Efficiency Of Different Strategies Research Articles

Аналітичні інструменти в економічних дослідженнях конкурентного середовища підприємства

This work is aimed at analyzing and highlighting the importance of using forecasting models in the study of the competitive environment of enterprise. Approaches to forecasting and modeling of the competitive environment of enterprise, which ensure the implementation of strategic planning and management, are studied. Various methods of forecasting are analyzed, which allow predicting future trends on the basis of available data. The key factors influencing the competitiveness of enterprise have been identified and their use for building a competitive model has been proposed. Forecasting requires a large amount of data and taking into account the versatility of the competitive environment. The use of several forecasting methods ensures high accuracy of predictions. No less important is the analysis of the impact of external factors on the activities of the enterprise, which allows you to quickly respond to changes. Forecasting is a dynamic process that requires constant updating and revision of results to maintain their relevance. Modeling of the competitive environment is based on the results of forecasting. The initial stage is to analyze the data and identify the key factors. Based on this, a model is built that reflects the interaction between these factors and allows you to evaluate the efficiency of different strategies. Models are subject to continuous evaluation and adjustment based on new data and changes in the environment, ensuring their adequacy and relevance. Particular attention is paid to the Irwin method, which is used to detect anomalies in data. This allows you to ensure high accuracy of the analysis and avoid the impact of random errors. On the basis of the carried out analysis, a table has been composed to systematize the directions of application of forecast models and methods in assessing the competitive environment of enterprise. It contains a brief description of forecasting methods and their characteristics, which contributes to a better understanding of the process and the choice of optimal approaches for specific situations. The importance of integrating forecasting and modeling to achieve effective enterprise management in a competitive environment is emphasized. The use of different methods and constant updating of models allow you to quickly adapt to changes, which is a key factor for success in today’s business environment.

Unveiling the power of adding sterols in wine: Optimizing alcoholic fermentation with strategic management

Excessive grape must clarification can result in sluggish alcoholic fermentation and sometimes alcoholic stuck fermentation, because of the lack of sterols for the yeasts growth. To avoid this risk, addition of sterols (ergosterol or phytosterols) can be performed, resulting in higher Saccharomyces cerevisiae viability and shorter fermentation duration. However, no dedicated study was implemented to evaluate the efficiency of different strategies of sterol management (considering sterol type, added concentration and timing of addition) during wine fermentation. So, first, to evaluate whether the response of wine yeast strains to sterol nutrition was similar according to the type and the concentration of the sterol present in the initial must, it was studied the response of a set of 10 S. cerevisiae strains in a synthetic grape must with low, medium and high concentrations of ergosterol or phytosterols. Then, the impact of the timing of sterol addition was evaluated on 2 S. cerevisiae strains with opposite behaviours. This work confirmed previous results concerning the role of ergosterol and phytosterols but also revealed new findings in this field. At first, it was confirmed that ergosterol played an important role in improving the maintenance of viable cells towards the end of fermentation, particularly in sterols-limited situations, while phytosterols demonstrated an ability to reduce acetate and glycerol production. But, in a second part, our study sheds new light on the beneficial impact of sterol addition on amino acid assimilation in yeast, leading to an increase in maximum fermentation rate, biomass production and percentage of viable cells. However, the main novelty of this research work concerns the timing of ergosterol addition. This addition at the start of fermentation in a phytosterols-free synthetic must enabled faster fermentations, as well as higher fermentative aroma synthesis, compared to addition during stationary phase Even if the impact of ergosterol additions were relatively similar for both strains tested, notable differences were found concerning amino acid assimilation and biomass production, suggesting differences in the regulation of nitrogen metabolism between both strains. These findings provide new insights into our understanding of sterol role in enological fermentation. It offers a basis for both the development of innovative strategies for sterol management and the selection of wine yeast strains under sterol starvation.

Accurate segmentation of neonatal brain MRI with deep learning.

An important step toward delivering an accurate connectome of the human brain is robust segmentation of 3D Magnetic Resonance Imaging (MRI) scans, which is particularly challenging when carried out on perinatal data. In this paper, we present an automated, deep learning-based pipeline for accurate segmentation of tissues from neonatal brain MRI and extend it by introducing an age prediction pathway. A major constraint to using deep learning techniques on developing brain data is the need to collect large numbers of ground truth labels. We therefore also investigate two practical approaches that can help alleviate the problem of label scarcity without loss of segmentation performance. First, we examine the efficiency of different strategies of distributing a limited budget of annotated 2D slices over 3D training images. In the second approach, we compare the segmentation performance of pre-trained models with different strategies of fine-tuning on a small subset of preterm infants. Our results indicate that distributing labels over a larger number of brain scans can improve segmentation performance. We also show that even partial fine-tuning can be superior in performance to a model trained from scratch, highlighting the relevance of transfer learning strategies under conditions of label scarcity. We illustrate our findings on large, publicly available T1- and T2-weighted MRI scans (n = 709, range of ages at scan: 26–45 weeks) obtained retrospectively from the Developing Human Connectome Project (dHCP) cohort.

Decision-level integration window strategy in satellite imagery-derived land surface temperature disaggregation

The purpose of this study is to present a new approach for satellite imagery-derived Land Surface Temperature (LST) disaggregation based on a decision level integration of various disaggregation strategies. Firstly, common disaggregation models including Global Window Strategy (GWS), Regular Local Window Strategy (RLWS), Object-based Window Strategy (OWS), and Conceptual Window Strategy (CWS) were used for LST disaggregation. Secondly, the Disaggregated LST (DLST) obtained from these strategies were integrated using the Decision-level Integration Window Strategy (DIWS). Finally, the efficiency of different strategies in LST disaggregation was evaluated using actual LST (ALST) maps and Actual Soil Temperature (AST) based on Pearson correlation coefficient (r) and Root Mean Square Error (RMSE). The mean r (RMSE) between ALST and DLST obtained from GWS, CWS, OWS, RLWS, and DIWS were 0.75 (1.87), 0.76 (1.90), 0.76 (1.80), 0.82 (1.38), and 0.89 (1.09 °C), respectively. The RMSE between AST and DLST obtained from these strategies were 3.28, 3.17, 2.87, 2.43, and 2.10 °C, respectively. The results showed that the effectiveness of DIWS in LST disaggregation was higher than other strategies.

Resilience efficiency importance measure for the selection of a component maintenance strategy to improve system performance recovery

To rapidly recover system performance after a failure event, a maintenance strategy is often determined to guide the repair of failed components. Due to the differences and interdependence between components, different maintenance sequences can lead to significant differences in the efficiency of system performance recovery. Hence, this paper analyzes a component maintenance strategy for the efficient recovery of system performance. First, two importance measures are proposed, namely the resilience efficiency importance measure (REIM) and the maintenance efficiency measure (MEM). The REIM measures the impact of the individual repair of a failed component on the efficiency of system performance recovery, and the MEM evaluates the repair efficiencies of different maintenance strategies. Both measures are used simultaneously to identify the importance of components for maintenance and guide the selection of maintenance sequences. The measures are then used to compare the maintenance efficiencies of different strategies, and the maintenance strategy that can accomplish the optimal overall efficiency of system performance recovery throughout the whole repair process is ultimately selected. Finally, the application of the proposed recovery process analysis method to a horizontal subsea Christmas tree system verifies its performance.

PhD data collection challenges due to the Covid-19 crisis – a report

The following report depicts the challenges I have faced in the process of collecting the necessary data for my PhD thesis titled Vocabulary learning strategies (VLSs) used by university students in an ESP context due to Covid-19 pandemics and switching to online teaching. In order to test the functionality of the VLSs qualitatively, and indicate which of them is responsible for acquisition of larger vocabulary quantities, my mentor (prof. Svetlana Dimitrova - Gyuzeleva) and I have designed an experiment which was originally intended to be fully implemented during the second semester of 2019/2020 (February to June). This experiment would provide a more detailed outline on the efficiency of different strategies use at the same time tackling maybe even a more important issue – raising learners’ awareness about their importance for individual language development. The respondents were students of Law and Finance. However, the Covid-19 pandemic interrupted the implementation of the experiment in the middle (in March) because the government introduced special measures and we were forced to convert to online teaching. Even though the teaching process was not dramatically challenged, since we were able to smoothly transfer to teaching via the platform Microsoft Teams, the experiment, or rather the results that were to be obtained with it, were significantly affected.

Analysis of the Deceleration Methods of Fatigue Crack Growth Rates under Mode I Loading Type in Pearlitic Rail Steel

The paper presents a comparison of the results of the fatigue crack growth rate for raw rail steel, steel reinforced with composite material—CFRP—and also in the case of counteracting crack growth using the stop-hole technique, as well as with an application of an “anti-crack growth fluid”. All specimens were tested using constant load amplitude methods with a maximum loading of Fmax = 8 kN and stress ratio R = σmin/σmax = 0.1 in order to analyze the efficiency of different strategies of fatigue crack growth rate deceleration. It has been shown that the fatigue crack grows fastest in the case of the raw material and slowest in the case of “anti-crack growth fluid” application. Additionally, the study on fatigue fracture surfaces using light and scanning electron (SEM) microscopy to analyze the crack growth mechanism was carried out. As a result of fluid activity, the fatigue crack closure occurred and significantly decreased crack driving force and finally resulted in fatigue crack growth decrease.

Coal biomethanation potential of various ranks from Pakistan: A possible alternative energy source

The present study investigated the possibility of microbial transformations of coal to gas (biogasification) as an alternative to conventional coal mining because this approach has the potential to be less expensive, cleaner, and provide greater access to deeper coal resources. Biogasification is often associated with low rank coal such as lignite and subbituminous coal that has produced enough coalbed methane to be commercially viable in the United States and Australia. However, little work has been done to analyze the potential of biogasification in higher rank coal. For this purpose, bioassay using a wetland-derived consortium and a coal-derived consortium were used to analyze coal samples from Pakistan belonging to different ranks (lignite to semi-anthracite). Among all samples a low volatile bituminous coal produced the maximum methane 34.95 μmol CH4/g coal with the wetland-derived microbial consortium, followed by subbituminous coal (30.18 μmol CH4/g coal). Lower methane levels were recorded with the coal-derived consortium, with subbituminous coal yielding the highest concentration (25.1 μmol CH4/g coal). Methane levels appeared to be increasing on the last measurement indicating the coal-derived consortium was slower than the wetland-derived consortium but could still catalyze biogasification in higher rank coals. Quantitative polymerase chain reaction analysis for mcrA functional genes indicated that the microbial community members that produce methane (methanogens) varied during the incubations. Energy conversion efficiency of different strategies (other biological and underground coal gasification processes) was also compared and discussed. This study was the first to compare bioassay using consortia of microbes non-indigenous and indigenous to coal and indicate the potential of biogasification from many different coalbeds across Pakistan.

Effectiveness and cost-effectiveness of Improving clinicians\u2019 diagnostic and communication Skills on Antibiotic prescribing Appropriateness in patients with acute Cough in primary care in CATalonia (the ISAAC-CAT study): study protocol for a cluster randomised controlled trial

BackgroundDespite their marginal benefit, about 60% of acute lower respiratory tract infections (ALRTIs) are currently treated with antibiotics in Catalonia. This study aims to evaluate the effectiveness and efficiency of a continuous disease-focused intervention (C-reactive protein [CRP]) and an illness-focused intervention (enhancement of communication skills to optimise doctor-patient consultations) on antibiotic prescribing in patients with ALRTIs in Catalan primary care centres.Methods/designA cluster randomised, factorial, controlled trial aimed at including 20 primary care centres (N = 2940 patients) with patients older than 18 years of age presenting for a first consultation with an ALRTI will be included in the study. Primary care centres will be identified on the basis of socioeconomic data and antibiotic consumption. Centres will be randomly assigned according to hierarchical clustering to any of four trial arms: usual care, CRP testing, enhanced communication skills backed up with patient leaflets, or combined interventions. A cost-effectiveness and cost-utility analysis will be performed from the societal and national healthcare system perspectives, and the time horizon of the analysis will be 1 year. Two qualitative studies (pre- and post-clinical trial) aimed to identify the expectations and concerns of patients with ALRTIs and the barriers and facilitators of each intervention arm will be run. Family doctors and nurses assigned to the interventions will participate in a 2-h training workshop before the inception of the trial and will receive a monthly intervention-tailored training module during the year of the trial period. Primary outcomes will be antibiotic use within the first 6 weeks, duration of moderate to severe cough, and the quality-adjusted life-years. Secondary outcomes will be duration of illness and severity of cough measured using a symptom diary, healthcare re-consultations, hospital admissions, and complications. Healthcare costs will be considered and expressed in 2021 euros (year foreseen to finalise the study) of the current year of the analysis. Univariate and multivariate sensitivity analyses will be carried out.DiscussionThe ISAAC-CAT project will contribute to evaluate the effectiveness and efficiency of different strategies for more appropriate antibiotic prescribing that are currently out of the scope of the actual clinical guidelines.Trial registrationClinicalTrials.gov, NCT03931577.

A comparison of search strategies to design the cokriging neighborhood for predicting coregionalized variables

Cokriging allows predicting coregionalized variables from sampling information, by considering their spatial joint dependence structure. When secondary covariates are available exhaustively, solving the cokriging equations may become prohibitive, which motivates the use of a moving search neighborhood to select a subset of data, based on their closeness to the target location and the screen effect approximation. This paper investigates the efficiency of different strategies for designing a sub-optimal neighborhood wherein the simplification of the cokriging equations is challenging. To do so, five alternatives (single search, multiple search, strictly collocated search, multi-collocated search and isotopic search) are tested and compared with the reference unique neighborhood, through synthetic examples with different data configurations and spatial joint correlation models. The results indicate that the multi-collocated and multiple searches bear the highest resemblance to the reference case under the analyzed spatial structure models, while the single and the isotopic searches, which do not differentiate the primary and secondary sampling designs, yield the poorest results in terms of cokriging error variance.

Comparison of strategies for scalable causal discovery of latent variable models from mixed data

Modern technologies allow large, complex biomedical datasets to be collected from patient cohorts. These datasets are comprised of both continuous and categorical data (“Mixed Data”), and essential variables may be unobserved in this data due to the complex nature of biomedical phenomena. Causal inference algorithms can identify important relationships from biomedical data; however, handling the challenges of causal inference over mixed data with unmeasured confounders in a scalable way is still an open problem. Despite recent advances into causal discovery strategies that could potentially handle these challenges; individually, no study currently exists that comprehensively compares these approaches in this setting. In this paper, we present a comparative study that addresses this problem by comparing the accuracy and efficiency of different strategies in large, mixed datasets with latent confounders. We experiment with two extensions of the Fast Causal Inference algorithm: a maximum probability search procedure we recently developed to identify causal orientations more accurately, and a strategy which quickly eliminates unlikely adjacencies in order to achieve scalability to high-dimensional data. We demonstrate that these methods significantly outperform the state of the art in the field by achieving both accurate edge orientations and tractable running time in simulation experiments on datasets with up to 500 variables. Finally, we demonstrate the usability of the best performing approach on real data by applying it to a biomedical dataset of HIV-infected individuals.

Efficiency of different strategies to mitigate ascertainment bias when using SNP panels in diversity studies

BackgroundSingle nucleotide polymorphism (SNP) panels have been widely used to study genomic variations within and between populations. Methods of SNP discovery have been a matter of debate for their potential of introducing ascertainment bias, and genetic diversity results obtained from the SNP genotype data can be misleading. We used a total of 42 chicken populations where both individual genotyped array data and pool whole genome resequencing (WGS) data were available. We compared allele frequency distributions and genetic diversity measures (expected heterozygosity (He), fixation index (FST) values, genetic distances and principal components analysis (PCA)) between the two data types. With the array data, we applied different filtering options (SNPs polymorphic in samples of two Gallus gallus wild populations, linkage disequilibrium (LD) based pruning and minor allele frequency (MAF) filtering, and combinations thereof) to assess their potential to mitigate the ascertainment bias.ResultsRare SNPs were underrepresented in the array data. Array data consistently overestimated He compared to WGS data, however, with a similar ranking of the breeds, as demonstrated by Spearman’s rank correlations ranging between 0.956 and 0.985. LD based pruning resulted in a reduced overestimation of He compared to the other filters and slightly improved the relationship with the WGS results. The raw array data and those with polymorphic SNPs in the wild samples underestimated pairwise FST values between breeds which had low FST (<0.15) in the WGS, and overestimated this parameter for high WGS FST (>0.15). LD based pruned data underestimated FST in a consistent manner. The genetic distance matrix from LD pruned data was more closely related to that of WGS than the other array versions. PCA was rather robust in all array versions, since the population structure on the PCA plot was generally well captured in comparison to the WGS data.ConclusionsAmong the tested filtering strategies, LD based pruning was found to account for the effects of ascertainment bias in the relatively best way, producing results which are most comparable to those obtained from WGS data and therefore is recommended for practical use.

The relevance of market prices for the design of transfer programs in response to food insecurity

Abstract This paper focuses on the use of market prices as discriminatory factors for the selection of strategies in response to conditions of food insecurity according to the comparative efficiency of different strategies. A classical production model has been used to define the conditions of relative advantage of different response options and to capture the effect of some contextual variables on such conditions. This type of approach can be quite useful when trying to optimise response strategy through its geographical diversification or adjustment over time. While such analytical approach reflects mainly a static cost-efficiency perspective, it can be sharpened through the partial inclusion of an effectiveness perspective.

Toward fault-tolerant parallel-in-time integration with PFASST

We introduce and analyze different strategies for the parallel-in-time integration method PFASST to recover from hard faults and subsequent data loss. Since PFASST stores solutions at multiple time steps on different processors, information from adjacent steps can be used to recover after a processor has failed. PFASST's multi-level hierarchy allows to use the coarse level for correcting the reconstructed solution, which can help to minimize overhead. A theoretical model is devised linking overhead to the number of additional PFASST iterations required for convergence after a fault. The potential efficiency of different strategies is assessed in terms of required additional iterations for examples of diffusive and advective type.

Using Monte-Carlo simulations to evaluate the efficiency of different strategies for nondestructive assessment of concrete strength

An international benchmark comparing a large variety of concrete strength assessment strategies was proposed by NDT experts of the RILEM committee TC-249. It was based on synthetic data and devoted to the estimation of average strength and concrete strength variability of a series of concrete columns of a single floor building using fixed budget. Based on information gathered after this benchmark, the main contribution of this paper is to simulate, using Monte Carlo simulation, the experts’ assessment strategies (i.e. NDT investigation program and data analysis process). These strategies are repeated a certain number of times in order to establish a representative assessment of each strategy. The quality of estimation by each strategy is evaluated using several indicators. The results show that this quality can be improved by using/changing several controlling factors: improving the quality of measurements, selecting core locations depending on the NDT test results (conditional cores), combining several NDT techniques and increasing the amount of resources.

External control strategies for self-propelled particles: Optimizing navigational efficiency in the presence of limited resources.

We experimentally and numerically study the dependence of different navigation strategies regarding the effectivity of an active particle to reach a predefined target area. As the only control parameter, we vary the particle's propulsion velocity depending on its position and orientation relative to the target site. By introducing different figures of merit, e.g., the time to target or the total consumed propulsion energy, we are able to quantify and compare the efficiency of different strategies. Our results suggest that each strategy to navigate towards a target has its strengths and weaknesses, and none of them outperforms the other in all regards. Accordingly, the choice of an ideal navigation strategy will strongly depend on the specific conditions and the figure of merit which should be optimized.

Discrete-continuous optimization of heat network operating conditions in parallel operation of similar pumps at pumping stations

The paper addresses an optimization problem of hydraulic conditions of heat supply systems. The research shows that when the main methods of operation control, including the control of the number of connected pumps at pumping stations, are used this problem is reduced to a mixed discrete-continuous programming problem which involves a nonlinear objective function, nonlinear equality constraints and simple inequalities. The paper presents the basic principles of the methods for calculation of feasible and optimal conditions on the basis of continuous variables as a constituent of the suggested technique for solving the general problem. Consideration is given to four possible strategies to fraction and cut the variants while searching for solutions on the basis of discrete variables. The results of computational experiments illustrating the comparative efficiency of different strategies are presented.

Conséquences de la pression économique sur les pratiques médicales en transfusion sanguine

The increasing pressure on healthcare resources affects blood donation and transfusion. We attempted a survey of the efficiency of different strategies, actual or proposed to improve the management of blood products. We found an important disconnect between the cost effectiveness ratio of strategies and their uptake by policy makers. In other words, the least efficient strategies are those which increase transfusion safety by increasing the number of biological markers and are those preferred by health authorities in developed countries. Other more efficient strategies are more slowly implemented and included a systematic use of transfusion guidelines, reducing blood losses or increasing pre operative blood levels in elective surgeries.

Economic efficiency analysis of different strategies to control post-weaning multi-systemic wasting syndrome and porcine circovirus type 2 subclinical infection in 3-weekly batch system farms

The study assessed the economic efficiency of different strategies for the control of post-weaning multi-systemic wasting syndrome (PMWS) and porcine circovirus type 2 subclinical infection (PCV2SI), which have a major economic impact on the pig farming industry worldwide.The control strategies investigated consisted on the combination of up to 5 different control measures. The control measures considered were: (1) PCV2 vaccination of piglets (vac); (2) ensuring age adjusted diet for growers (diets); (3) reduction of stocking density (stock); (4) improvement of biosecurity measures (bios); and (5) total depopulation and repopulation of the farm for the elimination of other major pathogens (DPRP). A model was developed to simulate 5 years production of a pig farm with a 3-weekly batch system and with 100 sows. A PMWS/PCV2SI disease and economic model, based on PMWS severity scores, was linked to the production model in order to assess disease losses. This PMWS severity scores depends on the combination post-weaning mortality, PMWS morbidity in younger pigs and proportion of PCV2 infected pigs observed on farms.The economic analysis investigated eleven different farm scenarios, depending on the number of risk factors present before the intervention. For each strategy, an investment appraisal assessed the extra costs and benefits of reducing a given PMWS severity score to the average score of a slightly affected farm. The net present value obtained for each strategy was then multiplied by the corresponding probability of success to obtain an expected value. A stochastic simulation was performed to account for uncertainty and variability.For moderately affected farms PCV2 vaccination alone was the most cost-efficient strategy, but for highly affected farms it was either PCV2 vaccination alone or in combination with biosecurity measures, with the marginal profitability between ‘vac’ and ‘vac+bios’ being small. Other strategies such as ‘diets’, ‘vac+diets’ and ‘bios+diets’ were frequently identified as the second or third best strategy. The mean expected values of the best strategy for a moderately and a highly affected farm were £14,739 and £57,648 after 5 years, respectively.This is the first study to compare economic efficiency of control strategies for PMWS and PCV2SI. The results demonstrate the economic value of PCV2 vaccination, and highlight that on highly affected farms biosecurity measures are required to achieve optimal profitability. The model developed has potential as a farm-level decision support tool for the control of this economically important syndrome.

A Small Post-Translocation Energy Bias Aids Nucleotide Selection in T7 RNA Polymerase Transcription

The RNA polymerase (RNAP) of bacteriophage T7 is a single subunit enzyme that can transcribe DNA to RNA in the absence of additional protein factors. In this work, we present a model of T7 RNAP translocation during elongation. Based on structural information and experimental data from single-molecule force measurements, we show that a small component of facilitated translocation or power stroke coexists with the Brownian-ratchet-driven motions, and plays a crucial role in nucleotide selection at pre-insertion. The facilitated translocation is carried out by the conserved Tyr639 that moves its side chain into the active site, pushing aside the 3′-end of the RNA, and forming a locally stabilized post-translocation intermediate. Pre-insertion of an incoming nucleotide into this stabilized intermediate state ensures that Tyr639 closely participates in selecting correct nucleotides. A similar translocation mechanism has been suggested for multi-subunit RNAPs involving the bridge-helix bending. Nevertheless, the bent bridge-helix sterically prohibits nucleotide binding in the post-transolocation intermediate analog; moreover, the analog is not stabilized unless an inhibitory protein factor binds to the enzyme. Using our scheme, we also compared the efficiencies of different strategies for nucleotide selection, and examined effects of facilitated translocation on forward tracking.