Search Results (17)

We develop an information-theoretic, cost-first framework for discrete dynamics in which the primitive operation is ratio-based comparison. Given two quantities compared via their ratio $x=a/b$, we assign a cost $F\left(x\right)$ measuring deviation from equilibrium ($x=1$). Adopting a reciprocal d’Alembert composition law motivated by coherent chaining, together with quadratic calibration at unity, uniquely determines a reciprocal comparison cost $J\left(x\right)=\frac{1}{2}\left(x+x^{-1}\right)-1$. Taking J as input, we model recognition events as deterministic updates on directed graphs recorded in a minimal ledger. Minimality (no intra-tick ordering metadata) together with non-commutativity of events implies atomic ticks: at most one event per tick. With conservation, pairwise locality, and quantization in $\delta \mathbb{Z}$, each event is recorded as a balanced double-entry posting. For graphs with cycles, assuming time-aggregated cycle closure over a finite clearing horizon, we show that cleared cycle closure is equivalent to path-independence and that the cumulative flow admits a scalar potential on each connected component (unique up to additive constant) via a discrete Poincaré lemma. On hypercube graphs $Q_d$, atomic single-edge updates impose a $2^d$-tick minimal period for timestamp-unique coverage, realized by cyclic Gray codes (explicitly for $d=3$). The framework links ratio-based cost functions, conservative graph flows, and discrete potential theory through explicitly stated axioms and structural assumptions. Full article

Advanced Persistent Threats (APTs) are characterized by stealth, infrequency, and long cycles, evading traditional security to endanger critical infrastructure. Complex semantic links between system entities can be accurately modeled using representation learning techniques based on heterogeneous provenance graphs, providing a novel method for uncovering hidden APT attack chains. However, in large-scale practical implementations, this approach still faces three major challenges: combinatorial explosion of long-range meta-paths, loss of semantic evolution during graph compression, and high computational overhead for dynamic environments. To address these, we propose APT-LMSPS, a detection system leveraging Long-Range Meta-path Progressive Sampling Search (LMSPS). The LMSPS algorithm uses dynamic pruning and semantic contribution assessment to convert meta-path combination explosion into constant-scale computation, accurately modeling long-range dependencies. Second, the Maintaining Global Semantics (MGS) approach intelligently filters events by tracking node semantic state changes, achieving an 8:1 compression ratio while preserving over 90% of critical pathways’ semantic integrity. Lastly, the meta-path encoding database uses a caching approach to avoid repeated encoding, doubling encoding effectiveness and enabling efficient, accurate, system-wide APT detection in large-scale scenarios. Evaluated on DARPA, StreamSpot, and ATLAS datasets, APT-LMSPS maintains competitive accuracy (F1-score ≥ 0.98) and improves long-range processing efficiency by an order of magnitude over baselines. Full article

Advanced Persistent Threats (APTs) exhibit covert behaviors, long attack cycles, and fragmented intelligence, creating challenges for correlation analysis and attribution. This work proposes a unified knowledge-graph-based framework for multi-level APT correlation. We construct an APT ontology and automatically extract entities and relations from threat reports using NER and relation extraction models. The resulting multi-source intelligence is normalized and integrated into a Neo4j knowledge graph containing 15,682 entities and 42,713 relations. Multi-level correlation analysis is then performed through explicit structural reasoning, semantic embedding models such as TransE and RotatE, and a temporal evolution module based on T-GCN to capture dynamic attack-path patterns. Experiments demonstrate that the proposed framework achieves an F1-score of 0.91 for relation extraction and improves APT correlation prediction accuracy by 17.3% over rule-based baselines. The system supports large-scale attack-chain reasoning and sector-oriented threat analysis, providing enhanced attribution and decision support for cybersecurity defense. Full article

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

Correlation analysis is one of the most prolific statistical methods used in data analysis problems, mining of knowledge focused on relationships of attributes in large datasets, and in various predictive tasks utilizing statistical, machine learning, and deep learning models. This approach to the analysis of functional relationships in multidimensional datasets is commonly used in conjunction with visual analysis approaches, which offer novel context for the relationships in data and clarify the results presented in large correlation matrices. One of such visualization methods uses graphical models called correlation graphs and chains, which visualize individual direct and indirect relationships between pairs of attributes in a dataset of interest as a graph structure, where vertices of the graph represent attributes of the dataset and edges between vertices represent the correlation of these attributes. This work focuses on the definition, identification, and exploration of so-called correlation cycles, which can be—through their deconstruction—used as an approach to lower error values in regression tasks. After the implementation of the correlation cycle identification and deconstruction, the proposed concept is evaluated on predictive analysis tasks in the context of three benchmarking datasets from the engineering field—the Sensor dataset, Superconductivity dataset, and Energy Farm dataset. The results obtained in this study show that when using simple, explainable regressors, the method utilizing deconstructed correlation cycles reaches a lower error rate in 83.3% of regression cases compared to the same regression models without the cycle incorporation. Full article

Lithium-ion batteries are major drivers to decarbonize road traffic and electric power systems. With the rising number of electric vehicles comes an increasing number of lithium-ion batteries reaching their end of use. After their usage, several strategies, e.g., reuse, repurposing, remanufacturing, or material recycling can be applied. In this context, remanufacturing is the favored end-of-use strategy to enable a new use cycle of lithium-ion batteries and their components. The process of remanufacturing itself is the restoration of a used product to at least its original performance by disassembling, cleaning, sorting, reconditioning, and reassembling. Thereby, disassembly as the first step is a decisive process step, as it creates the foundation for all further steps in the process chain and significantly determines the economic feasibility of the remanufacturing process. The aim of the disassembly depth is the replacement of individual cells to replace the smallest possible deficient unit and not, as is currently the case, the entire battery module or even the entire battery system. Consequently, disassembly sequences are derived from a priority matrix, a disassembly graph is generated, and the obstacles to non-destructive cell replacement are analyzed for two lithium-ion traction battery systems, to analyze the distinctions between battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) battery systems and identify the necessary tools and fundamental procedures required for the effective management of battery systems within the circular economy. Full article

This research introduces two novel topological indices, the entire Albertson index and the entire sigma index, as quantitative measures of molecular irregularity. The indices are defined by precise mathematical formulas and their behavior is analyzed across a diverse range of graph families. To evaluate the predictive capabilities of the proposed indices, we compare their performance with established irregularity indices in the modeling of molecular properties. Correlations with physicochemical properties, including the boiling point, melting point, and molecular volume, are investigated. Specific expressions for these indices are derived for various molecular structures, such as bridge molecules, polyomino chains of n-cycles, triangular benzenoid graphs, graphene, and dendrimer stars $D_3\left[n\right]$. The findings of this study contribute significantly to the field of chemical graph theory by providing novel tools to understand and predict molecular behavior. The entire irregularity indices have potential applications in drug discovery, materials science, and other areas where molecular properties are crucial. Full article

Gastric cancer (GC) is one of the most frequently diagnosed cancers in the world. Although the incidence is decreasing in developed countries, the treatment results are still unsatisfactory. The standard treatment for locally advanced gastric cancer (LAGC) is gastrectomy with perioperative chemotherapy. The association of selected microRNAs (miRNAs) with chemoresistance was assessed using archival material of patients with LAGC. Histological material was obtained from each patient via a biopsy performed during gastroscopy and then after surgery, which was preceded by four cycles of neoadjuvant chemotherapy (NAC) according to the FLOT or FLO regimen. The expression of selected miRNAs in the tissue material was assessed, including miRNA-21-3p, miRNA-21-5p, miRNA-106a-5p, miRNA-122-3p, miRNA-122-5p, miRNA-143-3p, miRNA-143-5p, miRNA-203a-3p, miRNA-203-5p, miRNA-551b-3p, miRNA-551b-5p, and miRNA-574-3p. miRNA expression was assessed using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The response to NAC was assessed using computed tomography of the abdomen and chest and histopathology after gastrectomy. The statistical analyses were performed using GraphPad Prism 9. The significance limit was set at p < 0.05. We showed that the expression of miR-143-3p, miR-143-5p, and miR-574-3p before surgery, and miR-143-5p and miR-574-3p after surgery, decreased in patients with GC. The expression of miR-143-3p, miR-143-5p, miR-203a-3p, and miR-551b-5p decreased in several patients who responded to NAC. The miRNA most commonly expressed in these cases was miRNA-551b-5p. Moreover, it showed expression in a patient whose response to chemotherapy was inconsistent between the histopathological results and computed tomography. The expression of miR-143-3p, miR-143-5p, miR-203a-3p, and miR-551b-5p in formalin-fixed paraffin-embedded tissue (FFPET) samples can help differentiate between the responders and non-responders to NAC in LAGC. miR-143-3p, miR-143-5p, and miR-574-3p expression may be used as a potential diagnostic tool in GC patients. The presence of miR-551b-5p may support the correct assessment of a response to NAC in GC via CT. Full article

The Zhang–Zhang polynomial of a benzenoid system is a well-known counting polynomial that was introduced in 1996. It was designed to enumerate Clar covers, which are spanning subgraphs with only hexagons and edges as connected components. In 2018, the generalized Zhang–Zhang polynomial of two variables was defined such that it also takes into account 10-cycles of a benzenoid system. The aim of this paper is to introduce and study a new variation of the Zhang–Zhang polynomial for phenylenes, which are important molecular graphs composed of 6-membered and 4-membered rings. In our case, Clar covers can contain 4-cycles, 6-cycles, 8-cycles, and edges. Since this new polynomial has three variables, we call it the multivariable Zhang–Zhang (MZZ) polynomial. In the main part of the paper, some recursive formulas for calculating the MZZ polynomial from subgraphs of a given phenylene are developed and an algorithm for phenylene chains is deduced. Interestingly, computing the MZZ polynomial of a phenylene chain requires some techniques that are different to those used to calculate the (generalized) Zhang–Zhang polynomial of benzenoid chains. Finally, we prove a result that enables us to find the MZZ polynomial of a phenylene with branched hexagons. Full article

Strontium (Sr) belongs to the same group in the periodic table as calcium (Ca). Sr level can serve as an index of rumen Ca absorption capacity; however, the effects of Sr on Ca²⁺ metabolism are unclear. This study aims to investigate the effect of Sr on Ca²⁺ metabolism in bovine rumen epithelial cells. The bovine rumen epithelial cells were isolated from the rumen of newborn Holstein male calves (n = 3, 1 day old, 38.0 ± 2.8 kg, fasting). The half maximal inhibitory concentration (IC50) of Sr-treated bovine rumen epithelial cells and cell cycle were used to establish the Sr treatment model. Transcriptomics, proteomics, and network pharmacology were conducted to investigate the core targets of Sr-mediated regulation of Ca²⁺ metabolism in bovine rumen epithelial cells. The data of transcriptomics and proteomics were analyzed using bioinformatic analysis (Gene Ontology and Kyoto Encyclopedia of genes/protein). Quantitative data were analyzed using one-way ANOVA in GraphPad Prism 8.4.3 and the Shapiro–Wilk test was used for the normality test. Results presented that the IC50 of Sr treatment bovine rumen epithelial cells for 24 h was 43.21 mmol/L, and Sr increased intracellular Ca²⁺ levels. Multi-omics results demonstrated the differential expression of 770 mRNAs and 2436 proteins after Sr treatment; network pharmacology and reverse transcriptase polymerase chain reaction (RT-PCR) revealed Adenosylhomocysteine hydrolase-like protein 2 (AHCYL2), Semaphoring 3A (SEMA3A), Parathyroid hormone-related protein (PTHLH), Transforming growth factor β2 (TGF-β2), and Cholesterol side-chain cleavage enzyme (CYP11A1) as potential targets for Sr-mediated Ca²⁺ metabolism regulation. Together these results will improve the current comprehension of the regulatory effect of Sr on Ca²⁺ metabolism and pave a theoretical basis for Sr application in bovine hypocalcemia. Full article

The concept of metric-related parameters permeates all of graph theory and plays an important role in diverse networks, such as social networks, computer networks, biological networks and neural networks. The graph parameters include an incredible tool for analyzing the abstract structures of networks. An important metric-related parameter is the partition dimension of a graph holding auspicious applications in telecommunication, robot navigation and geographical routing protocols. A fault-tolerant resolving partition is a preference for the concept of a partition dimension. A system is fault-tolerant if failure of any single unit in the originally used chain is replaced by another chain of units not containing the faulty unit. Due to the optimal fault tolerance, cycle-related graphs have applications in network analysis, periodic scheduling and surface reconstruction. In this paper, it is shown that the partition dimension (PD) and fault-tolerant partition dimension (FTPD) of cycle-related graphs, including kayak paddle and flower graphs, are constant and free from the order of these graphs. More explicitly, the FTPD of kayak paddle and flower graphs is four, whereas the PD of flower graphs is three. Finally, an application of these parameters in a scenario of installing water reservoirs in a locality has also been furnished in order to verify our findings. Full article

Our study is devoted to a subject popular in the field of matrix population models, namely, estimating the stochastic growth rate, λ_S, a quantitative measure of long-term population viability, for a discrete-stage-structured population monitored during many years. “Reproductive uncertainty” refers to a feature inherent in the data and life cycle graph (LCG) when the LCG has more than one reproductive stage, but when the progeny cannot be associated to a parent stage in a unique way. Reproductive uncertainty complicates the procedure of λ_S estimation following the defining of λ_S from the limit of a sequence consisting of population projection matrices (PPMs) chosen randomly from a given set of annual PPMs. To construct a Markov chain that governs the choice of PPMs for a local population of Eritrichium caucasicum, an short-lived perennial alpine plant species, we have found a local weather index that is correlated with the variations in the annual PPMs, and we considered its long time series as a realization of the Markov chain that was to be constructed. Reproductive uncertainty has required a proper modification of how to restore the transition matrix from a long realization of the chain, and the restored matrix has been governing random choice in several series of Monte Carlo simulations of long-enough sequences. The resulting ranges of λ_S estimates turn out to be more narrow than those obtained by the popular i.i.d. methods of random choice (independent and identically distributed matrices); hence, we receive a more accurate and reliable forecast of population viability. Full article

In its original manufacturing purpose, Expanded Polystyrene (EPS) is an industrial product with a short life cycle and waste with high negative environmental impact. Given this externality, this article contributes to the state of the art by proposing reuse EPS as a raw material valuable to the process of manufacturing hats in a Mexican company. The SCAMPER technique is used to formulate a hardener, which is optimized with a Taguchi design. Statistically, there is no difference between the quality of the conventional hardener and the mixture based on post-consumer EPS to make hats based on the standards defined by the company; a subjective analysis supported by the judgment of experts validates the quality of the hats. A contour graph and response surface reflect different combinations of solute and solvent to formulate the glue for the doping of the hat, with the same hardness results. This allows the artisan to assess the formulation from an economical point of view, as well as with respect to the arrangement of materials. These results specifically propose the sustainable alternative of integrating waste from the post–consumer EPS chain into the artisanal hat value chain, and are replicable to other similar products. Full article

We propose a novel linear time algorithm which, given any directed weighted graphs a and b with vertex degrees 1 or 2, constructs a sequence of operations transforming a into b. The total cost of operations in this sequence is minimal among all possible ones or differs from the minimum by an additive constant that depends only on operation costs but not on the graphs themselves; this difference is small as compared to the operation costs and is explicitly computed. We assume that the double cut and join operations have identical costs, and costs of the deletion and insertion operations are arbitrary strictly positive rational numbers. Full article

In order to increase the potential kidney transplants between patients and their incompatible donors, kidney exchange programs have been created in many countries. In the programs, designing algorithms for the kidney exchange problem plays a critical role. The graph theory model of the kidney exchange problem is to find a maximum weight packing of vertex-disjoint cycles and chains for a given weighted digraph. In general, the length of cycles is not more than a given constant L (typically $2\le L\le 5$), and the objective function corresponds to maximizing the number of possible kidney transplants. In this paper, we study the parameterized complexity and randomized algorithms for the kidney exchange problem without chains from theory. We construct two different parameterized models of the kidney exchange problem for two cases $L=3$ and $L\ge 3$, and propose two randomized parameterized algorithms based on the random partitioning technique and the randomized algebraic technique, respectively. Full article