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Aim: This research evaluated standard Weibull mixture cure (WMC) network meta-analysis (NMA) with Bayesian hierarchical (BH) WMC NMA to inform long-term survival of therapies. Materials & methods: Four trials in previously treated metastatic non-small-cell lung cancer with PD-L1 >1% were used comparing docetaxel with nivolumab, pembrolizumab and atezolizumab. Cure parameters related to a certain treatment class were assumed to share a common distribution. Results: Standard WMC NMA predicted cure rates were 0.03 (0.01; 0.07), 0.18 (0.12; 0.24), 0.07 (0.02; 0.15) and 0.03 (0.00; 0.09) for docetaxel, nivolumab, pembrolizumab and atezolizumab, respectively, with corresponding incremental life years (LY) of 3.11 (1.65; 4.66), 1.06 (0.41; 2.37) and 0.42 (-0.57; 1.68). The Bayesian hierarchical-WMC-NMA rates were 0.06 (0.03; 0.10), 0.17 (0.11; 0.23), 0.12 (0.05; 0.20) and 0.12 (0.03; 0.23), respectively, with incremental LY of 2.35 (1.04; 3.93), 1.67 (0.68; 2.96) and 1.36 (-0.05; 3.64). Conclusion: BH-WMC-NMA impacts incremental mean LYs and cost–effectiveness ratios, potentially affecting reimbursement decisions.

The use of pesticides to eliminate pests and weeds has raised concerns about water pollution and adverse effects on aquatic organisms, so many efforts have been made to increase the resistance of fish to these pesticides by using a proper nutrition strategy. Therefore, the aim of this study is to investigate the growth performance, antioxidant, and immune responses of fish exposed to Imidacloprid insecticide (C9H10ClN5O2) by different doses of Chlorella vulgaris dry powder to the diet of common carp (Cyprinus carpio). In this study, 600 common carp with a medium weight (18.10±0.2 g; Mean ± SE) were prepared and after adaptation and determination of lethal concentration of Imidacloprid, for 56 days in 6 treatments and each with 3 replications were classified and tested (Control (T1), 5% Chlorella vulgaris dry powder and no pollution (T2), 10% Chlorella vulgaris dry powder and no pollution (T3), No Chlorella vulgaris dry powder and 12.5% LC50 Imidacloprid (T4), 5% Chlorella vulgaris dry powder plus 12.5% LC50 imidacloprid (T5) and 10% Chlorella vulgaris dry powder plus 12.5% LC50 Imidacloprid (T6)). After 96 hours of exposure to distinct concentrations of the insecticide, the total mortality was measured and the Imidacloprid median lethal concentration (LC50) over 96 hours was calculated (266.2 mg/l) using Probit analysis. According to the result, common carp fed T2 had the highest final weight (FW), weight gain (WG), and specific growth rate (SGR), and the lowest feed conversion ratio (FCR) among the groups (P50) was significantly higher in T2 and T3 than other groups (PChlorella vulgaris protects common carp from Imidacloprid insecticide, since it improved growth performance, antioxidant and immune responses of fish.

Several microorganisms are there in chicken manure (CM) but Salmonella, Cryptosporidium and Escheridia coli are the most identified. Objective of this research includes, carrying out microbial count in the CM substrate for 40 days retention time in a digester, uterlizing kinetic expressions satisfying the process and fitting results obtained with 26 existing microbial growth kinetic models. Results shows that microbes inside the CM slurry, survived for a full period of 37 days divided into 7 days of acclimatization, 23 days of growth and another 7 days of equal rate of death and multiplication. Findings shows that the maximum specific growth rate, ????????????????, estimated from the basic Monod equation, of the organisms is 0.0076hr-1 and the half-saturation constant, ????????, is ????. ???????????? × ???????????? mg/l which indicates how sufficient the substrate concentration is for the bacteria to feed on. Not all 26 growth kinetic models found in the literature fit the measured experimental data. However, Monod with decay rate, Wayman and Tseng, Han and Levenspiel, Luong and Moser models fit the Monod values after regressing with POLYMATH 6.10 Educational Release.

Multi-attribute decision-making (MADM) methods are widely used by decision makers as decision support tools. Most MADM methods have shortcomings in the solution process that combined with other decision making methods can eliminate these shortcomings or improve the performance of the method. One of the methods that can be used to improve MADM methods is preferential voting, which is actually a linear programming (LP) model with weight restrictions. The Kemeny Median Indicator Ranks Accordance (KEMIRA) is one of the most modern MADM methods; in this paper, we provide an improved version in this relative, by utilizing the concept of preferential voting. The new model, in being implemented on a real-world problem, will be compared to the previous method and ultimately some of its advantages will be rendered.

Nowadays, architectural designers interact with children to better understand their demands so that to improve the emotional connection between the child and the architectural space. The present st...

Basic Data Envelopment Analysis (DEA) models are designed for non-negative data. However, negative data is inevitably used in many real-world issues. Also, multiple units with a maximum relative performance score (equal to one) can be obtained due to the benevolent view of evaluating Decision Making Units (DMUs) consistent performance. Therefore, the researchers proposed ranking models to differentiate efficient units. Cross efficiency is one of the most useful tools for DMUs ranking in the DEA. There are two major drawbacks to implementing this process. First, it gives different results in the presence of other optimal solutions; second, it does not provide a compelling reason to use the arithmetic mean to aggregate the results of the cross efficiency matrix. In this paper, first a new non-radial model is proposed to evaluate the performance of DMUs in the presence of negative data and then based on this model a new secondary goal model is proposed to eliminate the first drawback in the cross efficiency method. Also, to solve the second drawback in this method, a hybrid Multi-Attribute Decision Making (MADM)-DEA process with the help of fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje method is proposed. Finally, to show the applicability of the proposed methods, the results are used to select the supplier in a real-world problem.

Background: Obsessive-compulsive disorder (OCD) is a potentially debilitating disease that affects 1-4% of the general population. It is characterized by the presence of obsessions and compulsions which interfere with the individual’s life and functioning. Although conventional treatments such as drug therapies and cognitive behavioral therapy exist for OCD, these treatments are not universally successful and can cause side effects, which has created a demand for alternative and complementary therapies. Methods: In this review, we summarize randomized clinical trials on the effectiveness of herbal medicines for the treatment of OCD, and review the possible mechanisms of action for these medicines. A search in PubMed, Scopus, and The Cochrane Library found 1022 studies, of which 7 were included in our review. Results: The studies that we found were conducted over 6 to 12 weeks, and had an average sample size of 37. The plant species studied included Crocus sativus, Echium amoenum, Hypericum perforatum, Silybum marianum, Valeriana officinalis, and Withania somnifera. The trials demonstrated the effectiveness of all plants as treatments for OCD except H. perforatum. The phytochemicals found in these plants produce their effects through a variety of means such as inhibiting the reuptake of monoamines, GABAergic effects, and neuroendocrine modulation. The small number of studies and their small sample sizes, poor methodology, and lack of replication highlight the need for further research into herbal medicines for the treatment of OCD. Conclusion: Overall, herbal medicines can be used as stand-alone therapies for OCD or in conjunction with other methods.

Background Accidental awareness during general anaesthesia (AAGA) is a complex and rare outcome to investigate in surgical patient populations, particularly obstetric patients. We report the protocol of the Direct Reporting of Awareness in Maternity patients (DREAMY) study, illustrating how the research was designed to address practical and methodological challenges for investigating AAGA in an obstetric cohort. Methods This is the trial protocol of a prospective, multicentre cohort study of patients undergoing obstetric surgery under general anaesthesia. Accidental awareness during general anaesthesia will be detected using three repetitions of standardised direct questioning over 30 days, with responses indicating memories during general anaesthesia verified using structured interviews. Reports will be adjudicated, then classified, in accordance with pre-defined and pre-validated structures, including the Michigan Awareness Classification tool. Quantitative data will be collected on general anaesthesia conduct for all participants. This descriptive study is being conducted in England and aims to recruit a minimum of 2015 patients. Results The DREAMY study was prospectively registered (ClinicalTrials.gov Identifier: NCT03100396) and ethical approval granted. Participant recruitment began in May 2017 and one year follow up concluded in August 2019. Publication of the results is anticipated in 2020. Conclusions The DREAMY study will provide data on incidence, experience and implications of AAGA for obstetric patients, using a robust methodology that will reliably detect and translate subjective AAGA reports into objective outcomes. In addition, the study is expected to improve vigilance for AAGA in participating hospitals and encourage adoption of recommendations for support of patients experiencing AAGA.

Data envelopment analysis (DEA) is a mathematical programming technique for efficiency analysis. For dealing with the data in ratio form, the DEA model for ratio data known as DEA-R exists in the literature. However, some ratio data like financial risk may be negative naturally. In this paper, we contribute to the literature in two ways. In the first place, we deal with DEA-R models in the presence of negative ratio data by proposing an inverse DEA model for merger analysis. In the second place, we develop DEA-R models for merger analysis that can deal with negative data. We apply our models in a real-world application of efficiency and merger analysis of an Iranian bank with 66 branches. The proposed models maintain data confidentiality. This motivates managers to participate in the evaluation and merger process. Our models also provide a reasonable endogenous weight restriction framework without restricting weights exogenously.

Decision-making can be accounted for, as being one of the most essential and fundamental managerial tasks; and organizational research depends on its quality, so, from the point of view of connoisseurs, decision-making is the main essence of management. The KEmeny Median Indicator Ranks Accordance (KEMIRA) decision-making method is one of the latest Multi-Attribute Decision Making (MADM) methods, which has been conveyed in this paper by utilizing the preferential voting concept. A new Voting-KEMIRA model is expressed by rendering a linear programming model with weight control bylaw. In implementing this novel model on a real world problem, it is compared with the preceding method and finally a few of its advantages will be specified.

Oxcarbazepine as an anticonvulsant has been suggested as an effective drug in affective disorders. The present study was designed to compare the efficacy of oxcarbazepine and sodium valproate in the treatment of acute mania in the Iranian population. In a double-blind, randomized clinical trial, hospitalized bipolar patients in the acute manic phase who were admitted to Ibn-e-Sina psychiatric hospital in Mashhad city (north-eastern part of Iran) were enrolled. The diagnosis was confirmed using Structured Clinical Interview for DSM-IV-TR. Patients were then randomly allocated into two groups taking oxcarbazepine (900-2400 mg/day) and sodium valproate (about 20 mg/kg/day) for 6 weeks. Young Mania Rating Scale (YMRS), Clinical Global Impression Scale (CGI-S), and adverse effects of drugs were assessed at baseline and after 3 and 6 weeks. Mania symptoms based on mean scores of YMRS and CGI-S significantly decreased from baseline to endpoint in both treatments (P0.01). However, there was no significant difference between the two groups in terms of reduction of symptoms during times (P = 0.715 and P = 0.446, respectively) and adverse events (P0.05). This study confirmed the previous findings that indicate the efficacy of oxcarbazepine as same as sodium valproate. Moreover, its adverse effects resemble sodium valproate in the treatment of acutely manic patients.

The stop signal task (SST) paradigm with its original roots in 1948 has been proposed to study humans’ response inhibition. Several statistical software codes have been designed by researchers to simulate SST data in order to study various theories of modeling response inhibition and their assumptions. Yet, there has been a missing standalone statistical software package to enable researchers to simulate SST data under generalized scenarios. This paper presents the R statistical software package “SimSST”, available in Comprehensive R Archive Network (CRAN), to simulate stop signal task (SST) data. The package is based on the general non-independent horse race model, the copulas in probability theory, and underlying ExGaussian (ExG) or Shifted Wald (SW) distributional assumption for the involving go and stop processes enabling the researchers to simulate sixteen scenarios of the SST data. A working example for one of the scenarios is presented to evaluate the simulations’ precision on parameter estimations. Package limitations and future work directions for its subsequent extensions are discussed.

The initial issue that must be addressed in teamwork is the manner in which decisions are made by the group and its members. Voting is a procedure to aggregate individual votes to achieve a collective decision. Since individuals have varied opinions and preferences, preferential voting assists in conveying the priorities of the voters to the society or community. In many circumstances, such as voting-based managerial decisions, voters are of a voting preference of unequal voting power. This paper presents a method for the ranking of preferential voting with voters of unequal voting power, which, in addition to the utilization of preference voting models, employs the DEA and assurance region techniques. The deployment of DEA technique causes an increment in the competence of discriminating the ranking of candidates, and in the finale the proposed method is expressed for an empirical example to rank the petrochemical companies in the Tehran Stock Exchange.

In this paper, we prove that for a set of ten univariate stochastic orders including the usual order, a univariate stochastic order preserves either both, one or none of additivity and multiplication properties over the vector space of real-valued random variables. Then, classifying participant’s quickness in a mental chronometry trial to “weakly faster” and “strongly faster”, we use the above results for the usual stochastic order to establish necessary and sufficient conditions for a participant to be strongly faster than the other in terms of the fitted Wald, Exponentially modified Wald(ExW), and Exponentially modified Gaussian(ExG) distributional parameters. This research field remains uncultivated for other univariate stochastic orders and in several directions.

Background: The outbreak of coronavirus disease 2019 (COVID-19) pandemic has led to public panic and psychological problems. In this regard, few studies have reported the post-discharge mental health status of COVID-19 survivors. Objectives: This study aimed to evaluate the mental health status of COVID-19 survivors and determine the risk factors of adverse psychological outcomes. Methods: This cross-sectional study consisted of 188 COVID-19 survivors discharged from hospital. Data were recorded using social media applications. To evaluate the mental health status, Hospital Anxiety and Depression Scale (HADS) and Depressive Anxiety and Stress Scale 42 (DASS-42) were used. Ordinal regression with a logit link was used to assess the simultaneous effect of variables. Results: The mean age of participants was 56.4 ± 9.6 years, and the majority of patients were male (62.2%). According to HADS, 81 (43.1%) and 24 (12.8%) patients suffered from anxiety and depression, respectively. Using DASS-42, we reported at least a mild degree of depression [23 (12.2%)], anxiety [20 (10.6%)], and stress [74 (39.4%)] among the discharged patients. Depression, anxiety, and stress were significantly associated with length of hospital stay (P < 0.05). In addition, there was a direct association between stress and patients’ number of children (β = 0.38, P = 0.02). Conclusions: Depression, anxiety, and stress are relatively high among COVID-19 survivors. Length of hospital stay and number of children were identified as the predisposing factors for adverse psychological outcomes.

Background: The non-pharmacological interventions for behavioral problems of adolescents diagnosed with attention deficit hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) are of great interest to researchers. Objectives: This study aimed to examine the efficacy of group reality therapy in behavioral symptoms in ADHD and ODD. Methods: Forty patients diagnosed with ADHD and ODD between 12 and 18 years were randomly assigned to two groups. The patients in the intervention group were participated in group reality therapy for five 120-minute sessions, once a week. The patients in the control group were enrolled in five unstructured sessions without intervention. Conners Parent Short Form questionnaire, Strengths and Difficulties Questionnaire (SDQ), and depression, anxiety, and stress scale (DASS-21) were performed at baseline, the sixth week, and the tenth week for follow-up. Results: Conners test showed a statistically significant difference in the intervention group in hyperactivity (P = 0.005), conduct (P < 0.001), and total score (P < 0.001) in the sixth week, as well as in conduct (P = 0.001), and total score (P = 0.008) in the tenth week. SDQ test analyzed based on partial Eta squared test, indicated the effect size in the intervention group was 81% and in the control group was 27%. Moreover, the DASS test in the intervention group showed improvement in depression, anxiety, and stress scores (P < 0.001). Conclusions: Group reality therapy is effective in the improvement of disruptive behaviors and emotional symptoms in ADHD and ODD.

This open access book explores the global challenges and experiences related to digital entrepreneurial activities, using carefully selected examples from leading companies and economies that shape world business today and tomorrow. Digital entrepreneurship and the companies steering it have an enormous global impact; they promise to transform the business world and change the way we communicate with each other. These companies use digitalization and artificial intelligence to enhance the quality of decisions and augment their business and customer operations. This book demonstrates how cloud services are continuing to evolve; how cryptocurrencies are traded in the banking industry; how platforms are created to commercialize business, and how, taken together, these developments provide new opportunities in the digitalized era. Further, it discusses a wide range of digital factors changing the way businesses operate, including artificial intelligence, chatbots, voice search, augmented and virtual reality, as well as cyber threats and data privacy management.

For decades, intrapreneurship has been, and is still, promoted to employees as a way to capture the creativity and excitement of entrepreneurship, albeit with more resources and less risk. Intrapreneurship creates opportunities for individuals to be innovative and entrepreneurial within and for the organisation that employs them. The ways in which intrapreneurs act have not changed, unlike the business context surrounding them. Digitalisation has opened the path for new intrapreneurial opportunities; however, the amount of attention paid to the role of digital intrapreneurs within existing organisations is limited. We present our own definition of digital intrapreneurship and position our definition in the digital landscape where modern companies operate. This chapter outlines numerous ways to foster digital intrapreneurship, including a set of practical methods for managers to identify, and empower digital intrapreneurs. The chapter presents three case studies and discusses their practical implications for entrepreneurs and their teams.

For decades, creativity has been used to generate ideas among entrepreneurs and their teams. Although extensive research has been conducted on creativity, the majority of studies have focused on traditional ways of stimulating creativity, such as focus groups, the collective notebook method, brainstorming, brainwriting, reverse brainstorming and problem inventory analysis. However, the digital age appears to challenge much of this existing work on the nature of creativity. It is clear that online creativity and audiences are affecting the meaning, expression and impact of creativity. The traditional techniques of stimulating creativity have been replaced and aided by technology-driven innovations, such as artificial intelligence (AI), virtual reality (VR) and the Internet of things (IoT). This chapter explores ways to activate the creativity of entrepreneurs and their teams through the use of digital technologies. We believe that this chapter provides a rich source of examples on how technology is currently being used to support creativity by encouraging entrepreneurs and their teams to make connections, develop ideas, create meaning, collaborate and communicate. We present, in detail, three case studies and discuss practical implications for the future.

The Stop Signal Reaction Time (SSRT) is a latency measurement for the unobservable human brain stopping process, and was formulated by Logan (1994) without consideration of the nature (go/stop) of trials that precede the stop trials. Two asymptotically equivalent and larger indices of mixture SSRT and weighted SSRT were proposed in 2017 to address this issue from time in task longitudinal perspective, but estimation based on the time series perspective has still been missing in the literature. A time series-based state space estimation of SSRT was presented and it was compared with Logan 1994 SSRT over two samples of real Stop Signal Task (SST) data and the simulated SST data. The results showed that time series-based SSRT is significantly larger than Logan’s 1994 SSRT consistent with former Longitudinal-based findings. As a conclusion, SSRT indices considering the after effects of inhibition in their estimation process are larger yielding to hypothesize a larger estimates of SSRT using information on the reactive inhibition, proactive inhibition and their interplay in the SST data.

The stop signal reaction time (SSRT), a measure of the latency of the stop signal process, has been theoretically formulated using a horse race model of go and stop signal processes by the American...