bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synergistic Degradation of Methylene Blue By Novel Fe-Co Bimetallic Catalyst Supported On Waste Silica in Photo-Fenton-Like System
Authors: Quiton Khyle Glainmer , Lu Ming-Chun , Huang Yao-Hui 
Published: November 2021

Abstract
The environment is affected by agricultural, domestic, and industrial activities that lead to drastic problems such as global warming and wastewater generation. Wastewater pollution is of public concern, making the treatment of persistent pollutants in water and wastewater highly imperative. Several conventional treatment technologies have been applied to water and wastewater remediation, but each has numerous limitations. To address this issue, treatment using bimetallic systems has been extensively studied. Synergistic effects between the two metals are highly desirable because they usually offer enhanced activity, selectivity, and stability relative to their monometallic counterparts. In this study, a novel method to fabricate bimetallic Fe-Co catalyst supported on waste silica was investigated for the treatment of methylene blue dye as model pollutant. Under the optimum conditions of pHi 3.0, 3.0 mM H2O2, 1.0 g L-1 Fe-Co/SiO2 catalyst, and 20 mg L-1 initial dye concentration, the maximum response for the decoloration and mineralization efficiencies of methylene blue were 100% and 64.57%, respectively. Superoxide radical was unveiled to be the dominant reactive oxygen species in the photo-Fenton-like system over Fe-Co/SiO2 catalyst. Compared to the contrastive catalyst, the Fe-Co/SiO2 synthesized using fluidized-bed crystallization exhibited comparable decoloration and mineralization efficiencies, and relatively lower metal leaching.
In Silico Prediction and Structure Based Multitargeted Molecular Docking Analysis of Selected Bioactive Compounds Against Mucormycosis
Authors: Madanagopal Premnath , Ramprabhu Nagarjun , Jagadeesan Rahul 
Published: November 2021

Abstract
Background: During the second wave of the COVID-19 pandemic, an unusual increase in cases of mucormycosis was observed in India, owing to immunological dysregulation caused by the SARS-CoV-2 and the use of broad-spectrum antibiotics, particularly in patients with poorly controlled diabetes with ketoacidosisto have contributed to the rise and it has been declared an epidemic in several states of India.Because of the black colouring of dead and dying tissue caused by the fungus, it was dubbed "black fungus" by several Indian media outlets. In this study, attempts were taken to unmask novel therapeutic options to treat mucormycosis disease. Rhizopus species is the primary fungi responsible for 70% of mucormycosis cases.Results: We chose three important proteins from the Rhizopus delemar such as CotH3, Lanosterol 14 alpha-demethylase and Mucoricin which plays a crucial role in the virulence of Mucorales. Initially, we explored the physiochemical, structural and functional insights of proteins and later using AutoDock Vina, we applied computational protein-ligand binding modelling to perform a virtual screening around 300 selected compounds against these three proteins, including FDA-approved drugs, FDA-unapproved drugs, investigational-only drugs, and natural bioactive compounds. ADME parameters, Toxicity risk and biological activity of those compounds were approximated via in silico methods. Our computational studies identified six ligands as potential inhibitors against Rhizopus delemar, including 12,28-Oxamanzamine A, vialinin B and deoxytopsentin for CotH3; pramiconazole and saperconazole for Lanosterol 14 alpha-demethylase; and Hesperidin for Mucoricin. Interestingly, 12,28-Oxamanzamine A showed a maximum binding affinity with all three proteins(CotH3: -10.2 kcal/mol Lanosterol 14 alpha-demethylase: -10.9 kcal/mol Mucoricin:-8.6 kcal/mol).Conclusions: In summary, our investigation identified 12,28-Oxamanzamine A, vialinin B, deoxytopsentin, pramiconazole, saperconazole and hesperidin as potent bioactive compounds for treating mucormycosis that may be considered for further optimization techniques and in-vitro and in-vivo studies.
Return of norovirus and rotavirus activities in winter 2020/21 in an Asian city with strict zero COVID-19 control strategy, Hong Kong, China
Authors: Chan Martin Chi-Wai 
Published: November 2021

Abstract
We observed active norovirus and rotavirus circulation in winter 2020/21 in Hong Kong while a variety of non-pharmaceutical interventions to eliminate COVID-19 were effective. The initial collateral benefit of countermeasures for COVID-19 to reduce viral gastroenteritis burden is not sustainable even in a city with continual zero COVID-19 control strategy.
FAIR, ethical, and coordinated data sharing for COVID-19 response: a review of COVID-19 data sharing platforms and registries
Authors: Maxwell Lauren , Shreedhar Priya , Dauga Delphine , McQuilton Peter , Terry Robert , Denisiuk Alisa , Molnar-Gabor Fruzsina , Saxena Abha , Sansone Susanna-Assunta 
Published: November 2021

Abstract
Data sharing is central to the rapid translation of research into advances in clinical medicine and public health practice. In the context of COVID-19, there has been a rush to share data, marked by an explosion of population- and discipline-specific resources for collecting, curating, and disseminating participant-level data. We present a comprehensive overview of COVID-19-related platforms and registries that harmonize and share participant-level clinical, OMICs, and imaging data and metadata, and describe how these initiatives map to best practice for ethical, equitable, and FAIR management of data resources. Data sharing resources were concentrated in high income countries and siloed by comorbidity, body system, and data type. Resources for sharing clinical data were less FAIR than those for sharing OMICs or imaging data. We review gaps and redundancies in COVID-19 data sharing efforts and outline recommendations to build on existing synergies and align with frameworks for effective and equitable data reuse.
A Competitive Volleyball Algorithm to Solve Global Optimization with the Engineering Problems
Authors: Sun Shuo , Ma Liang , Liu Yong 
Published: November 2021

Abstract
In real life, many engineering problems are nonlineble NP problems, in order to solve some of these problems, we put forward a competitive volleyball algorithm.The algorithm proposed in this paper is a meta-heuristic technique based on swarm optimization. It is inspired by the competition between volleyball teams in a league and the improvement in players’ overall abilities in order to win the Most Valuable Player award. Several specific terms relating to competition, such as pre-match reinforcement, single round robin mechanism, optimal strategy constitute the structure of the algorithm. The sensitivity of several parameters of the algorithm is analyzed and tested for three types of benchmark functions: unimodal, high-dimensional multimodal and low-dimensional multimodal functions. Through the use of these three types of test functions, the performance of this algorithm is compared with nine classical metaheuristic algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Soccer League Competition (SLC), League Championship Algorithm (LCA) and Volleyball Premier League (VPL). CVA has been used to solve three real-world engineering problems. The results show that the performance of the CVA is behaviorally promising and better than the other classical metaheuristic algorithms.
Object Detection in Deep Surveillance
Authors: Thakur Narina , Nagrath Preeti , Jain Rachna , Saini Dharmender , Sharma Nitika , Hemanth Jude 
Published: November 2021

Abstract
Object detection is a key ability required by most computer visions and surveillance applications. Pedestrian detection is a key problem in surveillance, with several applications such as person identification, person count and tracking. The number of techniques to identifying pedestrians in images has gradually increased in recent years, even with the significant advances in the state-of-the-art deep neural network-based framework for object detection models. The research in the field of object detection and image classification has made a stride in the level of accuracy greater than 99% and the level of granularity. A powerful Object detector, specifically designed for high-end surveillance applications, is needed that will not only position the bounding box and label it but will also return their relative positions. The size of these bounding boxes can vary depending on the object and it interacts with the physical world. To address these requirements, an extensive evaluation of the state-of-the-art algorithms has been performed in this paper. The work presented in this paper performs detections on MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an Unmanned Aerial Vehicle (UAV). The experimental analysis has been performed on Faster-RCNN, SSD and YOLO models. The Yolov5 model is found to outperform all the other models with 61% precision and 44% of F measure value.
Geometrical Simulation Model for Milling of Carbon Fiber Reinforced Polymer (CFRP) Composites
Authors: Fu Xingyu , Song Kyeongeun , Kim Dong Min , Kang Zhengyang , Jun Martin Byung-Guk 
Published: November 2021

Abstract
This paper proposes a geometrical 3D milling simulation algorithm for Carbon Fiber Reinforced Polymer (CFRP) milling. In this simulation model, milling tools are simplified into layers of circles while CFRP lami- nates are simplified into layers of Dexel lines, which can realize simulations for various complex milling conditions. Significant geometrical parameters, for ex- ample, cutting angle and cutting length, can be computed with high efficiency. With some geometry-related physical models, the machining results can be pre- dicted for the entire milling process. The effectiveness of this simulation model has been validated by the milling force prediction and the delamination pre- diction. The performance of this simulation model benefits industrial CFRP manufacturing and provides a new method for online or long-time-interval simulation of CFRP machining.
The Ten-year Risk Prediction for Cardiovascular Disease in the National Population (Globorisk) of Malaysian Adults
Authors: Musa Kamarul Imran , Nawi Che Muhammad Nur Hidayat Che , Omar Mohd. Azahadi , Keegan Thomas , Yu Yong Poh 
Published: November 2021

Abstract
Globorisk is a novel risk prediction model that predicts cardiovascular disease (CVD) in the national population of all world countries. Using Malaysia's risk factor levels and CVD event rates, we calculated the laboratory-based and office-based risk scores to predict the 10-year risk for fatal CVD and fatal plus non-fatal CVD for the Malaysian adult population. We analysed data from 8253 participants from the 2015 nationwide Malaysian National Health and Morbidity Survey (NHMS 2015). The average risk for the 10-year fatal and fatal plus non-fatal CVD was calculated, and participants were further grouped into four categories: Low Risk (<10% risk for CVD), High-Risk A (≥10%), High-Risk B (≥20%) and High-Risk C (≥30%). Results were reported for all participants and were then stratified by sex, race, region, and state. The average risks for laboratory-based fatal CVD, laboratory-based fatal plus non-fatal CVD and office-based fatal plus non-fatal CVD were 0.07 (SD = 0.10), 0.14 (SD = 0.12) and 0.11 (SD = 0.09), respectively. There were substantial differences in terms of the sex-, race- and state-specific Globorisk risk scores obtained.
Observations on the Curative Effect of a Holmium Laser Under a Flexible Ureteroscope in the Treatment of Endogenous Renal Cysts Combined with Renal Calculi
Authors: Cai Qi , Liao Kaisen 
Published: November 2021

Abstract
Background: To explore and analyse the curative effects of a holmium laser under a flexible ureteroscope in the treatment of endogenous renal cysts complicated with renal calculi. Methods: Seven patients with endogenous renal cysts and renal calculi were admitted to our hospital from April 2019 to April 2021. The renal calculi was smashed by a holmium laser under a flexible ureteroscope, and the kidney cysts were opened by holmium laser endotomy. Window decompression was performed, indwelling the ureteral double-J tube for 1 month after the operation. After 1 month, when the patient was hospitalized and the double-J tube was removed, a flexible ureteroscope was inserted into the renal pelvis to confirm the effect of window opening and lithotripsy, and CT or B-ultrasound was reviewed. Results: Among the 7 patients with endogenous renal cysts and kidney stones, one patient was scheduled to undergo an internal incision after successful lithotripsy. The renal cyst wall tissue was surrounded by the renal artery, and the internal incision was terminated. One patient had an unsuccessful lithotripsy due to the poor angle of the kidney stone but was successfully treated for the renal cyst. The remaining 5 patients successfully underwent lithotripsy and internal incision surgery, of which 2 cases had difficulty finding kidney cysts and stones during the operation and needed assistance in locating kidney cysts and kidney stones under B-ultrasound monitoring. The average operation time was 42 minutes. When the catheter was extubated, the diameter of the renal cyst window was 0.5 cm to 2.5 cm under a flexible ureteroscope. The stone discharge was fair. There were residual gravels in the lower calyx of the kidney in two patients, and the stone basket was removed. After extubation, the maximum diameter of the renal cyst was reduced by more than half by CT or B-ultrasound, the effect of fenestration and decompression was achieved, and there were no obvious residual stones. Conclusions: A holmium laser under a flexible ureteroscope has a reliable curative effect in the simultaneous treatment of endogenous renal cysts with kidney stones, with little damage, quick recovery, and low cost. It can be used as the first choice for the treatment of endogenous renal cysts with kidney stones.
A Hybrid Fuzzy Decision Making Approach for Sitting a Solid Waste Energy Production Plant
Authors: ALBAYRAK Kezban 
Published: November 2021

Abstract
The rapid and uncontrolled growth of the world's population technological developments, increase in the social welfare and the transformation of societies into consumer societies have changed the dimensions of environmental problems. Nowadays waste management has become an important issue for the solution of environmental problems. Hence, we discussed the municipal solid waste management. Municipal solid waste management problem is a complex and it has many different aspects as political, social, technological and economical criteria have to consider. The evaluation of these criteria numerically is complicated and vague. This paper deals with this complexity by proposed methodology. Also the contribution of the article to the literature is that the proposed methodology is applied for the first time in municipal solid waste management problems. In this paper two fuzzy decision making approaches are combined for sitting a waste to energy plant in the Kırıkkale in Turkey. Four alternative locations and nine criteria are defined from the expert opinions and the literature survey. A new hybrid methodology that has not been applied before for this decision problem is proposed. In proposed methodology, there are two main stages. Criteria weights determination is the first stage and ranking of the alternative locations is the second stage of the methodology. In first stage Interval type 2 Fuzzy Analytic Hierarchy process (AHP) method is performed and in the second stage hesitant fuzzy Technique for order preference by similarity to an ideal solution (TOPSIS) method is used for ranking the alternative locations. Also decision makers have different experience level and knowledge about the problem and different decision makers’ weights are considered for group decision making. Two fuzzy methods are combined for the solid waste energy production plant location selection problem. As a result of the study, the second alternative (Bahsılı-Bedesten) is determined as the most suitable area for waste to energy production plant. Besides, with scenario analysis the effect of criteria on ranking of the alternatives is analyzed.
Deep Learning Based Attack Detection in IIoT using Two-Level Intrusion Detection System
Authors: Raja Kathiroli , Karthikeyan Krithika , B Abilash , Dev Kapal , Raja Gunasekaran 
Published: November 2021

Abstract
The Industrial Internet of Things (IIoT), also known as Industry 4.0, has brought a revolution in the production and manufacturing sectors as it assists in the automation of production management and reduces the manual effort needed in auditing and managing the pieces of machinery. IoT-enabled industries, in general, use sensors, smart meters, and actuators. Most of the time, the data held by these devices is surpassingly sensitive and private. This information might be modified, 1 stolen, or even the devices may be subjected to a Denial of Service (DoS) attack. As a consequence, the product quality may deteriorate or sensitive information may be leaked. An Intrusion Detection System (IDS), implemented in the network layer of IIoT, can detect attacks, thereby protecting the data and devices. Despite substantial advancements in attack detection in IIoT, existing works fail to detect certain attacks obfuscated from detectors resulting in a low detection performance. To address the aforementioned issue, we propose a Deep Learning-based Two Level Network Intrusion Detection System (DLTL-NIDS) for IIoT environment, emphasizing challenging attacks. The attacks that attain low accuracy or low precision in level-1 detection are marked as challenging attacks. Experimental results show that the proposed model, when tested against TON IoT, figures out the challenging attacks well and achieves an accuracy of 99.97%, precision of 95.62%, recall of 99.5%, and F1-score of 99.65%. The proposed DL-TLNIDS, when compared with state-of-art models, achieves a decrease in false alarm rate to 2.34% (flagging normal traffic as an attack) in IIoT.
Experimental calibration of an Fe3+/Fe2+-in-amphibole oxybarometer and its application to shallow magmatic processes at Shiveluch Volcano, Kamchatka
Authors: Goltz Andrea E. , Krawczynski Michael J. , McCanta Molly C. , Dyar M. Darby 
Published: November 2021

AbstractOxygen fugacity is an important but difficult parameter to constrain for primitive arc magmas. In this study, the partitioning behavior of Fe3+/Fe2+ between amphibole and glass synthesized in piston-cylinder and cold-seal apparatus experiments is developed as an oxybarometer, applicable to magmas ranging from basaltic to dacitic composition. The partitioning of Fe2+ is strongly dependent on melt polymerization; the relative compatibility of Fe2+ in amphibole decreases with increasing polymerization. The Fe2+/Mg distribution coefficient between amphibole and melt is a relatively constant value across all compositions and is, on average, 0.27. The amphibole oxybarometer is applied to amphibole in mafic enclaves, cumulates, and basaltic tephra erupted from Shiveluch volcano in Kamchatka with measured Fe3+/FeTotal. An average Fe3+/Fe2+ amphibole-glass distribution coefficient for basalt is used to convert the Fe3+/FeTotal of amphibole in samples from Shiveluch to magmatic oxygen fugacity relative to NNO. The fO2 of primitive melts at the volcano is approximately NNO+2 and is faithfully recorded in amphibole from an amphibole-rich cumulate and the basaltic tephra. Apparently, higher fO2 recorded by amphibole in mafic enclaves likely results from partial dehydrogenation of amphibole during residence in a shallow andesite storage region. We identify three pulses of mafic magma recharge within two weeks of, a month before, and two to three months before the eruption and find that, at each of these times, the host andesite was recharged by at least two magmas at varying stages of differentiation. Application of the amphibole oxybarometer not only gives insight into magmatic fO2 but also potentially details of shallow magmatic processes.
The role of graphite in the formation of unconformity-related uranium deposits of the Athabasca Basin, Canada: A case study of Raman spectroscopy of graphite from the world-class Phoenix uranium deposit
Authors: Song Hao , Chi Guoxiang , Wang Kewen , Li Zenghua , Bethune Kathryn M. , Potter Eric G. , Liu Yongxing 
Published: November 2021

Abstract
The unconformity-related uranium (URU) deposits in the Proterozoic Athabasca Basin (Canada) represent the richest, and one of the most important, uranium endowments in the world. Most of the URU deposits are associated with pre-existing graphitic basement faults that were reactivated after the formation of the basin. These graphite-rich structures have been widely used as a vector for exploration, but the nature of the association of the URU deposits with graphitic basement faults has been debated for over four decades. Proposed roles of graphite include: (1) as a direct reducing agent to reduce U6+ to U4+ and precipitate uraninite; (2) as a precursor of hydrocarbons (mainly CH4) produced in situ or nearby and then used as a reducing agent for uraninite precipitation; (3) as a precursor of hydrocarbons produced at depth that were remobilized to the site of mineralization and acted as a reducing agent for uraninite precipitation; and (4) as a lubricant facilitating faulting and fluid flow that led to uranium mineralization. This paper uses the Phoenix uranium deposit in the southeastern Athabasca Basin as a case study to address these uncertainties. Petrographic studies indicate that there is no direct contact between graphite and uraninite at microscopic scales, and the content of graphite in the graphitic metapelite along the ore-controlling WS Shear Zone does not show a systematic change with the distance from the unconformity surface. Raman spectroscopic studies of graphite suggest that the degree of structural disorder of graphite, expressed by various parameters related to the D bands and G band ratios, does not change systematically with the distance from the unconformity surface either. The minor irregularities in these parameters near the unconformity are better explained by paleo-weathering related to the unconformity and/or diagenetic processes than by hydrothermal activity related to uranium mineralization. Based on these observations and interpretations, the role of graphite as an in situ reducing agent, either directly or as a provider of hydrocarbons, is discounted. It is proposed that hydrocarbons derived from graphite at depth, tapped by episodic reactivation or seismicity of the basement faults that was facilitated by graphite as a lubricant, were responsible for URU mineralization.