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Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

This article explores the scientific construction of library olfactory space, based on the case of the olfactory space in the Jiangsu University library. It specifically focuses on understanding the interaction between the physical architectural space of the library and users’ olfactory perception and behavioral activities, with the ultimate goal of creating a deeply integrated olfactory experience in the Jiangsu University Library.

In this article, an empirical research method was used to gather perceptions from 30 university student users regarding the library olfactory space and to understand their olfactory preferences and requirements for its construction. Through qualitative analysis of the interview texts, the study identified correlations between user perceptions and elements of the library olfactory space.

The qualitative analysis of user interview texts and results from the library olfactory space design experiment contributed to the design proposal for the Jiangsu University Library olfactory space. The design proposal for the Jiangsu University Library olfactory space is provided and includes library architecture, activity context, functional services, olfactory experience design and technological applications.

This case study takes the environment, development strategy and user needs of the Jiangsu University Library as its unique research background and as such is not universal or generalizable to other libraries.

This article differs from others by advocating for the innovative architectural spatial design of libraries through olfactory experience, breaking the traditional perception of libraries as solely through visual and auditory senses.

The purpose of the research was to, first, investigate if the use of maps as instructional resources will boost scholarly performance and, second, examine if gender can moderate the effect of map usage on scholarly performance.

The study was a quasi-experimental pre-test and post-test. A sample of 260 JSS II Students from 8 schools were selected through the purposive sampling technique. A Social Studies Scholarly Performance Test (SSSPT) with a reliability index of 0.79 was the instrument for data collection. The students were assigned into two groups: control and experimental. Both groups were pre-tested taught for a timeline of six weeks and thereafter post-tested.

The study reported a significant increase in the scholarly performance of students taught with maps; a significant difference occurred in the scholarly performance of both groups and gender did not moderate the effect of maps.

The social studies teachers used for the study did not have previous knowledge or map skills; this could have affected the outcome. Secondly, the treatment took place for just six weeks, and the time allotted for social studies in the school timetable was used. This may not have given the students enough time to master map interpretation.

A major implication of the study is that results will show that maps can promote the scholarly performance of students in social studies. Secondly, the fact that gender did not moderate the effect of maps suggests that maps are gender-friendly.

The results of the study, if implemented, would make social studies teachers to become inventive and resourceful in the use of maps as instructional resources for junior secondary students' scholarly performance in social studies without taking gender into consideration.

This study is a product of the researcher’s doctoral thesis; therefore, it is original and has value. The results are the product of a painstaking study carried out by the author for a period of three years on the effect of instructional resources on social studies students’ scholarly performance. Maps were one of the instructional resources studied for the award of a Ph.D. degree.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

This paper aims to introduce a model using a digital twin concept in a cold heading manufacturing and develop a digital visual management (VM) system using Lean overall equipment effectiveness (OEE) tool to enhance the process performance and establish Fourth Industrial Revolution (I4.0) platform in small and medium enterprises (SMEs).

This work utilised plan, do, check, act Lean methodology to create a digital twin of each machine in a smart manufacturing facility by taking the Lean tool OEE and digitally transforming it in the context of I4.0. To demonstrate the effectiveness of process digitisation, a case study was carried out at a manufacturing department to provide the data to the model and later validate synergy between Lean and I4.0 platform.

The OEE parameter can be increased by 10% using a proposed digital twin model with the introduction of a Level 0 into VM platform to clearly define the purpose of each data point gathered further replicate in projects across the value stream.

The findings suggest that researchers should look beyond conversion of stored data into visualisations and predictive analytics to improve the model connectivity. The development of strong big data analytics capabilities in SMEs can be achieved by shortening the time between data gathering and impact on the model performance.

The novelty of this study is the application of OEE Lean tool in the smart manufacturing sector to allow SME organisations to introduce digitalisation on the back of structured and streamlined principles with well-defined end goals to reach the optimal OEE.

Using real-time support systems may help operators in road construction to improve paving and compaction operations. Nowadays, these systems transform from descriptive to prescriptive systems. Prescriptive or operator guidance systems propose operators actionable compaction strategies and guidance, based on the data collected. It is investigated how these systems mediate the perceptions and actions of operators in road pavement practice.

A case study is conducted on the specific application of an operator guidance system in a road pavement project. In this case study, comprehensive information is presented regarding the process of converting input in the form of data from cameras and sensors into useful output. The ways in which the operator guidance systems translate data into actionable guidance for operators are analyzed from the technological mediation perspective.

Operator guidance systems mediate actions of operators physically, cognitively and contextually. These different types of action mediation are related to preconditions for successful implementation and use of these systems. Coercive interventions only succeed if there is widespread agreement among the operators. Persuasive interventions are most effective when collective and individual interests align. Contextual influence relates to designs of the operator guidance systems that determine human-technology interactions when using them.

This is the first study that analyzes the functioning of an operator guidance system using the technological mediation approach. It adds a new perspective on the interaction between this system and its users in road pavement practice.

This paper presents an interpretation of freehand drawings produced by supply chain management undergraduates in response to the question: “What is sustainability?” Having to explain sustainability pictorially forced students to distill what the essence of sustainability meant to them and provided insights into how they perceived sustainability and their roles in achieving sustainability in the context of supply chain management.

Students were asked to draw and answer the question “What is sustainability?” These drawings were discussed/interpreted in class. All drawings were initially examined quantitatively, before a sample of four were selected for presentation here.

Freehand drawing can be used as part of a critical pedagogy to create a visual representation to bypass cognitive verbal processing routes. This allows students to produce clear, more critical and inclusive images of their understanding of a topic regardless of their vocabulary.

The authors offer this as a model for educators seeking alternative methods for engaging with sustainability and for creating a learning environment where students can develop their capacity for critical self-reflection.

This study shows how a collaborative learning experience facilitates learners demonstrating their level of understanding of sustainability.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0718

Solar cells could make textile-based wearable systems energy independent without the need for battery replacement or recharging; however, their laundry resistance, which is prerequisite for the product acceptance of e-textiles, has been rarely examined. This paper aims to report a systematic study of the laundry durability of solar cells embedded in textiles.

This research included small commercial monocrystalline silicon solar cells which were encapsulated with functional synthetic textile materials using an industrially relevant textile lamination process and found them to reliably endure laundry washing (ISO 6330:2012). The energy harvesting capability of eight textile laminated solar cells was measured after 10–50 cycles of laundry at 40 °C and compared with light transmittance spectroscopy and visual inspection.

Five of the eight textile solar cell samples fully maintained their efficiency over the 50 laundry cycles, whereas the other three showed a 20%–27% decrease. The cells did not cause any visual damage to the fabric. The result indicates that the textile encapsulated solar cell module provides sufficient protection for the solar cells against water, washing agents and mechanical stress to endure repetitive domestic laundry.

This study used rigid monocrystalline silicon solar cells. Flexible amorphous silicon cells were excluded because of low durability in preliminary tests. Other types of solar cells were not tested.

A review of literature reveals the tendency of researchers to avoid standardized textile washing resistance testing. This study removes the most critical obstacle of textile integrated solar energy harvesting, the washing resistance.

The aim of this article is to present a model for the orchestration of dynamic capabilities (ODCs) in cleantech companies that aim to obtain competitive advantage in the market.

The authors present herein descriptive research guided by a qualitative multiple case study approach carried out with 12 cleantech companies.

The results have showed that the ODC model is present in the product/process cycle, thus providing new capabilities and generating sustainable competitive advantage through the research categories presented.

This study contributes to the literature on the ODCs through microfoundations based on evidence of companies inserted in technological and intensively dynamic contexts.

This article demonstrates, through the ODC model, the main capabilities and characteristics of the assets of cleantech companies and how the process of renewing competencies to obtain competitive advantage occurs.

The ODC model utilizes technological resources in the product/process cycle. Asset specificity and the capacity for innovation allow cleantech companies to explore regulatory loopholes, making their sustainable model innovative and obtaining competitive advantage through the renewal of entrepreneurial capabilities and competencies.

The purpose of this study was to examine the experiences of multiple campus teams as they engaged in the assessment of their science, technology, engineering and mathematics (STEM) mentoring ecosystems within a peer assessment dialogue exercise.

This project utilized a qualitative multicase study method involving six campus teams, drawing upon completed inventory and visual mapping artefacts, session observations and debriefing interviews. The campuses included research universities, small colleges and minority-serving institutions (MSIs) across the United States of America. The authors analysed which features of the peer assessment dialogue exercise scaffolded participants' learning about ecosystem synergies and threats.

The results illustrated the benefit of instructor modelling, intra-team process time and multiple rounds of peer assessment. Participants gained new insights into their own campuses and an increased sense of possibility by dialoguing with peer campuses.

This project involved teams from a small set of institutions, relying on observational and self-reported debriefing data. Future research could centre perspectives of institutional leaders.

The authors recommend dedicating time to the institutional assessment of mentoring ecosystems. Investing in a campus-wide mentoring infrastructure could align with campus equity goals.

In contrast to studies that have focussed solely on programmatic outcomes of mentoring, this study explored strategies to strengthen institutional mentoring ecosystems in higher education, with a focus on peer assessment, dialogue and learning exercises.