Search Results (231)

We consider mixed Hamming packings, addressing the maximal cardinality of codes with a minimum codeword Hamming distance. We do not rely on any algebraic structure of the alphabets. We extend known-integer linear programming models of the problem to be efficiently tractable using standard ILP solvers. This is achieved by adopting the concept of contact graphs from classical continuous sphere packing problems to the present discrete context, resulting in a reduction technique for the models which enables their efficient solution as well as their decomposition to smaller subproblems. Based on our calculations, we provide a systematic summary of all lower and upper bounds for packings in the smallest Hamming spaces. The known results are reproduced, with some bounds found to be sharp, and the upper bounds improved in some cases. Full article

Current mainstream classification methods predominantly employ end-to-end multi-class frameworks. These approaches encounter inherent challenges including high-dimensional feature space complexity, decision boundary ambiguity that escalates with increasing class cardinality, sensitivity to label noise, and limited adaptability to dynamic model expansion. However, human beings may avoid these mistakes naturally. Research indicates that humans subconsciously employ a decision-making process favoring binary outcomes, particularly when responding to questions requiring nuanced differentiation. Intuitively, responding to binary inquiries such as “yes/no” often proves easier for humans than addressing queries of “what/which”. Inspired by the human decision-making hypothesis, we proposes a decision paradigm named the evolutionary binary decision framework (EBDF) centered around binary classification, evolving from traditional multi-classifiers in deep learning. To facilitate this evolution, we leverage the top-N outputs from the traditional multi-class classifier to dynamically steer subsequent binary classifiers, thereby constructing a cascaded decision-making framework that emulates the hierarchical reasoning of a binary decision tree. Theoretically, we demonstrate mathematical proof that by surpassing a certain threshold of the performance of binary classifiers, our framework may outperform traditional multi-classification framework. Furthermore, we conduct experiments utilizing several prominent deep learning models across various image classification datasets. The experimental results indicate significant potential for our strategy to surpass the ceiling in multi-classification performance. Full article

Background/Objectives: Plexiform neurofibromas (pNFs) are one of the cardinal presentations of NF1 patients, often arising during early childhood. Since selumetinib was approved by the FDA in 2020, the long-term side effects and various responses of mitogen-activated protein kinase inhibitors (MEKi) in pediatric patients necessitate a new strategy. We propose that combining selumetinib with heat shock protein 90 inhibitors (HSP90i) can enhance the inhibitory effects as well as reduce the dosage of selumetinib in combination. We validated the synergistic effects and the significantly improved treatment effects of the combination of selumetinib and HSP90i in pNFs. Methods: We used drug screen data mining to predict the combination of selumetinib and HSP90i. Using cell lines and in vivo mouse models for pNFs, we tested a series of combinations with different concentrations. We validated the in vivo inhibitory effects using the transplanted tumors from DhhCreNf1^f/f mouse models. Results: We demonstrated that combining selumetinib and SNX-2112 or retaspimycin can achieve better tumor inhibition with synergistic effects. The combination significantly delays the progression of mouse pNFs. Conclusions: The combination of selumetinib and HSP90i has significant synergistic effects, provides therapeutic inhibitor effects, and reduces the selumetinib dosage in combination. Full article

Accurate tree species identification through bark characteristics is essential for effective forest management, but traditionally requires extensive expertise. This study leverages artificial intelligence (AI), specifically the EfficientNet-B3 convolutional neural network, to enhance AI-based tree bark identification, focusing on northern red oak (Quercus rubra), hackberry (Celtis occidentalis), and bitternut hickory (Carya cordiformis) using the CentralBark dataset. We investigated three environmental variables—time of day (lighting conditions), bark moisture content (wet or dry), and cardinal direction of observation—to identify sources of classification inaccuracies. Results revealed that bark moisture significantly reduced accuracy by 8.19% in wet conditions (89.32% dry vs. 81.13% wet). In comparison, the time of day had a significant impact on hackberry (95.56% evening) and northern red oak (80.80% afternoon), with notable chi-squared associations (p < 0.05). Cardinal direction had minimal effect (4.72% variation). Bitternut hickory detection consistently underperformed (26.76%), highlighting morphological challenges. These findings underscore the need for targeted dataset augmentation with wet and afternoon images, alongside preprocessing techniques like illumination normalization, to improve model robustness. Enhanced AI tools will streamline forest inventories, support biodiversity monitoring, and bolster conservation in dynamic forest ecosystems. Full article

To explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives affect the fusion of nonlinear moving-target data and the spatial segmentation of such targets. We propose a differential-view nonlinear multi-target tracking approach that integrates the Gaussian mixture, jump Markov nonlinear system, and the cardinalized probability hypothesis density (GM-JMNS-CPHD). The method begins by partitioning the observation space based on the boundaries of distinct viewpoints. Next, it applies a combined technique—the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SOS (stochastic outlier selection)—to identify outliers near these boundaries. To achieve accurate detection, the posterior intensity is split into several sub-intensities, followed by reconstructing the multi-Bernoulli cardinality distribution to model the target population in each subregion. The algorithm’s computational complexity remains on par with the standard GM-JMNS-CPHD filter. Simulation results confirm the proposed method’s robustness and accuracy, demonstrating a lower error rate compared to other benchmark algorithms. Full article

Periodontitis is an inflammatory disease that affects the periodontal supporting tissues. Its cardinal clinical manifestations encompass gingival inflammation, periodontal pocket formation, and alveolar bone resorption. Urolithin A (UA), a gut microbiota-derived metabolite of ellagitannins, is known for its anti-inflammatory and osseous-protective properties. Nonetheless, the impact of UA on periodontitis remains unknown. To investigate the preventive effect of UA, we employed a lipopolysaccharide (LPS)-induced inflammation model in RAW 264.7 mouse macrophages, a receptor activator of nuclear factor-κB ligand (RANKL)-induced osteoclast differentiation model, and a ligature-induced periodontitis model in mice. The expression of inflammatory factors (tumor necrosis factor-α, TNF-α; interleukin-6, IL-6) was analyzed to assess anti-inflammatory efficacy. Bone loss in mice with periodontitis was assessed through histological and imaging techniques, including haematoxylin and eosin staining to evaluate alveolar bone morphology, Masson’s trichrome staining to visualize collagen fiber distribution, and micro-computed tomography scanning to quantify bone structural parameters. Additionally, we investigated the underlying mechanisms by examining osteoclast activity through tartrate-resistant acid phosphatase staining and the expression levels of proteins RANKL and osteoprotegerin (OPG). We found that UA reduced IL-6 and TNF-α levels in vitro and in vivo, inhibited osteoclast differentiation, and decreased the RANKL/OPG ratio in periodontitis mice. Full article

Polyhalogenated carbazoles (PHCZs) are emerging persistent organic pollutants that have attracted widespread attention due to their environmental occurrence and potential ecological risks. 1-Bromo-3,6-dichlorocarbazole (1-B-36-CCZ), which is a typical homolog of PHCZs produced as a byproduct in the dye industry, has been widely detected in various environmental media. In this study, we employed an integrated approach using an in vivo zebrafish model and network toxicology methods to systematically evaluate the vascular developmental toxicity of 1-B-36-CCZ and elucidate its underlying mechanisms. The experimental results revealed that the 96 h-LC₅₀ of 1-B-36-CCZ in zebrafish larvae was 4.52 mg/L. Sublethal exposures (0.045–45 μg/L) significantly induced an increase in heart rate (p < 0.05) and an enlargement of the pericardial edema area (p < 0.01). Using Tg(flk:eGFP) transgenic zebrafish embryos to assess vascular toxicity at concentrations of 0, 0.045, 0.45, 4.5, and 45 μg/L, we observed that 1-B-36-CCZ exposure significantly reduced the length and anastomosis rate of intersegmental vessels (ISVs) at 30 hpf, and inhibited the development of the common cardinal vein (CCV) at 48 and 72 hpf as well as the subintestinal vessel (SIV) at 72 hpf. Quantitative PCR (qPCR) analysis further revealed that the expression of key angiogenic genes (flk, kdr, and vegfa) was significantly downregulated, thus corroborating the phenotypic observations. Moreover, a “compound–target–pathway” network model predicted that SRC kinase is a key molecular target for 1-B-36-CCZ action. Enrichment analysis of target protein-coding genes and verapamil replication experiments indicated that 1-B-36-CCZ may cause damage to early vascular development in zebrafish larvae by altering intracellular calcium ion content through the activation of the SRC-mediated calcium ion signaling pathway. This study provides new experimental evidence for elucidating the toxic mechanisms of PHCZ-type pollutants and offers a theoretical basis for environmental health risk assessments. Full article

Decision-making in complex environments, especially sustainable ones, requires flexible methodologies to handle multiple criteria and stakeholder perspectives. This study introduces the SAPEVO-H² method (Simple Aggregation of Preferences Expressed by Ordinal Vectors—Hybrid and Hierarchical), an extensive model from the SAPEVO family, which offers multi-criteria analysis through a hierarchical structure of variables evaluated by groups partitioned into levels concerning their respective responsibilities. The proposal allows flexible analysis, considering inputs through ordinal and cardinal information. The validation of the methodology is demonstrated through a case study involving an automobile manufacturing company, which focuses on prioritizing sustainability projects based on multiple objectives aimed at minimizing polluting gas emissions. Within a hierarchical structure of five levels, the individual level results are presented. In addition, a sensitivity analysis is applied, exposing the most sensitive variables to changes concerning the highest levels. Then, we discuss the main contributions and limitations concerning the mathematical proposal and the conclusions and proposals for future work. Full article

As data-centric applications become increasingly complex, understanding effective query optimization in large-scale relational databases is crucial for managing this complexity. Yet, traditional cost-based and heuristic approaches simply do not scale, adapt, or remain accurate in highly dynamic multi-join queries. This research work proposes the reinforcement learning and graph-based hybrid query optimizer (GRQO), the first ever to apply reinforcement learning and graph theory for optimizing query execution plans, specifically in join order selection and cardinality estimation. By employing proximal policy optimization for adaptive policy learning and using graph-based schema representations for relational modeling, GRQO effectively traverses the combinatorial optimization space. Based on TPC-H (1 TB) and IMDB (500 GB) workloads, GRQO runs 25% faster in query execution time, scales 30% better, reduces CPU and memory use by 20–25%, and reduces the cardinality estimation error by 47% compared to traditional cost-based optimizers and machine learning-based optimizers. These findings highlight the ability of GRQO to optimize performance and resource efficiency in database management in cloud computing, data warehousing, and real-time analytics. Full article

Psychosis constitutes a cardinal component of schizophrenia and affects nearly fifty percent of those with bipolar disorder. We sought to molecularly characterize psychosis segregating in consanguineous families. Participants from eight multiplex families were evaluated using standardized testing tools. DNA was subjected to exome sequencing followed by Sanger sequencing. Effects of variants were modeled using in-silico tools, while cDNA from a patient’s blood sample was analyzed to evaluate the effect of a splice-site variant. Twelve patients in six families were diagnosed with schizophrenia, whereas four patients from two families had psychotic bipolar disorder. Two homozygous rare deleterious variants in INSR (c.2232-7T>G) and NFXL1 (c.1322G>A; p.Cys441Tyr) were identified, which segregated with severe treatment-resistant psychosis/schizophrenia in two families. There were none, or ambiguous findings in the other six families. The predicted deleterious missense variant affected a conserved amino acid, while the intronic variant was predicted to affect splicing. However, cDNA analysis from a patient’s blood sample did not reveal an aberrant transcript. Our results indicate that INSR and NFXL1 variants may have a role in psychosis that requires to be investigated further. Lack of molecular diagnosis in some patients suggests the need for genome sequencing to pinpoint the genetic causes. Full article

Imbalanced data are a non-trivial problem in deep learning. The high variability in the number of samples composing each category might force learning procedures to become biased towards classes with major cardinality and disregard classes with low instances. To overcome such limitations, common strategies involve data balancing using resampling techniques. The cardinality of overnumbered categories is often lowered by sample deletion, thus reducing the data space where the model can learn from. This paper introduces a new approach based on data balancing without sample deletion, allowing for biasing reduction in instance localization and classification tasks. The method is a multi-stage pipeline starting with data cleaning and data filtering steps and ending with the actual data balancing process, during which overnumbered samples are not deleted but divided into multiple sub-classes. In this way, the model can learn from balanced data distribution in which some classes have a high correlation factor. To evaluate the effectiveness of the method in real-life scenarios, a case study in the field of precision agriculture has been developed, motivated by the fact that the publicly available datasets for pest classification often reflect the real-world imbalanced distribution of pests, making the task challenging. Two models for the localization and recognition of pests belonging to several species are also indicated. The obtained results show the method’s validity as the performance both in the detection and classification tasks outperforms the state-of-the-art methods. The general nature of the conceived balancing technique may make the approach useful in other application fields. Full article

This paper proposes a dynamic unmanned aerial vehicle (UAV) clustering model for multi-target localization in complex 3D environments, where mobility-aware cluster formation is integrated to enhance collaborative localization accuracy. We derive the Cramér–Rao lower bound (CRLB) for localization performance analysis under measurement and motion-induced uncertainties. To solve the NP-hard clustering problem, we develop the MDQPSO-ASA algorithm, which combines multi-swarm discrete quantum-inspired particle swarm optimization with adaptive simulated annealing, incorporating a repair mechanism to satisfy spatial and cardinality constraints. Simulation results demonstrate the algorithm’s superiority in localization accuracy, computational efficiency, and adaptability to varying UAV/target scales compared to baseline methods. The developed algorithm provides an effective solution for resource-constrained collaborative localization tasks in practical scenarios. Full article

This paper introduces a two-phase decision support system based on information theory and financial practices to assist investors in solving cardinality-constrained portfolio optimization problems. Firstly, the approach employs a stock-picking procedure based on an interactive multi-criteria decision-making method (the so-called TODIM method). More precisely, the best-performing assets from the investable universe are identified using three financial criteria. The first criterion is based on mutual information, and it is employed to capture the microstructure of the stock market. The second one is the momentum, and the third is the upside-to-downside beta ratio. To calculate the preference weights used in the chosen multi-criteria decision-making procedure, two methods are compared, namely equal and entropy weighting. In the second stage, this work considers a portfolio optimization model where the objective function is a modified version of the Sharpe ratio, consistent with the choices of a rational agent even when faced with negative risk premiums. Additionally, the portfolio design incorporates a set of bound, budget, and cardinality constraints, together with a set of risk budgeting restrictions. To solve the resulting non-smooth programming problem with non-convex constraints, this paper proposes a variant of the distance-based parameter adaptation for success-history-based differential evolution with double crossover (DISH-XX) algorithm equipped with a hybrid constraint-handling approach. Numerical experiments on the US and European stock markets over the past ten years are conducted, and the results show that the flexibility of the proposed portfolio model allows the better control of losses, particularly during market downturns, thereby providing superior or at least comparable ex post performance with respect to several benchmark investment strategies. Full article

In view of the typical multi-target scenarios of underwater direction-of-arrival (DOA) tracking complicated by uncertain measurement noise in unknown underwater environments, a robust underwater multi-target DOA tracking method is proposed by incorporating Saga–Husa (SH) noise estimation and a backward smoothing technique within the framework of the cardinalized probability hypothesis density (CPHD) filter. First, the kinematic model of underwater targets and the measurement model based on the received signals of a hydrophone array are established, from which the CPHD-based multi-target DOA tracking algorithm is derived. To mitigate the adverse impact of uncertain measurement noise, the Saga–Husa approach is deployed for dynamic noise estimation, thereby reducing noise-induced performance degradation. Subsequently, a backward smoothing technique is applied to the forward filtering results to further enhance tracking robustness and precision. Finally, extensive simulations and experimental evaluations demonstrate that the proposed method outperforms existing DOA estimation and tracking techniques in terms of robustness and accuracy under uncertain measurement noise conditions. Full article

The complexity of large-scale group decision-making (LSGDM) in the digital society is becoming increasingly prominent. How to achieve efficient consensus through social networks (SNs) has become a core challenge in improving the decision quality. First, conventional clustering methods often rely on a single-distance metric, neglecting both numerical assessments and preference rankings. Second, ensuring the decision authenticity requires considering diverse behaviors, such as trust propagations, risk preferences, and minority opinion expressions, for scientific decision-making in SNs. To address these challenges, a consensus-reaching process (CRP) method based on an enhanced K-means++ clustering is proposed. The above method not only focuses on minority opinion handling (MOH), but also incorporates decision indicator sets (DISs) to analyze the degree of opinion divergences within groups. First, the Hamacher aggregation operator with a decay factor completes trust matrices, improving the trust representation. Second, a personalized distance metric that combines cardinal distances with ordinal distances is incorporated into the enhanced K-means++ clustering, enabling more precise clustering. Third, weights for decision-makers (DMs) and subgroups are determined based on trust levels and degree centrality indices. Fourth, minority opinions are appropriately handled via considering the diverse backgrounds and expertise of DMs, leveraging a difference-oriented DIS to detect and adjust these opinions via weight modifications until a consensus is reached. Fifth, the alternative ranking is objectively generated via DIS scores derived from multigranulation rough approximations. Finally, the feasibility of the proposed method is validated via a case study on unmanned aerial vehicle (UAV) selection using online reviews, supported by a sensitivity analysis and comparative experiments demonstrating superior performances over existing methods. The result shows that the proposed model can enhance clustering accuracies with hybrid distances, objectively measure the consensus via DISs, handle minority opinions effectively, and improve LSGDM’s overall efficiencies. Full article