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Ground vibration testing (GVT) results can be used as system parameters for predicting flutter, which is essential for aeroelastic clearance. This paper aims to compute GVT-based flutter in time domain, using unsteady air loads by matrix polynomial approximations.

The experimental parameters, namely, frequencies and mode shapes are interpolated to build an equivalent finite element model. The unsteady aerodynamic forces extracted from MSC NASTRAN are approximated using matrix polynomial approximations. The system matrices are condensed to the required shaker location points to build an aeroelastic reduced order state space model in SIMULINK.

The computed aerodynamic forces are successfully reduced to few input locations (optimal) for flutter simulation on unknown structural system (where stiffness and mass are not known) through a case study. It is demonstrated that GVT data and the computed unsteady aerodynamic forces of a system are adequate to represent its aeroelastic behaviour.

Airforce of every nation continuously upgrades its fleet with advanced weapon systems (stores), which demands aeroelastic flutter clearance. As the original equipment manufacturers does not provide the design data (stiffness and mass) to its customers, a new methodology to build an aeroelastic system of unknown aircraft is devised.

A hybrid approach is proposed, involving GVT data to build an aeroelastic state space system, using rationally approximated air loads (matrix polynomial approximations) computed on a virtual FE model for ground flutter simulation.

In the past decades, the desire to use natural source foods has increased because of environmental compatibility, safety and appropriate costs. Sonication is used in food industry owing to its short duration of process and saving energy. The purpose of this study is to investigate the effect of various maize starches in the batter on the oil absorption and quality assessment (moisture content) of chicken nuggets by using five mathematical models.

To determine the effects of different maize starches on oil absorption parameters, 5 per cent starches native, sonicated starch were substituted in batter instead of wheat flour. Suspensions contained native starch were treated with sonication (70 kHz, 5 min) using an ultrasound probe set. Samples were fried in a fryer at 150, 170 and 190°C for 1, 3and 5 min, respectively. Models were compared with R² and Arrhenius equation for estimating model prediction sufficiency.

Obtained results represented that between different formulated samples, maize starch with high temperature had main significant effect (p < 0.05) on moisture content of nuggets. During frying, the amount of oil loses significantly (p < 0.05) depended on temperature and time and sonication treatment.

Incorporation of sonication with maize starch at higher temperature on quality assessment has not been found.

The purpose of this paper is to evaluate the changes in descriptive sensory properties and overall consumer acceptability of soymilk prepared from roasted soybeans.

In total, 12 purposively selected post graduate students majoring in Food Science conducted descriptive sensory analysis after being trained for 18 h in sensory analysis, while 75 untrained students conducted consumer acceptability test of soymilk prepared by roasting soybeans at a temperature of 110°C for 20, 40, 60, 80 and 100 min and at 120°C for 20 min.

Results have revealed that roasting soybeans improved sensory properties by significantly (p<0.05) decreasing the objectionable green, beany flavours and increasing sweet taste, viscosity and roasted flavour. Furthermore, results from the principal component analysis revealed that aroma and sweet taste were the most critical sensory attributes. In addition, it was found out that soymilk samples prepared by roasting soybeans at 110°C for 40 and 60 min and at 120°C for 20 min were significantly more acceptable than the control soymilk.

The participants in this study were from one locality and predominantly soybean consuming community and therefore there is need to conduct the study in a different locality in order to validate the study findings.

The study can assist small scale processors that might not have access to lipoxygenase-free soybeans and other technologies for improving the quality of soymilk.

The study can be used as a guide for connecting the food processers with the external world of consumption.

For the first time, the study findings have demonstrated that controlled soybean roasting can be a useful strategy for improving soymilk sensory properties and consumer acceptability. The findings in this study can be usefully used in the quality control of soy bean-based products.

The purpose of this study is to monitor suspended particulate matter (SPM), PM2.5 and source apportionment study for the identification of possible sources during the year 2018–2019 at Raipur, India.

Source apportionment study was performed using a multivariate receptor model, positive matrix factorization (PMFv5.0) with a view to identify the various possible sources of particulate matter in the area. Back-trajectory analysis was also performed using NOAA-HYSPLIT model to understand the origin and trans-boundary movement of air mass over the sampling location.

Daily average SPM and PM2.5 aerosols mass concentration was found to be 377.19 ± 157.24 µg/m³ and 126.39 ± 37.77 µg/m³ respectively. SPM and PM2.5 mass concentrations showed distinct seasonal cycle; SPM – (Winter ; 377.19 ±157.25 µg/m?) > (Summer; 283.57 ±93.18 µg/m?) > (Monsoon; 33.20 ±16.32 µg/m?) and PM2.5 – (Winter; 126.39±37.77 µg/m³) > (Summer; 75.92±12.28 µg/m³). Source apportionment model (PMF) have been applied and identified five major sources contributing the pollution; steel production and industry (68%), vehicular and re-suspended road dust (10.1%), heavy oil combustion (10.1%), tire wear and brake wear/abrasion (8%) and crustal/Earth crust (3.7%). Industrial activities have been identified as major contributing factor for air quality degradation in the region.

Chemical characterization of aerosols and identification of possible sources will be helpful in abatement of pollution and framing mitigating strategies. It will also help in standardization of global climate model.

The findings provide valuable results to be considered for controlling air pollution in the region.

Machine learning methods have recently gained attention in business applications. We will explore the suitability of machine learning methods, particularly support vector regression (SVR) and radial basis function (RBF) approximation, in forecasting company sales. We compare the one-step-ahead forecast accuracy of these machine learning methods with traditional statistical forecasting techniques such as moving average (MA), exponential smoothing, and linear and quadratic trend regression on quarterly sales data of 43 Fortune 500 companies. Moreover, we implement an additive seasonal adjustment procedure on the quarterly sales data of 28 of the Fortune 500 companies whose time series exhibited seasonality, referred to as the seasonal group. Furthermore, we prove a mathematical property of this seasonal adjustment procedure that is useful in interpreting the resulting time series model. Our results show that the Gaussian form of a moving RBF model, with or without seasonal adjustment, is a promising method for forecasting company sales. In particular, the moving RBF-Gaussian model with seasonal adjustment yields generally better mean absolute percentage error (MAPE) values than the other methods on the sales data of 28 companies in the seasonal group. In addition, it is competitive with single exponential smoothing and better than the other methods on the sales data of the other 15 companies in the non-seasonal group.

The aim of this chapter is to discuss the key challenges that the academia and the academics of higher education have to face in relation to AI but also to make recommendations on the strategies that the academia can adopt so to optimize the use of AI in an ethical manner. Due to the lack of knowledge in the field of AI, there is limited literature on the field of AI, especially on issues related to academic integrity. For this reason, this chapter suggests several recommendations on how AI can be a foe not an enemy of academia. Those practices include the developing a culture of ethos for the use of AI among the stakeholder of higher education, the use of AI as a personalized tutor, and on grading. However, from a critical perspective, the most important issue of AI is academic integrity. At this case the stakeholders of higher education must take immediate action so to ensure the ethical use of IA in Higher Education. The authors of this chapter suggest making modifications on the way that students are assessed, including having more examinations and online quizzes along with written assignments which will promote critical reflections so to avoid the use of AI in written assignments.

Food‐processing operations produce many varied types of wastes which include solid and liquid effluents and, to a much lesser extent, volatile organic compounds, e.g. refrigerants. Economic, legislative and social pressures are forcing food industries to reconsider their attitude towards the generated effluents. Waste processing is no longer regarded as a series of operations intended to render wastes suitable for discharge into the atmosphere or a water body. It is an integral part of the mainstream activity of any industry. Discusses the characteristics of food‐processing wastes and their environmental impact and highlights strategies for efficient waste management.

A sample (N=81) of undergraduates participating in a semester-long team-project engineering course completed assessments of their leadership competence, motivation to lead, and leadership self-efficacy, as well as the leadership competence of their peers who served within their durable teams. Results indicated that peers scored students lower than students scored themselves; that males deflated the transactional leadership scores of the female peers they assessed; and that the strongest individual predictor of teammate- assigned scores was a student’s affective-identity motivation to lead (i.e. the degree to which they considered themselves a natural leader). Leadership self-efficacy failed to significantly predict teammate scores.

Artificial intelligence (AI) usage improves e-commerce logistics efficiency. However, many actors can play significant roles, such as supply chain consistency (SCC), last-mile logistics (LML) performance and collaboration and coordination among logistics firms. This study aims to assess how SCC and LML performance mediate and collaboration and coordination moderate the relationship between AI usage and logistics efficiency.

A structured questionnaire was used to collect the data. A total of 245 valid responses were received from Indian e-commerce businesses. The data were then analysed using AMOS v25 and structural equational modelling using SPSS for regression, PROCESS macro for mediation and moderated mediation analysis.

The findings show that AI usage independently impacts logistics efficiency, with SCC and last-mile delivery performance as mediating variables. Collaboration and coordination among logistic firms are also critical moderators in enhancing AI’s efficacy in logistic operations. The study findings suggest the integration of AI into logistic operations and provide implications to managers on the urgency of fostering a collaborative and synchronised environment to utilise the full potential of AI in e-commerce businesses.

This study not only contributes to the field of logistics theory by presenting empirical data on the various ramifications of AI but also offers practical guidance for logistics firms, particularly those operating in developing economies, on how to strategically employ AI to enhance operational efficiency and attain a competitive advantage in the era of e-commerce logistics in the digital age.