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Electric motor heating during biomass recovery and its handling on conveyor is a serious concern for the motor performance. Thus, the purpose of this paper is to design and develop a hardware prototype of master–slave electric motors based biomass conveyor system to use the motors under normal operating conditions without overheating.

The hardware prototype of the system used master–slave electric motors for embedded controller operated robotic arm to automatically replace conveyor motors by one another. A mixed signal based embedded controller (C8051F226DK), fully compliant with IEEE 1149.1 specifications, was used to operate the entire system. A precise temperature measurement of motor with the help of negative temperature coefficient sensor was possible due to the utilization of industry standard temperature controller (N76E003AT20). Also, a pulse width modulation based speed control was achieved for master–slave motors of biomass conveyor.

As compared to conventional energy based mains supply, the system is self-sufficient to extract more energy from solar supply with an energy increase of 11.38%. With respect to conventional energy based \ of 47.31%, solar energy based higher energy saving of 52.69% was reported. Also, the work achieved higher temperature reduction of 34.26% of the motor as compared to previous cooling options.

The proposed technique is free from air, liquid and phase-changing material based cooling materials. As a consequence, the work prevents the wastage of these materials and does not cause the risk of health hazards. Also, the motors are used with their original dimensions without facing any leakage problems.

This study aims to introduce a novel technique for nonlinear sensor time constant estimation and sensor dynamic compensation in hot-bar soldering using an extended Kalman filter (EKF) to estimate the temperature of the thermocouple.

Temperature optimal control is combined with a closed-loop proportional integral differential (PID) control method based on an EKF. Different control methods for measuring the temperature of the thermode in terms of temperature control, error and antidisturbance are studied. A soldering process in a semi-industrial environment is performed. The proposed control method was applied to the soldering of flexible printed circuits and circuit boards. An infrared camera was used to measure the top-surface temperature.

The proposed method can not only estimate the soldering temperature but also eliminate the noise of the system. The performance of this methodology was exemplary, characterized by rapid convergence and negligible error margins. Compared with the conventional control, the temperature variability of the proposed control is significantly attenuated.

An EKF was designed to estimate the temperature of the thermocouple during hot-bar soldering. Using the EKF and PID controller, the nonlinear properties of the system could be effectively overcome and the effects of disturbances and system noise could be decreased. The proposed method significantly enhanced the temperature control performance of hot-bar soldering, effectively suppressing overshoot and shortening the adjustment time, thereby achieving precise temperature control of the controlled object.

Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

Steel-reinforced concrete-filled steel tubular (SRCFST) columns have been increasingly popular in engineering practice for the columns' excellent seismic and fire performance. Significant design progress guidance has been made through continuous numerical and experimental research in recent years. This paper tested and analysed the residual loading capacity of SRCFST columns under axial loading after experiencing non-uniform ISO-834 standard fire.

The experimental research covered the main parameter of heating conditions, 1-side and 2-side fire, through two specimens. Two specimens were heated and loaded simultaneously in the furnace for 240 min. After cooling, the columns were moved to the hydraulic loading system and loaded to failure to determine the columns' residual capacity.

The experimental results indicated that the non-uniform heating area plays an essential role in the overall performance of SRCFST columns, the increasing heating area of columns results in lower residual loading capacity and stiffness. The SRCFST columns still had a high loading capacity after heating and loading in the fire.

The comparison of experimental data against design results showed that the design method generated a 16% safety margin for S2H4 and a 39% safety margin for S1H4.

Over the years, the significance of retrofitting has gained much attention with the unveiling of its different applications, such as energy retrofit and deep retrofit, to enhance the climate-resilience of buildings. However, no single study comprehensively assesses the climate-resilience of retrofitting. The purpose of this study is to address this gap via a systematic literature review.

Quality journal studies were selected using the PRISMA method and analysed manually and using scientometrics. Three dimensions of climate-resilience, such as robustness, withstanding and recovery, were used to evaluate the contribution of retrofit measures for achieving climate-resilient houses across four climate zones: tropical, arid, temperate and cold.

Most passive measures can enhance the robustness of residential buildings but cannot verify for withstanding against immediate shocks and timely recovery. However, some passive measures, such as night-time ventilation, show excellent performance over all four climate zones. Active measures such as heating, ventilation and air conditioning and mechanical ventilation with heat recovery, can ensure climate-resilience in all three dimensions in the short-term but contribute to greenhouse gas emissions, further exacerbating the long-term climate. Integrating renewable energy sources can defeat this issue. Thus, all three retrofit strategies should appropriately be adopted together to achieve climate-resilient houses.

Since the research is limited to secondary data, retrofit measures recommended in this research should be further investigated before application.

This review contributes to the knowledge domain of retrofitting by assessing the contribution of different retrofit measures to climate-resilience.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator.

This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants.

The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred.

The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff.

To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

The purpose of this paper is to introduce the simple synthesis process of thermal-insulation coating by using three different nanoparticles, namely, nano-zinc oxide (ZnO), nano-tin dioxide (SnO₂) and nano-titanium dioxide (TiO₂), which can reduce the temperature of solar cells.

The thermal-insulation coating is designed using sol-gel process. The aminopropyltriethoxysilane/methyltrimethoxysilane binder system improves the cross-linking between the hydroxyl groups, -OH of nanoparticles. The isopropyl alcohol is used as a solvent medium. The fabrication method is a dip-coating method.

The prepared S1B1 coating (20 Wt.% of SnO₂) exhibits high transparency and great thermal insulation property where the surface temperature of solar cells has been reduced by 13°C under 1,000 W/m² irradiation after 1 h. Meanwhile, the Z1B2 coating (20 Wt.% of ZnO) reduced the temperature of solar cells by 7°C. On the other hand, the embedded nanoparticles have improved the fill factor of solar cells by 0.2 or 33.33%.

Findings provide a significant method for the development of thermal-insulation coating by a simple synthesis process and low-cost materials.

The thermal-insulation coating is proposed to prevent exterior heat energy to the inside solar panel glass. At the same time, it can prevent excessive heating on the solar cell’s surface, later improves the efficiency of solar cell.

This study presents a the novel method to develop and compare the thermal-insulation coating by using various nanoparticles, namely, nano-TiO₂, nano-SnO₂ and nano-ZnO at different weight percentage.

Perspiration and heat are produced by the body and must be eliminated to maintain a stable body temperature. Sweat, heat and air must pass through the fabric to be comfortable. The cloth absorbs sweat and then releases it, allowing the body to chill down. By capillary action, moisture is driven away from fabric pores or sucked out of yarns. Convectional air movement improves sweat drainage, which may aid in body temperature reduction. Clothing reduces the skin's ability to transport heat and moisture to the outside. Excessive moisture makes clothing stick to the skin, whereas excessive heat induces heat stress, making the user uncomfortable. Wet heat loss is significantly more difficult to understand than dry heat loss. The purpose of this study is to provided a good compilation of complete information on wet thermal comfort of textile and technological elements to be consider while constructing protective apparel.

This paper aims to critically review studies on the thermal comfort of textiles in wet conditions and assess the results to guide future research.

Several recent studies focused on wet textiles' impact on comfort. Moisture reduces the fabric's thermal insulation value while also altering its moisture characteristics. Moisture and heat conductivity were linked. Sweat and other factors impact fabric comfort. So, while evaluating a fabric's comfort, consider both external and inside moisture.

The systematic literature review in this research focuses on wet thermal comfort and technological elements to consider while constructing protective apparel.