bluuebunny/crossref_metadata_2025 huggingface dataset:
Universal set of quantum gates for the flip-flop qubit in the presence of 1/f noise
Authors: Ferraro Elena , Rei Davide , Paris Matteo , De Michielis Marco
Published: January 2022

Impurities hosted in semiconducting solid matrices represent an extensively studied platform for quantum computing applications. In this scenario, the so-called flip-flop qubit emerges as a convenient choice for scalable implementations in silicon. Flip-flop qubits are realized implanting phosphorous donor in isotopically purified silicon, and encoding the logical states in the donor nuclear spin and in its bound electron. Electrically modulating the hyperfine interaction by applying a vertical electric field causes an Electron Dipole Spin Resonance (EDSR) transition between the states with antiparallel spins$\{|\downarrow \Uparrow \rangle ,|\uparrow \Downarrow \rangle \}${|↓⇑〉,|↑⇓〉}, that are chosen as the logical states. When two qubits are considered, the dipole-dipole interaction is exploited to establish long-range coupling between them. A universal set of quantum gates for flip-flop qubits is here proposed and the effect of a realistic 1/f noise on the gate fidelity is investigated for the single qubit$R_{z}(-\frac{\pi }{2})$Rz(−π2)and Hadamard gate and for the two-qubit$\sqrt{\mathit{iSWAP}}$iSWAPgate.
Multivariate Correlation-aware Spatio-temporal Graph Convolutional Networks for Multi-scale Traffic Prediction
Authors: Wang Senzhang , Zhang Meiyue , Miao Hao , Peng Zhaohui , Yu Philip S.
Published: January 2022

Traffic flow prediction based on vehicle trajectories collected from the installed GPS devices is critically important to Intelligent Transportation Systems (ITS). One limitation of existing traffic prediction models is that they mostly focus on predicting road-segment level traffic conditions, which can be considered as a fine-grained prediction. In many scenarios, however, a coarse-grained prediction, such as predicting the traffic flows among different urban areas covering multiple road links, is also required to help government have a better understanding on traffic conditions from the macroscopic point of view. This is especially useful in the applications of urban planning and public transportation planning. Another limitation is that the correlations among different types of traffic-related features are largely ignored. For example, the traffic flow and traffic speed are usually negatively correlated. Existing works regard these traffic-related features as independent features without considering their correlations. In this article, we for the first time study the novel problem of multivariate correlation-aware multi-scale traffic flow predicting, and we propose a feature correlation-aware spatio-temporal graph convolutional networks named MC-STGCN to effectively address it. Specifically, given a road graph, we first construct a coarse-grained road graph based on both the topology closeness and the traffic flow similarity among the nodes (road links). Then a cross-scale spatial-temporal feature learning and fusion technique is proposed for dealing with both the fine- and coarse-grained traffic data. In the spatial domain, a cross-scale GCN is proposed to learn the multi-scale spatial features jointly and fuse them together. In the temporal domain, a cross-scale temporal network that is composed of a hierarchical attention is designed for effectively capturing intra- and inter-scale temporal correlations. To effectively capture the feature correlations, a feature correlation learning component is also designed. Finally, a structural constraint is introduced to make the predictions on the two scale traffic data consistent. We conduct extensive evaluations over two real traffic datasets, and the results demonstrate the superior performance of the proposal on both fine- and coarse-grained traffic predictions.
Toward Accurate Spatiotemporal COVID-19 Risk Scores Using High-Resolution Real-World Mobility Data
Authors: Rambhatla Sirisha , Zeighami Sepanta , Shahabi Kameron , Shahabi Cyrus , Liu Yan
Published: January 2022

As countries look toward re-opening of economic activities amidst the ongoing COVID-19 pandemic, ensuring public health has been challenging. While contact tracing only aims to track past activities of infected users, one path to safe reopening is to develop reliable spatiotemporal risk scores to indicate the propensity of the disease. Existing works which aim at developing risk scores either rely on compartmental model-based reproduction numbers (which assume uniform population mixing) or develop coarse-grain spatial scores based on reproduction number (R0) and macro-level density-based mobility statistics. Instead, in this article, we develop a Hawkes process-based technique to assign relatively fine-grain spatial and temporal risk scores by leveraging high-resolution mobility data based on cell-phone originated location signals. While COVID-19 risk scores also depend on a number of factors specific to an individual, including demography and existing medical conditions, the primary mode of disease transmission is via physical proximity and contact. Therefore, we focus on developing risk scores based on location density and mobility behaviour. We demonstrate the efficacy of the developed risk scores via simulation based on real-world mobility data. Our results show that fine-grain spatiotemporal risk scores based on high-resolution mobility data can provide useful insights and facilitate safe re-opening.
Statistically-Robust Clustering Techniques for Mapping Spatial Hotspots: A Survey
Authors: Xie Yiqun , Shekhar Shashi , Li Yan
Published: January 2022

Mapping of spatial hotspots, i.e., regions with significantly higher rates of generating cases of certain events (e.g., disease or crime cases), is an important task in diverse societal domains, including public health, public safety, transportation, agriculture, environmental science, and so on. Clustering techniques required by these domains differ from traditional clustering methods due to the high economic and social costs of spurious results (e.g., false alarms of crime clusters). As a result, statistical rigor is needed explicitly to control the rate of spurious detections. To address this challenge, techniques for statistically-robust clustering (e.g., scan statistics) have been extensively studied by the data mining and statistics communities. In this survey, we present an up-to-date and detailed review of the models and algorithms developed by this field. We first present a general taxonomy for statistically-robust clustering, covering key steps of data and statistical modeling, region enumeration and maximization, and significance testing. We further discuss different paradigms and methods within each of the key steps. Finally, we highlight research gaps and potential future directions, which may serve as a stepping stone in generating new ideas and thoughts in this growing field and beyond.
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Authors: Sarwar Tabinda , Seifollahi Sattar , Chan Jeffrey , Zhang Xiuzhen , Aksakalli Vural , Hudson Irene , Verspoor Karin , Cavedon Lawrence
Published: January 2022

The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to improve healthcare practice. EHRs provide a rich set of information that includes demographics, medical history, medications, laboratory test results, and diagnosis. Data mining and analytics techniques have extensively exploited EHR information to study patient cohorts for various clinical and research applications, such as phenotype extraction, precision medicine, intervention evaluation, disease prediction, detection, and progression. But the presence of diverse data types and associated characteristics poses many challenges to the use of EHR data. In this article, we provide an overview of information found in EHR systems and their characteristics that could be utilized for secondary applications. We first discuss the different types of data stored in EHRs, followed by the data transformations necessary for data analysis and mining. Later, we discuss the data quality issues and characteristics of the EHRs along with the relevant methods used to address them. Moreover, this survey also highlights the usage of various data types for different applications. Hence, this article can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.
Evolutionary Dynamics and Molecular Mechanisms of HORMA Domain Protein Signaling
Authors: Gu Yajie , Desai Arshad , Corbett Kevin D.
Published: January 2022

Controlled assembly and disassembly of multi-protein complexes is central to cellular signaling. Proteins of the widespread and functionally diverse HORMA family nucleate assembly of signaling complexes by binding short peptide motifs through a distinctive safety-belt mechanism. HORMA proteins are now understood as key signaling proteins across kingdoms, serving as infection sensors in a bacterial immune system and playing central roles in eukaryotic cell cycle, genome stability, sexual reproduction, and cellular homeostasis pathways. Here, we describe how HORMA proteins’ unique ability to adopt multiple conformational states underlies their functions in these diverse contexts. We also outline how a dedicated AAA+ ATPase regulator, Pch2/TRIP13, manipulates HORMA proteins’ conformational states to activate or inactivate signaling in different cellular contexts. The emergence of Pch2/TRIP13 as a lynchpin for HORMA protein action in multiple genome-maintenance pathways accounts for its frequent misregulation in human cancers and highlights TRIP13 as a novel therapeutic target.
NiS2 Nanoparticles Grown on Reduced Graphene Oxide Co-Doped with Sulfur and Nitrogen for Enhanced Hydrogen Evolution Reaction in Acid Media
Authors: Lin Songmin , Yu Yuan , Sun Dongfeng , Meng Fangyou , Chu Wenhui , Ren Jie , Huang Linyin , Su Qingmei , Ma Shufang , Xu Bingshe
Published: January 2022

NiS2 nanoparticle electrocatalysts grown on sulfur and nitrogen co-doped reduced graphene oxide (NiS2/S,N-rGO) were explored. Since the hybrid catalyst of NiS2/S,N-rGO had a large surface area and high conductivity, it demonstrated outstanding electrocatalytic properties. This NiS2/S,N-rGO hybrid electrocatalyst required only 95 mV overvoltage to reach the current density of 10 mA cm−2 in hydrogen evolution reaction (HER) in acidic solution. In addition, the 5000 cycles test experiment showed the excellent HER electrochemical stability of the NiS2/S,N-rGO catalyst. This work also provided new strategies for increasing the catalytic activity of non-precious metal electrocatalysts.
Multi-Analyte Sensor Based on Pencil Graphite Electrode for Riboflavin and Pyridoxine Determination
Authors: Buleandră Mihaela , Popa Dana E. , Popa Adrian , Codreanu Nicoleta A. M. , David Iulia G.
Published: January 2022

Riboflavin (VB2) and pyridoxine (VB6) are two of the eight water-soluble B vitamins that play an essential role and are correlated in normal function of the body. In the present study, cyclic voltammetry demonstrated that in Britton-Robinson buffer (pH 5.00) VB2 undergoes a quasi-reversible electron transfer reaction and the oxidation of VB6 is irreversible, both electrochemical systems being diffusion controlled at the pencil graphite electrode (PGE) surface. Also, a sensitive and selective voltammetric procedure has been developed for the simultaneous determination of the two compounds using PGE and square wave voltammetry (SWV). Two well-defined oxidation peaks with a voltammetric separation of more than 1.10 V were obtained in Britton-Robinson buffer (pH 5.00). The SWV curves exhibited linear responses with riboflavin and pyridoxine concentrations. Thus, for VB2 two linear concentration ranges of 1.00 · 10−7–5.00 · 10−5 M and 5.00 · 10−5–7.50 · 10−4 M were obtained and in the case of VB6 the linear response was between 2.50 · 10−5 M and 2.50 · 10−3 M. The detection limits for VB2 and VB6 were 7.38 · 10−8 M and 1.10 · 10−5 M, respectively. The applicability of PGE was successfully proved for simultaneous determination of VB2 and VB6 in pharmaceutical tablets with good accuracy and precision.
Improved Cycling Performance of CrxV1−xSi2/Si Composite Electrode for Application to Lithium-Ion Battery Anodes
Authors: Kimura Yuta , Domi Yasuhiro , Usui Hiroyuki , Sakaguchi Hiroki
Published: January 2022

We have evaluated the anode properties of the silicide/Si composite electrodes for lithium-ion batteries and revealed that ternary silicide made by elemental substitution improved the electrochemical performance of the electrodes. In particular, a Cr0.5V0.5Si2/Si composite electrode exhibited a good cycle stability. Herein, we attempted mechanical grinding of the Cr0.5V0.5Si2/Si composite and addition of fluoroethylene carbonate (FEC) into the electrolyte to further improve the performance of the electrode. The electrode showed a superior cycling performance by these attempts as expected. The mechanical grinding should cause the formation of amorphous Si phase and fine dispersion of Cr0.5V0.5Si2in the Si phase, which suppresses the pulverization of the Cr0.5V0.5Si2/Si composite particle during charge-discharge. It is considered that the addition of FEC suppresses the continuous reductive decomposition of the electrolyte, which contributes to the improvement in the cyclability.
Accurate Measurement of the Contact Resistance During Internal Short Circuit in Lithium-Ion Batteries
Authors: Li Jiani , Li Wei , Song Jinyang , Chen Yong , Wang Lubing , Xu Jun
Published: January 2022

Accurate internal short circuit (ISC) resistance measurement is of vital importance to understanding and modeling the ISC process and consequent thermal runaway (TR) behaviors. The current measurement of ISC resistance usually cannot quantify the contact resistance of two directly-involved parties for the ISC, and the measured ISC resistance generally contains additional components (depending on the ISC modes). To this end, we propose a novel experimental methodology to accurately quantify the contact resistance during ISC. By a series of auxiliary experiments, we exclude the resistance contribution from non-direct-contact components (but are directly contacted with the direct-involving parties). Two representative scenarios of ISC, i.e., direct contact of electrodes and contact of conductive metallic plates with electrodes, are characterized by the proposed methodology. An additional ISC scenario is used to validate the effectiveness and accuracy of the proposed methodology. Results provide a powerful tool to characterize the contact resistance during ISC for future possible experiment design and theoretical modeling for the understanding of battery safety issues.
Effects of Salt Aggregation in Perfluoroether Electrolytes
Authors: Peters Brandon L. , Yu Zhou , Redfern Paul C. , Curtiss Larry A. , Cheng Lei
Published: January 2022

Electrolytes comprised of polymers mixed with salts have great potential for enabling the use of Li metal anodes in batteries for increased safety. Ionic conductivity is one of the key performance metrics of these polymer electrolytes and achieving high room-temperature conductivity remains a challenge to date. For a bottom-up design of the polymer electrolytes, we must first understand how the structure of polyelectrolytes on a molecular level determines their properties. Here, we use classical molecular dynamics to study the solvation structure and ion diffusion in electrolytes composed of a short-chain perfluoroether with LiFSI or LiTFSI salts. Density functional theory is also used to provide some insights into the structures and energies of the salt interactions with the perfluoroether. We observe the formation of aggregates of salts in the fluorinated systems even at low salt concentrations. The fluorine-fluorine attraction in the solvent is the governing factor for creating the salt aggregates. The aggregates’ size and lifetime change with concentration and anion. These simulations provide an insight into the structure and dynamics of perfluoroether based electrolytes that can be used to improve Li-ion batteries.
Plasmonic Heterosturcture for Full Solar Spectrum Harvesting
Authors: Yang Yang
Published: January 2022

Clean chemical fuel (hydrogen) generation from water using heterogeneous photocatalysts is an emerging technology for sustainable energy research. However, photo-to-hydrogen conversion efficiency of benchmarking photocatalysts still appear to be far from practice due to their limited light absorption of solar irradiation spectrum (UV-vis-NIR) and poor excited carrier lifetime. There is, therefore, an increasing interest in developing high efficiency photocatalysts with a broad spectral response and efficient excited carriers separation. In this work, a novel MoS2@TiO2 heterostructured film with periodically patterned morphology, exhibiting an enhanced light absorption within UV-vis-NIR wavelengths, was developed. Both experimental studies and theoretical modeling demonstrate that S-vacancy in the multilayered MoS2 nanoflakes is responsible for the localized surface plasmon resonance (LSPR, visible light absorption) and tunable band gaps (near infrared absorption). An outstanding H2 yield rate of 580 mmol h-1 g-1 was then achieved using the rationally designed novel MoS2@TiO2 photocatalyst. Present study also shows that the developed catalyst can even split sea water under solar light irradiation, which will open a new paradigm for direct solar energy harvesting.
(Invited) Rationally Designed Semiconductor/Nanocarbon Photoelectrodes for Solar Fuel Generation
Authors: Kecsenovity Egon , Endrodi Balazs , Janáky Csaba
Published: January 2022

Given that CO2 is a greenhouse gas, using the energy of sunlight to convert CO2 to transportation fuels (such as methanol or methane) represents a value-added approach to the simultaneous generation of alternative fuels and environmental remediation of carbon emissions. Electrochemistry and photoelectrochemistry have been proven to be a useful avenue for solar water splitting. CO2 reduction, however, is multi-electron in nature (e.g., 6 e- to methanol) with considerable kinetic barriers to electron transfer. It therefore requires the use of carefully designed electrode surfaces to accelerate e- transfer rates to levels that make practical sense. In this talk I will present hybrid photoelectrodes leading to enhanced efficiency, selectivity, and stability.

First, I will present the use of electrosynthetic (and photoelectrosynthetic) methods for preparing semiconductors on nanocarbon-modified electrode surfaces. Composites of nanocarbons with both inorganic and organic semiconductors represent an interesting class of new functional materials. Therefore, I will show how electrodeposition can be used to tune composition, crystal structure, and morphology of the nanocomposites for targeted applications.

In the second part of my talk, selected examples will be given for how these electrosynthesized hybrid assemblies can be deployed in various photoelectrochemical application schemes, most importantly CO2 conversion. I will present the controlled synthesis and photoelectrochemical behavior of Cu2O/CNT and Cu2O/graphene composites. A carefully designed, multiple-step electrodeposition protocol was developed that ensured homogeneous coating of the CNTs with the Cu2O nanocrystals. TiO2/ graphene nanocomposites were also obtained in a similar manner. This enhanced charge transport property for the hybrids resulted in a drastic increase in the photocurrents measured for the CO2 reduction. In addition to this superior performance, long term photoelectrolysis measurements proved that the Cu2O/nanocarbon hybrids were more stable than the oxide alone. Taking these observations together as a whole, a general model will be presented on the role of the nanocarbon scaffold.

Acknowledgements

This research was partially supported by the “Széchenyi 2020” program in the framework of GINOP-2.3.2-15-2016-00013 “Intelligent materials based on functional surfaces – from syntheses to applications” project.
Energy Efficiency and Reliability Considerations in Wireless Body Area Networks: A Survey
Authors: Ullah Farman , Khan M. Zahid , Mehmood Gulzar , Qureshi Muhammad Shuaib , Fayaz Muhammad
Published: January 2022

In this paper, we have reviewed and presented a critical overview of “energy-efficient and reliable routing solutions” in the field of wireless body area networks (WBANs). In addition, we have theoretically analysed the importance of energy efficiency and reliability and how it affects the stability and lifetime of WBANs. WBAN is a type of wireless sensor network (WSN) that is unique, wherever energy-efficient operations are one of the prime challenges, because each sensor node operates on battery, and where an excessive amount of communication consumes more energy than perceiving. Moreover, timely and reliable data delivery is essential in all WBAN applications. Moreover, the most frequent types of energy-efficient routing protocols include crosslayer, thermal-aware, cluster-based, quality-of-service, and postural movement-based routing protocols. According to the literature review, clustering-based routing algorithms are the best choice for WBAhinwidth, and low memory WBAN, in terms of more computational overhead and complexity. Thus, the routing techniques used in WBAN should be capable of energy-efficient communication at desired reliability to ensure the improved stability period and network lifetime. Therefore, we have highlighted and critically analysed various performance issues of the existing “energy-efficient and reliable routing solutions” for WBANs. Furthermore, we identified and compiled a tabular representation of the reviewed solutions based on the most appropriate strategy and performance parameters for WBAN. Finally, concerning to reliability and energy efficiency in WBANs, we outlined a number of issues and challenges that needs further consideration while devising new solutions for clustered-based WBANs.
The Sustainable Digitalization in the Manufacturing Industry: A Bibliometric Analysis and Research Trend
Authors: Shang Zhiming , Zhang Liming
Published: January 2022

Digital technologies are shown to perform a vital role in developing a resource-efficient industrial base. The effective adoption of digital technologies can help reduce costs and improve the sustainability and flexibility of manufacturing industries. However, how digital technologies favor the transition towards manufacturing systems has not been analyzed in detail yet, so more conceptual and empirical investigation is required in this field. This study investigates the digital transformation trend in the manufacturing industry by combining the bibliometric method with life cycle mode and literature mining. An integrated data analysis system (IDAS) is developed, which includes data collection, data analysis, and visualization processes. As a result, dual-map overlay, keywords clusters, Timeline, and Time zone views are visualized to identify the research trend with the three main aspects: design, larger integrated circuit, and sustainable manufacturing concept evolution. Besides, to recognize potentials for future development of the domain, life cycle model-based technological trajectory is identified, constructing an information society, the combination of tools, and the performance improvement of the whole digital transformation. Moreover, in response to sustainable development, a system considering business strategies and cultural delivery, technological integration, and partner participation and evaluation is developed, which could cope with complex manufacturing processes due to different industrial tasks in different regions.
An Early Warning of Atrial Fibrillation Based on Short-Time ECG Signals
Authors: Zhao Tianxia , Wang Xin’an , Qiu Changpei
Published: January 2022

This study introduces a method to classify single-lead ECG signals by extracting features through traditional methods and deep neural network methods. At first step, the statistical type features of the ECG signals are exacted by traditional methods, including time domain features, frequency domain features, and medical domain features. And then, deep neural networks are used to extract the deeper features of the ECG signal. The database of ECG signals is from Cinc 17, which have 8528 samples of short-time ECG signal. The huge amount of data makes the classification and identification more accurate by atrial fibrillation, normal sinus rhythm, noise, and indiscernible. Compare the base model built by the classified data and the data collected by the ECG device of CareON to enable daily early screening and a remote alert function with WeChat app. This method can extend the prevention, detection, and diagnosis of heart disease to the family, company, and other out-of-hospital scenarios, thus enabling faster treatment of heart patients and saving medical resources.
Ferroptosis: A New Regulatory Mechanism in Osteoporosis
Authors: Liu Pan , Wang Wenzhao , Li Zheng , Li Yao , Yu Xiaoping , Tu Ji , Zhang Zhengdong
Published: January 2022

Osteoporosis can be caused by a multitude of factors and is defined by a decrease in bone density and mass caused by the destruction of bone microstructure, resulting in increased bone brittleness. Thus, it is a systemic bone disease in which patients are prone to fracture. The role of ferroptosis in the pathogenesis of osteoporosis has become a topic of growing interest. In this review, we discuss the cell morphology, basic mechanisms of ferroptosis, the relationship between ferroptosis and osteoclasts and osteoblasts, as well as the relationship between ferroptosis and diabetic osteoporosis, steroid-induced osteoporosis, and postmenopausal osteoporosis. Emerging biomedical research has provided new insights into the roles of ferroptosis and osteoporosis, such as in cellular function, signaling pathways, drug inhibition, and gene silencing. The pathophysiology and mechanism of ferroptosis and osteoporosis need to be further studied and elucidated to broaden our understanding of iron metabolism and immune regulation. Studies using animal models of osteoporosis in vivo and cell models in vitro will help clarify the relationship between ferroptosis and osteoporosis and provide research ideas for the elucidation of new mechanisms and development of new technologies and new drugs for the treatment of osteoporosis in the future.
Research Advances in Antitumor Mechanism of Evodiamine
Authors: Li Luning , Zhang Cunxin , Huang Chen , Tian Xinchen , Sun Wenxue , Jiang Shulong
Published: January 2022

Evodiamine is a natural alkaloid extracted from Fructus Evodia. This bioactive alkaloid has been reported to have a wide range of biological activities, including anti-injury, antiobesity, vasodilator, and anti-inflammatory effects. In recent years, it has been found that evodiamine has tumor-suppressive effects on a variety of tumors. There is growing evidence that evodiamine can inhibit the rapid proliferation of tumor cells, induce cell cycle arrest at a certain phase, increase the incidence of apoptosis, promote autophagy, inhibit microangiogenesis and migration, and regulate immunotherapy. Evodiamine can inhibit Wnt/β-catenin, mTOR, NF-κB, PI3K/AKT, JAK-STAT, and other signaling pathways in various cancer cells, and it can significantly downregulate the expression of many tumor markers, such as VEGF and COX-2. These facts partially explain the antitumor mechanism of evodiamine. In this article, the antitumor mechanism of evodiamine was reviewed to provide the basis for its clinical application and therapeutic development in the future.
Evaluation of Nutrient Requirements of Sweet Lupine in Bread Wheat-Sweet Lupine under Additive Design Intercropping System in Northwest Ethiopia
Authors: Bayeh Birhanu , Alemayehu Getachew , Tadesse Tilahun , Alemayehu Melkamu
Published: January 2022

The application of optimal fertilizer rates for component crops improves productivity, land use efficiency, and profitability in an intercropping system. Two field experiments during the 2019 and 2020 cropping seasons were conducted in Adet and Debre Tabor districts with the objective of evaluating the nutrient requirements of sweet lupine in bread wheat-sweet lupine under additive design intercropping systems. Sweet lupine grown in bread wheat-sweet lupine intercropping was fertilized with seven fertilizer levels (0%, 25%, 50%, 75%, 100%, 125%, and 150% of the blanket-recommended NP fertilizer rate of sole lupine) and laid out in a randomized complete block design with three replications. The findings revealed that the highest grain yield of sweet lupine in wheat-sweet lupine intercropping system at Adet was achieved at 125% NP (0.51 t·ha−1) and 150% NP (0.52 t·ha−1), followed by 100% NP (0.43 t·ha−1) and 50% NP (0.35 t·ha−1) fertilizer levels of sweet lupine. Similarly, the highest grain yield of sweet lupine in Debre Tabor was recorded by the application of 125% NP (2.07 t ha−1) fertilizer level of sweet lupine followed by 150% NP (1.89 t·ha−1), 100% NP (1.71 t·ha−1), and 50% NP (1.70 t·ha−1) fertilizer levels. For every invested Ethiopian Birr in the treatments of 50% and 125% NP fertilizer levels of sweet lupine averaged additional profits of ETB 7.667 and ETB 4.537, respectively, can be obtained from sweet lupine that grew under bread wheat-sweet lupine intercropping system. Based on the averaged MRR across the different cost price ratio, application of 50% NP fertilizer level of sweet lupine can be recommended for profitable production of sweet lupine in bread wheat-sweet lupine under additive design intercropping system in Adet and Debre Tabor and areas with similar agroecology as it recorded the highest net return with acceptable marginal rate of return.
Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis
Authors: Alfaer Nada M. , Aljohani Hassan M. , Abdel-Khalek Sayed. , Alghamdi Abdulaziz S. , Mansour Romany F.
Published: January 2022

Natural computing refers to computational processes observed in nature and human-designed computing inspired by nature. In recent times, data fusion in the healthcare sector becomes a challenging issue, and it needs to be resolved. At the same time, intracerebral haemorrhage (ICH) is the injury of blood vessels on the brain cells, which is mainly liable for stroke. X-rays and computed tomography (CT) scans are widely applied for locating the haemorrhage position and size. Since manual segmentation of the CT scans by planimetry by the use of radiologists is a time-consuming process, deep learning (DL) is used to attain effective ICH diagnosis performance. This paper presents an automated intracerebral haemorrhage diagnosis using fusion-based deep learning with swarm intelligence (AICH-FDLSI) algorithm. The AICH-FDLSI model operates on four major stages namely preprocessing, image segmentation, feature extraction, and classification. To begin with, the input image is preprocessed using the median filtering (MF) technique to remove the noise present in the image. Next, the seagull optimization algorithm (SOA) with Otsu multilevel thresholding is employed for image segmentation. In addition, the fusion-based feature extraction model using the Capsule Network (CapsNet) and EfficientNet is applied to extract a useful set of features. Moreover, deer hunting optimization (DHO) algorithm is utilized for the hyperparameter optimization of the CapsNet and DenseNet models. Finally, a fuzzy support vector machine (FSVM) is applied as a classification technique to identify the different classes of ICH. A set of simulations takes place to determine the diagnostic performance of the AICH-FDLSI model using the benchmark intracranial haemorrhage data set. The experimental outcome stated that the AICH-FDLSI model has reached a proficient performance over the compared methods in a significant way.
Last-Mile Shuttle Planning for Improving Bus Commuters’ Travel Time Reliability: A Case Study of Beijing
Authors: Kou Weibin , Wang Jiayu , Liu Yanxi , Li Chenxu
Published: January 2022

This study proposes a multiobjective mixed integer nonlinear programming model for a last-mile shuttle service to improve bus commuters’ travel time reliability. The approach aims to assign the routes that pick up the transit passengers located at the different stops by shuttle service. A bilevel optimization model is established: the upper model of route design considers the tradeoff between time cost and fare cost when some of the passengers take the shuttles, and the lower model assigns the demand of transit passengers. The proposed model effectively captures the reliability of travel time because related parameters are estimated by a statistical fitting test with a large number of real-world bus geographic information system (GPS) data. Moreover, dynamic demand diverting from conventional transit to shuttle service and travel time reliability, including passenger in-vehicle time (IVT) and waiting time (WT), are fully considered in this model. Since the task is a nonlinear programming model, a two-stage algorithm combined with linearization processing is presented to find an optimal solution. Finally, from the case study of Zhongguancun Software Park zone in Beijing, it is indicated that when last-mile shuttle service is provided, bus passengers’ travel time reliability of last-mile trips can be improved by 14%. The study can be an important reference for improving the low reliability widely existing in the current transit commuters’ last-mile problem.
Decreased Adiponectin Levels Are a Risk Factor for Cognitive Decline in Spinal Cord Injury
Authors: Liu Fan-jie , Xu Hong-hao , Yin Ying , Chen Yuan-zhen , Xie Liang-yu , Li Hua-zhong , Wang Dan-dan , Shi Bin
Published: January 2022

Objective. Spinal cord injury (SCI) has become popular in recent years, and cognitive decline is a common complication. Adiponectin is a common protein hormone involved in the course of many diseases, but its relationship with SCI has not yet been elucidated. The purpose of our prospective study is to explore whether adiponectin can be used as a biomarker of cognitive decline in SCI. Methods. A total of 64 healthy volunteers and 92 patients with acute SCI were recruited by us. Serum adiponectin levels, demographic data (age and gender), lifestyle (smoking and drinking), medical history (diabetes and hypertension), and clinical baseline data (low-density lipoprotein, high-density lipoprotein, and fasting blood glucose) were recorded. Three months after enrollment, we used the Montreal Cognitive Assessment (MoCA) to evaluate cognitive function. Based on a quarter of the serum adiponectin levels, SCI patients were divided into 4 groups, and the differences in their MoCA scores were compared. In addition, we used multivariate linear regression to predict the risk factors of the MoCA score. Results. The serum adiponectin level (

6.1
±
1.1
 
μ
g
/
ml

) of SCI patients was significantly lower than that of the healthy control group (

6.7
±
0.9
 
μ
g
/
ml

), and there was a significant difference between the two (

p
<
0.001

). The group with higher serum adiponectin levels after 3 months of spinal cord injury had higher MoCA scores. Multivariate regression analysis showed that serum adiponectin level is a protective factor for cognitive function after SCI (

β
=
0.210

,

p
=
0.043

). Conclusions. Serum adiponectin levels can be used as an independent predictor of cognitive function in patients with acute SCI.
Identification System Based on Resolution Adjusted 2D Spectrogram of Driver’s ECG for Intelligent Vehicle
Authors: Choi Gyu Ho , Lim Kiho , Pan Sung Bum
Published: January 2022

Recently, traditional vehicles are being developed into intelligent vehicles as information is exchanged among various devices inside and outside the vehicles. In the connected car environment, the need for vehicle security is growing due to vehicle hacking accidents and possible threats to human life. Driver identification technology using electrocardiogram (ECG) signals has been studied to address vehicle security issues and driver-specific services. Existing driver identification systems tried to address the issues using a multidimensional feature extraction method. However, there are remaining issues, including accuracy concerns, because the resolution was adjusted without considering the ECG’s P, QRS Complexes, and T waves feature when analyzing the time-frequency multidimensional features. In this paper, we propose a driver identification system using a 2D spectrogram. It identifies a section where the resolution is optimally adjusted using a spectrogram that can simultaneously analyze the time-frequency features of an ECG. The experimental results show that the proposed method improved the identification performance compared to the existing multidimensional feature extraction methods such as EEMD and MFCCs. Besides, with a 2D spectrogram of 1/4 image size, the recognition performance is maintained in a CNN network and the training time is significantly reduced.
Positioning a Handshake Bay for Twin Stacking Cranes in an Automated Container Terminal Yard Block
Authors: Hu Zhi-Hua , Tian Xi-Dan , Yin Yu-Qi , Wei Chen
Published: January 2022

At automated container terminals (ACTs), twin automated stacking cranes (ASCs) can carry out the tasks—store and retrieve containers simultaneously in a yard block using a handshake bay, where a primary ASC stacks the container at the handshake bay and the other crane carries it to the destination bay. Although the handshake bay increases the degree of crane utilization, the ASCs will interfere with each other at the bay, decreasing the stacking efficiency. This study formulates a mixed-integer linear program (MILP) to position the handshake bay and simultaneously schedule the twin ASCs to minimize the tasks’ makespan. The proposed formulation considers the safe time interval to avoid crane collisions during adjacent crane movements. To solve the model, we developed a random-key genetic algorithm with a priority-based decoding scheme to optimize the task sequences and tasks assigned to the cranes. The priority-based GA can always generate feasible solutions by ranking the container-handling tasks. Numerical experiments prove that the safe temporal interval affects the makespan and the handshake bay’s position. An optimal handshake bay reduces 35% of the makespan compared with a nonoptimal bay, and the proposed algorithm is competitive compared with the on-the-shelf MILP solver and can solve medium- and large-scale instances in short computing time with gaps lower than 5% compared with ideal solutions.
[Retracted] Digital Effectiveness in Video Conference Methods on Internet Learning Environments of Higher Education
Authors: Wei Yanhong , Tang Huili
Published: January 2022

Information and communication technologies, especially modern transmission technologies, for instance, teleconferencing, widely known, provide premium educational and pedagogical opportunities, contributing significantly to the implementation of a collaborative learning environment. That the utilization of video conferencing differs significantly from conventional teaching should be emphasized in a way of leading adaptation to the requirements of the educational environment. In general, the digital efficiency of teleconferencing methods needs to be investigated as a very important factor when it is implemented effectively in online learning environments under the title of modern higher education. This paper has aimed at investigating the ways of effective utilization of video conferencing in an online learning environment and presents a case study following the principles of educational neuroscience and distance education that are related to appropriate utilization of available digital technologies to function as educational and learning means to promote the usage in the framework of higher education.
[Retracted] Herding Behavior in China’s Stock Market under the Background of Implementation of the SHKSC Policy
Authors: Gong Weili , Li Silin , Yu Hongxin
Published: January 2022

In the financial market, because of information asymmetry, investors often ignore private information. Herding behavior in financial markets has become an important research branch of behavioral finance. China stock market has implemented the Shanghai‐Hong Kong Stock Connect (SHKSC) policy and introduced mature funds, however, has it alleviated the herd effect? This article empirically verifies the existence of China's stock market before and after the implementation of the SHKSC policy. The research in this article found that the implementation of the SHKSC policy has not changed herding behavior. On the contrary, the herding effect has become more serious. It is further found that investors have no obvious investment preference for the industry to which the stock belongs. Investor investment is in line with the phenomenon of “slow rise and sharp fall.” When stock prices fall, the herd effect of the Chinese stock market is also more obvious.
Melatonin Improves the Resistance of Oxidative Stress-Induced Cellular Senescence in Osteoporotic Bone Marrow Mesenchymal Stem Cells
Authors: Chen Weikai , Lv Nanning , Liu Hao , Gu Chao , Zhou Xinfeng , Qin Wanjin , Chen Angela Carley , Chen Liang , Yang Huilin , Chen Xi , Liu Tao , He Fan
Published: January 2022

Accumulation of senescent bone marrow-derived mesenchymal stem cells (BMMSCs) has led to an age-related bone loss. However, the role of stem cell senescence in estrogen deficiency-induced osteoporosis remains elusive. Though melatonin plays a vital role in bone metabolism regulation, the underlying mechanisms of melatonin-mediated antiosteoporosis are partially elucidated. Therefore, this study purposed to explore (1) whether estrogen deficiency causes cellular senescence of BMMSCs, and if so, (2) the potential of melatonin in preventing bone loss via senescence signaling inhibition. BMMSCs derived from ovariectomized (OVX) rats (OVX BMMSCs) showed an impaired osteogenic capacity, albeit having comparable levels of senescence biomarkers than the sham cells. When exposed to low levels of hydrogen peroxide (H2O2), OVX BMMSCs rapidly exhibited senescence-associated phenotypes such as the increased activity of senescence-associated β-galactosidase (SA-β-gal) and upregulation of cell cycle inhibitors. Notably, the in vitro treatment with melatonin hindered H2O2-induced senescence in OVX BMMSCs and restored their osteogenic capacity. Treatment with either SIRT1 inhibitor (sirtinol) or melatonin receptor antagonists (luzindole and 4-P-PDOT) eliminated melatonin protective effects, thus indicating its potential in preventing stem cell senescence via SIRT1 activation through the melatonin membrane receptors. Following in vivo intravenous administration with melatonin, it successfully protected the bone microstructure and preserved the antisenescence property of BMMSCs in OVX rats. Collectively, our findings demonstrated that melatonin protected against estrogen deficiency-related bone loss by improving the resistance of BMMSCs to cellular senescence. Therefore, melatonin-mediated antisenescence effect on stem cells provides vital information to facilitate the development of a novel and effective strategy for treating postmenopausal OP.
An Effective Data Science Technique for IoT-Assisted Healthcare Monitoring System with a Rapid Adoption of Cloud Computing
Authors: M Abd El-Aziz Rasha , Alanazi Rayan , R Shahin Osama , Elhadad Ahmed , Abozeid Amr , I Taloba Ahmed , Alshalabi Riyad
Published: January 2022

Patients are required to be observed and treated continually in some emergency situations. However, due to time constraints, visiting the hospital to execute such tasks is challenging. This can be achieved using a remote healthcare monitoring system. The proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoT sensors are employed, which collect real healthcare data. These data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. This algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. Next, the optimum feature selection technique for extraction and selection of features is illustrated. A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. The proposed system’s performance is finally examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring.
Evaluation of Meibomian Gland Function after Therapy of Eyelid Tumors at Palpebral Margin with Super Pulse CO2 Laser
Authors: Zhao Songjiao , Duan Jueni , Zhang Jing , Gong Lan
Published: January 2022

Purpose. To investigate the effect on meibomian gland function of super pulse carbon dioxide (CO2) laser excision in the treatment of eyelid tumors at palpebral margin. Methods. 36 patients with 36

eyelid
 
tumor
 
size
≤
1
 
cm

and within 1 mm to palpebral margin were recruited in this study. Of which, 16 cases with tumors in the upper eyelid and 20 cases in the lower eyelid were involved. The eyelid tumors of all the patients were treated by super pulse CO2 laser with its power density varied between 0.6 and 21.1 W/mm2 and in repeat mode. The laser spot size ranged from 120 to 200 μm. Ocular surface parameters including tear film break-up time (BUT) and meibograde, meibum expressibility, and meibum quality were evaluated at pretherapy, 1 week, 1 month, and 3 months posttherapy in all 36 patients. Result. All the patients were satisfied with the therapy. No infective complications and recurrence occurred in any of the 36 patients at the following period. The eyelid wound recovered well with nearly normal appearing after 2 to 3 weeks. The morphology of limbi palpebralis, BUT, meibograde, meibum expressibility, and meibum quality of all the 36 patients showed no significant difference before and after the therapy. Conclusions. Super pulse CO2 laser had no effect on meibomian gland function and morphology in the excision of tumors at palpebral margins, which was an efficacy and well-tolerated therapy with lower complications and recurrence.