Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle cano...Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.展开更多
Low back pain(LPB)is a common and impactful health concern globally,affecting individuals across various demographics and imposing a significant burden on the health care system.Nonspecific chronic LBP(NCLBP),charac-t...Low back pain(LPB)is a common and impactful health concern globally,affecting individuals across various demographics and imposing a significant burden on the health care system.Nonspecific chronic LBP(NCLBP),charac-terized as pain lasting over 12 weeks without an identifiable cause,leads to notable functional limitations and reduced quality of life.Traditional rehabil-itation programs,often focusing on dynamic exercises for lumbar strengthening,typically do not target the deep stabilizing muscles crucial for lumbar support and effective recovery.Multi-angular isometric lumbar exercise(MAILE)offers a low-impact method for strengthening lumbar stabilizers through multi-angular isometric contractions,reducing risks from dynamic movements.This article examines MAILE’s potential in addressing motor control dysfunctions in NCLBP,highlighting studies on lumbar muscle activation,core stability,and isometric exercises.The article explores the prevalence and socioeconomic impact of NCLBP in the Middle East,highlighting the need for affordable treatment options in areas like Qatar and Saudi Arabia.This article aims to validate the efficacy of MAILE in reducing pain,enhancing mobility,and improving lumbar stability,offering a valuable option for NCLBP management.Future research should focus on large-scale clinical trials to substantiate these findings and guide clinical practice.展开更多
分布式相参雷达在抗干扰和目标探测性能方面具有明显优势,但分布式构型带来的栅瓣问题给目标到达方向(direction of arrival,DOA)估计带来很大的困难。在均匀分布式相参阵列的基础上,拓展和差波束形成(sum and difference beamforming,S...分布式相参雷达在抗干扰和目标探测性能方面具有明显优势,但分布式构型带来的栅瓣问题给目标到达方向(direction of arrival,DOA)估计带来很大的困难。在均匀分布式相参阵列的基础上,拓展和差波束形成(sum and difference beamforming,SDB)方法至分布式阵列的栅瓣区域,并采用双指向法分析鉴角曲线(angular response curve,ARC)特性,提出一种基于多载频自适应SDB(multi-frequency adaptive SDB,MF-ASDB)的解模糊测角方法。该方法在不同频点下利用密集栅瓣辅助扫描检测,通过ASDB方法计算模糊主值后,将其拓展得到包含目标真实角度的DOA模糊值;根据频率与栅瓣周期之间的角度偏移关系,使用最小二乘方法实现DOA解模糊。仿真结果验证了所提方法的有效性。展开更多
基金supported by the National Natural Science Foundation of China (31971791)the National Key Research and Development Program of China (2017YFD0300204)。
文摘Powdery mildew is a disease that threatens wheat production and causes severe economic losses worldwide. Its timely diagnosis is imperative for preventing and controlling its spread. In this study, the multiangle canopy spectra and disease severity of wheat were investigated at several developmental stages and degrees of disease severity. Four wavelength variable-selected algorithms: successive projection(SPA), competitive adaptive reweighted sampling(CARS), feature selection learning(Relief-F), and genetic algorithm(GA), were used to identify bands sensitive to powdery mildew. The wavelength variables selected were used as input variables for partial least squares(PLS), extreme learning machine(ELM), random forest(RF), and support vector machine(SVM) algorithms, to construct a suitable prediction model for powdery mildew. Spectral reflectance and conventional vegetation indices(VIs) displayed angle effects under several disease severity indices(DIs). The CARS method selected relatively few wavelength variables and showed a relatively homogeneous distribution across the 13 viewing zenith angles.Overall accuracies of the four modeling algorithms were ranked as follows: ELM(0.70–0.82) > PLS(0.63–0.79) > SVM(0.49–0.69) > RF(0.43–0.69). Combinations of features and algorithms generated varied accuracies, with coefficients of determination(R^(2)) single-peaked at different observation angles. The constructed CARS-ELM model extracted a predictable bivariate relationship between the multi-angle canopy spectrum and disease severity, yielding an R^(2)> 0.8 at each measured angle. Especially for larger angles,monitoring accuracies were increased relative to the optimal VI model(40% at-60°, 33% at +60°), indicating that the CARS-ELM model is suitable for extreme angles of-60° and +60°. The results are proposed to provide a technical basis for rapid and large-scale monitoring of wheat powdery mildew.
文摘Low back pain(LPB)is a common and impactful health concern globally,affecting individuals across various demographics and imposing a significant burden on the health care system.Nonspecific chronic LBP(NCLBP),charac-terized as pain lasting over 12 weeks without an identifiable cause,leads to notable functional limitations and reduced quality of life.Traditional rehabil-itation programs,often focusing on dynamic exercises for lumbar strengthening,typically do not target the deep stabilizing muscles crucial for lumbar support and effective recovery.Multi-angular isometric lumbar exercise(MAILE)offers a low-impact method for strengthening lumbar stabilizers through multi-angular isometric contractions,reducing risks from dynamic movements.This article examines MAILE’s potential in addressing motor control dysfunctions in NCLBP,highlighting studies on lumbar muscle activation,core stability,and isometric exercises.The article explores the prevalence and socioeconomic impact of NCLBP in the Middle East,highlighting the need for affordable treatment options in areas like Qatar and Saudi Arabia.This article aims to validate the efficacy of MAILE in reducing pain,enhancing mobility,and improving lumbar stability,offering a valuable option for NCLBP management.Future research should focus on large-scale clinical trials to substantiate these findings and guide clinical practice.
文摘分布式相参雷达在抗干扰和目标探测性能方面具有明显优势,但分布式构型带来的栅瓣问题给目标到达方向(direction of arrival,DOA)估计带来很大的困难。在均匀分布式相参阵列的基础上,拓展和差波束形成(sum and difference beamforming,SDB)方法至分布式阵列的栅瓣区域,并采用双指向法分析鉴角曲线(angular response curve,ARC)特性,提出一种基于多载频自适应SDB(multi-frequency adaptive SDB,MF-ASDB)的解模糊测角方法。该方法在不同频点下利用密集栅瓣辅助扫描检测,通过ASDB方法计算模糊主值后,将其拓展得到包含目标真实角度的DOA模糊值;根据频率与栅瓣周期之间的角度偏移关系,使用最小二乘方法实现DOA解模糊。仿真结果验证了所提方法的有效性。
