bluuebunny/crossref_metadata_2025 huggingface dataset:
Photo-Responsive Micelles with Controllable and Co-Release of Carbon Monoxide, Formaldehyde and Doxorubicin
Authors: Zheng Bin , Yu Lulu , Dong Huaze , Zhu Jinmiao , Yang Liang , Yuan Xinsong
Published: June 2022

Endogenous gases have attracted much attention due to their potent applications in disease therapies. The combined therapy, including gaseous molecules and other medicines that can create synergistic effects, is a new way for future treatment. However, due to the gaseous state, gas utilization in medical service is still limited. To pave the way for future usage, in this work, an amphiphilic block copolymer containing nitrobenzyl ether, 3-hydroxyflavone (3-HF) derivatives and ether linker was constructed. The nitrobenzyl ether group endows the polymer with a photo-responsive character. Upon light illumination, 3-HF derivatives can be triggered for carbon monoxide (CO) release. The ether linker can also be released emitting formaldehyde (FA). The self-assembly induced micelle can encompass medicine, e.g., doxorubicin (DOX), into it and a controlled release of DOX can be realized upon light illumination. As far as we know, there is no report on the combination donor of CO and DOX and this is the first attempt on the co-release of CO, FA and DOX.
Photodegradation and Mineralization of Phenol Using TiO2Coated γ-Al2O3: Effect of Thermic Treatment
Authors: Martinez-Gómez Claudia , Rangel-Vazquez Israel , Zarraga Ramon , del Ángel Gloria , Ruíz-Camacho Beatriz , Tzompantzi Francisco , Vidal-Robles Esmeralda , Perez-Larios Alejandro
Published: June 2022

It is well-known that γ-Al2O3 possesses large, specific areas and high thermal, chemical, and mechanical resistance. Due to this, it is the most-used support for catalysts, in this case TiO2, as it enables it to achieve better dispersion and improves the activity in catalytic photodegradation reactions. In a previous work, it was observed that the optimal content of TiO2 in γ-Al2O3 was around 15% since the degradation of phenol results were maximized and a synergistic effect was generated by the interaction of both oxides. In addition, an increase in acidity crystal size and the generation of localized, oxygen-vacant, electronic states in the forbidden band of γ-Al2O3, were observed. This study focuses on the effect of the calcination temperature on a γ-Al2O3-TiO2 catalyst (15% w/w of TiO2) and its impact on photocatalytic activity. The catalysts prepared here were characterized by X-ray diffraction, N2 adsorption–desorption, FTIR-pyridine adsorption, MAS-NMR, HRTEM-FFT, UV-vis, and fluorescence spectroscopy.
Application of Computer Microtomography and Hyperspectral Imaging to Assess the Homogeneity of the Distribution of Active Ingredients in Functional Food
Authors: Błoński Bartosz , Wilczyński Sławomir , Stolecka-Warzecha Anna
Published: June 2022

Functional foods represent one of the most intensively investigated and widely promoted areas in the food and nutrition sciences’ market today. The purpose of this work is to determine the possibility of using computed microtomography to assess the homogeneity of distribution of active pharmaceutical ingredients (vitamins K and D and calcium) throughout chocolate. Algorithms for analyzing of microtomographic images were proposed to quantify the distribution of active pharmaceutical ingredients (API) in chocolate: the Gray Level Co-Occurrence Matrix, quadtree decomposition and hyperspectral imaging. The use of the methods of analysis and processing of microtomographic images allows for a quantitative assessment of the homogeneity of the distribution of components throughout the sample, without a 3D reconstruction process. In computer microtomography analysis, it is possible to assess the distribution of those components whose density differs by at least a unit in the accepted scale of gray levels of images and for grain sizes not smaller than the voxel size. The proposed image analysis algorithms, Gray Level Co-Occurrence Matrix, quadtree decomposition and hyperspectral imaging, allow for the assessment of distribution of active ingredients in chocolate.
Assessing Alternative Supporting Organic Materials for the Enhancement of Water Reuse in Subsurface Constructed Wetlands Receiving Acid Mine Drainage
Authors: Oberholzer Martha M. , Oberholster Paul J. , Ndlela Luyanda L. , Botha Anna-Maria , Truter Johannes C.
Published: June 2022

Acid mine drainage (AMD) is a global problem with severe consequences for the environment. South Africa’s abandoned mines are a legacy from the country’s economic dependence on the mining sector, with consequent negative impacts on ecosystems. AMD remediation includes active and passive techniques. Constructed wetlands (a passive technique) have lower operational costs but require larger spaces and longer timeframes to achieve the remediation of AMD, and are supported by anaerobic sulphate-reducing bacteria (SRB), which capable of remediating high-sulphate-laden AMD while precipitating dissolved metals from the AMD. Organic substrates supporting these activities are often the limiting factor. When enhancing existing passive AMD remediation technologies, alternative waste material research that may support SRB activity is required to support the circular economy through the reduction in waste products. Chicken feathers show potential as a substrate enhancer, boosting organic carbon availability to SRB, which sustains passive AMD treatment processes by achieving pH elevation, sulphate and metal reductions in AMD water for reuse. Microbial biodiversity is essential to ensure the longevity of passive treatment systems, and chicken feathers are proven to have an association with SRB microbial taxa. However, the longer-term associations between the AMD water parameters, microbial diversity and the selected substrates remain to be further investigated.
Kyoto’s Gion Festival in Late Classical and Medieval Times: Actors, Legends, and Meanings
Authors: Teeuwen Mark
Published: June 2022

Kyoto’s Gion festival has arguably the best-documented history of all festivals (sairei) in Japan, and studies of its development have heavily influenced our understanding of festivals in general. Yet we must expect that our knowledge of this history is partial at most. Extant archives on its late classical and medieval history derive from a narrow group of festival actors, and are therefore intrinsically biased. This article looks at current reconstructions of the festival’s origin and development, addressing primarily the following questions: Which groups of actors are the historical record hiding from us? Is there a world of ritual action, beliefs, and concerns that we are missing entirely? Origin legends have been used throughout history to attribute meaning to the festival procedures. Today as in the past, these legends are always accompanied by narratives of continuity: at its core, it is implied, the festival remains unchanged. Such legends reflect the interests of actors and patrons of different ages, and changes in the festival’s context have required origin tales to be updated or even replaced. What do such narrative innovations reveal about the festival’s changing place in society at different historical junctures? Do such legends contain traces of the activities of actors who have since disappeared, taking their archives with them?
Mark’s Endings in Context: Paratexts and Codicological Remarks
Authors: Monier Mina
Published: June 2022

This article addresses the problem of the perception of Mark’s endings as expressed in its manuscript tradition. I will argue that, unlike the modern standardized image, manuscript evidence offers a complex phenomenon in which the endings were perceived in diverse ways that move across the threshold that separates a text from paratexts. Further, the manuscripts show an influence between the endings and their associated paratexts. I will show this phenomenon by examining (i) the hypotheses before the Gospel, (ii) marginalia that engaged Mark 16, and (iii) postscripts after the Gospel. In conclusion, the article recommends revisiting the standard perception of the “endings” within their larger paratextual ecosystem.
Air Pollution and Mortality Impacts
Authors: Dong Zhe Michelle , Shang Han Lin , Bruhn Aaron
Published: June 2022

This study quantifies the air quality impact on population mortality from an actuarial perspective, considering implications to the industry through the application of findings. The study focuses on the increase in mortality from air quality changes due to extreme weather impacts. We conduct an empirical study using monthly Californian climate and mortality data from 1999 to 2019 to determine whether adding PM2.5 as a factor improves forecast excess mortality. Expected mortality is defined using the rolling five-year average of observed mortality for each county. We compared three statistical models, namely a Generalised Linear Model (GLM), a Generalised Additive Model (GAM), and an Extreme Gradient Boosting (XGB) regression model. We find including PM2.5 improves the performance of all three models and that the GAM performs the best in terms of predictive accuracy. Change points are also considered to determine whether significant events trigger changes in mortality over extended periods. Based on several identified change points, some wildfires trigger heightened excess mortality.
High-Precision Depth Domain Migration Method in Imaging of 3D Seismic Data in Coalfield
Authors: Han Jianguang , Gu Bingluo , Zhu Guanghui , Liu Zhiwei
Published: June 2022

Fault structures developed in coal seams, which are often associated with roof collapse, water inrush, gas outburst, and other accidents, are common geological hazards in coal exploration and development. The accurate detection of micro-structures such as small faults has always been a research focus to ensure safety in coalfields. Three-dimensional (3D) seismic research is one of the most efficient methods for obtaining the structural characteristics of coal areas and identify small faults in coal seams, but it is difficult for traditional seismic data imaging technologies to meet the high-precision demand of current coal exploration. Aiming at the characteristics of 3D seismic data in coalfields, we calculated the difference coefficients based on the optimized inversion algorithm and proposed a variable-density acoustic equation optimized with a temporal–spatial staggered-grid finite difference forward algorithm. On this basis, by combining normalized cross-correlation imaging conditions and GPU/CPU collaborative parallel processing technology, we developed an efficient and high-precision 3D reverse time migration method suitable for 3D seismic data in coalfields. Numerical tests verified the accuracy and efficiency of the proposed migration method for the imaging of coal-measure strata with small fault structures and could effectively identify 5 m small faults in the coal seam. The migration test of 3D seismic data in real coalfields showed that our 3D reverse time migration method has good practicability for high-precision imaging of 3D seismic data in coalfields and is an effective method for the precise imaging of small faults in coal measures.
A New Probability Distribution for SAR Image Modeling
Authors: Sagrillo Murilo , Guerra Renata R. , Bayer Fábio M. , Machado Renato
Published: June 2022

This article introduces exponentiated transmuted-inverted beta (ET-IB) distribution, supported by a continuous positive real line, as a synthetic aperture radar (SAR) imagery descriptor. It is an extension of the inverted beta distribution, an important texture model for SAR imagery. The considered distribution extension approach increases the flexibility of the baseline distribution, and is a new probabilistic model useful in SAR image applications. Besides introducing the new model, the maximum likelihood method is discussed for parameter estimation. Numerical experiments are performed to validate the use of the ET-IB distribution as a SAR amplitude image descriptor. Finally, three measured SAR images referring to forest, ocean, and urban regions are considered, and the performance of the proposed distribution is compared to distributions usually considered in this field. The proposed distribution outperforms the competitor models for modeling SAR images in terms of some selected goodness-of-fit measures. The results show that the ET-IB distribution is suitable as a SAR descriptor and can be used to develop image-processing tools in remote sensing applications.
BPCNN: Bi-Point Input for Convolutional Neural Networks in Speaker Spoofing Detection
Authors: Yoon Sunghyun , Yu Ha-Jin
Published: June 2022

We propose a method, called bi-point input, for convolutional neural networks (CNNs) that handle variable-length input features (e.g., speech utterances). Feeding input features into a CNN in a mini-batch unit requires that all features in each mini-batch have the same shape. A set of variable-length features cannot be directly fed into a CNN because they commonly have different lengths. Feature segmentation is a dominant method for CNNs to handle variable-length features, where each feature is decomposed into fixed-length segments. A CNN receives one segment as an input at one time. However, a CNN can consider only the information of one segment at one time, not the entire feature. This drawback limits the amount of information available at one time and consequently results in suboptimal solutions. Our proposed method alleviates this problem by increasing the amount of information available at one time. With the proposed method, a CNN receives a pair of two segments obtained from a feature as an input at one time. Each of the two segments generally covers different time ranges and therefore has different information. We also propose various combination methods and provide a rough guidance to set a proper segment length without evaluation. We evaluate the proposed method on the spoofing detection tasks using the ASVspoof 2019 database under various conditions. The experimental results reveal that the proposed method reduces the relative equal error rate (EER) by approximately 17.2% and 43.8% on average for the logical access (LA) and physical access (PA) tasks, respectively.
Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods
Authors: Braz Fernando José , Ferreira João , Gonçalves Francisco , Weege Kawan , Almeida João , Baldo Fabiano , Gonçalves Pedro
Published: June 2022

Forecasting road flow has strong importance for both allowing authorities to guarantee safety conditions and traffic efficiency, as well as for road users to be able to plan their trips according to space and road occupation. In a summer resort, such as beaches near cities, traffic depends directly on weather conditions, variables that should be of great impact on the quality of forecasts. Will the use of a dataset with information on transit flows enhanced with meteorological information allow the construction of a precise traffic flow forecasting model, allowing predictions to be made in advance of the traffic flow in suitable time? The present work evaluates different machine learning methods, namely long short-term memory, autoregressive LSTM, and a convolutional neural network, and data attributes to predict traffic flows based on radar and meteorological sensor information. The models trained to predict the traffic flow have shown that weather conditions were essential for this forecast, and thus, these variables were employed in the evaluated deep-learning models. The results pointed out that it is possible to forecast the traffic flow at a reasonable error level for one-hour periods, and the CNN model presented the lowest prediction error values and consumed the least time to build its predictions.
TLS-Detectable Plane Changes for Deformation Monitoring
Authors: Kregar Klemen , Marjetič Aleš , Savšek Simona
Published: June 2022

TLS is nowadays often used for deformation monitoring. As it is not able to scan identical points in different time epochs, mathematical models of objects derived from point clouds have to be used. The most common geometric form to describe built objects is a plane, which can be described by four parameters. In this study, we aimed to find out how small changes in the parameters of the plane can be detected by TLS. We aimed to eliminate all possible factors that influence the scanning. Then, we shifted and tilted a finite physical representation of a plane in a controlled way. After each controlled change, the board was scanned several times and the parameters of the plane were calculated. We used two different types of scanning devices and compared their performance. The changes in the plane parameters were compared with the actual change values and statistically tested. The results show that TLS detects shifts in the millimetre range and tilts of 150″ (for a 1 m plane). A robotic total station can achieve twice the precision of TLS despite lower density and slower performance. For deformation monitoring, we strongly recommend repeating each scan several times (i) to check for gross errors and (ii) to obtain a realistic precision estimate.
Adversarial Autoencoder and Multi-Armed Bandit for Dynamic Difficulty Adjustment in Immersive Virtual Reality for Rehabilitation: Application to Hand Movement
Authors: Kamikokuryo Kenta , Haga Takumi , Venture Gentiane , Hernandez Vincent
Published: June 2022

Motor rehabilitation is used to improve motor control skills to improve the patient’s quality of life. Regular adjustments based on the effect of therapy are necessary, but this can be time-consuming for the clinician. This study proposes to use an efficient tool for high-dimensional data by considering a deep learning approach for dimensionality reduction of hand movement recorded using a wireless remote control embedded with the Oculus Rift S. This latent space is created as a visualization tool also for use in a reinforcement learning (RL) algorithm employed to provide a decision-making framework. The data collected consists of motions drawn with wireless remote control in an immersive VR environment for six different motions called “Cube”, “Cylinder”, “Heart”, “Infinity”, “Sphere”, and “Triangle”. From these collected data, different artificial databases were created to simulate variations of the data. A latent space representation is created using an adversarial autoencoder (AAE), taking into account unsupervised (UAAE) and semi-supervised (SSAAE) training. Then, each test point is represented by a distance metric and used as a reward for two classes of Multi-Armed Bandit (MAB) algorithms, namely Boltzmann and Sibling Kalman filters. The results showed that AAE models can represent high-dimensional data in a two-dimensional latent space and that MAB agents can efficiently and quickly learn the distance evolution in the latent space. The results show that Sibling Kalman filter exploration outperforms Boltzmann exploration with an average cumulative weighted probability error of 7.9 versus 19.9 using the UAAE latent space representation and 8.0 versus 20.0 using SSAAE. In conclusion, this approach provides an effective approach to visualize and track current motor control capabilities regarding a target in order to reflect the patient’s abilities in VR games in the context of DDA.
QADI as a New Method and Alternative to Kappa for Accuracy Assessment of Remote Sensing-Based Image Classification
Authors: Feizizadeh Bakhtiar , Darabi Sadrolah , Blaschke Thomas , Lakes Tobia
Published: June 2022

Classification is a very common image processing task. The accuracy of the classified map is typically assessed through a comparison with real-world situations or with available reference data to estimate the reliability of the classification results. Common accuracy assessment approaches are based on an error matrix and provide a measure for the overall accuracy. A frequently used index is the Kappa index. As the Kappa index has increasingly been criticized, various alternative measures have been investigated with minimal success in practice. In this article, we introduce a novel index that overcomes the limitations. Unlike Kappa, it is not sensitive to asymmetric distributions. The quantity and allocation disagreement index (QADI) index computes the degree of disagreement between the classification results and reference maps by counting wrongly labeled pixels as A and quantifying the difference in the pixel count for each class between the classified map and reference data as Q. These values are then used to determine a quantitative QADI index value, which indicates the value of disagreement and difference between a classification result and training data. It can also be used to generate a graph that indicates the degree to which each factor contributes to the disagreement. The efficiency of Kappa and QADI were compared in six use cases. The results indicate that the QADI index generates more reliable classification accuracy assessments than the traditional Kappa can do. We also developed a toolbox in a GIS software environment.
Near Field 3-D Millimeter-Wave SAR Image Enhancement and Detection with Application of Antenna Pattern Compensation
Authors: Song Shaoqiu , Lu Jie , Xing Shiqi , Quan Sinong , Wang Junpeng , Li Yongzhen , Lian Jing
Published: June 2022

In this paper, a novel near-field high-resolution image focusing technique is proposed. With the emergence of Millimeter-wave (mmWave) devices, near-field synthetic aperture radar (SAR) imaging is widely used in automotive-mounted SAR imaging, UAV imaging, concealed threat detection, etc. Current research is mainly confined to the laboratory environment, thus ignoring the adverse effects of the non-ideal experimental environment on imaging and subsequent detection in real scenarios. To address this problem, we propose an optimized Back-Projection Algorithm (BPA) that considers the loss path of signal propagation among space by converting the amplitude factor in the echo model into a beam-weighting. The proposed algorithm is an image focusing algorithm for arbitrary and irregular arrays, and effectively mitigates sparse array imaging ghosts. We apply the 3DRIED dataset to construct image datasets for target detection, comparing the kappa coefficients of the proposed scheme with those obtained from classic BPA and Range Migration Algorithm (RMA) with amplitude loss compensation. The results show that the proposed algorithm attains a high-fidelity image reconstruction focus.
Considerations and Limits of Embedding Sensor Nodes for Structural Health Monitoring into Fiber Metal Laminates
Authors: Bornemann Sarah , Lang Walter
Published: June 2022

The objective of this article is to present the results of our investigations concerning the environmental conditions that can be expected during the embedding process into fibre metal laminates and the consequences for a sensor node for structural health monitoring. The idea behind this investigation is to determine for which manufacturing conditions the integration of sensor nodes into the material can be done and to identify limits for this. The sensor nodes consist of commercially available integrated circuits and passive components soldered onto an adhesive-less flexible printed circuit board. They are tested under conditions above their specified limits, to find out if they are still working reliably after experiencing 155 min of 180 ∘C and 7 bar of pressure. Apart from occurring temperature damage, the effect of surrounding fibres potentially pushing away the components under the amount of pressure of the manufacturing process, as well as the potential of shorts due to conductive fibers are investigated and suitable solutions to prevent this are evaluated. One experiment exceeding the typical requirements of a fiber metal laminate embedding process for structural components will be conducted at 250 ∘C for 10 h, in order to determine the limits of embedding electronic sensor nodes. This time and temperature combination is expected to cause irreversible damage to the electronic system. Results show that it is possible to integrate electronics into materials under conditions far above their specifications when precautions are taken but also that there are limits that must not be exceeded during the embedding process.
GPU-Accelerated PD-IPM for Real-Time Model Predictive Control in Integrated Missile Guidance and Control Systems
Authors: Lee Sanghyeon , Lee Heoncheol , Kim Yunyoung , Kim Jaehyun , Choi Wonseok
Published: June 2022

This paper addresses the problem of real-time model predictive control (MPC) in the integrated guidance and control (IGC) of missile systems. When the primal-dual interior point method (PD-IPM), which is a convex optimization method, is used as an optimization solution for the MPC, the real-time performance of PD-IPM degenerates due to the elevated computation time in checking the Karush–Kuhn–Tucker (KKT) conditions in PD-IPM. This paper proposes a graphics processing unit (GPU)-based method to parallelize and accelerate PD-IPM for real-time MPC. The real-time performance of the proposed method was tested and analyzed on a widely-used embedded system. The comparison results with the conventional PD-IPM and other methods showed that the proposed method improved the real-time performance by reducing the computation time significantly.
Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography
Authors: Ye-Lin Yiyao , Prats-Boluda Gema , Galiano-Botella Marina , Roldan-Vasco Sebastian , Orozco-Duque Andres , Garcia-Casado Javier
Published: June 2022

Swallowing is a complex sequence of highly regulated and coordinated skeletal and smooth muscle activity. Previous studies have attempted to determine the temporal relationship between the muscles to establish the activation sequence pattern, assessing functional muscle coordination with cross-correlation or coherence, which is seriously impaired by volume conduction. In the present work, we used conditional Granger causality from surface electromyography signals to analyse the directed functional coordination between different swallowing muscles in both healthy and dysphagic subjects ingesting saliva, water, and yoghurt boluses. In healthy individuals, both bilateral and ipsilateral muscles showed higher coupling strength than contralateral muscles. We also found a dominant downward direction in ipsilateral supra and infrahyoid muscles. In dysphagic subjects, we found a significantly higher right-to-left infrahyoid, right ipsilateral infra-to-suprahyoid, and left ipsilateral supra-to-infrahyoid interactions, in addition to significant differences in the left ipsilateral muscles between bolus types. Our results suggest that the functional coordination analysis of swallowing muscles contains relevant information on the swallowing process and possible dysfunctions associated with dysphagia, indicating that it could potentially be used to assess the progress of the disease or the effectiveness of rehabilitation therapies.
The Impact of Using Novel Equations to Predict Nitrogen Excretion and Associated Emissions from Pasture-Based Beef Production Systems
Authors: Angelidis Angelos E. , McAuliffe Graham A. , Takahashi Taro , Crompton Les , Yan Tianhai , Reynolds Christopher K. , Stergiadis Sokratis , Misselbrook Tom
Published: June 2022

The excretion of nitrogen (N) in faeces and urine from beef cattle contributes to atmospheric pollution through greenhouse gas and ammonia emissions and eutrophication of land and aquatic habitats through excessive N deposition and nitrate leaching to groundwater. As N excretion by beef cattle is rarely measured directly, it is important to accurately predict losses by utilising a combined knowledge of diet and production parameters so that the effect of dietary changes on the potential environmental impact of beef production systems can be estimated. This study aimed to identify differences between IPCC and more detailed country-specific models in the prediction of N excretion and N losses at a system level and determine how the choice of model influences the interpretation of differences in diet at the system scale. The data used in this study were derived from a farm-scale experimental system consisting of three individual grazing farms, each with a different sward type: a permanent pasture, a high sugar ryegrass monoculture, and a high sugar ryegrass with white clover (~30% groundcover). Data were analysed using a mixed linear model (residual maximum likelihood analysis). The IPCC methods demonstrated significantly lower estimates of N excretion than country-specific models for the first housing period and significantly greater losses for the grazing and second housing periods. The country-specific models enabled prediction of N partitioning to urine and faeces, which is important for estimation of subsequent N losses through the production system, although the models differed in their estimates. Overall, predicted N losses were greater using the IPCC approaches compared to using more detailed country-specific approaches. The outcomes of the present study have highlighted that different models can have a substantial impact on the predicted N outputs and subsequent losses to the environment for pasture-based beef finishing systems, and the importance, therefore, of using appropriate models and parameters.
Low-Carbon Transition and Green Innovation: Evidence from Pilot Cities in China
Authors: Wang Taohong , Song Zhe , Zhou Jing , Sun Huaping , Liu Fengqin
Published: June 2022

Officially launched in 2008, China’s low-carbon city pilot project is aimed at creating green and low-carbon cities by restricting individual consumption and enterprise production behaviors as a means of controlling greenhouse gas emissions. Among other indicators, the impact of the pilot low-carbon initiative may be evaluated based on whether it induces enterprises to engage in green technology innovation. Using green patent application data from Chinese listed companies between 2009 and 2018, this paper applies a time-varying difference-in-difference (DID) model to conduct a multi-dimensional empirical test on the changes in listed companies’ degrees of green innovation before and after the publication of the list of three batches of pilot cities. Our findings were as follows: first, as a means of environmental regulation, the pilot low-carbon city initiative’s effect on enterprises’ green technology innovation conforms to the Porter hypothesis—that is, it encourages enterprises to improve their production technology and enhances the green innovation levels of listed companies in pilot cities; second, in terms of regional differences, the low-carbon cities pilot initiative can significantly induce green innovation activities among enterprises in China’s eastern region, but not in the central or western regions; third, from the perspective of enterprise ownership, the initiative promotes greater awareness of green innovation among non-state-owned enterprises than among state-owned enterprises. At the enterprise level, this paper provides theoretical support and empirical evidence for the success of the low-carbon city pilot initiative and highlights the implications for nationwide policy.
Analysis of the Impact of the COVID-19 Pandemic on the Future of Public Transport: Example of Warsaw
Authors: Bauer Marek , Bauer Kinga
Published: June 2022

The COVID-19 pandemic is completely changing the transport customs of city residents. It has decreased the number of travels and has affected changes in the division of transport means. This article presents a case study of the city of Warsaw, attempting to describe the process of changes in the use of public transport in daily trips in the following months of the pandemic. Statistical data on the public transport offer, number of passengers, and tickets sold in 2017–2021, which are available in monthly and annual bulletins issued by the public transport organizer, were used. The knowledge base was supplemented with the results of surveys conducted among the city’s residents. The obtained data were organized and analyzed using descriptive statistics methods. The study findings reveal that the lower use of public transport for travel during the COVID-19 pandemic is mainly due to the imposed limits on the number of passengers and is also linked to changes in the structure of the tickets purchased, especially a significant decrease in the sale of long-distance tickets, which implies the loss of a significant number of most valuable, regular users. It was also observed that the appraisal of public transport did not deteriorate, which allowed to expect with optimism the return of passengers after the pandemic. Therefore, a package of possible steps to be taken to restore confidence in public transport and to enable return of lost passengers is presented. The results of the analyses show how easily passengers can be lost and why it is so important to ensure the functioning of public transport even in crisis situations such as a pandemic. These results can also be applied in transport policy updates.
Interactive Effect of Biochar and Bio-Compost on Starting Growth and Physiologic Parameters of Argan
Authors: El Moussaoui Hassan , Bouqbis Laila
Published: June 2022

The argan tree, which is found in southern Morocco, is characterized by environmental, economic and nutritional benefits, but the growth of this tree is very slow. This makes it necessary to find methods to accelerate its growth. A pot experiment was conducted to evaluate the effects of biochar (BC) and bio-compost (CP) each applied at the rate of 0, 3 and 6% (M/M) on starting growth of argan in fine silty soil for sixteen months. Main stem length, diameter, durability ratio, total length of all stems and number of sprouted shoots were measured every two months with two photosynthetic measurements spaced five months apart for each argan seedling. Despite the strong signs of epigenetic sensitivity and genetic variability across the argan behavior of each treatment depending on the duration and environmental conditions of the crop and the large standard deviations marked in all the tests that were conducted on the argan, some treatments showed interesting results, even in terms of the interaction between climatic conditions, type of treatment and type of test. The argan plants which were grown in the substrate at 6% BC 3% CP showed significant results for all the growth parameters studied and throughout the test. This mixture marked an average water holding capacity (WHC) of around 0.66 g H2O/g dw; the argan seedlings showed the best perimeter average, which exceeded 2.7 cm in the last measurement, with a ratio (height/diameter) strictly less than 7, which removes any possible problem of argan filiform. However, argan plants from all treatments were not stable in the growth characteristics studied; each treatment has advantages and disadvantages regarding argan. Transplantation and monitoring in the field of argan seedlings that have had interesting results are strongly recommended to see if the good starting growth influences their development in the field or if it is a temporary effect.
Urban Intelligence for Carbon Neutral Cities: Creating Synergy among Data, Analytics, and Climate Actions
Authors: Lai Yuan
Published: June 2022

Cities are critical research subjects in carbon neutrality, considering they produce more than 70% of greenhouse gas emissions and their crucial role in taking climate actions. The pathway towards a greener society requires consensus, intelligence, and actions among global cities as a network of carbon neutral cities (CNC). Considering cities as complex system-of-systems, synergy among various sub-systems can create co-benefits through the progress towards carbon neutrality. Large volume, velocity, and variety of urban data provide new opportunities for quantifying, analyzing, and visualizing environmental–social–technical dynamics in urban systems. Rich data resources, advanced analytics, and climate actions collectively enable urban intelligence by leveraging data from heterogeneous sources with different spatial granularity and temporal frequency. Such intelligence can promote synergy across sub-systems and domains to support more responsive, precise, proactive planning, policy, and managerial actions. With a discussion on future innovation in urban intelligence for CNC, this paper presents conclusions on how urban intelligence can promote a smarter and greener society.
Optimal Design and Analysis for a New 1-DOF Compliant Stage Based on Additive Manufacturing Method for Testing Medical Specimens
Authors: Dang Minh Phung , Le Hieu Giang , Tran Nguyen Thanh Duy , Chau Ngoc Le , Dao Thanh-Phong
Published: June 2022

In situ nanoindentation is extensively employed for online observing deformation and mechanical behaviors of bio-materials. However, the existing designs of the positioning stages have limited performances for testing soft or hard biomaterials. Consequently, this paper proposes a new structural design of a compliant one degree of freedom (01-DOF) stage with faster response. In addition to a new design, this article applies an analytical method to estimate the kinematic and dynamic behaviors of the stage. Firstly, the 01-DOF stage is designed with two modules, including a displacement amplifier with six levers and a symmetric parallelogram mechanism. Secondly, a kinetostatic diagram of the stage is built by pseudo-rigid-body method. Then, the dynamic equation of the proposed stage is formulated using the Lagrange method. In order to speed up the response of the indentation system, the structural optimization of the stage is conducted via the Firefly algorithm. The results showed that the theoretical first-order resonant frequency is found at about 226.8458 Hz. The theoretical consequences are nearby to the verified simulation. Besides, this achieved frequency of the presented stage is greater than that of previous stages. In an upcoming study, the prototype will be fabricated by additive manufacturing method or a computerized wire cutting method in order to verify the analytical results with experimental results.
Monitoring the Ratio of Two Normal Variables Based on Triple Exponentially Weighted Moving Average Control Charts with Fixed and Variable Sampling Intervals
Authors: Hu Xuelong , Sun Guan , Xie Fupeng , Tang Anan
Published: June 2022

In statistical process control (SPC), the ratio of two normal random variables (RZ) is a valuable statistical indicator to be taken as the charting statistic. In this work, we propose a triple exponentially weighted moving average (TEWMA) chart for monitoring the RZ. Additionally, the variable sampling interval (VSI) strategy has been adopted to different control charts by researchers. With the application of this strategy, the VSI-TEWMA-RZ chart is then developed to further improve the performance of the proposed TEWMA-RZ chart. The run length (RL) properties of the proposed TEWMA-RZ and VSI-TEWMA-RZ charts are obtained by the widely used Monte-Carlo (MC) simulations. Through the comparisons with the VSI-EWMA-RZ and the VSI-DEWMA-RZ charts, the VSI-TEWMA-RZ chart is statistically more sensitive than the VSI-EWMA-RZ and the VSI-DEWMA-RZ charts in detecting small and moderate shifts. Moreover, it turned out that the VSI-TEWMA-RZ chart has better performance than the TEWMA-RZ chart on the whole. Furthermore, this paper illustrates the implementation of the proposed charts with an example from the food industry.
Explainable AI (XAI) Applied in Machine Learning for Pain Modeling: A Review
Authors: Madanu Ravichandra , Abbod Maysam F. , Hsiao Fu-Jung , Chen Wei-Ta , Shieh Jiann-Shing
Published: June 2022

Pain is a complex term that describes various sensations that create discomfort in various ways or types inside the human body. Generally, pain has consequences that range from mild to severe in different organs of the body and will depend on the way it is caused, which could be an injury, illness or medical procedures including testing, surgeries or therapies, etc. With recent advances in artificial-intelligence (AI) systems associated in biomedical and healthcare settings, the contiguity of physician, clinician and patient has shortened. AI, however, has more scope to interpret the pain associated in patients with various conditions by using any physiological or behavioral changes. Facial expressions are considered to give much information that relates with emotions and pain, so clinicians consider these changes with high importance for assessing pain. This has been achieved in recent times with different machine-learning and deep-learning models. To accentuate the future scope and importance of AI in medical field, this study reviews the explainable AI (XAI) as increased attention is given to an automatic assessment of pain. This review discusses how these approaches are applied for different pain types.
Magnetic Susceptibility Source Separation Solely from Gradient Echo Data: Histological Validation
Authors: Dimov Alexey V. , Gillen Kelly M. , Nguyen Thanh D. , Kang Jerry , Sharma Ria , Pitt David , Gauthier Susan A. , Wang Yi
Published: June 2022

Quantitative susceptibility mapping (QSM) facilitates mapping of the bulk magnetic susceptibility of tissue from the phase of complex gradient echo (GRE) MRI data. QSM phase processing combined with an R2* model of magnitude of multiecho gradient echo data (R2*QSM) allows separation of dia- and para-magnetic components (e.g., myelin and iron) that contribute constructively to R2* value but destructively to the QSM value of a voxel. This R2*QSM technique is validated against quantitative histology—optical density of myelin basic protein and Perls’ iron histological stains of rim and core of 10 ex vivo multiple sclerosis lesions, as well as neighboring normal appearing white matter. We found that R2*QSM source maps are in good qualitative agreement with histology, e.g., showing increased iron concentration at the edge of the rim+ lesions and myelin loss in the lesions’ core. Furthermore, our results indicate statistically significant correlation between paramagnetic and diamagnetic tissue components estimated with R2*QSM and optical densities of Perls’ and MPB stains. These findings provide direct support for the use of R2*QSM magnetic source separation based solely on GRE complex data to characterize MS lesion composition.
Gene Expression Analysis of the Stress Response to Lithium, Nickel, and Zinc in Paracentrotus lividus Embryos
Authors: Bonaventura Rosa , Costa Caterina , Deidda Irene , Zito Francesca , Russo Roberta
Published: June 2022

Many anthropogenic pollutants such as metals are discharged into the marine environment through modern sources. Among these, lithium (Li), nickel (Ni), and zinc (Zn) can interfere with biological processes in many organisms when their concentration rises. These metals are toxic to sea urchin embryos, affecting their development. Indeed, animal/vegetal and dorso/ventral embryonic axes are differently perturbed: Li is a vegetalizing agent, Ni can disrupt dorso-ventral axis, Zn can be animalizing. To address the molecular response adopted by embryos to cope with these metals or involved in the gene networks regulating embryogenesis, and to detect new biomarkers for evaluating hazards in polluted environments in a well-known in vivo model, we applied a high-throughput screening approach to sea urchin embryos. After fertilization, Paracentrotus lividus embryos were exposed to Li, Ni, and Zn for 24/48 h. At both endpoints, RNAs were analyzed by NanoString nCounter technology. By in silico analyses, we selected a panel of 127 transcripts encoding for regulatory and structural proteins, ranked in categories: Apoptosis, Defense, Immune, Nervous, Development, and Biomineralization. The data analysis highlighted the dysregulation of many genes in a metal-dependent manner. A functional annotation analysis was performed by the KEEG Orthology database. This study provides a platform for research on metals biomarkers in sea urchins.
Epidemiology and Response to the COVID-19 Pandemic in Kerala, India, 2020–2021: A Cross-Sectional Study
Authors: Jaya Ajan Maheswaran , Harries Anthony D. , Rahman Anisur , Khogali Mohammed , Chinnakali Palanivel , Gopalakrishnan Lakshmi Geetha , Pillai Mahesh Narayana
Published: June 2022

Kerala, a southern state in India, experienced a slightly different COVID-19 pandemic than the rest of India. Using data from daily COVID-19 bulletins and two other Kerala health information systems, this study reported on epidemiological characteristics and response measures of the COVID-19 pandemic between January 2020 and December 2021. After the first six months, Kerala experienced three distinct phases, with COVID-19 cases peaking in October 2020, May 2021, and August 2021. This contrasts with India, which experienced two main peaks in September 2020 and May 2021. The demographic profile of cases aligned with the national profile except for a slight increase of COVID-19 in persons aged ≥60 years. Monthly COVID-19 deaths increased dramatically from May 2021 onwards in line with case numbers but also reflecting changes in definitions of COVID-19 deaths. Case fatality for the two years was significantly higher in males than females, increased with increasing age groups, and varied between districts (p < 0.001). Trends in bed occupancy in field hospitals, hospitals for severe disease, intensive care units, and mechanical ventilation mirrored the different phases of the pandemic. The monitoring system in Kerala allowed certain aspects of the pandemic to be mapped, but it would benefit from further strengthening.
SARS-CoV-2 Viral Load in the Pulmonary Compartment of Critically Ill COVID-19 Patients Correlates with Viral Serum Load and Fatal Outcomes
Authors: Ynga-Durand Mario , Maaß Henrike , Milošević Marko , Krstanović Fran , Pribanić Matešić Marina , Jonjić Stipan , Protić Alen , Brizić Ilija , Šustić Alan , Čičin-Šain Luka
Published: June 2022

While SARS-CoV-2 detection in sputum and swabs from the upper respiratory tract has been used as a diagnostic tool, virus quantification showed poor correlation to disease outcome and thus, poor prognostic value. Although the pulmonary compartment represents a relevant site for viral load analysis, limited data exploring the lower respiratory tract is available, and its association to clinical outcomes is relatively unknown. Using bronchoalveolar lavage (BAL) and serum samples, we quantified SARS-CoV-2 copy numbers in the pulmonary and systemic compartments of critically ill patients admitted to the intensive care unit of a COVID-19 referral hospital in Croatia during the second and third pandemic waves. Clinical data, including 30-day survival after ICU admission, were included. We found that elevated SARS-CoV-2 copy numbers in both BAL and serum samples were associated with fatal outcomes. Remarkably, the highest and earliest viral loads after initiation of mechanical ventilation support were increased in the non-survival group. Our results imply that viral loads in the lungs contribute to COVID-19 disease severity, while blood titers correlate with lung virus titers, albeit at a lower level. Moreover, they suggest that BAL SARS-CoV-2 copy number quantification at ICU admission may provide a predictive parameter of clinical COVID-19 outcomes.