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This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.

Facility management (FM) is regarded as an emerging issue in civil engineering and is responsible for ensuring the building's expected performance. The purpose of this study is to analyze buildings' current FM processes for educational and high residential segments and propose an FM-building information modeling (BIM) (BIM6D) to understand the information flow and leading players with and without FM-BIM integration.

The research strategy was a case study with data from the FM process of two buildings. This study was carried out in three stages: diagnosis of FM of the two buildings, FM-BIM integration and information flow and leading players analysis. Maintenance activities were categorized according to periodicity and status criteria for each project element for FM-BIM integration and were visualized in the Revit design using Dynamo software.

The results of this study show differences in how FM is conducted, especially in formalization and preventive character, and similarities regarding the difficulty of foreseen and lack of control because of scattered, disconnected and incomplete information on both. The visual appeal of the FM-BIM integration facilitates information access. It optimizes the use of the digital model through the most prolonged phase of the life cycle of a building (post-occupation phase). However, FM-BIM challenges buildings that do not have digital model expertise as residential segments. This study suggests a more significant role for construction companies in these cases.

This study investigates BIM-FM integration of buildings in two different contexts and reveals the importance of a construction company's role in buildings in the residential segment. This study contributes with real-life cases on BIM in existing buildings, discussing the value and challenges of BIM in FM applications.

This paper seeks to investigate the differences in the teachers’ professional development (TPD) by mentorship in workplace. The authors examined the role of mentorship in the PD of teachers and conducted a meta-analysis of pertinent empirical data.

Using data from over 2,900 individuals, 66 experiments and 12 countries, the authors presented a meta-analysis of the association between workplace mentorship and TPD.

The authors concluded that mentoring activities could boost the TPD to some extent. It contributes positively to the discipline of science and language, kindergarten, individual mentoring and curriculum research. In addition, the periodicity should not exceed 1 year.

The results of the meta-analysis are restricted to short-term mentorship activities, and the sample size is modest. Building upon the findings from the literature review and meta-analysis, the authors delineated a research agenda for prospective investigations. This includes an imperative for further exploration into the nexus between mentoring and the PD of educators.

Based on the available literature and meta-analysis findings, the authors developed a framework for the “Experts in the classroom” TPD pattern.

This is the first meta-analysis evaluating the association between mentorship and TPD.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

This paper aims to analyze what Samarco and Brazilian magazines speak and say about Mariana’s environmental crime. Discover their doxa in this subject. Interpret the speakings, sayings and doxas through the theories of the treadmills of production, crime and law.

It is a qualitative and documental research and a narrative analysis. Regarding the documents: 45 were from public authorities, 14 from Samarco Mineração S.A. and 73 from Brazilian magazines. Theoretically, the authors resorted to Bourdieusian sociology (speaking, saying and doxa) and the treadmills of production, crime and law theories.

Samarco: speaking – mission statements; saying – detailed information and economic and financial concerns; doxa – assistance discourse. Brazilian magazines: speaking – external agents; saying – agreements; doxa – attribution, aggravations, historical facts, impacts and protests.

The absence of discussions that addressed this fatality, with its respective consequences, from an agenda that exposed and denounced how it exacerbated race, class and gender inequalities.

Regarding Mariana’s environmental crime: Samarco Mineração S.A. speaks and says through the treadmill of production theory and supports its doxa through the treadmill of crime theory, and Brazilian magazines speak and say through the treadmill of law theory and support their doxa through the treadmill of crime theory.

To provoke reflections on the relationship between the mining companies and the communities where they settle to develop their productive activities.

Concerning environmental crime in perspective, submit it to a theoretical interpretation based on sociological references, approach it in a debate linked to environmental criminology, and describe it through narratives exposed by the guilty company and by Brazilian magazines with high circulation.

To improve the speed and accuracy of turbine blade film cooling design process, the most advanced deep learning models were introduced into this study to investigate the most suitable define for prediction work. This paper aims to create a generative surrogate model that can be applied on multi-objective optimization problems.

The latest backbone in the field of computer vision (Swin-Transformer, 2021) was introduced and improved as the surrogate function for prediction of the multi-physics field distribution (film cooling effectiveness, pressure, density and velocity). The basic samples were generated by Latin hypercube sampling method and the numerical method adopt for the calculation was validated experimentally at first. The training and testing samples were calculated at experimental conditions. At last, the surrogate model predicted results were verified by experiment in a linear cascade.

The results indicated that comparing with the Multi-Scale Pix2Pix Model, the Swin-Transformer U-Net model presented higher accuracy and computing speed on the prediction of contour results. The computation time for each step of the Swin-Transformer U-Net model is one-third of the original model, especially in the case of multi-physics field prediction. The correlation index reached more than 99.2% and the first-order error was lower than 0.3% for multi-physics field. The predictions of the data-driven surrogate model are consistent with the predictions of the computational fluid dynamics results, and both are very close to the experimental results. The application of the Swin-Transformer model on enlarging the different structure samples will reduce the cost of numerical calculations as well as experiments.

The number of U-Net layers and sample scales has a proper relationship according to equation (8). Too many layers of U-Net will lead to unnecessary nonlinear variation, whereas too few layers will lead to insufficient feature extraction. In the case of Swin-Transformer U-Net model, incorrect number of U-Net layer will reduce the prediction accuracy. The multi-scale Pix2Pix model owns higher accuracy in predicting a single physical field, but the calculation speed is too slow. The Swin-Transformer model is fast in prediction and training (nearly three times faster than multi Pix2Pix model), but the predicted contours have more noise. The neural network predicted results and numerical calculations are consistent with the experimental distribution.

This paper creates a generative surrogate model that can be applied on multi-objective optimization problems. The generative adversarial networks using new backbone is chosen to adjust the output from single contour to multi-physics fields, which will generate more results simultaneously than traditional surrogate models and reduce the time-cost. And it is more applicable to multi-objective spatial optimization algorithms. The Swin-Transformer surrogate model is three times faster to computation speed than the Multi Pix2Pix model. In the prediction results of multi-physics fields, the prediction results of the Swin-Transformer model are more accurate.

This study investigates how enhancing information and communication technology (ICT) affects female economic participation in sub-Saharan African nations.

Three female economic participation indicators are used, namely female labour force participation, female unemployment and female employment rates. The engaged ICT variables are fixed broadband subscriptions, mobile phone penetration and Internet penetration. The Generalized Method of Moments is used for the empirical analysis.

The following main findings are established: First, there is a (1) negative net effect in the relevance of fixed broadband subscriptions in female labour force participation and female unemployment and (2) positive net effects from the importance of fixed broadband subscriptions on the female employment rate. Secondly, an extended analysis is used to establish thresholds at which the undesirable net negative effect on female labour force participation can be avoided. From the corresponding findings, a fixed broadband subscription rate of 9.187 per 100 people is necessary to completely dampen the established net negative effect. Hence, the established threshold is the critical mass necessary for the enhancement of fixed broadband subscriptions to induce an overall positive net effect on the female labour force participation rate.

This study complements the extant literature by assessing how increasing penetration levels of ICT affect female economic inclusion and by extension, thresholds necessary for the promotion of ICT to increase female economic inclusion.

This paper aims to develop a new 3D analytical model in cylindrical coordinates to study radial flux eddy current couplers (RFECC) while considering the magnetic edge and 3D curvature effects, and the field reaction due to the induced currents.

The analytical model is developed by combining two formulations. A magnetic scalar potential formulation in the air and the magnets regions and a current density formulation in the conductive region. The magnetic field and eddy currents expressions are obtained by solving the 3D Maxwell equations in 3D cylindrical coordinates with the variable separation method. The torque expression is derived from the field solution using the Maxwell stress tensor. In addition to 3D magnetic edge effects, the proposed model takes into account the reaction field effect due to the induced currents in the conducting part. To show the accuracy of the developed 3D analytical model, its results are compared to those from the 3D finite element simulation.

The obtained results prove the accuracy of the new developed 3D analytical model. The comparison of the 3D analytical model with the 2D simulation proves the strong magnetic edge effects impact (in the axial direction) in these devices which must be considered in the modelling. The new analytical model allows the magnetic edge effects consideration without any correction factor and also presents a good compromise between precision and computation time.

The proposed 3D analytical model presents a considerably reduced computation time compared to 3D finite element simulation which makes it efficient as an accurate design and optimization tool for radial flux eddy current devices.

A new analytical model in 3D cylindrical coordinates has been developed to find the electromagnetic torque in radial flux eddy current couplers. This model considers the magnetic edge effects, the 3D curvature effects and the field reaction (without correction factors) while improving the computation time.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This study aims to contribute to the extant literature by assessing how microfinance institutions (MFIs) affect female entrepreneurship, contingent on female unemployment levels.

The study focuses on 44 countries in sub-Saharan Africa for the period 2004–2018. The empirical evidence is based on interactive quantile regressions, which put emphasis on nations with high, low and intermediate levels of business constraints. The analysis is tailored to provide avoidable female unemployment levels in the implementation of policies designed for MFIs to promote female business ownership.

The hypotheses that MFIs are favorable for female business owners and some critical rates of female unemployment should be avoided in order for the favorable incidence to be maintained is exclusively valid in the 10th quantiles of the cost of business by females and time to start-up a business by females. Policy implications are discussed.

This study has complemented the extant literature by providing actionable female unemployment critical masses that governments can act upon in tailoring the relevance of MFIs in the doing of business by females.