92 research outputs found

    Empirical Research on the Relation Between Shares Reduction of Senior Executives and Earnings Management

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    Using listed companies with a presence of shares reduction of senior executives after the split share structure reform as research objects, we systematically study whether there are changes in earnings management behavior of senior executives’ shares reduction, as well as the relationship between the shares reduction degree and earnings management degree. Our analysis reveals that companies with a presence of shares reduction of senior executives have significantly positive controls over accounting earnings in the years of 2008 and 2009. However, there is no significant correlation between the level of earnings management of listed companies in China and the scale of shares reduction of senior executives

    Contrastive Cross-Domain Sequential Recommendation

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    Cross-Domain Sequential Recommendation (CDSR) aims to predict future interactions based on user's historical sequential interactions from multiple domains. Generally, a key challenge of CDSR is how to mine precise cross-domain user preference based on the intra-sequence and inter-sequence item interactions. Existing works first learn single-domain user preference only with intra-sequence item interactions, and then build a transferring module to obtain cross-domain user preference. However, such a pipeline and implicit solution can be severely limited by the bottleneck of the designed transferring module, and ignores to consider inter-sequence item relationships. In this paper, we propose C^2DSR to tackle the above problems to capture precise user preferences. The main idea is to simultaneously leverage the intra- and inter- sequence item relationships, and jointly learn the single- and cross- domain user preferences. Specifically, we first utilize a graph neural network to mine inter-sequence item collaborative relationship, and then exploit sequential attentive encoder to capture intra-sequence item sequential relationship. Based on them, we devise two different sequential training objectives to obtain user single-domain and cross-domain representations. Furthermore, we present a novel contrastive cross-domain infomax objective to enhance the correlation between single- and cross- domain user representations by maximizing their mutual information. To validate the effectiveness of C^2DSR, we first re-split four e-comerce datasets, and then conduct extensive experiments to demonstrate the effectiveness of our approach C^2DSR.Comment: This paper has been accepted by CIKM 202

    Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data

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    The prediction of gas well performance is crucial for estimating the ultimate recovery rate of natural gas reservoirs. However, physics-based numerical simulation methods require a significant effort to build a robust model, while the decline curve analysis method used in this field is based on certain assumptions, hence its applications are limited due to the strict working conditions. In this work, a deep learning model driven jointly by the decline curve analysis model and production data is proposed for the production performance prediction of gas wells. Due to the time-series characteristics of gas well production data, the long short-term memory neural network is selected to establish the architecture of artificial intelligence. The existing decline curve analysis model is first implicitly incorporated into the training process of the neural network and then used to drive the neural network construction along with the actual gas well production historical data. By applying the proposed innovative model to analyze the conventional and tight gas well performance predictions based on field data, it is demonstrated that the proposed long short-term memory neural network deep learning model driven jointly by the decline curve analysis model and production data can effectively improve the interpretability and predictive ability of the traditional long short-term memory neural network model driven by production data alone. Compared with the data-driven model, the jointly driven model can reduce the mean absolute error by 42.90% and 13.65% for a tight gas well and a carbonate gas well, respectively.Document Type: Original articleCited as: Xue, L., Wang, J., Han, J., Yang, M., Mwasmwasa, M. S., Nanguka, F. Gas well performance prediction using deep learning jointly driven by decline curve analysis model and production data. Advances in Geo-Energy Research, 2023, 8(3): 159-169. https://doi.org/10.46690/ager.2023.06.0

    Cognitive impairment in chronic migraine compared to pseudotumor cerebri

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    Introduction. We aimed to define the prevalence of objective cognitive impairment in a group of chronic migraineurs, and to define how migraineurs with cognitive impairment differed from those without impairment, and in doing so to compare cognitive impairment in chronic migraine to another chronic headache-related disorder already associated with cognitive impairment (i.e. pseudotumor cerebri syndrome). Objectives. Cognitive impairment in migraine, especially chronic migraine, has been too little studied. Only a few studies have been done, demonstrating that cognitive impairment exists in chronic migraineurs. It is not known how this compares to other headache-related conditions. Material and methods. We administered a cognitive battery consisting of the National Adult Reading Test, Mini-Mental Status Examination, Digit Span, Boston Naming Test, Rey Auditory Verbal Learning Test, Trail Making Test, Controlled Oral Word Association, and Category Fluency. Cognitive impairment was defined as mild single-domain with one test score, and mild multi- -domain with two scores more than two standard deviations below the mean for age-, gender-, and education-adjusted norms. The data from this study was compared to our previously published population of patients with pseudotumor cerebri syndrome. Results. One hundred prospectively recruited patients with chronic migraine were enrolled. Fifty-seven patients had normal cognitive profiles. Forty-three patients demonstrated mild cognitive impairment, and more than half (n = 24) showed impairment in multiple cognitive domains. Migraineurs with multi-domain impairment had higher pain intensity, shorter duration of disease, were taking narcotics, had more impaired vision-related mental health scores, and worse social health scores. We found an association between objective cognitive impairment and subjective perception of impairment only when controlling for pain. We found no associations with depression and topiramate use. The mean composite cognitive Z score was no different in chronic migraineurs and patients with pseudotumor cerebri. Conclusions and clinical implications. Most chronic migraineurs have normal cognitive profiles, but a large proportion of them do experience mild cognitive impairment, especially in multiple domains. The impairment seen in migraine is similar to that in pseudotumor cerebri syndrome, which has already been associated with mild cognitive impairment. Cognitively impaired migraineurs are different from non-impaired/less impaired migraineurs in several ways, which may be an important factor in influencing their migraine treatment

    Alzheimer’s disease genetic risk and cognitive reserve in relationship to long-term cognitive trajectories among cognitively normal individuals

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    Background: Both Alzheimer’s disease (AD) genetic risk factors and indices of cognitive reserve (CR) influence risk of cognitive decline, but it remains unclear whether they interact. This study examined whether a CR index score modifies the relationship between AD genetic risk factors and long-term cognitive trajectories in a large sample of individuals with normal cognition. Methods: Analyses used data from the Preclinical AD Consortium, including harmonized data from 5 longitudinal cohort studies. Participants were cognitively normal at baseline (M baseline age = 64 years, 59% female) and underwent 10 years of follow-up, on average. AD genetic risk was measured by (i) apolipoprotein-E (APOE) genetic status (APOE-ε2 and APOE-ε4 vs. APOE-ε3; N = 1819) and (ii) AD polygenic risk scores (AD-PRS; N = 1175). A CR index was calculated by combining years of education and literacy scores. Longitudinal cognitive performance was measured by harmonized factor scores for global cognition, episodic memory, and executive function. Results: In mixed-effects models, higher CR index scores were associated with better baseline cognitive performance for all cognitive outcomes. APOE-ε4 genotype and AD-PRS that included the APOE region (AD-PRSAPOE) were associated with declines in all cognitive domains, whereas AD-PRS that excluded the APOE region (AD-PRSw/oAPOE) was associated with declines in executive function and global cognition, but not memory. There were significant 3-way CR index score × APOE-ε4 × time interactions for the global (p = 0.04, effect size = 0.16) and memory scores (p = 0.01, effect size = 0.22), indicating the negative effect of APOE-ε4 genotype on global and episodic memory score change was attenuated among individuals with higher CR index scores. In contrast, levels of CR did not attenuate APOE-ε4-related declines in executive function or declines associated with higher AD-PRS. APOE-ε2 genotype was unrelated to cognition. Conclusions: These results suggest that APOE-ε4 and non-APOE-ε4 AD polygenic risk are independently associated with global cognitive and executive function declines among individuals with normal cognition at baseline, but only APOE-ε4 is associated with declines in episodic memory. Importantly, higher levels of CR may mitigate APOE-ε4-related declines in some cognitive domains. Future research is needed to address study limitations, including generalizability due to cohort demographic characteristics

    Outcomes of Refractive Surgery Consultations at an Academic Center: Characteristics Associated with Proceeding (or Not Proceeding) with Surgery

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    Objective. Refractive surgery volume has not rebounded despite economic recovery and literature describing safety, efficacy, and high patient satisfaction. We sought to examine characteristics of consultation seekers and status after consultation. Methods. Charts of patients seeking refractive surgery at Johns Hopkins University from 2013 through 2016 were retrospectively reviewed for age, gender, refractive characteristics, and outcome: surgery (photorefractive keratectomy, laser in-situ keratomileusis, implantable collamer lens, or refractive lens exchange); no surgery—“lost candidate” (good candidates who were lost after consultation); noncandidates based on technological limitations or contraindications; or no surgery—possessing expectations that surgery would not meet. Associations between characteristics and status after consultation were examined. Results. Twenty percent (142/712) of all patients were “lost candidates”; 57% (408/712) completed surgery. More women (56% or 401/712) sought consultation, but a greater percentage (63% or 195/311) of men completed surgery than women did (53% or 213/401) p=0.02. Of consultation seekers, 60% were low myopes, 29% were high myopes (>6 diopters of myopic spherical equivalent), and 11% were hyperopes. Surgical patients’ mean age was 34.2 ± 10.2 (standard deviation) years; for each additional year of age, patients were less likely to have surgery p<0.001. Hyperopes were ≥3 times more likely than myopes to have expectations not met by surgery or to be noncandidates than to have surgery p<0.005. Conclusions. Most patients seeking refractive surgery had 6 diopters or less of myopia. About 20% of patients were lost after consultation; better counseling and follow-up of candidates may be warranted. Expectations and technology limit eligibility for many, especially hyperopes. Low surgery volume may affect training of future refractive surgeons

    Optimized stratification approach enhances the weight-of-evidence method: Transparently uncovering wildfire probability and drivers-wildfire relationships in the southwest mountains of China

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    The mountainous region of southwest China serves as a significant treasure for resources and ecological security. Due to its topographic heterogeneity and cultural diversity, the wildfire regime in this area exhibits unique features. To uncover the distinct mechanisms that drive wildfire incidents and assess their occurrence probability, an enhanced weight-of-evidence (WofE) method was proposed by improving the stratification of evidence layers. This approach seeks to heighten effectiveness while maintaining procedural transparency and robustness. In our case study focusing on Yunnan Province, we extracted wildfire data from medium-resolution remote sensing imagery from 2006 to 2020. Various wildfire drivers were considered including wildfire environment, fuel conditions, and ignition sources. We utilized the WofE method grounded on Bayesian principles to clearly calculate the spatial association strength between these drivers and wildfires, thereby developing a reliable wildfire occurrence probability map. To enhance the explanatory power of the drivers within the WofE method, a discretization method based on the theory of spatial stratified heterogeneity was incorporated. Our results suggested that the WofE method can be optimized using the spatial stratified heterogeneity measured via GeoDetector, leading to an improved solution. Implementing optimized discrete drivers amplified the spatial explanatory power of wildfires by an average of 7.55%, supporting their inclusion as evidence layers within the WofE method. The optimized discrete WofE method yielded a wildfire occurrence probability map with an Area Under the Curve (AUC) value of 0.91, indicating high predictive accuracy. This map revealed notable spatial clustering and regional variations in wildfire occurrences across Yunnan Province. Additionally, we observed variable spatial correlation between each driver and wildfire occurrence, and the wildfire drivers indicative of ignition sources characteristics were relatively more dominant locally. This research contributes valuable insights towards enhancing the WofE method and provides a helpful reference for local wildfire management practices and resource allocation strategies
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