Search Results (16,412)

Yangjiatang Village traces its origins to the late Ming and early Qing dynasties. It has evolved over more than 400 years of history. There are 78 rammed-earth buildings left, making it one of the most complete and largest rammed-earth building complexes in East China. This study investigated the traditional rammed-earth houses in Yangjiatang Village, Songyang County, Zhejiang Province. By combining field investigation, microscopic characterization, and experimental simulation, we systematically revealed the erosion resistance of rammed earth in a subtropical humid climate was systematically revealed. Using a combination of advanced techniques including drone aerial photography, X-ray diffraction (XRD), microbial community analysis, scanning electron microscopy (SEM), and soil leaching simulations, we systematically revealed the anti-erosion mechanisms of rammed-earth surfaces in Yangjiatang Village. The study found that (1) rammed-earth walls are primarily composed of Quartz, Mullite, lepidocrocite, and Nontronite, with quartz and lepidocrocite being the dominant minerals across all orientations. (2) Regulating the community structure of specific functional microorganisms enhanced the erosion resistance of rammed-earth buildings. (3) The surface degradation of rammed-earth walls is mainly caused by four factors: structural cracks, surface erosion, biological erosion and roof damage. These factors work together to cause surface cracking and peeling (depth up to 3–5 cm). (4) This study indicates that the microbial communities in rammed-earth building walls show significant differences in various orientations. Microorganisms play a dual role in the preservation and deterioration of rammed-earth buildings: they can slow down weathering by forming protective biofilms or accelerating erosion through acid production. Full article

Understanding how trust in artificial intelligence evolves is crucial for predicting human behavior in AI-enabled environments. While existing research focuses on initial acceptance factors, the temporal dynamics of AI trust remain poorly understood. This study develops a temporal trust dynamics framework proposing three phases: formation through accuracy cues, single-error shock, and post-error repair through explanations. Two experiments in financial advisory contexts tested this framework. Study 1 (N = 189) compared human versus algorithmic advisors, while Study 2 (N = 294) traced trust trajectories across three rounds, manipulating accuracy and post-error explanations. Results demonstrate three temporal patterns. First, participants initially favored algorithmic advisors, supporting “algorithmic appreciation.” Second, single advisory errors resulted in substantial trust decline (η² = 0.141), demonstrating acute sensitivity to performance failures. Third, post-error explanations significantly facilitated trust recovery, with evidence of enhancement beyond baseline. Financial literacy moderated these patterns, with higher-expertise users showing sharper decline after errors and stronger recovery following explanations. These findings reveal that AI trust follows predictable temporal patterns distinct from interpersonal trust, exhibiting heightened error sensitivity yet remaining amenable to repair through well-designed explanatory interventions. They offer theoretical integration of appreciation and aversion phenomena and practical guidance for designing inclusive AI systems. Full article

(1) Background: Marine eutrophication represents a formidable challenge to sustainable global aquaculture, posing a severe threat to marine ecosystems and impeding the achievement of UN Sustainable Development Goal 14. Current methodologies for identifying eutrophication events and tracing their drivers from vast, heterogeneous text data rely on manual analysis and thus have significant limitations. (2) Methods: To address this issue, we developed a novel automated attribution analysis framework. We first pre-trained a domain-specific model (Aquaculture-BERT) on a 210-million-word corpus, which is the foundation for constructing a comprehensive Aquaculture Eutrophication Knowledge Graph (AEKG) with 3.2 million entities and 8.5 million relations. (3) Results: Aquaculture-BERT achieved an F1-score of 92.1% in key information extraction, significantly outperforming generic models. The framework successfully analyzed complex cases, such as Xiamen harmful algal bloom, generating association reports congruent with established scientific conclusions and elucidating latent pollution pathways (e.g., pond aquaculture–nitrogen input–Phaeocystis bloom). (4) Conclusions: This study delivers an AI-driven framework that enables the intelligent and efficient analysis of aquaculture-induced eutrophication, propelling a paradigm shift toward the deep integration of data-driven discovery with hypothesis-driven inquiry. The framework provides a robust tool for quantifying the environmental impacts of aquaculture and identifying pollution sources, contributing to sustainable management and achieving SDG 14 targets. Full article

Microbial forensics involves analyzing biological evidence to evaluate weaponized microorganisms or their toxins. This study aimed to detect and type Yersinia pestis from four simulated forensic samples—human plasma diluted in phosphate-buffered saline (#24-2), tomato juice (#24-5), grape juice (#24-8), and a surgical mask (#24-10). Notably, samples #24-10 may have contained live bacteria other than Y. pestis. A real-time polymerase chain reaction confirmed the presence of Y. pestis in all samples; however, whole-genome sequencing (WGS) coverage of the Y. pestis chromosome ranged from 0.46% to 97.1%, largely due to host DNA interference and low abundance. To address these limitations and enable strain-level identification, we designed a hybridization-based target enrichment approach focused on multilocus variable number tandem repeat analysis (MLVA). Next-generation sequencing (NGS) using whole-genome amplification revealed that the accuracy of the 25 MLVA profiles of Y. pestis for samples #24-2, #24-5, #24-8, and #24-10 was 4%, 100%, 52%, and 0%, respectively. However, all samples showed 100% accuracy with target-enriched NGS, confirming they all belong to the same strain. These findings demonstrate that a targeted enrichment strategy for MLVA loci can overcome common obstacles in microbial forensics, particularly when working with trace or degraded samples where conventional WGS proves challenging. Full article

Industrial wastewater can serve as a low-cost nutritional source for sustainable microalgal biomass production. This study investigated the biomass of Chlorella vulgaris var. vulgaris TISTR 8261 grown in untreated wastewater collected from four food industry factories in Phra Nakhon Sri Ayutthaya Province, Thailand. Among them, wastewater from a processed food production plant (PFPP) supported the highest algal growth. Supplementation with 17.4 mM sodium acetate significantly improved algal biomass yield. Further optimization with 3.7 mM NH₄Cl, 1.0 mM KH₂PO₄, 0.2 mM MgSO₄, and a moderate concentration of trace minerals enhanced the specific growth rate and chlorophyll concentration. Scaled-up cultivation in 3.5 L culture bottles in optimized PFPP yielded a maximum biomass yield of 8.436 ± 0.378 g L⁻¹, comparable to 6.498 ± 0.436 g L⁻¹ in standard TAP medium. Biomass composition analysis after 15 days of cultivation revealed 42.70 ± 1.40% protein, 17.10 ± 1.60% carbohydrate, and 1.90 ± 0.10% lipid on a dry weight basis. These findings demonstrate that optimized PFPP wastewater can effectively support high-density cultivation of C. vulgaris var. vulgaris TISTR 8261, yielding nutritionally rich biomass, and offering a cost-effective and environmentally sustainable strategy for industrial-scale microalgal production. Full article

Content delivery networks (CDNs) face steadily rising, uneven demand, straining heuristic cache replacement. Reinforcement learning (RL) is promising, but most work assumes a fully observable Markov Decision Process (MDP), unrealistic under delayed, partial, and noisy signals. We model cache replacement as a Partially Observable MDP (POMDP) and present the Miss-Triggered Cache Transformer (MTCT), a Transformer-decoder Q-learning agent that encodes recent histories with self-attention. MTCT invokes its policy only on cache misses to align compute with informative events and uses a delayed-hit reward to propagate information from hits. A compact, rank-based action set (12 actions by default) captures popularity–recency trade-offs with complexity independent of cache capacity. We evaluate MTCT on a real trace (MovieLens) and two synthetic workloads (Mandelbrot–Zipf, Pareto) against Adaptive Replacement Cache (ARC), Windowed TinyLFU (W-TinyLFU), classical heuristics, and Double Deep Q-Network (DDQN). MTCT achieves the best or statistically comparable cache-hit rates on most cache sizes; e.g., on MovieLens at $M=600$, it reaches $0.4703$ (DDQN $0.4436$, ARC $0.4513$). Miss-triggered inference also lowers mean wall-clock time per episode; Transformer inference is well suited to modern hardware acceleration. Ablations support $CL=50$ and show that finer action grids improve stability and final accuracy. Full article

Vehicular ad hoc networks (VANETs) demand authentication mechanisms that are both secure and privacy-preserving, particularly in light of emerging quantum-era threats. In this work, we propose a lattice-based identity authentication protocol that leverages pseudo-IDs to safeguard user privacy, while allowing the Trusted Authority (TA) to trace misbehaving vehicles when necessary. Compared with existing approaches, the proposed scheme strengthens accountability, improves scalability, and offers resistance against quantum attacks. A comprehensive complexity analysis is presented, addressing computational, communication, and storage overhead. Analysis results under practical parameter settings demonstrate that the protocol delivers robust security with manageable overhead, maintaining authentication latency within the real-time requirements of VANET applications. Full article

The endometrium is a highly dynamic tissue central to female reproductive function, undergoing nearly 500 cycles of proliferation, differentiation, shedding, and regeneration throughout a woman’s reproductive life. This remarkable regenerative capacity is driven by a reservoir of endometrial stem/progenitor cells (ESCs), which are crucial for maintaining tissue homeostasis. Dysregulation of these cells is linked to a variety of clinical disorders, including menstrual abnormalities, infertility, recurrent pregnancy loss, and serious gynecological conditions such as endometriosis and endometrial cancer. Recent advancements in organoid technology and lineage-tracing models have provided insights into the complex cellular hierarchy that underlies endometrial regeneration and differentiation. This review highlights the latest breakthroughs in endometrial stem cell biology, focusing particularly on 3D in vitro platforms that replicate endometrial physiology and disease states. By integrating these cutting-edge approaches, we aim to offer new perspectives on the pathogenesis of endometrial disorders and establish a comprehensive framework for developing precision regenerative therapies. Full article

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

Detailed description of phase composition and geochemistry of the Muschelkalk carbonate rocks of the Upper Silesian Province in Poland were presented in this article. The tests were carried out to determine mineralogical features and geochemical properties. The samples were collected from the formations of the Lower Muschelkalk (Gogolin Unit), Middle Muschelkalk (Diplopore Dolomite Unit) and Upper Muschelkalk (Tarnowice Unit, Boruszowice Unit). The following research methods were used: macroscopic description, X-Ray Diffraction, Fourier transform infrared spectroscopy, X-Ray Fluorescence and Atomic spectrometry with plasma intensification. The following carbonate phases were identified: a low-Mg calcite, a high-Mg calcite, a proto-dolomite, an ordered dolomite and a huntite. The results of XRD analysis allowed the determination of the chemical formulas of the mineral phases. XRF and ICP AES analyses allowed to establish the content of following trace elements: Sr, Ba, Al, Si, Fe, Mn, K, Na, S, Cl, Ti, Cr, Ni, Zn, Rb, Zr, Pb, As, V, Be, B, Co, Cu, Br, Mo and Cd. Apart from Sr and Ba, they are not fundamental components of carbonate rocks. They indicate the presence of minerals such as silicates, aluminosilicates, oxides and sulfides. Full article

Cranberry (Vaccinium macrocarpon Ait.) is an herbaceous, evergreen, dwarf shrub of the genus Vaccinium in the family Ericaceae, often used as a functional food. Cranberries are primarily distributed in the northern United States—including Massachusetts, Wisconsin, and Maine—as well as in Quebec, Canada; the state of Columbia; Chile in South America; and northeastern Europe. They are also found in China’s Greater Khingan Range and Fuyuan City, Jiamusi, Heilongjiang Province. The plants thrive in cool environments and exhibit considerable adaptability to soil conditions, preferring acidic soils. Cranberries are rich in a variety of biologically active components, such as polyphenols (proanthocyanidins, chlorogenic acid, flavonols, anthocyanins, caffeic acid, etc.), triterpenoids, and other nutrients. Studies have shown that the chemical components extracted from cranberry fruit have pharmacological effects such as antioxidant, anti-inflammatory, anti-cancer, and urinary tract infection prevention and treatment, and are commonly used clinically in the treatment of cardiovascular diseases, the prevention of urinary tract infections, blood pressure lowering, and the fight against Helicobacter pylori, among other clinical diseases. Cranberries also play a huge role in daily nutrition, and they are named for their richness in a variety of mineral elements, trace elements and vitamins. This work uses information from Pubmed, Web of Science, Scopus, CNKI (China National Knowledge Infrastructure), and related papers. In this paper, a comprehensive review of the phytochemical composition, pharmacological mechanism of action, clinical application value and nutritional significance of cranberry was conducted in recent years to provide references for the further extraction of chemical components in cranberry and rational clinical application, which can help to guide people to rationalize their diets and promote the formation of healthy diets. Full article

Organic fertilizer granulation represents a promising strategy for modifying nitrogen (N) release from compost in soil. Nevertheless, there is a lack of large-scale field trials exploring its impact on tobacco production and soil N supply. This research conducted a preliminary study by employing ¹⁵N tracing technology to investigate the effects of granular compost on soil N transformation and supply; on the yield and quality of tobacco leaves; and on the distribution of granular compost-derived N among the different soil N pools and tobacco plant organs. The results revealed that the 2 cm diameter granule organic fertilizer treatment (G2) significantly increased tobacco leaf yield by 15% compared to conventional fertilization (CK). However, the 4 cm diameter granule organic fertilizer (G4) treatment resulted in a reduction in leaf yield. Notably, the quality of tobacco leaves remained unaffected compared to conventional fertilization treatment; the N content ranged from 15 to 25 g kg⁻¹, which was within the high-quality range. The results also indicated that direct N supply to the tobacco from granular compost was limited. The G2 and G4 treatments provided 2.8% and 2.2% of the N in the fertilizer to the tobacco plants, respectively, with more than 93% of the N in the tobacco plants derived from the soil. Therefore, both of these particle sizes of granular compost facilitated the absorption of soil N by tobacco plants. At the end of the growth period, the N content derived from the G4 granular fertilizer in the soil was significantly higher than that from the G2 fertilizer. This may be due to the slower nutrient release mechanism and longer release period of the G4 fertilizer compared to G2. Our results suggested that granulated compost fertilizer (both G2 and G4) has the potential to enhance soil N supply. Despite the elevated nitrogen levels observed in leaves treated with 4 cm diameter granular fertilizer, an integrated assessment of yield performance demonstrates that the 2 cm diameter granular organic fertilizer delivers superior economic benefits. However, G2 may also have a higher potential for N loss. Further investigations under field conditions are necessary to validate the applicability of granular fertilizer of different particle sizes and its specific mechanisms of impact. Full article

The Mako Belt in the Kédougou-Kéniéba Inlier (eastern Senegal) preserves Paleoproterozoic (2.3–1.9 Ga) mafic and ultramafic rocks that record early crustal growth processes within the southern West African Craton (WAC). Basalt bulk rock compositions preserve primary melt signatures, whereas the associated ultramafic cumulates are variably serpentinized and are better assessed through mineral chemistry. Basalts occur as massive and pillow lavas, with MgO contents of 5.9–9.1 wt.% and flat to slightly LREE-depleted patterns (La/Smₙ = 0.73–0.88). Primitive mantle-normalized diagrams show subduction-related signatures, including enrichment in Ba, Pb, and Rb and depletion in Nb and Ta. Most basalts and all ultramafic rocks display (Nb/La)_PM > 1, consistent with enriched mantle melting in a back-arc setting. Harzburgites and lherzolites have cumulate textures, high Cr and Ni contents, and spinel with chromian cores (Cr# > 0.6) zoned sharply to Cr-rich magnetite rims that overlap basalt spinel compositions. Integration of the petrographic, mineralogical, and whole-rock geochemical data indicates the presence of mafic melts derived from a subduction-modified mantle wedge and likely formed in a back-arc basin above a subducting slab, rather than from a plume or mid-ocean ridge setting. Regional comparisons with other greenstone belts across the WAC suggest that the Mako Belt was part of a broader arc–back-arc system accreted during the Eburnean orogeny (~2.20–2.00 Ga). This study supports the view that modern-style plate tectonics—including subduction and back-arc magmatism—was already active by the Paleoproterozoic, and highlights the Mako Belt as a key archive of early lithospheric evolution in the WAC. Full article

Objectives: This study aimed to evaluate the reproducibility of artificial intelligence (AI) in identifying cephalometric landmarks, comparing its performance with manual tracing by an experienced orthodontist. Methods: A high-quality lateral cephalogram of a 26-year-old female patient, meeting strict inclusion criteria, was selected. Eighteen cephalometric landmarks were identified using the WebCeph software (version 1500) in three experimental settings: AI tracing without image modification (AInocut), AI tracing with image modification (AI-cut), and manual tracing by an orthodontic expert. Each evaluator repeated the procedure 10 times on the same image. X and Y coordinates were recorded, and reproducibility was assessed using the coefficient of variation (CV) and centroid distance analysis. Statistical comparisons were performed using one-way ANOVA and Bonferroni post hoc tests, with significance set at p < 0.05. Results: AInocut achieved the highest reproducibility, showing the lowest mean CV values. Both AI methods demonstrated greater consistency than manual tracing, particularly for landmarks such as Menton (Me) and Pogonion (Pog). Gonion (Go) showed the highest variability across all groups. Significant differences were found for the Posterior Nasal Spine (PNS) point (p = 0.001), where AI outperformed manual tracing. Variability was generally higher along the X-axis than the Y-axis. Conclusions: AI demonstrated superior reproducibility in cephalometric landmark identification compared to manual tracing by an experienced operator. While certain points showed high consistency, others—particularly PNS and Go—remained challenging. These findings support AI as a reliable adjunct in digital cephalometry, although the use of a single radiograph limits generalizability. Broader, multi-image studies are needed to confirm clinical applicability. Full article

Mineral water content strongly depends on the geologic layer characteristics. Therefore, the aim of the present study is to make a comparison between two renowned mineral water sources in Romania, Borsec and Tusnad. Two public springs were selected from each location: Boldizsar (about 6600 L/day) and Lazar (about 500 L/day) from Borsec and Mikes (about 5000 L/day) and Young’s spring (about 600 L/day) from Tusnad. All investigated springs are naturally carbonated. Water properties were measured in situ and in laboratory for the collected samples; the results found that Borsec mineral water has a pH of about 7.5, while Tusnad mineral water is slightly acid (pH = 6.5). TDS strongly depends on the spring’s flow (for instance, Boldizsar has a TDS of about 900 mg/L, while Lazar has a TDS of about 1529 mg/L due to its high mineralization, while Young’s spring has a TDS of 165 mg/L due to its low mineralization, although it has low flow). Borsec mineral water has a lower salinity of about 1.22 PSU, while Tusnad water has a salinity of about 2 PSU, caused by a high amount of Na and Fe ions. Mineral waters dissolve ions from the geological layers, which react with carbonic acid during drying, generating specific crystallized compounds. The crystallized matter was investigated using XRD coupled with mineralogical optical microscopy (MOM); their microstructural features were observed using SEM coupled with elemental spectroscopy. Borsec water generates mainly Ca, Mg, and Na minerals like calcite, aragonite, pseudo-dolomite, natron, and traces of halite. Tusnad mineral waters have significant amounts of Ca, but also have Fe and much more Cl, since calcite and aragonite are mixed up with large amounts of halite and iron compounds. It looks like the presence of iron ions in the Tusnad mineral water collected from Mikes and Young’s spring explains the acidic pH. All these aspects are useful for further investigation regarding specific therapeutic purposes like chronic colitis and biliary lithiasis symptom amelioration (Boldizsar), chronic colitis, and enterocolitis symptoms (Lazar). Tusnad waters, like the water from Mikes spring, are recommended for anemia and neurasthenia, while Young’s spring is recommended for renal lithiasis amelioration. Full article