacks is also provided. Simulation results confirm the effectiveness of the event triggered control schemes under multiple cyber-attacks.

An electrocardiogram (ECG) consists of complex P-QRS-T waves. Detecting long-term ECG recordings is time-consuming and error-prone for cardiologists . Deep neural networks (DNNs) can learn deep representations and empower automatic arrhythmia detection. However, when applying DNNs in practice, they usually suffer from domain shift that exits between the training data and testing data. Such shift can be caused by the high variability contained in ECG signals between patients and internal-variability of heartbeats for same patients, leading to degrading performance and impeding generalization of DNNs. To tackle this problem, we propose an unsupervised semantic-aware adaptive feature fusion network (USAFFN) to reduce such shift by alleviating the semantic distribution discrepancy between the feature spaces of two domains. Furthermore, an ECG contains rich information from different angles (beat, rhythm, and frequency levels), which is essential for arrhythmia detection. Therefore, a multi-perspective adaptive feature fusion (MPAFF) module is introduced to extract informative ECG representations. The experimental results show that the detection performance of our approach is highly competitive with the upper bound of alternative methods on the ARDB, and the generalization is confirmed on the INCART and LTDB.

Estimating the heat transfer in deep underground geothermal systems is a challenging task. The concept of explicitly describing the heat transfer between phases has been shown superior to the conventional approach but the heat transfer coefficient of fractures is still ambiguous. In this work, a definition of the lower and upper bound of the heat transfer coefficient between rock and flowing fluid in a single rock fracture is derived and validated with almost 250 experiments from other studies. The resulting values range between 8 and 260 W/m2 °C, with upper bounds roughly a factor 10 larger. Further, as shown in this work, the assumption of local thermal equilibrium can result in lower production temperatures than the assumption of heat transfer between phases depending on reservoir conditions. A dimensionless number is derived to distinct those cases and to determine the lower limit of the outflow temperature in any scenario. The results of this work enable a quantification of the value range of the heat transfer coefficient from laboratory experiments and allow a tabulation of the heat transfer coefficient for all kind of scenarios. The presented results can be used in discrete fracture models for an improved prognosis of production temperatures.

In many real-world applications, it is the fairness, not the accuracy, of a machine learning (ML) classifier that is the crucial factor. Post-processing approaches are widely considered as successful tools to improve the fairness of black-box ML classifiers. These aim to learn a relabeling function to modify initial predicted labels provided by a pre-trained “unfair” classifier, resulting in fair classification on a given test set. However, many post-processing methods require a training set with true labels to learn the relabeling function. To the best of our knowledge, there have been only two methods that learn the relabeling function without requiring the true labels of training samples. However, both of these methods require access to the predictions of the pre-trained classifier when performing on the test set, even after they learned the optimal relabeling function, and neither offers theoretical guarantees on the trade-off between accuracy loss and fairness improvement. In this paper, we propose a novel post-processing method based on Gaussian process (GP). We first train a GP with unlabeled samples, and use its posterior mean function to approximate the predictions of the pre-trained classifier. We then adjust the mean function (i.e. the relabeling function) to achieve two goals: (1) maximize the fairness and (2) minimize the difference between the relabeling function and the pre-trained classifier. By doing this, our method can improve fairness while maintaining high accuracy. We provide a theoretical analysis to derive an upper bound on accuracy loss for our method. We demonstrate our method on four real-world datasets, comparing with state-of-the-art baselines, to demonstrate its ability to achieve both fairness and accuracy.

The seismic protection of r.c. buildings by base-isolation systems is often achieved using elastomeric devices like the high-damping-rubber bearings (HDRBs). When these buildings are subjected to severe ground motions which can occur at sites located near active faults, the seismic demand of their superstructure can be amplified in a significant way and the interaction between shear and axial laws of the elastomeric bearings can become quite complex. Sophisticated models of the axial, shear and rotational behaviour of HDRBs, supported by experimental tests and numerical simulations, are proposed in the literature to describe their nonlinear response. However, few numerical studies are available to compare the influence of such models on the nonlinear response of base-isolated r.c. buildings under near-fault earthquakes. In this work, nonlinear incremental dynamic analyses of two typical r.c. framed structures, one retrofitted and the other one new, are carried out considering simplified and refined force–displacement laws of HDRBs. To this end, nine earthquakes, corresponding to three strong near-fault seismic events, extracted from the Pacific Earthquake Engineering Research center database, are selected to highlight effects of correlation between their horizontal and vertical components. Numerical results suggest that the nonlinear behaviour of HDRBs can be described in an equivalent way by using an upper and lower bound approach.

This paper focuses on the synchronization of fractional-order complex-valued neural networks (FOCVNNs) with reaction–diffusion terms in finite-time interval. Different from the existing complex-valued neural networks (CVNNs), the reaction–diffusion phenomena and fractional derivative are first considered into the system, meanwhile, the parameter switching (the system parameters will switch with the state) is considered, which makes the presented model more comprehensive. By choosing an appropriate Lyapunov function, the driver and response systems achieve Mittag-Leffler synchronization under a suitable controller. In addition, based on the fractional calculus theorem and the basic inequality methods, a criterion of synchronization for the error system in finite-time interval is derived and the upper bound of the corresponding finite synchronization time can be obtained. Finally, two examples are provided, one is a numerical example to explain the effectiveness of the main results, and the other shows that the results of this paper can be applied to image encryption for any size with high-security coefficient.

Learning disentangled and interpretable representations is an important aspect of information understanding. In this paper, we propose a novel deep learning model representing both discrete and continuous latent variable spaces which can be used in either supervised or unsupervised learning. The proposed model is trained using an optimization function employing the mutual information maximization criterion. For the unsupervised learning setting we define a lower bound to the mutual information between the joint distribution of the latent variables corresponding to the real data and those generated by the model. The maximization of this lower bound during the training induces the learning of disentangled and interpretable data representations. Such representations can be used for attribute manipulation and image editing tasks.

This study hybridizes the Fuzzy Quality Function Deployment (FQFD) and Evidential Reasoning (ER) approaches to prioritize the Transportation Design (TDs) based on Transportation Requirements (TRs) in Road High-demand Public Transportation (RHDPT). This six-step approach identifies the TRs and their Trapezoidal Weights (TWs) using the FQFD approach and based on Trapezoidal Fuzzy Numbers (TFNs) assigned by decision-makers. The second step identifies the TDs based on TRs aided to the FQFD approach. In the third step, the relation matrix between TDs and TRs is configured. Then, the fuzzy interaction weights of TDs are calculated, where TFNs consider the fulfillment of each one of the TDs on TRs. The output of the mentioned step is the Interaction Trapezoidal Weights (ITWs) of the TDs. Finally, the fourth step calculates the TWs of TDs. At the fifth step, we configure the Belief Structure (BS) of ER in order to convert the TWs of TDs to BS of ER. In fact, this step converts the output of the FQFD approach to input on ER. Finally, the priority of TDs is calculated based on the upper and lower bounds of expected values of TDs. Moreover, the superiority of TDs over each other is calculated. This paper not only smooths the chaos and ambiguity aspects of decision makers’ opinions via an efficient Lemma, but it also considers the weights of decision-makers, along with trapezoidal fuzzy numbers. A case study is implemented for the prioritization of TDs in Arba'een event as an RHDPT. The case study results and sensitivity analyses showed the excellent performance of our hybrid FQFD-ER approach to prioritize the TDs in RHDPT.

Network slicing is expected to become an integral part of future 5G systems providing a simple mechanism for physical network operators to diversify their business models. New Radio (NR) technology operating in millimeter wave (mmWave) band is one of the critical bearers for this functionality, providing extraordinary capacity at the air interface. This paper provides a mathematical tool for assessing the upper and lower bounds of NR BS density needed to maintain the requested slice rate guarantees. The upper bound corresponds to the full traffic isolation between slices while the lower one — to the full mixing of traffic from the slices. To this aim, we unite the tools of stochastic geometry and queuing theory formulating a performance evaluation framework that allows assessing the rate violation metrics in a dynamic network slicing environment. The developed framework captures specifics of mmWave NR technology, including antenna directivity at the UE and NR BS sides, propagation and blockage losses, as well as the service process with location-dependent resource requirements. Our results show that for considered schemes, the operational regime of the system changes abruptly with respect to the density of NR BSs. The difference between full isolation and full mixing schemes becomes bigger in environments with high session arrival intensities that naturally require dense deployments. Thus, at the initial market penetration phase, full isolation can be used without compromising the network performance. However, at mature stages, more complex schemes are needed to reduce the capital expenditures of the operators. acceptedVersion

This paper addresses the challenge of synchronized multiple spacecraft attitude reorientation in presence of pointing and boundary constraints with limited inter-spacecraft communication link. Relative attitude pointing constraint among the fleet of spacecraft has also been modeled and considered during the attitude maneuvers toward the desired states. Formation fling control structure that consists of decentralized path planners based on virtual structure approach joint with discrete time optimal local controller is designed to achieve the mission’s goals. Due to digital computing of spacecraft’s onboard computer, local optimal controller based on discrete time prediction and correction algorithm has been utilized. The time step of local optimal algorithm execution is designed so that the spacecraft track their desired attitudes with appropriate error bound. The convergence of the proposed architecture and stability of local controller’s tracking error within appropriate upper bound are proved. Finally, a numerical simulation of a stereo imaging scenario is presented to verify the performance of the proposed architecture and the effectiveness of the algorithm.

The problem of attitude takeover control of spacecraft by using cellular satellites with limited communication, actuator faults and input saturation is investigated. In order to lighten the communication burden of cellular satellites, an event-triggered control strategy is adopted. The filtered attitude information needs to be transmitted only when the defined measurement error reaches the event-triggered threshold in this strategy. Then, to deal with the unknown inertia matrix, actuator faults, external disturbances and the errors caused by event-triggered scheme, fuzzy logic systems is introduced to estimate the uncertainties directly. Combining fuzzy logic control strategy and the event-triggered method, the first event-triggered adaptive fuzzy control law is developed. Then, torque saturation of cellular satellites is further considered in the second control law, where the upper bound of the uncertainties is estimated by fuzzy logic systems. The resulting closed-loop systems under the two control laws are guaranteed to be bounded. Finally, the effectiveness of two proposed control laws is verified by the numerical simulations.

The performance-based seismic design has two primary concerns: (a) appropriate quantification of the uncertainties associated with the performance evaluation process, and (b) satisfactory characterization of the associated structural damage for direct incorporation into the design or performance evaluation methodology. This study attempts to address these primary concerns by evaluating the performance of reinforced concrete frame using nonlinear static procedures. For this, fifteen-moment resisting frames designed following the guidelines of Indian seismic codes were subjected to different lateral load patterns. The seismic performance is investigated in terms of fundamental periods, roof displacements, interstory drift ratio, base shear, and modification factor and was compared with various performance limits. The obtained results showed disagreement with Indian seismic code provisions, especially, towards the fundamental time period, upper and lower bound values of base shear drift ratio and modification factor.

We consider the problem of topology recognition in wireless (radio) networks modeled as undirected graphs. Topology recognition is a fundamental task in which every node of the network has to output a map of the underlying graph i.e., an isomorphic copy of it, and situate itself in this map. In wireless networks, nodes communicate in synchronous rounds. In each round a node can either transmit a message to all its neighbors, or stay silent and listen. At the receiving end, a node v hears a message from a neighbor w in a given round, if v listens in this round, and if w is its only neighbor that transmits in this round. Nodes have labels which are (not necessarily different) binary strings. Each node knows its label and can use it when executing the algorithm. The length of a labeling scheme is the largest length of a label. We concentrate on wireless networks modeled by trees, and we investigate two problems. • What is the shortest labeling scheme that permits topology recognition in all wireless tree networks of diameter D and maximum degree Δ? • What is the fastest topology recognition algorithm working for all wireless tree networks of diameter D and maximum degree Δ, using such a short labeling scheme? We are interested in deterministic topology recognition algorithms. For the first problem, we show that the minimum length of a labeling scheme allowing topology recognition in all trees of maximum degree Δ ≥ 3 is Θ ( log ⁡ log ⁡ Δ ) . For such short labeling schemes, used by an algorithm working for the class of trees of diameter D ≥ 4 and maximum degree Δ ≥ 3 , we show almost matching bounds on the time of topology recognition: an upper bound O ( D Δ ) , and a lower bound Ω ( D Δ ϵ ) , for any constant ϵ 1 . Our upper bounds are proven by constructing a topology recognition algorithm using a labeling scheme of length O ( log ⁡ log ⁡ Δ ) and using time O ( D Δ ) . Our lower bounds are proven by constructing a class of trees for which any topology recognition algorithm must use a labeling scheme of length at least Ω ( log ⁡ log ⁡ Δ ) , and a class of trees for which any topology recognition algorithm using a labeling scheme of length O ( log ⁡ log ⁡ Δ ) must use time at least Ω ( D Δ ϵ ) , on some tree of this class.

Structural performance under seismic loads is necessarily checked after the static load is considered in the design of a structure. If it fails to pass the specified safety requirements, the structure has to be redesigned, resulting in additional time and computational cost. Therefore, in this study, we present four shape optimization problems to obtain rational shapes for free-form shell structures with both high static and dynamic performances. The strain energy under static loads is the measure of static performance, and the lower bound on the fundamental natural frequency or strain energy under seismic loads is the measure of dynamic performance. An optimization problem is first formulated for minimizing the strain energy under frequency constraint, where the self-weight and live loads are applied. The strain energy corresponding to the equivalent seismic static load is next minimized. To prevent unfavorable shape when the design variables are dense, we also consider the surface curvature as the other objective function in a bi-objective optimization problem. Several numerical examples are given to demonstrate the effectiveness of the proposed method.

This paper investigates the finite-time attitude tracking control problem for rigid spacecraft exposed to the external disturbance and unknown inertial parameters. Initially, the basic controller is developed under the assumption that the inertial parameters and the upper bound of external disturbances are available to the designer. However, this assumption is inapplicable in practical applications. Considering this limitation, adaptive laws and Minimum-learning- parameter (MLP) algorithm are employed in the second controller to estimate the unknown system dynamics. Finite-time stability for tracking errors is achievable under the proposed methods. Though theoretical analysis and numerical simulations, effectiveness of the proposed controllers has been validated.

Localization is of great importance in several fields such as healthcare and security. To achieve localization, GPS technologies are common for outdoor localization but are insufficient for indoor localization. This is because the accuracy and precision of the users’ indoor locations are influenced by many factors (e.g., multi-path signal propagations and signal fading). As a result, the methodologies and technologies for indoor localization services need continuous refinement. Another challenge is the time complexity of the methodologies which impacts the performance of mobile phones’ limited resources. To address these challenges, a new set of fingerprinting algorithms called Fingerprinting Line-Based Nearest Neighbour is designed and implemented. The new set shifts some points such grid points possibly towards target nodes based on a deterministic range of percentages. Furthermore, the new algorithms run in 0(j * d) (i.e., the term y refers to the number of grid points and the term d refers to the number of Received Signal Strength Indicators collected at the online stage). This paper presents the following: 1) the accuracy of the new set of algorithms has a theoretical upper bound in terms of the distance errors, 2) the new set of algorithms (e.g., 90% shifting percentage) improves the accuracy compared to those for several existing Nearest Neighbour (NN)-based algorithms such as the Dual-Scanned Fingerprint Localization and the Soft-Range-Limited KNN (SRL-KNN) by 106% and 68%, respectively, 3) the algorithm improves the precision compared to those for the NN-based algorithms such as the Path-Loss-Based Fingerprint Localization and the SRL-KNN 76% and 73%, respectively, and 4) the new set has lower probabilities related to the position errors compared to those for the existing NN-based algorithms. Therefore, the new set of algorithms is considered reliable and efficient for indoor location services.

Adaptive gradient learning methods such as Adam, RMSProp, and AdaGrad play an essential role in training a very deep neural network. The learning rates of these optimizers are adaptively changed to accelerate the training process. And the convergence speed is much faster than SGD in many deep learning tasks such as classification and NLP tasks. However, recent works have pointed out that adaptive learning methods can not converge to a critical point under some situations and suffer poor generalization in many deep learning tasks. In our study, we propose AdaDB, an adaptive learning optimizer with data-dependent bound on the learning rate. Every element in the learning rate vector is constrained between a dynamic upper bound and a constant lower bound. And the upper bound is dependent on the data. We also give a theoretical proof of the convergence of AdaDB in the non-convex setting. Our experiments show that AdaDB is capable of eliminating the generalization gap between Adam and SGD. Experiments also reveal the convergence speed of AdaDB is much faster than Adam.

The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane classifier by minimizing an upper bound on the Vapnik–Chervonenkis (VC) dimension. The VC dimension measures the capacity or model complexity of a learning machine. Vapnik’s risk formula indicates that models with smaller VC dimension are expected to show improved generalization. On many benchmark datasets, the MCM generalizes better than SVMs and uses far fewer support vectors than the number used by SVMs. In this paper, we describe a neural network that converges to the MCM solution. We employ the MCM neurodynamical system as the final layer of a neural network architecture. Our approach also optimizes the weights of all layers in order to minimize the objective, which is a combination of a bound on the VC dimension and the classification error. We illustrate the use of this model for robust binary and multi-class classification. Numerical experiments on benchmark datasets from the UCI repository show that the proposed approach is scalable and accurate, and learns models with improved accuracies and fewer support vectors.

In this paper, the credit scoring problem is studied by incorporating networked information, where the advantages of such incorporation are investigated theoretically in two scenarios. Firstly, a Bayesian optimal filter is proposed to provide risk prediction for lenders assuming that published credit scores are estimated merely from structured financial data. Such prediction can then be used as a monitoring indicator for the risk management in lenders' future decisions. Secondly, a recursive Bayes estimator is further proposed to improve the precision of credit scoring by incorporating the dynamic interaction topology of clients. It is shown that under the proposed evolution framework, the designed estimator has a higher precision than any efficient estimator, and the mean square errors are strictly smaller than the Cram\'er-Rao lower bound for clients within a certain range of scores. Finally, simulation results for a special case illustrate the feasibility and effectiveness of the proposed algorithms., Comment: 19 pages, simulations and discussion added, motivation clarified, references updated

The evaluation of structural collapse capacity is an integral part of the estimation of collapse risk in performance based earthquake engineering. Several methods and procedures have been proposed in the past to quantify collapse capacity. However, there is no clear consensus on which method is most appropriate. Recently physics-based collapse criteria have been proposed, but their effectiveness is yet to be compared with codes of practice. In order to ensure safety and consistency, different code/standard based recommendations enforce the use of thresholds predominantly in terms of the engineering demand parameter (EDP) for the performance level of collapse prevention. These thresholds serve as limit-state criteria to evaluate the collapse capacity of a structure. This paper compares several performance measures that are derived using different criteria to understand their impact on the final collapse risk estimates. Four different criteria are studied, two of which are based on standards, and the other two are physics-based, which use energy formulations. Three different ASCE 7-16 code conforming RC buildings are designed and analysed. The effects of modelling uncertainties and of the ground motion spectral shape have been considered to impart more confidence in the results. The collapse risk estimates are interpreted in terms of different collapse performance measures. The estimates derived from all the four criteria are compared. It was found that the collapse risk is significantly affected by the choice of the criterion. For the same collapse risk, the physics-based criteria allow higher ultimate deformation at collapse. On the other hand, the code/standard based criteria tend to be conservative as they censor the deformation response of the structure using upper bound thresholds. It was also found that the physics-based criteria could underestimate the collapse risk, yet they can still be employed to produce more economical designs.

In this paper we put forward a method to construct balanced Boolean functions by modifying the Tang–Carlet–Tang functions with the help of functions in Generalized Maiorana–McFarland class. Essential cryptographic properties, for instance, nonlinearity, algebraic immunity and immunity to fast algebraic attacks are considered. A new lower bound on the nonlinearity of the functions that are contained in our construction is obtained. It is slightly better than the known result. Owing to a combinatorial fact, it can be demonstrated that the proposed functions have optimal algebraic immunity. Meanwhile, they also possess high nonlinearity and a moderate behavior against fast algebraic attacks. Apart from theoretical contributions, hardware implementation efficiency on the proposed functions is also analyzed. Since the participation of GMM class component, experimental results imply that functions constructed in this paper have a small implementation size and employ fewer logic gates compared with the construction containing an iterative structure.

In this study, the integrated fault estimation (FE) and fault tolerant control (FTC) design problem is investigated for the attitude system of a rigid spacecraft with inertia uncertainty, Lipschitz nonlinearity, external disturbance and multiple type actuator faults. An adaptive sliding mode observer design approach, which does not require the known upper bound of generalized perturbation, is proposed to obtain the estimation of unknown actuator faults. On this basis, a fault tolerant attitude tracking control scheme is developed for the faulty rigid spacecraft attitude system by combining the fractional order sliding mode control and backstepping control with adaptive fuzzy approximation technique. The stability of faulty closed loop attitude system under the designed FTC strategy is analyzed using Lyapunov approach. Finally, simulation results are given to show the superiority of the proposed integrated FE and FTC compared with some existing results.

The container relocation problem is one of important issues in seaport terminals which could bring a significant saving on the operating cost even with a slight improvement due to the huge number of containers processed across the world each year. Given a specific layout and container retrieval priorities, the container relocation problem aims to find the optimal movement sequence to minimize the total number of container relocation operations. In this paper, we propose novel machine learning-driven algorithms, which integrate optimization methods and machine learning techniques, to solve the problem. More specifically, we propose a new upper bound method called MLUB that incorporates branch pruners. These pruners are derived from some machine learning techniques through using the optimal solution values of many small-scale instances. The tightened upper bounds generated by MLUB are used subsequently both in the exact branch-and-bound algorithm called IB&B and the hybrid beam search heuristic called MLBS. Moreover, we also provide a tighter lower bound for the problem by additionally considering the interaction between consecutive target containers. Based on the benchmark data published recently in the literature, extensive experiments are conducted to test the performance of the proposed algorithms. The experimental results demonstrate that the proposed algorithms outperform the state-of-the-art algorithms reported in the literature, and some managerial insights regarding the load intensity of the bay and some algorithm parameters such as the look-ahead depth and the beam width are drawn from the results.

This study proposes an analytical method for the calculation of the safety factor against lateral spreading in embankments, both for cases with and without basal reinforcement. Hence, a new limit equilibrium approach is presented based on the lower bound plasticity theorem, which allows the computation of the required distribution of stresses along presumed slip surfaces at the limit state. Comparing the required distribution of stresses with the available resistances, the safety factor in the absence of basal reinforcement is calculated. If the safety factor is unsatisfactory, basal reinforcement can be introduced and its contribution to stability quantified. The lower bound approach allows the computation of mobilized resistance along the reinforcement. Accordingly, the proposed method allows the calculation of the safety factor against lateral spreading for problems involving basal reinforcements. Finally, the proposed method is compared with well-established methods in literature. Results suggest that the proposed method is reliable, practical, and applicable to safe and economical designs.

Given a graph G = ( V , E ) and an originator vertex v , broadcasting is an information disseminating process of transmitting a message from the vertex v to all vertices of the graph G as quickly as possible. A graph G on n vertices is called broadcast graph if the broadcasting from any vertex in the graph can be accomplished in ⌈ log n ⌉ time. A broadcast graph with the minimum number of edges is called minimum broadcast graph. The number of edges in a minimum broadcast graph on n vertices is denoted by B ( n ) . A long sequence of papers present different broadcast graph constructions and upper bounds on B ( n ) . In this paper, we improve the compounding method and construct new broadcast graphs with a better upper bound on B ( n ) . Consequently, we show that B ( n ) ≤ ( m − k + 1 ) n − ( 2 m − k + 1 − 2 ) ( m − 2 q + 1 ) 2 q − 1 for n ∈ [ 2 m − 1 + 1 , 2 m − 2 m 2 + 1 ] , where n = 2 m − 2 k − d , m ≥ 5 , 2 ≤ k ≤ m − 2 , 0 ≤ d ≤ 2 k − 1 , and q = m i n ( ⌊ m − 2 2 ⌋ , k − 2 ) .

We study the online problem of evacuating k robots on m concurrent rays to a single unknown exit. All k robots start on the same point s, not necessarily on the junction j of the m rays, move at unit speed, and can communicate wirelessly. The goal is to minimize the competitive ratio, i.e., the ratio between the time it takes to evacuate all robots to the exit and the time it would take if the location of the exit was known in advance, in the worst-case instance. When k = m , we show that a simple waiting strategy yields a competitive ratio of 4 and present a lower bound of 2 + 7 / 3 ≈ 3.52753 for all k = m ≥ 3 . For k = 3 robots on m = 3 rays, we give a class of parametrized algorithms with a nearly matching competitive ratio of 2 + 3 ≈ 3.73205 . We also present an algorithm for 1 k m , achieving a competitive ratio of 1 + 2 ⋅ m − 1 k ⋅ ( 1 + k m − 1 ) 1 + m − 1 k , obtained by parameter optimization on a geometric search strategy. Interestingly, the robots can be initially oblivious to the value of m > 2 . Lastly, by using a simple but fundamental argument, we show that for k m robots, no algorithm can reach a competitive ratio better than 3 + 2 ⌊ ( m − 1 ) / k ⌋ , for every k , m with k m .

We research the probability change of successful double spend attack on Proof-of-Work consensus protocol depending on network parameters in the model with continuous time. We analyze influence of block generation rate in networks with delayed message delivery on probability of double spend attack success, and provide strict analytical expressions for network security threshold and for the upper bound of block generation rate.

We study the single-message broadcast problem in dynamic radio networks. We show that the time complexity of the problem depends on the amount of stability and connectivity of the dynamic network topology and on the adaptiveness of the adversary providing the dynamic topology. More formally, we model communication using the standard graph-based radio network model. To model the dynamic network, we use a generalization of the synchronous dynamic graph model introduced in [Kuhn et al., STOC 2010]. For integer parameters $T\geq 1$ and $k\geq 1$, we call a dynamic graph $T$-interval $k$-connected if for every interval of $T$ consecutive rounds, there exists a $k$-vertex-connected stable subgraph. Further, for an integer parameter $\tau\geq 0$, we say that the adversary providing the dynamic network is $\tau$-oblivious if for constructing the graph of some round $t$, the adversary has access to all the randomness (and states) of the algorithm up to round $t-\tau$. As our main result, we show that for any $T\geq 1$, any $k\geq 1$, and any $\tau\geq 1$, for a $\tau$-oblivious adversary, there is a distributed algorithm to broadcast a single message in time $O\big(\big(1+\frac{n}{k\cdot\min\left\{\tau,T\right\}}\big)\cdot n\log^3 n\big)$. We further show that even for large interval $k$-connectivity, efficient broadcast is not possible for the usual adaptive adversaries. For a $1$-oblivious adversary, we show that even for any $T\leq (n/k)^{1-\varepsilon}$ (for any constant $\varepsilon>0$) and for any $k\geq 1$, global broadcast in $T$-interval $k$-connected networks requires at least $\Omega(n^2/(k^2\log n))$ time. Further, for a $0$ oblivious adversary, broadcast cannot be solved in $T$-interval $k$-connected networks as long as $T, Comment: 17 pages, conference version appeared in OPODIS 2015

We study the allocation problem of a compound request for a service chain in a software defined network that supports network function virtualization. Given a network that contains servers with limited processing power and links with limited bandwidth, a service chain is a sequence of virtual network functions (VNFs) that serve a certain flow request in the network. The allocation of a service chain consists of routing and VNF placement, namely each VNF from the sequence is placed in a server along a path. It is feasible if each server can handle the VNFs that are assigned to it, and if each link on the path can carry the flow that is assigned to it. A request for service is composed of a source and a destination in the network, an upper bound on the total latency, and a specification in the form of a directed acyclic graph (DAG) of VNFs that provides all service chains that are considered valid for this request. In addition, each pair of server and VNF is associated with a cost for placing the VNF in the server. Given a request, the goal is to find a valid service chain of minimum total cost that respects the latency constraint or to identify that such a service chain does not exist. We show that even the feasibility problem is NP-hard in general graphs. Hence we focus on DAGs. We show that the problem is still NP-hard in DAGs even for a very simple network, and even if the VNF specification consists of only one option (i.e., the virtual DAG is a path). On the other hand, we present an FPTAS for the case where the network is a DAG. In addition, based on our FPTAS, we provide algorithms for general instances in which the service chain passes through a bounded number of vertices whose degree is larger than two. Finally, we provide an FPTAS for a fault-tolerant version of the problem.

In this study, the time dependent free vibration analysis of composite concrete-filled steel tubular (CFST) arches with various uncertainties is thoroughly investigated within a non-stochastic framework. From the practical inspiration, both uncertain material properties and mercurial creep effect associated with such composite materials are simultaneously incorporated. Unlike traditional non-probabilistic schemes, both spatially independent (i.e., conventional interval models) and dependent (i.e., interval fields) interval system parameters can be comprised within a unified uncertain free vibration analysis framework for CFST arches. For the purpose of achieving a robust framework of the time-dependent uncertain free vibration analysis, a new computational approach, which has been developed within the scheme of the finite element method (FEM), has been proposed for determining the extreme bounds of the natural frequencies of practically motivated CFST arches. Consequently, by successfully solving two eigenvalue problems, the upper and lower bounds of the natural frequencies of such composite structures with various uncertainties can be rigorously secured. The unique advantage of the proposed approach is that it can be effectively integrated within commercial FEM software with preserved sharp bounds on natural frequencies for any interval field discretisation. The competence of the proposed computational analysis framework has been thoroughly demonstrated through investigations on both 2D and3D engineering structures.

In this study, the relative forging pressures of spur gears were evaluated. The precision forging of spur gears was analyzed by using the upper bound method considering corner filling and bulging effect. Numerical and experimental studies were performed to investigate the effects of various parameters, such as the number of teeth, modules, facewidth, bore diameter, and friction factor on the relative forging pressure of spur gears. The results were compared with the previous studies and a simplified formula was suggested to predict the relative pressure of precision forging of spur gears. The predicted relative forging pressures obtained by the suggested formula are shown much closer to the experimental results for the complete filling of the die cavity.

With the integration of more and more renewable energy in the power system, more reserves are needed to cope with the intermittent and stochastic characteristics of the renewable energy. To relieve the stress of the reserve supply by the generation side, air conditioning loads can be considered to participate in reserve allocation in dynamic economic dispatch owing to their high proportion in the demand and thermal energy storage characteristics. The lock time of air conditioners should not be ignored since it can avoid the short cycling of compressor. This paper proposes a hierarchical reserve allocation model with air conditioning loads considering lock time to coordinate the power system dispatch and the control of air conditioning loads. An aggregate model of air conditioning loads considering the lock time is proposed and linear constraints of the air conditioning loads are developed. Among these constraints, lockout constraints are linear slack constraints for convenient in computation and the validity of the slack form has been proved. These constraints are integrated into the reserve allocation in dynamic economic dispatch. In such a state, the problem has multi-time scale characteristics and thus, is difficult to solve; therefore, this problem is reconstructed as a three-level hierarchical problem, and Benders decomposition is applied. The first level (operation level) represents the operation of the power system in normal conditions. Each N-1 contingency is placed in the second level (contingency level). The third level (load control level) considers the constraints of the air conditioning loads. To increase the calculation efficiency, we propose three methods, which are adding necessary conditions, adjusting upper bound and applying parallel computing. The simulation results show that air conditioning loads can realize load reserve, and the proposed hierarchical solving method is effective to solve the reserve allocation problem with air conditioning loads considering the lock time.

Cross-laminated timber (CLT) is a popular construction material that is widely used in timber buildings. In this study, the mechanical properties of three types of CLT connections were investigated: (i) an angle bracket joint; (ii) a crossed self-tapping screw joint; and (iii) a simple butt joint with self-tapping screws. Three repetition tests for each type of CLT connection were conducted under two different cyclic-loading protocols. The experimental results showed that pull-out failure in the tension direction was dominant, whereas wood crushing and screw failure were typical failure modes in the shear direction. An empirical model was used to simulate the nonlinear behavior of the CLT connections under cyclic loading, where the pinching, cyclic stiffness, and strength degradation were considered. To estimate the model parameters associated with the uncertainty of these mechanical properties, the Bayesian approach was proposed, and the Cramer–Rao lower bound was derived as the theoretical lower bound. The parameter-identification results were evaluated according to the elastic stiffness, yielding point, and ductility via equivalent energy elastic–plastic analysis. Comparison of these mechanical properties between the test results and model estimates revealed good agreement. Additionally, the bounds of the estimated response were investigated according to the parameter variance. In comparison with the experimental data, the error bars covered two standard deviations, indicating the reliability of the estimated results. The values of the identified parameters associated with the standard deviation provide a good reference for further analysis when such types of CLT connections are incorporated into a finite-element model of a structural system. The proposed method of Bayesian parameter identification is also suitable for assessing other types of empirical models.

Current design and assessment guidelines define several seismic performance levels, aiming to ensure that structures exhibit adequate behaviour at different seismic intensity levels. Typically, the acceptance criteria at different performance levels are met by ensuring that local deformation demands are lower than pre-defined capacities. Despite the proven effectiveness of such an approach, it provides an ambiguous measure of the performance of the building, which, in most cases, is neither meaningful nor appropriate for building owners, stakeholders or decision-makers. The main objective of the research presented in this paper is to evaluate the expected direct economic seismic losses of steel moment-resisting frame structures designed according to Eurocode 8 (EC8). A set of 120 archetype buildings, representative of the current building stock in Portugal, were designed according to Part 1 of EC8 using three different behaviour factors, q: a) code-prescribed upper bound limits for medium and high ductility classes; b) behaviour factor defined according to an Improved Force-Based Design (IFBD) procedure. The PEER-PBEE methodology with the improvements proposed by Ramirez and Miranda [1] was employed for the estimation of expected seismic losses evaluated for the seismic intensity levels considered in Part 3 of EC8. The results obtained indicate that the buildings designed in accordance with EC8 comply with the non-collapse criteria. However, the level of damage could imply significant repair costs. Importantly, the results also highlight that a rational selection of the behaviour factor can result in a reduction of steel weight but still ensuring acceptable levels of expected annual losses.

Low-rank or sparse tensor recovery finds many applications in computer vision and machine learning. The recently proposed regularized multilinear regression and selection (Remurs) model assumes the true tensor to be simultaneously low-Tucker-rank and sparse, and has been successfully applied in fMRI analysis. However, the statistical performance of Remurs-like models is still lacking. To address this problem, a minimization problem based on a newly defined tensor nuclear-l1-norm is proposed, to recover a simultaneously low-Tucker-rank and sparse tensor from its degraded observations. Then, an M-ADMM-based algorithm is developed to efficiently solve the problem. Further, the statistical performance is analyzed by establishing a deterministic upper bound on the estimation error for general noise. Also, under Gaussian noise, non-asymptotic upper bounds for two specific settings, i.e., noisy tensor decomposition and random Gaussian design, are given. Experiments on synthetic datasets demonstrate that the proposed theorems can precisely predict the scaling behavior of the estimation error.

In this paper, a new nonlinear static analysis procedure is proposed to assess the seismic performance of tall buildings, which considers both the higher modes effects as well as the progressive changes in the dynamic characteristics of structures during the inelastic response. In this method, the applied load pattern computed using the upper-bound (UB) method is updated at each analysis step considering only the first two modes of vibration and corrected by combining this applied load pattern with a uniform load distribution. The accuracy of the proposed adaptive upper-bound (AUB) procedure is tested using four steel moment resisting frame buildings. The results from the (AUB) are compared to those of the (UB) pushover analysis, improved upper-bound (IUB) method, modal pushover analysis (MPA), displacement-based adaptive pushover (DAP) procedure, and the nonlinear time history analysis (NTHA). In most cases, the proposed procedure leads to more adequate results and closer to those obtained by NTHA.

In this study, we present two new mathematical models for mixed-model assembly line balancing. The line is semi-automated and characterized by various alternative formats for the operations performed, which range from human operators and assisting robots to robotics solutions, where the operators may have different skill levels and operators on adjacent stations may cooperate. Given the cycle time, the first model minimizes the fixed and variable costs associated with setting up the assembly line. However, the second mathematical model involves a cycle time, which is variable and it changes between a lower bound and an upper bound. These bounds reflect the expected future changes in demand. The objective is to minimize both the costs and the cycle time. To obtain more accurate and reliable results, we present a robust solution for the different confidence levels (α) determined by managers. To verify the mathematical models, we considered different test problems and compared the robust solutions under three different conditions. The results showed that the robust modeling approach obtains a more reliable design than the traditional cost trade-off alone. The model allows decision makers to select assembly operations based on a better understanding of their decision impacts in both the short term and long term under conditions of demand uncertainty.

A finite element model is described for the eaves joint of a cold-formed steel portal frame that comprises a single channel-section and bracket connected through both screws and bolts. Such a joint detail is commonly used in practice in New Zealand and Australia; as the bolt-holes are detailed for nominal clearance the intended purpose of the screws is to prevent slip of the joint during frame erection. The results of the finite element model are compared against experimental test results. In the experimental tests, the screws failed in shear followed by twisting of the joint bracket. It is shown that at first screw failure, the bolts had not engaged i.e. they still had not fully slipped. It is also shown that the gradient of load-displacement curve can be accurately predicted from the equations of screw stiffness of Zaharia and Dubina (2006). The finite element model is used to determine upper and lower bounds to the experimental failure load, and thus potentially can be used to assist designers when designing and detailing such eaves joints in practice.

Indoor Visible light Communication (VLC) systems use Light Emitting Diode (LED) luminaries for data transmission besides their main task as illumination devices. One of the challenges of VLC is minimizing the LED driving power without deteriorating the user experience. In this paper, we consider an indoor VLC network consisting of a number of LED luminaries serving a number of users. We compare the minimum LED driving power required to guarantee certain Quality of Service (QoS), represented by the minimum rate required per user, when using Non Orthogonal and Orthogonal Multiple Access ((N)OMA) techniques. We show that NOMA requires less driving power than OMA in order to satisfy the same QoS for the same number of users. Therefore, NOMA is more power efficient and gives the users more freedom to dim the light to their taste or comfort without deteriorating their QoS. We statistically characterize the NOMA minimum driving power when only the number of users and the number of LEDs are known (without knowledge of their exact locations). This is achieved by deriving the Probability Density Functions (PDF) for its upper and lower bounds. The PDF is the most comprehensive statistical characterization. It helps estimating the probability a room with a certain number of LEDs satisfies a pre-specified minimum rate for a certain number of users without causing power damage to the LEDs. It also helps estimating the average and the variance of the driving power which reflect on the illumination level.

This paper presents a novel and straightforward procedure for the derivation of homogenized failure surfaces for non-periodic masonry. The most innovative feature of this procedure is the automatic generation of a convenient finite element mesh directly from the sketch of the considered masonry panel, based on the so-called “pixel strategy” that converts each pixel into an element. An upper bound limit analysis problem coupled with homogenization is then solved by aptly formulating it as a linear programming problem. Another main feature is the implementation of a reduced formulation of such problem (called “master-slave approach”) so that the number of unknown variables is reduced and, consequently, the computational times needed for the extraction of the homogenized failure surfaces are shortened as well. A simple procedure is also implemented for a quick identification of the statistical Representative Element of Volume (or REV) for a non-periodic masonry panel. The REV is the smallest portion of a composite material that includes all the physical and geometrical characteristics needed for its complete description. The reliability of the procedure is tested by investigating six case studies displaying different degrees of non-periodicity, extracting and critically commenting the results obtained in terms of homogenized failure surfaces and failure modes.

The vehicle-routing problem with private fleet and common carrier (VRPPC) extends the capacitated VRP by considering the option of outsourcing customers to subcontractors at a customer-dependent cost instead of serving them with the private fleet. The VRPPC has important applications in small package shipping and manufacturing, but despite its relevance, no exact solution approach has been introduced so far. We propose a branch-price-and-cut algorithm that is able to solve small to medium-sized instances and provides tight lower bounds for larger instances from the literature. In addition, we develop a large neighborhood search that shows a decent solution quality and competitive run-times compared to the state-of-the-art on instances with homogeneous as well as heterogeneous vehicle fleet. In addition, several new best known solutions are found.

For cylindrical shells under axial compression, the essence of initial geometric imperfections is the superposition of local out-of-plane deformations of various forms, which may facilitate the development of buckling deformations, thus leading to a significant knockdown of the load-carrying capacity. It is very challenging for existing methods to provide an accurate prediction of the lower bound on a load-carrying capacity before the structure is fabricated. Therefore, it is crucial to find a type of assumed imperfection that will allow us to approximate lower bounds for shells in the design stage. Five 1-m-diameter unstiffened shells, termed W1-W5, are designed, analysed and tested. The measured imperfection approach, single-perturbation load approach (SPLA), worst multiple-perturbation load approach (WMPLA), and a Combined Approach for measured imperfections and superimposed radial point load imperfections are compared with test results. The results show that the SPLA-based methods produce higher KDFs than the test results and are sensitive to the distribution of the measured imperfections. In contrast, the KDFs predicted by the WMPLA and the Combined Approach are similar to one another and very close to the test results. From the comparison results, it can preliminarily be concluded that the WMPLA is able to envelop the small- and large-amplitude measured imperfections, which has the potential to predict a rational lower bound on the buckling loads of unstiffened cylindrical shells. The WMPLA should be extended to the design of other types of thin-walled structures with caution, because the manufacturing signature may be distinctly changed for different processes, and the buckling tests of other types of structures would be carried out in future study.

In this paper a numerical procedure for computing the full range of equilibrium thrust lines in masonry arches is presented. According to Heyman, the range can be obtained by shifting the thrust line upwards and downwards until it touches the upper and lower boundary of the arch. This thrust line is computed in such a way as to be the closest one to the geometrical axis. The two limit thrust lines, corresponding to the upper and lower bound of the range, are finally used to compute the degree of safety of an arch through the identification of a domain of equilibrium states and the Heymanian geometrical factor of safety, revisited in the form of a performance factor, that is more practical and immediate to understand.

This paper studies the prize-collecting vehicle routing problem (PCVRP), which is a new variant of the vehicle routing problem. In the PCVRP, besides the vehicle assignment and visiting sequencing, customer selection also must be determined because the available vehicles are insufficient to visit all the customers. Two types of PCVRP are often encountered in the real world, namely the PCVRP in which the number of vehicles is predetermined (PCVRP-P) and that in which the number of vehicles is not predetermined (PCVRP-NP). In general, multiple optimization objectives are required to be considered in both the PCVRP-P and PCVRP-NP. Unlike the commonly used weighted-sum method, a Pareto-based evolutionary algorithm featuring a genetic algorithm combined with a local search strategy (denoted as HGLS) is proposed for solving the multi-objective PCVRP-P. Subsequently, based on the proposed HGLS, a decomposition strategy is designed to solve the multi-objective PCVRP-NP problem by decomposing it into multiple PCVRP-P problems. In order to determine the number of PCVRP-P problems in the decomposition strategy, a vehicle-number-range determination approach is proposed to obtain a tight upper bound and lower bound of the number of vehicles. The superiority of the proposed algorithm is demonstrated by conducting several experiments on the benchmark problems.

This study deals with the bus & driver scheduling problems with mealtime windows for a single public transport bus route. Firstly, we develop three explicit Integer Linear Programming (ILP) models to formulate: the bus driver scheduling, bus & driver scheduling, and bus & driver scheduling with mealtime windows, respectively. These ILP models enable bus operators to solve their bus & driver scheduling problems by directly invoking an available optimization solver such as IBM ILOG CPLEX. However, our numerical experiments do not show remarkable improvements, as CPLEX takes more than 12, 17, and 35 h respectively to reduce the optimality gap to at most 0.5% for solving the above three models in an over 12-hour bus & driver scheduling problem. To improve the computational efficiency, we develop a valid inequality approach that can generate valid cuts incorporating with the CPLEX. An investigation into the valid inequality approach proves its high computational efficiency. In addition, we develop a self-adaptive search method for determining the upper and lower bounds of driver group and bus fleet sizes. Finally, we test our models and approaches on a real round-trip bus route. The results indicate that compared with the only use of CPLEX to solve the above three models, the valid inequality approach can address large-scale instances in a reasonable CPU time.

For cylindrical shell structures under axial compression, it is crucial to provide high-fidelity knockdown factors (KDFs) in preliminary design for aerospace and civil structures. In this paper, numerical studies of buckling response for composite cylindrical shell with geometric imperfections and embedded delamination imperfections are performed to predict the lower-bound buckling loads. Results indicate that composite shells with single dimple-shape geometric imperfection exhibit similar lower bound trend and buckling behavior as those with embedded delamination imperfection. It is found that the lower-bound buckling loads are much less conservative than the corresponding design recommendation from NASA SP-8007. And the effect of geometric imperfections can envelope that of delamination imperfections. Therefore, the worst multiple perturbation load approach (WMPLA) is performed to find the worst combination of dimple-shape geometric imperfections to predict the lower-bound buckling load, and the efficient global optimization (EGO) is employed to improve the computational efficiency of WMPLA. It is demonstrated that the improved WMPLA can provide an improved KDF by examples in open literature. Based on the improved KDFs, it is possible to increase the load-bearing efficiency of composite structures in practical engineering.

Surpassing the Shockley–Queisser limit, which places an upper bound on solar conversion efficiency for a single p–n junction solar cell at slightly more than 30%, is one of the grand challenges in the field of solar-energy research. Singlet fission (SF) in molecular acenes, the molecular analog of multiple exciton generation (MEG), has the potential to initiate a game-changing advance in solar-energy conversion. SF allows singlet-excited states (S1) to be down-converted into twice as many triplet-excited states (T1).