Search Results (13)

Kidney-paired donation programs assist patients in need of a kidney to swap their incompatible donor with another incompatible patient–donor pair for a suitable kidney in return. The kidney exchange problem (KEP) is a mathematical optimization problem that consists of finding the maximum set of matches in a directed graph representing the pool of incompatible pairs. Depending on the specific framework, these matches can come in the form of (bounded) directed cycles or directed paths. This gives rise to a family of KEP models that have been studied over the past few years. Several of these models require an exponential number of constraints to eliminate cycles and chains that exceed a given length. In this paper, we present enhancements to a subset of existing models that exploit the connectivity properties of the underlying graphs, thereby rendering more compact and tractable models in both cycle-only and cycle-and-chain versions. In addition, an efficient algorithm is developed for detecting violated constraints and solving the problem. To assess the value of our enhanced models and algorithm, an extensive computational study was carried out comparing with existing formulations. The results demonstrated the effectiveness of the proposed approach. For example, among the main findings for edge-based cycle-only models, the proposed (*PRE(i)) model uses a new set of constraints and a small subset of the full set of length-k paths that are included in the edge formulation. The proposed model was observed to achieve a more than 98% reduction in the number of such paths among all tested instances. With respect to cycle-and-chain formulations, the proposed (*ReSPLIT) model outperformed Anderson’s arc-based (AA) formulation and the path constrained-TSP formulation on all instances that we tested. In particular, when tested on a difficult sets of instances from the literature, the proposed (*ReSPLIT) model provided the best results compared to the AA and PC-based models. Full article

Extended connectivity in graphs can be analyzed through k-path Laplacian matrices, which permit the capture of long-range interactions in various real-world networked systems such as social, transportation, and multi-agent networks. In this work, we present several alternative methods based on machine learning methods (LSTM, xLSTM, Transformer, XGBoost, and ConvLSTM) to predict the final consensus value based on directed networks (Erdös–Renyi, Watts–Strogatz, and Barabási–Albert) and on the initial state. We highlight how different k-hop interactions affect the performance of the tested methods. This framework opens new avenues for analyzing multi-scale diffusion processes in large-scale, complex networks. Full article

As shallow coal resources in China become increasingly depleted, deep coal mining in complex geological areas has become an inevitable trend. However, the technical challenges associated with deep mining are becoming more significant, particularly the issues related to mine water hazards. This study utilized hydrogeological data from the III3 Mining Area in the Zhuxianzhuang Coal Mine, Anhui Province, and employed GMS (Groundwater Modeling System) software to construct a numerical karst water flow model under deep mining conditions. By simulating variations in the flow field, the study verified the drainage potential of the limestone water at the base of Seam 10 and assessed the water conductivity and connectivity of the F22 fault. The following conclusions were obtained: The simulation effectively captured the formation process of the karst water drawdown cone in the study area. The observed water level variations in different monitoring wells aligned well with the engineering reality after validation. The limestone water at the base of Seam 10 in the III3 Mining Area exhibited good transmissivity, weak recharge, and high drainage potential. Although the F22 fault is a normal fault with a maximum displacement of 550 m, offsetting formations from Seam 3 to the Ordovician limestone, its connectivity and water conductivity are poor, exhibiting significant water-blocking properties. The specific capacity (q) ranges from 1.40 × 10⁻⁴ to 3.26 × 10⁻³ m³/(s·m), and the hydraulic conductivity (K) ranges from 2.10 × 10⁻⁵ to 6.80 × 10⁻⁵. Under deep coal mining conditions, the extraction of coal disturbs the underlying limestone, generally resulting in an increase in its permeability coefficient compared to pre-mining conditions. The permeability coefficient (K) from the measured data before mining impact ranged from 0.000067 to 0.0022, while the simulated values after mining impact ranged from 0.0021 to 0.09. Additionally, mining activities affect the hydraulic head, flow rate, and flow paths of the karst water; the floor karst water is easily drainable, effectively reducing water pressure and the inrush coefficient, thus lowering water hazard risks. Although the mining area is affected by the large F22 fault, its water-resisting properties under sufficient drainage conditions prevent direct connectivity between the coal seam and the aquifer, avoiding water hazards. As global coal resources continue to be exploited, deep mining will inevitably become a common trend in coal extraction worldwide. This study develops a hydrogeological model tailored to deep mining under fault conditions, offering a solid theoretical foundation and practical reference for the prevention and management of mine water hazards on a global scale. This advancement contributes to the development of sustainable mining practices across the global industry. Full article

This paper proposes mathematical optimization models for solving the network planning problem using millimeter wave technology for 5G wireless communications networks. To this end, it is assumed that a set of users, $M=\{1,\dots ,m\}$, and a set of base stations, $N=\{1,\dots ,n\}$, are deployed randomly in a square area. In particular, the base stations should be connected, forming a star backbone so that users can connect to their nearest active base stations forming the backbone where the connections are symmetric. In particular, the first two models maximize the number of users connected to the backbone and minimize the distance costs of connecting users to the base stations, and distances of connecting the base stations themselves. Similarly, the last two models maximize and minimize the same objectives and the number of base stations to be activated to form the star backbone. Each user is allowed to connect to a unique active base station. In general, the millimeter wave technology presents a high path loss. Consequently, the transmission distances should be no larger than 300 m at most for different radial transmissions. Thus, a direct line of sight between users and base stations is assumed. Finally, we propose local search-based algorithms that allow finding near-optimal solutions for all our tested instances. Our numerical results indicate that we can solve network instances optimally with up to $k=100$, $n=200$, and $m=5000$ users. Full article

The current developments in the theory of quantum static triplet correlations and their associated structures (real r-space and Fourier k-space) in monatomic fluids are reviewed. The main framework utilized is Feynman’s path integral formalism (PI), and the issues addressed cover quantum diffraction effects and zero-spin bosonic exchange. The structures are associated with the external weak fields that reveal their nature, and due attention is paid to the underlying pair-level structures. Without the pair, level one cannot fully grasp the triplet extensions in the hierarchical ladder of structures, as both the pair and the triplet structures are essential ingredients in the triplet response functions. Three general classes of PI structures do arise: centroid, total continuous linear response, and instantaneous. Use of functional differentiation techniques is widely made, and, as a bonus, this leads to the identification of an exact extension of the “classical isomorphism” when the centroid structures are considered. In this connection, the direct correlation functions, as borrowed from classical statistical mechanics, play a key role (either exact or approximate) in the corresponding quantum applications. Additionally, as an auxiliary framework, the traditional closure schemes for triplets are also discussed, owing to their potential usefulness for rationalizing PI triplet results. To illustrate some basic concepts, new numerical calculations (path integral Monte Carlo PIMC and closures) are reported. They are focused on the purely diffraction regime and deal with supercritical helium-3 and the quantum hard-sphere fluid. Full article

Let $D=\left(V\right(D),A(D\left)\right)$ be a digraph of order n and let $r\in S\subseteq V\left(D\right)$ with $2\le \left|S\right|\le n$. A directed $(S,r)$-Steiner path (or an $(S,r)$-path for short) is a directed path P beginning at r such that $S\subseteq V\left(P\right)$. Arc-disjoint between two $(S,r)$-paths is characterized by the absence of common arcs. Let ${\lambda }_{S,r}^p\left(D\right)$ be the maximum number of arc-disjoint $(S,r)$-paths in D. The directed path k-arc-connectivity of D is defined as ${\lambda }_k^p\left(D\right)=min\{{\lambda }_{S,r}^p\left(D\right)\mid S\subseteq V\left(D\right),\left|S\right|=k,r\in S\}.$ In this paper, we shall investigate the directed path 3-arc-connectivity of Cartesian product ${\lambda }_3^p(G\square H)$ and prove that if G and H are two digraphs such that ${\delta }^0\left(G\right)\ge 4$, ${\delta }^0\left(H\right)\ge 4$, and $\kappa \left(G\right)\ge 2$, $\kappa \left(H\right)\ge 2$, then ${\lambda }_3^p(G\square H)\ge min\left\{2\kappa \left(G\right),2\kappa \left(H\right)\right\};$ moreover, this bound is sharp. We also obtain exact values for ${\lambda }_3^p(G\square H)$ for some digraph classes G and H, and most of these digraphs are symmetric. Full article

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber–Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks. Full article

Numerous Global Positioning System connected vehicles are collecting extensive data remotely in cities, enabling data-driven infrastructure planning. To truly benefit from this emerging technology, it is important to combine telematics and map data to make it easier to extract and mine useful information from the data. By performing map matching, data points that cannot be accurately located on the road network can be projected onto the correct road segment. As an important means of remote data processing, it has become an important pre-processing step in the field of data mining. However, due to the various errors of location devices and the complexity of road networks, map matching technology also faces great challenges. In order to improve the efficiency and accuracy of the map matching algorithm, this study proposes an offline method for low-frequency trajectory data map matching based on vehicle trajectory segmentation. First, the trajectory is segmented based on the vehicle’s travel direction. Then, the comprehensive probability of the corresponding road segment is calculated based on the spatial probability and the directional probability of each road segment around the location. Third, the k candidate matching paths under consideration are selected based on the comprehensive probability evaluation. Finally, the shortest path planning and the probability calculation of the different candidate paths are combined to find the optimal matching path. The experimental results on the real trajectory dataset in Shanghai and the road network environment show that the proposed algorithm has better accuracy, efficiency, and robustness than other algorithms. Full article

The roots of Wireless Sensor Networks (WSNs) are tracked back to US military developments, and, currently, WSNs have paved their way into a vast domain of civil applications, especially environmental, critical infrastructure, habitat monitoring, etc. In the majority of these applications, WSNs have been deployed to monitor critical and inaccessible terrains; however, due to their unique and resource-constrained nature, WSNs face many design and deployment challenges in these difficult-to-access working environments, including connectivity maintenance, topology management, reliability, etc. However, for WSNs, topology management and connectivity still remain a major concern in WSNs that hampers their operations, with a direct impact on the overall application performance of WSNs. To address this issue, in this paper, we propose a new topology management and connectivity maintenance scheme called a Tolerating Fault and Maintaining Network Connectivity using Array Antenna (ToMaCAA) for WSNs. ToMaCAA is a system designed to adapt to dynamic structures and maintain network connectivity while consuming fewer network resources. Thereafter, we incorporated a Phase Array Antenna into the existing topology management technologies, proving ToMaCAA to be a novel contribution. This new approach allows a node to connect to the farthest node in the network while conserving resources and energy. Moreover, data transmission is restricted to one route, reducing overheads and conserving energy in various other nodes’ idle listening state. For the implementation of ToMaCAA, the MATLAB network simulation platform has been used to test and analyse its performance. The output results were compared with the benchmark schemes, i.e., Disjoint Path Vector (DPV), Adaptive Disjoint Path Vector (ADPV), and Pickup Non-Critical Node Based k-Connectivity (PINC). The performance of ToMaCAA was evaluated based on different performance metrics, i.e., the network lifetime, total number of transmitted messages, and node failure in WSNs. The output results revealed that the ToMaCAA outperformed the DPV, ADPV, and PINC schemes in terms of maintaining network connectivity during link failures and made the network more fault-tolerant and reliable. Full article

The production of large-scale photovoltaics (PVs) is becoming increasingly popular in the field of power generation; they require the construction of power plants of several hundred megawatts. Nevertheless, the construction of these PV power plants with conventional low-voltage (LV) conversion systems is not an appropriate technological path. Particularly, large cross-section cables, a high quantity of semiconductors, and the bulky layout of 50/60-Hz step-up transformers make the PV system less competitive in terms of energy efficiency and cost. To overcome these drawbacks, this paper introduces new PV plant topologies with an intermediate medium-voltage direct current (MVDC) collector that requires galvanic isolation for connecting the PV arrays. Then, the design of a power electronic transformer (PET) is proposed, implementing 1.7-kV and 3.3-kV silicium carbide (SiC) power modules. The study confirms that this converter allows the use of medium-frequency (MF) transformers with high power densities while maintaining high efficiency, which facilitates the implementation of isolated medium-voltage (MV) topologies for utility-scale PV power plants. Full article

In this paper, we consider certain quantities that arise in the images of the so-called graph-tree indexes of graph groupoids. In text, the graph groupoids are induced by connected finite-directed graphs with more than one vertex. If a graph groupoid $\mathbb{G}$G contains at least one loop-reduced finite path, then the order of $\mathbb{G}$ is infinity; hence, the canonical groupoid index $\left[\mathbb{G}:\mathbb{K}\right]$ of the inclusion $\mathbb{K}\subseteq \mathbb{G}$ is either ∞ or 1 (under the definition and a natural axiomatization) for the graph groupoids $\mathbb{K}$ of all “parts” K of G. A loop-reduced finite path generates a semicircular element in graph groupoid algebra. Thus, the existence of semicircular systems acting on the free-probabilistic structure of a given graph G is guaranteed by the existence of loop-reduced finite paths in $\mathbb{G}$. The non-semicircularity induced by graphs yields a new index-like notion called the graph-tree index $\mathsf{\Gamma }$ of $\mathbb{G}$. We study the connections between our graph-tree index and non-semicircular cases. Hence, non-semicircularity also yields the classification of our graphs in terms of a certain type of trees. As an application, we construct towers of graph-groupoid-inclusions which preserve the graph-tree index. We further show that such classification applies to monoidal operads. Full article

In this paper, a reconfigurable mesh-tree with a disjoint hierarchical routing protocol for the Bluetooth sensor network is proposed. First, a designated root constructs a tree-shaped subnet and propagates parameters k and c in its downstream direction to determine new roots. Each new root asks its upstream master to start a return connection to convert the first tree-shaped subnet into a mesh-shaped subnet. At the same time, each new root repeats the same procedure as the designated root to build its own tree-shaped subnet, until the whole scatternet is formed. As a result, the reconfigurable mesh-tree constructs a mesh-shaped topology in one densely covered area that is extended by tree-shaped topology to other sparsely covered areas. To locate the optimum k layer for various sizes of networks, a peak-search method is introduced in the designated root to determine the optimum mesh-tree configuration. In addition, the reconfigurable mesh-tree can dynamically compute the optimum layer k when the size of the network changes in the topology maintenance phase. In order to deliver packets over the mesh-tree networks, a disjoint hierarchical routing protocol is designed during the scatternet formation phase to efficiently forward packets in-between the mesh-subnet and the tree-subnet. To achieve the energy balance design, two equal disjoint paths are generated, allowing each node to alleviate network congestion, since most traffic occurs at the mesh-subnet. Simulation results show that the joint reconfigurable method and routing algorithm generate an efficient scatternet configuration by achieving better scatternet and routing performance than BlueHRT (bluetooth hybrid ring tree). Furthermore, the disjoint routing with rechargeable battery strategy effectively improves network lifetime and demonstrates better energy efficiency than conventional routing methods. Full article

A novel topological approach for obtaining a family of new molecular descriptors is proposed. In this connection, a vector space E (molecular vector space), whose elements are organic molecules, is defined as a “direct sum“ of different ℜi spaces. In this way we can represent molecules having a total of i atoms as elements (vectors) of the vector spaces ℜi (i=1, 2, 3,..., n; where n is number of atoms in the molecule). In these spaces the components of the vectors are atomic properties that characterize each kind of atom in particular. The total quadratic indices are based on the calculation of mathematical quadratic forms. These forms are functions of the k-th power of the molecular pseudograph's atom adjacency matrix (M). For simplicity, canonical bases are selected as the quadratic forms' bases. These indices were generalized to “higher analogues“ as number sequences. In addition, this paper also introduces a local approach (local invariant) for molecular quadratic indices. This approach is based mainly on the use of a local matrix [M^k(G, F_R)]. This local matrix is obtained from the k-th power (M^k(G)) of the atom adjacency matrix M. M^k(G, F_R) includes the elements of the fragment of interest and those that are connected with it, through paths of length k. Finally, total (and local) quadratic indices have been used in QSPR studies of four series of organic compounds. The quantitative models found are significant from a statistical point of view and permit a clear interpretation of the studied properties in terms of the structural features of molecules. External prediction series and cross-validation procedures (leave-one-out and leave-group-out) assessed model predictability. The reported method has shown similar results, compared with other topological approaches. The results obtained were the following: a) Seven physical properties of 74 normal and branched alkanes (boiling points, molar volumes, molar refractions, heats of vaporization, critical temperatures, critical pressures and surface tensions) were well modeled (R>0.98, q2>0.95) by the total quadratic indices. The overall MAE of 5-fold cross-validation were of 2.11 oC, 0.53 cm³, 0.032 cm³, 0.32 KJ/mol, 5.34 oC, 0.64 atm, 0.23 dyn/cm for each property, respectively; b) boiling points of 58 alkyl alcohols also were well described by the present approach; in this sense, two QSPR models were obtained; the first one was developed using the complete set of 58 alcohols [R=0.9938, q²=0.986, s=4.006^oC, overall MAE of 5-fold cross-validation=3.824 ^oC] and the second one was developed using 29 compounds as a training set [R=0.9979, q²=0.992, s=2.97 ^oC, overall MAE of 5-fold cross-validation=2.580 ^oC] and 29 compounds as a test set [R=0.9938, s=3.17 ^oC]; c) good relationships were obtained for the boiling points property (using 80 and 26 cycloalkanes in the training and test sets, respectively) using 2 and 5 total quadratic indices: [Training set: R=0.9823 (q²=0.961 and overall MAE of 5-fold crossvalidation= 6.429 ^oC) and R=0.9927 (q²=0.977 and overall MAE of 5-fold crossvalidation= 4.801 ^oC); Test set: R=0.9726 and R=0.9927] and d) the linear model developed to describe the boiling points of 70 organic compounds containing aromatic rings has shown good statistical features, with a squared correlation coefficient (R²) of 0.981 (s=7.61 ^oC). Internal validation procedures (q²=0.9763 and overall MAE of 5-fold cross-validation=7.34 ^oC) allowed the predictability and robustness of the model found to be assessed. The predictive performance of the obtained QSPR model also was tested on an extra set of 20 aromatic organic compounds (R=0.9930 and s=7.8280 ^oC). The results obtained are valid to establish that these new indices fulfill some of the ideal requirements proposed by Randić for a new molecular descriptor. Full article