Research on bias in peer review examines scholarly communication and funding processes to assess the epistemic and social legitimacy of the mechanisms by which knowledge communities vet and self-regulate their work. Despite vocal concerns, a closer look at the empirical and methodological limitations of research on bias raises questions about the existence and extent of many hypothesized forms of bias. In addition, the notion of bias is predicated on an implicit ideal that, once articulated, raises questions about the normative implications (...) of research on bias in peer review. This review provides a brief description of the function, history, and scope of peer review; articulates and critiques the conception of bias unifying research on bias in peer review; characterizes and examines the empirical, methodological, and normative claims of bias in peer review research; and assesses possible alternatives to the status quo. We close by identifying ways to expand conceptions and studies of bias to countenance the complexity of social interactions among actors involved directly and indirectly in peer review. (shrink)

Model predictive control technology can effectively reduce the bad effect caused by inaccurate data prediction in microgrid energy management problem. However, the use of MPC technology needs to dynamically select an optimal solution from the Pareto solution set to implement, which needs the participant of the decision-makers frequently. In order to reduce the burden on decision-makers, we designed a knee point-based evolutionary multiobjective optimization algorithm, termed KBEMO. Knee point is the solution on Pareto front with the maximum marginal utility, which (...) is considered as the preferred solution if there is no other preference. This algorithm focuses on obtaining the knee region and automatically outputs knee points after the optimization. By combining this algorithm with MPC technology, it can effectively reduce the amount of computational consumption and obtain better convergence. Experimental results show that this method is more competitive than the traditional single-objective MPC method. (shrink)

Previous studies have shown the operational potential in political discourse analysis from the proximization perspective. This study adopts a cross-disciplinary approach to analyze political communication across transcultural contexts, especially in the cyber discourse space. Based on the spatial–temporal–axiological model, we compare the journalistic discourses on two social media platforms by China Xinhua News Agency, an official speaker for China worldwide. The corpuses are constructed with microblogs on Weibo in Chinese and Twitter in English containing key words of Sino–US trade war. (...) It is found that the speaker has shaped different realities of discourse in accordance with the cultural contexts and proximized from corresponding dimensions to increase political legitimacy. (shrink)

Montesquieu believes that human beings have three kinds of natures: self-preserving, imperfect knowledge and passions. The first and the third nature tend to conflict with each other, and the result is the state of war, in which human natures could not be satisfied. Montesquieu uses this theory of human nature to judge all the kinds of governments, and finds that the virtuous republic, despotism and monarchy all have important defects. Especially, the monarchy by nature tends to degenerate into despotism, which (...) is a realistic threat to him and his contemporaries. To solve this political problem, Montesquieu himself develops a two-fold solution: he wants to establish a certain kind of positive laws to satisfy the first nature, and wants to promote commerce to satisfy the third nature. Moreover, his ideal-government has important differences with the English political system. (shrink)

Strauss’s analysis of Machiavelli is both about his argument and action. He looks Machiavelli’s argument through the lens of classicalpolitical philosophy especially Plato’s political philosophy. He believed Machiavelli had not achieved important theoretical innovation. He looks Machiavelli’s action through the lens of modernity. He believed Machiavelli’s political thought did not perform a good function as it did in the last several centuries any more. Moreover, Strauss supplement Machiavelli’s political thought with a discussion of the problem of technique. Strauss’s caution of (...) the technical innovation makes sense, but his distinction between theoretical science and technique is not convincing. (shrink)

Aim To synthesize qualitative research on perspectives and understandings of Intensive Care Unit (ICU) patients, family members, and staff regarding respect and dignity in ICU, in order to explore the connotations and meanings of respect and dignity in ICU. Design A qualitative meta-synthesis. Methods The Chinese and English databases were systematically searched, including PubMed, Web of Science, CINAHL, Embase, Cochrane Library, CNKI, Wangfang Data, VIP, and CBM from each database’s inception to July 22, 2023. Studies were critically appraised using the (...) Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Qualitative Research. Qualitative data were extracted, summarized, and meta-synthesized. (PROSPERO: CRD42023447218). Results A total of 9 studies from 6 countries were included in the meta-synthesis. Thirty-six main themes and 67 sub-themes were extracted, which were eventually integrated into 9 categories and 4 themes: (1) integrity of humanity; (2) autonomy; (3) equality; (4) environmental support. Conclusion To maintain patient dignity, it is necessary to create an environment of respect within the ICU where healthcare professionals uphold the concept of preserving human integrity and respect patients' autonomy and equality. Healthcare professionals need to value the dignity of ICU patients and treat them as unique individuals during treatment and care. Hospital managers should also strive to create a respectful environment to provide environmental support for dignity care implementation. (shrink)

This study examines the influence of firm governance structures (board size, independence, CEO duality, director share ownership, and board meeting frequency) in relation to carbon emission disclosures by high-polluting Chinses firms. In addition, the study further examined the moderating role of earnings management on this relationship. In line with stakeholder and agency theories, our study identified that the large and independent boards exercise and demonstrate a higher degree of carbon emission disclosures. However, CEO duality and director share ownership are associated (...) with lower carbon emission disclosures. In addition, the study determined that higher earnings management results in a reduced level of carbon emission disclosures. Lastly, a firm earnings management strategy moderates the relationship between a firm governance structure and its carbon emission disclosures. The findings from the study are consistent with multiple econometric models and variables. The findings from the study contribute to the literature in the areas of firm corporate governance and carbon emission disclosures by documenting the moderating role of earnings management, which is not evident in previous studies; provide an enhanced perspective on the implications for firms, regulators, policymakers, and stakeholders who have an interest in reducing carbon emissions and advancing climate change mitigation goals in line with UN's Sustainable Development Goal (SDG) 7: climate action, and zero emissions goal by 2050. (shrink)

BackgroundPsychological distress is common among infertility patients. Total scale scores are often used to represent the severity of anxiety, depression, or stress, which ignores important differences between specific symptoms, and relationships between symptoms. This study aimed to identify patterns of psychological distress experienced by infertility patients and to identify the most central symptoms of anxiety, depression, and stress.MethodFrom June to September 2016, 740 infertility patients were included in this cross-sectional study. Infertility patients were asked to complete the Generalized Anxiety Disorder-7, (...) Patients Health Questionnaire-9 (PHQ-9), and Fertility Problem Inventory. Network analysis was used to examine the patterns of psychological distress in infertility patients and to test the most central symptoms of anxiety, depression, and stress.ResultsRestlessness was the most central symptom in infertility patients. “Feelings of guilt” had the highest strength among PHQ-9 symptoms. “Relationship concern stress” and “sexual concern stress” had the strongest connections in the network. Stability estimation indicated that the order of node strength centrality was more stable than the order of closeness and betweenness (the CS-coefficients were 0.75, 0.13, and 0.67, respectively). In addition, network structure and global strength were invariant across gender.LimitationsThe cross-sectional design did not permit identification of causal relationships. Patients in this study were recruited from one reproductive hospital; especially, most patients had low socioeconomic status, which limits generalizability of the findings.ConclusionThis study reinforces the need to better understand the underlying causes of psychological distress in infertile patients. A more detailed investigation of the relationship between these symptoms could provide information for psychosocial interventions aimed beyond “alleviating psychological distress.” We should consider the individual psychological symptom pattern and its potential causes in infertility patients instead of assuming a consistent psychological distress structure. (shrink)

In this paper, notions of fuzzy closure system and fuzzy closure L—system on L—ordered sets are introduced from the fuzzy point of view. We first explore the fundamental properties of fuzzy closure systems. Then the correspondence between fuzzy closure systems and fuzzy closure operators is established. Finally, we study the connections between fuzzy closure systems and fuzzy Galois connections. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hybrid renewable energy system arises regularly in real life. By optimizing the capacity and running status of the microgrid, HRES can decrease the running cost and improve the efficiency. Such an optimization problem is generally a constrained mixed-integer programming problem, which is usually solved by linear programming method. However, as more and more devices are added into MG, the mathematical model of HRES refers to nonlinear, in which the traditional method is incapable to solve. To address this issue, we first (...) proposed the mathematical model of an HRES. Then, a coevolutionary multiobjective optimization algorithm, termed CMOEA-c, is proposed to handle the nonlinear part and the constraints. By considering the constraints and the objective values simultaneously, CMOEA-c can easily jump out of the local optimal solution and obtain satisfactory results. Experimental results show that, compared to other state-of-the-art methods, the proposed algorithm is competitive in solving HRES problems. (shrink)

The scarcity of abnormal data leads to imbalanced data in the field of monitoring tool wear conditions. In this paper, a novel multi-index generative adversarial network is proposed to detect the tool wear conditions subject to imbalanced signal data. First, the generator in the MI-GAN is trained to produce fake normal signals, and the discriminator computes scores of testing signals and generated signals. Next, the generator detects abnormal signals based on the performance of imitating testing signals, and the discriminator will (...) compute the scores of testing signals and generated signals. Subsequently, two indexes, i.e., L 2 -norm and temporal correlation coefficient, are put forward to measure the similarity between generated signals and testing signals. Finally, our decision-making function further combines L 2 -norm and CORT with two discriminator scores to determine the tool conditions. Experimental results show that our method obtains 97% accuracy in tool wear detection based on imbalanced data without manual feature extraction, which outperforms traditional machine learning methods. (shrink)