Abstract: Building Information Modeling (BIM) represents a building as a static snapshot of the model’s state. The IFC standard does not define a formal mechanism that would link the same physical element across successive phases of a building’s life cycle. Design, construction, and operation are recorded in separate IFC files, and the same element is assigned different GUIDs in each. The result is fragmentation of the element’s identity, loss of the history of property changes, and the inability to formulate cross-phase queries. This paper proposes the BIM-Phase ontology, based on the fundamental DOLCE ontology, which solves this problem by introducing a distinction between a building element as an endurant and its life cycle phases as perdurants. The ontology comprises nine classes, six object relations, and six axioms expressed in OWL 2 DL. Phase properties and relations are represented using a reification pattern, which maintains full compatibility with the expressiveness of OWL 2 DL. The ontology was validated using the example of a single-family residential building developed in Autodesk Revit. Three structural elements (external wall, floor slab, column) were tracked across three phases of the life cycle. Eight competency questions covering scalar, constitutional, and mereological changes were defined and mapped to ontology constructs, confirming that BIM-Phase enables the recording of changes and the formulation of cross-phase queries that are impossible in classic IFC.

Abstract: Objectives: Borrelia burgdorferi sensu lato, a spirochete bacterium responsible for Lyme borreliosis - the most common tick-borne infection in North America and Europe - can trigger the production of antiphospholipid antibodies. These antibodies target host lipids such as cardiolipin (CL), phosphatidic acid (PA), phosphatidylcholine (PC), and phosphatidylserine (PS), which the spirochete incorporates into its membrane from the surrounding environment. Although antiphospholipid antibodies are typically associated with antiphospholipid syndrome (APS), they may also arise during infections, including Lyme borreliosis. This study aimed to develop and optimize several enzyme-linked immunosorbent assays (ELISAs) for measuring various antiphospholipid antibodies in patients with Lyme borreliosis. Methods: Thirty patients diagnosed with Lyme borreliosis were enrolled: ten with solitary erythema migrans (EM), ten with multiple EM (MEM), and ten with late manifestations known as acrodermatitis chronica atrophicans (ACA). Forty healthy blood donors served as controls. Four distinct antiphospholipid antibody ELISAs were developed, each using a different phospholipid coating: CL, PA, PC, and PS. Serum of APS patient was used as a positive control and for standard curve generation. Results: All four ELISAs were successfully established and demonstrated good measurement precision. Significant differences in antiphospholipid antibody levels and positivity rates were observed between Lyme borreliosis patients and healthy blood donors. Notably, levels of antibodies directed against PA (aPA), PC (aPC), and PS (aPS), both IgG and IgM, were significantly higher in patients with late Lyme borreliosis, manifested as ACA, compared to healthy blood donors. In contrast, anti-CL (aCL) levels did not differ significantly between groups. Patients with ACA also showed the highest frequency of multiple antiphospholipid antibody positivity, with 7 of 10 patients testing positive for three or more antiphospholipid antibodies. Conclusions: Accurate and precise in-house ELISAs for the detection of aCL, aPA, aPC, and aPS using APS sera as standard material were developed and validated for the analysis of samples of patients with Lyme borreliosis. Our data suggest that antiphospholipid antibody levels—specifically aPA, aPC, and aPS—differ across clinical manifestations of Lyme borreliosis, with the greatest increases observed in patients with ACA.

Abstract: Artificial intelligence (AI) is a human-built component of the technosphere, not an intelligence outside Earth-system limits. As AI systems scale, they increasingly shape the decisions, infrastructures, and capital flows through which human activity damages the biosphere. Dominant deployed foundation-model alignment methods, including reinforcement learning from human feedback (RLHF) and constitutional AI, treat human preferences as the primary alignment target while leaving biosphere integrity as context, externality, or secondary constraint. That framing is structurally incomplete. Human welfare, technological continuity, and AI operation all depend on biosphere function. Three convergent literatures support a corrective framework: planetary-boundary analysis showing seven of nine boundaries transgressed; energy-system analysis showing rapid and infrastructure-constrained data-center growth during the 2025-2030 buildout; and collective-action analysis showing that voluntary ecological restraint is unstable under competitive pressure. These literatures imply a design conclusion: ecological constraints must be formalized as hard inference-time refusal rules and reinforced through reward design. This paper presents Biosphere Sentinel as a reference architecture for reducing human and technospheric impacts on the biosphere through refusal rules, an eight-domain reward landscape, carbon-lock-in diagnostics, and a proposed Trophic Integrity Index pathway.

Abstract: Background/Objectives: High-sensitivity cardiac troponin (hs-cTn) assays are used in routine diagnostics to detect myocardial injury. However, a fraction of circulating cardiac troponin T (cTnT) enclosed within extracellular vesicles (EVs) goes widely undetected. This study introduces a combined lysis- and sonication-based protocol to release and quantify EV-bound cTnT in a time-efficient manner using a state-of-the-art hs-cTnT immunoassay. Methods: Plasma samples from patients with non-ST-segment elevation myocardial infarction (NSTEMI), unstable angina, pulmonary embolism, decompensated aortic stenosis, atrial fibrillation, myocarditis, and healthy controls were treated with lysis buffer and subsequently sonicated. Treated and untreated samples were assessed and compared to a conventional EV isolation method. Results: Following combined lysis and sonication, cTnT levels were significantly higher compared to native, unprocessed samples across all cohorts. Median increase post-processing ranged from 10% in decompensated aortic stenosis to 34% in healthy controls. In NSTEMI, EV-bound cTnT accounted for 15% of plasma cTnT and remained stable over 72 hours. EV cTnT/plasma cTnT ratios were comparable between the combined lysis and sonication approach and the conventional EV isolation method. Processing time prior to cTnT measurement was reduced from approximately 2.5 hours to approximately 10 minutes using combined lysis and sonication compared to the established EV isolation method. Conclusions: Our method allows for rapid liberation of a previously inaccessible EV-bound fraction of cTnT without the need for time-consuming and resource-intensive EV isolation workflows. The resulting total cTnT signatures indicate differential cTnT compartmentation depending on the underlying myocardial pathophysiology, enabling early differentiation of the mechanisms underlying troponin elevation. This approach is readily implementable alongside standard hs-cTnT testing at minimal additional time expense and may improve diagnostic sensitivity and specificity in acute clinical settings.

Abstract: The impact of dietary inert digestibility markers on gut microbiota and intestinal fermentation remains poorly understood. This study investigated the effects of titanium dioxide (TiO₂) supplementation at 4 kg/ton feed, representing a typical dose used in animal nutrition studies, on fermentation dynamics and microbial composition in broiler chickens using combined ex vivo and in vivo approaches. Ex vivo fermentations were conducted using ileal and caecal microbiota and substrates collected from 32-day-old broiler chickens, with direct TiO₂ supplementation, with gas production and volatile fatty acid (VFA) profiles as main measurements. In parallel, 392 broiler chickens were fed diets with or without TiO₂ for 32 days, and ileal and caecal digesta were analysed for fermentation end-products and microbial composition using shotgun metagenomic sequencing. A second ex vivo experiment was performed using microbiota adapted to dietary TiO₂. In the first ex vivo model, TiO₂ reduced gas production and acetic acid concentration in the ileum (p < 0.05), whereas in the caecum it increased gas production, total eubacterial counts, and branched-chain fatty acids (BCFA) (p < 0.05). In vivo, TiO₂ did not affect growth performance or organ development but significantly increased isobutyric acid and total BCFA concentrations in the caecum (p < 0.05). Metagenomic analysis revealed increased caecal alpha diversity (Shannon index) and enrichment of taxa associated with amino acid metabolism, including Massilicoli timonensis, Blautia merdavium, Rubneribacter badeniensis, and Mediterraneibacter caccavium. The second ex vivo experiment showed similar trends, with increased gas and BCFA production. Collectively, these findings indicate that TiO₂ can modulate intestinal fermentation and microbial composition in a segment-specific manner, suggesting that dietary markers may not be biologically inert.

Abstract: The Job Shop Scheduling Problem (JSSP) is a paradigmatic and strongly NP-hard combinatorial optimisation problem that underpins production planning in modern manufacturing systems, and constraint programming (CP) has become one of the leading methodologies for tackling it. However, comparative studies of CP solvers for the JSSP have so far been restricted to a single benchmark family, a single instance-size range, or a single hardware setting, which limits the practical guidance they offer to both researchers and practitioners. This paper presents a controlled empirical evaluation of four state-of-the-art CP solvers—IBM ILOG CP Optimizer, Google OR-Tools (CP-SAT), Hexaly, and OptalCP—on the makespan-minimisation JSSP. The four engines are run with default parameters and a uniform 600-second wall-clock time budget on 332 instances drawn from nine canonical benchmark families (Fisher–Thompson, Lawrence, Adams–Balas–Zawack, Applegate–Cook, Yamada–Nakano, Storer–Wu–Vaccari, Taillard, Demirkol–Mehta–Uzsoy, and Da Col–Teppan), spanning sizes from 6 × 5 up to 1000 × 1000 operations. OptalCP emerges as the most robust engine overall, certifying optimality on 57.5% of the instances with the smallest average optimality gap (3.55%), while Hexaly dominates on industrial-scale problems and produces the bulk of 31 new best-known upper bounds and one new best-known lower bound reported here. Solver competitiveness depends sharply on instance size and on the n/m ratio, with square instances confirmed as the hardest case. These findings support an instance-aware approach to CP solver selection in industrial scheduling.

Abstract: We propose a framework that isolates a precise complexity-theoretic bottleneck between counting complexity and the Birch--Swinnerton-Dyer conjecture (BSD) via Tunnell's theorem. The framework rests on two number-theoretic conjectures: a \emph{Reduction Conjecture} asserting the existence of a polynomial-time reduction from any \#P-complete problem to the counting of integer representations $D_n = \#\{(x,y,z) : n = 8x^2 + 2y^2 + 16z^2\}$ (with counts preserved up to a polynomial factor), and a \emph{Solution Density Conjecture} asserting that the values $\{D_n : n \text{ even square-free congruent}\}$ are sufficiently densely distributed (within the Eichler--Deligne ceiling $D_n = O(n^{1/2+\varepsilon})$) to support iterated polynomial descent. We do \emph{not} claim that $\text{P} = \text{NP}$ implies $\#\text{P} = \text{FP}$ (the natural binary-search route fails because the threshold predicate $[\#I \geq k]$ is PP-complete, not in NP, and PP is not known to collapse under $\text{P} = \text{NP}$). Instead, we prove a structural equivalence: under the two conjectures, BSD, and $\text{P} = \text{NP}$, $\#\text{P} \subseteq \text{FP}$ if and only if the specific family TunnellCount $:= \{n \mapsto D_n\}$ is in FP. The framework thus does not resolve the $\#\text{P} \stackrel{?}{=} \text{FP}$ question; it converts it into a concrete, falsifiable arithmetic question about the polynomial-time tractability of representation counts on one specific ternary quadratic form. We identify three concrete open problems---parsimony in Matiyasevich representations, the distribution of weight-$3/2$ Fourier coefficients via Waldspurger's formula, and the FP-tractability of $D_n$ itself---whose resolution would substantiate or refute the framework.

Abstract: Inflammasomes arise from complex protein assembly mechanisms and play a fundamental role in managing inflammation and the innate immune response. The molecules that trigger inflammasome assembly and activation are molecules derived from pathogens or DNA fragments released following cellular damage. The phenomena resulting from inflammasome activation range from the activation of caspases, such as caspase-1, to the secretion of pro-inflammatory cytokines, to cellular death by apoptosis or pyroptosis. Various pathologies have been linked to aberrant inflammasome activation, including several autoimmune diseases, leading scientists to direct experiments toward identifying the mechanisms responsible for aberrant inflammasome activation to develop new therapeutic strategies. In this review, we summarize the assembly mechanisms and involvement of two specific inflammasomes, NLRP3 and AIM2, in the autoimmune disease Sjögren's syndrome (SjD); NLRP3 and AIM2 aberrant activations appear to be involved in the exacerbation of inflammation, which becomes chronic, leading to dry mouth and dry eye and to an increased risk of developing B-cell non-Hodgkin's lymphoma in these patients. Understanding how different inflammasomes contribute to the pathogenesis of SjD could be fundamental to understanding the complex molecular mechanisms underlying this disease.

Abstract: Phishing detection models often report strong benchmark performance, yet their reliability under realistic deployment conditions remains uncertain. This study examines this problem by investigating three failure modes of cross-dataset phishing email detection: corpus generalisation failure, asymmetric prevalence-shift failure, and artifact-driven spurious learning. Using six public email corpora, CEAS_08, Enron, Ling, Nazario, Nigerian Fraud, and SpamAssassin, the study evaluates Term Frequency (TF) and Inverse Document Frequency (IDF)-based Logistic Regression and Linear Support Vector Classifier (SVC) models across pooled baseline testing, single-corpus cross-dataset transfer, leave-one-corpus-out pooled training, prevalence-shift simulation, training-prevalence manipulation, dataset-identification analysis, top-feature inspection, artifact-removal ablation, and targeted artifact masking. The findings show that single-corpus models are unstable under cross-dataset transfer, with F1-scores varying substantially across source–target combinations. In contrast, leave-one-corpus-out pooled training improves robustness, with Logistic Regression achieving sustained F1-scores between 0.8201 and 0.8994, and Linear SVC achieving F1-scores between 0.7607 and 0.8910 across unseen corpora. Prevalence-shift experiments reveal that failure is asymmetric and threshold-dependent. High-prevalence-trained models maintain high recall under fixed thresholds but suffer sharp recall degradation when operational alert-budget constraints are imposed. Conversely, low-prevalence-trained models become overly conservative in high-threat environments, producing high precision but substantially lower recall and poorer calibration. Artifact analyses further show that source corpus identity is highly learnable, with dataset-identification accuracy reaching 0.9722 for Logistic Regression and 0.9806 for Linear SVC. Top-feature and masking analyses indicate that models rely partly on corpus markers, date tokens, URL/domain terms, headers, and other artifact-like features rather than only general phishing indicators. The study contributes a deployment-aware and adversary-aware evaluation framework for phishing detection. It shows that benchmark accuracy alone is insufficient for assessing real-world robustness and that reliable phishing detection requires cross-corpus validation, prevalence-aware thresholding, and systematic testing for artifact-driven spurious learning.

Abstract: Earth blocks are attractive for low-cost housing because they use local soil, require less firing energy, and can provide good thermal mass, but their adoption in humid tropical regions is limited by moisture sensitivity. This review examines how agricultural waste ash stabilizers, with emphasis on palm kernel shell ash and related pozzolanic residues, influence moisture durability, dry/wet compressive strength behavior, and practical suitability of earth blocks for affordable housing. The paper synthesizes evidence from compressed earth block literature, pozzolanic material standards, and studies on ash-modified earthen masonry. It argues that wet-to-dry strength retention is a more realistic durability indicator than dry compressive strength alone because low-cost walls are exposed to wind-driven rain, capillary rise, damp surfaces, and imperfect maintenance. The review shows that ash stabilizers can improve particle bonding and pore refinement when properly processed, proportioned, compacted, and cured, but excessive ash, poor soil selection, or inadequate detailing can increase water absorption and reduce field reliability. The paper proposes a moisture-durability framework connecting material chemistry, block production, wall detailing, and tropical housing performance. It concludes that agricultural waste ash stabilized earth blocks are promising only when laboratory strength gains are tied to water-resistance testing and moisture-conscious architectural detailing.

Abstract: Long Range (LoRa) is a chirp spread spectrum (CSS) physical-layer technology that has become a leading candidate for low-power wide-area network (LPWAN) connectivity in the Internet of Things (IoT). At the receiver, the standard demodulator multiplies the incoming signal with a conjugate reference chirp and applies a one-dimensional discrete Fourier transform (DFT), reducing symbol detection to peak search in the frequency domain. While this non-coherent baseline is simple and robust under additive white Gaussian noise (AWGN), its symbol error rate (SER) degrades significantly in frequency-selective multipath channels, where parasitic spectral peaks distort the dominant tone. This paper presents a unified comparative study of seven LoRa detectors for spreading factor seven, six of which share a common one-dimensional DFT engine while a matched-filter bank operates directly in the time domain, with the six DFT detectors differing in their per-bin frequency-domain weighting and decision rule. The detector set spans the standard non-coherent DFT, a non-coherent matched filter bank, two coherent equalizers in the frequency domain (zero-forcing and minimum mean-square error), a phase-only equalizer, a maximal-ratio combiner with non-coherent decision, and an exhaustive maximum-likelihood detector that serves as a near‑optimal reference under the same preamble‑based CSI. To mitigate inter-symbol interference in the multipath case, every transmitted symbol is preceded by a cyclic prefix that converts the linear convolution with the channel into a circular convolution, enabling per-bin frequency-domain processing. Throughout the paper a deployment-realistic receiver model is adopted: the per-bin channel response is estimated by a frequency-domain least-squares estimator from a short preamble, and the noise variance is estimated blindly from the preamble residuals. The quality of the noise-variance estimator is reported separately as a diagnostic. Each detector is evaluated under both AWGN and a two-tap Rayleigh multipath channel through Monte Carlo simulation, and its execution time per call is recorded to provide a complementary view of computational cost. The framework introduced here clarifies how coherent processing, diversity combining, equalization, and exhaustive search trade detection performance against complexity within a single DFT-centric LoRa receiver architecture. The principal quantitative finding is that, under the two-tap Rayleigh multipath channel, the MMSE equalizer reaches SER ≈ 4.4×10⁻⁵ at SNR = −5 dB and tracks the exhaustive maximum-likelihood detector within 0.1 dB across the full SNR sweep, while costing only 1.26× the per-symbol time of the standard DFT receiver. Conversely, the standard non-coherent baseline hits an irreducible 16% error floor and the unregularized zero-forcing equalizer fails to reach the 10⁻² SER level at any SNR considered, isolating MMSE as the recommended choice in the multipath regime at every SNR for which a LoRa link is operationally viable.

Abstract: Electrochemical CO₂ reduction to ethanol is a promising route for circular-carbon fuel and chemical production, but practical implementation remains limited by coupled membrane, catalyst, transport and system-integration constraints. This Communication reassesses anion-exchange membranes (AEMs) and bipolar membranes (BPMs) using recent 2024–2026 literature. The central argument is that membrane selection is not a passive separation choice; it controls local pH, charge carriers, CO₂ availability, carbonate formation, water activity, proton/cation deliv-ery, product crossover and downstream techno-economic assessment (TEA) and life cycle assessment (LCA) burdens. AEM operation can create alkaline cathodic microenvironments that favor C–C coupling, but bicarbonate/carbonate formation imposes carbon-loss, salt-management and recovery penalties. BPM operation can improve pH separation and carbon management through water dissociation and bicarbonate acidification, but its viability depends on water-dissociation efficiency, co-ion exclusion, junction stability and voltage control. Recent ethanol-selective catalyst studies further show that copper oxidation state, grain boundaries, sub-surface dopants, ionomers, interfacial wettability and dynamic operation interact strongly with membrane-imposed microenvironments. The Communication pro-poses a membrane-centered decision framework linking AEM/BPM selection with ethanol selectivity, single-pass carbon utilization, energy efficiency, durability, TEA/LCA boundaries and future reactor design.

Abstract: Background: African Union (AU) guidance identifies decentralized diagnostics as central to epidemic preparedness. However, the epidemiological role of self-testing across epidemic-prone diseases remains underexplored. Drivers for potential impact of self-testing impact were examined conceptually using a transmission model. Methods: A deterministic SEIR model compared standard-of-care testing with additional self-testing. Global sensitivity analysis using Latin Hypercube sampling and partial rank correlation coefficients (PRCCs) examined parameters influencing reductions in peak disease prevalence (mitigation). Dynamics were illustrated using AU pathogen archetypes (Ebola, Influenza A, Cholera, Coronavirus, and Mpox), estimating the number needed to self-test (NNST) to avert one death as a measure of marginal efficiency. Results: Epidemic mitigation was minimal (median 1.9%; IQR: 0.4%–5.8%); correlated with isolation adherence (PRCC = 0.784), self-testing intensity (PRCC = 0.617), lower R0 (basic reproductive number; PRCC = -0.607) and greater duration of infectiousness (PRCC = 0.370). Achieving a 10% reduction in peak prevalence at R₀ = 1.1 required 34 self-tests per 10,000 people per day, exceeding AU COVID-19 operational benchmark of 10 per 10,000 per week. High-mortality, moderate-transmission archetypes (e.g., Ebola) were most responsive to mortality reductions (Median 1,512 NNST/death averted) compared to Mpox (Median 355,708 NNST/death averted). Adherence to post-test isolation exerted greater epidemiological impact than diagnostic accuracy. Conclusions: The epidemiological value of untargeted self-testing depends on pathogen characteristics and post-test behavioral adherence. Epidemic mitigation effects were generally limited under constrained health-system capacity. However, future demonstration studies evaluating rapid, early decentralized self-testing deployment during Ebola-archetype outbreaks may help identify operationally feasible targeted deployment strategies to support mitigation and mortality reduction.

Abstract: Recent work on neural scaling demonstrates consistent performance gains with increased data and model capacity, yet these improvements are typically assessed using surface-level metrics that do not capture factual reliability. In multi-document summarization (MDS), this limitation is particularly acute, as scaling has been shown to amplify hallucination and content distortion. In this paper, we investigate the empirical scaling behaviour of faithfulness-aware transformers under tightly controlled conditions, using LSHT as a fixed architectural and training baseline. Rather than proposing new scaling laws, we analyze how summarization quality, faithfulness and efficiency evolve as dataset size and model capacity are independently increased, while holding architecture, optimization, decoding and hardware constant. All experiments are conducted exclusively on the Multi-News benchmark to avoid cross-dataset confounds. Across ROUGE, coverage, repetition and faithfulness-oriented metrics, we show that lexical overlap and factual consistency follow distinct scaling dynamics. Faithfulness improves most rapidly during early data scaling (approximately 3–4% relative gain from 3k to 12k samples) but exhibits diminishing marginal returns at larger scales, whereas ROUGE continues to increase more smoothly. We further show that faithfulness is more sensitive to data diversity than to volume alone and identify practical scaling regimes that maximize faithfulness gains relative to computational cost. These results establish empirical expectations for scaling faithfulness-aware MDS systems and provide actionable guidance for reliable summarization under realistic resource constraints.

Abstract: Far-UVC radiation for disinfection in the presence of people in public indoor spaces through unshielded open radiation sources has been promoted for several years, claiming to be a simple solution to reduce infections from airborne pathogens such as bacteria and viruses. This literature review summarizes the existing research on the effectiveness of far-UVC radiation for inactivating pathogens, as well as potential risks to skin and eyes associated with exposure to far-UVC radiation. Further, it discusses radiation protection aspects of using far-UVC radiation in the presence of people, and addresses possible effects of far-UVC radiation on the human environment as well. The literature review shows that despite its antimicrobial and antiviral effectiveness, there is so far no sufficient evidence that far-UVC radiation can be used for disinfection in the presence of people in public indoor spaces without risks for humans and the environment. There are particular concerns about the safety of vulnerable groups such as children, the elderly and people with pre-existing medical conditions. The authors recommend further and extended studies in this field concerning potential risks of far-UVC radiation.

Abstract: Maintenance management of stationary combustion engines in the agricultural sector faces critical challenges owing to a reliance on manual methods, which increases the risk of unplanned downtime. This study developed a machine learning-based predictive model to anticipate failures within a 60-day horizon, facilitating the transition from a reactive to a proactive maintenance approach. Following the CRISP-DM methodology and drawing on a historical dataset of 2,250 records from 59 engines, feature engineering techniques were applied to derive 48 predictor variables, while K-Means clustering was employed to identify operational load profiles. The performance of two ensemble algorithms was then evaluated; LightGBM outperformed Random Forest under five-fold cross-validation with a 60/40 temporal split. The proposed model achieved an area under the ROC curve (AUC) of 0.91, a precision of 92.9%, and a recall of 76.1% for detecting actual failures. The findings indicate that gradient boosting techniques are highly effective for optimizing maintenance planning and reducing operating costs in the Ecuadorian agricultural context.

Abstract: Permutation flow shop scheduling is an important production planning problem handled in different contexts. Just-in-time measures have been significant in the optimization of real problems and one is specifically addressed here: the total earliness and tardiness of jobs. The most used approach in the literature to mathematically express this measure is to sum them up using unit weights thus obtainning a mono-objective function. In this paper it is shown that this is a simplification of a problem that is inherently multi-objective, highlighting how a more comprehensive approach can better support decision-making. A bi-objective mathematical optimization model and tools capable of analyzing the mono-objective solution within the multi-objective perspective are proposed. A computational study to analyze the benefits and difficulties of the solution using the bi-objective approach is presented. The results show that for large scale instances in which the tardiness factor is small, the conflict between the objectives of minimizing the total earliness and minimizing the total tardiness of jobs increases significantly. Therefore, the multi-objective approach has a greater potential to support decision-makers. Furthermore, we show that the choice of the solution method must be carefully considered, since the Pareto frontier associated with most instances has many non-supported points.

Abstract: With the aim of finding a generalization of the principle of general covariance applicable to certain models for a {\it fundamental dynamical}, a categorical approach to dynamics is developed. Given a category, the notion of pre-dynamics is introduced. The dynamics is then defined as a {\it finest pre-dynamics}. The {\it categorical recurrence principle}, which constitutes a categorical generalization of the principle of general covariance, is formulated. Natural consequences of the theory are the deterministic character of the fundamental dynamics and the evolving character of the physical dynamical law itself. One main problem considered is the way how time parameters can be defined in terms of the elements of the category and set theory, as a part of the implementation of the categorical recurrence principle. Aimed to address this question, a construction is presented showing how time parameters are defined from the elements of the theory recursively.

Abstract: Occupational exposure to hazardous chemicals remains a major concern across several industrial sectors, including, among others, manufacturing, agriculture, mining, and healthcare. Conventional exposure assessment methods, typically based on stationary environmental monitoring or periodic biological sampling, often fail to capture the dynamic and individualized nature of workplace exposures. In this context, wearable chemical sensors have emerged as promising tools for continuous and real-time exposure monitoring. This exploratory review provides an overview of recent advances in wearable chemical sensing technologies and discusses their potential applications in occupational exposure monitoring. Attention is given to electrochemical, optical, and hybrid sensing systems, as well as to the role of nanomaterials and flexible electronics in improving sensor sensitivity, selectivity, and wearability. Current applications in occupational settings, including monitoring of volatile organic compounds, toxic gases, and heavy metals, are discussed. The review also examines key challenges limiting the practical implementation of wearable sensors in occupational health programs, including long-term stability, environmental interference, power consumption, data integration, regulatory acceptance, and user compliance. Although wearable chemical sensors represent a rapidly evolving field with significant potential for improving occupational risk assessment and preventive strategies, further interdisciplinary research and long-term field validation studies are required before widespread implementation can be achieved.

Abstract: Managing safety and operational efficiency in large-scale events requires tools capable of capturing complex crowd dynamics while supporting rapid and informed decision-making. This paper presents a Generative AI-powered digital twin framework that integrates agent-based crowd simulation, an API-based execution pipeline, and a Large Language Model (LLM)-driven conversational interface within a unified system. The proposed approach enables dynamic configuration, execution, and analysis of crowd scenarios under varying operational conditions, including high-demand and emergency evacuation contexts. Experimental results demonstrate the system’s ability to reproduce nonlinear crowd dynamics, detect congestion patterns, and evaluate evacuation performance, providing actionable insights for planning and safety assessment. A key contribution lies in the introduction of an API-based execution paradigm that exposes the full simulation lifecycle (configuration, validation, execution, and output retrieval) through programmatic interfaces, enabling reproducible and scalable what-if analysis. Additionally, the integration of an LLM-based conversational interface allows non-technical users to interact with complex simulation models through natural language, significantly improving accessibility and usability. The framework is validated through a TRL-4 prototype, demonstrating robust performance, scalability, and interaction reliability. Overall, the proposed system advances digital twins from static analytical tools to executable, interactive, and user-centric platforms for decision support in complex urban environments.