Pricing Strategies in the Remanufacturing Market for the Uncertain Market Size in the Second Period

Our main endeavor is to investigate the effect of the uncertain market size in the second period on the pricing strategies in the remanufacturing market. Observing the previous research, we find that the market size in the second period is always supposed to be certain. However, there is substantial empirical and experimental evidence that the market size in reality deviates from this assumption. In fact, though the market size in the first period is definitized, it is difficult to confirm the change of the market scale in the second period, since this change is affected by all kinds of elements, such as the awareness of environmental protection, some consumers’ psychological factors, and the related governments’ policies. Hence, we pay attention to the case in which the change rate of the market scale in the second period is random variable. We suppose that this rate satisfies uniform distribution on [0,2]. Underlying this assumption, we further provide an insight into the game-playing relationship between original equipment manufacturers (OEMs) and remanufacturers. Moreover, we delicately and subtly incorporate the game theory, stochastic analysis, adversarial risk analysis (ARA), and optimization methods into the pricing strategies in the remanufacturing market. Last but not least, considerable efforts and attempts have been made to subtly test the sensitivity of an optimal solution to the different parameters

Association of atopy with disease severity in children with Mycoplasma pneumoniae pneumonia

BackgroundMycoplasma pneumoniae pneumonia (MPP) is common among children, but the impact of atopy on MPP severity in children is unknown. This study investigated whether atopic vs. nonatopic children had greater MPP severity.MethodsRetrospective analysis was conducted on 539 (ages 3–14 years) patients who were hospitalized in the First Affiliated Hospital of Anhui Medical University for MPP between January 2018 and December 2021, 195 were atopic and 344 were nonatopic. Of them, 204 had refractory MPP, and 335 had general MPP. And of atopic children, 94 had refractory MPP, and 101 had general MPP. Data on demographic and clinical characteristics, laboratory findings, clinical treatments were analyzed.ResultsSignificantly more boys with MPP were atopic than nonatopic (P < 0.05). More atopic (than nonatopic) children presented with prolonged fever and hospitalization, severe extra-pulmonary complications, asthma attaking, steroid and oxygen treatment, and increased IgE levels (all P < 0.05). In atopic (vs. nonatopic) children with MPP, the incidence of sputum plugs under the fiberoptic bronchoscopy and lobar pneumonia was significantly increased and required bronchoscopy-assisted and steroid therapy. Compared with nonatopic children, more atopic children developed refractory MPP (P < 0.05). Prolonged fever and hospitalization, severe extra-pulmonary complications, lymphocyte count, procalcitonin and lactate dehydrogenase levels, and percentages of atopy were all significantly higher (P < 0.05) among children with refractory MPP vs. general MPP. Moreover, Prolonged fever and hospitalization, lymphocyte count, procalcitonin and lactate dehydrogenase levels, and the treantment of steroid were all significantly higher (P < 0.05) among atopic children with refractory MPP vs. general MPP. Spearman correlation analysis showed strong associations between atopy and male sex, length of hospital stay, fever duration, IgE level, wheezing, lobar pneumonia, refractory MPP, and treatment with oxygen, hormones or bronchoscopy (P < 0.05).ConclusionsAtopy may be a risk factor for and was positively correlated with the severity of MPP in children

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Gendered STEM Workforce in the United Kingdom:The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Gendered STEM Workforce in the United Kingdom: The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress towards gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Detecting money laundering using filtering techniques: a multiple-criteria index

Money laundering is a dynamic activity attempting to circumvent anti-money laundering (AML) actions. We propose a money-laundering detection approach encompassing three separate detection measures applied simultaneously, providing a consolidated index to minimize circumvention. The index incorporates three detection measures: (1) deviations in trading volume and frequency; (2) unusual payments to or receipts from an atypical trade partner; and (3) Benford's Law, based on the number of times a specific digit occurs in a particular position in numbers to detect financial fraud. Finally, we design a numerical test that any reasonable detection approach should satisfy. Our results successfully discover possible fraud planted in the simulated data.money laundering, financial fraud, data mining techniques, outlier detection,