Design of a Geothermal Well Filled with Phase Change Materials for Daily and Seasonal Heat Storage and Supply

Geothermal use can be dated back thousands of years. From primitive geothermal direct use to more sophisticated ways of using the resource, now geothermal energy has been utilized in households, farms, buildings and industrial processes. There are typically three geothermal energy systems including direct use and district heating systems, electricity generation power plants and geothermal heat pumps. Besides, geothermal heat pumps have almost no negative effects on the environment, and even have positive effects as they reduce the usage of other environmentally unfriendly energy sources. This project aims at designing a geothermal well for daily and seasonal heat storage and supply for energy efficient buildings as well as other geothermal applications. Coupling with heating, ventilation, and air conditioning (HVAC) systems and building integrated photovoltaic thermal (BIPVT) systems, it can not only significantly boost the efficiency of HVAC and BIPVT systems, but also be used for inter-seasonal heat exchange of heating (winter) and cooling (summer) for energy efficient buildings. The system can be further expanded by integrating with greenhouses on farms. Please click Additional Files below to see the full abstract

GestureGPT: Zero-shot Interactive Gesture Understanding and Grounding with Large Language Model Agents

Current gesture recognition systems primarily focus on identifying gestures within a predefined set, leaving a gap in connecting these gestures to interactive GUI elements or system functions (e.g., linking a 'thumb-up' gesture to a 'like' button). We introduce GestureGPT, a novel zero-shot gesture understanding and grounding framework leveraging large language models (LLMs). Gesture descriptions are formulated based on hand landmark coordinates from gesture videos and fed into our dual-agent dialogue system. A gesture agent deciphers these descriptions and queries about the interaction context (e.g., interface, history, gaze data), which a context agent organizes and provides. Following iterative exchanges, the gesture agent discerns user intent, grounding it to an interactive function. We validated the gesture description module using public first-view and third-view gesture datasets and tested the whole system in two real-world settings: video streaming and smart home IoT control. The highest zero-shot Top-5 grounding accuracies are 80.11% for video streaming and 90.78% for smart home tasks, showing potential of the new gesture understanding paradigm

Nanoarchitectonics of the aggregation-induced emission luminescent molecule Tetraphenylethylene-COOH (TPE-COOH): Fluorescence imaging of targeted cancer microsphere cells

Cancer microspheres are an ideal cellular model for studying cancer stem cells. However, the lack of effective and rapid fluorescent imaging reagents for cancer microspheres has severely hampered cytological studies associated with cancer microspheres. Here, we have identified an aggregation-induced emission (AIE) probe, TPE-COOH, to bind to a targeting cyclic heptapeptide molecule to form a new TPE-Peptide probe. This probe has been employed for fluorescence imaging of normal, cancer and cancer microsphere cells. A novel analogue of TPAPy-1 was synthesized to verify the cell-specific targeting. For the first time, the fluorescence imaging of cancer microspheres was achieved and microspheres were imaged due to changes in viscosity, thereby providing a means to visualize and identify cancer microspheres

A Machine Learning-Based Predictive Model of Epidermal Growth Factor Mutations in Lung Adenocarcinomas

Data from 758 patients with lung adenocarcinoma were retrospectively collected. All patients had undergone computed tomography imaging and EGFR gene testing. Radiomic features were extracted using the medical imaging tool 3D-Slicer and were combined with the clinical features to build a machine learning prediction model. The high-dimensional feature set was screened for optimal feature subsets using principal component analysis (PCA) and the least absolute shrinkage and selection operator (LASSO). Model prediction of EGFR mutation status in the validation group was evaluated using multiple classifiers. We showed that six clinical features and 622 radiomic features were initially collected. Thirty-one radiomic features with non-zero correlation coefficients were obtained by LASSO regression, and 24 features correlated with label values were obtained by PCA. The shared radiomic features determined by these two methods were selected and combined with the clinical features of the respective patient to form a subset of features related to EGFR mutations. The full dataset was partitioned into training and test sets at a ratio of 7:3 using 10-fold cross-validation. The area under the curve (AUC) of the four classifiers with cross-validations was: (1) K-nearest neighbor (AUCmean = 0.83, Acc = 81%); (2) random forest (AUCmean = 0.91, Acc = 83%); (3) LGBM (AUCmean = 0.94, Acc = 88%); and (4) support vector machine (AUCmean = 0.79, Acc = 83%). In summary, the subset of radiographic and clinical features selected by feature engineering effectively predicted the EGFR mutation status of this NSCLC patient cohort

Tip-Enhanced Raman Spectroscopy with High-Order Fiber Vector Beam Excitation

We investigated tip-enhanced Raman spectra excited by high-order fiber vector beams. Theoretical analysis shows that the high-order fiber vector beams have stronger longitudinal electric field components than linearly polarized light under tight focusing conditions. By introducing the high-order fiber vector beams and the linearly polarized beam from a fiber vector beam generator based on an electrically-controlled acoustically-induced fiber grating into a top-illumination tip-enhanced Raman spectroscopy (TERS) setup, the tip-enhanced Raman signal produced by the high-order fiber vector beams was 1.6 times as strong as that produced by the linearly polarized light. This result suggests a new type of efficient excitation light beams for TERS