Dynamic Modeling for Product Family Evolution Combined with Artificial Neural Network Based Forecasting Model: A Study of iPhone Evolution

<div>Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The proposed model considers the influence of market demand, customer needs and technological requirements that are time-dependent. The methodology is a four-phase model. For each phase, the effectiveness of the developed approach is demonstrated with numerical simulation results and validated with a case study of Apple’s iPhone product family.</div>

Dynamic Modeling for Product Family Evolution Combined with Artificial Neural Network Based Forecasting Model: A Study of iPhone Evolution

<div>Products continuously evolve over time. Realizing the pattern of product family evolution along with proper estimation of features for future products has been regarded as a critical issue for business success. Focusing on this issue, a dynamic model for product family evolution combined with forecasting is proposed in this research work. The proposed model considers the influence of market demand, customer needs and technological requirements that are time-dependent. The methodology is a four-phase model. For each phase, the effectiveness of the developed approach is demonstrated with numerical simulation results and validated with a case study of Apple’s iPhone product family.</div>

DRY-DOCKING TIME AND LABOUR

The duration of stay of a ship in a drydock depends on the scope of routine underwater (submerged portion of the ship’s hull) repairing works (dry-docking works only) to be carried out. More specifically, the repairing works that are affected by outside water. These are, mainly, hull cleaning, coating (blasting and painting), rudder, propeller, stern tube aft seal, hull anodes, ICCP, sea valves, sea chests, tunnel thruster(s), bottom plugs, underwater structural steel (bottom and shipside) and so on. These dry-docking works dictate ships’ dry-docking time (days) and labour (man-days). Then what about the deadweight, age and type? Do these have any impact on dry-docking time and labour? An attempt has been made in this article to examine if there exist any possible relationship between a ships’ dry-docking time and its labour, and deadweight, age and type. Dry-docking time and related information for 586 cargo ships and dry-docking labour and related information for 50 cargo ships of various deadweights, ages and types were collected from a single shipyard. These were analyzed and presented in both tabular and graphical forms to demonstrate the possible relationship between dry-docking time and labour, and deadweight, age and type of ships. Ships’ dry-docking time and labour are very vital for both the ship owner and the shipyard because various charges in drydock are many folds higher than that at quay- side. As such, the reduction in ships’ dry-docking time and labour contributes in saving for the ship owner and increases the earning for the shipyard. A proper estimation of ships’ dry-docking time and labour can achieve this. In order to do so, the past information/data about ships’ dry-docking time and labour may serve a practical guide to prepare an achievable time and labour planning for dry-docking works. In this article, the authors have attempted to identify the independent variables that influence ships’ dry-docking time and labour (dependent variables) and suggested their possible inter-relationships. The inter-relationships between the independent variables (deadweight, age and type) and dependent variables (time and labour) appear to be linear.

Some aspects of plate number estimation of plate heat exchangers (PHEs). A case study

For the proper estimation of the plate number (N) of a plate heat exchanger (PHE) – in addition to the flow rates and thermophysical properties of fluids –, an appropriate correlation is needed for convective heat transfer coefficient (α) calculation. When one does not have a criterial equation for the corresponding plate shape, we propose a selecting method for α. With the suggested relationships from literature, we calculate the plate number of a geometrically known, similar heat duty PHE and choose those relationships that give the same plate number with the known heat exchanger. In our case study, the plate number determined by any of the screened equations for whole milk preheating has almost the same value (n = 10 ± 1) regardless of the method used to solve the PHE model (plate efficiency and Nconverg or Kconverg convergence methods). For liquids’ thermophysical property estimation, we recommend averaging the values given by equations from literature, followed by equation fitting.

Identification of linear response functions from arbitrary perturbation experiments in the presence of noise – Part 1: Method development and toy model demonstration

Existent methods to identify linear response functions from data require tailored perturbation experiments, e.g., impulse or step experiments, and if the system is noisy, these experiments need to be repeated several times to obtain good statistics. In contrast, for the method developed here, data from only a single perturbation experiment at arbitrary perturbation are sufficient if in addition data from an unperturbed (control) experiment are available. To identify the linear response function for this ill-posed problem, we invoke regularization theory. The main novelty of our method lies in the determination of the level of background noise needed for a proper estimation of the regularization parameter: this is achieved by comparing the frequency spectrum of the perturbation experiment with that of the additional control experiment. The resulting noise-level estimate can be further improved for linear response functions known to be monotonic. The robustness of our method and its advantages are investigated by means of a toy model. We discuss in detail the dependence of the identified response function on the quality of the data (signal-to-noise ratio) and on possible nonlinear contributions to the response. The method development presented here prepares in particular for the identification of carbon cycle response functions in Part 2 of this study (Torres Mendonça et al., 2021a). However, the core of our method, namely our new approach to obtaining the noise level for a proper estimation of the regularization parameter, may find applications in also solving other types of linear ill-posed problems.

ESTIMATION OF MECHANICAL LOAD ON RUBBER MIXING ROTORS BY USING A PARTIALLY FILLED FLOW SIMULATION IN CHAMBER

ABSTRACT Mixing characteristics and mechanical loads of rubber-mixing rotors are considered to be the two most important factors in actual rotor design. For the design of highly reliable production mixers, there is a great need for a proper estimation method of mechanical load, such as radial force or rotation torque of the rotors. The mechanical load of tangential mixing rotors and surrounding flow are mainly discussed by using partially filled numerical flow simulation. Operational parameters of the mixing condition were set to be fill factor and rotor phase angle of two rotors rotating at an even speed. The Carreau model was applied to the shear rate dependence of viscosity. The volume-of-fluid method was used for free surface simulation. Both two-dimensional and three-dimensional simulations were carried out to discuss mechanical load and its fluctuation mechanisms. For the numerical results, radial force on rotors, pressure, and the velocity distribution around the rotors and their fluctuations are presented and discussed. It was found that the radial force of the rotors could be estimated using this kind of flow simulation, and the fluctuation phenomena could be explained by the movement of a high-pressure region between the front of the rotor wings and the chamber wall.

Effect of the COVID-19 Lockdown on Air Pollution in the Ostrava Region

A proper estimation of anti-epidemic measures related to the influence of the COVID-19 outbreak on air quality has to deal with filtering out the weather influence on pollution concentrations. The goal of this study was to estimate the effect of anti-epidemic measures at three pollution monitoring stations in the Ostrava region. Meteorological data were clustered into groups with a similar weather pattern, and pollution data were divided into subsets according to weather patterns. Then each subset was evaluated separately. Our estimates showed a 4.1–5.7% decrease in NOx concentrations attributed to lower traffic intensity during the lockdown. The decrease of PM2.5 varied more significantly between monitoring stations. The highest decrease (4.7%) was detected at the traffic monitoring station, while there was no decrease detected at the rural monitoring station, which focuses mainly on domestic heating pollution. The key result of the study was the development of an analytical method that is able to take into account the effect of meteorological conditions. The method is much simpler and easy to replicate as an alternative to other published methods.

Using Proper Mean Generation Intervals in Modeling of COVID-19

In susceptible–exposed–infectious–recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., &gt;7 days. This discrepancy will lead to overestimated basic reproductive number and exaggerated expectation of infection attack rate (AR) and control efficacy. We argue that it is important to use suitable epidemiological parameter values for proper estimation/prediction. Furthermore, we propose an epidemic model to assess the transmission dynamics of COVID-19 for Belgium, Israel, and the United Arab Emirates (UAE). We estimated a time-varying reproductive number [R0(t)] based on the COVID-19 deaths data and we found that Belgium has the highest AR followed by Israel and the UAE.