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Derives a first order perturbation algorithm for the computation of mean values and (co‐) variances of the transient temperature field in conduction heated materials with random field parameters. Considers both linear as well as non‐linear heat conduction problems. The algorithm is advantageous in terms of computer time compared to the Monte Carlo method. The computer time can further be reduced by appropriate transformation of the random vectors resulting from the discretization of the random fields.

This paper aims to investigate the thermal performance involving larger heating rate, targeted heating, heating with least non-uniformity of the spatial distribution of temperature and larger penetration of heating within samples vs shapes of samples (circle, square and triangular).

Galerkin finite element method (GFEM) with adaptive meshing in a composite domain (free space and sample) is used in an in-house computer code. The finite element meshing is done in a composite domain involving triangle embedded within a semicircular hypothetical domain. The comparison of heating pattern is done for various shapes of samples involving identical cross-sectional area. Test cases reveal that triangular samples can induce larger penetration of heat and multiple heating fronts. A representative material (beef) with high dielectric loss corresponding to larger microwave power or heat absorption in contrast to low lossy samples is considered for the current study. The average power absorption within lossy samples has been computed using the spatial distribution and finite element basis sets. Four regimes have been selected based on various local maxima of the average power for detailed investigation. These regimes are selected based on thin, thick and intermediate limits of the sample size corresponding to the constant area of cross section, Ac involving circle or square or triangle.

The thin sample limit (Regime 1) corresponds to samples with spatially invariant power absorption, whereas power absorption attenuates from exposed to unexposed faces for thick samples (Regime 4). In Regimes 2 and 3, the average power absorption non-monotonically varies with sample size or area of cross section (A_c) and a few maxima of average power occur for fixed values of A_c involving various shapes. The spatial characteristics of power and temperature have been critically analyzed for all cross sections at each regime for lossy samples. Triangular samples are found to exhibit occurrence of multiple heating fronts for large samples (Regimes 3 and 4).

Length scales of samples of various shapes (circle, square and triangle) can be represented via Regimes 1-4. Regime 1 exhibits the identical heating rate for lateral and radial irradiations for any shapes of lossy samples. Regime 2 depicts that a larger heating rate with larger temperature non-uniformity can occur for square and triangular-Type 1 lossy sample during lateral irradiation. Regime 3 depicts that the penetration of heat at the core is larger for triangular samples compared to circle or square samples for lateral or radial irradiation. Regime 4 depicts that the penetration of heat is still larger for triangular samples compared to circular or square samples. Regimes 3 and 4 depict the occurrence of multiple heating fronts in triangular samples. In general, current analysis recommends the triangular samples which is also associated with larger values of temperature variation within samples.

GFEM with generalized mesh generation for all geometries has been implemented. The dielectric samples of any shape are surrounded by the circular shaped air medium. The unified mesh generation within the sample connected with circular air medium has been demonstrated. The algorithm also demonstrates the implementation of various complex boundary conditions in residuals. The numerical results compare the heating patterns for all geometries involving identical areas. The thermal characteristics are shown with a few generalized trends on enhanced heating or targeted heating. The circle or square or triangle (Type 1 or Type 2) can be selected based on specific heating objectives for length scales within various regimes.

The purpose of this paper is to initiate investigations to develop near infrared (NIR) spectroscopy coupled with spectral dimensionality reduction and multivariate calibration methods to rapidly measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. The raw spectra are transformed into wavelet coefficients. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models using 100 wavelet coefficients. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with the LS-SVM model.

The correlation coefficient of prediction (r_p) and root mean square errors of prediction were 0.99 and 4.37 percent, respectively. The results suggest that NIR spectroscopy, combining with the LS-SVM method, has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time-consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The paper aims to provide a better understanding of food service technologies, applied research and technical literacy needs in hospitality.

Reviews the applied research in food services and the technical literacy needed to interpret and benefit from it.

The summary of research is provided in the subject areas identified by QAAHE (2000): food safety management, food quality management and product development; equipment and facility layout/design; operational planning and modelling; as well as market and consumer related aspects. Underpinning scientific disciplines and operational/ strategic benefits of the described studies as well as the three tiers in the educational pathways in food production are described.

The research findings can be used as a source of competency‐building by practitioners and educators.

The paper identifies the main types of research and develops conceptual links between the scientific fundamentals of food service operations and industry practices.

The purpose of this paper is to develop near infrared (NIR) techniques coupled with multivariate calibration methods to rapid measure cotton content in blend fabrics.

In total, 124 and 41 samples were used to calibrate models and assess the performance of the models, respectively. Multivariate calibration methods of partial least square (PLS), extreme learning machine (ELM) and least square support vector machine (LS-SVM) were employed to develop the models. Through comparing the performance of PLS, ELM and LS-SVM models with new samples, the optimal model of cotton content was obtained with LS-SVM model. The correlation coefficient of prediction (r _p) and root mean square errors of prediction were 0.98 and 4.50 percent, respectively.

The results suggest that NIR technique combining with LS-SVM method has significant potential to quantitatively analyze cotton content in blend fabrics.

It may have commercial and regulatory potential to avoid time consuming work, costly and laborious chemical analysis for cotton content in blend fabrics.

The purpose of this paper is to present a quantitatively supported explanation of the intellectual development, the schools of thought and the sub-areas of the food cold chain (FCC) research to derive meaningful avenues for future research.

This study builds on bibliometric analysis and network analysis to systematically evaluate a sample of 1,189 FCC articles published over the past 25 years. The descriptive statistics and science mapping approaches using co-citation analysis were performed with VOSviewer software.

The findings reveal a state-of-the-art overview of the top contributing and influential countries, authors, institutions and articles in the area of FCC research. A co-citation analysis, coupled with content analysis of most co-cited articles, uncovered four underlying research streams including: application of RFID technologies; production and operation planning models; postharvest waste, causes of postharvest wastage and perishable inventory ordering polices and models; and critical issues in FCC. Current research streams, clusters and their sub-themes provided meaningful discussions and insights into key areas for future research in FCC.

This study might reshape practitioners’, researchers’ and policy-makers’ views on the multifaceted areas and themes in the FCC research field, to harness FCC’s benefits at both strategic and tactical level. Finally, the research findings offer a roadmap for additional research to yield more practical and modeling insights that are much needed to enrich the field.

The food processing industry is growing with retail and catering supply chains. With the rising complexity of food products and the need to address food customization expectations, food processing systems are progressively shifting from production line to job-shops that are characterized by high flexibility and high complexity. A food job-shop system processes multiple items (i.e. raw ingredients, toppings, dressings) according to their working cycles in a typical resource and capacity constrained environment. Given the complexity of such systems, there are divergent goals of process cost optimization and of food quality and safety preservation. These goals deserve integration at both an operational and a strategic decisional perspective. The twofold purpose of this paper is to design a simulation model for food job-shop processing and to build understanding of the extant relationships between food flows and processing equipment through a real case study from the catering industry.

The authors designed a simulation tool enabling the analysis of food job-shop processing systems. A methodology based on discrete event simulation is developed to study the dynamics and behaviour of the processing systems according to an event-driven approach. The proposed conceptual model builds upon a comprehensive set of variables and key performance indicators (KPIs) that describe and measure the dynamics of the food job-shop according to a multi-disciplinary perspective.

This simulation identifies the job-shop bottlenecks and investigates the utilization of the working centres and product queuing through the system. This approach helps to characterize how costs are allocated in a flow-driven approach and identifies the trade-off between investments in equipment and operative costs.

The primary purpose of the proposed model relies on the definition of standard resources and operating patterns that can meet the behaviour of a wide variety of food processing equipment and tasks, thereby addressing the complexity of a food job-shop. The proposed methodology enables the integration of strategic and operative decisions between several company departments. The KPIs enable identification of the benchmark system, tracking the system performance via multi-scenario what-if simulations, and suggesting improvements through short-term (e.g. tasks scheduling, dispatching rules), mid-term (e.g. recipes review), or long-term (e.g. re-layout, working centres number) levers.

The purpose of this paper is to analyze the unsteady conjugate mixed convective heat transfer characteristics in a vented porous cavity under the combined effects of moving conductive elliptic object and magnetic field.

The finite element method and arbitrary Lagrangian-Eulerian (ALE), impacts of Reynolds number, Hartmann number, aspect ratio of the conductive ellipse and moving speed of the object on the hydro-thermal performance are analyzed.

It was observed that the dynamic characteristics of the local and average Nu number of each hot wall are different. Magnetic field strength increment resulted in the enhancement of average Nu number for bot steady and transient case while the optimum case for best hydro-thermal performance is achieved for highest Ha number and non-dimensional time of 10. Higher value of average Nu and lower pressure coefficient are achieved for aspect ratio of 4 and non-dimensional time of 10. When the moving velocity of the conductive ellipse is considered, 42% enhancement in the average Nu is obtained at non-dimensional time of 20 and object velocity equals to 0.012 times entering fluid velocity in the negative y direction while the pressure coefficient is higher. The moving object is used as a useful tool to control the dynamic features of heat transfer in a vented cavity.

The present method of convective heat transfer control inside a vented cavity with a moving elliptic object is novel and can be used as an effective tool with magnetic field effects owing to diverse use of convection in cavities with vented ports in many practical thermal engineering systems.

Fresh fruits and vegetables (FV) are crucial global food resources, but the presence of heat loads during harvest adversely impacts their shelf life. While freezing technology provides an effective means of removing heat loads, it is an energy-intensive process and may consequently prove too costly for practical business viability. The growing interest in utilizing magnetic field (MF) technology during the freezing of fresh FV enhances the freezing rate and rapidly removes the heat loads of products.

In the present study, pulsed magnetic field (PMF) pretreatment employing specific field strengths (9 T, 14 T and 20 T) was examined as a preliminary step before freezing mango and tomato and compared to the conventional freezing method (untreated) at − 18 °C.

PMF pretreatment prior to freezing demonstrated a noteworthy enhancement in freezing rate by around 10 and 12% when compared with the conventional (untreated) freezing, which exhibited freezing rates of −0.08 °C/min and −1.10 °C/min for mango and tomato, respectively. The PMF pretreatment (at 20 T) provided a higher freezing rate (at p = 0.05) than the conventional freezing method reduced heat loads amounting to 1.1 × 107 J/kg oC and 2.9 × 106 J/kg oC, significantly (at p = 0.05) from mango and tomato, respectively. These reductions in heat loads were approximately more than 5% of the calculated heat loads removed during conventional freezing.

Mango and tomato samples were only tested; the results may lack generalizability. Therefore, researchers are encouraged to test for other products for further studies.

The paper includes implications for the development of a rapid freezing technique, the development of “pulsed magnetic field” and for eliminating the problem associated with conventional (slow) freezing.

The study holds significance for the production of postharvest freezing technology, providing insightful information on the PMF-assisted freezing of cellular foods.

The purpose of this paper is to develop a methodology for detecting tree trunks for autonomous agricultural applications performed using mobile robots.

The system is constructed by following the principles of Voronoi diagram method which is one of the machine learning algorithms used by the robotics, mechatronics and automation researchers.

To analyze the accuracy and performance and to make verification and evaluation, both simulation and experimental studies are conducted. The results indicate that the tree trunk detection system developed using Voronoi diagram method can be able to detect tree trunks with high precision.

A novel solution technique to detect tree trunks is proposed. The adaptation of Voronoi diagram method in an agricultural (orchard) task is presented.