Abstract: Genetic and Molecular Bases of Mental Well-being and Mindfulness is an exciting and quickly emerging research field that examines how the genetic and molecular bases of our biology (genetics and our molecular biology) can impact our mental well-being and our mindfulness practices, and aims to uncover the biological foundations of how we can begin to engage our mental well-being, stress-resilience, and mindfulness practices.

Abstract: Agent identity governance is advancing, though core agent identity and authorization questions remain unresolved: existing frameworks provision, authenticate, authorize, and retire non-human and agentic identities, governing the agent’s identity, credentials, and lifecycle while assuming the composition an agent was approved with remains the composition it runs with. This paper argues that assumption is the open seam. An agent’s effective composition—its tools, data sources, delegated authorities, policies, and child agents—is a runtime supply chain of capability, and that supply chain drifts. We introduce composition drift as the departure of an agent’s effective composition from the terms of its approval, and isolate its most consequential form, compositional drift: the accumulation of individually approved changes into capability that none authorized alone. We formalize this with a two-stage operator: a component-level diff detects that the composition changed (component divergence); a capability-closure stage detects when the change authorized something new (compositional drift)—a qualitative boundary, not a numeric threshold. The contribution is not the observation that approved changes can combine dangerously—long known to authorization security—but a temporal governance model for approved composition drift in agentic systems, linking emergent capability to reauthorization and inventory reconciliation. This drift produces shadow infrastructure: resources provisioned outside any inventory through benign, individually approved pathways. We propose composition attestation, a runtime composition-control layer complementary to identity governance. Paired positive and negative scenarios show the model discriminates, not labels. We bound our claims: the model establishes the phenomenon by construction and claims no deployment efficacy.

Abstract: Gabriel horn (G-horn) with finite volume but infinite surface area can characterize the frame-dragging effects of strong gravitational fields. Subject to the gravitational bounce depending on velocity direction, a G-horn black hole model is established on the Finsler geometric framework. Beyond considering curvature alone, the torsion determined by Cartan connection gives rise to Coriolis force and centrifugal force to counteract gravitational collapse. The G-horn topological structure determines the property that the mathematical singularity and the center of matter converging region need not coincide. Without hidden or naked singularities, the matter after entering this black hole is choked at a certain inner-surface zone of horn’s neck to form a regular “hollow” core based on spin-flip trigger of Cartan torsion. During geodesic fluctuations in the G-horn model, the existence of black hole remnants is rigorously derived. Due to the global geodesic completeness, those existing paradoxes or hypotheses such as black hole information loss, Penrose's cosmic censorship and firewalls are just the competition results between gravity of curvature and the resultant inertial forces of “centrifugal force ± Coriolis force” of torsion. The proposed G-horn map implies that the Schwarzschild, Kerr and Reissner‑Nordström black holes may be modified uniformly as this hyperbolic G-horn model.

Abstract: Olive is a major agricultural crop extensively cultivated throughout the Mediterranean region. However, olive trees are vulnerable to several diseases that can negatively affect productivity and yield. One of the most widespread foliar diseases is olive leaf peacock spot, caused by the fungus Cycloconium oleaginum. Early detection of this disease is essential for preventing leaf drop, limiting disease spread, maintaining tree health, and reducing treatment costs before the infection reaches an advanced stage. In this study, a multimodal hybrid deep learning framework is developed to detect peacock spot disease in olive leaves and assess disease severity based on visual and numerical features. The proposed framework integrates olive leaf images with soil conditions, environmental conditions, and vegetation and stress indices to provide a more comprehensive disease analysis than image-only approaches. A ResNet50-based convolutional neural network is used to extract visual features from leaf images, while a multilayer perceptron processes the numerical sensor-based and index-based data. These features are then fused within a unified learning framework to classify disease stages and estimate leaf damage severity, including lesion coverage and yellowing percentage. The performance of the proposed model was evaluated using standard performance metrics suitable for both classification and regression tasks. For classification, the model was evaluated on 494 testing samples and achieved an overall accuracy of 97.77 %, with a macro F1-score of 0.9809 and a weighted F1-score of 0.9776. In addition, the model achieved low regression errors, with mean absolute errors of 1.16 % for lesion coverage and 1.42 % for yellowing estimation. These results demonstrate the effectiveness of the proposed multimodal framework for accurate peacock spot detection and severity assessment, supporting its potential use in smart agricultural monitoring and disease management.

Abstract: Educational centres face new challenges in adapting to digital transformation. While the majority of scientific studies have focused on the individual perspective, fewer have examined it from an organisational perspective. This research addresses this gap. This paper analyses the digital transformation of secondary educational centres from the organisational psychology field, thus overcoming the traditional approach focused on teachers’ individual competencies. This study follows a qualitative methodology based on semi-structured interviews with 20 headteachers from educational centres, including questions related to the process and level of digitalisation, the difficulties encountered, and the main practices implemented, among others. The analysis identifies four overarching themes that emerge from the dynamic interplay between the digital culture, perceived resource availability, digitalisation practices and strategies. Considering the interrelations among these themes and the digital transformation stage, three models are identified: reactive, driven by internal initiative, and integrative, each representing a distinct level of maturity in the integration of digitalisation. Overall, the findings suggest the existence of different patterns of digitalisation across educational centres, distinguishing progressive phases of digital transformation. This framework provides a pathway for educational centres to undertake critical self-diagnosis, strategically design interventions, and continuously enhance their digitalisation processes.

Abstract: Thermal treatment of clay minerals induces a sequence of dehydration, dehydroxylation, and recrystallization reactions that control the properties of ceramic materials, calcined clays, and other high-temperature products. This review examines how vibrational spectroscopic techniques, particularly Fourier-transform infrared (FTIR), Raman, and infrared emission spectroscopy (IES), have advanced the molecular-level understanding of these transformations. Unlike conventional thermal analysis methods, these techniques directly monitor changes in hydroxyl groups, interlayer water, silicate frameworks, and newly formed phases during heating, providing real-time insight into reaction pathways and intermediate structures. The thermal behavior of major clay mineral groups, including kaolinite-group minerals, serpentines, smectites, illite, palygorskite, sepiolite, and mixed-layer clays, is compared in terms of their characteristic spectroscopic responses to increasing temperature. Particular attention is given to band shifts, intensity variations, band disappearance, and the appearance of new vibrational features associated with structural reorganization and phase development. The reviewed studies demonstrate that thermal stability is primarily governed by octahedral composition, cation–OH bond strength, vacancy distribution, and crystallinity. Integration of spectroscopic observations with complementary diffraction and thermal analysis data provides a unified framework for understanding clay mineral transformations and for optimizing thermal processing in ceramic manufacture and calcined clay applications.

Abstract: Circumscription is a classical non-monotonic formalism in which selected atoms are minimized while other atoms are fixed or allowed to vary. For propositional clause theories, checking whether a candidate interpretation is a circumscription model amounts to a global minimality test. We study this checking problem through the minimal reduct of the candidate interpretation. The reduct turns the global test into a residual entailment problem; we then decompose that entailment problem along the collapsed negative dependency graph. The checker verifies source components over their ancestor scopes, contracts atoms whose obligations have been certified, and records certificate fragments that refer back to clauses of the original input theory. We give two exact local certification strategies: a direct SAT check and a MUS-based extraction procedure. Experiments on solved random 3CNF instances and industrial CNF instances show that the decomposition-based checker agrees with the global reduct baseline and that its certificates can be replayed. The MUS variant produces much smaller supports, but it also spends more time on extraction.

Abstract: Few-shot domain adaptation (FSDA) has become a key approach for cross-domain intrusion detection, enabling models to leverage limited labeled target data under distribution shift. While a wide range of adaptation methods have been proposed, their effectiveness often varies significantly across different transfer scenarios, leading to inconsistent performance and limited interpretability. In this work, it is argued that such variability stems from an overlooked factor: transfer difficulty. This study proposes a transfer-difficulty-aware perspective on FSDA and shows that adaptation behavior is fundamentally dependent on cross-domain compatibility rather than solely on intrinsic domain structure. To this end, a distinction is made between intra-domain separability, which characterizes the internal structure of each domain, and transfer difficulty, which captures how well source-derived representations generalize to the target domain. A set of asymmetric transferability metrics is introduced to quantify this phenomenon and accompanied by a systematic analysis across multiple transfer directions. The results reveal that a domain with strong internal separability does not necessarily yield easy transfer, highlighting that intra-domain structure alone is insufficient to explain cross-domain performance. Furthermore, it is shown that different adaptation strategies exhibit distinct behaviors depending on transfer difficulty: target-only few-shot learning is effective in low-difficulty settings, whereas alignment-based approaches become essential in high-difficulty scenarios. These findings explain the inconsistent performance of existing methods and suggest that domain adaptation should be treated as a transfer-dependent problem rather than a uniform strategy. Ultimately, this work offers both theoretical insights and practical guidance for designing robust cross-domain intrusion detection systems.

Abstract: Over the past three decades, the United States has experienced a notable increase in weather-related disasters, including hurricanes, floods, torna-does, wildfires, and severe storms, posing growing challenges to healthcare preparedness and public health systems. This study analyzes Federal Emergency Management Agency (FEMA) disaster declarations from 1989 to 2019 to characterize temporal and geographic trends in weather-related events. Data after 2019 were excluded to avoid confounding effects asso-ciated with the COVID-19 pandemic, which disrupted disaster declara-tions, resource allocation, and healthcare system demands. Using descrip-tive statistics, generalized linear mixed models, and spatial clustering techniques, we identified substantial increases and nonlinear patterns in disaster declarations, with variation across hazard types and regions. These trends reflect evolving hazard exposure, regional differences, and policy-driven declaration practices. Although this study does not directly measure health outcomes or social vulnerability, the observed patterns have important implications for healthcare system capacity, workforce preparedness, and populations known to be disproportionately affected by disasters. The findings highlight the need for climate-informed training, data-driven preparedness planning, and integration of disaster trend analysis into nursing education and public health practice. Strengthening the ability of healthcare systems to anticipate and respond to evolving disaster patterns is critical for advancing resilience and promoting equita-ble health outcomes in the context of climate change.

Abstract: Salmonellosis is a major food safety concern, especially when Salmonella strains from meat show multidrug resistance. This study examined plant-derived extracts' antimicrobial potential. Extracts from Lepechinia caulescens (brenilla) and Sagina procumbens (pearl grass) were tested against multidrug-resistant Salmonella from meat in Guanajuato, Mexico. Of 25 strains, 9 were from chicken, 8 from beef, and 8 from pork. This study analyzed three extracts: two from L. caulescens—one isolated from leaves, flowers, and seeds (LCLFS) and the other from stems and roots (LCSR)—and a third extract from S. procumbens (SP). The plant material was dried, ground, and macerated in ethanol for one week in the dark at room temperature. It was then filtered, concentrated, and resuspended in sterile distilled water. The methodology involved performing minimum inhibitory concentration (MIC) tests and antimicrobial susceptibility testing using the disk diffusion method. The inhibition zones of the L. caulescens extracts (17–23 mm in diameter) yielded better results than those of S. procumbens (5–16 mm in diameter). The two extracts, LCLFS and LCSR, exhibited similar inhibitory concentrations of 30 µL/mL, whereas that of S. procumbens was 50 µL/mL. The LCSR extract consistently inhibited Salmonella strains present in all types of meat, unlike the SP extract. Statistical analysis showed that the type of extract and the source of Salmonella significantly affected the results. The LCLFS extract shows promise for the control of multidrug-resistant Salmonella in meat products.

Abstract: Histopathologic melanoma diagnosis extends beyond melanocytic cytology to encompass non-melanocytic features: solar elastosis patterns, stromal regression, adnexal relationships, epidermal reaction patterns, and the host inflammatory response. These “old school” low-power clues are particularly valuable on sun-damaged skin, where benign nevi, reactive melanocytic hyperplasia, and melanoma in situ share overlapping features. Quantitative data support two elastosis-based signs: the “umbrella sign” (reduced elastosis beneath the lesion’s central third; PPV for nevus 96%, NPV for melanoma 74%; calculated from raw cohort data) and the “purple fiber sign” (100% specificity, 30% sensitivity for nevus), both from a cohort of 81 actinically damaged lesions. Regression-identified by compressed elastic layers displaced to the reticular dermis, fibrosis, melanophages, and inflammation-aids diagnosis but complicates distinction from surgical scar. The maturation state of tertiary lymphoid structures (TLS) within the regression zone, ranging from immunosuppressive immature aggregates to anti-tumoral mature structures with germinal centers, may explain the variable prognostic significance of histologic regression. Epidermal hyperplasia over thick melanomas reflects angiogenesis-related changes, while effacement is a practical red flag in spitzoid lesions. Ancillary tests are most productive when morphology has already framed the differential. These non-melanocytic clues remain indispensable as the foundation for rational ancillary testing.

Abstract: Circular RNAs (circRNAs) have recently emerged as a class of abundant and remarkably stable non-coding RNAs preferentially enriched in the nervous system. In neurons, the fine-tuned spatial regulation of gene expression is critical for proper synaptic function; accordingly, several studies have demonstrated that circRNAs exhibit highly compartmentalized localization, specifically within dendrites, axons, and synapses. These spatial localization properties imply the presence of active transport mechanisms which control the intracellular trafficking of circRNAs. This review highlights current understanding of circRNA transport in neurons, focusing on molecular machinery driving synaptic enrichment. We specifically address the role of Ribonucleoprotein-based transport as a primary mechanism driving circRNA localization and examine how this spatial distribution influences synaptic plasticity and post-transcriptional gene regulation. Finally, we discuss the clinical implications of these processes, exploring the link between dysregulated RNA transport and the development of neuronal abnormalities.

Abstract: Background/Objectives: Microsatellite-stable colorectal cancer (MSS CRC) accounts for the vast majority of CRC cases and remains largely resistant to immune checkpoint inhibitors. Emerging evidence suggests that the gut microbiome is an important regulator of antitumor immunity and may contribute to immunotherapy resistance through multiple mechanisms involving the tumor microenvironment. This review aims to summarize current knowledge of the microbiome–immunity–therapy axis in MSS CRC and to explore microbiome-based strategies to enhance immunotherapy responsiveness. Methods: A narrative review of the recent literature was conducted, focusing on studies published within the last five years that investigated gut microbiota composition, microbial metabolites, tumor immune regulation, immunotherapy response, and microbiome-targeted therapeutic interventions in CRC. Evidence from mechanistic studies, translational research, clinical investigations, and multi-omics analyses was integrated. Results: Current evidence indicates that gut dysbiosis contributes to immune resistance in MSS CRC through immune exclusion, myeloid-driven immuno-suppression, T-cell dysfunction, chronic inflammation, and altered microbial metabolite signaling. Specific microorganisms, including Fusobacterium nucleatum, enterotoxigenic Bacteroides fragilis, pks-positive Escherichia coli, and other CRC-associated pathobionts, have been implicated in tumor progression and modulation of antitumor immunity. Microbial metabolites such as short-chain fatty acids, tryptophan-derived compounds, bile acids, succinate, and inosine represent key functional mediators linking microbial communities to host immune responses. Emerging microbiome-targeted interventions, including fecal microbiota transplantation, next-generation probiotics, postbiotics, selective microbial depletion, and engineered bacterial therapeutics, show potential to restore antitumor immunity and improve immunotherapy efficacy. In parallel, advances in metagenomics, metabolomics, spatial transcriptomics, and artificial intelligence are facilitating the development of precision immuno-microbiome oncology approaches. Conclusions: The gut microbiome functions as a critical regulator of immune resistance in MSS CRC through coordinated effects on microbial composition, metabolite production, and tumor immune remodeling. Microbiome-targeted interventions, combined with multi-omics-based patient stratification, may provide new opportunities to overcome immunotherapy resistance and expand the clinical benefits of immune checkpoint blockade in this traditionally refractory disease.

Abstract: This study proposes a Gaussian Process Regression (GPR) method for estimating the compressive strength of soil mixtures containing bentonite and basalt fibers. GPR is a preferred probabilistic machine learning approach, especially for limited datasets, due to its high generalization ability and its ability to directly calculate prediction uncertainty. In this study, experimentally obtained bentonite and basalt fiber ratio values were used as input parameters of the model, and unconfined compressive strength (q_u) was determined as the output variable. Model performance was evaluated using the Leave-One-Out Cross Validation (LOOCV) method. The performance of the proposed GPR model was evaluated with various metrics. Accordingly, the values of the MAE, RMSE, and R² metrics of the model were calculated as 3.6%, 5.2%, and 0.955, respectively. The results show that the GPR model provides high prediction accuracy and is a reliable prediction tool for small datasets. Furthermore, the prediction surfaces and uncertainty analyses obtained by the model contributed to a better understanding of the effect of mixture parameters on compressive strength.

Abstract: We present our morphological and transcriptomic evidence for a model of the bipartite grass cotyledon, wherein the scutellum comprises the distal, upper zone and the coleoptile is the sheathing, lower zone of the single grass cotyledon. We assert that homology is best evaluated by examining ontogeny, and not mature adult stages of development. The scutellum is the first lateral organ to arise in the grass embryo, accompanied by leaf homologous gene expression; thereafter the scutellum forms a highly-modified, non-foliar, digestive organ. Lateral organs in grasses exhibit distichous phyllotaxy, where successive organs arise on opposite sides of the shoot axis and in two ranks. Accordingly, scutellum and coleoptile arise on the same side of the shoot, as expected in a bipartite, single lateral organ that is fused medially. Likewise, transcriptomic data identifies expression of the sheath identity marker BOP1a in the coleoptile but not the scutellum, supporting the homology of the coleoptile and the grass sheathing leaf base. Taken together, these data support the bipartite model on the evolution of the grass cotyledon.

Abstract: Adrenal chromaffin cells are key effectors of the sympathoadrenal response and play a central role in physiological adaptation to stress. While acetylcholine and pituitary ad-enylate cyclase-activating polypeptide (PACAP) are recognized regulators of catechol-amine secretion, connexin-mediated gap junctional communication provides an addi-tional mechanism for coordinating chromaffin-cell activity. Whether early-life experience influences the capacity of chromaffin-cell networks to undergo stress-induced remodeling remains unknown. To address this question, we examined connexin 36 (Cx36) and connexin 43 (Cx43) expression in the adrenal medulla of adult rats exposed to neonatal maternal separation (MS; 3 h daily, postnatal days 2–15) and subsequently challenged with an 8-day unpredictable mild stress (UMS) protocol. Under basal adult conditions, MS did not alter adrenal medullary Cx36 or Cx43 immunoreactivity relative to ani-mal-facility-reared controls. In contrast, UMS increased connexin immunoreactivity, and this response was significantly enhanced in animals with a history of MS. MS+UMS animals also displayed augmented corticosterone responses to acute restraint stress, particularly in females. These findings indicate that neonatal maternal separation does not produce a constitutively altered adult chromaffin-cell phenotype but modifies the mag-nitude of subsequent stress-induced adrenal remodeling.

Abstract: To assess how Bayesian network structures are built and learned in applied work, we screened 5,993 recent papers (2020-2025) whose abstracts mention “Bayesian (belief) network” and deemed 3,661 relevant. Among these relevant papers, expert knowledge was used in 2,059 papers (56.2%): 1,785 (48.8%) relied on expert knowledge alone, whereas 274 (7.5%) combined expert input with algorithmic structure learning through edge modification or structural constraints. Data sharing was scarce: only 129 studies (3.5%) provided functional dataset links, which we curated into an open benchmark index. These findings support a data-first norm: share datasets, prefer automatic structure-learning baselines, and document expert input. Among 1,106 papers (30.2%) using algorithms without expert knowledge, score-based flags were most common (797, 72.1%; mainly hill climbing, K2, and tabu), followed by constraint-based methods (194, 17.5%; mainly PC and Grow-Shrink), fixed or restricted-topology BN classifiers (143, 12.9%; mainly TAN and naive Bayes), and hybrid methods (131, 11.8%; mainly MMHC); bootstrapping appeared in 223 papers (6.1%). Reported practice remains concentrated around familiar algorithms.

Abstract: High-speed electric rotating machines enhance power density and eliminate gearboxes in waste heat recovery microturbines, but conventional designs face high manufacturing costs and complex cooling requirements. This study presents the development, experimental validation, and comparative analysis of high-speed configurations. Initially, a lower-speed induction machine prototype operating at 13,000 rpm was built using standardized components to experimentally validate numerical loss models. Experimental testing of the initial prototype confirmed a total loss of 7.89 kW, closely matching the simulated 7.75 kW. Leveraging these findings, two next-generation topologies of decreased size, an induction machine and a surface permanent magnet machine, were designed and evaluated using finite element method and conjugate heat transfer simulations under sinusoidal and pulse-width modulation excitations. At a 14,000 rpm operational point, the surface permanent magnet prototype outperformed the induction machine configuration, ensuring the lower temperatures of the permanent magnet machine and achieving 63.2 kW of mechanical power and 96.21% efficiency compared to the induction machine's 52.4 kW and 94.64%.This paper builds upon the microturbine generator project introduced at the ICPGEEC 2025 conference, by presenting lighter, higher-speed machine designs.

Abstract: This study investigated the spatiotemporal dynamics of climate potential productivity and its correlation with changes in maize productivity in four agroclimatic zones of the Democratic Republic of the Congo (DRC) using Thornthwaite Memorial models and Mann–Kendall Trend Analysis from 1960 to 2024. Climate potential productivity showed contrasting trends between agroclimatic zones. In the Haut-Katanga and Tchuapa zones, the precipitation showed a strong positive relationship with climate potential productivity, emphasizing that water availability from rainfall is the primary driver of agricultural productivity. In Kongo Central and South Kivu zones, both precipitation and temperature indicated a positive relationship with climate potential productivity, highlighting that agricultural productivity in these zones, is highly sensitive to tropical climate variability. The climate resource utilization efficiency demonstrates markedly divergent trends across agroclimatic zones, possibly due to distinct agroclimatic conditions. Climate resource utilization efficiency indicated a fluctuating positive relationship with per-unit maize yield in the four agroclimatic zones. During the studied period, maize yield declined by 19.68%, 17.48%, 13.40%, and 15.65% in the agroclimatic zones of Haut-Katanga, Kongo Central, South Kivu, and Tchuapa, respectively. These findings can help in mitigating climate-related risks and contributing to agricultural resilience and food security.

Abstract: Norway spruce (Picea abies [L.] Karst.) is a paleoecologically relevant species in the Northern Hemisphere since the early Holocene. P. abies populations are sensitive to climate fluctuations; Asian treeline species are under nature protection. The thick wax and cuticle layers of spruce needles enhance their durability, making them frequent in glacial lake and peat bog cores. Their phytoliths are easily recognizable in sedimentary material and can be studied in subfossil tissues. The aim of this study is to assess the applicability of characteristic phytoliths from Norway spruce needles in paleoclimate research. We examined phytolith presence as a function of altitude in plant samples from three Carpathian Mountain ranges. Temperature data loggers were deployed at sampling sites to better characterize the thermal background of phytolith formation. Our results show a decreasing trend in phytolith presence with increasing elevation. The phytoliths formed by the transfusion tissue represent a sensitive type, indicating that spruce needle phytolith production responds to microclimatic variation. These findings are worth further investigation and validation. The study also provides a basis for refining the calculation method, contributing to the development of a potential “phytolith thermometer” for paleoclimatic reconstruction.