This study explores STEM students’ perspectives on service-learning and story-telling activities to enhance community engagement and advance gender equality, investigating their impact on students’ perceptions, experiences, and understanding of gender dynamics within rural communities. Through qualitative analysis of interviews, reflective journals, and participatory videos, this study explores the transformative potential of service-learning and storytelling initiatives in empowering rural women, challenging traditional societal roles, and advocating for equal opportunities, particularly in STEM disciplines. Findings reveal the multifaceted benefits of these activities, including the development of empathy, cultural awareness, leadership skills, and a commitment to social justice among participating students. This study highlights the importance of integrating service-learning and storytelling into STEM education to cultivate inclusive practices, promote community development, and advance gender equality in rural settings. Full article

The recent emergence of generative AI technologies is beginning to shape workforce hiring practices. The shift towards skills-based hiring over degree-based hiring has sparked concerns over the ability of college graduates to be prepared for their career roles. One approach to equip students to work with technology and adapt to rapidly changing environments is the development of an entrepreneurial mindset. One way to cultivate entrepreneurial thinking is through the participatory action research methodology, photovoice. This study explores how photovoice promotes discovery, evaluation, and exploitation of opportunities in university engineering classrooms. For this study, a literature review was conducted to identify, evaluate, and interpret available research. For the review, a five-step process was used. This process included defining a search criterion, constructing a Boolean logic search query, inserting the query into multiple academic search engines/databases, screening and selecting articles, and categorizing and mapping the literature. The review’s findings were organized based on the type of study, participants, duration of study and photovoice interventions used, study outcomes, and entrepreneurial mindset development. The results discussed in this paper offer insights, guidance, recommendations, and future directions for engineering education research. Full article

Wildfires pose a direct threat to the property, life, and well-being of the population of Montana, USA, and indirectly to their health through hazardous smoke and gases emitted into the atmosphere. Studies have shown that elevated levels of particulate matter cause impacts to human health ranging from early death, to neurological and immune diseases, to cancer. Although there is currently a network of ground-based air quality sensors (n = 20) in Montana, the geographically sparse network has large gaps and lacks the ability to make accurate predictions for air quality in many areas of the state. Using the random forest method, a predictive model was developed in the Google Earth Engine (GEE) environment to estimate PM2.5 concentrations using satellite-based aerosol optical depth (AOD), dewpoint temperature (DPT), relative humidity (RH), wind speed (WIND), wind direction (WDIR), pressure (PRES), and planetary-boundary-layer height (PBLH). The validity of the prediction model was evaluated using 10-fold cross validation with a R² value of 0.572 and RMSE of 9.98 µg/m³. The corresponding R² and RMSE values for ‘held-out data’ were 0.487 and 10.53 µg/m³. Using the validated prediction model, daily PM2.5 concentration maps (1 km-resolution) were estimated from 2012 to 2023 for the state of Montana. These concentration maps are accessible via an application developed using GEE. The product provides valuable insights into spatiotemporal trends of PM2.5 concentrations, which will be useful for communities to take appropriate mitigation strategies and minimize hazardous PM2.5 exposure. Full article

Document-oriented databases, a type of Not Only SQL (NoSQL) database, are gaining popularity owing to their flexibility in data handling and performance for large-scale data. MongoDB, a typical document-oriented database, is a database that stores data in the JSON format, where the upper field involves lower fields and fields with the same related parent. One feature of this
document-oriented database is that data are dynamically stored in an arbitrary location without explicitly defining a schema in advance. This flexibility violates the above property and causes difficulties for application program readability and database maintenance. To address these issues, we propose a reconstruction support method for document structures in MongoDB. The method uses the strength of the Has-A relationship between the parent and child fields, as well as the similarity of field names in the MongoDB documents in natural language processing, to reconstruct the data structure in MongoDB. As a result, the method transforms the parent and child fields into more
coherent data structures. We evaluated our methods using real-world data and demonstrated their MongoDBeffectiveness. Full article

In our study, we investigate the phenomenon of information loss, as measured by the Kullback–Leibler divergence, in a many-fermion system, such as the Lipkin model. Information loss is introduced as the number N of particles increases, particularly when the system is in a mixed state. We find that there is a significant loss of information under these conditions. However, we observe that this loss nearly disappears when the system is in a pure state. Our analysis employs tools from information theory to quantify and understand these effects. Full article

This paper presents a novel deep-learning network designed to detect intervals of jump discontinuities in single-variable piecewise smooth functions from their noisy samples. Enhancing the accuracy of jump discontinuity estimations can be used to find a more precise overall approximation of the function, as traditional approximation methods often produce significant errors near discontinuities. Detecting intervals of discontinuities is relatively straightforward when working with exact function data, as finite differences in the data can serve as indicators of smoothness. However, these smoothness indicators become unreliable when dealing with highly noisy data. In this paper, we propose a deep-learning network to pinpoint the location of a jump discontinuity even in the presence of substantial noise. Full article

Demographic changes have led to an increase in older people in prisons. Whereas the rehabilitative process of younger offenders is geared towards their reintegration into the labour market, successful ageing should be a policy aim for older prisoners. This study explores how older incarcerated persons view their ageing. A qualitative study using a written survey with only the single question What does ageing in prison mean to you? was conducted in Bavaria, Germany. A total of 64 prisoners (61 male, 3 female) supplied answers varying in length from a few words to several pages. The thematic analysis revealed that together with health concerns, social relations and everyday activities, the uncertainty of the future was a central focus point for the older adults in prison. The authors propose that a positive vision of the future needs to be included in any model of successful ageing. If successful ageing is used as an aim for older prisoners, more attention needs to be paid to support interventions during and after the release process. Full article

This study endeavors to automate the assessment of competencies within the domain of entrepreneurship, specifically targeting the augmentation of entrepreneurial cognition and conduct within universities in German rural regions, like Lower Franconia. Employing methods, including literature analyses and expert interviews, we formulated and validated an entrepreneurship competence profile and accompanying self-assessment tool. The ensuing evaluative framework is poised for seamless integration into learning management systems, thereby facilitating intelligent competence monitoring within educational environments. Purpose: The aim of this thesis is to develop an automated competence assessment procedure in the field of entrepreneurship. This can be used in the university environment in the long term to promote and teach entrepreneurial thinking and behavior in order to sustainably improve the quality of learning outcomes and achieve targeted promotion of entrepreneurship. Methodology: Based on a relevant literature analysis, four guideline-based expert interviews were created and conducted. The results of the interviews were compiled and validated in a structured competence profile (entrepreneurship competence profile). Based on this competence catalog for entrepreneurs, an empirically valid self-test was created using standard psychometric questionnaire construction methods. Results: The entrepreneurship competence profile and a consequential empirically validated self-test for competence assessment were created. This test provides the basis for the long-term competence development of students and can further be embedded automatically into a learning management system (LMS) as part of intelligent competence monitoring, which allows for the recording of competencies for each student and the individual incorporation of gap closure into the curriculum. Originality/value: In previous research, there were no competence profiles or competence assessment procedures in the field of entrepreneurship that derived relevant competencies directly from actors within this environment. This work illustrates the development of a competence assessment procedure for entrepreneurs and shows which methods can be used to close prevailing research gaps in the field of intelligent competence monitoring. Full article

A lot of time and effort is put into reducing waiting times in organizational life. However, jobs can include phases of waiting. The aim of this conceptual paper is to analyze waiting on the job level and provide a theoretical rationale for individual management of waiting times of employees. Wait crafting is introduced based on (job) crafting and its advantages for individuals and organizations outlined. Steps towards integrating the possibility of job crafting and needs for future research are indicated. Full article

Considering that the plasmonic properties of metallic nanoparticles (NPs) are strongly influenced by their dielectric environment, comprehension and manipulation of this interplay are crucial for the design and optimization of functional plasmonic systems. In this study, the plasmonic behavior of silver nanoparticles encapsulated in diverse copolymer dielectric environments was investigated, focusing on the analysis of the emerging localized surface plasmon resonances (LSPRs) through both experimental and theoretical approaches. Specifically, two series of nanostructured silver ultrathin films were deposited via magnetron sputtering on heated Corning Glass substrates at 330 °C and 420 °C, respectively, resulting in the formation of self-assembled NPs of various sizes and distributions. Subsequently, three different polymeric layers were spin-coated on top of the silver NPs. Optical and structural characterization were carried out by means of UV–Vis spectroscopy and atomic force microscopy, respectively. Rigorous Coupled Wave Analysis (RCWA) was employed to study the LSPRs theoretically. The polymeric environment consistently induced a red shift as well as various alterations in the LSPR amplitude, suggesting the potential tunability of the system. Full article

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R²), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures. Full article

Traditional methods for predicting sow reproductive cycles are not only costly but also demand a larger workforce, exposing workers to respiratory toxins, repetitive stress injuries, and chronic pain. This occupational hazard can even lead to mental health issues due to repeated exposure to violence. Managing health and welfare issues becomes pivotal in group-housed animal settings, where individual care is challenging on large farms with limited staff. The necessity for computer vision systems to analyze sow behavior and detect deviations indicative of health problems is apparent. Beyond observing changes in behavior and physical traits, computer vision can accurately detect estrus based on vulva characteristics and analyze thermal imagery for temperature changes, which are crucial indicators of estrus. By automating estrus detection, farms can significantly enhance breeding efficiency, ensuring optimal timing for insemination. These systems work continuously, promptly alerting staff to anomalies for early intervention. In this research, we propose part of the solution by utilizing an image segmentation model to localize the vulva. We created our technique to identify vulvae on pig farms using infrared imagery. To accomplish this, we initially isolate the vulva region by enclosing it within a red rectangle and then generate vulva masks by applying a threshold to the red area. The system is trained using U-Net semantic segmentation, where the input for the system consists of grayscale images and their corresponding masks. We utilize U-Net semantic segmentation to find the vulva in the input image, making it lightweight, simple, and robust enough to be tested on many images. To evaluate the performance of our model, we employ the intersection over union (IOU) metric, which is a suitable indicator for determining the model’s robustness. For the segmentation model, a prediction is generally considered ‘good’ when the intersection over union score surpasses 0.5. Our model achieved this criterion with a score of 0.58, surpassing the scores of alternative methods such as the SVM with Gabor (0.515) and YOLOv3 (0.52). Full article

Acrodermatitis chronica atrophicans (ACA) is not an infrequent condition in Europe. However, the characteristic skin lesions are often confused by non-dermatologists with other conditions. We report three unusual cases in which we made a definitive diagnosis of ACA complicated by cutaneous marginal zone lymphoma, juxta-articular fibrotic nodules, or bilateral sensory polyneuropathy. In all cases, correct diagnosis and adequate treatment was delayed over a period of at least 12 months. We initiated systemic antibiotics resulting in full recovery in these patients. The present case reports underscore that ACA may be associated with unusual clinical presentation which potentially result in delay of correct diagnosis and treatment. Hence, ACA diagnosis may be considerably delayed leading to inappropriate therapy exposure, prolonged patients’ suffering, and causing unnecessary cost. Thus, physicians who are not familiar with skin conditions should seek a timely consultation a dermatologist. Full article

With increased worldwide energy demand and carbon dioxide emissions from the use of fossil fuels, severe problems are being experienced in modern times. Energy is one of the most important resources for humankind, and its needs have been drastically increasing due to energy consumption, the rapid depletion of fossil fuels, and environmental crises. Therefore, it is important to identify and search for an alternative to fossil fuels that provides energy in a reliable, constant, and sustainable way that could use available energy sources efficiently for alternative renewable sources of fuel that are clean, non-toxic, and eco-friendly. In this way, there is a dire need to develop technologies for biofuel production with a focus on economic feasibility, sustainability, and renewability. Several technologies, such as biological and thermochemical approaches, are derived from abundant renewable biological sources, such as biomass and agricultural waste, using advanced conversion technologies for biofuel production. Biofuels are non-toxic, biodegradable, and recognized as an important sustainable greener energy source to conventional fossil fuels with lower carbon emissions, combat air pollution, empower rural communities, and increase economic growth and energy supply. The purpose of this review is to explain the basic aspects of biofuels and their sustainability criteria, with a particular focus on conversion technologies for biofuel production, challenges, and future perspectives. Full article

Long-duration energy storage is the key challenge facing renewable energy transition in the future of well over 50% and up to 75% of primary energy supply with intermittent solar and wind electricity, while up to 25% would come from biomass, which requires traditional type storage. To this end, chemical energy storage at grid scale in the form of fuel appears to be the ideal option for wind and solar power. Renewable hydrogen is a much-considered fuel along with ammonia. However, these fuels are not only difficult to transport over long distances, but they would also require totally new and prohibitively expensive infrastructure. On the other hand, the existing natural gas pipeline infrastructure in developed economies can not only transmit a mixture of methane with up to 20% hydrogen without modification, but it also has more than adequate long-duration storage capacity. This is confirmed by analyzing the energy economies of the USA and Germany, both possessing well-developed natural gas transmission and storage systems. It is envisioned that renewable methane will be produced via well-established biological and/or chemical processes reacting green hydrogen with carbon dioxide, the latter to be separated ideally from biogas generated via the biological conversion of biomass to biomethane. At the point of utilization of the methane to generate power and a variety of chemicals, the released carbon dioxide would be also sequestered. An essentially net zero carbon energy system would be then become operational. The current conversion efficiency of power to hydrogen/methane to power on the order of 40% would limit the penetration of wind and solar power. Conversion efficiencies of over 75% can be attained with the on-going commercialization of solid oxide electrolysis and fuel cells for up to 75% penetration of intermittent renewable power. The proposed hydrogen/methane system would then be widely adopted because it is practical, affordable, and sustainable. Full article

Considering the rapid evolution of lithium silicate-based glass ceramics (LSCs) in dentistry, this review paper aims to present an updated overview of the recently introduced commercial novel LSCs. The clinical and in vitro English-language literature relating to the microstructure, manufacturing, strengthening, properties, surface treatments and clinical performance of LSC materials was obtained through an electronic search. Findings from relevant articles were extracted and summarised for this manuscript. There is considerable evidence supporting the mechanical and aesthetic competency of LSC variants, namely zirconia-reinforced lithium silicates and lithium–aluminium disilicates. Nonetheless, the literature assessing the biocompatibility and cytotoxicity of novel LSCs is scarce. An exploration of the chemical, mechanical and chemo-mechanical intaglio surface treatments—alternative to hydrofluoric acid etching—revealed promising adhesion performance for acid neutralisation and plasma treatment. The subtractive manufacturing methods of partially crystallised and fully crystallised LSC blocks and the additive manufacturing modalities pertaining to the fabrication of LSC dental restorations are addressed, wherein that challenges that could be encountered upon implementing novel additive manufacturing approaches using LSC print materials are highlighted. Furthermore, the short-term clinical performance of zirconia-reinforced lithium silicates and lithium–aluminium disilicates is demonstrated to be comparable to that of lithium disilicate ceramics and reveals promising potential for their long-term clinical performance. Full article

Accurate feeding management in aquaculture relies on assessing the average weight of aquatic animals during their growth stages. The traditional method involves using a labor-intensive approach and may impact the well-being of fish. The current research focuses on a unique way of estimating red tilapia’s weight in cage culture via a river, which employs unmanned aerial vehicle (UAV) and deep learning techniques. The described approach includes taking pictures by means of a UAV and then applying deep learning and machine learning algorithms to them, such as convolutional neural networks (CNNs), extreme gradient boosting (XGBoost), and a Hybrid CNN-XGBoost model. The results showed that the CNN model achieved its accuracy peak after 60 epochs, showing accuracy, precision, recall, and F1 score values of 0.748 ± 0.019, 0.750 ± 0.019, 0.740 ± 0.014, and 0.740 ± 0.019, respectively. The XGBoost reached its accuracy peak with 45 n_estimators, recording values of approximately 0.560 ± 0.000 for accuracy and 0.550 ± 0.000 for precision, recall, and F1. Regarding the Hybrid CNN-XGBoost model, it demonstrated its prediction accuracy using both 45 epochs and n_estimators. The accuracy value was around 0.760 ± 0.019, precision was 0.762 ± 0.019, recall was 0.754 ± 0.019, and F1 was 0.752 ± 0.019. The Hybrid CNN-XGBoost model demonstrated the highest accuracy compared to using standalone CNN and XGBoost models and could reduce the time required for weight estimation by around 11.81% compared to using the standalone CNN. Although the testing results may be lower than those from previous laboratory studies, this discrepancy is attributed to the real-world testing conditions in aquaculture settings, which involve uncontrollable factors. To enhance accuracy, we recommend increasing the sample size of images and extending the data collection period to cover one year. This approach allows for a comprehensive understanding of the seasonal effects on evaluation outcomes. Full article

The rapid advancement of communication technologies underscores the urgent need for robust and adaptable emergency communication systems (ECSs), particularly crucial during crises and natural disasters. Although network-based ECSs have been extensively studied, integrating open-source technologies, such as software-defined wide area networks (SD-WAN) with private long-term evolution (LTE) base stations, is a relatively unexplored domain. This study endeavors to fill this gap by introducing an experimental ECS platform that utilizes a hybrid network, incorporating a VoIP network to enhance open-source and on-premises communications in targeted areas. Our hypothesis posits that a hybrid network architecture, combining SD-WAN and private LTE, can substantially improve the reliability and efficiency of ECSs. Our findings, supported by the open-source OMNeT++ simulator, illuminate the enhanced communication reliability of the network. Moreover, the proposed platform, characterized by autonomous wireless 4G/LTE base stations and an Asterisk VoIP server, demonstrates improved quality of service (QoS) and quality of experience (QoE), with minimal data loss. This research not only has immediate practical applications but also bears significant implications for the development of cost-effective, open-source communication networks, optimized for emergencies, critical infrastructure, and remote areas. Full article

Analyzing the isotopic composition of cosmic rays (CRs) provides valuable insights into the galactic environment and helps refine existing propagation models. A particular interest is devoted to secondary-to-primary ratios of light isotopic components of CRs, the measurement of which can provide complementary information with respect to secondary-to-primary ratios like B/C. Given the complexity of the concurrent measurement of velocity and momentum required to differentiate isotopes of the same Z, a task typically accomplished using magnetic spectrometers, existing measurements of these ratios only effectively characterize the low-energy region (below 1 GeV/nucl). This study introduces a novel technique for isotopic distinction in CRs at high energies up to 100 GeV/nucl based on multiple scattering, which, combined with the proposed measurement of velocity, represent an interesting alternative to magnetic spectrometers. The performance of this technique was assessed through a dedicated simulation using the GEANT4 package, with specific emphasis on Z = 1 isotopes. Full article

The effective action in the renormalizable quantum theory of gravity provides entropy because the total Hamiltonian vanishes. Since it is a renormalization group invariant that is constant in the process of cosmic evolution, we can show conservation of entropy, which is an ansatz in the standard cosmology. Here, we study renormalizable quantum gravity that exhibits conformal dominance at high energy beyond the Planck scale. The current entropy of the universe is derived by calculating the effective action under the scenario of quantum gravity inflation caused by its dynamics. We then argue that ghost modes must be unphysical but are necessary for the Hamiltonian to vanish and for entropy to exist in gravitational systems. Full article

The present research focuses on the effects of different processing routes on the physical and mechanical properties of nano Ti(CN)-based cermets with metallic binders. Tungsten carbide (WC) is added as a secondary carbide and Ni-Co is added as a metallic binder to nano Ti(CN)-based cermet processed via conventional and spark plasma sintering (SPS). A systematic comparison of the composition and sintering conditions for different cermets’ systems was carried out to design novel composition and sintering conditions. Nano TiCN powder was prepared by 30 h of ball milling. The highest density of >98.5% was achieved for the SPS-processed cermets sintered at 1200 °C and 1250 °C for 3 min at 60 MPa of pressure in comparison to the conventionally sintered cermets at 1400 °C for 1 h with a two-stage compaction process—uniaxially at 150 MPa and isostatically at 300 MPa of pressure. Comparative X-ray diffraction (XRD) analysis of the milled powders at different time intervals was performed to understand the characteristics of the as-received and milled powders. Peak broadening was observed after 5 h of ball milling, and it increased to 30 hr. Also, peak broadening and a refined carbide size was observed in the XRD and scanning electron microscope (SEM) micrographs of the SPS-processed cermet. Transmission electron microscope (TEM) analysis of the milled powder showed that its internal structure had a regular periodic arrangement of planes. SEM base scattered electron (BSE) images of all the cermets primarily showed three major microstructural phases of the core–rim–binder with black, grey, and white contrast, respectively. With the present sintering conditions, a high hardness of ~16 GPa and a fracture toughness of ~9 MPa m^1/2 were obtained for SPS-processed cermets sintered at higher temperatures. Full article