Search Results (1,632)

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

As China’s first systematic assessment of high-risk Amaranthaceae invaders, this study addresses a critical knowledge gap identified in the National Invasive Species Inventory, in which four invasive Amaranthaceae species (Dysphania ambrosioides, Celosia argentea, Amaranthus palmeri, and Amaranthus spinosus) are prioritized due to CNY 2.6 billion annual ecosystem damages in China. By coupling multi-species comparative analysis with a parameter-optimized Maximum Entropy (MaxEnt) model integrating climate, soil, and topographical variables in China under Shared Socioeconomic Pathways (SSP) 126/245/585 scenarios, we reveal divergent expansion mechanisms (e.g., 247 km faster northward shift in A. palmeri than D. ambrosioides) that redefine invasion corridors in the North China Plain. Under current conditions, the suitable habitats of these species span from 92° E to 129° E and 18° N to 49° N, with high-risk zones concentrated in central and southern China, including the Yunnan–Guizhou–Sichuan region and the North China Plain. Temperature variables (Bio: Bioclimatic Variables; Bio6, Bio11) were the primary contributors based on permutation importance (e.g., Bio11 explained 56.4% for C. argentea), while altitude (e.g., 27.3% for A. palmeri) and UV-B (e.g., 16.2% for A. palmeri) exerted lower influence. Model validation confirmed high accuracy (mean area under the curve (AUC) > 0.86 and true skill statistic (TSS) > 0.6). By the 2090s, all species showed net habitat expansion overall, although D. ambrosioides exhibited net total contractions during mid-century under the SSP126/245 scenarios, C. argentea experienced reduced total suitability during the 2050s–2070s despite high-suitability growth, and A. palmeri and A. spinosus expanded significantly in both total and highly suitable habitat. All species shifted their distribution centroids northward, aligning with warming trends. Overall, these findings highlight the critical role of temperature in driving range dynamics and underscore the need for latitude-specific monitoring strategies to mitigate invasion risks, providing a scientific basis for adaptive management under global climate change. Full article

As cyberattacks continue to rise in frequency and sophistication, extracting actionable Cyber Threat Intelligence (CTI) from diverse online sources has become critical for proactive threat detection and defense. However, accurately identifying complex entities from lengthy and heterogeneous threat reports remains challenging due to long-range dependencies and domain-specific terminology. To address this, we propose XLNet-CRF, a hybrid framework that combines permutation-based language modeling with structured prediction using Conditional Random Fields (CRF) to enhance Named Entity Recognition (NER) in cybersecurity contexts. XLNet-CRF directly addresses key challenges in CTI-NER by modeling bidirectional dependencies and capturing non-contiguous semantic patterns more effectively than traditional approaches. Comprehensive evaluations on two benchmark cybersecurity corpora validate the efficacy of our approach. On the CTI-Reports dataset, XLNet-CRF achieves a precision of 97.41% and an F1-score of 97.43%; on MalwareTextDB, it attains a precision of 85.33% and an F1-score of 88.65%—significantly surpassing strong BERT-based baselines in both accuracy and robustness. Full article

Frequency-hopping multiple access is widely adopted to blunt narrow-band jamming and limit spectral disclosure in cyber–physical systems, yet its practical resilience depends on three sequence-level properties. First, balancedness guarantees that every carrier is occupied equally often, removing spectral peaks that a jammer or energy detector could exploit. Second, a wide gap between successive hops forces any interferer to re-tune after corrupting at most one symbol, thereby containing error bursts. Third, a no-hit zone (NHZ) window with a zero pairwise Hamming correlation eliminates user collisions and self-interference when chip-level timing offsets fall inside the window. This work introduces an algebraic construction that meets the full set of requirements in a single framework. By threading a permutation over an integer ring and partitioning the period into congruent sub-blocks tied to the desired NHZ width, we generate balanced wide gap no-hit zone frequency-hopping (WG-NHZ FH) sequence sets. Analytical proofs show that (i) each sequence achieves the Lempel–Greenberger bound for auto-correlation, (ii) the family and zone sizes satisfy the Ye–Fan bound with equality, (iii) the hop-to-hop distance satisfies a provable WG condition, and (iv) balancedness holds exactly for every carrier frequency. Full article

Rangelands form the ecological and economic backbone of Namibia, yet the woody plant dynamics that sustain these landscapes remain sporadically quantified across the semi-arid interior. We investigated the characteristics (stand structure, regeneration, richness, diversity, composition, ecological importance, and indicator species) of woody communities along a pronounced south-to-north rainfall gradient (85–346 mm yr⁻¹) at five representative sites: Warmbad, Gibeon, Otjimbingwe, Ovitoto, and Sesfontein. Field sampling combined point-centered quarter surveys (10 points site⁻¹) and belt transects (15 plots site⁻¹). The basal area increased almost ten-fold along the gradient (0.4–3.4 m² ha⁻¹). Principal Coordinates Analysis (PCoA) arranged plots in near-perfect rainfall order, and Permutational Multivariate Analysis of Variance (PERMANOVA) confirmed significant site differences (F_3,56 = 9.1, p < 0.001). Nanophanerophytes dominated hyper-arid zones, while microphanerophytes appeared progressively with increasing rainfall. Mean annual precipitation explained 45% of the variance in mean height and 34% of Shannon diversity but only 5% of stem density. Indicator value analysis highlighted Montinia caryophyllacea for Warmbad (IndVal = 100), Rhigozum trichotomum (75.8) for Gibeon, Senegalia senegal (72.6) for Otjimbingwe, and Senegalia mellifera (97.3) for Ovitoto. Rainfall significantly influences woody structure and diversity; however, other factors also modulate density and regeneration dynamics. This quantitative baseline can serve as a practical toolkit for designing site-specific management strategies across Namibia’s aridity gradient. Full article

Background/Objectives: Selective serotonin reuptake inhibitors (SSRIs), widely prescribed for anxiety disorders, may negatively impact the gut microbiota, contributing to dysbiosis. Considering the gut–brain axis’s importance in mental health, probiotics could represent an effective adjunctive strategy. This study evaluated the effects of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 on microbiota composition, metabolic activity, and immune markers in fecal samples from patients with anxiety on SSRIs, using the SHIME^® (Simulator of the Human Intestinal Microbial Ecosystem) model. Methods: The fecal microbiotas of four patients using sertraline or escitalopram were inoculated in SHIME^® reactors simulating the ascending colon. After stabilization, a 14-day probiotic intervention was performed. Microbial composition was assessed by 16S rRNA sequencing. Short-chain fatty acids (SCFAs), ammonia, and GABA were measured, along with the prebiotic index (PI). Intestinal barrier integrity was evaluated via transepithelial electrical resistance (TEER), and cytokine levels (IL-6, IL-8, IL-10, TNF-α) were analyzed using a Caco-2/THP-1 co-culture system. The statistical design employed in this study for the analysis of prebiotic index, metabolites, intestinal barrier integrity and cytokines levels was a repeated measures ANOVA, complemented by post hoc Tukey’s tests to assess differences across treatment groups. For the 16S rRNA sequencing data, alpha diversity was assessed using multiple metrics, including the Shannon, Simpson, and Fisher indices to evaluate species diversity, and the Chao1 and ACE indices to estimate species richness. Beta diversity, which measures microbiota similarity across groups, was analyzed using weighted and unweighted UniFrac distances. To assess significant differences in beta diversity between groups, a permutational multivariate analysis of variance (PERMANOVA) was performed using the Adonis test. Results: Probiotic supplementation increased Bifidobacterium and Lactobacillus, and decreased Klebsiella and Bacteroides. Beta diversity was significantly altered, while alpha diversity remained unchanged. SCFA levels increased after 7 days. Ammonia levels dropped, and PI values rose. TEER values indicated enhanced barrier integrity. IL-8 and TNF-α decreased, while IL-6 increased. GABA levels remained unchanged. Conclusions: The probiotic combination of Lactobacillus helveticus R0052 and Bifidobacterium longum R0175 modulated gut microbiota composition, metabolic activity, and inflammatory responses in samples from individuals with anxiety on SSRIs, supporting its potential as an adjunctive strategy to mitigate antidepressant-associated dysbiosis. However, limitations—including the small pooled-donor sample, the absence of a healthy control group, and a lack of significant GABA modulation—should be considered when interpreting the findings. Although the SHIME^® model is considered a gold standard for microbiota studies, further clinical trials are necessary to confirm these promising results. Full article

The newly developed automorphism ensemble decoder (AED) leverages the rich automorphisms of Reed–Muller (RM) codes to achieve near maximum likelihood (ML) performance at short code lengths. However, the performance gain of AED comes at the cost of high complexity, as the ensemble size required for near ML decoding grows exponentially with the code length. In this work, we address this complexity issue by focusing on the factor graph permutation group (FGPG), a subgroup of the full automorphism group of RM codes, to generate permutations for AED. We propose a uniform partitioning of FGPG based on the affine bijection permutation matrices of automorphisms, where each subgroup of FGPG exhibits permutation invariance (PI) in a Plotkin construction-based information set partitioning for RM codes. Furthermore, from the perspective of polar codes, we exploit the PI property to prove a subcode estimate convergence (SEC) phenomenon in the AED that utilizes successive cancellation (SC) or SC list (SCL) constituent decoders. Observing that strong SEC correlates with low noise levels, where the full decoding capacity of AED is often unnecessary, we perform path pruning to reduce the decoding complexity without compromising the performance. Our proposed SEC-aided path pruning allows only a subset of constituent decoders to continue decoding when the intensity of SEC exceeds a preset threshold during decoding. Numerical results demonstrate that, for the FGPG-based AED of various short RM codes, the proposed SEC-aided path pruning technique incurs negligible performance degradation, while achieving a complexity reduction of up to 67.6%. Full article

In this paper, voxel-based Asymptotic Homogenization (AH) is employed to calculate the thermal expansion and thermal conductivity characteristics of lattice materials that have a Representative Volume Element (RVE) with non-orthogonal periodic bases. The non-orthogonal RVE of the cellular lattice is discretized using voxel elements (iso-parametric hexahedral element, on a cartesian grid). A homogenization framework is developed in python that uses a fast-nearest neighbor algorithm to approximate the (non-orthogonal) periodic boundary conditions of the discretized RVE. Validation studies are performed where results of the homogenized Thermal Expansion Coefficient (TEC) and thermal conduction performed in this paper are compared with results generated by commercially available software. These included comparison with the results for (a) bi-material unidirectional composite with orthogonal RVE cell envelope; (b) bi-material hexagon lattice with orthogonal cell envelope; (c) bi-material hexagon lattice with non-orthogonal cell envelope; and (d) bi-material square lattice. A novel approach of visualizing the contribution of each voxel towards the individual terms within the homogenized thermal conductivity matrix is presented, which is necessary to mitigate any potential errors arising from the numerical model. Additionally, the effect of the thermal expansion and thermal conductivity for bi-material hexagon lattice (orthogonal and non-orthogonal RVE cell envelope) are presented for varying internal cell angles and all permutations of material assignments for a relative density of 0.3. It is found that when comparing the non-orthogonal RVE with the Orthogonal RVE as a reference model, the numerical error due to approximating the periodic boundary condition for the non-orthogonal bi-material hexagon is generally less than 2% as the numerical error is pseudo-cyclically dependent on the discretization along the cartesian axis. Full article

The escalating threat of climate change has intensified the global urgency to accurately predict carbon dioxide (CO₂) emissions for sustainable development, particularly in developing economies experiencing rapid industrialization and globalization. Traditional Extreme Learning Machines (ELMs) offer rapid learning but often yield unstable performance due to random parameter initialization. This study introduces a novel hybrid model, Red-Billed Blue Magpie Optimizer-tuned ELM (RBMO-ELM) which harnesses the intelligent foraging behavior of red-billed blue magpies to optimize input-to-hidden layer weights and biases. The RBMO algorithm is first benchmarked on 15 functions from the CEC2015 test suite to validate its optimization effectiveness. Subsequently, RBMO-ELM is applied to predict Indonesia’s CO₂ emissions using a multidimensional dataset that combines economic, technological, environmental, and globalization-driven indicators. Empirical results show that the RBMO-ELM significantly surpasses several state-of-the-art hybrid models in accuracy (higher R²) and convergence efficiency (lower error). A permutation-based feature importance analysis identifies social globalization, GDP, and ecological footprint as the strongest predictors underscoring the socio-economic influences on emission patterns. These findings offer both theoretical and practical implications that inform data-driven Artificial Intelligence (AI) and Machine Learning (ML) applications in environmental policy and support sustainable governance models. Full article

Herein, a matrix representation of the Hamilton quaternion group by 4 × 4 square matrices with entries equal to −1, 0, or 1 is defined. It is proven that this group, denoted as $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$, is a group of rotational symmetries of the four-dimensional hypercube ${\mathbb{Z}}_2^4,$ that is, a subgroup of the special orthogonal group ${\mathrm{SO}}_4\left(\mathbb{Z}\right).$ As a consequence, $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ is a group of rotational symmetries for each of the biological hypercubes RNY, YNY, YNR, and RNR. It is also proven that $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ is a group of permutations of the eight cubes contained in the four-dimensional hypercube ${\mathbb{Z}}_2^4.$ The latter is a novel result. It is also proven that the matrix quaternion group $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ is a normal subgroup of ${\mathrm{SO}}_4\left(\mathbb{Z}\right)$ and that the latter is a semidirect product of $\left({\mathrm{Q}}_{\mathrm{M}},\circ \right)$ with a copy of the special orthogonal group ${\mathrm{SO}}_3\left(\mathbb{Z}\right),$ also called an octahedral group because it is a group of rotational symmetries of a regular octahedron or of a three-dimensional cube. Full article

Metaheuristics are powerful optimization techniques that are well-suited for addressing complex combinatorial problems across diverse scientific and industrial domains. However, their application to computationally expensive problems remains challenging due to the high cost and significant number of fitness evaluations required during the search process. Surrogate modeling has recently emerged as an effective solution to reduce these computational demands by approximating the true, time-intensive fitness function. While surrogate-assisted metaheuristics have gained attention in recent years, their application to complex scheduling problems such as the Permutation Flowshop Scheduling Problem (PFSP) under learning, deterioration, and maintenance effects remains largely unexplored. To the best of our knowledge, this study is the first to investigate the integration of surrogate modeling within the artificial bee colony (ABC) framework specifically tailored to this problem context. We develop and evaluate two distinct strategies for integrating surrogate modeling into the optimization process, leveraging the ABC algorithm. The first strategy uses a Kriging model to dynamically guide the selection of the most effective search operator at each stage of the employed bee phase. The second strategy introduces three variants, each incorporating a Q-learning-based operator in the selection mechanism and a different evolution control mechanism, where the Kriging model is employed to approximate the fitness of generated offspring. Through extensive computational experiments and performance analysis, using Taillard’s well-known standard benchmarks, we assess solution quality, convergence, and the number of exact fitness evaluations, demonstrating that these approaches achieve competitive results. Full article

This paper examines temporal symmetry breaking and structural duality in a class of time-changed McKean–Vlasov neutral stochastic differential equations. The system features super-linear drift coefficients satisfying a one-sided local Lipschitz condition and incorporates a fundamental duality: one drift component evolves under a random time change $E_t$, while the other progresses in regular time t. Within the symmetric framework of mean-field interacting particle systems, where particles exhibit permutation invariance, we establish strong convergence of the tamed Euler–Maruyama method over finite time intervals. By replacing the one-sided local condition with a globally symmetric Lipschitz assumption, we derive an explicit convergence rate for the numerical scheme. Two numerical examples validate the theoretical results. Full article

Background and Objectives: Investigating the adherence to antihypertensive medication and identifying patients with low adherence allows targeted interventions to improve therapeutic outcomes. Artificial intelligence (AI) offers advanced tools for analyzing medication adherence data. This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. Materials and Methods: A cross-sectional, multi-center study was conducted on 3095 hypertensive patients from community pharmacies. A structured questionnaire was administered, collecting data on sociodemographic factors, medical history, self-monitoring behaviors, and informational exposure, alongside medication adherence measured using the Romanian-translated and validated ARMS (Adherence to Refills and Medications Scale). Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. Results: A total of 79.13% of respondents had an ARMS Score ≥ 15, indicating a high prevalence of suboptimal adherence. Better adherence was statistically associated (adjusted for age and sex) with more frequent blood pressure self-monitoring, a reduced salt intake, fewer daily supplements, more frequent reading of medication leaflets, and the receipt of specific information from pharmacists. Among the ML models, CatBoost achieved the highest ROC AUC Scores across the non-adherence classifications, although none exceeded 0.75. Conclusions: Several machine learning models were developed and validated to estimate levels of medication non-adherence. While the performance was moderate, the results demonstrate the potential of AI in identifying and stratifying patients by adherence profiles. Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling. Full article

The article presents the quantum signal-induced heap transform (QsiHT) method of the QR-decomposition of multi-qubit operations. This transform can be generated by a given signal, by using different paths, or orders, of processing the data. We propose using the concept of the fast path of calculation of the QsiHT and applying such transforms on each stage of the matrix decomposition. This allows us to build quantum circuits for multi-qubit unitary operation without permutations. Unitary operations with real and complex matrices are considered. The cases of 3- and 4-qubit operations are described in detail with quantum circuits. These circuits use a maximum of 28 and 120 Givens rotation gates for 3- and 4-qubit real operations, respectively. All rotations are performing only on adjacent bit planes. For complex unitary operation, each of the Givens gates is used in pairs with two Z-rotation gates. These two types of rotations and the global phase gate are the universal gate set for multi-qubit operations. The presented approach can be used for implementing quantum circuits for $n$-qubits when $n\ge 2$, with a maximum of ($4^n/2-2^{n-1})$ Givens rotations and no permutations. Full article

Monitoring river water quality is essential for environmental sustainability and public health. This study proposes a machine learning (ML)-based framework to model, predict, and classify the Water Quality Index (WQI) using river water samples collected across India. The dataset includes eight physicochemical and microbial parameters: Temperature, pH, Dissolved Oxygen, Biological Oxygen Demand (BOD), Conductivity, Nitrate/Nitrite, Fecal Coliform, and Total Coliform. The WQI was calculated using weighted aggregation and categorized into Excellent, Good, Medium, and Poor classes. Regression and classification models—such as Linear Regression, Random Forest, Gradient Boosting, and Logistic Regression—were evaluated using MAE, RMSE, R², Accuracy, Precision, Recall, and F1-score. Spatial mapping and exploratory data analysis were conducted to identify regional patterns. Feature importance (Gini and permutation-based) and error analysis enhanced interpretability. The framework achieved over 95% agreement with manual WQI classification, highlighting its effectiveness for real-time, scalable water quality monitoring and policy support. Full article