bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flat rent price prediction in Berlin with web scraping
Authors: Meyberg Camilo , Rendtel Ulrich , Leerhoff Holger 
Published: June 2024

AbstractInternet data pose a challenge to the traditional system of official statistics, which relies on more conventional sources such as surveys and registers, not readily adaptable to rapid changes. Expanding this system to include internet data is currently at an experimental stage, exploring these sources’ potentials and benefits. This paper describes a project conducted within the ESSnet Trusted Smart Statistics – Web Intelligence Network framework. It investigates the use of online apartment listings to analyze the rental market. We used web scraping to extract information from two online real estate portals for flats in the city of Berlin. Using this data, we developed a model to predict rental prices per square meter based on the accommodation’s features and location within the city. We detected offers which appear in both portals by means of statistical matching and removed duplicate offers. Missing values were treated by multiple imputation. The prediction model is a semi-parametric approach where the postal districts are used to describe the location effect. Comparisons with microcensus results and the local rent index reveal significant differences between the market of online flat offers and the stock of existing flat contracts. Interested readers will find the commented programming code in the internet supplement.
Data synchronization techniques and their impact on the prediction performance of automated recalibrated soft sensors in bioprocesses
Authors: Siegl Manuel , Geier Dominik , Andreeßen Björn , Max Sebastian , Mose Esther , Zavrel Michael , Becker Thomas 
Published: June 2024

AbstractInnovative soft sensor concepts can recalibrate automatically when the prediction performance decreases due to variations in raw materials, biological variability, and changes in process strategies. For automatic recalibration, data sets are selected from a data pool based on distance-based similarity criteria and then used for calibration. Nevertheless, the most appropriate data sets often are not reliably selected due to variances in the location of landmarks and process length of the bioprocesses. This can be overcome by synchronization methods that align the historical data sets with the current process and increase the accuracy of automatic selection and recalibration. This study investigated two different synchronization methods (dynamic time warping and curve registration) as preprocessing for the automatic selection of data sets using a distance-based similarity criterion for soft sensor recalibration. The prediction performance of the two soft sensors without synchronization was compared to the variants with synchronization and evaluated by comparing the normalized root mean squared errors. Curve registration improved the prediction performance on average by 24% (Pichia pastoris) and 9% (Bacillus subtilis). Using dynamic time warping, no substantial improvement in prediction performance could be achieved. A major factor behind this was the loss of information due to singularities caused by the changing process characteristics. The evaluation was performed on two target variables of real bioprocesses: biomass concentration prediction in P. pastoris and product concentration prediction in B. subtilis.
Reducing Social Media Attention Inequality in Disasters: The Role of Official Media During Rainstorm Disasters in China
Authors: Zheng Longfei , Chen Lei , Long Fenjie , Liu Jianing , Li Lei 
Published: June 2024

AbstractUnequal social media attention can lead to potentially uneven distribution of disaster-relief funds, resulting in long-term inequality among regions after disasters. This study aimed to measure inequalities in social media attention to regions during disasters and explore the role of official media in reducing such inequality. This is performed by employing social media, official media, and official aggregated statistics regarding China’s rainstorm disasters. Through a set of panel-data regressions and robustness tests, three main conclusions were drawn: (1) There were inequalities among regions regarding social media attention they received during rainstorm disasters. For disasters of the same magnitude, regions with low economic outcome per capita received less attention on social media. (2) Official media can reduce inequality in social media attention during disasters. Official media statements can encourage netizens to pay attention to disaster-stricken areas, and especially the overlooked underdeveloped areas. (3) Of all the measures taken by official media, timely, accurate, and open disclosure of disaster occurrences proved to be the most potent means of leveling the playing field in terms of social media attention; contrarily, promotional or booster-type messages proved futile in this regard. These findings revealed the vulnerabilities within social media landscapes that affect disaster relief response, shedding light on the role of official guidance in mitigating inequalities in social media attention during such crises. Our study advises social media stakeholders and policymakers on formulating more equitable crisis communication strategies to bridge the gap in social media attention and foster a more balanced and just relief process.
Overview of integrated electrocoagulation-adsorption strategies for the removal of heavy metal pollutants from wastewater
Authors: Twizerimana Pontien , Wu Yang 
Published: June 2024

AbstractRemoval of heavy metal pollutants from wastewater is critical for preserving a clean environmental setting and guaranteeing universal well-being. Physical, chemical, and biological approaches have been devised for the removal of heavy metals from various wastewater sources. Methods used include electrocoagulation (EC) and adsorption (AD). Although both methods are reported to be efficiently applied in wastewater treatment, the EC method is impeded by high energy consumption, particularly when treating concentrated effluents, as it demands substantial current for coagulant formation. Alternatively, the AD process is hindered by adsorbent saturation and the competitive effects of specific chemicals. Here, we only found limited studies on integrated EC-AD combined process, where the AD and EC were either used in separate or combined system vessels to augment the removal efficiency of heavy metal ions from wastewater or synthetic solutions. It is imperative to conduct more studies on synergistic approaches that combine adsorption with other wastewater treatment methods to address current limits and optimize removal processes. The review identified current density, pH, time, temperature, and adsorbent dosages as factors influencing the EC-AD process in heavy in the removal of heavy metals from wastewater. Although electrocoagulation combined with adsorption has been explored in several studies which have been confined to synthetic effluents, limiting their relevance to real-world scenarios. Therefore, this review proposes for development and design of EC-AD combined technologies to exploit their strengths and minimize associated limitations. Overall, the combined strategies proved more effective and economical compared to individual adsorption and electrocoagulation methods.
An Evolutionary Multitasking Ant Colony Optimization Method Based on Population Diversity Control for Multimodal Transport Problems
Authors: Cheng Meiying , Dong Liming 
Published: June 2024

AbstractMultimodal transport is a challenging, NP-hard problem in combinational optimization and has been solved using evolutionary algorithms, which excel at solving large-scale problems. However, few studies have used evolutionary algorithms, particularly swarm intelligence algorithms, to concurrently handle multiple multimodal transport instances. Ant colony optimization (ACO), which is a population intelligence technique that is adept at identifying the optimal paths in graphs, has been primarily used to address tasks separately rather than concurrently. Therefore, in this study, we introduce a multipopulation-based multitask environment where task-specific populations run in parallel, and ACO serves as the optimizer for each task. A variance-based population diversity measure is then calculated to characterize the distribution differences among individuals. If the population diversity of a specific task falls below a predetermined threshold, the valuable routing traits extracted from other tasks are transferred to the stagnant population. Our method is called population diversity-controlled multitask ACO (PDMTACO). We use multiple benchmark traveling salesman problem (TSP) instances at different scales to validate the efficacy of PDMTACO. Subsequently, we extend PDMTACO to address a series of multimodal transport problems. Our experimental results demonstrate that the use of information transferred by our method significantly reduces its logistics costs and carbon emissions in all multimodal transport tasks.
An Empirical Model of the Occurrence Rate of Low Latitude Post‐Sunset Plasma Irregularities Derived From CHAMP and Swarm Magnetic Observations
Authors: Stolle C. , Siddiqui T. A. , Schreiter L. , Das S. K. , Rusch I. , Rother M. , Doornbos E. 
Published: June 2024

AbstractThe prediction of post‐sunset equatorial plasma depletions (EPDs), often called ionospheric plasma bubbles, has remained a challenge for decades. In this study, we introduce the Ionospheric Bubble Probability (IBP) model, an empirical model to predict the occurrence probability of EPDs derived from 9 years of CHAMP and 9 years of Swarm magnetic field measurements. The model predicts the occurrence probability of EPDs for a given longitude, day of year, local time and solar activity, for the altitude range of about 350–510 km, and low geographic latitudes of ±45°. IBP has been found to successfully reconstruct the distribution of EPDs as reported in previous studies from independent data. IBP has been further evaluated using 1‐year of untrained data of the Ionospheric Bubble Index (IBI). IBI is a Level 2 product of the Swarm satellite mission used for EPD identification. The relative operating characteristics (ROC) curve shows positive excursion above the no‐skill line with Hanssen and Kuiper's Discriminant (H&KSS) score of 0.52, 0.51, and 0.55 at threshold model output of 0.16 for Swarm A, B, and C satellites. Additionally, the reliability plots show proximity to the diagonal line with a decent Brier Skill Score (BSS) of 0.249, 0.210, and 0.267 for Swarm A, B, and C respectively at 15% climatological occurrence rate. These tests indicate that the model performs significantly better than a no‐skill forecast. The IBP model offers compelling glimpses into the future of EPD forecasting, thus demonstrating its potential to reliably predict EPD occurrences. The IBP model is publicly available.
Developing an Explainable Variational Autoencoder (VAE) Framework for Accurate Representation of Local Circulation in Taiwan
Authors: Hsieh Min‐Ken , Wu Chien‐Ming 
Published: June 2024

AbstractThis study develops an explainable variational autoencoder (VAE) framework to efficiently generate high‐fidelity local circulation patterns in Taiwan, ensuring an accurate representation of the physical relationship between generated local circulation and upstream synoptic flow regimes. Large ensemble semi‐realistic simulations were conducted using a high‐resolution (2 km) model, TaiwanVVM, where critical characteristics of various synoptic flow regimes were carefully selected to focus on the effects of local circulation variations. The VAE was constructed to capture essential representations of local circulation scenarios associated with the lee vortices by training on the ensemble data set. The VAE's latent space effectively captures the synoptic flow regimes as controlling factors, aligning with the physical understanding of Taiwan's local circulation dynamics. The critical transition of flow regimes under the influence of southeasterly synoptic flow regimes is also well represented in the VAE's latent space. This indicates that the VAE can learn the nonlinear characteristics of the multiscale interactions involving the lee vortex. The latent space within VAE can serve as a reduced‐order model for predicting local circulation using synoptic wind speed and direction. This explainable VAE binds the physical reasoning to the predictions of the local circulation that ensures the physical examination of the uncertainty in accelerating the local weather assessments under various climate change scenarios.
Sustainable synthesis of magnetic petroleum coke/nonanyl chitosan composite for efficient removal of o-nitrophenol
Authors: Omer Ahmed M. , Eltaweil Abdelazeem S. , Abdelhamed Aly M. , Abd El-Monaem Eman M. , El-Subruiti Gehan M. 
Published: June 2024

AbstractWorldwide industrialization has grown at a rapid pace, contaminating water resources, particularly with phenolic pollutants that pose a risk to aquatic systems and human health. The goal of this study is to create an inexpensive magnetic composite that can effectively remove nitrophenol (o-NP) using adsorptive means. In this instance, a nonanyl chitosan (N-Cs) derivative was synthesized and then combined with activated petroleum coke (AP-coke) and magnetic Fe3O4 to boost its adsorbability towards o-NP and to facilitate its separation. Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffractometer (XRD), Vibrating sample magnetometer (VSM), X-ray photoelectron spectroscopy (XPS), and zeta potential were employed to characterize the magnetic composite. The experimental results indicated that the Fe3O4/AP-coke/N-Cs composite possesses a greater affinity toward o-NP with a maximal efficiency reached 88% compared to 22.8, 31.2, and 45.8% for Fe3O4, AP-coke and N-Cs, respectively. The equilibrium adsorption data coincided with the Langmuir, Freundlich, and Temkin isotherm models, with a maximum adsorption capacity of 291.55 mg/g at pH 6, whereas the pseudo second order kinetic model offered the best fit to the experimental data. Besides, the developed adsorbent preserved satisfactory adsorption characteristics after reuse for five successive cycles. The proposed adsorption mechanism involves the H-bonding, π-π interaction, hydrophobic interactions and electron donor-acceptor interactions. These findings hypothesize that the constructed magnetic composite could efficiently remove nitrophenols from polluted water with high performance and ease-separation.
Tongue image fusion and analysis of thermal and visible images in diabetes mellitus using machine learning techniques
Authors: Thirunavukkarasu Usharani , Umapathy Snekhalatha , Ravi Vinayakumar , Alahmadi Tahani Jaser 
Published: June 2024

AbstractThe study aimed to achieve the following objectives: (1) to perform the fusion of thermal and visible tongue images with various fusion rules of discrete wavelet transform (DWT) to classify diabetes and normal subjects; (2) to obtain the statistical features in the required region of interest from the tongue image before and after fusion; (3) to distinguish the healthy and diabetes using fused tongue images based on deep and machine learning algorithms. The study participants comprised of 80 normal subjects and age- and sex-matched 80 diabetes patients. The biochemical tests such as fasting glucose, postprandial, Hba1c are taken for all the participants. The visible and thermal tongue images are acquired using digital single lens reference camera and thermal infrared cameras, respectively. The digital and thermal tongue images are fused based on the wavelet transform method. Then Gray level co-occurrence matrix features are extracted individually from the visible, thermal, and fused tongue images. The machine learning classifiers and deep learning networks such as VGG16 and ResNet50 was used to classify the normal and diabetes mellitus. Image quality metrics are implemented to compare the classifiers’ performance before and after fusion. Support vector machine outperformed the machine learning classifiers, well after fusion with an accuracy of 88.12% compared to before the fusion process (Thermal-84.37%; Visible-63.1%). VGG16 produced the classification accuracy of 94.37% after fusion and attained 90.62% and 85% before fusion of individual thermal and visible tongue images, respectively. Therefore, this study results indicates that fused tongue images might be used as a non-contact elemental tool for pre-screening type II diabetes mellitus.
Experimental investigation on enhanced combustion of methanol/heavy fuel oil by droplet puffing at elevated temperatures
Authors: Chen Xiaoyu , Long Wuqiang , Wang Yang , Xiao Ge , Dong Pengbo , Wang Zixin , Xi Xi 
Published: June 2024

AbstractTo achieve high-efficiency combustion of heavy fuel oil (HFO), this study investigated the combustion characteristics of methanol/HFO droplets with methanol content from 10 to 30% using the suspension method under ambient temperature from 923 to 1023 K. The combustion of methanol/HFO droplets was summarized as a two-phase process consisting of six typical stages, emphasizing liquid phase. Especially, the fluctuation evaporation stage, induced by frequent and intense puffing, was identified as prominent character. Both the ignition delay and lifetime of HFO and methanol/HFO droplets decreased with increasing ambient temperatures. For the methanol/HFO droplet, the ignition delay and droplet lifetime increased with the increasing methanol content. Prominently, compared to HFO, HM10 had the most significant reduction in droplet lifetime and TINL under the same operating conditions, which indicated that the addition of 10% methanol accelerated the combustion process and reduced soot generation. Additionally, the thermos-dynamic characteristics of methanol/HFO droplets were investigated. Puffing was primarily attributed to superheating of methanol and pyrolysis of heavy components in HFO, which resulted in active and passive rupture of bubbles. Similarity and maximum deformation were employed to qualitatively distinguish between them. The obtained findings aimed to develop a promising alternative fuel to reduce emissions and preserve energy.
Assessment of cerebrovascular alterations induced by inflammatory response and oxidative–nitrative stress after traumatic intracranial hypertension and a potential mitigation strategy
Authors: Zhang Shangming , Chen Yehuang , Chen Qizuan , Chen Hongjie , Wei Liangfeng , Wang Shousen 
Published: June 2024

AbstractThe rapid perfusion of cerebral arteries leads to a significant increase in intracranial blood volume, exposing patients with traumatic brain injury to the risk of diffuse brain swelling or malignant brain herniation during decompressive craniectomy. The microcirculation and venous system are also involved in this process, but the precise mechanisms remain unclear. A physiological model of extremely high intracranial pressure was created in rats. This development triggered the TNF-α/NF-κB/iNOS axis in microglia, and released many inflammatory factors and reactive oxygen species/reactive nitrogen species, generating an excessive amount of peroxynitrite. Subsequently, the capillary wall cells especially pericytes exhibited severe degeneration and injury, the blood–brain barrier was disrupted, and a large number of blood cells were deposited within the microcirculation, resulting in a significant delay in the recovery of the microcirculation and venous blood flow compared to arterial flow, and this still persisted after decompressive craniectomy. Infliximab is a monoclonal antibody bound to TNF-α that effectively reduces the activity of TNF-α/NF-κB/iNOS axis. Treatment with Infliximab resulted in downregulation of inflammatory and oxidative–nitrative stress related factors, attenuation of capillary wall cells injury, and relative reduction of capillary hemostasis. These improved the delay in recovery of microcirculation and venous blood flow.
Cuticular hydrocarbon profiles reveal geographic chemotypes in stingless bees (Hymenoptera: Meliponini)
Authors: Rodrigues Méndez Melody Patricia , Muñoz-Rodríguez David , Aragón-Pech Rosendo Arturo de Jesús , Macías Macías José Octavio , Quezada-Euán José Javier G. 
Published: June 2024

AbstractCuticular hydrocarbon (CHCs) variation has been detected in various insect taxa, but the potential contribution of cuticular compounds for analyzing intraspecific diversity at the population level has been little explored. Here we assess for the first time intraspecific variation in the CHC profile of stingless bees, using the species Melipona beecheii and Nannotrigona perilampoides. The objective is determining whether intraspecific variation can be useful for population identification. We found species-specific chemical patterns and extensive variation within each species. Notably, chemotypes were significantly associated to geographic origin in N. perilampoides but less so in M. beecheii and we discuss possible explanations for these patterns. Our results support the use of CHCs in conjunction with other methods in emerging problems such as undetected colony mobilization across regions. As CHCs are involved in several aspects of stingless bee recognition and interactions, it would be essential to unravel how these chemical signatures evolve across populations.
Establishment and risk factor assessment of the abnormal body temperature probability prediction model (ABTP) for dairy cattle
Authors: Lee Tsair-Fwu , Chiu Chien-Liang , Liu Yen-Hsien , Chang Chu-Ho , Shao Jen-Chung , Guo Shih-Sian , Liao Yi-Lun , Chen Chia-Hui , Tseng Chin-Dar , Chao Pei-Ju , Lee Shen-Hao 
Published: June 2024

AbstractThe study aims to develop an abnormal body temperature probability (ABTP) model for dairy cattle, utilizing environmental and physiological data. This model is designed to enhance the management of heat stress impacts, providing an early warning system for farm managers to improve dairy cattle welfare and farm productivity in response to climate change. The study employs the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to analyze environmental and physiological data from 320 dairy cattle, identifying key factors influencing body temperature anomalies. This method supports the development of various models, including the Lyman Kutcher-Burman (LKB), Logistic, Schultheiss, and Poisson models, which are evaluated for their ability to predict abnormal body temperatures in dairy cattle effectively. The study successfully validated multiple models to predict abnormal body temperatures in dairy cattle, with a focus on the temperature-humidity index (THI) as a critical determinant. These models, including LKB, Logistic, Schultheiss, and Poisson, demonstrated high accuracy, as measured by the AUC and other performance metrics such as the Brier score and Hosmer–Lemeshow (HL) test. The results highlight the robustness of the models in capturing the nuances of heat stress impacts on dairy cattle. The research develops innovative models for managing heat stress in dairy cattle, effectively enhancing detection and intervention strategies. By integrating advanced technologies and novel predictive models, the study offers effective measures for early detection and management of abnormal body temperatures, improving cattle welfare and farm productivity in changing climatic conditions. This approach highlights the importance of using multiple models to accurately predict and address heat stress in livestock, making significant contributions to enhancing farm management practices.