Search Results (25,821)

The scarcity of resources has constrained the supply of conventional feedstuffs for livestock production. Consequently, mulberry (Morus spp.), known for its high protein content and bioactive compounds, has been developed as a promising alternative feed. However, the optimal water–fertilizer ratio for cultivating feed mulberry and the underlying physiological and agronomic mechanisms remain poorly understood. To address this, a two-year field experiment (2023–2024) was conducted to investigate the effects of water–fertilizer coupling on feed mulberry yield, water use efficiency (WUE), and soil quality. This experiment employed a split-plot design with three irrigation levels (I1 = 45, I2 = 90, and I3 = 135 mm) and four fertilizer rates (F1 = 0, F2 = 150, F3 = 225, and F4 = 300 kg·ha⁻¹). The results demonstrated the following: (1) The variation trends in SWC were consistent with those of soil available N, P, and K contents. Under water–fertilizer coupling, the total water consumption peaked in the I3F3 treatment, with values of 639.9 mm and 703.5 mm in the two years, respectively. (2) The I3F3 treatment produced both the highest yield (37.19 and 41.66 t·ha⁻¹) and the highest leaf N, P, and K contents among all treatments. (3) Water and fertilizer use efficiencies exhibited parabolic trends in response to increasing irrigation and fertilizer inputs. The highest agronomic nitrogen efficiency (AEN) was observed in I2F2. (4) The AMOS 26 model further revealed that soil nutrient content had the strongest direct positive effect on yield (standardized coefficient = 0.68), followed by total water consumption (0.33). And irrigation significantly enhanced soil nutrient availability (standardized coefficient = 0.29). In summary, the I3F3 combination achieved the highest yield and water use efficiency, whereas the I2F2 treatment exhibited the highest AEN. This trade-off suggests that the optimal strategy depends on management objectives (yield maximization vs. resource conservation) in the North China Plain. Full article

Aquaponics is an integrated and resource-efficient production system that combines aquaculture and hydroponics in a closed-loop environment with reduced water consumption and nutrient losses. The present study evaluated the nutritional composition, mineral profile, microbiological quality, and antioxidant-related phytochemical characteristics of red amaranth (Amaranthus cruentus L.) cultivated in a recirculating aquaponic system under controlled environmental conditions. Leaf biomass was analyzed for proximate composition, macro- and micronutrient content, total phenolic and flavonoid compounds, betalains, chlorophyll pigments, and antioxidant activity using standard analytical and spectrophotometric methods. The results demonstrated high crude protein content and substantial accumulation of essential minerals, particularly calcium, potassium, and magnesium. The analyzed biomass also exhibited elevated levels of phenolic compounds, flavonoids, betalains, and chlorophyll pigments associated with considerable antioxidant potential. The pigment profile suggested good physiological adaptation of plants to aquaponic cultivation conditions. In addition, microbiological analysis confirmed acceptable hygienic quality and safety of the harvested plant material. Overall, the findings indicate that red amaranth can be successfully cultivated in aquaponic systems while maintaining high nutritional value and functional food potential. The study highlights aquaponic cultivation as a sustainable approach to producing nutrient-dense leafy vegetables within environmentally responsible agricultural systems. Full article

Video anomaly detection (VAD) aims to identify abnormal events in videos. Due to the lack of high-quality training data with detailed annotations, current VAD methods can only produce video-level predictions. To remedy this, several methods attempt to synthesize pseudo video anomalies. However, these methods suffer from low realism and coarse annotations, which limits their performance in real-world scenarios. In this paper, we propose a framework for unsupervised anomaly video generation from solely normal videos, leveraging VLMs to generate structured textual descriptions of anomalies conditioned on the perception of this video. Then, abnormal segments are synthesized using VLMs based on the synthetic textual descriptions. As our framework is highly controllable, video-level and region-level labels can be obtained to provide fine-grained annotations. On top of the synthetic data, we develop a fine-grained VAD network to simultaneously produce video-level, frame-level, and region-level predictions. Experiments show that our method achieves remarkable fine-grained VAD performance. Full article

Two major factors influence the expected recovery of the ozone layer: a decline in ozone-depleting substances (ODSs) due to implementation of the Montreal Protocol and stratospheric cooling due to increasing greenhouse gas (GHG) concentration. We investigate the largest spring Arctic ozone anomalies revealed in three ensemble calculations of the chemistry–climate model (CCM) SOCOLv3 under moderate (SSP2-4.5) and severe (SSP5-8.5) scenarios of GHG growth, accounting for the expected decline in ODS concentrations over 2015–2099. During the first half of the 21st century, the coldest winters could still produce Arctic total ozone content (TOC) anomalies comparable with the record spring 2020 values, despite the overall recovery of the ozone layer by mid-century. In March and April, TOC may occasionally drop below 220 Dobson Units. According to estimates from the Moscow State University (MSU) radiation model, the lowest TOC values under cloudless midday conditions could increase surface UV radiation by a factor of 1.5–2, reaching a UV index of 5–6 (and up to ~8 in April)—levels requiring sun protection measures. Full article

Rotational friction welding offers an adhesive-free approach for producing wood dowel joints, but pull-out performance and process consistency are strongly affected by the welding parameters. This study investigated the effects of the hole-to-dowel diameter ratio, rotational speed, and plunging rate on rotationally friction-welded beech (Fagus sylvatica L.) dowel joints. An L9 orthogonal design was combined with supplementary testing, curve-based validity assessment, post-peak analysis, post-pull-out surface imaging, and independent validation. Range analysis ranked the parameter effects as plunging rate, hole-to-dowel diameter ratio, and rotational speed. Type III analysis of variance confirmed significant effects of the hole-to-dowel diameter ratio and plunging rate, whereas rotational speed was not significant within 1600–2000 rpm. The predicted combination was a ratio of 0.80, 1800 rpm, and 14 mm·s⁻¹. The validation group reached 2567.22 N, 34.96% above T3, but its coefficient of variation of 35.93% showed that considerable variability remained. All joints failed by complete dowel withdrawal; the exposed dowel surfaces indicated mixed interfacial separation, sliding, and localized wood-fiber tearing. Darkened regions occurred at different speed levels, without consistent evidence of extensive burning at 2000 rpm. High-capacity joints also showed more abrupt post-peak degradation, indicating a trade-off between capacity, consistency, and failure suddenness. Full article

Complex diseases often involve distributed interactions among biological regions, physiological systems, imaging phenotypes, and clinical variables that are not fully captured by anatomical proximity, isolated biomarkers, or conventional feature-based representations. In oncology, neuroimaging, critical care, and systems medicine, distant or apparently separate biomedical sectors may show strong statistical or functional coupling associated with multimodal imaging signatures, inflammatory responses, metabolic constraints, treatment-induced changes, or shared disease-state organization. In this work, we introduce a proof-of-concept relational graph framework for representing such candidate hidden connectivity in terms of correlation-induced accessibility bridges. The novelty of the framework is that it does not treat biomedical correlation, graph distance, and network connectivity as separate descriptors but explicitly couples non-factorizable inter-sector correlation to localized accessibility compression in an emergent disease-state geometry. The proposed framework represents a biomedical system as a weighted relational graph in which nodes correspond to clinically relevant entities, such as tissue regions, imaging-derived features, biomarker modules, physiological variables, or disease states, while weighted edges encode constraints on functional, statistical, or pathological accessibility. Within this structure, coarse-grained biomedical sectors are defined as organized subsystems, and non-factorizable coupling between sectors is quantified using mutual-information-type measures. Candidate biomedical bridges are then defined operationally as localized, high-gain reductions in effective inter-sector accessibility distance. We introduce explicit coupling rules linking sector-level correlation to bridge-specific accessibility compression, including an effective distance-compression model and an ensemble-based formulation. Numerical proof-of-concept simulations on randomized modular graph ensembles show that increasing correlation strength systematically reduces effective inter-sector distance and increases bridge gain. The strongest compression occurs when correlation modulates a designated bridge architecture, exceeding the effects observed under random non-bridge or generic inter-sector modulation. These simulations are not intended to validate a disease-specific biological mechanism but to test whether the proposed correlation–compression rule produces bridge-specific effects distinguishable from null graph perturbations. The resulting structures should not be interpreted as physical anatomical tunnels or direct causal pathways unless supported by additional biological evidence. Rather, they represent correlation-induced accessibility bridges: localized, high-gain routes in a patient- or disease-specific relational geometry. The framework may therefore provide a theoretical and computational basis for prioritizing candidate hidden connectivity patterns in radiomics, multimodal prognosis, physiological deterioration, recurrence modeling, and systems-level disease networks. Full article

Steam injection is an energy-intensive enhanced-oil-recovery method for high-viscosity reservoirs, and its performance is controlled by coupled heat delivery, steam condensation, temperature-dependent viscosity reduction, mobility change and reservoir filtration response. This study develops an experimentally validated physics-motivated residual-learning framework for forecasting oil production and selecting thermally rational steam-supply regimes. The model combines a physics-motivated semi-empirical baseline describing useful steam-related heat input, calibrated viscosity transformation, mobility growth, steam–oil ratio and a thermal-energy efficiency index with a residual-learning block fitted to measured regime-level records. The supervised forecasting task was performed at the regime level using 200 operating-regime records treated as the effective modelling units, with nested logs aggregated within regimes and within-group dependence examined through campaign-, reservoir-state- and well-availability-based checks. The 4800 steam-injection log entries and 4800 production-response log entries were treated as nested time-resolved measurements used only for regime-level aggregation, feature construction and quality-control checks; they were not counted as independent training samples. Blind-test validation produced R² values of 0.974 for oil rate, 0.988 for cumulative oil production, 0.731 for steam–oil ratio and 0.828 for the thermal-energy efficiency index; the corresponding MAPE values were 4.56%, 3.86%, 4.48% and 3.29%, respectively. The error structure shows higher uncertainty for composite indicators than for direct production responses, which is consistent with the measurement chain. Response-surface and Pareto analyses identify bounded steam-supply operating regions where production gain remains balanced against specific steam consumption and the thermal-energy efficiency index. Full article

Large language models produce semantically rich embeddings, yet direct employment of generic LLM embeddings fails to satisfy news recommendation demands due to inherent semantic mismatches with task targets. Existing fine-tuning methods including LoRA can narrow such gaps but bring prohibitive computational overhead, restricting real-world deployment. This work proposes lightweight task-adaptive modules (TAMs). It keeps LLM parameters fixed and transforms offline embeddings into task-specialized representations without full-model backpropagation, drastically cutting training costs. Evaluated on MIND benchmarks across ten mainstream recommendation architectures, TAMs achieves comparable accuracy to LoRA, with computational cost reduced to 1/10 of LoRA’s level. TAMs outperform GloVe-based models by 1.3–12.4% in AUC and scale effectively to MINDlarge. Ablation experiments confirm that the nonlinear projection is pivotal to performance improvement, and statistical validation across three random seeds confirms result robustness. This paradigm provides an efficient low-cost solution for LLM-based news recommendation under resource constraints. Full article

This study presents the Multi-dimensional Flood Vulnerability Index (M-FLOVI), calculated by using an index-based method specifically tailored to capture the impact of nature-based solutions (NbSs) on vulnerability. It aggregates five vulnerability dimensions (physical, economic, environmental, social and institutional) into a single index within a multi-level framework. Each dimension is calculated from a set of indicators that can be computed with moderate data demands. These calculations generally require information about buildings, infrastructure, land cover, population, protected areas and cultural heritage, which can partly be obtained from open-access data. M-FLOVI ranges between 0 and 1, and it can be readily mapped and combined with flood hazard to produce flood risk maps. This paper elaborates a step-by-step M-FLOVI calculation in the Tamnava River Basin, Serbia, where various NbSs were proposed. Under the baseline conditions, most of the study area exhibits either moderate (83.5%) or low vulnerability (15.8%). These NbSs decrease future vulnerability in 6.6 km² (1.34%) of the study area. Afforestation (0.59%) and retention ponds (0.42%) decrease environmental vulnerability, while flood plain restoration (0.33%), which is expected to create a protected bird habitat, increases environmental vulnerability. These results suggest that M-FLOVI can effectively capture NbSs’ impacts on future vulnerability to floods. Full article

Sertoli cells are essential for testicular development and spermatogenesis, but the post-transcriptional mechanisms regulating their function in sheep remain incompletely understood. This study investigated the regulatory relationship between miR-132-y and Yes-associated protein 1 (YAP1), a core effector of the Hippo pathway, in primary Sertoli cells isolated from Southdown × Hu F1 sheep. Target prediction and dual-luciferase reporter assays supported a direct interaction between miR-132-y and the YAP1 3′ untranslated region. YAP1 overexpression was associated with increased CCK-8-based cell viability and altered mRNA expression of selected viability-associated, YAP1-related, and Sertoli cell function-associated genes, whereas YAP1 silencing showed opposite trends. Conversely, miR-132-y overexpression reduced YAP1 mRNA abundance and was associated with decreased CCK-8-based cell viability and corresponding transcriptional changes, while miR-132-y inhibition produced the opposite pattern. Rescue experiments showed that ectopic YAP1 expression partially attenuated miR-132-y-associated changes. Overall, these findings provide in vitro, cell-based evidence that miR-132-y targets YAP1 at the transcript level and is associated with viability-related transcriptional responses in sheep Sertoli cells. Full article

Calcium (Ca²⁺) and magnesium (Mg²⁺) are essential divalent cations whose homeostasis is essential for cardiovascular, muscular and metabolic function. Absolute or relative imbalances between Ca and Mg can lead to cardiovascular, metabolic and neurological pathologies. Ionized calcium (Ca²⁺) is a biologically active fraction of plasma calcium that is tightly regulated by protein binding, phosphate complexation, magnesium modulation and acid–base status. Ionized calcium plays a central role in multiple physiological processes and is strongly influenced by plasma pH, phosphate concentration and magnesium levels. However, the combined effects of these parameters are difficult to evaluate intuitively because of their nonlinear interactions. In this study, a numerical simulation framework was used to explore how simultaneous variations in pH, phosphate and magnesium may influence ionized calcium under typical physiological plasma conditions as a phenomenological framework linking ionic equilibrium with viscosity-dependent flow parameters under well-mixed plasma conditions. The simulations reveal phenomenological changes in which concurrent increases in pH and phosphate or reductions in magnesium produce disproportionately large decreases in ionized calcium. Within the physiological ranges examined, the results also indicate a region of relative stability for ionized calcium corresponding to Ca/Mg ratios close to 3, while this value should be interpreted as an emergent feature of the modeled parameter space rather than a universal physiological constant. These findings illustrate the importance of considering multiple electrolyte interactions simultaneously when evaluating calcium homeostasis and may provide a conceptual framework for further experimental investigation. Full article

Background/Objectives: Laboratory automation is a key strategy for increasing productivity and reducing sample turnaround time (TAT), a common indicator of laboratory performance. However, owing to the statistical distribution of TAT values, conventional descriptors such as mean, standard deviation, and percentiles cannot capture the processing history of individual samples. In this study, sample flow within a highly automated laboratory system was analyzed by analogy with the Brownian motion of molecules in solution, using an ad hoc modified Dynamic Light Scattering (DLS) correlation function. Methods: Seven processing histories, each consisting of 1000 samples and representing different TAT scenarios, were generated, and the corresponding correlation functions were calculated. Each sample was assumed to remain correlated with its initial state (value = 1) until its TAT was reached; thereafter, once the result was produced, the sample was considered uncorrelated and its status value became 0. The correlation function was defined as the normalized progressive sum, over time, of the status values of all analyzed samples at each time point. Results: The DLS-inspired autocorrelation function enabled the derivation of parameters describing both overall system performance and sample processing status. These parameters provide quantitative indicators for near-real-time monitoring of automation chain efficiency and reveal system features that are not accessible through conventional TAT statistics. Conclusions: This approach allows the definition of measurable metrics describing the system’s capacity to buffer and mitigate operational disruptions at both the global and individual-sample levels. The proposed framework provides a novel tool for evaluating, monitoring, and comparing the performance of laboratory automation systems. Full article

Endodontic irrigant solutions act as crucial pretreatment conditioning agents in dentin biomimetic remineralization, preparing the collagen scaffold for calcium phosphate infiltration and subsequent tooth structure reconstruction. In this study, root dentin discs were exposed for 10 min to five irrigant solutions: sodium hypochlorite (NaClO, 3%), EDTA (17%), citric acid (CA, 10%), chlorhexidine (CHX, 2%), and an innovative experimental formulation containing citric acid (7%) and surfactants. Samples were then aged in Hank’s Balanced Salt Solution (HBSS) at 37 °C for three months to simulate long-term clinical conditions. Physicochemical modifications of the collagen and apatite phases were assessed at each experimental stage using ATR-FTIR spectroscopy, with the A_CaP/A_{Amide I} and A₈₇₀/A_CaP absorbance ratios as markers of the degree of mineralization and apatite carbonate content, respectively. Results indicated that CHX- and EDTA-treated dentin exhibited the highest remineralization after ageing, while NaClO impeded remineralization due to collagen degradation. The experimental irrigant produced the most pronounced demineralization, followed by CA; however, it also facilitated significant remineralization, attributed to citrate–collagen binding and surfactant-enhanced apatite nucleation. NaClO selectively degraded collagen and increased apatite crystallinity; CA inhibited apatite nucleation through adsorbed citrate ions, and CHX and EDTA induced minimal alterations. These findings provide molecular-level evidence linking short-term irrigant effects to the long-term potential for dentin biomineralization, with direct implications for irrigant selection in regenerative endodontic protocols. It should be noted that this study was conducted on dentin discs obtained from a single tooth; all findings should therefore be regarded as preliminary and require confirmation in studies with larger, biologically independent sample sizes. Full article

Antimicrobial resistance (AMR) is a global health crisis addressed through a One Health framework. However, recent European Union (EU) surveillance data reveals a marked divergence in progress between the human and animal sectors. This study analyzes the most recent monitoring reports (European Surveillance of Antimicrobial Consumption Network and European Sales and Use of Antimicrobials for Veterinary Medicine, 2024) to compare the effectiveness of mitigation strategies across sectors. The findings expose a clear paradox: while the veterinary sector has achieved a structural 24.3% reduction in antimicrobial sales in the EU since 2018, human medicine has recorded a 2% increase in overall consumption, diverging from established reduction targets. From a qualitative perspective, veterinary medicine has nearly eliminated the use of critically important antimicrobials in the AntiMicrobial Expert Group (AMEG) (category B), including polymyxins and third-generation cephalosporins, which now account for only 0.24% of total sales. In contrast, human medicine continues to struggle to contain antimicrobial resistance in key sentinel pathogens, notably Klebsiella pneumoniae and Escherichia coli. Furthermore, companion animals, representing 97.9% of non-food-producing animal biomass, emerge as a reservoir of antimicrobial-resistant bacteria due to the intensive use of broad-spectrum oral formulations. The results indicate that the veterinary regulatory model, centered on binding volume reduction and preventive strategies, has been more effective in reducing overall antimicrobial consumption compared to the voluntary, guideline-based stewardship approaches currently used in human medicine. Achieving meaningful control of antimicrobial resistance will require human medicine to adopt the same level of structural rigor already implemented in animal production systems. Full article

Engineered cementitious composites (ECCs) reinforced with imported polyvinyl alcohol (PVA) or polyethylene (PE) fibers exhibit high tensile deformability, but the fiber cost limits the wider application of ECCs. In this study, locally produced PVA and PE fibers were used to develop lower-cost ECC, and PVA–PE fiber hybridization was adopted to improve tensile deformability. Based on matrices with various fly ash volumes, the single-fiber pullout behavior was first investigated at the micromechanical level. The results showed that PVA and PE fibers failed mainly by rupture and pullout, respectively, and that the chemical bonding between PVA fibers and the surrounding matrix decreased with increasing fly ash volume. The effects of single-fiber addition and hybrid-fiber addition on the macromechanical properties of ECC were then examined. The results indicated that ECC reinforced with hybrid PVA–PE fibers exhibited enhanced tensile performance compared with ECC reinforced with either PVA or PE fibers alone, with an ultimate tensile strain exceeding 5.3%, an average crack width below 39 μm, and hybrid reinforcing effect coefficients of 1.17–1.30, indicating a positive hybrid effect. Overall, the lower-cost ECC incorporating hybrid local fibers developed in this study demonstrates promising tensile deformability and crack-control capacity. Full article