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Food choices profoundly affect one's dietary, nutritional and health outcomes. Using alcoholic beverages as a case study, the authors assess the potential of genetic data in predicting consumers' food choices combined with conventional socio-demographic data.

A discrete choice experiment was conducted to elicit the underlying preferences of 484 participants from seven provinces in China. By linking three types of data (—data from the choice experiment, socio-demographic information and individual genotyping data) of the participants, the authors employed four machine learning-based classification (MLC) models to assess the performance of genetic information in predicting individuals' food choices.

The authors found that the XGBoost algorithm incorporating both genetic and socio-demographic data achieves the highest prediction accuracy (77.36%), significantly outperforming those using only socio-demographic data (permutation test p-value = 0.033). Polygenic scores of several behavioral traits (e.g. depression and height) and genetic variants associated with bitter taste perceptions (e.g. TAS2R5 rs2227264 and TAS2R38 rs713598) offer contributions comparable to that of standard socio-demographic factors (e.g. gender, age and income).

This study is among the first in the economic literature to empirically demonstrate genetic factors' important role in predicting consumer behavior. The findings contribute fresh insights to the realm of random utility theory and warrant further consumer behavior studies integrating genetic data to facilitate developments in precision nutrition and precision marketing.

The purpose of this study is to segment Turkish wine customers based on their level of involvement. This study profiles them based on their wine drinking motivations, wine attributes, information sources, wine purchasing and consumption behaviour and socio-demographic characteristics.

For this study, a structured online questionnaire was used to collect data from the listed email addresses of institutes, universities and commercial websites. The sample size was 708 people. After splitting consumers into three groups based on their involvement levels in wine (high, moderate and low), the study profiles them by implementing ANOVA, principal component and chi-square analyses.

The study identifies the differences between groups with different involvement levels in wine regarding drinking motivations, wine attributes, information sources, consumption and purchasing behaviour as well as socio-demographic characteristics.

Although there are a few studies in the literature evaluating wine consumers from various nations, to the best of the authors’ knowledge, this is the first study investigating wine consumers based on involvement levels in Turkey, where alcoholic beverages are excessively taxed, and advertising is banned and promoting them is limited.

This paper aims to investigate the effect of product price image (PPI) on perceived value (PV) and repurchase intention (RI) of Brazilian customers' craft beer. Moreover, this research also verifies the moderating effect of customer experience (CE) and price sensitivity.

The survey data analysis was performed using Smart-PLS 3.3.9 and Process 4.1 software with 329 customers.

The results show that PV is a full mediation variable in the relationship between PPI and RI. As a mediated moderation, lower CE and price sensitivity better explain the indirect effect of PPI on RI via PV.

Thus, managers may reinforce the PV of low price sensitivity and low CE. These customers learn about companies' prices compared with higher price-sensitive customers and higher CE that already PV.

The article discusses the implications of PV as a mediator, low price sensitivity and low CE as moderators for craft beer.

1. The world's most widely consumed alcoholic beverage, following water and tea, the third-most-popular drink on earth is beer;

2. Managers should create strategies to reinforce the PV and consequently the RI by offering PPI and benefits (PV) for customers with low experience and low-price sensitivity about craft beer;

3. Low customer experience and low-price sensitive's customers are learning about companies' prices compared to higher price sensitive, and higher customer experience that already PV, PL, PF, and PEs and NEs;

4. Small producers craft beer that connotated the product's high quality and benefits; the higher may be the level of RI and consumption from customers.

The world's most widely consumed alcoholic beverage, following water and tea, the third-most-popular drink on earth is beer;

Managers should create strategies to reinforce the PV and consequently the RI by offering PPI and benefits (PV) for customers with low experience and low-price sensitivity about craft beer;

Low customer experience and low-price sensitive's customers are learning about companies' prices compared to higher price sensitive, and higher customer experience that already PV, PL, PF, and PEs and NEs;

Small producers craft beer that connotated the product's high quality and benefits; the higher may be the level of RI and consumption from customers.

This study aims to understand the degree of predictability and value in analyzing consumer purchase patterns in the US wine retail market. The study considers whether brands in US wine retailing follow the well-established Duplication of Purchase Law and Double Jeopardy Law.

Over 20,000 customer panel wine purchases were analyzed from a number of locations within a supermarket chain based on the West Coast of the USA. Cross-purchasing behavior for the top 20 wine brands by market penetration was analyzed to assess whether the well-established Duplication of Purchase Law and Double Jeopardy Law hold up in this wine retail setting in the USA. The degree of predictability and the existence of anomalies in expected cross-purchasing behavior were identified in the analysis.

Results confirmed a Double Jeopardy pattern and that wine cross-purchasing patterns for the most part followed the Duplication of Purchase Law. However, exceptions to these patterns were found, which indicated areas in need of managerial attention due to the potential to remedy, develop or monitor the most prominent variations between predicted and realized cross-purchasing behavior. Repeated identification of variations has been identified in other product categories, known as market partitions.

Although it is commonly believed that wine is a unique product category, the results of this study demonstrate that consumer behavior toward wine is similar to other fast-moving consumer goods. The exceptions suggest that while similar consumer purchase patterns are evident, consumers are more likely to cross purchase wine brands and grape types more than would be expected given Duplication of Purchase Law benchmarks.

Natural gas hydrate (NGH) has been regarded as one of the most important resources due to NGH's large amounts of reserve. However, NGH development still faces many technical challenges, such as low production rate and reservoir instability resulting from NGH decomposition. Therefore, developing a fully coupled THMC model for simulating the hydrate decomposition and studying its mechanical behavior is very important and necessary. The purpose of this article is to develop and solve a multi-phase, strong nonlinearity and large-scale fully coupled thermal-hydro-mechanical–chemical (THMC) model for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH decomposition.

In this paper, a multi-phase, strong nonlinearity and large-scale fully coupled THMC model is developed for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH dissociation. The fully coupled THMC model is solved by using a fully implicit finite element method, in which the gas pressure, water pressure, temperature and displacement are taken as basic unknown variables. The proposed model is validated against with the experimental data, showing high accuracy and reliability.

A multi-phase, strong nonlinearity and large-scale fully coupled THMC model is developed for simulating the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and rock deformation during NGH decomposition. The proposed model is validated against with the experimental data, showing high accuracy and reliability.

Some assumptions are made to make the model tractable, including (1) the composition gas of hydrate is pure methane; (2) the gas-liquid multi-phase flow in the pore obeys Darcy's law; (3) hydrate occurs on the surface of soil particles, both of them form the composite consolidation material; (4) the small-strain assumption is applied to composite solid materials, which are treated as skeletons and cannot be moved; (5) momentum change caused by phase change is not considered.

NGH has been regarded as one of the most important resources due to its large amounts of reserve. However, NGH development still faces many technical challenges, such as low production rate and reservoir instability resulting from NGH decomposition. Most of the existing studies decouple the process with solid deformation and seepage behavior, but the accuracy of the numerical results will be sacrificed to certain extent. Therefore, it is very important and necessary to develop a fully coupled THMC model for simulating the hydrate decomposition and studying its mechanical behavior.

NGH, widely distributed in shallow seabed or permanent frozen region, has the characteristics of high energy density and high combustion efficiency (Yan et al., 2020). A total of around 7.5 × 1,018 m³ has been proved to exist around the world and 1 m³ of NGH can release about 160–180 m³ of natural gas (Kvenvolden and Lorenson) under normal conditions. Safely and sustainably extracting NGH commercially can effectively relieve global energy pressure and contribute to achieving carbon reduction goals.

The novelty of the present work lies in mainly two aspects. First, a fully coupled THMC model is developed for studying the multi-physics processes involving solid-liquid-gas flow, heat transfer, NGH phase change and solid deformation during NGH dissociation. Second, the numerical solution is obtained by using a fully implicit finite element method (FEM) and is validated against experimental data.

The purpose of this paper is to present the methodology that was used to perform system identification of a dynamically unstable tilt-rotor from flight test data. The method incorporated wavelet transform into the maximum likelihood principle formulation, emphasizing both time and frequency responses. Using wavelets allowed to additionally filter noise in the data, and this increased the estimation quality. This approach did not require measurement and process noise modeling in contrast to the Kalman filter usage for parameter estimation.

In the study, lateral-directional stability and control derivatives of an unstable tiltrotor in hover were estimated. This was performed by applying the maximum likelihood output error method. The estimated model response was decomposed using the Mallat pyramid and matched to wavelet coefficients obtained directly from measurements. In addition, a coherence-based weighting function was used to put more emphasis on the most reliable data. For comparison, the same set of data was used to identify a model with the same structure using the maximum likelihood principle with an incorporated Kalman filter.

It was found that maximum likelihood principle and wavelet transform allowed for estimating aerodynamic coefficients of a dynamically unstable aircraft. The estimation was performed with high accuracy.

The designed method can be used for system identification of unstable aircraft and when additional noise is present (e.g. when noise due to turbulence was observable during the flight test or higher noise levels were present in the sensors data).

The paper presents verification of a wavelet-based maximum likelihood principle output error method using flight test data.

Knowledge about the prevalence and impact of Adverse Childhood Experiences (ACEs) is pivotal to trauma-informed approaches, yet the impact of ACEs training is rarely investigated. This study reports a qualitative investigation of police perceptions of ACEs training in relation to conceptualisations of ACEs and trauma-informed working, practical applications of ACE knowledge and service-level support.

Four focus groups were conducted with 29 police officers, who had participated in an ACEs-awareness training. Based on the qualitative data, themes were generated using reflexive thematic analysis (Braun and Clarke, 2019).

Analysis generated seven themes, conceptualised into three domains of conceptual understanding, police culture and operationalising ACEs.

The sample is limited to Scottish police officers and is ethnically non-diverse. Further evaluation of higher quality interventions is warranted.

The study highlighted that a lack of conceptual framework, officer concerns and police culture may present barriers to officers incorporating ACEs knowledge into their day-to-day work. Future trainings should address these issues to achieve maximum benefits.

To the authors’ knowledge, this is the first in-depth qualitative study of police officers' perceptions of ACEs training. Focus groups facilitated the expression of cultural norms. The results provide insight into tailoring trauma-informed interventions in police in future, as well as raising broader service-level issues.

The purpose of this article is to treat the nonlinear optimal control problem in EV traction systems which are based on 5-phase induction motors. Five-phase permanent magnet synchronous motors and five-phase asynchronous induction motors (IMs) are among the types of multiphase motors one can consider for the traction system of electric vehicles (EVs). By distributing the required power in a large number of phases, the power load of each individual phase is reduced. The cumulative rates of power in multiphase machines can be raised without stressing the connected converters. Multiphase motors are also fault tolerant because such machines remain functional even if failures affect certain phases.

A novel nonlinear optimal control approach has been developed for five-phase IMs. The dynamic model of the five-phase IM undergoes approximate linearization using Taylor series expansion and the computation of the associated Jacobian matrices. The linearization takes place at each sampling instance. For the linearized model of the motor, an H-infinity feedback controller is designed. This controller achieves the solution of the optimal control problem under model uncertainty and disturbances.

To select the feedback gains of the nonlinear optimal (H-infinity) controller, an algebraic Riccati equation has to be solved repetitively at each time-step of the control method. The global stability properties of the control loop are demonstrated through Lyapunov analysis. Under moderate conditions, the global asymptotic stability properties of the control scheme are proven. The proposed nonlinear optimal control method achieves fast and accurate tracking of reference setpoints under moderate variations of the control inputs.

Comparing to other nonlinear control methods that one could have considered for five-phase IMs, the presented nonlinear optimal (H-infinity) control approach avoids complicated state-space model transformations, is of proven global stability and its use does not require the model of the motor to be brought into a specific state-space form. The nonlinear optimal control method has clear implementation stages and moderate computational effort.

In the transportation sector, there is progressive transition to EVs. The use of five-phase IMs in EVs exhibits specific advantages, by achieving a more balanced distribution of power in the multiple phases of the motor and by providing fault tolerance. The study’s nonlinear optimal control method for five-phase IMs enables high performance for such motors and their efficient use in the traction system of EVs.

Nonlinear optimal control for five-phase IMs supports the deployment of their use in EVs. Therefore, it contributes to the net-zero objective that aims at eliminating the emission of harmful exhaust gases coming from human activities. Most known manufacturers of vehicles have shifted to the production of all-electric cars. The study’s findings can optimize the traction system of EVs thus also contributing to the growth of the EV industry.

The proposed nonlinear optimal control method is novel comparing to past attempts for solving the optimal control problem for nonlinear dynamical systems. It uses a novel approach for selecting the linearization points and a new Riccati equation for computing the feedback gains of the controller. The nonlinear optimal control method is applicable to a wider class of dynamical systems than approaches based on the solution of state-dependent Riccati equations.

This paper aims to address a pollution-routing problem with one general period of congestion (PRP-1GPC), where the start and finish times of this period can be set freely.

In this paper, three sets of decision variables are optimized, namely, travel speeds before and after congestion and departure times on given routes, aiming to minimize total cost including green-house gas emissions, fuel consumption and driver wages. A two-phase algorithm is introduced to solve this problem. First, an adaptive large neighborhood search heuristic is used where new removal and insertion operators are developed. Second, an analysis of optimal speed before congestion is presented, and a tailored speed-and-departure-time optimization algorithm considering congestion is proposed by obtaining the best node to be served first over the congested period.

The results show that the newly developed operator of congested service-time insertion with noise is generally used more than other insertion operators. Besides, compared to the baseline methods, the proposed algorithm equipped with the new operators provides better solutions in a short time both in PRP-1GPC instances and time-dependent pollution-routing problem instances.

This paper considers a more general situation of the pollution-routing problem that allows drivers to depart before the congestion. The PRP-1GPC is better solved by the proposed algorithm, which adds operators specifically designed from the new perspective of the traveling distance, traveling time and service time during the congestion period.

Organisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.

The study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.

This study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.

This study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.

By considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.

This study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.