bluuebunny/crossref_metadata_2025_split huggingface dataset:
X-ray guided anatomy-based fitting: The validity of OTOPLAN
Authors: Alahmadi Asma , Abdelsamad Yassin , Hafez Ahmed , Hagr Abdulrahman 
Published: November 2024

Background
Anatomy-based fitting (ABF) for cochlear implant users is a new era that seeks improved outcomes. Recently, different imaging modalities, such as plain X-rays, have been proposed to build the ABF as an alternative to the computed tomography (CT) scan. This study aimed to assess the feasibility and validity of OTOPLAN® software in building ABF using plain X-ray imaging.

Patients and methods
A retrospective evaluation of postoperative CT scans and plain X-ray post-op images of 54 patients was analyzed using the OTOPLAN® software. The post-op analysis was done for the angular insertion depth (AID) and center frequency of each electrode contact using both imaging modalities. Moreover, inter-rater reliability was assessed for measurements obtained from CT scans and plain X-ray images.

Results
Non-significant statistical and clinical mismatches were detected when comparing the AID and center frequency measurements assessed using CT and X-rays. The absolute difference between CT and X-ray approaches ranged from 0.0 to 4.6 degrees for AID and 0.2 to 0.5 semitone for frequency. Moreover, the AID and the frequency measurements from CT and X-ray images demonstrated almost perfect agreement between the raters. The inter-observer reliability for CT scans showed that the intraclass correlation coefficient (ICC) exceeded 0.97 for AID and 0.95 for the frequency across all electrode contacts.

Conclusion
Our results demonstrated the validity and reliability of using post-operative X-ray images by OTOPLAN® software to build Anatomy-based Fitting maps.
Circulating CCRR serves as potential novel biomarker for predicting acute myocardial infarction
Authors: Xuan Lina , Luo Huishan , Wang Shu , Wang Guangze , Yang Xingmei , Chen Jun , Guo Jianjun , Duan Xiaomeng , Li Xiufang , Yang Hua , Wang Shengjie , Zhang Hailong , Zhang Qingqing , Liu Shulei , She Yongtao , Kang Kai , Sun Lihua 
Published: November 2024

Abstract

Objective
Cold regions exhibit a high prevalence of cardiovascular disease, particularly acute myocardial infarction (AMI), which is one of the leading causes of death associated with cardiovascular conditions. Cardiovascular disease is closely linked to the abnormal expression of long non-coding RNA (lncRNA). This study investigates whether circulating levels of lncRNA cardiac conduction regulatory RNA (CCRR) could serve as a biomarker for AMI.

Materials and methods
We measured circulating CCRR from whole blood samples collected from 68 AMI patients and 69 non-AMI subjects. An AMI model was established using C57BL/6 mice. Quantitative reverse transcription PCR (qRT-PCR) was used to assess CCRR expression. Exosomes were isolated from cardiomyocytes, and their characteristics were evaluated using electron microscope and nanoparticle tracking analysis. The exosome inhibitor GW4869 was employed to examine the effect of exosomal CCRR on cardiac function using echocardiography. Protein expression was detected using Western blot and immunofluorescence staining.

Results
The circulating level of CCRR was significantly higher in AMI patients (1.93 ± 0.13) than in non-AMI subjects (1.00 ± 0.05, P < 0.001). The area under the ROC curve (AUC) of circulating CCRR was 0.821. Similar changes in circulating CCRR levels were consistently observed in an AMI mouse model. Exosomal CCRR derived from hypoxia-induced cardiomyocytes and cardiac tissue after AMI were increased, a change that was reversed by GW4869. Additionally, CCRR-overexpressing exosomes improved cardiac function in AMI.

Conclusion
Circulating lncRNA CCRR is a potential predictor of AMI. Exosomal CCRR plays a role in the communication between the heart and other organs through circulation.
Clinical study of a new nutritional index for predicting long-term prognosis in patients with coronary atherosclerotic heart disease following percutaneous coronary intervention
Authors: Chu Xinqiu , Yuan Yuewen , Chen Jiya , Yu Yanwei , Li Yang 
Published: November 2024

Abstract

Background and Objective
Some patients continue to experience major adverse cardiovascular and cerebrovascular events (MACCE) after percutaneous coronary intervention (PCI) in frigid places. Indexes of inflammation and nutrition alone were shown to predict outcomes in patients with PCI. However, the clinical predictive value of mixed indicators is unclear. This study aimed to assess the predictive value of the albumin/neutrophil/lymphocyte ratio (NLR) on the long-term prognosis of patients with coronary heart disease (CHD) following percutaneous coronary intervention (PCI).

Methods
A total of 608 post-PCI CHD patients were categorized into low- and high-index groups based on the optimal cut-off values for albumin and NLR. The primary outcome was a composite endpoint comprising all-cause mortality and major adverse cerebrovascular events. The secondary outcome was the comparison of the predictive efficiency of the new nutritional index, albumin/NLR, with that of albumin or NLR alone.

Results
Over the five-year follow-up period, 45 patients experienced the composite endpoint. The incidence of endpoint events was significantly higher in the low-index group (12%) compared to the high-index group (4.9%). Receiver operating characteristic (ROC) curve analysis revealed that the albumin/NLR index had a larger area under the curve (AUC: 0.655) than albumin (AUC: 0.621) or NLR (AUC: 0.646), indicating superior predictive efficiency. The prognostic nutritional index had an AUC of 0.644, further supporting the enhanced predictive value of the albumin/NLR index over individual nutritional and inflammatory markers.

Conclusion
The albumin/neutrophil/lymphocyte ratio is independently associated with the long-term prognosis of CHD patients post-PCI and demonstrates superior predictive efficiency compared to individual nutritional and inflammatory markers.
Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
Authors: Jović Branislav , Lukić Tin , Jović Danica , Radnović Nikola , Schaetzl Randall J. , Marković Slobodan B. 
Published: November 2024

Abstract
In this study, in a pioneering effort, glomalin proteins were extracted and geochemically characterized from soil in Serbia. Standard chemical (dry combustion, Walkley-Black) and spectroscopic methods (fourier transform infrared spectroscopy (FTIR), dynamic light scattering (DLS), scanning electron microscope (SEM)) were used to gain insight into the glomalin proteins. Samples from the City of Novi Sad (Serbia) were used for characterization and comparison. The soil contained an average of 17 mg/g of glomalin, representing ≈21% of the soil organic matter. DLS zeta potential analysis and the FTIR spectra reveal two significant differences before and after glomalin extraction, indicating a relationship between sand fractions and organic matter that affects particle aggregation and erosion potential. A comparison of SEM images and DLS results reveals that the glomalin extract consists of two particle size groups (0.08–1 and 6 µm), suggesting that the extraction process isolates only a smaller fraction of proteins and less tightly bound particles, indicating that soil aggregation is influenced by both mineralogy and the characteristics of organic matter. Hence, our research raises important questions about the role of glomalin in mitigating soil degradation processes, particularly soil erosion. Our study will enhance the overall understanding of glomalin, inspire future research, and prove beneficial for the sustainable restoration of degraded lands.
Computational role of homogeneous–heterogeneous chemical reactions and a mixed convective ternary hybrid nanofluid in a vertical porous microchannel
Authors: Roja Ajjanna , Saadeh Rania , Madhukesh Javali Kotresh , Shamshuddin MD. , Prasad Koushik Vijaya , Khan Umair , Prakash Chander , Hussain Syed Modassir 
Published: November 2024

Abstract
This article mainly scrutinizes the heat transfer and flow characteristics of a mixed convection ternary hybrid nanofluid in a porous microchannel considering the catalytic chemical reaction and nonuniform heat absorption/generation. Using appropriate similarity transformations, the modeled equations are converted into reduced ones and then solved via the Runge–Kutta–Fehlberg 4th/5th order method. To strengthen this analysis, the convection mechanism has been deployed. The effect of pertinent physical parameters on the fluid motion and thermal field is displayed, including some important engineering variables like the Nusselt number, Sherwood number, and drag force. The novel outcomes display that the flow reduces with porous permeability and nanoparticle volume fraction. The temperature of the nanofluid improves with nonuniform heat absorption/generation. The concentration decreases in the presence of both homogeneous and heterogeneous reaction intensities. The heat transfer rate enhances for the Eckert number, and a similar influence on the mass transfer rate is noticed for homogeneous reaction parameters. Further, the drag force declines for the Grashof number. The outcomes show that, in all cases, the ternary hybrid nanofluid shows a greater impact than the nanofluid. The attained findings represent applications in the era of cooling and heating systems, thermal engineering, and energy production.
Adsorption removal of copper(II) from water by zero valent iron loaded dendritic mesoporous silica
Authors: Yu Junjie , Tobilko Viktoriia 
Published: November 2024

The object of research is synthesized dendritic mesoporous nanoscale silica (DMSN) modified with zero-valent iron (Fe0@DMSN). This material exhibits a high adsorption capacity for heavy metal ions, in particular copper, whose increased content in the aquatic environment poses a threat to living organisms. In this regard, the main physicochemical features of the removal of copper cations from the aqueous medium using the obtained sample were investigated.
The morphology of the obtained dendritic silicas was studied by electron microscopy and the presence of a layer of zero-valent iron was confirmed by X-ray diffraction analysis and infrared spectroscopy. The parameters of the porous structure of the synthesized materials were determined. It was found that after modification of mesoporous silica with particles of zero-valent iron, the value of its specific surface area decreased from 504 m2/g to 312 m2/g. This may be due to the formation of a Fe0 layer not only on their surface but also in the channels of the inorganic matrix, which has a unique dendritic structure characteristic of this type of particles. At the same time, the number of active centers increases due to the enrichment of the silica surface with functional modifier groups that show a high affinity for metal cations.
The adsorption capacity of Fe0@DMSN towards Cu2+ ions has been studied and it has been shown that the maximum adsorption value is 39.8 mg/g, which is significantly higher than that of the initial synthesized DMSN sample (0.7 mg/g).
The experimental data obtained indicate that the obtained sorption material based on dendritic mesoporous silica nanoparticles with a layer of reactive zero-valent iron can be used for the purification of water contaminated with metal ions. In addition, the magnetic properties of such materials, known and proven by various scientists, will make it easy to separate the solid phase in the processes of sorption water purification using magnetic separation.
A Decline Curve Pitfall Using Least-Squares Solution
Authors: Schrider Leo A. , Cerullo Richard E. 
Published: November 2024

In the solution of the exponential and hyperbolic decline curves by least squares, petroleum engineers normally attempt to pick for these equations constants that "best" agree with the production decline. Unfortunately, these solutions for the constants taken from the least-squares line are not necessarily the "best" fit to the data.
As an example of this type of solution, let us consider an exponential decline-curve equation familiar to most petroleum engineers:
Equation (1)
This equation is frequently referred to as the semilog decline-curve equation. The semilog title arises from the fact that production data satisfying this type of exponential equation plot as a straight line on semilog paper.
The Third Wave: Industry Restructuring Yet To Come
Authors: Koerner Roy H. 
Published: November 2024

Summary
Reorganizations are not new to the oil industry. However, they have been commonplace for approximately the past 20 years. All U.S. industries have undergone change since the early 1980's to compete on a global basis. This paper describes the successive evolution of the three types of reorganization or "waves." Apparent positive results on business measures resulted from the first two waves. Successive iterations of reorganizations resulted in apparent deleterious effects on the surviving employees. There are indications that bottom-line effects may be leveling, and the reaction from that may be more second-wave-type reorganizations. Many may perceive new or different organizations resulting because of their manifestations. However, they will be just variations on a basic reorganization theme. The third wave, yet to come, will require companies to "soul search" their reason for being and to formulate clear visions of their future role in the industry. This paper describes some character attributes and cognition requirements for companies and industry professionals to "ride" the third wave of reorganization successfully.
Anomaly Detection in weekly SIRGAS GNSS Time Series: A TimesNet assessment in real data
Authors: de Andrade Luiz Claudio Oliveira , de Paulo Maurício Carvalho Mathias , Marques Haroldo Antonio 
Published: November 2024

Abstract

Time series anomaly detection is essential in a range of fields because it allows for the identification of atypical patterns that differ from the expected behavior. Time series data of GNSS coordinates of SIRGAS stations are valuable for research purposes, including the determination of tectonic drift (velocity) and strain rate deformation. Changes in the patterns of a GNSS time series at a station due to abnormal events such as earthquakes, equipment changes, or malfunctions can significantly impact the accuracy of velocity estimation. In this work, we assess the efficacy of a neural network called TimesNet for detecting anomalies in real GNSS time series data from 95 SIRGAS stations in Brazil and Ecuador. As TimesNet achieved state-of-the-art efficiency on many benchmark datasets, it was chosen as the primary model for this research. Hyperparameter optimization was employed for TimesNet using two stations: BRAZ, in a low seismic activity area in Brazil, and CHEC in a high seismic activity area in Ecuador. The results with both hyperparameters sets were compared against three established anomaly detection filters (Gaussian process, Kalman and Moving Average) and a LSTM neural network that were paired with three anomaly scorer methods (norm, K-means and difference). We tested two methods to predict labels from the anomaly scores: 1) Using a fixed percentile (Top 1%) threshold, and 2) Using the Top S scores, where S is the number of anomalies of each station. The TimesNet with hyperparameters optimized in the CHEC station obtained better results than BRAZ, both in Brazil and Ecuador stations, showing that the choice of hyperparameters may present an impact in the anomaly detection results. The best F1Top S and MSE in Brazil were obtained with the Kalman filter (F1Top S=57.60 | MSE = 0.0094), closely followed by TimesNet (F1Top S=55.67 | MSE = 0.0095). In Ecuador, the best F1Top S and MSE were obtained by TimesNet (F1Top S=63.64 | MSE = 0.0145), followed by the Kalman filter (F1Top S=59.09 | MSE = 0.0344). Therefore, the anomaly scores produced by TimesNet were less noisy than those produced by the other methods on Ecuador. To foster further research, the source code for this analysis is publicly available at https://github.com/mauriciodev/deep_anomaly_gnss.
Dependency-aware Task Collaborative Offloading and Resource Allocation in UAV Enabled Edge Computing
Authors: Huang Zhenqi , Kuang Zhufang , Xu Bin , Bi Yuanguo , Liu Anfeng 
Published: November 2024

Abstract

Edge computing, a new wireless communications technology, can provide abundant resources for computing and storage. Recently, it is a challenging problem to jointly consider the offloading and resource allocation of generally dependent tasks in multi Unmanned Aerial Vehicles (UAVs) multi Terminal Devices (TDs) edge computing system. In this paper, the joint problem of offloading decisions, collaborative decisions and resource allocation of general dependent tasks is investigated, considering and highlighting the cooperation between TDs. The corresponding optimization problem, which is a Mixed Integer Nonlinear Programming problem, is formulated. To make this problem solved, an iterative method based on Deep Reinforcement Learning (DRL) and Convex optimization is proposed to decompose the original problem into two subproblems. Given the resource allocation scheme, the Deep Q network (DQN) algorithm is employed to solve the offloading and collaborative decisions of all tasks. Then, given the offloading decision and cooperation decision of all tasks, the convex optimization algorithm is used to solve the optimal resource allocation scheme in the UAV enabled edge computing system. Two subproblems iterative alternately. The simulation results demonstrated that our proposed method can significantly reduce the system energy consumption compared to other schemes.
An Eastern Conception of Wisdom: Representation from Indian Sample
Authors: Dewangan Roshan Lal , Ghosh Rajib 
Published: November 2024

Abstract

Background
Wisdom is universally perceived as the pinnacle of human development across various religions, cultures, and literary traditions. However, research on wisdom has primarily been centered in Western countries. The Eastern understanding of wisdom draws heavily from research conducted in, or involving participants from, Taiwan, China, and Japan. This study endeavours to explore the implicit conception of wisdom among Indian scholars.
Methods
Forty scholars from various disciplines, including Science, Fine Arts, Humanities, and spiritual organizations (Sages), responded to questions regarding their perspectives on wisdom. Interview were transcribed and thematically analyzed. Wisdom characteristics provided by the Indian scholars were further validated by a sample of 353 adults. The findings were analyzed in comparison to both Western and Eastern (Taiwan, China, and Japan) research on wisdom.
Results
Indian scholars have conceptualized wisdom under six major themes: integration of various human abilities, gained through experience, inclusion of prosocial values, depth of understanding, spiritual/transcendental elements, and rarity but achievability by all. The characteristics of wisdom identified by these scholars show strong agreement among other Indians.
Conclusion
Indian scholars’ understanding of wisdom mirrors that of their counterparts worldwide. Their multifaceted view emphasizes wisdom’s role in ethical decision-making, spiritual evolution, and societal advancement. The insights gathered from this study are expected to enrich the global discourse on wisdom, offering a deeper understanding of Indian perspectives.
Optimizing 5G network management
Authors: Gao Huijun , Lei Dongxu , Zhuang Songlin , Barzel Baruch , Boccaletti Stefano 
Published: November 2024

Abstract

Modern society takes connectivity for granted. We, therefore, rely quite heavily on
our communication networks, both to sustain interpersonal connections, but also to
support our technological infrastructure, e.g., health, power or transportation. This
dependence will further strengthen as 5G technology becomes more pervasive and
communication traffic sharply increases. It is, therefore, crucial to develop methods
to optimize the efficiency and reliability of our ever-expanding networks. Such
methods must account for the interplay between the static network infrastructure
and the dynamic user connection preferences. The problem is that the user preferences
arise from each individual’s mobility and communication patterns - data
that are strictly protected by privacy concerns, and hence cannot be used for the
network optimization. To address this challenge we develop CLUSTER, an interpretable
Bayesian non-parametric framework, that uses aggregate, low resolution,
user data, to detect user groups with predictably correlated connection patterns.
We show that CLUSTER offers actionable insights on the network management,
such as setting each base-station’s activation cycles, detecting critical stations and
guiding the deployment of new stations. All, without violating user privacy. More
broadly, CLUSTER illustrates a general approach to extract meaningful information
from privacy protected data.
Global potential geographic distribution of Anthonomus eugenii under climate change: A comprehensive analysis based on an ensemble modeling approach
Authors: Wang Peilin , Yang Ming , Zhao Haoxiang , Wei Dandan , Zhang Guifen , Jiang Hongbo , Zhang Chi , Xian Xiaoqing , Huang Hongkun , Zhang Yibo 
Published: November 2024

Abstract

Climate warming is affecting the ranges and population dynamics of invasive species, including insects, which have become a global problem, causing biodiversity declines and agricultural economic losses. Anthonomus eugenii as an important invasive pest on pepper is now mainly located in the USA and Mexico. However, the global potential geographic distribution (PGD) of A. eugenii with climate change remains unknown, which makes it difficult to monitor and control. In this study, based on the global distribution areas and important environmental variables, we constructed an ensemble model to predict the global PGD of A. eugenii under the current climate and three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) in the 2030s and 2050s. The mean true skill statistics (TSS) and the area under the receiver operating characteristic curve (AUC) of the EM were 0.825 and 0.988, respectively, indicating that the EM was reliable. The mean temperature of the driest (bio9) and wettest (bio8) quarter and precipitation of the coldest quarter (bio19) were the most important environmental variables affecting the PGD of A. eugenii. Under the current climate, the PGD of A. eugenii was mainly concentrated in southern North America and central South America. The suitable areas of A. eugenii could increase significantly, reaching the maximum under SSP5-8.5 in the 2030s, approximately 1911.7 × 104 km². Moreover, the distribution centroid would shift to higher latitudes with global warming. It also had the potential invasion risk in Russia, China, the Republic of the Congo, and Romania, which should enhance quarantine control and early warning.