Abstract: A dual-band filtering antenna is provided, which includes: a base, a set of baluns arranged crosswise on the base, and a first radiation arm and a second radiation arm each connected to one of the set of baluns, where copper foil is printed on front and back sides of the first radiation arm and the second radiation arm, the copper foil is divided into a plurality of small modules, a connection module is further arranged between every two small modules, a C-shaped gap is formed on a front side of each small module, and an inverted L-shaped branch is arranged at an edge of each small module.

Abstract: The present disclosure relates to a composition that includes a perovskite having a stoichiometry comprising A1-xFAxSn1-yBy(I1-zXz)3, where A is a first cation, B is a second cation, X is a halide, and 0.5?x?0.9, 0.5?y?0.9, and 0?z?1. In some embodiments of the present disclosure, A may include at least one of cesium, guanidinium, and/or methylammonium. In some embodiments of the present disclosure, X may include at least one of bromide and/or chloride. In some embodiments of the present disclosure, z may be equal to zero.

Abstract: Embodiments are directed to managing data for a predictive learner recommendation platform. A platform that includes applications hosted in an application layer may be provided. The applications may be employed to provide a request to determine a pathway prediction for a learner such that the pathway prediction may be associated with a role offered by employers. Prediction engines associated with the request may be determined based on the service layer interface and the request such that the request may be provided to the determined prediction engines via the service layer interface. The prediction engines may be employed to generate the pathway prediction based on a learner profile that corresponds with the learner, a role success profile that corresponds to the employers, and models that are trained to predict matches between the learner profile and the role success profile.

Abstract: Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.

Abstract: Methods of encapsulating perovskites, such as metal halide perovskites, that may include depositing a nitride or an oxide on a film that includes a perovskite. Composite materials that include a perovskite layer and a layer of a nitride or an oxide. Devices, such as electronic devices, that include composite materials.

Abstract: Embodiments are directed to managing data for a predictive learner recommendation platform. A platform that includes applications hosted in an application layer may be provided. The applications may be employed to provide a request to determine a pathway prediction for a learner such that the pathway prediction may be associated with a role offered by employers. Prediction engines associated with the request may be determined based on the service layer interface and the request such that the request may be provided to the determined prediction engines via the service layer interface. The prediction engines may be employed to generate the pathway prediction based on a learner profile that corresponds with the learner, a role success profile that corresponds to the employers, and models that are trained to predict matches between the learner profile and the role success profile.

Abstract: The present disclosure relates to a composition that includes a perovskite having a stoichiometry comprising A1-xFAxSn1-yBy(I1-zXz)3, where A is a first cation, B is a second cation, X is a halide, and 0.5?x?0.9, 0.5?y?0.9, and 0?z?1. In some embodiments of the present disclosure, A may include at least one of cesium, guanidinium, and/or methylammonium. In some embodiments of the present disclosure, X may include at least one of bromide and/or chloride. In some embodiments of the present disclosure, z may be equal to zero.

Abstract: Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.

Abstract: Embodiments are directed to managing data correlation over a network. Role success models that correspond to roles and to success criteria may be provided. A student profile that includes skill vectors may be provided based on student information. Role success models may be employed to determine intermediate scores based on the skill vectors and the success criteria. A predictive score for the student that corresponds with a predicted performance of the student in the roles may be generated based on the one or more intermediate scores. Actions for the student may be determined based on a mismatch of the skill vectors and role skill vectors that correspond to the roles. In response to the student performing the actions: updating the one or more skill vectors based on a completion of the actions; and updating the predictive score based on the role success models and the updated skill vectors.