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Energy evolution and damage characteristics of coal rock under graded cyclic loading and unloading with confining pressure
Characterization of core promoter activation by the Drosophila insulator-binding protein BEAF
Brazil must beef up its COP30 scheme to preserve tropical forests
Enhancing pumping unit diagnosis with similarity splicing data augmentation and wavelet denoising
The race for deep-sea minerals could cause geopolitical and ecological harm
Spatiotemporal climatic signals in cereal yield variability and trends in Ethiopia
Abstract Climatic variability and recurrent drought can strongly affect the variability of crop yield and are therefore frequently considered a risk to food security in Ethiopia. A better understanding of how crop yields vary in space and time, and their relationship to climatic and other driving factors, can assist in enhancing agricultural production and adapting to and mitigating the impacts of climate change. We applied a multiple linear regression model to examine the spatiotemporal climatic signal (air temperature, precipitation, and solar radiation) in the yields of the most important crops (maize, sorghum, tef, and wheat) over the period 1995–2018. An analysis of the climatic data indicated that growing season temperature increased significantly in most regions, but the trends in precipitation were not significant. The yields of maize, sorghum, tef, and wheat tended to increase across most crop-growing areas, particularly in the west, but was highly variable. The results highlight large spatial differences in the contribution of climatic trends to crop-yield variability across Ethiopian regions. The trends in climatic variability did not significantly affect crop yields in some areas, whereas in the main crop-growing areas, up to − 39.2% of yield variability could be attributed to the climatic trends. Specifically, the climatic trends negatively affected maize yields but positively affected sorghum, tef, and wheat yields. Nationally, the average impacts of climatic trends on crop yields was relatively small, ranging from a 3.2% decrease for maize to a 0.7% increase for wheat. In contrast, technological advancements contributed substantially more to yield gains, with annual increases ranging from 4.3% for wheat to 5.1% for sorghum. These findings highlight the dominant role of non-climatic drivers, particularly improved agricultural technology, in shaping crop yield trends. Our findings underscore the spatial heterogeneity of climate impacts on agriculture and highlight the critical importance of technological progress in enhancing crop productivity. They also provide actionable insights for designing crop- and location-specific adaptation strategies, and stress the need for integrated, climate-resilient development pathways in the region.
Are these the happiest PhD students in the world?
A multi stage deep learning model for accurate segmentation and classification of breast lesions in mammography
Research on fault diagnosis method of fiber optic sensing roller driven by dynamic model
Quantum discord and entanglement in radiative capture reactions
Why we still don’t understand the Universe — even after a century of dispute
Self efficacy as a key mediator for cervical cancer screening behavior
Three-dimensional nanofibrous PCL/gelatin scaffold fabricated using centrifugal force assisted wet electrospinning technique
Abstract A three-dimensional (3D) scaffold that enables optimal cell–matrix interactions is essential for developing physiologically relevant neural tissue models. In this study, wet electrospinning was optimized to fabricate nanofibrous PCL/gelatin scaffolds with a well-controlled 3D architecture, using centrifugal force to tune scaffold morphology, porosity, and mechanical properties. The effects of centrifugal force intensity (5000 vs. 10,000 rpm) and application time (5 vs. 10 min) were systematically investigated. Scaffolds fabricated at 5000 rpm exhibited poor structural integrity and were excluded from further analysis. Among scaffolds produced at 10,000 rpm, blends of PCL/gelatin at 70:30 and 60:40 demonstrated excellent porosity (98.1 ± 1.9% and 97.3 ± 1.1%, respectively) and favorable fiber architecture. The 70:30–10 min scaffold achieved the highest tensile strength (57.03 ± 1.50 kPa) and modulus (53.00 ± 2.00 kPa), aligning with the physiological range of neural tissues. MTT assays confirmed robust biocompatibility, with C6 glial cell viability increasing by + 4.30% on the 70:30–10 min scaffold and + 5.88% on the 60:40–10 min scaffold over 14 days. Although the 70:30–5 min scaffold showed the highest proliferation (+ 12.16%), the 10-min variant was selected for detailed morphological evaluation due to its superior mechanical performance and structural uniformity. DAPI and H&E staining further validated the enhanced cell aggregation, ECM deposition, and neural-like morphology within the 70:30–10 min scaffold. These results collectively highlight the critical role of scaffold composition and processing parameters in engineering 3D neural tissue scaffolds, with the optimized 70:30–10 min scaffold emerging as a promising candidate for advanced neural tissue engineering applications.
Why simply ending animal testing isn’t the answer in biomedical research
Feasibility and safety of sailing based rehabilitation for rare skeletal disorders using wearable sensors and patient reported outcomes
Molecular subtypes of human skeletal muscle in cancer cachexia
Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties
Abstract In this study, we develop a conditional diffusion model that proposes the optimal process parameters and predicts the microstructure for the desired mechanical properties. In materials development, it is costly to try many samples with different parameters in experiments and numerical simulations. The use of data-driven inverse design method can reduce the cost of materials development. This study develops an inverse analysis model that predicts process parameters and microstructures. This method can be used for any material, but in this study it is applied to polymeric material, which is the matrix resin of carbon fiber reinforced thermoplastics as an example. Matrix resins contain a mixture of dendrites, which are crystalline phases, and amorphous phases even after crystal growth is complete, and it is important to consider the microstructures consisting of the crystalline structure and the remaining amorphous phase to achieve the desired mechanical properties. Typically, the temperature during forming affects the microstructures, which in turn affect the macroscopic mechanical properties. The trained diffusion model can propose not only the processing temperature but also the microstructure when Young’s modulus and Poisson’s ratio are given. The capability of our conditional diffusion model to represent complex dendrites is also noteworthy. This model can be applied to other process parameters and mechanical properties. Furthermore, multiple process parameters and mechanical properties can be handled together.
Utilizing ALD to fabricate ZnO ETL to improve the luminous efficiency of perovskite light-emitting diodes
Effects of increasing cognitive demands through expanding movement options on biomechanics during changes of direction in female football players
Abstract Anterior cruciate ligament injuries often occur during changes-of-direction (CODs), particularly when combined with cognitively demanding decision-making tasks. This study investigated the effects of increasing movement options during CODs in response to a real opponent on whole-body biomechanics in female football players. Twenty-nine female football players (15 with high and 14 with low expertise) performed 90° CODs in response to a real opponents’ action under four conditions: anticipated with one option (ANT-1), unanticipated with two (UNANT-2), three (UNANT-3) or four (UNANT-4) movement options. Three-dimensional motion analysis captured whole-body biomechanics at initial contact and during weight acceptance. Continuous biomechanical data were analyzed using a statistical parametric mapping approach. No significant condition effects were observed for peak knee mechanics. However, at initial contact the pelvis was significantly less tilted and rotated towards the running direction in the UNANT-4 condition than in ANT-1. The hip was significantly more abducted and internally rotated in all unanticipated CODs. Furthermore, trunk rotation to the cutting leg was reduced in all unanticipated conditions compared to ANT-1. No significant differences were found between expertise groups. Increasing cognitive demands in a simulated match-play scenario primarily influenced proximal segment biomechanics during CODs in female football players. The authors therefore recommend integrating whole-body control and cognitively demanding stimuli into testing and injury prevention strategies.