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Tracking trends in new technology funding patterns is essential for venture scaling. The emerging advanced digital technologies (ADT) such as virtual reality (VR), artificial intelligence (AI), blockchain and Internet-of-things (IoT) promote business innovation adaptations, and in turn, reshape the industrial landscape. To attract nascent funding for such prospective projects among the public, well-articulated project pitches that are equipped with effective marketing communication convey the projects' importance and marketability. Specifically, when the entrepreneurs and the crowdfunding platform users interact via different types of crowdfunding platforms, pitch framing, including the signaling of ADT terms, project location and fundraising goal, becomes imperative to help facilitate crowdfunding success.

Drawing on data collected from six leading US-based equity and reward-based crowdfunding platforms in 2020, an empirical study was performed. Using the text analysis approach, the authors examined the positive effects of incorporating technology orientation on crowdfunding success. While the effect between the project description's signaling of geographic location, fundraising goal and articulation style on fundraising success, while controlling for project and platform characteristics.

The results suggested that the technology-orientated projects are more likely to achieve better fundraising outcomes. Taking crowdfunding platform types, project locations, minimum fundraising goals and articulation with analytical and authentic into consideration, the results still hold.

Building on the theoretical framework of signaling theory, the authors consider the crowdfunding-specific contextual factors to enhance the understanding of the positivity impact of technology orientation. By such addition, it facilitates more effective strategic composition of entrepreneurs' fundraising conversations.

Introduces a new analytical review procedure that measures the degree to which a data set’s digit distribution deviates from a Benford digit distribution. This deviation can indicate potential manipulation and can be used to signal the need for further audit testing. An artificial neural network is used to distinguish between “normal” and “manipulated” financial data. The results show that if data have been contaminated (at a 10 per cent level or more) a Benford analytical review procedure will detect this 68 per cent of the time. If the data are not contaminated, the test will indicate that the data are “clean” 67 per cent of the time. Because analytical review procedures are not used in isolation, these results probably understate the effectiveness and potential of a digits‐based analytical review procedure. This procedure’s fraud detection results compare favorably to traditional analytical review procedures. Importantly, its unique analysis procedure allows it to complement traditional analytical review procedures. A key limitation of this study is that it uses simulated data, rather than actual data. Such an enhancement will be a critical step in future research. This method appears to have potential merit and provides many opportunities for new research.

The purpose of this paper is to develop a system for estimating the position of the active magnetic bearing (AMB) shaft. A new approach using the static and dynamic inductances and complex analytic signal to simplify the estimation procedure. Finite element (FE) simulations are introduced as a part of the system synthesis.

The paper presents an AMB displacement estimation system. The system is created with three inductive sensors. The position is computed from refined static and dynamic inductance obtained from complex analytic signals of flux and current. FE simulation is used to relate refined inductances to the displacement and to verify the model.

This paper shows the applicability of complex analytic signal transformation on estimation systems. The use of new refined inductance is presented in contrast to the classical approach of static and dynamic inductances. The paper shows that classical approach of static and dynamic inductance is not usable for the presented estimation system.

For the practical implementation of the presented system, it is necessary to know the exact dimensions of the AMB stator and the voltage and frequency used to supply the inductance estimation system.

The paper presents a system for estimating the displacement of AMB. The paper introduces the application of complex analytic signal to the estimation of AMB displacement. The mentioned signal is used to compute the new refined inductances. The comparison to the classical approach of static and dynamic inductances is given in this paper. The paper introduces FE simulations to the estimation system synthesis.

This study aims to perform the experimental work on a laboratory-constructed steel truss bridge model on which hammer blows are applied for excitation. The vibration response signals of the bridge structure are collected using sensors placed at different nodes. The different damaged states such as no damage, single damage, double damage and triple damage are introduced by cutting members of the bridge. The masked noise with recorded vibration responses generates challenge to properly analyze the health of bridge structure.

The analytical modal properties are obtained from finite element model (FEM) developed using SAP2000 software. The response signals are analyzed in frequency domain by power spectrum and in time-frequency domain using spectrogram and Stockwell transform. Various low pass signal-filtering techniques such as variational filter, lowpass sparse banded (AB) filter and Savitzky–Golay (SG) differentiator filter are also applied to refine vibration signals. The proposed methodology further comprises application of Hilbert transform in combination with MUSIC and ESPRIT techniques.

The outcomes of SG filter provided the denoised signals using appropriate polynomial degree with proper selected window length. However, certain unwanted frequency peaks still appeared in the outcomes of SG filter. The SG-filtered signals are further analyzed using fused methodology of Hilbert transform-ESPRIT, which shows high accuracy in identifying modal frequencies at different states of the steel truss bridge.

The sequence of proposed methodology for denoising vibration response signals using SG filter with Hilbert transform-ESPRIT is a novel approach. The outcomes of proposed methodology are much refined and take less computational time.

This research presents the theory and implementation detail for the extraction of angular speed variations. In particular, band‐pass filtering, frequency shifting and analytic representation are addressed with simple mathematical equations. Finally, some case studies on a 3kW induction motor are investigated for healthy and faulty conditions. Some vital key features and characteristics can be extracted from the angular speed variations. Pole pass speed side bands around the rotor speed can be used as a feature for broken rotor bar faults. The number of poles multiplied by the synchronous running speed cycles and the rise of 100 Hz components are key characteristics for voltage imbalance faults.

This paper aims to present an optimization method of the input driving signal of a piezoelectric inkjet printhead to improve droplet consistency and increase jetting frequency.

The optimization target is the transient pressure in the nozzle caused by the input driving signal, which directly generates the droplets. After demonstrating the linearity of the driving input and system pressure, an analytic model as a transfer function was developed, allowing calculation of the pressure vibration in the nozzle for an arbitrary input. Different patterns of input signal were parameterized and applied into the optimizing function, which represents the difference between the ideal and the actual pressure vibration. By determining the function minimum, the optimized parameters of the input signal were estimated.

Optimization results of different input patterns were compared and verified by the numerical model of the printhead, and it was revealed that the optimization method that combined the quenching pulse and an increased falling time interval was more effective than use of a single method.

After the process of optimization, a new type of input signal to the piezoelectric inkjet printhead was showed. By this method, the frequency of the printhead could be increased without losing consistency of droplets.

International business research is showing an increasing interest in the link between international business and human rights. Despite extensive coverage of corporate social responsibility (CSR) and sustainability, the analysis and discussion of why multinational corporations include human rights in corporate reporting is still in its early stages. This chapter develops an analytical framework on corporate human rights reporting, with special emphasis on international business. The conceptual part of the framework draws on legitimacy theory, stakeholder theory and signalling theory. The analytical part distinguishes between factors inside the corporation and the external environment of business organisations operating internationally.

To provide further evidence on the effectiveness of analytical procedures (APs) used in auditing. Computer simulation experiments are used to examine the error detection ability of a set of APs. Two different types of errors are examined and compared on the basis of the Type I and Type II errors they produce. The results of the experiments support earlier performance assessments of APs based upon simulated data. Higher noise levels reduce performance but a more detailed modeling of the process generating the data appears to produce a compensatory increase in performance. Contrary to earlier findings, some annual APs performed better than their related monthly counterparts. Case study and experimental results are better reconciled than in previous studies. The findings are based upon simulated data and deal with two types of error only. The experiments model the data generating process underlying accounting numbers but are simplifications of the real situation. Future research based upon the same approach but using more sophisticated experimental models and dealing with a wider class of errors would be useful. The findings echo earlier recommendations that APs should not be relied upon as lone, substantive testing devices for error and fraud. The simulation experiments use Statistical Activity Cost Theory to generate accounting numbers from specified, underlying stochastic processes. This allows errors to be related to transactions, i.e. the level at which they typically occur, whereas in prior experimental work errors have only been related to accounts.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

This paper aims to present a new method for identification of some flight modes, including natural and non-standard modes, and extraction of their characteristics, directly from measurements of flight parameters in the time domain.

The Hilbert-Huang transform (HHT), as a novel prevailing tool in the signal analysis field, is used to attain the purpose. The study shows that the HHT has superior potential capabilities to improve the airplane flying quality analysis and to conquer some drawbacks of the classical method in flight dynamics.

The proposed method reveals the existence of some non-standard modes with small damping ratios at non-linear flight regions and obtains their characteristics.

The paper examines only airplane longitudinal dynamics. Further research is needed regarding lateral-directional dynamic modes and coupling effects of the longitudinal and lateral modes.

Application of the proposed method to the flight test data may result in real-time flying quality analysis, especially at the non-linear flight regions.

First, to utilize the empirical mode decomposition (EMD) capabilities in real time, a local-online algorithm is introduced which estimates the signal trend by the Savitzky-Golay sifting process and eliminates it from the signal in the EMD algorithm. Second, based on the local-online EMD algorithm, a systematic method is proposed to identify flight modes from flight parameters in the time domain.