Search Results (1,215)

This study aimed to estimate life expectancy at birth and survival patterns within a multigenerational family from Romania (102 individuals), whose members lived across the period 1900–2024. Life expectancy was estimated using abridged synthetic cohort life tables, and the results were interpreted through survival curve analysis. Life expectancy at birth was estimated at approximately 84 years for females and 80 years for males, while the overall life expectancy for the total family population was 81 years, representing a weighted estimate derived from sex-specific life tables, with weights corresponding to the proportion of females and males in the studied population, rather than a simple arithmetic mean, following standard demographic practice. The resulting survival curves exhibited a clear Type I survival pattern, characterized by low mortality at younger ages and an increasing concentration of deaths at older ages. When contextualized using recent Eurostat data, the life expectancy estimated for the analyzed family exceeds current national-level values reported for Romania and is close to the European Union average, particularly for females. These findings indicate a favorable survival profile at the familial level and illustrate the usefulness of life tables for investigating longevity patterns in small populations. Full article

In the context of the transition of the European energy sector and economy towards sustainable systems, this study aims to investigate the influence of digital enablers on affordable and clean energy in the European Union, using an econometric approach based on panel data regression. In accordance with the literature review and the main programmatic documents that mark the sustainable transition of the energy system, as well as the role of digitalization in this process, 4 research hypotheses and 16 sub-hypotheses were developed regarding the influence of digital enablers specific to the digitalization of the population and enterprises on clean and affordable energy. To confirm the hypotheses, a panel data regression was used for the period 2016–2024 in the European Union states. From a methodological perspective, the panel data regression was carried out using estimation of fixed effects and random effects models, Hausman tests for model selection, diagnostic testing, and correction of standard errors using Driscoll–Kraay estimators. The panel data regression analysis was carried out using R software, version 4.5.1. The results obtained showed that not all independent variables that express the digitalization of the population have the same influence on the share of renewable energy. The performed analysis shows the influence of the level of digitalization of enterprises on the share of renewable energy in the final energy consumption value, but also of the digitalization of the population on the price of energy, as a synthetic expression of affordable energy. Therefore, an essential contribution of the research is represented by highlighting the differentiated impact of digital enablers on clean and affordable energy, using a dual perspective of digitalization, at both the population and enterprise levels. Full article

Pelvic organ prolapse (POP) is a common benign gynecological disorder that substantially affects quality of life, particularly in aging female populations. Current management strategies, including standardized vaginal pessaries and synthetic surgical meshes, are often limited by poor anatomical adaptability, mechanical mismatch with native pelvic tissues, and long-term safety concerns. These limitations have driven increasing interest in personalized and biomechanically compatible therapeutic solutions. Three-dimensional (3D) printing, also known as additive manufacturing, has emerged as a promising bioengineering technology to address these unmet clinical needs. By enabling layer-by-layer fabrication directly from digital models, 3D printing allows for precise control over device geometry, mechanical properties, and material composition, facilitating patient-specific design. This narrative review summarizes recent progress in 3D printing for POP management across three major application domains: (i) next-generation meshes based on biodegradable polymers, elastomeric materials, natural biomaterials, and hydrogel systems; (ii) customized vaginal pessaries tailored to individual pelvic anatomy using imaging-assisted workflows; and (iii) imaging-based pelvic models and prototype devices for surgical planning, education, and exploratory assessment. Overall, existing studies demonstrate that 3D printing enables improved biomechanical compatibility, enhanced tissue integration, and multifunctional device design, including drug delivery capability. Although current evidence is largely pre-clinical or based on pilot studies, additive manufacturing holds strong potential to advance POP management toward safer, personalized, and functionally optimized clinical solutions. Full article

Urban seismic vulnerability poses severe challenges for disaster preparedness in Lima, Peru, where a long-standing seismic gap increases risk to a metropolitan population of approximately ten million residents. This study presents an adaptive multi-objective optimization framework that dynamically adjusts shelter allocation priorities across earthquake intensity scenarios. The methodology integrates spatial data on population distribution, infrastructure vulnerability, and seismic hazard zones to optimize three competing objectives through the NSGA-III algorithm: inter-shelter spacing, population coverage, and safety. Model parameters were calibrated using controlled synthetic scenarios and subsequently validated with real-world data from Lima. Under the high-impact scenario used by the Municipality of Lima, the official set of 356 designated shelters was compared with an optimized configuration selected from 5855 potential sites under identical hazard and demand conditions. The optimized solution increased population coverage by 66.82% and reduced the average distance to critical resources by 24.55%, while reducing service gaps in peripheral districts. Scenario-adaptive optimization improved the robustness of shelter planning by producing configurations that were better aligned with operational priorities as hazard severity escalated, supporting more equitable access in a resource-constrained urban context. This research contributes an evidence-based decision-support tool for emergency management, translating multi-objective trade-offs into actionable shelter layouts for Lima. Full article

This paper presents a unified analytical and computational framework for the study of typhoid fever transmission dynamics governed by a Caputo fractional-order compartmental model of order $\kappa \in (0,1]$. The population is stratified into five epidemiological classes, namely susceptible $\left(\mathcal{S}\right)$, asymptomatic $\left(\mathcal{A}\right)$, symptomatic $\left(\mathcal{I}\right)$, hospitalised $\left(\mathcal{H}\right)$, and recovered $\left(\mathcal{R}\right)$, and the governing system explicitly incorporates asymptomatic transmission, treatment dynamics, and temporary immunity with waning. The use of the Caputo fractional derivative is motivated by the well-documented existence of chronic asymptomatic Salmonella Typhi carriers, whose heavy-tailed sojourn times in the carrier state are naturally encoded by the Mittag–Leffler waiting-time distribution arising from the fractional operator. A complete qualitative analysis of the fractional system is carried out: the basic reproduction number ${\mathcal{R}}_0$ is derived via the next-generation matrix method; local and global asymptotic stability of both the disease-free equilibrium ${\mathcal{E}}_0$ (when ${\mathcal{R}}_0\le 1$) and the endemic equilibrium ${\mathcal{E}}^{*}$ (when ${\mathcal{R}}_0>1$) are established using fractional Lyapunov theory and the LaSalle invariance principle; and the normalised sensitivity indices of ${\mathcal{R}}_0$ are computed to identify transmission-amplifying and transmission-suppressing parameters. Existence, uniqueness, and Ulam–Hyers stability of solutions are established via Banach and Leray–Schauder fixed-point arguments. To complement the analytical results, a fractional physics-informed neural network ($\mathcal{PINN}$) framework is developed to simultaneously reconstruct compartmental trajectories and identify unknown biological parameters from sparse synthetic observations. $\mathcal{PINN}$ embeds the L1-Caputo discretisation directly into the training residuals and employs a four-stage Adam–L-BFGS optimisation strategy to recover five trainable parameters $\Theta$ = $\{\varphi ,\mu ,\sigma ,\psi ,\beta \}$ across three fractional orders $\kappa \in \{1.0,0.95,0.9\}$. The estimated parameters show strong agreement with the true values at the classical limit $\kappa =1.0$ ($\mathrm{MAPE}=2.27\%$), with the natural mortality rate $\mu$ recovered with $\mathrm{APE}\le 0.51\%$ and the transmission rate $\beta$ with $\mathrm{APE}\le 3.63\%$ across all fractional orders, confirming the structural identifiability of the model. Pairwise correlation analysis of the learned parameters establishes the absence of equifinality, validating that $\beta$ can be reliably included in the trainable set. Noise robustness experiments under Gaussian perturbations of $1\%$, $3\%$, and $5\%$ demonstrate graceful degradation ($\mathrm{MAPE}$: $0.82\%\to 3.10\%\to 7.31\%$), confirming the reliability of the proposed framework under realistic observational conditions. Full article

Over the past 20 years, honey bee colony declines have been driven by multiple factors, notably diseases and parasites. The parasitic mite Varroa destructor, which weakens the bees’ immune systems, has been particularly harmful. While various synthetic acaricides are used, the chemicals may accumulate in the beeswax, endangering colony health and allowing Varroa populations to develop resistance to these acaricides. These problems have prompted interest in organic alternatives like thymol and oxalic acid. In this study, colony health was assessed through the proteins-to-phenolics spectral ratio in honey and beeswax, determined by fluorescence spectroscopy, as a ratiometric indicator of infection level in treated hives. Over two months, hives were treated with either oxalic acid, thymol, or remained untreated as controls. Neither treatment significantly affected the proteins-to-phenolics ratios in honey, ranging from 0.30 to 0.83, or in beeswax, ranging from 1.40 to 1.83, suggesting that the incorporation of these vital constituents remains stable despite acaricide application. While thymol demonstrates potential adverse effects on bee health, careful management of treatment concentrations is essential to ensure both the efficacy of Varroa control and the preservation of honey quality. These findings provide valuable insights for beekeepers regarding the safe application of organic acaricides. Full article

The rational design of supported Lewis acid catalysts is frequently impeded by an incomplete understanding of how the support’s synthetic history governs its intrinsic acidity and catalytic efficacy. Herein, we elucidate the structure–property–performance relationship linking the aging dynamics of a boehmite precursor to the activity of the resultant chlorinated alumina (Al₂O₃–Cl) catalyst in n-butane isomerization. Using n-butane as the probe feedstock, we investigated how alumina supports with distinct physicochemical properties regulate the performance of Al₂O₃–Cl catalysts for n-butane isomerization. By systematically adjusting the aging parameters (stirring rate, temperature, and time), we reveal that the structural evolution of the alumina support transitions from initial particle aggregation to Ostwald ripening and surface reconstruction. A decisive structure–performance correlation is identified: precursor synthesis conditions govern both the population and accessibility of specific surface hydroxyls (notably Type II terminal OH groups), which act as anchoring sites for the generation of active Lewis acid centers upon chlorination. Optimal aging parameters (300 rpm, 90 °C, 6 h) promote the formation of a hierarchical pore architecture with a maximized density of accessible hydroxyls, thereby affording enhanced Lewis acidity and superior isomerization activity. This work provides a fundamental framework for tailoring solid acid catalysts by precisely engineering the precursor architecture. Full article

Every year, floods disrupt the lives of hundreds of millions of people worldwide. Their impacts are further intensified by climate change, rapid urbanization, and land-use changes, making it crucial to identify areas most susceptible to flooding. While machine learning (ML) models have proven effective in identifying flood susceptibility, their validity and the integration of human risk remain underexplored in geomorphologically complex and highly flood-prone regions. This study developed an ensemble ML framework for flood susceptibility mapping in the Kosi Megafan, located in Nepal and India. We compared its performance with established ML models and a one-dimensional convolutional neural network (1D-CNN), validated results using Dartmouth Flood Observatory (DFO) and Sentinel-1 SAR (Synthetic Aperture Radar) data, and assessed the population exposed to high-risk zones. A total of 13 (8 retained) flood conditioning factors (FCFs) were derived from remote sensing datasets, and a flood inventory was created to train multiple ML models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), 1D-CNN, and a Stacked Ensemble model. Among these, the stacked ensemble model achieved the highest performance (AUC = 0.76, accuracy = 0.70, precision = 0.69, recall = 0.72, F1-score = 0.70). The resulting susceptibility map identified high-risk zones mainly in the southern and southwestern Megafan, showing strong spatial agreement with the Sentinel-1-derived flood inventory and the DFO flood data (1992–2022). This study highlights the effectiveness of combining SAR-derived flood evidence with ensemble ML approaches for accurate and scalable flood susceptibility mapping in data-scarce, hazard-prone basins. Ultimately, the research supports efforts to build resilience and mitigate the long-term impact of flooding in the region. Full article

Fibropapillomatosis, a disease associated with Scutavirus chelonidalpha5, commonly known as Chelonid alphaherpesvirus 5 (ChHV5), manifests as benign tumors that impair the motor, visual, and physiological functions of affected sea turtles. In this study, blood and tissue samples were collected from turtles exhibiting fibropapilloma-like lesions as well as from clinically healthy individuals. A nested PCR approach was employed to amplify the viral UL30 and UL28 genes for the detection and characterization of the virus variants. The mitochondrial control region was used to assess the relationship between the turtle population and the viral variant. Among the 19 turtles analyzed, six tested positive for ChHV5, including both symptomatic and asymptomatic turtles. Phylogenetic analysis revealed that three positive samples belonged to the Western Atlantic/Caribbean clade, whereas the other three grouped within the Atlantic clade. New oligonucleotides and probes were designed for ChHV5 qPCR detection, accounting for the globally accumulated genetic variability. The qPCR test parameters demonstrated an optimized assay with an efficiency of 101.4% and a detection limit of 2.4 genome copy equivalents (GCE)/μL. This study confirms the presence of two ChHV5 viral variants in rescued turtles from the Caribbean region of Colombia, including both clinically affected and asymptomatic individuals. Therefore, these results support the association between ChHV5 and fibropapillomatosis. Furthermore, analysis of the mitochondrial control region supports the hypothesis of horizontal transmission of the virus. A novel qPCR protocol with a synthetic control is proposed to improve early diagnosis and strengthen conservation and prevention strategies. Full article

Cellular senescence represents a critical biological paradox in oncology. Although it evolved as a safety mechanism to halt tumorigenesis through stable cell cycle arrest, its persistence in tissues can alter the microenvironment, promoting tumor recurrence. In the context of glioblastoma (GBM), this phenomenon is critically important, as current standard therapies, such as radiotherapy and chemotherapy, inadvertently induce a state of senescence known as “therapy-induced senescence” (TIS). Senescent cells remain metabolically active and acquire a unique Senescence-Associated Secretory Phenotype (SASP), characterized by the release of pro-inflammatory cytokines, proteases, and growth factors. SASP reshapes the tumor microenvironment (TME) through paracrine signals, promoting immunosuppression, invasiveness, drug resistance and tumor recurrence. Different glial populations, including astrocytes, microglia, and oligodendrocyte precursor cells (OPCs), respond differently to senescence, specifically contributing to the creation of a permissive niche for tumor recurrence. To contrast the effects of this phenomenon, a promising therapeutic strategy has emerged, the “one-two punch,” which induces initial DNA damage followed by selective elimination of senescent cells with senolytic drugs. In this review, we analyze in detail the efficacy of targeted synthetic agents, such as the Bcl-2 family inhibitor Navitoclax, and natural bioactive compounds such as Quercetin and Fisetin. The analysis focuses on the molecular mechanisms through which these agents disrupt anti-apoptotic pathways (SCAPs) and inhibit the PI3K/AKT/mTOR axis, restoring sensitivity to apoptosis. We propose that the integration of senolytic adjuvants into standard clinical protocols may represent a crucial frontier for eliminating residual disease reservoirs and we also suggest the possibility of combining them with molecules with neuroprotective action to significantly improve the prognosis in GBM. Full article

Corporate reputation teams rely on media monitoring and qualitative research, both limited in speed and coverage when digital narratives form rapidly. This paper proposes SAPIENT (Sentinel-Augmented Population Intelligence for Emerging Narrative Tracking), a multi-agent system that links a sentinel layer over public text streams with a simulation layer that runs moderated, repeatable in silico focus-group sessions. The sentinel layer ingests social media, news, and forum text to produce a compact signal state (topics, sentiment, anomaly scores, risk labels), which conditions the simulation layer through an orchestrator. Persona agents and a moderator follow an Agentic Focus Group (AFG) protocol with repeated runs, variance reporting, and human review gates. We describe four sustainability communication scenarios: greenwashing backlash prediction, greenhushing risk assessment, campaign pre-testing, and crisis communication simulation. Nine experiments span 280 AFG runs across 20 conditions, three LLM backends (Claude Sonnet 4, GPT-4o, and Gemini 2.5 Flash), and a preregistered pilot human validation study with 54 participants. Signal conditioning improved simulation specificity ($p=0.012$). Cross-lingual sessions revealed a sentiment asymmetry between English and Turkish ($p=0.001$) with preserved persona rank ordering ($r=0.81$, $p=0.015$). Cross-model comparison showed consistent persona differentiation across all three backends (Pearson $r>0.92$, $p<0.002$ for all pairs). Sentiment was robust to prompt paraphrasing ($p=0.061$, n.s.), though credibility was sensitive to prompt wording ($p<0.001$). All significant results from Experiments 1–8 survived Benjamini–Hochberg correction. A preregistered pilot with 54 human participants on Prolific replicated the predicted credibility ranking across framing variants ($p=0.004$) but not the sentiment ranking, identifying a specific calibration target for future work. Full article

This study systematically investigated the dynamic diversity, potential sources, and antifungal activities of small molecular peptides during the pile-fermentation process of post-fermented tea. By analyzing the damaging effects of small molecular peptide extracts from tea samples at different pile-fermentation stages on the spore cell membranes of Aspergillus carbonarius (A. carbonarius) and the inhibitory activity against β-1,3-glucan synthase (β-1,3-GS), it was confirmed that some small molecular peptides exhibit significant antifungal effects. The main findings are as follows: (1) The number of identified small molecular peptides showed a trend of first increasing and then decreasing with the progress of pile-fermentation, peaking at 4453 species on the 35th day of pile-fermentation, and were dominated by hexapeptides and heptapeptides with molecular weights ranging from 600 to 800 Da. (2) Based on orthogonal partial least squares discriminant analysis (OPLS-DA), the samples were divided into three characteristic stages according to the differences in small molecular peptide composition at different stages, and 156 characteristic peptides with a relative abundance higher than 0.1% were screened out. Their precursor proteins were derived from 148 proteins belonging to 16 genera, including Camellia, Aspergillus, Saccharomyces, Penicillium, and Bacillus. (3) BLAST alignment results showed that five out of the 156 characteristic peptides were degradation fragments of known antifungal peptides originating from Aspergillus and Bacillus. (4) Combining molecular docking screening and in vitro verification of synthetic peptides, a total of 27 small molecular peptides with antifungal activity were obtained, and their mechanism of action was the inhibition of β-1,3-GS activity. (5) The small molecular peptides related to antifungal activity could be classified into two categories: enzymatic hydrolysates of known antifungal peptides, and the enzymatic hydrolysates of tea-derived proteins or macromolecular peptides. Both categories were mainly distributed in the three stages of pile-fermentation, and there was a significant positive correlation among the population size of dominant microorganisms, microbial peptidase activity, and the abundance of small molecular peptides. This study reveals the dynamic generation pattern and antifungal potential of small molecular peptides during the pile-fermentation of post-fermented tea, providing a new scientific basis for evaluating the dynamic changes in microbial communities in tea and effectively controlling the contamination of harmful fungi during the pile-fermentation process. Full article

The need to investigate ecological and sustainable approaches to weed management, as well as to reduce the negative environmental impact of chemical herbicides, is becoming increasingly important in modern agriculture and land management. Among alternative strategies, allelopathy is a natural mechanism by which particular plant species release bioactive compounds that can influence the germination, growth, and development of neighboring plants. Harnessing allelopathic interactions offers an opportunity to develop environmentally friendly alternatives to synthetic herbicides and helps preserve ecological balance within agroecosystems. This review examines the potential of allelopathic plant-derived substances for weed control in agricultural systems, with particular emphasis on managing weed populations in vegetable crops and gardens in urban and peri-urban areas. This study introduces the concept of allelopathy with definitions and general information. Subsequently, the paper analyzes the phenomenon’s presence at the plant level, its interactions, and the extracts obtained from allelopathic plants. The paper focuses on essential oils and fatty acid-derived compounds, such as pelargonic acid, which have demonstrated significant inhibitory effects on weed germination and biomass accumulation. Overall, the presented results establish a scientific basis for developing bioherbicides and support implementing sustainable, environmentally responsible horticultural practices. Full article

Physical inactivity is a major preventable cause of non-communicable disease and premature mortality. Mobile health interventions can promote physical activity, but their effectiveness depends on the ability to adapt to user’s context and motivation. Reinforcement learning (RL), particularly contextual bandits (CBs), offers a promising framework for such adaptive personalization. However, in practice, RL-based models face the cold start problem (CSP), due to the lack of initial training data. This study examines whether theory-driven simulated data can mitigate the CSP in training RL systems for personalized physical activity recommendations. A scoping review of 18 empirical studies on the Integrated Behavioral Change Model (IBC) provided population parameters for key constructs, used to simulate 2000 virtual users via multivariate modeling and structural equation calibration. A CB algorithm with an ε-greedy policy was trained with this dataset and compared with data from real world pilot using the Apptivate mHealth web-app (n = 588). Results showed close alignment between simulated and real behaviors. Our findings demonstrate that behaviorally informed synthetic data can effectively be used to train RL algorithms, offering an interpretable, sustainable, scalable, and privacy-safe solution to the CSP in personalized digital health interventions. Full article

Sectional warping requires selecting a final operating length when only a small sample of residual cone masses can be measured. This paper proposes a Bayesian chance-constrained planning rule that combines a conjugate log-space model with fast posterior predictive simulation of the population minimum to recommend a risk-limited band length. The method provides a transparent risk parameter, efficient computation, and direct comparison with heuristic, bootstrap, distribution-free, and tail-model baselines. In an industrial-like synthetic study, the Bayesian policy reduced the mean remainder relative to a tuned sample-minimum rule while maintaining controlled shortage risk, and the results clarify why fully distribution-free guarantees are impractical under typical sampling budgets. Full article