bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generation and Classification of Land Use and Land Cover Datasets in the Indian States: A Comparative Study of Machine Learning and Deep Learning Models
Authors: Rengma Nyenshu Seb , Yadav Manohar 
Published: August 2023

Abstract
Land use and land cover (LULC) analysis is highly significant for various environmental and social applications. As remote sensing (RS) data becomes more accessible, LULC benchmark datasets have emerged as powerful tools for complex image classification tasks. These datasets are used to test state-of-the-art artificial intelligence models, particularly convolutional neural networks (CNNs), which have demonstrated remarkable effectiveness in such tasks. Nonetheless, there are existing limitations, one of which is the scarcity of benchmark datasets from diverse settings, including those specifically pertaining to the Indian scenario. This study addresses these challenges by generating medium-sized benchmark LULC datasets from two Indian states and evaluating state-of-the-art CNN models alongside traditional ML models. The evaluation focuses on achieving high accuracy in LULC classification, specifically on the generated patches of LULC classes. The dataset comprises 4000 labelled images derived from Sentinel-2 satellite imagery, encompassing three visible spectral bands and four distinct LULC classes. Through quantitative experimental comparison, the study demonstrates that ML models outperform CNN models, exhibiting superior performance across various LULC classes with unique characteristics. Notably, using a traditional ML model, the proposed novel dataset achieves an impressive overall classification accuracy of 96.57%. This study contributes by introducing a standardized benchmark dataset and highlighting the comparative performance of deep CNNs and traditional ML models in the field of LULC classification.
Global Left Ventricular Myocardial Work by Echocardiography: A Novel Method to Predict Major Adverse Cardiovascular Events from Maintenance Hemodialysis Patients
Authors: Guo Yao , Sun Minmin , Cao Xuesen , Tan Xiao , Shu Xianhong , Pan Cuizhen 
Published: August 2023

Abstract
Background
To compare non-invasive myocardial work (MW) parameters between maintenance hemodialysis (MHD) and healthy controls and further determine its value in predicting major adverse cardiovascular events (MACE) in MHD patients.
Methods
We performed a prospective single-institution study of 92 MHD patients (57.2 ± 12.3 years) and 40 age- and sex-matched healthy controls. Conventional echocardiographic data, global longitudinal strain (GLS), and MW parameters [Global work index (GWI), global constructive work (GCW), global work efficiency (GWE), global wasted work (GWW)] were derived and compared between MHD and the control. Logistic regression was used to determine the association and predictive value of these parameters for MACE.
Results
With a median follow-up of 3.3 ± 0.72 years (61.9% male), 23 (25%) MHD patients experienced MACE. MHD patients demonstrated significantly reduced GWE (P < 0.001) and elevated GWW (P < 0.001) compared with healthy individuals. Baseline models included age, E/E’ and LVMI. Two different nested models which added GWE and GLS respectively showed that age (P < 0.005), GWE (P = 0.034) and GLS (P = 0.014) were all independent predictors of MACE.
Conclusions
Myocardial work is a novel tool for assessing LV myocardial performance. GWE is an independent predictor of MACE in MHD patients and superior to other MW indices.
Azoxystrobin induced genotoxicity in Pethia conchonius, a freshwater fish of river Teesta, India
Authors: Ray Arpita , Dutta Debojit , Ghosh Bappaditya , Bahadur Min 
Published: August 2023

Abstract
Purpose
Azoxystrobin is one of the globally used strobilurin fungicides. Indiscriminate use of azoxistrobin can contaminate the water bodies by surface run-off. The bioaccumulation of azoxistrobin may adversely affect the aquatic organisms. This study has aimed to evaluate the genotoxic effects of azoxystrobin using Micronucleus (MN) and Comet assays in the fish Pethia conchonius as an experimental organism captured from the river Teesta of the northern part of West Bengal.
Methods
The 96h LC50 of azoxystrobin was estimated to be 0.514 mg/L. Three Sub-lethal concentrations (SLCs), namely SLC I = 0.025 mg/L, SLC II = 0.0514 mg/L and SLC III = 0.103 mg/L were used for the assay. The fishes were exposed to the above concentrations of azoxystrobin for 96h. The whole blood and gills were collected for MN and Comet assays, respectively at 24h interval.
Results
Nuclear abnormalities like micronuclei, notched and blebbed nuclei, and the DNA damage in terms of %Head DNA, %Tail DNA, Tail Length and Olive Tail moment were estimated by MN and Comet assays. The frequency of the nuclear abnormalities and DNA damages in the exposed groups were significantly (p˂0.05) higher than the control with increasing concentrations and the duration of exposure of azoxystrobin. The frequency of micronuclei (1.156 ± 0.011), notched (0.889 ± 0.011), blebbed (0.811 ± 0.011) nuclei and DNA damage parameters, %Head DNA (26.288 ± 1.981), %Tail DNA (73.712 ± 1.981), Tail Length (287.760 ± 11.641) and Olive Tail Moment (116.301 ± 7.636) were highest for SLC III at 96h of exposure.
Conclusion
The results strongly indicated that azoxystrobin is highly genotoxic in fish causing severe DNA damage.
Approximate Q-Learning-based (AQL) Network Slicing in Mobile Edge-Cloud environments for Delay-sensitive Services Using Markov Decision Process
Authors: Bharanidharan Chandrakasan , Malathi Subramanian , Sathya V , Rajakumaran m 
Published: August 2023

Abstract
Network slicing in mobile edge-cloud environments has gained significant attention due to its ability to provide customized network services tailored to the specific requirements of different applications. However, ensuring efficient resource allocation and meeting the tough delay requirements of delay-sensitive services remain major challenges in network slicing. In this paper, we propose an Approximate Q-Learning-based (AQL) approach for network slicing in mobile edge-cloud systems, specifically targeting delay-sensitive services. The AQL approach utilizes Q-Learning, a reinforcement learning technique, to optimize resource allocation and decision-making in network slicing. By formulating the resource allocation problem as a Markov Decision Process (MDP), the proposed AQL model learns to make intelligent decisions on resource allocation to minimize delays and maximize resource utilization. The model takes into account various factors such as network congestion, service priority, and available resources to dynamically adapt the network slice configuration. To evaluate the performance of the AQL approach, we conducted extensive simulations comparing it with traditional resource allocation methods. The results demonstrate that the AQL-based network slicing approach effectively reduces delay and improves resource utilization, outperforming conventional methods. The approach also demonstrates its adaptability to dynamic network conditions and varying service requirements. Furthermore, we investigate the impact of different factors on the AQL model's performance, such as the number of network slices, resource availability, and service priority. Our analysis provides valuable insights into the optimal configuration and operation of network slicing for delay-sensitive services. Overall, this paper contributes to the field of network slicing in mobile edge-cloud systems by introducing an AQL-based approach that efficiently addresses the resource allocation challenges for delay-sensitive services. The proposed approach offers a flexible and intelligent solution that can be applied in real-world scenarios, enabling network operators to provide reliable and high-performance network services for a variety of delay-sensitive applications.
Preparation of High-Temperature Resistant Aluminum-Doped Silica Aerogel from Aluminum Sol Source by Ambient Pressure Drying
Authors: Gao Shuai , Yang Ting , Liu Shuning , Liu Kai , Cao Zeqi , Pang Wanjun , Jiang Hongyi 
Published: August 2023

Abstract
Abstract
The preparation of aluminum-doped silica aerogel (ASA) typically utilizes alumina alkoxides or inorganic aluminum salts as precursors usually hindered by high cost or precursors instability. In this study, we innovatively developed a high-temperature resistant ASA via the sol-gel and atmospheric pressure drying (APD) method, utilizing an inexpensive aluminum sol as the aluminum source, tetraethyl orthosilicate (TEOS) and basic silica sol as the composite silicon source. The effects of the Al/Si molar ratio and calcination temperature on the structure, morphology, and thermal stability of ASA were investigated. The results revealed that ASA-0.2 (with an Al/Si molar ratio of 0.2) exhibited a specific surface area of 616.3m2/g at room temperature, whereas ASA-0 was 508.2m2/g. After calcination at 1000°C for 2h, ASA-0.2 remained a significantly higher specific surface area (290.0m2/g) compared to ASA-0 (23.9m2/g). Mechanism analysis indicated that the addition of aluminum sol not only improved the strength of the aerogel skeleton but also inhibited the phase transition of silica, effectively enhancing the high-temperature resistance of the aerogel. The utilization of inexpensive and stable aluminum sol as a precursor presents a promising approach for the industrial production of heat-resistant aerogels.
Inflammatory biotype of ADHD is linked to chronic stress: a data-driven analysis of the inflammatory pro-teome
Authors: Schiweck Carmen , Schnorr Isabel , Siegl Anne , Luckhardt Sonja , Wenz Söri , Friedrichsen Hendrik , Jomaa Hiba El , Steinmann Annebirth , Kilencz Tünde , Arteaga-Henríquez Gara , Ramos-Sayalero Carolina , Ibanez-Jimenez Pol , Rosales-Ortiz Silvia , Bitter Istvan , Fadeuilhe Christian , Ferrer Marc , Lavebratt Catharina , Réthelyi János , Richarte Vanesa , Rommelse Nanda , Ramos-Quiroga Josep Antoni , Vasquez Alejandro Arias , Resch Eduard , Reif Andreas , Matura Silke 
Published: August 2023

Abstract
The association between Attention Deficit Hyperactivity Disorder (ADHD) and low-grade inflammation has been explored in children but rarely in adults. Inflammation is characteristic of some, but not all, patients with ADHD and might be influenced by ADHD medication but also lifestyle factors including nutrition, smoking, and stress. It is also still unclear if any specific symptoms are related to inflammation. Therefore, we assessed 96 inflammatory proteins in a deeply pheno-typed cohort of 126 adult ADHD participants with a stable medication status using OLINK technology. A data-based, unsupervised hierarchical clustering method could identify two distinct biotypes within the 126 ADHD participants based on their inflammatory profile: a higher inflammatory potential (HIP) and a lower inflammatory protein potential (LIP) group. Biological processes that differed strongest between groups were related to the NF-κB pathway, chemokine signaling, IL-17 signaling, metabolic alterations, and chemokine attraction. A comparison of psychological characteristics revealed that the HIP group was more likely to have higher levels of chronic stress (p < 0.001), a higher clinical global impression scale score (p = 0.030), and a higher risk for suicide (p = 0.032). Medication status did not influence protein levels significantly (p ≥ 0.074), but psychotropic co-medication (p ≤ 0.009) did. In conclusion, our data suggest the presence of two distinct biotypes in adults with ADHD. Higher levels of inflammatory proteins in ADHD are linked to higher levels of chronic perceived stress in a linear fashion. Further research on inflammation in adults with ADHD should take stress levels into account.
AUCReshaping: Improved sensitivity at high-specificity
Authors: Bhat Sheethal , Mansoor Awais , Georgescu Bogdan , Panambur Adarsh , Ghesu Florin , Islam Saahil , Packhäuser Kai , Rodr´iguez-Salas Dalia , Grbic Sasa , Maier Andreas 
Published: August 2023

Abstract
The performance of deep-learning (DL) systems are generally measured using the Area under the Receiver-Operating-Curve
(AU-ROC); however, the holistic nature of this metric fails to account for the performance, at a specific range(s) of sensitivity and specificity, at which system is intended to operate. Therefore, it is seen that two systems with the same AU-ROC perform very differently in the field. The issue is especially exaggerated for anomaly detection tasks: a very popular application of DL systems in various areas of research spanning medical imaging, industrial automation, manufacturing, cyber security, fraud detection, drug research, etc. This is primarily due to the fact that the datasets used to train these systems are heavily imbalanced, and the abnormality class usually has a highly skewed misclassification cost compared to the normal class. Traditionally, DL systems account for this by weighting the cost function or designing for different operating points on the ROC curve. This approach achieves reasonable results in most instances but, the system does not actively attempt to maximize performance for the chosen operating point. In this paper, we propose a novel function called AUCReshaping which reshapes the ROC curve only in the desired region by optimizing the sensitivity at a desired specificity. The reshaping is implemented through an adaptive and iterative boosting manner that allows the network to focus on relevant samples during learning. We mainly studied the impact of AUCReshaping on an abnormality detection Chest X-Ray (CXR) task, followed by a breast mammogram and credit card fraud detection task. The results demonstrate an increase of 2-40% in sensitivity at high-specificity, for a binary classification task.
Automotive Urban Minerals Formation in China and Japan
Authors: Xu Guochang , Liu Fei , Miao Youping , Wang Binzhe , Zhang Zhengyang , Li Jinhui , Matsubae Kazuyo , Zeng Xianlai 
Published: August 2023

Abstract
Automotive electrification is sound for mitigating climate change, but it heavily relies on critical metals and brings serious challenges to material supply chains. Accordingly, we evaluated the resource pressure and risk of the automotive industries in China and Japan (two global automotive industry leaders) based on an urban metabolism analysis. The results show that the formation of automotive urban minerals in China and Japan will increase from 12.0 Mt and 4.4 Mt in 2022 to 47 Mt and 4.8 Mt in 2050, respectively. The industrial circularity degree of most recyclable metals increases over time in China, while Japan reflects a more stable trend. Regarding resource re-supply potential of urban material stocks to future demand, lithium, cobalt, and nickel are the most critical metals in China in 2030, while indium, cerium, and palladium are the most critical metals in Japan. Compared to exploiting geological mines, urban mines are more accessible in cities of China and Japan and less vulnerable to external factors. Improving the overall collection, separation, and recycling ratio is essential for the effective development of UM. Establishing a material circulation automotive industry poses challenges and opportunities for China and Japan. China and Japan, as exemplars of the fast-developing and developed worlds, can provide valuable lessons for the countries that lack automotive electrification targets to define their future aims.
Voltage Source Inverter based sub synchronous resonance analysis in wind farm using genetic widow optimization algorithm with photo voltaic cell
Authors: Arockiaraj  , B.V.Manikandan  
Published: August 2023

Abstract
The extensive and widespread incorporation of wind farms into the transmission network presents many objections. One of them is the requirement to improve power transmission ability to transport large levels of wind energy. The progression of series compensator is a proven method of increasing the power transmit capacity of offered conduction lines and it is increasingly being provided much importance for grid integration of outsized wind farms. On the other hand, series reactive compensation lines create sub-synchronous resonance (SSR) in the turbo generator, as a result of which electrical unsteadiness may occur at sub-synchronous frequency components and possible failure in turbo generator tube of shaft. This research paper proposes a novel approach for alleviating SSR using a voltage source inverter (VSI) in combination with a genetic window optimization algorithm (GWO) and a photovoltaic (PV) farm. The aim is to mitigate the negative effects of SSR while maximizing the utilization of renewable energy sources. It also provides a detailed description of the problem formulation, fitness function design, GWA initialization, and evolution process. Simulation results and analysis demonstrate the effectiveness and efficiency of the GA-based approach in SSR mitigation for practical wind farms. Analyzes of eigenvalues and participation factors are performed under various operating conditions. The analysis takes into account changes in wind speed, distance compensation range and wind velocity. Comprehensive small-signal analysis is used to examine the effects of SSR in wind turbines. Simulations of electromagnetic transients are performed using PSCAD/EMTDC software for the first IEEE reference system.
Smart IoT-based water treatment with a Supervisory Control and Data Acquisition (SCADA) system process
Authors: B. Dwarakanath , P. Kalpana Devi , Kumar A Ranjith , M. Metwally Ahmed Sayed , Ashraf Ghulam Abbas , Thamineni Bheema Lingaiah 
Published: August 2023

Abstract
Water treatment is necessary to ensure the availability of clean and safe water for various uses. Integrating Internet of Things (IoT) technology with water purification systems has shown enormous potential in recent years for enhancing the efficiency and efficacy of the treatment process. Monitoring the disposal of sewage in treatment facilities is the primary obstacle. As a result, a Supervisory Control And Data Acquisition (SCADA) system, including the IoT, has been proposed to ensure the proper operation of these sewer systems and limit the risk of overflow and malfunction. In this paper, we suggest a novel approach that blends Deep Belief Networks (DBNs) with an IoT-based water treatment system equipped with a SCADA system for increased monitoring and control. An IoT–SCADA system can be implemented at various wastewater collection and treatment phases. Secondly, incorporating DBNs enhances the system's predictive capabilities, enabling proactive maintenance and decision-making to prevent potential failures and optimize resource allocation. The proposed technique computes the efficacy of the effluent treatment facility and ensures that chemical emissions do not exceed permissible limits. Furthermore, Complex Event Processing (CEP) can be utilized to evaluate and analyze the massive influx of real-time data sets provided by IoT sensors.
Pyrrolo[2,3-D]Pyrimidines as EGFR and VEGFR Kinase Inhibitors: A Comprehensive SAR Review
Authors: Metwally Kamel , Abo-Dya Nader E. 
Published: August 2023

:
Tyrosine kinases are implicated in a wide array of cellular physiological processes,
including cell signaling. The discovery of the BCR-ABL tyrosine kinase inhibitor
imatinib and its FDA approval in 2001 paved the way for the development of small molecule
chemical entities of diverse structural backgrounds as tyrosine kinase inhibitors for
the treatment of various ailments. Two of the most prominent tyrosine kinases as drug targets
are the epidermal growth factor receptor (EGFR) and the vascular endothelial growth
factor receptor (VEGFR), as evidenced by the clinical success of their many inhibitors in
the drug market. Among several other physiological roles, EGFR regulates epithelial tissue
development and homeostasis, while VEGFR regulates tumor-induced angiogenesis.
The pyrrolo[2,3-d]pyrimidine nucleus represents a deaza-isostere of adenine, the nitrogenous
base of ATP. The recent introduction of many pyrrolo[2,3-d]pyrimidines to the drug
market as tyrosine kinase inhibitors makes them a hot topic in the medicinal chemistry research
area at the present time. This review article comprehensively sheds light on the
structure-activity relationship (SAR) of pyrrolo[2,3-d]pyrimidines as EGFR and VEGFR
tyrosine kinase inhibitors, aiming to provide help medicinal chemists in the design of future
pyrrolopyrimidine kinase inhibitors.
Role of Cav2.3 (R-type) Calcium Channel in Pain and Analgesia: A
Scoping Review
Authors: de Amorim Ferreira Marcella , Ferreira Juliano 
Published: August 2023

Background:
Voltage-gated calcium channels (VGCCs) play an important role in pain development
and maintenance. As Cav2.2 and Cav3.2 channels have been identified as potential drug
targets for analgesics, the participation of Cav2.3 (that gives rise to R-type calcium currents) in pain
and analgesia remains incompletely understood.

Objective:
Identify the participation of Cav2.3 in pain and analgesia.

Methods:
To map research in this area as well as to identify any existing gaps in knowledge on the potential
role of Cav2.3 in pain signalling, we conducted this scoping review. We searched PubMed and
SCOPUS databases, and 40 articles were included in this study. Besides, we organized the studies into
5 types of categories within the broader context of the role of Cav2.3 in pain and analgesia.

Results:
Some studies revealed the expression of Cav2.3 in pain pathways, especially in nociceptive
neurons at the sensory ganglia. Other studies demonstrated that Cav2.3-mediated currents could be inhibited
by analgesic/antinociceptive drugs either indirectly or directly. Some articles indicated that
Cav2.3 modulates nociceptive transmission, especially at the pre-synaptic level at spinal sites. There
are studies using different rodent pain models and approaches to reduce Cav2.3 activity or expression
and mostly demonstrated a pro-nociceptive role of Cav2.3, despite some contradictory findings and
deficiencies in the description of study design quality. There are three studies that reported the association
of single-nucleotide polymorphisms in the Cav2.3 gene (CACNA1E) with postoperative pain and
opioid consumption as well as with the prevalence of migraine in patients.

Conclusion:
Cav2.3 is a target for some analgesic drugs and has a pro-nociceptive role in pain.