bluuebunny/crossref_metadata_2025 huggingface dataset:
Innovative Solutions for Worn Fingerprints: A Comparative Analysis of Traditional Fingerprint Impression and 3D Printing
Authors: Mao Wenhui , Zhao Yadong , Pavlenko Petro , Chen Yihan , Shi Xuezhi
Published: April 2024

Fingerprint recognition systems have achieved widespread integration into various technological devices, including cell phones, computers, door locks, and time attendance machines. Nevertheless, individuals with worn fingerprints encounter challenges when attempting to unlock original fingerprint systems, which results in disruptions to their daily activities. This study explores two distinct methods for fingerprint backup: traditional fingerprint impression and 3D printing technologies. Unlocking tests were conducted on commonly available optical fingerprint lock-equipped cell phones to assess the efficacy of these methods, particularly in unlocking with worn fingerprints. The research findings indicated that the traditional fingerprint impression method exhibited high fidelity in reproducing fingerprint patterns, achieving an impressive unlocking success rate of 97.8% for imprinting unworn fingerprints. However, when dealing with worn fingerprints, the traditional fingerprint impression technique showed a reduced unlocking success rate, progressively decreasing with increasing degrees of finger wear. In contrast, 3D-printed backup fingerprints, with image processing and optimization of ridge height, mitigated the impact of fingerprint wear on the unlocking capability, resulting in an unlocking success rate of 84.4% or higher. Thus, the utilization of 3D printing technology proves advantageous for individuals with severely worn or incomplete fingerprints, providing a viable solution for unforeseen circumstances.
Identification of the Biomechanical Response of the Muscles That Contract the Most during Disfluencies in Stuttered Speech
Authors: Marin Edu , Unsihuay Nicole , Abarca Victoria E. , Elias Dante A.
Published: April 2024

Stuttering, affecting approximately 1% of the global population, is a complex speech disorder significantly impacting individuals’ quality of life. Prior studies using electromyography (EMG) to examine orofacial muscle activity in stuttering have presented mixed results, highlighting the variability in neuromuscular responses during stuttering episodes. Fifty-five participants with stuttering and 30 individuals without stuttering, aged between 18 and 40, participated in the study. EMG signals from five facial and cervical muscles were recorded during speech tasks and analyzed for mean amplitude and frequency activity in the 5–15 Hz range to identify significant differences. Upon analysis of the 5–15 Hz frequency range, a higher average amplitude was observed in the zygomaticus major muscle for participants while stuttering (p < 0.05). Additionally, when assessing the overall EMG signal amplitude, a higher average amplitude was observed in samples obtained from disfluencies in participants who did not stutter, particularly in the depressor anguli oris muscle (p < 0.05). Significant differences in muscle activity were observed between the two groups, particularly in the depressor anguli oris and zygomaticus major muscles. These results suggest that the underlying neuromuscular mechanisms of stuttering might involve subtle aspects of timing and coordination in muscle activation. Therefore, these findings may contribute to the field of biosensors by providing valuable perspectives on neuromuscular mechanisms and the relevance of electromyography in stuttering research. Further research in this area has the potential to advance the development of biosensor technology for language-related applications and therapeutic interventions in stuttering.
Assessing the Effect of Data Quality on Distance Estimation in Smartphone-Based Outdoor 6MWT
Authors: Caramaschi Sara , Olsson Carl Magnus , Orchard Elizabeth , Molloy Jackson , Salvi Dario
Published: April 2024

As a result of technological advancements, functional capacity assessments, such as the 6-minute walk test, can be performed remotely, at home and in the community. Current studies, however, tend to overlook the crucial aspect of data quality, often limiting their focus to idealised scenarios. Challenging conditions may arise when performing a test given the risk of collecting poor-quality GNSS signal, which can undermine the reliability of the results. This work shows the impact of applying filtering rules to avoid noisy samples in common algorithms that compute the walked distance from positioning data. Then, based on signal features, we assess the reliability of the distance estimation using logistic regression from the following two perspectives: error-based analysis, which relates to the estimated distance error, and user-based analysis, which distinguishes conventional from unconventional tests based on users’ previous annotations. We highlight the impact of features associated with walked path irregularity and direction changes to establish data quality. We evaluate features within a binary classification task and reach an F1-score of 0.93 and an area under the curve of 0.97 for the user-based classification. Identifying unreliable tests is helpful to clinicians, who receive the recorded test results accompanied by quality assessments, and to patients, who can be given the opportunity to repeat tests classified as not following the instructions.
Comparative Analysis of Anomaly Detection Approaches in Firewall Logs: Integrating Light-Weight Synthesis of Security Logs and Artificially Generated Attack Detection
Authors: Komadina Adrian , Kovačević Ivan , Štengl Bruno , Groš Stjepan
Published: April 2024

Detecting anomalies in large networks is a major challenge. Nowadays, many studies rely on machine learning techniques to solve this problem. However, much of this research depends on synthetic or limited datasets and tends to use specialized machine learning methods to achieve good detection results. This study focuses on analyzing firewall logs from a large industrial control network and presents a novel method for generating anomalies that simulate real attacker actions within the network without the need for a dedicated testbed or installed security controls. To demonstrate that the proposed method is feasible and that the constructed logs behave as one would expect real-world logs to behave, different supervised and unsupervised learning models were compared using different feature subsets, feature construction methods, scaling methods, and aggregation levels. The experimental results show that unsupervised learning methods have difficulty in detecting the injected anomalies, suggesting that they can be seamlessly integrated into existing firewall logs. Conversely, the use of supervised learning methods showed significantly better performance compared to unsupervised approaches and a better suitability for use in real systems.
Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals
Authors: Nan Jason , Herbert Matthew S. , Purpura Suzanna , Henneken Andrea N. , Ramanathan Dhakshin , Mishra Jyoti
Published: April 2024

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.
Understanding the Nonlinear Response of SiPMs
Authors: Moya-Zamanillo Víctor , Rosado Jaime
Published: April 2024

A systematic study of the nonlinear response of Silicon Photomultipliers (SiPMs) was conducted through Monte Carlo (MC) simulations. The MC code was validated against experimental data for two different SiPMs. Nonlinearity mainly depends on the balance between the photon rate and the pixel recovery time. Additionally, nonlinearity has been found to depend on the light pulse shape, the correlated noise, the overvoltage dependence of the photon detection efficiency, and the impedance of the readout circuit. Correlated noise has been shown to have a minor impact on nonlinearity, but it can significantly affect the shape of the SiPM output current. Considering these dependencies and a previous statistical analysis of the nonlinear response of SiPMs, two phenomenological fitting models were proposed for exponential-like and finite light pulses, explaining the roles of their various terms and parameters. These models provide an accurate description of the nonlinear responses of SiPMs at the level of a few percentages for a wide range of situations.
Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis
Authors: Mercorelli Paolo
Published: April 2024

Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data.
Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination
Authors: Sun Chuanming , Wu Guangning , Pan Guixiang , Zhang Tingyu , Li Jiali , Jiao Shibo , Liu Yong-Chao , Chen Kui , Liu Kai , Xin Dongli , Gao Guoqiang
Published: April 2024

Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models.
The Removal of As(III) Using a Natural Laterite Fixed-Bed Column Intercalated with Activated Carbon: Solving the Clogging Problem to Achieve Better Performance
Authors: Ouedraogo Régie Dimanche , Bakouan Corneille , Sakira Abdoul Karim , Sorgho Brahima , Guel Boubié , Somé Touridomon Issa , Hantson Anne-Lise , Ziemons Eric , Mertens Dominique , Hubert Philippe , Kauffmann Jean-Michel
Published: April 2024

Natural laterite fixed-bed columns intercalated with two types of layers (inert materials, such as fine sand and gravel, and adsorbent materials, such as activated carbon prepared from Balanites aegyptiaca (BA-AC)) were used for As(III) removal from an aqueous solution. Investigations were carried out to solve the problem of column clogging, which appears during the percolation of water through a natural laterite fixed-bed column. Experimental tests were conducted to evaluate the hydraulic conductivities of several fixed-bed column configurations and the effects of various parameters, such as the grain size, bed height, and initial As(III) concentration. The permeability data show that, among the different types of fixed-bed columns investigated, the one filled with repeating layers of laterite and activated carbon is more suitable for As(III) adsorption, in terms of performance and cost, than the others (i.e., non-intercalated laterite; non-intercalated activated carbon, repeating layers of laterite and fine sand; and repeating layers of laterite and gravel). A study was carried out to determine the most efficient column using breakthrough curves. The breakthrough increased from 15 to 85 h with an increase in the bed height from 20 to 40 cm and decreased from 247 to 32 h with an increase in the initial As(III) concentration from 0.5 to 2 mg/L. The Bohart–Adams model results show that increasing the bed height induced a decrease in the kAB and N0 values. The critical bed depths determined using the bed depth service time (BDST) model for As(III) removal were 15.23 and 7.98 cm for 1 and 20% breakthroughs, respectively. The results show that the new low-cost adsorptive porous system based on laterite layers with alternating BA-AC layers can be used for the treatment of arsenic-contaminated water.
Visionary Nature-Based Solutions Evaluated through Social Return on Investment: The Case Study of an Italian Urban Green Space
Authors: Vasiliu Elisa-Elena , Torabi Moghadam Sara , Bisello Adriano , Lombardi Patrizia
Published: April 2024

Cities are facing challenges in adaptation to, and mitigation of climate change. Urban Green Spaces (UGS) have a pivotal role in this transformative process and are almost always coupled with digital tools. The deployment of digital solutions, encompassing Information and Communication Technology (ICT) and the Internet of Things (IoT), seeks to increase awareness of UGS benefits across a wider range of users. This study is part of a Horizon 2020 project that aims to measure the social impact of Visionary Solutions (VS), i.e., combined Nature Based Solutions (NBSs) and Digital Solutions (DSs), in UGSs located in seven European cities. The project proposes a novel application of the Social Return on Investment (SROI) methodology to forecast the impact of VS implementation in the case of an Italian demonstration. The three main objectives are: (i) establishing a causal chain for transformation through the Theory of Change (ToC) tool; (ii) quantifying the expected change by developing two monetary alternatives; and (iii) comparing these alternatives to assess which is more influential in stakeholders’ decision-making. The authors reviewed a range of financial proxies of social outcomes from other SROI case studies. The result of the Italian demonstration is that, for each euro invested in project solutions, two euros of social return are generated. The analysis reveals these monetized intangible outcomes.
The Teachability of Global Citizenship to Children through Empirical Environmental Education: Reflections from a Horticultural Project in a Spanish School
Authors: Pérez-Ortega Isabel , González-Fuente Iñigo
Published: April 2024

In a context defined by the internationalisation of educational policies and the supranational nature of school programmes, we highlight the desirability of promoting local strategies for teaching environmental sustainability in order to contribute to the formation of global citizenship in children. Based on the experience of a horticultural curricular project in a school in northern Spain, the aim of this article is to reflect on the need for socio-educational communities to transform the objectives of environmental education into tools with which children can co-responsibly build connections to modify or enrich their everyday concepts of caring for the planet. To do this, semiotic analysis of different official school documents is used as a key methodology. Our findings invite consideration of the fact that pedagogies designed to train children in global citizenship competencies should not be limited to the classroom or to reproducing the proposals of institutional documents. Rather, they should be based on the prior knowledge and experiences of all members of the community, above all, of the children. From this perspective, the promotion of empirical learning situations is essential for the acquisition of meaningful and appropriate environmental contents, in the sense that they allow children, as future global citizens, to recognise the ethical repercussions of their own actions and decisions.
Calculating Load and Intensity Using Muscle Oxygen Saturation Data
Authors: Vasquez-Bonilla Aldo , Yáñez-Sepúlveda Rodrigo , Gómez-Carmona Carlos D. , Olcina Guillermo , Olivares-Arancibia Jorge , Rojas-Valverde Daniel
Published: April 2024

The study aimed to calculate training intensity and load using muscle oxygen saturation (SmO2) during two differentiated physical tasks. 29 university athletes participated in a 40-m Maximal Shuttle Run Test (MST, 10 × 40-m with 30 s recovery between sprints) and a 3000-m time trial run. Distance and time were used to calculate external load (EL). Internal load indicators were calculated based on percentage of maximum heart rate (%HRMAX) and SmO2 variables: muscle oxygen extraction (∇%SmO2) and the cardio-muscle oxygen index (CMOI) was also provided by relating ∇%SmO2 ÷ %HRMAX, and the training load were calculated as the product of speed (m/min × IL) and the efficiency index [Effindex (m/min ÷ IL)]. A student t test was applied based on Bayesian factor analysis. As expected, EL differed in the 40-m MST (331 ± 22.8) vs. 3000-m trials (222 ± 56.8) [BF10 = 6.25e+6; p = <0.001]. Likewise, IL showed higher values in 40-m MST (39.20 ± 15.44) vs. 3000-m (30.51 ± 8.67) in CMOI: [BF10 = 1.70; p = 0.039]. Training load was greater in 40-m MST (85.77 ± 27.40) vs. 3000-m (15.55 ± 6.77) [(m/min × ∇%SmO2): BF10 = 12.5; p = 0.003] and 40-m MST (129.27 ± 49.44) vs. 3000-m (70.63 ± 32.98) [(m/min × CMOI): BF10 = 169.6; p = <0.001]. Also, the Effindex was higher in 40-m MST (10.19 ± 4.17) vs. 3000-m (6.06 ± 2.21) [(m/min × ∇%SmO2): BF10 = 137.03; p = <0.001] and 40-m MST (9.69 ± 4.11) vs. 3000-m (7.55 ± 1.87) [(m/min × CMOI): BF10 = 1.86; p = 0.035]. This study demonstrates calculations of training intensity and load based on SmO2 as an internal load indicator along with speed as an external load indicator during two differentiated exercises.
Assessment of the Physical Activity of Children with Asthma Bronchiale
Authors: Balatoni Ildikó , Kiss Tímea , Balla György , Papp Ágnes , Csernoch László
Published: April 2024

Physical activity is an especially important part of everyday life for children with chronic diseases. The aim of the study was to show whether asthma is a barrier to physical activity in our society. The correlations between the severity of the disease, body mass index, and physical activity were analyzed, and parents’ opinions on whether children should participate in active sports were assessed. Physical activity of children with asthma was analyzed by questionnaires; 93 parents and their 93 children were involved in the survey. The age of children was 12.6 ± 3.5 years (mean ± SD), 69.9% were boys, 30.1% were girls. A total of 93.4% of the respondents participated in a physical education program and 56.5% also attended sporting activities on a regular basis. In terms of disease severity, 61.2% of the children had mild asthma, 37.6% moderate, and 1.2% severe, and 6.5% of the respondents also stated that their children’s illness had been consistently or frequently limiting their performance concerning their school or home duties over the past four weeks. Of the parents surveyed, 12% felt that physical activity was not appropriate in the context of this disease. We concluded that fear of the consequences of physical activity depends largely on education, which should involve parents, teachers, and coaches.
Relative Age Effect Analysis in the History of the Ballon d’Or (1956–2023)
Authors: Saavedra-García Miguel A. , Santiago-Alonso Miguel , Vila-Suárez Helena , Montero-Seoane Antonio , Fernández-Romero Juan J.
Published: April 2024

Ballon d’Or is the most important individual award in football, and is a significant measure of excellence. From our knowledge, this is the first study that explored the relative age effect (RAE) throughout the history of the Ballon d’Or. A total of 1899 football players nominated for the award from the first edition in 1956 to the most recent edition (2023) were analyzed. To assess the RAE, the birthdate distributions were categorized into four trimesters. The comparison involved correcting for the uniform distribution using chi-square analysis, with Cramer’s V serving as a measure of effect size. Standardized residuals were computed to identify quarters that exhibited significant deviation from the expected values. Odds Ratio and 95% confidence intervals were used to identify discrepancies between trimesters. The results indicated a pronounced presence of an RAE at the global level. However, the longitudinal analysis revealed variations in the behavior of the RAE over time. In the initial decades, there is an overrepresentation of players born in the last months of the year. Subsequently, there is no discernible RAE. In the most recent decades, there has been a clear resurgence of RAE, with an overrepresentation of players born in the first quarters of the year.
Factors Affecting Supply Chain Integration in Omni-Channel Retailing
Authors: Liu Yue , Song Guang
Published: April 2024

China’s retail industry has vigorously developed an omni-channel retail strategy in the last few years. However, quantitative research on this development remains relatively scarce. This article addresses this by building a theoretical model of the relationships between internal supply chain integration, external supply chain integration and financial performance in omni-channel retailing and then verifies it by using a structural equation model to analyze 356 omni-channel retailers. The results show that in the case of omni-channel retailers, the supply chain’s internal information integration has a significant positive impact on its external information integration and that the supply chain’s internal process and internal organization integration significantly positively impact its external process and external organization integration. And it also shows the supply chain’s external information integration and external organization integration positively impact omni-channel retailers’ financial performance. However, the supply chain’s external process integration is not found to have the same effect on financial performance. This study addresses a theoretical gap in the research on the impact of supply chain integration on firm performance in omni-channel retailing. In addition, it provides suggestions about how omni-channel strategies can be effectively implemented in China’s retail industry.
Research on Alternative Relationship between Chinese Renewable Energy and Imported Coal for China
Authors: Liu Pingkuo , Guo Kailing , Wu Jiahao
Published: April 2024

The issue of energy security in the new development paradigm featuring dual circulation has been paid attention to by all sectors, but at present, there are few results from relevant quantitative analyses. With a focus on China’s actual energy trade, this research examines the core elements of energy security in international and Chinese cycles. In this context, the “gravity model” and “Allen substitution elasticity” are optimized and expanded. An integrated assessment methodology is developed as a result of this effort. This methodology consists of an international-cycle trade gravity model and a Chinese-cycle price elasticity model. Additionally, it empirically analyzes the effects of China’s renewable energy substitution from the perspective of the “dual cycles” of energy security, and illustrates the current state of China’s energy security through the analysis of energy substitution relationship data. The results show that Chinese renewable energy does have a buffer effect on imported coal in terms of trade efficiency, scale, and behavior, as well as performance, and the energy trade price has a direct guiding significance for this buffer function, but the enhancement function of economy-driven efficiency is indirect. Furthermore, as far as the absolute price elasticity and net price elasticity are concerned, although Chinese wind power generation is a substitute for imported coal, its price elasticity also confirms that Chinese wind power generation is not a “normal commodity”. Moreover, at present, Chinese photovoltaic power generation shows the attribute of a “normal commodity”, but it has a certain degree of complementarity with imported coal, although this complementarity will weaken in the near future with the trend of changing to substitution.
Experimental Study on the Effect of Soil Reinforcement and Slip Resistance on Shallow Slopes by Herbaceous Plant Root System
Authors: Lv Jinguo , Wang Wenqi , Dai Te , Liu Baoyong , Liu Guangwei
Published: April 2024

In this study, Setaria viridis was selected as the research object, the soil reinforcement mode of roots was analyzed, and the general mechanical model of soil reinforcement was proposed. The direct shear tests of root–soil composite and root tensile tests were carried out, and the relationship between the root additional cohesion and root depth was studied. Furthermore, numerical simulations were established to explore the variation law of shallow displacement and slope stability as a function of the root ratio. The results show that the effect of herbaceous plant roots on the soil was composed of material modification and root binding force on the soil. The shear strength improvement of the root–soil composite was mainly reflected by the increase in cohesion. Furthermore, the composite cohesion was positively correlated with the root content but negatively correlated with the water content. With the increase in root diameter, the tensile strength of the Setaria viridis root increased linearly, but its tensile strength decreased and showed a power relation. The greater the total cohesion of the root–soil composite layer, the smaller the total displacement and the higher the slope stability. Thus, the slope’s herbaceous vegetation could effectively control the deformation and slip of the shallow soil, which has good application value for reducing soil erosion in mining areas.
Vineyard Microclimatic Zoning as a Tool to Promote Sustainable Viticulture under Climate Change
Authors: Fonseca André , Cruz José , Fraga Helder , Andrade Cristina , Valente Joana , Alves Fernando , Neto Ana Carina , Flores Rui , Santos João A.
Published: April 2024

Understanding microclimate spatial variability is crucial for sustainable and optimised grape production within vineyard plots. By employing a combination of a microclimate model (NicheMapR) and multiple climate data sources, this study aimed to achieve microclimatic analysis in two vineyard plots, Quinta do Bomfim (northern Portugal) and Herdade do Esporão (southern Portugal). This approach provides an innovative 10 m spatial resolution for climate variables. This study incorporated local station hourly data with quantile mapping bias correction on the ERA5-land data. The microclimate model output was employed to perform bias correction on a EURO-CORDEX model ensemble. Climate extreme and bioclimatic indices specifically targeted to viticulture were calculated for each vineyard plot. The 10 m scale was analysed to identify potential shifts in temperature extremes, precipitation patterns, and other crucial climatic variables for grape cultivation within each specific plot. The significance of microclimate analyses was higher in areas with intricate topography, while in areas with smooth slopes, the variation of climatic variables was determined to be negligible. There was a projected increase in the median temperature of approximately 3.5 °C and 3.6 °C and a decrease in precipitation of approximately 98 mm and 105 mm in Quinta do Bomfim and Herdade do Esporão, respectively, when comparing a future scenario for the period 2071–2100 against the historical period (1981–2010). Hence, this study offers a comprehensive and future-oriented method for analysing microclimates in vineyard plots. By incorporating geospatial data, ERA5-land data, and the microclimate NicheMapR model, this research aimed to enhance the understanding of current microclimates and future climate scenarios for viticulturists.
Spatial Interaction of Local Government Debt: Evidence from China
Authors: Han Ning , Guo Huiyan , Diao Weitao
Published: April 2024

The amount of local government debt has an important impact on the economic and social sustainability of a country. The rapid increase in local government debt in China over the past decade and the associated risks have profound implications for financial and economic sustainable development. While existing research has investigated governmental strategic interactions of tax and spending, little attention has been given to the spatial interaction of local government debt. This study employs Two-Regime Spatial Lag Models to investigate the spatial interaction of the debt among China’s 332 prefectural-level local governments over the period of 2015 to 2019. The findings show significant interaction effects between neighbouring governments, both in the acquisition and utilisation of debt quotas, and the interaction during the acquisition process is higher than that during the utilisation process. In addition, the interaction between neighbouring governments within the same province is more pronounced than that between governments adjacent but located in different provinces. Furthermore, the interaction of special debt is more manifest than that of general debt. These findings pass various robustness tests. Additionally, the mechanism test shows that fiscal competition is one of the driving forces behind the observed interdependence of local governments’ debt strategies.
Towards Advancing Translators’ Guidance for Organisations Tackling Innovation Challenges in Manufacturing within an Industry 5.0 Context
Authors: Noeske Paul-Ludwig Michael , Simperler Alexandra , Leite Cavalcanti Welchy , Beber Vinicius Carrillo , Alliott Tasmin , Schiffels Peter , Goldbeck Gerhard
Published: April 2024

Following the vision of the European Commission, organisations and workers establishing Industry 5.0 approaches aspire to more future-proof, resilient, sustainable, and human-centred European industries. In this contribution, we explore how technological innovations that contribute to a “win–win” interaction with involved stakeholders may be advanced in a human-centred and transparent proceeding supported by impartial expert translators who provide information or knowledge-based guidance for decision-makers, initiators and implementers in manufacturing innovation driven by sustainability. We elaborate a stepwise procedure for agreeing on milestones and conjointly treading the path towards solving innovation challenges during a translation process. We exemplify the technological aspects of such a process using an innovation case aiming at identifying parameters for enhancements in a vacuum-bagging process applied to the manufacturing of composite parts from prepregs based on condensation-curing matrix resins made from renewable resources. In detail, we present a straightforward design of an experimental approach varying the dwelling temperature and the temperature ramps during the curing of stacked prepregs. In this way, we demonstrate that for cured composites comprising a poly(furfuryl alcohol)-based matrix, the porosity and connected mechanical properties achieved with autoclave-free curing processes sensitively depend on these process parameters. Applying the resulting data-based model is shown to support decision-making for sustainable composite manufacture.
The Impacts of Government Subsidies on Public Transportation Customer Complaints: A Case Study of Taichung City Bus Subsidy Policy
Authors: Yen Chia-Chun , Liu Weng Shih Kun , Tien Chuen-Lin , Hwu Tian-Jong
Published: April 2024

The government has implemented an operational subsidy program with the expectation that bus companies can provide better services to passengers. However, does the change in government subsidy policies affect the service levels of bus companies? This study analyzed passenger complaint data to objectively express the results of changes in bus subsidy policies. Taking Taichung City buses as an example, there was a change in transportation policies after a change in the municipal government. Under the old policy, Taichung City buses implemented a subsidy policy based on passengers (SPP), where the first eight kilometers of a route were subsidized. Later, the SPK subsidy policy, based on kilometers (SPK), extended the subsidy coverage by bus mileage to 10 km. This study collected bus passenger complaint data before and after the change in subsidy policies, within six months. A t-Test was conducted to verify if the average customer complaint types were consistent between the two policies. The results showed significant differences in six categories of customer complaints, which were then presented in GIS. Finally, we drew two main conclusions: (1) SPP policy, bus companies focus on operating in CBD; (2) SPK policy, bus companies focus on operating in suburban areas.
Green Horizons: Navigating the Future of Agriculture through Sustainable Practices
Authors: Dönmez Dicle , Isak Musab A. , İzgü Tolga , Şimşek Özhan
Published: April 2024

Sustainable agriculture seeks to balance human nutritional needs with the preservation of environmental quality and the economic viability of agricultural systems. Principles of resource conservation, economic resilience, social equity, and competitiveness underpin this approach. Despite its potential to address crucial issues like food security, energy sustainability, and environmental stewardship, sustainable agriculture faces challenges. These include the scalability of its practices, potential economic constraints in the short term, and the need for significant shifts in policy and consumer behavior. A more explicit definition could encapsulate sustainable agriculture as integrating innovative practices that are environmentally benign, economically feasible, and socially equitable. This encompasses practices that reduce water scarcity, combat soil degradation, and address climate change impacts—crucial steps toward a robust and future-proof agricultural system. By acknowledging these challenges and specifying issues like water scarcity, soil health, and climate change, this abstract presents a balanced view of sustainable agriculture’s role in a sustainable future, highlighting its necessity and the urgent need for integrated efforts to overcome its implementation barriers.
Best Proximity Point Results for Multi-Valued Mappings in Generalized Metric Structure
Authors: Khan Asad Ullah , Samreen Maria , Hussain Aftab , Sulami Hamed Al
Published: April 2024

In this paper, we introduce the novel concept of generalized distance denoted as Jθ and call it an extended b-generalized pseudo-distance. With the help of this generalized distance, we define a generalized point to set distance Jθ(u,H★), a generalized Hausdorff type distance and a PJθ-property of a pair (H★,K★) of nonempty subsets of extended b-metric space (U★,ρθ). Additionally, we establish several best proximity point theorems for multi-valued contraction mappings of Nadler type defined on b-metric spaces and extended b-metric spaces. Our findings generalize numerous existing results found in the literature. To substantiate the introduced notion and validate our main results, we provide some concrete examples.
Multi-Dimensional Data Analysis Platform (MuDAP): A Cognitive Science Data Toolbox
Authors: Li Xinlin , Wang Yiming , Bi Xiaoyu , Xu Yalu , Ying Haojiang , Chen Yiyang
Published: April 2024

Researchers in cognitive science have long been interested in modeling human perception using statistical methods. This requires maneuvers because these multiple dimensional data are always intertwined with complex inner structures. The previous studies in cognitive sciences commonly applied principal component analysis (PCA) to truncate data dimensions when dealing with data with multiple dimensions. This is not necessarily because of its merit in terms of mathematical algorithm, but partly because it is easy to conduct with commonly accessible statistical software. On the other hand, dimension reduction might not be the best analysis when modeling data with no more than 20 dimensions. Using state-of-the-art techniques, researchers in various research disciplines (e.g., computer vision) classified data with more than hundreds of dimensions with neural networks and revealed the inner structure of the data. Therefore, it might be more sophisticated to process human perception data directly with neural networks. In this paper, we introduce the multi-dimensional data analysis platform (MuDAP), a powerful toolbox for data analysis in cognitive science. It utilizes artificial intelligence as well as network analysis, an analysis method that takes advantage of data symmetry. With the graphic user interface, a researcher, with or without previous experience, could analyze multiple dimensional data with great ease.
The Three Faces of U(3)
Authors: LaChapelle John
Published: April 2024

U(n) is a semi-direct product group characterized by nontrivial homomorphisms mapping U(1) into the automorphism group of SU(n). For U(3), there are three nontrivial homomorphisms that induce three separate defining representations. In a toy model of U(3) Yang–Mills (endowed with a suitable inner product) coupled to massive fermions, this renders three distinct covariant derivatives acting on a single matter field. Employing a mod3 permutation induced by a large gauge transformation acting on the defining representation vector space, the three covariant derivatives and one matter field can alternatively be expressed as a single covariant derivative acting on three distinct species of matter fields possessing the same U(3) quantum numbers. One can interpret this as three species of matter fields in the defining representation.
Fixed Point Dynamics in a New Type of Contraction in b-Metric Spaces
Authors: Navascués María A. , Mohapatra Ram N.
Published: April 2024

Since the topological properties of a b-metric space (which generalizes the concept of a metric space) seem sometimes counterintuitive due to the fact that the “open” balls may not be open sets, we review some aspects of these spaces concerning compactness, metrizability, continuity and fixed points. After doing so, we introduce new types of contractivities that extend the concept of Banach contraction. We study some of their properties, giving sufficient conditions for the existence of fixed points and common fixed points. Afterwards, we consider some iterative schemes in quasi-normed spaces for the approximation of these critical points, analyzing their convergence and stability. We apply these concepts to the resolution of a model of integral equation of Urysohn type. In the last part of the paper, we refine some results about partial contractivities in the case where the underlying set is a strong b-metric space, and we establish some relations between mutual weak contractions and quasi-contractions and the new type of contractivity.
Enhancing Logistics Performance through Increased Trust and Collaboration in Supply Chain Risk Management: A Focus on the Distribution Network of Manufacturing Companies
Authors: Kim Soohyo , Kim Choyeon
Published: April 2024

This study aims to explore the significance of trust among companies within the supply chain and investigate its effect on collaborative supply chain risk management. In the current uncertain business environment, it is crucial for companies to establish trust relationships with their trading partners and collaboratively manage risks. This research seeks to understand how such trust relationships influence collaborative risk management and, subsequently, the effect of risk management collaboration on logistics performance. This study surveyed manufacturing companies in the Korean supply chain, and data were analyzed using SPSS 23.0 and AMOS 23.0. The results of this study imply that cognitive trust, related to the partner’s expertise and capabilities, is more crucial than emotion-based affective trust in fostering collaborative supply chain risk management among companies within the supply chain, especially in the current unpredictable and complex business environments. Given the supply chain disruptions caused by uncertainties in the business environment, it is imperative to effectively manage risks and restructure the disrupted supply chain. Therefore, companies need to strive to demonstrate their expertise and capabilities to build cognitive trust and enhance logistics performance through supply chain risk management collaboration.
Transitioning to Agile Organizational Structures: A Contingency Theory Approach in the Financial Sector
Authors: Pacheco-Cubillos Darley Biviana , Boria-Reverter Josefa , Gil-Lafuente Jaime
Published: April 2024

The primary objective of this investigation is to explore the key factors within a large corporation that facilitate the transition from a conventional hierarchical organizational structure to a more agile one. Although there are studies in the literature on the different agile organizational structures, there are no clear guidelines for a large company to transform to an agile framework. This study employs contingency theory as its framework and specifically focuses on the financial sector to identify both opportunities and challenges encountered during this transformation process. Qualitative research methods were employed, involving an analysis of five case studies in which managers served as respondents. This research contributes to the identification of a model as a guideline for the transformation of the organizational structure towards agility that can provide valuable information for companies undergoing such a transformation. The managers surveyed provided their experience and the competencies of their profiles were validated, in addition to the application of the Delphi method to obtain more objective information. The findings underscore that large companies should refrain from adopting a completely flat hierarchical structure. Moreover, there exists a set of strategic-level elements that serve as a guiding framework for the transformation process. Furthermore, it is essential to acknowledge that the transformation process itself does not follow a one-size-fits-all approach; rather, it is contingent upon the unique context of each individual case, and it is also a cultural challenge.
An End-to-End Lightweight Multi-Scale CNN for the Classification of Lung and Colon Cancer with XAI Integration
Authors: Hasan Mohammad Asif , Haque Fariha , Sabuj Saifur Rahman , Sarker Hasan , Goni Md. Omaer Faruq , Rahman Fahmida , Rashid Md Mamunur
Published: April 2024

To effectively treat lung and colon cancer and save lives, early and accurate identification is essential. Conventional diagnosis takes a long time and requires the manual expertise of radiologists. The rising number of new cancer cases makes it challenging to process massive volumes of data quickly. Different machine learning approaches to the classification and detection of lung and colon cancer have been proposed by multiple research studies. However, when it comes to self-learning classification and detection tasks, deep learning (DL) excels. This paper suggests a novel DL convolutional neural network (CNN) model for detecting lung and colon cancer. The proposed model is lightweight and multi-scale since it uses only 1.1 million parameters, making it appropriate for real-time applications as it provides an end-to-end solution. By incorporating features extracted at multiple scales, the model can effectively capture both local and global patterns within the input data. The explainability tools such as gradient-weighted class activation mapping and Shapley additive explanation can identify potential problems by highlighting the specific input data areas that have an impact on the model’s choice. The experimental findings demonstrate that for lung and colon cancer detection, the proposed model was outperformed by the competition and accuracy rates of 99.20% have been achieved for multi-class (containing five classes) predictions.
First Synthesis of Ergotamine-13CD3 and Ergotaminine-13CD3 from Unlabeled Ergotamine
Authors: Herter Sven-Oliver , Haase Hajo , Koch Matthias
Published: April 2024

Ergot alkaloids (EAs) formed by Claviceps fungi are one of the most common food contaminants worldwide, affecting cereals such as rye, wheat, and barley. To accurately determine the level of contamination and to monitor EAs maximum levels set by the European Union, the six most common EAs (so-called priority EAs) and their corresponding epimers are quantified using high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS). The quantification of EAs in complex food matrices without appropriate internal standards is challenging but currently carried out in the standard method EN 17425:2021 due to their commercial unavailability. To address the need for isotopically labeled EAs, we focus on two semi-synthetic approaches for the synthesis of these reference standards. Therefore, we investigate the feasibility of the N6-demethylation of native ergotamine to yield norergotamine, which can subsequently be remethylated with an isotopically labeled methylating reagent, such as iodomethane (13CD3-I), to yield isotopically labeled ergotamine and its C8-epimer ergotaminine. Testing the isotopically labeled ergotamine/-inine against native ergotamine/-inine with HPLC coupled to high-resolution HR-MS/MS proved the structure of ergotamine-13CD3 and ergotaminine-13CD3. Thus, for the first time, we can describe their synthesis from unlabeled, native ergotamine. Furthermore, this approach is promising as a universal way to synthesize other isotopically labeled EAs.