bluuebunny/crossref_metadata_2025 huggingface dataset:
Lemongrass Essential Oil Components with Antimicrobial and Anticancer Activities
Authors: Mukarram Mohammad , Choudhary Sadaf , Khan Mo Ahamad , Poltronieri Palmiro , Khan M. Masroor A. , Ali Jamin , Kurjak Daniel , Shahid Mohd
Published: December 2021

The prominent cultivation of lemongrass (Cymbopogon spp.) relies on the pharmacological incentives of its essential oil. Lemongrass essential oil (LEO) carries a significant amount of numerous bioactive compounds, such as citral (mixture of geranial and neral), isoneral, isogeranial, geraniol, geranyl acetate, citronellal, citronellol, germacrene-D, and elemol, in addition to other bioactive compounds. These components confer various pharmacological actions to LEO, including antifungal, antibacterial, antiviral, anticancer, and antioxidant properties. These LEO attributes are commercially exploited in the pharmaceutical, cosmetics, and food preservations industries. Furthermore, the application of LEO in the treatment of cancer opens a new vista in the field of therapeutics. Although different LEO components have shown promising anticancer activities in vitro, their effects have not yet been assessed in the human system. Hence, further studies on the anticancer mechanisms conferred by LEO components are required. The present review intends to provide a timely discussion on the relevance of LEO in combating cancer and sustaining human healthcare, as well as in food industry applications.
Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
Authors: Avramidis George P. , Avramidou Maria P. , Papakostas George A.
Published: December 2021

Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis. These works mainly focus on medical images as they use X-ray and ultrasound images as input for their models. In this study, we review the conducted works and compare the methods that use deep learning with the procedure that is commonly followed by a medical doctor for the RA diagnosis. The results show that 93% of the works use only image modalities as input for the models as distinct from the medical procedure where more patient medical data are taken into account. Moreover, only 15% of the works use direct explainability methods, meaning that the efforts for solving the trustworthiness issue of deep learning models were limited. In this context, this work reveals the gap between the deep learning approaches and the medical doctors’ practices traditionally applied and brings to light the weaknesses of the current deep learning technology to be integrated into a trustworthy context inside the existed medical infrastructures.
Design Optimization of Plate-Fin Heat Exchanger in a Gas Turbine and Supercritical Carbon Dioxide Combined Cycle with Thermal Oil Loop
Authors: Cao Yue , Zhan Jun , Zhou Jianxin , Si Fengqi
Published: December 2021

This paper presents an investigation on the optimum design for a plate-fin heat exchanger (PFHE) of a gas and supercritical carbon dioxide combined cycle which uses thermal oil as intermediate heat-transfer fluid. This may promote the heat transfer from low heat-flux exhaust to a high heat-flux supercritical carbon dioxide stream. The number of fin layers, plate width and geometrical parameters of fins on both sides of PFHE are selected as variables to be optimized by a non-dominated sorting genetic algorithm-II (NSGA-II), which is a multi-objective genetic algorithm. For the confliction of heat transfer area and pressure drop on the exhaust side, which are the objective indexes, the result of NSGA-II is a Pareto frontier. The technique for order of preference by similarity to ideal solution (TOPSIS) approach is applied to choose the optimum solution from the Pareto frontier. Finally, further simulation is performed to analyze the effect of each parameter to objective indexes and confirm the rationality of optimization results.
A Novel Approach to Determine Static Falling down Sidelong Angle of Tractor Using a 3D Printed Miniature Model
Authors: Jang Moon-Kyeong , Hwang Seok-Joon , Shin Chang-Seop , Nam Ju-Seok
Published: December 2021

In this study, we have conducted a quasi-static lateral overturning test and derived a static falling down sidelong angle by using a miniature tractor model. We analyzed the lateral overturning condition of tractors theoretically by literature review and derived a method to manufacture a miniature model based on the analysis result. A miniature model was manufactured using a 3D printer at a scale of 1/20, relative to the actual dimension of a tractor. In addition, a test platform was constructed to perform lateral overturning tests. The miniature model was placed on the test platform, and the inclination angle of the ground was gradually increased. Then the static falling down sidelong angle, that is the inclination angle at the moment of lateral overturning occurred, was measured. The test result indicated that the difference of average static falling down sidelong angle was 1.0%. comparing to the accredited certification test result of the original tractor. Therefore, by utilizing the method applied in this study, the static falling down sidelong angle of tractors could be derived quickly and conveniently from a miniature model. As future research, the in-depth verification for the developed method is required by applying to a variety of agricultural machines.
Improved Bio-Oil Quality from Pyrolysis of Pine Biomass in Pressurized Hydrogen
Authors: Wang Jingliang , Wang Shanshan , Lu Jianwen , Yang Mingde , Wu Yulong
Published: December 2021

The pyrolysis of pine sawdust was carried out in a fixed bed reactor heated from 30 °C to a maximum of 700 °C in atmospheric nitrogen and pressurized hydrogen (5 MPa). The yield, elemental composition, thermal stability, and composition of the two pyrolysis bio-oils were analyzed and compared. The result shows that the oxygen content of the bio-oil (17.16%) obtained under the hydrogen atmosphere was lower while the heating value (31.40 MJ/kg) was higher than those of bio-oil produced under nitrogen atmosphere. Compounds with a boiling point of less than 200 °C account for 63.21% in the bio-oil at pressurized hydrogen atmosphere, with a proportion 14.69% higher than that of bio-oil at nitrogen atmosphere. Furthermore, the hydrogenation promoted the formation of ethyl hexadecanoate (peak area percentage 19.1%) and ethyl octadecanoate (peak area percentage 15.42%) in the bio-oil. Overall, high pressure of hydrogen improved the bio-oil quality derived from the pyrolysis of pine biomass.
Fate of Soil Carbon Transported by Erosional Processes
Authors: Lal Rattan
Published: December 2021

The accelerated process of soil erosion by water and wind, responsible for transport and redistribution of a large amount of carbon-enriched sediments, has a strong impact on the global carbon budget. The breakdown of aggregates by erosivity of water (raindrop, runoff) and wind weakens the stability of soil C (organic and inorganic) and aggravates its vulnerability to degradation processes, which lead to the emission of greenhouse gases (GHGs) including CO2, CH4, and N2O, depending on the hydrothermal regimes. Nonetheless, a part of the eroded soil C may be buried, reaggregated and protected against decomposition. In coastal steep lands, (e.g., Taiwan, New Zealand) with a short distance to burial of sediments in the ocean, erosion may be a sink of C. In large watersheds (i.e., Amazon, Mississippi, Nile, Ganges, Indus, etc.) with a long distance to the ocean, however, most of the C being transported is prone to mineralization/decomposition during the transit period and is a source of GHGs (CO2, CH4, N2O). Land use, soil management and cropping systems must be prudently chosen to prevent erosion by both hydric and aeolian processes. The so-called plague of the soil, accelerated erosion by water and wind, must be effectively curtailed.
Internal Damage Analysis of Braided Composites Embedded in Carbon Nanotube Yarn
Authors: Jia Minrui , Wan Zhenkai
Published: December 2021

Carbon nanotube (CNT) yarn sensors were embedded in 3D braided composites in the form of arrays to detect the internal damage of specimens and study the internal damage monitoring of the 3D braided composites. The signals collected by the sensor array of CNT yarn were preprocessed using the dynamic wavelet threshold algorithm. The exact position of the damage was calculated based on the main features of the resistance signal matrix, which was calculated using the quadratic matrix singular value. The results show that the internal damage localization of the specimens was consistent with the actual damage. The localizations in this study can provide a basis for enhancing the structural health monitoring of smart 3D braided composites.
Power Management for Connected EVs Using a Fuzzy Logic Controller and Artificial Neural Network
Authors: Angundjaja Clint Yoannes , Wang Yu , Jiang Wenying
Published: December 2021

In recent years, the electric vehicles (EVs) power management strategy has been developed in order to reduce battery discharging power and fluctuation when an EV requires high and rapid discharging power due to frequent stop-and-go driving operations. A combination of lithium-ion battery and supercapacitor (SC) as the EV’s energy sources, known as a hybrid energy storage system (HESS), is a promising solution for fast discharging conditions. Effective power management to extensively utilize HESS can be developed if future power demand is accessible. A vehicular network as a typical form of the currently developed internet of things (IoT) has made future information obtainable by collecting information on surrounding data. This paper proposes a power management strategy for the HESS with the support of IoT. Since the obtained information from the vehicular network could not directly be used to improve HESS, a two-level control structure has been developed to perform future data prediction and power distribution. A fuzzy logic controller (FLC) is utilized in level one control structure to manage a HESS power split based on future information. Since FLC requires future information as a reference input, the future information is obtained by using an artificial neural network (ANN) in level two control structure. The ANN provides a direct prediction that could approximate the future power demand prediction with the assumption that the vehicular network scenario is deployed to obtain surrounding information. Simulation results demonstrate that the average discharging battery power and power variation are reduced by 46.1% and 52.3%, respectively, compared to the battery-only case.
A Review on Contaminants of Emerging Concern in the Environment: A Focus on Active Chemicals in Sub-Saharan Africa
Authors: Ripanda Asha S. , Rwiza Mwemezi Johaiven , Nyanza Elias C. , Njau Karoli N. , Vuai Said A. H. , Machunda Revocatus L.
Published: December 2021

Active chemicals are among the contaminants of emerging concern that are rarely covered in regulatory documents in sub-Saharan Africa. These substances are neither in the list of routinely monitored substances nor in the guidelines for routine environmental monitoring activities. This has been of concern to public health officials, toxicologists, communities, and governments, hence the need for risk assessment and regulation of these substances. In this review article, the presence of active chemicals in the sub-Saharan African environment was investigated. The results indicate the availability of few studies in some countries, while in other countries no reports of active chemicals were found, hence the need for further research targeting such countries. It was further observed that mixtures of active chemicals from different therapeutic categories—such as antibiotics and analgesics—were reported. The natural environment is increasingly at risk due to the presence of these substances, their metabolites, and their transformation byproducts. These substances are characterized by persistence as a result of their non-biodegradable nature; hence, they circulate from one environmental compartment to another through the food chain, causing harm along the way. Most studies that evaluated the toxicity of these substances considered the effects of a single drug, but observations indicated the presence of drug mixtures, hence the need for further evaluation of the effects of drug–drug interactions—including synergistic and additive effects—for environmental sustainability. The presence of ACs in several environmental compartments at quantifiable quantities was discovered in this investigation, indicating the potential for ecosystem injury as a result of bioaccumulation, bioconcentration, and biomagnification through the food chain. This necessitates further research on the subject in order to ensure a healthier environment.
Detecting Web-Based Attacks with SHAP and Tree Ensemble Machine Learning Methods
Authors: Ndichu Samuel , Kim Sangwook , Ozawa Seiichi , Ban Tao , Takahashi Takeshi , Inoue Daisuke
Published: December 2021

Attacks using Uniform Resource Locators (URLs) and their JavaScript (JS) code content to perpetrate malicious activities on the Internet are rampant and continuously evolving. Methods such as blocklisting, client honeypots, domain reputation inspection, and heuristic and signature-based systems are used to detect these malicious activities. Recently, machine learning approaches have been proposed; however, challenges still exist. First, blocklist systems are easily evaded by new URLs and JS code content, obfuscation, fast-flux, cloaking, and URL shortening. Second, heuristic and signature-based systems do not generalize well to zero-day attacks. Third, the Domain Name System allows cybercriminals to easily migrate their malicious servers to hide their Internet protocol addresses behind domain names. Finally, crafting fully representative features is challenging, even for domain experts. This study proposes a feature selection and classification approach for malicious JS code content using Shapley additive explanations and tree ensemble methods. The JS code features are obtained from the Abstract Syntax Tree form of the JS code, sample JS attack codes, and association rule mining. The malicious and benign JS code datasets obtained from Hynek Petrak and the Majestic Million Service were used for performance evaluation. We compared the performance of the proposed method to those of other feature selection methods in the task of malicious JS code content detection. With a recall of 0.9989, our experimental results show that the proposed approach is a better prediction model.
The Local Ordered Charging Strategy of Electric Vehicles Based on Energy Routers under Three-Phase Balance of Residential Fields
Authors: Gao Hui , Yang Lutong , Duan Haowei
Published: December 2021

With the popularization of electric private cars and the increase of charging facilities in residential areas, disorderly charging will affect the power supply efficiency of their distribution transformers and the quality of electricity used by users in residential areas. In severe cases, it may even cause vibration of the power grid, causing serious three-phase imbalance problems such as single-phase burnout of transformers or insulation breakdown of household appliances. This paper analyzes the influencing factors of the unbalanced operation of each phase of the distribution transformer and the electrical load characteristics of typical residential areas. Based on the photovoltaic output of the station area, the charging and discharging capacity of the energy storage system, and the orderly charging plan of residential electric vehicles, a local orderly charging strategy for electric vehicles based on energy routers under the three-phase balance of the residential area is proposed. This strategy can realize the three-phase balance control of the distribution transformer. The effectiveness of the method is verified by a typical scenario example. The control method is changed to minimize the three-phase imbalance in residential areas and improve the low utilization rate of the distribution network and the comprehensive utilization efficiency of adjustable resources in residential areas.
Increasing the Time between Failures of Electric Submersible Pumps for Oil Production with High Content of Mechanical Impurities
Authors: Shishlyannikov Dmitriy , Zverev Valeriy , Ivanchenko Anna , Zvonarev Ivan
Published: December 2021

The main principles of hydroabrasive wear of parts of the pumping stages of well’s electric submersible pumps are considered in this article. The concentration, grain-size distribution and shape of solid particles have the greatest impact on the abrasion ability of particles of mechanical impurities interacting with the parts of pumping equipment. The implementation of filters is the most effective and affordable way to protect borehole electric submersible pumps from hydroabrasive wear. Filters reduce the concentration and decrease the average grain-size of mechanical impurities going through the pumping stages. The authors propose variants of constructive and schematic solutions of self-cleaning slot filters, providing an increase in the operating time of electric centrifugal pump units during production of well fluid with a high content of mechanical impurities. The operating principle of the proposed filters is described. The results of calculations of deformation of tubing string during the increase in pressure at the oil wellhead are presented, confirming the possibility of restoring the permeability of spring filter elements without lifting the pump unit to the surface.
Wind Turbine Fault Diagnosis by the Approach of SCADA Alarms Analysis
Authors: Wei Lu , Qian Zheng , Pei Yan , Wang Jingyue
Published: December 2021

Wind farm operators are overwhelmed by a large amount of supervisory control and data acquisition (SCADA) alarms when faults occur. This paper presents an online root fault identification method for SCADA alarms to assist operators in wind turbine fault diagnosis. The proposed method is based on the similarity analysis between an unknown alarm vector and the feature vectors of known faults. The alarm vector is obtained from segmented alarm lists, which are filtered and simplified. The feature vector, which is a unique signature representing the occurrence of a fault, is extracted from the alarm lists belonging to the same fault. To mine the coupling correspondence between alarms and faults, we define the weights of the alarms in each fault. The similarities is measured by the weighted Euclidean distance and the weighted Hamming distance, respectively. One year of SCADA alarms and maintenance records are used to verify the proposed method. The results show that the performance of the weighted Hamming distance is better than that of the weighted Euclidean distance; 84.1% of alarm lists are labeled with the right root fault.
INSPECTORMAP: A Spatial Data Infrastructure Applied to the Maintenance of Solar Plants Using Free Satellite Imagery
Authors: Ruiz de Oña Esteban , Sánchez-Aparicio María , Del Pozo Susana , González-Aguilera Diego
Published: December 2021

Photovoltaic solar plants are one of the main facilities away from urban centers for the generation of clean energy. Since its appropriate maintenance ensures its suitable operation, optimizing their maintenance tasks in a preventive way is key. This article presents a spatial data infrastructure called INSPECTORMAP that, based on the analysis of free satellite images within the optical spectrum, can detect unusual vegetation and bodies of water in the vicinity of photovoltaic plants that can affect their correct operation. Thanks to the implementation of a monitoring and alert system, it is possible to know and map the status of the photovoltaic plant in terms of unusual coverages appearing, both natural and artificial, at any moment. Thus, maintenance workers would travel to the solar plant to carry out their maintenance tasks in this regard only when the system detects a risk.
A Method for Correcting the Interference of Overlapping Absorption Lines Using Second Harmonic Spectral Reconstruction
Authors: Hou Yue , Huang Kejin
Published: December 2021

The measurement accuracy of trace gas detection based on infrared absorption spectroscopy is influenced by the overlap of absorption lines. A method for correcting the interference of overlapping absorption lines using second harmonic spectral reconstruction (2f-SR) is proposed to improve the measurement accuracy. 2f-SR includes three parts: measurement of gas temperature and use of the differences in temperature characteristics of absorption lines to correct the temperature error, 2f signal restoration based on laser characteristics to eliminate the influence of waveform change on overlapping absorption lines, and fast multi-peak fitting for the separation of interference from overlapping absorption lines. The CH4 measurement accuracy based on overlapping absorption lines is better than 0.8% using 2f-SR. 2f-SR has a lower minimum detection limit (MDL) and a higher detection accuracy than the separation of overlapping absorption lines based on the direct absorption method. The MDL is reduced by two to three orders of magnitude and reaches the part per million by volume level. 2f-SR has clear advantages for correcting the interference of overlapping absorption lines in terms of both MDL and measurement accuracy.
Optimization of Costs and Production Process of Fire Dampers
Authors: Kádárová Jaroslava , Kočišová Michaela , Teplická Katarína , Suhányiová Alžbeta , Lachvajderová Laura
Published: December 2021

Currently, companies are trying to optimize production, even more so as the economic situation is gradually deteriorating in many countries around the world, which is currently caused mainly by the COVID 19 pandemic. A specific type of production is that on production lines, which are individual workplaces where various production and assembly operations take place. The aim of the article is to analyze the impact of the choice of the control method for the production process on the optimization of costs and the process. The subject of the research is the production of fire-fighting throttles on a production line that has three workplaces. The problem of waste on the production line was identified by direct measurements of time consumption at the individual workplaces and by using images of the working day schedule. The article compares three approaches to production line management and waste reduction. The first approach was based on counting the number of finished products on the production line. The second approach analyzed in detail the duration of the individual operations into which the workplaces were divided. In the third approach, a method of balancing workplaces was used, which brought time and cost savings. The achieved results, using different methods of production management, are analyzed in detail, and the conclusions evaluated. The article contributes a comparison of the achieved results of cost optimization through the course of production of fire dampers. The research results were applied in practice. The research used methods of industrial engineering in the field of ergonomics, technical and economic analysis, and production management. Visualization of hourly performances, downtimes and failures, execution of work procedures, Shop Floor Management and many others, are recommended as ways to implement waste elimination when using the methods of industrial engineering.
Comparative Analysis of the Cooling Efficiency in Tropical Climate of Three Alternative Materials for Evaporative Cooling Pads
Authors: Obando Vega Felipe Andrés , Montoya Rios Ana Paola , Osorio Saraz Jairo Alexander , Damasceno Flávio Alves , Barbari Matteo
Published: December 2021

A comparison between commercial cellulose evaporative cooling pad and pads manufactured with three alternative materials was made for environmental conditions typical of tropical countries. Wind tunnel tests were carried out to compute the convective heat and mass transfer coefficients and to measure the pressure drop across each pad, obtaining expressions that can be used as reference for future simulations. Using a mathematical model, a detailed analysis of the efficiency, Temperature–Humidity Index, temperature drop and relative humidity through the pads was performed in terms of different operative and constructive conditions, such as air velocities, dry-bulb temperatures, relative humidity and pad thickness. Significant differences between the pressure drop across the pads were found, which increases with the inlet air velocity and water flow. A weak relation between the analysed variables and the inlet air velocity was found, while a high correlation between the analysed variables, pad thickness and the external environmental conditions was established. Cooling efficiencies above 40% and a Temperature–Humidity Index below the mild zone of thermal stress were obtained for the alternative panels. Although the cellulose cooling pad showed the best behaviour in all aspects, the alternative pads can be used as substituted in specific applications to reduce the Temperature–Humidity Index inside agricultural facilities.
Synthesis and Structural Confirmation of the Thiazole Alkaloids Derived from Peganum harmala L.
Authors: Sim Myeonghyeon , Lee Sujin , Han Youngtaek
Published: December 2021

Peganumal A and B are thiazole alkaloids isolated from the seeds of Peganum harmala L. Thiazole moieties are rarely found in natural products, but diverse compounds possessing thiazole moieties have attracted attention owing to their broad range of biological activities. Peganumals are the first natural thiazole compounds isolated from the genus Peganum. It was difficult to define the exact structure of peganumal A via spectroscopic analysis. In this paper, we report the first total synthesis of peganumal A and B. The 5-benzylthiazole skeleton possessing methyl or hydrogen at the 2-position of the peganumals was efficiently constructed via the Hantzsch thiazole synthesis of the α-bromoaldehyde intermediate. Moreover, the spectral data of the synthetic 2H–thiazole compound were identical to those previously reported for peganumal A. The synthesis allowed the confirmation of the structure of peganumal A.
Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems
Authors: Pokuta Waldemar , Zatwarnicki Krzysztof
Published: December 2021

Cloud computing systems revolutionized the Internet, and web systems in particular. Quality of service is the basis of resource configuration management in the cloud. Load balancing mechanisms are implemented in order to reduce costs and increase the quality of service. The usage of those methods with adaptive intelligent algorithms can deliver the highest quality of service. In this article, the method of load distribution using neural networks to estimate service times is presented. The discussed and conducted research and experiments include many approaches, among others, application of a single artificial neuron, different structures of the neural networks, and different inputs for the networks. The results of the experiments let us choose a solution that enables effective load distribution in the cloud. The best solution is also compared with other intelligent approaches and distribution methods often used in production systems.
A Proposed Algorithm to Compute the Stress-Strain Plastic Region and Displacement of a Deep-Lying Tunnel Considering Intermediate Stress and Strain-Softening Behavior
Authors: Li Jinwang , He Xiufeng , Shen Caihua , Zheng Xiangtian
Published: December 2021

Past studies on deep-lying tunnels under the assumption of plane strain have generally neglected the influence of intermediate principal stress even though this affects the surrounding rocks in the plastic zone. This study proposes a finite difference method to compute the stress strain plastic region and displacement of a tunnel based on the Drucker–Prager (D–P) yield criterion and non-associated flow rule and considering the influences of intermediate principal stress and the strain-softening behavior of surrounding rock. The computed results were compared with those of other well-known solutions and the accuracy and validity of the method were confirmed through some examples. Parameter analysis was conducted to investigate the effects of intermediate principal stress on stress-strain, the plastic region, the ground response curve, and the dilatability of surrounding rock. The results showed that the plastic radius Rp, the residual radius Rd, and radial displacement of surrounding rock first decreased and then increased with increasing intermediate principal stress coefficient b from 0 to 1, with the minimums occurring at b = 0.75. On the contrary, the peak and rate of variation of the dilatancy coefficient first increased and then decreased with increasing b and the dilatancy coefficient Kψ gradually transitioned from nonlinear to linear variation. Meanwhile, the inhibition of the plastic radius and radial displacement gradually weakened with increasing support pressure, whereas the dilatancy coefficient of the tunnel opening gradually increased.
Involvement of the Orexinergic System in Feeding
Authors: Marcos Pilar , Coveñas Rafael
Published: December 2021

To know the processes involved in feeding, the dysregulation of hypothalamic neuropeptides promoting anorexigenic/orexigenic mechanisms must be investigated. Many neuropeptides are involved in this behavior and in overweight/obesity. Current pharmacological strategies for the treatment of obesity are unfortunately not very effective and, hence, new therapeutic strategies must be investigated and developed. Due to the crucial role played by orexins in feeding behavior, the aim of this review is to update the involvement of the orexinergic system in this behavior. The studies performed in experimental animal models and humans and the relationships between the orexinergic system and other substances are mentioned and discussed. Promising research lines on the orexinergic system are highlighted (signaling pathways, heterogeneity of the hypothalamic orexinergic neurons, receptor-receptor interaction, and sex differences). Each of the orexin 1 and 2 receptors plays a unique role in energy metabolism, exerting a differential function in obesity. Additional preclinical/clinical studies must be carried out to demonstrate the beneficial effects mediated by orexin receptor antagonists. Because therapies applied are in general ineffective when they are directed against a single target, the best option for successful anti-obesity treatments is the development of combination therapies as well as the development of new and more specific orexin receptor antagonists.
Therapeutic Applications of Halloysite
Authors: Mobaraki Mohammadmahdi , Karnik Sonali , Li Yue , Mills David K.
Published: December 2021

In recent years, nanomaterials have attracted significant research interest for applications in biomedicine. Many kinds of engineered nanomaterials, such as lipid nanoparticles, polymeric nanoparticles, porous nanomaterials, silica, and clay nanoparticles, have been investigated for use in drug delivery systems, regenerative medicine, and scaffolds for tissue engineering. Some of the most attractive nanoparticles for biomedical applications are nanoclays. According to their mineralogical composition, approximately 30 different nanoclays exist, and the more commonly used clays are bentonite, halloysite, kaolinite, laponite, and montmorillonite. For millennia, clay minerals have been extensively investigated for use in antidiarrhea solutions, anti-inflammatory agents, blood purification, reducing infections, and healing of stomach ulcers. This widespread use is due to their high porosity, surface properties, large surface area, excellent biocompatibility, the potential for sustained drug release, thermal and chemical stability. We begin this review by discussing the major nanoclay types and their application in biomedicine, focusing on current research areas for halloysite in biomedicine. Finally, recent trends and future directions in HNT research for biomedical application are explored.
MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
Authors: Chen Ruiqi , Wu Tianyu , Zheng Yuchen , Ling Ming
Published: December 2021

In Internet of Things (IoT) scenarios, it is challenging to deploy Machine Learning (ML) algorithms on low-cost Field Programmable Gate Arrays (FPGAs) in a real-time, cost-efficient, and high-performance way. This paper introduces Machine Learning on FPGA (MLoF), a series of ML IP cores implemented on the low-cost FPGA platforms, aiming at helping more IoT developers to achieve comprehensive performance in various tasks. With Verilog, we deploy and accelerate Artificial Neural Networks (ANNs), Decision Trees (DTs), K-Nearest Neighbors (k-NNs), and Support Vector Machines (SVMs) on 10 different FPGA development boards from seven producers. Additionally, we analyze and evaluate our design with six datasets, and compare the best-performing FPGAs with traditional SoC-based systems including NVIDIA Jetson Nano, Raspberry Pi 3B+, and STM32L476 Nucle. The results show that Lattice’s ICE40UP5 achieves the best overall performance with low power consumption, on which MLoF averagely reduces power by 891% and increases performance by 9 times. Moreover, its cost, power, Latency Production (CPLP) outperforms SoC-based systems by 25 times, which demonstrates the significance of MLoF in endpoint deployment of ML algorithms. Furthermore, we make all of the code open-source in order to promote future research.
Predicting the Hydrological Impacts of Future Climate Change in a Humid-Subtropical Watershed
Authors: Rashid Haroon , Yang Kaijie , Zeng Aicong , Ju Song , Rashid Abdur , Guo Futao , Lan Siren
Published: December 2021

Future climate change is expected to impact the natural systems. This study used future climate data of general circulation models (GCMs) to investigate the impacts of climate change during the future period (2062–2095) relative to the historical period (1981–2014) on the hydrological system of the Minjiang river watershed, China. A previously calibrated soil and water assessment tool (SWAT) was employed to simulate the future hydrology under the impacts of changes in temperature, precipitation, and atmospheric CO2 concentration for four shared socioeconomic pathways (SSP 1, 2, 3, and 5) of the CMIP6. The study revealed that the impacts of increase in future temperature, i.e., increase in ET, and decrease in surface runoff, water, and sediment yield will be countered by increased atmospheric [CO2], and changes in the hydrological parameters in the future will be mostly associated to changes in precipitation. Data of the GCMs for all the SSPs predicts increase in precipitation of the watershed, which will cause increase in surface runoff, water yield, and sediment yield. Surface runoff will increase more in SSP 5 (47%), while sediment and water yield will increase more in SSP 1, by 33% and 23%, respectively. At the seasonal scale, water yield and surface runoff will increase more in autumn and winter in SSP 1, while in other scenarios, these parameters will increase more in the spring and summer seasons. Sediment yield will increase more in autumn in all scenarios. Similarly, the future climate change is predicted to impact the important parameters related to the flow regime of the Minjiang river, i.e., the frequency and peak of large floods (flows > 14,000 m3/s) will increase along the gradient of scenarios, i.e., more in SSP 5 followed by 3, 2, and 1, while duration will increase in SSP 5 and decrease in the other SSPs. The frequency and duration of extreme low flows will increase in SSP 5 while decrease in SSP 1. Moreover, peak of extreme low flows will decrease in all scenarios except SSP 1, in which it will increase. The study will improve the general understanding about the possible impacts of future climate change in the region and provide support for improving the management and protection of the watershed’s water and soil resources.
Generalized Rough Sets via Quantum Implications on Quantum Logic
Authors: Dai Songsong
Published: December 2021

This paper introduces some new concepts of rough approximations via five quantum implications satisfying Birkhoff–von Neumann condition. We first establish rough approximations via Sasaki implication and show the equivalence between distributivity of multiplication over join and some properties of rough approximations. We further establish rough approximations via other four quantum implication and examine their properties.
Graph Entropy Based on Strong Coloring of Uniform Hypergraphs
Authors: Fang Lusheng , Deng Bo , Zhao Haixing , Lv Xiaoyun
Published: December 2021

The classical graph entropy based on the vertex coloring proposed by Mowshowitz depends on a graph. In fact, a hypergraph, as a generalization of a graph, can express complex and high-order relations such that it is often used to model complex systems. Being different from the classical graph entropy, we extend this concept to a hypergraph. Then, we define the graph entropy based on the vertex strong coloring of a hypergraph. Moreover, some tightly upper and lower bounds of such graph entropies as well as the corresponding extremal hypergraphs are obtained.
Proteomic Analysis of Exosomes Secreted from Human Alpha-1 Antitrypsin Overexpressing Mesenchymal Stromal Cells
Authors: Wei Hua , Green Erica , Ball Lauren , Fan Hongkuan , Lee Jennifer , Strange Charlie , Wang Hongjun
Published: December 2021

Extracellular vesicles (EVs) mediate many therapeutic effects of stem cells during cellular therapies. Bone marrow-derived mesenchymal stromal cells (BM-MSCs) were manufactured to overexpress the human antiprotease alpha-1 antitrypsin (hAAT) and studied to compare the EV production compared to lentivirus treated control MSCs. The goal of this study was to compare protein profiles in the EVs/exosomes of control and hAAT-MSCs using unbiased, high resolution liquid chromatography and mass spectrometry to explore differences. Nanoparticle tracking analysis (NTA) showed that the particle size of the EVs from control MSCs or hAAT-MSCs ranged from 30 to 200 nm. Both MSCs and hAAT-MSCs expressed exosome-associated proteins, including CD63, CD81, and CD9. hAAT-MSCs also expressed high levels of hAAT. We next performed proteomic analysis of EVs from three healthy donor cell lines. Exosomes collected from cell supernatant were classified by GO analysis which showed proteins important to cell adhesion and extracellular matrix organization. However, there were differences between exosomes from control MSCs and hAAT-MSCs in cytokine signaling of the immune system, stem cell differentiation, and carbohydrate metabolism (p < 0.05). These results show that hAAT-MSC exosomes contain a different profile of paracrine effectors with altered immune function, impacts on MSC stemness, differentiation, and prevention of cell apoptosis and survival that could contribute to improved therapeutic functions.
Effects of Melissa officinalis L. Essential Oil in Comparison with Anaesthetics on Gill Tissue Damage, Liver Metabolism and Immune Parameters in Sea Bass (Lateolabrax maculatus) during Simulated Live Transport
Authors: Wang Qi , Mei Jun , Cao Jie , Xie Jing
Published: December 2021

In the current study, Melissa officinalis L. essential oil (MOEO), a novel sedative and anaesthetic, was employed in transport water to obtain a lower stress effect and higher survival rate for live marine fish. The effect of MOEO and various types of anaesthetics, administered at a low temperature on gill morphology, liver function and immunological parameters of living sea bass (Lateolabrax maculatus) subjected to transport stress, was evaluated to optimize the anaesthetic and sedative concentrations during live sea bass transport. Light microscopy and scanning electron microscopy of sea bass, subjected to simulated live transport for 72 h, demonstrated that the changes in the morphological characteristics of gill tissue treated with 40 mg/L MOEO (A3 group) were minimal in comparison to those observed in untreated sea bass. The results of pyruvate kinase (PK), phosphofructokinase (PFK), hexokinase (HK), hepatic glycogen (Gly), superoxide dismutase (SOD), lipid peroxides (MDA) and Caspase-3 assays indicated that the glycolysis rate, energy consumption, lipid peroxidation and hepatocyte apoptosis were the lowest in the A3 group. The values of the two immune parameters, lysozyme (LZM) and fish immunoglobulin M (IgM), indicated the strongest immunity ability in the A3 group. After 12 h recovery, sea bass treated with 30 mg/L MS-222 (B group) displayed a 100% survival rate, sea bass treated with 20 mg/L (A2 group) and 40 mg/L (A3 group) MOEO displayed a 96% survival rate, sea bass treated with 20 mg/L eugenol (C group) had a 94% survival rate, and untreated sea bass (CK group) had a 50% survival rate. Therefore, the addition MOEO to the transport water had anaesthetic and sedative effects similar to MS-222 and eugenol. The results confirmed that the addition of MOEO to the transport water could reduce tissue damage, energy metabolism, and the oxidative stress response in sea bass during transport.
Dynamic Phenotypic Switching and Group Behavior Help Non-Small Cell Lung Cancer Cells Evade Chemotherapy
Authors: Nam Arin , Mohanty Atish , Bhattacharya Supriyo , Kotnala Sourabh , Achuthan Srisairam , Hari Kishore , Srivastava Saumya , Guo Linlin , Nathan Anusha , Chatterjee Rishov , Jain Maneesh , Nasser Mohd W. , Batra Surinder Kumar , Rangarajan Govindan , Massarelli Erminia , Levine Herbert , Jolly Mohit Kumar , Kulkarni Prakash , Salgia Ravi
Published: December 2021

Drug resistance, a major challenge in cancer therapy, is typically attributed to mutations and genetic heterogeneity. Emerging evidence suggests that dynamic cellular interactions and group behavior also contribute to drug resistance. However, the underlying mechanisms remain poorly understood. Here, we present a new mathematical approach with game theoretical underpinnings that we developed to model real-time growth data of non-small cell lung cancer (NSCLC) cells and discern patterns in response to treatment with cisplatin. We show that the cisplatin-sensitive and cisplatin-tolerant NSCLC cells, when co-cultured in the absence or presence of the drug, display dynamic group behavior strategies. Tolerant cells exhibit a ‘persister-like’ behavior and are attenuated by sensitive cells; they also appear to ‘educate’ sensitive cells to evade chemotherapy. Further, tolerant cells can switch phenotypes to become sensitive, especially at low cisplatin concentrations. Finally, switching treatment from continuous to an intermittent regimen can attenuate the emergence of tolerant cells, suggesting that intermittent chemotherapy may improve outcomes in lung cancer.
The Importance of BHB Testing on the Post-Mortem Diagnosis of Ketoacidosis
Authors: Ahlström Stina , Ahlner Johan , Jönsson Anna K. , Green Henrik
Published: December 2021

Although beta-hydroxybutyrate (BHB) analysis has proved its importance in forensic pathology, its effects on cause-of-death diagnostics are unaddressed. Therefore, this study aims at evaluating the effects of BHB analysis on the number of deaths by DKA (diabetes ketoacidosis), AKA (alcoholic ketoacidosis), HHS (hyperosmolar hyperglycaemic state), hypothermia, diabetes, alcoholism, and acidosis NOS (not otherwise specified). All 2900 deaths from 2013 through 2019 in which BHB was analysed at the National Board of Forensic Medicine, and 1069 DKA, AKA, HHS, hypothermia, diabetes, alcoholism, and acidosis cases without BHB analysis were included. The prevalence of BHB-positive cases for each cause of death, and trends and proportions of different BHB concentrations, were investigated. The number of BHB analyses/year increased from 13 to 1417. AKA increased from three to 66 and acidosis from one to 20. The deaths from alcoholism, DKA, and hypothermia remained stable. It is unclear why death from alcoholism remained stable while AKA increased. The increase in unspecific acidosis deaths raises the question why a more specific diagnosis had not been used. In conclusion, BHB analysis is instrumental in detecting AKA and acidosis. The scientific basis for the diagnosis of DKA and hypothermia improved, but the number of cases did not change.
Shock-Induced Damage Mechanism of Perineuronal Nets
Authors: Mahmud Khandakar Abu Hasan Al , Hasan Fuad , Khan Md Ishak , Adnan Ashfaq
Published: December 2021

The perineuronal net (PNN) region of the brain’s extracellular matrix (ECM) surrounds the neural networks within the brain tissue. The PNN is a protective net-like structure regulating neuronal activity such as neurotransmission, charge balance, and action potential generation. Shock-induced damage of this essential component may lead to neuronal cell death and neurodegenerations. The shock generated during a vehicle accident, fall, or improvised device explosion may produce sufficient energy to damage the structure of the PNN. The goal is to investigate the mechanics of the PNN in reaction to shock loading and to understand the mechanical properties of different PNN components such as glycan, GAG, and protein. In this study, we evaluated the mechanical strength of PNN molecules and the interfacial strength between the PNN components. Afterward, we assessed the PNN molecules’ damage efficiency under various conditions such as shock speed, preexisting bubble, and boundary conditions. The secondary structure altercation of the protein molecules of the PNN was analyzed to evaluate damage intensity under varying shock speeds. At a higher shock speed, damage intensity is more elevated, and hyaluronan (glycan molecule) is most likely to break at the rigid junction. The primary structure of the protein molecules is least likely to fail. Instead, the molecules’ secondary bonds will be altered. Our study suggests that the number of hydrogen bonds during the shock wave propagation is reduced, which leads to the change in protein conformations and damage within the PNN structure. As such, we found a direct connection between shock wave intensity and PNN damage.
Genome Editing among Bioethics and Regulatory Practices
Authors: Mandrioli Mauro
Published: December 2021

In the last decade, genome editing technologies became very effective and several clinical trials have been started in order to use them for treating some genetic diseases. Interestingly, despite more than 50 years of discussion about the frontiers of genetics in human health and evolution, the debate about the bioethics and the regulatory practices of genome editing is still far from satisfactory answers. This delay results from an excessive emphasis on the effectiveness of the genome editing technologies that is relevant for the regulatory practices, but not at a bioethical level. Indeed, other factors (such as accessibility and acceptability) could make these techniques not accepted at the bioethical level, even in the presence of their 100% effectiveness.