自制教具亮出物理课的风采

初中物理探究实验可以有效提高学生的科学思维和自主探究精神,教师在实验教学中结合学生的认知和兴趣,突出教学的逻辑性、复杂性、灵活性和创造性,使学生在实验数据处理过程中,能准确把握和理解问题的本质,就会让学生逐步深化对物理知识的理解,有效提高学生的科学思维观察和实验探究能力.本文以教科版九年级《磁现象》一节为例,阐述自制教具的开发和设计思路和过程.

Landscape changes and their ecological eff ects of Miaodao Archipelago with human disturbances and under natural conditions in the past 30 years

The modern landscape patterns of islands usually show obvious spatial heterogeneity and complex ecological effects due to the vulnerability of ecosystems with natural characteristics under increasing human activities.In this work,we studied the variation in landscape pattern of the Miaodao Archipelago in Bohai Sea,North China,from 1990 to 2019,and an evaluation index system was established to explore the impacts of natural conditions and human disturbances on the ecological effects in the pressure-state-response(PSR)framework.Empirical analysis was conducted on the natural conditions,human disturbances,and ecological effects.The results show that forest was the main component of the landscape pattern in the archipelago.Both of the areas offorest and construction land were increasing,and the areas of cropland and grassland were declining.Other landscape types changed slightly,and the landscape fragmentation was increasing.The natural condition exhibited positive effects while human disturbance showed negative effects on the local ecology.Human disturbances come mainly from shoreline use while the natural conditions were mainly from the elevation change.The ecological effects were resulted mainly from the net primary productivity and water yield.

Accuracy of predicting metabolizable energy from in vitro digestible energy determined with a computer-controlled simulated digestion system in feed ingredients for ducks

Two experiments were conducted to study the accuracy of predicting true metabolizable energy(TME)of ingredients for ducks from in vitro digestible energy(IVDE)determined with a computer-controlled simulated digestion system.Experiment 1 was to establish TME prediction models from the IVDE of 9 energy feed ingredients and 12 protein feed ingredients using regression analysis.Experiment 2 was to validate the accuracy of the predicted ME of 10 ingredients randomly selected from Exp.1.Ten diets were formulated with 2 to 6 of 10 ingredients.Dietary in vivo TME values were compared with calculated values based on the TME predicted in Exp.1.In Exp.1,the correlation coefficients between TME and IVDE were 0.9339(P<0.05)in 9 energy feed ingredients and 0.8332(P<0.05)in 12 protein feed ingredients.No significant difference was observed on the slope and intercept of TME regression models between 9 energy feed ingredients and 12 protein feed ingredients.Therefore,the regression model of TME on IVDE for 21 feed ingredients was TME=0.7169×IVDE+1,224(R^(2)=0.7542,P<0.01).Determined and predicted TME differed by less than 100 kcal/kg of DM in 11 ingredients,and the difference ranged from 100 to 200 kcal/kg of DM in 5 ingredients.However,the difference between determined and predicted TME varied from 410 to 625 kcal/kg of DM in rice bran,rapeseed meal,corn gluten meal,and citric acid meal.In Exp.2,the determined and calculated TME were comparable(3,631 vs.3,639 kcal/kg of DM)and highly correlated(r=0.9014;P<0.05)in 10 diets.Determined and calculated TME differed by less than 100 kcal/kg of DM in 7 diets and by 106 to 133 kcal/kg of DM in 3 diets.These results have demonstrated that TME can be accurately predicted from IVDE in most feed ingredients,but it is less accurate for rice bran,rapeseed meal,corn gluten and citric acid meal.

Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted,Soft Epidermal Electronics

The facial expressions are a mirror of the elusive emotion hidden in the mind,and thus,capturing expressions is a crucial way of merging the inward world and virtual world.However,typical facial expression recognition(FER)systems are restricted by environments where faces must be clearly seen for computer vision,or rigid devices that are not suitable for the time-dynamic,curvilinear faces.Here,we present a robust,highly wearable FER system that is based on deep-learning-assisted,soft epidermal electronics.The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions,releasing the constraint of movement,space,and light.The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample.The proposed wearable FER system is superior for wide applicability and high accuracy.The FER system is suitable for the individual and shows essential robustness to different light,occlusion,and various face poses.It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place.This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment,enabling promising human-computer interaction applications.

Highly Robust and Wearable Facial Expression Recognition via Deep-Learning-Assisted, Soft Epidermal Electronics

The facial expressions are a mirror of the elusive emotion hidden in the mind,and thus,capturing expressions is a crucial way of merging the inward world and virtual world.However,typical facial expression recognition(FER)systems are restricted by environments where faces must be clearly seen for computer vision,or rigid devices that are not suitable for the time-dynamic,curvilinear faces.Here,we present a robust,highly wearable FER system that is based on deep-learning-assisted,soft epidermal electronics.The epidermal electronics that can fully conform on faces enable high-fidelity biosignal acquisition without hindering spontaneous facial expressions,releasing the constraint of movement,space,and light.The deep learning method can significantly enhance the recognition accuracy of facial expression types and intensities based on a small sample.The proposed wearable FER system is superior for wide applicability and high accuracy.The FER system is suitable for the individual and shows essential robustness to different light,occlusion,and various face poses.It is totally different from but complementary to the computer vision technology that is merely suitable for simultaneous FER of multiple individuals in a specific place.This wearable FER system is successfully applied to human-avatar emotion interaction and verbal communication disambiguation in a real-life environment,enabling promising human-computer interaction applications.