Search Results (49,797)

Xiangyun Bay Marine Ranch, in the Bohai Sea, China, is an important fishery resource area but has long been affected by eutrophication, habitat destruction, and overfishing. To support resource conservation and management, this study combined environmental DNA (eDNA) metabarcoding, acoustic survey, and a traditional net survey to assess fish resources. The traditional net survey collected 18 fish species (2143 individuals), with Speartailed goby (Chaeturichthys stigmatias) and Indian flathead (Platycephalus indicus) as the dominant species (~70% of total). An acoustic survey revealed that fish resource density in the reef area ranged from 6.05 × 10⁻⁵ to 9.38 × 10⁻⁵ ind/m², with vertical distribution concentrated in the 1–3 m water layer; strong scatterers (−47 to −42 dB) at 3 m depth indicated benthic large individuals. The eDNA analysis generated 1,091,731 high-quality sequences assigned to 39 fish species, with dominant species including Kammal thryssa (Thryssa kammalensis), Yellow-spotted slipmouth (Nuchequula flavaxilla), and Large-mouth naked goby (Gymnoglobus macrognathos). In conclusion, the acoustic survey demonstrates unique advantages in assessing spatial distribution of fishery resources, while eDNA technology serves as an effective supplement to traditional net surveys. Their combined application provides more accurate scientific evidence for the production management and sustainable utilization of marine ranches. Full article

Orosanga japonica (Hemiptera: Ricaniidae) is an invasive, polyphagous planthopper established in Türkiye since 2007 that threatens economically important perennial and field crops across the Black Sea region. Although its establishment and rapid spread suggest considerable adaptive capacity, the genomic basis of this potential remains unexplored. We combined whole-genome sequencing with RNA-Seq data to generate a functionally annotated genome, recovering 92.5% of conserved single-copy orthologs. Utilizing these resources, we performed comparative genomic analyses across 20 additional hemipteran and thysanopteran species, investigating gene family dynamics and genome-wide positive selection. These analyses revealed 52 gene families with statistically significant, lineage-specific size changes (24 expansions and 28 contractions): expanded families were enriched for C2H2 zinc-finger transcription factors, α/β-hydrolases, major facilitator transporters, serpins, and chemosensory proteins, whereas contracted families included a cytochrome P450 family. Additionally, branch site tests identified 122 candidate genes under lineage-specific positive selection. These loci fall into three functional categories potentially associated with invasion-related traits: host detection and neuronal excitability, stress tolerance (xenobiotic, oxidative, and proteostatic), and barrier remodeling. Together, these genomic signatures highlight candidate molecular mechanisms that may contribute to host detection, xenobiotic tolerance, and ecological plasticity, providing a framework for evolutionary analysis and the targeted management of this emerging agricultural pest. Full article

Human Digital Twins (HDTs) extend traditional Digital Twin (DT) concepts by modeling both humans and hospital processes to support smarter and more human-centered healthcare. By integrating Industry 4.0 (I4.0) technologies with the human-centric principles of Industry 5.0 (I5.0), HDTs offer new opportunities to improve hospital operations. This study presents a PRISMA-based systematic literature review to examine the role of HDTs in hospital operations. A total of 329 papers were identified through the initial search, and after the screening process, 22 studies were included for in-depth analysis. The review combines bibliometric analysis to examine publication trends, leading authors, contributing countries, and keyword co-occurrence with a content analysis to identify the main research themes. Three major themes emerged: (1) HDT architectures and data integration, (2) human-centric and governance aspects, including explainable artificial intelligence and privacy, and (3) operational and clinical outcomes, including patient flow, resource utilization, and staff support. Based on these findings, the study proposes a four-layer HDT framework for practical implementation in hospital operations. Although the reviewed studies indicate that HDTs have considerable potential to improve operational efficiency and strengthen human involvement, most existing research remains conceptual or simulation-based. Future research should therefore prioritize real-world implementation and validation while incorporating ethical, explainable, and sustainable design principles. Full article

The clinical integration of artificial intelligence has outpaced the development of robust evaluative frameworks, raising critical safety concerns. This perspective establishes a clear taxonomy distinguishing probabilistic language models from deterministic classifiers and applies a multi-dimensional combinatorial model to calculate the requirements for complete diagnostic coverage. Our analysis demonstrates that comprehensive diagnostic coverage requires between 50,000 and 150,000 distinct, task-specific classifiers under subspecialty-level clinical granularity; conservative aggregated estimates (4500–18,750 binary classifiers) do not reflect the multiplicative expansion introduced by subtype differentiation, severity staging, temporal variants, demographic stratification, and equipment variation, whereas currently cleared devices cover less than one percent of this clinical space. More fundamentally, although population-trained models can generate conditional patient-specific risk estimates when predictors are informative and calibration is adequate, these statistical parameters optimized on population-scale data cannot provide the categorical certainty required for individual diagnostic decisions, which is a gap that clinical judgment must bridge. Because clinical AI tools are inherently statistical and perform reliably only on common, highly represented presentations while failing on rare, atypical cases rare in their training data, attempting to automate routine tasks leaves human clinicians with only the most challenging diagnostics. Furthermore, selective automation of these low-complexity cases introduces severe occupational hazards, including cognitive surrender, diagnostic complacency, and rapid expertise atrophy. Rather than pursuing the computationally and logistically unfeasible goal of complete diagnostic classification, developers should prioritize predictive, prognostic trajectory modeling. This paradigm shift aligns the probabilistic nature of machine learning with clinical utility, reinforcing clinical judgment as the irreplaceable diagnostic integrator. Full article

Data obtained from experiments and surveys in human social systems are inevitably influenced by systematic measurement errors, and network data are no exception. Despite the prevalence of error in social network data, current research often lacks rigorous estimation of its expected precision, which may lead to biased conclusions. Signed networks, which encode both positive and negative relationships, constitute an important component of network science, and conducting measurement error analysis on them can substantially enhance the accuracy of social network analysis. This paper proposes a set of error measurement tools based on the Expectation-Maximization (EM) algorithm, specifically designed to estimate errors in signed network data. We extend traditional experimental error estimation to the network domain, derive a general error estimation method for signed networks, and validate its scientific validity and practical utility through extensive simulation experiments on both synthetic and real-world networks. The experiments reveal that network density and the ratio of positive to negative edges significantly influence the posterior probability distribution of the adjacency matrix. Specifically, as density increases, edge estimation accuracy exhibits a U-shaped trend, and the proportion of negative edges shows a nonlinear relationship with accuracy. The proposed method is applicable to repeatedly measured signed networks and provides a reliable framework for reconstructing network structures as faithfully as possible. Full article

Effective modal shift policies require accurate valuation of the external costs associated with freight transport. However, South Korea’s logistics policy currently relies on valuation parameters that have not been systematically updated since 2013. This study recalibrates the unit benefits of shifting freight from roads to rails using a hybrid estimation framework that combines bottom-up emission estimation with a top-down allocation of aggregate external costs, grounded in national statistics harmonized to a 2022 base year (with some inputs drawn from the most recent earlier survey years and price-adjusted to 2022) and link-level national traffic data (KOTI View-T 3.0). We quantify the reductions in average external costs per ton/kilometer across five impact domains: air pollution, climate change, noise, traffic accidents, and congestion. The analysis yields a total unit benefit of KRW 151.81 per ton/kilometer, a figure approximately 3.7 times the inflation-adjusted benchmark currently in use. A decomposition of this difference shows that updated emission inventories, accident statistics, and valuation parameters alone raise the unit benefit to KRW 69.6 KRW per ton/kilometer (approximately 1.7 times the benchmark), while the revision of the road freight traffic share, from the 3.5% assumed in earlier travel demand models to the 21.2% observed at the link level, accounts for the remainder. Scenario analyses covering the principal allocation and valuation assumptions place the total unit benefit between KRW 38.6 and 217.7 per ton/kilometer: the benefit exceeds the benchmark in every scenario except the single most conservative variant, which combines all unfavorable assumptions simultaneously—including the legacy freight share—and falls approximately 5% below it. These findings indicate that the parameters currently used in policy appraisal may materially underestimate the social benefits of rail freight under the assumptions adopted here. We recommend that policymakers utilize these updated unit values to recalibrate freight subsidies and investment appraisals to align with the 2050 Carbon-Neutral Strategy. Full article

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies. Full article

To address the challenges of relying on experience for vehicle allocation in concrete transportation for dam construction—as well as the difficulty in coordinating vehicle capacity, loading/unloading capabilities, and system queuing status—this study focuses on a closed-loop transportation system comprising “batching plant—transport route—pouring area” and develops a vehicle allocation model based on a BCMP (Baskett–Chandy–Muntz–Palacios) closed-loop queuing network. The loading process at the mixing plant and the unloading process at the silo yard are modeled as finite-service nodes, while the transportation of loaded vehicles and the return of empty vehicles are modeled as infinite-service nodes. The Buzen convolution algorithm is used to solve for the system’s steady-state performance. With the number of vehicles N and the silo yard diversion ratio p as joint decision variables, the feasible region for vehicle configuration and recommended solutions are determined subject to constraints on silo yard feed demand and node utilization. The results of the case study show that, using reference operating condition S0 as a benchmark, a decrease in the loading efficiency of the batching plant increased the recommended number of vehicles by 28.57%; a simultaneous increase in the unloading efficiency of the dual-bin system reduced the recommended number of vehicles by 42.86%; enhanced coordination between the loading and unloading systems reduced the recommended number of vehicles by 50.00% and decreased the expected queuing time per cycle by 97.26%; furthermore, the system bottleneck shifted as loading and unloading capacities changed. Further independent validation was conducted using discrete-event simulation (DES); the maximum relative error between BCMP and DES in terms of system throughput, expected queuing wait time per cycle, and maximum node utilization was less than 1.5%. Sensitivity analysis indicates that concrete pouring demand and unloading capacity at the bin area are key factors affecting vehicle allocation. When demand increases or unloading capacity decreases beyond the system’s capacity limits, simply adding more vehicles does not result in a feasible solution. The study demonstrates that the proposed method can quantitatively reveal the relationships among vehicle fleet size, traffic diversion at the bin area, loading and unloading capacity, and queueing conditions, thereby providing a decision-making basis for the coordinated allocation of vehicles and loading/unloading resources in concrete construction for dams. Full article

Starch, a natural source of energy in the form of glucose chains, is widely utilized in various industrial and scientific fields. In its native state, starch is thermally unstable and undergoes gelatinization. Physicochemical modifications of starch aim to increase its thermal and structural stability while simultaneously enhancing its reactivity by introducing new functional groups. The primary objective of this study was to develop thermally stable and economically viable starch-based drug carriers capable of the controlled release of a negatively charged component sourced from aronia extract. Potato starch underwent a series of chemical modifications, specifically quaternary amine etherification, citric acid esterification, and/or hydrothermal modification. The characterization involved determining several parameters: the degree of amino substitution groups; starch particle size using a laser particle size analyzer; viscosity and pH; gelation temperature and heat capacity measured by scanning calorimetry (DSC); mass degradation analyzed via thermogravimetric analysis (TG); crystallinity determined by X-ray diffraction (XRD); potential intermolecular interactions studied by Fourier-Transform Infrared Spectroscopy with Attenuated Total Reflectance (FTIR-ATR); and the rate of chlorogenic acid release from aronia extract tablets quantified by spectrophotometry. The highest cationization results were achieved using citrate starches, reaching up to 86%. The combined application of citric acid esterification and cationization, coupled with an annealing process, resulted in increased viscosity, amorphousness, and enzyme resistance of the starch. Citric acid esterification significantly improved the thermal stability of the starch. Furthermore, FTIR studies revealed the formation of electrostatic interactions between the functional groups of the starch and the components of aronia extract. The amount of chlorogenic acid released showed significant variation (70–100%) depending on the type of starch modification. Collectively, these studies confirmed that both hydrothermal and chemical modifications influence the thermal and structural stability of the starch. Utilizing all combination modification strategies ensured the production of highly promising carriers for active substances. Full article

Background: Albumin-based biomarkers have emerged as practical tools for prognostic assessment in coronary artery disease (CAD), but their comparative prognostic patterns and potential clinical utility have not been systematically evaluated. Methods: We performed a systematic review and meta-analysis of albumin-based biomarkers, including the C-reactive protein-to-albumin ratio (CAR/hsCAR), neutrophil percentage-to-albumin ratio (NPAR), lactate-to-albumin ratio (LAR), and C-reactive protein–albumin–lymphocyte (CALLY) index, in patients with CAD. Pooled associations with mortality and MACE were assessed, together with discriminative performance and clinical utility. Results: Thirty-four studies involving 47,763 patients were included. Higher CALLY values were associated with lower all-cause mortality (HR 0.46, 95% CI 0.24–0.87) and a directionally protective but non-significant association with MACE (HR 0.44, 95% CI 0.18–1.08). Elevated CAR/hsCAR was associated with increased mortality (OR 1.61, 95% CI 1.09–2.38) and MACE (HR 1.37, 95% CI 1.21–1.56) in acute coronary syndrome (ACS)-related cohorts. NPAR showed the most consistent association with mortality (HR 1.88, 95% CI 1.71–2.08; I² = 4.8%), whereas its association with MACE was less stable (HR 1.86, 95% CI 0.56–6.13). LAR showed the largest pooled effect estimate for mortality (HR 2.50, 95% CI 1.78–3.51). In discriminative analyses, CAR showed the most balanced performance for long-term MACE, NPAR showed the strongest rule-in profile for long-term MACE, and CAR and CALLY showed the best discrimination for mortality. Conclusions: Albumin-based biomarkers are linked to adverse CAD outcomes, especially in ACS, with distinct prognostic patterns by biomarker and endpoint. They provide complementary risk stratification information, and further prospective studies are needed to confirm their incremental clinical utility. Full article

The jujube tree fruit remains a primary fruit in northern China, yet its geographical distribution and yield are significantly constrained by freezing stress during winter. Numerous studies have highlighted the pivotal regulatory function of microRNAs (miRNAs) in plant responses to low-temperature stress. Nevertheless, the specific miRNAs involved in the response to low temperatures and their associated gene networks in Ziziphus jujuba Mill are not well understood. In this investigation, we utilized high-throughput sequencing to analyze small RNA libraries from branches subjected to temperatures of 4 °C and −30 °C. Our analysis identified a total of 342 miRNAs, comprising 123 known miRNAs and 219 novel miRNAs. The differential expression analysis revealed that under low-temperature conditions, 177 miRNAs underwent significant changes. Among them, specific upregulation of miR319 in the less cold-resistant variety and miR6483 in sensitive variety was observed. By employing degradome sequencing, we identified a total of 1551 target genes corresponding to 3059 unique miRNA target interaction pairs involving 299 miRNAs. Functional analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways indicated that these target genes are primarily associated with transcriptional regulation, metabolic pathways, and genetic information processing. Through a comprehensive analysis, we pinpointed 11 genes corresponding to 9 miRNAs that are implicated in jujube tree cold stress, and 7 target genes of 7 miRNAs were confirmed by 5′-RACE analysis. These miRNAs are likely to exert crucial regulatory functions in the context of jujube tree cold stress. This study is the first to systematically identify miRNAs and their target genes in the response of Ziziphus jujuba Mill to low-temperature stress, which provides important resources for in-depth analysis of the molecular mechanism of jujube tree cold resistance and for cold-resistant breeding. Full article

High average-utility itemset mining is a significant research direction in data mining. The traditional average-utility (AU) measure employs itemset length as the normalization benchmark, which mitigates the bias toward long itemsets; however, it does not adequately account for the discrepancies between unit profit and actual sales volume. This paper introduces two novel measures: the weighted average utility based on external utility (E_WAU) and that based on internal utility (I_WAU), and examines the theoretical relationships among AU, E_WAU, and I_WAU. Subsequently, we establish a three-dimensional evaluation framework, namely, “category average–profit conversion–unit profitability”. Based on this framework, we develop classification decision matrices for pairwise comparisons of AU, E_WAU, and I_WAU. Within these matrices, products are grouped according to combinations of any two measures, yielding distinct product classifications that support customized marketing strategies. This approach further underscores the practical value of E_WAU and I_WAU in facilitating real-world decision-making. Finally, we conduct empirical validation and analysis on three real-world datasets. Experimental results demonstrate that, compared with using AU alone, the combined use of E_WAU and I_WAU is more effective in identifying product categories and devising appropriate sales strategies. Full article

The deployment of hydrogen-powered public transport requires operators to decide not only whether to adopt fuel-cell buses, but also how hydrogen should be supplied. This study compares two sourcing strategies, as follows: on-site hydrogen production (Make) and external procurement (Buy). An operator-centered framework combining Levelized Cost of Hydrogen (LCOH) and Total Cost of Ownership (TCO) is applied to two Italian cases, using project-specific procurement and technical data from Monfalcone/Gorizia and Ferrara together with explicit modeling assumptions. Under base-case assumptions, Make yields an LCOH of €13.67/kg H₂ and a fleet TCO of €2.34/km, compared with €2.51/km for Buy at a delivered green hydrogen price of €12/kg. The deterministic break-even price is €10.25/kg, while both hydrogen configurations remain more expensive than diesel (€1.24/km). A probabilistic analysis based on 10,000 Latin Hypercube simulations and six uncertain parameters shows that Make is less costly in 78.8% of cases and Buy in 21.2%. The median probabilistic break-even price is €10.38/kg, with a 5th–95th percentile range of €8.79–12.38/kg. Delivered hydrogen price is the main driver, followed by Make CAPEX, fleet utilization, and financing conditions. Hydrogen sourcing therefore remains a strategic investment choice, with implications for capital exposure, renewable-energy integration, economic sustainability, and supply resilience. Full article

This study investigates how the Bio-Circular-Green (BCG) economy model and the Sufficiency Economy Philosophy (SEP) can support the development of creative tourism for community-based conservation and well-being in Ban Pa Khlu Community, Nakhon Si Thammarat Province, Southern Thailand. A participatory mixed-methods case study approach informed by PAR principles was employed. Qualitative data were collected via stakeholder interviews, focus group discussions, participatory observation, and community workshops, while quantitative data were obtained from tourist satisfaction surveys during pilot tourism activities. The data were analyzed using thematic analysis and descriptive statistics. The findings suggest that integrating SEP principles with BCG concepts provided a basis for community participation and the sustainable utilization of nipa palm resources. Creative tourism activities based on local knowledge and cultural heritage provided opportunities for environmental learning, cultural knowledge sharing, and value-added use of nipa palm resources, while offering potential supplementary livelihood opportunities for the community. The Ban Pa Khlu case illustrates how local natural and cultural resources can be developed into community-based tourism experiences while maintaining a focus on ecological conservation and local cultural values. This study provides an integrated approach for applying SEP and BCG principles in community-based creative tourism development. Full article

Coronary artery disease (CAD) and non-obstructive azoospermia (NOA) have distinct aetiologies. We examined whether separately analysed public transcriptomic cohorts contained overlapping exploratory candidate signals without assuming a shared causal mechanism. We re-analysed five Gene Expression Omnibus datasets using differential-expression screening, weighted gene co-expression network analysis (WGCNA), an archived neural-network candidate ranking, xCell enrichment scoring, and single-cell transcriptomic mapping. Nominal differential-expression screening identified 978 CAD-associated and 2562 NOA-associated candidate transcripts. WGCNA showed a moderate correlation between the MElightyellow module and NOA status (r = 0.58, p = 0.007) in GSE45887, a small, imbalanced, non-independent subset of GSE45885. An archived neural-network (NNET) ranking prioritised HSPA1B, PLCL2, ISLR2, STRN, and AQP7 for descriptive analyses; the ranking was generated from the same 20 specimens and is treated as heuristic. xCell produced marker-gene enrichment scores rather than direct measurements of cell abundance or function. Single-cell mapping was descriptive because GSE149512 combined heterogeneous NOA aetiologies with paediatric and adult comparator tissues. The analyses generate hypotheses from separate CAD and NOA cohorts. They do not establish a shared causal pathway, a sex- or age-independent association, temporal sequence, clinical diagnostic utility, or direct correspondence between testicular and coronary cell states. Full article