bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Object Detection in SAR Images Based on Computation-Aware Neural Architecture Search
Authors: Li Chuanyou , Li Yifan , Hu Huanyun , Shang Jiangwei , Zhang Kun , Qian Lei , Wang Kexiang
Published: October 2022

Remote sensing techniques are becoming more sophisticated as radar imaging techniques mature. Synthetic aperture radar (SAR) can now provide high-resolution images for day-and-night earth observation. Detecting objects in SAR images is increasingly playing a significant role in a series of applications. In this paper, we address an edge detection problem that applies to scenarios with ship-like objects, where the detection accuracy and efficiency must be considered together. The key to ship detection lies in feature extraction. To efficiently extract features, many existing studies have proposed lightweight neural networks by pruning well-known models in the computer vision field. We found that although different baseline models have been tailored, a large amount of computation is still required. In order to achieve a lighter neural network-based ship detector, we propose Darts_Tiny, a novel differentiable neural architecture search model, to design dedicated convolutional neural networks automatically. Darts_Tiny is customized from Darts. It prunes superfluous operations to simplify the search model and adopts a computation-aware search process to enhance the detection efficiency. The computation-aware search process not only integrates a scheme cutting down the number of channels on purpose but also adopts a synthetic loss function combining the cross-entropy loss and the amount of computation. Comprehensive experiments are conducted to evaluate Darts_Tiny on two open datasets, HRSID and SSDD. Experimental results demonstrate that our neural networks win by at least an order of magnitude in terms of model complexity compared with SOTA lightweight models. A representative model obtained from Darts_Tiny (158 KB model volume, 28 K parameters and 0.58 G computations) yields a faster detection speed such that more than 750 frames per second (800×800 SAR images) could be achieved when testing on a platform equipped with an Nvidia Tesla V100 and an Intel Xeon Platinum 8260. The lightweight neural networks generated by Darts_Tiny are still competitive in detection accuracy: the F1 score can still reach more than 83 and 90, respectively, on HRSID and SSDD.
Improved Graph-Based Arabic Hotel Review Summarization Using Polarity Classification
Authors: Amoudi Ghada , Almansour Amal , Alghamdi Hanan Saleh
Published: October 2022

The increasing number of online product and service reviews has created a substantial information resource for individuals and businesses. Automatic review summarization helps overcome information overload. Research in automatic text summarization shows remarkable advancement. However, research on Arabic text summarization has not been sufficiently conducted. This study proposes an extractive Arabic review summarization approach that incorporates the reviews’ polarity and sentiment aspects and employs a graph-based ranking algorithm, TextRank. We demonstrate the advantages of the proposed methods through a set of experiments using hotel reviews from Booking.com. Reviews were grouped based on their polarity, and then TextRank was applied to produce the summary. Results were evaluated using two primary measures, BLEU and ROUGE. Further, two Arabic native speakers’ summaries were used for evaluation purposes. The results showed that this approach improved the summarization scores in most experiments, reaching an F1 score of 0.6294. Contributions of this work include applying a graph-based approach to a new domain, Arabic hotel reviews, adding sentiment dimension to summarization, analyzing the algorithms of the two primary summarization metrics showing the working of these measures and how they could be used to give accurate results, and finally, providing four human summaries for two hotels which could be utilized for another research.
Influence of Low Yield Point Reinforcement and High Toughness Concrete on Seismic Performance of the Frame
Authors: Cheng Kaihua , Cheng Li , Jiao Weixuan , Zhang Bowen , Zhao Heng
Published: October 2022

Currently, ordinary concrete and steel bars are mostly used in frames, but there is no systematic research on the impact of high toughness concrete and low yield point steel bars on the seismic performance of frames. The low-cycle repeated load test of three 1/3 ratio two-story and two-span cast-in-situ concrete frame specimens is carried out. The resilience model hysteresis characteristics, displacement ductility, and energy dissipation capacity are studied and analyzed by experiment. In addition, a comparative analysis is made between the ordinary frame and the frame with low yield point reinforcement. The restoring force model of reinforced concrete (RC) frame with low yield point reinforcement and high toughness concrete is given. The results show that the frame with low yield point reinforcement has better ductility and lateral stiffness than the ordinary reinforced concrete frame. The ductility of the frame will be improved by using high toughness concrete. The deformation ability of the members will be improved by using low yield point steel and high toughness concrete in the frame.
Heat Conduction Plate Layout Optimization Using Physics-Driven Convolutional Neural Networks
Authors: Sun Yang , Elhanashi Abdussalam , Ma Hao , Chiarelli Mario Rosario
Published: October 2022

Optimizing heat conduction layout is essential during engineering design, especially for sensible thermal products. However, when the optimization algorithm iteratively evaluates different loading cases, the traditional numerical simulation methods usually lead to a substantial computational cost. To effectively reduce the computational effort, data-driven approaches are used to train a surrogate model as a mapping between the prescribed external loads and various geometry. However, the existing model is trained by data-driven methods, which require intensive training samples from numerical simulations and do not effectively solve the problem. Choosing the steady heat conduction problems as examples, this paper proposes a physics-driven convolutional neural networks (PD-CNNs) method to infer the physical field solutions for randomly varied loading cases. After that, the particle swarm optimization (PSO) algorithm is used to optimize the sizes, and the positions of the hole masks in the prescribed design domain and the average temperature value of the entire heat conduction field is minimized. The goal of reducing heat transfer is achieved. Compared with the existing data-driven approaches, the proposed PD-CNN optimization framework predicts field solutions that are highly consistent with conventional simulation results. However, the proposed method generates the solution space without pre-obtained training data. We obtained thermal intensity results for holes 1, hole 2, hole 3, and hole 4 with 0.3948, 0.007, 0.0044, and 0.3939, respectively, by optimization PD-CNN model.
Geo-Spatial Context Provision for Digital Twin Generation
Authors: Jost Thomas Ernst , Stary Christian , Heininger Richard
Published: October 2022

Light detection and ranging technology allows for the creation of detailed 3D point clouds of physical objects and environments. Therefore, it has the potential to provide valuable information for the operation of various kinds of cyber-physical systems that need to be aware of, and interact with, their surroundings, such as autonomous vehicles and robots. Point clouds can also become the basis for the creation of digital representations of different assets and a system’s operational environment. This article outlines a system architecture that integrates the geo-spatial context information provided by LiDAR scans with behavioral models of the components of a cyber-physical system to create a digital twin. The clear distinction between behavior and data sets the proposed digital twin architecture apart from existing approaches (that primarily focus on the data aspect), and promotes contextual digital twin generation through executable process models. A vaccine logistics automation use case is detailed to illustrate how information regarding the environment can be used for the operation of an autonomous robot carrying out transport preparation tasks. Besides supporting operation, we propose to combine context data retrieved from the system at different points in the logistics process with information regarding instances of executable behavior models as part of the digital twin architecture. The twin can subsequently be used to facilitate system and process monitoring through relevant stakeholders and structure context data in a user-centric fashion.
Mechanism of Coal Burst Triggered by Disturbing Mining-Induced Stress: An Experimental Investigation
Authors: Bai Jinzheng , Dou Linming , Li Xuwei , Cao Jinrong , Wang Kangkang , Chai Yanjiang , Kan Jiliang
Published: October 2022

The true triaxial test can accurately simulate the dynamic and static load superposition environment of deep mining and then reproduce the spatial and temporal evolution process of coal-rock dynamic disasters. This study used a self-developed true triaxial coal-rock dynamic behavior test system to investigate the dynamic failure characteristics and mechanism of coal bursts under different mining-induced stress disturbances. The results show that the perturbation duration of the coal samples under quasi-static load decreases with the increase of the disturbance rate, and the perturbation stress level increases first and then decreases. The coal samples can accumulate higher strain energy and show progressive and dynamic failure. The perturbation duration and stress peak of the coal sample under the cycle load decreased with the increase of the cycle amplitude and frequency, and the coal sample first spalled off on the free surface. The damage then developed internally until the coal burst. The perturbation duration and stress peak of coal samples decrease with the increase of transient stress and the perturbation stress levels. The dynamic failure process of coal samples is straightforward, and the strength of coal burst is violent and is more difficult to predict. The conclusions obtained help to deepen the understanding of the triggering mechanism of coal bursts.
Enrichment of Metals in the Sediments of an Industrially Impacted Estuary: Geochemistry, Dispersion and Environmental Considerations
Authors: Mangas-Suarez Mario , Garcia-Ordiales Efren , Pérez Julio Antonio , Álvarez Rodrigo , Villa Alberto , Ordoñez Almudena , Roqueñí Nieves
Published: October 2022

The city of Avilés is one of the most industrialized cities in the north of Spain and, accordingly, its estuary and coastal area have been subjected to great anthropic impacts in the last 100 years. This research attempts to establish a preliminary scenario of the geochemical and environmental status of both coastal and estuarine sediments in this area. For this study, a total of 96 sediment samples were collected, processed and analyzed to quantify the granulometric distribution of the sediments, as well as their concentrations of organic carbon and main metal(oids) that may cause an environmental risk. The results show that the estuarine sediments present important concentrations of Cd, Hg, Pb and Zn that allow them to be clearly differentiated from the coastal sediments; this information, along with the sedimentology, implies that the estuary acts as a sink of pollution and not as source to the coastal area. Inside the estuary, the high levels of contamination produce a significant potential ecological risk due to contaminant transfers to other environmental compartments and to the biota. Although direct discharges of industrial effluents have been gradually eliminated, the current state of the sediment requires the implementation of measures that are more consistent than natural regeneration in order to ensure low risk levels for the ecosystem.
Aggressive Nasopalatine Cyst with Nasal Involvement in an Edentulous Patient
Authors: Perez Alexandre , Lenoir Vincent , Lombardi Tommaso
Published: October 2022

The purpose of this study was to report a rare case of an aggressive nasopalatine cyst with nasal involvement in an edentulous patient. A 61-year-old edentulous Caucasian male presented with an anterior maxillary lesion that prevented him from wearing his complete upper denture. Clinical examination showed vestibular and palatal swelling around the edentulous sites of teeth #11, 12 and 13. Radiological examination revealed a well-limited cyst of the anterior maxilla that was aggressive in nature because of its size, erosion of the palatal and buccal cortical bone, and extension to the nasal floor. The provisional diagnosis was an aggressive nasopalatine duct cyst, but because the patient had been edentulous for years, the differential diagnosis also included a residual cyst. To remove the lesion, we used a slightly modified surgical procedure compared with the conventional approach: a flap was raised on the palatal side and another on the vestibular side instead of the classical palatal approach. The diagnosis of a nasopalatine duct cyst was confirmed after a histopathological examination. Large destructive nasopalatine duct cysts involving the nasal cavity are rare. In an edentulous patient, this lesion may be misdiagnosed as a residual cyst. In this case, the surgical risk was the inadvertent creation of a naso-antral communication. The lesion was completely enucleated without complications and healing was uneventful.
In Vitro Antiproliferative Activity of Ptaeroxylon obliquum Leaf Extracts, Fractions and Isolated Compounds on Several Cancer Cell Lines
Authors: Khunoana Edward T. , Eloff Jacobus N. , Ramadwa Thanyani E. , Nkadimeng Sanah M. , Selepe Mamoalosi A. , McGaw Lyndy J.
Published: October 2022

Several cancers are induced by microbial infections or chronic inflammation. Ptaeroxylon obliquum is traditionally used to treat various infections characterized by inflammation. The in vitro antiproliferative and antioxidant activity of P. obliquum leaf extracts, fractions and isolated compounds were determined. Antiproliferative activity was assessed against normal Vero cells, and several cancerous human cells, including human breast cancer (MCF-7), hepatocarcinoma (HepG2), lung adenocarcinoma (A549) and human cervical cancer cells (HeLa) using a colorimetric tetrazolium bromide assay. Radical scavenging activity was tested using the 2,2-diphenyl-1-instrpicrylhydrazyl (DPPH) and 2,2’-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) assays. Obliquumol, O-methylalloptaeroxylin and a mixture of lupeol and β-amyrin were isolated from the chloroform fraction using silica gel open column chromatography. Acetone extracts were toxic to HepG2 cells with IC50 values from 8 to 200 µg/mL but were less toxic to other cells with selectivity index as high as 14. Aqueous extracts and fractions were non-toxic at concentrations tested against all the cell lines (IC50 > 100 µg/mL). Isolated compounds had IC50 values ranging from 52 to 539 µg/mL and 189 to 247 µg/mL against HepG2 and HeLa cells, respectively. Light microscopy showing changes in HepG2 and HeLa cell morphology supported the cytotoxicity of the acetone extracts. Water extracts scavenged ABTS and DPPH radicals with IC50 values as low as 29.06 µg/mL and 43.4 µg/mL. P. obliquum extracts may be useful as sources of anticancer therapy, as they have selective cytotoxicity against cancer cell lines.
Special Issue “Application of Non-Linear Dynamics”
Authors: Starosta Roman , Awrejcewicz Jan
Published: October 2022

Nonlinear phenomena occur in engineering structures, biological systems, social processes, and in economics [...]
Ultrasound-Assisted Extraction of β-Asarone from Sweet Flag (Acorus calamus) Rhizome
Authors: Omer Noridayu , Choo Yeun-Mun , Ashokkumar Muthupandian , Yusof Nor Saadah Mohd
Published: October 2022

In this study, the extraction efficiency of β-asarone from Malaysian Acorus calamus from Acoraceae family using conventional solvent extraction and ultrasound-assisted extraction techniques was compared. The results showed that the ultrasound-assisted extraction technique significantly improves the extraction yields and process feasibility without changing the structure of the active compound, i.e., β-asarone. The extraction yield increment was found to be ~2.5-fold and ~1.6-fold at 1:100 and 1:50 solid-to-solvent ratio, at 30% applied sonication power. The positive impact of sonication can also be observed for both mechanistic stages of extraction, i.e., the washing and diffusion stages, due to the favorable physical effect of acoustic cavitations. The observation was supported by the SEM images of the plant residue. The characterization of the extract was carried out using HPLC, NMR, UV and IR techniques. In conclusion, ultrasound assistance increases the extraction efficiency by ~2.5-fold even at only 30% applied ultrasonic power at a 1:100 solid-to-solvent ratio. The present study also provides an efficient and simple method for accurate direct dosing of Acorus calamus extracts to an application.
Transfer Learning-Assisted Evolutionary Dynamic Optimisation for Dynamic Human-Robot Collaborative Disassembly Line Balancing
Authors: Jin Liang , Zhang Xiao , Fang Yilin , Pham Duc Truong
Published: October 2022

In a human-robot collaborative disassembly line, multiple people and robots collaboratively perform disassembly operations at each workstation. Due to dynamic factors, such as end-of-life product quality and human capabilities, the line balancing problem for the human-robot collaborative disassembly line is a dynamic optimisation problem. Therefore, this paper investigates this problem in detail and commits to finding the evolutionary dynamic optimisation. First, a task-based dynamic disassembly process model is proposed. The model can characterise all feasible task sequences of disassembly operations and the dynamic characteristics of tasks affected by uncertain product quality and human capabilities. Second, a multiobjective optimisation model and a feature-based transfer learning-assisted evolutionary dynamic optimisation algorithm for the dynamic human-robot collaborative disassembly line balancing problem are developed. Third, the proposed algorithm uses the balanced distribution adaptation method to transfer the knowledge of the optimal solutions between related problems in time series to track and respond to changes in the dynamic disassembly environment. Then, it obtains the optimal solution sets in a time-varying environment in time. Finally, based on a set of problem instances generated in this study, the proposed algorithm and several competitors are compared and analysed in terms of performance indicators, such as the mean inverted generational distance and the mean hypervolume, verifying the effectiveness of the proposed algorithm on dynamic human-robot collaborative disassembly line balancing.
Assessing Impact of Temperature Variability of Climate Change on Mortality Based on Multiple GCM Projections in China
Authors: Zheng Xiangyi , Ma Qingyuan , Wang Ying , Wang Xia , Zhang Xinren , Luan Qingzu
Published: October 2022

Gradually increasing durations of high temperature caused by climate change harm the health of individuals and then lead to death. This study aimed to investigate the relationship between durations of different daily mean air-temperature categories and mortality in China and forecast future mortality changes in China for 2020–2050 under Representative Concentration Pathways (RCP)4.5 and RCP8.5 scenarios. The daily mean air temperature was divided into 10 categories, and the days under each air-temperature category were counted during the period of 2000–2015. Then, the connection between the days of each of the 10 air-temperature categories and mortality was established using the semi parametric regression model. Results indicate that the days of the >32 °C category have the largest impact on mortality in China, with the death rate increasing by 23‰ for one additional day. Predictions reveal that mortality in China will increase 25.48% and 26.26% under the RCP4.5 and RCP8.5 scenarios, respectively. Moreover, the mortality of 86 regions in western China will increase 30.42%. Therefore, in the future, the increasing duration of days of high temperatures will raise the mortality rate in China and aggravate the mortality gap between developed and underdeveloped regions.
Projected Changes of Day-to-Day Precipitation and Choco Low-Level Jet Relationships over the Far Eastern Tropical Pacific and Western Colombia from Two CMIP6 GCM Models
Authors: Valencia Juliana , Mejía John F.
Published: October 2022

The far Eastern Tropical Pacific (EPAC) and Western Colombia are one of the rainiest places on Earth, and the Choco low-level jet (ChocoJet) is one of the processes that influence the formation of copious precipitation and convection organization in this region. This study investigates the projected changes in precipitation in this region using historical and future simulations based on model output from two models contributing to the Coupled Model Intercomparison Project phase 6 (CMIP6). In close agreement with observations, models simulate that ChocoJet intensity is directly proportional to precipitation in the region. This relationship is also found far inland in Central America, the northwestern part of South America Pacific Coast, and the intermountain valleys of the Colombian Andes. Late 21st century simulations show a southward migration in mean and regional daily precipitation consistent with a decreased ChocoJet intensity. The weaker ChocoJet is related to a projected increase in EPAC tropical sea surface temperatures (SSTs) and an increased frequency and intensity of the warm phase of the Niño 1+2 SST interannual variability.
Influence of the Atlantic Multidecadal Oscillation on South American Atmosphere Dynamics and Precipitation
Authors: Maksic Jelena , Shimizu Marília H. , Kayano Mary T. , Chiessi Cristiano M. , Prange Matthias , Sampaio Gilvan
Published: October 2022

The Atlantic Multidecadal Oscillation (AMO) is coherently linked to climate variations over many parts of the globe. Despite recent achievements, the mechanism by which the AMO influences regional precipitation over South America is not well understood. In this study, we isolate the atmospheric response to the AMO using a water isotope-enabled version of the Community Earth System Model version 1.2 (iCESM1.2) and determine its influence on (sub)tropical South American regional precipitation and atmospheric circulation. The results suggest an interhemispheric seesaw in Hadley circulation strength and that the section of the Atlantic Hadley cell is marked by a stronger upward air component south of the equator during the cold AMO phase. We also find that the precipitation anomalies over (sub)tropical South America during AMO phases are mainly related to changes in the Atlantic Intertropical Convergence Zone (ITCZ) core strength, where in the cold (warm) AMO phase the core region strengthens (weakens) from February to July, while from July to November the core region weakens (strengthens). Our results stress the importance of acknowledging the dynamics of season- and regional-dependent ITCZ responses, as they are sufficient to produce observed AMO-related signals even in the absence of marked changes in the ITCZ position.
Effects of Weather and Anthropogenic Precursors on Ground-Level Ozone Concentrations in Malaysian Cities
Authors: Baidrulhisham Syaza Ezzati , Noor Norazian Mohamed , Hassan Zulkarnain , Sandu Andrei Victor , Vizureanu Petrica , Ul-Saufie Ahmad Zia , Zainol Mohd Remy Rozainy Mohd Arif , Kadir Aeslina Abdul , Deák György
Published: October 2022

Ground-level ozone (O3) is a significant source of air pollution, mainly in most urban areas across the globe. Ground-level O3 is not emitted directly into the atmosphere. It results from photo-chemical reactions between precursors and is influenced by weather factors such as temperature. This study investigated the spatial and temporal analysis of ground-level ozone and analyzed the significant anthropogenic precursors and the weather parameters associated with ground-level ozone during daytime and nighttime at three cities in peninsular Malaysia, namely, Kuala Terengganu, Perai, and Seremban from 2016 to 2020. Secondary data were acquired from the Department of Environment (DOE), Malaysia, including hourly data of O3 with trace gases and weather parameters. The secondary data were analyzed using temporal analysis such as descriptive statistics, box plot, and diurnal plot as well as spatial analysis such as contour plot and wind rose diagram. Spearman correlation was used to identify the association of O3 with its precursors and weather parameters. The results show that a higher concentration of O3 during the weekend due to “ozone weekend effects” was pronounced, however, a slightly significant effect was observed in Perai. The two monsoonal seasons in Malaysia had a minimal effect on the study areas except for Kuala Terengganu due to the geographical location. The diurnal pattern of O3 concentration indicates bimodal peaks of O3 precursors during the peak traffic hours in the morning and evening with the highest intensity of O3 precursors detected in Perai. Spearman correlation analysis determined that the variations in O3 concentrations during day and nighttime generally coincide with the influence of nitrogen oxides (NO) and temperature. Lower NO concentration will increase the amount of O3 concentration and an increasing amount of O3 concentration is influenced by the higher temperature of its surroundings. Two predictive models, i.e., linear (multiple linear regression) and nonlinear models (artificial neural network) were developed and evaluated to predict the next day and nighttime O3 levels. ANN resulted in better prediction for all areas with better prediction identified for daytime O3 levels.
Aerodynamic Shape Optimization of a Square Cylinder with Multi-Parameter Corner Recession Modifications
Authors: Wang Zhaoyong , Zheng Chaorong , Mulyanto Joshua Adriel , Wu Yue
Published: October 2022

Corner modifications can reduce wind loads acting on supertall buildings and modify the corresponding flow structures. The present study investigated the aerodynamic shape optimization of the corner recession square cylinders with multiple geometric parameters in a large design space via the GA-GRNN surrogate model updating-based multi-objective optimization framework. Six typical optimal aerodynamic shape sections M1~M6 were selected from the Pareto optimal front, and the effects of multiple geometric parameters of these sections on the aerodynamic performance and flow field were analyzed. The results showed that the present multi-objective optimization framework can significantly reduce the computational load and time cost, and significantly improve the optimization efficiency in solving complex engineering problems. The optimal corner recession sections can obviously reduce the mean drag coefficient CD and root mean square lift coefficient CσL while significantly increasing the Strouhal number St of the square cylinder, and it is concluded that the aerodynamic shape optimization can significantly improve the aerodynamic performance of square-sectional supertall buildings. When compared with the benchmark section, the CD and CσL of the optimal section M1 can be reduced up to 45.7% and 84.5%, respectively. Based on the analysis of the flow structures around the optimal sections, the flow mechanism can be attributed to the fact that the corner recession modifications postpone the flow separation, and deflect the separated shear layer towards the side surfaces and suppress the development of vortex shedding in the wake, which leads to significant elongation of the wake length and reduction of the width of the recirculation region. The proposed multi-objective optimization framework in this study can provide an important reference for the aerodynamic shape optimization of building structures and relevant studies.
Are Adaptation Measures Used to Alleviate Heat Stress Appropriate to Reduce Ammonia Emissions?
Authors: Scherllin-Pirscher Barbara , Mikovits Christian , Baumann-Stanzer Kathrin , Piringer Martin , Schauberger Günther
Published: October 2022

The emission of ammonia (NH3) is predominantly caused by agriculture, especially by livestock keeping. The health effects of NH3 and the related formation of particulate matter are the reasons for solid efforts to reduce their ambient concentrations. In addition, the impact of global warming on livestock is increasing due to heat stress, likely also increasing NH3 emissions. Therefore, adaptation measures are under discussion to reduce the heat stress of animals inside livestock units. Because of the relationship between temperature increase and NH3 release, the impact of the adaptation measures to cool the indoor air of livestock units (three different energy-saving air preparation systems, an inversion of the feeding and resting times by half a day, a reduction of the stocking density and doubling the maximum volume flow rate) was investigated. The NH3 release was calculated by the following predictors: indoor air temperature; ventilation rate describing the turbulence inside the livestock building; and the diurnal variation caused by the animal activity. These parameters were calculated by a simulation model for the indoor climate of livestock buildings. The monthly mean of the NH3 emission for several adaptation measures, which were applied to reduce heat stress, were compared with the emission of a reference building for 1800 fattening pigs, divided into nine sections with 200 animals each for an all-in-all-out production cycle to calculate the mitigation potential. The higher the cooling power of such adaptation measures, the higher the mitigation potential for NH3. In particular, those adaptation measures which cool the inlet air (e.g., cooling pads reduce the emission by −2%, earth-air heat exchangers by −3.1%) show the best performance to mitigate the NH3 emission of livestock buildings.
Towards On-Site Implementation of Multi-Step Air Pollutant Index Prediction in Malaysia Industrial Area: Comparing the NARX Neural Network and Support Vector Regression
Authors: Mustakim Rosminah , Mamat Mazlina , Yew Hoe Tung
Published: October 2022

Malaysia has experienced public health issues and economic losses due to air pollution problems. As the air pollution problem keeps increasing over time, studies on air quality prediction are also advancing. The air quality prediction can help reduce air pollution’s damaging impact on public health and economic activities. This study develops and evaluates the Nonlinear Autoregressive Exogenous (NARX) Neural Network and Support Vector Regression (SVR) for multi-step Malaysia’s Air Pollutant Index (API) prediction, focusing on the industrial areas. The performance of NARX and SVR was evaluated on four crucial aspects of on-site implementation: Input pre-processing, parameter selection, practical predictability limit, and robustness. Results show that both predictors exhibit almost comparable performance, in which the SVR slightly outperforms the NARX. The RMSE and R2 values for the SVR are 0.71 and 0.99 in one-step-ahead prediction, gradually changing to 6.43 and 0.68 in 24-step-ahead prediction. Both predictors can also perform multi-step prediction by using the actual (non-normalized) data, hence are simpler to be implemented on-site. Removing several insignificant parameters did not affect the prediction performance, indicating that a uniform model can be used at all air quality monitoring stations in Malaysia’s industrial areas. Nevertheless, SVR shows more resilience towards outliers and is also stable. Based on the trends exhibited by the Malaysia API data, a yearly update is sufficient for SVR due to its strength and stability. In conclusion, this study proposes that the SVR predictor could be implemented at air quality monitoring stations to provide API prediction information at least nine steps in advance.
Compact Non-Dispersive Infrared Multi-Gas Sensing Platform for Large Scale Deployment with Sub-ppm Resolution
Authors: Wastine Benoit , Hummelgård Christine , Bryzgalov Maksym , Rödjegård Henrik , Martin Hans , Schröder Stephan
Published: October 2022

We report on a novel, cost-effective non-dispersive infrared (NDIR) multi-gas sensor aimed at environmental air pollution monitoring. The rugged design of the K96 sensor core combines highest compactness and low-power consumption with our unique multi-channel cell design, featuring the detection of up to three different gases simultaneously, including CO2, CH4, N2O, and H2O. Our sensing platform allows the selection of the target gases as well as the concentration ranges, thus providing highly customizable gas sensor systems targeting application-specific gas monitoring settings. The sensor core comes with an implemented calibration model, and can address in real time any cross-sensitivity between the NDIR gas-sensing channels. We provide an immensely versatile sensing system while ensuring high sensing stability combined with high precision (<0.1 ppm for both CO2 and N2O, <0.5 ppm for CH4). The K96 multi-gas sensor core offers a resilient sensor solution for the increasing demand of compact monitoring systems in the field of environmental monitoring at reasonable costs for medium-to-high volumes.
Spatial Distribution, Material Composition and Provenance of Loess in Xinjiang, China: Progress and Challenges
Authors: Saimaiti Akemu , Fu Chaofeng , Song Yougui , Shukurov Nosir
Published: October 2022

The loess in the arid area of Xinjiang is located at the eastern end of the Central Asia Loess Belt, and paleoclimate research about it is of great importance for understanding the mechanism of interaction of the Eurasian Westerly monsoon system and the aridity of Central Asia. This review focuses on recent progress concerning the spatial distribution, material composition and provenance of loess in Xinjiang and points out the shortcomings of and challenges to provenance and dust circulation. Field investigation and previous studies indicate that loess sediments have been mainly distributed on the river terraces and windward piedmont of the Tianshan Mountains and the Kunlun Mountains since the late Pliocene (mainly late Pleistocene). Grain size and age data show that Xinjiang loess deposits at some locations are rapid and discontinuous or sedimentary hiatus. The Siberian High system largely controlled dust mobilization and loess accumulation in northern Xinjiang but not southern Xinjiang. In southern Central Asia, the intensity of dust activity may be determined by the Caspian Sea–Hindu Kush Index (CasHKI) and local circulation. However, there is not enough evidence that the CasHKI index can affect the Tarim Basin area. Consequently, ascertaining the driving mechanism of mid-latitude Westerly winds and the dynamic process of loess deposition in Xinjiang is a specific suggestion for critical future research. Many indicators have shown that the loess dust sources in Xinjiang are composed of mainly proximal materials plus some remote materials. Alluvial plains and local proluvial fans contributed more to loess deposition, while Central Asian deserts comprise a small proportion of loess deposition in northern Xinjiang. In future provenance research, new technologies and new methods should be continuously tested to facilitate an objective understanding of the provenance of the loess in Xinjiang.
Atmospheric Circulation Drivers of Extreme High Water Level Events at Foggy Island Bay, Alaska
Authors: Bieniek Peter A. , Erikson Li , Kasper Jeremy
Published: October 2022

The northern coast of Alaska is experiencing significant climatic change enhancing hazards from reduced sea ice and increased coastal erosion. This same region is home to offshore oil/gas activities. Foggy Island Bay is one region along the Beaufort Sea coast with planned offshore oil/gas development that will need to account for the changing climate. High water levels impact infrastructure through coastal erosion and flooding hazards. In this study, 21 high water level events exceeding the top 95th percentile were identified at the gauge in Prudhoe Bay, Alaska (adjacent to Foggy Island Bay) over 1990–2018. All 21 events, and many non-extreme days with elevated water levels, were associated with strong westerly winds according to station records. Storm systems were generally found to be a key driver of westerly winds in the region according to downscaled reanalysis and storm track data. A dynamically downscaled global climate model projection from CMIP5 indicated that days with westerly wind events may become more frequent as storms strengthen and have longer durations by 2100 in the Foggy Island Bay region. Coupled with the anticipated continued decline in sea ice, the northern coast of Alaska may experience more frequent high water events over the next ~80 years.
A Bivariate Nonstationary Extreme Values Analysis of Skew Surge and Significant Wave Height in the English Channel
Authors: Chapon Antoine , Hamdi Yasser
Published: October 2022

Coastal flooding compound events can be caused by climate-driven extremes of storm surges and waves. To assess the risk associated with these events in the context of climate variability, the bivariate extremes of skew surge (S) and significant wave height (HS) are modeled in a nonstationary framework using physical atmospheric/oceanic parameters as covariates (atmospheric pressure, wind speed and sea surface temperature). This bivariate nonstationary distribution is modeled using a threshold-based approach for the margins of S and HS and a dynamic copula for their dependence structure. Among the covariates considered, atmospheric pressure and related wind speed are primary forcings for the margins of S and HS, but temperature is the main positive forcing of their dependence. This latter relation implies an increasing risk of compound events of S and HS for the studied site in the context of increasing global temperature.
A Short Review of Simple Analytical Methods for the Evaluation of PAHs and PAEs as Indoor Pollutants in House Dust Samples
Authors: Orecchio Santino , Amorello Diana , Indelicato Roberta , Barreca Salvatore , Orecchio Silvia
Published: October 2022

Studies on indoor air quality are indispensable when considering that people spend approximately 85% of their time in confined environments. This short review mostly takes into consideration research that uses passive samplers to evaluate the quality of indoor environments (houses, school, cars, etc.). This short review summarizes most analytical methods to detect and quantify PAHs and PAEs in house dust used as a passive sampler. The objective of house dust analysis is to identify the presence, amount and distribution of specific hazardous substances in confined spaces and, if possible, to identify their sources. Household dust and the compounds present in it can enter the human body by inhalation, non-food ingestion and absorption through the skin. The observed differences in concentrations of house dust may also indicate important differences in the chemical and physical nature of pollutants caused by air filtration and absorption during the migration of ambient air into the indoor environment.
Prediction of Real Driving Emission of Light Vehicles in China VI Based on GA-BP Algorithm
Authors: Yu Hao , Chang Hong , Wen Zengjia , Ge Yunshan , Hao Lijun , Wang Xin , Tan Jianwei
Published: October 2022

In the China VI regulations for light-duty vehicles, the (RDE) test is introduced as a supplementary test procedure. In the actual test process, the RDE results are more significantly influenced by driving behavior and vehicle type. To reduce the test cost, the NOx and PN prediction models are established based on the GA-BP method. The results showed that the coefficients of determination of the GA-BP model for NOx and PN predictions are all greater than 0.9 and are linearly highly correlated at the instantaneous emission level. At the overall emission level, the overall error of the GA-BP model is less than 7% for NOx prediction and less than 6% for PN prediction. The model has high accuracy for both instantaneous and overall emissions of light-duty vehicles. This provides practical engineering value for guiding the RDE test.
Combinatorial Interpretation of Numbers in the Generalized Padovan Sequence and Some of Its Extensions
Authors: Vieira Renata Passos Machado , Alves Francisco Regis Vieira , Catarino Paula Maria Machado Cruz
Published: October 2022

There is ongoing research into combinatorial methods and approaches for linear and recurrent sequences. Using the notion of a board defined for the Fibonacci sequence, this work introduces the Padovan sequence combinatorial approach. Thus, mathematical theorems are introduced that refer to the study of the Padovan combinatorial model and some of its extensions, namely Tridovan, Tetradovan and its generalization (Z-dovan). Finally, we obtained a generalization of the Padovan combinatorial model, which was the main result of this research.
Development of the Generalized Multi-Dimensional Extended Partitioned Bonferroni Mean Operator and Its Application in Hierarchical MCDM
Authors: Banerjee Debasmita , Guha Debashree , Mesiar Radko , Karmakar Mondol Juliet
Published: October 2022

In this article, we propose the generalized version of the extended, partitioned Bonferroni mean (EPBM) operator with a systematic investigation of its behavior and properties. It can aggregate data of various dimensions in one formulation by modeling mandatory conditions along with partitioned structure interrelationships amongst the criterion set. In addition, we generate the condition for weight vectors satisfied by the weighting triangle associated with the proposed extended aggregation operator. We employed the proposed operator to aggregate a dataset following a hierarchical structure. We found that by implementing the proposed operator one can even rank the alternatives more intuitively with respect to any intermediate perspective of the hierarchical system. Finally, we present an application of the proposed extended aggregation operator in a case-based example of a child’s home environment quality evaluation with detailed analysis.
Selection of Business Process Modeling Tool with the Application of Fuzzy DEMATEL and TOPSIS Method
Authors: Jin Guangying , Jin Guangzhe , Huo Haibo
Published: October 2022

The business process modeling tool selection problem has a significant impact on the overall performance of enterprise business process modeling, which will directly affect the development of enterprise information systems. Apart from that, the process to select the business process modeling tool from all alternatives is a Multi-Criteria Decision Making (MCDM) problem. This paper develops a methodology based on the hybrid fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) method to help companies select the optimal business process modeling tool, where the business process modeling process is more efficient, economic and safe. The proposed method has the following state-of-the-art contributions and features: (1) the latest application of the MCDM methodology to the field of BPM tool selection, (2) addressing the direct and indirect impact between criteria in the selection of BPM tools, and (3) considering the hybrid fuzzy (uncertainty) decision-making issue in the BPM tool selection process. Meanwhile, the mathematical formula in TOPSIS can be regarded as a formula for solving a symmetric problem. The hybrid fuzzy DEMATEL method is used to obtain the weight for the criteria to be considered in the BPM tool selection process, and the TOPSIS method is used to obtain the final business process modeling tool.
Some New Integral Inequalities Involving Fractional Operator with Applications to Probability Density Functions and Special Means
Authors: Kodamasingh Bibhakar , Sahoo Soubhagya Kumar , Shaikh Wajid Ali , Nonlaopon Kamsing , Ntouyas Sotiris K. , Tariq Muhammad
Published: October 2022

Fractional calculus manages the investigation of supposed fractional derivatives and integrations over complex areas and their applications. Fractional calculus is the purported assignment of differentiations and integrations of arbitrary non-integer order. Lately, it has attracted the attention of several mathematicians because of its real-life applications. More importantly, it has turned into a valuable tool for handling elements from perplexing frameworks within different parts of the pure and applied sciences. Integral inequalities, in association with convexity, have a strong relationship with symmetry. The objective of this article is to introduce the notion of operator refined exponential type convexity. Fractional versions of the Hermite–Hadamard type inequality employing generalized R–L fractional operators are established. Additionally, some novel refinements of Hermite–Hadamard type inequalities are also discussed using some established identities. Finally, we present some applications of the probability density function and special means of real numbers.
A Fractional-Order SIR-C Cyber Rumor Propagation Prediction Model with a Clarification Mechanism
Authors: Li Linna , Li Yuze , Zhang Jianke
Published: October 2022

As communication continues to develop, the high freedom and low cost of the communication network environment also make rumors spread more rapidly. If rumors are not clarified and controlled in time, it is very easy to trigger mass panic and undermine social stability. Therefore, it is important to establish an efficient model for rumor propagation. In this paper, the impact of rumor clarifiers on the spread of rumors is considered and fractional order differentiation is introduced to solve the problem that traditional models do not take into account the “anomalous propagation” characteristics of information. A fractional-order Susceptible-Infected-Removal-Clarify (SIR-C) rumor propagation prediction model featuring the clarification mechanism is proposed. The existence and asymptotic stability conditions of the rumor-free equilibrium point (RFEP) E0; the boundary equilibrium points (BEPs) E1 and E2 are also given. Finally, the stability conditions and practical cases are verified by numerical simulations. The experimental results confirm the analysis of the theoretical study and the model fits well with the real-world case data with just minor deviations. As a result, the model can play a positive and effective role in rumor propagation prediction.
Enhancing the Catalytic Activity of Layered Double Hydroxide Supported on Graphene for Lithium–Sulfur Redox Reactions
Authors: Xu Junjie , Tang Rui , Liu Minghui , Xie Shuai , Zhang Dawei , Kong Xianghua , Jin Song , Ji Hengxing , Zhang Tierui
Published: October 2022

The lithium–sulfur battery is one of the next-generation rechargeable battery candidates due to its high theoretical energy density and low cost. However, the sluggish conversion kinetics of soluble lithium polysulfides into insoluble Li2S2/Li2S leads to low sulfur utilization, retarded rate responses, and rapid capacity decay. Here, we enhance the sulfur reduction kinetics by designing and synthesizing a lamellar-structured NiFeLDH and reduced graphene oxide (rGO) composite. The assembly of a two-dimensional NiFeLDH with rGO, which has high conductivity and electrocatalytic activity, significantly enhances the electrochemical steps of sulfur reduction. The S@NiFeLDH/rGO cathode delivers an initial discharge capacity of 1014 mAh g−1 at 0.2 C and a capacity of 930 mAh g−1 after 100 cycles at 0.2 C. Even at a high current density of 1 C, the S@NiFeLDH/rGO could maintain a high capacity of 554 mAh g−1 after 400 cycles.