38139 research outputs found
Sort by
A nitrogen-oxygen triazine flame retardant for simultaneously improving flame retardancy and mechanical performance of nylon 6
No full textThe rapid advancement of modern industries has placed higher demands on the comprehensive performance of nylon 6 (PA6) and addressing its flammability issue has also received significant attention. Therefore, developing flame-retardant PA6 with superior overall performance has become a key research objective. In this work, a novel and highly efficient triazine-based flame retardant, phthalimidoxy-1,3,5-triazine (TPT), was successfully synthesized, and it was found to have a radical quenching mechanism analogous to that of hindered amine light stabilizers (HALS). Incorporating only 1.5 wt% TPT significantly improved the limiting oxygen index (LOI) of PA6/1.5TPT to 28% and increased both tensile strength and flexural strength to 80.49 and 93.25 MPa, respectively. Compared to pure PA6, the time to ignition (TTI) of PA6/1.5TPT was extended by 46.7%, and the total smoke production (TSP) was reduced by 42%. The hygrothermal aging results demonstrated that the PA6 composites maintained outstanding flame-retardant performance and mechanical integrity even after aging. Moreover, density functional theory (DFT) calculations and gas-phase mechanism analysis indicated that TPT generated stable radicals during thermal decomposition, which effectively captured hydrogen (H·) and carbon (C·) radicals produced in the initial degradation stage of PA6, thereby suppressing the combustion. This work presents a promising strategy for creating high-efficiency, multifunctional flame retardants for PA6, thus broadening its application potential
Utilizing soil characteristics and hybrid machine learning for interpretable potato yield prediction: A study with satin-bowerbird optimization and deep neural network
No full textContext
Yield forecasting is crucial to the agricultural planning enterprise, such as input control, farm logistics and reduction of economic risks. The soils in the Maritime provinces of Canada have a great difference in their properties which affect the productivity of crops. Such variability requires a strong prediction model that could address the different characteristics of soil.
Objective
This research proposal is expected to establish a stable potato yield prediction model based on the soil property data of New Brunswick and Prince Edward Island and determine whether the application of optimization techniques with deep learning can enhance the prediction accuracy over the conventional machine learning approach.
Methods
Soil samples were taken at eight experimental sites in the 2017 and 2018 growing seasons, with 18 soil properties being captured. The feature selection techniques were used to create three input scenarios (Comb1, Comb2, Comb3). To optimize the selection of input variables, a hybrid prediction model, DNN-SBO (Deep Neural Network -Satin Bowerbird Optimization), was suggested and refined with the Boruta feature selection and Best Subset Regression-WASPAS. The performance of the model was tested in comparison with Kernel Ridge Regression (KRR), Elastic Net, K-Nearest Neighbors (KNN) and Support Vector Regression (SVR), on the evaluation metrics of Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The model interpretability was done using SHAP (Shapley Additive exPlanation) analysis.
Results and Conclusions
Comb2 was the best input scenario that consisted of Total Base Saturation, Sulfur, Magnesium, Potash, Aluminum, Zinc, Phosphate, Manganese, Organic Matter, Iron, and Copper. DNN-SBO model had the best predictive power with R= 0.903 (train) and RMSE= 4.165 t/ha and MAPE= 6.766 % and R= 0.853(test) and RMSE= 5.522 t/ha and MAPE= 9.707 %. The SHAP analysis has shown that Iron was the most significant predictor (mean SHAP = +5.49), next was Copper, Zinc, Phosphorus, and Organic Matter.
Significance
The paper sheds light on the promise of deep learning that is based on bio-inspired optimization and feature selection techniques in order to achieve a significant increase in crop yield prediction. The findings can lead to the wider use of the similar methods in precision agriculture, which will result in smarter and data-driven farming in variably soiled areas
Effects of Soaking, Roasting, and Germination on Saponin Reduction and Nutritional Enhancement in Quinoa (Chenopodium quinoa)
No full textThis study evaluates the impact of soaking, roasting, and germination—alone and in combination—on saponin reduction and
nutritional enhancement in quinoa (Chenopodium quinoa Willd), integrating both biochemical analysis and engineering metrics for industrial scalability. Commercial quinoa seeds were subjected to controlled soaking (12–24 h, 3:1–5:1 v/w, 22°C, gentle agitation), roasting (180°C±2°C, 3.5 min, rotary drum roaster), germination (72 h, 25°C, 90% RH), and their equivalent combinations. Proximate composition (AOAC methods), saponin content (gravimetric assay), and total phenolic content (Folin–Ciocalteu assay) were determined, with all measurements conducted in triplicate. Statistical analysis employed one-way ANOVA to assess significant differences among treatments (p < 0 05). Results demonstrated germination and combined germination–roasting as the most effective treatments, achieving a saponin reduction of 53% (0 56 ± 0 02 g/100 g vs. raw 1 20 ± 0 05 g/100 g, F = 238 88, p = 0 0001), the highest protein content (17 40% ± 0 03% vs. raw 14 73% ± 0 61%, p = 0 012), and total phenolic content (2 01 ± 0 18 mg GAE/g, 65% increase, F = 30 18, p = 0 00001). Roasting alone most effectively reduced moisture (3 57% ± 0 16%), supporting shelf stability, but caused notable thermal degradation of phenolics (−48%). Engineering assessments revealed roasting as the most energy-intensive step (1.2 kWh/kg), whereas soaking presented a throughput bottleneck (12–24 h, 8–16 kg/h). The findings suggest a scalable, integrated processing strategy combining biological and
thermal treatments, optimizing nutritional value, antinutritional factor removal, and industrial feasibility for broader quinoa utilization
Remote sensing-based 2D hydrodynamic modeling and assessment of the 2014 flood in the Jhelum river basin
No full textFlood modeling and hazard mapping are essential for effective disaster management. This paper models the 2014 flood along a 72 km stretch of the Jhelum River between Mangla Dam and Rasul Barrage in Pakistan using a 2D Hydrologic Engineering Center River Analysis System (HEC–RAS) framework integrated with remote sensing (RS) and Geographic Information System (GIS) techniques. Three digital elevation models (DEMs), namely SRTM, ALOS, and ASTER, along with MODIS and Landsat imagery, were used for terrain modeling and flood extent validation. The simulated flood depths and velocities were compared with observed flow data at the Rasul Barrage gauge station, while flood extents were validated with satellite-derived inundation maps. The proposed model displayed superior performance when comparing the maximum flood extents of SRTM and ALOS, with 72% and 71% fitness. It matched the area with a percentage of 84% and 83% and overestimation with a rate of 16% and 17%, respectively. The performance of ASTER, on the other hand, was below par, with a measure of fit at 50%, a matching area of 80%, and overestimation at a rate of 42%. Overall, the average flood extents of SRTM and ALOS showed superior performance to that of ASTER. The results highlight the suitability of SRTM and ALOS DEMs for reliable flood modeling and hazard assessment in data-scarce regions. Relevant disaster management authorities can use this approach for effective disaster management
An enhanced predictive energy management of a green hydrogen integrated microgrid based on correlation analysis considering uncertain conditions
No full textThe increasing adoption of renewable energy sources presents unique challenges due to their unpredictable nature and the need for efficient energy storage management. Maintaining grid stability requires robust energy storage solutions to manage the variability as the power industry shifts towards renewable generation. This paper focuses on developing a reliable model based on experimental predictive correlation for a green hybrid grid-integrated microgrid that utilizes hydrogen storage to mitigate energy fluctuation. The goal is to maintain frequency and voltage stability across various operating scenarios using hydrogen as the primary energy storage system. A comprehensive framework is presented for modelling a hybrid energy system that combines solar, hydrogen storage, fuel cells, and lithium battery storage. The initial step in this study involves experimental investigation to formulate a predictive correlation index (PCI) for hydrogen-based energy systems, concentrating on the relationship between hydrogen flowrate, pressure, temperature, and the resulting electrical outputs, voltage, and current. This formulation is then used to develop an enhanced energy management coordination strategy based on the correlation index, leveraging the capabilities of model predictive control to anticipate fluctuations in energy demand and supply. A cloud-based Internet of Things (IoT) platform is utilized to monitor system performance in real-time under various conditions, manage storage efficiently, and enhance security by minimizing vulnerabilities. Numerical findings confirm that the hybrid predictive scheme reduces frequency deviation within ±0.16 % and constrains voltage variation to approximately ±4 %. Here, the experimental PCI values identify an optimal hydrogen operating range of 0.3–0.4 bar for reliable performance. Performance benchmarking further demonstrates that, compared with conventional droop-based methods reported in earlier studies, the proposed correlated hybrid control strategy achieves improved transient response and lower steady-state error, ensuring more reliable coordination of hydrogen and battery storage. The results prove that the proposed predictive coordination strategy effectively mitigates voltage and frequency fluctuations during transient situations by optimally controlling the hydrogen storage within the microgrid. This outcome underscores the positive impact of the proposed predictive coordination strategies in enhancing continuous power supply and improving the overall efficiency of grid-integrated systems
Hip offset parameters and functional outcomes following total hip arthroplasty: association with performance, strength, and patient-reported outcomes
No full textBackground
Restoring native hip offset is considered important for optimizing function following total hip arthroplasty (THA), yet the relationships between offset parameters and postoperative outcomes remain inconsistently reported. This study investigated the associations between femoral offset (FO), acetabular offset (AO), and global offset (GO) with functional mobility, hip abductor strength, and postoperative pain.
Methods
A total of 69 patients (mean age: 69.6 years) with unilateral THA were assessed at an average follow-up of 3.3 years. Offset parameters were measured radiographically and classified as decreased, restored, or increased relative to the contralateral hip. Functional outcomes were assessed using the Timed Up and Go (TUG) test and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Hip abductor strength was measured via manual dynamometry, and pain was evaluated using a visual analog scale (VAS).
Results
No significant associations were found between offset parameters and TUG or WOMAC scores. However, patients in the decreased GO group exhibited significantly reduced hip abductor strength in the operated limb, with this asymmetry persisting over time. Additionally, both FO and AO in the non-operated hip were significantly associated with VAS pain scores, and their combined effect appeared to amplify pain perception. These relationships also changed over time during the follow-up period.
Conclusions
While offset restoration did not relate to global functional tests such as TUG or WOMAC, patients with decreased global offset exhibited persistent abductor weakness, and contralateral offset parameters were associated with pain perception. These findings highlight the complexity of the relationship between offset and functional recovery and emphasize the importance of accurate offset restoration and bilateral biomechanical assessment in optimizing long-term outcomes following THA
The Peruvian Miracle: Published Research on Transcendental Meditation, Health and Education
No full textThis book, ‘The Peruvian Miracle’, rigorously and systematically documents how the group practice of Transcendental Meditation and the TM–Sidhi program has
contributed to an unprecedented social, educational, and economic transformation in Peru. The book describes regarding the efforts made to disseminate the technique before, during, and after the pandemic. The entities we manage—the educational institutions and the Regional Education Office—signed agreements to promote this practice throughout the region, achieving the results detailed in the book.
This book brings us closer to an innovative experience whose results are evident, offering us hope for a more balanced, healthy, and uplifted society, focused on achieving coherence and the long-awaited peace with social justice. It is an important contribution to the development of human nature and opens doors to new research on this topic
Development, Cultural Adaptation, and Content Validation of Urdu Pain Neuroscience Education Materials for Low Back Pain in Pakistan
No full textBackground: Pain neuroscience education (PNE) can support understanding of low back pain and facilitate engagement with active care. Most PNE materials have been developed in English, and there is little culturally adapted content for Urdu-speaking populations. Locally relevant educational resources may help improve clarity, acceptability, and communication in clinical settings. Objective: To develop, culturally adapt, and content-validate Urdu PNE materials for individuals with LBP and for use by healthcare professionals in Pakistan. Methods: A four-stage adaptation process was used. Phase 1 involved drafting a ten-module English PNE booklet and clinician guide based on contemporary pain-science literature. Phase 2 included forward–backward translation into Urdu and cultural adaptation by translators and a bilingual pain researcher. In Phase 3, three focus-group sessions with clinicians and a person with LBP informed iterative revisions. In Phase 4, a multidisciplinary panel (clinicians and individuals with LBP, n = 32) assessed seven domains of the final Urdu materials for clarity, relevance, and cultural appropriateness using Lawshe’s content validity ratio (CVR). Results: Focus-group feedback led to simplification of Urdu phrasing, refinement of metaphors, and adjustments to illustrations. All seven domains exceeded the minimum CVR threshold (0.30) for n = 32, with a mean overall CVR of 0.69 ± 0.12. Cultural appropriateness (CVR = 0.88) and content accuracy (CVR = 0.86) showed the highest agreement. Conclusions: The adapted Urdu PNE materials were judged to be clear, relevant, and culturally appropriate by clinicians and individuals with LBP. These materials may be useful for supporting pain-related education in clinical and community settings. These findings establish preliminary content validity; further studies are needed to evaluate feasibility, implementation, and clinical outcomes
Online Teacher Education and Interactive Technologies
No full textThis Element focuses on the role of interactive technologies in enhancing pre-service teachers' engagement with learning in online environments. It begins with a brief overview of the current state of teacher education, focusing on online teaching. This is followed by analysing the concept of engagement, underscoring its importance for pre-service teachers studying online. The Element explores various dimensions of engagement – cognitive, behavioural, affective, and other – and how interactive technologies can enhance these dimensions in online learning. A key feature of this Element is its exploration of key challenges that teacher educators and pre-service teachers encounter when using interactive technologies with practical recommendations for addressing them. The concluding section shifts the focus to the future, offering recommendations for how teacher education can use interactive technologies to 'grow' teacher educators who can engage their students. Throughout the Element, practical examples complement theoretical discussions to bridge the gap between theory and practice
Jurisprudence and Theology: The Australian School
No full textThomas Aquinas’s distinction between four kinds of law—eternal, natural, human and divine—has become a staple of Western jurisprudence. This chapter explicates the distinction between these categories of law, beginning with and building upon Aquinas’s analysis. In so doing, it emphasises the essential role of theological doctrines in fully grasping the relationships between the categories. Aquinas’s exposition of the four kinds of law assumes a distinction between divine and human perspectives on law, as well as a specific conception of the sources and limits of the human capacity for reliable legal knowledge, and God’s role in making this knowledge possible. These theological dimensions of Aquinas’s understanding of law have often been obscured in more recent jurisprudential discussions, but they are essential for grasping some of the subtle and enduring insights in his taxonomy