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Despite the unstoppable global drive towards electric mobility, the electrification of sub-Saharan Africa’s ubiquitous informal multi-passenger minibus taxis raises substantial concerns. This is due to a constrained electricity system, both in terms of generation capacity and distribution networks. Without careful planning and mitigation, the additional load of charging hundreds of thousands of electric minibus taxis during peak demand times could prove catastrophic. This paper assesses the impact of charging 202 of these taxis in Johannesburg, South Africa. The potential of using external stationary battery storage and solar PV generation is assessed to reduce both peak grid demand and total energy drawn from the grid. With the addition of stationary battery storage of an equivalent of 60 kWh/taxi and a solar plant of an equivalent of 9.45 kWpk/taxi, the grid load impact is reduced by 66%, from 12 kW/taxi to 4 kW/taxi, and the daily grid energy by 58% from 87 kWh/taxi to 47 kWh/taxi. The country’s dependence on coal to generate electricity, including the solar PV supply, also reduces greenhouse gas emissions by 58%.
Automatic content creation system for augmented reality maintenance applications for legacy machines
(2024)
Augmented reality (AR) applications have great potential to assist maintenance workers in their operations. However, creating AR solutions is time-consuming and laborious, which limits its widespread adoption in the industry. It therefore often happens that even with the latest generation machines, instead of an AR solution, the user only receives an electronic manual for the equipment operation and maintenance. This is commonplace with legacy machines. For this reason, solutions are required that simplify the creation of such AR solutions. This paper presents an approach using an electronic manual as a basis to create fast and cost-effective AR solutions for maintenance. As part of the approach, an application was developed to automatically identify and subdivide the chapters of electronic manuals via the bookmarks in the table of contents. The contents are then automatically uploaded to a central server and indexed with a suitable marker to make the data retrievable. The prepared content can then be accessed for creating context-related AR instructions via the marker. The application is characterized by the fact that no developers or experts are required to prepare the information. In addition to complying with common design criteria, the clear presentation of the contents and the intuitive use of the system offer added value for the performance of maintenance tasks. Together, these two elements form a novel way to retrofit legacy machines with AR maintenance instructions. The practical validation of the system took place in a factory environment. For this purpose, the content was created for a filter change on a CNC milling machine. The results show that inexperienced users can extract appropriate content with the software application. Furthermore, it is shown that maintenance workers, can access the content with an AR application developed for the Microsoft HoloLens 2 and complete simple tasks provided in the manufacturer's electronic manual.
The introduction of smart contracts has expanded the applicability of blockchains to many domains beyond finance and cryptocurrencies. Moreover, different blockchain technologies have evolved that target special requirements. As a result, in practice, often a combination of different blockchain systems is required to achieve an overall goal. However, due to the heterogeneity of blockchain protocols, the execution of distributed business transactions that span several blockchains leads to multiple interoperability and integration challenges. Therefore, in this article, we examine the domain of Cross-Chain Smart Contract Invocations (CCSCIs), which are distributed transactions that involve the invocation of smart contracts hosted on two or more blockchain systems. We conduct a systematic multi-vocal literature review to get an overview of the available CCSCI approaches. We select 20 formal literature studies and 13 high-quality gray literature studies, extract data from them, and analyze it to derive the CCSCI Classification Framework. With the help of the framework, we group the approaches into two categories and eight subcategories. The approaches differ in multiple characteristics, e.g., the mechanisms they follow, and the capabilities and transaction processing semantics they offer. Our analysis indicates that all approaches suffer from obstacles that complicate real-world adoption, such as the low support for handling heterogeneity and the need for trusted third parties.
Human pose estimation (HPE) is integral to scene understanding in numerous safety-critical domains involving human-machine interaction, such as autonomous driving or semi-automated work environments. Avoiding costly mistakes is synonymous with anticipating failure in model predictions, which necessitates meta-judgments on the accuracy of the applied models. Here, we propose a straightforward human pose regression framework to examine the behavior of two established methods for simultaneous aleatoric and epistemic uncertainty estimation: maximum a-posteriori (MAP) estimation with Monte-Carlo variational inference and deep evidential regression (DER). First, we evaluate both approaches on the quality of their predicted variances and whether these truly capture the expected model error. The initial assessment indicates that both methods exhibit the overconfidence issue common in deep probabilistic models. This observation motivates our implementation of an additional recalibration step to extract reliable confidence intervals. We then take a closer look at deep evidential regression, which, to our knowledge, is applied comprehensively for the first time to the HPE problem. Experimental results indicate that DER behaves as expected in challenging and adverse conditions commonly occurring in HPE and that the predicted uncertainties match their purported aleatoric and epistemic sources. Notably, DER achieves smooth uncertainty estimates without the need for a costly sampling step, making it an attractive candidate for uncertainty estimation on resource-limited platforms.
Tech hubs (THs) and cognate structures are nowadays ubiquitous in the innovation ecosystem of Sub-Saharan African (SSA) countries. However, the concept of THs is fuzzy due to the lack of a clear and universally accepted definition. This ambiguity is further compounded by the diverse range of organizations that self-identify as hubs, or are categorized as such by others. As a result, research on THs in SSA remained limited. Against the backdrop of established research on the interconnectedness of technology, innovation and entrepreneurship in different organizational forms, this paper is meant to provide fresh insights into the study of THs in SSA. To advance future research, first, it reveals what is special about THs in SSA and how they are related to existing concepts. I particularly argue that they contour a fourth-wave model of incubation. Second, four main categories are unfolded to delineate THs in SSA which is the cornerstone for future research.
Salivary gland tumors (SGTs) are a relevant, highly diverse subgroup of head and neck tumors whose entity determination can be difficult. Confocal Raman imaging in combination with multivariate data analysis may possibly support their correct classification. For the analysis of the translational potential of Raman imaging in SGT determination, a multi-stage evaluation process is necessary. By measuring a sample set of Warthin tumor, pleomorphic adenoma and non-tumor salivary gland tissue, Raman data were obtained and a thorough Raman band analysis was performed. This evaluation revealed highly overlapping Raman patterns with only minor spectral differences. Consequently, a principal component analysis (PCA) was calculated and further combined with a discriminant analysis (DA) to enable the best possible distinction. The PCA-DA model was characterized by accuracy, sensitivity, selectivity and precision values above 90% and validated by predicting model-unknown Raman spectra, of which 93% were classified correctly. Thus, we state our PCA-DA to be suitable for parotid tumor and non-salivary salivary gland tissue discrimination and prediction. For evaluation of the translational potential, further validation steps are necessary.
Comparative analysis of the chemical and rheological curing kinetics of formaldehyde-based wood adhesives is crucial for assessing their respective performance. Differential scanning calorimetry (DSC) and rheometry are the conventional techniques used for monitoring the curing processes leading to crosslinking polymerization of the adhesives. However, the direct comparison of these techniques is inappropriate due to the intrinsic differences in their underlying procedures. To address this challenge, the two adhesive samples were sequentially cured, firstly with rheometry and followed by DSC. The observed higher curing degree in the subsequent DSC procedure underpins the incomplete curing of the samples during initial rheometry. Furthermore, the comparative assessment of the activation energies, molar ratios, and active groups of the two adhesives highlights the importance of the pre-exponential factor in addition to the activation energies, as it attributes to the probability of active groups coinciding at the appropriate spatial arrangement.
Accurate monitoring of a patient's heart rate is a key element in the medical observation and health monitoring. In particular, its importance extends to the identification of sleep-related disorders. Various methods have been established that involve sensor-based recording of physiological signals followed by automated examination and analysis. This study attempts to evaluate the efficacy of a non-invasive HR monitoring framework based on an accelerometer sensor specifically during sleep. To achieve this goal, the motion induced by thoracic movements during cardiac contractions is captured by a device installed under the mattress. Signal filtering techniques and heart rate estimation using the symlets6 wavelet are part of the implemented computational framework described in this article. Subsequent analysis indicates the potential applicability of this system in the prognostic domain, with an average error margin of approximately 3 beats per minute. The results obtained represent a promising advancement in non-invasive heart rate monitoring during sleep, with potential implications for improved diagnosis and management of cardiovascular and sleep-related disorders.
Software scripts for sensor data extraction in Rasberry Pi: user-space and kernel-space comparison
(2024)
This paper compares two popular scripting implementations for hardware prototyping: Python scripts execut from User-Space and C-based Linux-Driver processes executed from Kernel-Space, which can provide information to researchers when considering one or another in their implementations. Conclusions exhibit that deploying software scripts in the kernel space makes it possible to grant a certain quality of sensor information using a Raspberry Pi without the need for advanced real-time operational systems.
The massive use of patient data for the training of artificial intelligence algorithms is common nowadays in medicine. In this scientific work, a statistical analysis of one of the most used datasets for the training of artificial intelligence models for the detection of sleep disorders is performed: sleep health heart study 2. This study focuses on determining whether the gender and age of the patients have a relevant influence to consider working with differentiated datasets based on these variables for the training of artificial intelligence models.
Purpose
As a response to the increased frequency of disruptive events and intense competition, organizational agility has become a key concept in organizational research. Fostering organizational agility requires leveraging knowledge that exists both outside (exploration) and inside (exploitation) the organization. This research tests the so-called ambidexterity hypothesis, which claims that a balance between exploration and exploitation leads to increased organizational outcomes, including the development of organizational agility. Complementing previously established measurement models on ambidexterity, this research proposes an alternative measurement model to analyze how ambidexterity can enhance organizational agility and, indirectly, performance, taking into consideration the moderating effect of environmental competitiveness.
Design/methodology/approach
A review of existing measurement models for ambidexterity shows that tension, a crucial aspect of ambidexterity, is often neglected. The authors, therefore, develop a new measurement model of ambidexterity to incorporate ambidexterity-induced tension. Using this measurement model, they examine the effect of ambidexterity on the development of entrepreneurial and adaptive agility as well as performance.
Findings
Ambidexterity positively influences both entrepreneurial and adaptive agility, indicating that a balance between exploration and exploitation has superior organizational effects. This finding confirms the ambidexterity hypothesis with respect to organizational agility. Furthermore, both entrepreneurial and adaptive agility drive organizational performance. These two indirect effects via agility fully mediate the impact of ambidexterity on organizational performance. Finally, environmental competitiveness positively moderates the relationship between ambidexterity and adaptive agility.
Originality/value
The findings extend research on ambidexterity by showing its positive effects on organizational agility. Furthermore, the study proposes an alternative operationalization to capture the ambidexterity construct that may lay the groundwork for further applications of the ambidexterity concept.
Sleep disorders can impact daily life, affecting physical, emotional, and cognitive well-being. Due to the time-consuming, highly obtrusive, and expensive nature of using the standard approaches such as polysomnography, it is of great interest to develop a noninvasive and unobtrusive in-home sleep monitoring system that can reliably and accurately measure cardiorespiratory parameters while causing minimal discomfort to the user’s sleep. We developed a low-cost Out of Center Sleep Testing (OCST) system with low complexity to measure cardiorespiratory parameters. We tested and validated two force-sensitive resistor strip sensors under the bed mattress covering the thoracic and abdominal regions. Twenty subjects were recruited, including 12 males and 8 females. The ballistocardiogram signal was processed using the 4th smooth level of the discrete wavelet transform and the 2nd order of the Butterworth bandpass filter to measure the heart rate and respiration rate, respectively. We reached a total error (concerning the reference sensors) of 3.24 beats per minute and 2.32 rates for heart rate and respiration rate, respectively. For males and females, heart rate errors were 3.47 and 2.68, and respiration rate errors were 2.32 and 2.33, respectively. We developed and verified the reliability and applicability of the system. It showed a minor dependency on sleeping positions, one of the major cumbersome sleep measurements. We identified the sensor under the thoracic region as the optimal configuration for cardiorespiratory measurement. Although testing the system with healthy subjects and regular patterns of cardiorespiratory parameters showed promising results, further investigation is required with the bandwidth frequency and validation of the system with larger groups of subjects, including patients.
Purpose
Digital transformation of organizations has major implications for required skills and competencies of the workforce, both as a prerequisite for implementation, and, as a consequence of the transformation. The purpose of this study is to analyze required skills and competencies for digital transformation using the context of robotic process automation (RPA) as an example.
Design/methodology/approach
This study is based on an explorative, thematic coding analysis of 119 job advertisements related to RPA. The data was collected from major online job platforms, qualitatively coded and subsequently analyzed quantitatively.
Findings
The research highlights the general importance of specific skills and competencies for digital transformation and shows a gap between available skills and required skills. Moreover, it is concluded that reskilling the existing workforce might be difficult. Many emerging positions can be found in the consulting sector, which raises questions about the permanent vs temporary nature of the requirements, as well as the difficulty of acquiring the required knowledge.
Originality/value
This paper contributes to knowledge by providing new empirical findings and a novel perspective to the ongoing discussion of digital skills, employment effects and reskilling demands of the existing workforce owing to recent technological developments and automation in the overall context of digital transformation.
The dawn of the 21st Century has witnessed a tremendous increase in trade pacts among nations, resulting in renewed hopes for sustainable enterprise development in emerging economies worldwide. Ghana and other sub-Saharan African (SSA) countries have signed onto several North-South and South-South free trade agreements with the hope of strengthening their presence in the international trade arena, and to promote economic growth in SSA. For over two decades, however, very little has changed, and many have dashed their high hopes as enterprises continue to struggle in SSA. Not even the African Continental Free Trade Agreement (AfCFTA) could renew the hopes of sceptics. Several studies opined that enterprises in SSA could improve their domestic and international competitiveness by establishing mutually beneficial partnerships with their counterparts from the Global North and South. This study delved into the issues that affect North-South and South-South business collaborations and recommends key success factors that could help promote mutually beneficial cross-border business partnerships. The research includes both literature and empirical information on the key success factors of business partnerships between African enterprises as well as between African enterprises and firms from the Global North. We approached the study qualitatively using a phenomenological research design. Research participants included important stakeholders in Africa and Europe's international trade and sustainable enterprise development ecosystem. The study identified several challenges with the current business collaborations and recommended new ways of making such partnerships more beneficial.
In order to ensure sufficient recovery of the human body and brain, healthy sleep is indispensable. For this purpose, appropriate therapy should be initiated at an early stage in the case of sleep disorders. For some sleep disorders (e.g., insomnia), a sleep diary is essential for diagnosis and therapy monitoring. However, subjective measurement with a sleep diary has several disadvantages, requiring regular action from the user and leading to decreased comfort and potential data loss. To automate sleep monitoring and increase user comfort, one could consider replacing a sleep diary with an automatic measurement, such as a smartwatch, which would not disturb sleep. To obtain accurate results on the evaluation of the possibility of such a replacement, a field study was conducted with a total of 166 overnight recordings, followed by an analysis of the results. In this evaluation, objective sleep measurement with a Samsung Galaxy Watch 4 was compared to a subjective approach with a sleep diary, which is a standard method in sleep medicine. The focus was on comparing four relevant sleep characteristics: falling asleep time, waking up time, total sleep time (TST), and sleep efficiency (SE). After evaluating the results, it was concluded that a smartwatch could replace subjective measurement to determine falling asleep and waking up time, considering some level of inaccuracy. In the case of SE, substitution was also proved to be possible. However, some individual recordings showed a higher discrepancy in results between the two approaches. For its part, the evaluation of the TST measurement currently does not allow us to recommend substituting the measurement method for this sleep parameter. The appropriateness of replacing sleep diary measurement with a smartwatch depends on the acceptable levels of discrepancy. We propose four levels of similarity of results, defining ranges of absolute differences between objective and subjective measurements. By considering the values in the provided table and knowing the required accuracy, it is possible to determine the suitability of substitution in each individual case. The introduction of a “similarity level” parameter increases the adaptability and reusability of study findings in individual practical cases.
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, DeepSeg, nnU-Net, and DeepSCAN for automatic glioma boundaries detection in pre-operative MRI. It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0.9294, 0.8788, and 0.8803, and Hausdorf distance of 5.23, 13.54, and 12.05, for the whole tumor, tumor core, and enhancing tumor, respectively. Furthermore, the proposed ensemble method ranked first in the final ranking on another unseen test dataset, namely Sub-Saharan Africa dataset, achieving mean Dice scores of 0.9737, 0.9593, and 0.9022, and HD95 of 2.66, 1.72, 3.32 for the whole tumor, tumor core, and enhancing tumor, respectively.
AI-based prediction and recommender systems are widely used in various industry sectors. However, general acceptance of AI-enabled systems is still widely uninvestigated. Therefore, firstly we conducted a survey with 559 respondents. Findings suggested that AI-enabled systems should be fair, transparent, consider personality traits and perform tasks efficiently. Secondly, we developed a system for the Facial Beauty Prediction (FBP) benchmark that automatically evaluates facial attractiveness. As our previous experiments have proven, these results are usually highly correlated with human ratings. Consequently they also reflect human bias in annotations. An upcoming challenge for scientists is to provide training data and AI algorithms that can withstand distorted information. In this work, we introduce AntiDiscriminationNet (ADN), a superior attractiveness prediction network. We propose a new method to generate an unbiased convolutional neural network (CNN) to improve the fairn ess of machine learning in facial dataset. To train unbiased networks we generate synthetic images and weight training data for anti-discrimination assessments towards different ethnicities. Additionally, we introduce an approach with entropy penalty terms to reduce the bias of our CNN. Our research provides insights in how to train and build fair machine learning models for facial image analysis by minimising implicit biases. Our AntiDiscriminationNet finally outperforms all competitors in the FBP benchmark by achieving a Pearson correlation coefficient of PCC = 0.9601.
Purpose
In recognising the key role of business intelligence and big data analytics in influencing companies’ decision-making processes, this paper aims to codify the main phases through which companies can approach, develop and manage big data analytics.
Design/methodology/approach
By adopting a research strategy based on case studies, this paper depicts the main phases and challenges that companies “live” through in approaching big data analytics as a way to support their decision-making processes. The analysis of case studies has been chosen as the main research method because it offers the possibility for different data sources to describe a phenomenon and subsequently to develop and test theories.
Findings
This paper provides a possible depiction of the main phases and challenges through which the approach(es) to big data analytics can emerge and evolve over time with reference to companies’ decision-making processes.
Research limitations/implications
This paper recalls the attention of researchers in defining clear patterns through which technology-based approaches should be developed. In its depiction of the main phases of the development of big data analytics in companies’ decision-making processes, this paper highlights the possible domains in which to define and renovate approaches to value. The proposed conceptual model derives from the adoption of an inductive approach. Despite its validity, it is discussed and questioned through multiple case studies. In addition, its generalisability requires further discussion and analysis in the light of alternative interpretative perspectives.
Practical implications
The reflections herein offer practitioners interested in company management the possibility to develop performance measurement tools that can evaluate how each phase can contribute to companies’ value creation processes.
Originality/value
This paper contributes to the ongoing debate about the role of digital technologies in influencing managerial and social models. This paper provides a conceptual model that is able to support both researchers and practitioners in understanding through which phases big data analytics can be approached and managed to enhance value processes.
Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.
Flame-retardant finishing of cotton fabrics using DOPO functionalized alkoxy- and amido alkoxysilane
(2023)
In the present study, DOPO-based alkoxysilane (DOPO-ETES) and amido alkoxysilane (DOPO-AmdPTES) were synthesized by one-step and without by-products as halogen-free flame retardants. The flame retardants were applied on cotton fabric utilizing sol–gel method and pad-dry-cure finishing process. The flame retardancy, the thermal stability and the combustion ehaviour of treated cotton were evaluated by surface and bottom edge ignition flame test (according to EN ISO 15025), thermogravimetric analysis (TGA) and micro-scale combustion calorimeter (MCC). Unlike CO/DOPO-ETES sample, cotton treated with DOPO-AmdPTES nanosols exhibits self-extinguishing ehaviour with high char residue, an improvement of the LOI value and a significant reduction of the PHRR, HRC and THR compared to pristine cotton. Cotton finished with DOPO-AmdPTES reveals a semi-durability after ten laundering cycles keeping the flame-retardant properties unchanged. According to the results obtained from TGA-FTIR, Py-GC/MS and XPS, the major activity of flame retardant occurs in the condensed phase via catalytic induced char formation as physical barrier along with the activity in the gas phase derived mainly from the dilution effect. The early degradation of CO/DOPO-AmdPTES compared to CO/DOPO-ETES, triggered by the cleavage of the weak bond between P and C=O, as the DFT study indicated, provides the beneficial effect of this flame retardant on the fire resistance of cellulose.