A strategy for a university cafe during holidays

With no existing strategy for a cafe business and a highly competitive market, the organisation requires a planned strategy. This research proposes to research a café to determine the best strategy for the organisation. A questionnaire will collect quantitative and qualitative data and the organisation will be observed to determine business strategies

INFERENCE, POWER AND SAMPLE SIZE FOR ADAPTIVE TWO-STAGETREATMENT STRATEGIES

An adaptive treatment strategy (ATS) is defined as a sequence of treatments and intermediate responses. ATS' arise when chronic diseases such as cancer and depression are treated over time with various treatment alternatives depending on intermediate responses to earlier treatments. For example, in two-stage adaptive treatment strategies, patients receive one of the induction treatments followed by a maintenance therapy given that the patients responded to the induction treatment they received. Clinical trials are often designed to compare adaptive treatment strategies based on appropriate designs such as sequential randomization designs. One of the main objectives of these trials is to compare two or more treatment strategies in terms of largest patient benefit, such as prolonged survival.Statistical inference from such trials needs to account for the sequential randomization structure of the design. Recent literature suggests several methods of estimation. A comparative review of currently available inferential procedures for analyzing data from such trials is presented. A sample size formula is introduced for comparing the survival probabilities under two treatment strategies sharing the same initial treatment. The formula is based on the large sample properties of inverse-probability- weighted estimator. Monte Carlo simulation study shows strong evidence that the proposed sample size formula guarantees desired power, regardless of the true distributions of survival times. To test for a difference in the effects of different induction and maintenance treatment combinations, a supremum weighted log-rank test is proposed. The test is applied to a dataset from a two-stage randomized trial and the results are compared to those obtained using a standard weighted log-rank test. A sample-size formula is derived based on the limiting distribution of the supremum weighted log-rank statistic. Simulation studies show that the proposed test provides sample sizes which are close to those obtained by standard weighted log-rank test under a proportional hazard alternative. However, the proposed test is more powerful than the standard weighted log-rank test under non-proportional hazard alternatives.The public health significance of this work is to provide a practical guidance of sample size determination and a test procedure in clinical trials that adopt two stage randomization designs

What is the best strategy?

The research question of this study is “What is the best strategy for Momento-Uni, especially in holiday”. Momento-Uni is a café and restaurant which is located in the Waikato University in Hamilton. In this study, it is aim to develop and identify a proper strategy for Momento-Uni to create competitive advantages and increase its sales in normal period of time, furthermore, a specific strategy also needs to be developed when there is a holiday period to balance its loss and profits. On the other hand, because of the special location of the Momento-Uni, the strategy needs to be created for its sustainable and long-term development and its unique marketing position. In this study, a mixed research method is used which includes quantitative and qualitative study design and observation. A questionnaire is developed to collect the information and data about the customers’ evaluation and recommendation for Momento-Uni’s service and products. An interview was held with their manager to understand Momento-Uni’s business goals, current situation, internal working environment and future development. The observation was developed by the researcher to evaluate their competitor and internal working issues. As a result, it shows that the main customers are students from 18-30 years old and coffee and drinks and snacks take up most profit of the whole sales. Through the result, a focus strategy is developed because students are the mainly targeted segments and to meet their needs. However, Momento-Uni does not have a clear differentiation and cost-leadership strategy to meet customers’ variable needs. As students do not have a high consumption level, in the future, the challenges will be existed. The results also shows that in holidays, the number of customers are declined steeply which increase the difficulties in the future growth. According to the results, it recommends that Momento-Uni needs to develop a clear strategy with differentiation and cost-leadership strategy to offer customer differentiated service and products, creates its products varieties and improve its service quality. For the customer psychology and behavior, by better understanding the potential thought, feeling and intentions of the customers, a proper strategy needs to be developed about validation of existing insights and the creation of new and novel insight to follow the marketing trend to increase its marketing competitiveness. For the human resource management, Momento-Uni needs to have a reasonable staff management during the normal time, especially in holidays, the opening hour for each site in holiday will be adjusted, Lake site and Ms2 site will be closed in a few weeks. Lake site always have overmuch staff, the staff in lake site will be reduced in normal time. For the future growth, the new working policy and business plan will be developed and improved to achieve its business goals for the future success

Improved SVD + + Recommendation Algorithm Based on Fusion Time Factor

Collaborative filtering algorithm is widely used in recommendation system. Aiming at the problems of data sparsity and low recommendation accuracy in traditional collaborative filtering algorithm, an improved recommendation algorithm is proposed PT _ SVD++. Firstly, the attribute information of users and the implicit feedback information of items are introduced to improve the SVD++ algorithm, which solves the insufficient utilization of information and alleviates the problem of sparse data；Secondly the time effect model is established to further improve the accuracy of the prediction results. The experimental results on MovieLens dataset show that compared with other algorithms, the average absolute error and root mean square error of this algorithm are lower, and its recommendation accuracy is higher

Two helices from one chiral centre – self organization of disc shaped chiral nanoparticles

Gold nanoparticles (AuNPs) have been prepared and surfacefunctionalizedwith a mixture of 1-hexanethiol co-ligands and chiraldiscogen ligands separated from a disulfide function via a flexiblespacer. Polarized optical microscopy together with differentialscanning calorimetry showed that the organic corona of thenanocomposite forms a stable chiral discotic nematic (ND*) phasewith a wide thermal range. Synchrotron X-ray diffraction showedthat gold NPs form a superlattice with p2 plane symmetry. Analysisindicated that the corona takes up the shape of a flexiblemacrodisk. Synchrotron radiation-based circular dichroism signalsof thin films are significantly enhanced on the isotropic-LCtransition in line with the formation of a chiral nematic phase of theorganic corona. At lower temperatures the appearance of CDsignals associated with the NPs is indicative of the formation of asecond helical structure. The decreased volume required and thechiral environment of the disc ligands drives the nanoparticles intocolumns that arrange helically parallel to the shortest axis of thetwo dimensional lattice

Statistical significance and publication reporting bias in abstracts of reproductive medicine studies

Funding Information: We thank Dr David Chavalarias from Complex Systems Institute of Paris Ile-de-France for sharing scripts in extracting P-values. B.W.M. is supported by an National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437); B.W.M. reports consultancy, research grants, and travel support from Merck. W.L. is supported by an NHMRC Investigator Grant (GNT2016729). Q.F. reports receiving a PhD scholarship from Merck. The other author has no conflict of interest to declare. Funding Information: B.W.M. is supported by an National Health and Medical Research Council (NHMRC) Investigator grant (GNT1176437); B.W.M. reports consultancy, research grants, and travel support from Merck. W.L. is supported by an NHMRC Investigator Grant (GNT2016729). Q.F. reports receiving a PhD scholarship from Merck. The other author has no conflict of interest to declare. Publisher Copyright: © 2024 Oxford University Press. All rights reserved.Peer reviewe

GPT-NAS: Neural Architecture Search with the Generative Pre-Trained Model

Neural Architecture Search (NAS) has emerged as one of the effective methods to design the optimal neural network architecture automatically. Although neural architectures have achieved human-level performances in several tasks, few of them are obtained from the NAS method. The main reason is the huge search space of neural architectures, making NAS algorithms inefficient. This work presents a novel architecture search algorithm, called GPT-NAS, that optimizes neural architectures by Generative Pre-Trained (GPT) model. In GPT-NAS, we assume that a generative model pre-trained on a large-scale corpus could learn the fundamental law of building neural architectures. Therefore, GPT-NAS leverages the generative pre-trained (GPT) model to propose reasonable architecture components given the basic one. Such an approach can largely reduce the search space by introducing prior knowledge in the search process. Extensive experimental results show that our GPT-NAS method significantly outperforms seven manually designed neural architectures and thirteen architectures provided by competing NAS methods. In addition, our ablation study indicates that the proposed algorithm improves the performance of finely tuned neural architectures by up to about 12% compared to those without GPT, further demonstrating its effectiveness in searching neural architectures