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The purpose of this paper is to study the relationship between arc sound signal and arc height through arc sound features of GTAW welding, which is aimed at laying foundation work for monitoring the welding penetration and quality by using the arc sound signal in the future.

The experiment system is based on GTAW welding with acoustic sensor and signal conditioner on it. The arc sound signal was first processed by wavelet analysis and wavelet packet analysis designed in this research. Then the features of arc sound signal were extracted in time domain, frequency domain, for example, short‐term energy, AMDF, mean strength, log energy, dynamic variation intensity, short‐term zero rate and the frequency features of DCT coefficient, also the wavelet packet coefficient. Finally, a ANN (artificial neural networks) prediction model was built up to recognize different arc height through arc sound signal.

The statistic features and DCT coefficient can be absolutely used in arc sound signal processing; and these features of arc sound signal can accurately react the modification of arc height during the GTAW welding process.

This paper tries to make a foundation work to achieve monitoring arc length through arc sound signal. A new way to remove high frequency noise of arc sound signal is produced. It proposes some effective statistic features and a new way of frequency analysis to build the prediction model.

This paper aims to deal with the integration of energy storage devices (ultracapacitors) in wind energy applications to absorb the short terms fluctuations. The originality of this contribution is focused on energy management related to wind power frequency distribution between the hybrid sources. The robust and simplified control strategies are proposed and applied to DC‐DC converters without AC signals measurements. A novel MPPT method is introduced to operate the wind generator at the maximum power regardless of the wind speed variations. The fluctuating part of this power is mitigated by using a UC. The reference current of this last is obtained from a low pass filter. An innovative limitation algorithm of the UC voltage is proposed with aims to ensure optimal operation of the system. The control algorithms are implemented in a PIC18F4431 microcontroller. Some experimental results from this new approach are presented and analyzed.

This study is organized according to the following main and sub‐topics after introduction: frequency distribution principle; wind energy generation; short‐term fluctuations storage system; and experimental setup and results.

The simulations results highlight the interest of using ultracapacitors in a wind‐diesel system. The experimental results show that the short term fluctuations induced by the wind generator current are effectively mitigated by the ultracapacitors.

In this paper, an interesting MPPT method is presented. The fluctuations mitigation is realised by using the frequency distribution according to ultracapacitors dynamics. The ultracapacitors voltage control method is proposed with the aim of maintaining optimal operation conditions, and is validated by experimental tests.

Although the UN Sustainable Development Goals (SDGs) emphasize the importance of leaving no one behind, the opposite is happening for the world's 89 million forcibly displaced people who are mostly left out of SDGs’ reporting and progress. A key reason for this poor outcome is that host country governments plan refugee camps as short-term shelters, but refugees stay in these camps for more than a decade on average due to ongoing conflicts in their home country. This disparity between intent and reality prevents sustainable living conditions for refugee populations. Operational innovations are needed to find sustainable solutions that ensure a higher quality of life and progress toward sustainability in refugee camps.

Through an abductive case study, the authors develop a theoretical framework on sustainable operational innovations for refugee camps. The authors use this framework to analyze four sustainable operational innovations implemented in three refugee camps in Jordan.

The authors develop three research propositions that describe the conditions required for these operational innovations to succeed: they need to include specific needs and cultural preferences of refugees, they must accommodate host governments' restrictions that limit permanent settlement, and finally, technological innovations require careful data management policies to protect refugees. Doing this, the authors account for the broader political-economic and ecological environments that refugee camps are embedded in.

This paper opens a new area of research on sustainable innovation in humanitarian operations. It provides insights into key contingency factors moderating the link between operational innovations and sustainability outcomes. It represents one of the few studies that build their theorizing upon field data collected in refugee camps.

Deep learning (DL) technology is used to design a voice evaluation system to understand the impact of learning aids on DL and mobile platforms on students’ learning behavior.

DL technology is used to design a speech evaluation system.

The experimental results show that the speech evaluation system designed has a high accuracy rate, the highest agreement rate with manual evaluation of pronunciation is 89.5%, and the correct speech recognition rate is 96.64%. The designed voice evaluation system and the manual voice rating system have a maximum error rate of 2%. The experimental results suggest that it is necessary to further optimize the learning aids for mobile platform. The learning aids of the mobile platform need to be further optimized to promote the improvement of student learning efficiency.

The results show that the speech evaluation system designed has good practical application value, and it provides a certain reference value for the future study of learning tools on DL.

Collaborative learning with recorded lectures and presentations can be supported by allowing users to anchor notes in the documents and exchange them with other learners. While the traditional modality for annotation and discussion is text, there are a number of reasons in favour of supporting other media and modalities as well. We describe the extension of a lecture‐on‐demand annotation and discussion system that allows learners to use spoken notes. Our main focus is on the development of a suitable user interface that facilitates the retrieval of speech data employing signal‐processing algorithms while at the same time being simple and easy to use.

Although phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.

The authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.

The findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.

The findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

The purpose of this paper is to identify the most appropriate multidisciplinary data sources related with energy optimization decision support as well as the related methodologies, tools and techniques for data capturing and processing for each of them.

A review is conducted on the state-of-play of decision support systems for energy optimization, focussing on the municipal sector, followed by an identification of the most appropriate multidisciplinary data sources related with energy optimization decision support. An innovative methodology is outlined to integrate semantically modeled data from multiple sources, to assist city authorities in energy management.

City authorities need to lead relevant actions toward energy-efficient neighborhoods. Although there are more and more energy and other related data available at the city level, there are no established methods and tools integrating and analyzing them in a smart way, with the purpose to support the decision-making process on energy use optimization.

A novel multidimensional approach is proposed, using semantic technologies to integrate data from multiple sources, to assist city authorities to produce short-term energy plans in an integrated, transparent and comprehensive way.