bluuebunny/crossref_metadata_2025_split huggingface dataset:
Scaling theory of fractal complex networks: Bridging local self-similarity and global scale-invariance
Authors: Fronczak Agata , Fronczak Piotr , Samsel Mateusz , Makulski Kordian , Łepek Michał , Mrowinski Maciej J. 
Published: July 2023

Abstract
We show that fractality in complex networks arises from the geometric self-similarity of their built-in hierarchical community-like structure, which is mathematically described by the scale-invariant equation for the masses of the boxes with which we cover the network when determining its box dimension. This approach - grounded in both scaling theory of phase transitions and renormalization group theory - leads to the consistent scaling theory of fractal complex networks, which reveals a collection of scaling exponents and different relationships between them. The exponents can be divided into two groups: microscopic (hitherto unknown) and macroscopic, characterizing respectively the local structure of fractal complex networks and their global properties. Interestingly, exponents from both groups are related to each other and only a few of them (three out of seven) are independent, thus bridging the gap between local self-similarity and global scale-invariance of fractal networks. We successfully verify our findings in real networks situated in various fields (information – the World Wide Web, biological – the human brain, and social – scientific collaboration networks) and in several fractal network models.
Immune signature of the Chlamydia vaccine CTH522/CAF®01 translates from mouse to human and induces durable protection
Authors: Olsen Anja , Rosenkrands Ida , Jacobsen Christina , Cheeseman Hannah , Kristiansen Max , Dietrich Jes , Shattock Robin , Follmann Frank 
Published: July 2023

Abstract
The clinical development of an effective Chlamydia vaccine requires in-depth understanding of how well protective pre-clinical immune signatures translate to humans. Here we report the first comparative immunological characterization of CTH522/CAF®01 in mice and humans. We find a range of immune signatures that translate from mouse to humans, including a Th1/Th17 cytokine profile and antibody functionality. Interestingly, we identified vaccine-induced T cell epitopes, conserved among serovars and previously found in infected individuals. Using the mouse model, we show that the common immune signature protected against ascending infection in mice and vaccine induced antibodies could delay bacterial ascension to the oviduct, as well as development of pathology, in a T cell depleted mouse model. Finally, we demonstrate long-lasting immunity and protection of mice one year after vaccination. Based on the results obtained in the present study, we propose to further investigate CTH522/CAF®01 in a phase IIb study.
Enhanced antibiotic removal by waste coffee grounds prepared via water washing and KOH activation
Authors: Lee Min Gyu , Kim Younghun 
Published: July 2023

Abstract
This paper proposed a method to remove amoxicillin (AMX) from waste coffee grounds (CGs) using environmentally friendly activated biochar-based adsorbents. KOH-activated adsorbents (activated CGs or ACGs) were prepared by washing with water to avoid using toxic chemicals. The maximum adsorption capacity and kinetics of the ACGs were evaluated using the Freundlich isotherm, Langmuir isotherm, adsorption density, and pseudo-second-order equation. ACG-4a (activated by KOH after washing with water) exhibited a significantly higher adsorption capacity (740.7 mg/g), whereas ACG-4b (activated by KOH after washing with NaOH) exhibited a lower capacity (549.5 mg/g). The specific surface area of the adsorbent is key to the adsorption performance. A comparison of the adsorbents revealed that the ACGs exhibited the highest AMX-adsorbing capacity. Their use has important environmental implications for water pollution mitigation and ecosystem preservation.
Sensibility of LISEM with different disaggregation efficiency equations to simulate sediment yield
Authors: Schlesner Alexandre Augusto , Bernardi Felipe K. , Werle Larissa , Dambroz Alice P. B. 
Published: July 2023

Abstract
The Limburg Soil Erosion Model (LISEM) is an environmental planning and diagnostic tool that, through the simulation of the processes of sediment disintegration, transport and deposition provided by runoff, calculates erosion and sediment yield (SY) in watersheds on an event scale. Currently, the model presents options for adjusting the disaggregation process using efficiency coefficients in the form of equations that act as modulators of the disaggregation provided by the main disaggregation equation of the model, which obeys the principle of the transport capacity by the power of the flow. In simulations for catchments with cohesive soils and rough reliefs, these coefficients may be limiting for the good estimation of SY in erosion modeling. Through hydrological monitoring of a hydrographic basin with agricultural activity of 1.2 km² hydrological variables of flow, suspended sediment concentration and precipitation intensity were obtained for a period of three years, as well as the physical characterization of the relief, soil and land use over the basin. Through the monitored hydrological database, 20 events were calibrated using the three disaggregation efficiency equations and the SY modeling performance was evaluated, in order to choose the one that best represents the disaggregation processes for cohesive soil conditions. From the three equations tested, the one that is used in the EUROSEM model represented by y = 0.79 * exp (-0.85 * COH) satisfactorily predict the SY for 17 of the 20 simulated events, compared to the one originally used in the LISEM model, developed for non-cohesive materials, and also in relation to that used in the MMF model, results obtained respecting the soil cohesion values observed in the field. For three simulated events the adjustment was not satisfactory due to the characteristics of the SY of these events, which do not follow the predictive logic of the erosive process proposed by the model. Thus, the availability of better equations to represent the conditions of the disaggregation for cohesive soils opens new perspectives for the use of the model in studies of the erosive processes in basin scale, and to determine the relationship between the estimated SY as a reflection of the management practices and conservationists adopted on the slopes.
Mildew Detection for Stored Wheat using Gas Chromatography–Ion Mobility Spectrometry and Broad Learning Network
Authors: Fu Mai-xia , Lian Fei-yu 
Published: July 2023

Abstract

Most of the existing methods for wheat mildew detection are biochemical methods, which have the problems of complicated procedures and slow speed. In this paper, a novel wheat mildew detection and classification model is proposed by combining gas chromatography-ion mobility spectrometry (GC-IMS) with a broad learning network (BLN) model. Firstly, the GC-IMS fingerprint spectrum of wheat samples with different degrees of mildew are collected by GC-IMS spectrometer, and then an effective and efficient incremental learning system without the need for deep architecture is constructed to identify these fingerprint spectrums. In the BLN model, ridge regression of the pseudo-inverse is designed to find the desired connection weights, and the new weights can be updated easily by only computing the pseudo-inverse of the corresponding added node. To improve the classification accuracy of the BLN model, incremental learning and the spatial attention mechanism (SAM) are introduced into the model. Experimental results show that the training time of the proposed model is greatly reduced compared to existing deep learning models. Under the small sample set condition, the mean average accuracy (mAP) of wheat mildew types reaches 90.32%, the identification precision of early wheat mildew reaches 95.34%. The comprehensive index shows that the neural network model proposed in this paper can be used as an alternative model for deep learning in similar areas of image recognition. The experiment also proved that GC-IMS combined with a broad learning model is an efficient and accurate method for wheat mildew detection.
Thermal ratcheting in granular materials with irregular particle shapes
Authors: Pan Yize , Gong Xiaohui , Loria Alessandro Rotta 
Published: July 2023

Abstract
Granular materials with irregular particle shapes, such as sands, undergo a myriad of temperature variations in natural and engineered systems. However, the impacts of cyclic temperature variations on the mechanics of granular materials remain poorly understood. In this context, little is known about the mechanical response of such materials to cyclic temperature variations for central variables that characterize granular systems: particle shapes, applied stress levels, relative densities, and temperature amplitudes. This paper presents advanced laboratory experiments to explore the impacts of cyclic temperature variations on the mechanics of sands. The results show that cyclic temperature variations applied to sands induce thermal ratcheting: the cumulative growth of irreversible bulk deformations of such materials due to microstructural rearrangements caused by thermal expansions and contractions of constituting particles. The deformations of granular materials caused by thermal ratcheting strongly depend on particle shape, stress level, relative density, and temperature amplitude. These deformations are limited for individual thermal cycles but accumulate and become significant for multiple thermal cycles. Thermal ratcheting leads to substantial compaction in sands and other granular materials, which can affect various natural and engineered systems.
Impact of Different Sulfur Sources on the Structure and Function of Sulfur Autotrophic Denitrification Bacteria
Authors: Chen Zhenguo , Lou Minlan , Fang Peizhen , Xiao Dunquan , Zhu Wenting , Chen Hongwei , Qian Wei 
Published: July 2023

Abstract
Nitrate pollution in surface water has become a significant environmental concern. Sulfur autotrophic denitrification (SAD) technology is gaining attention for its cost-effectiveness and efficiency in nitrate removal. This study aimed to investigate the structure and function of sulfur autotrophic denitrification microbial communities in systems using sodium thiosulfate (Group A) and elemental sulfur (Group B) as the sole electron donors. Metagenomic amplicon sequencing and physicochemical analysis were performed to examine the microbial communities. The results revealed that on day 13, the nitrate nitrogen removal rate in Group A was significantly higher (89.2%) compared to Group B (74.4%). The dominant genus in both Groups was Thiobacillus, with average abundances of 34.15% and 16.34% in Groups A and B, respectively. β-diversity analysis based on species level showed significant differences in bacterial community structure between the two Groups (P < 0.001). Group A exhibited a greater potential for nitrate reduction and utilized both thiosulfate and elemental sulfur (P < 0.01) compared to Group B. This study provides valuable technical insights into applying autotrophic denitrification with different sulfur sources and offers new prospects for in-depth mechanistic analysis.
Agricultural Pest Small Target Detection Algorithm Based on Improved YOLOv5 Architecture
Authors: Song Haiying , Yan Yiying , Xie Min , Duan Dejie , Xie Zhuokai , Li Yiliang 
Published: July 2023

Abstract
How to accurately and timely detect harmful organisms without artificial intervention is an important research issue in agricultural technology. When using machine vision methods to detect pests and diseases, the targets of agricultural pests are often too small or obstructed, resulting in targets occupying too small pixels to be accurately recognized. To address this issue, this article proposes an effective pest detection algorithm for small targets——YOLO-Pest. This method is based on the backbone of YOLOv5s as the backbone network, mainly replacing the C3 module of YOLOv5 backbone layer and the PANet structure of the neck layer with C3CBAM module and BiFPN structure respectively, and adding CAC3 module to the neck layer to improve the detection accuracy of small targets. In addition, based on the IP102 dataset, this article selected 40 species of pests as the dataset for model evaluation. The experimental results show that the improved YOLOv5s achieves MAP0.5 and MAP0.5:0.95 of 64.6% and 40.1% respectively in the dataset selected in this article. Compared with the original algorithm, MAP0.5 and MAP0.5:0.95 have been improved by 1.6% and 0.1%, respectively.
Artificial Intelligence Applications in Decision Making for Disease Management
Authors: Abdekhoda Mohammadhiwa , Ranjbaran Fatemeh 
Published: July 2023

Abstract
Background: Artificial intelligence (AI) has several potential applications in medicine, creating opportunities for reliable and evidence based decision making in disease management. Thus, the practical aspects of AI in decision-making should be identified. This study was conducted to identify AI applications in decision making for disease management.
Method: This study was a systematic review using the PRISMA-ScR checklist. Data collection was carried out by searching the related keywords in WOS and Scopus in May 2023.
Results: Regarding the AI applications in decision making for disease management, we found 80 sub-themes which were categorized into six themes, i.e. 1) Processing and managing data, 2) Characterization and analysis, 3) Prediction and risk stratification, 4) Screening, 5) Prognosis, and 6) Diagnosis.
Conclusion: AI has considerable capability in disease treatment and would be an integral part of medicine in the future. This study clearly identified six main themes that addressed AI capability in decision making for disease management. The use of AI can help in making medical decisions with more trust and confidence and thus make medical interventions more accurate and effective.
Water quality and gender intersections: Assessing differential impacts on health and well-being in Abeokuta, Nigeria
Authors: OLUWASANYA Grace , OMONIYI Ayodetimi , PERERA Duminda , LAYI-ADIGUN Barakat , THUY Laurens , QADIR Manzoor 
Published: July 2023

Abstract
It is often assumed that humans experience the effect of poor water quality like multiple health and socioeconomic impacts in the same way. But these impacts are not gender-neutral due to inequalities caused by physiological composition, age marginalization, and socioeconomic conditions, among others. We analyzed the intersections between water quality and gender and applied a mixed-method approach in collecting local-specific data and information. The assessment shows that without point-of-use water treatment, the water sources in the area are not safe for potable purposes, as the waters are hard in the sequence of borehole > surface water > hand-dug well > sachet water, with elevated levels of calcium (> 75mg/L) and not free from microbial contamination. Among the area population segments, men and boys (relative to women and girls) are more susceptible (55%) to the compounding health effects associated with the hardness and high calcium concentration in water. Girls are the most affected by the associated impacts of water collection. Men and boys are more vulnerable to the consequences of poor hygiene, while women are more susceptible to the health effects of toilet cleaning and sharing of sanitation facilities. Though most women and girls prefer disposable sanitary pads, women change more frequently and practice better menstrual hygiene than girls. We conclude that there are differential impacts of unsafe water, WASH services, and practices on human health. Gendered statistics through sex-disaggregated data is crucial to unmasking the differential impacts, which are neither gender-neutral nor evenly distributed between women and men, and boys and girls.
Binary gannet optimization algorithm for feature selection using time-varying transfer function
Authors: Xu Lin , Geng Fang-Dong , Hu Rui-Bin , Wang Ruo-Bin 
Published: July 2023

Abstract
The Gannet Optimization Algorithm (GOA) has shown good competence in solving constrained optimization problems, whereas the algorithm is unable to solve binary optimization problems. Hence, this paper proposes a new S-shaped time-varying transfer function and implements a binary version of the Gannet Optimization Algorithm (BGOA). The performance of the proposed time-varying transfer function was verified by comparing it with five other popular S-shaped transfer functions on 23 benchmark test functions. Eventually, BGOA is used in the feature selection domain to find feature subsets, which minimizes the number of selected features and maximizes the classification accuracy. An assessment metric to evaluate the performance of BGOA over 15 different datasets from the UCI repository and compare it with Binary Particle Swarm Optimization Algorithm (BPSO), and Binary Grey Wolf Optimization Algorithm (BGOA). The results show that the proposed BGOA is capable of searching for the optimal subset of features in the binary search space.
MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection
Authors: Hailu Tesfay Gidey , Abdulkadir Taye 
Published: July 2023

Abstract

In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user's requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.
Long-term outcomes of Baerveldt glaucoma implant surgery in Japanese patients
Authors: Iwasaki Kentaro , Komori Ryohei , Arimura Shogo , Orii Yusuke , Takamura Yoshihiro , Inatani Masaru 
Published: July 2023

Abstract
This study evaluated the long-term surgical outcomes of Baerveldt glaucoma implant (BGI) surgery in patients with refractory glaucoma (233 eyes/204 patients). Surgical failure was defined by: <20% reduction in preoperative intraocular pressure (IOP), or criterion A (IOP > 21 mmHg), criterion B (IOP > 17 mmHg), or criterion C (IOP > 14 mmHg). Reoperation, loss of light perception vision, or hypotony also denoted failure. The probability of success at 5 years postoperatively using criteria A, B, and C was 71.5%, 48.4%, and 22.0%, respectively. The mean IOP decreased significantly from 32.8 ± 9.4 mmHg preoperatively to 13.4 ± 4.1 mmHg at 5 years; the mean number of glaucoma medications also decreased from 3.7 ± 1.2 to 1.9 ± 1.9 (both P < 0.01). The number of previous intraocular surgeries was significantly associated with failure in the multivariable analysis for criterion B (hazard ratio: 1.28; P < 0.01). Early and late postoperative complications occurred in 93 (39.9%) and 33 (14.2%) eyes, respectively. Postoperative interventions were performed in 55 eyes (23.6%). BGI surgery resulted in significant long-term decreases in IOP and the number of glaucoma medications. BGI surgery is effective for refractory glaucoma. However, postoperative interventions due to complications are required in numerous cases.
Cancer Prevalence Across Vertebrates
Authors: Compton Zachary , Harris Valerie , Mellon Walker , Rupp Shawn , Mallo Diego , KAPSETAKI Stefania , Wilmot Mallory , Kennington Ryan , Noble Kathleen , Baciu Cristina , Ramirez Lucia , Peraza Ashley , Martins Brian , Sudhakar Sushil , Aksoy Selin , Furukawa Gabriela , Vincze Orsolya , Giraudeau Mathieu , Duke Elizabeth , Spiro Simon , Flach Edmund , Davidson Hannah , Zehnder Ashley , Graham Trevor , Troan Brigid , Harrison Tara , Tollis Marc , Schiffman Joshua , Aktipis Athena , Abegglen Lisa , Maley Carlo , Boddy Amy 
Published: July 2023

Abstract
Cancer is pervasive across multicellular species, but what explains differences in cancer prevalence across species? Using 16,049 necropsy records for 292 species spanning three clades (amphibians, sauropsids and mammals) we found that neoplasia and malignancy prevalence increases with adult weight (contrary to Peto’s Paradox) and somatic mutation rate, but decreases with gestation time. Evolution of cancer susceptibility appears to have undergone sudden shifts followed by stabilizing selection. Outliers for neoplasia prevalence include the common porpoise (<1.3%), the Rodrigues fruit bat (<1.6%) the black-footed penguin (<0.4%), ferrets (63%) and opossums (35%). Discovering why some species have particularly high or low levels of cancer may lead to a better understanding of cancer syndromes and novel strategies for the management and prevention of cancer.
A Correlation Study of Preoperative Lumbar Paraspinal Muscle Quality and L5-S1 Lumbar Foraminal Stenosis Degeneration after L4-5 TLIF
Authors: Chen Minghang , Zhang Peng , Lai Jiaxin , Li Sheng , Yu Weijie , Fan Shikang , Teng Honglin 
Published: July 2023

Abstract
Study design: Retrospective study.
Objectives: Adjacent segment degeneration (ASD) is a major complication associated with spinal fusion. The lumbar paraspinal muscle is an essential factor influencing the occurrence of ASD. This study aimed to investigate the effect of preoperative lumbar paraspinal muscle quality on L5-S1 adjacent lumbar foraminal stenosis degeneration (ASLFSD) after L4-5 transforaminal lumbar interbody fusion (TLIF).
Methods: 113 patients diagnosed with lumbar spinal stenosis at L4-5 were treated with TLIF. Lumbar paraspinal muscle measurements were obtained preoperatively and bilaterally from axial T2-weighted MR images. The measurementsincluded the total cross-sectional area of psoas (PS-tCSA), of erector spinae (ES-tCSA), and of multifidus (MF-tCSA); and fatty infiltration of psoas (PS-FI), of erector spinae (ES-FI) and of multifidus (MF-FI). Foraminal measurements, including posterior disc height (PDH), disc-to-facet distance (D-F), foraminal height (FH), and foraminal area (FA), were obtained bilaterally using a computed tomography system. The association between lumbar paraspinal muscle quality and changes in foraminal measurements was also studied.
Results: We observed that the FH and FA significantly reduced at 1 year postoperatively at the mean follow-up period of 41.56 ± 8.38 months (range. 43–50 months), and PDH, D-F, FH, and FA all significantly reduced at final follow-up. These changes in foraminal measurements were significantly and negatively correlated with PS-FI, ES-FI, and MF-FI.
Conclusion: During the clinical follow-up, we found that patients with a higher degree of paraspinal muscle FI were more likely to develop L5-S1 ASLFSD after L4-5 TLIF.