Statistical calibration and exact one-sided simultaneous tolerance intervals for polynomial regression

Statistical calibration using linear regression is a useful statistical tool having many applications. Calibration for infinitely many future y-values requires the construction of simultaneous tolerance intervals (STI’s). As calibration often involves only two variables x and y and polynomial regression is probably the most frequently used model for relating y with x, construction of STI’s for polynomial regression plays a key role in statistical calibration for infinitely many future y-values. The only exact STI’s published in the statistical literature are provided by Mee et al. (1991) and Odeh and Mee (1990). But they are for a multiple linear regression model, in which the covariates are assumed to have no functional relationships. When applied to polynomial regression, the resultant STI’s are conservative. In this paper, one-sided exact STI’s have been constructed for a polynomial regression model over any given interval. The available computer program allows the exact methods developed in this paper to be implemented easily. Real examples are given for illustration

Counting by weighing:know your numbers with confidence

Counting by weighing is often more efficient than counting manually, which is time consuming and prone to human errors, especially when the number of items (e.g. plant seeds, printed labels or coins) is large. Papers in the statistical literature have focused on how to count, by weighing, a random number of items that is close to a prespecified number in some sense. The paper considers the new problem, arising from a consultation with a company, of making inference about the number of 1p coins in a bag with known weight for infinitely many bags, by using the estimated distribution of coin weight from one calibration data set only. Specifically, a lower confidence bound has been constructed on the number of 1p coins for each of infinitely many future bags of 1p coins, as required by the company. As the same calibration data set is used repeatedly in the construction of all these lower confidence bounds, the interpretation of coverage frequency of the lower confidence bounds that is proposed is different from that of a usual confidence set

One reference genome is not enough.

A recent study on human structural variation indicates insufficiencies and errors in the human reference genome, GRCh38, and argues for the construction of a human pan-genome

Training and Validation of the Fast PCRTM_Solar Model

In this work, we extended PCRTM to including the contribution from solar radiation, including the nonlocal thermal equilibrium (NLTE) effect

Optimization of Location-Routing for the Waste Household Appliances Recycling Logistics under the Uncertain Condition

Waste household appliances and electronic products usually contain harmful substances which need scientific and reasonable collection, classification, processing, recovery and disposal to achieve sustainable and effective recycling and utilization. In recent years, due to the poor management of waste household appliances recycling logistics system, safety accidents occur frequently, which seriously harm the health and life safety of the society. This paper studies the risk management of recycling waste household appliances under uncertain conditions and establishes a risk measurement model under fuzzy population density. Considering the multi-stage and classification diversity of waste household appliances recycling logistics, the multi-objective location routing model and location - routing model are established respectively. Based on the model complexity analysis, the solution method of multi-objective model is designed. Finally, the validity of the model and algorithm is verified by examples and tests

FROM SERVICE TO SUPERIORITY: UNVEILING THE IMPACT OF ARTIFICIAL INTELLIGENCE ON CUSTOMER EXPERIENCE IN GIANT HYPERMARKETS, KLANG VALLEY, MALAYSIA

The integration of Artificial Intelligence (AI) in retail has redefined customer experience, offering new opportunities for personalisation, service quality enhancement, and continuous service delivery. This study investigates the impact of AI on customers’ experience at Giant Hypermarkets in Klang Valley, Malaysia, focusing on four key areas: personalisation, service quality, hassle-free service, and customer service. Using quantitative methods, data were gathered from 365 respondents to assess the influence of AI-driven services on customer experience. The findings reveal that hassle-free service significantly enhances customer experience, while personalisation, service quality, and customer service show no significant impact. These results suggest that while AI is effective in streamlining operations and reducing customer effort, it may fall short in providing the emotional engagement needed to enhance the overall customer experience. This study highlights the importance of balancing AI efficiency with human interaction, particularly in relational services. This research offers valuable insights for retailers, academics, and small business owners, emphasizing the need to adopt AI technologies that focus on customer convenience and experience while maintaining a personal touch. This study clearly highlights the implications for the retail industry, demonstrating that AI can significantly enhance customer experience, but its strategic integration is crucial to address both operational efficiency and emotional engagement.  Article visualizations

[2-(2-Carboxy­phen­yl)benzoato]bis­(1,10-phenanthroline)zinc(II) 2-(2-carboxy­phen­yl)benzoate monohydrate

In the title compound, [Zn(C14H9O4)(C12H8N2)2](C14H9O4)·H2O, the ZnII atom of the complex cation is six-coordinated in an octa­hedral geometry by four N atoms from two 1,10-phenanthroline ligands and two O atoms of a carboxyl­ate group from a singly deprotonated diphenic acid. The phenanthroline and carboxylate ligands act as chelating ligands. The dihedral angles between the two benzene rings in the deprotonated diphenic acid groups are 81.05 (2) (ligand) and 89.10 (2)° (anion). O—H⋯O and C—H⋯O hydrogen bonds link the components into a three-dimensional network