Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing Lean Six Sigma in financial services: the effect of motivations, selected methods and challenges on LSS program- and organizational performance

PurposeThe purpose of this paper is to contribute to the limited body of empirical knowledge on the impact of Lean Six Sigma (LSS) program implementations on organizational performance in financial services by investigating how antecedents of Lean Six Sigma program success (motivations, selected LSS methods and challenges) affect organizational performance enhancement via LSS program performance.Design/methodology/approachA sample of 198 LSS professionals from 7 countries are surveyed. Structural equation modeling (SEM) is performed to test the questioned relations.FindingsThis study’s findings comprise: (1) LSS program performance partially mediates the relationship between motivations for LSS implementation and organizational performance, (2) selected LSS method applications has a fully (mediated) indirect impact on organizational performance, (3) LSS implementation challenges also have an indirect (mediated) impact on organizational performance and (4) LSS program performance has a positive impact on organizational performance.Originality/valueThe findings of this research predominantly provide nuances and details about LSS implementation antecedents and effects, useful for managers in advising their business leaders about the prerequisites and potential operational and financial benefits of LSS implementation. Furthermore, the paper provides evidence and details about the relationship between important antecedents for LSS implementation identified in existing literature and their impact on organizational performance in services. Thereby, this research is the first in providing empirical, cross-sectional, evidence for the antecedents and effects of LSS program implementations in financial services.
On Cost Functions in the Hybrid Variational–Ensemble Method

AbstractIn the hybrid variational–ensemble data assimilation schemes preconditioned on the square root of background covariance , is a linear map from the model space to a higher-dimensional space. Because of the use of the nonsquare matrix , the transformed cost function still contains the inverse of . To avoid this inversion, all studies have used the diagonal quadratic form of the background term in practice without any justification. This study has shown that this practical cost function belongs to a class of cost functions that come into play whenever the minimization problem is transformed from the model space to a higher-dimension space. Each such cost function is associated with a vector in the kernel of (Ker), leading to an infinite number of these cost functions in which the practical cost function corresponds to the zero vector. These cost functions are shown to be the natural extension of the transformed one from the orthogonal complement of Ker to the full control space.In practice, these cos.
The effect of thioredoxin and prochymosin coexpression on the refolding of recombinant alpaca chymosin

The milk-clotting enzyme chymosin is a member of the group of aspartate proteinases. Chymosin is the main component of rennet traditionally obtained from the stomachs of dairy calves and widely used to coagulate milk in the production of various types of cheese. Another source of chymosin, which does not require the killing of animals, is based on recombinant DNA technology. Recombinant alpaca chymosin has a number of valuable technological properties that make it attractive for use in cheese-making as an alternative to recombinant bovine chymosin. The purpose of this work is to study the effect of coexpression of thioredoxin and prochymosin on the refolding of the recombinant zymogen and the activity of alpaca chymosin. To achieve this goal, on the basis of the pET32a plasmid, an expression vector was constructed containing the thioredoxin A gene fused to the N-terminal sequence of the marker enzyme zymogen, alpaca prochymosin. Using the constructed vector, pET-TrxProChn, a strain-producer of the recombinant chimeric protein thioredoxin-prochymosin was obtained. The choice of prochymosin as a model protein is due to the ability of autocatalytic activation of this zymogen, in which the pro-fragment is removed, together with the thioredoxin sequence attached to it, with the formation of active chymosin. It is shown that Escherichia coli strain BL21 transformed with the pET-TrxProChn plasmid provides an efficient synthesis of the thioredoxin-prochymosin chimeric molecule. However, the chimeric protein accumulates in inclusion bodies in an insoluble form. Therefore, a renaturation procedure was used to obtain the active target enzyme. Fusion of thioredoxin capable of disulfide-reductase activity to the N-terminal sequence of prochymosin provides optimal conditions for zymogen refolding and increases the yield of recombinant alpaca chymosin immediately after activation and during long-term storage by 13 and 15 %, respectively. The inclusion of thioredoxin in the composition of the chimeric protein, apparently, contributes to the process of correct reduction of disulfide bonds in the prochymosin molecule, which is reflected in the dynamics of the increase in the milk-clotting activity of alpaca chymosin during long-term storage.
A statistical physics approach to inference problems on random networks

Recent advances in measurement technologies have resulted in the availability of large datasets from a variety of fields spanning the natural and social sciences. This posed the challenge to develop new statistical tools to extract relevant information from the data. A paradigmatic model that has been successfully applied to analyze large datasets is the Ising model of binary spins interacting through pairwise connections. In this thesis, we use methods of statistical physics to tackle several open problems related to modelling the stochastic dynamics of the Ising model and reconstructing the unknown network of interactions from data. First, we derive a novel mean-field solution to the discrete time parallel dynamics of the Ising model, based on a weak coupling expansion of the log-generating function with constrained first and second order moments over time, the result of which outperforms other mean field techniques in predicting single site magnetization. Next, for both the equilibrium and kinetic models, we analyze the inverse problem of learning the couplings between the variables based on a set of observations on spin configurations. Using the cavity and replica methods of statistical physics, we compare the performance of different inference algorithms, by analytical computation of the estimation error as a function of the size of the dataset, and study its deviation from asymptotic optimality. We also derive optimal algorithms for learning the couplings. Finally, we consider the case where a subset of the spin trajectories is observed while the rest are hidden. This enabled us to model systems where only a finite fraction of the system is experimentally accessible, but allowed the hidden variables to affect the dynamics of the observed variables. A central question is the prediction of the hidden spin state when the couplings are known. For the average case scenario, we investigate the theoretically optimal performance for predicting hidden spins by computing the error of the Bayes optimal predictor. We also derive a meanfield formalism to accurately estimate the single-site magnetisation of hidden spins for single instances of the network.
Defining Sports Performance by Using Automated Machine Learning System

: We wanted to determine whether we could use an automated machine learning system called Azure for the selection process and placement of conscript training in such a way that AI can make decisions for the right conscript training program individually. To test this, we had four separate datasets and access to the Microsoft Azure automated machine learning environment. According to the test sets we performed, we see that, by using an automated machine learning environment, it was possible to reach the precision level of the decisions we wanted. The main obstacle was not the used automated machine learning environment itself, but the quality of the data used for learning. We also made improvement suggestions regarding how data could be collected and what kind of data we should measure to make predictive data analysis better and be more usable in the future.
Structural Deterioration Detection Using Enhanced Autoregressive Residuals

This paper presents a study on detecting structural deterioration in existing buildings using ambient vibration measurements. Deterioration is a slow and progressive process which reduces the structural performance, including load-bearing capacity. Each building has unique vibration characteristics which change in time due to deterioration and damage. However, the changes due to deterioration are generally subtler than the changes due to damage. Examples of deterioration include subtle loss of steel-concrete bond strength, slight corrosion of reinforcement and onset of internal cracks in structural members. Whereas damage can be defined as major sudden structural changes, such as major external cracks of concrete covers. Herein, a deterioration detection method which uses structural health monitoring (SHM) data is proposed to address the deterioration assessment problem. The proposed novel vibration-based deterioration identification method is a parametric-based approach, incorporated with a nonparametric statistical test, to capture changes in the dynamic characteristics of structures. First, autoregressive (AR) time-series models are fitted to the vibration response time histories at different sensor locations. A sensitive deterioration feature is proposed for detecting deterioration by applying statistical hypotheses of two-sample [Formula: see text]-test on the model residuals, based on which a function of the resulting [Formula: see text]-values is calculated. A novel AR model order estimation procedure is proposed to enhance the sensitivity of the method. The performance of the proposed method is demonstrated through comprehensive simulations of deterioration at single and multiple locations in finite element models (FEM) of 3- and 20-storey reinforced concrete (RC) frames. The method shows a promising sensitivity to detect small levels of structural deterioration prior to damage, even in the presence of noise.
Comparison of Analgesic Effects of Ketorolac Plus Apotel to Meperidine in Patients Undergoing Right Thoracotomy: A Double-Blind Randomized Clinical Trial

Background Postoperative pain management using nonsteroidal anti-inflammatory drugs with no narcotic-attributed adverse effects, such as respiratory depression, nausea, and vomiting, is still the subject of extensive research. However, concerns about bleeding and nephrotoxicity have limited routine use. Objectives The present trial aimed to compare the effects of ketorolac/apotel and meperidine on postoperative pain relief in patients undergoing thoracotomy. Methods This randomized controlled trial enrolled 122 patients who were candidates for right thoracotomy. The patients were randomly divided into two groups that received ketorolac (30 mg)/apotel (1 g) or meperidine (0.5 - 1 mg/kg) at the beginning of recovery, respectively. This study assessed the Numeric Rating Scale pain score immediately and 30 and 60 minutes after the surgery in the recovery room, blood pressure, oxygen saturation (SpO2), sweating, and pulse rate. Results The average pain score at recovery time was significantly lower in the ketorolac/apotel group (2.06 ± 1.40) than in the meperidine group (2.76 ± 1.61) (P = 0.011). In contrast to the ketorolac/apotel group, an increasing trend was observed in pain scores in the meperidine group throughout the time sequence (P < 0.05). However, a slight non-significant increase was also observed in the ketorolac/apotel group. No statistical differences were observed in blood pressure (P = 0.826), SpO2 (P = 0.826), and pulse rate (P = 0.811) between the two study groups. Conclusions This study provides support that the combination of ketorolac/apotel offers a slightly superior analgesic effect for patients undergoing thoracotomy, compared to meperidine. Pain management is crucial during recovery, and the current study’s findings suggest that administering ketorolac/apotel provides effective analgesia during recovery after thoracic surgery.
Teaching locomotion biomechanics: from concepts to applications

Our daily activities involve different types of locomotion, including walking and running. In this paper we present a series of biomechanical models to describe such locomotor activities with a set only a few characteristic parameters. With this we aim at providing an introduction to the modelling of human locomotion which is equally appropriate for high-school and university-level students. We share our experiences with integrating these biomechanical models into classes with students of different disciplines comprising engineering, computer science, physics and sport science. Supplementing these modelling approaches, we use experimental methods as well as robotic testbeds to evaluate and justify the biomechanical simulation models. By this, we enable students from different fields to gain first experiences in the understanding and application of human movement systems and potential extensions to related technologies, such as robotics and assistive devices (e.g. prostheses and exoskeletons).
qPCR Assay as a Tool for Examining Cotton Resistance to the Virus Complex Causing CLCuD: Yield Loss Inversely Correlates with Betasatellite, Not Virus, DNA Titer

Cotton leaf curl disease (CLCuD) is a significant constraint to the economies of Pakistan and India. The disease is caused by different begomoviruses (genus Begomovirus, family Geminiviridae) in association with a disease-specific betasatellite. However, another satellite-like molecule, alphasatellite, is occasionally found associated with this disease complex. A quantitative real-time PCR assay for the virus/satellite components causing CLCuD was used to investigate the performance of selected cotton varieties in the 2014–2015 National Coordinated Varietal Trials (NCVT) in Pakistan. The DNA levels of virus and satellites in cotton plants were determined for five cotton varieties across three geographic locations and compared with seed cotton yield (SCY) as a measure of the plant performance. The highest virus titer was detected in B-10 (0.972 ng·µg−1) from Vehari and the lowest in B-3 (0.006 ng·µg−1) from Faisalabad. Likewise, the highest alphasatellite titer was found in B-1 (0.055 ng·µg−1) from Vehari and the lowest in B-1 and B-2 (0.001 ng·µg−1) from Faisalabad. The highest betasatellite titer was found in B-23 (1.156 ng·µg−1) from Faisalabad and the lowest in B-12 (0.072 ng·µg−1) from Multan. Virus/satellite DNA levels, symptoms, and SCY were found to be highly variable between the varieties and between the locations. Nevertheless, statistical analysis of the results suggested that betasatellite DNA levels, rather than virus or alphasatellite DNA levels, were the important variable in plant performance, having an inverse relationship with SCY (−0.447). This quantitative assay will be useful in breeding programs for development of virus resistant plants and varietal trials, such as the NCVT, to select suitable varieties of cotton with mild (preferably no) symptoms and low (preferably no) virus/satellite. At present, no such molecular techniques are used in resistance breeding programs or varietal trials in Pakistan.
“My math lessons are all about learning from your mistakes”: how mixed-attainment mathematics grouping affects the way students experience mathematics

ABSTRACT Mixed-attainment mathematics teaching is not a common practice in England, despite evidence that ability grouping is not an effective strategy for improving educational outcomes. This study compares mathematics in School M (mixed-ability groupings) and School S (sets) in relation to student beliefs, and teacher beliefs and practices. Questionnaire data from 286 students and 12 teachers were triangulated with lesson observations and interviews. This article suggests grouping practices could indeed influence students’ mindsets, teachers’ mindsets and teachers’ beliefs and practices. An above average proportion of students in both schools reported growth orientations, although these beliefs were held more strongly by students in the mixed-attainment grouping. School M teachers also held stronger growth-mindsets than School S teachers. Mathematics teachers in both schools reported connectionist beliefs, but the students’ experiences differed. Most students in School M perceive typical mathematics lessons as involving a substantial problem or challenge worked on collaboratively in pairs or small groups, and having several entry points. Students in both schools valued learning from mistakes, but School M students were more likely to both believe this would help them, and have access to this type of learning opportunity. Students taught in sets experienced mathematics as procedures delivered by teachers and reproduced by students. This has implications for further research as mixed-attainment groupings may be a factor in determining the way in which students experience learning mathematics.
Arteriolosclerosis CSVD: a common cause of dementia and stroke and its association with cognitive function and total MRI burden

Objective Arteriolosclerosis cerebral small vessel disease (CSVD) is a common type of CSVD. This study aimed to explore the factors associated with cognitive function and total MRI burden related to the disease. Methods The demographic characteristics, clinical manifestations, cognitive function score, Barthel Index (BI), blood test index, and follow-up results of arteriolosclerosis CSVD patients treated for the first time in our hospital from January 2014 to August 2022 were collected. White matter hyperintensity (WMH) Fazekas score, total MRI burden, and cerebral atrophy grade were evaluated according to brain MRI findings. Factors associated with CSVD cognitive function were analyzed by binary logistic regression. The correlative factors related to the total MRI burden of CSVD were analyzed by ordered multiple logistic regression. Results A total of 146 patients were included in this study, of which 132 cases (90.4%) had hypertension. There were 108 patients (74.0%) with cognitive dysfunction, 97 patients (66.4%) with balance and gait disorders, and 83 patients (56.8%) with moderate-to-severe dependence in daily life (BI ≤ 60 points). Of 146 patients, 79 (54.1%) completed clinical and imaging follow-ups for a median of 3 years. The number of patients with cognitive impairment and BI ≤ 60 points after follow-up significantly increased compared with the first admission (P < 0.001). There were also significant differences in total MRI burden (P = 0.001), WMH Fazekas score, and cerebral atrophy grade (P < 0.001). Mean age (P = 0.012), median deep WMH Fazekas score (P = 0.028), and median deep (P < 0.001) and superficial (P =0.002) cerebral atrophy grade of patients with cognitive impairment at first admission were all higher than those with non-cognitive impairment. Multivariate analysis showed that deep cerebral atrophy was independently and significantly associated with cognitive impairment of CSVD (P = 0.024), and hypertension was significantly and independently associated with total MRI burden (P = 0.001). Conclusion The disease course of arteriolosclerosis CSVD may be related to cognitive function and total MRI burden. Deep cerebral atrophy was an independent risk factor for cognitive dysfunction in arteriolosclerosis CSVD, and hypertension was an independent risk factor for total MRI burden.
Determinants of Tax Productivity Performance in Tanzania: A Time Series Analysis

The study was focused on assessing the determinants of tax productivity performance in Tanzania adopting a time series analysis based on data spanning from the year 1996 to 2020. A time series using the multivariate regression model of the Ordinary Least Square Method (OLSM) was employed to analyze the data. The findings of the study show that GDP as a proxy of economic factors, the share of agriculture in GDP as a proxy of structural factors and regulatory quality as a proxy of institutional factors have a positive significant relationship with the dependent variable (i.e., total tax as a percent of GDP). On the other hand, the industrial sector, trade volume (i.e., export and import), and control of corruption were insignificant factors influencing the dependent variable (i.e., total tax as a percent of GDP). As part of the policy implication, we recommend that there should be sustainable initiatives to formalize the agricultural sector. Improve the industrial sector, trade volume and institutional environment to widen the tax base and increase tax revenue to align with the country’s economic development
Incentive mechanisms for price and advertising coordination in dynamic marketing channels

This paper examines the issue of price and advertising coordination in bilateral monopolies from a dynamic perspective. Its main objectives are to design incentive mechanisms that the manufacturer can use in order to reduce double marginalization, and to investigate whether these mechanisms can be implemented by the manufacturer and accepted by the retailer. I consider a differential game where a manufacturer controls its wholesale price and its national advertising effort. The carry-over effects of national advertising build up brand goodwill, which represents the model's state variable. The retailer controls the product's retail price and the local advertising effort, which affect consumer demand, in conjunction with the brand goodwill. I compute strategies and outcomes under three scenarios: (a) a fully coordinated scenario where channel members act as if the channel was vertically integrated; (b) a noncoordinated scenario, considered as the status quo, where the channel is decentralized; and (c) a scenario where incentive mechanisms are offered by the manufacturer. The main results indicate that two incentive mechanisms, namely, a retail-price incentive and an advertising allowance offered by the manufacturer to the retailer can mitigate the double-marginalization problem. With these incentives, and an appropriate choice of the channel-coordinating wholesale price, the manufacturer can induce the retailer to fix its control variables at their channel-coordinating levels without committing to implement the channel-coordinating level of national advertising. Our results highlight the role of the channel-coordinating wholesale price level on the Pareto-optimality and the implementation of the incentives. I compute the values for the channel-coordinating wholesale price that can be used as benchmarks by channel members in order to evaluate if the incentives should be implemented or not. I illustrate our results with numerical simulations indicating the channel-coordinating wholesale price values where we observe an increase in both channel members' individual profits when the incentives are implemented (i.e., with respect to the status quo). These results can be explained by the decrease in retail price and the increase in local and national advertising. Both effects explain the increase in brand goodwill and demand when the incentives are implemented.