Semantic representation and comparative analysis of physical activity sensor observations using MOX2-5 sensor in real and synthetic datasets: a proof-of-concept-study.

The widespread use of devices like mobile phones and wearables allows for automatic monitoring of human daily activities, generating vast datasets that offer insights into long-term human behavior. A structured and controlled data collection process is essential to unlock the full potential of this information. While wearable sensors for physical activity monitoring have gained significant traction in healthcare, sports science, and fitness applications, securing diverse and comprehensive datasets for research and algorithm development poses a notable challenge. In this proof-of-concept study, we underscore the significance of semantic representation in enhancing data interoperability and facilitating advanced analytics for physical activity sensor observations. Our approach focuses on enhancing the usability of physical activity datasets by employing a medical-grade (CE certified) sensor to generate synthetic datasets. Additionally, we provide insights into ethical considerations related to synthetic datasets. The study conducts a comparative analysis between real and synthetic activity datasets, assessing their effectiveness in mitigating model bias and promoting fairness in predictive analysis. We have created an ontology for semantically representing observations from physical activity sensors and conducted predictive analysis on data collected using MOX2-5 activity sensors. Until now, there has been a lack of publicly available datasets for physical activity collected with MOX2-5 activity monitoring medical grade (CE certified) device. The MOX2-5 captures and transmits high-resolution data, including activity intensity, weight-bearing, sedentary, standing, low, moderate, and vigorous physical activity, as well as steps per minute. Our dataset consists of physical activity data collected from 16 adults (Male: 12; Female: 4) over a period of 30-45 days (approximately 1.5 months), yielding a relatively small volume of 539 records. To address this limitation, we employ various synthetic data generation methods, such as Gaussian Capula (GC), Conditional Tabular General Adversarial Network (CTGAN), and Tabular General Adversarial Network (TABGAN), to augment the dataset with synthetic data. For both the authentic and synthetic datasets, we have developed a Multilayer Perceptron (MLP) classification model for accurately classifying daily physical activity levels. The findings underscore the effectiveness of semantic ontology in semantic search, knowledge representation, data integration, reasoning, and capturing meaningful relationships between data. The analysis supports the hypothesis that the efficiency of predictive models improves as the volume of additional synthetic training data increases. Ontology and Generative AI hold the potential to expedite advancements in behavioral monitoring research. The data presented, encompassing both real MOX2-5 and its synthetic counterpart, serves as a valuable resource for developing robust methods in activity type classification. Furthermore, it opens avenues for exploration into research directions related to synthetic data, including model efficiency, detection of generated data, and considerations regarding data privacy.

Fabrication of High-Performance Solar Cells and X-ray Detectors Using MoX2@CNT Nanocomposite-Tuned Perovskite Layers.

The interface design of inorganic and organic halide perovskite-based devices plays an important role to attain high performance. The modification of transport layers (ETL and HTL) or the perovskite layer is given the crucial inspiration to realize superior power conversion efficiencies (PCEs). The highly conducting 2D materials of CNT, graphene/GO, and transition-metal dichalcogenides (TMDs) are suitable substitutes to tune the electronic structure/work function of perovskite devices. Herein, the nanocomposites composed of molybdenum dichalcogenides (MoX2 = MoS2, MoSe2, and MoTe2) stretched CNT was embedded with HTL or perovskite layer to improve the resulted characteristics of perovskite devices of solar cells and X-ray detectors. A superior solar cell efficiency of 12.57% was realized for the MoTe2@CNT nanocomposites using a modified active layer-composed device. Additionally, X-ray detectors with MoTe2@CNT-modulated active layers achieved 13.32 µA/cm2, 3.99 mA/Gy·cm2, 4.81 × 10-4 cm2/V·s, and 2.13 × 1015 cm2/V·s of CCD-DCD, sensitivity, mobility, and trap density, respectively. Density functional theory approximation was used to realize the improved electronics properties, optical properties, and energy band structures in the MoX2@CNT-doped perovskites evidently. Thus, the current research paves the way for the improvement of highly efficient semiconductor devices based on perovskite-based structures with the use of 2D nanocomposites.

Slater-Koster parametrization for the phonons of monolayer MoX2(X = S, Se or Te).

With the help of the Slater-Koster parametrization, we construct simplified force constant (FC) models to describe the phonons of several two-dimensional (2D) transition metal dichalcogenides (TMDs) (MoX2, X = S, Se or Te) by only considering the FCs to fourth-nearest-neighbor interactions. By fitting the phonon dispersions derived from first-principles calculations, we find these models can well describe the symmetry characters and semimetal states of MoX2's phonons. Combining the basis of the FC model and the theory of tensor representation, we derive the origin of the irreducible representations at the high symmetry points Γ,KandM. Moreover, by using the compatibility relation between high symmetry points and high symmetry lines, we find the semimetal states of MoX2are protected by vertical and horizontal mirrors. Our work provides an effective tool to further study the phonons of 2D TMDs.