Abstract: Climate change poses significant risks to Mediterranean aquaculture, with sea surface temperature (SST) identified as a critical stressor affecting cultivated species. This study aims to assess climate-related risks for coastal aquaculture in the Valencian Community (Spain) by analyzing SST spatiotemporal variability and predicting future trends. A multi-method approach was employed, combining ARIMA models for 10-year predictions at eight coastal locations, Bayesian hierarchical models (BHM) fitted via INLA for spatiotemporal analysis of maximum SST and temperature range (2000–2024), and Generalized Additive Models (GAM) to evaluate relationships with climate indices (NAO, AMO, ENSO). Results revealed a consistent warming trend since the 1990s, with ARIMA predictions indicating maximum SST values of 27.2±0.1 °C in September over the next decade. The spatiotemporal model showed effective spatial correlation ranges of 246 km for maximum SST and 207 km for SST range. Anomalous warming years (2003, 2006, 2018, 2023–2024) coincided with documented marine heatwave events. The GAM explained 98.2% of deviance, with AMO showing significant influence (>0.001) while ENSO was not statistically significant. Notably, the area north of San Antonio Cape exhibited lower warming trends, suggesting potential climate refuge characteristics. Southern locations (Altea, Campello) currently experience the highest temperatures, but projections indicate Valencia and Sagunto will become the warmest areas. These findings provide essential information for marine spatial planning and recommend a precautionary approach when considering aquaculture relocation towards northern coastal areas.

Abstract: Ionizing radiation affects enzymes, which are critical for most cellular functions, by inducing chemical alterations in their molecular structures, often resulting in the inhibition of their activities. Understanding the molecular and kinetic mechanisms underlying these effects requires suitable experimental protocols and models tailored to specific enzymes and their substrates. In this study, we present a convenient experimental approach utilizing a medical linear accelerator (LINAC) suite as a precise means for irradiating enzyme solutions. Additionally, we examine the effects of ionizing radiation on the catalytic activities and potential structural changes of two enzymes: invertase (β-fructofuranosidase) and collagenase.

Abstract: To address the challenges of insufficient frequency regulation resources and diverse response capabilities in the Yunnan power grid caused by large-scale integration of renewable energy, this paper proposes a cooperative frequency regulation strategy for a hybrid energy storage system incorporating electrolytic aluminum load. First, the frequency regulation model is established for the integrated system comprising electrolytic aluminum load, abandoned mine pumped storage power station, and electrochemical energy storage. A frequency regulation method for electrochemical energy storage is designed, considering control mode weighting factors and state-of-charge (SOC) recovery characteristics. Subsequently, an improved filter with variable filtering time constants is developed based on the area control error (ACE). The high-frequency and low-frequency signals output by the filter are compensated by electrochemical energy storage and abandoned mine pumped storage, respectively. Furthermore, a frequency regulation strategy accounting for frequency regulation zone division is designed. Finally, simulation results under typical scenarios demonstrate that the proposed strategy effectively improves the SOC characteristics of electrochemical energy storage and enhances the frequency regulation performance of the hybrid energy storage system (HESS), while preventing overcharging and over discharging to extend the lifespan of energy storage devices.

Abstract: Human sex recognition is a universal human ability, yet its biological and developmental basis remains incompletely characterized. Research on pheromonal signaling in animals has been essential for elucidating principles of sex recognition, social development, and multisensory integration across species, and has profoundly informed hypotheses about human behavior. However, in humans, no specific sex pheromone has been identified, and the anatomical and genetic substrates required for pheromonal communication—such as a functional vomeronasal organ and intact vomeronasal receptor gene families—are vestigial or absent. These observations suggest that additional or alternative mechanisms may play a central role in human sex recognition.This manuscript proposes the Human Sex Recognition (HSR) model, a developmental framework in which the human voice functions as the earliest and most reliable biological cue to sex. Drawing on converging observations reported across the scientific literature, the model integrates evidence that infants exhibit early sensitivity to vocal signals; that children form stable auditory–visual associations linking voices with sexed visual features; and that pubertal hypothalamic changes confer motivational and emotional significance on these learned associations. The manuscript is organized in two parts. Part I presents the formulation and developmental foundations of the HSR model. Part II evaluates empirical, biological, and comparative evidence bearing on human sex recognition and examines how the HSR framework complements existing pheromone-based sex recognition (HPSR) accounts.Although no new dataset is introduced, the synthesis of human developmental, perceptual, and neurobiological research indicates that HSR is supported by extensive real-life exposure, large effect sizes, and biologically plausible learning mechanisms. The HSR model incorporates mechanisms well established by pheromonal research while proposing additional developmental processes in domains where pheromonal explanations appear insufficient, thereby generating testable predictions for future experimental and developmental studies.

Abstract: The Wheeler–DeWitt equation imposes a Hamiltonian constraint that removes explicit temporal evolution from the quantum state of the universe, producing a frozen mathematical description that conflicts with the observed dynamical nature of physics. This timelessness is not a paradox but a sign of structural incompleteness: the theory lacks a physical time field that provides flow, memory, and energy exchange. We introduce a minimal extension in which an explicit scalar time field, Theta, enters the Hamiltonian and drives evolution through its conjugate momentum, mediated by a universal stability constant chi, approximately 0.551, derived from the Chronos framework. This restores continuous dynamics without violating diffeomorphism invariance or abandoning constraint quantization. The traditional Wheeler–DeWitt form appears naturally when the time field reaches equilibrium, while deviations from equilibrium reproduce the temporal flow seen in nature. This framework links quantum behavior, thermodynamic progression, and cosmic evolution within a unified structure. It also clarifies why artificial intelligence and information systems, which operate on discrete states, lack inherent continuity. By reinstating time as a scalar field, the Wheeler–DeWitt constraint becomes a generator of physical evolution rather than a statement of stasis, aligning the mathematics of quantum gravity with the dynamism of the universe.

Abstract: Background: Chronic non-specific low back pain (CNSLBP) is a leading cause of disability worldwide. Clinical guidelines recommend a biopsychosocial approach and avoidance of unnecessary imaging and passive treatments. Physiotherapists play a key role in CNSLBP management; however, guideline adherence remains inconsistent. No previous study has examined how Slovenian physiotherapists understand and apply CNSLBP guidelines. Methods: A cross-sectional survey was conducted among physiotherapists working in Slovenian primary healthcare settings (public and private sectors). An online questionnaire assessed guideline familiarity, beliefs about pain, activity, work, and spinal vulnerability, and clinical reasoning using a case vignette. Three validated instruments were used: HC-PAIRS, Back-PAQ, and guideline-related clinical decision-making questions. Responses were summarized descriptively and dichotomized as guideline-concordant or discordant. Results: A total of 104 physiotherapists (≈14% of the eligible primary care workforce) participated. Although most reported familiarity with CNSLBP guidelines, substantial discordance with evidence-based recommendations was observed. Only 54% provided guideline-concordant advice regarding physical activity, while recommendations concerning work restrictions (89% discordant) and bed rest (64% discordant) deviated markedly from guidelines. Back-PAQ responses revealed prevalent fear-based beliefs, with 68% agreeing or being unsure that the back is easily injured and 31% endorsing spinal fragility. HC-PAIRS scores indicated a predominantly biomedical orientation. Similar patterns were observed in responses to the clinical vignette. Conclusions: Slovenian physiotherapists in primary care demonstrate a significant gap between guideline recommendations and clinical beliefs. Misconceptions about spinal vulnerability, activity, and work appear to drive guideline-discordant reasoning, highlighting the need for targeted education and improved dissemination of biopsychosocial guidelines for CNSLBP management.

Abstract: We introduce SORT-COSMO, the cosmology application module of the Supra-Omega Resonance Theory (SORT). SORT-COSMO provides a projection-based structural framework for analysing large-scale cosmological phenomena without modifying gravitational dynamics, introducing new fields, or performing empirical parameter fitting. The framework treats cosmological observables as structural projections of a shared operator–kernel backbone composed of idempotent resonance operators, a global consistency projector, and a non-local projection kernel. Within this geometry, scale-dependent drift, long-range coherence, and emergent structural amplification arise as projection effects rather than as consequences of altered expansion histories or additional physical degrees of freedom. SORT-COSMO formalises a set of structural diagnostics applicable to multiple cosmological anomaly classes, including scale-dependent Hubble drift, early galaxy overdensity, early supermassive black-hole seeds, low-multipole CMB modulation, and large-scale cosmic coherence. Each use case is treated as a diagnostic scenario within projection space, not as a phenomenological model or empirical fit. The framework is intentionally non-dynamical and fully compatible with standard cosmological theories, including General Relativity and \(\Lambda\)CDM. SORT-COSMO is positioned as a complementary structural analysis layer within the modular SORT v6 architecture, enabling cross-domain consistency with SORT-AI, SORT-QS, and SORT-CX while remaining agnostic to underlying microphysical assumptions.

Abstract: This article proposes an interpretable, multi-layered recruitment model that balances predictive performance with decision transparency in AI-supported HR processes, ad-dressing risks related to opacity, auditability, and ethically sensitive decision-making. The architecture combines an expert rule layer for minimum-threshold screening, an unsupervised clustering layer to structure candidate profiles and generate pseudo-labels, and a supervised classification layer trained and evaluated via repeated k-fold cross-validation. Model behavior is explained using SHAP to identify feature contribu-tions to cluster assignment, and cluster quality is additionally diagnosed using Necessary Condition Analysis (NCA) to assess minimum competency requirements for attaining a target overall quality level. The approach is illustrated in a Data Scientist recruitment case study, where centroid-based clustering predominates (K-Means is most frequently se-lected), while linear classifiers show the highest effectiveness and stability (logistic re-gression performs best). SHAP highlights competencies that differentiate candidates beyond the initial threshold, and NCA further distinguishes candidates within the recommended cluster by identifying profiles that meet (or fail) the necessary-condition bottleneck. The proposed framework is replicable and supports transparent, auditable recruitment decisions.

Abstract: Following Livadiotis G. and McComas D. J. (2023) [1], we propose a new type of DNA frameshift mutations that occur spontaneously due to information exchange between the DNA sequence of length bases (n) and the mutation sequence of length bases (m), and respect the kappa-addition symbol ⊕κ. We call these proposed mutations Kappa-Frameshift Background (KFB) mutations. We find entropy defects originate in the interdependence of the information length systems (or their interconnectedness, that is, the state in which systems with a significant number of constituents (information length bases) depend on, or are connected with each) by the proposed KFB-mutation). We also quantify the correlation among DNA information length bases (n) and (m) due to information exchange. In the presence of entropy defects, the Landauer’s bound and minimal metabolic rate for a biological system are modified. We observe that the different n and κ scales are manifested in the double evolutionary emergence of the proposed biological system through subsystems correlations. For specific values of the kappa parameter we can expect deterministic laws associated with a single biological polymer in the short term before the polymer explores over time all the possible ways it can exist.

Abstract: Carvacrol, a phenolic monoterpene predominantly found in Origanum species, has been reported to exhibit antimicrobial, anti-inflammatory, antioxidant, and anticancer effects. Synergistic formulations such as Vacrol and S-Mix, enriched with carvacrol and complementary essential oil compounds, may enhance therapeutic efficacy while reducing toxicity. Essential oil components were analyzed via GC-MS. Cell viability was assessed using the sulforhodamine B (SRB) assay at different concentrations and incubation periods. An in ovo chorioallantoic membrane (CAM) assay was performed to investigate tumor volume changes and histopathological alterations. Vacrol and S-Mix demonstrated concentration- and time-dependent cytotoxic effects in MDA-MB-231 cells, with significant reductions in viability at higher concentrations (100 µM–1 mM). In ovo, S-Mix induced ~40% reduction in tumor volume and promoted apoptotic morphology compared to controls. Synergistic effects of carvacrol with α-pinene, eugenol, and β-terpineol likely contributed to enhanced bioactivity. Vacrol and S-Mix exhibit promising antiproliferative and pro-apoptotic activity against triple-negative breast cancer models. These findings support further preclinical and mechanistic investigations to validate their therapeutic potential.

Abstract: Young people with major depressive disorder can present with trauma, paranoia, and active suicidal thoughts, a profile that often resists selective-serotonin re-uptake inhibitors and other monoaminergic drugs whose clinical effects emerge only after several weeks. Interest has therefore turned to the glutamatergic system; intravenous ketamine, for example, blocks N-methyl-D-aspartate (NMDA) receptors and rapidly improves mood, but its cost and monitoring needs limit use. Cheung’s oral strategy—dextromethorphan boosted by a CYP2D6-inhibiting antidepressant plus piracetam to enhance α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) throughput—aims to reproduce ketamine’s neuroplastic cascade with readily available medicines.This retrospective case series reviews three consecutive patients, two adolescents and one young adult, treated with that combination in routine outpatient care after poor response to previous interventions. The regimen was introduced stepwise, beginning with low-dose fluoxetine and bedtime dextromethorphan, followed by daytime piracetam. Depressive severity was tracked with the Patient Health Questionnaire-9, and clinicians noted functional changes and adverse effects. All three patients showed marked improvement: PHQ-9 scores fell into the mild range within two to four weeks, suicidal ideation ceased, and paranoid ruminations resolved. No clinically significant adverse events or signs of serotonin excess occurred. These observations suggest that an inexpensive oral approach directed at the NMDA–AMPA axis may deliver rapid relief in complex, treatment-resistant depression among adolescents and young adults. Although limited to three cases, the favourable tolerability and speed of response support further systematic study of glutamatergic combinations in this population.

Abstract: Background/Objectives: Missing data in clinical observational studies, such as out-of-hospital cardiac arrest (OHCA) registries, can compromise statistical validity. Single imputation methods are simple alternatives to complete-case analysis (CCA) but do not account for imputation uncertainty. Multiple imputation (MI) is the standard for handling missing at random (MAR) data, yet its implementation remains challenging. This study evaluated the performance of MI in association analysis compared with CCA and single imputation methods. Methods: Using a simulation framework with real-world Singapore OHCA registry data (N=13,274 complete cases), we artificially introduced 20%, 30% and 40% missingness under MAR. MI was implemented using predictive mean matching (PMM), random forest (RF), and classification and regression trees (CART) algorithms, with 5-20 imputations. Performance was assessed based on bias and precision in a logistic regression model evaluating the association between alert issuance and bystander CPR. Results: CART outperformed PMM, providing more accurate β coefficients and stable CIs across missingness levels. Although K-Nearest Neighbours (KNN) produced similar point estimates, it underestimated imputation uncertainty. PMM showed larger bias, wider and less stable CIs, and in some settings performed similarly to CCA. MI methods produced wider CIs than single imputation, appropriately capturing imputation uncertainty. Increasing the number of imputations had minimal impact on point estimates but modestly narrowed CIs. Conclusion: MI performance depends strongly on the chosen algorithm. CART and RF methods offered the most robust and consistent results for OHCA data, whereas PMM may not be optimal and should be selected with caution. MI using tree-based methods (CART/RF) remains the preferred strategy for generating reliable conclusions in OHCA research.

Abstract: This paper addresses the challenge of efficiently identifying and classifying resource contention behaviors in cloud computing environments. It proposes a deep neural network method based on multi-scale temporal modeling and attention-based feature enhancement. The method takes time series resource monitoring data as input. It first applies a Multi-Scale Dilated Convolution (MSDC) module to extract features from resource usage patterns at different temporal resolutions. This allows the model to capture the multi-stage dynamic evolution of resource contention behaviors. An Attention-based Feature Weighting (AFW) module is then introduced. It learns attention weights along both the temporal and feature dimensions. This enables the model to emphasize key time segments and core resource metrics through saliency modeling and feature enhancement. The overall architecture supports end-to-end modeling. It can automatically learn temporal patterns of resource contention without relying on manual feature engineering. To evaluate the effectiveness of the proposed method, this study constructs a range of contention scenarios based on real-world cloud platform data. The model is assessed under different structural configurations and task conditions. The results show that the proposed model outperforms existing mainstream temporal classification models across multiple metrics, including accuracy, recall, F1-score, and AUC. It demonstrates strong feature representation and classification capabilities, especially in handling high-dimensional, multi-source, and dynamic data. The proposed approach offers practical support for resource contention detection, scheduling optimization, and operational management in cloud platforms.

Abstract: Environmental contaminants are increasingly recognized as potential drivers of microbial adaptation. This study investigates whether two complex microbial traits, biofilm formation and polycaprolactone (PCL) degradation, are primarily driven by taxonomic affiliation or environmental exposure. A collection of bacterial strains was isolated from distinct environments, including poultry litter (antibiotics) and plastic-contaminated soils. Biofilm formation was quantified while PCL degradation was assessed via clearing-zone assays. Isolates from both taxonomic and metabolic perspective and multivariate analyses to explore the association between trait and environment. Biofilm formation was predominantly observed in multidrug-resistant strains from poultry farming, while PCL degradation was exclusive to strains from plastic-rich environments. Exposure to environmental pollution appears to promote the emergence of microbial traits through ecological selection and plastic responses.

Abstract: Background Emerging and re-emerging infectious diseases that infect wildlife have highlighted the necessity for wildlife health surveillance (WHS) due to the interconnectedness of wildlife in maintaining the health of natural resources, agriculture, and humans. While global policies and guidelines exist, a critical gap remains in local-to-national implementation of WHS systems. A group of local, national, and global actors in WHS have formed a working group to address this gap. Methods and Findings The working group reports on a theory of change (ToC) developed to implement WHS from local to global scales. Using proven methods for developing a collaborative ToC, we leveraged the expertise of working group members and identified six transformative pathways to be implemented via collaborations across scales and contexts: mindset change, policy and investment, evidence-based practice, user-driven technologies, capacity enhancement, and mobilization of a global community of practice. Interpretation This ToC serves as a roadmap to develop effective WHS systems that support adaptive management and implementation. WHS is fundamental to understanding the impacts of health threats to biodiversity, domestic animals, and humans. This ToC presents an approach to operationalize the integration of wildlife health into collaborative One Health surveillance.

Abstract: This paper introduces the Proximal Chemical Mandate Principle, a unified theoretical framework asserting that all motivated behavior in organisms with neurochemical systems is driven by two invariant, instantaneous optimization processes: Reward Signal Maximization (R↑) and Stress Signal Minimization (S↓). The mandates operate through a deterministic, expanded hierarchical causal chain, including variables like the Completion Approach Rate (cAr), linking sensory input (I-s) to ultimate, environmentally contingent outcomes (Uo). The framework defines three environmental domains—Natural Selection Field (NSF), Natural Culturophiliart Field (NC★F), and Natural Counterproductive Field (NCF)—to explain why identical neurochemical optimization processes yield adaptive, meaning-seeking, or maladaptive outcomes. This contingency is formalized by the Sophistication–Velocity Principle, which models how the efficiency of mandate implementation (Š) interacts with predictive fitness consequences stress (fĈs) to determine behavioral trajectories. The principle is further supported by the Stimulus's Fitness Principle, which quantifies individual variation in fitness outcomes based on the unique balance between neurochemical response and health consequences. The theory integrates extensive empirical evidence, including high-temporal-resolution studies of dopamine dynamics and the Readiness Potential (RP), validating the principle of instantaneous mandate execution (Mandoinstafill). It provides a deterministic interpretation of consciousness as a state reflection of ongoing optimization, and resolves key evolutionary and behavioral paradoxes by explaining them as sophisticated implementations of the mandates under specific environmental and neurochemical conditions: Addiction (hijacked R↑), Altruism/Emergency Response (immediate S↓ priority), Voluntary Childlessness (favorable R↑/S↓ balance in modern contexts), Suicide (extreme S↓ fulfillment), Hard Work/Delayed Gratification (PFC modeling of long-term R↑), Hyper-Palatable Foods (supernormal R↑ exploitation), and Music/Evolutionary Acoustics (sophisticated implementation exploiting evolved OsC for R↑). The framework ultimately suggests that complex human capacities serve as tools for sophisticated mandate fulfillment, with applications in personalized behavioral optimization, mental health, and the ethical alignment of Artificial General Intelligence (AGI).

Abstract: Population data at small area scales are essential for effective decision-making, influencing public health, disaster response, and resource allocation, amongst others. While national censuses remain the cornerstone of population data, they are often constrained by high costs, infrequent collection cycles, and coverage gaps, which can hinder timely data availability. To address these challenges, geospatial statistical approaches using limited microcensus surveys have been demonstrated as a reliable source, but the field has advanced substantially in recent years, with significant developments in both data sources and modelling methodologies. New approaches now leverage routine health intervention campaign data, satellite-derived settlement maps, and bespoke modelling approaches to produce reliable small area population estimates where enumeration is difficult or outdated. Various countries are applying these techniques to support census operations, health program planning, and humanitarian response. This manuscript reviews recent advances in ‘bottom-up’ population mapping approaches, highlighting innovations in input data, modelling methods, and validation techniques. We examine ongoing challenges, including partial observation of buildings under forest canopy, population displacement, and institutional uptake. Finally, we discuss emerging opportunities to enhance these approaches through better integration with traditional data ecosystems, capacity strengthening, and co-production with national institutions.

Abstract: In this review, we examine boron-containing agents for boron neutron capture therapy (BNCT) with a focus on absorption, distribution, metabolism, excretion and toxicity (ADMET) and model-informed design. BNCT is a binary radiotherapeutic modality in which high linear energy transfer particles are generated in the vicinity of ^10B, ideally within boron-loaded tumour cells, so the therapeutic outcome depends critically on the pharmacokinetics and biodistribution of boron carriers. We survey low-molecular-weight compounds, peptide conjugates, polymeric and nanostructured platforms and cell-based vectors, and discuss how physicochemical properties, transporter engagement and nano–bio interactions govern tumour uptake, subcellular localisation and normal-tissue exposure. We also describe a shift from maximising boron content towards optimising exposure profiles using positron emission tomography (PET), physiologically based pharmacokinetic (PBPK) modelling and in silico ADMET tools to define irradiation windows. Classical agents such as boronophenylalanine (BPA) and sodium borocaptate (BSH) are contrasted with newer polymeric and metallacarborane-based carriers, with attention to brain penetration, endosomal escape, linker stability, biodegradation and elimination routes, as well as platform-specific toxicities. We argue that further progress in BNCT will depend on integrating imaging-derived kinetics with PBPK-informed dose planning and engineering subcellularly precise yet degradable carriers, and that ADMET-guided design and spatiotemporal coordination are central to achieving reproducible clinical benefit from BNCT’s spatial selectivity.

Abstract: Federated Learning (FL) and Split Learning (SL) maintain client data privacy during collaborative training by keeping raw data on distributed clients and only sharing model updates (FL) or intermediate results (SL) with the centralized server. However, this level of privacy is insufficient, as both FL and SL remain vulnerable to security risks like poisoning and various inference attacks. To address these flaws, we introduce SplitML, a secure and privacy-preserving framework for Federated Split Learning (FSL). SplitML generalizes and formalizes FSL using IND−CPAD secure Fully Homomorphic Encryption (FHE) combined with Differential Privacy (DP) to actively reduce data leakage and inference attacks. This framework allows clients to use different overall model architectures, collaboratively training only the top (common) layers while keeping their bottom layers private. For training, clients use multi-key CKKS FHE to aggregate weights. For collaborative inference, clients can share gradients encrypted with single-key CKKS FHE to reach a consensus based on Total Labels (TL) or Total Predictions (TP). Empirical results show that SplitML significantly improves protection against Membership Inference (MI) attacks, reduces training time, enhances inference accuracy through consensus, and incurs minimal federation overhead.

Abstract: The main aim in this paper is to present a simplified (temperature-dependent) version of the quantum statistical model for computing the equation of state of electrons in materials. For this purpose, the Englert-Schwinger approximation scheme within the quantum statistical model is extended to finite temperatures. This procedure leads to a modified Thomas-Fermi-Dirac model. Schwinger and co-workers had originally demonstrated this procedure for the case of zero-temperature, and applied it to compute the electronic properties of cold free atoms. In this paper, a new algorithm is developed to solve the modified Thomas-Fermi-Dirac model, and the numerical results obtained for Cu and Al are compared with those of the exact quantum statistical model. Good agreement is found particularly for thermal component of electron equation of state. The present approach, at much less efforts, would be useful in high-energy-density physics as thermal component of electron properties alone are needed in equation of state theory. Derivation of explicit expressions of different contributions (viz. kinetic, gradient, exchange and exchange-correlation terms) to the free energy functional, its stationary property, finite-temperature corrections to energy of strongly bound electrons, and (iv) details of the new algorithm are provided in the Appendix.