Research hotspots and trends of brain-computer interface technology in stroke: a bibliometric study and visualization analysis

BackgroundThe incidence and mortality rates of stroke are escalating due to the growing aging population, which presents a significant hazard to human health. In the realm of stroke, brain-computer interface (BCI) technology has gained considerable attention as a means to enhance treatment efficacy and improve quality of life. Consequently, a bibliometric visualization analysis was performed to investigate the research hotspots and trends of BCI technology in stroke, with the objective of furnishing reference and guidance for future research.MethodsThis study utilized the Science Citation Index Expanded (SCI-Expanded) within the Web of Science Core Collection (WoSCC) database as the data source, selecting relevant literature published between 2013 and 2022 as research sample. Through the application of VOSviewer 1.6.19 and CiteSpace 6.2.R2 visualization analysis software, as well as the bibliometric online analysis platform, the scientific knowledge maps were constructed and subjected to visualization display, and statistical analysis.ResultsThis study encompasses a total of 693 relevant literature, which were published by 2,556 scholars from 975 institutions across 53 countries/regions and have been collected by 185 journals. In the past decade, BCI technology in stroke research has exhibited an upward trend in both annual publications and citations. China and the United States are high productivity countries, while the University of Tubingen stands out as the most contributing institution. Birbaumer N and Pfurtscheller G are the authors with the highest publication and citation frequency in this field, respectively. Frontiers in Neuroscience has published the most literature, while Journal of Neural Engineering has the highest citation frequency. The research hotspots in this field cover keywords such as stroke, BCI, rehabilitation, motor imagery (MI), motor recovery, electroencephalogram (EEG), neurorehabilitation, neural plasticity, task analysis, functional electrical stimulation (FES), motor impairment, feature extraction, and induced movement therapy, which to a certain extent reflect the development trend and frontier research direction of this field.ConclusionThis study comprehensively and visually presents the extensive and in-depth literature resources of BCI technology in stroke research in the form of knowledge maps, which facilitates scholars to gain a more convenient understanding of the development and prospects in this field, thereby promoting further research work

Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption

Building energy modelling (BEM) is crucial for achieving energy conservation in buildings, but occupant energy-related behaviour is often oversimplified in traditional engineering simulation methods and thus causes a significant deviation between energy prediction and actual consumption. Moreover, the conventional fixed schedule-setting method is not applicable to the recently developed data-driven BEM which requires a more flexible and data-related multi-timescales schedule-setting method to boost its performance. In this paper, a data-based schedule setting method is developed by applying K-medoid clustering with Principal Component Analysis (PCA) dimensional reduction and Dynamic Time Warping (DTW) distance measurement to a comprehensive building energy historical dataset, partitioning the data into three different time scales to explore energy usage profile patterns. The Year–Month data were partitioned into two clusters; the Week–Day data were partitioned into three clusters; the Day–Hour data were partitioned into two clusters, and the schedule-setting matrix was developed based on the clustering result. We have compared the performance of the proposed data-driven schedule-setting matrix with default settings and calendar data using a single-layer neural network (NN) model. The findings show that for the data-driven predictive BEM, the clustering results-based data-driven schedule setting performs significantly better than the conventional fixed schedule setting (with a 25.7% improvement) and is more advantageous than the calendar data (with a 9.2% improvement). In conclusion, this study demonstrates that a data-related multi-timescales schedule matrix setting method based on cluster results of building energy profiles can be more suitable for data-driven BEM establishment and can improve the data-driven BEMs performance

Evaluating multimodal ChatGPT for emergency decision-making of ocular trauma cases

PurposeThis study aimed to evaluate the potential of ChatGPT in diagnosing ocular trauma cases in emergency settings and determining the necessity for surgical intervention.MethodsThis retrospective observational study analyzed 52 ocular trauma cases from Ningbo Eye Hospital. Each case was input into GPT-3.5 turbo and GPT-4.0 turbo in Chinese and English. Ocular surface photographs were independently incorporated into the input to assess ChatGPT’s multimodal performance. Six senior ophthalmologists evaluated the image descriptions generated by GPT-4.0 turbo.ResultsWith text-only input, the diagnostic accuracy rate was 80.77%–88.46% with GPT-3.5 turbo and 94.23%–98.08% with GPT-4.0 turbo. After replacing examination information with photography, GPT-4.0 turbo’s diagnostic accuracy rate decreased to 63.46%. In the image understanding evaluation, the mean completeness scores attained 3.59 ± 0.94 to 3.69 ± 0.90. The mean correctness scores attained 3.21 ± 1.04 to 3.38 ± 1.00.ConclusionThis study demonstrates ChatGPT has the potential to help emergency physicians assess and triage ocular trauma patients properly and timely. However, its ability in clinical image understanding needs to be further improved

The role of the tumor microenvironment in HNSCC resistance and targeted therapy

The prognosis for head and neck squamous cell carcinoma (HNSCC) remains unfavorable, primarily due to significant therapeutic resistance and the absence effective interventions. A major obstacle in cancer treatment is the persistent resistance of cancer cells to a variety of therapeutic modalities. The tumor microenvironment (TME) which includes encompasses all non-malignant components and their metabolites within the tumor tissue, plays a crucial role in this context. The distinct characteristics of the HNSCC TME facilitate tumor growth, invasion, metastasis, and resistance to treatment. This review provides a comprehensive overview of the HNSCC TME components, with a particular focus on tumor-associated macrophages (TAMs), regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), cancer-associated fibroblasts (CAFs), the extracellular matrix, reprogrammed metabolic processes, and metabolic products. It elucidates their contributions to modulating resistance to chemotherapy, radiotherapy, targeted therapy, and immunotherapy in HNSCC, and explores novel therapeutic strategies targeting the TME for HNSCC management

Experimental study on a novel photovoltaic thermal system using amorphous silicon cells deposited on stainless steel

Amorphous silicon (a-Si) cells are able to perform better as temperature increases due to the effect of thermal annealing. a-Si cells have great potential to solve or ease the problems of high power temperature coefficient, large thermal stress caused by temperature fluctuation and gradient, and thick layer of conventional crystalline silicon cell-related photovoltaic/thermal (PV/T) collectors. In this paper, an innovative a-Si PV/T system is developed. It is the first time that a-Si cells deposited on stainless steel have been used in a practical PV/T system. The system comprises of two PV/T collectors. In each collector, there are 8 pieces of solar cells in series. Long-term outdoor performance has been monitored. Experimental results on the thermal efficiency Image 1, electrical efficiency Image 2 and I-V characteristic are presented. The peak instantaneous Image 3 was about 42.49% with the maximum Image 4 of 5.92% on April 2, 2017. The daily average Image 5 and Image 6 were 32.8% and 5.58%. Accordingly, Image 7 ,Image 8, Image 9 and Image 10 on October 27 were 43.47%, 5.69%, 38.65% and 5.22 %. During more than half a year operation, no technical failure of the system has been observed. The feasibility of the a-Si PV/T is preliminarily demonstrated by the prototype

Effects of Different Organic Amendments on Aggregate-Associated Humus Carbons and Nutrients in a Paddy Soil

The degradation of soil structure in paddy fields is critical, and the application of organic amendments is an effective way to enhance soil structure and function. However, the mechanisms by which different organic amendments influence soil aggregate-associated humus carbon and nutrients remain unclear. Considering this, four treatments were employed in a randomized complete block design with three replications: (1) chemical fertilizer (CK); (2) chemical fertilizer plus organic amendment (MC); (3) chemical fertilizer plus organic amendment containing Bacillus subtilis (FT); and (4) Chemical fertilizer plus organic amendment containing polyacrylamide (PM). The results showed that all soil improvement measures significantly increased the proportion of macroaggregates (>2 mm and 2–0.25 mm), primarily the 2–0.25 mm fraction (34.53–48.46%), and the mean weight diameter (MWD), compared to CK. Soil organic carbon (SOC), humic acid carbon (HAC), fulvic acid carbon (FAC), humin carbon (HUC), total nitrogen (TN), and total phosphorus (TP) were predominantly concentrated within the macroaggregates. Relative to CK, the PM increased the HUC content in large aggregates (>2 mm) and significantly enhanced HAC by 19.53% within the same fraction, while the FT significantly boosted FAC by 31.78% in the >2 mm fraction. Furthermore, MC, FT, and PM treatments significantly enhanced SOC, TN, and TP contents within large macroaggregates compared to CK, with PM generally showing the highest SOC and TN levels, and FT being the highest in terms of TP in large aggregates (though differences among treatments were non-significant). Correlation analysis revealed that only in large aggregates did SOC show significant positive correlations with humus carbon fractions (except HAC), as well as with TN and TP. The amendments, particularly PM, effectively enhanced nutrient and humus carbon accumulation within large aggregates and improved aggregate stability. Notably, PM strengthened the direct pathways for the formation of SOC and humus carbon. In summary, the combined application of chemical fertilizer and organic amendments, containing polyacrylamide positively influenced aggregate stability and nutrient accumulation in paddy soil

Performance investigation of a novel low-carbon solar-assisted multi-source heat pump heating system demonstrated in a public building in Hull

Global climate change has raised great attention from governments and prompted a wave of application of low-carbon heating technologies. However, solar-assisted heat pump, as an attractive technology, has challenges of unstable performance and complex structure and control strategy in practical application. Following the carbon-neutral strategy, a novel low-carbon solar-assisted multi-source heat pump heating system (LSMHS) is therefore proposed to improve the application potential of SAHP, which is demonstrated in Hull Central Library by replacing the library's original gas boiler heating system (GBHS). The LSMHS integrates eight novel multi-throughout-flowing solar collector arrays with an innovative two-stage heat recovery heat pump which can automatically switch different operation modes according to weather conditions. The practical operation results revealed that the LSMHS maximized the advantages of each component and achieved a high monthly average system COPsys ranging from 2.12 to 2.68 in the three-month demonstration. Eventually, the LSMHS provided a bill saving of 0.73 % with a significant carbon reduction of 63.69 % when compared to the GBHS in practice, achieving an equivalent bill saving of £6.7 for every tone of carbon reduction. The remarkable demonstration results showcased the application potential of the novel LSMHS and gave valuable guidance for low-carbon building heating