Search Results (15,442)

This study analyzes the relationship between economic inequality, social capital, and crime across Mexican municipalities in 2020, adopting a mediation approach and an exploratory territorial analysis. Using data from the National Council for the Evaluation of Social Development Policy (CONEVAL), the National Institute of Statistics and Geography (INEGI), the Executive Secretariat of the National Public Security System (SESNSP), the National Statistical Directory of Economic Units (DENUE), and the National Electoral Institute (INE), a structural social capital index was constructed through Principal Component Analysis (PCA), a structural social capital index was constructed through Principal Component Analysis (PCA), incorporating indicators of civic participation, associational density, and local social infrastructure. The empirical strategy combines ordinary least squares (OLS) regression models, Exploratory Spatial Data Analysis (ESDA), bootstrap mediation models with 5000 resamples, and spatial regression robustness checks. The results show that inequality is positively and significantly associated with municipal crime rates; the Gini index is associated with vehicle theft (β = 0.115, p < 0.001) and homicide (β = 0.073, p < 0.001). After structural covariates are included, inequality loses statistical significance for property crime but remains associated with homicide (β = 0.098, p < 0.001). Social capital shows a significant negative association with both crime outcomes. The mediation analysis indicates a statistically significant indirect pathway linking inequality and crime through structural social capital; however, the form of mediation differs by crime type. For homicide, the evidence is consistent with complementary or partial mediation, whereas the covariate-adjusted vehicle theft model shows a competitive mediation or suppression pattern. The spatial analysis reveals territorial autocorrelation in all variables. Spatial regression robustness checks further show that structural social capital remains negatively associated with both crime outcomes after accounting for spatial dependence. The findings suggest that the inequality–crime relationship depends on contextual factors and local social structures and should be interpreted as statistical associations rather than causal effects. Full article

A naive Bayesian classifier (NBC) combined with principal component analysis (PCA) effectively differentiates the dose-dependent effects of certain groups of psychoactive drugs based on their impact on the amplitude–spectral characteristics of electrocorticograms (ECoG) in rats. This approach has been shown to be useful for pharmacological screening of agents with unknown or poorly understood activity. Despite previously obtained optimistic results, classification determination for some drugs was inaccurate, necessitating the search for possible ways to improve the predictive effectiveness of the proposed algorithm. One possible approach would be to use as input quantitative data not only the impact of the psychoactive drugs studied on the amplitude–spectral characteristics of ECoG but also connectivity changes, including the average coherence power of different pairs of leads. The aim of this study was to compare the accuracy of NBC in classifying the pharmacological mechanism of action of agents with well-known mechanisms (test set) using pharmaco-EEG data on changes in the amplitude–spectral characteristics of ECoG, coherence, and the combined use of two data sets. Materials and methods. Experiments were performed on Wistar rats with chronically implanted ECoG electrodes. The training set, relative to which the effects of the pharmacological agents from the test set were classified, were the matrices of effects of 12 pharmacological agents: the NMDA antagonist dizocilpine, the D2/D3 antagonists haloperidol and sulpiride, the M-anticholinergic tropicamide, the H1/5HT2A receptor blocker hydroxyzine, the acetylcholinesterase inhibitor galantamine, the alpha-2 adrenergic agonist dexmedetomidine, the alpha-2 adrenergic antagonist atipamezole, the adenosine receptor blocker caffeine and the GABA-mimetics aminophenylbutyric acid (phenibut), bromdihydrochlorophenylbenzodiazepine (phenazepam) and 5-ethyl-5-phenyl-2,4,6(1H,3H,5H)-pyrimidinetrione. The test set included various drugs with tropism for the targets of the training set drugs: dopamine receptor antagonists chlorpromazine, droperidol, tiapride and raclopride, H1-histamine blockers diphenhydramine and promethazine, 5-HT2-receptor blockers ritanserin and glemenserin, acetylcholinesterase inhibitor ipidacrine, alpha2-adrenergic receptor antagonist yohimbine, alpha2-adrenergic agonists medetomidine and xylazine, GABA-mimetics 5-ethyl-5-(1-methylbutyl)-2,4,6(1H,3H,5H)-pyrimidinetrione and chloral hydrate. The analysis of the ECoG signal included the calculation of 132 amplitude–spectral characteristics and 75 coherence indicators, which, using the PCA, led to new integrative indicators used for further classification of the NBC. Results and discussion. For each drug in the test set, the median similarity probability with a particular group from the training set was calculated, which was used to assess the classification quality. It was found that, when using the amplitude–spectral characteristics of ECoG, the proposed methodological approach allows for the identification of the ECoG effects of several groups of psychoactive drugs, including D2/D3-dopamine, M-cholinergic, H1-histamine, and 5-HT2-serotonin receptor blockers, AChE inhibitors, GABA-mimetics, and alpha-2-adrenergic receptor agonists and antagonists. This approach enabled the correct classification of 18 of 24 groups in the test set. When using changes in coherence indices as the initial data, the classification accuracy also amounted to 18 of 24 groups. When combining the two data sets, the number of correctly identified NBC groups was 20 of 24 groups. When comparing the classification during training (confusion matrix), it was found that coherence data or adding coherence data to the data based on changes in amplitude–spectral characteristics leads to a statistically significant (p < 0.01 in both cases) increase in accuracy. Conclusions. The obtained data demonstrated high accuracy in classifying the pharmacological activity of the test sample drugs using any of the three compared approaches. Despite the lack of statistically significant differences between them, classification based on the combined dataset demonstrated a higher number of “correct” similarities. This allows us to recommend the approach based on combined data of drug effects on amplitude–spectral characteristics and coherence as the most promising for further studies using pharmaco-EEG screening. Full article

Structural analysis using X-rays and electron beams has long provided the average arrangement of atoms and molecules—that is, “structural information”—with high precision. By contrast, static measurements cannot directly yield dynamic information on how a material changes over time; instead, information on motion is convolved into a single numerical value such as the B-factor (atomic displacement parameter). Taking this limitation as its starting point, this review surveys the recent trend of introducing a time axis into measurements to observe material dynamics directly. First, we outline the technological foundations that have made the transition from static to time-resolved measurement possible. It rests on the dramatic shortening of exposure times, enabled by the increased brilliance of X-ray and electron sources and by advances in detection technology such as direct photon-counting detectors. Next, we survey dynamic measurement techniques, including time-resolved X-ray crystallography, coherent X-ray scattering, neutron scattering, and time-resolved electron microscopy. We also point out the essential limitation that most of them still return ensemble or volume averages. Building on this, we systematically describe diffracted X-ray tracking (DXT), diffracted X-ray blinking (DXB), small-angle X-ray blinking (SAXB), transmitted X-ray blinking (TXB), and electron-beam molecular dynamics (EBMD), which use gold nanocrystals and gold nanoparticles as motion probes. We distinguish throughout between methods that follow individual objects—DXT and EBMD, which yield trajectories of single labeled molecules or single particles—and methods that analyze intensity fluctuations arising from many contributors within one pixel or illuminated volume—DXB, SAXB and TXB. The latter are not single-molecule measurements; rather, they replace a global ensemble average by a spatially localized statistical one, retaining local heterogeneity that a bulk measurement would average away. Finally, we discuss the implementation and prospects of the large-volume data analysis—principal component analysis, Bayesian inference, machine learning, and autonomous measurement—needed to handle the explosively increasing amount of information that the time axis introduces. We close with the outlook that time-resolved measurement incorporating AI and big-data analysis will become established as a new measurement platform that complements and extends conventional static structural analysis. Full article

Rotating machinery prognostics is essential for ensuring the reliability and operational safety of industrial systems. Although gated recurrent units (GRUs) have achieved competitive performance in remaining useful life (RUL) prediction, whether their internal dynamics are consistent with irreversible degradation mechanisms remains largely unexplored. This study revisits GRU-based prognostics from a physics-consistency perspective and analyzes the potential mismatch between gating mechanisms and degradation evolution. A full-life benchmarking framework is developed based on the XJTU-SY bearing run-to-failure dataset. A training-based health indicator (HI) is constructed through multi-domain vibration feature extraction and principal component analysis, where the degradation-state representation and RUL prediction objective are explicitly distinguished to avoid physically inconsistent supervision. Several representative approaches, including statistical models and deep learning architectures (LSTM, GRU, TCN, and Transformer), are evaluated using both prediction accuracy metrics (RMSE, MAE, and R²) and physical consistency criteria (monotonicity index, monotonicity violation index, and degradation trend consistency). Experimental results demonstrate that superior prediction accuracy does not necessarily guarantee physically consistent degradation modeling. Although GRU provides competitive RUL prediction performance, its hidden-state evolution and gating responses exhibit noticeable non-monotonic behaviors during degradation progression. These findings reveal a potential discrepancy between prediction-oriented recurrent learning mechanisms and irreversible degradation dynamics, highlighting the importance of incorporating physics-consistency evaluation into reliable data-driven prognostic models. Full article

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article

Background/Objectives: Exercise medicine is gaining significant importance in adult oncology to improve physical activity and quality of life (QoL). However, few studies have investigated the effects of adapted physical activities in the pediatric population. The aim of this secondary analysis is to compare the movement quality of a hematological/oncological pediatric population before and after an individualized exercise program (IEP). Methods: Participants who met the inclusion criteria (aged 5–18 years, oncological/hematological diagnosis, consent) underwent an IEP delivered both in the hospital and via telemedicine at home. The subject’s movement quality was assessed before and after six months of the IEP using instrumented functional tests (i.e., 6 min walking test, Timed Up and Go, T25-FW, and turning test). To reduce the number of variables obtained, principal component analysis (PCA) was performed, and the resulting scores were then compared. Results: A total of 18 subjects (median age 12 years; eight females) were included in the analysis. Through the PCA, four dimensions were identified: Gait Efficiency, Gait Quality, Dynamic Balance, and Functional Abilities. The pre–post comparison showed significant improvements (p = 0.002) in Dynamic Balance and a trend (p = 0.05) in Gait Efficiency. Conclusions: The presented results indicate the crucial Principal Components (PCs) of movement in children treated for cancer. Moreover, in our setting, under the IEP we observed a significant improvement in Dynamic Balance. The results are consistent with those observed in previous studies. However, the limited number of subjects and the study design preclude definitive conclusions. Full article

Mobile robots and autonomous vehicles require tightly regulated direct current (DC) power under rapidly varying load conditions, motivating control strategies that combine fast nonlinear regulation with predictive optimization. This paper proposes a Hybrid Sliding Mode Control and Model Predictive Control (Hybrid SMC + MPC) strategy for a DC-DC buck converter supplying a representative mobile-robot mission load. The controller employs a cascade SMC structure for fast inner-loop regulation and an MPC component that provides finite-horizon duty-cycle correction using planned load information. The MPC problem is formulated in condensed form and solved analytically without an external optimization solver. A Lyapunov-based analysis establishes a sufficient reaching condition for the sliding variable under the ideal averaged-model assumptions, and the condition is verified for the simulated mission. The proposed approach is evaluated in MATLAB using a 10-phase, 10 s load profile with resistance varying from 7 Ω to 100 Ω and is compared with SMC-only, MPC-only, PID, constant-duty, and reconstructed fuzzy-logic benchmarks. In the averaged-model study, the Hybrid SMC + MPC achieves a maximum absolute voltage deviation of 0.388 V, an RMSE of 0.0115 V, and a final-phase mean absolute error of 0.0076 V. It provides the lowest maximum voltage deviation among the principal closed-loop controllers, while PID achieves the lowest RMSE and final-phase error and SMC-only exhibits the shortest mean settling time. Relative to MPC-only, the Hybrid controller reduces the maximum voltage deviation by approximately 43.6% and the mean settling time by approximately 66.1%. An ablation study shows that the MPC contribution substantially improves overall and steady-state regulation accuracy, while load preview primarily reduces the worst-case voltage deviation. Switching-level MATLAB/Simulink validation with explicit 20 kHz PWM and converter parasitics confirms that the output remains within ±2% of the 25 V reference throughout the complete mission, with a maximum absolute deviation of 0.443 V and a maximum steady-state switching ripple of 21.6 mV peak-to-peak. These results demonstrate that the proposed Hybrid SMC + MPC architecture provides a favorable balance between worst-case transient regulation, steady-state accuracy, and predictive control capability for dynamically varying robotic power loads. Full article

The increasing tourism pressure on the UNESCO World Heritage Altai Mountains calls for efficient environmental monitoring tools. This study presents a Google Earth Engine framework that couples the Remote Sensing Ecological Index (RSEI) with land use/land cover (LULC) mapping to assess ecological changes in the Lake Manzherok area between 2020 and 2025. RSEI was derived from Sentinel-2 and Landsat imagery by combining four indicators (NDVI, MNDWI, NDBSI, LST) through principal component analysis. LULC classification was carried out using Random Forest trained exclusively on Sentinel-2 spectral bands. The results confirm that RSEI effectively captures ecological gradients in complex mountainous terrain, with the first principal component explaining 57–62% of the total variance. While 92% of the study area remained stable, 5.9% showed a decline in ecological status, spatially coinciding with a near doubling of built-up and bare surfaces from 9.89 km² to 18.17 km². The largest negative RSEI changes were associated with transitions from forestland (ΔRSEI = −0.29) and grassland (ΔRSEI = −0.20) to built-up/bare land, whereas reverse transitions displayed positive ΔRSEI values. These spatial patterns are consistent with the visible development related to tourism. However, because the built-up/bare land class also includes naturally bare surfaces, and because interannual climate variability may affect the RSEI components, it is important to interpret the ΔRSEI values as relative changes rather than absolute measurements of tourism impact. The proposed framework provides a reproducible and transferable tool for monitoring ecological quality in data-scarce mountain regions, delivering spatially explicit evidence that can support conservation and land-use planning. Full article

Szechuan pepper pungency is traditionally assessed by subjective sensory panels or laboratory instruments, hindering on-site detection. Perceived numbing sensation does not always correspond directly to alkylamide content because multiple electroactive constituents, including polyphenols and sanshools, may contribute to the overall sensory response. Chromatographic methods quantify individual compounds but may not fully reflect integrated human pungency perception. Electrochemical detection bypasses separation, as the voltammetric response integrates oxidative signals from multiple electroactive species. We developed a portable electrochemical system with a custom programmable-gain potentiostat, three-electrode detector, and STM32-controlled software for differential pulse voltammetry (DPV) measurement. Coupled with machine learning, it assessed Zanthoxylum bungeanum (red peppers) and Zanthoxylum schinifolium (green peppers). Fifteen replicate scans from each of 22 origins yielded 330 DPV curves calibrated against general Labeled Magnitude Scale (gLMS) scores from a trained panel. An artificial neural network (ANN) achieved R² = 0.937 for red peppers, while principal component analysis–support vector regression (PCA–SVR) achieved R² = 0.860 for green peppers. Competitive adaptive reweighted sampling (CARS) identified three characteristic potential intervals for each type: 0.17–0.21, 0.57–0.62, and 0.69–0.80 V for red peppers, and 0.24–0.27, 0.56–0.68, and 0.69–0.77 V for green peppers. These intervals indicate that pungency-related electrochemical information is distributed across multiple potential regions. The system shows potential for rapid and objective quality assessment of Szechuan pepper. Full article

The rule of law can be considered as a multidimensional institutional phenomenon, which emerges through interplay between legal, governance and administrative institutions. The paper offers an exploratory statistical modeling approach to find empirical patterns in national rule-of-law profiles according to the 2024 World Justice Project (WJP) Rule of Law Index. Eight dimensions of the index are considered to identify differences between countries and similarities of their multidimensional institutional performance. Principal Component Analysis reveals strong associations between eight dimensions, which are structured along the same performance institutional scale; the first principal component explains 85.7% of the overall variation and two principal components explain 92.5% of it. K-Means, hierarchical and DBSCAN clustering methods are then used to examine the empirical similarities between countries. While the six-cluster solution of K-Means offers distinct group descriptions, low bootstrap stability of this solution suggests that these groups cannot be regarded as fixed rule-of-law regimes. In addition, the Random Forest analysis reveals Regulatory Enforcement, Absence of Corruption, and Criminal Justice as the three dimensions, which contribute to the empirical differentiation of the described profiles the most. In general, the results imply that international variations in rule-of-law performance are viewed as heterogeneous locations in a multidimensional institution space, rather than as stable and distinct legal systems. The above-presented methodology allows for an exploratory approach to analyze international variations in rule-of-law performance that considers the limitations of cross-section data and instability of clusters. Full article

Context: Forage bermudagrass (Cynodon dactylon) is widely used in warm-season livestock production systems because of its high productivity and adaptability. However, systematic evaluation of forage-type germplasm remains limited, restricting the identification of superior breeding materials. Aims: This study aimed to evaluate phenotypic variation, identify key yield-related traits, and identify high-performing forage bermudagrass germplasm with contrasting interannual phenotypic responses derived from a ‘Wrangler’ × ‘CD-21’ hybrid population. Methods: Two evaluation populations were established. A single-genotype population of 621 individuals was used to assess plant and canopy height variation, whereas 16 representative entries were evaluated for biomass yield and major agronomic traits during 2024–2025. Frequency distribution, principal component, correlation, and path analyses were conducted. Key results: Stem height and canopy height showed unimodal, approximately normal distributions, indicating continuous phenotypic variation and supporting their characterization as quantitative traits. Biomass yield was positively associated with stem height (r = 0.79), canopy height (r = 0.82), and internode length (r = 0.63). Path analysis indicated that stem height had the largest estimated direct effect (β = 0.45) on biomass yield within the proposed path model. Multivariate analyses revealed distinct phenotypic differences among entries and years, allowing classification into high-performing, environmentally responsive, and leaf-structure efficient groups. Conclusions: Stem height, canopy height, and internode length were identified as key traits associated with forage biomass production. Integrating multivariate and path analyses effectively differentiated forage bermudagrass germplasm based on yield performance and agronomic traits. Implications: The identified germplasm and trait relationships provide useful information for further breeding evaluation and selection decisions and support the development of improved forage bermudagrass cultivars. Full article

Machine learning (ML) integrated with metabolomics has emerged as a promising strategy to advance precision pharmacotherapy, enabling data-driven prediction of drug response. This review provides an overview of commonly applied ML methodologies in metabolomics-based pharmacological studies, including supervised models (Random Forest, Extreme Gradient Boosting, Support Vector Machine, Logistic Regression, K-Nearest Neighbors), unsupervised models (K-Means Clustering, Principal Component Analysis), and deep learning approaches. We summarize recent progress in the application of metabolomics-driven ML to personalized medication, with a focus on drug dosage optimization, therapeutic efficacy prediction, and adverse drug reaction assessment. Despite these advances, significant challenges remain, including limited explainability, insufficient prospective clinical validation, lack of standardization and reproducibility, and data dimensionality and quality issues. Addressing these issues will be essential for the clinical translation of ML-metabolomics integration. Looking ahead, continued methodological innovation, large-scale multi-center prospective validation, and integration with other omics platforms will be key to unlocking the full potential of metabolomics combined with ML in precision healthcare. Full article

Raspberry ketone (RK) is a valuable natural flavor compound, but its extraction from plants is inefficient because of its low natural abundance. Microbial production of RK offers a promising alternative; however, insufficient availability of malonyl-CoA can limit RK biosynthesis. In this study, we introduced a malonate-dependent malonyl-CoA supply module into an engineered Escherichia coli strain designed for de novo RK production through heterologous expression of malonyl-CoA synthetase MatB and malonate transporter MatC. In the presence of malonate, the introduction of the MatB–MatC module increased RK production by 2.3-fold. To optimize malonate supplementation, RK pathway metabolites, including intracellular acyl-CoA intermediates, were quantified by liquid chromatography–mass spectrometry, and the resulting metabolite profiles were analyzed by principal component analysis, hierarchical clustering, and correlation analysis. This study indicated that 50-mM malonate was optimal, yielding 24 mg/L RK during 96-deep-well plate cultivation. Fed-batch optimization increased RK production to 301 mg/L in a 100-mL jar fermenter, and scale-up cultivation in a 2-L jar fermenter produced 340 mg/L RK. In this study, optimizing MatB–MatC-dependent malonyl-CoA supply, combined with pathway-level metabolic profiling and controlled fed-batch cultivation, enhanced de novo RK production in E. coli. Full article

Background: Wampee (Clausena lansium (Lour.) Skeels) is an understudied Rutaceae crop native to southern China, whose fruit features a complex aroma profile with simultaneous sour, sweet, bitter and astringent notes. Although bioactive compounds including flavonoids, alkaloids and volatile oils in wampee fruit have been partially characterized, the tissue-specific metabolic and transcriptional basis underlying its distinctive aroma formation remains largely unclear. Methods: We performed an integrated volatile metabolomic and transcriptomic analysis on the pericarp (peel) and flesh of wampee fruit across three cultivars (Shanyellowpi, Heijingang, Bingtangxin). Volatile metabolites were profiled via headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS), and transcriptome profiles were generated by RNA-Seq. Multi-omics integration was conducted using Procrustes analysis, gene–metabolite correlation network construction and weighted gene co-expression network analysis (WGCNA). Results: A total of 288 volatile metabolites were identified, representing the most comprehensive volatile inventory for C. lansium reported to date. Principal component analysis and partial least squares discriminant analysis revealed distinct volatile profiles between pericarp and flesh; terpenoids were the dominant chemical class, accounting for 71.56–91.48% of total volatiles in pericarp and 61.88–67.22% in flesh. Notably, organoheterocyclic compounds were significantly enriched in Shanyellowpi flesh (40.45%), forming a cultivar-specific metabolic signature absent in the other two cultivars. Transcriptomic analysis showed that phenylpropanoid biosynthesis was the most significantly enriched pathway among differentially expressed genes, followed by monoterpene biosynthesis and sesquiterpenoid biosynthesis. Procrustes analysis demonstrated a strong global concordance between the two omics layers (M² = 0.535, p < 0.001). Gene–metabolite correlation networks identified terpene synthase (TPS) genes (HP075360, HP217350) and oxidoreductase genes (SOD1, GST, 10HGO) as candidate co-regulators of terpenoid biosynthesis. WGCNA further prioritized TPS genes, cytochrome P450 genes and MYB transcription factor genes as key regulators driving volatile metabolic divergence between tissues. Conclusion: This study provides a comprehensive volatile and transcriptomic atlas of wampee fruit, and identifies tissue-specific and cultivar-specific metabolic signatures as well as their candidate regulatory genes. These findings advance our understanding of quality differentiation in Rutaceae fruits and lay a foundation for molecular breeding and flavor improvement of wampee. Full article

The Old City of Sana’a, a UNESCO World Heritage Site, remains morphologically unclassified at the urban block scale, with understanding still grounded in descriptive narrative rather than quantitative data—leaving conservation decisions without a measurable, reproducible spatial evidence base. This study introduces a numerical taxonomy of 115 historic urban blocks built from four standardized morphometric indicators—area, compactness, elongation, and rectangularity—together with axially encoded orientation, reduced through Principal Component Analysis (74.7% variance, three components) and partitioned by K-means into five morphologically distinct block types ($n=36,29,19,16,15$). Kruskal–Wallis tests confirmed significant differentiation across all types for every rankable indicator (all $p<0.001$; Dunn’s post-hoc: 28 of 50 pairwise contrasts significant). Qibla deviation—a culturally specific measure of angular proximity to Mecca, deliberately withheld from clustering—emerged as the strongest discriminator between types (${\eta }_H^2=0.714$), showing that shape and orientation alone recover a coherent pattern in which one type’s mean orientation axis falls within $4.5^{\circ }$ of the Qibla axis. However, this statistical discriminator reflects the distinctiveness of a single type rather than a universal cultural organizing principle across the fabric. Types are distributed across all three historic zones rather than clustering spatially, and shape regularity shows no association with distance from the Great Mosque. Building on these signatures, a percentile-based screening instrument offers a reproducible, block-level morphometric baseline to help prioritize this UNESCO-listed site’s conservation under active conflict-related threat. Full article