Search Results (7,661)

Plant extracts are widely used as natural pesticides, cosmetic ingredients, and in pharmaceutical applications. However, their poor water solubility and stability limit their usability. Lipid nanoparticles (LNPs) offer an effective encapsulation strategy to overcome these challenges. This study demonstrates the encapsulation of three representative substances from these industries: quercetin as a pesticide, irones as a cosmetic ingredient, and nucleic acids for pharmaceutical use. Ultrasonic treatment was used for the encapsulation of quercetin and irones, and a concept for continuous encapsulation in a plug flow reactor was proposed for process intensification. Inline multi-angle light scattering and dynamic light scattering measurements proved effective for real-time monitoring and enabled the replacement of traditional batch measurements. In the pharmaceutical area, mRNA-based therapies require LNP encapsulation to prevent nucleic acid degradation. Plant-based β-sitosterol was used as an alternative helper lipid to cholesterol, resulting in an average particle diameter of 72 nm and an encapsulation efficiency of 91%, comparable to commercial formulations such as the Comirnaty vaccine. Furthermore, a novel process model based on population balances was developed to simulate the entire manufacturing process, from rapid mixing in a T-mixer to particle stabilization via buffer exchange during diafiltration. By applying a quantitative and distinctive model validation workflow, the model was shown to be as accurate and precise as the experimental data, enabling its use as a digital twin for autonomous continuous operation. In summary, this study contributes to reducing the facility footprint and cost of goods through the implementation of continuous processing and model-based control. This approach improves productivity by 20% and reduces process time by a factor of two. Full article

Milk production in dairy herds is determined by both intrinsic and extrinsic factors, with reproductive efficiency serving as a primary determinant. Infectious, nutritional, and management-related challenges can reduce this efficiency. Following parturition, cows are more susceptible to clinical disorders due to a temporary loss of integrity in the cervix, vagina, and vulva, which allows environmental bacteria to ascend and alter the vaginal microbiota. These microbial changes may disrupt endocrine responses related to conception and contribute to repeat breeder cow syndrome (RBCS), which is defined as failure to conceive after three or more inseminations. This study investigated associations among cultivable vaginal bacteria, circulating progesterone and glucose concentrations, and reproductive performance in 30 fourth-parity Holstein cows with a body condition score of 3.5. Cows were classified by reproductive history as repeat breeders (RBCS; n = 14) or controls (CTL; n = 16). Vaginal mucosal samples were collected at insemination and cultured on blood agar and MacConkey media under aerobic and microaerobic conditions. Bacterial identification was conducted using Gram staining and standard biochemical assays. Blood samples were collected at insemination, on day 5 post-insemination, and every two days thereafter to measure progesterone and glucose concentrations. Fertility outcomes were analyzed using PROC GLIMMIX, and hormonal data were analyzed using mixed models with repeated measures. The bacterial genera identified included Bacillus, Escherichia coli, Staphylococcus, Klebsiella, Proteus, Streptococcus, and Actinomyces. Progesterone and glucose concentrations did not differ significantly between groups (p > 0.05). However, the fertility rate (p < 0.05; CTL:87.50% vs. RBCS:57.14%) and number of attempts to conceive (p < 0.001; CTL:2.5 vs. RBCS:6.7) differed statistically between treatments. A higher prevalence of S. hyicus was detected in RBCS cows, and E. coli, S. hyicus, and Proteus spp. were more frequently detected in non-pregnant cows. These findings suggest that the identified cultivable vaginal bacteria are associated with reproductive status in dairy cows. Full article

Microbiome-based therapies are reshaping the therapeutic landscape for ulcerative colitis (UC), offering new avenues for disease management beyond conventional immunomodulatory and biologic treatments. UC remains a chronic, relapsing condition with significant unmet clinical needs, as many patients fail to achieve sustained remission or experience adverse effects with current therapies. The gut microbiome has emerged as a central contributor to UC pathogenesis, influencing epithelial barrier integrity, immune homeostasis, and metabolic signaling. Interventions such as fecal microbiota transplantation (FMT) and defined microbial consortia have demonstrated proof-of-concept efficacy in early-phase clinical trials, each leveraging distinct mechanistic strategies. FMT, as a broad ecological intervention, restores microbial diversity and functional redundancy, potentially addressing multiple pathogenic mechanisms simultaneously. In contrast, defined consortia enable precise targeting of specific metabolic and immunological pathways, including short-chain fatty acid production, bile-acid remodeling, epithelial barrier reinforcement, immune modulation, and succinate degradation. Recent clinical evidence suggests that consortia with broader mechanistic coverage may achieve more consistent biological activity than narrowly focused designs. This review synthesizes mechanistic and clinical insights across broad and defined microbial consortia, integrates evidence from randomized controlled trials and early-phase LBP studies, and outlines a precision-medicine framework to guide therapy selection. We highlight the importance of aligning therapeutic mechanisms with patient-specific microbial, metabolic, and immune profiles, and discuss future directions including biomarker-guided stratification, hybrid consortia, and adaptive trial designs. Advancing both broad and defined approaches, while incorporating ecological principles, mechanistic understanding, and patient stratification, will be essential to realizing the full therapeutic potential of microbiome-based therapies in UC. Full article

Salmonella enterica remains a major threat to animal and human health because of its broad host range, increasing antimicrobial resistance (AMR), and capacity to form biofilms. Biofilm formation enhances bacterial persistence in host tissues, farm environments, food-processing systems, and clinical reservoirs, while also contributing to their tolerance against antibiotics, disinfectants, and other stresses. However, biofilm capacity is not uniform across serovars and is influenced by host adaptation, niche specialization, and accessory genome content. This review synthesizes current knowledge on the relationship between biofilm formation, AMR, and serovar-specific adaptation in Salmonella. It examines biofilm-associated traits across various hosts (e.g., gastrointestinal tract and gallbladder, and environmental (e.g., food-production and clinical) niches, and discusses comparative evidence from genomic, transcriptomic, proteomic, and metabolomic studies. Particular attention is given to the emerging concept of comparative biofilmomics, which integrates phenotypic and multi-omics data across diverse serovars and host sources to identify conserved and niche-specific determinants of persistence. This framework may help define high-risk lineages that couple multidrug resistance (MDR) with enhanced biofilm-forming capacity. A better understanding of these linked traits will support the development of more targeted interventions for controlling persistent Salmonella in veterinary, food production, and public health settings. Full article

The paper deals with the concept of how to regulate structure formation in the interfacial transition zone (ITZ) between the Ordinary Portland Cement (OPC) matrix and basalt to ensure the durability of basalt fiber-reinforced concretes. It has been demonstrated that the alkali–silica reaction (ASR) can be transformed from a destructive (negative) process into a constructive one in OPC concrete through activation by sodium water glass combined with the incorporation of an Al₂O₃-containing additive, namely metakaolin. Alkaline activation increased the compressive strength of OPC basalt fiber-reinforced concrete by 1.6–1.9 times. The formation of stable zeolite-like hydration products within the Na₂O-CaO-Al₂O₃-SiO₂-H₂O system promoted self-healing of the ITZ. This resulted in a 5.6-fold increase in ITZ microhardness compared to the cement matrix, as well as transforming expansion into shrinkage of concrete with a final value of 0.01 mm/m after 360 days. The structure-forming processes in the ITZ ensured a 1.14-fold increase in the compressive strength of 180-day alkali-activated OPC basalt fiber-reinforced concrete compared to its 30-day strength, in contrast to a 0.92-fold decrease in the strength of the non-modified OPC analog under conditions accelerating the development of ASR. Full article

This paper argues that the most significant challenge artificial intelligence poses to theological anthropology is not ontological but epistemic. Rather than asking whether machines can think, feel, or bear the image of God, this paper redirects attention to the prior question of what happens to the human when core epistemic capacities, judgment, discernment, interpretive authority, and moral reasoning are progressively delegated to computational systems. Drawing on the concept of epistemic automation, understood as the systematic transfer of knowledge-producing functions from human agents to algorithmic processes, this paper develops a threefold analytical framework. First, it distinguishes epistemic authority from ontological status as the more productive locus for theological anthropological inquiry. Second, it introduces the distinction between fluency and understanding as an anthropological boundary condition that AI renders newly visible. Third, it analyses delegated cognition as a form of agency deformation with theological significance. The paper concludes that theological anthropology must move beyond reactive commentary on AI and instead generate a theory of the human under conditions of epistemic transformation. The argument engages constructively with philosophy of technology, social epistemology, and Christian theological traditions to offer a framework applicable across confessional boundaries. Full article

Paper-based microfluidic devices (μPADs) have attracted significant attention for point-of-care testing (POCT), environmental monitoring, and food safety due to their low cost, ease of use, and minimal instrument dependence. However, fabricating high-resolution and reproducible microchannels on paper remains challenging. Conventional methods such as wax printing, photolithography, and inkjet printing are limited by resolution or equipment cost. Here, we present a low-cost, high-resolution fabrication method for μPADs, termed wax soft lithography, which combines wax printing with soft lithography. Through this method, microchannels with a minimum width of 234 ± 62 μm were consistently produced, and complex patterns were successfully fabricated, demonstrating high precision and reproducibility. As a proof-of-concept demonstration of device functionality, the fabricated μPADs were used to detect glucose in spiked urine samples, showing a concentration-dependent colorimetric response. This method provides an effective route for rapid production of high-resolution μPADs in resource-limited settings. With further validation before practical applications, this method shows promise for future development in POCT. Full article

This paper examines how forest by-products (potash, tar, resin and charcoal—PoTaRCh), with a special focus on charcoal production, are presented in contemporary heritage tourism and how different communication frameworks influence the audience’s perceptions and intentions in the context of low-carbon development. The research is based on a combined methodological approach. Qualitative analysis of 70 communication units from the field of heritage tourism identified three dominant communication frames: traditional heritage, ecological-educational frame and future-oriented low-carbon innovation. These findings served as the basis for the experimental part of the research, conducted through an online A/B test on a sample of 212 adult respondents interested in travel, cultural tourism and heritage-based experiences. The results of the experiment indicate that the low-carbon communication framework leads to statistically significantly higher levels of perceived relevance of PoTaRCh, visit intention and positive attitude towards sustainability compared to the traditional framework, with perceived relevance partially mediating these effects. The findings suggest that, although traditional communication patterns still dominate heritage tourism, the future-oriented low-carbon framework shows greater communication potential for attracting a sustainability- and future-oriented audience. By combining the analysis of communication content from several European countries and the experimental testing of communication frameworks, the research provides an empirical contribution to the understanding of the transition from the concept of heritage-as-preservation to heritage-as-transition in contemporary discourses of sustainable tourism. Full article

This study addresses the growing need for structured, measurable organizational development (OD) models amid digital transformation, geopolitical uncertainty, and increasing managerial complexity. Contemporary middle- and top-level managers are expected to ensure productivity, strategic clarity, resilience, and data-driven decision-making; however, existing leadership methodologies are often examined separately and lack integrated evaluation frameworks. The research analyses two prominent approaches: the American Action Science methodology and the Scandinavian (particularly Finnish) consensus-based leadership concept. While Action Science emphasizes explicit reasoning, double-loop learning, accountability, and measurable performance outcomes, the Finnish consensus model prioritizes trust, participation, and relational cohesion. The aim of the study is to develop and empirically test the original digital model LeadWinO (LEADership for WINning Organizations) for evaluating the organizational development activities of middle- and top-level managers. The model was empirically tested on managers in Lithuania. The novelty of the research lies in combining management and informatics perspectives by embedding organizational development evaluation into a digital, indicator-based, and potentially predictive framework. The type of study is quantitative research integrating questionnaire analysis in the case of multi-profile sections. Analytical tool used for data simulation is Feedforward Neural Network for constructing sufficient gapless sets of digitalized data. Research results showed that the American Action Science methodology is most effectively used by managers working in very small and small enterprises in the service and maintenance sectors. The findings are expected to contribute to the operationalization of leadership effectiveness under uncertainty and provide organizations with an auditable structure linking managerial behaviour, decision-making processes, and organizational performance outcomes. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

In recent years, the study of generalized fuzzy structures in algebraic systems has attracted considerable attention due to their ability to represent uncertainty and bipolar information. In this paper, we introduce the notion of cubic bipolar ideals in the framework of Sheffer stroke Hilbert algebras. This concept integrates the descriptive capability of cubic sets with the dual representation of bipolar information, providing a broader perspective for investigating algebraic structures associated with the Sheffer stroke operation. We establish the definition of cubic bipolar ideals and investigate several of their fundamental properties. In particular, the structural behavior of these ideals is examined within Sheffer stroke Hilbert algebras. Furthermore, the preservation of cubic bipolar ideals under algebraic homomorphisms is analyzed through the study of images and preimages. The Cartesian product of cubic bipolar ideals is also discussed, and conditions ensuring the stability of the resulting structures are obtained. The results presented here contribute to the development of fuzzy algebraic theory and extend existing approaches to Sheffer stroke-based algebraic systems. Full article

Economic entropy, as an emerging concept in econophysics, has gained increasing relevance in the analysis of complex systems characterized by uncertainty, nonlinearity, and out-of-equilibrium dynamics. However, its integration into conventional economic modeling—particularly in production functions such as the Cobb–Douglas function—remains fragmented and lacks systematic empirical validation. This study conducts a scientometric analysis of 345 Scopus-indexed documents (1973–2024) addressing the intersection between entropy, econophysics, and production functions, with the aim of mapping the intellectual structure of the field, characterizing its growth trends, identifying its core contributions, and highlighting its main research gaps. The results reveal that the field has experienced sustained growth since 2004, with a notable acceleration between 2020 and 2023, although it exhibits a fragmented authorship structure that does not conform to Lotka’s Law, suggesting that the field is still in a stage of scientific consolidation. The Cobb–Douglas function emerges as a niche topic within the econophysics literature, with limited integration between entropy-based approaches—informational, thermodynamic, and maximum entropy—and the empirical modeling of production. Furthermore, weak citation linkages between econophysics and conventional economics are observed, confirming the interdisciplinary fragmentation of the field. These findings provide a structured reference for researchers interested in advancing toward analytical frameworks that explicitly incorporate uncertainty, information, and physical constraints into economic analysis, thereby contributing to the development of econophysics as an integrative discipline. Full article

In 1988, two seminal studies were published almost simultaneously in the same scientific journal. Both spurred the field of brain energy metabolism research in new directions, culminating in a long-lasting debate that appeared to split its practitioners into two factions that seem unwilling to agree on what metabolic processes are fueling the active brain with adenosine triphosphate (ATP). The first study used rat hippocampal slices to demonstrate the ability of lactate to support neuronal function as the sole oxidative mitochondrial substrate. The second study demonstrated that upon brain stimulation, glucose consumption is not accompanied by respective oxygen consumption, but a non-oxidative glucose utilization or what has become known as “aerobic glycolysis”. Consequently, for almost four decades, researchers in this field have been divided between those who profess that brain activity is supported by oxidative lactate metabolism and those who insist that non-oxidative glucose metabolism supports it. Hypotheses for both concepts were offered, “The Astrocyte Neuron Lactate Shuttle Hypothesis” and “The Efficiency Tradeoff Hypothesis,” respectively. To bridge the gap between the two groups, a recent editorial, authored by over twenty leading investigators, was published. The editorial received two separate responses from investigators who supported the non-oxidative glucose consumption as the main process supporting neural activity, signaling that the gap between the two groups remained. The present perspective highlights the principal disagreements that divide this utmost important field of research. It argues that the main reason for these disagreements is rooted in the assumption that pyruvate is the end-product of aerobic glycolysis, even when many among those who adhere to this assumption accept that in the active brain glycolysis is the main provider of the necessary ATP and the end-product is lactate under aerobic conditions. The consideration of a paradigm shift, according to which lactate is the real end-product of glycolysis, independent of the presence or absence of oxygen, could bridge the great divide between those who separate glycolysis into two outcomes and those who profess that there is only one, prefix-less glycolytic pathway that always ends with the production of lactate. Full article

As modern society grows increasingly complex, access to essential services such as healthcare, legal aid, tailored education, and psychological support remains heavily gated by socio-economic, neurological, and systemic barriers. This paper explores the transformative potential of Large Language Models (LLMs) and Generative Artificial Intelligence not merely as industrial productivity enhancers, but as vital “social scaffolds” capable of fostering a more inclusive society. Crucially, we propose a paradigm shift in the concept of Ethical Computing—moving from a passive defensive framework of non-maleficence (“do no harm”) to an active mandate of beneficence, where AI systems are explicitly developed to serve marginalized and un(der)served populations. Through this expanded ethical lens, we systematically analyze the democratizing impact of AI across four primary axes of inclusivity: socio-economic (providing zero-cost medical triage and legal translation for undocumented populations), neurospicy (acting as a non-judgmental communicative bridge for individuals with Autism Spectrum Disorder), pedagogical (delivering hyper-personalized executive function support for Special Educational Needs), and psychological (serving as an accessible, first-level triage system for mental health crises). By framing LLMs as a modern social safety net, we outline a clear trajectory for future research, advocating for an “ethical-by-design” development paradigm that explicitly prioritizes equity, accessibility, and the active dismantling of historical barriers for the digitally and socially disenfranchised. Full article

Clothing pattern-making is an important part of transforming design concepts into finished products; however, the traditional manual pattern-making process is not only time-consuming, but also suffers from inefficiency, which seriously restricts the automation and precision of clothing production. This study proposes an automated clothing pattern-making method, the core of which lies in the organic combination of an improved Pix2Pix model and a conditional diffusion model. The improved Pix2Pix model effectively captures the complex structural information in clothing patterns by introducing a multi-scale discriminator and a new composite loss function. Due to limited data, the improved Pix2Pix falls short in terms of image generation quality, so a conditional diffusion model was introduced to enhance the detail and overall integrity of the generated images. Experiments were conducted on pattern-making tasks for the sleeves and back panels of various typical clothing styles. The sleeve components primarily validated the model’s basic generation capabilities. The results showed that the improved Pix2Pix-generated initial template could capture the basic contour structure, and after diffusion model repair, the lines became clearer and the details more complete; the back panels components validated the model’s robustness. Quantitative results showed that the proposed method achieved SSIM, PSNR, and LPIPS values of 0.869, 22.31, and 0.1318, respectively. Compared with the results of other advanced models, the proposed method exhibits the highest accuracy and clarity in the generated images, confirming its practicality and effectiveness in automated apparel pattern-making. Full article