Abstract: Placental abruption is detachment of the placenta before delivery from the implantation site that may have a potential to develop life-threating emergency clinic syptoms. The multifactorial nature of this disorder and no lab testing or procedures that can diagnose placental abruption. makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-making and enable precise assessments. This study purposed on predictive 15 ML models for placental abruption high-lighting input characteristics, performance metrics, and validation. The medical records of 564 patients were analyzed between 2021 and 2025 for studies using AI to develop predictive models for placental abruption. Findings were analyzed with Python software and Pycaret library. The model integrated data for 5 variables (features) for the prediction. Among 15 machine learning algorithms, Logistic regression was chosen as the best model. The performance metrics were determined as follows: accuracy rate of 0.85, AUC of 0.91, recall of 0.85, precision of 0.85, and F1 score of 0.85. In the ranking based on their importance in the classification model, gestational age at delivery was observed to have the highest importance for classification. Twenty-eight unseen cases were utilized for an extra validation step. The model achieved a high accuracy on this set, with 21 cases correctly predicted. The presented 15 ML models in our study had significant accuracy in predicting placental abruption , but these models require further development before they can be applied in a clinical setting.

Abstract: Bacillus amyloliquefaciens is an important agricultural microbial resource. This study focuses on the whole genome analysis and functional characterization of B. amyloliquefaciens SH-53, isolated from the Wuliang Mountain National Nature Reserve in Dali, Yunnan. The genomic feature analysis revealed that the genome of SH-53 contains 27 ribosomal RNA operons, 4,078 protein-coding genes, and 250 prophage-related genes. Additionally, 12 biosynthetic gene clusters (BGCs) for secondary metabolites were predicted, of which 7 are novel gene clusters with unknown functions, showing significant differences compared to the known BGCs of conventional biocontrol strains.Functional potential analysis indicates that SH-53 possesses potential antagonistic activity against plant pathogenic bacteria and can colonize the plant rhizosphere through various mechanisms to exert growth-promoting effects. It is capable of synthesizing multiple antibacterial secondary metabolites, indole-3-acetic acid (IAA), iron carriers, secreting amylase, and efficiently utilizing sulfur sources. The genome also harbors a complete core gene network related to the induced systemic resistance (ISR) and supporting genes that maintain secondary metabolism homeostasis.In conclusion, B. amyloliquefaciens SH-53 exhibits rich biocontrol-related characteristics and unique secondary metabolic potential, indicating promising prospects for its development as an excellent biocontrol agent.

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Deep Learning (DL) has undergone widespread adoption for medical image analysis and diagnosis. Numerous studies have explored mammographic image analysis for breast cancer screening. For this study, we assessed the hypothesis that stratifying mammography images based on the presence or absence of a corresponding region of interest (ROI) improves classification accuracy for both normal–abnormal and benign–malignant classifications. Our methodology involves independently training models and performing predictions on each subgroup with subsequent integration of the results. We used several DL models, including ResNet, EfficientNet, SwinTransformer, ConvNeXt, and MobileNet. For experimentation, we used the publicly available VinDr., CDD-CESM, and DMID datasets. Our comparison with prediction results obtained without ROI-based stratification demonstrated that the utility of considering ROI presence to enhance diagnostic accuracy in mammography increases along with the data volume. These findings support the usefulness of our stratification approach, particularly as a dataset size grows.

Abstract: Let 1 < a1 < a2 < · · · be integers with \( \sum_{k=1}^\infty a_k^{-1}<\infty \), and set \( F(s)=1+\sum_{k=1}^\infty a_k^{-s}, \qquad \Re s>1. \) A question of Erdős and Ingham, recorded as Erdős Problem #967 in a compilation by T. F. Bloom (accessed 2025--12--01), asks whether one always has \( F(1+it)\neq 0 \) for all real t. This paper does not resolve the problem; instead, it develops a modern dynamical-systems framework for its study. Using the Bohr transform, we realise $F$ as a Hardy-function on a compact abelian Dirichlet group and interpret \( F(1+it) \)as an observable along a Kronecker flow. Within this setting we establish a quantitative reduction of the nonvanishing question to small-ball estimates for the Bohr lift, formulated as a precise conjecture, and we obtain partial results for finite Dirichlet polynomials under Diophantine conditions on the frequency set. The approach combines skew-product cocycles, ergodic and large-deviation ideas, and entropy-type control of recurrence to small neighbourhoods of -1, aiming at new nonvanishing criteria on the line \( \Re s=1 \).

Abstract: Background: Invisible or humanized care (High-Touch) is inherent to the nursing profession. Professionals with sensory processing sensitivity perceive and process more sensory information simultaneously and more deeply than usual, which may be more closely linked to invisible care. Objective: To analyze the influence of sensory processing sensitivity on nursing professionals' perception of invisible care. Method: A cross-sectional descriptive observational study. Seventy-nine professionals from a level III hospital completed an online form assessing various situations encountered by nursing staff in their daily practice related to the different dimensions of invisible care (Care-Q) and the sensory processing sensitivity temperament trait (HSPS). Results: Showed that 15% (12) of nursing professionals were highly sensitive. A statistically significant relationship was also found between the components of invisible care and the overall Care-Q score for professionals in general and for highly sensitive professionals. The invisible care component "maintains a trusting relationship" with the HSPS overall (ρ = 0.224), but no significant correlations were observed when professionals were identified as highly sensitive with scores ≥ 160 points with the different Care-Q components. Conclusion: The perception of invisible care is inherent to the nursing profession and is not strongly influenced by the SPS trait. Therefore, invisible care is an essential component of nursing practice.

Abstract: Environmental, Social and Governance (ESG) risk is increasingly influenced by inter-firm relationships embedded in global supply chains. Challenging firm-level approaches that treat ESG exposure as independent across companies. This study examines whether firms’ structural positions within supply-chain networks are associated with ESG risk exposure and whether incorporating network information improves ESG risk prediction. The analysis draws on an international dataset integrating validated supplier-buyer relationships, shipment-level trade data. ESG incident records and sentiment derived from ESG-related news. Network-based econometric models and graph-oriented learning approaches are evaluated against conventional firm-level benchmarks. The results indicate that ESG risk clusters within connected groups of firms, with higher exposure observed among firms occupying central or intermediary positions in supply networks. In addition, ESG-related media sentiment exhibits predictive power for subsequent ESG incidents, supporting its role as an early warning signal. Overall, models that explicitly account for network structure deliver more accurate and better-calibrated predictions than standard econometric and machine-learning approaches. These findings highlight the value of a network-informed perspective for ESG risk assessment in complex international production systems.

Abstract: Travel-associated Legionnaires’ disease (TALD) events can generate public concern when environmental surveillance findings are communicated without adequate ex-planation of the results. This study examined how surveillance data on Legionella spp. were framed and amplified during a TALD-related investigation in Crete, Greece, between June and July 2025. A mixed infodemiology and environmental surveillance approach was applied, including analysis of 95 online media items across nine lan-guages, Google Trends search-interest data, and hotel water-system surveillance data from epidemiologically linked facilities. Sampling conducted in a limited number of hotels associated with TALD cases indicated that approximately 50% of water samples exceeded the laboratory reporting limit of ≥50 CFU/L, a numerically correct but con-text-specific finding. Numerical misframing occurred in 83.7%, 41.7%, and 18.2% of Greek, German, and English language items, respectively, with significant differences across language markets (χ² (8) = 43.75, p < 0.0001; Cramér’s V = 0.679). Public search-interest signals were transient and geographically limited. Environmental sur-veillance showed no increase in Legionella pneumophila risk, with similar proportions of samples ≥50 CFU/L in the pre-/peri-infodemic (Jan–Jul 2025) and post-infodemic (Aug–Nov 2025) periods (23.11% [95% CI: 18.21–28.87] vs. 24.45% [19.34–30.41]) and similar exceedance of ≥1000 CFU/L (13.45% [9.69–18.36] vs. 14.41% [10.45–19.55]). Overall, loss of contextual interpretation of surveillance results and conflation of laboratory re-porting limits with regulatory thresholds were associated with inconsistent public risk perception, without evidence of increased environmental hazard.

Abstract: The importance of the infrastructure is associated with the value of the infrastructure, the greater the importance of infrastructure, the greater its value. The concept of the importance of road infrastructure can take on a different value instead of different points of view. For example, roads can be evaluated from an economic, social, political, and military, among others. In 2021, the Lazio Regional Road Authority (ASTRAL) requested assistance from the Research Center for Transport and Logistics (CTL) to develop a composite scoring index (Regional Index, Ri) that would rank the relative importance of ASTRAL–maintained roadway network. The Ri index is expressed numerically between values from 1 to 5 (with 5 representing the highest importance). It includes the following variables: Population density, AADT, road traffic crashes, accessibility to point of interest, maintenance cost, air emissions, and noise pollution. The methodology includes the following steps. First, the variables were selected on the basis of their reliability, measurability, coverage and relevance to the phenomenon to be measured. Then, the data collection and normalization of the variables on a scale of 1 to 5 were carried out. Subsequently, through a multicriteria analysis, the variables were weighted and added. Finally, a sensitivity analysis was performed to evaluate which variables had the most influence on the final output of the formula. The methodology proposed has been implemented on the Region Lazio roadway network in order to obtain the Ri of the road segments.

Abstract: The Bateman-Horn conjecture is a conjecture on prime values in polynomials. We prove it by Golomb's method.

Abstract: The increased demand for food worldwide has led to the widespread use of synthetic chemical fertilizers. Since the Green Revolution, the use of such chemical fertilizers has been in high demand as a nutrient input in agriculture. The increased application of ferti-lizer to upsurge crop yields is not suitable for the long term and leads to nutrient loss, as well as severe environmental and ecological consequences. Contrasted to conventional fertilizers, nano-fertilizers, which are designed at the 1–100 nm size, provide focused nu-trient delivery, decreased leaching, and improved plant absorption. They accomplish this by greatly increasing crop yields, enhancing fertilizer usage efficiency, and facilitating sustainable farming in the face of obstacles, including resource scarcity, climate change, and a projected 10 billion people by 2050. In comparison to typical NPK fertilizers at equal nutrient rates, nano-fertilizers enhanced crop yields by an average of 20-23% across cere-als, legumes, and horticulture crops, according to studies conducted between 2015 and 2024. In particular, using nano-urea to rice increased grain yield by 28.6% with 44% less nitrogen input, and applying nano-zinc to wheat increased yields by 31.2% and improved grain Zn content by 41%. Through targeted foliar or soil application, nano fertilizers in-crease nutrient use efficiency (NUE) by frequently more than 50% as opposed to 30-50% for conventional fertilizers. Nano fertilizer is prepared based on the encapsulation of plant essential minerals and nutrients with a suitable polymer matrix as a carrier and delivered as nano-sized particles or emulsions to the plants. Natural plant openings like stomata and lenticels in plant parts facilitate the uptake and diffusion, leading to higher NUE. This review provides an overview of current knowledge on the development of advanced nano-based and smart agriculture using nano fertilizer that has improved nutritional management. Furthermore, nano-scale fertilizers and their formulation, and nano-based approaches to increase crop production, along with the different types of fertilizers that are currently available and the mechanism of action of the nano fertilizers, are discussed. Thus, it is expected that a properly designed nano fertilizer could synchronize the release of nutrients in crop plants as and when needed.

Abstract: Cardio-renal syndrome (CRS) is a term referring to a bidirectional group of disorders in which there is a concomitant compromise of both organs, the heart and the kidney, leading to a significant increase in morbidity and mortality. In recent years, numerous publications have addressed this complex entity from different points of view.For better understanding, five subtypes have been established: depending on its form of presentation, acute or chronic; the organ initially affected and whether there is another responsible systemic disease.CRS represents a complex interaction between both organs with several neurohormonal, inflammatory and hemodynamic pathophysiological mechanisms involved. Its different forms of presentation and the difficulty of its management requires a multidisciplinary and comprehensive therapeutic approach targeting all the mechanisms involved in its pathogenesis. Throughout this review we will analyze all relevant aspects of CRS from its classification to current diagnosis and treatment.

Abstract:

To address the practical challenges of diverse anomaly patterns, strongly coupled dependencies, and high labeling costs in large-scale complex infrastructures, this paper presents an unsupervised anomaly detection method that integrates graph neural networks with Transformer models. The approach learns normal system behavior and identifies deviations without relying on anomaly labels. Infrastructure components are abstracted as nodes in a dependency graph, where nodes are characterized by multiple source observability signals. A graph encoder aggregates neighborhood information to produce structure-enhanced node representations. Self-attention mechanisms are introduced along the temporal dimension to capture long-range dynamic dependencies. This design enables joint modeling of structural relations and temporal evolution. A reconstruction-based training strategy is adopted to constrain the learning of normal patterns. Reconstruction error is used to derive anomaly scores for detection. To ensure reproducibility and ease of deployment, complete specifications of data organization, training procedures, and key hyperparameter settings are provided. Comparative experiments on public benchmarks demonstrate overall advantages across multiple evaluation metrics and confirm the effectiveness of the proposed framework in representing anomaly propagation and temporal drift characteristics in complex systems.

Abstract: Acute myeloid leukemia (AML) continues to pose significant therapeutic challenges, with high relapse rates driven largely by leukemic stem cells (LSCs), a rare, therapy-resistant population with self-renewal capacity, niche adaptation, and the ability to re-initiate disease. In this state-of-the-art review, we synthesize recent advances in LSC biology, addressing (i) how LSCs differ functionally and phenotypically from normal hemato-poietic stem cells, (ii) practical approaches for LSC quantification using multiparameter flow cytometry and LSC-enriched marker panels, (iii) the metabolic and epigenetic programs that enable LSC persistence under chemotherapy and contribute to measurable residual disease, and (iv) current therapeutic strategies targeting LSC eradication, in-cluding antibody-based therapies, apoptosis and metabolic inhibitors, and emerging epigenetic agents. We also examine the key translational barriers, particularly antigen overlap with normal progenitors, microenvironmental protection, and the need for assay harmonization, and propose a practical framework for integrating LSC assessment into risk stratification and therapeutic development.

Abstract: In the modern microservice environment, library dependencies for inter-system communication have become bloated, and conflicts and complications during build and operation have become problems. In particular, in the conventional communication architecture that depends on the MySQL database, the multi-layer dependencies included in \texttt{libmysqlclient} restrict the flexibility of system design. In this study, a replication-protocol-compatible patch was applied to the lightweight MySQL client library Trilogy, and a loosely coupled, low-footprint IPC library connecting the control plane and the data plane was implemented. The proposed method eliminates dependencies on the internal static library group of MySQL Server, while enabling binary log events to be processed directly at the application layer. Stable operation has been achieved for more than one year in a commercial system environment, and its effectiveness has been verified through long-term operation.

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This paper discusses the theory of productivity maximisation in relation to human productive potential. If productivity is considered as means to attain certain outcomes, it must have practical implications. Herein, human productive potential is considered as a neurocognitive concept having its significance felt in personal and professional frontier, for human beings are always in search to maximise their productivity by tapping untapped potential latent within. This paper addresses this issue, while at the same time, it examines of the role of cognitive constraints in constraining human potential, which has important implications for the individual and industrial frontiers. In this respect, we have also discussed, in brief, the concept of anti-productivity, its nature, and practical implications.

Abstract: Quiescence is a reversible, non-proliferative cellular state that enables survival under nutrient limitation while preserving the capacity to resume growth. Rather than representing a passive default, quiescence is an actively regulated program conserved from unicellular eukaryotes to metazoans. This review focuses on the nuclear mechanisms underlying quiescence entry, maintenance, and exit, drawing on mechanistic insights from yeast models while highlighting conserved principles in multicellular systems. Across species, quiescence is characterized by global transcriptional repression, chromatin compaction, and extensive reorganization of nuclear architecture, coordinated by nutrient-sensing pathways centered on TOR/mTOR signaling. We discuss how transcriptional reprogramming is achieved through redistribution of RNA polymerases, dynamic transcription factor activities, and large-scale remodeling of histone modifications, alongside repressive chromatin formation. In parallel, post-transcriptional mechanisms—including intron retention, alternative polyadenylation, and accumulation of non-coding RNAs—fine-tune gene expression while limiting biosynthetic output. We further examine how changes in nuclear organization, such as nucleolar condensation, condensin-mediated chromosome rearrangements, and telomere hyperclusters, support long-term viability and genome stability. Collectively, this review highlights nuclear dynamics as an integrative regulatory layer that links metabolic state to cellular identity, adaptability, and long-term survival, with broad implications for development, stem cell function, and disease.

Abstract: This study investigated how complexes formed by lauric acid (LA) and polyphenols (gallic acid, GA; 3,4-dihydroxyphenylacetic acid, DOPAC; caffeic acid, CA) with bread-fruit starch affect starch’s digestibility and properties. The digestibility resistance of ternary complexes was not superior to that of binary complexes but remained higher than native starch. Among them, the CA complex (CB) showed the highest RS and the lowest hydrolysis, indicating CA's prominent role in enhancing digestibility resistance. A decreased breakdown value and increased gelatinization temperatures were revealed, indicating inhibited gelatinization. During cooling phase, samples with LA showed viscosity peaks, confirming V-type complex formation, which was absent in polyphe-nol-only samples. R1047/1022 and Rc values were increased in complexes. Ternary complexes exhibited a lower R1047/1022 ratio but a higher Rc than binary complexes, suggesting LA mainly influenced long-range order, while polyphenols affected both short- and long-range order. This may explain the antagonistic effect on digestibility in ternary complexes.

Abstract: Models of neutron and strange stars are considered in the approximation of a uniform density distribution. A universal algebraic equation, valid for any equation of state, is used to find the approximate mass of a star of a given density without resorting to the integration of differential equations. Equations of state for neutron stars had been taken for degenerate neutron gas and for more realistic ones, used by Bethe, Malone, Johnson (1975). Models of homogeneous strange stars for the equation of state in the "quark bag model" have a simple analytical solution. The solutions presented in the paper for various equations of state differ from the exact solutions obtained by the numerical integration of differential equations by at most ∼ 20%. The formation of strange stars is examined as a function of the deconfinement boundary (DB), at which quarks become deconfined. Existing experimental data indicate that matter reaches very high densities in the vicinity of the DB. This imposes strong constraints on the maximum mass of strange stars and prohibits their formation at the final stages of stellar evolution, because the limiting mass of neutron stars is substantially higher and corresponds to considerably lower matter densities.

Abstract:

Gastric cancer (GC0 is primarily caused by Helicobacter pylori infection and smoking, with a higher incidence in families with multiple GC cases owing to lifestyle and genetic factors. The use of medications to eradicate H. pylori can reduce the incidence of GC. Furthermore, GC is the fourth most common cancer, affecting one in 11 men (9.1%) and one in 23 women (4.38%). The incidence of GC increases after 50 years of age, particularly among men. However, the reason for difference in incidence rates between both sexes remains unclear. We investigated the incidence of GC in families with hereditary breast and ovarian cancer (HBOC). The results showed that the incidence of GC in families with HBOC was 4.2 times higher than that in other families. Furthermore, the incidence of gastric cancer in families with HBOC and other families was 74.57% and 53.67% in men, respectively. Overall, the higher incidence of gastric cancer in men than that in women may be due to the underlying cause of hereditary GC.

Abstract: This study numerically examined the anchorage mechanism of rebar hooks under varying straight development lengths, including high stress levels. A Three-Dimensional Rigid Body Spring Model (3D-RBSM) was used for the investigation, which the model has successfully reproduced the experimental pullout test stress–slip relationships and inner–outer strain distributions for the case of bonded hook part with and without a straight development length. The numerical model, which considered both hook and straight development length was able to output local concrete stresses and internal crack propagation enabling a clear interpretation of how straight development length influences the anchor-age mechanism. The results revealed that increasing straight development length increases stiffness, reduces rebar strains and concrete stresses in the hook region, promotes crack formation around the rebar surface and forms maximum tensile stresses closer to the top surface, ultimately resulting in earlier splitting failure at high rebar stress levels. A comparison of cases with and without hooks shows that combining the hook with straight development length improves stress distribution, delays crack propagation and increases anchorage by reducing tensile stress concentrations near the top surface and side faces. The findings offer insights to support rebar hook anchorage design and review of existing standards.