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This paper highlights an assignment in a combination upper-division undergraduate and graduate civic leadership class at a Midwestern state comprehensive university. The three-part assignment challenges students’ critical thinking skills and research capabilities while simultaneously necessitating the exploration of contrasting viewpoints on contentious issues.

Intentionally exposing students to diverse perspectives in a controlled environment.

We posit that the severity and frequency of these issues can be mitigated through focused efforts.

Students are better prepared to engage in civil debate on controversial topics, which continuously divide our communities, after completing a class using this pedagogical strategy.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.

Science mapping is an essential application of visualization technology widely used in safety, construction management and environmental science. The purpose of this study was to explore thermal comfort in residential buildings (TCinRB) research in India, identify research trends using a science mapping approach and provide a perspective for recommending future research in TCinRB.

This study used the VOSviewer tool to conduct a systematic analysis of the development trend in TCinRB studies in India based on Scopus Index articles published between 2001 and 2020. The annual numbers of articles, geographical locations of studies, major research organizations and authors, and the sources of journals on TCinRB were presented based on the analysis. Then, using co-authorship analysis, the collaborations among the major research groups were reported. Furthermore, research trends on TCinRB studies were visually explored using keyword co-occurrence analysis. The emerging research topics in the TCinRB research community were discovered by analyzing the authors’ keywords.

The findings revealed that studies had been discovered to pay more attention to north-east India, vernacular architecture, Hyderabad apartments and temperature performance in the past two decades. Thermal adaptation, composite climate, evaporative cooling and clothing insulation are emerging research areas in the TCinRB domain. The findings summarized mainstream research areas based on Indian climatic zones, addressed current TCinRB research gaps and suggested future research directions.

This review is particularly significant because it could help researchers understand the body of knowledge in TCinRB and opens the way for future research to fill an important research gap.

In the context of a developing country, Indian buildings need further research to channelize energy needs optimally to reduce energy wastage, thereby reducing carbon emissions. Also, reduction in smart devices’ costs with sequential advancements in Information and Communication Technology have resulted in an environment where model predictive control (MPC) strategies can be easily implemented. This study aims to propose certain preemptive measures to minimize the energy costs, while ensuring the thermal comfort for occupants, resulting in better greener solutions for building structures.

A simulation-based multi-input multi-output MPC strategy has been proposed. A dual objective function involving optimized energy consumption with acceptable thermal comfort has been achieved through simultaneous control of indoor temperature, humidity and illumination using various control variables. A regression-based lighting model and seasonal auto-regressive moving average with exogenous inputs (SARMAX) based temperature and humidity models have been chosen as predictor models along with four different control levels incorporated.

The mathematical approach in this study maintains an optimum tradeoff between energy cost savings and satisfactory occupants’ comfort levels. The proposed control mechanism establishes the relationships of output variables with respect to control and disturbance variables. The SARMAX and regression-based predictor models are found to be the best fit models in terms of accuracy, stability and superior performance. By adopting the proposed methodology, significant energy savings can be accomplished during certain hours of the day.

This study has been done on a specific corporate entity and future analysis can be done on other corporate or residential buildings and in other geographical settings within India. Inclusion of sensitivity analysis and non-linear predictor models is another area of future scope.

This study presents a dynamic MPC strategy, using five disturbance variables which further improves the overall performance and accuracy. In contrast to previous studies on MPC, SARMAX model has been used in this study, which is a novel contribution to the theoretical literature. Four levels of control zones: pre-cooling, strict, mild and loose zones have been used in the calculations to keep the Predictive Mean Vote index within acceptable threshold limits.

The purpose of this paper is to develop a building information modelling (BIM)-based multi-objective optimization (MOO) framework for volumetric analysis of buildings during early design stages. The objective is to optimize volumetric spaces (3D) instead of 2D spaces to enhance space utilization, thermal comfort, constructability and rental value of buildings

The integration of two fundamental concepts – BIM and MOO, forms the basis of proposed framework. In the early design phases of a project, BIM is used to generate precise building volume data. The non-sorting genetic algorithm-II, a MOO algorithm, is then used to optimize extracted volume data from 3D BIM models, considering four objectives: space utilization, thermal comfort, rental value and construction cost. The framework is implemented in context of a school of architecture building project.

The findings of case study demonstrate significant improvements resulting from MOO of building volumes. Space utilization increased by 30%, while thermal comfort improved by 20%, and construction costs were reduced by 10%. Furthermore, rental value of the case study building increased by 33%.

The proposed framework offers practical implications by enabling project teams to generate optimal building floor layouts during early design stages, thereby avoiding late costly changes during construction phase of project.

The integration of BIM and MOO in this study provides a unique approach to optimize building volumes considering multiple factors during early design stages of a project

The mental healthcare is experiencing an ever-growing surge in understanding the consumer (e.g., patient) engagement paradox, aiming to vouch for the quality of care. Despite this surge, scant attention has been given in academia to conceptualize and empirically investigate this particular aspect. Thus, drawing on the Stimulus-Organism-Response (S-O-R) paradigm, the study explores how patients engage with healthcare service providers and how they perceive the quality of the healthcare services.

Data were collected from 279 respondents, and the derived conceptual model was tested by using Smart PLS 3.2.7 and PROCESS. To complement the findings of partial least squares (PLS)-based structural equation modeling (SEM), the present study also applied fuzzy set qualitative comparative analysis (fsQCA) to identify the necessary and sufficient conditions to explore substitute conjunctive paths that emerge.

Findings show that patients’ perceived intimacy (PI), cohesion and privacy enhance the quality of mental healthcare service providers. The results also suggest that patients’ PI, cohesion and privacy have indirect effects on the perceived quality of care (PQC) by the service providers through consumer engagement. The fsQCA results derive that the relationship among conditions leading to patients’ perception of the quality of care in regard to mental healthcare service providers is complex and is best reflected as multiple and conjectural causation configurations.

The findings from this research contribute to the advancement of studies on patients’ experiences by empirically examining the unique dynamics of interaction between consumers (patients) and mental healthcare service providers, thereby enriching both the literature on social interactions and the understanding of the consumer–provider relationship.

The results of this study provide practical implications for mental healthcare service providers on how to combine the study variables to enhance the quality of care and satisfy more patients.

A significant research gap has ascertained the inter-relationship between PI, cohesion, privacy, engagement and PQC from the perspective of mental healthcare service providers. This research is one of the primary studies from a managerial and methodological standpoint. The study contributes by combining symmetric and asymmetric statistical tools in service marketing and healthcare research. Furthermore, the application of fsQCA helps to understand the interactions that might not be immediately obvious through traditional symmetric methods.

Drawing on the dynamic capability view (DCV), this research determines the suitable configurations of resilience strategies for sustainable tourism supply chain performance amidst “extreme” disruptive events affecting the entire supply chain.

This research applies a multi-study and multi-method approach. Study 1 utilizes in-depth interviews to identify a list of tourism supply chain sustainability risks and resilience strategies. Study 2, using quality function deployment (QFD) technique, determines the most important resilience strategies corresponding to highly significant risks. Study 3, on the other hand, adopts a fuzzy set qualitative comparative analysis (fsQCA) to determine the best recipe of resilience strategies and risks to make the tourism supply chain performance sustainable.

The findings reveal that sustainable tourism performance during an extreme disruptive event (e.g. COVID-19 health crisis) depends on the combined effect of tourism resilience strategies and risks instead of their individual effect.

The research findings offer significant managerial implications. Managers may experiment with multiple causal conditions of risks and resilience strategies to engender the expected outcome.

This research extends current knowledge on tourism supply chain and offers insights for managers to mitigate the risks and ensures sustainable performance in the context of extreme disruptive events.

The purpose of this article is to investigate whether relationships with media figures can impact the psychological and sociocultural adjustment of educational tourists.

The article uses the qualitative method of autoethnography to provide a highly personalised account of the first author’s relationships with podcast hosts during his time as an educational tourist, in order to fulfil its previously mentioned purpose.

An analysis of the author’s narrative account demonstrates how he formed parasocial relationships (PSRs) with his favourite podcast hosts. These relationships increased self-efficacy, reduced stress and provided a sense of social support, which, in turn contributed to psychological and sociocultural adjustment.

By demonstrating how relationships with media figures can impact their psychological and sociocultural adjustment, this article argues that research on educational tourists should explore their PSRs, in addition to their other kinds of friendships which have already been examined.

Tactile communication that relies on the human sense of touch replicated using vibration motors is increasingly being explored for seamless communication on construction jobsite. However, the technological efficacy cannot secure the users’ acceptability of the tactile communication devices. This study aims to assess the factors affecting the wearability of such a portable tactile device based on the responses from practicing professionals.

The investigation adapted a three-step phenomenological interviewing approach to seek feedback from construction personnel in Texas, the USA, regarding the viability of wearable tactile communication. The interviewees expressed various opinions about the on-body placement upon exposure to a portable tactile feedback prototype developed for this study, which was used to derive inferences regarding the factors affecting its on-field acceptability.

All the participants of the round-table study (11 out of 11) considered tactile feedback as a viable mode of communication on construction jobsite. Seven professionals supported the integration of a tactile device with the hard hat, whereas the rest preferred tactile eyeglasses. Weatherability, rechargeability, traceability, safety and social receptivity were identified as the major factors affecting the on-body placement of the wearable tactile communication device.

This paper presents a roadmap to gain construction industry opinion on the factors that can affect the on-body placement of a wearable tactile communication device. The five aforementioned factors impacting tactile communication acceptability were used to evaluate 10 potential on-body placements. The findings have implications for research and development of wearable tactile devices and the subsequent acceptability of such a device on the jobsite.

This paper aims to apply a mixed-methods approach to redefine essential work skill sets, propose components of these skill sets that are necessary for workers in the contexts of emerging market economies (EMEs), examine potential selection tools that should be applied across firms and explain implications for the labour process theory (LPT) of work.

During the first phase of this study, qualitative data was collected from methods, including semi-structured interviews with human resources (HR) experts from the Personnel Management Association of Thailand, top and HR executives and managers of firms across industries in Thailand. Non-participant observations were made during the pre-interview, interview and post-interview stages. Analysis of archival documents and Web-based resources was then conducted. The authors used the qualitative data obtained from the first phase to develop survey instruments for conducting quantitative research during the second phase of this study.

The empirical findings demonstrate that essential work skill sets are “soft skills” that help workers survive and thrive in the business arena in EMEs. These essential work skill sets have implications for the LPT of work in that they play an important role in transferring the power of negotiation from employers to workers in the labour market. Essential work skill sets here can be divided into eight skill domains: (1) ideation and system thinking, (2) information and digital literacy, (3) social skills with appreciation for diversity and inclusion, (4) communication and language, (5) creativity and innovation, (6) emotional quotient (EQ) for self-management and development, (7) growth/outward mindset and (8) cognitive skills for the job role. Within each essential work skill set, there are several skills that workers in the current world of work need to possess (42 skills in total). Additionally, potential selection tools include behavioural observation, behavioural-based interviews, STAR (Situation, Task, Action and Results) interviews, role plays, case studies and simulations, high-pressure interview questions, project assignments, assessment centres, in-depth interview questions and special methods, such as face reading and fingerprint reading. Top and HR managers across industries strongly agree that the eight essential work skill sets and 42 skills are necessary for workers to survive in the business arena in EMEs. They also strongly agree that talent selection tools, especially behavioural-based interviews, are used by their firms to select high-skilled job candidates in the labour market.

Because the eight essential skill sets proposed in this paper are based primarily on the qualitative data obtained from top/HR managers in firms across some industries, generalization to respondents across other industries or across other EMEs may be limited. It is possible that the context of other EMEs may be different from that of Thailand. In this regard, some of the essential work skill sets that are suitable in the case of Thailand may not be suitable in the case of other EMEs. Future studies should thus explore how institutional contexts of other countries/economies shape the definition of essential skill sets and their components, as well as potential selection tools that shall be applied to select high-skilled labour in those contexts.

This paper provides practical implications for top managers and/or HR managers of firms across various industries in EMEs. In particular, managers should internally train and develop their employees/workers to possess the eight essential skill sets: (1) ideation and system thinking, (2) information and digital literacy, (3) social skills with appreciation for diversity and inclusion, (4) communication and language, (5) creativity and innovation, (6) EQ for self-management and development, (7) growth/outward mindset and (8) cognitive skills for the specific job role so that their employees/workers can survive and thrive in the era of the brittleness, anxiety, non-linearity and incomprehensibility of the business world under pandemic conditions. Additionally, top managers and/or HR managers of these firms should apply the potential selection tools proposed in this paper to probe into job candidates’ past experience and behaviours to better predict such candidates’ success at work. In this regard, job candidates/workers should prepare themselves to possess these essential work skill sets so that they can be successful in the business arena and should understand potential selection tools that firms may apply to recruit and select them.

This paper provides social/policy implications for the government and/or relevant public agencies of Thailand and of other EMEs. These governments should encourage firms across industries to invest resources in training and developing their employees/workers to possess those essential work skill sets so that these employees/workers are industry-ready, leading to the alleviation of the problems of skill and mismatch in the labour market.

This paper contributes to the prior literature on human resource management (HRM), the comparative institutional perspective on employment systems based on the varieties of capitalism (VoC) framework and the LPT of work in the following ways: first, this paper fills in the research gap in the field of HRM that calls for studies that explore how the COVID-19 pandemic shapes essential skill sets and skills development among workers within firms (Cooke et al., 2021). Second, this paper provides implications for the LPT of work regarding how essential work skill sets are likely to return the power of negotiation from employers to workers in EMEs during the current situation. Third, the VoC framework tends to focus on only two types of economies, liberal market economies and coordinated market economies. However, this paper examines essential work skill sets and potential screening tools in the context of the underresearched country of Thailand, an EME. In fact, the Thai labour market is quite different from that of other EMEs labour markets, as it is impacted by an aging workforce. This paper contributes to the literature on comparative institutional perspectives on employment systems as it redefines essential work skill sets, proposes various components of these skill sets among workers and examines potential selection tools that are applied across firms located in EMEs.