Daily electric vehicle charging dataset for training reinforcement learning algorithms.

Reinforcement learning algorithms are increasingly utilized across diverse domains within power systems. One notable challenge in training and deploying these algorithms is the acquisition of large, realistic datasets. It is imperative that these algorithms are trained on extensive, realistic datasets over numerous iterations to ensure optimal performance in real-world scenarios.

Synthesis, antileishmanial activity and molecular modeling of new 1-aryl/alkyl-3-benzoyl/cyclopropanoyl thiourea derivatives.

Due to the lack of effective vaccine(s) against leishmania and also pharmacokinetics issues of current drugs, it is necessary to discover new antileishmanial agents. Within this particular study, a series of novel 1-aryl/alkyl-3-benzoyl/cyclopropanoyl thiourea derivatives were synthesized (yields 69-84%) and evaluated as antileishmanial compounds (1-11). Synthetic derivatives were subjected to in vitro antileishmanial assessment against Leishmania major promastigotes by colorimetric MTT assay.

Selective Microwave Zeroth-Order Resonator Sensor Aided by Machine Learning.

Microwave sensors are principally sensitive to effective permittivity, and hence not selective to a specific material under test (MUT). In this work, a highly compact microwave planar sensor based on zeroth-order resonance is designed to operate at three distant frequencies of 3.5, 4.