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This paper represents a hybrid Vision/INS system for tool tracking applications. The proposed system incorporates low cost MEMS sensors and low cost vision type sensors for tracking industrial tools. Vision systems alone have to deal with the problem of “line of sight” and the INS sensor alone will encounter an exponential drift, which render both systems useless for the proposed application.

The Vision/INS system with the integration of the extended Kalman filter calculates 6D position‐orientation of a tool during its operation within the required accuracy tolerance specific to the application at hand. In this paper, a tool motion modeling approach is proposed to limit the error in an acceptable range for a short period of missing data. The motion of the tool is modeled and updated at any time that the instrument is in the camera view field. This model is applied to the estimation algorithm whenever the camera is not in line of site and the optical data is missing.

The result of applying motion modeling is shown that the resulted error due to absence of the vision measurement system was bounded and decreased (see the experimental results).

In this paper, the tool motion modeling is proposed to bind the error in the acceptable range for a short period of missing data. The motion of the tool is modeled and updated at any time that the instrument is in the camera view field. This model is applied to the estimation algorithm whenever the camera is not in line of site and the optical data is missing.

Semi-active suspension systems with electromagnetic dampers allow energy regeneration and the required control strategies are easier to implement than the active suspensions are. This paper aims to address the application of a tubular linear permanent magnet synchronous machine for a semi-active suspension system.

Classical rules of mechanics and electromagnetics were applied to describe a dynamic model combining vibration and electrical machines theories. A multifaceted MATLAB®/Simulink model was implemented to incorporate equations and simulate global performance. Experimental tests on an actual prototype were carried out to investigate displacement transmissibility of the passive case. In addition, simulation results were shown for the dissipative semi-active case.

The application of the developed model suggests convergent results. For the passive case, numerical and experimental outcomes validate the parameters and confirm system function and proposed methodology. MATLAB®/Simulink results for the semi-active case are consistent, showing an improvement on the displacement transmissibility. These agree with the initial conceptual thoughts.

The use of linear electromagnetic devices in suspension systems is not a novel idea. However, most published papers on this subject outline active solutions, neglect semi-active ones and focus on experimental studies. However, here a dynamic mechanical-electromagnetic coupled model for a semi-active suspension system is reported. This is in conjunction with a linear electromagnetic damper.

Shaft orbit is an important characteristic for vibration monitoring and diagnosing system of hydroelectric generating set. Because of the low accuracy and poor reliability of traditional methods in identifying the shaft orbit moving direction (MD), the purpose of this paper is to present a novel automatic identification method based on trigonometric function and polygon vector (TFPV).

First, some points on shaft orbit were selected with inter‐period acquisition method and joined together orderly to form a complex plane polygon. Second, by using the coordinate transformation and rotation theory, TFPV were applied comprehensively to judge the concavity or convexity of the polygon vertices. Finally, the shaft orbit MD is identified.

The simulation and experiment demonstrate that the method proposed can effectively identify the common shaft orbit MD.

In order to identity the shaft orbit MD effectively, a novel automatic identification method based on TFPV is proposed in this paper. The problem of identifying the shaft orbit MD is transformed into the problem about orientation of complex polygons, which are formed orderly by points on orbit shaft, and TFPV are applied comprehensively to judge the concavity or convexity of the polygon vertices.

This study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time.

Unlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time.

The results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety.

Despite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found.

The identities of Muslim women tend to be essentialized into binaries of what she is and what she ought to be (Golnaraghi & Dye, 2016). For far too long Muslim women’s voices in North America have been marginalized by hegemonic Orientalist (Said, 1978) and traditionalist (Clarke, 2003) Islamic discourses. When it comes to issues of agency, empowerment, and self-expression, it is either imposed by Western ideals or regulated by traditionalist politics of Islam (Zine, 2006). As such, Muslim women activists must engage and negotiate within the dual and narrow oppressions of Orientalist and traditionalist Islamic representations of her (Khan, 1998; Zine, 2006). Given the scarcity of space provided in print media (Golnaraghi & Dye, 2016; Golnaraghi & Mills, 2013) for Muslim women to construct, appropriate, and remake their own identities, some have turned to social media to challenge these dichotomies through activism and resistance. Such a space is necessary in order to recover, resurface, and reauthorize the hybrid voices, experiences, and identities of the Muslim woman on their own terms in order to challenge hegemonic discourse. Highlighting the nuances of feminist activism, particularly that of Muslim postcolonial feminists that can make a difference to Critical Management Studies (CMS) as a community concerned with social justice and challenging marginalization and oppression. The “Somewhere in America #Mipsterz” (Muslim hipsters) video launched in 2013, the site for our critical discourse analysis, is one case where this resistance can be seen, showcasing fashionable veiled Muslim women artistically expressing themselves to the beats of Jay Z.

The purpose of this article is to present a new application of pursuit‐based analysis for diagnosing rolling element bearing faults.

Intelligent diagnosis of rolling element bearing faults in rotating machinery involves the procedure of feature extraction using modern signal processing techniques and artificial intelligence technique‐based fault detection and identification. This paper presents a comparative study of both the basis and matching pursuits when applied to fault diagnosis of rolling element bearings using vibration analysis.

Fault features were extracted from vibration acceleration signals and subsequently fed to a feed forward neural network (FFNN) for classification. The classification rate and mean square error (MSE) were calculated to evaluate the performance of the intelligent diagnostic procedure. Results from the basis pursuit fault diagnosis procedure were compared with the classification result of a matching pursuit feature‐based diagnostic procedure. The comparison clearly illustrates that basis pursuit feature‐based fault diagnosis is significantly more accurate than matching pursuit feature‐based fault diagnosis in detecting these faults.

Intelligent diagnosis can reduce the reliance on experienced personnel to make expert judgements on the state of the integrity of machines. The proposed method has the potential to be extensively applied in various industrial scenarios, although this application concerned rolling element bearings only. The principles of the application are directly translatable to other parts of complex machinery.

This work presents a novel intelligent diagnosis strategy using pursuit features and feed forward neural networks. The value of the work is to ease the burden of making decisions on the integrity of plant through a manual program in condition monitoring and diagnostics particularly of complex pieces of plant.

The purpose of this paper is to develop the different phases of the implementation of vibration-based maintenance (VBM). Then, the focus will be on the first two stages, namely, the inventory and feasibility study where each step will be translated into a very detailed implementation process through an industrial case study.

The study is based on a state of art on the implementation of the VBM; a survey of national and international experts in the field of VBM and finally an analysis of 30 years of VBM practice in a large Moroccan company in the field of chemical processing, via a collective approach called Diagnostic Court Autonome.

The study showed that improving productivity by reducing downtimes due to vibration defects through effective vibration monitoring is possible and investment in equipment and vibration monitoring personnel is largely justified in the company studied.

This paper presents in detail the two preliminary phases with all procedures describing in a practical way the operating rules to apply and organize the roles of different actors. The work will be useful both to researchers and maintenance managers interested in structuring their vibration monitoring cells.

The purpose of this paper is to propose a few steps to enhance maintenance effectiveness in practice.

The author reviews the strengths and limitations of different approaches to maintenance management (MM). Some relevant practical observations of reliability engineering are discussed. Based on these, and drawing on the research on MM in relevant literature, the author suggests a few steps, suitable for implementation, for effective MM.

Review of available approaches to MM and some factors of reliability engineering points to some ways to improve maintenance effectiveness in the practical sense.

Implementation of the steps, as suggested in the paper, should enhance the effectiveness of MM leading to higher machine uptimes with less of maintenance costs. In such steps, the ease of implementation in practical situations has been given due importance.

In the steps, called as an “Actionable Program for Maintenance,” that the author suggests, he has highlighted some issues, and has made some suggestions, which are new.

The US fossil fuel industry is vulnerable to opposition from other sectors of the ruling class. Non-fossil fuel capitalists might conclude that climate breakdown jeopardizes their interests. State actors such as judges, regulators, and politicians may come to the same conclusion. However, these other elite actors are unlikely to take concerted collective action against fossil fuels in the absence of growing disruption by grassroots activists. Drawing from the history of the Obama, Trump, and Biden presidencies, I analyze the forces determining government climate policies and private-sector investments. I focus on how the climate and Indigenous movements have begun to force changes in the behavior of certain ruling-class interests. Of particular importance is these movements' progress in two areas: eroding the financial sector's willingness to fund and insure fossil fuels, and influencing judges and regulators to take actions that further undermine investors' confidence in fossil fuels. Our future hinges largely on whether the movements can build on these victories while expanding their base within labor unions and other strategically positioned sectors.

The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.