bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Analysis of a Novel GFRP Stiffened Pipe
Authors: Kong Liulin , Shi Qiang , Zhang Bo
Published: October 2022

This study presents a new GFRP pipe structure fabricated by a combination of continuous and spacing arrangement of filament winding, where the latter was used to form the stiffened part and the former to form the structural part. Results of comparative analysis through a series of flatwise compression tests indicate that, with the same material consumptions, up to 38.7% increase in ring stiffness was achieved by the stiffened outer pipes with diameter of 400 mm, compared with the plain ones. The failure mode of this proposed pipe is ductile fracture during the loading process, and its bearing capacity did not decrease rapidly after fiber failure. The stiffened inner pipes with diameter of 500 mm exhibited an average of 16.7% increase in ring stiffness compared with the plain ones, while both types of specimens showed the same failure mode with delamination occurring between the stiffened layer and outer structural layer. From the experimental investigation, it is concluded that suitable geometric shape can significantly improve the ring stiffness of the specimens, which provides a guidance for structural design of pipelines and their geometric optimization.
Control of Physical Processes in an Extrusion Line Polymer Sleeves Production
Authors: Nikonova Tatyana , Gierz Łukasz , Zharkevich Olga , Dandybaev Essim , Baimuldin Murat , Daich Leonid , Sichkarenko Andrey , Kotov Evgeniy
Published: October 2022

This article describes the operation principle of an extrusion line for producing a polypropylene film for sleeves. The advantages and disadvantages of the technological and structural nature for an extrusion line are presented, as well as the stages of polymer sleeves with the calculation of technological parameters and a description of the main purpose of each stage. The film quality is described, such as the turbidity, coefficient of friction, glossiness, and breaking strength. The dependence of the milkiness on the average cooling temperature of the extruder head is defined. The glossiness dependence is calculated on the cooling temperature of the extruder head. The dependence of the friction coefficient on the cooling temperature of the extruder head was established. The influence of the crystallization line height on the glossiness of the film is defined. The influence of the crystallization line height on the milkiness is shown. The dependence of fluctuations in the average thickness (thickness difference) of the film on the thickness of the forming slot is found. The dependence of fluctuations in the average thickness (thickness difference) of the film and degree of inflation of the sleeve is discovered. The relationship between the quality parameter of the film and technological parameters of its production process has been determined. An automated system for temperature control has been developed in the production of polyethylene film by installing a temperature sensor with feedback.
Enhancing Food Ingredient Named-Entity Recognition with Recurrent Network-Based Ensemble (RNE) Model
Authors: Komariah Kokoy Siti , Sin Bong-Kee
Published: October 2022

Food recipe sharing sites are becoming increasingly popular among people who want to learn how to cook or plan their menu. Through online food recipes, individuals can select ingredients that suit their lifestyle and health condition. Information from online food recipes is useful in developing food-related systems such as recommendations and health care systems. However, the information from online recipes is often unstructured. One way of extracting such information into a well-structured format is the technique called named-entity recognition (NER), which is the process of identifying keywords and phrases in the text and classifying them into a set of predetermined categories, such as location, persons, time, and others. We present a food ingredient named-entity recognition model called RNE (recurrent network-based ensemble methods) to extract the entities from the online recipe. RNE is an ensemble-learning framework using recurrent network models such as RNN, GRU, and LSTM. These models are trained independently on the same dataset and combined to produce better predictions in extracting food entities such as ingredient names, products, units, quantities, and states for each ingredient in a recipe. The experimental findings demonstrate that the proposed model achieves predictions with an F1 score of 96.09% and outperforms all individual models by 0.2% to 0.5% in percentage points. This result indicates that RNE can extract information from food recipes better than a single model. In addition, this information extracted by RNE can be used to support various information systems related to food.
Simplified Double-Integral Sliding-Mode Control of PWM DC-AC Converter with Constant Switching Frequency
Authors: Al-Baidhani Humam , Kazimierczuk Marian K.
Published: October 2022

In this paper, a simplified double-integral sliding-mode control method for pulse-width-modulated dc-ac buck conversion is introduced. The control equation is derived based on the equivalent control method, in which the control-oriented model is developed using the averaged dynamics of the power converter in continuous conduction mode. In contrast with the conventional sliding-mode control schemes, the complexity of adding a capacitor current sensor, variable ramp voltage, and other relevant components is avoided. Furthermore, the control equation is translated into a simple electronic circuit with minimal added components, which reduces the practical implementation cost. The proposed control method rejects large disturbances, tracks the reference signal, and maintains a constant switching frequency. Systematic design procedure, control parameters selection, and stability conditions are presented. The design methodology is verified via simulating the proposed control circuit using Simscape Electrical in MATLAB. The control method is also compared with the conventional double-integral sliding-mode control scheme under load disturbances. The results show that the simplified control approach provides a fast transient response and robust tracking performance.
Development of a Procedure for Risk-Based Qualification of Additively Manufactured Components: Adopting to Oil and Gas Industrial Applications
Authors: Abbaszadeh Behzad , Ratnayake R. M. Chandima , Eskandarzade Mehdi , Ajri Masoud , Rasouli Hassanali , Najafi Ershadi Meysam
Published: October 2022

Recent advances in additive manufacturing (AM) technology provide the potential for on-demand and rapid production of spare parts during urgent repair times. Recently, big oil and gas companies have shown early progress in using additive technology in manufacturing specific heat exchangers, downhole cleanout tool nozzles, offshore risers, gas turbine nozzles, and subsea chemical stick injection tools. Despite the mentioned progress, the current adoption level of additive technology for the offshore oil and gas industry is very limited. Non-destructive and destructive evaluation methods of additively manufactured metallic components have been studied extensively. However, the technique selection procedure and scope of the required test methods have not been studied sufficiently. This paper discusses various elements related to the qualification of additively manufactured components for application in the oil and gas industry. A risk-based qualification method for identifying the scopes of required non-destructive and destructive tests and the resulting qualification procedure for additively manufactured spare components in offshore oil and gas applications is suggested.
Comparison and Selection of Conventional PCR Primer Sets for Studies Associated with Nitrogen Cycle Microorganisms in Surface Soil
Authors: Lee Siwon , Jung Yong-Ju , Moon Jinah , Lee Jin-Young , Kim Heejung , Yang Jae-E , Lee Hyunji , Jung Jaewon , Kim Ha-Rang
Published: October 2022

The nitrogen cycle is a biogeochemical cycle primarily associated with the microbial activity that occurs in various environments, including soil. Various genes related to the nitrogen cycle have been studied for different purposes by many researchers; therefore, the polymerase chain reaction (PCR) conditions and gene compositions differ among reports, making comparisons difficult. In this study, we compare the PCR methods to amplify 13 nitrogen cycle-related genes (amo (amoA and amoB), norB (cnorB and qnorB), hzs, napA, narG, nifH, nirK, nirS, nosZ, nrfA, and nxrA) in the soil samples collected from four land use types and selected a method with excellent applicability. However, the PCR method for five nitrogen cycle-related genes (amoC, hao, hzo, nirB, and nxrB) could not be presented. In addition, the nitrogen cycle-related genes from the four land use types (field, forest, bare land, and grassland) and the seasonally collected samples were analyzed and discussed. In the grassland samples, all the nitrogen cycle-related genes reviewed were amplified. These results vary from those of the field, forest, and bare land samples, and it was estimated that grassland, among the land use types, could play an important role in the nitrogen cycle in soil. However, an association between the seasons and the rainy season was not confirmed. Thus, this study may be used for future research in various fields related to the nitrogen cycle.
Collaborative Mobile Robotics for Semantic Mapping: A Survey
Authors: Achour Abdessalem , Al-Assaad Hiba , Dupuis Yohan , El Zaher Madeleine
Published: October 2022

Ensuring safety in human–robot collaboration is one of the main challenges in mobile robotics today. Semantic maps are a potential solution because they provide semantic knowledge in addition to the geometric representation of the environment. They allow robots to perform their basic tasks using geometric representation, mainly localization, path planning and navigation, and additionally allow them to maintain a cognitive interpretation of the environment in order to reason and make decisions based on the context. The goal of this paper is to briefly review semantic mapping for a single mobile robot in indoor environments, and then focus on collaborative mobile semantic mapping. In both contexts, the semantic mapping process is divided into modules/tasks, and recent solutions for each module are discussed. Possible system architectures are also discussed for collaborative semantic mapping. Finally, future directions are highlighted.
The Temperature and Pore Pressure Distribution of Lightweight Aggregate Concrete Slabs Exposed to Elevated Temperatures
Authors: Tang Chao-Wei
Published: October 2022

Concrete has good fire resistance. However, once exposed to rapidly increasing temperatures, concrete may suffer from thermal stress-induced spalling or pore pressure-induced spalling. Compared with normal-weight concrete (NWC), lightweight aggregate concrete (LWAC) has a low thermal conductivity and is more prone to cause a higher temperature gradient under the action of high temperatures. This poses a hidden concern to the fire safety of general LWAC structures. Therefore, this study aimed to investigate the temperature and the pore pressure distribution of LWAC slabs under one-dimensional heating tests and to compare them with NWC slabs. The test variables were the moisture content of the specimen (oven-dried and air-dried), target temperature (600 and 800 °C), and heating rate (5 and 10 °C/min). The temperature and the pore pressure distributions were measured separately by thermocouples and pressure gauges embedded in different positions of the specimen. The test results show that the maximum pore pressure (Pmax) of the LWAC slabs was generally higher than that of the NWC slab at a distance of 10 mm from the heated surface when the specimen was in an oven-dried state. However, at 30 and 50 mm from the heated surface, the Pmax of the NWC slab tended to be higher. This shows that the Pmax distribution of the LWAC slab was closer to the heated surface when the specimen was in an oven-dried state, while the Pmax of the NWC slab occurred further from the heated surface. Further, as the heating rate increased, a higher pore pressure was generated inside the specimen and the pressure rose more rapidly. In particular, at a target temperature of 800 °C and a heating rate of 10 °C/min, the corner spalling phenomenon appeared on the air-dried LWAC slab.
Design of Mode-Locked Fibre Laser with Non-Linear Power and Spectrum Width Transfer Functions with a Power Threshold
Authors: Xie Ziyi , Peng Junsong , Sorokina Mariia , Zeng Heping
Published: October 2022

There is a growing demand for higher computational speed and energy efficiency of machine learning approaches and, in particular, neural networks. Optical implementation of neural networks can address this challenge. Compared to other neuromorphic platforms, fibre-based technologies can unlock a wide bandwidth window and offer flexibility in dimensionality and complexity. Moreover, fibre represents a well-studied, low-cost and low-loss material, widely used for signal processing and transmission. At the same time, mode-locked fibre lasers offer flexibility and control, while the mode-locking effect can be crucial for unlocking ultra-short timescales and providing ultra-fast processing. Here, we propose a mode-locked fibre laser with a non-linear power threshold in both power and spectrum. The advantage of the proposed system is a spectrum width two-branch function dependent on the input signal power. The effect is caused by a transition between two operating regimes and is governed by the input signal power. The proposed design enables receiving a non-linear transfer function in amplitude with a power threshold as an optical analogue of biological neurons with the additional advantage of a non-linear two-branch transfer function in spectrum width. The latter property is similar to the frequency-varied response dependent on stimulus properties in biological neurons. Thus, our work opens new avenues in research into novel types of artificial neurons with a frequency spectrum width variable response and, consequently, spiking neural networks and neural-rate-based coding with potential applications in optical communications and networks with flexible bandwidth, such as 5G and emerging 6G.
Prediagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
Authors: Zhang Huanyu , Wang Ruwei , Zhou Hong , Xia Shudong , Jia Sixiang , Wu Yiteng
Published: October 2022

Heart failure (HF) is a devastating condition that impairs people’s lives and health. Because of the high morbidity and mortality associated with HF, early detection is becoming increasingly critical. Many studies have focused on the field of heart disease diagnosis based on heart sound (HS), demonstrating the feasibility of sound signals in heart disease diagnosis. In this paper, we propose a non-invasive early diagnosis method for HF based on a deep learning (DL) network and the Korotkoff sound (KS). The accuracy of the KS-based HF prediagnosis was investigated utilizing continuous wavelet transform (CWT) features, Mel frequency cepstrum coefficient (MFCC) features, and signal segmentation. Fivefold cross-validation was applied to the four DL models: AlexNet, VGG19, ResNet50, and Xception, and the performance of each model was evaluated using accuracy (Acc), specificity (Sp), sensitivity (Se), area under curve (AUC), and time consumption (Tc). The results reveal that the performance of the four models on MFCC datasets is significantly improved when compared to CWT datasets, and each model performed considerably better on the non-segmented dataset than on the segmented dataset, indicating that KS signal segmentation and feature extraction had a significant impact on the KS-based CHF prediagnosis performance. Our method eventually achieves the prediagnosis results of Acc (96.0%), Se (97.5%), and Sp (93.8%) based on a comparative study of the model and the data set. The research demonstrates that the KS-based prediagnosis method proposed in this paper could accomplish accurate HF prediagnosis, which will offer new research approaches and a more convenient way to achieve early HF prevention.
Optimization of Machining Parameters for Corner Accuracy Improvement for WEDM Processing
Authors: Wang Shih-Ming , Wu Jia-Xuan , Gunawan Hariyanto , Tu Ren-Qi
Published: October 2022

Corner accuracy occurring in wire electrical discharge machining (WEDM) is influenced by machining parameters such as wire vibration, wire lag, and excessive discharge, etc. This study proposed an optimization method which can improve the corner accuracy of the WEDM process. The parameters of pulse-on time (ON), pulse-off time (OFF), open circuit voltage (OV), servo voltage (SV), wire tension (WT), and flushing pressure (WA) were selected to investigate the influences of the major parameters on the machining accuracy in this study. Three different corner angles of 30°, 60°, and 90° were chosen for the verification experiments. The response surface methodology (RSM) was used to analyze and investigate the effect of each parameter on the corner error. After integrating the response surface value and algorithm, an optimization system with a friendly human–machine interface, which has a procedure guiding function, was developed with use of C# language. The system can predict the corner error and also recommend optimal machining parameters for smaller corner error and faster machining speed based on the original machining parameters. Finally, cutting experiments were conducted to verify the proposed system, and the results showed that the proposed method can effectively improve the corner accuracy by 39%, 20%, and 33%.
Application of Hyperspectral Technology Combined with Genetic Algorithm to Optimize Convolution Long- and Short-Memory Hybrid Neural Network Model in Soil Moisture and Organic Matter
Authors: Wang Huan , Zhang Lixin , Zhao Jiawei , Hu Xue , Ma Xiao
Published: October 2022

A method of soil moisture and organic matter content detection based on hyperspectral technology is proposed. A total of 800 different soil samples and hyperspectral data were collected in the laboratory and from the field. A hyperspectral database was established. After wavelet denoising and principal component analysis (PCA) preprocessing, the convolutional neural network (CNN) module was first used to extract the wavelength features of the data. Then, the long- and short-memory neural network (LSTM) module was used to extract the feature bands and nearby hidden state vectors. At the same time, the genetic algorithm (GA) was used to optimize the hyperparametric weight and bias value of the LSTM training network. At the initial stage, the data were normalized, and all features were analyzed by grey correlation degree to extract important features and to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model proposed in this paper was used to predict soil moisture and organic matter. The prediction performance was compared with CNN, support vector regression (SVR), and CNN-LSTM hybrid neural network model without GA optimization. The GA-CNN-LSTM algorithm was superior to other models in all indicators. The highest accuracy rates of 94.5% and 92.9% were obtained for soil moisture and organic matter, respectively. This method can be applied to portable hyperspectrometers and unmanned aerial vehicles to realize large-scale monitoring of moisture and organic matter distribution and to provide a basis for rational irrigation and fertilization in the future.
Abdomen Fat and Liver Segmentation of CT Scan Images for Determining Obesity and Fatty Liver Correlation
Authors: Gulzar Yonis , Alkinani Ahmed , Alwan Ali A. , Mehmood Abid
Published: October 2022

This research aimed to investigate the relationship and the correlation between abdomen fat accumulation and the level of diffused fat in the human liver using computerized methods. Computed tomography data sets of 125 subjects were employed in this study. This investigation was implemented through three phases. For each subject, the quantity of abdomen fat was measured, and then the liver was segmented and analyzed to evaluate the fat content. Finally, the collected results were utilized to evaluate the correlation. In addition, the liver intensities were measured to evaluate the level of the diffused fat in the liver. Experimental results show that there was a medium negative correlation between the visceral fat to the abdomen size ratio and the mean of the liver intensity values. The same correlation was found between the mean of the liver intensity values and the total abdomen fat to the abdomen size ratio. The ratios mentioned above were used to obtain relative measurements for the size contribution of fat (for each type) to the whole size of the abdominal area, due to the wide variety of sizes of subjects used in this research. This study supports the research community with objective measurements that reveal a direct proportional relationship between obesity and the level of diffused fat in the liver. The findings of this research provide evidence of a detrimental health effect of abdominal obesity (especially visceral fat) on liver fat content, which is associated with an increased risk of serious health problems, such as diabetes, high blood pressure, kidney disease, and developing heart problems. The research helps to identify a cause for fat accumulation in the liver, which can help in the process of preventing and treating fatty liver disease.
Hierarchical Prototypes Polynomial Softmax Loss Function for Visual Classification
Authors: Xiao Chengcheng , Liu Xiaowen , Sun Chi , Liu Zhongyu , Ding Enjie
Published: October 2022

A well-designed loss function can effectively improve the characterization ability of network features without increasing the amount of calculation in the model inference stage, and has become the focus of attention in recent research. Given that the existing lightweight network adds a loss to the last layer, which severely attenuates the gradient during the backpropagation process, we propose a hierarchical polynomial kernel prototype loss function in this study. In this function, the addition of a polynomial kernel loss function to multiple stages of the deep neural network effectively enhances the efficiency of gradient return, and only adds multi-layer prototype loss functions in the training stage without increasing the calculation of the inference stage. In addition, the good non-linear expression ability of the polynomial kernel improves the characteristic expression performance of the network. Verification on multiple public datasets shows that the lightweight network trained with the proposed hierarchical polynomial kernel loss function has a higher accuracy than other loss functions.
Arctic Climate Extremes
Authors: Overland James E.
Published: October 2022

There are multiple extreme events underway in the Arctic that are beyond previous records: rain in Greenland, Alaska weather variability, and ecosystem reorganizations in the Barents and the northern Bering Sea associated with climate change and sea-ice loss. Such unique extreme events represent a philosophical challenge for interpretation, i.e., a lack of statistical basis, as well as important information for regional adaptation to climate change. These changes are affecting regional food security, human/wildlife health, cultural activities, and marine wildlife conservation. Twenty years ago, the Arctic was more resilient to climate change than now, as sea ice had a broader extent and was three times thicker than today. These new states cannot be assigned probabilities because one cannot a priori conceive of these states. They often have no historical analogues. A way forward for adaptation to future extremes is through scenario/narrative approaches; a recent development in climate change policy is through decision making under deep uncertainty (DMDU).
Risky Travel? Subjective vs. Objective Perceived Risks in Travel Behaviour—Influence of Hydro-Meteorological Hazards in South-Eastern Europe on Serbian Tourists
Authors: Blešić Ivana , Ivkov Milan , Tepavčević Jelena , Popov Raljić Jovanka , Petrović Marko D. , Gajić Tamara , Tretiakova Tatiana N. , Syromiatnikova Julia A. , Demirović Bajrami Dunja , Aleksić Milica , Vujačić Duško , Kričković Emina , Radojković Milan , Morar Cezar , Lukić Tin
Published: October 2022

In terms of climate related security risks, the region of South-Eastern Europe (SEE) can be identified as one of the world’s hot spots. As weather-related hazards continue to increase in numbers and spatial distribution, risk perception in the tourism industry becomes even more important. Additionally, people’s perception of natural hazards is one of the key elements in their decision-making process when choosing a travel destination. Although a vast number of studies have examined aspects of risk perception, an integrated approach which considers both objective and subjective factors related to the tourism industry and hydro-meteorological hazards remains relatively scarce. This pioneering study inspects the causality between objective perceived risks, as well as subjective risk factors. A methodological approach and the obtained results present a certain novelty since the previous conceptualized Psychological Preparedness for Disaster Threat Scale (PPDTS) was applied for the first time in the tourism industry. The obtained results reveal the presence of a statistically significant relationship between objective risks and certain subjective risk factors (gender, age, education, prior experience, anticipation, and awareness). Therefore, this study may offer a conceptual platform for both theoretical and practical implications for enhanced approaches oriented toward more qualitative risk management at a given travel destination, in regions prone to hydro-meteorological hazards.
Classification and Prediction of Nitrogen Dioxide in a Portuguese Air Quality Critical Zone
Authors: Ribeiro Vitor Miguel , Gonçalves Rui
Published: October 2022

This study presents classification and prediction exercises to evaluate the future behavior of nitrogen dioxide in a critical air quality zone located in Portugal using a dataset, the time span of which covers the period between 1 September 2021 and 23 July 2022. Three main results substantiate the importance of this research. First, the classification analysis corroborates the idea of a neutrality principle of road traffic on the target since the respective coefficient is significant, but quantitatively close to zero. This result, which may be the first sign of a paradigm shift regarding the adoption of electric vehicles in addition to reflect the success of previously implemented measures in the city of Lisbon, is reinforced by evidence that the carbon monoxide emitted mostly by diesel vehicles exhibits a significant, negative and permanent effect on satisfying the hourly limit value associated with the target. Second, robustness checks confirm that the period between 8 h and 16 h is particularly remarkable for influencing the target. Finally, the predictive exercise demonstrates that the internationally patented Variable Split Convolutional Attention model has the best predictive performance among several deep learning neural network alternatives. Results indicate that the concentration of nitrogen dioxide is expected to be volatile and only a redundant downward trend is likely to be observed. Therefore, in terms of policy recommendations, additional measures to avoid exceeding the legal nitrogen dioxide ceiling at the local level should be focused on reducing carbon monoxide emissions, rather than just being concerned about halting the intensity of road traffic.
Sound Quality Factors Inducing the Autonomous Sensory Meridian Response
Authors: Shimokura Ryota
Published: October 2022

The acoustical characteristics of auditory triggers often recommended to generate the autonomous sensory meridian response (ASMR) on Internet platforms were investigated by parameterizing their sound qualities following Zwicker’s procedure and calculating autocorrelation (ACF)/interaural cross-correlation (IACF) functions. For 20 triggers (10 human- and 10 nature-generated sounds), scores (on a five-point Likert scale) of the ASMR, perceived loudness, perceived pitch, comfort, and perceived closeness to the sound image were obtained for 26 participants by questionnaire. The results show that the human-generated sounds were more likely to trigger stronger ASMR than nature-generated sounds, and the primary psychological aspect relating to the ASMR was the perceived closeness, with the triggers perceived more closely to a listener having higher ASMR scores. The perceived closeness was evaluated by the loudness and roughness (among Zwicker’s parameter) for the nature-generated sounds and the interaural cross-correlation coefficient (IACC) (among ACF/IACF parameters) for the human-generated sounds. The nature-generated sounds with higher loudness and roughness and the human-generated sounds with lower IACC were likely to evoke the ASMR sensation.
Price Risk Strategy Analysis for Budget Hotels in the Post-Pandemic Era
Authors: Chen I-Fei , Kuo Pi-Ying , Tsaur Ruey-Chyn
Published: October 2022

The supply chain of the tourism industry, including air transportation, travel agencies, souvenirs, and hotel services, is almost at a breaking point, causing a rise in unemployment with huge losses during the COVID-19 pandemic period. In order to overcome these losses, we propose that luxury hotels should consider offering budget hotels at a lower cost but with satisfactory accommodation in order to create some turn-arounds in the post-pandemic era. However, budget hotels that branch off from luxury hotels cannot post the same room rates because there are some uncertain factors that affect the traveler experience when staying in budget hotels. In this study, we define four types of risk factors for the self-selection of the consumer model, and then find that the optimal room price appears to be independent of the performance risk for the service quality, brand image, and shuttle buses, but is dependent on physical risk in terms of priority number risk, the financial risk of refund rates, and the privacy risk of investment in the system. Finally, we discuss how government subsidies can encourage branched budget hotels by describing three sensitivity scenarios. The results show that subsidies that go towards staff training and higher-frequency shuttle buses will cause consumers to book more stays in budget hotels and, thereby, contribute to a higher profit. By lobbying the policy on government subsidies, budget hotels that branch off from luxury hotels are a profitable business model for a reduction in the huge losses occurred during the period of the spread of COVID-19.
Diffusion Effect in Quantum Hydrodynamics
Authors: Bonilla-Licea Moise , Schuch Dieter , Bonilla Estrada Moises
Published: October 2022

In this paper, we introduce (at least formally) a diffusion effect that is based on an axiom postulated by Werner Heisenberg in the early days of quantum mechanics. His statement was that—in quantum mechanics—kinematical quantities such as velocity must be treated as complex matrices. In the hydrodynamic formulation of quantum mechanics according to Madelung, the complex Schrödinger equation is rewritten in terms of two real equations—a continuity equation and a modified Hamilton–Jacobi equation. Considering seriously Heisenberg’s axiom, the velocity occurring in the continuity equation should be replaced by a complex one, thus introducing a diffusion term with an imaginary diffusion coefficient. Therefore, in quantum mechanics, there should be a diffusion effect showing up in the dynamics. This is the case in the time evolution of the free-motion wave packet under time reversal. The maximum returns to the initial position; however, the width of the wave packet does not shrink to its initial width. This effect is obvious but—as far as we know—it is not mentioned in any textbook. In our paper, we discuss this effect in detail and show the connection with a complex version of quantum hydrodynamics.
A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes
Authors: Jaradat Ashraf , Safieddine Fadi , Deraman Aziz , Ali Omar , Al-Ahmad Ahmad , Alzoubi Yehia Ibrahim
Published: October 2022

Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence.
Spatial Heterogeneity of Root Water Conduction Strategies of Zygophyllaceae Plants in Arid Regions of China
Authors: Chen Ying , Dong Yanjun , Liu Jie , Li Zongshan , Wang Xiaochun , Keyimu Maierdang , Wang Cong , Gao Guangyao , Feng Xiaoming
Published: October 2022

Desert plants are the main component of species diversity in desert ecosystems, and studying the anatomy and function of desert plant xylem is of great significance for understanding climate sensitivity and adaptation mechanisms to arid ecosystems. In this study, 11 sampling points were selected in the region starting from the Loess Plateau to the Jungar Basin, the taproot anatomy materials of 9 samples of Zygophyllaceae plants were collected, and the water conduction strategies and spatial distribution characteristics of these species were analyzed. The age, growth rate, vessel number, vessel fraction, vessel area within a fixed measurement range (TVA), MVA, water conductivity (TKp, MKp) and vessel diameter ranged between 1 and 27 years, 43.67 and 678.10 μm/year, 20 and 1952, 4.43 and 26.58%, 8009.62 and 192069.12 μm², 27.63 and 2164.33 μm², 0.417 and 364.97 kg m−1 MPa−1 s−1, 0.000624 and 7.60 kg m−1 Mpa−1 s−1, and 5.57 and 73.87 μm, respectively. The number of root vessels (R = 0.27, p > 0.05) of Zygophyllaceae plants decreased with the decrease in precipitation, and the average vessel area (R = −0.28, p > 0.05) and hydraulic diameter (R = −0.29, p > 0.05) showed an upward trend. This shows that the water hydraulic efficiency priority strategy may be adopted in the root system of Zygophyllaceae plants in severe drought stress condition, and the water hydraulic safety priority strategy may be used in mild drought stress conditions. With the increase in temperature, the root age of Zygophyllaceae plants showed an increasing trend, and the growth rate showed a downward trend, indicating that the radial growth of the roots of Zygophyllaceae plants is mainly affected by temperature. Altitude influences plant growth by affecting temperature and precipitation in arid habitats. The findings of the present study on root xylem anatomical characteristics and life history strategies provides a scientific basis for the ecological restoration of vegetation in arid and semi-arid areas of China.
Diversity of the Paedomorphic Snail-Eating Click-Beetle Genus Malacogaster Bassi, 1834 (Elateridae: Agrypninae: Drilini) in the Mediterranean
Authors: Hoffmannova Johana , Kundrata Robin
Published: October 2022

The soft-bodied click-beetle genus Malacogaster Bassi, 1834 from the Mediterranean region has never been taxonomically revised to date. Information on its morphology, intra- and interspecific variability, systematics and distribution is fragmented and most species have not been properly studied since their description. Therefore, in this study we summarize all available information on the genus Malacogaster. Altogether, we recognize 10 valid species from the area including the Canary Islands, Iberian Peninsula, Balearic Islands, northern coast of Africa, Sardinia, and Sicily. Malacogaster ruficollis Dodero, 1925, stat. nov., which was originally described as a variety of M. bassii Lucas, 1870 and later synonymized with it, is considered a separate species. Malacogaster parallelocollis Reitter, 1894, syn. nov. and M. olcesei var. reductus Pic, 1951, syn. nov. are synonymized with M. maculiventris Reitter, 1894. Malacogaster notativentris Pic, 1951, syn. nov. and M. olcesei Pic, 1951, syn. nov. are synonymized with M. passerinii Bassi, 1834. Lectotypes are designated for M. maculiventris Reitter, 1894, M. nigripes heydeni Reitter, 1894, M. parallelocollis Reitter, 1894, M. thoracica Redtenbacher, 1858, M. olcesei Pic, 1951, and M. rubripes Peyerimhoff, 1949 to fix their identity.
Biochemical and Structural Characterization of Chi-Class Glutathione Transferases: A Snapshot on the Glutathione Transferase Encoded by sll0067 Gene in the Cyanobacterium Synechocystis sp. Strain PCC 6803
Authors: Mocchetti Eva , Morette Laura , Mulliert Guillermo , Mathiot Sandrine , Guillot Benoît , Dehez François , Chauvat Franck , Cassier-Chauvat Corinne , Brochier-Armanet Céline , Didierjean Claude , Hecker Arnaud
Published: October 2022

Glutathione transferases (GSTs) constitute a widespread superfamily of enzymes notably involved in detoxification processes and/or in specialized metabolism. In the cyanobacterium Synechocsytis sp. PCC 6803, SynGSTC1, a chi-class GST (GSTC), is thought to participate in the detoxification process of methylglyoxal, a toxic by-product of cellular metabolism. A comparative genomic analysis showed that GSTCs were present in all orders of cyanobacteria with the exception of the basal order Gloeobacterales. These enzymes were also detected in some marine and freshwater noncyanobacterial bacteria, probably as a result of horizontal gene transfer events. GSTCs were shorter of about 30 residues compared to most cytosolic GSTs and had a well-conserved SRAS motif in the active site (10SRAS13 in SynGSTC1). The crystal structure of SynGSTC1 in complex with glutathione adopted the canonical GST fold with a very open active site because the α4 and α5 helices were exceptionally short. A transferred multipolar electron-density analysis allowed a fine description of the solved structure. Unexpectedly, Ser10 did not have an electrostatic influence on glutathione as usually observed in serinyl-GSTs. The S10A variant was only slightly less efficient than the wild-type and molecular dynamics simulations suggested that S10 was a stabilizer of the protein backbone rather than an anchor site for glutathione.
Digging into the 3D Structure Predictions of AlphaFold2 with Low Confidence: Disorder and Beyond
Authors: Bruley Apolline , Mornon Jean-Paul , Duprat Elodie , Callebaut Isabelle
Published: October 2022

AlphaFold2 (AF2) has created a breakthrough in biology by providing three-dimensional structure models for whole-proteome sequences, with unprecedented levels of accuracy. In addition, the AF2 pLDDT score, related to the model confidence, has been shown to provide a good measure of residue-wise disorder. Here, we combined AF2 predictions with pyHCA, a tool we previously developed to identify foldable segments and estimate their order/disorder ratio, from a single protein sequence. We focused our analysis on the AF2 predictions available for 21 reference proteomes (AFDB v1), in particular on their long foldable segments (>30 amino acids) that exhibit characteristics of soluble domains, as estimated by pyHCA. Among these segments, we provided a global analysis of those with very low pLDDT values along their entire length and compared their characteristics to those of segments with very high pLDDT values. We highlighted cases containing conditional order, as well as cases that could form well-folded structures but escape the AF2 prediction due to a shallow multiple sequence alignment and/or undocumented structure or fold. AF2 and pyHCA can therefore be advantageously combined to unravel cryptic structural features in whole proteomes and to refine predictions for different flavors of disorder.
Metabolomic Profile of Indonesian Betel Quids
Authors: Zhang Pangzhen , Sari Elizabeth Fitriana , McCullough Michael J. , Cirillo Nicola
Published: October 2022

Consumption of areca nut alone, or in the form of betel quid (BQ), has negative health effects and is carcinogenic to humans. Indonesia is one of the largest producers of areca nuts worldwide, yet little is known about the biomolecular composition of Indonesian areca nuts and BQs. We have recently shown that phenolic and alkaloid content of Indonesian BQs exhibits distinct geographical differences. Here, we profiled for the first time the metabolomics of BQ constituents from four regions of Indonesia using non-targeted gas chromatography–mass spectrometry (GC–MS) analysis. In addition to well-known alkaloids, the analysis of small-molecule profiles tentatively identified 92 phytochemicals in BQ. These included mainly benzenoids and terpenes, as well as acids, aldehydes, alcohols, and esters. Safrole, a potentially genotoxic benzenoid, was found abundantly in betel (Piper betle) inflorescence from West Papua and was not detected in areca nut samples from any Indonesian region except West Papua. Terpenes were mostly detected in betel leaves and inflorescence/stem. Areca nut, husk, betel leaf, the inflorescence stem, and BQ mixture expressed distinctive metabolite patterns, and a significant variation in the content and concentration of metabolites was found across different geographical regions. In summary, this was the first metabolomic study of BQs using GC–MS. The results demonstrate that the molecular constituents of BQs vary geographically and suggest that the differential disease-inducing capacity of BQs may reflect their distinct chemical composition.
Receptor Transporter Protein 4 (RTP4) in the Hypothalamus Is Involved in the Development of Antinociceptive Tolerance to Morphine
Authors: Fujita Wakako , Uchida Hitoshi , Kawanishi Masashi , Kuroiwa Yusuke , Abe Manabu , Sakimura Kenji
Published: October 2022

Receptor transporter protein 4 (RTP4), one of the receptor chaperone proteins, contributes to the maturation and membrane trafficking of opioid receptor heteromers consisting of mu (MOPr) and delta (DOPr) opioid receptors (MOPr-DOPr). Although MOPr-DOPr is known to mediate the development of morphine tolerance, the extent to which RTP4 plays a role in this process has not been elucidated. Given that RTP4 can be upregulated by repeated administration of morphine, especially in the hypothalamus, here we investigated the effect of hypothalamus-selective ablation of RTP4 on the development of antinociceptive tolerance to morphine. In this study, we generated RTP4flox mice and selectively knocked-out RTP4 using local injection of adeno-associated virus expressing Cre recombinase (AAV-Cre) into the hypothalamus. The AAV-Cre injection partially, but significantly, decreased the level of RTP4 expression, and suppressed the development of antinociceptive tolerance to morphine. Next, we examined the mechanism of regulation of RTP4 and found that, in neuronal cells, Rtp4 induction is via Gi and MAPK activation, while, in microglial cells, the induction is via Toll-like receptor 4. Together, these studies highlight the role of MOR activity in regulating RTP4, which, in turn, plays an important role in modulating morphine effects in vivo.
Biomimetic Artificial Proton Channels
Authors: Andrei Iuliana-Marilena , Barboiu Mihail
Published: October 2022

One of the most common biochemical processes is the proton transfer through the cell membranes, having significant physiological functions in living organisms. The proton translocation mechanism has been extensively studied; however, mechanistic details of this transport are still needed. During the last decades, the field of artificial proton channels has been in continuous growth, and understanding the phenomena of how confined water and channel components mediate proton dynamics is very important. Thus, proton transfer continues to be an active area of experimental and theoretical investigations, and acquiring insights into the proton transfer mechanism is important as this enlightenment will provide direct applications in several fields. In this review, we present an overview of the development of various artificial proton channels, focusing mostly on their design, self-assembly behavior, proton transport activity performed on bilayer membranes, and comparison with protein proton channels. In the end, we discuss their potential applications as well as future development and perspectives.
Seasonal Changes in the Biochemical Constituents of Green Seaweed Chaetomorpha antennina from Covelong, India
Authors: Vinuganesh A. , Kumar Amit , Korany Shereen Magdy , Alsherif Emad A. , Selim Samy , Prakash Sanjeevi , Beemster Gerrit T. S. , AbdElgawad Hamada
Published: October 2022

Seaweeds are well known for having a wealth of nutritional benefits and providing ecological support to associated fauna. Seasonality influences the biochemical characteristics, affecting their ecological and economic values. In the present study, we evaluated pigments, primary and secondary metabolites, minerals, and antioxidant properties of green seaweed Chaetomorpha antennina growing on the intertidal rocks along the Covelong coast, India, in different seasons (from June 2019 to March 2020). Significant variations were found in the levels of antioxidants, minerals, and metabolites in different seasons, e.g., amino acid levels were the highest in post-monsoon and the lowest in summer. In monsoon, we found the highest concentration of fatty acids in the thalli. Lipid peroxidation and total antioxidant activity were at their maximum levels during post-monsoon, which indicated oxidative damage responses. No significant variations were found in the levels of photosynthetic pigments. The outcomes indeed suggested seasonal variations in the biochemical and nutrient profile of C. antennina. We suggest that the harvesting/collection of C. antennina for different nutrients and metabolites should be performed in the respective seasons.