bluuebunny/crossref_metadata_2025 huggingface dataset:
Causes and Risk Factors of Pediatric Spontaneous Intracranial Hemorrhage—A Systematic Review
Authors: Ciochon Urszula , Bindslev Julie , Hoei-Hansen Christina , Truelsen Thomas , Larsen Vibeke , Nielsen Michael , Hansen Adam
Published: June 2022

Previous studies suggest that the most common cause of spontaneous intracerebral hemorrhage in children and adolescents is arteriovenous malformations (AVMs). However, an update containing recently published data on pediatric spontaneous intracranial hemorrhages is lacking. The aim of this study is to systematically analyze the published data on the etiologies and risk factors of pediatric spontaneous intracranial hemorrhage. This systematic review was performed in compliance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. A search in PubMed, Embase, Scopus, Web of Science and Cochrane Library was conducted aiming for articles published in year 2000 and later, containing data on etiology and risk factors of spontaneous intracranial hemorrhages in unselected cohorts of patients aged between 1 month and 18 years. As a result, forty studies were eligible for data extraction and final analysis. These included 7931 children and adolescents with 4009 reported etiologies and risk factors. A marked variety of reported etiologies and risk factors among studies was observed. Vascular etiologies were the most frequently reported cause of pediatric spontaneous intracranial hemorrhages (n = 1727, 43.08% of all identified etiologies or risk factors), with AVMs being the most common vascular cause (n = 1226, 70.99% of all vascular causes). Hematological and systemic causes, brain tumors, intracranial infections and cardiac causes were less commonly encountered risk factors and etiologies.
Prisoner’s Dilemma Game with Cooperation-Defection Dominance Strategies on Correlational Multilayer Networks
Authors: Li Qin , Zhao Guopeng , Feng Minyu
Published: June 2022

As multilayer networks are widely applied in modern society, numerous studies have shown the impact of a multilayer network structure and the network nature on the proportion of cooperators in the network. In this paper, we use Barabási–Albert scale-free networks (BA) and Watts and Strogatz networks (WS) to build a multilayer network structure, and we propose a new strategy-updating rule called “cooperation-defection dominance”, which can be likened to dominant and recessive traits in biogenetics. With the newly constructed multilayer network structure and the strategy-updating rules, based on the simulation results, we find that in the BA-BA network, the cooperation dominance strategy can make the networks with different rs show a cooperative trend, while the defection dominance strategy only has an obvious effect on the network cooperation with a larger r. When the BA network is connected to the WS network, we find that the effect of strategy on the proportion of cooperators in the network decreases, and the main influencing factor is the structure of the network. In the three-layer network, the cooperation dominance strategy has a greater impact on the BA network, and the proportion of the cooperators is enhanced more than under the natural evolution strategy, but the promotion effect is still smaller than that of the two-layer BA network because of the WS network. Under the defection dominance strategy, the WS layer appears different from the first two strategies, and we conclude through simulation that when the payoff parameter is at the middle level, its cooperator proportion will be suppressed, and we deduce that the proportion of cooperators and defectors, as well as the payoff, play an important role.
Teachers’ Attitudes towards Inclusive Education at Greek Secondary Education Schools
Authors: Mouchritsa Maria , Romero Ainara , Garay Urtza , Kazanopoulos Spyridon
Published: June 2022

Advocates of inclusive education believe in the right of all learners to education and the many benefits it delivers. Teachers’ attitudes and beliefs toward inclusive education are instantly reflected in their classroom activities and practices. This research will concentrate on special and general education teachers in Greek secondary schools. It will investigate their attitudes toward inclusive education and how these attitudes alter as a consequence of variables such as age, gender, teaching experience, and inclusive education training. Quantitative, main, and correlational research was obtained between groups using a non-experimental technique. The sample was taken from 307 educators, who were almost equally divided between general and special education. The SACIE-R questionnaire was used to assess teachers’ attitudes toward inclusive education. The outcomes of the research demonstrated that attitudes toward inclusive education were impacted by the kind of special education received, as well as age and general education teaching experience. Furthermore, positive attitudes were impacted by gender, but negative sentiments and concerns were influenced by general education and special education teaching experience. Finally, the study revealed low levels of negative sentiments, medium levels of concerns, and high levels of favourable attitudes toward inclusive education.
Multi-Intensity Optimization-Based CT and Cone Beam CT Image Registration
Authors: Xie Lisiqi , He Kangjian , Gong Jian , Xu Dan
Published: June 2022

Cancer is a highly lethal disease that is mainly treated by image-guided radiotherapy. Because the low dose of cone beam CT is less harmful to patients, cone beam CT images are often used for target delineation in image-guided radiotherapy of various cancers, especially in breast and lung cancer. However, breathing and heartbeat can cause position errors in images taken during different periods, and the low dose of cone beam CT also results in insufficient imaging clarity, rendering existing registration methods unable to meet the CT and cone beam CT registration tasks. In this paper, we propose a novel multi-intensity optimization-based CT and cone beam CT registration method. First, we use a multi-weighted mean curvature filtering algorithm to preserve the multi-intensity details of the input image pairs. Then, the strong edge retention results are registered using and intensity-based method to obtain the multi-intensity registration results. Next, a novel evaluation method called intersection mutual information is proposed to evaluate the registration accuracy of the different multi-intensity registration results. Finally, we determine the optimal registration transformation by intersection mutual information and apply it to the input image pairs to obtain the final registration results. The experimental results demonstrate the excellent performance of the proposed method, meeting the requirements of image-guided radiotherapy.
A New Adaptive High-Degree Unscented Kalman Filter with Unknown Process Noise
Authors: Xu Daxing , Wang Bao , Zhang Lu , Chen Zhiqiang
Published: June 2022

Vehicle state, including location and motion information, plays an essential role on the Internet of Vehicles (IoV). Accurately obtaining the system state information is the premise of realizing precise control. However, the statistics of system process noise are often unknown due to the complex physical process. It is challenging to estimate the system state when the process noise statistics are unknown. This paper proposes a new adaptive high-degree unscented Kalman filter based on the improved Sage–Husa algorithm. First, the traditional Sage–Husa algorithm is improved using a high-degree unscented transform. A noise estimator suitable for the high-degree unscented Kalman filter is obtained to estimate the statistics of the unknown process noise. Then, an adaptive high-degree unscented Kalman filter is designed to improve the accuracy and stability of the state estimation system. Finally, the target tracking simulation results verify the proposed algorithm’s effectiveness.
Integrating LoRa-Based Communications into Unmanned Aerial Vehicles for Data Acquisition from Terrestrial Beacons
Authors: Arroyo Patricia , Herrero José Luis , Lozano Jesús , Montero Pablo
Published: June 2022

The Internet of Things (IoT) is a paradigm that has rapidly evolved in recent years. The ability to connect many devices is driving the development of new types of applications that allow the remote monitoring of a wide variety of devices. One of the great challenges that has been identified in this field is the monitoring of sensors scattered in wide areas or located in environments with poor or even no communications coverage. To deal with this problem, different approaches based on cellular or satellite communications have been considered. However, these alternatives are complex and very expensive. To overcome these drawbacks, we designed a system based on unmanned aerial vehicles and LoRa technology that enables data transfer from sensors to a central system. Furthermore, machine learning techniques were applied to process and classify the data retrieved from the sensors. Finally, a Java-based application was developed, providing services such as data storage, processing, and visualization. To verify the correct operation of the proposed system, manual and autonomous flight tests were carried out, verifying the correct transmission of the data from the sensors to the central system.
Leader-Based Trajectory Following in Unstructured Environments—From Concept to Real-World Implementation
Authors: Nestlinger Georg , Rumetshofer Johannes , Solmaz Selim
Published: June 2022

In this paper, the problem of vehicle guidance by means of an external leader is described. The objective is to navigate a four-wheeled vehicle through unstructured environments, characterized by the lack of availability of typical guidance infrastructure like lane markings or HD maps. The trajectory-following approach is based on an estimate of the leader’s path. For that, position measurements are stored over time with respect to an inertial frame. A new strategy is proposed to rate the significance of position measurements and ensure that a certain threshold of stored samples is not exceeded. Having an estimate of the leader path is essential to prevent the cutting-corner phenomenon and for exact path following in general. A spline-approximation technique is applied to obtain a smooth reference path for the underlying lateral and longitudinal motion controllers. For longitudinal tracking, a constant time-headway policy was implemented, to follow the leader with a constant time gap along the estimated path. The algorithm was first developed and tested in a simulation framework and then deployed in a demonstrator vehicle for validation under real operating conditions. The presented experimental results were achieved using only on-board sensors of the demonstrator vehicle, while high-accuracy differential GPS-based position measurements serve as the ground truth data for visualization.
Experimental Study of Electric Power Generation with Concentrated Solar Thermoelectric Generator
Authors: Bensafi Mohammed , Ameur Houari , Kaid Noureddine , Hoseinzadeh Siamak , Memon Saim , Sohani Ali
Published: June 2022

Although thermoelectric technology is little-known in the public domain, it presents an exciting alternative solution in many cases where lost heat energy can be quickly recovered to produce electricity. In the present paper, an attempt is undertaken to exploit this energy. For this purpose, an experimental study is conducted to produce electricity with the thermoelectric effect by utilizing a device placed on a parabolic concentrator. The device is placed on a solar tracker. The results obtained for two days of two distinct months, January and June, revealed that the production in June was higher than that in January by around 92.86%. This distinction is due to the concentrated solar beam being different on a day in each month. A vital product was recorded by utilizing the concentrator. This gadget permitted us to take advantage of the limit of sun-based radiation to produce power. The power may be stored with a legitimate stockpiling procedure.
A Reverse Order Hierarchical Integrated Scheduling Algorithm Considering Dynamic Time Urgency Degree of the Process Sequences
Authors: Cao Wangcheng , Xie Zhiqiang , Yang Jing , Zhan Xiaojuan , Pei Lirong , Yu Xu
Published: June 2022

Aiming at the general integrated scheduling problem of tree-structured complex single-product machining and assembling, a reverse order hierarchical integrated scheduling algorithm (ROHISA) is proposed by considering the dynamic time urgency degree (TUD) of process sequences (PSs). The strategy of process sorting is put forward, and the TUD of PS is defined. The process tree is reversed using leaf alignment, and according to the order from leaf to root, the scheduling order of leaf nodes in the same layer is determined layer by layer according to the TUD values of the PSs to which the leaf nodes belong. In turn, the sorted leaf nodes in each layer are stored in a corresponding layered array (LA). Finally, the elements in each LA are reversed, and the LAs’ arranging order is reversed. A reverse order hierarchical scheduling strategy is proposed. Starting from the root node, every LA is taken as a unit to conduct trial scheduling each time. Under the condition of meeting the craft constraints, a set of quasi-scheduling schemes of same-layer processes (QSSSLP) is obtained, and the one with the minimum end time is selected from it as the scheduling scheme of the same layer processes (SSSLP). If it is not unique, the QSSSLP that machines all the same layer processes (SLP) as early as possible is selected. The research shows that the ROHISA optimizes the integrated scheduling results of single-product manufacturing enterprises and improves its production efficiency.
Resource Allocation on Blockchain Enabled Mobile Edge Computing System
Authors: Zheng Xinzhe , Zhang Yijie , Yang Fan , Xu Fangmin
Published: June 2022

Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.
An Appropriate Index to Assess the Global Cancellation Level of the Harmonic Currents Consumed by a Set of Single-Phase Uncontrolled Rectifiers and a Set of Fluorescent Lamps
Authors: Mesas Juan José , Sainz Luis , Monjo Lluís , Pedra Joaquín
Published: June 2022

An in-depth study of harmonic current reduction in European commercial buildings due to the harmonic cancellation effect when a set of single-phase uncontrolled rectifiers and a set of fluorescent lamps are connected at the same voltage level is essential, since both types of non-linear loads are very present in commercial and residential sectors. This paper provides an appropriate index to assess the global cancellation level of the harmonic currents for this study. The equivalent circuit per phase of the typical three-phase power system of European commercial installations is presented and simplified for the cancellation analysis of the harmonic currents consumed by a set of multiple identical single-phase uncontrolled rectifiers and a set of multiple identical fluorescent lamps connected at the same voltage level. The suitability and usefulness of the proposed index are shown by applying it to that analysis, which leads to some results of practical interest. This index can be generalized to any number of sets of multiple identical non-linear loads and can be applied in graphical and optimization studies that will allow a greater benefit from the harmonic cancellation effect to be obtained given the global nature of the index.
A Study on the Vulnerability Cascade Propagation of Integrated Energy Systems in the Transportation Industry Based on the Petri Network
Authors: Yin Xiaohong , Li Lin , Liu Qiang
Published: June 2022

In order to solve the vulnerability problem of the integrated energy system in the transportation industry, a vulnerability cascade propagation model based on the Petri net is proposed. The article comprehensively considers the configuration of the energy system, constructs the cascade propagation-based function Petri net model using a hierarchical modelling approach, and performs vulnerability cascade propagation analysis using Matlab on this basis. However, the integrated energy system of the transportation industry is complex and extensive, and it is not easy to model the Petri network of the whole transportation industry, which will be continued in-depth in the subsequent research. The study results show that the energy system’s vulnerability keeps changing with the growth of time, and the factors in maintaining the equilibrium vary from one subsystem to another. In addition, the ringed structure is more vulnerable compared to the acyclic structure, and the vulnerability cascade propagates faster for the ringed structure than the acyclic structure. The results of the study contribute to the scientific development of integrated energy system planning and construction for the transportation industry and provide a reference for the rehabilitation and construction of energy systems.
Experimental and Numerical Simulation of Water Adsorption and Diffusion in Coals with Inorganic Minerals
Authors: Xu Yong , Chen Xuexi , Zhao Wei , Chen Peng
Published: June 2022

The study on the adsorption and micropore filling of water vapor in coal is significant for predicting coalbed methane content in coal seams. The primary purpose of this study is to explain the effects of coal pore structure and its surface chemistry on water vapor monolayer adsorption, micropore filling, and diffusion coefficient. First, X-ray diffraction (XRD) and mercury intrusion porosimetry (MIP) analyzed inorganic mineral components of two kinds of coal samples and pore fissures structures. Then, we divide pores and fissures according to the theory of fractal dimensions. Furthermore, we carried out the water vapor adsorption and desorption experiments on two kinds of coal; in particular, we set 14 points of relative pressure between 0 and 0.2. Guggenheim–Anderson–de Boer (GAB), Frenkel–Halsey–Hill (FHH), and Freundlich models were used to analyze the data of water vapor adsorption to obtain the boundary pressure points of the monolayer, multilayer adsorption, and capillary condensation. Finally, the parameters of the models were obtained by fitting the adsorption data of water vapor according to the combined GAB, Freundlich, DA, and bidisperse adsorption (BDA) models to analyze the interaction mechanism between coal and water. We explain why the strongly adsorbed water minerals, such as pyrite, illite, and nacrite coal, can improve water vapor’s adsorption and diffusion capacity in coal pore fissures.
Experimental and Numerical Investigation of Extinguishing Effectiveness of Inert-Gas Agents in a Leaky Enclosure
Authors: Hu Xiaoqin , Kraaijeveld Arjen
Published: June 2022

Gas-fire-suppression systems are currently applied to some specific buildings in Norway, as sprinkler systems may not provide sufficient protection in some cases. The application of inert-gas-fire-suppression systems for hazard class 6 buildings needs further intensive validation by experimental and numerical study. Due to the presence of cracks and ventilation systems, it becomes doubtful whether inert-gas agents can extinguish a deep-seated fire located in a leaky enclosure. In this study, tests and numerical simulations were both conducted to investigate the extinguishing effectiveness of inert-gas agents for a closet fire in a leaky apartment. The results show that the location of cracks plays a nonnegligible role in determining the oxygen level in the leaky apartment. The tests and simulations demonstrated that the gas-fire-suppression system successfully extinguished the closet fire even if the activation of the gas-fire-suppression system was postponed or the path available for the inert-gas agent to reach the fire source was narrowed. However, the sprinkler system failed to achieve this. The experimental data also demonstrated that the calculation method proposed in our previous work can be used to estimate the oxygen level in a leaky enclosure.
Optimization of Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Based on Dynamic Programming
Authors: Du Changqing , Huang Shiyang , Jiang Yuyao , Wu Dongmei , Li Yang
Published: June 2022

Fuel cell hybrid electric vehicles have attracted a large amount of attention in recent years owing to their advantages of zero emissions, high efficiency and low noise. To improve the fuel economy and system durability of vehicles, this paper proposes an energy management strategy optimization method for fuel cell hybrid electric vehicles based on dynamic programming. Rule-based and dynamic-programming-based strategies are developed based on building a fuel cell/battery hybrid system model. The rule-based strategy is improved with a power distribution scheme of dynamic programming strategy to improve the fuel economy of the vehicle. Furthermore, a limit on the rate of change of the output power of the fuel cell system is added to the rule-based strategy to avoid large load changes to improve the durability of the fuel cell. The simulation results show that the equivalent 100 km hydrogen consumption of the strategy based on the dynamic programming optimization rules is reduced by 6.46% compared with that before the improvement, and by limiting the rate of change of the output power of the fuel cell system, the times of large load changes are reduced. Therefore, the strategy based on the dynamic programming optimization rules effectively improves the fuel economy and system durability of vehicles.
Financial Hazard Assessment for Electricity Suppliers Due to Power Outages: The Revenue Loss Perspective
Authors: Khosa Ikramullah , Taimoor Naveed , Akhtar Jahanzeb , Ali Khurram , Rehman Ateeq Ur , Bajaj Mohit , Elgbaily Mohamed , Shouran Mokhtar , Kamel Salah
Published: June 2022

The electrical power infrastructure of the modern world is advanced, efficient, and robust, yet power outages still occur. In addition to affecting millions of people around the world, these outage events cost billions of dollars to the global economy. In this paper, the revenue loss borne by electricity-supplying companies in the United States due to power outage events is estimated and predicted. Various factors responsible for power outages are considered in order to present an exploratory data analysis at the U.S. level, followed by the top ten affected states, which bear over 85% of the total revenue loss. The loss is computed using historic observational data of electricity usage patterns and the tariff offered by the energy suppliers. The study is supplemented with reliable and publicly available records, including electricity usage patterns, the consumer category distribution, climatological annotations, population density, socio-economic indicators and land area. Machine learning techniques are used to predict the revenue loss for future outage events, as well as to characterize the key parameters for efficient prediction and their partial dependence. The results show that the revenue loss is a function of several parameters, including residential sales, percentage of industrial customer, time-period of the year, and economic indicators. This study may help energy suppliers make risk-informed decisions, while developing revenue generation strategies as well as identifying safer investment avenues for long-term returns.
Provision of Ecosystem Services in Riparian Hemiboreal Forest Fixed-Width Buffers
Authors: Saklaurs Mārcis , Lībiete Zane , Donis Jānis , Kitenberga Māra , Elferts Didzis , Jūrmalis Edgars , Jansons Āris
Published: June 2022

The importance of riparian forest protection is widely acknowledged. However, scientific discussions are still ongoing as to the most suitable and effective protection activities for these forests. The quality of the provision of different ecosystem services in protected riparian forest buffers could provide an insight into the impact of protection regulations. Cultural ecosystem services in riparian forests have an important social-ecological context, especially with the growing interest in recreation activities in forests. The aim of our study was to compare provision of different ecosystem services in riparian forest buffers located both adjacent to (0–50 m) and distant from (51–200 m) the stream. In our study, four small-to-medium-sized rivers in Latvia were used. In total, six different indicators of ecosystem services were estimated, based on data from the National Forest Inventory and the European Soil Data Centre. Bayesian ordinal regression was employed to assess the differences between the two buffer strips. Our results showed that the majority of assessed ecosystem service indicators (Recreation potential of the forest ecosystem, Visual quality of the forest landscape, Potential for the presence of medicinal plants and Potential for the presence of nectar plants) were of higher quality in the adjacent (0–50 m) buffer. Only one indicator (Flora with phytoremediation potential) had significantly higher values in the distant buffer strips (estimate 0.24, CI: [0.11, 0.38]). The observed distribution of quality classes showed that, only for the indicator Potential of medicinal plants, the highest quality class was the most common (>60%), for other indicators dominated average quality class estimations. The obtained results suggested that the current protection status that riparian forest buffers have facilitated maintain the provision of several cultural and regulation & maintenance ecosystem services.
Identification of Maize with Different Moldy Levels Based on Catalase Activity and Data Fusion of Hyperspectral Images
Authors: Wang Wenchao , Huang Wenqian , Yu Huishan , Tian Xi
Published: June 2022

Maize is susceptible to mold infection during growth and storage due to its large embryo and high moisture content. Therefore, it is essential to distinguish the moldy sample from healthy groups to prevent the spread of mold and avoid huger economic losses. Catalase is a metabolite in the growth of microorganisms; hence, all maize samples were accurately divided into four moldy grades (health, mild, moderate, and severe levels) by determining their catalase activity. The visible and shortwave near-infrared (Vis-SWNIR) and longwave near-infrared (LWNIR) hyperspectral images were investigated to jointly identify the moldy levels of maize. Spectra and texture information of each maize sample were extracted and used to build the classification models of maize with different moldy levels in pixel-level fusion and feature-level fusion. The result showed that the feature-level fusion of spectral and texture within Vis-SWNIR and LWNIR regions achieved the best results, overall prediction accuracy reached 95.00% for each moldy level, all healthy maize was correctly classified, and none of the moldy samples were misclassified as healthy level. This study illustrated that two hyperspectral image systems, with complementary spectral ranges, combined with feature selection and data fusion strategies, could be used synergistically to improve the classification accuracy of maize with different moldy levels.
Metabolomic Analysis Reveals Insights into Deterioration of Rice Quality during Storage
Authors: Wang Qian , Zhang Dong , Zhao Luyao , Liu Jianlei , Shang Bo , Yang Weiqiao , Duan Xiaoliang , Sun Hui
Published: June 2022

To determine the changes in the quality of rice during storage, this study investigated the comprehensive metabolomic profiles of Nanjing 9108 (typical japonica rice) and Jianzhen 2 (typical indica rice) varieties in China, using metabolomics. A total of 13 categories of 593 metabolites including lipids (134 species), phenolic acids (78 species), flavonoids (70 species), alkaloids (67 species), organic acids (64 species), amino acids and derivatives (64 species), saccharides and alcohols (44 species), nucleotides and derivatives (37 species), vitamins (14 species), lignans and coumarins (9 species), tannins (2 species), terpenoids (2 species), and others (8 species) were identified in both varieties. The result showed significant changes in 204 metabolites in Nanjing 9108, while only 26 were altered in Jianzhen 2 during storage. These metabolites involved 46 metabolic pathways. The TCA cycle, linoleic, and α-linolenic acid metabolic pathways were unique in Nanjing 9108. Finally, the results of quantitative mass spectrometry of 11 metabolites provided insight into biomarkers associated with quality deterioration of rice. This study provides insights into the mechanism of deterioration in the quality of rice during storage.
Consumer Perception of Beef Quality and How to Control, Improve and Predict It? Focus on Eating Quality
Authors: Liu Jingjing , Ellies-Oury Marie-Pierre , Stoyanchev Todor , Hocquette Jean-François
Published: June 2022

Quality refers to the characteristics of products that meet the demands and expectations of the end users. Beef quality is a convergence between product characteristics on one hand and consumers’ experiences and demands on the other. This paper reviews the formation of consumer beef quality perception, the main factors determining beef sensory quality, and how to measure and predict beef eating quality at scientific and industrial levels. Beef quality is of paramount importance to consumers since consumer perception of quality determines the decision to purchase and repeat the purchase. Consumer perception of beef quality undergoes a multi-step process at the time of purchase and consumption in order to achieve an overall value assessment. Beef quality perception is determined by a set of quality attributes, including intrinsic (appearance, safety, technological, sensory and nutritional characteristics, convenience) and extrinsic (price, image, livestock farming systems, commercial strategy, etc.) quality traits. The beef eating qualities that are the most valued by consumers are highly variable and depend mainly on the composition and characteristics of the original muscle and the post-mortem processes involved in the conversion of muscle into meat, the mechanisms of which are summarized in this review. Furthermore, in order to guarantee good quality beef for consumers in advance, the prediction of beef quality by combining different traits in scenarios where the animal, carcass, and muscle cuts can be evaluated is also discussed in the current review.
A New Food Ingredient Rich in Bioaccessible (Poly)Phenols (and Glucosinolates) Obtained from Stabilized Broccoli Stalks
Authors: Costa-Pérez Antonio , Moreno Diego A. , Periago Paula M. , García-Viguera Cristina , Domínguez-Perles Raúl
Published: June 2022

Broccoli (Brassica oleracea var. italica) stalks account for up to 35% of the broccoli harvest remains with the concomitant generation of unused waste that needs recovery to contribute to the sustainability of the system. However, due to its phytochemical composition, rich in bioactive (poly)phenols and glucosinolates, as well as other nutrients, the development of valorization alternatives as a source of functional ingredients must be considered. In this situation, the present work aims to develop/obtain a new ingredient rich in bioactive compounds from broccoli, stabilizing them and reducing their degradation to further guarantee a high bioaccessibility, which has also been studied. The phytochemical profile of lyophilized and thermally treated (low-temperature and descending gradient temperature treatments), together with the digested materials (simulated static in vitro digestion) were analysed by HPLC-PDA-ESI-MSn and UHPLC-3Q-MS/MS. Broccoli stalks and co-products were featured by containing phenolic compounds (mainly hydroxycinnamic acid derivatives and glycosylated flavonols) and glucosinolates. The highest content of organosulfur compounds corresponding to the cores of the broccoli stalks treated by applying a drying descendant temperature gradient (aliphatic 18.05 g/kg dw and indolic 1.61 g/kg dw, on average, while the breakdown products were more abundant in the bark ongoing low temperature drying 11.29 g/kg dw, on average). On the other hand, for phenolics, feruloylquinic, and sinapoylquinic acid derivatives of complete broccoli stalk and bark, were more abundant when applying low-temperature drying (14.48 and 28.22 g/kg dw, on average, respectively), while higher concentrations were found in the core treated with decreasing temperature gradients (9.99 and 26.26 g/kg dw, on average, respectively). When analysing the bioaccessibility of these compounds, it was found that low-temperature stabilization of the core samples provided the material with the highest content of bioactives including antioxidant phenolics (13.6 and 33.9 g/kg dw of feruloylquinic and sinapoylquinic acids, on average, respectively) and sulforaphane (4.1 g/kg dw, on average). These processing options enabled us to obtain a new product or ingredient rich in bioactive and bioaccessible compounds based on broccoli stalks with the potential for antioxidant and anti-inflammatory capacities of interest.
Properties of Oaxaca Cheese Elaborated with Ultrasound-Treated Raw Milk: Physicochemical and Microbiological Parameters
Authors: Huerta-Jimenez Mariana , Herrera-Gomez Brissa , Dominguez-Ayala Eduardo A. , Chavez-Martinez America , Juarez-Moya Juliana , Felix-Portillo Monserrath , Alarcon-Rojo Alma D. , Carrillo-Lopez Luis M.
Published: June 2022

The effect of ultrasound-treated fresh raw milk upon yield, physicochemical and microbiological quality of Oaxaca cheese was evaluated under a factorial design. The ultrasound frequencies tested were 25 and 45 kHz, during 15 or 30 min. The cheeses made with the ultrasonicated milk (30 min, high-intensity ultrasound, HIU) had greater luminosity without significant changes in hue or chroma, as compared to the controls with no HIU. The yield improved significantly (by up to 2.8 kg/100 L of milk), as the ultrasound treatment time increased. Such cheese yield is attributable to the higher protein content, which was up to 1.5% higher, after sonication. Long-treatment time (30 min) at 25 kHz significantly lowered mesophilic bacteria counts down to limits allowed by current regulations and favors the growth of lactic acid bacteria (LAB) while lowering mold and yeast counts. The absence of E. coli and Salmonella spp. and the decrease in S. aureus counts in Oaxaca cheese were attributed to the mixing of the paste with hot water, inherent to the traditional elaboration process, and to the antagonistic effect of the ultrasound-triggered increased LAB on pathogenic bacteria. Since the artisanal elaboration of Oaxaca cheese does not comply with the current Mexican regulations regarding mesophiles, ultrasound could be a suitable technology to protect its genuine elaboration process with raw milk.
Smart Antifreeze Hydrogels with Abundant Hydrogen Bonding for Conductive Flexible Sensors
Authors: Dai Bailin , Cui Ting , Xu Yue , Wu Shaoji , Li Youwei , Wang Wu , Liu Sihua , Tang Jianxin , Tang Li
Published: June 2022

Recently, flexible sensors based on conductive hydrogels have been widely used in human health monitoring, human movement detection and soft robotics due to their excellent flexibility, high water content, good biocompatibility. However, traditional conductive hydrogels tend to freeze and lose their flexibility at low temperature, which greatly limits their application in a low temperature environment. Herein, according to the mechanism that multi−hydrogen bonds can inhibit ice crystal formation by forming hydrogen bonds with water molecules, we used butanediol (BD) and N−hydroxyethyl acrylamide (HEAA) monomer with a multi−hydrogen bond structure to construct LiCl/p(HEAA−co−BD) conductive hydrogel with antifreeze property. The results indicated that the prepared LiCl/p(HEAA−co−BD) conductive hydrogel showed excellent antifreeze property with a low freeze point of −85.6 °C. Therefore, even at −40 °C, the hydrogel can still stretch up to 400% with a tensile stress of ~450 KPa. Moreover, the hydrogel exhibited repeatable adhesion property (~30 KPa), which was attributed to the existence of multiple hydrogen bonds. Furthermore, a simple flexible sensor was fabricated by using LiCl/p(HEAA−co−BD) conductive hydrogel to detect compression and stretching responses. The sensor had excellent sensitivity and could monitor human body movement.
Transforming Capillary Alginate Gel (Capgel) into New 3D-Printing Biomaterial Inks
Authors: Panarello Andrew Philip , Seavey Corey Edward , Doshi Mona , Dickerson Andrew K. , Kean Thomas J. , Willenberg Bradley Jay
Published: June 2022

Three-dimensional (3D) printing has great potential for creating tissues and organs to meet shortfalls in transplant supply, and biomaterial inks are key components of many such approaches. There is a need for biomaterial inks that facilitate integration, infiltration, and vascularization of targeted 3D-printed structures. This study is therefore focused on creating new biomaterial inks from self-assembled capillary alginate gel (Capgel), which possesses a unique microstructure of uniform tubular channels with tunable diameters and densities. First, extrusions of Capgel through needles (0.1–0.8 mm inner diameter) were investigated. It was found that Capgel ink extrudes as slurries of fractured and entangled particles, each retaining capillary microstructures, and that extruded line widths W and particle sizes A were both functions of needle inner diameter D, specifically power-law relationships of W~D0.42 and A~D1.52, respectively. Next, various structures were successfully 3D-printed with Capgel ink, thus demonstrating that this biomaterial ink is stackable and self-supporting. To increase ink self-adherence, Capgel was coated with poly-L-lysine (PLL) to create a cationic “skin” prior to extrusion. It was hypothesized that, during extrusion of Capgel-PLL, the sheared particles fracture and thereby expose cryptic sites of negatively-charged biomaterial capable of forming new polyelectrolyte bonds with areas of the positively-charged PLL skin on neighboring entangled particles. This novel approach resulted in continuous, self-adherent extrusions that remained intact in solution. Human lung fibroblasts (HLFs) were then cultured on this ink to investigate biocompatibility. HLFs readily colonized Capgel-PLL ink and were strongly oriented by the capillary microstructures. This is the first description of successful 3D-printing with Capgel biomaterial ink as well as the first demonstration of the concept and formulation of a self-adherent Capgel-PLL biomaterial ink.
Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images
Authors: Kim Gi , Zhang Ho , Cho Yong , Ryu Seung
Published: June 2022

Doctors in primary hospitals can obtain the impression of lumbosacral radiculopathy with a physical exam and need to acquire medical images, such as an expensive MRI, for diagnosis. Then, doctors will perform a foraminal root block to the target root for pain control. However, there was insufficient screening medical image examination for precise L5 and S1 lumbosacral radiculopathy, which is most prevalent in the clinical field. Therefore, to perform differential screening of L5 and S1 lumbosacral radiculopathy, the authors applied digital infrared thermographic images (DITI) to the machine learning (ML) algorithm, which is the bag of visual words method. DITI dataset included data from the healthy population and radiculopathy patients with herniated lumbar discs (HLDs) L4/5 and L5/S1. A total of 842 patients were enrolled and the dataset was split into a 7:3 ratio as the training algorithm and test dataset to evaluate model performance. The average accuracy was 0.72 and 0.67, the average precision was 0.71 and 0.77, the average recall was 0.69 and 0.74, and the F1 score was 0.70 and 0.75 for the training and test datasets. Application of the bag of visual words algorithm to DITI classification will aid in the differential screening of lumbosacral radiculopathy and increase the therapeutic effect of primary pain interventions with economical cost.
Access to Digital Information and Protective Awareness and Practices towards COVID-19 in Urban Marginalized Communities
Authors: Pattanasri Siwarat , Nguyen Thi Phuoc Lai , Vu Thanh Bien , Winijkul Ekbordin , Ahmad Mokbul Morshed
Published: June 2022

Due to digital inequality, poor living, and health care conditions, marginalized people are the most vulnerable group to the COVID-19 pandemic. This study examined how digital information influences knowledge, practices, threat appraisals, and motivation behaviors of urban marginalized communities. It examined slum people’s digital competencies, their access to COVID-19 online information, and their trust in COVID-19 information provided by both online and offline media. A total of 453 slum people in Bangkok city, Thailand were surveyed, and multiple regression was performed to examine whether socio-demographic factors influence the access to online communication of slum people. We hypothesized that access to online information might affect marginalized people’s awareness of COVID-19 and resulted in greater levels of their practices and protective behaviors. The finding showed that slum people who had access to online information tended to have a better awareness of self-protection against COVID-19, while elderly, female, and foreign migrant workers faced a number of constraints in accessing COVID-19 online information. Such results are important considering the pandemic is compelling societies to turn toward digital technologies to confront the COVID-19 pandemic and address pandemic-related issues. We also discuss how to enhance the role of digital communication in helping urban marginalized communities during and after the pandemic.
Comparison of the Effect of Endurance, Strength, and Endurance-Strength Training on Inflammatory Markers and Adipokines Levels in Overweight and Obese Adults: Systematic Review and Meta-Analysis of Randomised Trials
Authors: Makarewicz Aleksandra , Jamka Małgorzata , Geltz Jakub , Śmidowicz Angelika , Kokot Marta , Kaczmarek Nina , Mądry Edyta , Walkowiak Jarosław
Published: June 2022

The aim of this meta-analysis was to compare the effects of endurance, strength, and combined training on inflammatory markers and adipokine concentrations in overweight and obese adults. We performed a literature search of the Cochrane Library, PubMed, Scopus, and Web of Science databases and identified 24 randomised control trials published prior to June 2021. Our findings indicate that endurance training was significantly more beneficial than strength training in reducing C-reactive protein (CRP) (standard mean difference (SMD): −1.317, 95% confidence intervals (CI): −2.565, −0.070, p = 0.0385), interleukin 6 (IL-6) (SMD: −0.363, 95% CI: −0.648, −0.078, p = 0.0126), and visfatin (SMD: −0.618, 95% CI: −1.015, −0.222, p = 0.0023) concentrations. Moreover, combined training was more beneficial than strength training alone in lowering tumour necrosis factor-alpha (TNF-α) levels (SMD: 0.890, 95% CI: −0.301, 1.478, p = 0.0030). There were no differences between the effects of different types of training programmes on adiponectin and leptin concentrations. In conclusion, compared with strength training, endurance training is more effective in lowering CRP, IL-6, and visfatin concentrations, while combined training is more beneficial in reducing TNF-α levels in overweight and obese adults. Further studies are needed to determine which type of training has a better effect on adiponectin and leptin concentrations in this population.
The Impact of Instant Reimbursement of Cross-Regional Medical Services on Hospitalization Costs Incurred by the Floating Population—Evidence from China
Authors: Zhang Xiaodong , Zhang Lei
Published: June 2022

Background: The medical cost reimbursement function of medical insurance can reduce the medical burden of individuals and thus improve their medical service utilization level. This study aimed to explore the impact of different cross-regional reimbursement types of medical insurance (instant reimbursement and manual reimbursement) on the hospitalization costs incurred by the floating population. Methods: The data used in this study was from the 2018 China Migrants Dynamic Survey (CMDS) conducted by the National Health Commission of China. The multiple linear regression model and Propensity Score Matching method (PSM) were used to analyze the impact of instant and manual reimbursement on hospitalization costs. Results: (1) Instant reimbursement and manual reimbursement could significantly reduce the floating population’s out-of-pocket proportion of hospitalization costs by 33.2% and 26.9%, respectively; (2) the average proportion and amounts of out-of-pocket hospital costs of instant reimbursement for the floating population were lower than those of manual reimbursement by 6.35% and 19.6%, respectively, and this impact would gradually increase as the flow distance expanded; (3) there was no significant impact of instant reimbursement on the total hospitalization costs relative to manual reimbursement. Conclusions: Our study suggests that instant reimbursement can effectively reduce the out-of-pocket medical burden of the floating population at the individual level, but it will not have an obvious impact on the total hospitalization costs at the social level.
miR-494 in Extracellular Vesicles as a Potent Biomarker of Chronic Myeloid Leukemia Treatment with Tyrosine Kinase Inhibitors
Authors: Shibuta Tatsuki , Shimizu Honoka , Takada Yukichi , Fuku Asuka , Tomiyasu Satoshi , Umemura Tsukuru
Published: June 2022

Extracellular vesicles (EVs) are nano-sized particles released from cells and transferring molecules (proteins, lipids and nucleic acids such as mRNA, tRNA and miRNA) to recipient cells. Surface antigens and components are important for the functions as cell-to-cell communication of EVs. Thus, EVs are useful biomarkers for various diseases including leukemias and other types of malignancies. We evaluated whether miRNAs in EVs released from chronic myelogenous leukemia (CML) cells could be used for diagnosis. Microarray analysis of miRNAs in EVs obtained from the culture supernatants of two CML cell lines showed that miR-494 and miR-373-5p were significantly decreased by tyrosine kinase inhibitor for BCR-ABL1. Validation analysis with Taqman-based qRT-PCR of whole serum obtained patients with CML in the chronic phase (n = 5) did not show a significant difference in miR-494 levels compared to the CML accelerated phase and blast crisis patients (n = 5). However, the levels of miR-494 were 2.9-fold higher in the accelerated phase or blast crisis than in the chronic phase (p < 0.05). These results indicate that it is important to measure miR-494 using only EVs rather than whole serum. Our data suggest that EV-miR-494 is a useful biomarker of CML progression and evaluation of response to tyrosine kinase inhibitors.