Sci

Lithological, metasomatic, and structural heterogeneity in ore-hosting rocks limits the reliability of applying a single set of mechanical parameters without domain subdivision. This study evaluates intact rock properties, shear resistance along natural discontinuities, and conditional upscaling to the rock mass scale. The database comprised 232 laboratory records from 36 geotechnical borehole identifiers: 73 Brazilian tensile tests, 68 uniaxial compression tests, 49 triaxial compression tests, and 42 direct shear tests. Four domains were defined: D1, beresite-altered granodiorites; D2, diorites; D3, granodiorites; and D4, lamprophyre dikes. The lowest mean uniaxial compressive strength occurred in D1 (91.75 ± 40.65 MPa), whereas D2 showed the highest mean value, although its small sample size precludes confirmation as a domain characteristic. D3 provided the most representative dataset and exhibited intra-domain variability, while D4 showed the greatest variability in Young’s modulus (CV = 82.21%). Mean apparent cohesion along natural discontinuities was 0.125 ± 0.039 MPa for D1 and 0.140 ± 0.083 MPa for D3; D2 and D4 remain preliminary. GSI values of 48–58 were used only in scenario-based Hoek–Brown calculations. A ±5-point change in GSI altered equivalent rock mass strength by approximately −25% to −26% and +33% to +34%. The results support domain-based parameterization but require in situ verification. Full article

With the development of quantum computing, classical cryptosystems (RSA, ECDSA) that ensure the security of distributed ledgers face an existential threat. This paper examines protocols for protecting personal data (PD) in blockchain, taking into account the “Harvest Now, Decrypt Later” strategy. We propose and formalize a family of protocols designed for storing and exchanging personal data in blockchain systems. The article describes in detail approaches to software implementations of smart contracts for the Ethereum (using ECIES (Elliptic Curve Integrated Encryption Scheme) and Keccak-256) and Hyperledger Fabric 2.5 (integrating NIST post-quantum standards: ML-KEM (Module-Lattice-Based Key Encapsulation Mechanism) and ML-DSA (Module-Lattice-Based Digital Signature Algorithm)) platforms based on the developed protocols. For all developed protocols, a Threat Agent Model (TAM) is presented, threat scenarios are examined, and resilience to typical attack scenarios is demonstrated. A comparative analysis of computational efficiency and overhead is conducted. The results show that using lattice cryptography provides high performance, but the 50-fold increase in signature size makes direct implementation of PQC (Post-Quantum Cryptography) in Layer 1 public networks economically unfeasible. A hybrid model and the use of Layer 2 to ensure quantum resistance are proposed. Full article

The brown marmorated stink bug (Halyomorpha halys) is a major invasive pest threatening fruit production across Europe. This study integrates spatiotemporal geostatistical modelling with degree-day and photoperiod analyses to characterise its seasonal dynamics in Emilia-Romagna (Italy) from 2020 to 2022. Weekly pheromone trap data were used to quantify developmental succession among Small nymphs (early instars, N1–N3), Large nymphs (late instars, N4–N5), and Adults. Time-series and cross-correlation analyses confirmed consistent developmental delays across years, with Small preceding Large by approximately two weeks and Adults emerging after an additional two to three weeks. However, global inter-stage correlations were moderate (r ≈ 0.4–0.5), indicating substantial spatial heterogeneity among monitoring sites and suggesting that regional averages do not fully capture local population dynamics. To address this variability, a three-dimensional spatiotemporal geostatistical model (space × time) was implemented using Direct Sequential Simulation. The model successfully reproduced seasonal population waves and interannual differences in onset and persistence. The identification of persistent hotspots and stage-specific temporal windows is biologically relevant because it highlights where and when H. halys populations are most likely to increase. As such, from an IPM perspective, these outputs can support earlier monitoring, more precise timing of management interventions, and spatial prioritization of control efforts. These findings demonstrate that combining stage-specific temporal analysis with spatially explicit modelling improves forecasting accuracy and supports more precise timing of biological and chemical interventions. The proposed framework provides a scalable tool for climate-responsive integrated pest management in fruit-growing systems. Full article

Smart-home environments require secure and reliable user authentication mechanisms to prevent unauthorized access and spoofing attacks. Traditional password- and PIN-based methods remain vulnerable to theft, replay attacks, and credential compromise. To address these challenges, this study proposes an intelligent voice-based authentication and anomaly detection framework for secure smart-home environments. The framework utilizes benchmark ASVspoof 2019 and ASVspoof 2021 datasets containing bona fide and spoofed speech samples. After preprocessing, discriminative acoustic features, including Mel-Frequency Cepstral Coefficients (MFCC) and Constant-Q Cepstral Coefficients (CQCC), are extracted and provided to a Hybrid CNN-LSTM model for speaker verification. An integrated anomaly detection module further enhances security by identifying replay, spoofing, and synthetic speech attacks. Access is granted only when the input voice is authenticated and classified as non-anomalous. Experimental results demonstrate the effectiveness of the proposed framework, achieving an overall accuracy of 97.2% and a macro-AUC of 0.972. The model also achieves low Equal Error Rates of 3.8%, 2.9%, and 2.1% across the evaluated classes, indicating robust spoof detection and anomaly generalization capabilities. These results highlight the suitability of the proposed framework for secure and intelligent smart-home access control applications. Full article

Chronic diabetic foot ulcers remain a major clinical challenge due to persistent inflammation, impaired tissue repair, and microbial dysbiosis. We report the case of a 62-year-old male with diabetes mellitus presenting with two chronic diabetic foot ulcers managed using different local treatment strategies within the same patient. One wound received conventional wound care, while the second was treated with topical probiotic therapy containing Enterococcus faecium and Saccharomyces boulardii. Wound progression was assessed over eight weeks using the RESVECH 2.0 scale (Results Expected from Chronic Wound Healing), serial wound measurements, and microbiological cultures obtained before and after treatment. Although baseline wound characteristics differed between lesions—with the probiotic-treated ulcer being smaller and less severe at presentation—the probiotic-treated plantar ulcer demonstrated progressive reduction in RESVECH 2.0 scores and complete closure by week 8, whereas the conventionally treated ulcer remained partially open at the end of follow-up. Serial microbiological assessment demonstrated persistent colonization with Providencia stuartii and changes in antimicrobial susceptibility profiles during follow-up. Although limited to a single-patient observation, these findings support further investigation of microbiome-targeted approaches as adjunctive strategies in chronic wound management. Full article

Anuran skin secretions are rich sources of bioactive metabolites whose composition may vary according to environmental conditions. In this study, skin secretions from two populations of Dendropsophus columbianus exposed to contrasting environmental conditions (Clarete and Las Guacas, Popayán, Cauca, Colombia) were evaluated for antifungal activity against Hemileia vastatrix, the causal agent of coffee leaf rust. Secretion from the Clarete population showed no significant inhibition of urediniospore germination (p = 0.9999). In contrast, secretion from Las Guacas, a polluted and poorly conserved environment, exhibited significant dose-dependent antifungal activity (p = 0.0004–<0.0001). Chromatographic profiles from Las Guacas were more complex than those from Clarete. Two fractions, designated 5* and 7*, reduced urediniospore germination to 17.6% and 9.6%, respectively, compared to the negative control (75.37%; p < 0.0001). LC-MS analysis detected low-molecular-weight singly charged compounds (400–1500 m/z) consistent with alkaloid-like secondary metabolites. Although no definitive structural identification was achieved, these findings highlight the chemical plasticity and biotechnological potential of D. columbianus for the sustainable control of coffee leaf rust. Full article

Agent-based neural models often encode transmission within neuron state updates, which can make it difficult to separately log and quantify spatial recruitment patterns, delay structure, and environment-mediated feedback effects. We present LANA (Local Adaptive Neural Agents), a dual-agent neural agent-based model in which neurons and propagating signals are represented as distinct interacting entities embedded in a dynamic environmental field. The model combines discrete leaky integrate-and-fire neuron dynamics, mobile signal agents, synaptic links with distance-dependent delays, and a bounded environment-to-neuron feedback mechanism. LANA is intended as a normalized phenomenological mesoscopic framework for mechanism-level comparison rather than as a circuit-specific biophysical reconstruction. To support interpretability and reproducibility, we report a compact internal verification block for the implemented operators, including delay propagation, environmental decay and diffusion, threshold activation, and refractory enforcement. We then compare the full LANA model against a matched neuron-only baseline and summarize spatial recruitment using first-spike maps, cumulative recruitment times, and wavefront speed as a secondary descriptive metric. Finally, we evaluate two controlled operating regimes, a resting regime (S1) and a hyperexcitable regime (S2), under fixed network size, stimulation schedule, and matched random seeds. Relative to the baseline, the full model sustains and spreads activity more effectively and provides spatially resolved recruitment summaries, including first-spike timing and cumulative recruitment measures, that are not available in the same form when transmission is represented only through neuron-level updates. Relative to S1, S2 exhibits earlier activation, higher firing activity, stronger environmental accumulation, and faster cumulative recruitment. Local and factorial sensitivity analyses further identify the parameters that most strongly govern these regime differences. Together, these results position LANA as a normalized mesoscopic and computationally tractable framework for studying how excitability, transport state dynamics, delayed coupling, and environment-mediated feedback jointly shape emergent activity in controlled simulation settings. Full article

Maritime accident analysis increasingly uses machine learning to support safety management, but many existing studies focus on single-output prediction, such as accident-occurrence probability, severity class, near-miss frequency, or one specific consequence. This study proposes a data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences. A dataset of 582 recorded accident cases is constructed by integrating SafePASS project records with consequence, severity, and structural-damage information from the literature. The dataset includes 15 input variables covering ship characteristics, operational context, environmental conditions, accident type, and geographical zone and 15 consequence outputs covering structural damage, casualties, emergency-response indicators, total loss, and secondary consequence/escalation mechanisms. The ANN is trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated under different network configurations and data-partitioning strategies. The best-performing model uses 30 hidden neurons with a 60/20/20 split, achieving a correlation coefficient (R) equal to 0.9249 and a mean squared error (MSE) equal to 0.0240 for testing, and a R equal to 0.9278 and a MSE equal to 0.0231 for validation. Ten-fold cross-validation further confirms internal predictive stability, with mean testing R equal to 0.8803 ± 0.0827 and MSE equal to 0.0445 ± 0.0478. Permutation-based sensitivity analysis shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence. The framework should be interpreted as predicting the relative likelihood, severity, or magnitude of accident consequences in recorded or scenario-defined accident cases, not the probability of accident occurrence. Future work should address dataset imbalance, include near-miss and nonserious records, incorporate richer AIS and metocean data, integrate exposure data, and validate the framework using independent accident datasets. Full article

Hydrodynamic cavitation is assessed as a conditional process-intensification platform for the sustainable recovery and transformation of bioactive and functional fractions from agri-food residues, food-processing by-products, and plant-derived matrices. The analysis focuses on fractions enriched in polyphenols, flavonoids, pectins, carotenoids, proteins, pigments, essential oils, and other value-added compounds with potential relevance for food, nutraceutical, formulation-oriented, and related high-value applications. Rather than being considered an inherently green or universally superior technology, hydrodynamic cavitation is evaluated according to the specific process functions it can provide, including matrix disruption, mass-transfer enhancement, solvent-use reduction, recovery of pectin-associated fractions, protein extraction, macromolecular restructuring, dispersion, and process integration. Quantitative and scale-relevant indicators are considered where available, including recovery yield, target-compound content, solvent use, operating conditions, treated volume, energy input, fraction quality, and reporting limits. Comparison with ultrasound-assisted extraction, microwave-assisted extraction, pulsed electric fields, subcritical water extraction, natural deep eutectic solvents, and enzyme-assisted extraction indicates that its advantage is most defensible when hydrodynamic effects address a clearly identified matrix or process limitation. The available evidence supports substantial potential for wet matrices, plant by-products, aqueous suspensions, and liquid food systems. However, critical gaps remain in energy reporting, selectivity, recovered-fraction stability, scale-up, downstream processing, and application-oriented validation. Recovered fractions should therefore be regarded as candidate ingredients or functional intermediates, rather than as direct evidence of efficacy in final products. Full article

To mitigate the environmental footprint of the aviation sector, this study proposes an integrated cascading biorefinery scheme to produce Sustainable Aviation Fuel (SAF) precursor bloodstock via enzymatic transesterification of coconut oil. Utilizing a synergistic binary alcohol system (ethanol-butanol) and the liquid lipase Eversa Transform 2.0, a strategic molecular reconfiguration of fatty acid esters was achieved. Optimization through Response Surface Methodology (RSM) identified critical parameters—5% catalyst loading, total binary alcohol-to-oil molar ratio of 7:1 (specifically comprised of a 2.5:4.5:1 ethanol/butanol/coconut oil matrix), and an operation temperature of 57.5 °C—yielding a 97% conversion efficiency. A sequential vacuum fractional distillation process was implemented to partition the ethyl-butyl esters into high-value streams. Notably, the light distillate fraction, characterized by a specific carbon chain distribution (C6: 27.2%, C8: 52.5%, C10: 6%, and C12: 13.6%), perfectly aligns with the molecular window of aviation kerosene. This fraction exhibits excellent cold-flow properties, viscosity, and volatility profiles, positioning it as an ideal high-performance SAF precursor blendstock to increase the renewable content of current aviation fuels. Simultaneously, the remaining C16–C18 residue serves as a high-density energy source for internal refinery processes, while C8–C14 species are recovered as high-purity chemical feedstocks. This circular model maximizes carbon atom economy and economic viability by cogenerating high added-value biochemicals alongside jet-grade blendstocks. These findings provide a scalable, enzymatic framework for the next generation of decarbonized aviation fuels. Full article

This study develops two independent energy transition indices, the Global North energy transition index (GNETI) and the Global South energy transition index (GSETI), using principal component analysis (PCA) to evaluate energy transition performance during the period 2013–2022. Each index was calculated independently using a balanced panel of 70 observations, corresponding to 7 countries observed over 10 years. The Global North sample includes Denmark, Germany, Sweden, Japan, Canada, the United Kingdom, and the United States; while the Global South sample includes India, Brazil, South Africa, Indonesia, Mexico, Colombia, and Ghana. For the Global North, PCA results show that $P{C}_1$ explains 59.68% of the total variance, while $P{C}_2$ and $P{C}_3$ describe 21.10% and 17.42% of the total variance, respectively. The first two components account for 80.78% of the total variance, while the first three components explain 98.20%, confirming the robustness of the index structure. The 2022 GNETI values indicate that Sweden has the highest performance (100.00), followed by Denmark (71.01), Canada (66.50), the United Kingdom (40.51), Germany (36.46), the United States (34.45), and Japan (11.86). The PCA results show that $P{C}_1$, $P{C}_2$, and $P{C}_3$ explain 58.76%, 21.99%, and 9.63%, respectively, of the total variance. The cumulative variance described confirms the adequacy of the PCA approach for constructing the GSETI. In 2022, the highest GSETI values were observed in Ghana (90.39), Colombia (85.06), Brazil (70.98), India (59.00), Indonesia (52.97), Mexico (47.72), and South Africa (18.70). The findings indicate significant regional differences in the pathways of energy transition, based on variability in technological capability, renewable energy uptake and resources, energy security levels, and the fundamental structure of state energy systems. Full article

Imaging of the lungs is traditionally based on chest X-ray as a first-line imaging method for lung aeration assessment. However, radiation exposure limits its use for patients in the pediatric intensive care unit. Lung ultrasonography (LUS) is a suitable alternative, but its interpretation is highly observer-dependent and requires sufficient experience and skill. We sought to develop a model based on deep learning to assist the clinician in the interpretation of LUS observations. In this retrospective, single-center, proof-of-concept study, all patients, age 0–18 years old admitted at the PICU of the Leiden University Medical Center (LUMC) between January and May 2022 who underwent an LUS were included. LUS video frames were analyzed using a deep learning tool; a conditional generative adversarial network (cGAN) was developed to generate segmentation masks containing clinical features from individual LUS frames. A total of 31 patients, with a median age of 2.5 months (IQR 0–11 months), were analyzed. A total of 98 LUS assessments and 506 4-s videos were collected. The median LUS score was 12 (IQR 8–17). The two best-performing frame-based segmentation models achieved mean Dice similarity coefficients of 0.97 ± 0.03 and 0.96 ± 0.03, with mean squared errors of 0.025 ± 0.025 and 0.030 ± 0.026, respectively. These findings demonstrate that a pediatric-specific cGAN can segment key LUS features from individual frames. However, the results derive from a small, single-center cohort with a frame-level rather than patient-level data split, and no formal clinical validation; independent, prospectively collected validation cohorts are required before any clinical implementation. Full article

Background: Post-COVID-19 conditions (PCCs) present a significant public health challenge due to a vast array of new or persistent health symptoms across subjects. The complex, multi-systemic nature of PCCs makes these conditions difficult to differentiate from other non-COVID-19 related medical conditions. While the Military Health System Data Repository (MDR) provides a robust supply of population-level encounter data, its high-dimensional structure poses challenges for knowledge discovery and outcome research. Objectives: The primary aim of this study was to identify novel manifestations of PCCs among active-duty service members, and model the probabilistic relationships between PCC-related diagnoses. We propose a machine learning workflow as an effective tool for knowledge discovery to statistically validate candidate PCCs from large datasets. Methods: We conducted a retrospective cohort study using MDR records from July 2018 to June 2023. From an initial pool of 311,367 eligible Active-Duty Tricare beneficiaries, we isolated 101,789 COVID-19 infections and matched them 1:1 with uninfected controls (N = 203,578 total) based on age, sex, and propensity for COVID-19. Encounter data was mapped to 392 clinical categories using the Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software Refined (CCSR). Candidate PCC categories were isolated using a cross-validated lasso regression model optimized with a Tree of Parzen Estimators algorithm. A consensus Bayesian Network structure was fitted to model potential probabilistic dependency structures between identified PCCs and prior COVID-19 diagnosis. Finally, conditional Cox proportional hazards models were used to statistically validate selected novel conditions using larger cohorts drawn from the same initial eligible pool by matching cases 1:2 with controls. Results: Feature selection reduced the diagnosis set by 97.96%, isolating 8 clinical categories from the initial 392. The model confirmed known PCCs, such as respiratory symptoms and malaise, and identified two potentially novel candidate PCCs: tinnitus and personality disorders. Survival analysis validated the selection of tinnitus, showing a significant association with COVID-19 (HR: 1.17, 95% CI: 1.12–1.22). No significant association was found between COVID-19 infection and personality disorders (HR: 1.11, 95% CI: 0.97–1.26). Conclusions: This study demonstrates an effective analytical pathway for addressing the limitations of analyzing complex, high-dimensional healthcare billing data. The methodology successfully generated testable hypotheses, identifying tinnitus as a relevant sequela, and is generalizable to future research involving unknown health outcomes related to prior infection. Full article

Gluten-related diseases (GRDs), affecting approximately 1–6% of the global population, require long-term adherence to a gluten-free (GF) diet for effective disease management. Food label literacy plays a critical role in ensuring dietary safety; however, consumer attitudes, behaviors, and perceptions regarding GF food labeling remain insufficiently characterized. This study systematically reviewed and synthesized evidence on these factors across different GRDs. A comprehensive search of PubMed/MEDLINE, EMBASE, Cochrane Library, Scopus, and Web of Science was conducted for studies published between January 2000 and December 2025. Studies evaluating attitudes, beliefs, and behaviors related to GF food labeling among individuals with GRDs were included. A total of 82 studies involving 61,284 participants from 27 countries were included, with 44 studies contributing to the meta-analysis. Consistent GF label reading was reported by 79.2% of participants, while 60.3% expressed confidence in label accuracy. However, 40.9% reported dietary infractions due to misleading labeling. Label reading behavior varied across disease groups and regulatory settings. Key barriers included ambiguous wording, inconsistent cross-contamination disclosures, and lack of standardized symbols. These findings highlight important gaps in labeling practices and emphasize the need for standardized regulations and targeted educational interventions to improve dietary safety and health outcomes. Full article

Can China feed itself by 2100? This study begins with a critical reexamination of the bayesTFR recovery assumption embedded in UN WPP 2024 and reconstructs population scenarios using an “empirical-base ± empirical-offset” methodology anchored at China’s 2023 official TFR of 1.01. For food security analysis, we narrow the population to three scenarios (low, medium, and policy target), and we set three scenarios each for the demand side and the supply side, producing an integrated 27-scenario analysis. The demand side comprises three trajectories: East Asian saturation type (650 kg/person/year), EU type (780 kg/person/year), and EU + $\alpha$ type (850 kg/person/year). The supply side comprises three trajectories: optimistic (cropland area maintains the red line, with yield reaching the technological ceiling), medium (climate change reduces yield by $-10\%$, and cropland area breaches the red line by $-5\%$), and pessimistic (climate change reduces yield by $-20\%$, and cropland area breaches the red line by $-10\%$). Based on NBS empirical data, projection results show that all 27 scenarios achieve surplus by 2100 (even the worst case retains 0.265 Gt surplus), confirming the robustness of long-term food security. However, during the medium term (2030–2050), the worst case scenario retains 26% import dependency. Even under the U.S.-type full emulation scenario (1100 kg/person/year) examined as a supplementary stress test, all nine sub-scenarios maintain surplus. The challenge for China’s food security lies not in long-term absolute shortage but in medium-term import dependency management and policy transition to the surplus era. By integrating demographic projection, agricultural-economic demand modeling, and a layered food-system accounting framework, this study offers a transferable cross-disciplinary methodology for long-term food security assessment under demographic transition, relevant beyond China to other aging, post-peak societies. Full article

Cancer stem cells (CSCs) drive therapeutic resistance and tumor relapse by exploiting redundant regulatory networks that integrate Wnt/β-catenin, Notch, and Hedgehog signaling with metabolic reprogramming, epigenetic plasticity, and tumor microenvironment crosstalk, a network architecture that renders single-pathway inhibition strategies insufficient. This review systematically examines evidence that natural compounds (curcumin, sulforaphane, resveratrol, EGCG, berberine, and quercetin) act as multitarget modulators of CSC plasticity, analyzing their molecular mechanisms of action in specific cancer models. Each compound engages distinct regulatory nodes: curcumin suppresses β-catenin nuclear translocation and STAT3 phosphorylation in lung cancer CSC models; sulforaphane represses ΔNp63α-driven stemness transcription in colorectal cancer and reduces CSC self-renewal in prostate and head and neck models; resveratrol dissociates the β-catenin–GLI-1 interaction in oral and lung CSC populations and induces Wnt/β-catenin-dependent autophagy in breast CSCs; EGCG inhibits DNMT and HDAC activity in glioblastoma and colorectal models; berberine activates AMPK-mediated suppression of mTORC1 in colorectal cancer; and quercetin suppresses PI3K/AKT/mTOR signaling while downregulating EMT transcription factors in breast and colorectal systems. We critically assess persistent methodological limitations, including bulk cell-line models, supraphysiological concentrations, and the absence of functional tumor-initiating validation, that currently prevent stronger translational conclusions. Natural compounds from Latin American biodiversity are identified as an underexplored source of CSC-active molecules. We conclude by defining the experimental standards required to reposition natural compounds as clinically relevant network-level modulators of CSC plasticity. Full article

This paper introduces an IIoT-based hybrid predictive maintenance system for industrial steam boilers, which responds to the increased demands for making intelligent and accurate decisions by leveraging data-driven analytics in complex industrial environments. The proposed approach presents comparative hybrid predictive monitoring frameworks based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models integrated with Statistical Process Control (SPC) and Cumulative Sum (CUSUM) monitoring techniques for industrial boiler monitoring; it allows accurate system behavior prediction coupled with enhanced anomaly detection across interconnected subsystems. To ensure practicability, the framework is implemented in an integrated operation technology and information technology (OT–IT) architecture with one year of real operation data from an industrial steam boiler in an oil refinery. A two-phase validation strategy is employed to overcome the gap between offline model development and application. During the initial phase, predictive models are developed and tested based on multivariate time-series data to model both the time dependence of the processes and the mechanical variables. The second phase involves the online deployment of the predictive monitoring framework through a Hardware-in-the-Loop (HiL) implementation with Programmable Logic Controller (PLC)-based and Open Platform Communications Unified Architecture (OPC UA) communication to enhance realistic system validation under emulated boiler process conditions without disrupting live plant operations. The experimental results indicate that the GRU model outperforms the LSTM, achieving good R² (0.8956) and mean absolute percentage error (MAPE, 0.6345%), demonstrating strong predictive accuracy across key operational variables. In addition, SPC is used to set up adaptive operational thresholds based on normal industrial process behavior, and then CUSUM is applied to the prediction residuals to improve the detection of the gradual degradation of the system. Real-time validation ensures system stability, low latency, and bidirectional data transfer between the OT and IT layers, enabling continuous monitoring and real-time decision-making. The proposed solution provides a practical and scalable predictive maintenance framework in an industrial context, particularly in oil and gas operations, that helps to transition to Industry 4.0 and intelligent asset management. Full article

Excitation–emission matrix (EEM) fluorescence spectroscopy, when combined with machine learning algorithms, has emerged as a highly promising tool for non-invasive biomedical diagnosis, demonstrating significant potential across various applications. This systematic review offers a comprehensive analysis of recent advancements in integrating EEM with chemometric techniques and machine learning models for the detection of infectious diseases, cancer, neurological, and metabolic disorders, as well as for monitoring bioactive compounds and hormonal contaminants. The review examines multivariate approaches alongside spectral preprocessing strategies, highlighting their ability to resolve overlapping signals and extract relevant information from complex biological matrices. The reviewed studies report promising high sensitivity, specificity, and accuracy values across diverse biological matrices and disease targets, supporting the scalability and versatility of this diagnostic platform. A critical evaluation of methodological approaches is also provided, identifying common pipeline-level challenges and drawing a constructive distinction between proof-of-concept studies, which establish the discriminative potential of EEM spectral data and studies aimed at clinical validation, a distinction that helps contextualize reported performance and guides future research design. Future perspectives focus on the development of open-access spectral databases, portable devices, standardized preprocessing protocols, and the integration of deep learning and explainable artificial intelligence, all of which represent concrete pathways toward the clinical translation of EEM-based diagnostics. This review confirms the value of EEM spectroscopy coupled with machine learning as a versatile, scalable, and high-impact platform for biomedical diagnostics, with significant potential for applications in public health and personalized medicine. Full article

The resurgence of trade policy interventions in the global economy has renewed interest in the long-run macroeconomic implications of commercial barriers. While previous research has largely focused on the short-term effects of tariff reforms and trade liberalization, relatively less attention has been paid to the persistence of trade policy regimes over time. This study addresses this gap by analysing the relationship between trade policy persistence—proxied by the trajectory of the Nominal Average Tariff (NAT)—and Peru’s real GDP during the period 1980–2025. Using a Johansen cointegration framework combined with a Vector Error Correction Model (VECM), the study evaluates both the existence of a long-run equilibrium relationship and the dynamics of adjustment following deviations from that equilibrium. The econometric evidence confirms the existence of a stable long-run relationship between the NAT and aggregate GDP. The normalized cointegrating vector suggests that higher and persistent levels of tariff protection are associated with lower levels of real GDP in the long run. The estimated error-correction mechanism further indicates that deviations from equilibrium are gradually corrected through adjustments in the trajectory of real GDP, whereas the tariff equation does not exhibit a statistically significant adjustment process at conventional levels. This asymmetric structure suggests that trade policy persistence operates as a relatively stable macroeconomic condition, while aggregate GDP gradually adjusts to long-run disequilibria. By framing tariffs not only as policy instruments but also as indicators of persistent policy orientations, the study contributes to the trade and growth literature from a persistence-based perspective. The findings additionally highlight the potential relevance of policy consistency and predictability in small open economies characterized by high external dependence and prolonged processes of trade liberalization. Full article