ABSTRACT
This paper investigates a government's subsidy strategy for motivating a manufacturer to set up a flexible production line for emergency supplies. Four subsidy strategies are proposed to ensure a desired service level in case of an emergency: zero subsidy, a fixed subsidy, a marginal subsidy, and a hybrid subsidy. We develop a game theoretical model to examine how the government can induce a manufacturer to set up a flexible production line that can respond promptly to an emergency, based on the manufacturer's cost structure (fixed and marginal costs). We find that when the marginal profit of an emergency product is higher than that of the manufacturer's regular product, a fixed (marginal) subsidy is the dominant strategy if the manufacturer's fixed (marginal) cost is high, while a hybrid subsidy strategy is dominant if both costs are high. When the marginal profit of an emergency product is lower than that of the manufacturer's regular product, neither a fixed subsidy nor a zero subsidy will be the dominant strategy. We also find that a marginal subsidy can ensure the effectiveness of the strategy, while a fixed subsidy helps improve strategy efficiency. We use government subsidy strategies implemented for Chinese COVID-19 emergency supplies as examples to demonstrate the effectiveness and efficiency of the subsidy strategies under the proposed framework. We also extend the discussion by considering the manufacturer's social consciousness.
ABSTRACT
We use a detailed micro dataset on product availability and stockouts to construct a direct high-frequency measure of consumer product shortages during the 2020–2022 pandemic. We document a widespread multi-fold rise in stockouts in nearly all sectors early in the pandemic. Over time, the composition evolved from temporary to more permanently discontinued products, concentrated in fewer sectors. We show that unexpected shocks to stockout levels have significant inflationary effects within three months. These effects are larger and more persistent for imported goods and import-intensive sectors. We develop a model of inventories in a sector facing both demand and cost disturbances, and use the observed joint dynamics of stockouts and prices to show that these effects can be associated with elevated costs of replenishing inventories and higher exposure to trade.
ABSTRACT
To avoid the spread of COVID-19, China has implemented strict lockdown policies and control measures, resulting in a dramatic decrease in air pollution and improved air quality. In this study, the air quality model WRF-Chem and the latest MEIC2019 and MEIC2020 anthropogenic emission inventories were used to simulate the air quality during the COVID-19 lockdown in 2020 and the same period in 2019. By designing different emission scenarios, this study explored the impact of the COVID-19 lockdown on the concentration of air pollutants emitted by different sectors (industrial sector and transportation sector) in Nanjing for the first time. The results indicate that influenced by the COVID-19 lockdown policies, compared with the same period in 2019, the concentrations of PM2.5, PM10, and NO2 in Nanjing decreased by 15%, 17.1%, and 20.3%, respectively, while the concentration of O3 increased by 45.1% in comparison;the concentrations of PM2.5, PM10 and NO2 emitted by industrial sector decreased by 30.7%, 30.8% and 14.0% respectively;the concentrations of PM2.5, PM10 and NO2 emitted by transportation sector decreased by 15.6%, 15.7% and 26.2% respectively. The COVID-19 lockdown has a greater impact on the concentrations of PM2.5 and PM10 emitted by the industrial sector, while the impact on air pollutants emitted by the transportation sector is more reflected in the concentration of NO2. This study provides some theoretical basis for the treatment of air pollutants in different departments in Nanjing.
ABSTRACT
Presentations spanned a range of applications: the public health impacts of poor air quality and environmental justice;greenhouse gas measuring, monitoring, reporting, and verification (GHG MMRV);stratospheric ozone monitoring;and various applications of satellite observations to improve models, including data assimilation in global Earth system models. The combination of methane (CH4), carbon dioxide (CO2), carbon monoxide (CO), and NO2 retrievals can improve confidence in emissions inventories and model performance, and together these data products would be of use in future air quality management tools. The ability to retrieve additional trace gases (e.g., ethane, isoprene, and ammonia) in the thermal IR along with those measured in the UV–Vis–NIR region would be extremely useful for air quality applications, including source apportionment analysis (e.g., for oil/natural gas extraction, biogenic, and agricultural sources). Ground-level ozone is one of six criteria pollutants for which the EPA sets National Ambient Air Quality Standards (NAAQS) to protect against human health and welfare effects.
ABSTRACT
As classic psychedelics are increasingly legalized and reintroduced into the psychotherapeutic frame, a deeper understanding of their effect on personality and overall wellbeing - as well as their clinical contraindications and potential pitfalls - will prove essential. As a result, this study represents a preliminary investigation into the effect of psilocybin exposure on a range of personality constructs.Methods: Data was collected through a collaboration with The Psychedelic Society, an organization that legally administers psilocybin in the Netherlands to self-selecting retreat attendees who have been screened for mental health disorders in accordance with Johnson et al.'s (2008) widely accepted safety guidelines. Participants completed the following measures via online questionnaire the day before psilocybin exposure, two days after psilocybin exposure, and at a one-month follow-up: Brief Symptom Inventory (BSI), Experiences in Close Relationships-Revised (ECR-R), Mentalization Scale (MentS), Inventory of Personality Organization (IPO), Pathological Narcissism Inventory (PNI), Pro-Environmental Behavior (PEB) and the Ryff Scale of Psychological Well-Being (Ryff). As rigorous double-blind clinical studies have linked psychedelic-occasioned mystical experiences to sustained improvement in personality-related domains (Maclean et al., 2011;Griffiths et al., 2006;Griffiths et al., 2008, Griffiths et al., 2011), the Mystical Experience Questionnaire (MEQ) was also administered. Lastly, participants provided self-report responses to open-ended questions regarding subjective experiences following psilocybin exposure. Results: Due to the Covid-19 pandemic, data collection was prematurely cut short, and only preliminary conclusions can be drawn. Those provisional conclusions include significantly improved overall symptomatology (BSI GSI), identity diffusion (IPO-ID) and reality testing (IPO-RT) following psilocybin exposure. Significant interactions were also identified for primitive defenses (IPO-PD) and narcissistic grandiosity (PNI-G), with the non-mystical experience group trending toward improvement, and the mystical-experience group remaining largely unchanged over time. Due to methodological constraints encountered, these findings remain preliminary. Nevertheless, the presence of multiple significant findings (however nascent) within the context of so few participants suggests the possibility of more robust findings in the context of a larger sample size. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
We updated the anthropogenic emissions inventory in NOAA's operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model's prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA's Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.
ABSTRACT
Fluxes of nitrogen oxides (NOx=NO+NO2) and carbon dioxide (CO2) were measured using eddy covariance at the British Telecommunications (BT) Tower in central London during the coronavirus pandemic. Comparing fluxes to those measured in 2017 prior to the pandemic restrictions and the introduction of the Ultra-Low Emissions Zone (ULEZ) highlighted a 73 % reduction in NOx emissions between the two periods but only a 20 % reduction in CO2 emissions and a 32 % reduction in traffic load. Use of a footprint model and the London Atmospheric Emissions Inventory (LAEI) identified transport and heat and power generation to be the two dominant sources of NOx and CO2 but with significantly different relative contributions for each species. Application of external constraints on NOx and CO2 emissions allowed the reductions in the different sources to be untangled, identifying that transport NOx emissions had reduced by >73 % since 2017. This was attributed in part to the success of air quality policy in central London but crucially due to the substantial reduction in congestion that resulted from pandemic-reduced mobility. Spatial mapping of the fluxes suggests that central London was dominated by point source heat and power generation emissions during the period of reduced mobility. This will have important implications on future air quality policy for NO2 which, until now, has been primarily focused on the emissions from diesel exhausts.
ABSTRACT
Precise and continuous monitoring on long-term carbon dioxide (CO2) and methane (CH4) over the globe is of great importance, which can help study global warming and achieve the goal of carbon neutrality. Nevertheless, the available observations of CO2 and CH4 from satellites are generally sparse, and current fusion methods to reconstruct their long-term values on a global scale are few. To address this problem, we propose a novel spatiotemporally self-supervised fusion method to establish long-term daily seamless XCO2 and XCH4 products from 2010 to 2020 over the globe at grids of 0.25°. A total of three datasets are applied in our study, including GOSAT, OCO-2, and CAMS-EGG4. Attributed to the significant sparsity of data from GOSAT and OCO-2, the spatiotemporal Discrete Cosine Transform is considered for our fusion task. Validation results show that the proposed method achieves a satisfactory accuracy, with the σ (R²) of ~ 1.18 ppm (> 0.9) and 11.3 ppb (0.9) for XCO2 and XCH4 against TCCON measurements, respectively. Overall, the performance of fused results distinctly exceeds that of CAMS-EGG4, which is also superior or close to those of GOSAT and OCO-2. Especially, our fusion method can effectively correct the large biases in CAMS-EGG4 due to the issues from assimilation data, such as the unadjusted anthropogenic emission inventories for COVID-19 lockdowns in 2020. Moreover, the fused results present coincident spatial patterns with GOSAT and OCO-2, which accurately display the long-term and seasonal changes of globally distributed XCO2 and XCH4. [ABSTRACT FROM AUTHOR] Copyright of Earth System Science Data Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
COVID-19 pandemic and the Ukraine war demonstrate the inevitability of supply chain disruptions. A lack of effective supply chain resilience (SCR) causes mismatches between demand and supply, and the destabilization of normal operational policies in production, distribution, and inventory control. Existing research mainly provides different definitions and measurements of supply chain resilience for different product supply chains. In this paper, we look at how Industry 4.0 (14.0) technologies enhance supply chain resilience. An 14.0 enabled architecture is designed for a factory with multiple suppliers and extended inventory for improving SCR. Different simulation scenarios for a LED factory are designed for demonstrating 14.0 technologies supporting SCR in different phases. © 2022 IEEE.
ABSTRACT
When people face difficult life situations, close interpersonal interactions that are positive (supportive, warm, and intimate) and negative (critical, withdrawing, and unhelpful) can be assessed with the Interpersonal Resilience Inventory and should be distinct from social support indices (structural support and perceived support schema), associated with stress and well-being, and salient across different stress contexts. Online participants completed the Interpersonal Resilience Inventory when facing family or financial stressors (n = 327) and the COVID-19 pandemic (n = 180). Confirmatory factor analysis, discrimination, correlations, and models regressing stress and well-being on positive and negative interactions indicated that scales are distinct and explain unique variance in stress and well-being beyond general social support. Results highlight the unique function of perceived interactions. This study expands previous medical and couple-specific work on perceptions of positive and negative interactions by assessing them across important relationships, in two unique stress contexts (family and financial hardships, and COVID-19), and after controlling for other types of social support (support schemas and structural support). This work is important for building parsimonious theories of perceived interactions that may be generalizable across relationships and stress contexts and may illuminate social support pathways to well-being. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
The article examines what medical suppliers and providers learned about facial protection products after the Covid-19 pandemic of 2020-2022. Topics discussed include remarks from Jason Burnham, Senior Director of Facial Protection at Owens & Minor, evidence of pandemic-relaxed behaviors morphing into workflow acceptance, and statement from Gary Harris, Vice President of Sales and Marketing at Prestige Ameritech, about emergency use guidelines for personal protective equipment (PPE).
ABSTRACT
NOx is an important primary air pollutant, dominantly produced by anthropogenic, mostly combustion based, activities from sectors such as industry, traffic and transport. NOx is directly linked to negative health and environmental impacts. Currently, the construction of emission inventories to keep track of NOx emissions is based on official national reported emissions and prOxies such as activity data as well as direct measurements. The effort to properly construct an accurate inventory is significant and time consuming which causes a reporting offset between one and five years with respect to the current date. Next to this temporal lag difficulties in composed inventories can arise from legislative and protocol differences between countries and over time in reporting of emissions. Satellite based atmospheric composition measurements provide a unique opportunity to fill this gap and independently estimate emissions on a large scale in a consistent, transparent and comprehensible way. They give the possibility to check for compliance with emission reduction targets in a timely manner as well as to observe rapid emission reductions such as experienced during the COVID-19 lock-downs. In this study we apply a consistent methodology to derive NOx emissions over Germany for the years of 2019-2021. For the years where reporting is available differences between satellite estimates and inventory totals were within 100kt. The large reduction of NOx emissions related to the COVID-19 lock-downs were observed in both the inventory and satellite derived emissions. The recent projections for the inventory emissions pointed to a recovery of the emissions towards pre-COVID19 levels this increase was not observed. While emissions from the larger power-plants did rebound to earlier levels, others sectors such as road transport and shipping did not and could be linked to a reduction in the number of heavier transport trucks. This again illustrates the value of having a consistent satellite based methodology for faster projections to guide and check the conventional emission inventory reporting. The method described in this manuscript also meet the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues. Transparency and comparability, two key values for emission reporting, are thus bolstered by this technique. [ FROM AUTHOR]
ABSTRACT
Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future. [ FROM AUTHOR]
ABSTRACT
The impact of the COVID-19 health crisis on the mental health of the population requires the implementation of new primary screening strategies of mental health disorders to intervene in a timelier manner, and technology may provide solutions. We aimed to evaluate the usefulness of the mobile app Mentali (version 1.1.2; creators: Jorge Alfonso Solís Galván Sodel Vázquez Reyes, Margarita de la Luz Martínez Fierro, Perla Velasco Elizondo, Idalia Garza Veloz, Alejandro Mauricio González and Claudia Caldera Villalobos, Zacatecas, México) as a primary screening tool for anxiety and depression disorders in medical students and to assess the triggering risk factors. This was a descriptive and longitudinal study and included 155 Mexican medical students. Participants interacted with Mentali for 6 months. The mobile app integrated the Beck anxiety and depression inventories together with a mood module. At the end of the interaction, the students received psychological and psychiatric interventions to confirm their primary diagnoses. Symptoms of moderate/severe anxiety and depression were present in 62.6% and 54.6% of the studied population. When corroborating the diagnoses, Mentali obtained a sensitivity of 100%, 95%, and 43% to classify a mental health disorder, anxiety, and depression, respectively. The most important triggers found were as follows: belonging to a dysfunctional family, being introverted, and having suffered from bullying. The proportion of users with excellent/good mood decreased from 78.7% to 34.4% at the end of the semester, and the proportion of users who claimed to have bad/very bad mood increased from 7.4% to 34.4% at the end of the semester (p < 0.05). Mentali was useful for identifying users with anxiety and/or depression, and as an auxiliary tool to coordinate the provision of specialized interventions, allowing us to increase the proportion of patients who needed psychological care and received it by 30%. The efficacy of Mentali in identifying activities through time with an impact on the mood and mental health of the users was confirmed. Our results support the use of Mentali for the primary screening of mental health disorders in young adults, including medical students.
ABSTRACT
We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of the COVID-19 pandemic. This model was designed to understand the upstream and downstream propagation of the industry-specific demand and supply shocks caused by COVID-19, which were exceptional in their severity, suddenness and heterogeneity across industries. The model, which was inspired in part by previous work on the response to natural disasters, includes the introduction of a new functional form for production functions, which allowed us to create bespoke production functions for each industry based on a survey of industry analysts. We also introduced new elements for modeling inventories, consumption and labor. The resulting model made accurate real-time forecasts for the decline of sectoral and aggregate economic activity in the United Kingdom in the second quarter of 2020. We examine some of the theoretical implications of our model and find that the choice of production functions and inventory levels plays a key role in the propagation of pandemic shocks. Our work demonstrates that an out of equilibrium model calibrated against national accounting data can serve as a useful real time policy evaluation and forecasting tool.
ABSTRACT
The Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite is a valuable source of information to monitor the NOx emissions that adversely affect air quality. We conduct a series of experiments using a 4×4 km2 Comprehensive Air Quality Model with Extensions (CAMx) simulation during April–September 2019 in eastern Texas to evaluate the multiple challenges that arise from reconciling the NOx emissions in model simulations with TROPOMI. We find an increase in NO2 (+17 % in urban areas) when transitioning from the TROPOMI NO2 version 1.3 algorithm to the version 2.3.1 algorithm in eastern Texas, with the greatest difference (+25 %) in the city centers and smaller differences (+5 %) in less polluted areas. We find that lightningNOx emissions in the model simulation contribute up to 24 % of the column NO2 in the areas over the Gulf of Mexico and 8% in Texas urban areas. NOx emissions inventories, when using locally resolved inputs, agree with NOx emissions derived from TROPOMI NO2 version 2.3.1 to within 20 % in most circumstances, with a small NOx underestimate in Dallas–Fort Worth (-13 %) and Houston (-20 %). In the vicinity of large power plant plumes (e.g., Martin Lake and Limestone) we find larger disagreements, i.e., the satellite NO2 is consistently smaller by 40 %–60 % than the modeled NO2, which incorporates measured stack emissions. We find that TROPOMI is having difficulty distinguishingNO2 attributed to power plants from the background NO2 concentrations in Texas – an area with atmospheric conditions that cause short NO2 lifetimes. Second, the NOx/NO2 ratio in the model may be underestimated due to the 4 km grid cell size. To understand ozone formation regimes in the area, we combine NO2 column information with formaldehyde (HCHO) column information. We find modest low biases in the model relative to TROPOMI HCHO, with -9 % underestimate in eastern Texas and -21 % in areas of central Texas with lower biogenic volatile organic compound (VOC) emissions. Ozone formation regimes at the time of the early afternoon overpass are NOx limited almost everywhere in the domain, except along the Houston Ship Channel, near the Dallas/Fort Worth International airport, and in the presence of undiluted power plant plumes. There are likely NOx-saturated ozone formation conditions in the early morning hours that TROPOMI cannot observe and would be well-suited for analysis with NO2 and HCHO from the upcoming TEMPO (Tropospheric Emissions: Monitoring Pollution) mission. This study highlights that TROPOMI measurements offer a valuable means to validate emissions inventories and ozone formation regimes, with important limitations.
ABSTRACT
Despite continuing criticism regarding the psychometric credibility of projective techniques, evident in the scholarly literature (e.g., Imuta et al., 2013;Lawrence et al., 2021;Ryan et al., 2019), mounting recent evidence indicates that projective tests are considered an integral part of acceptable methods used or applied in research studies (Eby, 2020;Piotrowski, 2022). Perhaps reflecting the acceptability and relevance of projective assessment, chapter coverage in recent texts on psychological assessment clearly depicts the importance of projective techniques in clinical and child psychology applied to the mental health evaluation process (Saklofske et al., 2013;Sellbom & Suhr, 2020;Verdon & Azoulay, 2020;Weiner & Kleiger, 2021;Wright, 2020;Yalof & Bram, 2021). All this bodes-well for research, but what about the status of projective testing in practice/applied settings, particularly as a foundation for forming a conceptual therapeutic framework in mental health treatment? Hence, the purpose of this commentary is to address this specific issue by examining recent test usage findings, based on empirical data, as reported by practitioners. Of particular interest are the findings of 2 recent dissertations regarding the use of projective tests by practicing psychologists. The first study surveyed a sample of 510 members of APA Division 12 (both clinical child and pediatric psychologists) on the use of drawings (H-T-P, DAP, KFDs) in assessment (Longest, 2006). The most interesting observation is that 25% of the practitioners responded to the survey. Such attention to drawings in clinical practice shows a continued professional interest in projective assessment (see Piotrowski, 2016). More recent data on the use of a variety of projective techniques by psychologists were reported in a dissertation on current assessment practices in the context as an aid in therapy (Hanigan, 2021). This study surveyed licensed psychologists in practice across a myriad of applied settings in the USA. Of the 293 respondents, 29% were engaged in assessment for 20+ hours/per week (this is a slight increase from survey data reported over the past 2 decades). In addition, 55% of these practitioners use at least one projective technique (45% don't use any projective measures). Overall, the continued presence of projective measures in the research literature, the introduction of a host of novel projective techniques in recent years, and contemporary survey data on test usage clearly confirm the fact that projective techniques remain a significant part of the assessment armamentarium of mental health professionals. The current pandemic has had a very detrimental impact on the assessment enterprise (Gicas et al., 2021;Krach et al., 2020), particularly with regard to the use of the Rorschach. This disengagement of personality assessment practice has been corroborated in an analysis of the recent mental health literature related to COVID-19 (Piotrowski & Watt, 2021).Hence, we must await the findings of future studies (post-COVID) before an accurate appraisal on the nature and extent of psychological testing (particularly projective techniques) can be determined (Krishnamurthy et al., 2022). But, for now, projective assessment appears alive and (somewhat well) in the field of mental health evaluation. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
ABSTRACT
Objective: Given the global uncertainty faced due to the Covid-19 pandemic, health services were forced to adjust inventory management and purchase projections. This publication aims to describe the strategies taken and their impact on the supply chain indicators by the pharmacy service in the management of drug purchases during the pandemic to expose the importance of pharmacist in charge of the supply chain. Methods: This observational study describes the drug purchasing system in a general hospital and the strategies used to manage drug supply. The actions proposed by the pharmacy department are listed chronologically related to inventory issues and purchasing decisions. The accuracy of the purchase forecast was evaluated by calculating indicators such as the mean absolute standard deviation (MAD) and the variance of the forecast error (MSE). Inventory days and inventory turnover indicators were also compared in pre-pandemic and pre-pandemic periods. Findings: In general, the forecast error given by MAD and MSE tended to decrease. Specifically, from the 82 drug categories, during the pandemic period, this indicator decreased in 72 (88%), increased in 9 (11%), and remained the same in only 1 (1%) of the categories. In financial terms, comparing the 2018-2019 and 2020-2021 periods, a favorable result was obtained when evaluating the inventory turnover indicators, which decreased by 0.01 points and the days of inventory increased on average by two days. Conclusions: The implementation and use of these indicators prevented drug shortages, reducing inventory forecast errors. A pharmacist with knowledge in inventory management allows managing a process of continuous improvement and tactics for efficient inventory management without neglecting the benefit to patients or the economic profitability of the service. There were limitations since digital operating systems do not generate centralized or organized data for this type of analysis.
ABSTRACT
The issuance of consumption coupons during the epidemic period to stimulate the economy must take full account of the level of probabilistic consumption and inventory optimization. In this paper, an improved minimum-cost maximum-flow model is constructed to dynamically adjust the inventory capacity of node enterprises with the change of probabilistic consumption level, and three scenarios are simulated by numerical assumptions. The results show that: (1) The model can better solve the problem of consumption coupons, probabilistic consumption and inventory optimization;(2) Consumer welfare remains unchanged, the largest number of government consumption coupons is issued, and the number of enterprise inventories reaches the lowest;(3) Enterprise inventories are minimized with different decisions on consumer probability consumption, and the government’s issuance of consumption coupons and the satisfaction of consumer demand have reached a dynamic balance. Corresponding suggestions are put forward, hoping to better help the government to implement the consumption coupons policy to stimulate the economy.
ABSTRACT
Malingered psychosis is problematic in many legal/forensic contexts and can have serious consequences such as wrongful competency to stand trial and criminal responsibility determinations, inaccurate violence/dangerousness risk assessments, unjust punishments/sentences, inappropriate prison placements, reduced culpability in prison rule violations, high recidivism risk, and inappropriate parole/probation readiness decisions. Given that malingering is most prevalent in forensic settings (Grossman & Wasyliw, 1988) and psychosis is the mental illness most frequently malingered by forensic populations (Broughton & Chesterman, 2001) the accurate assessment and identification of malingered versus true psychosis is imperative for appropriate responses/outcomes in these settings. Currently available measures used to detect malingered psychopathology are lengthy, time consuming, difficult to interpret, and lack straightforward consistent cutoff scores across different diagnostic categories. A within-subjects repeated measures design was used to validate using the newly published Inventory of Problems-29 (IOP-29) False Disorder Probability Scale (FDS) and five Modified Response Special Scores variables of the Rorschach Performance Assessment System (R-PAS) to detect feigned psychosis in a forensic sample of 13 adults on probation for lower-level sexual, domestic violence, and/or drug/alcohol-related convictions in the United States. Participants were instructed to complete the IOP-29 and R-PAS under two different test conditions: (1) Genuine condition (GEN) with standard administration instructions, and (2) Feigning condition (FGN) during which participants were instructed to feign psychosis based on a case vignette description of the Diagnostic and Statistical Manual of Mental Disorders-5th edition (DSM-5) schizophrenia criteria. As hypothesized, results were consistent with previous international studies on detecting psychosis on the IOP-29 in that FDS scores were significantly higher during the FGN condition compared to the GEN condition. The a priori cutoff of > .50 distinguished FGN from GEN. Specificity (92.3%) and sensitivity (84.6%) were high and consistent with past research. It was also predicted that raw scores on five Modified Response Special Scores R-PAS variables would be higher in the FGN condition than in the GEN condition and that a priori cutoffs would correctly distinguish FGN from GEN. These hypotheses were partially supported in that raw scores were significantly higher in the FGN condition and a priori cutoffs correctly distinguished FGN from GEN on three of the five Modified Response Special Scores variables. Limitations include small sample size resulting from COVID-19 pandemic in-person restrictions, absence of genuine psychotic patient group, and demographic characteristics (e.g., only 1 female, non-incarcerated, low level criminal offenses, etc.), which may reduce replicability and generalizability of the findings. Future research should replicate this study with larger sample sizes and examine gender differences and ecological validity in different types of forensic settings. Overall, the results of this study support the use of the IOP-29 and three R-PAS Modified Response Special Scores variables to detect malingered psychosis in forensic populations. The IOP-29 may be preferable to other available measures since it performs just as well, and has the benefits of being shorter, more straightforward, and easier to interpret with a single consistent cutoff score across diagnostic categories. (PsycInfo Database Record (c) 2022 APA, all rights reserved)