The study of the conceptual framework examined climate change, water civilization, and socio-economic development in ancient Dalmatia (Croatia). The study examined the cultural and social development centered on managing and depending on water from 350 BCE to 900 CE. Significance was highlighted on water resources and water civilization as the basis of human society, influencing culture, governance, and survival. This research utilized a thermodynamic framework to illustrate the dynamic interrelationships that affect the development of civilization, establishing continuous cycles of reciprocal influence and adjustment. The findings indicate that the main external dangers to civilizations in Dalmatia were significant alterations in climate patterns and abrupt variations in water cycles brought on by solar radiation and cooling due to volcanic eruptions. Within the organization, there was an insufficient water management expertise required to manage and regulate water resource systems. With the failure of water networks and dissipative structures, local communities were deprived of water and food resources. Hunger, warfare, and illness frequently ensued, resulting in large-scale movement or the desertion of cities. The results suggest that the connection among climate change, water societies, and socio-economic elements underpins both human history and contemporary global issues.
This study integrates microbial biotechnology, bioprocess optimization, and circular bioeconomy principles to valorize oilseed agro-industrial coproducts as low-cost inducing substrates for lipase production. Palm kernel cake (PKC), canola cake, andiroba cake, and brewer’s spent grain were evaluated as carbon sources for extracellular lipase production by the wild-type strain Trichoderma sp. LEPM-711 under submerged fermentation. Qualitative screening in olive oil/rhodamine B medium confirmed the strain’s lipolytic potential. Among the substrates tested, PKC showed the strongest induction, reaching 4.68 U mL−1, and was therefore selected for nitrogen supplementation and statistical optimization. Peptone supplementation increased lipolytic activity to 8.41 U mL−1 after 72 h. A Plackett–Burman design identified CaCl2·2H2O, CoCl2·6H2O, and peptone as significant medium components, while response surface methodology established the optimal operational conditions as pH 6.0, 106 spores mL−1, and 2% (w/v) PKC. Under these conditions, the crude enzymatic extract reached 18.31 U mL−1 at pH 7.0 and 45 °C, corresponding to an approximately 3.9-fold increase over the initial PKC fermentation. These findings support the use of Trichoderma sp. LEPM-711 and oilseed coproducts as a sustainable platform for enzyme production and residue upgrading within applied natural sciences.
Background: Removing explicit protected health information (PHI) does not necessarily make a clinical narrative non-identifiable. Rare diagnoses, social circumstances, locations, temporal patterns, and treatment trajectories may still permit re-identification. Methods: This PRISMA-ScR-informed scoping review combined PubMed/MEDLINE, OpenAlex, and targeted NLP searches with OpenAlex title-and-abstract, Web of Science, and Scopus sensitivity checks. Eligible publications addressed clinical or biomedical text de-identification from 1 January 2018 to 31 March 2026. Results: The searches yielded 275 records and 258 unique records after deduplication. Eligibility assessment produced 110 core records, 11 background or review records, and 22 exclusions. All 143 eligibility-stage records underwent independent double screening and reconciliation; structured extraction covered all 110 core records. Abstract screening of 47 additional Scopus candidates found no task function or implementation paradigm outside the proposed classification. The resulting two-axis framework separates a method’s role in the workflow from its technical implementation. Conclusions: Clinical text de-identification cannot be reduced to named-entity recognition. Local hybrid or transformer pipelines remain the most defensible baseline for routine PHI detection. LLMs are better suited to defined tasks in augmentation, transformation, and assurance, with local validation and explicit control of data exposure.
Background: This study explored the observations and experiences of athletic trainers (ATs) regarding food security constraints and drivers among collegiate student-athletes through the framework of the social determinants of health (SDOH). Methods: Seventeen ATs from nine public Mountain West Division I schools participated in one of six 90 min, virtual focus groups conducted in July–December 2023. A phenomenological design and constructivist theoretical framework were employed to analyze transcripts, compile themes, infer food security constraints and drivers, and map them to SDOH domains. Results: Emerging constraints and drivers mapped to seven of the eight SDOH domains (education, employment, health systems and services, housing, income and wealth, physical environment, and social environment); no themes related to the public safety domain. Conclusions: ATs identified a considerable number of food security constraints and drivers; however, based on the findings of other studies, their observations were not comprehensive. Incorporating the findings of those other studies, food security constraints and drivers exist across all eight SDOH domains. Four concepts spanned multiple domains: student-athlete normative expectations, nutrition/food security prioritization, financial disparities, and residing off campus. To ensure food security among collegiate student-athletes, AT perceptions suggest that structural changes facilitating the implementation of necessary food drivers may be warranted.
mRNA vaccines are a relevant approach in cancer immunotherapy, using messenger RNA to induce immune responses against tumor-associated antigens. In this review, BNT111 and BNT122 are compared as representative off-the-shelf and personalized models. BNT111 is a fixed mRNA vaccine that has demonstrated significant antitumor efficacy against shared melanoma antigens, particularly when combined with immune checkpoint inhibitors. It allows a standardized production via in vitro transcription (IVT) in a cell-free system. Conversely, BNT122 is a personalized vaccine designed to match an individual’s tumor mutations by targeting patient-specific neoantigens to elicit more robust immune responses. It has significant suitability in the adjuvant setting to target minimal residual disease. Despite favorable safety and immunogenicity, the effectiveness of these vaccines is influenced by various factors, including tumor heterogeneity, differences in antigen expression, off-target effects on mRNA-LNP distribution, molecular instability, and complex manufacturing constraints. Neither approach can be directly considered as the definitive optimal vaccine. A comprehensive analysis of their strengths and limitations is vital for a balanced and objective future research direction. Collectively, this emphasizes the need for further improvements in vaccine design and strategies, prioritizing high-quality, safe, and accessible treatments for every cancer-based patient and ensuring their successful integration into healthcare.
Waterflooding in oilfields for oil displacement and reservoir pressure maintenance has led to the production of scale in several reservoirs. The formation of scale occurs both in the porous media of the reservoir and in the production equipment, leading to production disruptions that result in a decline in revenue. The aim of this paper is to investigate the effects of mixing samples of seawater and aquifer water. This is achieved by conducting turbidity, salinity, pH, and zeta potential measurements. The risk of self-precipitation of the prepared samples was assessed using the PHREEQC program. A PVT cell was used to assess the impact of temperature and pressure on the prepared seawater and aquifer samples. When 40% of the seawater sample was combined with 60% of the aquifer water sample, the turbidity findings indicated maximum precipitation. The amount of precipitation dropped as temperature and pressure increased. To assess the impact of scale formation on the permeability of a Berea sandstone core, a core flooding experiment was conducted employing liquid and gas as the flowing fluid. Additionally, SEM and EDS analyses were used to examine the shape and composition of scale. It was found that SO42− and Ca2+ ions predominated in scale precipitation.
Bentonite slurry is frequently used as a support fluid in the construction of drilled shafts. During the piling process, the slurry acts as a sealant and slightly penetrates the nearby soil. However, the degree to which bentonite slurry penetrates the soil affects the resulting frictional capacity of the bored piles. This experimental study examines the extent of this phenomenon, arising from the formation of what is typically known as the bentonite filter cake or mud cake. The frictional properties of the filter cake are examined through three groups of direct shear tests, employing three pre-cast concrete blocks positioned on a sand layer that has been subject to bentonite slurry for varying durations. To ensure comparison, a similar pre-cast concrete block was utilized in each test series, resulting in uniform surface roughness in the concrete. A handheld surface roughness device was utilized to measure the roughness profile of each concrete block, assessing the surface roughness of all concrete surfaces. The outcomes of the direct shear test performed were subsequently normalized based on the assessed roughness of the concrete surface. Experimental results showed that the friction capacity of the soil–concrete interface for granular materials (“sand–concrete interface”) decreases with longer exposure to bentonite slurry. Specifically, the shear strength is inversely proportional to the square root of the bentonite slurry exposure time. Tests on the internal friction angle of Québec Valcartier granitic sand and the friction angles at sand–concrete interfaces with and without bentonite slurry exposure revealed that the non-exposed sand–concrete interface achieves a peak friction angle equal to 77% of the peak internal friction angle of Québec Valcartier granitic sand. This value represents 69% and 60% of the peak friction angle of the sand tested for bentonite exposure durations of 2 and 4 h, respectively.
Frailty is a geriatric syndrome associated with adverse outcomes, particularly in super-aged societies. Hospitalization-related inactivity may contribute to frailty progression and worsen prognosis and post-discharge quality of life; however, frailty identification at admission is challenging. This study used diagnosis procedure combination (DPC) data to examine associations between patient and medication factors at admission and hospitalization-induced frailty. The cohort comprised patients aged ≥65 years hospitalized at Shonantobu General Hospital with records of oral medication prescribed or brought in at admission. Four PRISMA-7 frailty risk index items were evaluated. Patients meeting at least three criteria were defined as the frailty risk group. Bivariate and multivariate analyses assessed the influence of patient background and medication categories on frailty risk. The frailty risk group included 888/1948 cases. Risk and nonrisk groups differed significantly in sex, age, and body mass index (BMI). Logistic regression analysis showed that low BMI, antithrombotics, mineral supplements, and antiparkinsonian drugs were associated with frailty risk, although the findings for male sex and advanced age should be interpreted cautiously because these variables were included as components of the modified frailty-risk definition. Patient background and medication information from DPC data at admission may contribute to future frailty risk estimation. Future prospective studies using detailed clinical data and machine learning models are needed to clarify the relationship between medication use and frailty.
Precision Livestock Farming (PLF) has emerged as an approach in modern animal production, integrating advanced technologies such as sensors, automation, data analytics, and artificial intelligence to enable continuous, individualised monitoring of livestock and their environment. This review examines the impact of PLF technologies on three critical dimensions of livestock systems: productivity, animal welfare, and environmental sustainability. PLF applications, including wearable and environmental sensors, automated feeding and milking systems, and video-based monitoring, allow for early detection of health and behavioural deviations, optimisation of feed efficiency, and improved reproductive and disease management. These technologies support proactive, data-driven decision-making that enhances productivity while promoting animal welfare and reducing the environmental footprint of livestock production. Despite these benefits, the adoption of PLF faces significant challenges, including high initial investment costs, technical limitations, system integration issues, data ownership and privacy concerns, and ethical considerations related to automation. Future research and policy efforts should focus on developing cost-effective, scalable solutions, standardised data frameworks, and supportive regulatory measures to enable equitable and responsible implementation across diverse production systems. By addressing these challenges, PLF offers a pathway towards more efficient, welfare-oriented, and environmentally sustainable livestock production, contributing to global food security and resilient agricultural systems.
Lifestyle-related diseases such as diabetes are closely influenced by daily habits, yet the complex interactions between lifestyle factors and blood glucose variation remain insufficiently quantified. This study proposes a natural language processing (NLP) framework that analyzes long-form illness blogs to identify lifestyle factors associated with elevated blood glucose levels. Diabetes-related narratives were collected from a Japanese illness blog portal (TOBYO) and processed through GPT-4o-based automated labeling, BERT-series contextual embeddings, and LightGBM classification. For Type 2 Diabetes classification, the model achieved an F1-score of 0.73 using JMedRoBERTa embeddings, outperforming baseline models (BERT = 0.70; Twitter-RoBERTa = 0.65). Key factors contributing to glucose elevation were identified through feature importance analysis, with dietary behavior, lack of exercise, poor sleep, and stress emerging as major contributors. These findings demonstrate the potential of combining large language models with structured machine learning to extract health-relevant knowledge from patient narratives. The proposed approach contributes to preventive healthcare by offering interpretable, data-driven insights into lifestyle–glycemic relationships, and provides a foundation for personalized diabetes risk monitoring and AI-based health management applications.
All-inorganic cesium lead iodide (CsPbI3) has been investigated for more than a decade as an absorber for perovskite photovoltaics thanks to its attractive bandgap, thermal robustness compared with hybrid perovskites, and compatibility with tandem concepts. Yet, despite remarkable efficiency progress, CsPbI3 remains far from widespread commercialization. The core roadblock is the metastability of the photoactive black perovskite phases (α/γ/β) against transformation to the photoinactive yellow δ-phase under realistic conditions, amplified by defect chemistry, ion migration, and interfacial reactions. Additional barriers arise from scale-up constraints (film uniformity, throughput, solvent management), long-term operational stability (humidity, heat, UV, bias), and environmental/safety requirements, especially lead containment, sequestration, and end-of-life strategies. This review critically analyzes the intertwined physical, chemical, and engineering factors that still limit CsPbI3 deployment, with emphasis on how solutions in one domain can fail without co-design in others. This review summarizes state-of-the-art stabilization strategies (size/strain engineering, additive/doping routes, surface/interface passivation, and encapsulation), highlight scalable manufacturing pathways including solvent-minimized and vacuum-assisted approaches, and discuss lead-mitigation technologies such as Pb-adsorbing functional layers. Finally, I argue that artificial intelligence (AI)—from machine-learning stability models to process monitoring, robotic optimization, and digital twins—has become essential to navigate the enormous parameter space of CsPbI3 materials and manufacturing. It concludes with actionable recommendations and future directions toward bankable, scalable, and sustainable CsPbI3 photovoltaics.
Cyclic triaxial tests are often used to evaluate the behavior of soils under seismic loads. The stress conditions imposed on a soil specimen during a cyclic triaxial test, however, are very different than those acting on an element of soil during an earthquake. One major difference is that the element in the field is subjected to a change in total confining stress, whereas in a conventional cyclic triaxial test the total confining stress (as applied through the cell pressure) is held constant. This use of constant cell pressure is usually justified by the assumption that in a saturated specimen the change in total stress is offset by a change in pore pressure, thus resulting in no change in the effective confining stress or liquefaction susceptibility. A laboratory study using cyclic triaxial tests was conducted on several soils to assess the validity of this assumption. For each soil, two series of stress-controlled cyclic triaxial tests were run: one set with a constant cell pressure, and thus a constant total confining stress, and a second set with a variable total stress/cell pressure. These tests were then compared in terms of both the resulting cyclic resistance curves and the amount of energy dissipated to trigger liquefaction. It was found that the two conditions of confining stress yielded results that were not statistically different. Therefore, the assumption that the change in pore pressure caused by the variation in total stress is offset by the change in pore pressure and thus results in no change in effective stress or liquefaction susceptibility appears valid. Based on these findings, cyclic triaxial tests performed with constant cell pressure, and thus a constant total confining stress, provide valid results for liquefaction analyses.
The dystrophin gene encodes multiple dystrophin isoforms with tissue-specific functions, including several shorter isoforms expressed in the central nervous system and retina. While Duchenne muscular dystrophy (DMD) has historically been characterized as a primary myopathy resulting from loss of the full-length dystrophin Dp427, increasing clinical evidence indicates that dysfunction of shorter dystrophin isoforms contributes to significant extramuscular pathology, including retinal disease. In particular, loss of the Dp71 isoform has been implicated in retinal inflammation, blood–retinal barrier breakdown, and pathological angiogenesis. In this study, we investigated whether low-level residual expression of Dp71 is sufficient to mitigate retinal inflammation in the mdx3Cv mouse model, which displays reduced—but not absent—expression of multiple dystrophin isoforms. Western blot analysis revealed that mdx3Cv retinas express approximately 4% of wild-type Dp71 protein levels. Despite this marked reduction, mdx3Cv mice did not exhibit the inflammatory phenotype previously observed in Dp71-null mice. Retinal VEGF protein levels and VEGF receptor (FLT-1 and KDR) mRNA expression were preserved, while VEGF mRNA levels were modestly reduced. Furthermore, expression of inflammatory markers ICAM-1 and ALOX5AP, leukocyte adhesion to retinal vasculature, Aquaporin-4 expression, and BRB permeability to albumin were all comparable to wild-type littermates. Together, these findings demonstrate that minimal residual expression of Dp71 is sufficient to preserve retinal vascular homeostasis and prevent inflammatory and permeability defects in the mdx3Cv retina. These results further suggest that partial dystrophin restoration—at levels achievable with current exon-skipping or gene-based therapies—may be adequate to prevent or attenuate retinal pathology in DMD, providing a realistic and clinically relevant therapeutic target.
This study presents a passive mechanical filter designed to enhance sub-Hertz Venusquake detection by shaping the seismic transfer path. The mechanism uses a tunable, high-Q pendulum mounted inside a cylindrical enclosure on a three-ring gimbal to ensure self-leveling and alignment in gravity on uneven terrain. Unlike approaches that rely on broadband digitization and require active control and a stable power supply, this housing–gimbal mechanism performs mechanical filtering for sub-Hz signal amplification and higher frequency attenuation without power. Response spectrum analysis shows that the transmissibility can be tuned to achieve peak sensitivities in the 0.5–0.8 Hz range. When tuned to 50–55 mm pendulum length and under assumed undamping, the pendulum-mounted mechanism improves detectability at best by 10–100× relative to a bare sensor for moderate magnitude (Ms = 3–6) in a 12 h observation window, with signal-to-noise (SNR) ratio of 3, and amplitude spectrum density (ASD) of 10−8 m/s2/√Hz. Furthermore, we extrapolate that the predicted minimum detectable event rates follow Nmmin∝SNR1.2ASD1.2fs0.6, where fs is the quake wave frequency. The damping ratio, considering both structural damping and viscous drag, is estimated to be in the order of 10−3 to 10−2. A probabilistic sensitivity analysis is performed to account for the inherent uncertainty in the spectral mismatch between the narrowband sub-Hz resonance of the designed mechanical filter and the peak frequencies of seismic events; the derived probability model suggests strategies for improving the detection probability in the 0.01–1 Hz range.
This work demonstrates that simple calibration procedures can reduce the errors in determining relative humidity with a psychrometer by up to 75 times. The psychrometer uses two temperature transducers to measure relative humidity. The calibration consists of comparing both transducers before assembling the psychrometer. A psychrometer assembly is presented, built from readily available parts, which provides temperature data for comparison with a theoretical model. The results are compared against a reference psychrometer to assess errors with and without calibration. A twenty-two-fold decrease in error was observed for a relative humidity of 62%.
The Internet of Things (IoT) is increasingly deployed at the edge under resource and environmental constraints, which limits the practicality of traditional intrusion detection systems (IDSs) on IoT hardware. This paper presents two IDS configurations. First, we develop a baseline IDS with fixed hyperparameters, achieving 99.20% accuracy and ~0.002 ms/sample inference latency on a desktop machine; this configuration is suitable for high-performance platforms but is not intended for constrained IoT deployment. Second, we propose a lightweight, edge-oriented IDS that applies ANOVA-based filter feature selection and uses a genetic algorithm (GA) for the bounded hyperparameter tuning of the classifier under stratified cross-validation, enabling efficient execution on Raspberry Pi-class devices. The lightweight IDS achieves 98.95% accuracy with ~4.3 ms/sample end-to-end inference latency on Raspberry Pi while detecting both low-volume and high-volume (DoS/DDoS) attacks. Experiments are conducted in a Raspberry Pi-based real lab using an up-to-date mixed-modal dataset combining system/network telemetry and heterogeneous physical sensors. Overall, the proposed framework demonstrates a practical, hardware-aware, and reproducible way to balance detection performance and edge-level latency using established techniques for real-world IoT IDS deployment.
Due to the limitations of conventional antibiotics, antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives for the prevention and treatment of oral infections. This study systematically evaluated in vitro evidence regarding the antimicrobial and anti-biofilm activity of natural AMPs against oral pathogens. A systematic search using the PICOT strategy was conducted in PubMed, EMBASE, and Scopus, retrieving 7711 articles. After title and abstract screening, 109 studies were selected for full-text analysis, resulting in 26 articles that met the eligibility criteria. Among the AMPs evaluated, nisin (n = 15) and LL-37 (n = 5) were the most frequently investigated, while other peptides included lactoferrin, lactoferricin, melittin, lysozyme, histatin-5, cystatin C, chromogranin A, parasin-1, protamine, AmyI-1-18, and DCD-1L. Natural AMPs of human and animal origin demonstrated antimicrobial activity against bacteria associated with oral infections, particularly Streptococcus mutans and Enterococcus faecalis. These peptides were tested in different formulations, including solutions, incorporation into dental materials and polymers, and application in sonodynamic antimicrobial therapy. Overall, the findings indicate that natural AMPs represent a promising class of biomolecules for controlling oral biofilms; however, further clinical studies are required to validate their long-term efficacy and safety.
Optical synaptic transistors employing polymer dielectrics have emerged as promising building blocks for neuromorphic computing due to their low power consumption and rich photo-induced memory behaviors. While extensive experimental studies have demonstrated various synaptic functions, a unified physical understanding of the coupled charge trapping and ionic polarization processes governing device dynamics remains incomplete. In this work, we develop a unified physical model to investigate optical synaptic behaviors in polymer-based transistors with oxide interlayers. The model explicitly describes the time-dependent evolution of photo-induced charge trapping at the semiconductor–dielectric interface and ionic polarization within the polymer dielectric, which jointly modulate the effective threshold voltage of the transistor channel. Based on this framework, key synaptic functions including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and pulse-dependent potentiation are quantitatively reproduced. The model further reveals how dielectric structure and trapping strength govern the transition between short-term and long-term plasticity. This study provides a physically intuitive and experimentally relevant modeling framework for understanding optical synaptic transistors, offering guidance for the rational design and optimization of polymer-based neuromorphic devices. Although simplified, the proposed model captures the essential physics governing optical synaptic behaviors and provides a general framework applicable to a wide class of ion–electronic neuromorphic devices. Experimental measurements are used as physically motivated proxies to validate the multi-timescale structure of the model rather than direct numerical fitting.
Since Reynolds’ work, turbulence has been one of the most important subjects in fluid dynamics. Although its complete understanding seems still out of reach, there is at least one established physical basis that turbulence is a phenomenon of a random but non-trivially correlated assembly of vortices. The knowledge of vortices has thus become a prerequisite for promoting our understanding of the nature of turbulence. In this article, we first review the simple, compact vortex solutions to the Navier–Stokes equations for incompressible viscous fluids and a unified view of a certain type of vortices including Burgers, Sullivan and Bellamy-Knights solutions. The non-equivalence of the inviscid limit of the Navier–Stokes equations and the Euler equations is emphasized. Introducing the notion of observational non-uniqueness, which differs from the non-uniqueness in a certain class of differential equations, of solutions to the Navier–Stokes equations, the observation problem associated with the dense distribution of non-equivalent solutions is argued. The origin of the extreme sensitivity of the solutions to the boundary conditions is clarified. A few examples of vortex phenomena in the real world are also surveyed. We next review the works of constructing turbulence as a random assembly of simple, compact vortices. An attempt to combine the vortex model of turbulence with the Kármán–Howarth equation for the velocity correlation functions of anisotropic turbulence is presented. It is pointed out that the studies in this direction suggested that Kolmogorov’s 2/3 scaling law was generally compatible with anisotropy. A few quantities are proposed as candidates to measure anisotropy in turbulence experiments.
Background: Non-classical MHC class I molecules MICA and MICB are stress-inducible NKG2D ligands that contribute to immune surveillance, non-HLA antibody formation, and alloreactivity in solid organ and hematopoietic stem cell transplantation; population-level data for Southern Europe remain limited. Methods: High-resolution MICA and MICB genotyping was performed in 1364 unrelated individuals from southern Portugal using a hybrid-capture next-generation sequencing workflow, and allele calls were analyzed with standard population-genetic metrics (allele and genotype frequencies, heterozygosity, Hardy–Weinsberg equilibrium, and LD-like D, D′, r2) and multilocus allele presence/absence encodings explored by k-means clustering, spectral clustering, principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection. Results: Forty-two MICA and twenty-two MICB alleles were identified; MICA*002:01, MICA*004:01, MICA*008:01, MICA*008:04 and MICB*002:01, MICB*004:01, MICB*005:02, MICB*008:01 were most frequent, and most individuals carried at least two distinct MICA and two distinct MICB allotypes. Co-occurrence and LD-like analyses revealed conserved MICA–MICB combinations, including a strong association between MICA*009:02 and MICB*005:06, while unsupervised analyses identified partially overlapping multilocus genotype backgrounds and recurrent four-allele constellations. Conclusions: These findings provide a detailed non-classical MHC reference for southern Portugal and a multilocus framework to support interpretation of non-HLA antibodies and MICA/MICB-aware donor evaluation in selected clinical scenarios, as well as the development of machine learning-based immunologic risk models.
