THE INFLUENCE MECHANISMS OF ILLEGITIMATE TASKS ON EMPLOYEES’ SILENCE BEHAVIORS AGAINST THE BACKDROP OF ARTIFICIAL INTELLIGENCE AND FUZZY ALGORITHMS

Employee silence can degrade the working environment and decrease employees’ motivation and commitment to an organization. As a result, it not only affects employees but also reduces the productivity of the organization. However, few studies have investigated the influencing mechanisms of employee silence empirically. This paper studies how illegitimate tasks affect employee silence based on artificial intelligence and fuzzy algorithms. We surveyed 325 employees in several medium-sized enterprises in Jiangsu and Anhui, China. According to the findings, emotional exhaustion partially mediates the relationship between illegitimate tasks and employees’ silence behaviors, and leadership humor can moderate the positive effect of illegitimate tasks on emotional exhaustion. Therefore, situating the mechanisms underlying employees’ silence behaviors in the context of artificial intelligence and fuzzy algorithm research helps researchers understand the relationship between illegitimate tasks and employees’ silence behaviors, thus improving related research on silence behaviors

Constrained shuffled complex evolution algorithm and its application in the automatic calibration of Xinanjiang model

The Shuffled Complex Evolution—University of Arizona (SCE-UA) is a classical algorithm in the field of hydrology and water resources, but it cannot solve constrained optimization problems directly. Using penalty functions has been the preferred method to handle constraints, but the appropriate selection of penalty parameters and penalty functions can be challenging. To enhance the universality of the SCE-UA, we propose the Constrained Shuffled Complex Evolution Algorithm (CSCE) to conveniently and effectively solve inequality-constrained optimization problems. Its performance is compared with the SCE-UA using the adaptive penalty function (SCEA) on 14 test problems with inequality constraints. It is further compared with seven other algorithms on two test problems with low success rates. To demonstrate its effect in hydrologic model calibration, the CSCE is applied to the parameter optimization of the Xinanjiang (XAJ) model under synthetic data and observed data. The results indicate that the CSCE is more advantageous than the SCEA in terms of the success rate, stability, feasible rate, and convergence speed. It can guarantee the feasibility of the solution and avoid the problem of deep soil tension water capacity (WDM)<0 in the optimization process of the XAJ model. In the case of synthetic data, the CSCE can accurately find the theoretical optimal parameters of the XAJ model under the given constraints. In the case of observed data, the XAJ model optimized by the CSCE can effectively simulate the hourly rainfall-runoff events of the Hexi Basin and achieves mean Nash efficiency coefficients greater than 0.75 in the calibration period and the validation period

Automatic velocity picking with restricted weighted k-means clustering using prior information

Automatic picking of seismic velocity can be performed using k-means clustering. In simple k-means clustering, the number of clusters needs to be predetermined, while the picking result is affected by the initial value of each cluster center. In this study, we present an unsupervised weighted k-means clustering velocity-picking method that picks the centers of the energy clusters instead of the geometric centers of the clusters. This method works on the semblance velocity spectrum and requires an initial velocity function and three user-defined thresholds to limit the search area. The number of cluster centers and their initial times are obtained according to a rectangular signal resulting from the three thresholds, while the initial velocities of the cluster centers can be subsequently obtained using their initial times and the initial velocity function. Inaccurate selection of thresholds may merge two clusters wrongly, in which case only a stronger event is selected. In the weighted k-means clustering algorithm, weights are calculated by using the amplitudes of the velocity points. Meanwhile, points far from the center are gradually removed to ensure that each cluster center coincides with the respective energy cluster center. We also propose a method for ignoring non-primary velocities, such as multiples, by removing points that create sudden changes in the slope of the reference velocity beyond a user-defined limit. The processing of the model and real data show that the proposed seismic velocity-picking method has high efficiency and picking accuracy

A Patent Bibliometric Analysis of Carbon Capture, Utilization, and Storage (CCUS) Technology

Large amounts of CO2 from human socioeconomic activities threaten environmental sustainability. Moreover, uncontrolled resource use and lack of relevant technology exacerbate this issue. For this reason, carbon capture, utilization, and storage (CCUS) technology has gained worldwide attention. Many scholars have researched CCUS, but few have used CCUS patent bibliometric analysis from a unified perspective. This article aims to provide a conclusive analysis for CCUS researchers and policymakers, as well as summarize the innovation trends, technological distribution, and topic evolution. Based on 11,915 pieces of patent data from the Derwent Innovations Index, we used bibliometric analysis and data mining methods to conduct research on four dimensions: overall trend, geographical distribution, patentees, and patent content. The results of this article are as follows. CCUS has entered a rapid development stage since 2013. Patents are mainly distributed geographically in China, the US, and Japan, especially in heavy industries such as energy and electricity. Large enterprises hold patents with a relatively stable network of cooperators and attach great importance to international patent protection. A total of 12 topics were identified through clustering, and these topics gradually shifted from technicalities to commercialization, and from industrial production to all aspects of people’s daily lives

Changing Patterns in Cancer Mortality from 1987 to 2020 in China

Background: China has the highest number of new cancer cases and deaths worldwide, posing huge health and economic burdens to society and affected families. This study comprehensively analyzed secular trends of national cancer mortality statistics to inform future prevention and intervention programs in China. Methods: The annual estimate of overall cancer mortality and its major subtypes were derived from the National Health Commission (NHC). Joinpoint analysis was used to detect changes in trends, and we used age-period-cohort modeling to estimate cohort and period effects in Cancers between 1987 and 2020. Net drift (overall annual percentage change), local drift (annual percentage change in each age group), longitudinal age curves (expected longitudinal age-specific rate), and period (cohort) relative risks were calculated. Results: The age-standardized cancer mortality in urban China has shown a steady downward trend but has not decreased significantly in rural areas. Almost all cancer deaths in urban areas have shown a downward trend, except for colorectal cancer in men. Decreasing mortality from cancers in rural of the stomach, esophagus, liver, leukemia, and nasopharynx was observed, while lung, colorectal cancer female breast, and cervical cancer mortality increased. Birth cohort risks peaked in the cohorts born around 1920–1930 and tended to decline in successive cohorts for most cancers except for leukemia, lung cancer in rural, and breast and cervical cancer in females, whose relative risks were rising in the very recent cohorts. In addition, mortality rates for almost all types of cancer in older Chinese show an upward trend. Conclusions: Although the age-standardized overall cancer mortality rate has declined, and the urban-rural gap narrowed, the absolute cancer cases kept increasing due to the growing elderly population in China. The rising mortality related to lung, colorectal, female breast, and cervical cancer should receive higher priority in managing cancer burden and calls for targeted public health actions to reverse the trend

Whole-body PET tracking of a D-dodecapeptide and its radiotheranostic potential for PD-L1 overexpressing tumors

Abstract Peptides that are composed of dextrorotary (D)-amino acids have gained increasingattention as a potential therapeutic class. However, our understanding of the in vivo fate of D-peptidesis limited. This highlights the need for whole-body, quantitative tracking of D-peptides to betterunderstand how they interact with the living body. Here, we used mouse models to track themovement of a programmed death-ligand 1 (PD-L1)-targeting D-dodecapeptide antagonist (DPA)using positron emission tomography (PET). More specifically, we profiled the metabolic routes of[64Cu]DPA and investigated the tumor engagement of [64Cu/68Ga]DPA in mouse models. Our resultsrevealed that intact [64Cu/68Ga]DPA was primarily eliminated by the kidneys and had a notableaccumulation in tumors. Moreover, a single dose of [64Cu]DPA effectively delayed tumor growthand improved the survival of mice. Collectively, these results not only deepen our knowledge of thein vivo fate of D-peptides, but also underscore the utility of D-peptides as radiopharmaceuticals.KEY WORDS D-peptide; PET imaging; Radiotheranostics; In vivo fate; PD-L1Abbreviations: 2D, two-dimensional; 3D, three-dimensional; CPM, radioactivity per minute; DAB,3,3-diaminobenzidine; DMEM, Dulbecco’s modified Eagle medium; DMSO, dimethyl sulfoxide;DMF, dimethylformamide; DOTA, 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid; DPA,D-dodecapeptide antagonist; FBS, fetal bovine serum; FITC, fluorescein isothiocyanate; HCT,hematocrit; H&E, Hematoxylin and Eosin; HPLC, high performance liquid chromatography; mAb,monoclonal antibody; MD, molecular dynamics; MIP, maximum intensity projection; MIPD,mirror-image phage display; PCNA, proliferating cell nuclear antigen; PD-L1, programmed deathligand 1; PET, positron emission tomography; PLC, platelet; RBC, red blood cells; TAC, time‒activity curves; TFA, trifluoroacetic acid; TLC, thin layer chromatography; TRT, targetedradionuclide therapy; WBC, white blood cell