bluuebunny/crossref_metadata_2025 huggingface dataset:
High Plasma Levels of Activated Factor VII-Antithrombin Complex Point to Increased Tissue Factor Expression in Patients with SARS-CoV-2 Pneumonia: A Potential Link with COVID-19 Prothrombotic Diathesis
Authors: Martinelli Nicola , Rigoni Anna Maria , De Marchi Sergio , Osti Nicola , Donini Martino , Montagnana Martina , Castagna Annalisa , Pattini Patrizia , Udali Silvia , De Franceschi Lucia , Tinazzi Elisa , Mazzi Filippo , Moruzzi Sara , Argentino Giuseppe , Delfino Lorenzo , Sartori Giulia , Azzini Anna Maria , Tacconelli Evelina , Van Dreden Patrick , Lippi Giuseppe , Girelli Domenico , Olivieri Oliviero , Friso Simonetta , Pizzolo Francesca
Published: November 2022

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causal agent of coronavirus disease 2019 (COVID-19), in which coagulation abnormalities and endothelial dysfunction play a key pathogenic role. Tissue factor (TF) expression is triggered by endothelial dysfunction. Activated factor VII-antithrombin (FVIIa-AT) complex reflects indirectly FVIIa-TF interaction and has been proposed as a potential biomarker of prothrombotic diathesis. FVIIa-AT plasma concentration was measured in 40 patients (30 males and 10 females; 64.8 ± 12.3 years) admitted with SARS-CoV-2 pneumonia during the first pandemic wave in Italy. Two sex- and age-matched cohorts without COVID-19, with or without signs of systemic inflammation, were used to compare FVIIa-AT data. The FVIIa-AT plasma levels in COVID-19 patients were higher than those in non-COVID-19 subjects, either with or without inflammation, while no difference was observed among non-COVID-19 subjects. The association between COVID-19 and FVIIa-AT levels remained significant after adjustment for sex, age, C-reactive protein, renal function, fibrinogen, prothrombin time and activated partial thromboplastin time. Our results indicate that SARS-CoV-2 infection, at least during the first pandemic wave, was characterized by high FVIIa-AT levels, which may suggest an enhanced FVIIa-TF interaction in COVID-19, potentially consistent with SARS-CoV-2-induced endotheliopathy.
Application of Artificial Intelligence in Pathology: Trends and Challenges
Authors: Kim Inho , Kang Kyungmin , Song Youngjae , Kim Tae-Jung
Published: November 2022

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.
How Mindsets, Academic Performance, and Gender Predict Finnish Students’ Educational Aspirations
Authors: Laurell Jenni , Gholami Khalil , Tirri Kirsi , Hakkarainen Kai
Published: November 2022

This study examined Finnish eighth graders’ (N = 1136) educational aspirations and how those can be predicted by mindsets, academic achievement, and gender. Multinomial logistic regression analyses were conducted to investigate how two mindset constructs (intelligence and giftedness), domain-specific academic performance (mathematics and reading), and gender relate to students’ educational aspirations on three levels (academic, vocational, and unknown). The growth mindset about giftedness was found to predict unknown aspirations, whereas the growth mindset about intelligence did not predict educational aspirations. High performance in math predicted students’ academic aspirations, but performance in reading did not predict educational aspirations. Gender-related differences were found, as boys seem to have vocational aspirations, but the effect did not penetrate all schools. Lastly, students’ aspirations differed between schools: from some schools, students are more likely to apply to university, while from other schools, students are more likely to apply to vocational education. Overall, the study demonstrated that a growth mindset does not directly predict academic aspirations, and the relationship between implicit beliefs and educational outcomes might be more complex than suggested.
Is Parental Mathematics Anxiety Associated with Young Children’s Arithmetical Performance?
Authors: Bellon Elien , van Bergen Elsje , Dowker Ann Derore
Published: November 2022

It has been suggested that parental mathematics anxiety may influence their children’s mathematics anxiety, attitudes, and performance. It remains an open question whether these parent-child associations differ by parental sex or parental involvement. We tested 249 Dutch-speaking Belgian participants, forming 83 (biological) mother–father–child trios. The 83 children (age: M = 5.74; SD = 0.30) attended Kindergarten. We tested their nonsymbolic comparison, symbolic numerical magnitude processing, numeral recognition, arithmetic, and matrix reasoning. We assessed both parents’ arithmetic skills, math anxiety, educational level, and division of care. More math-anxious parents tended to be less highly educated (r~0.42) and poorer at math (r~0.30). Compared to fathers, mothers had lower arithmetic, higher math anxiety, and higher educational level. Assortative mating (i.e., a significant spousal correlation) was found for educational level and arithmetic. Mothers’ (but not fathers’) educational level predicted children’s arithmetic scores (r = 0.31). Other parent-offspring correlations were non-significant. Most of the children’s test scores are intercorrelated. The parental characteristic that best predicted five- and six-year-olds’ arithmetic performance was maternal educational level rather than mathematical anxiety or performance. We discuss these findings in relation to the used measures, parental gender and involvement, children’s age, statistical power, and genetic and environmental transmission. The field is just starting to understand whether and how mathematics anxiety and the skills of parents influence those of their offspring.
Student Perceptions of Online Education during COVID-19 Lockdowns: Direct and Indirect Effects on Learning
Authors: Dinu Larisa M. , Baykoca Ardic , Dommett Eleanor J. , Mehta Kosha J. , Everett Sally , Foster Juliet L. H. , Byrom Nicola C.
Published: November 2022

The COVID-19 pandemic forced an abrupt transition to fully online learning in universities that typically provided campus-based teaching. We examined the learning experience of undergraduate and postgraduate students during this transition at a UK university. Qualitative surveys and interview responses revealed both direct effects of the transition to online learning and indirect effects caused by the COVID-19 induced lockdown. Direct effects related to interaction and communication altered study-related opportunities and digital tool use. In all cases, students expressed a range of views, for example, with some reporting greater opportunities and others fewer. However, there was a clear consensus that the online learning had brought greater flexibility for students. For indirect effects, students noted altered time available for study, challenges and benefits to studying at home, greater monotony and required autonomy as well as altered priorities, concerns about employment, finances and career prospects. These reflections on students’ experiences of online learning can inform academics and education providers to design appropriate strategies in order to better facilitate and support students’ education via fully online or blended learning approaches.
Heterogeneous CPU-GPU Accelerated Subgridding in the FDTD Modelling of Microwave Breakdown
Authors: Feng Jian , Song Kaihong , Fang Ming , Chen Wei , Xie Guoda , Huang Zhixiang , Wu Xianliang
Published: November 2022

Microwave breakdown is crucial to the transmission of high-power microwave (HPM) devices, where a growing number of studies have analyzed the complex interactions between electromagnetic waves and the evolving plasma from theoretical and analytical perspectives. In this paper, we propose a finite-difference time-domain (FDTD) scheme to numerically solve Maxwell’s equation, coupled with a fluid plasma equation for simulating the plasma formation during HPM air breakdown. A subgridding method is adopted to obtain accurate results with lower computational resources. Moreover, the three-dimensional subgridding Maxwell–plasma algorithm is efficiently accelerated by utilizing heterogeneous computing technique based on graphics processing units (GPUs) and multiple central processing units (CPUs), which can be applied as an efficient method for the investigation of the HPM air breakdown phenomena.
RSCNet: An Efficient Remote Sensing Scene Classification Model Based on Lightweight Convolution Neural Networks
Authors: Chen Zhichao , Yang Jie , Feng Zhicheng , Chen Lifang
Published: November 2022

This study aims at improving the efficiency of remote sensing scene classification (RSSC) through lightweight neural networks and to provide a possibility for large-scale, intelligent and real-time computation in performing RSSC for common devices. In this study, a lightweight RSSC model is proposed, which is named RSCNet. First, we use the lightweight ShuffleNet v2 network to extract the abstract features from the images, which can guarantee the efficiency of the model. Then, the weights of the backbone are initialized using transfer learning, allowing the model to learn by drawing on the knowledge of ImageNet. Second, to further improve the classification accuracy of the model, we propose to combine ShuffleNet v2 with an efficient channel attention mechanism that allows the features of the input classifier to be weighted. Third, we use a regularization technique during the training process, which utilizes label smoothing regularization to replace the original loss function. The experimental results show that the classification accuracy of RSCNet is 96.75% and 99.05% on the AID and UCMerced_LandUse datasets, respectively. The floating-point operations (FLOPs) of the proposed model are only 153.71 M, and the time spent for a single inference on the CPU is about 2.75 ms. Compared with existing RSSC methods, RSCNet achieves relatively high accuracy at a very small computational cost.
Optimization and Techno-Economic Appraisal of Parabolic Trough Solar Power Plant under Different Scenarios: A Case Study of Morocco
Authors: Ait Lahoussine Ouali Hanane , Alami Merrouni Ahmed , Chowdhury Shahariar , Techato Kuaanan , Channumsin Sittiporn , Ullah Nasim
Published: November 2022

Morocco is a country with a lack of fossil fuel resources and an increasing demand for energy. This inspired the country to increase the use of renewable energy in the energy mix. The objective of this study was to conduct an optimization and techno-economic appraisal of a concentrated solar power plant (CSP) using different scenarios that took Ouarzazate city in the south of Morocco as a case study. To achieve this, several parameters were assessed, including the impacts of solar collector assemblies (SCAs), receiver types, heat transfer fluids, cooling systems, solar multiples, and thermal storage hours, with regard to the profitability of the CSP plant. Then, performance and sensitivity analyses were conducted to select the best integration scenarios based on different economic indicators, including levelized cost of electricity (LCOE) and net present value (NPV). The findings revealed that the use of the Luz LS-3 as the collector/SCA, Solel UVAC 3 as receiver, and Dowtherm Q as heat transfer fluid exhibited the highest performance in terms of the annual energy production yield and capacity factor, as well as the lowest real and nominal LCOEs with a wet cooled condenser. Furthermore, the LCOE is extremely sensitive to changes in the number of hours of storage and the solar multiple, and the optimal real and nominal LCOEs are determined by a highly specific combination of the solar multiple and the number of hours of storage. As a consequence, the maximum and minimum net electricity outputs for the best configuration of the Parabolic Trough Collector (PTC) plant were 24.6 GWh and 7.4 GWh in May and December, respectively. Likewise, the capacity factor and the gross-to-net conversion factor for the optimized plant were found to be 47.9%, and 93.5%, respectively. Concerning the economic study, the expected energy cost was 0.1303 USD per kWh and the NPV value for Ouarzazate city was positive (more than USD 20 million), which indicates that the studied PTC plant was estimated to be financially and economically feasible. The results of this analysis are highly significant and may persuade decision makers, financiers, and solar energy industry players to increase their investments in the Kingdom of Morocco.
A Review of the Life Cycle Analysis Results for Different Energy Conversion Technologies
Authors: Motuzienė Violeta , Čiuprinskas Kęstutis , Rogoža Artur , Lapinskienė Vilūnė
Published: November 2022

Technologies that use renewable energy sources (RES) are crucial to achieving decarbonization goals, but a significant number of studies show their relatively high environmental impact during the production phase. Therefore, technologies need to be compared in terms of their life-cycle environmental impact. The life cycle analysis (LCA) methodology is well known and widely employed. However, problems related to the methodological choices prevent taking full advantage of the LCA, as the results of numerous studies are often incomparable. The presented review aims to critically compare the impact of different energy generation technologies—RES (as well as non-RES) energy generators and co-generators. The numeric results are structured and analyzed in terms of the global warming potential (GWP) and non-RES primary energy consumption. The results show that RES technologies are superior compared to conventional fossil-fuel-based systems in most cases, and the high impact during the production and installation phases is compensated in the operational phase. The high variations in GWP from similar technologies result from different methodological choices, but they also show that the wrong choice of the technology in a certain location might cause serious environmental drawbacks when the impact of the RES technology exceeds the impact of fossil fuel-based technologies. Cogeneration technologies using waste as a fuel may even have a negative GWP impact, thus showing even higher potential for decarbonization than RES technologies.
Ecological Footprint-Environmental Regulations Nexus: The Case of the Union for the Mediterranean
Authors: Karşılı Hüseyin , Erkut Burak
Published: November 2022

The environmental regulations–ecological footprint nexus is occupying an important space in the current debate of energy economics. As a counter measure to environmental degradation, implementing environmental regulations remains on the agenda of scholars and policymakers alike, but whether these regulations have a reducing impact on the ecological footprint remains open since the literature on the topic, and empirical evidence, remains fragmented and dissimilar. The current approach aimed to investigate this for five member countries of the Union for the Mediterranean with panel data econometric techniques. Panel data from France, Italy, Portugal, Spain, and Türkiye were considered for 1992–2015 and were tested for cross-sectional dependence, unit roots, and cointegration. Panel fixed effect regression estimations were conducted, also with Newey-West and Driscoll-Kraay standard errors. In addition, a country-level analysis was conducted by using fully modified ordinary least squares estimation. The results showed that energy consumption and trade increased the environmental footprint, but for environmental regulations, no conclusive effect was identified. The country-level analysis indicated that there is a divergent situation for environmental regulations among the five member countries, where only one out of five member countries showed a significant negative effect. This new empirical evidence for Union for the Mediterranean member countries highlights the importance of a common regulatory policy framework to combat the negative impacts of environmental degradation.
Application of Artificial Intelligence to Improve the Thermal Energy and Exergy of Nanofluid-Based PV Thermal/Nano-Enhanced Phase Change Material
Authors: Sayed Enas Taha , Rezk Hegazy , Olabi Abdul Ghani , Gomaa Mohamed R. , Hassan Yahia B. , Rahman Shek Mohammad Atiqure , Shah Sheikh Khaleduzzaman , Abdelkareem Mohammad Ali
Published: November 2022

Photovoltaic-thermal (PVT) technologies have demonstrated several attractive features, such as higher power and comparative efficiencies. Improving the thermal recovery from the PVT system would further improve the power output and the efficiency of the PVT system. This paper identifies the best operating factors of nanofluid-based PV thermal/nano-enhanced phase change material using artificial intelligence. The target is the maximization of thermal energy and exergy outputs. The suggested approach combines ANFIS modelling and particle swarm optimization (PSO). Four operating factors are taken into consideration: PCM (phase change material) layer thickness, HTF (heat transfer fluid) mass flow rate, MFNPCM (“mass fraction of nanoparticles in PCM”) and MFNfluid (“mass fraction of nanoparticles in nanofluid”). Using a dataset, an “adaptive neuro-fuzzy inference system” (ANFIS) model has been established for simulating the thermal energy and exergy outputs in terms of the mentioned operating factors. Then, using PSO, the best values of PCM thickness, mass flow rate, MFNPCM and MFNfluid are estimated. The proposed model’s accuracy was examined by comparing the results with those obtained by response surface methodology and the experimental dataset.
Contrast Estimation in Vibroacoustic Signals for Diagnosing Early Faults of Short-Circuited Turns in Transformers under Different Load Conditions
Authors: Huerta-Rosales Jose R. , Granados-Lieberman David , Amezquita-Sanchez Juan P. , Garcia-Perez Arturo , Bueno-Lopez Maximiliano , Valtierra-Rodriguez Martin
Published: November 2022

The transformer is one of the most important electrical machines in electrical systems. Its proper operation is fundamental for the distribution and transmission of electrical energy. During its service life, it is under continuous electrical and mechanical stresses that can produce diverse types of damage. Among them, short-circuited turns (SCTs) in the windings are one of the main causes of the transformer fault; therefore, their detection in an early stage can help to increase the transformer life and reduce the maintenance costs. In this regard, this paper proposes a signal processing-based methodology to detect early SCTs (i.e., damage of low severity) through the analysis of vibroacoustic signals in steady state under different load conditions, i.e., no load, linear load, nonlinear load, and both linear and nonlinear loads, where the transformer is adapted to emulate different conditions, i.e., healthy (0 SCTs) and with damage of low severity (1 and 2 SCTs). In the signal processing stage, the contrast index is analyzed as a fault indicator, where the Unser and Tamura definitions are tested. For the automatic classification of the obtained indices, an artificial neural network is used. It showed better results than the ones provided by a support vector machine. Results demonstrate that the contrast estimation is suitable as a fault indicator for all the load conditions since 89.78% of accuracy is obtained if the Unser definition is used.
Predicting the Potential Energy Yield of Bifacial Solar PV Systems in Low-Latitude Region
Authors: Yakubu Rahimat O. , Ankoh Maame T. , Mensah Lena D. , Quansah David A. , Adaramola Muyiwa S.
Published: November 2022

The validation of the potential energy yield of bifacial PV systems of various configurations at low latitudes under West African climatic conditions is critical for evaluating performance and for promoting market expansion of the technology since validation has mostly occurred in high-latitude regions. In this paper, the potential energy yield from an inclined south-facing bifacial PV module and a vertically mounted east–west bifacial PV module are compared to an inclined south-facing monofacial PV module using an analytical model, field-measured data, and simulations. For measured/modelled and PVsyst/modelled monofacial systems, the model predicts RMSE values of 1.49 and 9.02, respectively. An inclined bifacial PV system has RMSEs of 1.88 and 7.97 for measured/modelled and PVsyst/modelled, respectively, and a vertically installed system has RMSEs of 10.03 for measured/modelled and 3.76 for PVsyst/modelled. Monthly energy yield is predicted by the model, with deviations from measured data ranging from 0.08% to 1.41% for monofacial systems, from 0.05% to 4.06% for inclined bifacial systems, and from 4.63% to 9.61% for vertical bifacial systems. The average bifacial gains from the modelled, measured, and simulated data of an inclined south-facing stand-alone bifacial PV system over an inclined south-facing stand-alone monofacial system are 9.05%, 10.15%, and 5.65%. Finally, at 0.25 albedo, the inclined monofacial PV system outperforms and yields more energy than the vertically installed bifacial PV system.
AC–AC Modular Multilevel Converter—Hexverter
Authors: Arbugeri Cesar A. , Mussa Samir A. , Heldwein Marcelo L.
Published: November 2022

This article proposes the study, control and analysis of the topology of an AC–AC modular multilevel converter, known in the literature as the Hexverter. The Hexverter is capable of converting the energy between two AC systems with a reduced number of elements, if compared with other modular multilevel topologies, which makes this topology attractive in AC–AC applications. However, there are few studies about the Hexverter in the literature, so this work presents its operation principle, conducts modeling, and proposes a control scheme for the converter’s proper operation, validating the operation and control via hardware-in-the-loop emulation.
Study on a Novel Variable Valve Timing and Lift Mechanism for a Miller Cycle Diesel Engine
Authors: Liu Fanshuo , Liu Bolan , Zhang Junwei , Wan Peng , Li Ben
Published: November 2022

Thermal efficiency and power density improvement are the main research foci of the literature on diesel engines. The Miller cycle is considered to be one of the most promising methods of diesel engine operation. In this study, a fully variable valve timing and lift mechanism (CD-HFVVS) was studied to determine the possibility of a Miller operation. Firstly, the valve seat impact buffer in the mechanism was tested, which proved that the buffer can effectively eliminate the valve seat impact. Then the influences of different speeds and oil temperatures were studied. The results show that the valve opening duration is prolonged when engine speed increases, and the valve lift and duration are reduced while the oil temperature increased. The valve timing and lift can be fully adjusted by changing the oil discharge position and the initial plunger position, which further proves that CD-HFVVS can achieve the performance optimization of the Miller cycle. By using the mechanism, a single cylinder test was performed. By using variable inlet valve timing, the fuel efficiency can be effectively improved and the peak pressure and in-cylinder average temperature can both be suppressed.
Fault-Tolerant Control for Microgrids—Recent Developments and Future Directions
Authors: Hosseinzadeh Mehdi
Published: November 2022

Microgrids are defined as a cluster of loads, distributed energy resources, and storage devices, and they are receiving worldwide attention due to the increasing rate of consumption of nuclear and fossil fuels and the community demand for reducing pollutant emission in electricity generation fields [...]
Comparative Assessment of Polycyclic Aromatic Hydrocarbons and Toxicity in a Diesel Engine Powered by Diesel and Biodiesel Blends with High Concentrations of Alcohols
Authors: Yilmaz Nadir , Atmanli Alpaslan , Vigil Francisco M. , Donaldson Burl
Published: November 2022

Higher carbon alcohols such as n-propanol, n-butanol, and n-pentanol that can be produced from biomass can be used as alternative fuels in diesel engines. These alcohols can mix with both diesel fuel and biodiesel without any phase separation. Currently, unregulated emissions such as toxicity and total polycyclic aromatic hydrocarbon (PAH) from the use of these alcohols are not monitored. Investigating the effects of increasing the alternative fuel concentration for use in a diesel engine on PAH emissions will contribute to the protection of the environment and extend the engine’s operating life. In this study, the effects of adding 35% (by volume) n-propanol, n-butanol and n-pentanol to diesel and biodiesel on unregulated emissions in a diesel engine were compared. In the total PAH emission of biodiesel, the mixture containing n-pentanol stood out compared to other mixtures with a decrease of 39.17%. In higher alcohol-diesel mixtures, the highest reduction was observed in the n-butanol mixture as 80.98%. With respect to toxic emissions, very close values were obtained in biodiesel blends up to 94.15%, although n-butanol showed a maximum reduction of 84.33% in diesel blends. All these reductions also prevented the formation of high-cycle PAHs. The results obtained showed that the use of high carbon alcohols in a high mixing ratio contributed to the improvement of the fuel properties of biodiesel and to an increase in the alternative fuel mixing ratio with the reduction of PAH emissions from diesel fuel.
A Comparative Study of Shallow Machine Learning Models and Deep Learning Models for Landslide Susceptibility Assessment Based on Imbalanced Data
Authors: Xu Shiluo , Song Yingxu , Hao Xiulan
Published: November 2022

A landslide is a type of geological disaster that poses a threat to human lives and property. Landslide susceptibility assessment (LSA) is a crucial tool for landslide prevention. This paper’s primary objective is to compare the performances of conventional shallow machine learning methods and deep learning methods in LSA based on imbalanced data to evaluate the applicability of the two types of LSA models when class-weighted strategies are applied. In this article, logistic regression (LR), random forest (RF), deep fully connected neural network (DFCNN), and long short-term memory (LSTM) neural networks were employed for modeling in the Zigui-Badong area of the Three Gorges Reservoir area, China. Eighteen landslide influence factors were introduced to compare the performance of four models under a class balanced strategy versus a class imbalanced strategy. The Spearman rank correlation coefficient (SRCC) was applied for factor correlation analysis. The results reveal that the elevation and distance to rivers play a dominant role in LSA tasks. It was observed that DFCNN (AUC = 0.87, F1-score = 0.60) and LSTM (AUC = 0.89, F1-score = 0.61) significantly outperformed LR (AUC = 0.89, F1-score = 0.50) and RF (AUC = 0.88, F1-score = 0.50) under the class imbalanced strategy. The RF model achieved comparable outcomes (AUC = 0.90, F1-score = 0.61) to deep learning models under the class balanced strategy and ran at a faster training speed (up to 63 times faster than deep learning models). The LR model performance was inferior to that of the other three models under the balanced strategy. Meanwhile, the deep learning models and the shallow machine learning models showed significant differences in susceptibility spatial patterns. This paper’s findings will aid researchers in selecting appropriate LSA models. It is also valuable for land management policy making and disaster prevention and mitigation.
Soil Degradation Due to Conversion from Natural to Plantation Forests in Indonesia
Authors: Widyati Enny , Nuroniah Hani Sitti , Tata Hesti Lestari , Mindawati Nina , Lisnawati Yunita , Darwo  , Abdulah Lutfy , Lelana Neo Endra , Mawazin  , Octavia Dona , Prameswari Diana , Rachmat Henti Hendalastuti , Sutiyono  , Darwiati Wida , Wardani Marfuah , Kalima Titi , Yulianti  , van Noordwijk Meine
Published: November 2022

Soil organic matter (SOM) is a crucial component of soil, through which physical, chemical, and biological characteristics interact in a local context. Within the forest category, the conversion of natural forests to monoculture plantations has raised concerns in Indonesia over the loss of soil functions, similar to conversion to agriculture. In natural forests, SOM can accumulate as part of a closed nutrient cycle with minimal nutrient losses; in plantation forestry, SOM decline and recovery can alternate over time, associated with larger nutrient losses. We reviewed existing studies to quantify how shifts from natural forests to short-rotation plantation forests (SRPF) affect SOM dynamics, soil nutrient contents, and soil-borne pathogens that cause disease. The review combines descriptive and quantitative methods (meta-analysis). The results show that conversion affects the soil C balance, soil structure and water balance, soil nutrient balance, and soil-borne diseases. Contributing factors include the reduced diversity of plant and rhizosphere communities, lower annual litter production, more uniform litter quality, and nutrient removal at the harvest cycle. Conversion from natural to plantation forest conditions also increases plant disease incidence by changing biological control mechanisms.
Contamination Status and Health Risk Assessment of Soil Heavy Metals in the Northern Slope of Eastern Tianshan Mountains Industrial Belt in Xinjiang, Northwest China
Authors: Abliz Abdugheni , Shi Qingdong , Abulizi Abudukeyimu
Published: November 2022

In recent years, soil heavy metal pollution has become an important issue of general concern because it is an important factor that threatens the soil environment. To assess the risk of the human health of the people living in the economic belt on the northern slope of the eastern Tianshan Mountains, and provide guidance for pollution control and risk prevention, the northern slope of the eastern Tianshan Mountains was selected as the study area, and six heavy metals (i.e., Zn, Cu, Cr, Pb, and Hg) were measured. The results revealed that the Cu, Pb, Hg, and As contents of the soil exceeded the soil background value of Xinjiang by 1.02, 4.10, 1.76, and 7.98 times, respectively. The Zn and Cr contents were lower than the limits of regional soil standards. Based on the pollution assessment using the pollution index (PI), values indicate that the levels of Zn and Cr pollution were low, those of Cu and Hg pollution were moderate, and those of Pb and As pollution were high. The health risk assessment results revealed that there are non-carcinogenic risks to children. The carcinogenic health risks posed by the heavy metals in this region are higher than the threshold (10−4), indicating that they pose a hazard to human health. The results of this research provide a theoretical basis and reference for soil heavy metal pollution control and human health risk management in this area.
A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks
Authors: Su Yue , Nguyen Kien , Sekiya Hiroo
Published: November 2022

Many IoT-blockchain systems in which blockchain connections run on an infrastructure-based network, such as Wi-Fi or LTE, face a severe problem: the single point of failure (SPoF) (i.e., depending on the availability, an access point of an LTE base station). Using infrastructure-less networks (i.e., ad hoc networks) is an efficient approach to prevent such highly disruptive events. An ad hoc network can automatically restore blockchain communication using an ad hoc routing protocol, even if a node fails. Moreover, an ad hoc routing protocol is more efficient when considering the IoT nodes’ mobility. In this paper, we first construct IoT-blockchain systems on emulated and real ad hoc networks with Ethereum and three ad hoc routing protocols (i.e., OLSR, BATMAN, and BABEL). We then evaluate the blockchain recovery time in static and mobile scenarios. The results show that BATMAN achieves the best blockchain recovery performance in all investigated scenarios because BATMAN only determines whether to switch a route by comparing the number of OGM packets received from a different next-hop. More specifically, in the small-scale real IoT-blockchain, BATMAN recovers at least 73.9% and 59.8% better than OLSR and BABEL, respectively. In the medium-scale emulated IoT-blockchain, the recovery time of BATMAN is at least 69% and 60% shorter than OLSR or BABEL, respectively.
BERT- and BiLSTM-Based Sentiment Analysis of Online Chinese Buzzwords
Authors: Li Xinlu , Lei Yuanyuan , Ji Shengwei
Published: November 2022

Sentiment analysis of online Chinese buzzwords (OCBs) is important for healthy development of platforms, such as games and social networking, which can avoid transmission of negative emotions through prediction of users’ sentiment tendencies. Buzzwords have the characteristics of varying text length, irregular wording, ignoring syntactic and grammatical requirements, no complete semantic structure, and no obvious sentiment features. This results in interference and challenges to the sentiment analysis of such texts. Sentiment analysis also requires capturing effective sentiment features from deeper contextual information. To solve the above problems, we propose a deep learning model combining BERT and BiLSTM. The goal is to generate dynamic representations of OCB vectors in downstream tasks by fine-tuning the BERT model and to capture the rich information of the text at the embedding layer to solve the problem of static representations of word vectors. The generated word vectors are then transferred to the BiLSTM model for feature extraction to obtain the local and global semantic features of the text while highlighting the text sentiment polarity for sentiment classification. The experimental results show that the model works well in terms of the comprehensive evaluation index F1. Our model also has important significance and research value for sentiment analysis of irregular texts, such as OCBs.
A Cost-Aware Framework for QoS-Based and Energy-Efficient Scheduling in Cloud–Fog Computing
Authors: Suleiman Husam
Published: November 2022

Cloud–fog computing is a large-scale service environment developed to deliver fast, scalable services to clients. The fog nodes of such environments are distributed in diverse places and operate independently by deciding on which data to process locally and which data to send remotely to the cloud for further analysis, in which a Service-Level Agreement (SLA) is employed to govern Quality of Service (QoS) requirements of the cloud provider to such nodes. The provider experiences varying incoming workloads that come from heterogeneous fog and Internet of Things (IoT) devices, each of which submits jobs that entail various service characteristics and QoS requirements. To execute fog workloads and meet their SLA obligations, the provider allocates appropriate resources and utilizes load scheduling strategies that effectively manage the executions of fog jobs on cloud resources. Failing to fulfill such demands causes extra network bottlenecks, service delays, and energy constraints that are difficult to maintain at run-time. This paper proposes a joint energy- and QoS-optimized performance framework that tolerates delay and energy risks on the cost performance of the cloud provider. The framework employs scheduling mechanisms that consider the SLA penalty and energy impacts of data communication, service, and waiting performance metrics on cost reduction. The findings prove the framework’s effectiveness in mitigating energy consumption due to QoS penalties and therefore reducing the gross scheduling cost.
A Framework for Generating Radial and Surface-Oriented Regularized Stokeslets
Authors: Chisholm Nicholas G. , Olson Sarah D.
Published: November 2022

Error in the method of regularized Stokeslets is highly dependent on the choice of the blob or regularization function that is utilized to handle singularities in the flow. In this work, we develop a general framework to choose regularizations at the level of the vector potential via smoothing factors. We detail the derivation for radial smoothing factors and specify properties which ensure that the solution is a regularized flow satisfying the incompressible Stokes equations. Error analysis is completed for both the far-field flow (away from the location of the forces) as well as at the location of the forces, relating our newly derived smoothing factors to commonly used blob functions and moment conditions. When forces are on a surface, we extend the radial smoothing factor case to the case of non-radial regularizations that are surface-oriented. We illustrate the utility of this framework by computing the forward and inverse problems of a translating sphere using radial and surface-oriented regularizations.
Effects of Chitosan/Collagen Peptides/Cinnamon Bark Essential Oil Composite Coating on the Quality of Dry-Aged Beef
Authors: Zhang Songshan , Sun Xiaobin , Lei Yuanhua , Sun Baozhong , Xie Peng , Liu Xiaochang
Published: November 2022

The aim of this study was to evaluate the effects of the chitosan/collagen peptides/cinnamon bark essential oil composite coating on dry-aged beef. Chitosan (2%, w/v), collagen peptides (1%, w/v), and cinnamon bark essential oil (1%, v/v) were homogenized to obtain the coating. Beef samples were divided into three groups (traditional dry-ageing, in-bag dry-ageing, and coating and then dry-ageing) and dry-aged for 42 days. Physiochemical, microbial, and sensorial parameters of samples were determined during the dry-ageing process. There were no significant differences (p > 0.05) in pH values, shear force values, cooking loss, color, juiciness, tenderness, and flavor across groups. The total volatile base nitrogen value of the coating group was lower than those of the other two groups. Compared to traditional dry-ageing, in-bag and coating dry-ageing reduced (p < 0.05) many volatile compounds such as alcohols, aldehydes, ketones, and acetate. In-bag and coating dry-ageing had no impact on the fungal community, but changed the bacterial community by inhibiting Pseudomonas. This study demonstrates that the chitosan/collagen peptides/cinnamon bark essential oil coating reduces microbial spoilage during dry-ageing, and has a small influence on product quality.
Physicochemical Characteristics and Flavor Properties of Texturized Dual-Proteins Extrudates: Effect of Surimi to Soybean Flour Ratio
Authors: Li Xiaodong , Zhang Tonghao , An Yueqi , Yin Tao , Xiong Shanbai , Rong Hongshan
Published: November 2022

This study investigated the effects of surimi to soybean flour ratio (0:10, 1:9, 2:8, 3:7, 4:6) on the physicochemical characteristics and flavor properties of dual-proteins extrudates. The increasing ratio of surimi improved the color of extrudates and raised the apparent viscosity of the mixed raw materials, which led to the decrease of extrudates’ thickness. The excess ratio of surimi and soybean flour (more than 2:8) was bad for extrudates’ physicochemical characteristics with sharply decreased tensile strength, macroscopic longitudinal fracture, broken and unevenly distributed microstructure, increased water mobility and decreased free water content. However, the increasing ratio of surimi had no effect on the protein secondary structure of extrudates. Sensory evaluation, E—tongue and E—nose analysis suggested that adding surimi significantly changed the flavor properties of extrudates, with increased sweetness and umami taste, and an appropriate ratio (2:8 or 3:7) could reduce the beany flavor and without an obvious fishy off-flavor.
Ginger Extract-Loaded Sesame Oil-Based Niosomal Emulgel: Quality by Design to Ameliorate Anti-Inflammatory Activity
Authors: Abdallah Marwa H. , Elghamry Hanaa A. , Khalifa Nasrin E. , Khojali Weam M. A. , Khafagy El-Sayed , Lila Amr S. Abu , El-Horany Hemat El-Sayed , El-Housiny Shaimaa
Published: November 2022

Ginger, a natural plant belonging to the Zingeberaceae family, has been reported to have reasonable anti-inflammatory effects. The current study aimed to examine ginger extract transdermal delivery by generating niosomal vesicles as a promising nano-carrier incorporated into emulgel prepared with sesame oil. Particle size, viscosity, in vitro release, and ex vivo drug penetration experiments were performed on the produced formulations (ginger extract loaded gel, ginger extract loaded emulgel, ginger extract niosomal gel, and ginger extract niosomal emulgel). Carrageenan-induced edema in rat hind paw was employed to estimate the in vivo anti-inflammatory activity. The generated ginger extract formulations showed good viscosity and particle size. The in vitro release of ginger extract from niosomal formulation surpassed other formulations. In addition, the niosomal emulgel formulation showed improved transdermal flux and increased drug permeability through rabbit skin compared to other preparations. Most importantly, carrageenan-induced rat hind paw edema test confirmed the potential anti-inflammatory efficacy of ginger extract niosomal emulgel, compared to other formulations, as manifested by a significant decrease in paw edema with a superior edema inhibition potency. Overall, our findings suggest that incorporating a niosomal formulation within sesame oil-based emulgel might represent a plausible strategy for effective transdermal delivery of anti-inflammatory drugs like ginger extract.
PCR-Based Screening of Spinal Muscular Atrophy for Newborn Infants in Hyogo Prefecture, Japan
Authors: Noguchi Yoriko , Bo Ryosuke , Nishio Hisahide , Matsumoto Hisayuki , Matsui Keiji , Yano Yoshihiko , Sugawara Masami , Ueda Go , Wijaya Yogik , Niba Emma , Shinohara Masakazu , Bouike Yoshihiro , Takeuchi Atsuko , Okamoto Kentaro , Saito Toshio , Shimomura Hideki , Lee Tomoko , Takeshima Yasuhiro , Iijima Kazumoto , Nozu Kandai , Awano Hiroyuki
Published: November 2022

Spinal muscular atrophy (SMA) is a common devastating neuromuscular disorder, usually involving homozygous deletion of the SMN1 gene. Newly developed drugs can improve the motor functions of infants with SMA when treated in the early stage. To ensure early diagnosis, newborn screening for SMA (SMA-NBS) via PCR-based genetic testing with dried blood spots (DBSs) has been spreading throughout Japan. In Hyogo Prefecture, we performed a pilot study of SMA-NBS to assess newborn infants who underwent routine newborn metabolic screening between February 2021 and August 2022. Hyogo Prefecture has ~40,000 live births per year and the estimated incidence of SMA is 1 in 20,000–25,000 based on genetic testing of symptomatic patients with SMA. Here, we screened 8336 newborns and 12 screen-positive cases were detected by real-time PCR assay. Multiplex ligation-dependent probe amplification assay excluded ten false positives and identified two patients. These false positives might be related to the use of heparinized and/or diluted blood in the DBS sample. Both patients carried two copies of SMN2, one was asymptomatic and the other was symptomatic at the time of diagnosis. SMA-NBS enables us to prevent delayed diagnosis of SMA, even if it does not always allow treatment in the pre-symptomatic stage.
Mitochondrial Factor C20orf7 Facilitates the EMT-Mediated Cancer Cell Migration and the Proliferation of Colon Cancer In Vitro and In Vivo
Authors: Liu Hou-Hsien , Lee Chia-Hwa , Hsieh Yi-Chen , Zheng Jia-Huei , Liu Yun-Ru , Chang Chia-Hsuan , Cho Er-Chieh
Published: November 2022

Colon cancer is a major malignant neoplasm with a low survival rate for late-stage patients. Therefore, the investigation of molecules regulating colon cancer progression and the discovery of novel therapeutic targets is critical. Mitochondria play a vital role in maintaining the homeostasis of cells. Abnormal mitochondrial metabolism alterations and the induction of glycolysis can facilitate tumor growth; therefore, targeting mitochondrial molecules is suggested to be a promising strategy for cancer treatment. In this study, we investigated the role of this largely unknown mitochondrial factor, chromosome 20 open reading frame 7 (C20orf7), in colon cancer progression. Clustered regularly interspaced short palindromic repeats (CRISPR) technology was utilized for C20orf7 depletion, and functional assays were performed to examine the regulation of C20orf7 in colon cancer cells. We demonstrated that C20orf7 facilitates epithelial–mesenchymal transition (EMT)-mediated cell migration and promotes the proliferation of colon cancer. The anti-cancer drug 5-fluorouracil (5FU) was also applied, and C20orf7 was targeted with a combination of 5FU treatment, which could further enhance the anti-cancer effect in the colon cancer cell line and the xenograft mice model. In summary, this study demonstrated, for the first time, that C20orf7 plays a promotional role in cancer tumorigenesis and could be a promising therapeutic target in colon cancer treatment.
Genome-Wide Identification and In Silico Analysis of ZF-HD Transcription Factor Genes in Zea mays L.
Authors: Islam Md. Abir Ul , Nupur Juthy Abedin , Khalid Muhammad Hayder Bin , Din Atta Mohi Ud , Shafiq Muhammad , Alshegaihi Rana M. , Ali Qurban , Ali Qurban , Kamran Zuha , Manzoor Mujahid , Haider Muhammad Saleem , Shahid Muhammad Adnan , Manghwar Hakim
Published: November 2022

Zinc finger-homeodomain proteins are amongst the most prominent transcription factors (TFs) involved in biological processes, such as growth, development, and morphogenesis, and assist plants in alleviating the adverse effects of abiotic and biotic stresses. In the present study, genome-wide identification and expression analyses of the maize ZHD gene family were conducted. A total of 21 ZHD genes with different physicochemical properties were found distributed on nine chromosomes in maize. Through sequence alignment and phylogenetic analysis, we divided ZHD proteins into eight groups that have variations in gene structure, motif distribution, and a conserved ZF domain. Synteny analysis indicated duplication in four pairs of genes and the presence of orthologues of maize in monocots. Ka/Ks ratios suggested that strong pure selection occurred during evolution. Expression profiling revealed that the genes are evenly expressed in different tissues. Most of the genes were found to make a contribution to abiotic stress response, plant growth, and development. Overall, the evolutionary research on exons and introns, motif distributions, and cis-acting regions suggests that these genes play distinct roles in biological processes which may provide a basis for further study of these genes’ functions in other crops.