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In selected-response assessments such as attitude surveys with Likert-type rating scales, examinees often select from rating scale categories to reflect their locations on a construct. Researchers have observed that some examinees exhibit "response styles," which are systematic patterns of responses in which examinees are more likely to select certain response categories, regardless of their locations on the construct (Baumgartner & Steenkamp, 2001; Paulhus, 1991; Roberts, 2016; Van Vaerenbergh & Thomas, 2013). To identify and minimize construct-irrelevant impacts of response styles, researchers have proposed tools such as the Partial Credit Model -- Response Style (PCMRS; Tutz et al., 2018) as an extension of the Partial credit model (PCM; Masters, 1982) to model the tendency for examinees to exhibit response styles. The PCMRS directly models response styles as a person-specific gamma parameter and corrects estimates of item difficulty for the presence of response styles. In this study, the authors describe details about the PCMRS model parameters and explore the correspondence between indicators of response styles from the PCMRS and person fit statistics that reflect the Rasch measurement framework to identify examinees who exhibit midpoint and extreme response styles. Findings suggest that researchers and practitioners who aim to identify response styles as a type of construct-irrelevant variance can do so using measurement models such as the PCM.

Abstract Hydrogen energy is playing an increasingly important role in integrated energy systems (IES) due to its multiple uses. Green hydrogen can be produced from electricity generated from renewable energy, reducing carbon emissions and environmental pollution. Previous work usually only considers electric vehicles (EVs) as flexible loads, and there are few studies on the production and application process of green hydrogen. In this paper, hydrogen electrolysis centers (HECs) and EVs are simultaneously schedulable loads, integrated into IES, and optimized. The distribution network model is established, and the optimization goal is to minimize network loss while ensuring the safe operation of the power grid. Through the power flow optimization method of mixed‐integer second‐order cone programming, the space optimization scheduling of EVs in the IES is carried out, and the optimal location of the HEC is reasonably planned and set. A simulation instance of IEEE 33 nodes is used to verify the proposed method. The final results show that this method can minimize network loss in the range of safe operation of the system for the spatial scheduling of electrolytic hydrogen centers and EVs. At the same time, it can also provide a reference for HEC addressing.

In this paper, firstly, the relevant parameters of influent C/N quantification were selected, and then, on the basis of the relevant parameters, the apparent shear force formula was deduced, and the apparent shear force was used to reflect the hydraulic condition of the reactor. Next, the experimental device, operation method, influent water quality and analysis method were determined, and the influences of influent C/N on the sludge granulation process were derived from the influences of group I C/N ratio on anaerobic granular sludge and group II C/N ratio on anaerobic granular sludge. Then, the return sludge from the Guangzhou sewage treatment plant in Guangdong Province was selected as the experimental seed sludge, and the methanol concentration of 0.13g/L (COD=200mg/L) was used as the experimental water quality, and the influences of influent C/N on nutrient N2O denitrification were illustrated by the experimental analyses. The results showed that the NO2− − N accumulation rate was maintained above 81% in all five water intake modes, and when the ratio of water intake (1:1) and (2:1), a higher concentration of NO2− − N was accumulated in the first nitrification stage of 26.5 mg/L and 24.9 mg/L, respectively, which could give full play to the advantages of nitrifying bacteria to achieve nitrification rapidly and also reduce the production of nutrient salt N2O. This study combines macro-experiment and micro-analysis to provide a theoretical basis for granular sludge technology.

Honeybees (Apis mellifera) can be exposed via numerous potential pathways to ambient nanoparticles (NPs), including rare earth oxide (REO) NPs that are increasingly used and released into the environment. Gut microorganisms are pivotal in mediating honeybee health, but how REO NPs may affect honeybee health and gut microbiota remains poorly understood. To address this knowledge gap, honeybees were fed pollen and sucrose syrup containing 0, 1, 10, 100, and 1000mgkg-1 of nano-La2O3 for 12days. Nano-La2O3 exerted detrimental effects on honeybee physiology, as reflected by dose-dependent adverse effects of nano-La2O3 on survival, pollen consumption, and body weight (p

We report a case of a 59-year-old male diagnosed with extensive-stage small cell lung cancer (SCLC). He received first-line platinum doublet chemotherapy and second-line topotecan-based regimen, but experienced disease progression after each line of therapy. He was then treated with Sintilimab, a PD-1 inhibitor, in combination with nab-paclitaxel in the third-line setting, which resulted in significant tumor shrinkage. Restaging scans showed a partial response per RECIST criteria with 62% reduction in tumor burden. This case highlights the application and efficacy of immune checkpoint inhibitors in extensive-stage SCLC.

Abstract Hydrogen fuel cell vehicles (HFCVs) are promising environmentally friendly technologies that are increasingly supported by governments around the world. In the integrated energy system (IES) of this paper, by adding the method of electrolysis to hydrogen production, the electrical energy is converted into chemical energy, which cannot only absorb the new energy with fluctuating output but also meet the use of more and more HFCVs. Aiming at the newly introduced hydrogen energy conversion and consumption pathways, this paper uses a statistical analogy to model HFVCs, taking into account the energy conversion efficiency and the needs of vehicle owners. Using the GUROBI solver in the YALMIP toolbox, the IES optimization problem is formed into a MILP problem and solved, and the corresponding co‐optimization scheduling method is given. Based on meeting the needs of HFCVs and electric vehicle (EV) owners, the system can schedule the number and timing of charging and discharging of EVs and the power of hydrogen production from electrolyzers on the time scale, and finally smooth the total power curve. Simulation results show that the proposed collaborative optimal energy scheduling method can meet the current demand for new energy in the system and improve the economy of the IES.

Practical constraints in rater-mediated assessments limit the availability of complete data. Instead, most scoring procedures include one or two ratings for each performance, with overlapping performances across raters or linking sets of multiple-choice items to facilitate model estimation. These incomplete scoring designs present challenges for detecting rater biases, or differential rater functioning (DRF). The purpose of this study is to illustrate and explore the sensitivity of DRF indices in realistic sparse rating designs that have been documented in the literature that include different types and levels of connectivity among raters and students. The results indicated that it is possible to detect DRF in sparse rating designs, but the sensitivity of DRF indices varies across designs. We consider the implications of our findings for practice related to monitoring raters in performance assessments.

Abstract Background GeneXpert enterovirus Assay is a PCR-based assay for Enterovirus meningitis diagnosis. However, there is currently no research about the performance of GeneXpert enterovirus assay in the diagnosis of enterovirus meningitis. Thus, a systematic review and meta-analysis is significant on the topic. Methods Embase, Cochrane Library, Web of Science, and PubMed were systematically reviewed with retrieval types. Some criteria were used to filter the studies. Only studies published in English, that made a comparison between GeneXpert enterovirus assay and RT-PCR, and could be formulated in a 2*2 table, were included. The quality of the included studies was evaluated by QUADAS-2. The effect of the GeneXpert enterovirus assay was assessed by the Sensitivity, Specificity, Positive Likelihood Ratio, Negative Likelihood Ratio, Diagnosis Odds Ratio, and summary receiver operating characteristic (SROC) curve. Publication bias and heterogeneity were evaluated by the Deeks' funnel test and Bivariate Box plot respectively. Results 7 studies were recruited in the analysis. The Pooled Sensitivity was 0.96 [95% CI (0.94–0.97)], Pooled Specificity was 0.99 [95% CI (0.98–0.99)], Positive Likelihood Ratio was 130.46 [95% CI (35.79–475.58)], Negative Likelihood Ratio was 0.04 [95% CI (0.02–0.10)], and Diagnostic Odds Ratio was 3648.23 (95% CI [963.99–13,806.72)]. In SROC Curve, Area Under Curve (AUC) was 0.9980, and Q*= 0.9849. In Deeks' funnel test, the P-value was 0.807 (P > 0.05), indicating no publication bias. The Bivariate Box plot indicated no evident heterogeneity. Conclusions The GeneXpert enterovirus assay demonstrated high diagnostic accuracy in diagnosing enterovirus meningitis.

IntroductionParasitic plants can damage crop plants and consequently cause yield losses and thus threaten food security. Resource availability (e.g., phosphorus, water) has an important role in the response of crop plants to biotic attacks. However, how the growth of crop plants under parasitism are affected by environmental resource fluctuation is poorly understood.MethodsWe conducted a pot experiment to test the effects of the intensity of Cuscuta australis parasitism and the availability of water and phosphorus (P) on soybean shoot and root biomass.Results and discussionWe found that low-intensity parasitism caused ~6% biomass reduction, while high-intensity parasitism caused ~26% biomass reduction in soybean. Under 5–15% water holding capacity (WHC), the deleterious effect of parasitism on soybean hosts was ~60% and ~115% higher than that under 45–55% WHC and 85–95% WHC, respectively. When the P supply was 0 μM, the deleterious effect of parasitism on soybean was 67% lower than that when the P supply was 20 μM. Besides, the biomass of C. australis was highest when both the water and the P availability were lowest. Cuscuta australis caused the highest damage to soybean hosts under 5 μM P supply, 5–15% WHC, and high-intensity parasitism. Additionally, C. australis biomass was significantly and negatively related to the deleterious effect of parasitism on soybean hosts and to the total biomass of soybean hosts under high-intensity parasitism, but not under low-intensity parasitism. Although high resource availability can promote soybean growth, the two resources have different impacts on the response of hosts to parasitism. Higher P availability decreased host tolerance to parasites, while higher water availability increased host tolerance. These results indicate that crop management, specifically water and phosphorus supply, can efficiently control C. australis in soybean. To our best knowledge, this appears to be the first study to test the interactive effect of different resources on the growth and response of host plants under parasitism.