Search Results (20,365)

During the installation of prefabricated vertical drains (PVDs) in soft soil foundations, the smear effect induced by mandrel shoe penetration can severely damage the soil structure and reduce permeability, thereby becoming a key factor restricting foundation consolidation efficiency. Previous studies have generally neglected the smear disturbance caused by the geometry of the mandrel shoe. Although existing studies have conducted numerical and theoretical analyses on the smear effect induced by PVD installation, most of them are still based on equivalent circular simplifications and are therefore unable to characterize the anisotropic disturbance induced by a triangular mandrel shoe. To address this limitation, a three-dimensional CEL penetration model considering the real triangular geometry was established, and the traditional cavity expansion theory was directionally modified. The effects of penetration rate, geometric angular structure, and soil type of the triangular mandrel shoe on the smear zone were systematically investigated. The results show that, with increasing penetration rate, the near-field peak stress and far-field displacement increase simultaneously; from slow penetration to fast penetration, the near-field peak stress increases by approximately 42%. By quantitatively defining the critical threshold corresponding to a sharp 50% attenuation in radial displacement as the boundary of the strong smear zone, it was found that increasing the size of the mandrel shoe significantly amplifies the geometric corner effect, and the near-field disturbance range increases by about 21% compared with that of the small-sized case. The larger the size, the more pronounced the anisotropic disturbance characteristics become: the stress concentration effect and displacement splitting in the vertex direction are further enhanced, causing the disturbance range in that direction to far exceed that in the side direction. Soil properties are the key medium parameters controlling the smear zone. Owing to its relatively high stiffness index and skeleton strength, Clayey Silt shows the largest displacement range, whereas Common Clay exhibits the smallest smear zone because of its stronger structural constraint. The modified theoretical model agrees well with the CEL numerical simulation results, verifying its effectiveness under conditions that consider the geometric characteristics of the mandrel shoe. This study provides a theoretical basis and numerical support for the structural design of mandrel shoes in soft-ground PVD construction. Full article

As part of a NASA University Leadership Initiative (ULI) program, this work supports the continued development and evaluation of a fatigue-based process window for stress-relieved Ti-6Al-4V specimens produced via laser powder bed fusion (PBF-LB/M). Four-point bend and axial fatigue specimens were fabricated by NASA ULI collaborators across a range of scan velocities (800–2000 mm/s) at a constant power of 370 W using an EOS M290 system. All fatigue specimens were low-stress-ground by a commercial vendor and tested at Case Western Reserve University (CWRU) under load-controlled cyclic loading at a stress ratio of R = 0.1. This paper presents a curated dataset linking PBF-LB/M process parameters to fatigue outcomes across 175 specimens. Of these, 136 fractured and this study includes fatigue crack initiation site identification and defect morphology metrics derived from post mortem SEM analysis. Specimens that reached runout (10⁷ cycles) and did not fracture under subsequent fatigue testing are retained in the dataset, with fractographic fields marked as ‘NA’ to indicate non-applicability. The dataset includes specimen metadata, processing parameters, fatigue life data, fatigue initiation site classification (e.g., keyhole, gas-entrapped pore (GeP), lack-of-fusion (LoF), contamination), defect size and shape descriptors, and spatial location relative to the free surface. These data are intended to support defect-based fatigue life prediction, probabilistic modeling, process–structure–property studies, and machine learning frameworks linking process parameters to fatigue performance in PBF-LB/M Ti-6Al-4V. Full article

The aim of this work was to explore and test the concept of a novel direct nucleation control method with external dissolution of fine crystals (E-DNC). It was postulated that the heating cycles of the internal DNC method could be transferred from the crystallizer jacket to an electrically heated recirculation tube, thereby using less energy and requiring a shorter time compared to internal DNC. The conceptual model was explored and developed by reviewing previous research on the topic and addressing known drawbacks. Engineering experience and a heuristic approach led to the conclusion that five commonly used controllers would suffice for a straightforward implementation of the method. A simplified simulation was developed to compare the time and energy requirements for the internal and external DNC methods, and it was concluded that external DNC uses 37% less energy and 19% less time compared to internal DNC. The laboratory system was then constructed by modifying the internal DNC apparatus with inexpensive and commonly used components. A linear cooling experiment was performed to establish the baseline for comparison with E-DNC experiments and to set the expected count range. The E-DNC experiments were then conducted with the aim of obtaining larger crystal sizes, and it was shown that the process could be designed in only three experiments. Adjusting the heating rate and count limit led to a significant increase in median crystal size (22.5%) compared to linear cooling. Full article

International tourism plays an important role in the global service economy, contributing to trade, employment, and regional development. For this reason, identifying the factors that influence tourist flows is an important issue for tourism policy, market strategy, and infrastructure planning. A large body of research has applied gravity models to analyze tourism flows between countries. While this approach provides a clear economic interpretation, it is usually based on linear specifications and may therefore capture only part of the relationships present in tourism data. This study examines the economic and geographic determinants of international tourism flows to Mongolia using a framework that combines a traditional gravity model with machine learning techniques. Mongolia serves as an instructive empirical setting, a landlocked, geographically peripheral destination whose inbound demand determinants have received limited systematic empirical attention. The analysis uses panel data for 27 origin countries covering the period from 2000 to 2024. In the first stage, a gravity model is estimated to assess how tourism flows relate to economic size and geographic distance. The results show that tourism flows tend to increase with the economic size of origin and destination countries, while greater geographical distance is associated with lower tourism flows. The estimated distance elasticity ranges from approximately −1.85 to −2.10 across model specifications, which is larger in absolute terms than the values typically reported in cross-country studies. This result is consistent with the relatively high travel cost barriers associated with Mongolia’s geographic location. These findings are consistent with the distance decay relationship commonly reported in the tourism literature. In the second stage, machine learning algorithms, including Random Forest, LightGBM, and XGBoost, are used as complementary interpretive instruments rather than forecasting tools to explore possible nonlinear relationships among the explanatory variables. To make the results more interpretable, the contribution of individual variables is examined using SHAP (Shapley Additive Explanations). The machine learning results indicate that some relationships in tourism demand may be nonlinear and not fully captured by the linear gravity specification. Specifically, distance sensitivity is approximately 6.5 times greater in nearby markets than in long-haul markets, with a structural inflexion at around 5700 km. Further analysis suggests that the influence of geographical distance is not uniform across all markets. In particular, tourism flows originating from middle-income countries appear to be more sensitive to increases in travel distance than those from higher-income countries. Overall, the findings indicate that economic size and geographical distance remain key determinants of international tourism flows to Mongolia. At the same time, the use of machine learning methods provides additional insight into potential nonlinear patterns in tourism demand. By combining econometric modelling with explainable machine learning techniques, the study offers an integrated analytical perspective for examining international tourism flows at geographically peripheral destinations where standard gravity assumptions may be insufficient. Full article

Sub-15 nm line structures are key building blocks for advanced device prototyping, nanoscale electrodes, and lithography templates such as etch/deposition masks. Although ultrahigh-voltage (≥100 kV) electron-beam lithography (EBL) can more readily achieve extremely small critical dimensions, its tool and infrastructure requirements limit widespread adoption in many laboratories. In contrast, 30 kV field-emission SEM platforms are far more accessible; however, resolution-limit patterning at 30 kV is more sensitive to beam current, exposure dose, and development conditions, motivating the establishment of a reproducible process flow and a well-defined process window. Here, we investigate the resolution limit of isolated lines using a Zeiss Gemini 460 system operated at 30 kV and an in-house pattern generator with 950 k PMMA C2 resist. To demonstrate device-level applicability, we develop a stable lift-off process, and all critical dimensions are evaluated on metal lines after e-beam evaporation and lift-off. By screening beam current and scanning dose to build the dose-to-size relationship, we show that reducing beam current significantly improves the achievable minimum line width. Under 35 pA, using CD ≤ 15 nm as the criterion for sub-15 nm window extraction, the usable dose range is [700, 804.3] µC/cm², corresponding to a dose latitude of ~14.9%. The best performance is obtained at 700 µC/cm², yielding a transferred metal line width of 13.85 nm after lift-off. This work provides a practical resolution-limit process flow and a quantitative process window for performing sub-15 nm patterning on accessible 30 kV platforms, supported by product-level lift-off validation. Full article

Precision agriculture has become a major direction of agricultural technological development in recent decades, addressing efficiency, environmental, and economic challenges simultaneously. Input optimization based on site-specific data collection—particularly variable-rate nutrient application, precision irrigation systems, and targeted crop protection—has been shown to generate measurable cost and resource savings. The aim of the study is to explore and systematically evaluate the economic impacts influencing precision technology in crop production. Although the technical and environmental benefits of precision technologies are widely documented, their economic performance and farm-level profitability remain inconsistently interpreted. The study is based on a systematic literature review of peer-reviewed English-language journal articles retrieved from the Web of Science, Scopus, ScienceDirect, and JSTOR databases. Study selection and evaluation were conducted in accordance with the PRISMA 2020 methodological framework. The literature indicates that precision technologies achieve average input savings of 8–20% and yield increases of 2–6%, while reported return on investment (ROI) values typically range between 5% and 15%. Economic viability is strongly dependent on farm size, with most studies identifying profitability above 100–200 ha. Additional benefits include improved management of soil heterogeneity, enhanced nutrient-use efficiency, and reduced excess input application, although adoption remains constrained by high investment costs and technological complexity. Full article

During the adaptive reuse of industrial heritage buildings, existing opening systems and envelope performance often pose major constraints. These restrictions make it difficult for the building to meet the requirements of the updated indoor environment, resulting in insufficient daylight and increased energy consumption. Therefore, optimizing lighting and energy performance has become the primary goal of the retrofit design. However, with limited interventions, the retrofit of heritage buildings to achieve significant overall performance improvement is still a challenge. From a sustainability perspective, improving daylight utilization and reducing energy demand are essential strategies for achieving low-carbon and resource-efficient building retrofit. This study proposes a grid-based parametric multi-objective optimization approach to optimize the window openings of the building envelope. The approach defines the position, size and material properties of the roof and facade openings as design variables. Implemented via the Honeybee and Octopus platforms, it integrates a genetic algorithm with EnergyPlus and Radiance simulations to co-optimize daylight performance, circadian frequency, and energy use intensity. Taking a single-story typical industrial heritage building in China’s cold climate zone as a case study, it is shown that coordinated multi-objective constraints significantly improve the overall performance across various evaluation metrics. The optimization results also provide interpretable window configuration strategies and recommended parameter ranges, which fully consider the climate adaptability of the surrounding environment. These findings offer useful guidance for sustainable retrofit design decision-making in similar single-story industrial heritage buildings. Full article

Background/Objectives: Postoperative recurrence is a critical issue in the treatment of resectable pancreatic ductal adenocarcinoma (rPDAC). Moreover, the prognosis after early recurrence is extremely poor. This study aimed to develop a recurrence prediction model and to define early recurrence after upfront surgery (UFS) for rPDAC. Methods: This multicenter retrospective study included patients who underwent UFS for anatomically rPDAC between January 2013 and December 2017. Multivariate analyses were conducted to identify the risk factors for recurrence-free survival and to construct a recurrence prediction model. Subsequently, a minimum p value approach was used to determine the optimal cutoff values for early and late recurrence. Results: The cohort included 603 patients (325 men and 278 women). During the median follow-up period of 25 months (interquartile range, 15–38 months), 381 patients (63.2%) experienced a recurrence. Multivariate analyses revealed carbohydrate antigen 19-9 ≥37 U/mL (hazard ratio [HR], 1.58; p < 0.001), tumor size ≥ 2.2 cm (HR, 1.59; p < 0.001), lymph node metastasis (HR, 1.86; p < 0.001), R1 resection (HR, 1.56; p = 0.002), and no adjuvant chemotherapy (HR, 1.54; p < 0.001) as independent predictors. The recurrence prediction model demonstrated an area under the curve of 0.72–0.75. The optimal threshold for early and late recurrences was a recurrence-free interval of five months. Carbohydrate antigen 19-9 ≥ 156 U/mL was a significant predictor of early recurrence (OR, 3.28; p < 0.001). Conclusions: This study identified the prognostic risk factors for recurrence and developed a recurrence prediction model for patients undergoing UFS for rPDAC. Moreover, a recurrence-free interval of five months was identified as the optimal threshold for distinguishing between early and late recurrences. Full article

Compared to antennas bearing a single port, MIMO antennas with several ports enable higher data throughput by exploiting spatial diversity. This capability is essential for next-generation implantable medical devices, where high channel capacity is a key requirement. A quad-element implantable MIMO antenna is designed and practically validated at 1420 MHz in this paper. It occupies a compact volume of $7\times 8\times 0.1 {\mathrm{mm}}^3$ ($5.6 {\mathrm{mm}}^3$). The compactness is realized by combining high-permittivity substrate (Rogers 3010 with relative permittivity of 10.2) with meandered radiator paths, which increase the effective current length while maintaining a small physical size. All antennas have very small mutual coupling with isolation of more than $31.78$ dB, which is mainly due to the spacing of 1 mm between the elements and the substrate, which is thin. The peak realized gain for each antenna element is $-27.3$ dBi. The simulation is performed within a capsule-like structure, which is embedded in the stomach tissue model. The experimental verification is carried out by embedding antenna within minced meat. The ECC, channel capacity, and link margin are also evaluated and found to be satisfactory. The proposed antenna ensures reliable communication performance, with the transmission range being as high as $2.5$ m, link margin being 15 dB, and the data rate being 120 Mb/s. The proposed antenna ensures a good level of ECC, which is less than $0.1$. The SAR is $52.3$ W/kg at 1420 MHz. This design is favorable for implants because of the small size, good impedance matching, high isolation, low correlation, good level of gain, and good link performance. Full article

This study systematically investigated the dynamic diversity, potential sources, and antifungal activities of small molecular peptides during the pile-fermentation process of post-fermented tea. By analyzing the damaging effects of small molecular peptide extracts from tea samples at different pile-fermentation stages on the spore cell membranes of Aspergillus carbonarius (A. carbonarius) and the inhibitory activity against β-1,3-glucan synthase (β-1,3-GS), it was confirmed that some small molecular peptides exhibit significant antifungal effects. The main findings are as follows: (1) The number of identified small molecular peptides showed a trend of first increasing and then decreasing with the progress of pile-fermentation, peaking at 4453 species on the 35th day of pile-fermentation, and were dominated by hexapeptides and heptapeptides with molecular weights ranging from 600 to 800 Da. (2) Based on orthogonal partial least squares discriminant analysis (OPLS-DA), the samples were divided into three characteristic stages according to the differences in small molecular peptide composition at different stages, and 156 characteristic peptides with a relative abundance higher than 0.1% were screened out. Their precursor proteins were derived from 148 proteins belonging to 16 genera, including Camellia, Aspergillus, Saccharomyces, Penicillium, and Bacillus. (3) BLAST alignment results showed that five out of the 156 characteristic peptides were degradation fragments of known antifungal peptides originating from Aspergillus and Bacillus. (4) Combining molecular docking screening and in vitro verification of synthetic peptides, a total of 27 small molecular peptides with antifungal activity were obtained, and their mechanism of action was the inhibition of β-1,3-GS activity. (5) The small molecular peptides related to antifungal activity could be classified into two categories: enzymatic hydrolysates of known antifungal peptides, and the enzymatic hydrolysates of tea-derived proteins or macromolecular peptides. Both categories were mainly distributed in the three stages of pile-fermentation, and there was a significant positive correlation among the population size of dominant microorganisms, microbial peptidase activity, and the abundance of small molecular peptides. This study reveals the dynamic generation pattern and antifungal potential of small molecular peptides during the pile-fermentation of post-fermented tea, providing a new scientific basis for evaluating the dynamic changes in microbial communities in tea and effectively controlling the contamination of harmful fungi during the pile-fermentation process. Full article

Metaheuristic optimization algorithms have attracted considerable research interest for solving complex optimization problems, yet many existing algorithms suffer from premature convergence and an inadequate balance between exploration and exploitation. The Coati Optimization Algorithm (COA) is a recently proposed nature-inspired metaheuristic that models the hunting and escape behaviors of coatis; however, it exhibits limited search diversity and tends to stagnate in local optima on high-dimensional, multimodal landscapes. This paper proposes an Improved Coati Optimization Algorithm (ICOA) that integrates four complementary enhancement strategies: (1) a Dynamic Adaptive Step-Size strategy that combines Lévy flights with Student’s t-distribution perturbations for heavy-tailed exploration; (2) a Population-Adaptive Dynamic Perturbation strategy that incorporates differential evolution operators with fitness-proportional scaling; (3) an Iterative-Cyclic Differential Perturbation strategy that employs sinusoidal scheduling and population-differential guidance; and (4) a Cosine-Adaptive Gaussian Perturbation strategy for refined exploitation with time-decaying intensity. ICOA is evaluated on 29 CEC2017, 10 CEC2020, and 12 CEC2022 benchmark functions across dimensions ranging from 10 to 100, compared against seven state-of-the-art algorithms in each benchmark suite. A statistical analysis using the Friedman test and the Wilcoxon rank-sum test confirms that ICOA achieves overall rank 1 on all three benchmark suites, with Friedman mean ranks of 1.207 (CEC2017, $D=100$), 1.000 (CEC2020, $D=10$), and 2.208 (CEC2022, $D=10$); the CEC2020 result should be interpreted in the context of its low dimensionality. A scalability analysis across four dimensionalities (10D, 30D, 50D, 100D) demonstrates consistent first-place rankings with mean ranks between 1.000 and 1.207. An ablation study and a sensitivity analysis of the strategy activation probability validate the contribution of each individual strategy and the optimality of the 50% activation setting. Furthermore, ICOA achieves the best results on all six constrained engineering design problems tested, with all improvements confirmed as statistically significant ($p<0.05$). Full article

The catalytic conversion of CO₂ into methane (CH₄) offers a sustainable solution to the worsening global warming scenario, especially for controlling CO₂ levels. This study reports silicalite-1 supported Ni catalysts with different loadings for CO₂ conversion to CH₄, prepared via wet impregnation. The X-ray diffraction pattern revealed an increase in crystallite size at higher Ni loadings, which was further supported by N₂ sorption, where the specific surface area and microporosity of the catalysts were decreased. There was a slight shift in the reducibility of the catalysts, potentially indicating the impact of loading on dispersion and spatial distribution. The catalyst performance was evaluated over a range of temperatures at 5 bar and a GHSV of 20,000 mL g_cat⁻¹ h⁻¹. Surprisingly, the Ni(5)@Silicalite-1 exhibited higher CO₂ conversion efficiency across the range of temperatures compared to Ni(10)@Silicalite-1. The NiO(5)@Silicalite-1 demonstrated a maximum CO₂ conversion of 88% at 450 °C, which was approximately 14% higher than that of the catalyst with a 10 wt.% loading. Notably, the CH₄ selectivity pattern was quite identical across the catalysts, underscoring that the reaction pathways were unaffected by the loadings. The higher performance of NiO(5)@Silicalite-1 could be ascribed to smaller NiO crystallites and improved textural properties. Full article

Objective: To compare the efficacy and safety of gas-based therapies for chronic wounds using a systematic review and network meta-analysis (NMA). Methods: Following PRISMA 2020, we systematically searched PubMed, Embase, Web of Science, Cochrane CENTRAL, and CBM from inception to 1 October 2025, screened studies in duplicate, and resolved disagreements by arbitration (κ = 0.87). Randomized controlled trials (RCTs) enrolling adults with chronic wounds were eligible; the primary endpoint was complete wound healing. Pairwise meta-analysis used risk ratios (RRs) with 95% CIs; heterogeneity was assessed with Q/I² and random-effects models were applied when appropriate. A frequentist NMA synthesized direct and indirect evidence, and treatments were ranked with SUCRA. Publication bias (Egger/Begg) and evidence certainty (GRADE) were evaluated. Results: Twenty-seven RCTs comprising 1673 participants were included. In pairwise pooling, gas therapies significantly increased complete healing versus standard care (random-effects RR = 2.17, 95% CI 1.61–2.94), with substantial heterogeneity (I² = 75.7%); results were directionally consistent and robust to sensitivity analyses. Prespecified subgroup analyses suggested effect modification by intervention type and wound etiology. In the NMA, most gas modalities showed beneficial trends versus standard care; however, SUCRA ranking placed standard care highest (93.9%), a finding attributed by the authors to network structure and between-study variability. Ozone therapy and topical oxygen ranked next, whereas HBOT and cold atmospheric plasma ranked mid-range; CO₂ therapy ranked lowest due to sparse evidence. Small-study effects were likely (Egger p < 0.001; Begg p = 0.013), and overall certainty was graded as moderate, limited primarily by heterogeneity, imprecision, and potential publication bias. Conclusions: Across RCTs, gas therapies as a class improve the probability of complete healing in chronic wounds relative to standard care, but effect sizes vary by modality and wound type. Given heterogeneity, possible publication bias, and inconsistencies within the evidence network, these findings should be applied with caution. HBOT remains the modality supported by the broadest evidence base, while large, high-quality, multicenter RCTs are needed to refine comparative effectiveness and safety rankings across gas therapies. Full article

Malaysia’s aspiration to attain high-income status necessitates not only economic growth but also a deeper understanding of poverty that goes beyond financial indicators. The Multidimensional Poverty Index (MPI) for Malaysia is designed to be comprehensive, considering a wide range of factors relevant to the diverse population of the country. Unlike traditional income-based approaches, our study goes beyond money to capture how poverty affects households across multiple dimensions. The MPI reveals important insights that standard measures often miss—showing which families struggle with education, health, housing, or digital access, not just income. Therefore, this study aims to enhance the Multidimensional Poverty Index for the Malaysian context by identifying and incorporating new dimensions and indicators to better capture the complexity of poverty in the country based on an empirical study in Kuala Lumpur, Malaysia. The MPI represents a significant advancement, offering a multidimensional framework for poverty measurement. Based on results in Kuala Lumpur, 38.7% of households were found to be multidimensionally poor. This means that nearly 4 out of every 10 households in this study experienced deprivations in multiple basic needs, not just income. Household size also significantly influences the risk of multidimensional poverty, with households of more than six members being over three times more likely to be poor, primarily due to higher dependency ratios and greater consumption burdens. Full article

Background: Musculoskeletal disorders (MSDs) are a major contributor to global disability. Exercise-based rehabilitation is widely recommended as first-line management; however, in clinical practice, it is frequently combined with adjunct therapeutic modalities, and the incremental effectiveness of these approaches remains unclear. The present review addressed the research question: Do adjunct modalities provide additional benefits beyond exercise-based rehabilitation alone in individuals with musculoskeletal disorders? Methods: This systematic review was conducted according to PRISMA 2020 guidelines and prospectively registered in the PROSPERO database (registration number CRD420261309183). Electronic searches were performed in PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Central Register of Controlled Trials to identify controlled clinical trials evaluating exercise-based rehabilitation delivered alone or combined with adjunct modalities. Outcomes included pain, functional disability, physical performance, strength, structural or imaging-based measures, biomechanical variables, injury risk, and work-related outcomes. Due to methodological heterogeneity across studies, a structured narrative and tabular synthesis were performed. Results: Twenty-one controlled clinical trials were included, encompassing tendinopathies (n = 7), knee osteoarthritis (n = 5), post-ACL reconstruction (n = 2), chronic spinal pain (n = 3), sarcopenia (n = 2), low bone mass (n = 2), and occupational musculoskeletal conditions (n = 1), with sample sizes ranging from 22 to 823 participants. Pain outcomes were reported in 18 studies (86%) and functional outcomes in 16 studies (76%). Exercise-based rehabilitation consistently produced clinically meaningful improvements across studies, whereas adjunct modalities demonstrated short-term advantages in a limited number of trials but rarely showed sustained long-term superiority. Conclusions: Evidence from controlled clinical trials indicates that exercise-based rehabilitation is an effective primary intervention for improving pain, functional capacity, and physical performance across diverse musculoskeletal conditions. Adjunct modalities may provide condition-specific or short-term benefits but do not consistently enhance long-term outcomes beyond structured exercise programs. Full article