bluuebunny/crossref_metadata_2025 huggingface dataset:
Using Simulated Pest Models and Biological Clustering Validation to Improve Zoning Methods in Site-Specific Pest Management
Authors: Méndez-Vázquez Luis Josué , Lasa-Covarrubias Rodrigo , Cerdeira-Estrada Sergio , Lira-Noriega Andrés
Published: February 2022

Site-specific pest management (SSPM) is a component of precision agriculture that relies on spatially enabled agronomic data to facilitate pest control practices within management zones rather than whole fields. Recent integration of high-resolution environmental data, multivariate clustering algorithms, and species distribution modeling has facilitated the development of a novel approach to SSPM that bases zone delineation on environmentally independent subfield units with individual potential to host pest populations (eSSPM). Although the potential benefits of eSSPM are clear, methods currently described for its implementation still demand further evaluation. To offer clear insight into this matter, we used field-level environmental data from a Tahiti lime orchard and realistic simulations of six citrus pests to: (1) generate a series of virtual (i.e., controlled) infestation scenarios suitable for methodological testing purposes, (2) evaluate the utility of nested (i.e., within-cluster) partitioning essays to improve the accuracy of current eSSPM methods, and (3) implement two biological clustering validators to evaluate the performance of 10 clustering algorithms and choose appropriate numbers of management zones during field partitioning essays. Our results demonstrate that: (1) nested partitioning essays outperform zoning methods previously described in eSSPM, (2) more than one clustering algorithm tend to be necessary to generate field partition models that optimize site-specific pest control practices within crop fields, and (3) biological clustering validation is an essential addition to eSSPM zoning methods. Finally, the generated evidence was integrated into an improved workflow for within-field zone delineation with pest control purposes.
Algorithm for an Effective Ratio of the Transverse Bending Rigidity Based on the Segment Joint Bending Stiffness
Authors: Huang Dawei , Jiang Hao , Luo Wenjun , Xiong Hao , Tang Baizan , Xu Jinhui
Published: February 2022

An algorithm for calculating the effective ratio of the transverse bending rigidity is established based on the segment longitudinal joint bending stiffness. With the knowledge of this effective ratio, the bending rigidity of a modified uniform rigidity ring is fully defined. To verify this developed algorithm, the effective ratios and convergence deformations of the modified uniform rigidity rings obtained with different methods are compared. Moreover, the responses of the modified uniform rigidity ring model under loading obtained from this algorithm are compared to those obtained with the existing generally accepted beam-spring model. The results show that although the bending moments obtained from these two models are different, the axial forces, horizontal convergent deformations, and vertical convergent deformations are quite consistent with each other. The modified uniform rigidity ring model built on the developed effective ratio algorithm is applicable for the analysis of the tunnel convergence deformation and the interaction between the tunnel structure and the ground during operation. This modified uniform rigidity ring model is simpler and easier to use than the beam-spring model; thus, the significance of the developed algorithm for the effective ratio of the transverse bending rigidity is demonstrated.
Artificial Subjectivity: Personal Semantic Memory Model for Cognitive Agents
Authors: Munir Aumm-e-hani , Qazi Wajahat Mahmood
Published: February 2022

Personal semantic memory is a way of inducing subjectivity in intelligent agents. Personal semantic memory has knowledge related to personal beliefs, self-knowledge, preferences, and perspectives in humans. Modeling this cognitive feature in the intelligent agent can help them in perception, learning, reasoning, and judgments. This paper presents a methodology for the development of personal semantic memory in response to external information. The main contribution of the work is to propose and implement the computational version of personal semantic memory. The proposed model has modules for perception, learning, sentiment analysis, knowledge representation, and personal semantic construction. These modules work in synergy for personal semantic knowledge formulation, learning, and storage. Personal semantics are added to the existing body of knowledge qualitatively and quantitatively. We performed multiple experiments where the agent had conversations with the humans. Results show an increase in personal semantic knowledge in the agent’s memory during conversations with an F1 score of 0.86. These personal semantics evolved qualitatively and quantitatively with time during experiments. Results demonstrated that agents with the given personal semantics architecture possessed personal semantics that can help the agent to produce some sort of subjectivity in the future.
A Clustering Approach to Improve IntraVoxel Incoherent Motion Maps from DW-MRI Using Conditional Auto-Regressive Bayesian Model
Authors: Scalco Elisa , Mastropietro Alfonso , Rizzo Giovanna , Lanzarone Ettore
Published: February 2022

The Intra-Voxel Incoherent Motion (IVIM) model allows to estimate water diffusion and perfusion-related coefficients in biological tissues using diffusion weighted MR images. Among the available approaches to fit the IVIM bi-exponential decay, a segmented Bayesian algorithm with a Conditional Auto-Regressive (CAR) prior spatial regularization has been recently proposed to produce more reliable coefficient estimation. However, the CAR spatial regularization can generate inaccurate coefficient estimation, especially at the interfaces between different tissues. To overcome this problem, the segmented CAR model was coupled in this work with a k-means clustering approach, to separate different tissues and exclude voxels from other regions in the CAR prior specification. The proposed approach was compared with the original Bayesian CAR method without clustering and with a state-of-the-art Bayesian approach without CAR. The approaches were tested and compared on simulated images by calculating the estimation error and the coefficient of variation (CV). Furthermore, the proposed method was applied to some illustrative real images of oncologic patients. On simulated images, the proposed innovation reduced the average error of 47%, 21% and 58% for D, f and D*, respectively, compared to the state-of-the-art Bayesian approach, and of 48% and 34% for D and f, respectively, compared to the original CAR, while it achieved the same error for D*. The clustering approach was also able to consistently reduce the CV for each coefficient. On real images, the novel approach did not alter the IVIM maps obtained by the original CAR method, with the advantage of reducing their typical blotchy appearance at the boundaries. The proposed approach represents a valuable improvement over the state-of-the-art Bayesian CAR method and provides more reliable IVIM coefficient estimation, and is less sensitive to bias and inconsistency at tissue/tissue and tissue/background interfaces.
Identification and Temporal Characteristics of Earthquake Clusters in Selected Areas in Greece
Authors: Bountzis Polyzois , Papadimitriou Eleftheria , Tsaklidis George
Published: February 2022

The efficiency of earthquake clustering investigation is improved as we gain access to larger datasets due to the increase of earthquake detectability. We aim to demonstrate the robustness of a new clustering method, MAP-DBSCAN, and to present a comprehensive analysis of the clustering properties in three major seismic zones of Greece during 2012–2019. A time-dependent stochastic point model, the Markovian Arrival Process (MAP), is implemented for the detection of change-points in the seismicity rate and subsequently, a density-based clustering algorithm, DBSCAN, is used for grouping the events into spatiotemporal clusters. The two-step clustering procedure, MAP-DBSCAN, is compared with other existing methods (Gardner-Knopoff, Reasenberg, Nearest-Neighbor) on a simulated earthquake catalog and is proven highly competitive as in most cases outperforms the tested algorithms. Next, the earthquake clusters in the three areas are detected and the regional variability of their productivity rates is investigated based on the generic estimates of the Epidemic Type Aftershock Sequence (ETAS) model. The seismicity in the seismic zone of Corinth Gulf is characterized by low aftershock productivity and high background rates, indicating the dominance of swarm activity, whereas in Central Ionian Islands seismic zone where main shock-aftershock sequences dominate, the aftershock productivity rates are higher. The productivity in the seismic zone of North Aegean Sea vary significantly among clusters probably due to the co-existence of swarm activity and aftershock sequences. We believe that incorporating regional variations of the productivity into forecasting models, such as the ETAS model, it might improve operational earthquake forecasting.
Semi-Active Cable Damping to Compensate for Damping Losses Due to Reduced Cable Motion Close to Cable Anchor
Authors: Weber Felix , Spensberger Simon , Obholzer Florian , Distl Johann , Braun Christian
Published: February 2022

The relative motion of transverse cable dampers is smaller than predicted by the taut string model because of the effects of bending stiffness and fixed support conditions. As a result of the reduced damper motion, the dissipated energy per cycle is reduced as well, which may explain why damping measurements on real stay cables with transverse dampers often show lower cable damping ratios than expected from the taut string theory. To compensate for the reduced damper motion and damper efficiency, respectively, a semi-active cable damper is proposed. The controllable damper is realized by a hydraulic oil damper with real-time controlled bypass valve whereby the resulting damper force is purely dissipative. The proposed control law is clipped viscous damping with negative stiffness. The viscous coefficient is adjusted in real time to the actual frequency of vibration to generate optimum modal damping while the negative stiffness component partially compensates for the reduced damper motion due to the flexural rigidity and fixed support conditions of the cable. The measurements of the prototype semi-active hydraulic damper are used to derive a precise model of the semi-active damper force including the control force constraints due to the fully open and fully closed bypass valve. This model is used to compute the cable damping ratios of the first four cable modes, for typical damper positions, for a taut string model and for a cable model with flexural rigidity and fixed supported ends. The obtained cable damping ratios are compared to those resulting from the passive linear viscous damper being optimized to the first four cable modes. The results demonstrate that the proposed semi-active cable damper with the consideration of the minimum and maximum control force constraints significantly enhances the cable damping of the first four modes compared to the linear viscous damper.
Composition of the Wild Rabbit Nest and Its Implication for Domestic Rabbit Breeding
Authors: Bilkó Ágnes , Petróczi Imre , Bárdos Boróka , Nagy István , Altbacker Vilmos
Published: February 2022

Wild European rabbits (Oryctolagus cuniculus) give birth in an underground nursery burrow where the kits are hermetically closed into the nest chamber for several weeks except for the three minutes of daily nursing. Given this confinement, the nest quality seems to be crucial in affecting the kits’ survival. The situation is less critical in rabbitries where the domesticated descendants are kept and give birth in separate nest boxes where the temperature and humidity conditions are less extreme and nests consist of fur and wood shavings or straw. However, nesting behaviour of domestic does still reflects several aspects of the ancestors, and providing conditions close to their biological needs is advised. The present study aimed to understand the factors contributing to the nest material choice of rabbits by analysing the composition of wild rabbit nests and testing naive domestic does in controlled laboratory settings for their preferences in constructing their nests. We were able to collect 21 just abandoned nests in their natural habitat and found they contained rabbit fur and hay, of which 85% was dry grass, mostly long leaves of Calamagrostis sp. Using domesticated rabbit does, we then tested if there is a preference for the features of the wild rabbit nests or whether the above-mentioned finding was either an accidental consequence of grass availability or ageing of the nest material. Two-way choice tests revealed that primiparous domestic does preferred the dry grass over green and long leaves over short grass bunches for nest building. Ageing of the nest material was rather negligible and did not contribute to the high level of dry grass content. We concluded that long dry grass is recommended as it seems to fulfil basic needs of this domesticated animal.
Application of Biochar for the Restoration of Metal(loid)s Contaminated Soils
Authors: Graziano Marta , Martín-Peinado Francisco José , Delgado-Moreno Laura
Published: February 2022

Biochar has recently aroused great interest for the restoration of contaminated soils since it improves soil properties and induces the immobilization of pollutants. This study evaluates the use of biochar from plant pruning, applied as an amendment, for immobilizing metal(loid)s in a highly contaminated soil as well as for reducing the phytotoxicity of these pollutants by promoting natural revegetation. For this purpose, a bioassay with Trifolium pratense L. was used to test the effectiveness of the soil amendment in greenhouse conditions. Three treatments were carried out including soil contaminated with metal(loid)s (RA), and this soil was amended with biochar at different dosage: 4% (RA4B) and 8% (RA8B). A non-contaminated soil (NC) from a nearby area not affected by contamination was used as a control. The results show that biochar increased soil pH by several units depending on the dose used, 8% being the most effective one. Biochar treatments also reduced soluble and bioavailable forms of Zn and Cu. Likewise, phytotoxicity was significantly reduced, promoting seed germination and biomass with plant growth values similar to the non-polluted soil. In light of the results obtained, the evaluation of the bioremediation potential of biochar under field conditions can be considered.
A Scheduling Approach for the Train-Set Circulation Plan and Timetable for the Longer Distance High-Speed Railway in Transition Time
Authors: Wang Zeyu , Bai Zixi , Zhou Leishan , Guo Bin , Han Chang , Zhou Hanxiao , Zhao Wenqiang
Published: February 2022

Nowadays, high-speed railway (HSR) has become one of the main choices for passengers. As the number of passengers increases, their travel demands become diverse and the fluctuation range of passenger travel demands will also increase. In order to adapt to the change of passenger travel demands, the switching frequency of timetables needs to be increased. When switching the timetable, the train-set circulation plan also needs to be considered. In this paper, a scheduling approach for quickly solving the timetable and the train-set circulation plan in the transition time is proposed. A section sequence is constructed in the integer programming model, and the primary train-set circulation plan is obtained. Then a stop plan is obtained on the basis of passenger travel demands. To obtain the final train-set circulation plan and the timetable, a genetic algorithm (GA) is designed, and a method that can ensure that the timetable meets the safety operation requirements is proposed. The scheduling approach is tested on the Beijing-Shanghai HSR. The results show that by extending the transition time, the scheduling approach can switch the train-set position from the old state to new state, without additional consumption of resources, on the premise of meeting the travel demands of passengers.
Pedestrian Counting Based on Piezoelectric Vibration Sensor
Authors: Yu Yang , Qin Xiangju , Hussain Shabir , Hou Weiyan , Weis Torben
Published: February 2022

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.
Biomolecules from Microalgae and Cyanobacteria: Applications and Market Survey
Authors: Hachicha Rihab , Elleuch Fatma , Ben Hlima Hajer , Dubessay Pascal , de Baynast Helene , Delattre Cedric , Pierre Guillaume , Hachicha Ridha , Abdelkafi Slim , Michaud Philippe , Fendri Imen
Published: February 2022

Nowadays, microalgae and cyanobacteria have become a promising and sustainable source of useful products, thanks to their richness in bioactive metabolites of high value (antibiotics, toxins, pharmaceutically active compounds, plant growth regulators, and others). These photoautotroph microorganisms generate biomass using photosynthesis. This review, which distinguishes microalgae and Cyanobacteria, often called blue-green microalgae, aims to present their classification and taxonomic diversity as the ecological niches occupied by them. In addition, the usages of open ponds and photobioreactors to produce various microalgae and Cyanobacteria strains and the high-value bioactive compounds from these microorganisms are summarized. Finally, the numerous commercial applications of these phytoplanktons in different fields, such as food, dietary supplements, feed, cosmetic, and biofuel applications, are reviewed.
Order Releasing and Scheduling for a Multi-Item MTO Industry: An Efficient Heuristic Based on Drum Buffer Rope
Authors: Yue Lei , Xu Guangyan , Mumtaz Jabir , Chen Yarong , Zou Tao
Published: February 2022

Increasing productivity and efficiency in factories with make-to-order (MTO) production systems have attracted growing interest of academia and industry. In MTO companies, efficient order release and schedule are essential for succeeding in today’s marketplace. However, dynamic demand of customers and constrained resources make it difficult to achieve, as well as limiting the profits. Thus, to overcome the problem of order releasing and multi-item scheduling considering the capacity constrained resources investigated, a heuristic approach is proposed based on the drum-buffer-rope (DBR) method. The proposed heuristic is tested on different types of problems based on due date tightness and demand of products. The performance of the proposed heuristic is compared with other famous heuristic methods in literature. End results indicate that the proposed heuristic based on the DBR method outperforms against the other competitors, and it gives more significant results when optimal buffer size is adopted.
Verification and Comparison of Direct Calculation Method for the Analysis of Support–Ground Interaction of a Circular Tunnel Excavation
Authors: Lee Yu-Lin , Hsu Wen-Kuei , Chou Po-Yu , Hsieh Pei-Wen , Ma Chi-Huang , Kao Wei-Cheng
Published: February 2022

A direct algorithmic process can deal with the solution of the support–ground interaction in a circular tunnel excavation through the convergence-confinement method (CCM) with the concept of increment. This process is the so-called direct calculation method (DCM) that can find solutions, the mobilized support pressure and the convergence, in the analysis of CCM. To achieve the solution, using two linear equations in the elastic region and Newton’s recursive method to find the roots in the plastic region are proposed and realized by a calculated spreadsheet. The validity of the algorithmic process for the analytical solutions was investigated and verified by the finite element computation, and compared with the published results, Rocksupport (2004), Oreste (2009), and Gschwandtner-Galler (2012). The results obtained between DCM and related studies show no significant differences.
Phytochemical Profile of Eight Categories of Functional Edible Oils: A Metabolomic Approach Based on Chromatography Coupled with Mass Spectrometry
Authors: Socaciu Carmen , Dulf Francisc , Socaci Sonia , Ranga Floricuta , Bunea Andrea , Fetea Florinela , Pintea Adela
Published: February 2022

Functional vegetable oils are highly considered not only for their nutritional value, but also for their health benefits. The profile of phytochemicals responsible for their quality is useful also for the identification of possible mislabeling or adulteration. The comparative composition of eight categories (sunflower, pumpkin, hempseed, linseed, soybean, walnut, sea buckthorn and olive) of commercial vs. authentic oils was determined. Fatty acids, volatiles, carotenoids, tocopherols, and phenolic components were analyzed by gas- and liquid chromatography-based techniques coupled with diode array, mass spectrometry, or fluorescence detection. Classification models, commonly used in metabolomics, e.g., principal component analysis, partial least squares discriminant analysis, hierarchical clusters and heatmaps have been applied to discriminate each category and individual samples. Carotenoids, tocopherols, and phenolics contributed mostly, qualitatively, and quantitatively to the discrimination between the eight categories of oils, as well as between the authentic and the commercial ones. This metabolomic approach can be easily implemented and the heatmaps can be considered as “identity” cards of each oil category and the quality of commercial oils, comparative to the authentic ones of the same botanical and geographical origin.
Computational Evaluation of Cardiac Function in Children Supported with Heartware VAD, HeartMate 2 and HeartMate 3 Left Ventricular Assist Devices
Authors: Şafak Koray K. , Aluç Mehmet Can , Bozkurt Selim
Published: February 2022

Heart failure is one of the principal causes of morbidity and mortality in children. Treatment techniques may not work, and heart transplantation may be required as a result. The current state of donor-organ supply means that many patients cannot undergo transplantation. In these patients, ventricular assist devices (VADs) may be used to bridge the time until the transplantation. Continuous-flow VADs are increasingly being implanted to paediatric patients. The aim of this study was to evaluate cardiac function in children supported with Heartware HVAD, HeartMate2 and HeartMate3 devices using computational simulations. A lumped-parameter model simulating cardiac function in children around 12 years of age was used to simulate dilated cardiomyopathy and heart-pump support. The operating speeds in HVAD, HeartMate2 and HeartMate3 were selected as 2600 rpm, 8700 rpm and 5200 rpm constant speed, respectively, while the Lavare cycle and artificial-pulse modes were used to generate mean pump outputs at around 4.40 L/min and mean arterial pressures at around 82 mmHg in each device. Aortic pulse pressure was 11 mmHg, 14 mmHg and 6 mmHg under HVAD, HeartMate2 and HeartMate3 support, respectively. HVAD’s Lavare cycle and HeartMate3’s artificial pulse increased aortic pulse pressure to 15 mmHg and 20 mmHg. HeartMate3 with artificial-pulse mode may be more beneficial in reducing arterial-pulsatility-associated problems.
A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
Authors: Liu Yang , Li Tingyu , Bao Jia , Hu Xiaomin , Zhao Xin , Shao Lixin , Li Chenglong , Lu Mengyuan
Published: February 2022

In recent years, an increasing amount of short-chain perfluoroalkyl substance (PFAS) alternatives has been used in industrial and commercial products. However, short-chain PFASs remain persistent, potentially toxic, and extremely mobile, posing potential threats to human health because of their widespread pollution and accumulation in the water cycle. This study systematically summarized the removal effect, operation conditions, treating time, and removal mechanism of various low carbon treatment techniques for short-chain PFASs, involving adsorption, advanced oxidation, and other practices. By the comparison of applicability, pros, and cons, as well as bottlenecks and development trends, the most widely used and effective method was adsorption, which could eliminate short-chain PFASs with a broad range of concentrations and meet the low-carbon policy, although the adsorbent regeneration was undesirable. In addition, advanced oxidation techniques could degrade short-chain PFASs with low energy consumption but unsatisfied mineralization rates. Therefore, combined with the actual situation, it is urgent to enhance and upgrade the water treatment techniques to improve the treatment efficiency of short-chain PFASs, for providing a scientific basis for the effective treatment of PFASs pollution in water bodies globally.
Comparison of X-ray Fluorescence (XRF) and Atomic Absorption Spectrometry (AAS) Results for an Environmental Assessment at a Mercury Site in Kyrgyzstan
Authors: Spearman Sandra , Bartrem Casey , Sharshenova Ainash , Salymbekova Kasiet , Isirailov Makhmud , Gaynazarov Saparbai , Gilmanov Roman , von Lindern Ian , von Braun Margrit , Möller Gregory
Published: February 2022

Khaidarkan, Batken Province, Kyrgyzstan is home to one of the world’s largest and last primary mercury mines. Doctors without Borders (MSF) and the Ministry of Health (MOH) of Kyrgyzstan have found that the Batken region has an elevated rate of non-communicable diseases (NCD) within the country. NCD can be caused by environmental pollution. A human health risk assessment was conducted to investigate heavy metal exposure. Using a hand-held X-ray fluorescence (XRF) spectrometer for soil screening is faster and less expensive than reliance on bench-scale methods. To establish a site-specific mercury conversion factor between XRF and the local MOH lab’s Atomic Absorption Spectrometry (AAS) with a Pyrolyzer attachment, soil samples were collected in Khaidarkan and surrounding villages. Samples were analyzed by XRF in three stages: in situ, ex situ-bulk, and ex situ-sieved. The ex situ-sieved samples were analyzed by AAS. Analysis results indicate that in situ readings can be used as a qualitative tool for screening, and a conversion factor of 1.7 was most appropriate for converting ex situ-bulk/ex situ-sieved and AAS results. This analysis enables the MOH laboratory and others to use XRF as a quick and cost-effective monitoring tool for Hg contamination in soil.
Multi-Attribute Decision Making Method for Node Importance Metric in Complex Network
Authors: Zhang Yongheng , Lu Yuliang , Yang Guozheng , Hang Zijun
Published: February 2022

Correctly measuring the importance of nodes in a complex network is critical for studying the robustness of the network, and designing a network security policy based on these highly important nodes can effectively improve security aspects of the network, such as the security of important data nodes on the Internet or the hardening of critical traffic hubs. Currently included are degree centrality, closeness centrality, clustering coefficient, and H-index. Although these indicators can identify important nodes to some extent, they are influenced by a single evaluation perspective and have limitations, so most of the existing evaluation methods cannot fully reflect the node importance information. In this paper, we propose a multi-attribute critic network decision indicator (MCNDI) based on the CRITIC method, considering the H-index, closeness centrality, k-shell indicator, and network constraint coefficient. This method integrates the information of network attributes from multiple perspectives and provides a more comprehensive measure of node importance. An experimental analysis of the Chesapeake Bay network and the contiguous USA network shows that MCNDI has better ranking monotonicity, more stable metric results, and is highly adaptable to network topology. Additionally, deliberate attack simulations on real networks showed that the method exhibits high convergence speed in attacks on USAir97 networks and technology routes networks.
Crack Growth and Delamination Analysis in GFRP Composite Materials
Authors: Tsivolas Eleftherios , Gergidis Leonidas N. , Paipetis Alkiviadis S.
Published: February 2022

The modeling of the structural behavior of composite materials is an interesting but complex task since the response of the material to loading structural may be difficult to predict, and the failure may be manifested in different forms. In cross-ply fiber-reinforced composites, the major failure mechanisms include: (i) the failure of the matrix material (transverse cracking), (ii) delamination and (iii) the breakage of the fibers. The process of the transverse cracking is a well studied damage mechanism and can be used in numerical simulations, in order to study the effects of various parameters on the crack density. In this paper, the finite element modeling of a cross-ply composite under uniaxial loading in tension is performed using ABAQUS software, considering all the potential damage mechanisms. The model takes into account shear-lag effect for the determination of the stress transfer and furthermore it adopts a homogenization procedure for the calculation of elastic and viscoelastic material properties. Stochasticity is introduced by assigning various interfacial strengths that follow a Gaussian distribution, so as to predict the cracking sequence up to saturation in the transverse to the 0∘ layers. The results are directly compared with available experimental measurements showing reasonable agreement. Finally, a cross-ply RVE model was created and loaded in uniaxial tension and crack propagation is modelled with the Extended Finite Element Method (XFEM). The stress concentration calculations around the crack tips are in agreement with the mesoscale model.
Deep Graph Reinforcement Learning Based Intelligent Traffic Routing Control for Software-Defined Wireless Sensor Networks
Authors: Huang Ru , Guan Wenfan , Zhai Guangtao , He Jianhua , Chu Xiaoli
Published: February 2022

Software-defined wireless sensor networks (SDWSN), where the data and control planes are decoupled, are more suited to handling big sensor data and effectively monitoring dynamic environments and events. To overcome the limitations of using static routing tables under high traffic intensity, such as network congestion, high packet loss rate, low throughput, etc., it is critical to design intelligent traffic routing control for the SDWSNs. In this paper we propose a deep graph reinforcement learning (DGRL) model-based intelligent traffic control scheme for SDWSNs, which combines graph convolution with deterministic policy gradient. The model fits well for the task of intelligent routing control for the SDWSN, as the process of data forwarding can be regarded as the sampling of continuous action space and the traffic data has strong graph features. The intelligent control policies are made by the SDWSN controller and implemented at the sensor nodes to optimize the data forwarding process. Simulation experiments performed on the Omnet++ platform show that, compared with the existing traffic routing algorithms for SDWSNs, the proposed intelligent routing control method can effectively reduce packet transmission delay, increase packet delivery ratio, and reduce the probability of network congestion.
Stock Market Crisis Forecasting Using Neural Networks with Input Factor Selection
Authors: Fuchs Felix , Wahl Markus , Zagst Rudi , Zheng Xinyi
Published: February 2022

Artificial neural networks have gained increasing importance in many fields, including quantitative finance, due to their ability to identify, learn and regenerate non-linear relationships between targets of investigation. We explore the potential of artificial neural networks in forecasting financial crises with micro-, macroeconomic and financial factors. In this application of neural networks, a huge amount of available input factors, but limited historical data, often leads to over-parameterized and unstable models. Therefore, we develop an input variable reduction method for model selection. With an iterative walk-forward forecasting and testing procedure, we create out-of-sample predictions for crisis periods of the S&P 500 and demonstrate that the model selected with our method outperforms a model with a set of input factors taken from the literature.
Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
Authors: Sultonov Furkat , Park Jun-Hyun , Yun Sangseok , Lim Dong-Woo , Kang Jae-Mo
Published: February 2022

Automatic road extraction from unmanned aerial vehicle (UAV) imagery has been one of the major research topics in the area of remote sensing analysis due to its importance in a wide range of applications such as urban planning, road monitoring, intelligent transportation systems, and automatic road navigation. Thanks to the recent advances in Deep Learning (DL), the tedious manual segmentation of roads can be automated. However, the majority of these models are computationally heavy and, thus, are not suitable for UAV remote-sensing tasks with limited resources. To alleviate this bottleneck, we propose two lightweight models based on depthwise separable convolutions and ConvMixer inception block. Both models take the advantage of computational efficiency of depthwise separable convolutions and multi-scale processing of inception module and combine them in an encoder–decoder architecture of U-Net. Specifically, we substitute standard convolution layers used in U-Net for ConvMixer layers. Furthermore, in order to learn images on different scales, we apply ConvMixer layer into Inception module. Finally, we incorporate pathway networks along the skip connections to minimize the semantic gap between encoder and decoder. In order to validate the performance and effectiveness of the models, we adopt Massachusetts roads dataset. One incarnation of our models is able to beat the U-Net’s performance with 10× fewer parameters, and DeepLabV3’s performance with 12× fewer parameters in terms of mean intersection over union (mIoU) metric. For further validation, we have compared our models against four baselines in total and used additional metrics such as precision (P), recall (R), and F1 score.
Assessment of Impacts of Climate Change on Hydropower-Dominated Power System—The Case of Ethiopia
Authors: Mekonnen Tewodros Walle , Teferi Solomon Tesfamariam , Kebede Fitsum Salehu , Anandarajah Gabrial
Published: February 2022

The Ethiopia energy mix is dominated by hydro-generation, which is largely reliant on water resources and their availability. This article aims to examine the impacts of severe drought on electric power generation by developing a Drought Scenario. OSeMOSYS (an open source energy modelling tool) was used to perform the analyses. The results were then compared with an existing reference scenario called “New Policy Scenario”. The study looked at how power generation and CO2 emissions would be altered in the future if reservoir capacity was halved due to drought. Taking this into account, the renewable energy share decreased from its 90% in 2050 to 81% in 2065, which had been 98% to 89% in the case of New Policy Scenario. In another case, CO2 emissions also increased from 0.42 Mt CO2 in 2015 to 7.3 Mt CO2 in 2065, a 3.3 Mt CO2 increase as compared to the New Scenario. The results showed how a prolonged period of drought would reduce the river flows and lead to an energy transition that may necessitate the installation of other concurrent alternative power plants. The study suggested ways to approach energy mix, particularly for countries with hydro-dominated power generation and those experiencing drought.
Cutting Flute and Thread Design on Self-Tapping Pedicle Screws Influence the Insertion Torque and Pullout Strength
Authors: Wu Lien-Chen , Hsieh Yueh-Ying , Tsuang Fon-Yih , Kuo Yi-Jie , Chiang Chang-Jung
Published: February 2022

Self-tapping screws are commonly used in trauma and maxillofacial surgery and are increasingly used for pedicle screw insertions. In order to evaluate how the quantity and length of cutting flutes on self-tapping pedicle screws affect the insertion torque and pullout strength, eight different self-tapping pedicle screw designs were evaluated. All screws had a threaded length of 35 mm and featured variations in the number of leads, as well as the length and quantity of cutting flutes. Five samples of each design were inserted into pre-drilled, untapped holes (ø2.7 mm, length 35 mm) in sawbone blocks of density 20 PCF. The insertion torque and pullout strength were measured according to ASTM F543. The results showed that screws with a longer cutting flute of 9.5 mm had a lower mean maximum insertion torque than screws with shorter 2.9 mm cutting flutes. Pedicle screws with a double-lead thread design had a greater insertion torque than their single-lead counterparts, and the use of three cutting flutes produced a lower torque than two cutting flutes. The results demonstrated a greater pullout strength in screws with a single-lead thread rather than a double-lead, three cutting flutes instead of two, and a longer length for the cutting flute. In conclusion, to provide immediate stability and reduce the surgical insertion time, a single-lead, self-tapping pedicle screw incorporating three long cutting flutes is recommended because of the significantly greater pullout strength. This design could also reduce the risk of implant loosening in comparison to double-lead, self-tapping pedicle screw designs.
Application of Deep Learning to Construct Breast Cancer Diagnosis Model
Authors: Lin Rong-Ho , Kujabi Benjamin Kofi , Chuang Chun-Ling , Lin Ching-Shun , Chiu Chun-Jen
Published: February 2022

(1) Background: According to Taiwan’s ministry of health statistics, the rate of breast cancer in women is increasing annually. Each year, more than 10,000 women suffer from breast cancer, and over 2000 die of the disease. The mortality rate is annually increasing, but if breast cancer tumors are detected earlier, and appropriate treatment is provided immediately, the survival rate of patients will increase enormously. (2) Methods: This research aimed to develop a stepwise breast cancer model architecture to improve diagnostic accuracy and reduce the misdiagnosis rate of breast cancer. In the first stage, a breast cancer risk factor dataset was utilized. After pre-processing, Artificial Neural Network (ANN) and the support vector machine (SVM) were applied to the dataset to classify breast cancer tumors and compare their performances. The ANN achieved 76.6% classification accuracy, and the SVM using radial functions achieved the best classification accuracy of 91.6%. Therefore, SVM was utilized in the determination of results concerning the relevant breast cancer risk factors. In the second stage, we trained AlexNet, ResNet101, and InceptionV3 networks using transfer learning. The networks were studied using Adaptive Moment Estimation (ADAM) and Stochastic Gradient Descent with Momentum (SGDM) based optimization algorithm to diagnose benign and malignant tumors, and the results were evaluated; (3) Results: According to the results, AlexNet obtained 81.16%, ResNet101 85.51%, and InceptionV3 achieved a remarkable accuracy of 91.3%. The results of the three models were utilized in establishing a voting combination, and the soft-voting method was applied to average the prediction result for which a test accuracy of 94.20% was obtained; (4) Conclusions: Despite the small number of images in this study, the accuracy is higher compared to other literature. The proposed method has demonstrated the need for an additional productive tool in clinical settings when radiologists are evaluating mammography images of patients.
Exposure and Health Effects of Bacteria in Healthcare Units: An Overview
Authors: Monteiro Ana , Cardoso Jéssica , Guerra Nuno , Ribeiro Edna , Viegas Carla , Cabo Verde Sandra , Sousa-Uva António
Published: February 2022

Healthcare units consist of numerous people circulating daily, such as workers, patients, and companions, and these people are vehicles for the transmission of microorganisms, such as bacteria. Bacteria species may have different allergenic, pathogenic, infectious, or toxic properties that can affect humans. Hospital settings foment the proliferation of bacteria due to characteristics present in the indoor hospital environment. This review article aims to identify the potential health effects caused by bacterial contamination in the context of healthcare units, both in patients and in workers. A search was carried out for articles published in PubMed, Web of Science and Scopus, between 1 January 2000 and 31 October 2021, using the descriptor hospital exposure assessment bacteria. This bibliographic research found a total of 13 articles. Bacteria transmission occurs mainly due to the contact between healthcare workers and patients or through the handling of/contact with contaminated instruments or surfaces. The most common bacterial contaminants are Escherichia coli, Pseudomonas aeruginosa, Staphylococcus spp., Staphylococcus aureus and Micrococcus luteus, and the principal health effects of these contaminants are hospital-acquired infections and infections in immunocompromised people. A tight control of the disinfection methods is thus required, and its frequency must be increased to remove the microbial contamination of wards, surfaces and equipment. A better understanding of seasonal variations is important to prevent peaks of contamination.
Lung Segmentation in CT Images: A Residual U-Net Approach on a Cross-Cohort Dataset
Authors: Sousa Joana , Pereira Tania , Silva Francisco , Silva Miguel C. , Vilares Ana T. , Cunha António , Oliveira Hélder P.
Published: February 2022

Lung cancer is one of the most common causes of cancer-related mortality, and since the majority of cases are diagnosed when the tumor is in an advanced stage, the 5-year survival rate is dismally low. Nevertheless, the chances of survival can increase if the tumor is identified early on, which can be achieved through screening with computed tomography (CT). The clinical evaluation of CT images is a very time-consuming task and computed-aided diagnosis systems can help reduce this burden. The segmentation of the lungs is usually the first step taken in image analysis automatic models of the thorax. However, this task is very challenging since the lungs present high variability in shape and size. Moreover, the co-occurrence of other respiratory comorbidities alongside lung cancer is frequent, and each pathology can present its own scope of CT imaging appearances. This work investigated the development of a deep learning model, whose architecture consists of the combination of two structures, a U-Net and a ResNet34. The proposed model was designed on a cross-cohort dataset and it achieved a mean dice similarity coefficient (DSC) higher than 0.93 for the 4 different cohorts tested. The segmentation masks were qualitatively evaluated by two experienced radiologists to identify the main limitations of the developed model, despite the good overall performance obtained. The performance per pathology was assessed, and the results confirmed a small degradation for consolidation and pneumocystis pneumonia cases, with a DSC of 0.9015 ± 0.2140 and 0.8750 ± 0.1290, respectively. This work represents a relevant assessment of the lung segmentation model, taking into consideration the pathological cases that can be found in the clinical routine, since a global assessment could not detail the fragilities of the model.
Deformation Analysis of the Rock Surrounding a Tunnel Excavated through a Gently Dipping Bed
Authors: Gao Zhen , Luo Jianjun , Wu Xu , Li Ke
Published: February 2022

To investigate the deformation and stability of the rock surrounding a tunnel excavated through layered rock, a three-dimensional numerical simulation was conducted to compare and analyze the right line of the YD2K100+560–630 two-stage docking cross-section of the Yuelongmen Tunnel using a ubiquitous joint model. Based on 3-D numerical simulations, the following conclusions were drawn: (1) The excavation on the construction entrance side significantly disturbed the construction exit side, and the exit side was more disturbed in the steeply dipping layer than in the gently dipping layer. (2) The influence of the dip angle of the laminae on the uplift of the tunnel invert was greater than the influence on the tunnel vault settlement when the laminae were oriented perpendicular to the trend of the tunnel and two-stage docking excavation was used. (3) When two-stage excavation was applied to gently inclined laminated rock, there was an obvious disturbance to both the top of the tunnel (less than one time the diameter of the through face; and to the bottom of the tunnel within 0.5 time the diameter of the through face on the side with the stopping construction exit side during the tunnelling process. When tunnel excavation was conducted against the trend of the layer, there was obvious disturbance to the top of the tunnel within 0.5 time the diameter of the through face and to the bottom of the tunnel within one time the diameter of the entrance face) on the construction exit side during the tunnelling process due to the presence of laminae. The excavation on the construction entrance side significantly disturbed the construction exit side. When docking and penetrating layered rock tunnels, priority should be given to docking and penetrating in the direction corresponding to the layers, and temporary support on the exit side should be strengthened to ensure the deformation stability of the surrounding rock mass.
Numerical Investigation of Climate Change Effects on Storm Surges and Extreme Waves on Canada’s Pacific Coast
Authors: Cousineau Julien , Murphy Enda
Published: February 2022

Storm surges and waves are key climate-driven parameters affecting the design and operation of ports and other infrastructure on the coast. Reliable predictions of future storm surges and waves are not yet available for the west coast of Canada, and this data gap hinders effective climate risk assessment, planning and adaptation. This paper presents numerical simulations of storm surges and waves in British Columbia coastal waters under a future climate (Representative Concentration Pathway) scenario (RCP8.5). The numerical models were first forced by wind and surface pressure fields from the ERA-5 global reanalysis, and calibrated and validated using historical wave and water level records. The models were then driven by atmospheric data from four regional climate models (RCMs) to investigate potential changes in the frequency and magnitude of storm surges and extreme waves over the 21st century. The model outputs were analyzed to determine the potential impacts of climate change on storm surges and wave effects at key ports and transportation assets in western Canada. The study is the first of its kind to utilize unstructured, computational models to simulate storm surges and waves for the entire western Canada coastal region, while maintaining the high spatial resolution in coastal sub-basins needed to capture local dynamic responses.