bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel Survival Features Generated by Clinical Text Information and Radiomics Features May Improve the Prediction of Ischemic Stroke Outcome
Authors: Guo Yingwei , Yang Yingjian , Cao Fengqiu , Li Wei , Wang Mingming , Luo Yu , Guo Jia , Zaman Asim , Zeng Xueqiang , Miu Xiaoqiang , Li Longyu , Qiu Weiyan , Kang Yan
Published: July 2022

Background: Accurate outcome prediction is of great clinical significance in customizing personalized treatment plans, reducing the situation of poor recovery, and objectively and accurately evaluating the treatment effect. This study intended to evaluate the performance of clinical text information (CTI), radiomics features, and survival features (SurvF) for predicting functional outcomes of patients with ischemic stroke. Methods: SurvF was constructed based on CTI and mRS radiomics features (mRSRF) to improve the prediction of the functional outcome in 3 months (90-day mRS). Ten machine learning models predicted functional outcomes in three situations (2-category, 4-category, and 7-category) using seven feature groups constructed by CTI, mRSRF, and SurvF. Results: For 2-category, ALL (CTI + mRSRF+ SurvF) performed best, with an mAUC of 0.884, mAcc of 0.864, mPre of 0.877, mF1 of 0.86, and mRecall of 0.864. For 4-category, ALL also achieved the best mAuc of 0.787, while CTI + SurvF achieved the best score with mAcc = 0.611, mPre = 0.622, mF1 = 0.595, and mRe-call = 0.611. For 7-category, CTI + SurvF performed best, with an mAuc of 0.788, mPre of 0.519, mAcc of 0.529, mF1 of 0.495, and mRecall of 0.47. Conclusions: The above results indicate that mRSRF + CTI can accurately predict functional outcomes in ischemic stroke patients with proper machine learning models. Moreover, combining SurvF will improve the prediction effect compared with the original features. However, limited by the small sample size, further validation on larger and more varied datasets is necessary.
A Dynamically Consistent Nonstandard Difference Scheme for a Discrete-Time Immunogenic Tumors Model
Authors: Khan Muhammad Salman , Samreen Maria , Khan Muhammad Asif , De la Sen Manuel
Published: July 2022

This manuscript deals with the qualitative study of certain properties of an immunogenic tumors model. Mainly, we obtain a dynamically consistent discrete-time immunogenic tumors model using a nonstandard difference scheme. The existence of fixed points and their stability are discussed. It is shown that a continuous system experiences Hopf bifurcation at one and only one positive fixed point, whereas its discrete-time counterpart experiences Neimark–Sacker bifurcation at one and only one positive fixed point. It is shown that there is no chance of period-doubling bifurcation in our discrete-time system. Additionally, numerical simulations are carried out in support of our theoretical discussion.
Embedding Soft Thresholding Function into Deep Learning Models for Noisy Radar Emitter Signal Recognition
Authors: Pan Jifei , Zhang Shengli , Xia Lingsi , Tan Long , Guo Linqing
Published: July 2022

Radar emitter signal recognition under noisy background is one of the focus areas in research on radar signal processing. In this study, the soft thresholding function is embedded into deep learning network models as a novel nonlinear activation function, achieving advanced radar emitter signal recognition results. Specifically, an embedded sub-network is used to learn the threshold of soft thresholding function according to the input feature, which results in each input feature having its own independent nonlinear activation function. Compared with conventional activation functions, the soft thresholding function is characterized by flexible nonlinear conversion and the ability to obtain more discriminative features. By this way, the noise features can be flexibly filtered while retaining signal features, thus improving recognition accuracy. Under the condition of Gaussian and Laplacian noise with signal-to-noise ratio of −8 dB to −2 dB, experimental results show that the overall average accuracy of soft thresholding function reached 88.55%, which was 11.82%, 8.12%, 2.16%, and 1.46% higher than those of Sigmoid, PReLU, ReLU, ELU, and SELU, respectively.
Self-Supervised Learning for Time-Series Anomaly Detection in Industrial Internet of Things
Authors: Tran Duc Hoang , Nguyen Van Linh , Nguyen Huy , Jang Yeong Min
Published: July 2022

Industrial sensors have presently emerged as a very important device for monitoring environmental conditions in the manufacturing system. However, abnormal behavior of these smart sensors may cause some failure or potential risk during system operation, thereby increasing the high availability of the entire manufacturing process. An anomaly detection tool in industrial monitoring system must detect any abnormal behavior in advance. Recently, self-supervised learning demonstrated comparable performance with other methods while eliminating manually labeled processes in training. Moreover, this technique decreases the complexity of the training model in lightweight devices to increase the processing time and detect accurately the health of equipment assets. Therefore, this paper proposes an anomaly detection method using a self-supervised learning framework in a time-series dataset to improve the model performance in terms of high accuracy and lightweight method. With the consideration of time-series data augmentation for generating pseudo-label, a classifier using one-dimension convolutional neural network (1DCNN) is applied to learn the characteristics of normal data. This classification model output will effectively measure the degree of abnormality. The experimental results indicate that our proposed method outperforms classic anomaly detection methods. Furthermore, the model deployment in a real testbed is performed to illustrate the efficiency of the self-supervised learning method for time-series anomaly detection.
Probabilistic Forecasting of German Electricity Imbalance Prices
Authors: Narajewski Michał
Published: July 2022

The imbalance market is very volatile and often exhibits extreme price spikes. This makes it very hard to model; however, if predicted correctly, one could make significant gains by participating on the right side of the market. In this manuscript, we conduct a very short-term probabilistic forecasting of imbalance prices, contributing to the scarce literature in this novel subject. The forecasting is performed 30 min before the delivery, so that the trader might still choose the trading place. The distribution of the imbalance prices is modelled and forecasted using methods well-known in the electricity price forecasting literature: lasso with bootstrap, gamlss, and probabilistic neural networks. The methods are compared with a naive benchmark in a meaningful rolling window study. The results provide evidence of the efficiency between the intraday and balancing markets as the sophisticated methods do not substantially overperform the intraday continuous price index. On the other hand, they significantly improve the empirical coverage. Therefore, the traders should avoid participating in the balancing market, which is inline with the objective and current regulations of the market. The analysis was conducted on the German market; however, it could be easily applied to any other market of a similar structure.
Innovative Optimal Nonstandard Tripping Protection Scheme for Radial and Meshed Microgrid Systems
Authors: Abeid Salima , Hu Yanting , Alasali Feras , El-Naily Naser
Published: July 2022

The coordination of optimal overcurrent relays (OCRs) for modern power networks is nowadays one of the critical concerns due to the increase in the use of renewable energy sources. Modern grids connected to inverter-based distributed generations (IDGs) and synchronous distributed generations (SDGs) have a direct impact on fault currents and locations and then on the protection system. In this paper, a new optimal OCR coordination scheme has been developed based on the nonstandard time–current characteristics (NSTCC) approach. The proposed scheme can effectively minimize the impact of distributed generations (DGs) on OCR coordination by using two optimization techniques: genetic algorithm (GA) and hybrid gravitational search algorithm–sequential quadratic programming (GSA–SQP) algorithm. In addition, the proposed optimal OCR coordination scheme has successfully employed a new constraint reduction method for eliminating the considerable number of constraints in the coordination and tripping time formula by using only one variable dynamic coefficient. The proposed protection scheme has been applied in IEEE 9-bus and IEC MG systems as benchmark radial networks as well as IEEE 30-bus systems as meshed structures. The results of the proposed optimal OCR coordination scheme have been compared to standard and nonstandard characteristics reported in the literature. The results showed a significant improvement in terms of the protection system sensitivity and reliability by minimizing the operating time (OT) of OCRs and demonstrating the effectiveness of the proposed method throughout minimum and maximum fault modes.
Adaptation of Users to Future Climate Conditions in Naturally Ventilated Historic Buildings: Effects on Indoor Comfort
Authors: Fiorito Francesco , Vurro Giandomenico , Carlucci Francesco , Campagna Ludovica Maria , De Fino Mariella , Carlucci Salvatore , Fatiguso Fabio
Published: July 2022

User behaviour can significantly affect indoor thermal comfort conditions, as well as energy consumption, especially in existing buildings with high thermal masses where natural cross ventilation is the main strategy to reduce cooling loads. The aims of this paper were: (i) to compare how behavioural changes evaluated by means of rule-based and stochastic models lead to changes in indoor thermal comfort levels, and (ii) to define the patterns of indoor thermal comfort in historic residential buildings in future scenarios. To this end, a historic building located in Molfetta (Southern Italy) was analysed using a dynamic energy simulation engine in five weather scenarios (Typical Meteorological Year, current extreme weather file 2018, predicted weather files for 2020, 2050, and 2080 generated by morphing method), and stochastic and rule-based models for window openings were adopted and compared. The results showed that the stochastic model was more accurate than the rule-based one, resulting in a reduction of discomfort conditions during the summer period between 30% and 50% in all climate scenarios. However, although the differences between predicted discomfort levels using rule-based and stochastic models tended to increase, discomfort levels still appeared to be not acceptable in the 2050 and 2080 scenarios due to the rising temperature driven by climate change.
Numerical Simulation of Gaseous Detonation Performance and Wall Reflection Effect of Acetylene-Rich Fuel
Authors: Gao He , Tang Hao
Published: July 2022

The efficient utilization of energy in rich fuel detonation processes and the effective control method of soot are important topics in combustion research. In this paper, we numerically study the detonation wave behavior of acetylene–air systems in rich fuel condition by using a reduced reaction mechanism. The non-stiff terms of the governing equations are solved explicitly using the gas kinetic scheme, and the stiff terms are solved implicitly. Our results show that the acetylene pyrolysis is the dominant reaction process. The oxidation reaction is exploited to initiate the reaction induction process, providing the required energy to overcome the potential energy barrier. The secondary detonation structure is due to the stable interaction of the transverse waves and the combined action of the vinyl reaction system, thus effectively improve the energy release rate and providing a powerful solution for the fuel-rich high-energy release of advanced heat engines. The area of the unreacted pocket increases with the acetylene concentration, resulting in an irregular wave-front and detonation cell. The reflected shock wave impacting on the wall induces the secondary reaction of the detonation products. The concentration of polycyclic aromatic hydrocarbons decreases significantly and regenerates near the wall. Our approach provides an effective tool for controlling detonation soot and the preparation of carbon particles.
Two-Phase Volumetric Expanders: A Review of the State-of-the-Art
Authors: van Heule Xander , De Paepe Michel , Lecompte Steven
Published: July 2022

Two-phase expansion is the process where a fluid undergoes a pressure drop through or in the liquid–vapor dome. This operation was historically avoided. However, currently it is studied for a multitude of processes. Due to the volume increase in volumetric expanders, a pressure drop occurs in the fluid resulting in flashing phenomena occurring. These phenomena have been studied before in other processes such as two-phase flows or static flash. However, this has not been extensively studied in volumetric expanders and is mostly neglected. Even if data has shown this is not always neglectable depending on the expander type. The thermal non-equilibrium occurring can be modeled on different principles of flashing flows, such as the mixture model, boiling delay model, and homogeneous relaxation model. The main application area in current literature for volumetric two-phase expansion machines, is in low-temperature two-phase heat-to-power cycles. These cycles have shown benefit over classic options if expanders are available with efficiencies in the range of at least 75%. Experimental investigation of expanders in two-phase operation, though lacking in quantity, has shown that this is an achievable goal. However, the know-how to accomplish this requires more studies, both experimentally and in modeling techniques for the different phenomena occurring within these expanders. The present work provides a brief but comprehensive overview of the available experimental data, applicable flashing modeling techniques, and available models of volumetric two-phase expanders.
Abnormal Phenomena and Mathematical Model of Fluid Imbibition in a Capillary Tube
Authors: Deng Wenquan , Liang Tianbo , Yuan Shuai , Zhou Fujian , Li Junjian
Published: July 2022

At present, the imbibition behavior in tight rocks has been attracted increasing attention since spontaneous imbibition plays an important role in unconventional oil and gas development, such as increasing swept area and enhancing recovery rate. However, it is difficult to describe the imbibition behavior through imbibition experiment using tight rock core. To characterize the imbibition behavior, imbibition and drainage experiments were conducted among water, oil, and gas phases in a visible circular capillary tube. The whole imbibition process is monitored using a microfluidic platform equipped with a high frame rate camera. This study conducts two main imbibition experiments, namely liquid-displacing-air and water-displacing-oil experiments. The latter is a spontaneous imbibition that the lower-viscosity liquid displaces the higher-viscosity liquid. For the latter, the tendency of imbibition rate with time does not match with previous model. The experimental results indicate that it is unreasonable to take no account of the effect of accumulated liquid flowing out of the capillary tube on imbibition, especially in the imbibition experiments where the lower-viscosity liquid displaces the higher-viscosity liquid. A mathematical model is established by introducing an additional force to describe the imbibition behavior in capillary tube, and the model shows a good prediction effect on the tendency of imbibition rate with time. This study discovers and analyzes the effect of additional force on imbibition, and the analysis has significances to understand the imbibition behavior in tight rocks.
Determinants of Electricity Demand in Cote D’Ivoire, Ghana, Nigeria and Senegal
Authors: Koranteng Nkansah Horace , Suleman Shafic , Ackah Ishmael , Amarh Benjamin Ashitey , Eduah Dominic , Jinapor John Abdulai
Published: July 2022

It has been established that a consistent supply of electricity to sectors of an economy is vital for economic growth. Countries in West Africa have not realized their full economic potential due to limited access to electricity. The problem with limited energy consumption has been compounded by an unstable supply of power. In this regard, diverse studies have sought to ascertain the factors that influence the consumption of electricity. However, in West Africa, there are very limited empirical works carried out to establish the determinants of electricity consumption and this has necessitated this study. The pooled OLS method is used to examine the determinant consumption of electricity for the period 1980 to 2018. In addition, the study focuses on four countries in West Africa: Nigeria, Ghana, Cote d’Ivoire and Senegal. The results indicate that gross domestic product, foreign direct investment, trade openness, industry output and population growth show a positive and significant relationship with electricity consumption. However, consumer price index has a negative and significant influence on the quantum of electricity consumed. The study recommends that projects focused to increase electricity generation capacity in West Africa should be encouraged and energy from clean sources should be harnessed to provide electricity.
Optimizing the Design of a Biomass-to-Biofuel Supply Chain Network Using a Decentralized Processing Approach
Authors: Psathas Fragkoulis , Georgiou Paraskevas N. , Rentizelas Athanasios
Published: July 2022

When designing biomass-to-biofuel supply chains, the biomass uncertainty, seasonality and geographical dispersion that affect economic viability need to be considered. This work presents a novel methodology that can optimize the design of biofuel supply chains by adopting a decentralized network structure consisting of a mix of fixed and mobile processing facilities. The model considers a variable biomass yield profile and the mobile fast pyrolysis technology. The mixed-integer linear programming model developed identifies the optimal biofuel production and biomass harvesting schedule schemes under the objective of profit maximization. It was applied in the case study of marginal lands in Scotland, which are assumed to be planted with Miscanthus. The trade-offs observed between economies of scale against the transportation costs, the effect of the relocation costs and the contribution of storage capacity were investigated. The results showed that, in most cases, harvesting is most concentrated during the month of the highest biomass yield, provided that storage facilities are available. Storage capacity plays an important role to widen the operational time window of processing facilities since scenarios with restricted or costly storage resulted in facilities of higher capacity operating within a narrower time window, leading to higher investment costs. Relocation costs proved to have a minor share in the total transportation costs.
Study of Grid-Connected PV System for a Low Voltage Distribution System: A Case Study of Cambodia
Authors: Vai Vannak , Eng Samphors
Published: July 2022

The low voltage (LV) distribution systems are extended year by year due to the increase in energy demand. To overcome this issue, distribution system utilities have been focusing on designing and operating an appropriate distribution system with minimum capital and operational expenditure for supplying electricity to users. This article compares different algorithms to design an LVAC distribution system in a rural area, which focuses on minimizing the total length of lines and the power losses and balancing the loads among the three phases including the economic evaluation of the grid-connected PV system. Firstly, the shortest path (SP) algorithm is established to search for the minimization of the conductor used. Secondly, three different algorithms which are repeated phase sequence (RPABC), first fit bin packing (FFBP), and mixed-integer quadratic programming (MIQP) algorithms are developed to balance the load and minimize power losses. Next, a comparative result of three different algorithms is provided. Finally, the techno-economic analysis of the grid-connected PV system with different electricity tariffs with hybrid optimization of multiple energy resources (HOMER) software is studied in the planning period. To validate a proposed method, the 129-buses low voltage distribution in a rural village, in Cambodia, is tested. The simulation result confirms the optimal solution of the MIQP algorithm and PV system integration in designing a distribution system in a particular case study.
Design Methodology Based on Prebuilt Components for Modular Multilevel Converters with Partial Integration of Energy Storage Systems
Authors: Errigo Florian , De Oliveira Porto Leandro , Morel Florent
Published: July 2022

To provide ancillary services in HVDC applications, modular multilevel converters (MMCs) with integration of energy storage systems are a promising solution as they take advantage of the modularity and the controllability of the stored energy. In these solutions, an energy storage system is connected to the DC capacitor of a submodule (SM) to make an energy storage submodule (ES-SM). An MMC with partial integration (MMC-PIES) is an MMC with each arm made of a mix of SMs and ES-SMs. In this paper, we propose a novel design methodology for these converters considering they are built based on existing prebuilt submodules, while design methodologies in the literature consider the SM and ES-SM characteristics to be degrees of freedom. Therefore, the proposed approach is closer to an industrial standpoint and computes the minimum number of ES-SMs to comply with requirements. We also include a new optimization method for the circulating currents needed to balance the energy in the SM and ES-SM capacitors. Design scenarios are presented. The results show that the value of the DC capacitance and the current limitation of the switches highly influence the design, restricting the possible operating points. In addition, half-bridge ES-SMs seem to be a more promising solution than full-bridge ES-SMs, reducing the number of ES-SMs.
Hardware Implementation of a Home Energy Management System Using Remodeled Sperm Swarm Optimization (RMSSO) Algorithm
Authors: Ramalingam Senthil Prabu , Shanmugam Prabhakar Karthikeyan
Published: July 2022

A remodeled sperm swarm optimization (RMSSO) algorithm for a home energy management (HEM) system is proposed, and its real-time efficacy was evaluated using a hardware experimental model. This home environment comprised sixteen residential loads, a smart meter and a Raspberry Pi controller to optimize the energy consumption cost (ECC) in response to the Indian day-ahead pricing (DAP) scheme. A wired/wireless communication network was considered to communicate with the smart meter and controller. To address this optimization problem, the sperm swarm optimization (SSO) algorithm’s constriction coefficient was remodeled to improve its global searching capability and proposed as RMSSO. For the first time, salp swarm optimization (SSA), SSO, and RMSSO algorithms were employed to schedule home appliances in the Indian scenario. To validate the proposed technique’s outcome, the results were compared to those of the conventional SSO and SSA algorithms. This problem was solved using the Python/GUROBI optimizer tool. As a consequence, consumers can use this control strategy in real-time to reduce energy consumption costs.
Seepage Law of Nearly Flat Coal Seam Based on Three-Dimensional Structure of Borehole and the Deep Soft Rock Roadway Intersection
Authors: Zhang Lei , Jing Chen , Li Shugang , Bao Ruoyu , Zhang Tianjun
Published: July 2022

Exploring the evolution characteristics of gas seepage between boreholes during the drainage process is critical for the borehole’s layout and high-efficiency gas drainage. Based on the dual-porous medium assumption and considering the effect of stress redistribution on coal seam gas seepage characteristics, a coal seam gas seepage model with a three-dimensional roadway and borehole crossing structure has been established and numerically calculated, concluding that the coal seam is between the drainage boreholes. The temporal and spatial evolution characteristics of gas pressure and permeability help elucidate the gas seepage law of the nearly flat coal seam associated with the deep soft rock roadway and borehole intersection model. The results indicate that: (1) The roadway excavation results in localized stress in some areas of the surrounding rock, reducing the strength of the coal body, increasing the expansion stress, and increasing the adsorption of gas by the coal body. (2) Along the direction of the coal seam, the permeability decreases initially and then increases. The gas pressure in the coal seam area in the middle of the borehole is higher than the pressure in the coal seam around the borehole, and the expansion stress and deformation increase, reducing the permeability of the coal body; when near the next borehole, the greater the negative pressure, the faster the desorption of the gas attracts the matrix shrinkage effect and causes the coal seam permeability rate to keep increasing. (3) The improvement of gas drainage with the overlapping arrangement of two boreholes firstly increases and then decreases as time goes on. (4) When the field test results and numerical simulation of the effective area of gas extraction are compared, the effectiveness of the model is verified. Taking the change of the porosity and the permeability into the model, it is able to calculate the radius of gas drainage more accurately.
Application of Selected Machine Learning Techniques for Identification of Basic Classes of Partial Discharges Occurring in Paper-Oil Insulation Measured by Acoustic Emission Technique
Authors: Boczar Tomasz , Borucki Sebastian , Jancarczyk Daniel , Bernas Marcin , Kurtasz Pawel
Published: July 2022

The paper reports the results of a comparative assessment concerned with the effectiveness of identifying the basic forms of partial discharges (PD) measured by the acoustic emission technique (AE), carried out by application of selected machine learning methods. As part of the re-search, the identification involved AE signals registered in laboratory conditions for eight basic classes of PDs that occur in paper-oil insulation systems of high-voltage power equipment. On the basis of acoustic signals emitted by PDs and by application of the frequency descriptor that took the form of a signal power density spectrum (PSD), the assessment involved the possibility of identifying individual types of PD by the analyzed classification algorithms. As part of the research, the results obtained with the use of five independent classification mechanisms were analyzed, namely: k-Nearest Neighbors method (kNN), Naive Bayes Classification, Support Vector Machine (SVM), Random Forests and Probabilistic Neural Network (PNN). The best results were achieved using the SVM classification tuned with polynomial core, which obtained 100% accuracy. Similar results were achieved with the kNN classifier. Random Forests and Naïve Bayes obtained high accuracy over 97%. Throughout the study, identification algorithms with the highest effectiveness in identifying specific forms of PD were established.
Municipal Sewage Sludge Composting in the Two-Stage System: The Role of Different Bulking Agents and Amendments
Authors: Kulikowska Dorota , Bernat Katarzyna , Zaborowska Magdalena , Zielińska Magdalena
Published: July 2022

This study assessed the effect of different lignocellulosic amendments and bulking agents on compost stability (based on a 4 day respiration activity test, AT4, and self-heating factor, SHF) and maturity (based on the nitrification index Initr and the ratio of C in humic acids, HA, to total organic carbon, TOC, in compost, CHA/TOC). With all feedstock compositions (FCs), the share of sewage sludge was 79% (wet mass). For FC1, wood chips (13.5%) and wheat straw (7.5%) were used as bulking agents and amendments; for FC2, instead of wood chips, energy willow was added; for FC3, pine bark (13.5%) and conifer sawdust (7.5%) were used. All FCs produced stable and mature compost; however, with FC2, the thermophilic phase last 3 days longer than with the other FCs. Moreover, an AT4 value below 10 g O2/kg dry mass (d.m.) was obtained the earliest with FC2 (after 45 days, ca. 15–20 days earlier than with other FCs). With FC2, Initr below 0.5 was obtained in ca. 60 days, 10 days earlier than with FC3 and 30 days earlier than with FC1. The highest net increases in HS (86.0 mg C/g organic matter (OM)) and HA (56.3 mg C/g OM) were also noted with FC2; with other FCs, the concentrations of these compounds were from 1.3- to 1.5-fold (HS) and from 1.4- to 1.9-fold (HA) lower. With FC2, the highest CHA/TOC (15.5%) was also noted, indicating that this compost contained the largest share of the most stable form of organic carbon. The rates of OM removal in the bioreactor ranged from 7.8 to 10.1 g/(kg d.m.·day). The rates of SH and HA formation ranged from 1.63 to 4.83 mg C/(g OM·day) and from 1.23 to 1.80 mg C/(g OM·day), respectively. This means that, through the choice of the amendments and bulking agents, the length of the composting time needed to obtain a stable and mature product can be controlled.
Rockburst Intensity Level Prediction Method Based on FA-SSA-PNN Model
Authors: Xu Gang , Li Kegang , Li Mingliang , Qin Qingci , Yue Rui
Published: July 2022

To accurately and reliably predict the occurrence of rockburst disasters, a rockburst intensity level prediction model based on FA-SSA-PNN is proposed. Crding to the internal and external factors of rockburst occurrence, six rockburst influencing factors (σθ, σt, σc, σc/σt, σθ/σc, Wet) were selected to build a rockburst intensity level prediction index system. Seventy-five sets of typical rockburst case data at home and abroad were collected, the original data were preprocessed based on factor analysis (FA), and the comprehensive rockburst prediction indexes, CPI1, CPI2, and CPI3, obtained after dimensionality reduction, were used as the input features of the SSA-PNN model. Sixty sets of rockburst case data were extracted as the training set, and the remaining 15 sets of rockburst case data were used as the test set. After the model training was completed, the model prediction results were analysed and evaluated. The research results show that the proposed rockburst intensity level prediction method based on the FA-SSA-PNN model has the advantages of high prediction accuracy and fast convergence, which can accurately and reliably predict the rockburst intensity level in a short period of time and can be used as a new method for rockburst intensity level prediction, providing better guidance for rockburst prediction problems in deep rock projects.
A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
Authors: Nguyen Tien M. , Pham Khanh D. , Nguyen John , Chen Genshe , Lee Charles H. , Behseta Sam
Published: July 2022

This review provides a comprehensive review of past and existing works on 5G systems with a laser focus on 5G Satellite Integration (SATis5) for commercial and defense applications. The holistic survey approach is used to gain an in-depth understanding of 5G-Terrestrial Network (5G-TN), 5G-Non-Terrestrial Network (5G-NTN), SATis5 testbeds, and projects along with related SATis5 architectures. Based on the survey results, the review provides (i) outlook perspectives on potential SATis5 architectures for current and future integrated defense and commercial satellite communication (SATCOM) with 5G systems, and (ii) a thorough understanding of problems associated with anticipated outlooks and corresponding studies addressing these problems. The commercial SATis5 architectures discussed here can be extended to civilian SATCOM applications.
Different-Sized Vessels of Quercus variabilis Blume Respond Diversely to Six-Year Canopy and Understory N Addition in a Warm-Temperate Transitional Zone
Authors: Zhang Shaokang , Yu Biyun , Zhou Peng , Huang Jianguo , Fu Shenglei , Zhang Wei
Published: July 2022

Nitrogen is a necessary macroelement in plant growth and is usually considered a limiting factor in many forest ecosystems. Increasing N deposition has been reported to affect tree growth. However, the effects still remain controversial due to variable N fertilization methods used. In order to study the realistic responses of tree growth to increasing N deposition, we investigated effects of canopy and understory N addition on tree-ring growth and vessel traits of Quercus variabilis Blume. Since 2013, 50 kg N ha−1 year was applied monthly from April to December to either the canopy (CN) or understory (UN) of trees in a warm-temperate forest in Central China. During 2013–2018, tree-ring growth and vessel-related traits (mean vessel area, theoretical xylem hydraulic conductivity (KH), relative ratio of KH, etc.) were analyzed. Tree rings were negatively impacted by both CN and UN treatments, but only the effect of UN was significant. Neither CN nor UN significantly impacted the detected vessel traits. However, some diverging influencing trends were still showed in some vessel traits. Both CN and UN treatments positively affected the percentage of annual total vessel area and vessel density, with the effect of UN on vessel density being more severe. All the detected vessel traits of the large vessels formed at the beginning of the tree-ring responded positively to CN, whereas the opposite response to UN was showed on mean vessel area and the relative ratio of KH. All these diverging responses in different vessel traits likely reflected the compensation and trade-off between maximizing growth and adapting to CN and UN treatments. Six-year long N addition negatively and positively affected tree-ring growth and vessel traits of Q. variabilis in Central China, respectively. UN treatment could not fully simulate the real effect on tree growth, especially on the hydraulic architecture.
Identification of Urban Green Space Types and Estimation of Above-Ground Biomass Using Sentinel-1 and Sentinel-2 Data
Authors: Xiao Jue , Chen Longqian , Zhang Ting , Li Long , Yu Ziqi , Wu Ran , Bai Luofei , Xiao Jianying , Chen Longgao
Published: July 2022

High-quality urban green space supports the healthy functioning of urban ecosystems. This study aimed to rapidly assess the distribution, and accurately estimate the above-ground biomass, of urban green space using remote sensing methods, thus providing a better understanding of the urban ecological environment in Xuzhou for more effective management. We performed urban green space classifications and compared the performance of Sentinel-2 MSI data and Sentinel-1 SAR data and combinations, for estimating above-ground biomass, using field data from Xuzhou, China. The results showed the following: (1) incorporating an object-oriented method and random forest algorithm to extract urban green space information was effective; (2) compared with stepwise regression models with single-source data, biomass estimation models based on multi-source data provide higher estimation accuracy (R2 = 0.77 for coniferous forest, R2 = 0.76 for shrub-grass vegetation, R2 = 0.75 for broadleaf forest); and (3) from 2016 to 2021, urban green space coverage in Xuzhou decreased, while the total above-ground biomass increased, with higher average above-ground biomass in broadleaf forests (133.71 tons/ha) compared to coniferous forests (92.13 tons/ha) and shrub-grass vegetation (21.65 tons/ha). Our study provides an example of automated classification and above-ground biomass mapping for urban green space using multi-source data and facilitates urban eco-management.
Applications of Artificial Intelligence to Eosinophilic Esophagitis
Authors: Smith Eric Reuben , Shah Jay
Published: July 2022

Eosinophilic Esophagitis (EoE) is a chronic immune-related inflammation, and challenges to its diagnosis and treatment evaluation persist. This literature review evaluates all AI applications to EOE, including 15 studies using AI algorithms for counting eosinophils in biopsies, as well as newer diagnostics using mRNA transcripts in biopsies, endoscopic photos, blood and urine biomarkers, and an improved scoring system for disease classification. We also discuss the clinical impact of these models, challenges faced in applying AI to EoE, and future applications. In conclusion, AI has the potential to improve diagnostics and clinical evaluation in EoE, improving patient outcomes.
Pilot Study Showing Feasibility of Phosphoproteomic Profiling of Pathway-Level Molecular Alterations in Barrett’s Esophagus
Authors: Moore Jarrod , Hekman Ryan , Blum Benjamin C. , Lawton Matthew , Lehoux Sylvain , Stachler Matthew , Pleskow Douglas , Sawhney Mandeep S. , Cummings Richard D. , Emili Andrew , Qureshi Alia
Published: July 2022

(1) Background: Barrett’s esophagus is a major risk factor for esophageal adenocarcinoma. In this pilot study, we employed precision mass spectrometry to map global (phospho)protein perturbations in Barrett’s esophagus lesions and adjacent normal tissue to glean insights into disease progression. (2) Methods: Biopsies were collected from two small but independent cohorts. Comparative analyses were performed between Barrett’s esophagus samples and adjacent matched (normal) tissues from patients with known pathology, while specimens from healthy patients served as additional controls. (3) Results: We identified and quantified 6810 proteins and 6395 phosphosites in the discovery cohort, revealing hundreds of statistically significant differences in protein abundances and phosphorylation states. We identified a robust proteomic signature that accurately classified the disease status of samples from the independent patient cohorts. Pathway-level analysis of the phosphoproteomic profiles revealed the dysregulation of specific cellular processes, including DNA repair, in Barrett’s esophagus relative to paired controls. Comparative analysis with previously published transcriptomic profiles provided independent evidence in support of these preliminary findings. (4) Conclusions: This pilot study establishes the feasibility of using unbiased quantitative phosphoproteomics to identify molecular perturbations associated with disease progression in Barrett’s esophagus to define potentially clinically actionable targets warranting further assessment.
Altered Transcriptional Regulation of Glycolysis in Circulating CD8+ T Cells of Rheumatoid Arthritis Patients
Authors: Harshan Shilpa , Dey Poulami , Raghunathan Srivatsan
Published: July 2022

Peripheral T lymphocytes of rheumatoid arthritis (RA) patients show pathological changes in their metabolic pathways, especially glycolysis. These changes may drive the increased proliferation and tissue invasiveness of RA T cells. In order to study the transcriptional regulation underlying these alterations, we analysed publicly available RNA sequencing data from circulating T lymphocyte subsets of healthy individuals, untreated RA patients, and patients undergoing treatment for RA. Differential co-expression networks were created using sample-wise edge weights from an analysis called “linear interpolation to obtain network estimates for single sample” (lionessR), and annotated using the Gene Transcription Regulation Database (GTRD). Genes with high centrality scores were identified. CD8+ effector memory cells (Tem) and CD8+CD45RA+ effector memory cells (Temra) showed large changes in the transcriptional regulation of glycolysis in untreated RA. PFKFB3 and GAPDH were differentially regulated and had high centrality scores in CD8+ Tem cells. PFKFB3 downregulation may be due to HIF1A post transcriptional inhibition. Tocilizumab treatment partially reversed the RA-associated differential expression of several metabolic and regulatory genes. MYC was upregulated and had high centrality scores in RA CD8+ Temra cells; however, its glycolysis targets were unaltered. The upregulation of the PI3K-AKT and mTOR pathways may explain MYC upregulation.
Toward Sustainable Urban Drainage Planning? Geospatial Assessment of Urban Vegetation Density under Socioeconomic Factors for Quito, Ecuador
Authors: Ulloa-Espíndola René , Lalama-Noboa Elisa , Cuyo-Cuyo Jenny
Published: July 2022

Natural or anthropogenic urban vegetation is an important resource for urban planning, risk assessment, and sustainable development of a city. Quito is a megadiverse city due to its location and topography, but the socioeconomic diversity generates more contrasting conditions of certain behaviors and habits related to urban infrastructure. The contrasts of vegetation and green spaces in the different sectors of Quito also reflect the diversity of the city. This study examines the effects of socioeconomic conditions on the loss or increase of urban vegetation. The exploratory regression method (spatial) and logit model (non-spatial) were used to explain the socioeconomic effects on urban vegetation density at the level of urban parishes. On the one hand, the Normalized Difference Vegetation Index (NDVI) was calculated as the dependent variable based on the 2021 sentinel images. On the other hand, the independent variables were structured based on the socioeconomic level, the land valuation areas of Quito (AIVAS), and the quality of life index. This article contributes to establishing baseline information that helps structure the conditions, strategies, and investments to design and implement plans and programs for urban drainage, ecosystem benefits, and sustainable development in the city of Quito.
Amplification in Time and Dilution in Space: Partitioning Spatiotemporal Processes to Assess the Role of Avian-Host Phylodiversity in Shaping Eastern Equine Encephalitis Virus Distribution
Authors: Humphreys John M.
Published: July 2022

Eastern equine encephalitis virus (EEEv) is an arthropod-borne virus and the causative agent of neurologic disease in humans, horses, poultry, and wildlife. Although EEEv is known to be transmitted in cycles involving avian hosts and ornithophilic mosquitoes, there is ongoing debate about the role avian-host phylodiversity plays in diluting or amplifying virus prevalence across geographic space and through time. This study leveraged seventeen years of non-human EEEv detections to quantify possible EEEv dilution and amplification effects in response to avian-host phylodiversity. In assessing EEEv and avian-host diversity relationships, comparisons were performed to illustrate how modeling decisions aimed at capturing spatial patterns, temporal trends, and space–time interactions impacted results and the interpretations drawn from those results. Principal findings indicated that increased avian phylodiversity promotes EEEv dilution across geographic space, but this dilution effect is scale-dependent and masked by amplification effects that occur through time. Findings further demonstrated that the decisions made when modeling complex spatiotemporal dynamics can readily contribute to contrasting statistical outcomes and results misinterpretation, even when arithmetic and mathematics are strictly correct.
Landslides and Gullies Interact as Sources of Lake Sediments in a Rifting Context: Insights from a Highly Degraded Mountain Environment
Authors: Belayneh Liuelsegad , Dewitte Olivier , Gulie Guchie , Poesen Jean , O’Hara Daniel , Kassaye Alemayehu , Endale Tizita , Kervyn Matthieu
Published: July 2022

Like many other lakes in the world, the interconnected Abaya and Chamo lakes in the Southern Main Ethiopian Rift are affected by rapid sediment accumulation. Although land degradation is a well-known issue in this part of the African continent, the main sediment sources, their spatial distribution and interaction in the Abaya–Chamo lakes’ basin have not yet been documented. Here, we present a systematic inventory, characterization, and spatial analysis of landslides and gullies as concentrated sediment sources, for four representative river catchments impacted by landscape rejuvenation. Using Google Earth imagery and field surveys, we mapped with high accuracy a total of 7336 gullies and 430 landslides. Recent landslides observed during the last decade were few, small and shallow, and appear to have played a minor role in the current sediment dynamics. Large landslides are old and inactive. Although they do not contribute to the current sediment budget, they contribute indirectly to landscape dynamics by favoring the occurrence of gullies. Overall, large percentages of severe to extremely degraded areas of gully erosion are located in rejuvenated landscapes, especially at the level of the old landslides. Many active gullies are connected to the river network, as such acting as the source of sediment. Our analysis is a step towards understanding the nature and control of anthropic activities on sediment production in the region. We also highlight the importance of considering the interactions between sediment sources and the connectivity of the geomorphological system.
Seismic Microzonation of the Pompeii Archaeological Park (Southern Italy): Local Seismic Amplification Factors
Authors: Amato Vincenzo , Covolan Marina , Dessales Hélène , Santoriello Alfonso
Published: July 2022

Pompeii Archaeological Park is the best laboratory for the study of the seismic site effects on cultural heritage: the ancient site was destroyed and buried by the 79 AD Vesuvian eruption and, furthermore, it was also affected by the 62–63 AD strong earthquake. Large sectors of the city were reconstructed after this earthquake while other parts were still under reconstruction when the fall-out and pyroclastic density currents of the eruption buried the Roman city. In order to evaluate the distribution of the damage and reconstructions due to the earthquake, detailed mappings of the structures were carried out using multidisciplinary approaches. In addition, analyses of the topographical features, subsoil stratigraphies, and geophysical surveys, responsible for local seismic amplification (site effects), allow us to define the sectors of the ancient city where the Amplification Factors (AFs) were the main ones responsible for damage. Selected areas and examples of compromised and reconstructed buildings show that the ancient topography and subsoil features (both lithological and seismic) are the main AFs. In particular, the damages caused by the 62–63 AD earthquakes seem to be mainly due to topographical factors such as steep scarps and slopes, ridges, peaks, and terraces, as well as to the major thickness of the soft sediments (loose volcanoclastic layers, paleosols, weathered lavas, and anthropogenic infillings) located over the well-lithified lavas. It is not uncommon to also have the combination of these two factors. For the first time, this multidisciplinary approach allows us to draw a seismic microzonation map for one of the most important archaeological sites of the world.