Search Results (11,821)

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

The rising global temperature and changing climate patterns have increased the frequency and intensity of extreme heat events, droughts, and heavy precipitation, significantly affecting agriculture, water resources, and ecosystems. Solar radiation management (SRM) has been proposed as a geoengineering strategy to mitigate these effects by reducing incoming solar radiation. This study evaluated future trends and variability in rainfall and temperature extremes in the Philippines under GeoMIP (G6Solar and G6Sulfur) and ScenarioMIP (SSP2-4.5 and SSP5-8.5) projections. Using five General Circulation Models (GCMs) and a suite of 10 climate indices recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), changes in extreme precipitation and temperature across different climate zones in the Philippines were assessed. Climate projections for the future (2041–2070) scenario were analyzed using bias correction, downscaling, and spatial interpolation techniques. Trend analysis was evaluated using the Mann–Kendall test and Sen’s slope estimator, while variability was assessed through statistical methods. The results show widespread warming and increased extreme precipitation, but these trends vary significantly across regions. Non-uniform responses emerge across scenarios, with some northern regions experiencing decreases in specific precipitation indices despite the broader warming trend under SRM and non-SRM conditions. These findings provide critical insights into the potential impacts of SRM on future climate extremes in the Philippines and guidance on climate policy recommendations for decision-makers and stakeholders. Full article

Adaptive Reinforced Gray Langur Optimization (ARGLO), an enhanced variant of the Gray Langurs Optimizer, is developed for high-dimensional, multimodal, and nonlinear search landscapes susceptible to local trapping. Although the original GLO performs multi-population cooperative search by simulating the social structures of gray langurs, it still suffers from uneven random initialization, insufficient adaptive population partitioning, weak local perturbation, and premature convergence. ARGLO incorporates three strategies: good point set-based oppositional and quasi-oppositional learning initialization, hierarchical equilibrium adaptive population partitioning, and elite-guided hybrid mutation. Collectively, these mechanisms generate a higher-quality starting population, coordinate global search with local refinement, and reduce the risk of entrapment in suboptimal regions. Evidence from component-wise experiments together with the CEC test suite indicates that ARGLO delivers higher solution precision, steadier convergence, as well as more consistent performance, especially as dimensionality increases. Moreover, ARGLO is applied to near-infrared spectral feature selection and SVR parameter optimization for polysaccharide content prediction in Dendrobium huoshanense. Compared with unoptimized SVR, ARGLO-SVR reduces RMSE by 35.35% and improves R² by 21.92%; compared with GLO-SVR, it reduces RMSE by 6.05% and improves R² by 2.30%. These results demonstrate the effectiveness and application potential of ARGLO in complex optimization and rapid nondestructive quality detection of traditional Chinese medicinal materials. Full article

The Galinge deposit in East Kunlun, China is a large Fe-polymetallic skarn system with a significant by-product, Co, but the respective roles of magmatism, skarn evolution, and wall rock interaction in Co enrichment remain incompletely understood. We integrate zircon and garnet U–Pb geochronology, zircon Lu–Hf isotopes and trace elements, whole-rock geochemistry, and SEM-EDS and EPMA mineral chemistry. Granodiorite and diorite porphyry yield zircon U–Pb ages of 230.09 ± 0.91 Ma and 229.4 ± 1.3 Ma, respectively, whereas skarn garnet yields 224.4 ± 9.3 Ma, placing intrusion and skarn formation within a Late Triassic magmatic–hydrothermal system. Both suites are metaluminous, LREE-enriched, and Nb–Ta–Ti-depleted; zircon ε_Hf(t) values of −9.4 to −1.8 indicate the predominant reworking of older crustal material with variable input from a more radiogenic component. Strictly screened Ti-in-zircon temperatures and lattice strain Ce anomalies yield median apparent ΔFMQ values of +3.36 for granodiorite and +3.04 for diorite porphyry, indicating comparably oxidized magmatic conditions. The analyzed intrusions contain 2.12–13.4 ppm Co, whereas cobaltite and Co-bearing arsenopyrite contain 32.83–34.14 wt% and 0.38–4.53 wt% Co, respectively. Spatial and paragenetic relations place Co enrichment after magnetite deposition, during an early sulfide-stage hydrothermal sulfarsenide event within the skarn system. We infer that Late Triassic intrusions supplied heat, fluids, and ligands, whereas structural focusing and cooling, coupled with carbonate wall rock reactions and a reduction in carbonaceous or Fe²⁺-bearing domains, promoted As–S-rich Co precipitation; the leaching of intermediate–mafic wall rocks may have supplemented the Co inventory. Full article

Organic dairy farming has expanded rapidly over the past three decades, driven by regulatory reforms, consumer demand, and growing recognition of its environmental, animal welfare, and potential human health benefits. Despite this growth, evidence comparing organic and conventional dairy systems remains fragmented across genetics, phenomics, animal health, and human health outcomes. This review synthesizes current knowledge through the lens of genotype × environment interactions, integrating evidence from four complementary domains: (1) genomic architecture and breeding strategies; (2) phenotypic performance, including milk production and composition, meat quality, nutrition, and reproductive traits; (3) animal health, disease resistance, antimicrobial use, and welfare; and (4) implications for human health. Holstein–Friesian cattle remain the predominant breed in both systems; however, organic production favors animals with greater robustness, longevity, grazing efficiency, and disease resilience. Genetic studies further demonstrate that highly heritable production traits share similar genetic architecture across production systems, whereas health, fertility, longevity, and other low-heritability functional traits exhibit stronger genotype × environment interactions and more system-specific genomic signatures. These findings suggest that breeding strategies developed for high-input conventional systems are unlikely to maximize performance under organic management. Collectively, the evidence supports a shift from selection focused primarily on milk yield toward genomic improvement of robustness, disease resistance, reproductive resilience, grazing adaptation, and lifetime productivity. We propose Genomically Optimized Organic Dairy (GOOD) as an emerging framework that integrates genomic selection, precision phenotyping, health monitoring, and environmental adaptation to develop dairy cattle better suited to organic production. Full article

During software testing, memory errors in C/C++ can silently corrupt the program state. Address-based memory sanitizers, while offering practical performance and compatibility, are fundamentally unable to distinguish spatial errors that skip redzones or temporal errors that occur after memory reuse. Moreover, their reuse-delay quarantine mechanisms impose significant space and time overhead. We propose a taxonomy of memory sanitizers based on validity encoding and violation detection. Guided by this taxonomy, we introduce fused metadata, a single 8-byte word that encodes an object’s end address and a 6-bit identity tag. MixSan, a prototype built on RangeSanitizer (RSan), stores the same identity tag in pointer high bits through Intel Linear Address Masking (LAM) U57 and validates both the tag and the spatial bound with a unified 3-ALU-op check. On SPEC CPU2006, MixSan incurs a 1.58× geomean runtime overhead, comparable to RSan’s 1.61× overhead. On the Larson allocator benchmark, MixSan and the uninstrumented tcmalloc both scale with thread count, whereas RSan throughput falls; MixSan’s multi-thread plateau is more than 30× that of RSan. In a custom 97-test suite targeting post-reuse temporal errors and redzone-skipping spatial errors, MixSan’s mean single-run detection rate is 98.41% over ${10}^4$ independent executions per program, matching the theoretical $63/64$ rate given a uniform 6-bit tag distribution, whereas ASan and RSan do not detect these constructed cases. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

Accurate precipitation data are critical for hydrological and climatic studies in Guangdong Province, where complex terrain and frequent extreme rainfall pose substantial challenges. However, the performance of gridded precipitation products is still not well understood. This study evaluates nine products, including gauge-based (CHM_PRE, CN05.1, GMCP, NOAA CPC), satellite-based (IMERG-E, IMERG-F, TMPA RT, TMPA 3B42), and ERA5 reanalysis against NCDC observations from 2001 to 2019 using metrics including trend significance, correlation (R), root mean square error (RMSE), categorical statistics (POD, FAR, ETS), and relative bias across rainfall intensities. The results indicate that, based on validation against NCDC observations, CHM_PRE performs the best across all temporal scales, capturing significant increasing trends (p < 0.05) and achieving the highest consistency with observations at the annual (R = 0.99), monthly (R = 0.99), and daily (R = 0.89) scales. Using CHM_PRE as the reference, CN05.1 shows the highest spatial consistency with it, especially for extreme events. NOAA CPC exhibits the best performance in monthly event detection (ETS = 0.42; BIAS ≈ 1). Satellite products show acceptable performance at the monthly scale but exhibit intensity-dependent biases and high daily variability, with pronounced “light rain overestimation and heavy rain underestimation.” ERA5 shows limitations, particularly in its severe underestimation of extreme precipitation. CHM_PRE is thus identified as the most suitable dataset for Guangdong based on its agreement with NCDC observations. With CHM_PRE as the reference, CN05.1 provides a reliable alternative for spatial analyses; NOAA CPC performs the best in monthly event detection. Satellite products suit monthly use but require daily-scale caution; ERA5 shows a relatively poor performance. Full article

To address the limited cooling range of ventilation in deep, high-temperature development headings and the lack of coordinated design between coil-based cooling and the ventilation system, this study proposes a coupled “twin-forcing–coil” cooling scheme. Building on conventional overlap (forcing–exhausting) ventilation, a rear-mounted second forcing duct is added to the conventional overlap (force–exhaust combined) auxiliary ventilation system, forming a dual-duct forcing, single-exhausting configuration—hereafter termed the “twin-forcing–single-exhausting” (TFSE) system—that provides a booster (relay) air supply to mitigate the along-path attenuation of cooling capacity and the short-circuiting of cold air; an in situ heat-exchange coil wall further provides supplementary cooling where ventilation-based temperature control weakens. Using a development heading at the 790 m level of a metal mine in Yunnan as the engineering background, a three-dimensional numerical model coupling the roadway, ventilation system, and coil wall was established and validated against nine field monitoring points, showing average relative errors of approximately 1% for temperature and 2–3% for humidity, comparable to the measurement uncertainty of the field instrumentation. Because the numerical model does not account for evaporative and condensation phase-change processes, two supplementary development headings with standing water at the face were used for validation; results showed that model error increases with water accumulation and heading length, indicating the model’s applicability is limited to conditions with intact surrounding rock and minimal seepage. Six operating cases were designed with duct placement and coil spacing as variables. Results show that single-duct ventilation cooling decays markedly beyond 30 m from the face, whereas twin-forcing booster (relay) air supply effectively extends the cooling range, reducing the 30–70 m section temperature by 2.7–2.9 K; the second duct should be positioned where the first duct’s cooling capacity begins to attenuate but is not yet depleted. Based on only two spacing configurations tested (10 m and 15 m), coil-staggered spacing showed limited effect on cooling performance under the field conditions examined; this preliminary finding requires validation across a broader range of spacings. Among the chilled-water conditions tested, an inlet temperature of 280.65 K and a flow velocity of 0.5 m/s offered a reasonable trade-off between cooling uniformity and economic efficiency. Under the boundary conditions and equipment parameters of this case, energy consumption estimates further indicate that the cooling effect per unit electricity consumption of twin-forcing ventilation is roughly 6–8 times that of coil-based cooling, primarily due to pumping losses over the ~240 m chilled-water delivery distance. This energy penalty indicates that coil-based cooling is better suited as a localized, short-distance supplementary measure rather than as a means of extending the cooling range over long distances. Full article

The rapid expansion of fiber-reinforced polymers (FRPs) in wind energy, transportation, and aerospace is generating increasing amounts of accompanied waste, making effective valorization essential to a circular economy. This review compares FRP recovery technologies in terms of recovered-fiber quality, operating conditions, post-treatment, environmental impacts, and industrial applicability. Then, it also examines direct reuse, FRP remanufacturing, and reuse in cementitious composites. Quantitative synthesis indicates that high-quality recycled carbon fibers (rCFs) generally retain more than 90% of their original strength, whereas mechanically recovered glass fibers (rGFs) typically retain approximately 70–90%. The preferred pathway depends on the intrinsic value, damage state, morphology, and residual properties. Components with sufficient residual capacity should be directly reused; high-quality fibers are better suited to polymer remanufacturing; and heterogeneous or lower-grade glass-FRP (GFRP) fractions are more compatible with cementitious applications, where mechanically recycled GFRP can provide interfacial bond strengths comparable to conventional engineering macrofibers. Future research should establish quantitative links among recovered material quality, processing, interfacial behavior, and end-use performance, while adopting consistent environmental and economic assessment boundaries. A graded utilization framework is therefore required to support both large-scale and value-added reuse of FRP waste. Full article

Federated, semi-supervised novelty detection is well suited for intrusion detection on resource-constrained Internet of Things (IoT) nodes: each device learns a model of benign traffic, shares only summary statistics, and does not transmit raw traffic samples. A previously published cross-layer federated detector, Baseline K-Means, showed that periodically merging worker statistics through a coordinator raises the detection rate, but it also exhibited a systematic side effect: after every merge, the precision decays, and the false-positive rate (FPR) climbs because the coordinator recomputes its threshold from streaming distances filtered by the closest observed anomaly, so tightens after every merge, flagging progressively more benign traffic; the threshold was also hand-tuned. We present AF-BKM, an Adaptive Federated Baseline K-Means that repairs the federated mechanism with two label-free, statistics-only enhancements, denoted as E1 and E2: (i) an adaptive decision threshold read from the benign Mahalanobis-distance distribution, requiring no manual percentile search and no attack labels (E1), and (ii) a robust, benignly anchored aggregation that blends worker means under quality weighting and outlier-worker filtering and recalibrates the threshold on a trusted benign anchor to a stable, anchor-referenced false-positive level, which a target-FPR rule can make operator-selectable instead of tightening it toward the nearest anomaly (E2). With MinMax scaling fit only on benign baseline data and non-IID federated streams on NSL-KDD, UNSW-NB15 and the N-BaIoT corpus of real traffic from commercial IoT devices, AF-BKM removes the merge-induced precision decay (the first-to-last-epoch precision change improves from $-0.134$ to $-0.002$ on NSL-KDD, from $-0.121$ to $-0.014$ on UNSW-NB15, and from $-0.170$ to $-0.009$ on N-BaIoT) and reduces the mean FPR by 30–$64\%$, depending on the dataset; all central improvements are significant across 10 seeds (Wilcoxon $p=0.002$, large effect sizes). AF-BKM preserves recall on NSL-KDD and N-BaIoT and, on the harder UNSW-NB15, exposes an explicit precision–recall trade-off through a benign target-FPR knob. In fp32, the deployed model serializes to $5.5$–52 KB, a packet is classified in 11–27 µs on a desktop CPU, and each merge round uploads a $d+3$-value summary (160–472 B) $94.6$–$98.3\%$ smaller than the same summary extended with the covariance upper triangle. A robustness study covering selected faulty-worker updates, contamination of the commissioning anchor, and detector-level white-box evasion reports the measured degradation patterns: fabricated threshold candidates have no direct path to the threshold, although a fabricated mean still reaches it indirectly through the blended centroid, and the anchor-referenced false-positive level remains stable under percent-level anchor contamination, while recall sensitivity is dataset-dependent and the evasion budget tracks the benign–attack margin of each dataset. We frame the contribution with a focused taxonomy that identifies merge-induced precision decay under non-IID workers as an open gap. Code is released for reproducibility. Full article

This exploratory descriptive-correlational study analyses the perceptions of 257 adult students (doctoral, master’s, and teachers) at DPPD, USV Iași, during the 2025–2026 academic year regarding hybrid education, teaching quality, and environmental sustainability. Using a structured Likert-scale questionnaire, the analysis indicates good-to-excellent internal consistency, with Cronbach’s alpha values ranging between 0.886 and 0.901, and a high overall global average score of 4.64. The findings reveal strong support for the hybrid model. Perceived teaching quality received the highest subscale rating (M = 4.80), closely followed by the perceived ecological impact (M = 4.65). The analysis indicates strong Pearson correlations, specifically between the hybrid learning experience and perceived teaching quality (r = 0.818), as well as between the perceived ecological impact and pro-sustainability attitudes (r = 0.809). Regarding academic mobility, the estimate indicates 81,283 km of avoided commuting travel and approximately 12,295 kg of avoided commuting-related CO₂ emissions, based on self-reported distance, means of transport, and number of physical attendances replaced by online activities. These findings suggest that hybrid learning may represent a relevant managerial option for university sustainability policies. The model appears well suited to postgraduate programmes addressed to employed adults, although the ecological benefits should be read as partial and do not displace perceived teaching quality as the central factor. Full article

Background: The development of osteoarthritis (OA), a whole-joint disorder that is increasingly recognized, depends on subchondral trabecular bone. Variations in the viscoelastic characteristics of trabecular bone have been proposed to affect load distribution and potentially lead to joint degradation. The viscoelastic response of human trabecular bone in both healthy and osteoarthritic situations was investigated using constitutive modeling and stress-relaxation testing. Fifteen tibial trabecular bone specimens were evaluated using Standard Linear Solid (SLS) models and two-branch generalized Maxwell models following uniaxial stress-relaxation testing. Mechanical, energy, and relaxation-related traits were retrieved and compared between groups. Results: Healthy bone tended to relax stress more slowly and to bear mechanical loads over time to a slightly greater extent than osteoarthritic bone, which tended to relax stress more quickly and had poorer mechanical endurance; these differences were not statistically significant. The Generalized Maxwell model suited the experimental data better than the SLS model (R² > 0.98), capturing both short- and long-term relaxation mechanisms. Sensitivity analysis revealed higher parameter variability in OA specimens, suggesting possible differences in mechanical heterogeneity and load-dissipation behavior that require further investigation. Conclusions: Although the observed differences were not statistically significant in this exploratory study, the results suggest potential trends toward altered viscoelastic behavior between healthy and osteoarthritic trabecular bone. Future studies with larger cohorts are needed to further investigate osteoarthritis-related biomechanical alterations. Multi-branch viscoelastic modeling may provide sensitive mechanical descriptors for characterizing the relaxation behavior of subchondral bone. Full article

Background: Postoperative admission to critical care facilities is frequently employed following complex abdominal and gynecologic oncology surgery; however, the clinical value of routine short-stay (≤24 h) high-acuity care remains uncertain as the existing evidence is limited, heterogeneous and derived primarily from non-randomized studies. Consequently, the present critical narrative review aims to discuss the rationale for planned short-duration postoperative high-acuity care, summarize the contemporary evidence, and identify priorities for future research. Methods: Relevant studies evaluating planned postoperative admission to critical care facilities for ≤24 h following major abdominal surgery were identified through a targeted review of contemporary literature using a structured search of MEDLINE, Scopus, Google Scholar, the Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov. Evidence from observational studies investigating intensive care units, high-dependency units, post-anesthesia care units, and advanced recovery pathways was critically synthesized, with particular attention to clinical outcomes, healthcare utilization, and current knowledge gaps. Results: Nine primary studies were included, predominantly observational in design, with the majority lacking a comparative study design, thereby limiting direct evaluation of the clinical effectiveness of planned short-stay postoperative high-acuity care. None of the studies were designed to evaluate a gynecologic oncology population, limiting the direct applicability of the available evidence to this setting. As such, the current evidence is better suited to critical clinical interpretation of the rationale, patient selection, and potential role of postoperative high-acuity care than to drawing definitive conclusions regarding intervention effectiveness. Overall, the available data suggest that planned short-stay high-acuity postoperative care is not consistently associated with reductions in postoperative mortality or overall morbidity but may decrease unplanned intensive care unit admissions in selected patient populations. Most studies reported little or no effect on overall hospital length of stay, whereas increased healthcare costs were primarily observed in capacity-driven or unplanned postoperative critical care pathways. Interpretation of the available literature remains limited by the predominance of observational studies, heterogeneous patient populations, variability in critical care models, and inconsistent outcome reporting. Conclusions: Current evidence does not support routine postoperative admission to critical care facilities following major abdominal surgery, while evidence specifically addressing gynecologic oncology remains insufficient to establish specialty-specific effectiveness. However, planned, time-limited high-acuity postoperative care was not associated with an observed increase in adverse clinical outcomes in the available observational studies and may offer clinical and system-level benefits in selected high-risk patients; these findings should be interpreted cautiously given the limitations of the available evidence. Prospective studies with standardized outcome reporting and clearly defined populations are needed to inform evidence-based perioperative care strategies. Full article

Domain-specific information systems often maintain their data model, rule base, and application infrastructure as separate artifacts, complicating maintenance and pre-deployment verification. This study investigates whether these artifacts can be unified in a verifiable ontology-to-code pipeline without changing the expected classifications. The proposed Model-Driven Architecture uses the Business Application Builder framework and a Web Ontology Language 2 Description Logic ontology to represent domain structure, classification rules, and generation metadata. HermiT verifies consistency, satisfiability, and subsumption under open-world semantics before code generation. The generator produces persistence, business-logic, data-transfer, and presentation layers, while the generated Java application evaluates stored records under closed-world semantics and resolves overlapping categories using ontology-declared priorities. In a Serbian research-evaluation case study, the generated system reproduced the M30 and M33 classifications of an established Jess implementation. An internal secondary experiment generated and executed a prenatal-diagnosis application; all six runtime classifications matched the HermiT entailments and expected outcomes. The public artifact independently reproduces the ontology-level experiments but excludes the proprietary generator and generated source code. The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies. No performance superiority is claimed. Full article