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Neurons are the fundamental units of the brain and nervous system. Developing a good modeling of human neurons is very important not only to neurobiology but also to computer science and many other fields. The McCulloch and Pitts neuron model is the most widely used neuron model, but has long been criticized as being oversimplified in view of properties of real neuron and the computations they perform. On the other hand, it has become widely accepted that dendrites play a key role in the overall computation performed by a neuron. However, the modeling of the dendritic computations and the assignment of the right synapses to the right dendrite remain open problems in the field. Here, we propose a novel dendritic neural model (DNM) that mimics the essence of known nonlinear interaction among inputs to the dendrites. In the model, each input is connected to branches through a distance-dependent nonlinear synapse, and each branch performs a simple multiplication on the inputs. The soma then sums the weighted products from all branches and produces the neuron’s output signal. We show that the rich nonlinear dendritic response and the powerful nonlinear neural computational capability, as well as many known neurobiological phenomena of neurons and dendrites, may be understood and explained by the DNM. Furthermore, we show that the model is capable of learning and developing an internal structure, such as the location of synapses in the dendritic branch and the type of synapses, that is appropriate for a particular task — for example, the linearly nonseparable problem, a real-world benchmark problem — Glass classification and the directional selectivity problem.

Construction processes planning and effective management are extremely important for success in construction business. Head of a design must be well experienced in initiating, planning, and executing of construction projects. Therefore, proper assessment of design projects' managers is a vital part of construction process. The paper deals with an effective methodology that might serve as a decision support aid in assessing project managers. Project managers' different characteristics are considered to be more or less important for the effective management of the project. Qualifying of managers is based on laws in force and sustainability of project management involving determination of attributes value and weights by applying analytic hierarchy process (AHP) and expert judgement methods. For managers' assessment and decision supporting is used additive ratio assessment method (ARAS). The model, presented in this study, shows that the three different methods combined (ARAS method aggregated together with the AHP method and the expert judgement method) is an effective tool for multiple criteria decision aiding. As a tool for the assessment of the developed model, was developed multiple criteria decision support system (MCDSS) weighting and assessment of ratios (WEAR) software. The solution results show that the created model, selected methods and MCDSS WEAR can be applied in practice as an effective decision aid.

Flexible dielectric composites with high permittivity have been extensively studied due to their potential applications in high-density energy capacitors. In this review, effects of interface characteristics on the dielectric properties in the polymer-based nanocomposites with high permittivity are analyzed. The polymer-based dielectric composites are classified into two types: dielectric–dielectric (DD, ceramic particle-polymer) composites and conductor–dielectric (CD, conductive particle-polymer) composites. It is highly desirable for the dielectric–dielectric composites to exhibit high permittivity at low content of ceramic particles, which requires a remarkable interface interaction existing in the composite. For conductor–dielectric composites, a high permittivity can be achieved in composite with a small amount of conductor particle, but associated with a high loss. In this case, the interface between conductor and polymer with a good insulating characteristic is very important. Different methods can be used to modify the surface of ceramic/conductor particles before these particles are dispersed into polymers. The experimental results are summarized on how to design and make the desirable interface, and recent achievements in the development of these nanocomposites are presented. The challenges facing the fundamental understanding on the role of interface in high-permittivity polymer nanocomposites should be paid a more attention.

An analytical model is presented that predicts the average heat transfer rate for forced convection, air cooled, plate fin heat sinks for use in the design and selection of heat sinks for electronics applications. Using a composite solution based on the limiting cases of fully-developed and developing flow between isothermal parallel plates, the average Nusselt number can be calculated as a function of the heat sink geometry and fluid velocity. The resulting model is applicable for the full range of Reynolds number, , and accurately predicts the experimental results to within an RMS difference of 2.1%.

Digital disruptions are substantially impacting businesses and reshaping our economy worldwide, attracting increasing attention in research and practice. However, research lacks theoretical framing and understanding of the emergence, development, and impact of digital disruption. This study analyses and structures the fragmented knowledge on digital disruption by means of a systematic literature review, identifying five relevant key dimensions, i.e., disruption characteristics, market factors, organisational factors, value constellation, and impact/outcomes. Based on this analysis and classic disruption theory, we develop a theory-informed integrative framework, proposing nine relevant layers of digital disruption and deriving corresponding theoretical propositions for future research. The study makes an initial contribution towards theory development and a comprehensive understanding of the digital disruption concept. It may serve as the starting point for further theory development and a guiding scheme for managers on how to create or deal with digital disruption.

There is a growing body of work on the ex vivo tensile testing of tendon repairs, an appreciable amount of which are performed on cadaveric porcine flexor tendons. However, there is little information in the literature on exactly how to perform the dissections necessary to obtain flexor tendons from porcine trotters. We present a simple method to rapidly harvest tendons from the porcine foot, allowing large amounts of material to be harvested in little time for the purpose of tensile testing of tendon repairs.

The uncertainties in scientific studies for climate risk management can be investigated at three levels of complexity: “ABC”. The most sophisticated involves “Analyzing” the full range of uncertainty with large multi-model ensemble experiments. The simplest is about “Bounding” the uncertainty by defining only the upper and lower limits of the likely outcomes. The intermediate approach, “Crystallizing” the uncertainty, distills the full range to improve the computational efficiency of the “Analyze” approach. Modelers typically dictate the study design, with decision-makers then facing difficulties when interpreting the results of ensemble experiments. We assert that to make science more relevant to decision-making, we must begin by considering the applications of scientific outputs in facilitating decision-making pathways, particularly when managing extreme events. This requires working with practitioners from outset, thereby adding “D” for “Decision-centric” to the ABC framework.

Aiming at the defects of pronunciation errors and limited collection of pronunciation data resources in traditional artificial neural networks, an English pronunciation judgment and detection model based on deep learning neural networks data stream fusion is proposed. Taking Chinese English pronunciation as the research object, three groups of phonetic data were selected as experimental auxiliary data, based on the convolutional neural network, through the preset reset of the pronunciation detection system of the model, the sampling and recognition extraction of the speech system, the wrong speech detection and the feature analysis of the multi-level data stream tandem, the experiments are carried out with CU-CHLOE language learning database, WSJ1 database and 863 Mandarin database. The experimental results show that the recognition accuracy of this model is higher than that of the traditional neural network model, the accuracy of error type diagnosis is significantly improved, and its noise robustness is the best.

In this paper, we study the uncertain multiple attribute decision making problems with preference information on alternatives (UMADM-PIA, for short), in which the information on attribute weights is not precisely known, but value ranges can be obtained. A projection method is proposed for the UMADM-PIA. To reflect the decision maker's preference information, a projection model is established to determine the weights of attributes, and then to select the most desirable alternative(s). The method can reflect both the objective information and the decision maker's subjective preferences, and can also be performed on computer easily. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and practicality.

Previous studies reported that children with autism spectrum disorder (ASD) show a certain interest in social robots. This makes social robots potential to be a model to teach social skills. This exploratory study aims to investigate whether three types of joint attention skills (i.e., eye-contact, pointing, gaze-following) could be improved for five preschoolers with ASD using an evidence-based robot-modeling intervention with a humanoid social robot NAO. Our observation shows that these children were motivated when interacting with NAO by following and responding correctly to NAO’s joint attention behaviors. Although some improvements were found, no pattern or systematic effect could be revealed. In the future, more evidence-based studies are needed to investigate the benefits of robot-assisted therapy more deeply.

Androgen deprivation therapy is a common treatment for advanced or metastatic prostate cancer. Like the normal prostate, most tumors depend on androgens for proliferation and survival but often develop treatment resistance. Hormonal treatment causes many undesirable side effects which significantly decrease the quality of life for patients. Intermittently applying androgen deprivation in cycles reduces the total duration with these negative effects and may reduce selective pressure for resistance. We extend an existing model which used measurements of patient testosterone levels to accurately fit measured serum prostate specific antigen (PSA) levels. We test the model's predictive accuracy, using only a subset of the data to find parameter values. The results are compared with those of an existing piecewise linear model which does not use testosterone as an input. Since actual treatment protocol is to re-apply therapy when PSA levels recover beyond some threshold value, we develop a second method for predicting the PSA levels. Based on a small set of data from seven patients, our results showed that the piecewise linear model produced slightly more accurate results while the two predictive methods are comparable. This suggests that a simpler model may be more beneficial for a predictive use compared to a more biologically insightful model, although further research is needed in this field prior to implementing mathematical models as a predictive method in a clinical setting. Nevertheless, both models are an important step in this direction.

0-3 dielectric composites with high dielectric constants have received great interest for various technological applications. Great achievements have been made in the development of high performance of 0-3 composites, which can be classified into dielectric–dielectric (DDCs) and conductor–dielectric composites (CDCs). However, predicting the dielectric properties of a composite is still a challenging problem of both theoretical and practical importance. Here, the physical aspects of 0-3 dielectric composites are reviewed. The limitation of current understanding and new developments in the physics of dielectric properties for dielectric composites are discussed. It is indicated that the current models cannot explain well the physical aspects for the dielectric properties of 0-3 dielectric composites. For the CDCs, experimental results show that there is a need to find new equations/models to predict the percolative behavior incorporating more parameters to describe the behavior of these materials. For the DDCs, it is indicated that the dielectric loss of each constituent has to be considered, and that it plays a critical role in the determination of the dielectric response of these types of composites. The differences in the loss of the constituents can result in a higher dielectric constant than both of the constituents combined, which breaks the Wiener limits.

The uniformity and homogeneously dispersed nanoparticles in base fluids contribute to enhanced thermal conductivity of the mixture. By considering the uniformity and geometrical structures (e.g., body-centered cubic) of homogeneously dispersed nanoparticles in base fluids, a model for determining the effective thermal conductivity (ETC) of such nanoparticle-fluid suspensions, commonly known as nanofluids is proposed in this study. The theoretical results of the effective thermal conductivities of TiO₂/Deionized (DI) water and Al₂O₃/DI water-based nanofluids are presented, and they are found to be in good agreement with our experimental results and also with those reported in the literature. The new model presented in this study shows a better prediction of the effective thermal conductivity of nanofluids compared to other classical models attributed to Maxwell, Hamilton–Crosser, and Bruggeman.

This paper presents the development of a numerical, iterative and nonisentropic model for the thermodynamic processes of a reciprocating compressor of a refrigeration system operating at steady state. The mathematical model was implemented using the scientific software Engineering Equation Solver (EES) and it is based on the application of the energy equations in four regions of the compressor: inlet duct and chambers of pre-compression, compression, and post-compression. The model was validated with experimental data collected from an open-drive reciprocating compressor, operating with the refrigerant R-134a at different suction and discharge pressures and with different compressor rotational speeds. Model validation was made comparing the values of the mass flow rate and the discharge temperature of the compressor generated by the model with their corresponding experimental values for 33 experimental tests, the mean relative difference was $-$0.2% for the discharge temperature and 2.9% for mass flow rate. In this validation, the output variables of the model were calculated considering the uncertainties from the input variables. The theoretical mean standard uncertainty is 2% for discharge temperature and 6% for mass flow rate. An analysis of the capacitive and thermal performance of the compressor was made using the model, which demonstrates a decrease in the capacitive and thermal efficiencies for increasing the pressure ratio or clearance volume.

Due to the low vibration frequency and weak vibration energy in natural environment, the vibration energy harvester is faced with the problem of low power and low adaptability and becoming particularly difficult in actual conditions. It is necessary to improve the harvesting capacity and efficiency by optimizing the parameters of the harvester, making full use of the energy of low and unstable atmospheric vibrations. In this paper, a mathematical model is established for the cantilever magnetostrictive vibration harvester under the base excitation, including the mechanical deformation of the composite beam, and the electromagnetic results produced thereof. The mechanical-magneticelectric energy conversion relationship is duly taken into account. The additional weight, coil parameters, external resistance and other parameters of the harvester are optimized and analyzed through numerical simulation. In addition, the theoretical results are analyzed and discussed via comparison with experiments. Finally, the effects of the above factors are assessed, which allows us to obtain the optimal winding length, number of turns of the coil, and optimal tip additional mass. The experiment result shows that the optimized magnetostrictive harvester can output 12.07mW power to the external resistor under the condition of 1g acceleration mechanical vibration, with normalized power density reaching 40.2mW/cm³/g. Moreover, the optimized magnetostrictive harvester can successfully supply power for the LED display screen of the temperature sensor and a low-power thermometer.

A wavelet-based forecasting method for time series is introduced. It is based on a multiple resolution decomposition of the signal, using the redundant "à trous" wavelet transform which has the advantage of being shift-invariant.

The result is a decomposition of the signal into a range of frequency scales. The prediction is based on a small number of coefficients on each of these scales. In its simplest form it is a linear prediction based on a wavelet transform of the signal. This method uses sparse modelling, but can be based on coefficients that are summaries or characteristics of large parts of the signal. The lower level of the decomposition can capture the long-range dependencies with only a few coefficients, while the higher levels capture the usual short-term dependencies.

We show the convergence of the method towards the optimal prediction in the autoregressive case. The method works well, as shown in simulation studies, and studies involving financial data.

Many studies in new product development (NPD) single out the use of information (especially market information) as a key predictor of NPD performance, but knowledge is lacking about what type of information is needed in each phase of the NDP process to enable high NPD performance. Based on a literature review and a pilot case study, this article increases the understanding of managing information in NPD. It is argued that the capability of managing information consists of three components: acquiring, sharing, and using information. By focusing on three different phases of the NPD process, 11 propositions regarding which information, information sources and means of cross-functional integration patterns that are most important to high NPD performance have been derived in each respective phase. In addition, the article also discusses antecedents and consequences of managing information. The article concludes with implications for managers, identifies limitations and proposes an agenda for further research into this area.

Thin-film deposition processes have gained much popularity due to their unique capability to enhance the physical and chemical properties of various materials. Identification of the best parametric combination for a deposition process to achieve desired coating quality is often considered to be challenging due to the involvement of a large number of input process parameters and conflicting responses. This study discusses the development of adaptive neuro-fuzzy inference system-based models for the prediction of quality measures of two thin-film deposition processes, i.e., SiCN thin-film coating using thermal chemical vapor deposition (CVD) process and Ni–Cr alloy thin-film coating using direct current magnetron sputtering process. The predicted response values obtained from the developed models are validated and compared based on actual experimental results which exhibit a very close match between both the values. The corresponding surface plots obtained from the developed models illustrate the effect of each process parameter on the considered responses. These plots will help the operator in selecting the best parametric mix to achieve enhanced coating quality. Also, analysis of variance results identifies the importance of each process parameter in the determination of response values. The proposed approach can be applied to various deposition processes for modeling and prediction of observed response values. It will also assist as an operator in selecting the best parametric mix for achieving desired response values.

The characteristics of the electromechanical response observed in an ionic-electroactive polymer (i-EAP) are represented by the time ($t$) dependence of its bending actuation ($y$). The electromechanical response of a typical i-EAP — poly(ethylene oxide) (PEO) doped with lithium perchlorate (LP) — is studied. The shortcomings of all existing models describing the electromechanical response obtained in i-EAPs are discussed. A more reasonable model: $y=y_{max}{e}^{-\tau ∕t}$ is introduced to characterize this time dependence for all i-EAPs. The advantages and correctness of this model are confirmed using results obtained in PEO-LP actuators with different LP contents and at different temperatures. The applicability and universality of this model are validated using the reported results obtained from two different i-EAPs: one is Flemion and the other is polypyrrole actuators.

The development of Web applications requires a variety of tasks, some of them involving aesthetic and cognitive aspects. As a consequence, there is a need for appropriate models and methodologies which allow the heterogeneus members of hypermedia projects to effectively communicate and guide them during the development process. In this chapter, we describe some hypermedia models and methodologies proposed for the development of hypermedia applications.