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To investigate the effects of the temperature of synthesis and of the thermal treatment of zinc chromate on the properties of the zinc chromate pigments.

Zinc chromate pigments was prepared using zinc salt solution, sodium hydroxide and potassium dichromate solutions. The reaction temperature was varied in order to evaluate the effect of the reaction temperature on the properties of the pigment synthesised. The pigment obtained was heated to 10°C above the critical temperature in order to investigate the effect of thermal treatment on the pigment properties. SEM, XRD, XFR and STA were employed for the characterisation of the pigments synthesised.

Reaction temperature had a significant effect on the properties of the resulting pigment. Thermal treatment of the pigment caused the decomposition of the zinc chromate phase to ZnO, ZnCr₂O₄ and K₂CrO₄ phases. The glaze containing zinc chromate had satisfactory colour characteristics.

The study focused on the preparation and evaluation of zinc chromate as a substitute for praseodymium zircon in glaze. The study could be extended to investigation of similar inorganic pigment for similar purposes.

The information on the effects of the reaction temperature of pigment synthesis and of thermal treatment of the pigment is useful for industrial production of the pigment of satisfactory properties.

Novel use of zinc chromate as a lower‐cost substitute for praseodymium zircon in glaze and understanding of the effects of the production conditions on the properties of the resulting pigment and the glaze.

Zinc potassium chromate (4Zn·O₄CrO₃·K₂O·3H₂O) was prepared using zinc salt solution, sodium hydroxide and potassium dichromate solutions. The pigment synthesised was investigated by various methods. Upon heating, this pigment underwent decomposition to form ZnCr₂O₄, K₂CrO₄ and ZnO. The pigment obtained was successfully used in the preparation of glaze and then on a tile. The L*, a* and b* values of the fired glaze were 71.607, 2.695 and 67.327, respectively.

Clinical governance (CG) is crucial for healthcare quality of care improvement and safeguarding high standards of care. Little is known about CG in sub-Saharan Africa. The authors assessed health workers' perceptions of CG and hospital performance in Ghana's psychiatric hospitals.

A cross-sectional survey was conducted among 230 health workers across two psychiatric hospitals in Ghana. Data were collected with a structured questionnaire and analyzed with Statistical Package for Social Sciences (SPSS), version 23.0.

The majority (59.5%) of the respondents were females. The authors found that less than five in ten respondents felt that the hospitals have adopted measures to promote quality assurance (43.2%) and research and development (43.7%). However, a little above half of the respondents felt that the hospitals have adopted measures to promote education and training (57.7%); clinical audit (52.7%); risk management (50.7%) and clinical effectiveness (68.6%). The authors also found a statistically significant association between CG and hospital performance (p < 0.05).

There was a positive relationship between CG and hospital performance. Therefore, investing in CG may help to increase hospital performance.

This is the maiden study to investigate CG and hospital performance in Ghana's psychiatric hospitals and one of the few studies in Africa. This study makes a modest contribution to the global discourse on the subject matter.

Despite recognizing the significance of risk-based frameworks in fire safety engineering, the usual approach in structural fire design is largely member/component level, wherein effect of uncertainties influencing the fire resistance of structures are not explicitly considered. In this context, a probabilistic framework is presented to investigate the vulnerability of a reinforced concrete (RC) members and structure under fire loading scenario.

The RC structures exposed to fire are modeled in a finite element (FE) platform incorporating material and geometric nonlinearity, in which the transient thermo-mechanical analysis is carried out by suitably incorporating the temperature variation of thermal and mechanical properties of both concrete and steel rebar. The stochasticity in the system is considered in structural resistance, thermal and fire model parameters, and the subsequent fragility curves are developed considering threshold limit state of deflection.

The fire resistance of RC structure is reported to be significantly lower in comparison to the RC members, thereby illustrating the current prescriptive design approaches based on studies of structural member behavior to be crucial from a safety and reliability point of view.

The framework developed for the vulnerability assessment of RC structures under fire hazard through FE analysis can be effectively used to estimate the structural fire resistance for other similar structure to enhance safety and reliability of structures under such extreme threats.

The paper proposes a novel methodology for vulnerability assessment of three-dimensional RC structures under fire hazard through FE analysis and provides comparison of the structural fragility with fragility developed for structural members. Moreover, the research emphasizes to assume 3D behavior of the structure rather than the approximate 2D behavior.

The purpose of this study is to investigate the effect of standard fire on the strength and microstructure properties of concrete with different strength grades.

Different strength grades of concrete used for the investigation are M20, M30, M40 and M50. An electrical bogie hearth furnace was developed to simulate the International Standards Organization 834 standard fire curve.Concrete samples were subjected to high temperatures of 925, 1,029, 1,090 and 1,133°C for the duration of 1, 2, 3 and 4 h, respectively, as per standard fire curve. Compressive strength, tensile strength, thermal crack pattern and spalling of heated concrete specimens were evaluated by experimental investigation. Scanning electron microscopy and thermo-gravimetric analysis were performed to investigate the microstructure properties of heated concrete specimens.

Test results indicated reduction in the strength and changes in the microstructure properties of concrete exposed to elevated temperature. The degree of weight and the strength loss were found to be higher for concrete with higher grades. An empirical relation is proposed to determine the residual strength of concrete with different strength grade using regression analysis.

Results of this research will be useful for the design engineers to understand the behavior of concrete exposed to elevated temperature as per standard fire.

When concrete is exposed to elevated temperature, its internal microstructure changes, thereby strength and durability of concrete deteriorates. The performance of concrete with different strength grade exposed to standard fire is well understood. This research’s findings will be useful for the designers to understand more about fire resistance of concrete. A simple relationship is proposed to determine the residual strength of concrete exposed to various durations of heating.

The primary purpose of this research was to expand the knowledge base regarding the behavior of steel columns during exposure to fire. This paper presents the numerical study of the effect of heat on the performance of parking steel column in a seven-story steel building under cyclic loading.

In this research, the forces and deformations developed during a fire are estimated by using detailed 3D finite-element models. The analyses are in the form of a coupled thermo-mechanical analysis in two types of loading: concurrent loading (fire and cyclic loading) and non-concurrent loading (first fire and then cyclically), and the analyses have been conducted in both states of the fire loading with cooling and without cooling using the ABAQUS software. Further, it was investigated whether, during the fire loading, the specimen was protected by a 3-cm-thick concrete coating and how much it changes the seismic performance. After verification of the specimen with the experimental test results, the column model was investigated under different loading conditions.

The result of analyses indicates that the effect of thermal damage on the performance of steel columns, when cooling is happening late, is more than the state in which cooling occurs immediately after the fire. In this paper, thermal–seismic performance of parking steel columns has been specified and the effect of the fire damage has been investigated for the protected steel by concrete coating and to the non-protected steel, under both cooling and non-cooling states.

This study led to recommendations based on the findings and suggestions for additional work to support performance-based fire engineering. It is clear that predicting force and deformation on steel column during fire is complex and it is affected by many variables. Here in this paper, those variables are examined and proper results have been achieved.

This study aims to bring awareness to the developing of fault detection systems using the data collected from sensor devices/physical devices of various systems for predictive maintenance. Opportunities and challenges in developing anomaly detection algorithms for predictive maintenance and unexplored areas in this context are also discussed.

For conducting a systematic review on the state-of-the-art algorithms in fault detection for predictive maintenance, review papers from the years 2017–2021 available in the Scopus database were selected. A total of 93 papers were chosen. They are classified under electrical and electronics, civil and constructions, automobile, production and mechanical. In addition to this, the paper provides a detailed discussion of various fault-detection algorithms that can be categorised under supervised, semi-supervised, unsupervised learning and traditional statistical method along with an analysis of various forms of anomalies prevalent across different sectors of industry.

Based on the literature reviewed, seven propositions with a focus on the following areas are presented: need for a uniform framework while scaling the number of sensors; the need for identification of erroneous parameters; why there is a need for new algorithms based on unsupervised and semi-supervised learning; the importance of ensemble learning and data fusion algorithms; the necessity of automatic fault diagnostic systems; concerns about multiple fault detection; and cost-effective fault detection. These propositions shed light on the unsolved issues of predictive maintenance using fault detection algorithms. A novel architecture based on the methodologies and propositions gives more clarity for the reader to further explore in this area.

Papers for this study were selected from the Scopus database for predictive maintenance in the field of fault detection. Review papers published in this area deal only with methods used to detect anomalies, whereas this paper attempts to establish a link between different industrial domains and the methods used in each industry that uses fault detection for predictive maintenance.

The purpose of this paper is to improve the degradation efficiency of Rhodamine B (RhB) by new photocatalytic materials.

Binary Z-scheme g-C₃N₄/Bi₂WO₆ photocatalytic material was synthesized by the one-step hydrothermal reaction. The construction of Z-scheme heterojunction led to the rapid separation of photogenerated electrons and holes, which would degrade RhB into small molecular substances to achieve the purpose of degradation.

It was found that Bi₂WO₆/25%g-C₃N₄ displayed the highest photocatalytic activity, which was about 1.44 and 1.34 times higher than that of pure Bi₂WO₆ and g-C₃N₄, respectively. According to the trapping experiments, the superoxide radical (·O²⁻) was the major active species of the RhB decomposition in Bi₂WO₆/g-C₃N₄ catalysts.

The successful synthesis of Z-scheme Bi₂WO₆/g-C₃N₄ provides new ideas and references for the design of catalysts with high photocatalytic activity, which should have wide applications in the future.

This study aims to investigate the effect of high temperature (600°C) on the compressive strength of concrete covered with a mixture of polypropylene fiber and gypsum plaster (PFGP).

To study the compressive strength of concrete specimens exposed to temperature, 16 cubic specimens (size: 150 mm × 150 mm × 150 mm) were made. After 28 days of processing and gaining the required strength of specimens, first, polypropylene fiber was mixed with gypsum plaster (CaSO4.2H2O) and then the concrete specimens were covered with this mixture. To cover the concrete specimens with the PFGP, the used PFGP thickness was 15 mm or 25 mm. The polypropylene rates mixed with the gypsum plaster were 1, 3 and 5 per cent. A total of 14 specimens, 12 of which were covered with PFGP, were exposed to high temperature in two target times of 90 and 180 min.

The results show that the PFGP as covering materials can improve the compressive strength lost because of the heating of the concrete specimens. The results also show that the presence of polypropylene fiber in gypsum plaster has the effect on the compressive strength lost because of the heating of the PFGP-covered concrete. The cover of PFGP having 3 per cent polypropylene fiber had the best effect on remained strength of the specimens.

The cover of PFGP having 3 per cent polypropylene fiber has the best effect on remained strength of the PFGP covered specimens exposed to temperature.

Rotating functionally graded (FG) disks of variable thickness generates vibration. This study aims to analyze the vibration generated by the rotating disks using a finite element program and compare the results obtained with the regression methods.

Transverse vibration values of rotating FG disks with variable thickness were modeled using different regression methods. The accuracies of the obtained models are compared. In the context of comparing regression methods, multiple linear regression (MLR), extreme learning machine (ELM), artificial neural networks (ANNs) and radial basis function (RBF) were used in this study. The error graph between the observed value and the predicted value of each regression method was obtained. The error values of the regression methods used with scientific error measures were calculated.

The analysis of the transverse vibration of rotating FG disks with the finite element program is consistent with the studies in the literature. When the variables and vibration value determined on the disk are modeled with ELM, MLR, ANN and RBF regression methods, it is concluded that the most accurate model order is RBF, ANN, MLR and ELM.

There are studies on the vibration value of rotating discs in the literature, but there are very few studies on modeling. This study shows that ELM, MLR, ANN and RBF, which are machine learning methods, can be used in modeling the vibration value of rotating discs.