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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%.
Menopause is the permanent cessation of menstruation occurring naturally in women's aging. The most frequent symptoms associated with menopausal phases are mucosal dryness, increased weight and body fat, and changes in sleep patterns. Oral symptoms in menopause derived from saliva flow reduction can lead to dry mouth, ulcers, and alterations of taste and swallowing patterns. However, the oral health phenotype of postmenopausal women has not been characterized. The aim of the study was to determine postmenopausal women's oral phenotype, including medical history, lifestyle, and oral assessment through artificial intelligence algorithms. We enrolled 100 postmenopausal women attending the Dental School of the University of Seville were included in the study. We collected an extensive questionnaire, including lifestyle, medication, and medical history. We used an unsupervised k-means algorithm to cluster the data following standard features for data analysis. Our results showed the main oral symptoms in our postmenopausal cohort were reduced salivary flow and periodontal disease. Relying on the classical assessment of the collected data, we might have a biased evaluation of postmenopausal women. Then, we used artificial intelligence analysis to evaluate our data obtaining the main features and providing a reduced feature defining the oral health phenotype. We found 6 clusters with similar features, including medication affecting salivation or smoking as essential features to obtain different phenotypes. Thus, we could obtain main features considering differential oral health phenotypes of postmenopausal women with an integrative approach providing new tools to assess the women in the dental clinic.
Rare but extreme events, such as pandemics, terror attacks, and stock market collapses, pose a risk that could undermine cooperation in societies and groups. We extend the public goods game (PGG) to investigate the relationship between rare but extreme external risks and cooperation in a laboratory experiment. By incorporating risk as an external random variable in the PGG, independent of the participants’ contributions, we preserve the economic equilibrium of non-cooperation in the original game. Furthermore, we examine whether cooperation can be restored by the relatively simple intervention of informing about countermeasures while keeping the actual risk constant. Our experimental results reveal that on average extreme risks indeed decrease contributions by about 20%; however, countermeasure information increases contributions by about 10%. Specifically, in the first interactions, cooperation levels can even reach those observed in the riskless baseline. Our results suggest that countermeasure information could help reinforce social cohesion and resilience in the face of rare but extreme risks.
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.
In this paper, the essential sponsorship basics are presented and the communication instrument of sports sponsorship is illustrated. Building on this, both the perspectives of sponsors and sponsees are examined in detail. In addition, the special features of sports event sponsorships are highlighted. Finally, current developments in sports sponsorship in the context of the FIFA Soccer World Cup 2022 in Qatar and the UEFA European Soccer Championship 2024 in Germany are compared and discussed.
Wave-like differential equations occur in many engineering applications. Here the engineering setup is embedded into the framework of functional analysis of modern mathematical physics. After an overview, the –Hilbert space approach to free Euler–Bernoulli bending vibrations of a beam in one spatial dimension is investigated. We analyze in detail the corresponding positive, selfadjoint differential operators of 4-th order associated to the boundary conditions in statics. A comparison with free string wave swinging is outlined.
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.
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.
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.
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.
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.
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.
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.
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.
Evaluation of a contactless accelerometer sensor system for heart rate monitoring during sleep
(2024)
The monitoring of a patient's heart rate (HR) is critical in the diagnosis of diseases. In the detection of sleep disorders, it also plays an important role. Several techniques have been proposed, including using sensors to record physiological signals that are automatically examined and analysed. This work aims to evaluate using a contactless HR monitoring system based on an accelerometer sensor during sleep. For this purpose, the oscillations caused by chest movements during heart contractions are recorded by an installation mounted under the bed mattress. The processing algorithm presented in this paper filters the signals and determines the HR. As a result, an average error of about 5 bpm has been documented, i.e., the system can be considered to be used for the forecasted domain.
Based on social information processing theory, this research examines whether and how an employee’s proactive personality influences intrinsic and extrinsic career growth. It also examines the mediating effects of two types of proactive behaviors (voice behavior and taking charge) and the moderating effect of a leader’s proactive personality. A sample of 307 employee-leader dyads participated in this survey. Structural equation modeling was used to test the hypotheses, and the bootstrap procedure was used to test the indirect effects. Results show that an employee’s proactive personality has significant positive effects on both intrinsic and extrinsic career growth. The mediating effect of taking charge was confirmed, while the mediating effect of voice behavior was not. Leader proactive personality weakens the relationship between employee proactive personality and the two types of proactive behaviors. Employee proactive personality is more positively related to intrinsic and extrinsic career growth via proactive behaviors when a leader’s proactive personality is low. This study extends the literature on proactive personality, proactive behavior, and career development by examining the underlying determination, mediation, and moderation mechanisms.
The article pleads for Education for Sustainable Development (ESD) in the textile and fashion sector and shows possibilities how this can be implemented from elementary school to higher education and vocational training. It begins by highlighting the non-sustainable practices and deficits that can be found in the fashion and textile sector worldwide and explains the sustainability goals in the context of the UN Roadmap ESD for 2030. In order to raise the awareness for sustainability and implement these goals, education is needed. The article introduces the concept of ESD as a guiding principle with the core element design competence, implemented by the interdisciplinary method of Design Thinking (DT). In order to successfully teach the ESD-relevant design competence, various didactic principles are required. It can be shown that they are very similar to the principles and phases of DT. Within a research project DT and its potential for implementing ESD has been investigated in teaching-learning situations at elementary schools as well as in an interdisciplinary seminar for student teachers. These findings have been transferred to the EU project Fashion DIET, which pursues the goal of implementing ESD in the textile and fashion sector. By means of an online pilot workshop, the methods and principles of DT were presented and explained to lecturers, teachers and educators, who gave their feedback on the potential of DT as a method to implement ESD as a guiding principle in their curricula.
Student-faculty interactions that promote learning are essential contributors to student retention, academic success and satisfaction. But the factors that causally initiate and frame these interactions are not well understood. Only if students evaluate these interactions as positive will they seek them. We conducted a survey experiment with students (n = 375) from a tuition-fee-free German business school, using conditional process analysis to assess which factors frame effective interactions. We focus on out-of-classroom standard and non-standard requests that students make to faculty, then investigate how faculty and student gender and students’ academic entitlement influence the interaction. Our study examines how students evaluate the interaction with faculty: when they seek interaction, their expectations of getting their requests approved, and their disappointment when their requests are declined. We find a significant influence of the request type along with moderating effects of faculty gender, student gender and student entitlement, particularly for non-standard work requests. We conclude with policy implications for university management: developing target-group-specific measures that facilitate the desired and positively evaluated student-faculty interactions might benefit all university stakeholders.
For optimization of production processes and product quality, often knowledge of the factors influencing the process outcome is compulsory. Thus, process analytical technology (PAT) that allows deeper insight into the process and results in a mathematical description of the process behavior as a simple function based on the most important process factors can help to achieve higher production efficiency and quality. The present study aims at characterizing a well-known industrial process, the transesterification reaction of rapeseed oil with methanol to produce fatty acid methyl esters (FAME) for usage as biodiesel in a continuous micro reactor set-up. To this end, a design of experiment approach is applied, where the effects of two process factors, the molar ratio and the total flow rate of the reactants, are investigated. The optimized process target response is the FAME mass fraction in the purified nonpolar phase of the product as a measure of reaction yield. The quantification is performed using attenuated total reflection infrared spectroscopy in combination with partial least squares regression. The data retrieved during the conduction of the DoE experimental plan were used for statistical analysis. A non-linear model indicating a synergistic interaction between the studied factors describes the reactor behavior with a high coefficient of determination (R²) of 0.9608. Thus, we applied a PAT approach to generate further insight into this established industrial process.