bluuebunny/crossref_metadata_2025 huggingface dataset:
Marker Peptides for Indicating the Spoilage of Milk—Sample Preparation and Chemometric Approaches for Yielding Potential Peptides in a Raw Milk Model
Authors: Class Lisa-Carina , Kuhnen Gesine , Schmid Jasmin , Rohn Sascha , Kuballa Jürgen
Published: October 2024

The diminishing of food waste is gaining increasing importance, especially in context with a growing population and a need for the sustainable use of food resources. A more precise determination of the best-before date can contribute to this general aim. As proteoforms can be regarded as indicators for ecophysiological influences, their suitability for determining the spoilage and, consequently, the shelf-life of food is suggested. Proteoforms reflect the spoilage of food more accurately. The aim of the present study was to develop an efficient proteomics workflow to determine the shelf-life of milk as a prominent target. In this case, raw milk was chosen as model, as it degrades much faster. The integration of different multivariate analysis techniques was used to analyze the spoilage of raw milk with regard to aspects of its proteome. As the feasibility of such an approach has already been demonstrated in previous studies, it is further necessary to enable a robust and reproducible workflow, primarily gaining appropriate numbers and amounts of peptides when the research question differs and other dairy products are evaluated. In the present study, two approaches for gaining peptides were considered: In addition to a direct hydrolysis of a protein-rich sample solution, in-gel hydrolysis is another common approach in proteomics. By separating the proteins in a traditional gel electrophoresis before hydrolysis, the change in the individual proteins and, consequently, potential peptides can be monitored more specifically during storage. However, the traditional approach offers not only possibilities but also limitations that must be considered. The study showed that it is beneficial to apply a combination of different application strategies, as they complement each other and can thus increase the information content of a sample or confirm a theory. Mass spectrometric features, which represent a chemical–structural change of all kinds of compounds during storage, were selected, and three of them were identified as peptides, originating from α-s1-casein.
Purification and Characterization of β-Mannanase Derived from Rhizopus microsporus var. rhizopodiformis Expressed in Komagataella phaffii
Authors: Qu Jinghua , Long Jie , Li Xingfei , Zhou Xing , Chen Long , Qiu Chao , Jin Zhengyu
Published: October 2024

The demand for food-grade β-mannanases, ideal for high-temperature baking, is increasing. Using the Komagataella phaffii (P. pastoris) expression system for β-mannanase production, this study aimed to enhance purification methods. We evaluated better conditions for production and purification of β-mannanase (PpRmMan134A) from recombinant P. pastoris X-33, focusing on a higher purity and reducing the production of endogenous secretory proteins in fermentation. By adjusting carbon and nitrogen sources, culture time, and temperature, we controlled cell growth to reduce the production of endogenous secretory proteins. The better-evaluated conditions involved culturing recombinant P. pastoris in 70 mL buffered glycerol complex medium for 24 h at 30 °C, then in modified buffered methanol-complex medium with 0.91% (w/v) methanol, 0.56% (w/v) sorbitol, and 0.48% (w/v) mannitol for another 24 h, which improved the PpRmMan134A yield and reduced endogenous secretory proteins, shortening the fermentation time by 72 h. An affordable purification method using ultrafiltration and salt-out precipitation was utilized. PpRmMan134A showed thermostability up to 100 °C and effectively degraded locust bean gum into smaller fragments, mainly producing mannotriose. In conclusion, with its enhanced purity due to reduced levels of endogenous secretory proteins, purified PpRmMan134A emerges as a promising enzyme for high-temperature baking applications.
Impact of Functional Ingredients on the Technological, Sensory, and Health Properties of Bakery Products
Authors: Tolve Roberta , Simonato Barbara
Published: October 2024

Improving well-being, allowing for sustainable lifestyles, and enhancing waste control are aims of the United Nations in the 2030 Agenda for Sustainable Development [...]
Development and Evaluation of the Acaricidal Activity of Xantan Gum-Based Hydrogel and Polymeric Nanoparticles Containing Achyrocline satureioides Extract
Authors: Fantatto Rafaela Regina , Gomes Annelize Rodrigues , Constantini João Vitor Carvalho , Rodero Camila Fernanda , Chorilli Marlus , Chagas Ana Carolina de Souza , Melero Ana , Pietro Rosemeire Cristina Linhari Rodrigues
Published: October 2024

The Rhipicephalus microplus tick causes enormous economic losses in livestock farming around the world. Despite several promising studies carried out with plant extracts such as Achyrocline satureioides against this ectoparasite, a major obstacle is related to pharmaceutical presentation forms. There is no study showing xantan gum-based hydrogel and polycaprolactone nanoparticles containing A. satureioides extract against R. microplus larvae. The objective of this study was to incorporate A. satureioides extract to develop a nanoformulation (AScn) and a hydrogel (ASlh) and evaluate them against R. microplus larvae with the purpose of increasing the contact time of the extract with the larvae and improve the effectiveness. The ethanolic extracts were incorporated in polycaprolactone nanoparticles and characterized via analysis of the mean hydrodinamic diameter and polidispersity index. The xanthan gum-based hydrogel formulation was prepared with crude extract of A. satureioides 40 mg/mL, 0.25% xanthan gum, and 8% poloxamer, to determine the bioadhesiveness of the formulation in bovine leather and the flow rate of the formulation in the animal. The results in larvae demonstrated that when evaluated in the form of a hydrogel (ASlh), mortality was higher, with 91.48% mortality at a concentration of 20 mg/mL presenting itself as an interesting alternative for controlling this ectoparasite.
The Impact of Social Participation on Frailty among Older Adults: The Mediating Role of Loneliness and Sleep Quality
Authors: Wang Yanting , Zheng Feiyang , Zhang Xinping
Published: October 2024

Background: Frailty has become a common health issue among older adults, imposing a burden on both society and individuals. The relationship between social participation and frailty has received widespread attention, but the mechanism remains to be explored. The aim of this study is to explore the impact of social participation on frailty among older adults and to analyze the mediating role of loneliness and sleep quality, providing suggestions to alleviate frailty. Methods: Data related to social participation, loneliness, sleep quality, and frailty from 7779 older adults were collected from the Chinese Longitudinal Healthy Longevity Survey (CLHLS 2018). The chain mediation model was conducted to explore the relationship between variables, and the Bootstrap method was used to examine the path coefficients. Results: Social participation negatively affected frailty (β = −0.00391049, 95% CI = [−0.042296, −0.035465]); the indirect effect of social participation on frailty mediated by loneliness was −0.0019505 (95% CI = [−0.002551, −0.001371]); the indirect effect of social participation on frailty mediated by sleep quality was −0.0011104 (95%CI = [−0.001692, −0.000557]); the effect mediated by both loneliness and sleep quality was −0.0004263 (95% CI = [−0.000593, −0.000304]). Conclusions: Social participation negatively affected frailty. Loneliness and sleep quality not only mediated independently, but also played a chain mediating role. This suggested that encouraging older adults to engage in more social participation, reducing loneliness, and improving sleep quality are feasible measures to improve frailty.
Activities of Daily Living Disability Transition Patterns in Older Adults with Chronic Diseases: A Four-Year Cohort Study in China
Authors: Guo Tian , Zhang Yunwei , Xu Gang , Liu Wenxian , Ding Hansheng , Chen Shaofan
Published: October 2024

Background: Older adults with chronic diseases often experience higher rates of Activities of Daily Living (ADL) disability, with research primarily examining the transition between states of ADL disability and non-disability. The current study aims to analyze the patterns and factors of mutual transitions between multiple different ADL disability states in older adults with chronic diseases. Methods: This longitudinal study utilized data from the Shanghai Elderly Care Unified Needs Assessment (SECUNA) spanning 2014 to 2017, with 2014 being the baseline. The study included older adults aged 60 years and older with chronic diseases. Using the Markov model, individuals were classified into three states: no ADL disability, mild ADL disability, and severe ADL disability. Transition patterns were analyzed by calculating the frequency, intensity, and probability of transition, and the influencing factors of six transition scenarios were evaluated. Results: Older adults with mild ADL disability were more likely to experience improvement (transition intensity: 0.4731) rather than deterioration (transition intensity: 0.2226) in their ADL disability states. However, those with severe ADL disability faced challenges in improving their states (transition intensities: 0.0068 and 0.1204). Among the six ADL disability transition scenarios, place of residence was associated with four scenarios, age and economic sources were associated with three scenarios, sex was associated with two scenarios, and other factors were associated with one scenario. Conclusions: The transition patterns and factors differ among individuals with varying ADL disability states. It is essential for relevant agencies to implement tailored preventive healthcare strategies to effectively manage the health status of older adults with chronic diseases.
Application of Ethephon Manually or via Drone Enforces Bud Dormancy and Enhances Flowering Response to Chilling in Litchi (Litchi chinensis Sonn.)
Authors: Wen Bingyi , Deng Cailian , Tian Qi , Ouyang Jianzhong , Zeng Renfang , Wang Huicong , Huang Xuming
Published: October 2024

Ethephon (2-chloroethylphosphonic acid) is frequently used for flush management in order to maximize flowering in litchi. However, the optimal dosage of ethephon, which balances between flush control effect and the detrimental effect on leaves, is unknown. This study aimed to identify the optimal ethephon dosage and test more efficient ethephon application methods, using a drone for flush control and flowering promotion in litchi. The effects of a single manual full-tree spray of 250, 500 or 1000 mg/L of ethephon in early November on the bud break rate, leaf drop rate, net photosynthetic rate, LcFT1 expression and floral induction (panicle emergence rate and panicle number) in ‘Jingganghongnuo’ litchi were examined in the season of 2021–2022. In the season of 2022–2023, the effects of drone application of 1000 mg/L of ethephon in early November on bud growth and floral induction were observed. The results showed that the manual ethephon treatments were effective at enforcing bud dormancy and elongating the dormancy period and that the effects were positively dependent on dosage. One manual spray of 1000 mg/L of ethephon in late autumn enabled a dormancy period of 6 weeks. The treatments advanced seasonal abscission of old leaves in winter and caused short-term suppression on photosynthesis within 2 weeks after treatment. Ethephon treatments, especially at 1000 mg/L, enhanced the expression of LcFT1 in the mature leaves and promoted floral induction reflected by earlier panicle emergence and increased panicle emergence rate and number in the terminal shoot. The floral promotion effect was also positively dosage dependent. The cumulative chilling hours below 15 °C from the date of treatment to the occurrence of a 20% panicle emergence rate were lowered in ethephon treatments. A drone spray of 1000 mg/L of ethephon solution consumed a sixth of the manual spray solution volume and was two thirds less effective in suppressing bud break compared with manual spraying. However, it achieved a significant flowering promotion effect comparable to traditional manual spraying. The results suggest that ethephon application enhanced flowering responsiveness to chilling as well as enforced bud dormancy. The application of ethephon with a drone proved to be an efficient method for flush control and flower promotion.
Protecting Infinite Data Streams from Wearable Devices with Local Differential Privacy Techniques
Authors: Zhao Feng , Fan Song
Published: October 2024

The real-time data collected by wearable devices enables personalized health management and supports public health monitoring. However, sharing these data with third-party organizations introduces significant privacy risks. As a result, protecting and securely sharing wearable device data has become a critical concern. This paper proposes a local differential privacy-preserving algorithm designed for continuous data streams generated by wearable devices. Initially, the data stream is sampled at key points to avoid prematurely exhausting the privacy budget. Then, an adaptive allocation of the privacy budget at these points enhances privacy protection for sensitive data. Additionally, the optimized square wave (SW) mechanism introduces perturbations to the sampled points. Afterward, the Kalman filter algorithm is applied to maintain data flow patterns and reduce prediction errors. Experimental validation using two real datasets demonstrates that, under comparable conditions, this approach provides higher data availability than existing privacy protection methods for continuous data streams.
Left Atrial Low-Voltage Extent Predicts the Recurrence of Supraventricular Arrhythmias
Authors: Teumer Yannick , Gold Luca , Katov Lyuboslav , Bothner Carlo , Rottbauer Wolfgang , Weinmann-Emhardt Karolina
Published: October 2024

The incidence of left atrial (LA) supraventricular arrhythmias is increasing. Even after LA ablation, recurrence of these tachycardias is common. MRI studies show that LA cardiomyopathy is a significant risk factor for recurrence and correlates with low voltage areas detected via 3D electroanatomic mapping (EAM). There are limited data on the impact of low voltage extent detected by EAM on recurrence-free survival. Voltage thresholds defining low voltage vary across different studies. This study aims to investigate the impact of the extent of low voltage areas in the LA on recurrence-free survival and to assess whether defining low voltage areas using thresholds of 0.5, 0.4, or 0.3 mV offers better predictive performance. Patients with atrial arrhythmia who underwent LA EAM at Ulm University Heart Center between September 2018 and September 2022 were included from the ATRIUM registry. ROC analysis determined the voltage threshold for predicting recurrence-free survival. Kaplan–Meier and logistic regression models adjusted for patient variables were used to analyze recurrence-free survival. Of 1089 screened patients, 108 met the inclusion criteria. ROC analysis indicated that a 0.4 mV threshold for low voltage provided the best predictive performance. Logistic regression showed a 1.039-fold increase in recurrence risk per percent increase in LA low voltage area (odds ratio = 1.039, 95% CI 1.014–1.064). Low voltage extent in EAM correlates with 1-year recurrence rate after ablation of left atrial supraventricular arrhythmias. The threshold of 0.4 mV is the most suitable for predicting recurrences of those examined.
Prognostic Impact of Pulmonary Diseases in 952 Patients with Thoracic and/or Abdominal Aortic Aneurysm
Authors: Kobayashi Yoichi , Ishiguro Takashi , Kagiyama Naho , Sumi Makoto , Takayanagi Noboru
Published: October 2024

Background/Objectives: Pulmonary diseases are common in patients with thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA). Although high prevalences of chronic obstructive pulmonary disease and lung cancer (LC) are known, the prevalence of these and other pulmonary diseases regarding their relation to the outcome of TAA and/or AAA are not determined. Methods: Pulmonary diseases present at aortic aneurysm (AA) diagnosis and follow-up periods and cause of death of 952 patients with TAA, AAA, or TAA + AAA (including thoracoabdominal AA) treated at our institution in Japan were retrospectively analyzed. Cox regression analysis was used to investigate potential risk factors of mortality. Results: The mean patient age was 72.4 years, and the median follow-up was 4.92 years. At diagnosis, 528 (55.5%) patients had pulmonary diseases, including emphysema without interstitial lung disease (ILD) or LC, LC, idiopathic pulmonary fibrosis (IPF) without LC, non-IPF ILD without LC, and interstitial lung abnormalities (ILAs) without LC in 250, 85, 65, 15, and 58 patients, respectively. During follow-up, LC and acute exacerbation (AE) of IPF developed in 50 and 12 patients, respectively. In 213 patients who died, there were 45 (21.1%) aortic disease-related deaths. Other causes of death included LC (27.7%), cardiovascular events (9.4%), pneumonia (5.6%), and interstitial lung disease (4.7%). In a multivariate Cox regression hazard model, age; larger maximum aneurysm diameter; and coexisting LC, IPF, or concomitant cancer were associated with poor prognosis. Conclusions: In patients with AA, not only age and aneurysm diameter but also coexisting LC and IPF were prognostic factors for mortality.
TG-PGAT: An AIS Data-Driven Dynamic Spatiotemporal Prediction Model for Ship Traffic Flow in the Port
Authors: Ma Jianwen , Zhou Yue , Chang Yumiao , Zhu Zhaoxin , Liu Guoxin , Chen Zhaojun
Published: October 2024

Accurate prediction of ship traffic flow is essential for developing intelligent maritime transportation systems. To address the complexity of ship traffic flow data in the port and the challenges of capturing its dynamic spatiotemporal dependencies, a dynamic spatiotemporal model called Temporal convolutional network-bidirectional Gated recurrent unit-Pearson correlation coefficient-Graph Attention Network (TG-PGAT) is proposed for predicting traffic flow in port waters. This model extracts spatial features of traffic flow by combining the adjacency matrix and spatial dynamic coefficient correlation matrix within the Graph Attention Network (GAT) and captures temporal features through the concatenation of the Temporal Convolutional Network (TCN) and Bidirectional Gated Recurrent Unit (BiGRU). The proposed TG-PGAT model demonstrates higher prediction accuracy and stability than other classic traffic flow prediction methods. The experimental results from multiple angles, such as ablation experiments and robustness tests, further validate the critical role and strong noise resistance of different modules in the TG-PGAT model. The experimental results of visualization demonstrate that this model not only exhibits significant predictive advantages in densely trafficked areas of the port but also outperforms other models in surrounding areas with sparse traffic flow data.
Energy Use and Carbon Footprint Assessment in Retrofitting a Novel Energy Saving Device to a Ship
Authors: Uyan Eren , Atlar Mehmet , Gürsoy Osman
Published: October 2024

The Gate rudder system (GRS) was recently introduced as an innovative energy-saving device (ESD) for ships, and it is the most attractive ESD currently used in the market, with double figures of fuel savings in full-scale (>10–35%) compared with a ship with a conventional rudder system (CRS). Although there are few new ship applications of GRS, the recently completed EC-H2020 GATERS project successfully demonstrated its unique energy-saving and manoeuvrability benefits as a “retrofit” solution for an existing general cargo vessel for the first time. The project results suggested that the GRS holds significant potential for retrofitting existing ships to enhance fuel efficiency (~35%) and improve manoeuvrability. Nevertheless, the application was a comprehensive undertaking requiring various work tasks such as component manufacturing, removing existing systems, and modification and upgrading works, with substantial energy consumption and environmental impacts. Therefore, it was insightful to study energy use and environmental impacts in a GRS retrofit process. This study developed and implemented a comprehensive energy consumption and carbon footprint assessment framework for the GRS retrofit in the GATERS project. A detailed assessment of energy consumption and related carbon emissions was performed during the major stages of manufacturing, system removals, and modifications and assembly in the GRS retrofit. Also, the potential savings in energy use and emissions were addressed. The results demonstrated that the manufacturing stage was the most energy-intensive phase, being responsible for 91.4% of total electricity and 46.7% of fuel-based thermal energy use. The system removals accounted for 53.3% of the fuel-based thermal energy, whereas the modification and assembly work accounted for about 7.7% of the total electricity use. Additionally, various measures such as clean electrification, energy efficiency, mould/tool reuse, and component reuse to reduce the energy consumption and related carbon emissions in future GRS retrofit applications were addressed and discussed together with their reduction potentials.
Spatiotemporal Point–Trace Matching Based on Multi-Dimensional Feature Fuzzy Similarity Model
Authors: Liu Yi , Wu Ruijie , Guo Wei , Huang Liang , Li Kairui , Zhu Man , van Gelder Pieter
Published: October 2024

Identifying ships is essential for maritime situational awareness. Automatic identification system (AIS) data and remote sensing (RS) images provide information on ship movement and properties from different perspectives. This study develops an efficient spatiotemporal association approach that combines AIS data and RS images for point–track association. Ship detection and feature extraction from the RS images are performed using deep learning. The detected image characteristics and neighboring AIS data are compared using a multi-dimensional feature similarity model that considers similarities in space, time, course, and attributes. An efficient spatial–temporal association analysis of ships in RS images and AIS data is achieved using the interval type-2 fuzzy system (IT2FS) method. Finally, optical images with different resolutions and AIS records near the waters of Yokosuka Port and Kure are collected to test the proposed model. The results show that compared with the multi-factor fuzzy comprehensive decision-making method, the proposed method can achieve the best performance (F1 scores of 0.7302 and 0.9189, respectively, on GF1 and GF2 images) while maintaining a specific efficiency. This work can realize ship positioning and monitoring based on multi-source data and enhance maritime situational awareness.
YOLO-GE: An Attention Fusion Enhanced Underwater Object Detection Algorithm
Authors: Li Qiming , Shi Hongwei
Published: October 2024

Underwater object detection is a challenging task with profound implications for fields such as aquaculture, marine ecological protection, and maritime rescue operations. The presence of numerous small aquatic organisms in the underwater environment often leads to issues of missed detections and false positives. Additionally, factors such as the water quality result in weak target features, which adversely affect the extraction of target feature information. Furthermore, the lack of illumination underwater causes image blur and low contrast, thereby increasing the difficulty of the detection task. To address these issues, we propose a novel underwater object detection algorithm called YOLO-GE (GCNet-EMA). First, we introduce an image enhancement module to mitigate the impact of underwater image quality issues on the detection task. Second, a high-resolution feature layer is added into the network to improve the problems of missed detections and false positives for small targets. Third, we propose GEBlock, an attention-based fusion module that captures long-range contextual information and suppresses noise from lower-level feature layers. Finally, we combine an adaptive spatial fusion module with the detection head to filter out conflicting feature information from different feature layers. Experiments on the UTDAC2020, DUO and RUOD datasets show that the proposed method achieves an optimal detection accuracy.
Exogenous Nitric Oxide Induces Pathogenicity of Alternaria alternata on Huangguan Pear Fruit by Regulating Reactive Oxygen Species Metabolism and Cell Wall Modification
Authors: Wang Di , Zhang Haijue , Meng Lingkui , Tan Xinyu , Liu Rong , Gao Qingchao , Wu Yan , Zhu Yuhan , Ren Xueyan , Li Yongcai , Kong Qingjun
Published: October 2024

Black spot caused by Alternaria alternata is one of the most common postharvest diseases in fruit and vegetables. A comprehensive investigation into its pathogenicity mechanism is imperative in order to propose a targeted and effective control strategy. The effect of nitric oxide (NO) on the pathogenicity of A. alternata and its underlying mechanism was studied. The results showed that treatment with 0.5 mM L−1 of sodium nitroprusside (SNP) (NO donor) increased the lesion diameter of A. alternata in vivo and in vitro, which was 22.8% and 13.2% higher than that of the control, respectively. Exogenous NO treatment also induced endogenous NO accumulation by activating nitric oxide synthase (NOS). In addition, NO triggered an increase in reactive oxygen species (ROS) levels. NO enhanced activities and gene expression levels of NADPH oxidase (NOX), superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX), and glutathione reductase (GR). Moreover, NO stimulated cell wall degrading enzymes by activating the corresponding gene expression in vivo and in vitro. These results suggested that exogenous NO promoted the pathogenicity of A. alternata by inducing ROS accumulation and activating antioxidants and cell wall degrading enzymes. The present results could establish a theoretical foundation for the targeted control of the black spot disease in pear fruit.
Land Cover Mapping in West Africa: A Collaborative Process
Authors: Mensah Foster , Mushtaq Fatima , Bartel Paul , Abramowitz Jacob , Cherrington Emil , Mahamane Mansour , Mamane Bako , Dieye Amadou Moctar , Sanou Patrice , Enaruvbe Glory , Mar Ndeye Fatou
Published: October 2024

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa.
Spatiotemporal Evolution of Territorial Spaces and Its Effect on Carbon Emissions in Qingdao City, China
Authors: He Jiali , Liu Xiangfei , Wang Xuetong , Li Xueyang , Yu Linger , Niu Beibei
Published: October 2024

Land use change has always been a significant factor affecting global carbon emissions. Dissecting the characteristics of territorial space evolution and its impact on carbon emissions is crucial for developing low-carbon-oriented territorial space optimization and governance strategies. This paper calculates the carbon emissions associated with territorial spaces in Qingdao from 2000 to 2020, utilizing land use data alongside various statistical data. Based on the accounting results, the evolution characteristics of territorial spaces and their corresponding carbon emissions, as well as the carbon transition dynamics resulting from space transfer, are analyzed. A carbon transition decomposition formula is then proposed to quantify the differential and spatially heterogeneous impacts of changes in space types and socio-economic development on emissions. The results indicate that: (1) the evolution of territorial spaces in Qingdao during 2000–2020 is characterized by an expansion of living space and a contraction of production and ecological spaces; (2) net carbon emissions rose from 313.98 × 104 tons to 1068.58 × 104 tons, with urban production space contributing the most (69.96% in 2020) due to its significantly high emission density. The spatial distribution of carbon emissions exhibited a stable “northwest–southeast” pattern, with increased dispersion and weakened directionality; (3) the transformation of territorial spaces promoted carbon emissions in Qingdao, with the conversion of urban production space to other uses yielding the most favorable carbon transitions, while the expropriation of agricultural production spaces for urban production and residents’ living has resulted in the most detrimental carbon transitions; (4) socio-economic development shapes the overarching pattern of regional emission density changes, whereas space transfers account for local variations. This paper also identifies priorities for spatial optimization and key sectors for emission reduction. The findings contribute to a deeper understanding of the carbon emission consequences of territorial space transformation in Qingdao, thereby providing valuable insights for regional spatial planning and optimization aimed at promoting low-carbon development.
Application of Protection Features to a Biosphere Reserve for the Control and Development of Tourist Activities: The Case of the Gêres–Xurés Park
Authors: Cruz María Montserrat , Sánchez Francisco Javier , Suárez Vanessa María
Published: October 2024

In this study, we consider the importance of applying legal protection and management measures to natural tourist areas. We assess the sensitivity of these areas with respect to tourist demand, which drives the need for protective and corrective measures, such as carrying capacities controlled via admissions; in particular, we analyse the protective features that exist in a transboundary biosphere reserve, the Gêres–Xurés Baixa Limia Reserve, which occupies territory in northern Spain, southern Galicia (Spain) and northern Portugal. Our objective is to assess the relevance and effectiveness of the features used by both countries to protect their natural spaces while guaranteeing sustainability and environmental development. These measures ensure that the value of their resources is not compromised for future generations or negatively affected by tourist use. To carry out the analysis, we first conducted a geographical study of the area to characterize the different areas found in this biosphere reserve. Subsequently, we analysed the regulatory norms used for protection in both countries, and we explain the management mechanisms that have been implemented in both countries. This approach enables us to compare the relative importance assigned by each country for the sustainable management of the chosen territory and tourist space. Furthermore, we can compare the use of control measures for sustainable and respectful tourism planning, such as guiding plans, use plans and management and protective regulations.
Pioneers of Origin of Life Studies—Darwin, Oparin, Haldane, Miller, Oró—And the Oldest Known Records of Life
Authors: Schopf J. William
Published: October 2024

The two basic approaches to elucidating how life began both date from Darwin. The first, that of the experimentalists, stems from Darwin’s famous “warm little pond” letter to Joseph Hooker of 1871. This approach, an attempt to replicate the sequential events leading to life’s origin, is exemplified by the “primordial soup” hypothesis of A.I. Oparin (1924) and J.B.S. Haldane (1929); the Miller–Urey laboratory synthesis of amino acids under possible primitive Earth conditions (1953); and Joan Oró’s nonbiological synthesis of the nucleic acid adenine (1959). The second approach, that of the observationalists who search for relevant evidence in the geological record, dates from Darwin’s 1859 On the Origin of Species, in which he laments the “inexplicable” absence of a pre-Cambrian fossil record. Darwin’s concern spurred a century of search that was ultimately rewarded by Stanley Tyler’s 1953 discovery of diverse microscopic fossils in the ~1900 Ma Gunflint Chert of southern Canada. Tyler’s find was soon followed by a cascade of discoveries worldwide; the establishment of a new field of science, Precambrian paleobiology; and, more recently, the discovery of 3400 and ~3465 Ma Paleoarchean microfossils, establishing that primordial life evolved early, far, and fast. Though progress has been made, much remains to be learned in the foci of this Origin of Life 2024 volume, for which this essay is the history-reviewing “stage setter”.
Efficient Nonlinear Model Predictive Path Tracking Control for Autonomous Vehicle: Investigating the Effects of Vehicle Dynamics Stiffness
Authors: Zhu Guozhu , Hong Weirong
Published: October 2024

Motion control is one of the three core modules of autonomous driving, and nonlinear model predictive control (NMPC) has recently attracted widespread attention in the field of motion control. Vehicle dynamics equations, as a widely used model, have a significant impact on the solution efficiency of NMPC due to their stiffness. This paper first theoretically analyzes the limitations on the discretized time step caused by the stiffness of the vehicle dynamics model equations when using existing common numerical methods to solve NMPC, thereby revealing the reasons for the low computational efficiency of NMPC. Then, an A-stable controller based on the finite element orthogonal collocation method is proposed, which greatly expands the stable domain range of the numerical solution process of NMPC, thus achieving the purpose of relaxing the discretized time step restrictions and improving the real-time performance of NMPC. Finally, through CarSim 8.0/Simulink 2021a co-simulation, it is verified that the vehicle dynamics model equations are with great stiffness when the vehicle speed is low, and the proposed controller can enhance the real-time performance of NMPC. As the vehicle speed increases, the stiffness of the vehicle dynamics model equation decreases. In addition to the superior capability in addressing the integration stability issues arising from the stiffness nature of the vehicle dynamics equations, the proposed NMPC controller also demonstrates higher accuracy across a broad range of vehicle speeds.
Data Quality-Aware Client Selection in Heterogeneous Federated Learning
Authors: Song Shinan , Li Yaxin , Wan Jin , Fu Xianghua , Jiang Jingyan
Published: October 2024

Federated Learning (FL) enables decentralized data utilization while maintaining edge user privacy, but it faces challenges due to statistical heterogeneity. Existing approaches address client drift and data heterogeneity issues. However, real-world settings often involve low-quality data with noisy features, such as covariate drift or adversarial samples, which are usually ignored. Noisy samples significantly impact the global model’s accuracy and convergence rate. Assessing data quality and selectively aggregating updates from high-quality clients is crucial, but dynamically perceiving data quality without additional computations or data exchanges is challenging. In this paper, we introduce the FedDQA (Federated learning via Data Quality-Aware) (FedDQA) framework. We discover increased data noise leads to slower loss reduction during local model training. We propose a loss sharpness-based Data-Quality-Awareness (DQA) metric to differentiate between high-quality and low-quality data. Based on the DQA, we design a client selection algorithm that strategically selects participant clients to reduce the negative impact of noisy clients. Experiment results indicate that FedDQA significantly outperforms the baselines. Notably, it achieves up to a 4% increase in global model accuracy and demonstrates faster convergence rates.
Stability and Optimality Criteria for an SVIR Epidemic Model with Numerical Simulation
Authors: Ismail Halet , Debbouche Amar , Hariharan Soundararajan , Shangerganesh Lingeshwaran , Kashtanova Stanislava V.
Published: October 2024

The mathematical modeling of infectious diseases plays a vital role in understanding and predicting disease transmission, as underscored by recent global outbreaks; to delve deep into the dynamic of infectious disease considering latent period presciently is inevitable as it bridges the gap between realistic nature and mathematical modeling. This study extended the classical Susceptible–Infected–Recovered (SIR) model by incorporating vaccination strategies during incubation. We introduced multiple time delays to an account incubation period to capture realistic disease dynamics better. The model is formulated as a system of delay differential equations that describe the transmission dynamics of diseases such as polio or COVID-19, or diseases for which vaccination exists. Critical aspects of the study include proving the positivity of the model’s solutions, calculating the basic reproduction number (R0) using next-generation matrix theory, and identifying disease-free and endemic equilibrium points. The local stability of these equilibria is then analyzed using the Routh–Hurwitz criterion. Due to the complexity introduced by the delay components, we examine the stability by studying the roots of a fourth-degree exponential polynomial. The effects of educational campaigns and vaccination efficacy are also investigated as control measures. Furthermore, an optimization problem is formulated, based on Pontryagin’s maximum principle, to minimize the number of infections and associated intervention costs. Numerical simulations of the delay differential equations are conducted, and a modified Runge–Kutta method with delays is used to solve the optimal control problem. Finally, we present a few simulation results to illustrate the analytical findings.
Pricing of a Binary Option Under a Mixed Exponential Jump Diffusion Model
Authors: Lu Yichen , Song Ruili
Published: October 2024

This paper focuses on the pricing problem of binary options under stochastic interest rates, stochastic volatility, and a mixed exponential jump diffusion model. Considering the negative interest rates in the market in recent years, this paper assumes that the stochastic interest rate follows the Hull–White (HW) model. In addition, we assume that the stochastic volatility follows the Heston volatility model, and the price of the underlying asset follows the jump diffusion model in which the jumps follow the mixed exponential jump model. Considering these factors comprehensively, the mixed exponential jump diffusion of the Heston–HW (abbreviated as MEJ-Heston–HW) model is established. Using the idea of measure transformation, the pricing formula of binary call options is derived by the martingale method, eigenfunction, and Fourier transform. Finally, the effects of the volatility term and the parameters of the mixed-exponential jump diffusion model on the option price in the O-U process are analyzed. In the numerical simulation, compared with the double exponential jump Heston–HW (abbreviated as DEJ-Heston–HW) model and the Heston–HW model, the mixed exponential jump model is an extension of the double exponential jump model, which can approximate any distribution in the sense of weak convergence, including arbitrary discrete distributions, normal distributions, and various thick-tailed distributions. Therefore, the MEJ-Heston–HW model adopted in this paper can better describe the price of the underlying asset.
A Study on the Identification of the Water Army to Improve the Helpfulness of Online Product Reviews
Authors: Li Chuyang , Zhang Shijia , Liu Xiangdong
Published: October 2024

Based on the perspective of identifying the water army, this paper uses the methods of machine learning and data visualization to analyze the helpfulness of online produce reviews, portray product portraits, and provide real and helpful product reviews. In order to identify and eliminate the water army, the Term Frequency-Inverse Document Frequency Model (TF-IDF) and Latent Semantic Index Model (LSI) are used. After eliminating the water army, three classification methods were selected to perform sentimental analysis, including logistics, SnowNLP, and Convolutional Neural Network for text(TextCNN). The TextCNN has the highest F1 score among the three classification methods. At the same time, the Latent Dirichlet Allocation mode (LDA) is used to extract the topics of various reviews. Finally, targeted countermeasures are proposed to manufacturers, consumers, and regulators.
A Sixth-Order Cubic B-Spline Approach for Solving Linear Boundary Value Problems: An In-Depth Analysis and Comparative Study
Authors: Lodhi Ram Kishun , Darweesh Moustafa S. , Aydi Abdelkarim , Kolsi Lioua , Sharma Anil , Ramesh Katta
Published: October 2024

This research presents an efficient and highly accurate cubic B-spline method (CBSM) for solving second-order linear boundary value problems (BVPs). The method achieves sixth-order convergence, supported by rigorous error analysis, ensuring rapid error reduction with mesh refinement. The effectiveness of the CBSM is validated through four numerical examples, showcasing its accuracy, reliability, and computational efficiency, making it well-suited for large-scale problems. A comparative analysis with existing methods confirms the superior performance of the CBSM, positioning it as a practical and powerful tool for solving second-order BVPs.
Soft Weakly Quasi-Continuous Functions Between Soft Topological Spaces
Authors: Al-Ghour Samer , Abuzaid Dina , Naghi Monia
Published: October 2024

As an extension of quasi-continuity in general topology, we define soft quasi-continuity. We show that this notion is equivalent to the known notion of soft semi-continuity. Next, we define soft weak quasi-continuity. With the help of examples, we prove that soft weak quasi-continuity is strictly weaker than both soft semi-continuity and soft weak continuity. We introduce many characterizations of soft weak quasi-continuity. Moreover, we study the relationship between soft quasi-continuity and weak quasi-continuity with their analogous notions in general topology. Furthermore, we show that soft regularity of the co-domain of a soft function is a sufficient condition for equivalence between soft semi-continuity and soft weakly quasi-continuity. Furthermore, we provide several results of soft composition, restrictions, preservation, and soft graph theorems in terms of soft weak quasi-continuity.
A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction
Authors: Zhang Zongjun , Deng Qian , He Wei , Yang Cuiping
Published: October 2024

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.
AI-Powered Approaches for Hypersurface Reconstruction in Multidimensional Spaces
Authors: Yotov Kostadin , Hadzhikolev Emil , Hadzhikoleva Stanka , Milev Mariyan
Published: October 2024

The present article explores the possibilities of using artificial neural networks to solve problems related to reconstructing complex geometric surfaces in Euclidean and pseudo-Euclidean spaces, examining various approaches and techniques for training the networks. The main focus is on the possibility of training a set of neural networks with information about the available surface points, which can then be used to predict and complete missing parts. A method is proposed for using separate neural networks that reconstruct surfaces in different spatial directions, employing various types of architectures, such as multilayer perceptrons, recursive networks, and feedforward networks. Experimental results show that artificial neural networks can successfully approximate both smooth surfaces and those containing singular points. The article presents the results with the smallest error, showcasing networks of different types, along with a technique for reconstructing geographic relief. A comparison is made between the results achieved by neural networks and those obtained using traditional surface approximation methods such as Bézier curves, k-nearest neighbors, principal component analysis, Markov random fields, conditional random fields, and convolutional neural networks.
On the Generalized (p,q)-ϕ-Calculus with Respect to Another Function
Authors: Etemad Sina , Stamova Ivanka , Ntouyas Sotiris K. , Tariboon Jessada
Published: October 2024

In the present paper, we generalized some of the operators defined in (p,q)-calculus with respect to another function. More precisely, the generalized (p,q)-ϕ-derivatives and (p,q)-ϕ-integrals were introduced with respect to the strictly increasing function ϕ with the help of different orders of the q-shifting, p-shifting, and (q/p)-shifting operators. Then, after proving some related properties, and as an application, we considered a generalized (p,q)-ϕ-difference problem and studied the existence property for its unique solutions with the help of the Banach contraction mapping principle.