bluuebunny/crossref_metadata_2025 huggingface dataset:
RESEARCH ON VALIDITY MEASUREMENT OF MEGACITIES RENEWAL POLICIES AND SUITABILITY SCALE FOR URBAN REMOTE SENSING DATA APPLICATIONS: TAKING BEIJING’S DUAL CONTROL POLICY AS AN EXAMPLE
Authors: Wu Y. , Li A.
Published: October 2022

Abstract. Urban renewal has become the application hotspot of urban remote sensing development in recent years. However, scientific distribution of weight relationship between indicators reflecting progress and effectiveness of different urban renewal goals and tasks, and comprehensive evaluation of effectiveness of policies in the existing indicator system require further in-depth research. In particular, there is no clear standard for application caliber of remote sensing data of different cities in spatial indicators. Taking Beijing’s dual control policy as an example, this paper measures effectiveness of mega-city renewal policies represented by Beijing based on analytic hierarchy process, and analyzes types of remote sensing data suitable for urban renewal. In addition, the suitability scale of urban remote sensing data application is given through case studies. By analyzing the actual validity of Beijing’s dual-control policy implementation, it is concluded that the application scale of urban remote sensing data for urban renewal suitability is remote sensing image big data > remote sensing data artificial intelligence algorithm > remote sensing image historical data > remote sensing big data. It is necessary to focus on automatic identification and exploration of spatial changes based on remote sensing, design and calculation of spatial indicators of urban physical examination based on remote sensing in the future. Furthermore, work needs to be carried out to further improve evaluability, accuracy and objectivity of practical application of remote sensing data in different cities for urban renewal, and explore establishment of a clear urban physical examination system and remote sensing data application system guidelines.
MAPPING FOREST DISTURBANCE USING PURE FOREST INDEX TIME SERIES AND CCDC ALGORITHM
Authors: Cai Y. , Shi Q. , Liu X.
Published: October 2022

Abstract. Forest dynamics are closely related to climate change, natural disasters, and ecological diversity. The accumulated Landsat archive provides an unprecedented opportunity for long-term forest dynamics monitoring globally. However, using Landsat time series to detect small-scale and low-intensity disturbance events is still challenging since the moderate spatial resolution of Landsat images and the mixed pixel problem. Towards improving the ability of vegetation index (VI) in characterizing sub-pixel forest dynamics, this paper introduced the spectral mixture analysis (SMA) to develop a novel Pure Forest Index (PFI). The Continuous Change Detection and Classification (CCDC) algorithm was used to detect forest disturbance based on the PFI time series. Cross-comparison shows that PFI is far superior to other conventional VI in indicating forest conditions since it can enhance the spectral signal of the forest and suppress noises from the background. Time series analysis further demonstrates the superiority of PFI in accurately characterizing forest dynamics. The high overall accuracy of 0.96 for the forest disturbance map generated by the proposed approach was achieved. This study highlights a novel VI for accurately tracking subtle forest changes in a heterogeneous landscape.
SPATIOTEMPORAL ANALYSIS METHOD OF URBAN ENVIRONMENTAL FACTORS ALONG STREETS CONSTRAINED BY ROAD NETWORK
Authors: Hu L.
Published: October 2022

Abstract. Since people and vehicles in the city are mostly concentrated in the area along the road, there are few researches on the spatiotemporal analysis of environmental factors in the street area. This paper mainly focuses on the spatial and temporal analysis theory of environmental factors based on geographically weighted regression model taking PM2.5 as an example, breaking through the temporal and spatial analysis method of environmental factors along the street constrained by the road network, a spatiotemporal analysis and prediction based on the weighted impact of the road network buffer area and neighboring stations is proposed. Taking the distribution of PM2.5 in Beijing as an example, an experiment was conducted to analyze the spatial and temporal characteristics of PM2.5 along the street to verify the accuracy and reliability of the method proposed in this paper. Further improve the geospatial scale of the spatiotemporal analysis of environmental factors to achieve more refined spatiotemporal prediction of environmental factors.
A NEW METHOD FOR DEHAZING OF UAV REMOTE SENSING IMAGES BASED ON IMPROVED DARK CHANNEL PRIOR
Authors: Liu X. L. , Zhang T. , Liu Y. H. , Wang R. J.
Published: October 2022

Abstract. Aiming to solve the problem of loss of important image information, such as blurred details and low contrast, caused by fog, haze and other meteorological influences in the slope monitoring process of UAV remote sensing images, a new method of improving dark channel image dehazing based on channel-weighted analysis and compensation function is proposed by a training of multilayer perceptron (MLP) in this study. First, based on the dark channel prior principle, the original hazy UAV image is mapped to obtain the estimated values of atmospheric light and rough transmittance. Next, by counting the RGB three-channel values of the pixels in the high-brightness regions, and analyzing the scattering of the RGB three-channel values in the haze, a color recovery module of atmospheric light is constructed, and the estimated value of atmospheric light is optimized. Then, according to the global transmittance, the compensation boundary value is determined and a functional relationship between different brightness regions and the increment of transmittance is established as a compensation function to optimize the rough transmittance. Finally, perform secondary optimization on the rough transmittance with the multi-layer perceptron (MLP) to obtain a smoother transmittance value. The experimental results show that the image processed by the proposed method has good contrast. The color saturation and authenticity are effectively maintained. And the detailed information of the mountain recorded by the image is better restored, which can provide a real data basis for slope monitoring.
A MULTI-STEP DE-NOISING METHOD FOR INTERFEROGRAM IN PS-INSAR
Authors: Wang R. J. , Xu K. , Liu X. L.
Published: October 2022

Abstract. In order to reduce the influences of the noises in Synthetic Aperture Radar (SAR) image on accurate extraction of permanent scattered (PS) points in PS-InSAR technology, a multi-step de-noising approach considering the characteristics of the noises is proposed in this study. The method can deal with the noises from one-dimensional signals perspective. First, the two-dimensional interferogram is transformed into one-dimensional phase signals by extracting the phase values of each row in S-shaped. Next, the phase signals are decomposed by the extreme-point symmetric mode decomposition (ESMD) method. Then, for the purpose of reserving more useful phase information, the Lee filtering method is applied to the high-frequency noisy part identified based on the Spearman's correlation coefficients in ESMD decomposition. Finally, the de-noised interferogram is recovered from the phase signals by ways in S-shaped in reverse. The experimental results show that the proposed method is effective in dealing with the noises and can achieve a higher accuracy of interferogram for PS-InSAR.
LARGE-SCALE FLOOD DETECTION IN THE PEARL RIVER BASIN BASED ON GEE AND TIME-SERIES SENTINEL-1 SAR IMAGES
Authors: Zhao B. , Sui H.
Published: October 2022

Abstract. Due to its wide scope, rapid development and high hazard, remote sensing images with a single time phase and local area coverage cannot meet the needs of large-scale flood detection. The problem of large-scale flooding emergency detection can be well solved by using Google Earth Engine (GEE) platform, which has the characteristics of fast data computing speed, easy image retrieval and rich remote sensing data. In this paper, we propose a method for flood detection using Sentienl-1 imagery based on the GEE platform, which can quickly accomplish a large scale flood detection. The method was validated in a flooding event that occurred around the Pearl River basin, China in June 2022. It shown that using the proposed method can not only meet the needs of basin-wide flood detection, but also extract information on the temporal dimension of the flood development status.
POINT CLOUD SLICING-BASED EXTRACTION OF INDOOR COMPONENTS
Authors: Zhao J. , Cui Y. , Niu X. , Wang X. , Zhao Y. , Guo M. , Zhang R.
Published: October 2022

Abstract. Indoor point cloud data is easy to obtain and contains a large amount of data, but there is no connectivity, no structural attributes, and no semantic information between point clouds. It is very difficult to extract interior components. In order to solve the problem that it is difficult to extract the closed door in the indoor point cloud, and the indoor scanning point cloud data is noisy and redundant, a point cloud segmentation method is proposed to reduce the amount of data and the impact of noise. Because most of the indoor components are not on the same horizontal plane, different component information can be obtained by slicing the point cloud at different heights. In these slices, the component that always exists is the wall. By comparing other data with the point cloud data with a more complete wall, the required component can be extracted. The experimental results show that this method can accurately extract the indoor door component information and beam component information, providing a data basis for building model refinement.
SIMULATION OF ECOLOGICAL RISK IN BEIJING USING MOP-PLUS MODEL
Authors: Du M. , Liu C. , Wang W. , Yang H.
Published: October 2022

Abstract. The accelerated urbanisation, threatening the integrity of ecological environment. The lack of future simulation in ecological risk assessment in current studies. Especially in metropolis, to address this problem, this study uses the Multiple objective programming (MOP) and the improved patch-generating land use simulation (PLUS) models to simulate land use in Beijing in 2035 under the Natural Development (ND) scenario and the Liveable City (LC) scenario; the changes in land types and land transfers under different scenarios are analysed. In addition, a landscape ecological risk assessment method was applied to analyse the ecological risks caused by land use in each scenario. The study found that, in general, the dominant trend of land use change in Beijing is the shift from construction land to ecological land. The spatial pattern of ecological risk is polarized from east to west, and the conversion of a large amount of built-up land to arable land or grassland, and the high vulnerability of arable land and grassland to human destruction, are the reasons for the continuous increase in ecological risk in Beijing. The area of construction land in the LC scenario is closer to the planned area than in the ND scenario, and the ecological risks faced by the LC scenario are slightly lower than those of the ND scenario. Therefore, it is reasonable to use the LC scenario to simulate the land use situation in Beijing in 2035.
A DEEP NEURAL NETWORK FOR SPATIOTEMPORAL PREDICTION OF THEFT CRIMES
Authors: Lv X. , Jing C. , Wang Y. , Jin S.
Published: October 2022

Abstract. Accurate crime prediction plays an important role in public safety, providing technical guidance and decision support for the police and government departments. Due to the dynamics and imbalance of crime distribution, it is difficult to build predictive models for it. Specifically, the fine-grained and non-linear spatiotemporal dependencies of crime data cannot be captured accurately. In this paper, a neural network model ST-ACLCrime based on ConvLSTM and SE block was proposed to predict the number of theft crimes in hotspot areas. By overlaying ConvLSTM layers, fine-grained spatiotemporal dependencies are captured while preserving spatial location information. To further enhance the global channel feature representation, SE block is used to recalibrate the channel features and enhance the channel inter-dependencies. In addition, the closeness and the period components are set to dynamically capture the dependence of different time trends. We choose the city of Chicago as the study case, and use a multi-level spatial grid to divide the whole city area. The experimental results show that the proposed model exceeds all baseline model, such as HA, CNN, LSTM, CNN-LSTM and ConvLSTM. It was effectively capturing spatiotemporal dependence and improving prediction accuracy.
INDOOR AND OUTDOOR STRUCTURED MONOMER RECONSTRUCTION OF CITY 3D REAL SCENE BASED ON NONLINEAR OPTIMIZATION AND INTEGRATION OF MULTI-SOURCE AND MULTI-MODAL DATA
Authors: Shao Z. , Cheng G. , Yi Y.
Published: October 2022

Abstract. 3D Real Scene is an important part of new infrastructure construction, which provides a unified spatial base for economic and social development and informatization of various departments. According to the demand of real 3D Real Scene digital construction, this paper aims to study the method of indoor and outdoor structured monomer reconstruction of city 3D Real Scene, develop digital twin platform, and strive to lead the development of 3D Real Scene. The main research contents of this paper are as follows: 1) Spatio-temporal-spectral-angular remote sensing observation system and data fusion model; 2) Rapid construction method of vector monomer 3D model of indoor and outdoor entity object; 3) 3D structural reconstruction technology of urban component level based on vector images; 4) A universal digital twins platform, LuojiaDT. The technologies we have developed have been widely used in the national 3D Real Scene construction of China, including smart city, smart transportation, cultural heritage protection, public security and police, urban underground pipe network, indoor and outdoor location service. This research will promote the continuous development of digital twins technology.
ANALYSIS OF NEW RETAIL LOCATION BASED ON GIS SPATIAL ANALYSIS—TAKE STARBUCKS AND LUCKIN COFFEE FOR EXAMPLE
Authors: You Y. , Zhang J.
Published: October 2022

Abstract. This paper selects nine administrative districts in Shenyang, and compares the spatial distribution patterns and factors influencing the location of the new retail brand Luckin Coffee and the classic traditional retail brand Starbucks in Shenyang. The results show that the traditional retail Starbucks tends to be distributed in a single-center pattern, while the new retail brand Luckin Coffee tends to be distributed in a multi-center decentralized pattern. According to the significance of the influence on spatial layout, the most important factor influencing the location of traditional retail Starbucks Coffee is the density of commercial plazas, while the most important factor influencing the location of new retail brand Luckin Coffee is the density of commercial office buildings. The new retail brand Luckin Coffee, with its Internet gene, weakens the spatial resistance of its store location.
A NOVEL ADAPTIVE REMOTE SENSING PANSHARPENING ALGORITHM BASED ON THE ICM
Authors: Zhao H. T. , Li X. J. , Li Y. K. , Ge J. F. , Xu X. Y.
Published: October 2022

Abstract. In the paper, a novel Intersecting Cortical Network Model (ICM) based adaptive pansharpening algorithm is proposed to solve the deficiency of spectral distortion and texture detail missing in the remote sensing image fusion. The Shuffled Frog Leaping Algorithm (SFLA) is used in the proposed method to adaptively optimize the ICM model parameters. The fitness function of SFLA is constructed by fusion evaluation index Q4 and SAM, which can generate the irregular optimal segmentation regions. Then, these regions are used to adaptively extract the detail information of the panchromatic image. Finally, the sharpened higher resolution image is obtained with the weighted details and the multispectral upsampling image. Experiments are carried out with the WorldView-2 and GF-2 high-resolution datasets. The experimental results shown that the proposed algorithm performs better compared with the existing pansharpening fusion methods both in the spectral preservation and spatial detail enhancement, which verifies the effectiveness of the algorithm.
Sentiment Analysis in Social Media for Depression Identification Using BiLSTM
Authors: Subathra M. , Meenakshisundaram K.
Published: October 2022

Social networks have evolved into an excellent platform for users to engage with their interested friends and share their thoughts, photographs, and videos that show their emotions, feelings, and sentiments. This opens up the possibility of analyzing social network data for user feelings and sentiments in order to examine their moods and attitudes when talking through these online platforms. Although the diagnosis of depression using social network data has gained popularity around the world, there are other dimensions that have yet to be discovered. In this study, aim to perform a depression analysis on social media data collected from an online public source. The proposed is to explore the impact of Rule based Vader Analyzer and a BiLSTM . The results are calculated and shown using the performance metrics such as recall, precision and accuracy.
NCPR Reducing Path-Discovery Overload in Mobile Adhoc Networks (MANET’s)
Authors: Patil Ajit R. , Ingole Avinash M.
Published: October 2022

In this research manuscript we propose in improved NCPR protocol with security mechanism against attack. less broadcast traffic than flood broadcast method minimizing congestion and collision with maximum data packet delivery the scheme stands in with high density network which is main performance parameter. Due to soaring mobility of nodes in MANETs there exist recurrent connection breakages which direct to recurrent path crash and route Findings. The overhead of a path discovery cannot be deserted. In a route discovery, broadcasting is an original and successful data distribution method, where a movable node blindly rebroadcasts the first established route appeal packets unless it has a route to the target, and thus it causes the broadcast tempest difficulty. In this Manuscript we propose a neighbor coverage- based probabilistic rebroadcast protocol (NCPR0 for reducing routing overhead in MANETs. In arrange to successfully exploit the neighbor coverage knowledge, we recommend a novel rebroadcast delay to resolve the rebroadcast categorize, and then we can gain the more exact supplementary coverage ratio by sensing neighbor coverage knowledge. We also classify a connectivity factor to provide the node density adaptation. By combining the additional coverage ratio and connectivity factor, we set a reasonable rebroadcast probability. Our approach combines the advantages of the neighbor coverage knowledge and the probabilistic mechanism, which can appreciably diminish the number of retransmissions so as to reduce the routing overhead and can also progress the routing performance.
Security and Privacy Concerns for the Modern Technology of Internet of Things
Authors: Majid Baba Samiya , Bala Indu
Published: October 2022

The Internet of Things (IoT) is present in every aspect of our lives. They are used in our households, in hospitals, and outside to monitor and report environmental improvements, deter fires, and perform a variety of other useful functions. However, both of these advantages can come at the expense of significant security and risks privacy. Several academic research have been conducted to counteract these issues and figure out a better way to remove or minimise the threats to the user’s privacy and protection specifications in IoT devices. The survey is divided into four parts. The first section would look at the most important shortcomings of IoT devices and how to overcome them. The description of IoT attacks will be presented in the second section. The final section would look at security problems at various layers.
Application of Artificial Intelligence in Applied Biology and Health Sciences
Authors: Kurian Binoy
Published: October 2022

In modern world, artificial intelligence will substitute or magnify human competency in Applied Biology and Health Sciences. AI is the cognitive brilliance manifested by machines or software. AI is growing as an outstanding field in information technology as it enriched the mankind in many circumstances. The application of AI across different disciplines promises an alternative sustainable solution for all human problems. AI is developed in last 10 years as a life changer; when it stated solving human problems easily. This includes solving protein architecture, drug discovery and design, drug recycling, drug safety, diagnose patients, to choose a suitable crop, to determine when to fertilize crops, to avoid adulteration of seeds, to diagnose the crop infections, for proper application of pesticides and herbicides, to identify and removal of weeds, to identify natural ripening of fruits, for the detection of food nutrient value, agri-products for the health and weather forecasting.
Effect of Different Drying Methods on Antioxidant Activity and Availability of Phytochemicals in Leaves of Costus speciosus, Coccinia grandis and Gymnema sylvestre
Authors: Maheshika Peries Colombage , Navarthne Seneviratne , Wijesinghe Janaka , Priyashantha Henagamage Amila , Coorey Ranil
Published: October 2022

Drying is an important method for preserving phytochemicals in plant materials though the stability is altered with the type of drying method. This study aimed at evaluating the effectiveness of different drying methods on antioxidant activity and phytochemical availability in leaf ethanolic extracts of Costus speciosus, Coccinia grandis, and Gymnema sylvestre. Plant leaves of the selected species were subjected to shade drying at room temperature (25 + 5℃), oven drying at 45 ℃ and freeze-drying methods. The extracts were obtained by a maceration method with 95% ethanol. Obtained ethanolic extracts were analyzed for antioxidant activity in terms of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, 2,2-azino-bis-(3-ethylbenzothiazoline-6-sulfonic acid (ABTS) radical scavenging activity, total reducing power and total antioxidant capacity. Phytochemical availability was determined qualitatively and quantitatively in the extracts. All the plant species and drying methods had a significant difference (p<0.05) for the tested antioxidant activities. Among the drying techniques, the freeze-drying method was more effective to preserve the antioxidant compounds and phytochemicals compared to oven drying and shade drying. The lowest antioxidant activity and phytochemical availability were shown by the leaves subjected to oven drying at 45 ℃. The freeze-dried leaves of C. speciosus ethanolic extract demonstrated the best radical scavenging activity for DPPH and ABTS assays with IC50 values of 57.27 + 2.08 μg/ml, 21.86+ 3.55 μg/ml respectively. Furthermore, the highest content of total phenolics (82.88+2.31 mg GAE/g), tannins (9.58 +2.71mg TAE/ g), terpenoids (45.10+3.46 %), and alkaloids (19.37+3.54%) were recorded in C. speciosus among all dried leaves from the selected species. It can be concluded that the freeze-dried leaves of all plant species showed a higher value of antioxidant activity and phytochemical availability and that can be potentially used for the production of pharmaceuticals and novel functional foods.
Modeling of Climate Effect on Arable Crops Production of Oryza Sativa and Zea Mays in Nigeria Using Fuzzy Logic
Authors: Aimufua G. I. O. , Akimoh J. O. , Jegede A. J.
Published: October 2022

In Nigeria, farmers account for more than a third of the labor force. Despite the government's and other relevant stakeholders' strategies and measures to address climate change's impact on agricultural activities in Nigeria, the country has continued to be threatened by climate change, which has had negative consequences for food production, particularly rice and maize, which are the country's main staple foods. High precipitation owing to moisture levels negates agricultural yields, according to the impacts of temperature and moisture levels on agriculture productivity. Weather disasters, deforestation, disease transmission, and species extinction are all climate variables that limit agricultural production rates. The goal of this research is to use machine learning algorithms to determine the best weather conditions for arable crops. To model and predict agricultural yields, fuzzy logic was used. Secondary data, pre-processing and processing, modeling, and the user interface are the five sections that make up the system design. The harvesting outcomes for each of the months specified From January through December, the majority of the data obtained revealed Harvest values of low (54.8%), fair (23.6%), and good (21.5%) quality. Agricultural activities should be carried out between MAY and SEP, whereas crops that require a lot of rain should be sown between JUL and AUG when rainfall is at its highest. The study concluded that agricultural operations should be carried out between MAY and SEP in order to achieve good harvest quality, based on the data collected, extracted, and assessed. The findings of the study will guide policymakers and farmers toward a bumper harvest, increased income, and a rise in the country's GDP. Finally, crops that require a lot of rain, like rice, should be planted between July and August, according to the research.
Precipitation of Fine Carbides, Transformation of the Retained Austenite and Corrosion Properties on EN-31 Steel by Cryogenic Treatment Process
Authors: Sri Siva R. , Shunmuga Priyan M.
Published: October 2022

Investigate of precipitation carbides transformation of the retained austenite and corrosion properties on EN-31 steel by cryogenic treatment method. In this work to identify the percentage of retained austenite and corrosion rate, was presented on EN 31 bearing steel by which cryogenic treatment process. Further, the hardness values of CHT and DCT were compared by using the microstructure view of the CHT and DCT samples. The optimized cryotreated samples were prepared for metallographic examination as per ASTM E3-01. Then the specimen was subjected to factor level setting like cooling rate, soaking period, soaking temperature and tempering temperature at various conditions. Moreover, the precipitation of fine carbides and the transformation of retained austenite to martensite showed considerable variations in the hardness of the optimized DCT samples compared to the CHT samples. The mean hardness value of this sample is 861HV and 19.20%, 847HV and 17.25%, 838HV and 17.10%, 857HV and 18.40%, 790HV and 13.45% improvement in the hardness compared to CHT.
A Comprehensive Review and Comparative Study of Various Rib Configurations Used in Rectangular Duct for Heat Transfer Enhancement
Authors: Thikane Swapnil , Mashyal Suresh
Published: October 2022

This research study provides a comprehensive review of various heat transfer enhancement techniques that are being used by various researchers for enhancing heat transfer rate and overall thermal performance factor of the heat exchangers. Based on the present review study, it can be concluded that intensive study has been carried out by various researchers to determine the effect of various rib geometries on the heat transfer enhancement and friction factor characteristics of rectangular duct. The enhancement in the heat transfer rate is obtained by introducing the blockages or restrictions in the fluid flow path by providing various rib turbulator configurations. This research paper puts highlights on the various heat transfer enhancement techniques are used for increasing overall thermal performance factor in the heat exchangers. Introduction of ribs as flow disturber results not only into increase in heat transfer surface area but also into increased turbulence, recirculation of working media, viscous effect and secondary flow that results into swirl. The interruptions or blockages created by ribs provided in the flow channel results into increased pressure drop and disruptions in boundary layer development. Provision of ribs also causes secondary flow which ensures enhanced thermal contact between working fluid and surfaces of the flow channel. This results into increase in heat transfer coefficient and thermal performance of heat exchanger respectively.
Urban Sprawl and its effect on Sustainable Urban Development: A Case Study of Shimla City
Authors: Lal Ram , Verma Gulshan , Sharma Aastha
Published: October 2022

A fast-growing developing country like India is witnessing the phenomena of Urban Sprawl and land-use changes in almost all of its megacities which are the epicenters of economic growth, administration, and tourism prospects. The proximity to the growth center, availability of space, and increasing migration mainly lead to the gradual spreading of the urban landscape towards its adjacent regions, resulting in the loss of productive and agricultural lands, open green spaces, and surface water bodies. Considering the speed of urban sprawl growth rate, the city's scale and role have changed frequently. Thus, there is a need to study, surveil, and quantify the urban sprawl for sustainable development of the urban areas. The current study examines the importance of sustainable urban development, which seeks to create cities and towns to improve the long-term health of the planet's human beings and ecological systems. The main focus of this paper is to illustrate the analytical approach and challenges resulting from increasing population growth that has created a need for high-performing, cost-effective, resource-efficient, and environmentally friendly sustainable infrastructure. The task comprises steps delineation of urban area for consecutive years, comparison between urban areas, identification of the urban sprawl pattern, recognition of magnitude and direction of changing sprawl, and its effect on land use/land cover.
Geotechnical Evaluation of the Causes of Road Pavement Failure Along the Mbaitoli-Ikeduru-Mbaise Highway, Southeastern Nigeria
Authors: Chijioke Nwachukwu Martin , Godwin Aleke Chikwado , Pius Aghamelu Okechukwu , Uchenna Joesph Ezea
Published: October 2022

The incessant failed spots in the road pavement along the 27.61km Mbaitolu-Ikeduru-Ahiara Mbaise highway in Imo state, southeastern Nigeria, has necessitated this geotechnical study to evaluate the impact of the subgrade material quality on the road pavement failure. The study entailed both physical and geological assessment of the predominantly sandy to clayey silt Benin Formation underlying the area, as well as field and laboratory engineering tests on the subgrade materials underlying the road alignment. The engineering tests carried out included the grain size distribution, specific gravity, natural moisture content, Atterberg (liquid and plastic) limits, compaction (to determine maximum dry density and optimum moisture content), California bearing ratio (CBR), and shear strength (to determine cohesion and angle of internal friction). The engineering test results indicate that the natural moisture content values range between 10.4 and 18.9%, liquid limit between 36.0 and 48.6%, plastic limit ranges from 26.1 to 31.6, with corresponding plasticity index values that range from 9.2 to 17.0%. The grain size distribution test results connote the predominance of sand, with number of fines-sized particles that ranges from 22 to 38% and amount coarse to medium sand-sized particles ranging from 62 to 78%.Maximum dry density ranges from 1.29to 1.93 kg/m3. (CBR) ranges from 3.7 and 91.9%. Specific gravity ranges between 2.56and 2.61, while cohesion and angle of internal friction range from 39 to 42 kPa, and 14 to 20°, respectively. Some failed portion especially in Inyishi area were likely due to moisture fluctuation in the subgrade due to closeness of the water table. Proper compaction, chemical stabilization, and appropriate drainage would likely boost the efficiency of the subgrade material and the durability of the road pavement.
Exploration of Approximate Probabilistic Distributions in GeoGebra: An Experience in the Faculty of Engineering
Authors:   Universidad Nacional de Entre Ríos, Battisti Marisa , Kohan Diana ,   Universidad Nacional de Entre Ríos
Published: October 2022

This article is a report of the change made in the teaching and learning methodology of the topic Approximate Probability Distributions, in the framework of the subject Probability and Statistics, given in the undergraduate courses of the Faculty of Engineering of the National University of Entre Ríos, Argentina. In order to achieve better learning processes, two GeoGebra teaching tools were developed and used in both theoretical and practical classrooms to solve a series of problematic situations. It is perceived that the activity allowed spaces in which the student had to question the implementation of certain resolution methodologies, which usually develops mechanically without asking if the procedures used align with the theoretical foundations. El presente artículo es un reporte del cambio efectuado en la metodología de enseñanza y aprendizaje del tópico Distribuciones Probabilísticas Aproximadas, en el marco de la asignatura Probabilidad y Estadística, dictada en las carreras de grado la Facultad de Ingeniería de la Universidad Nacional de Entre Ríos, Argentina. Con intenciones de alcanzar mejores procesos de aprendizaje se realizaron dos instrumentos de enseñanza en GeoGebra que fueron utilizados tanto en la clase teórica como en la práctica para resolver una serie de situaciones problemáticas. Se percibe que la actividad posibilitó espacios en los cuales el estudiante debió cuestionarse la implementación de ciertas metodologías de resolución, que habitualmente desarrolla de manera mecánica sin preguntarse si los procedimientos empleados se alinean con los fundamentos teóricos.
Strategies For Teaching Statistics In Public Schools In Southern Colombia During The Pandemic Period
Authors:   Universidad del Cauca, Riascos Forero Yilton
Published: October 2022

The COVID-19 pandemic has been “an unprecedented crisis across the board.” In education, 160 million students in Latin America and the Caribbean were affected, according to UNESCO. For this study, in Colombia, three elementary and middle school teachers were interviewed about strategies for teaching statistics in rural areas under these conditions. Despite deficiencies in their disciplinary training, they sought to implement distance education without good results because in addition to difficulties in technological connectivity evidenced ignorance of the technology and weaknesses to use it, opting for strategies with workshops and paper guides as evidence to follow the performance of their students, no efforts were observed to diversify and implement synchronous and asynchronous activities in virtual applications. La pandemia del COVID-19 ha sido “una crisis sin precedentes en todos los ámbitos.” En educación, en América Latina y el Caribe se afectó a 160 millones de estudiantes según la UNESCO. Para este estudio, en Colombia, se entrevistó a tres docentes de básica y media sobre estrategias para la enseñanza de la estadística en zonas rurales en estas condiciones. A pesar de deficiencias en su formación disciplinar, buscaron implementar educación a distancia sin buenos resultados porque además de dificultades en la conectividad tecnológica evidenciaron desconocimiento de la tecnología y debilidades para utilizarla, optando por estrategias con talleres y guías en papel como evidencias para seguir el desempeño de sus estudiantes, no se observaron esfuerzos por diversificar e implementar actividades sincrónicas y asincrónicas en aplicaciones virtuales.
Empowering Non-specialists to Interpret and Disseminate Statistics Through Structured Assessments
Authors:   University College London, Honnor Thomas , Abourashchi Niloufar ,   University College London, Rassias Matina ,   University College London
Published: October 2022

Assessment is an important tool for quantifying each student’s relative ability but when carefully designed can also be used to educate and empower students. Because the importance of statistical understanding is becoming increasingly recognised, there is a subsequent growth in non-specialist students taking statistics service courses. The assessments for such courses can define students’ personal course aims and level of engagement and set the tone for their future interactions with the subject. We have designed and implemented a modernised assessment pattern, with assessments structured to build upon each other and lead students from the foundations of probability to interpretation and communication of authentic statistical analyses. We discuss our experiences having implemented this new assessment pattern across three courses, totaling more than 600 students.
Statistical Literacy and Curriculum Guidelines in Brazil for the Early Years of Elementary School: A Reflection
Authors:   Universidade Federal do Rio de Janeiro, Baccar Maria Helena , Landim Flávia ,   Universidade Federal do Rio de Janeiro, Rangel Letícia ,   Universidade Federal do Rio de Janeiro, Leal Vanessa ,   Universidade Federal do Rio de Janeiro, Oliveira Raquel ,   Universidade Federal do Rio de Janeiro, Novaes André ,   Universidade Federal do Rio de Janeiro, Reis Valeria ,   Universidade Federal do Rio de Janeiro
Published: October 2022

This work has been developed by the Fundão Project: Statistics and Probability, a collaborative project of the Federal University of Rio de Janeiro (UFRJ), composed of professors, undergraduate students, and school level mathematics teachers. The research is part of a study whose focus is the teaching and learning of statistics and probability at elementary school. Our goal is the professional development of mathematics teachers. Focusing on the initial stage of statistical literacy and based on an integrated approach, we considered the (recent) Brazilian normative orientations of Common National Curriculum Basis (BNCC) in statistics and probability to propose didactic sequences along the lines of the resolution of statistical investigation problems by the Guidelines for Assessment and Instruction in Statistics Education (GAISE).
Where Is Data Ethics in Postsecondary Data Science Courses?
Authors:   Middlebury College, Lyford Alex , Mei Katelyn ,   Middlebury College
Published: October 2022

The number of postsecondary data science courses, especially at the introductory level, has increased dramatically over the past decade. As the prevalence and complexity of data-related challenges increases, so too does the need to teach students how to handle these issues in an ethically-responsible manner. In this work, we use a combination of course syllabi and course descriptions from 361 data science courses across 81 postsecondary institutions in the United States. We find that fewer than half of all data science courses contain any elements of data ethics, broadly defined. We conclude with examples of the different ways that data ethics is integrated into data science courses across the United States and a vision for data ethics moving forward.
Workplace Statistics Training Moved Online
Authors:   University College London, Wade Angie , Koutoumanou Eirini ,   University College London
Published: October 2022

The COVID-19 pandemic forced (or quickened the advent of) online short course statistics training for non-statistical professionals. Participants in traditional statistics programs generally form a cohort who progress together over some protracted timescale, forging support networks and friendship groups, some of which will be maintained throughout professional careers. By contrast, short courses for professional purposes are often standalone and half a day to one week in duration. Participant focus lies with obtaining skills for usage within the foreseeable future, such that creating social networks with other participants is of little or no importance. We present our experiences with two years of online, examination-free, short course statistics training for the period from March 2020 to 2022.
Validity Evidence for Statistics Education Instruments: Findings and Best Practices
Authors:   Midwestern University, Bolch Charlotte , Tjoe Hartono ,   The Pennsylvania State University, Casey Stephanie ,   Eastern Michigan University, Whitaker Douglas ,   Mount Saint Vincent University, Harrell-Williams Leigh ,   The University of Memphis, Engledowl Chris ,   New Mexico State University, Mulé Taylor ,   The University of Memphis, Piontek Justine ,   The University of Memphis
Published: October 2022

Appropriate validity evidence is essential when interpreting scores from tests and instruments. In statistics education, the application of modern measurement theory is limited and not well integrated into research as support for instrument interpretation. The Validity Evidence for Measurement in Mathematics Education project is documenting validity evidence for mathematics and statistics education instruments through a structured literature review to create a searchable instrument database. This paper highlights the status of the work documenting validity evidence for statistics education instruments measuring constructs such as teacher knowledge and attitudes. Many “custom” single-study measures incorporated items from multiple validated instruments and/or added new items without providing evidence for the new instrument. Preliminary information about the types of validity claims and evidence identified from standardized coding is reported.
Stochastic Knowledge of Future Chilean Mathematics Teachers
Authors:   Pontificia Universidad Católica de Valparaíso, Ruz Felipe , Chance Beth ,   California Polytechnic State University, Medina Elsa ,   California Polytechnic State University
Published: October 2022

Mathematics teachers are key agents in teaching stochastic at school, so we are interested in analyzing their knowledge of the content after completing their initial training. We conceptualized the knowledge of the content from the Teacher's Didactic-Mathematical Knowledge Model and following a quantitative methodology, we analyzed a sample of 269 future Chilean mathematics teachers. The results were generally low, although higher in terms of stochastic knowledge promoted at school level (common knowledge) compared to the skills developed during initial teacher training (extended knowledge). We conclude with recommendations to incorporate modern and effective methodologies for teaching and learning stochastic, which we hope can be an input for those responsible for stochastic teacher education. Los profesores de matemática son agentes claves en la enseñanza de la estocástica en la escuela, por lo que nos interesamos en analizar su conocimiento del contenido tras haber completado su formación inicial. Conceptualizamos al conocimiento del contenido desde el Modelo de Conocimiento Didáctico-Matemáticos del profesor y siguiendo una metodología cuantitativa, analizamos una muestra de 269 futuros profesores de matemáticas chilenos. Los resultados fueron en general bajos, aunque superiores en lo relativo al conocimiento estocástico promovido a nivel escolar (conocimiento común) en comparación a las habilidades desarrolladas durante la formación inicial docente (conocimiento ampliado). Finalizamos con recomendaciones para incorporar metodologías modernas y efectivas para la enseñanza y aprendizaje de la estocástica, que esperamos puedan ser un insumo para los responsables de la educación estocástica del profesorado.
Learning From COVID-19
Authors:   Univerity College London and Klagenfurt University, Kapadia Ramesh , Bilgin Ayse Aysin ,   Macquarie University
Published: October 2022

People’s lives have been severely disrupted by COVID-19, with high numbers in hospitals and many deaths after the initial outbreak in China. If the efforts of many statistics educators were fulfilled to increase the number of statistically literate citizens who can make evidence-based decisions based on accurate data, we may have had lower infection and death rates. Unfortunately, the data is rather unreliable, especially with the use of self-testing and reporting. In this paper, we assert that statistics requires context to quantify risk. We present data and a summary of developments in Australia and England with the hope that case studies can be developed for students to understand risk better.
How Do Future Elementary School Teachers Deal With Students’ Mistakes in Probability Assignments About Fair Play?
Authors:   Universidad de Granada, Burgos María , Albanes Verónica ,   Universidad de Granada, López-Martín Mª del Mar ,   Universidad de Almería, Aguayo-Arriagada Carmen ,   Universidad de Almería
Published: October 2022

An aspect of interest in teacher training is the development of knowledge and skills that allow organizing teaching and the proposal of actions to overcome the difficulties encountered by students. The non-reversibility of random phenomena, the determination of the sampling space and the different meanings associated with probability, among others, generate certain biases that hinder the acquisition of adequate probabilistic reasoning. In this paper we propose to explore and characterize the ways of action of future primary education teachers when facing situations in which the mistakes of fictional students are addressed in front of an equitable play task. Un aspecto de interés en la formación docente es el desarrollo de conocimientos y competencias que permitan organizar la enseñanza y la propuesta de acciones para superar las dificultades que encuentran los estudiantes. La no reversibilidad de los fenómenos aleatorios, la determinación del espacio muestral y los distintos significados asociados a la probabilidad, entre otros, generan ciertos sesgos que dificultan la adquisición de un adecuado razonamiento probabilístico. En el presente trabajo nos proponemos explorar y caracterizar las formas de actuación de futuros profesores de Educación Primaria al enfrentarse a situaciones en las que se abordan los errores de estudiantes ficticios frente a una tarea de juego equitativo.
Statistical Curriculum In Bolivia, Changes In The Era Of Big Data
Authors:   Universidad Mayor de San Andrés, Chirino Gutierrez Alvaro , Mamani Huanca Sherli ,   Fundación Aru, Mamani Mamani Beatriz ,   Fundación Aru
Published: October 2022

This paper presents the development of the statistical curriculum in the school and university system in Bolivia, a review of the current regulations is performed, and the contents related to statistics in the primary and secondary cycle are described. For the undergraduate degree in the university, the process of the last curriculum at the Universidad Mayor de San Andrés is described and the statistical profile described through interviews with the career directors of the only two universities that offer the program in statistics is included El presente documento presenta el desarrollo de la currícula estadística en el sistema escolar y universitario en Bolivia, se realiza una revisión de la normativa vigente, se describen los contenidos vinculados a la estadística en el ciclo primario y secundario. Para el pregrado en la universidad se describe el proceso del último plan de estudios en la Universidad Mayor de San Andrés y se incluye el perfil estadístico descrito mediante entrevistas a los directores de carrera de las dos únicas universidades que ofrecen el programa en estadística