Search Results (7,433)

The presence of sea ice threatens low ice-class vessels’ navigation safety in the Arctic, and traditional Navigation Risk Assessment Models based on sea ice parameters have been widely used to guide safe passages for ships operating in ice regions. However, these models mainly rely on empirical coefficients, and the accuracy of these models in identifying sea ice navigation risk remains insufficiently validated. Therefore, under the binary classification framework, this study used Automatic Identification System (AIS) data along the Northeast Passage (NEP) as positive samples, manual interpretation non-navigable data as negative samples, a total of 10 machine learning (ML) models were employed to capture the complex relationships between ice conditions and navigation risk for Polar Class (PC) 6 and Open Water (OW) vessels. The results showed that compared to traditional Navigation Risk Assessment Models, most of the 10 ML models exhibited significantly improved classification accuracy, which was especially pronounced when classifying samples of PC6 vessel. This study also revealed that the navigability of the East Siberian Sea (ESS) and the Vilkitsky Strait along the NEP is relatively poor, particularly during the month when sea ice melts and reforms, requiring special attention. The navigation risk output by ML models is strongly determined by sea ice thickness. These findings offer valuable insights for enhancing the safety and efficiency of Arctic maritime transport. Full article

Background/Objectives: Loneliness is the sensation of feeling alone or emotionally isolated, even when one is surrounded by other people. It is associated with conditions such as depression, anxiety, harmful habits, and cardiovascular problems. The main objective of the present study was to develop and determine the psychometric properties of the Multidimensional Loneliness Scale (MLS), which is a self-report instrument. Methods: The present study is instrumental in nature, as it aims to analyze the psychometric properties of a new assessment instrument. A total of 484 adults, both men and women, aged between 18 and 55 years, participated in this research. Results: Exploratory factor analysis revealed the presence of four dimensions: social disconnection, family estrangement, loss of attachment figure, and intrapersonal emptiness. Confirmatory factor analysis demonstrated that the four-dimensional model exhibited a good fit (CFI = 0.91; RMSEA = 0.07; SRMR = 0.07; AIC = 737.87). The concurrent validity was evidenced by significant correlations with the De Jong Gierveld scale and the Revised UCLA Loneliness Scale. The reliability analysis demonstrated satisfactory internal consistency, with omega coefficients ranging from 0.84 to 0.92 and alpha coefficients from 0.84 to 0.93. Conclusions: The MLS is a self-report instrument designed to assess loneliness, and it has satisfactory psychometric properties. Full article

The rice-wheat rotation is the main agricultural cropping system in Jiangsu Province, playing a vital role in ensuring food security and promoting economic development. However, current research on rice-wheat systems mainly focuses on in-situ controlled experiments at the point scale, with limited studies addressing carbon footprint (CF) and energy balance (EB) at the regional scale and long time series. Therefore, we analyzed the evolution patterns of the CF and EB of the rice-wheat system in Jiangsu Province from 1980 to 2022, as well as their influencing factors. The results showed that the sown area and total yield of rice and wheat exhibited an increasing–decreasing–increasing trend during 1980–2022, while the yield per unit area increased continuously. The CF of rice and wheat increased by 4172.27 kg CO₂ eq ha⁻¹ and 2729.18 kg CO₂ eq ha⁻¹, respectively, with the greenhouse gas emissions intensity (GHGI) showing a fluctuating upward trend. Furthermore, CH₄ emission, nitrogen (N) fertilizer, and irrigation were the main factors affecting the CF of rice, with proportions of 36%, 20.26%, and 17.34%, respectively. For wheat, N fertilizer, agricultural diesel, compound fertilizer, and total N₂O emission were the primary contributors, accounting for 42.39%, 22.54%, 13.65%, and 13.14%, respectively. Among energy balances, the net energy (NE) of rice exhibited an increasing and then fluctuating trend, while that of wheat remained relatively stable. The energy utilization efficiency (EUE), energy productivity (EPD), and energy profitability (EPF) of rice showed an increasing and then decreasing trend, while wheat decreased by 46.31%, 46.31%, and 60.62% during 43 years, respectively. Additionally, N fertilizer, agricultural diesel, and compound fertilizer accounted for 43.91–45.37%, 21.63–25.81%, and 12.46–20.37% of energy input for rice and wheat, respectively. Moreover, emission factors and energy coefficients may vary over time, which is an important consideration in the analysis of long-term time series. This study analyzes the ecological and environmental effects of the rice-wheat system in Jiangsu Province, which helps to promote the development of agriculture in a green, low-carbon, and high-efficiency direction. It also offers a theoretical basis for constructing a low-carbon sustainable agricultural production system. Full article

Efficient and sustainable extraction of bioactive benzopyrans from Hypericum polyanthemum Klotzsch ex Reichardt (Hypericaceae) remains underexplored, despite their potential applications. The current study aimed to optimize this process by integrating computational simulation and experimental extraction with suitable solvents. The COSMO-RS model was employed to screen deep eutectic solvents (DESs), indicating lactic acid/glycine/water 3:1:3 (DES 1) as a highly promising candidate based on activity coefficients at infinite dilution for target benzopyrans (HP1, HP2, HP3). Ultrasound-assisted extraction (UAE) was then conducted using the proposed DES as well as hexane, and the extracts were analyzed via high-performance liquid chromatography (HPLC) and spectrophotometry for total phenolic content (TPC). The results for DES 1 showed yields for benzopyrans HP1 (1.43 ± 0.09 mg/g plant) and HP2 (0.55 ± 0.04 mg/g plant) close to those obtained in the hexane extract (1.65 and 0.78 mg/g plant, respectively), corroborating the use of COSMO-RS for solvent screening. Kinetic analysis using an adapted Crank diffusion model successfully described the mass transfer process for DES 1 (R² > 0.98, mean average percent error < 9%), indicating diffusion control and allowing estimation of effective diffusion coefficients. This work confirms COSMO-RS as a valuable tool for solvent selection and demonstrates that UAE with the identified DES provides an efficient, greener approach for extracting valuable benzopyrans, offering a foundation for further process optimization. Full article

In agricultural practice, in addition to determining the nitrogen (N_f) dose, it is necessary to effectively control its effect on currently grown crops. Meeting these conditions requires not only the use of phosphorus (P) and potassium (K), but also nutrients such as magnesium (Mg) and sulfur (S). This hypothesis was verified in a single-factor field experiment with winter wheat (WW) carried out in the 2015/2016, 2016/2017, and 2017/2018 growing seasons. The experiment consisted of seven variants: absolute control (AC), NP, NPK-MOP (K as Muriate of Potash), NPK-MOP+Ki (Kieserite), NPK-KK (K as Korn–Kali), NPK-KK+Ki, and NPK-KK+Ki+ES (Epsom Salt). The use of K as MOP increased grain yield (GY) by 6.3% compared to NP. In the NPK-KK variant, GY was 13% (+0.84 t ha⁻¹) higher compared to NP. Moreover, GYs in this fertilization variant (FV) were stable over the years (coefficient of variation, CV = 9.4%). In NPK-KK+Ki+ES, the yield increase was the highest and mounted to 17.2% compared to NP, but the variability over the years was also the highest (CV ≈ 20%). The amount of N in grain N (GN) increased progressively from 4% for NPK-MOP to 15% for NPK-KK and 25% for NPK-KK+Ki+ES in comparison to NP. The nitrogen harvest index was highly stable, achieving 72.6 ± 3.1%. All analyzed NUE indices showed a significant response to FVs. The PFP-N_f (partial factor productivity of N_f) indices increased on NPK-MOP by 5.8%, NPK-KK by 12.9%, and NPK-KK+Ki+ES by 17.9% compared to NP. The corresponding N_f recovery of N_f in wheat grain was 47.2%, 55.9%, and 64.4%, but its total recovery by wheat (grain + straw) was 67%, 74.5%, and 87.2%, respectively. In terms of the theoretical and practical value of the tested indexes, two indices, namely, NUP (nitrogen unit productivity) and NUA (nitrogen unit accumulation), proved to be the most useful. From the farmer’s production strategy, FV with K applied in the form of Korn–Kali proved to be the most stable option due to high and stable yield, regardless of weather conditions. The increase in the number of nutritional factors optimizing the action of nitrogen in winter wheat caused the phenomenon known as the “scissors effect”. This phenomenon manifested itself in a progressive increase in nitrogen unit productivity (NUP) combined with a regressive trend in unit nitrogen accumulation (NUA) in the grain versus the balance of soil available Mg (Mg_b). The studies clearly showed that obtaining grain that met the milling requirements was recorded only for NUA above 22 kg N t⁻¹ grain. This was possible only with the most intensive Mg treatment (NPK-KK+Ki and NPK-KK+Ki+ES). The study clearly showed that three of the six FVs fully met the three basic conditions for sustainable crop production: (i) stabilization and even an increase in grain yield; (ii) a decrease in the mass of inorganic N in the soil at harvest, potentially susceptible to leaching; and (iii) stabilization of the soil fertility of P, K, and Mg. Full article

Background/Objectives: In non-muscle-invasive bladder cancer (NMIBC), the decision for immediate postoperative single-dose intravesical chemotherapy (SI) is based on clinical and presumed pathological features, as a definitive pathology is unknown at the time of surgery. This study aims to assess how accurately urologists can predict the pathological features of bladder tumors based solely on cystoscopic appearance and evaluate their ability to identify patients eligible for SI. Methods: A total of 104 patients with bladder masses were included. Seven senior urologists and four residents participated. Before transurethral resection, both groups predicted tumor stage, grade, and the presence of carcinoma in situ (CIS). Resident predictions were collected for all 104 patients, while senior predictions were collected for 72 patients. Based on these predictions, patient eligibility for SI was determined according to the EAU NMIBC guidelines. After final pathology reports, risk scores were recalculated and compared with the surgeons’ predictions. Cohen’s Kappa (κ) coefficient was used to assess agreement between predictions and pathology. Positive and negative predictive values were also calculated for both groups. Results: Strong agreement with final pathology could not be demonstrated for stage, grade, or CIS for either group. Urology residents’ predictions were slightly more accurate than those of senior urologists. Overall, 19.4% (14/72) (based on senior urologists’ predictions) and 18.2% (19/104) (based on resident predictions) of patients were misclassified and either overtreated or undertreated. Conclusions: Cystoscopic visual prediction alone is insufficient for determining eligibility for immediate postoperative intravesical chemotherapy, regardless of the urologist’s experience. More objective criteria are needed to improve the selection of appropriate patients for SI. Full article

This paper proposes an incentive scheme based on a public–private partnership (PPP) to encourage low-carbon travel behavior by inducing the mode choice shift from private cars to public transit. The scheme involves three key entities: travelers, the government, and the private sector. Travelers can choose between private cars and public transit, producing different emissions. As the leader, the government aims to reduce total emission to a certain level with limited budgets. The private sector, as an intermediary, invests subsidies in low-carbon rewards to attract green travelers and benefits from a larger user pool. A two-layer multi-objective optimization model is proposed, which includes travel time, monetary cost, and emission. The objective of the upper level is to maximize the utilities of the private sector and minimize social costs to the government. The lower layer is the user equilibrium of the travelers. The numerical results obtained through heuristic algorithms demonstrate that the proposed scheme can achieve a triple-win situation, where all stakeholders benefit. Moreover, sensitivity analysis finds that prioritizing pollution control strategies will be beneficial to the government only if the unit pollution control cost coefficient is below a low threshold. Contrary to intuition, larger government subsidies do not necessarily lead to better promotion of low-carbon travel. Full article

Accurate calibration of mesoscopic contact model parameters is essential for ensuring the reliability of Particle Flow Code in Three Dimensions (PFC3D) simulations in geotechnical engineering. Trial-and-error approaches are often used to determine the parameters of the contact model, but they are time-consuming, labor-intensive, and offer no guarantee of parameter validity or simulation credibility. Although conventional machine learning techniques have been applied to invert the contact model parameters, they are hampered by the difficulty of selecting the optimal hyperparameters and, in some cases, insufficient data, which limits both the predictive accuracy and robustness. In this study, a total of 361 PFC3D uniaxial compression simulations using a linear parallel bond model with varied mesoscopic parameters were generated to capture a wide range of rock and geotechnical material behaviors. From each stress–strain curve, eight characteristic points were extracted as inputs to a multi-objective Automated Machine Learning (AutoML) model designed to invert three key mesoscopic parameters, i.e., the elastic modulus (E), stiffness ratio (k_s/k_n), and degraded elastic modulus (E_d). The developed AutoML model, comprising two hidden layers of 256 and 32 neurons with ReLU activation function, achieved coefficients of determination (R²) of 0.992, 0.710, and 0.521 for E, k_s/k_n, and E_d, respectively, demonstrating acceptable predictive accuracy and generalizability. The multi-objective AutoML model was also applied to invert the parameters from three independent uniaxial compression tests on rock-like materials to validate its practical performance. The close match between the experimental and numerically simulated stress–strain curves confirmed the model’s reliability for mesoscopic parameter inversion in PFC3D. Full article

Chromium slag sites pose severe environmental risks due to hexavalent chromium (Cr(VI)) contamination, characterized by high mobility and toxicity. This study focused on chromium-contaminated soil from a historical chromium slag site in North China, where long-term accumulation of chromate production residues has led to serious Cr(VI) pollution, with Cr(VI) accounting for 13–22% of total chromium and far exceeding national soil risk control standards. To elucidate Cr(VI) transformation mechanisms and elemental linkages, a combined approach of macro-scale condition experiments and micro-scale analysis was employed. Results showed that acidic conditions (pH < 7) significantly enhanced Cr(VI) reduction efficiency by promoting the conversion of CrO₄²⁻ to HCrO₄⁻/Cr₂O₇²⁻. Among reducing agents, FeSO₄ exhibited the strongest effect (reduction efficiency >30%), followed by citric acid and fulvic acid. Temperature variations (−20 °C to 30 °C) had minimal impact on Cr(VI) transformation in the 45-day experiment, while soil moisture (20–25%) indirectly facilitated Cr(VI) reduction by enhancing the reduction of agent diffusion and microbial activity, though its effect was weaker than chemical interventions. Soil grain-size composition influenced Cr(VI) distribution unevenly: larger particles (>0.2 mm) in BC-35 and BC-36-4 acted as main Cr(VI) reservoirs due to accumulated Fe-Mn oxides, whereas BC-36-3 showed increased Cr(VI) in smaller particles (<0.074 mm). μ-XRF and correlation analysis revealed strong positive correlations between Cr and Ca, Fe, Mn, Ni (Pearson coefficient > 0.7, p < 0.01), attributed to adsorption–reduction coupling on iron-manganese oxide surfaces. In contrast, Cr showed weak correlations with Mg, Al, Si, and K. This study clarifies the complex factors governing Cr(VI) behavior in chromium slag soils, providing a scientific basis for remediation strategies such as pH adjustment (4–6) combined with FeSO₄ addition to enhance Cr(VI) reduction efficiency. Full article

This study aims to address the challenges posed by uneven energy amplitude and a low signal-to-noise ratio (SNR) in the total focus imaging of coarse-crystalline elastic anisotropic materials. A novel method for acoustic field correction vector-coherent total focus imaging, based on the materials’ properties, is proposed. To demonstrate the effectiveness of this method, a test specimen, an austenitic stainless steel nozzle weld, was employed. Seven side-drilled hole defects located at varying positions and depths, each with a diameter of 2 mm, were examined. An ultrasound simulation model was developed based on material backscatter diffraction results, and the scattering attenuation compensation factor was optimized. The acoustic field correction function was derived by combining acoustic field directivity with diffusion attenuation compensation. The phase coherence weighting coefficients were calculated, followed by image reconstruction. The results show that the proposed method significantly improves imaging amplitude uniformity and reduces the structural noise caused by the coarse crystal structure of austenitic stainless steel. Compared to conventional total focus imaging, the detection SNR of the seven defects increased by 2.34 dB to 10.95 dB. Additionally, the defect localization error was reduced from 0.1 mm to 0.05 mm, with a range of 0.70 mm to 0.88 mm. Full article

Increasing soil carbon storage is an important strategy for achieving sustainable development. Enhancing soil carbon sequestration capacity can effectively reduce the concentration of atmospheric carbon dioxide, which not only contributes to the carbon neutrality goal but also helps maintain ecosystem stability. Based on 146 soil samples collected at plot locations selected across Beijing, we examined relationships between soil organic carbon (SOC) and key characteristics of urban forests, including their spatial structure and species complexity. The results showed that SOC in the topsoil with a depth of 20 cm was highest over forested plots (6.384 g/kg–20.349 g/kg) and lowest in soils without any vegetation cover (5.586 g/kg–6.783 g/kg). The plots with herbaceous/shrub vegetation but no tree cover had SOC values in between (5.586 g/kg–15.162 g/kg). The plot data revealed that SOC was better correlated with the physical structure than the species diversity of Beijing’s urban trees. The correlation coefficients (r) between SOC and five physical structure indicators, including average diameter at breast height (DBH), average tree height, basal area density, and the diversity of DBH and tree height, ranged from 0.32 to 0.52, whereas the r values for four species diversity indicators ranged from 0.10 to 0.25, two of which were not statistically different from 0. Stepwise linear regression analyses revealed that the species diversity indicators were not very sensitive to SOC variations among a large portion of the plots and were about half as effective as the physical structure indicators for explaining the total variance of SOC. These results suggest that urban planning and greenspace management policies could be tailored to maximize the carbon co-benefits of urban land. Specifically, trees should be planted in urban areas wherever possible, preferably as densely as what can be allowed given other urban planning considerations. Protection of large, old trees should be encouraged, as these trees will continue to sequester and store large quantities of carbon in above- and belowground biomass as well as in soil. Such policies will enhance the contribution of urban land, especially urban forests and other greenspaces, to nature-based solutions (NBS) to climate change. Full article

Background: Postoperative pulmonary embolism (PPE) is a critical complication that can significantly affect patient outcomes. This study aimed to assess knowledge, attitudes, and practices (KAP) of thoracic healthcare professionals toward PPE. Methods: A cross-sectional study was conducted from September to December 2022. Results: A total of 222 thoracic healthcare professionals participated in the study; the majority were aged 30–40 years (40.54%) and had over 10 years of work experience (47.75%). Participants completed a self-designed questionnaire assessing demographic data and KAP scores: knowledge (0–11), attitudes (11–55), and practices (9–45). The main measures included the mean scores for knowledge, attitudes, and practices, along with correlation analyses and path analysis to assess relationships among the KAP components. Mean scores were 9.03 ± 1.13 for knowledge, 50.09 ± 4.23 for attitudes, and 35.78 ± 7.85 for practices. Participants showed strong awareness of PPE definitions and risk factors, but only 24.77% correctly identified its classic clinical triad. Attitudinally, while most expressed a willingness to engage in PPE training and risk assessment, 55.41% remained cautious about anticoagulation due to bleeding risks. In practice, although 72.52% consistently supported postoperative mobilization, only 30.63% frequently acquired updated PPE knowledge. Significant positive correlations were found between knowledge and attitudes (r = 0.218, p < 0.001) and between attitudes and practices (r = 0.234, p < 0.001). Path analysis showed that knowledge positively influenced attitudes (path coefficient 0.748, p = 0.002), and attitudes positively influenced practices (path coefficient 0.374, p = 0.003). Conclusions: Thoracic healthcare professionals exhibited adequate knowledge, positive attitudes, and proactive practices regarding PPE, indicating a strong foundation for enhancing postoperative care. Full article

The continuous variability in the microbiological quality of surface waters presents significant challenges for ensuring the production of safe drinking water in compliance with public health regulations. Inadequate treatment of surface waters can lead to the presence of pathogenic microorganisms in the drinking water supply, posing serious risks to public health. This research presents an in-depth data analysis using machine learning tools for the induction of models to describe and predict microbiological water quality for the sustainable management of the Butoniga drinking water treatment facility in Istria (Croatia). Specifically, descriptive and predictive models for total coliforms and E. coli bacteria (i.e., classes), which are recognized as key sanitary indicators of microbiological contamination under both EU and Croatian water quality legislation, were developed. The descriptive models provided useful information about the main environmental factors that influence the microbiological water quality. The most significant influential factors were found to be pH, water temperature, and water turbidity. On the other hand, the predictive models were developed to estimate the concentrations of total coliforms and E. coli bacteria seven days in advance using several machine learning methods, including model trees, random forests, multi-layer perceptron, bagging, and XGBoost. Among these, model trees were selected for their interpretability and potential integration into decision support systems. The predictive models demonstrated satisfactory performance, with a correlation coefficient of 0.72 for total coliforms, and moderate predictive accuracy for E. coli bacteria, with a correlation coefficient of 0.48. The resulting models offer actionable insights for optimizing operational responses in water treatment processes based on real-time and predicted microbiological conditions in the Butoniga reservoir. Moreover, this research contributes to the development of predictive frameworks for microbiological water quality management and highlights the importance of further research and monitoring of this key aspect of the preservation of the environment and public health. Full article

The integration of financial technology expenditures and green total factor productivity (GTFP) constitutes a critical impetus for sustainable economic advancement. This study employs provincial panel data from China (2012–2020) and uses the SBM model with undesirable outputs, the PVAR model, moderation effect analysis, and threshold regression to investigate the underlying mechanisms and threshold effects of financial technology expenditure on GTFP. The results show that (1) financial technology expenditure has a significant promoting effect on the growth of GTFP, with a coefficient of 0.614 (p < 0.05), indicating the need for further increases in fiscal investment in science and technology; (2) the effect of financial technology expenditure on GTFP varies across the eastern, central, and western regions of China, with stronger effects observed in the eastern region, suggesting that the government should formulate differentiated financial technology expenditure policies on the basis of local conditions; and (3) that educational investment and industrial upgrading play strong moderating roles in the impact of financial technology expenditure on GTFP, with interaction term coefficients of 0.059 (p < 0.05) and 0.206 (p < 0.1), respectively. Threshold analysis further reveals that the positive effect strengthens significantly once educational investment surpasses a log value of 9.3674 and industrial upgrading exceeds a ratio of 0.0814. However, currently, China’s education investment and industrial structure upgrading are still insufficient, necessitating further increases in education investment and promoting the transformation and upgrading of the industrial structure. Full article

To accurately assess the water quality, ecosystem status, distribution of large benthic organisms, and ecological restoration under human intervention, an analysis of benthic organisms on Caofeidian in September and November 2023 and January and May of the following year was conducted in this work. By performing CCA (canonical correspondence analysis) and cluster and correlation coefficient (Pearson) analyses, the temporal variation characteristics of benthic abundance, dominant species, community structure and biodiversity were analyzed. A total of 79 species of macro-benthic animals were found in four months, including 32 species of polychaetes, cnidarians, 1 species of Nemertean, 19 species of crustaceans, and 24 species of molluscs. The use of conventional grab-type mud collectors revealed that the Musculus senhousei dominated the survey (Y > 0.02). While only a small number of Ruditapes philippinarum were collected from bottom-dwelling species, a certain number of bottom-dwelling species (Ruditapes philippinarum and Scapharca subcrenata) were also collected during the trawl survey. Additionally, a significant population of Rapana venosa was found in the area. It is speculated that the dual effects of predation and competition are likely the primary reasons for the relatively low abundance of bottom-dwelling species. The density and biomass of macro-benthos were consistent over time, which was the highest in May, the second highest in January, and the lowest in September and November. The main environmental factors affecting the large benthic communities in the surveyed sea areas were pH, DO, NO₂⁻-N, T, SAL and PO₄³⁻-P. Combined with historical data, it was found that although the environmental condition in the Caofeidian sea area has improved, the Musculus senhousei has been dominant. In addition, the abundance of other species is much less than that of the Musculus senhousei, and the diversity of the benthic community is still reduced. Our work provides valuable data support for the management and improvement of bottom Marine pasture and promotes the transformation of Marine resources from resource plunder to a sustainable resource. Full article