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Background: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare.

This paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger.

The system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50.

Several novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2.

This paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.

This research employed two neurophysiological techniques (electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning algorithms to capture and analyze relationship-oriented leadership (ROL) and task-oriented leadership (TOL). By grounding the study in the theoretical perspectives of transformational leadership and embodied leadership, the study draws connections to the human body's role in activating ROL and TOL styles.

EEG and GSR signals were recorded during resting state and event-related brain activity for 52 study participants. Both leadership styles were assessed independently using a standard questionnaire, and brain activity was captured by presenting subjects with emotional stimuli.

ROL revealed differences in EEG baseline over the frontal lobes during emotional stimuli, but no differences were found in GSR signals. TOL style, on the other hand, did not present significant differences in either EEG or GSR responses, as no biomarkers showed differences. Hence, it was concluded that EEG measures were better at recognizing brain activity associated with ROL than TOL. EEG signals were also strongest when individuals were presented with stimuli containing positive (specifically, happy) emotional content. A subsequent machine learning model developed using EEG and GSR data to recognize high/low levels of ROL and TOL predicted ROL with 81% accuracy.

The current research integrates psychophysiological techniques like EEG with machine learning to capture and analyze study variables. In doing so, the study addresses biases associated with self-reported surveys that are conventionally used in management research. This rigorous and interdisciplinary research advances leadership literature by striking a balance between neurological data and the theoretical underpinnings of transformational and embodied leadership.

Historically Black Colleges and Universities (HBCUs) were established during an era of legal segregation in the United States and, by providing access to higher education, added considerably to the progress of millions of Black Americans. Moreover, to the benefit of their students, faculties, staffs, alumni, and local communities, most HBCUs sponsor intercollegiate athletic teams. No doubt on these campuses, student-athletes are under pressure to meet academic and athletic demands. In this chapter, the central narrative is on the academic and athletic experiences of Black male student-athletes matriculating at HBCUs with National Collegiate Athletic Association affiliation. This chapter adds to the extant literature on the athletic status and academic plight of Black male student-athletes at HBCUs.

The purpose of this study is to assess the impact of nonfinancial sustainability reporting (NFSR) on enterprise value moderated by the management legitimate authority (MLA) for companies listed on the Tehran Stock Exchange.

To this aim, 190 firms were assessed during 2014–2019. This study used Arianpoor and Salehi’s indicators. The scoring method for NFSR, environmental sustainability reporting (ESR), social sustainability reporting (SSR) and governance sustainability reporting (GSR) was based on Zimon et al. Also, the CEO pay slice index was used to calculate the management’s legitimate authority. Tobin’s Q was used as a standard measure for the firm value, providing a suitable means of comparison.

The results revealed that NFSR affects enterprise value positively. In addition, ESR and SSR positively affect the enterprise value. However, GSR did not affect the enterprise value. MLA affects the relationship between NFSR/ESR/SSR/GSR and enterprise value, resulting from the effect of MLA on firm-related information quality and transparency.

Linking NFSR and management’s legitimate authority to firm value will enable managers to lead in helping firms enhance transparency and disclosure, improving their reporting standards and increasing the enterprise value. This, in turn, will ultimately result in better sustainability and governance practices.

The results can help understand that analysts and investors somehow consider discussions related to the NFSR in decisions related to the company’s value, and positive market reactions to these practices’ disclosures can motivate firms to improve value and performance.

The majority of prior research in this field has focused on developing countries. An international perspective is critical, and this study helps draw a more contextualized picture of sustainability than before. In addition, the present research explored the management’s legitimate authority role, which is considered an innovative aspect.

To compare the performance of a new hybrid manufacturing system (HMS) with a conventional cellular manufacturing system (CMS). The hybrid system is a combination of the cellular manufacturing and job shop.

A hypothetical manufacturing facility with eight machines and 20 parts is used as a case. Simulation models are developed for two manufacturing systems. A multi‐factor comparison is carried out to test the performance of the systems under different scenarios.

It was found that group scheduling rules (GSR) and the manufacturing system design factors have significant impact on the performance of the system. In particular, the hybrid system shows its best performance when the MSSPT GSR is applied, whereas the cellular system is superior when DDSI is implemented. The results also demonstrate that, by adding non‐family parts to the production schedule of the HMS, significant benefits in the performance measures can be attained.

The conclusion cannot be generalized, as the result is dependent upon the input data and the size of the problem.

The application may be limited to certain industry sectors. Further studies may be needed to identify the appropriate industry.

While the majority of the literature focuses on either a job shop or a pure CMS, this paper has a distinctive approach that allows the combined use of both systems. This could be a useful transitional approach from one system to the other.

The authors aim to develop a conceptual framework for longitudinal estimation of stress-related states in the wild (IW), based on the machine learning (ML) algorithms that use physiological and non-physiological bio-sensor data.

The authors propose a conceptual framework for longitudinal estimation of stress-related states consisting of four blocks: (1) identification; (2) validation; (3) measurement and (4) visualization. The authors implement each step of the proposed conceptual framework, using the example of Gaussian mixture model (GMM) and K-means algorithm. These ML algorithms are trained on the data of 18 workers from the public administration sector who wore biometric devices for about two months.

The authors confirm the convergent validity of a proposed conceptual framework IW. Empirical data analysis suggests that two-cluster models achieve five-fold cross-validation accuracy exceeding 70% in identifying stress. Coefficient of accuracy decreases for three-cluster models achieving around 45%. The authors conclude that identification models may serve to derive longitudinal stress-related measures.

Proposed conceptual framework may guide researchers in creating validated stress-related indicators. At the same time, physiological sensing of stress through identification models is limited because of subject-specific reactions to stressors.

Longitudinal indicators on stress allow estimation of long-term impact coming from external environment on stress-related states. Such stress-related indicators can become an integral part of mobile/web/computer applications supporting stress management programs.

Timely identification of excessive stress may improve individual well-being and prevent development stress-related diseases.

The study develops a novel conceptual framework for longitudinal estimation of stress-related states using physiological and non-physiological bio-sensor data, given that scientific knowledge on validated longitudinal indicators of stress is in emergent state.

For professional sustainability work, graduates need to be able to work in teams and collaborate with stakeholders; in other words, they need to have developed interpersonal competence. There is growing evidence that project-based sustainability courses facilitate interpersonal competence development. However, research so far has focused on single case studies and on assessing learning outcomes. The purpose of this study is to deepen the understanding of how graduate students learn interpersonal competence in project-based sustainability courses.

This study adopts a multi-case study approach triangulating observations, semi-structured interviews and focus groups supported by Photovoice method. A comparison of three project-based sustainability courses in graduate programs at universities in the USA, Germany, Switzerland and Spain is conducted to gain generalizable insights on how interpersonal competence can be developed through project-based sustainability courses.

Receiving inputs, experiencing, reflecting and experimenting are four learning processes supportive of interpersonal competence development. Interpersonal attitudes seem to be mostly learned through a combination of experiencing and reflecting, followed by experimenting; not surprisingly, interpersonal knowledge is mostly developed through a combination of receiving inputs, experiencing and (collective) reflection; and interpersonal skills seem to be mostly learned through a combination of receiving inputs and experimenting, or, more directly, experiencing and experimenting.

These findings support the unique learning opportunities offered through project-based sustainability courses and can help instructors to better facilitate students’ development of interpersonal competence.

The value of this study is three-fold: (i) it provides a comprehensive picture of interpersonal competence, including attitudes, knowledge, and skills; (ii) it spells out specific teaching and learning processes; and (iii) it links these to specific interpersonal competence facets and components.

Advanced skills in communication, teamwork and stakeholder engagement are widely recognized as important success factors for advancing sustainability. While project-based learning formats claim to advance such skills, there is little empirical evidence that demonstrates how interpersonal competence is being developed. This study aims to describe and explains teaching and learning processes of project-based sustainability courses that contribute to the development of interpersonal competence as one of the key competencies in sustainability.

This study on an international project-based learning course adopted a multi-method case study approach, triangulating observations, semi-structured interviews and focus groups supported by Photovoice method through which students tracked their learning processes. Data collection and analysis followed a grounded theory approach.

Learning through and from conflicts within a learning community can foster competence development in teamwork, communication and stakeholder engagement. This study identified inner and outer conflicts (within individuals versus between individuals or groups) as potential drivers of learning processes, depending on strategies applied to address these conflicts.

The value of this study is fourfold: it demonstrates how conflicts can be leveraged for students’ competence development; it provides in-depth empirical data from multiple perspectives, it discusses the findings in the context of teaching and learning theories, and it demonstrates an application of the Photovoice method to track and improve teaching and learning processes.

In this paper, an attempt has been made to develop an algorithm equipped with geometric pattern registration techniques to perform exact, robust and fast robot localization purely based on laser range data.

The expected pose of the robot on a pre-calculated map is in the form of simulated sensor readings. To obtain the exact pose of the robot, segmentation of both real laser range and simulated laser range readings is performed. Critical points on two scan sets are extracted from the segmented range data and thereby the pose difference is computed by matching similar parts of the scans and calculating the relative translation.

In contrast to other self-localization algorithms based on particle filters and scan matching, the proposed method, in common positioning scenarios, provides a linear cost with respect to the number of sensor particles, making it applicable to real-time resource-limited embedded robots. The proposed method is able to obtain a sensibly accurate estimate of the relative pose of the robot even in non-occluded but partially visible segments conditions.

A comparison of state-of-the-art localization techniques has shown that geometrical scan registration algorithm is superior to the other localization methods based on scan matching in accuracy, processing speed and robustness to large positioning errors. Effectiveness of the proposed method has been demonstrated by conducting a series of real-world experiments.