bluuebunny/crossref_metadata_2025 huggingface dataset:
Broadband Optical Frequency Comb Generation Utilizing a Gain-Switched Weak-Resonant-Cavity Fabry–Perot Laser Diode under Multi-Wavelength Optical Injection
Authors: Tao Yuhong , Wang Qiupin , Ou Pu , Xia Guangqiong , Wu Zhengmao
Published: September 2024

We propose and experimentally demonstrate an approach for generating a wideband optical frequency comb (OFC) featuring multiple comb lines and wavelength tunability based on a gain-switched weak-resonant-cavity Fabry–Perot laser diode (WRC-FPLD) under multi-wavelength optical injection. The longitudinal mode interval of the utilized WRC-FPLD is about 0.28 nm (35.0 GHz), and its relaxation oscillation frequency is about 2.0 GHz at 1.15 times the threshold current. Under current modulation with a power of 20.00 dBm and a frequency of 2.0 GHz, the WRC-FPLD is driven into the gain-switched state. By further introducing multi-wavelength injection light (MWIL) containing four power equalization comb lines with an interval of 0.56 nm, a wideband OFC featuring multiple comb lines and wavelength tenability can be obtained. The experimental results demonstrate that by gradually increasing the injection’s optical power, the number of produced OFC lines initially increases and then decreases. By meticulously adjusting the wavelengths of the MWIL and carefully selecting the matched injection power, the broadband OFC can be tuned across an extensive spectral range. Under optimized operation parameters, an OFC with 147 lines, and a bandwidth of approximately 292 GHz within a 10 dB amplitude, variation is achieved. In this case, the measured single-sideband phase noise at the fundamental frequency is about −115 dBc/Hz @ 10 kHz, indicating that the comb lines possess good stability and strong coherence.
Prediction of Dielectric Constant in Series of Polymers by Quantitative Structure-Property Relationship (QSPR)
Authors: Ascencio-Medina Estefania , He Shan , Daghighi Amirreza , Iduoku Kweeni , Casanola-Martin Gerardo M. , Arrasate Sonia , González-Díaz Humberto , Rasulev Bakhtiyor
Published: September 2024

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material’s capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability.
Bacterial Cellulose Production within a Circular Economy Framework: Utilizing Organic Waste
Authors: Moreno-Díaz Cristina , González-Arranz Salvador , Martínez-Cerezo Carmen
Published: September 2024

Bacterial cellulose (BC) has emerged as a sustainable biomaterial with diverse industrial applications. This paper examines BC production through a circular economy framework, focusing on organic waste as a primary feedstock. It compares static and agitated cultivation methods for BC production, highlighting their advantages and limitations. Static cultivation using Gluconacetobacter xylinum yields high-quality cellulose films but is constrained by lower yields and longer incubation times. Agitated cultivation accelerates production but may affect fiber uniformity. This paper emphasizes sustainability by exploring organic waste materials such as coffee grounds, tea leaves, and food scraps as cost-effective nitrogen and carbon sources. These materials not only lower production costs but also support circular economy principles by converting waste into valuable products. BC produced from these waste sources retains key properties, making it suitable for applications in the textile and other industries. In addition, BC production can align with vegan principles, provided that all additives and processing methods are free of animal-derived components. The paper discusses BC’s potential to replace synthetic fibers in textiles and reduce environmental impact. Case studies show successful BC integration into textile products. In conclusion, this paper calls for more research to optimize BC production processes and explore new industrial applications. Using organic waste in BC production can help industries adopt sustainable practices, reduce environmental footprints, and create high-value materials.
Thermomechanical and Structural Analysis of Manufactured Composite Based on Polyamide and Aluminum Recycled Material
Authors: Gnatowski Adam , Gołębski Rafał , Stachowiak Krystian , Petrů Jana , Měsíček Jakub
Published: September 2024

The paper presents an analysis of the filler’s effect on the machining process and on changes in the thermomechanical properties of polymer composites based on aluminum chips. Composite research samples with a polymer matrix in the form of polyamide 6 were made by the pressing method. Comparative studies were carried out on the changes in thermomechanical properties and structure of the obtained molders with different filler contents and different fractions after the machining process. In order to determine the changes in thermal and mechanical properties, analysis was carried out using the differential scanning calorimetry (DSC) method, thermal analysis of dynamic mechanical properties (DMTA) and a detailed stereometric analysis of the surface. After mechanical processing, roughness amplitude parameters and volumetric functional parameters were determined. In order to analyze the structure, tomographic examinations of the manufactured composite were conducted. In relation to the polymer matrix, a significant increase in the storage modulus of the composites was noted in the entire temperature range of the study. An increase in the enthalpy of melting of the matrix was noted in composites with a lower filler content and a shift in the melting range of the crystalline phase. Significant differences were noted in the study of the composite surfaces in the case of using fillers obtained after machining with different fractions. The dependencies of the functional and amplitude parameters of the surfaces after machining of composite samples prove the change in the functional properties of the surface. The use of aluminum chips in the composite significantly changed the surface geometry.
AI: Anarchic Intelligence: On Epinoia
Authors: Marder Michael
Published: September 2024

With a few notable exceptions, the word “epinoia” has not been heard with a philosophical ear since the time of Epicurus and the Stoics. In addition to the scarce mentions it had received in philosophy, epinoia was strewn across the plays of Euripides and Aristophanes and, more so, across the canonical body of Christian theology, from Patristics—Origen, Cyril of Alexandria, Basil of Caesarea, Gregory of Nyssa, Maximus the Confessor—to the late Byzantine period. Straddling the divide between the authorities of the nascent Church and those they suspected of heresy, it made a spectacular appearance in Gnostic texts (The Apocryphon of John), cryptically embodying the reconciliation of knowledge and life. On the margins of the Christian tradition, first-century CE controversial religious figures such as Simon Magus associated epinoia with the great goddess and the womb of existence, even as, three centuries later, Eunomius of Cyzicus—the theological arch-enemy of the Cappadocian Fathers, Basil and Gregory—deplored it for its hollowness and pure conventionality. In this paper, I argue that epinoia is the figure of anarchic intelligence in theology and philosophy alike. The anarchy of epinoia is its note of defiance: the escape from power it plots is the most serious challenge to power, the royal road to liberation from the oppressive unity of Being, Mind, or Concept.
Infrared Weak Target Detection in Dual Images and Dual Areas
Authors: Zhuang Junbin , Chen Wenying , Guo Baolong , Yan Yunyi
Published: September 2024

This study proposes a novel approach for detecting weak small infrared (IR) targets, called double-image and double-local contrast measurement (DDLCM), designed to overcome challenges of low contrast and complex backgrounds in images. In this approach, the original image is decomposed into odd and even images, and the gray difference contrast is determined using a dual-neighborhood sliding window structure, enhancing target saliency and contrast by increasing the distinction between the target and the local background. A central unit is then constructed to capture relationships between neighboring and non-neighboring units, aiding in clutter suppression and eliminating bright non-target interference. Lastly, the output value is derived by extracting the lowest contrast value of the weak small targets from the saliency map in each direction. Experimental results on two datasets demonstrate that the DDLCM algorithm significantly enhances real-time IR dim target detection, achieving an average performance improvement of 32.83%. The area under the ROC curve (AUC) decline is effectively controlled, with a maximum reduction limited to 3%. Certain algorithms demonstrate a notable AUC improvement of up to 43.96%. To advance infrared dim target detection research, we introduce the IFWS dataset for benchmarking and validating algorithm performance.
Opposing Impacts of Greenspace Fragmentation on Land Surface Temperature in Urban and Surrounding Rural Areas: A Case Study in Changsha, China
Authors: Wang Weiye , Li Xiaoma , Li Chuchu , Gan Dexin
Published: September 2024

Managing the amount of greenspace (i.e., increasing or decreasing greenspace coverage) and optimizing greenspace configuration (i.e., increasing or decreasing greenspace fragmentation) are cost-effective approaches to cooling the environment. The spatial variations in their impacts on the thermal environment, as well as their relative importance, are of great importance for greenspace planning and management but are far from thoroughly understood. Taking Changsha, China as an example, this study investigated the spatial variations of the impacts of greenspace amount (measured as a percent of greenspace) and greenspace fragmentation (measured by edge density of greenspace) on the Landsat-derived land surface temperature (LST) using geographically weighted regression (GWR), and also uncovered the spatial pattern of their relative importance. The results indicated that: (1) Greenspace amount showed significantly negative relationships with LST for 91.73% of the study area. (2) Both significantly positive and negative relationships were obtained between greenspace fragmentation and LST, covering 14.90% and 13.99% of the study area, respectively. (3) The negative relationship between greenspace fragmentation and LST is mainly located in the urban areas, while the positive relationship appeared in the rural areas. (4) Greenspace amount made a larger contribution to regulating LST than greenspace fragmentation in 93.31% of the study area, but the latter had stronger roles in about 6.69% of the study area, mainly in the city center. These findings suggest that spatially varied greenspace planning and management strategies should be adopted to improve the thermal environment.
High-Resolution Dipole Remote Detection Logging Based on Optimal Nonlinear Frequency Modulation Excitation
Authors: Cao Xueshen , Chen Hao , Li Chao , Zhou Yinqiu , Chen Dehua , Zhao Jiaheng
Published: September 2024

In order to improve the detection range and imaging resolution of dipole remote detection logging, an optimal nonlinear frequency modulation (ONLFM) excitation method is proposed in this paper. In this method, the optimal waveform model of the sound source is designed with objective functions of SNR and resolution to obtain the highest resolution under the condition of the required SNR. This optimal model is a multi-constraint optimization problem, and the simulated annealing method has been used to solve it. Solving the optimal model can obtain the optimal spectrum, and the ONLFM waveform can be designed by using the stationary phase principle. Compared with the electric pulse sound source used in traditional technology, this ONLFM sound source can improve the energy and extend the frequency band range of the reflected wave, which can provide the higher resolution and SNR. The simulation results show that the ONLFM sound source can effectively improve the SNR and resolution of the reflected wave, and the detection range and imaging resolution of dipole shear wave remote detection will be improved.
Blind Deblurring Method for CASEarth Multispectral Images Based on Inter-Band Gradient Similarity Prior
Authors: Zhu Mengying , Liu Jiayin , Wang Feng
Published: September 2024

Multispectral remote sensing images contain abundant information about the distribution and reflectance of ground objects, playing a crucial role in target detection, environmental monitoring, and resource exploration. However, due to the complexity of the imaging process in multispectral remote sensing, image blur is inevitable, and the blur kernel is typically unknown. In recent years, many researchers have focused on blind image deblurring, but most of these methods are based on single-band images. When applied to CASEarth satellite multispectral images, the spectral correlation is unutilized. To address this limitation, this paper proposes a novel approach that leverages the characteristics of multispectral data more effectively. We introduce an inter-band gradient similarity prior and incorporate it into the patch-wise minimal pixel (PMP)-based deblurring model. This approach aims to utilize the spectral correlation across bands to improve deblurring performance. A solution algorithm is established by combining the half-quadratic splitting method with alternating minimization. Subjectively, the final experiments on CASEarth multispectral images demonstrate that the proposed method offers good visual effects while enhancing edge sharpness. Objectively, our method leads to an average improvement in point sharpness by a factor of 1.6, an increase in edge strength level by a factor of 1.17, and an enhancement in RMS contrast by a factor of 1.11.
A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio
Authors: Florez Ruben , Palomino-Quispe Facundo , Alvarez Ana Beatriz , Coaquira-Castillo Roger Jesus , Herrera-Levano Julio Cesar
Published: September 2024

Currently, the number of vehicles in circulation continues to increase steadily, leading to a parallel increase in vehicular accidents. Among the many causes of these accidents, human factors such as driver drowsiness play a fundamental role. In this context, one solution to address the challenge of drowsiness detection is to anticipate drowsiness by alerting drivers in a timely and effective manner. Thus, this paper presents a Convolutional Neural Network (CNN)-based approach for drowsiness detection by analyzing the eye region and Mouth Aspect Ratio (MAR) for yawning detection. As part of this approach, endpoint delineation is optimized for extraction of the region of interest (ROI) around the eyes. An NVIDIA Jetson Nano-based device and near-infrared (NIR) camera are used for real-time applications. A Driver Drowsiness Artificial Intelligence (DD-AI) architecture is proposed for the eye state detection procedure. In a performance analysis, the results of the proposed approach were compared with architectures based on InceptionV3, VGG16, and ResNet50V2. Night-Time Yawning–Microsleep–Eyeblink–Driver Distraction (NITYMED) was used for training, validation, and testing of the architectures. The proposed DD-AI network achieved an accuracy of 99.88% with the NITYMED test data, proving superior to the other networks. In the hardware implementation, tests were conducted in a real environment, resulting in 96.55% and 14 fps on average for the DD-AI network, thereby confirming its superior performance.
Collaborative Joint Perception and Prediction for Autonomous Driving
Authors: Ren Shunli , Chen Siheng , Zhang Wenjun
Published: September 2024

Collaboration among road agents, such as connected autonomous vehicles and roadside units, enhances driving performance by enabling the exchange of valuable information. However, existing collaboration methods predominantly focus on perception tasks and rely on single-frame static information sharing, which limits the effective exchange of temporal data and hinders broader applications of collaboration. To address this challenge, we propose CoPnP, a novel collaborative joint perception and prediction system, whose core innovation is to realize multi-frame spatial–temporal information sharing. To achieve effective and communication-efficient information sharing, two novel designs are proposed: (1) a task-oriented spatial–temporal information-refinement model, which filters redundant and noisy multi-frame features into concise representations; (2) a spatial–temporal importance-aware feature-fusion model, which comprehensively fuses features from various agents. The proposed CoPnP expands the benefits of collaboration among road agents to the joint perception and prediction task. The experimental results demonstrate that CoPnP outperforms existing state-of-the-art collaboration methods, achieving a significant performance-communication trade-off and yielding up to 11.51%/10.34% Intersection over union and 12.31%/10.96% video panoptic quality gains over single-agent PnP on the OPV2V/V2XSet datasets.
A UAV Thermal Imaging Format Conversion System and Its Application in Mosaic Surface Microthermal Environment Analysis
Authors: Jiang Lu , Zhao Haitao , Cao Biao , He Wei , Yun Zengxin , Cheng Chen
Published: September 2024

UAV thermal infrared remote sensing technology, with its high flexibility and high temporal and spatial resolution, is crucial for understanding surface microthermal environments. Despite DJI Drones’ industry-leading position, the JPG format of their thermal images limits direct image stitching and further analysis, hindering their broad application. To address this, a format conversion system, ThermoSwitcher, was developed for DJI thermal JPG images, and this system was applied to surface microthermal environment analysis, taking two regions with various local zones in Nanjing as the research area. The results showed that ThermoSwitcher can quickly and losslessly convert thermal JPG images to the Geotiff format, which is further convenient for producing image mosaics and for local temperature extraction. The results also indicated significant heterogeneity in the study area’s temperature distribution, with high temperatures concentrated on sunlit artificial surfaces, and low temperatures corresponding to building shadows, dense vegetation, and water areas. The temperature distribution and change rates in different local zones were significantly influenced by surface cover type, material thermal properties, vegetation coverage, and building layout. Higher temperature change rates were observed in high-rise building and subway station areas, while lower rates were noted in water and vegetation-covered areas. Additionally, comparing the temperature distribution before and after image stitching revealed that the stitching process affected the temperature uniformity to some extent. The described format conversion system significantly enhances preprocessing efficiency, promoting advancements in drone remote sensing and refined surface microthermal environment research.
Proteomic Comparison between Periodontal Pocket Tissue and Other Oral Samples in Severe Periodontitis: The Meeting of Prospective Biomarkers
Authors: Bellei Elisa , Monari Emanuela , Bertoldi Carlo , Bergamini Stefania
Published: September 2024

Periodontitis is characterized by gingival regression, alveolar bone resorption and the development of deep periodontal pockets that, if left untreated, can lead to tooth loss. Currently, specific biomarkers are needed for the early, objective diagnosis, monitoring, and management of periodontal patients. In this proteomic study, periodontal pocket tissues from patients with severe periodontitis were analyzed in comparison to periodontally healthy sites with the aim of discovering distinctive protein targets. Gingival tissues were fragmented using a motorized mechanical method and mixture protein was separated via mono-dimensional gel electrophoresis. The examination of protein bands using definite 1D image analysis software allowed for the detection of 22 differentially expressed proteins between pathological and healthy samples that were identified through mass spectrometry. A comparative assessment of these proteins with those previously reported in other studies conducted on periodontal diseases in various types of oral specimens, such as gingival crevicular fluid, dentin, tooth pulp, root canal content, salivary gland secretions, saliva, periodontal ligament cells, and dental stem cells, highlighted a great number of significant common matches. The discovery of a selective cluster of periodontitis-related biomarkers could become particularly important before the clinical manifestation of the disease to promptly stop its progression for a timely preventive diagnosis.
The Impact of Corporate Governance on Sustainability Disclosures: A Comparison from the Perspective of Financial and Non-Financial Firms
Authors: Erben Yavuz Asuman , Kocaman Bade Ekim , Doğan Mesut , Hazar Adalet , Babuşcu Şenol , Sutbayeva Raikhan
Published: September 2024

This study explores the impact of corporate governance on firms’ environmental, social, and governance (ESG) performance, with a focus on board characteristics and ownership structures. Using a panel dataset of 6 financial and 16 non-financial firms listed on the Borsa Istanbul (BIST) from 2013 to 2021, the study investigates how ownership (blockholder, foreign, or institutional) and board composition (size, gender diversity, and foreign directors) influence ESG disclosures. The analysis distinguishes between financial and non-financial firms, revealing that corporate governance mechanisms affect ESG performance differently across sectors. Foreign ownership and the presence of foreign and female board members are positively associated with higher ESG disclosures, while ownership concentration is negatively correlated with ESG performance. These findings suggest caution when comparing firms across sectors based solely on ESG disclosures, as governance factors influence outcomes differently in financial and non-financial contexts. This study provides a detailed analysis of effective corporate governance mechanisms in Türkiye, emphasizing the crucial roles of ownership structure and board composition in enhancing ESG transparency. The results offer valuable insights for regulators and investors, contributing to a nuanced understanding of how governance structures shape ESG performance in both financial and non-financial firms in Türkiye.
Research on Vehicle-Driving-Trajectory Prediction Methods by Considering Driving Intention and Driving Style
Authors: Shao Liming , Ling Meining , Yan Ying , Xiao Guangnian , Luo Shiqi , Luo Qiang
Published: September 2024

With the rapid advancement of autonomous driving technology, the accurate prediction of vehicle trajectories has become a research hotspot. In order to accurately predict vehicles’ trajectory, this study comprehensively explores the impact of driving style and intention on trajectory prediction, proposing a novel prediction method. Firstly, the dataset AD4CHE was selected as the research data, from which the required trajectory data of vehicles were extracted, including 1202 lane-changing and 1137 car-following driving trajectories. Secondly, a long short-term memory (LSTM) network based on the Keras framework was constructed by using the TensorFlow deep-learning platform. The LSTM network integrates driving intention, driving style, and historical trajectory data as inputs to establish a vehicle-trajectory prediction model. Finally, the mean absolute error (MAE) and root-mean-square error (RMSE) were selected as the evaluation indicators for the models, and the prediction results of the models were compared under two conditions: not considering driving style and considering driving style. The results demonstrate that models incorporating driving style significantly outperformed those that did not, highlighting the critical influence of driving style on vehicle trajectories. Moreover, compared to traditional kinematic models, the LSTM-based approach exhibits notable advantages in long-term trajectory prediction. The prediction method that accounts for both driving intention and style effectively reduces RMSE, significantly enhancing prediction accuracy. The findings of this research provide valuable insights for vehicle-driving risk assessment and contribute positively to the advancement of autonomous driving technology and the sustainable development of road traffic.
Collectively Remembering Environmental Disasters: The Vaia Storm as a Case Study
Authors: Menegatto Marialuisa , Freschi Gloria , Bulfon Margherita , Zamperini Adriano
Published: September 2024

This study investigated the relatively unexplored topic of the collective memory of environmental disasters from a psychosocial perspective. To achieve this, we conducted an exploratory case study on the Vaia storm, which hit the Italian Alps in October 2018, causing significant social and ecological damage. We carried out thirteen in-depth semi-structured interviews with members of the enunciatory community of Vaia as follows: groups of people who either experienced the disaster firsthand, studied it, or had a particular interest in it. Through a thematic analysis of the interview transcripts, the four following key themes emerged that illustrate the forms taken by the collective memory of the disaster: (1) a sensory dimension; (2) an emotional dimension; (3) a narrative dimension; and (4) a material dimension. Our findings suggest a non-anthropocentric, hybrid understanding of collective memory, aligning with emerging concepts in the psychosocial literature on the emotional impacts of environmental disasters. We also advance potential future research questions at the intersection of memory studies, resilience, and sustainability and stress the practical implications of collective memory in fostering sustainable practices and enhancing climate resilience.
Understanding Resilience and Sustainability in Organizations: A Cutting-Edge Framework and the Research Agenda
Authors: Kantabutra Sooksan , Ketprapakorn Nuttasorn
Published: September 2024

Given the conceptual confusion between resilience and sustainability in organizations in the literature, the present study examined the literature on the relationship between the two by reviewing the past 27 years of global literature on resilience and sustainability in organizations, to clarify the relationship between the two. It adapted the Integrated Systematic Literature Review technique (ISLR) to identify the most influential scholars in the knowledge domain of resilience and sustainability in organizations, uncover the intellectual structure that defines the knowledge base on resilience and sustainability in organizations, identify the most influential documents in the knowledge domain, derive the most interesting topics in the knowledge domain, and derive the cutting-edge body of knowledge on resilience and sustainability in organizations in response to the dynamic nature of resilience and sustainability problems. The findings reveal that Ivanov, Bansal, and Sarkis are the most influential scholars in the knowledge domain, and the most influential document was authored by Ortiz-de-Mandojana and Bansal. This study uncovers five schools of thought: sustainable business strategy, predictive analytics, sustainable supply chain, ecological resilience, and sustainable leadership. Informed by the analyses, a cutting-edge framework, comprising sustainability practices, sustainability outputs, and sustainability and resilience outcomes, was derived to guide future research and inform practitioners on how to ensure resilience and sustainability in their organizations.
Does Intelligent Manufacturing Contribute to the Enhancement of Carbon Emission Performance? Evidence from Total Factor Carbon Emission Performance
Authors: Jin Weibo , Zhang Yuqi , Xu Yao , Zhang Yi , Kim Yanggi , Yan Yi
Published: September 2024

The deep integration of intelligent technology and the manufacturing industry is a crucial driving force for promoting green and low-carbon development, which is a key strategy for achieving sustainable development. Using panel data from 30 provinces in mainland China from 2010 to 2022, this study measures the level of intelligent development and the total factor carbon emission performance (TFCEP). Additionally, a mediating effect model is constructed to explore the impact of intelligent manufacturing (IM) on carbon emission performance (CEP) and its underlying mechanisms. The findings reveal that (1) the intellectualization of the manufacturing industry significantly enhances CEP, a conclusion that remains robust under various tests; (2) the impact of IM on CEP varies by regional geographical locations, the degree of economic agglomeration (EA), and whether the province is a low-carbon pilot area; and (3) the mechanism analysis indicates that IM improves CEP by promoting EA. Given that China is the world’s largest manufacturing country and the largest carbon emitter, analyzing the impact of its IM on CEP provides valuable theoretical insights and practical experiences for China and other manufacturing countries aiming to achieve a win–win situation of sustainable economic development and environmental improvement.
On Symmetrical Equivelar Polyhedra of Type {3, 7} and Embeddings of Regular Maps
Authors: Bokowski Jürgen
Published: September 2024

A regular map is an abstract generalization of a Platonic solid. It describes a group, a topological cell decomposition of a 2-manifold of type {p, q} with only p-gons, such that q of them meet at each vertex in a circular manner, and we have maximal combinatorial symmetry, expressed by the flag transitivity of the symmetry group. On the one hand, we have articles on topological surface embeddings of regular maps by F. Razafindrazaka and K. Polthier, C. Séquin, and J. J. van Wijk.On the other hand, we have articles with polyhedral embeddings of regular maps by J. Bokowski and M. Cuntz, A. Boole Stott, U. Brehm, H. S. M. Coxeter, B. Grünbaum, E. Schulte, and J. M. Wills. The main concern of this partial survey article is to emphasize that all these articles should be seen as contributing to the common body of knowledge in the area of regular map embeddings. This article additionally provides a method for finding symmetrical equivelar polyhedral embeddings of type {3, 7} based on symmetrical graph embeddings on convex surfaces.
Numerical Investigation of Symmetrical and Asymmetrical Characteristics of a Preloading Spiral Case and Concrete during Load Rejection
Authors: Zhang Zhenwei , Luo Yutong , Yang Guisheng , Zhang Shaozheng , Wang Zhengwei
Published: September 2024

During the transient process of load rejection, the hydraulic pressure applied to the pump-turbine and plant concrete changes dramatically and induces high dynamic stress on the spiral case. The preloading spiral case has been widely used in large-scale pumped-storage power stations due to its excellent load-bearing capacity. However, studies on the impact of preloading pressure on the structural response during load rejection are still few in number. In this paper, 3D flow domain and structural models of a prototype pump-turbine are designed to analyze the hydraulic characteristics and flow-induced dynamic behavior of the preloading steel spiral case under different preloading pressures during load rejection. The results show that the asymmetric design of the logarithmic spiral lines ensures an axially symmetric potential flow within the spiral case domain with uniform pressure distribution. Higher preloading pressure provides larger preloading clearance, leading to greater flow-induced deformation and stress, with their maximum values located at the mandoor and the inner edge, respectively. The combined effect of the asymmetrical shape, internal hydraulic pressure and unbalanced hydraulic force leads to an asymmetrical preloading clearance distribution, resulting in an asymmetrical distribution along the axial direction but a symmetrical characteristic near the waistline of the structural response. Stress variations at sections and between sections share similar characteristics during load rejection. It follows the same trend as the hydraulic pressure under lower preloading pressures, while there is a delayed peak of stress due to the delayed contact phenomenon when the preloading pressure reaches the maximum static head. The conclusions provide scientific guidance for optimizing the preloading pressure selection and structural design for the stable operation of units.
Enhancing Spreading Factor Assignment in LoRaWAN with a Geometric Distribution Approach for Practical Node Distributions
Authors: Tempiem Phanupong , Silapunt Rardchawadee
Published: September 2024

This paper proposes the GD (Geometric Distribution) algorithm, a novel approach to enhance the default Adaptive Data Rate (ADR) mechanism in the Long-Range Wide Area Network (LoRaWAN). By leveraging the Probability Mass Function (PMF) of the GD model, the algorithm effectively addresses biased node distributions encountered in real-world scenarios. Its ability to finely adjust the weight factor (w) or the probability of success in allocating SFs enables the optimization of spreading factor (SF) allocation, thereby achieving the optimal Data Extraction Rate (DER). To evaluate the algorithm’s performance, simulations were conducted using the fixed node pattern derived from actual dairy farm locations in Ratchaburi province, Thailand. Comparative analyses were performed against the uniform random node pattern and existing algorithms, including the ADR, EXPLoRa, QCVM, and SD. The GD algorithm significantly outperformed existing methodologies for both fixed and uniform random node patterns, achieving a 14.3% and 4.8% improvement in DER over the ADR, respectively. While the GD algorithm consistently demonstrated superior DER values across varying coverage areas and payload sizes, it incurred a slight increase in energy consumption due to node allocations to higher SFs. Therefore, the trade-off between DER and energy consumption must be carefully weighed against the specific application.
Comparison of Traumatic Brain Injury in Adult Patients with and without Facial Fractures
Authors: Lupascu Iulia Tatiana , Hostiuc Sorin , Minoiu Costin Aurelian , Hostiuc Mihaela , Popa Bogdan Valeriu
Published: September 2024

Objectives: Facial fractures and associated traumatic brain injuries represent a worldwide public health concern. Therefore, we aimed to determine the pattern of brain injury accompanying facial fractures by comparing adult patients with and without facial fractures in terms of demographic, clinical, and imaging features. Methods: This single-center, retrospective study included 492 polytrauma patients presenting at our emergency department from January 2019 to July 2023, which were divided in two groups: with facial fractures (FF) and without facial fractures (non-FF). The following data were collected: age, sex, mechanism of trauma (road traffic accident, fall, and other causes), Glasgow Coma Scale (GCS), the evolution of the patient (admitted to a medical ward or intensive care unit, neurosurgery performed, death), and imaging features of the injury. Data were analyzed using descriptive tests, Chi-square tests, and regression analyses. A p-value less than 0.05 was considered statistically significant. Results: In the FF group, there were 79% (n = 102) men and 21% (n = 27) women, with a mean age of 45 ± 17 years, while in the non-FF group, there were 70% (n = 253) men and 30% (n = 110) women, with a mean age 46 ± 17 years. There was a significant association between brain injuries and facial fractures (p < 0.001, AOR 1.7). The most frequent facial fracture affected the zygoma bone in 28.1% (n = 67) cases. The most frequent brain injury associated with FF was subdural hematoma 23.4% (n = 44), and in the non-FF group, the most common head injury was intraparenchymal hematoma 29% (n = 73); Conclusions: Both groups shared similarities regarding gender, age, cause of traumatic event, and outcome but had significant differences in association with brain injuries, ICU admission, and clinical status.
Editorial to the Special Issue “Universe: Feature Papers 2023—Cosmology”
Authors: Bamba Kazuharu
Published: September 2024

According to recent observational data, including Supernovae Ia (SNe Ia) [...]
Integrated Sensing and New Radio Communications for Air Vehicle Positioning
Authors: Mbaye Ibrahima , Al-Rubaye Saba , Conrad Christopher , Inalhan Gokhan
Published: September 2024

Aerial vehicles are increasingly relying on connectivity to cellular networks, with 5G new radio (NR) and 6G technologies deemed critical for the next generation of indoor and outdoor positioning systems. Conventional time of arrival approaches require time synchronisation between base stations and vehicles, and a clock bias greater than 30 ns can result in a positioning inaccuracy above 10 m. This work, thereby, proposes an integrated positioning technique based on RF fingerprinting using ray-tracing data and reinforced with machine learning. The system leverages advanced sensing technologies, NR communications, and AI-driven random forests to enhance the precision and reliability of air vehicle positioning, contributing to safer and more efficient air travel and autonomous flight operations. The developed solution is evaluated in a representative urban canyon environment, in which the performance of conventional radio-based positioning systems is often degraded. Notably, a supervised learning algorithm based on the received signal strength and time of arrival is shown to exhibit an accuracy of under 3 m in 75% of the areas studied.
The Diatom Genus Navicula in Spring Ecosystems with the Description of Navicula aquaesuavis sp. nov.
Authors: Cid-Rodríguez María , Cantonati Marco , Angeli Nicola , Bilous Olena , Al-Harbi Maha , Lange-Bertalot Horst , Levkov Zlatko , Piana Lucia , Spitale Daniel , Saber Abdullah A.
Published: September 2024

Given the limited understanding of species diversity and ecological preferences of diatoms of the genus Navicula in spring ecosystems, herein we present and describe as species new to science, Navicula aquaesuavis Lange-Bert., Levkov, Cid-Rodríguez, A.A.Saber and Cantonati sp. nov. This species was collected from a mountain spring located above the tree line at 1613 m a.s.l. in the Northern Apennines. The Fontana del Vescovo (Bishop’s spring), which is the locus classicus of the new species, has a low conductivity (60–70 µS cm−1), temperature of ca. 5 °C, circumneutral pH (7.3–7.5), relatively low nitrate (ca. 1 mg L−1), and also suffered from a discharge reduction from 1 to 0.1 L s−1 from 2011 to 2023. The putative new species was confirmed by a second finding in Northern Macedonia, and we thoroughly document this second population as well. We seized the opportunity to describe this new Navicula and review the global literature on the diatom genus Navicula in spring ecosystems. Using the results of this review and our own databases on springs and wells in central Europe and Egypt, we discuss the main Navicula species and their environmental preferences in spring habitats.
TECHNOLOGIE FOR THE USE OF MODERN STIFFENINGFABRICS FOR UNIFORMS
Authors: Akindinova Tatyana L. , Zamyshlyaeva Veronika V.
Published: September 2024

The article presents the technology of choosing modern stiffening fabrics used in the manufacture of stiffening gasket in the ceremonial military uniform. The selection technology is based on the results of experimental studies of the manufacturability of stiffening fabrics during bending, which complement the standard weft stiffness index with a set of characteristics reflecting the dynamics of single-cycle tests. Elas-ticity, bending work, and the coefficient of stability of the structure, which are determined by the methodol-ogy implemented on an automated system, are highlighted as the main indicators of the manufacturability of stiffening fabrics. The new reference information obtained on a wide range of indicators of the manufactura-bility of stiffening fabrics is recommended for use in digitalization of confection and improvement of CAD operation. The proposed technology for selecting side fabrics for uniforms is formalized in the form of a de-veloped algorithm that allows you to find side fabrics of rational hardness and high elasticity.
FINDS OF 2002-2003 IN USHAKOVO VILLAGE (GURYEVSKY DISTRICT, KALININGRAD REGION)
Authors: Kulakov V. I.
Published: September 2024

This article is a publication of finds made in 2002-2003 at the destroyed Ushakovo-1 burial ground. Fragments of vessels with corded ornamentation suggest the presence of a Neolithic site or settlement here. Material from Roman times directly indicates the existence of C2 phase burial ground near the later church of St. Nicholas. Early pottery indicates the possibility of the existence of a Prussian settlement of the “late pagan” period (11th-12th centuries) here. The first wooden church, following the example of the early Order cemeteries in Alt-Wehlau and Arnau, could be accompanied by burials of the 14th-15th centuries with almost pagan equipment. Early Order pottery was left by a settlement of the Lischke type, which arose south of the castle and its forburg at the beginning of the 14th century on the site of a Prussian village, next to the church of St. Nicholas.
TIP FAKÜLTESİ ÖĞRENCİLERİNİN BEYİN GÖÇÜNE BAKIŞI - TANIMLAYICI BİR ARAŞTIRMA
Authors: Altun Güzelderen Yazgı Beriy , Yurdakul Şefik , Altundal Eylem Gözde , Ünal Egemen , Mollahaliloğlu Salih
Published: September 2024

Çalışmada tıp fakültesi öğrencilerinin beyin göçü ile ilgili düşünceleri, tutumları ve bu düşüncelerinde etkili faktörlerin incelenmesi amaçlandı. Kesitsel tipteki bu çalışma Ankara Yıldırım Beyazıt Üniversitesi Tıp Fakültesi 1, 3 ve 6 ıncı dönem öğrencileri ile yürütüldü. Çalışmaya katılmaya gönüllü olan katılımcılara çevrim içi ve yüz yüze olarak sosyodemografik özellikler, Minnesota İş Tatmin Ölçeği ve beyin göçüne yönelik sorulardan oluşan anket formu uygulandı. Dönem 1, 3 ve 6 öğrencilerinin %41,7’sine ulaşıldı. Katılımcıların %28,2’si beyin göçü düşüncesi olmadığını belirtti. İngilizce tıp bölümünde okuyanların, yabancı uyruklu öğrencilerin, maddi durumu kötü olanların, yabancı dil bilgisi iyi olanların, daha önceden yurt dışına çıkmış olanların, yabancı dil kursuna gidenlerin, çevresinde yurt dışında çalışan hekim olanların, dönem 1 ve 3 olanların beyin göçü düşüncesinin yüksek olduğu saptandı. Yurtdışına gitme düşüncesi olan öğrencilerin Minnesota İş Tatmin Ölçeği puan ortalaması (SD) 61,58 (±13,90), yurtdışına gitme düşüncesi olmayan öğrencilerin puan ortalaması 66,17(±13,22) olup yurt dışına gitmek isteyenlerin mesleki tatmini daha düşük olduğu saptanmıştır.
FACTORS ASSOCIATED WITH NON-ADHERENCE TO MEDICATION IN ELDERLY WITH HYPERTENSION
Authors: Akıncı Gülçin Elmas , Dündar Cihad
Published: September 2024

Non-adherence to medication, estimated to be approximately 10%–60%, is quite common in the elderly and is an important cause of morbidity. In this study, we aimed to determine the rates of non-adherence to antihypertensive medications and related factors in the elderly. This cross-sectional study was conducted in primary healthcare facilities in Samsun, Turkey, during May and June 2017, involving a face-to-face interview with patients. The inclusion criteria were a diagnosis of hypertension, use of antihypertensive medication for at least one year, age 65 years and older, and ability to communicate verbally. The Hypertension Medication Adherence Scale was used to assess adherence to treatment. Multivariate logistic regression analysis was used to determine the evaluated variables related to poor adherence. The mean age of the study group was 71.8 ± 6.7 years. The frequency of non-adherence to medication was 10.9%. According to the results of the logistic regression analysis, cognitive impairment, visual impairment, having no social support for medicine intake, male gender, and being single were significantly associated with non-adherence to antihypertensive medication. To improve medication adherence in primary care, simplified treatment options should be offered and a patient-oriented treatment plan should be planned.
Güçlendirme İşlerinde Miktar Hatasına Bağlı Dengesiz Teklifin Önlenmesi için Öneriler
Authors: Topuz Suat , Bettemir Önder Halis
Published: September 2024

Yıkıcı 6 Şubat Kahramanmaraş depremlerinin ardından on binlerce orta hasarlı binanın güçlendirilmesi ve az hasarlı binaların tamiratı gerçekleştirilmektedir. Güçlendirme ve onarım süreci ihale ve sözleşme üzerine deneyimsiz apartman yöneticileri tarafından yürütüldüğü için deneyimli yükleniciler tarafından sunulabilecek dengesiz tekliflere karşı etkisiz kalınabilmektedir. Ayrıca deprem sonrası bölge halkında oluşan yüksek düzeydeki endişe ve korku nedeniyle yüklenicilerin iş artışı talepleri işveren tarafından kabul edilerek sözleşme bedelinin çok üzerinde iş artışları görülebilmektedir. Bu çalışmada güçlendirme işlerinde dengesiz teklife dayalı olası iş artışlarından dolayı yüklenicilerin haksız kazanç elde etmelerinin önüne geçebilecek revize fiyat uygulamaları önerilmiş ve sonuçları değerlendirilmiştir. Kamu ihale kanunu eklerinde yer alan revize birim fiyat formülünün dengesiz teklif üzerindeki etkinliği %0 ile %20 iş artışından sonra uygulanma durumu incelenmiştir. Buna ek olarak daha hızlı fiyat eksiltmesi sağlayabilmek için artış oranının üssü alınarak revize birim fiyat formülü modifiye edilmiştir. Modifiye revize fiyat formülünün %0 ile %20 iş artışından sonra uygulanması incelenmiş ve dengesiz teklif sunan yüklenicinin iş artışından dolayı elde edeceği kârı azaltarak iş artışının cazip olmaması amaçlanmıştır. Bununla beraber dengeli teklif veren yüklenicilerin hızlı düşen birim fiyat uygulamasından mağdur olmayacak düzeyde revize birim fiyat uygulanması önerilmektedir. Vaka analizi çalışmasında elde edilen bulgulara göre modifiye revize birim fiyat formülünün %10 iş artışından sonra uygulanmasının dengesiz teklif sunan yüklenicinin aşırı kâr etmesini önlerken dengeli teklif sunan yüklenicinin makul kâr elde edebilmesine olanak verdiği belirlenmiştir. Bu çalışmada uygulaması kolay ve tarafların haklarını gözeten adaletli bir revize fiyat uygulaması önerilerek güçlendirme işlerinin adil ve güvenli biçimde gerçekleştirilmesinin sağlanması amaçlanmıştır. Bu sayede dengesiz teklif sunan yükleniciler için iş artışı cazip olmayacak ve iş artışları gerekli haller dışında ortaya çıkmayacağı için deprem sonrası maddi durumu zayıflayan konut sahiplerinin finansal sağlıklarını aşırı etkilemeyecek biçimde güçlendirme işleri gerçekleştirilebilecektir.