A preliminary study on exploratory search behavior of undergraduate students in China

Purpose: This study attempts to investigate how a user's search behavior changes in the exploratory search process in order to understand the characteristics of the user's search behavior and build a behavioral model.Design/methodology/approach: Forty-two matriculated full-time senior college students with a female-to-male ratio of 1 to 1 who majored in medical science in Jilin University participated in our experiment. The task of the experiment was to search for information about &ldquo;the influence of environmental pollution on daily life&rdquo; in order to write a report about this topic. The research methods include concept map, query log analysis and questionnaire survey.Findings: The results indicate that exploratory search can significantly change the knowledge structure of searchers. As searchers were moving through different stages of the exploratory search process, they experienced cognitive changes, and their search behaviors were characterized by quick browsing, careful browsing and focused searching.Research limitations: The study used only one search topic, and there is no comparision or control group. Although we took search habits, personal thinking habits, personality characteristics and professional background into account, a more detailed study to analyze the effects of these factors on exploratory search behavior is needed in our further research.Practical implications: This study can serve as a reference for other researchers engaged in the same effort to construct the supporting system of exploratory search.Originality/value: Three methods are used to investigate the behavior characteristics during exploratory search.</p

Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products

A system review of central nervous system tumors on children in China: epidemiology and clinical characteristics

Abstract Background Central nervous system (CNS) tumors are the most common solid tumors in children and the leading cause of cancer-related death in the latter. Currently, the incidence rate exceeds that of leukemia and ranks first in the incidence of malignant tumors in children. Methods The epidemiological data on childhood CNS tumors were collected from the Chinese Cancer Registry Annual Report. The annual percent change (APC) of incidence and mortality-rate changes were estimated via Joinpoint regression. Due to a lack of pertinent data, we performed a system review on the clinical-pathological characteristics in Chinese publications. Results There was no significant increase in the incidence rate (APC: -0.1, 95% CI: -1.5 to 1.3), but there was a significant increase in the mortality rate (APC: 1.8, 95% CI: 0.3 to 3.4) for childhood CNS tumors. In the subgroup analysis, there were significant increases in both the incidence and mortality rates in rural areas (APC in the incidence: 6.2, 95% CI: 2.4 to 10.2; APC in mortality: 4.4, 95% CI: 0.4 to 8.4). The most common location and type of childhood CNS were, respectively, the cerebral hemisphere (25.5%, 95% CI: 21.7% to 29.4%) and astrocytomas (26.8%, 95% CI: 23.9% to 29.6%). Conclusions The epidemiological trends, and the relevant prediction, highlighted the need to pay continual attention to childhood CNS tumors, and the clinicopathology evinced its own distinctive characteristics. Timely detection and effective treatment must be further promoted regarding childhood CNS tumors with a view to decreasing the disease burden, especially in rural areas

Table_1_Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.DOCX

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.</p

Image_1_Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.TIF

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.</p

Image_2_Fusing hyperspectral imaging and electronic nose data to predict moisture content in Penaeus vannamei during solar drying.TIF

The control of moisture content (MC) is essential in the drying of shrimp, directly impacting its quality and shelf life. This study aimed to develop an accurate method for determining shrimp MC by integrating hyperspectral imaging (HSI) with electronic nose (E-nose) technology. We employed three different data fusion approaches: pixel-, feature-, and decision-fusion, to combine HSI and E nose data for the prediction of shrimp MC. We developed partial least squares regression (PLSR) models for each method and compared their performance in terms of prediction accuracy. The decision fusion approach outperformed the other methods, producing the highest determination coefficients for both calibration (0.9595) and validation sets (0.9448). Corresponding root-mean square errors were the lowest for the calibration set (0.0370) and validation set (0.0443), indicating high prediction precision. Additionally, this approach achieved a relative percent deviation of 3.94, the highest among the methods tested. The findings suggest that the decision fusion of HSI and E nose data through a PLSR model is an effective, accurate, and efficient method for evaluating shrimp MC. The demonstrated capability of this approach makes it a valuable tool for quality control and market monitoring of dried shrimp products.</p

Generation of westerly wind bursts by forcing outside the tropics

Abstract The westerly wind burst (WWB) is an important triggering mechanism of El Niño and typically occurs in the western Pacific Ocean. The Fourier spectrum of the wind field over the western tropical Pacific is characterised by a large variety of peaks distributed from intra-seasonal to decadal time scales, suggesting that WWBs could be a result of nonlinear interactions on these time scales. Using a combination of observations and simulations with 15 coupled models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we demonstrate that the main drivers initiating WWBs are quantifiable physical processes rather than atmospheric stochastic signals. In this study, ensemble empirical mode decomposition (EEMD) from the Holo-Hilbert spectral analysis (HHSA) is used to decompose daily zonal winds over the western equatorial Pacific into seasonal, interannual and decadal components. The seasonal element, with prominent spectral peaks of less than 12 months, is not ENSO related, and we find it to be strongly associated with the East Asian monsoon (EAM) and cross-equatorial flow (CEF) over the Australian monsoon region. The CEF is directly related to the intensity of the Australian subtropical ridge (STR-I). Both the EAM and CEF are essential sources of these high-frequency winds over the western Pacific. In contrast, the interannual wind component is closely related to El Niño occurrences and usually peaks approximately two months prior to a typical El Niño event. Finally, the decadal element merely represents a long-term trend and thus has little to no relation to El Niño. We identified EAM- and CEF-induced westerly wind anomalies in December–January–February (DJF) and September–October–November (SON). However, these anomalies fade in March–April–May (MAM), potentially undermining the usual absence of WWBs in the boreal spring. Similar results are found in CMIP6 historical scenario data