bluuebunny/crossref_metadata_2025 huggingface dataset:
The Influence of Organizational Culture and Job Competence on Employee Work Productivity at PT Patriot Intan Abadi, Tanah Laut Regency
Authors:   Pancasetia College of Economics Banjarmasin, Melania Melania , Saputra Ketut Adi ,   Pancasetia College of Economics Banjarmasin, Kadi Abdul ,   Pancasetia College of Economics Banjarmasin, Amrulloh Rifqi ,   Pancasetia College of Economics Banjarmasin
Published: December 2023

This study aims to examine the influence of organizational culture and job competence on employee productivity at PT Patriot Intan Abadi, Tanah Laut Regency. The population in this study comprises all employees of PT Patriot Intan Abadi, totaling 137 employees. The sampling technique used in this research is probability sampling, employing the Taro Yamane or Slovin formula for calculation. Based on the Slovin formula calculation, the determined sample size is 58 respondents/employees. The data analysis used in this research is multiple linear regression with statistical tools facilitated by the IBM SPSS v.25 program. The results of this study indicate that organizational culture and job competence have an influence on the work productivity of employees at PT Patriot Intan Abadi. The job competence variable has a dominant influence on the work productivity of employees at PT Patriot Intan Abadi, Tanah Laut Regency.
Saudi School Children's Oral Health and Hygiene Literacy and Behaviors
Authors:   Ministry of Health, Saudi Arabia, Moied Alwadie Saleh Ahmed , Ali Al Mastur Abdulaziz ,   Ministry of Health, Saudi Arabia, Jawad Alkhars Fatemah Ahmed ,   Ministry of Health, Saudi Arabia, Ageel Alenezi Seham ,   Ministry of Health, Saudi Arabia, Mohammed Alqhtani Ebtisam Saeed ,   Ministry of Health, Saudi Arabia, Alaqade Amerah Mohammed ,   Ministry of Health, Saudi Arabia, Ayad Alanazi Turki Ibrahim ,   Ministry of Health, Saudi Arabia, Dukiman Alharbi Fawwaz Mugbel ,   Ministry of Health, Saudi Arabia, Almutairi Ali Radhi Ali ,   Ministry of Health, Saudi Arabia
Published: December 2023

Background: Awareness, attitude, and routines greatly affect oral health. This survey examined Saudi schoolchildren's oral hygiene knowledge, attitude, and behavior in Al Qassim. Materials and Methods: A cross-sectional study was conducted in the Al Qassim region, focusing on school children. The study utilized a pretested online questionnaire as the primary data collection tool. The initial segment of the questionnaire documented the participants' age and dental background, while also capturing their respective experiences. The subsequent section addressed the topics of dental hygiene and dental appointments. The third section of the study was the assessment of students' knowledge on oral hygiene. Results: The study revealed that 39% of the students possessed a satisfactory level of oral hygiene knowledge. However, it was observed that students who brushed their teeth regularly or twice a day exhibited a considerably greater level of oral hygiene knowledge (p<0.001). Approximately 56.8% of the student population indicated experiencing oral issues, whereas 20.7% disclosed never having sought dental care during their lifetime. There was a statistically significant difference in the frequency of dental visits seen among students aged 12 years and older last year (p=0.010). Conclusion: It is imperative for educational institutions in Saudi Arabia to initiate the integration of a comprehensive oral health education program that encompasses both students and their parents.
Analysing Market Trends with AI
Authors:   Tech Lead at Nationwide, Sangarsu Raghavendra
Published: December 2023

Businesses now operate differently due to disruptive technologies such as the Internet of Things, big data analytics, blockchain, and artificial intelligence. The latest disruptive technology is artificial intelligence (AI), which has the greatest potential to change marketing. International practitioners are trying to identify the best AI solutions for their marketing function. However, a thorough review of the literature can demonstrate the value of artificial intelligence (AI) in marketing and suggest future areas of research.
Demand Planning Integration Best Practices: SAP SCM Perspective – PART 3
Authors:   Technology Professional, Department of Information and Technology, Stryker, USA, Azmeera Rajesh
Published: December 2023

SAP Supply Chain Management is one of the key modules in SAP ERP and controls Production Planning, business forecasting and demand planning. It helps the organization to manage their supply chain process in a dynamic environment. SAP SCM is a complete software to cover key processes such as supply chain networking, supply chain planning and coordination, and supply chain execution
Machine Learning for Software Developers: An Introductory Guide
Authors:   8921 Satterlee Ave Se, Snoqualmie, WA, USA, Thatikonda Vamsi
Published: December 2023

The paper acts as a guide into the fundamental principles of Machine Learning, and its significance for software developers. It offers insight into types of machine learning, important concepts, and relevant practical applications of the concepts to provide a deeper understanding of the context. Tools and frameworks have also been presented, which are necessary to implement a machine learning algorithm within the organization. The paper however, emphasizes over the importance of data preparation, model building and training for successful implementation. Additional resources and practical examples that might be useful towards learning and application have also been provided to make it a seamless process of information exchange. It also addresses common challenges, ethical concerns and latest trends emerging within the field, to ensure that a complete overview has been presented to software developers to prepare for the future effectively
Utilizing AI and Machine Learning for Human Emotional Analysis through Speech-to-Text Engine Data Conversion
Authors:   USA, Shukla Abhishek
Published: December 2023

Artificial Intelligence (AI) and Machine Learning (ML) technologies have revolutionized various domains, and one of their fascinating applications is in analyzing human emotions through speech-to-text data conversion. This article delves into the innovative use of AI and ML to process spoken words into text and subsequently evaluate human emotions embedded within the speech. We explore the development of speech-to-text technologies, the essential function of natural language processing (NLP), and the use of deep learning models to understand emotional cues. Our research discoveries illustrate how these technologies are altering fields like mental health, customer service, and market research, granting deeper understanding of human emotional states. By scrutinizing emotions conveyed in spoken language, artificial intelligence (AI) and machine learning (ML).
Effect of Food Storage Conditions on Shelf-Life in Uganda
Authors: Namono Akello
Published: December 2023

Purpose: The aim of the study was to investigate effect of food storage conditions on shelf-life in Uganda

Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.

Findings: The shelf-life of food products is influenced by factors like temperature, humidity, and microbial activity, making proper food storage crucial for combating food spoilage and waste, especially in regions with high food insecurity. A World Bank survey highlighted that low access to essential goods was primarily due to financial constraints and rising prices, with a significant portion of the population experiencing food insecurity. Recent developments in food storage include the use of natural antimicrobials, nanotechnology applications, advanced pathogen detection methods, improved storage management practices, and biotechnological processes for waste conversion. These advancements aim to extend food product shelf life, reduce spoilage, enhance food safety, and contribute to food security in Uganda.

Unique Contribution to Theory, Practice and Policy: Arrhenius equation theory, water activity theory & oxidation theory may be used to anchor future studies on effect of food storage conditions on shelf-life in Uganda. Create user-friendly, informative food labels that clearly highlight allergen information and provide consumer-friendly guidance on reading labels. Mandate standardized allergen labeling formats and allocate funding for public awareness campaigns on allergen safety.
ACHIEVING GOOD GOVERNANCE AND SUSTAINABLE DEVELOPMENT IN NIGERIA THROUGH EMOTIONAL INTELLIGENCE AND LEADERSHIP DEVELOPMENT IN EDUCATION
Authors:   School of Education, Department of Educational Psychology,  Federal College of Education Okene, Kogi State, Sabdat Idris
Published: December 2023

This study aims to investigate how the integration of emotional intelligence and leadership development in education can contribute to achieving good governance and promoting sustainable development in Nigeria. The study employs qualitative research method in which analytic approach was used to review literature on emotional intelligence and its important, influence of emotional intelligence on leadership and sustainable development, the role emotional intelligent play on good governance, and how emotional intelligence can be used to nurture leadership development in Nigeria education system. Drawing from analysis of relevant literature, the author advocates the integration of emotional intelligence into the curriculum. It is believed that integration of emotional intelligence would equip educators and students with self-awareness, empathy, effective communication, and conflict resolution skills. This would empower future leaders to navigate complex social landscapes. This, in turn, would enable them to address societal issues with a greater understanding of the human dimensions involved. The researcher made further recommendations which include inter alia, that: The Nigerian government should prioritize the integration of emotional intelligence and leadership development programs among Educators. Educators should emphasize ethics, social responsibility, and sustainability across various subjects and cultivation of leadership skills among students. Furthermore, more trained Guidance Counsellors should be employed in the Nigerian educational sectors to cushion the effect of unwholesome development of emotional intelligence and enhancing good governance and sustainable development in Nigeria.
ASSESSING THE ATTITUDINAL BEHAVIOUR OF STUDENTS WITHIN JOS METROPOLIS TOWARDS HOMOSEXUALITY AND ITS IMPLICATION ON PSYCHOLOGICAL ADJUSTMENT
Authors:   Department of General Studies,                                                                                                                                                                                                    Plateau State Polytechnic, Jos, Nigeria, Chingle Isaac
Published: December 2023

A 2x2 factorial design is adopted for the study, one hundred and twenty (120) participants who are students are drawn from four different schools all within Jos metropolis. University of Jos, Plateau State Polytechnic, Jos campus, College of Heath Technology Zawan and School of Nursing Jos Vom. The participant had age range of between 15-20 years, with frequency 52, representing 43.3%, age range of 21-25 years has frequency 47 representing 39.2%, age range of 26 -30 years has a frequency of 21, representing 17.5%. 58 of the participants are Christians, representing 48.3%, while 62 are Muslims, representing 51.7%. 102 of the participants were singles representing 85%, while 18 were married representing 15%. Males has a frequency of 66, representing 55%, while females are 54, representing 45%. Two instruments are used for the study; the homosexuality attitude scale (HAS) and the psychological adjustment test report scale (PATRS), Three hypothesis were tested in the study. Hypothesis one result show that participants with positive attitude toward homosexuality had higher psychological adjustment than participants with poor attitudes towards homosexuality, t-.266 (118), =0.4.63, P=0.789. The hypothesis was therefore not supported. The second hypothesis result shows that females significantly adjusted psychologically than their male counterparts, t 118(1), p=0.020. The hypothesis was therefore accepted. The third hypothesis result shows that Christian participants had higher psychological adjustment compared to Muslim participants, the hypothesis was not supported. Given the role some society and schools have played against homosexuality especially in regard to negative attitudes, there is an urgent need for the society and relevant agencies to play proactive role in fighting traditional sexual prejudice and the need to identify those who are victims and rehabilitate them through available psychological interventions. The study, therefore, recommend the need to sensitize the general public on building positive attitudes towards homosexuals is sacrosanct. This is because these people are suffering from conscious and unconscious psychological disorders which may prompt them into such negative attitude. Therefore, only a society that understands them and show them love can have a positive influence on them.
YAHOO BOYS ACTIVITIES AND THE NIGERIAN ECONOMY: MATTERS ARISING
Authors:   Department of Sociology & Anthropology, Faculty of Social Sciences, Federal University Otuoke, Bayelsa State, Nigeria., Ene Warikiente , Olomu Michael ,   School of Postgraduate Studies, Department of Sociology & Anthropology,  Faculty of Social Sciences, Federal University Otuoke, Bayelsa State, Nigeria.
Published: December 2023

This study examines the impact of yahoo boys’ (young fraudsters) activities on the Nigerian economy, drawing from the perspectives of Merton's theory on structural strain and crime, as well as Emile Durkheim's anomie theory. To accomplish this, the research employed a content analysis methodology, primarily relying on secondary data sources. The findings of this study illuminate the substantial economic repercussions of these individuals' actions, with Nigeria incurring significant financial losses running into billions of Naira. These "yahoo boys" not only fail to contribute productively to the economy but also engage in illicit activities to accumulate wealth. Consequently, their activities lead to financial losses for both individuals and businesses, exert pressure on economic policies, and present formidable challenges for the government, such as combating inflation, managing welfare crises, and addressing business disruptions, among other issues. The driving forces behind the activities of these young fraudsters include greed, the scarcity of employment opportunities, and the perceived failure of the government to effectively penalize criminal behavior. As a result, this study recommends that the government should implement policies aimed at fostering youth employment prospects. Additionally, stringent measures should be established to regulate and curtail the misuse of internet technology, with a particular focus on discouraging illegal activities. Furthermore, government and other relevant organizations should engage in youth-oriented programs aimed at redirecting their skills towards constructive endeavors while instilling core values of honesty, integrity, and hard work. These initiatives are vital steps towards mitigating the adverse effects of "yahoo boys" activities on the Nigerian economy and society as a whole.
The course of the postpartum period in cows in the presence of concomitant pathology
Authors: Chekan Oleksandr , Dopa Vyacheslav , Musiienko Yurii , Plyuta Larysa , Risovaniy Vitaliy
Published: December 2023

The decrease in productivity in cows is directly related to the pathologies of the organs of the reproductive system that occur at the end of the transit period, therefore it is important to improve the methods of diagnosis and preventive therapy of cows in this period. The purpose of the work was to establish the forms of pathologies that occur in the transit period in cows, their interrelationship and the causes that cause them in terms of seasonal dynamics. When conducting research, morphological, clinical (examination, palpation), laboratory (bacteriological studies of the uterus), and statistical (statistical reliability) methods were used. The research was conducted in the limited liability company “Milk of the Fatherland” in 2 departments where unattached housing is used. The occurrence of subinvolution of the uterus against the background of litter retention and persistent corpus luteum was established by 9.5% more in the 1st department. A 43.5% correlation of the prevalence of delayed involution with endometritis was revealed. An increase, 12.8% in January and up to 14.1 in February, of cases of subinvolution of the uterus was established, while in May the similar indicator was 5.0%. The prevalence of persistent corpus luteum was established up to 16.8%, luteal cyst – up to 1.7%, ovarian follicular cyst – 2.1%. The presence of microflora in the uterine cavity (E. coli, Ps. aeruginosa and S. aureus – 49.8% S. aureus and E. coli – 33.5%, Pr. vulgaris and E. coli – 16.7%) provoked the occurrence inflammatory processes: chronic endometritis – 10.4%, vulvovaginitis – 18.1%, cervicitis – 21.4%. Research results can be used to develop new and improve existing methods of treatment of cows with pathology of genital organs in the transit period in farms of different forms of ownership
Ağır Yük Araçları Park Alanlarının Karakteristikleri: Türkiye’ye Yönelik Bir İnceleme
Authors: Taşcı Sevil Ayça , Tektaş Necla
Published: December 2023

Çekici, römork, yarı-römork, kamyon, tır ve diğer ağır yük araçlarının yol kenarları, rampalar, boş alanlar ve ara sokaklar gibi yerlere park etmesi, güvenlik riski oluştururken altyapıyı da olumsuz olarak etkilemektedir. Uzun mesafeli yük taşımacılığı için kullanılan iyi aydınlatılmış, güvenli, emniyetli, konforlu ve yerleri belirlenmiş park alanları sayesinde ağır vasıta araç sürücülerinin stresinin azalması, sürücü, yük ve trafik güvenliği ile emniyetinin artması mümkün olabilmektedir. Uygun park alanlarının yetersiz olması veya bulunmaması, tedarik zinciri ve lojistik hizmetlerinin verimliliği ile rekabet gücünü etkileyen ulusal ölçekte bir kavram olarak karşımıza çıkabilmektedir. Bu doğrultuda üst seviye yönetim iradesi ile ulusal ölçekte ve bütüncül bir yaklaşımla ihtiyaç, fayda ve maliyet analizi sonuçları dikkate alınarak merkezi çözümler geliştirilmesinin ağır yük araçlarının park ihtiyacının giderilmesine yönelik uygun bir yöntem olacağı değerlendirilmektedir. Bu çözümlerin sağlanmasında düzenleme, teşvik ve destekler önemli imkanlar sunacaktır. Uygun planlanma ve tasarımla, doğru yerlerde konumlandırılan nitelikli ve yeterli tır parklarının, sürdürülebilir ve sağlıklı toplum hedefleri ile ekonomik kalkınmaya katkısı olacağı öngörülmektedir. Bu çalışmanın amacı, yük taşımacılığı sürecinin gerektirdiği dinlenme, güvenlik, bakım gibi konularda ihtiyacı karşılayacak yeterli tır parkı alanı sağlanması için mevcut uygulama ve başarılı örnekleri değerlendirerek bir yol haritası ortaya koymaktır. Literatür taramasıyla ulusal ve uluslararası ölçekte mevzuat, proje ve raporlar ile teknik şartnameler, resmi ve özel kurumların internet sayfaları incelenerek araştırma makalesi olarak düzenlenen bu çalışmanın; taşımacılık sektöründe yer alan işletme, girişim ve çalışanlar ile politika yapıcılara ve sektörün diğer tüm paydaşlarına faydalı olması ve literatüre katkı sağlaması hedeflenmektedir.
The Effect of Different Planting Density on The Vegetative, Pomological qnd Chemical Properties of Some Sea Buckthorn (Hippophae rhamnoides) Varieties
Authors: OLEİCHENKO Sergey , ABLAY Akan , DEMİRTAŞ İsmail
Published: December 2023

This study was carried out in the conditions of Okenov farm, Issyk-Kul region, Cety-Oghuz district. As materials, Athena, Inya, Etna, Dwarf (male) Elizabeth (K) and False oleaster varieties developed by the Scientific Research Institute of Horticulture of Siberia were used. The varieties were evaluated in terms of yield, fruit weight, biochemical contents, morphological and phenological characteristics. At the end of the study, the data obtained from the varieties were evaluated and an economic analysis was made. As a result of the study, Athena and Inya varieties were found to be superior to the control variety in terms of yield (26%) and dry matter (24%). In the experiment, the largest fruits were detected in the Athena variety, which produced 14% larger fruits than the control variety, and the smallest fruits were determined in the Etna variety, which produced 10.2% smaller fruits than the control variety. It was determined that the Etna variety had the highest value in terms of biochemical contents such as Vitamin C, carotene and pectin compared to the other varieties included in the study. Considering all evaluations, Athena and Inya' varieties have been determined as promising varieties for this region.
Atmospheric electricity observations at Eskdalemuir Geophysical Observatory
Authors: Harrison R. Giles , Riddick John Carter
Published: December 2023

Abstract. Atmospheric electricity measurements, principally of the hourly Potential Gradient (PG), were made continuously at Eskdalemuir Observatory, Scotland, (55.314° N, 356.794° E) between 1911 and 1981. Air ion properties were also determined. The sensing apparatus for PG measurement at Eskdalemuir initially used a Kelvin water dropper potential equaliser (1911–1936), followed by a radioactive probe from 1936 and, from 1965, a horizontal stretched wire sensor at 0.5 m, all attached to recording devices. Monthly mean PG data from these instruments is now available digitally. Originally, the data was classified into undisturbed and disturbed days, using the chart record (electrogram). This approach has limitations at Eskdalemuir due to mist, fog and calm conditions, which can influence the mean PG despite the day appearing undisturbed on the electrogram. Nevertheless, a correlation with Pacific Ocean temperature fluctuations is apparent in the Eskdalemuir PG data between 1911 and 1950. As at Lerwick, there was an abrupt decrease in the PG caused by nuclear weapon detonations in the late 1950s and early 1960s. The 1950s PG decrease began at Eskdalemuir before that at Lerwick, for which possible additional local factors are evaluated.
A DECADE OF PROGRESS IN TOPO-BATHYMETRIC LASER SCANNING EXEMPLIFIED BY THE PIELACH RIVER DATASET
Authors: Mandlburger G. , Pfennigbauer M. , Schwarz R. , Pöppl F.
Published: December 2023

Abstract. Topo-bathymetric laser scanning featuring high spatial resolution and a seamless transition within the littoral zone from land to water evolved considerably in the last decade due to progress in both sensor technology and processing methods. Unlike early systems that focused solely on maximizing depth of penetration, topo-bathymetric scanners enable detailed description of coastal and inland waters at a level of detail that opens up applications in hydromorphology, hydraulic engineering, ecohydraulics, and hydrobiology. Since 2013, a near-natural river section of the pre-alpine Pielach River has been repeatedly surveyed with bathymetric LiDAR sensors from manned and unmanned aerial platforms. The captured time series not only constitutes a valuable data basis for analyzing morphometric change in response to recurring flood peaks, but also allows to trace the progress in sensor, platform and data processing technology. In this contribution we demonstrate that over the last ten years, the depth performance could be increased by approximately 60 %, starting from 1 Secchi depths to more than 2 Secchi depths with sub-m spatial resolution. We furthermore focus on current approaches for improving the geometric sensor calibration, which allow integrated processing of GNSS-, IMU- and LiDAR observations for concurrent calculation of both flight trajectories and 3D point clouds with an absolute accuracy better than 5 cm. This is specifically important for repeat surveys and monitoring of fluvial processes. While this contribution confirms substantial progress in the field, further topics like precise modeling of dynamic water surfaces, full waveform processing in complex target situations including littoral vegetation and submerged deadwood, and detection and modeling of underwater vegetation are identified as future research areas.
AN IMPROVED MASK R-CNN: EXTRACTION OF DOOR AND WINDOW INSTANCES ON VILLAGE BUILDING FAÇADE IMAGES
Authors: Zhong D. , He L. , Lin Y.
Published: December 2023

Abstract. Rapid access to the basic structure of village buildings is conducive to the investigation of the load-bearing bodies of village houses and provides data support for disaster assessment and post-disaster rescue and reconstruction. The development of computer vision technology provides new ideas and tools for identifying and extracting basic structures of housing buildings. Considering that the original Mask R-CNN ignores the spatial association and relationship of door and window elements, an advanced deep learning model based on Mask R-CNN network is proposed in this paper to detect and segment the door and window structure from the façade images. The improved network architectures integrate the attention mechanism with the original network, containing an improved Coordinate Attention(CA) module and a relationship module-based head network. The experimental results show that the Average Precision(AP) value of the backbone combined with the improved CA module is increased by 0.7% and 0.7% on regression and segmentation tasks respectively, compared with the original Mask R-CNN network. In the head network based on the relationship module, the calculation strategy of the relational module proposed in this paper increases the AP values of detection and segmentation from 76.7% and 77.7% to 80.6% and 80.0%, respectively.
ENHANCING PEDESTRIAN TARGET RECOGNITION IN OPEN COMMUNITY MULTI-SCENE SPACES USING THE YOLO-STP NETWORK
Authors: Liu C. , Li Y. , Gu J. , Lou Y. , Shen T.
Published: December 2023

Abstract. Addressing the challenge of quantitatively analyzing and presenting pedestrian elements within open community spaces is of significant importance. Focusing on the indoor scene spaces of open communities, this study introduces the TJ-Person pedestrian target recognition image dataset. Furthermore, we design a deep learning-based community pedestrian activity analysis network model and incorporate various attention mechanisms, such as SA, CA, CBAM, SE, and SK, into the YOLO v5s deep learning target recognition network framework for comparative evaluation of pedestrian target recognition in open communities. Utilizing the optimized YOLO Swin Transformer Person (YOLO-STP) network, precise identification of pedestrian targets across multiple scenarios was achieved. We conducted experimental verification using four typical scenarios within Shanghai's NICE2035 open community as case studies. The results demonstrated that the proposed YOLO-STP community pedestrian activity analysis network model achieved an optimal detection accuracy of up to 98.47%. In all four tested scenarios, the YOLO-STP method consistently exhibited competitive performance. Moreover, in the COCO-2017 open-source dataset testing, the YOLO-STP method outperformed other networks of the same type, showcasing its significant advantages. Overall, the research presented in this study provides a crucial technical foundation for the analysis and recognition of pedestrian targets in future community scenarios.
DESIGN AND IMPLEMENTATION OF GEOPARK MANAGEMENT SYATEM IN SOUTHWEST CHINA BASED ON GIS
Authors: Li J. , Liu Y. , Jing C. , Feng Y.
Published: December 2023

Abstract. Geoparks contain various geological relics and peculiar geological landscapes, which have good ornamental value and are non-renewable precious wealth. Today, with the deterioration of the natural environment and the impact of human activities, the geopark has been destroyed to a certain extent, but the current research methods on the geopark are still mostly traditional propaganda methods with a single method. This study takes the geoparks in Southwest China as the research object, establishes a geopark management system, realizes the display, query, statistical rendering, distribution analysis and impact factor analysis of geoparks in the region, not only intuitively reflects the distribution of geoparks, The GIS analysis method is also introduced to explore the regularity of their distribution and the factors affecting the distribution, so as to provide reference for the protection, planning and construction of subsequent geoparks.
ANALYSIS OF EXTERIOR ORIENTATION PARAMETERS ON MONOPLOTTING PROCEDURE FOR AVOIDING OBSTACLES IN UAV REAL-TIME ROUTING
Authors: Simões D. P. , de Oliveira H. C. , dos Santos R. L.
Published: December 2023

Abstract. Unmanned Aerial Vehicles (UAVs) are adopted in different applications, such as mapping, logistics, and surveillance. For some applications, it is important to identify people or objects to be tracked in the image and define their coordinates in the object space. It can be done by using a photogrammetric process called Monoplotting. Moreover, by applying the Propagation of Variance and Covariance, the standard deviation from planimetric coordinates can be defined as an Exterior Orientation Parameter (EOP) function. This work evaluates the planimetric accuracy achieved using points defined in UAV images and the impact of EOPs precision. Two UAV images, the Digitals Surface Model (DSM) from the corresponding areas, the EOPs (X0, Y0, Z0, ω, φ, κ), and the platform/sensors characteristics were used for the experiments. Results showed that planimetric precision is low when using UAVs with a low-cost positioning system, especially if the points of interest in the image are far from the nadir. For UAVs with a high-precision positioning system, Inertial Measurement Unit (IMU) system is the one with a higher influence on the accuracy of planimetric coordinates, especially for oblique photographs (essential for UAV routing when avoiding obstacles). The impact of the IMU system precision increases in oblique photographs, this alerts to attention for UAV flight real-time routing guided by obstacles mapping using images.
A CRITICAL ANALYSIS OF INTERNAL RELIABILITY FOR UNCERTAINTY QUANTIFICATION OF DENSE IMAGE MATCHING IN MULTI-VIEW STEREO
Authors: Huang D. , Qin R.
Published: December 2023

Abstract. Nowadays, photogrammetrically derived point clouds are widely used in many civilian applications due to their low cost and flexibility in acquisition. Typically, photogrammetric point clouds are assessed through reference data such as LiDAR point clouds. However, when reference data are not available, the assessment of photogrammetric point clouds may be challenging. Since these point clouds are algorithmically derived, their accuracies and precisions are highly varying with the camera networks, scene complexity, and dense image matching (DIM) algorithms, and there is no standard error metric to determine per-point errors. The theory of internal reliability of camera networks has been well studied through first-order error estimation of Bundle Adjustment (BA), which is used to understand the errors of 3D points assuming known measurement errors. However, the measurement errors of the DIM algorithms are intricate to an extent that every single point may have its error function determined by factors such as pixel intensity, texture entropy, and surface smoothness. Despite the complexity, there exist a few common metrics that may aid the process of estimating the posterior reliability of the derived points, especially in a multi-view stereo (MVS) setup when redundancies are present. In this paper, by using an aerial oblique photogrammetric block with LiDAR reference data, we analyze several internal matching metrics within a common MVS framework, including statistics in ray convergence, intersection angles, DIM energy, etc. We associate these metrics to the per-point errors evaluated through LiDAR reference data and discuss their potential contributions in estimating internal reliabilities of point clouds derived from DIM algorithms. The experimental results show that ray convergence and DIM energy are relevant indicators for the accuracy of the generated point clouds. Initial investigation shows that these two indicators could be further utilized to infer the measurement errors without reference data, which could potentially estimate the reliabilities of point clouds through error propagation.
TREE DIGITISATION FROM POINT CLOUDS WITH UNREAL ENGINE
Authors: González-Domínguez S. , Balado J. , Novo A. , Arias P.
Published: December 2023

Abstract. Trees are fundamental parts of urban areas and green urbanism. Although much effort is being put into the digitisation of urban areas, trees present great complexity and are usually replaced by predefined models. On the one hand, trees are elements composed of trunk, branches, and leaves, each with a completely different structure and geometry. On the other hand, the tree parts are closely related to each species. Therefore, in order to obtain a realistic digital urban environment, in 3D models such as CityGML or Metaverse, it is necessary that the trees correspond faithfully to reality. The aim of this work is to propose a method to digitise trees from Mobile Laser Scanning and Terrestrial Laser Scanning data. The process takes advantage of the differentiation between trunks and leaves for their segmentation by point cloud geometric features. Unreal Engine is then used to digitise each part. Trunk and branches are geometrically preserved. For dense canopy trees, predefined leaves according to the species are imported and the alpha shape of the crown is filled. For non-dense canopy trees, the canopy is imported and modified to fit the branches. The method was tested on four real case studies. The results show realistic trees, with correct trunk and foliage segmentation, but highly dependent on the life/canopy repositories. Unreal Engine was a very complete and useful tool for the digitisation of trees generating realistic textures and lighting options.
LIDAR SLAM-AIDED VEHICULAR NAVIGATION SYSTEM FOR GNSS-DENIED ENVIRONMENTS
Authors: Abdelaziz N. , El-Rabbany A.
Published: December 2023

Abstract. Typically, in situations where Global Navigation Satellite System (GNSS) signals are unavailable, navigation systems rely on integrating GNSS and inertial navigation system (INS) data. While such integration can provide accurate positioning during short GNSS signal outages, it cannot sustain prolonged GNSS outages. The reason for this is that the system's performance depends solely on the INS and can result in significant errors over time. To address this issue, additional onboard sensors are necessary. This study proposes a navigation system that integrates INS and LiDAR simultaneous localization and mapping (SLAM) using an extended Kalman filter (EKF). The system was tested using the raw KITTI dataset in various outdoor driving scenarios without GNSS signals. It is shown that the proposed system significantly outperformed the INS-only system, with an average RMSE improvement of around 93% and 58% in the horizontal and the up directions, respectively.
DEEP LEARNING FOR OBJECT DETECTION USING RADAR DATA
Authors: Reda A. M. , El-Sheimy N. , Moussa A.
Published: December 2023

Abstract. Recently, Deep learning algorithms are becoming increasingly instrumental in autonomous driving by identifying and acknowledging road entities to ensure secure navigation and decision-making. Autonomous car datasets play a vital role in developing and evaluating perception systems. Nevertheless, the majority of current datasets are acquired using Light Detection and Ranging (LiDAR) and camera sensors. Utilizing deep neural networks yields remarkable outcomes in object recognition, especially when applied to analyze data from cameras and LiDAR sensors which perform poorly under adverse weather conditions such as rain, fog, and snow due to the sensor wavelengths. This paper aims to evaluate the ability to use RADAR dataset for detecting objects in adverse weather conditions, when LiDAR and Cameras may fail to be effective. This paper presents two experiments for object detection using Faster-RCNN architecture with Resnet-50 backbone and COCO evaluation metrics. Experiment 1 is object detection over only one class, while Experiment 2 is object detection over eight classes. The results show that as expected the average precision (AP) of detecting one class is (47.2) which is better than the results from detecting eight classes (27.4). Comparing my results from experiment 1 to the literature results which achieved an overall AP (45.77), my result was slightly better in accuracy than the literature mainly due to hyper-parameters optimization. The outcomes of object detection and recognition based on RADAR indicate the potential effectiveness of RADAR data in automotive applications particularly in adverse weather conditions, where vision and LiDAR may encounter limitations.
USER IMPLEMENTATION AND ASSESSMENT OF BDS-3 PRECISE POINT POSITIONING AUGMENTATION SERVICE WITH NO ECONOMIC COST
Authors: Ouyang C. , Shi J. , Peng W. , Dong X. , Guo J. , Yao Y.
Published: December 2023

Abstract. The satellite-based precise point positioning (PPP) service enables users with high accuracy positions without Internet connections. Currently, two types of satellite-based PPP services are available. One type is commercial service, such as Trimble CenterPoint RTX service. The second type is non-commercial and cost-free service, including the BDS-3 satellite-based PPP service based on PPP-B2b signals. This service is currently open access to all receiver manufacturers. This contribution firstly presents a detailed instruction on how to apply PPP-B2b signals into positioning. Afterwards, the performance of BDS-3 PPP service is validated with both static and kinematic experiments carried out in Wuhan, China, during day of year 168 and 172, 2022. Results indicate that PPP-B2b signals mainly improve BDS-3 clock precision and GPS orbit accuracy. With BDS-3 PPP-B2b augmentation messages, the static satellite-based PPP shows 3.9/6.9/10.3 mm daily solution repeatability in E/N/U directions, respectively. An average of 13.9 min convergence time and 3.42/2.16/6.65 cm E/N/U positioning accuracy are achieved in the simulated kinematic mode. During a 28-min stepper motor driven movement test, 3.48/14.88 cm 2D/3D kinematic positioning accuracy is obtained using the BDS-3 PPP service.
MODELING LAND DEFORMATIONS IN MOUNTAINS BY COMBINING TIME-SERIES L-BAND SAR IMAGES AND SPATIOTEMPORAL STATISTICAL MODELS
Authors: Susaki J. , Kakinami R.
Published: December 2023

Abstract. Predicting land deformations using remote-sensing technology is extremely challenging because the simulation models that are involved have many parameters, including geological features, that must be calibrated against areas of interest, and no universal model is available. Instead, this paper proposes a method for modeling land deformations with time-series L-band synthetic aperture radar (SAR) images and spatiotemporal statistical models for prediction. First, time-series SAR analysis generates temporal land deformation. Then, water-related products are obtained from the Today’ Earth system of the Japan Aerospace Exploration Agency. The three-day consecutive maximum values are calculated and then interpolated spatially. Finally, a descriptive or dynamic spatiotemporal statistical model is applied. The low-rank Gaussian process model, a descriptive model, is found to be highly accurate for explaining land deformations, even for actual slid areas, as long as the areas of analysis are limited, e.g., 150 m × 50 m. It is concluded that the proposed method is effective for predicting land deformations spatially and temporally and so can be used to identify possible landslide locations.
OPTICAL AND SAR IMAGE FUSION BASED ON VISUAL SALIENCY FEATURES
Authors: Zhang J. , Ren X. , Li J. , Wang L. , Ye Y.
Published: December 2023

Abstract. With the expansion of optical and SAR image fusion application scenarios, it is necessary to integrate their information in land classification, feature recognition, and target tracking. Current methods focus excessively on integrating multimodal feature information to enhance the information richness of the fused images, while neglecting the highly corrupted visual perception of the fused results by modal differences and SAR speckle noise. To address this problem, in this paper we propose a novel optical and SAR image fusion framework named Visual Saliency Features Fusion (VSFF). We improved the decomposition algorithm of complementary feature to reduce most of the speckle noise in the initial features, and divide the image into main structure features and detail texture features. For the fusion of main structure features, we reconstruct a visual saliency features map that contains significant information from optical and SAR images, and input it together with the optical image into a total variation constraint model to compute the fusion result and achieve the optimal information transfer. Meanwhile, we construct a new feature descriptor based on Gabor wavelet, which separates meaningful detail texture features from residual noise and selectively preserves features that can improve the interpretability of fusion result. Further a fast IHS transform fusion is used to supplement the fused image with realistic color information. In a comparative analysis with five state-of-the-art fusion algorithms, VSFF achieved better results in qualitative and quantitative evaluations, and our fused images have a clear and appropriate visual perception.
DRONE-BASED CROP TYPE IDENTIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS: AN EVALUATION OF THE PERFORMANCE OF RESNET ARCHITECTURES
Authors: Ajayi O. G. , Olufade O. O.
Published: December 2023

Abstract. This study investigates the application of deep learning techniques, specifically ResNet architectures, to automate crop type identification using remotely sensed data collected by a DJI Mavic Air drone. The imagery was captured at an altitude of 30 meters, maintaining an average airspeed of 5 m/s, and ensuring a front and side overlap of 75% and 65%, respectively. The pre-flight planning and image acquisition was facilitated through the Drone Deploy platform, yielding a dataset consisting of 1488 aerial photographs covering the study area. These images possess an average ground sampling distance (GSD) of 22.2 millimetres. The dataset was meticulously labelled with "maize" and employed to train three distinct ResNet architectures, namely ResNet-50, ResNet-101, and ResNet-152. The evaluation of these models was based on accuracy and processing time. Notably, ResNet-50 emerged as the most proficient, achieving an accuracy rate of 82% with a precision score of 0.5 after just two hours of initial training, while ResNet-101 and ResNet-152 architectures achieved 27% and 24% accuracy, respectively. These outcomes underscore the potential of ResNet-50 architecture, even with a limited dataset, as a valuable tool for precise crop-type classification within the precision agriculture domain.
A Comprehensive Review of Artificial Intelligence Algorithms Applications for Distributed Generation Sizing and Location Optimization to Enhance Efficiency and Reliability Indices in Radial Distribution Systems
Authors:   Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, AlZahrani Nawaf , Awedh Mohammad ,   Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia, Rushdi Ali ,   Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
Published: December 2023

The research aimed to understand radial distribution systems by defining them and exploring their strategies, and to explore methods for applying artificial intelligence algorithms in radial distribution systems, It also aimed to clarify types of indicators in radial distribution systems, highlighting the importance of providing a scientific and applied framework for integrating artificial intelligence in the field of energy distribution. The significance of the research lies in presenting a comprehensive and up-to-date approach based on the applications of artificial intelligence algorithms to improve the design and organization of distributed production systems in radial distribution systems. By employing a descriptive and analytical approach, the researcher was able to reach several conclusions, with the most prominent being that the application of artificial intelligence algorithms in radial distribution systems contributes to improving energy efficiency by directing it more accurately and effectively, the ability of artificial intelligence to provide precise data-dependent analyses enhances decision-making processes in radial distribution systems, The integration of artificial intelligence techniques contributes to improving overall efficiency in radial distribution systems, reducing energy losses, and enhancing energy distribution effectively. The research recommends enhancing integration between artificial intelligence techniques and radial distribution systems to ensure effective and smooth interaction, thereby increasing efficiency, Priority should be given to the safety and protection of data, and effective cybersecurity measures should be implemented to address the growing challenges in this context.
Current approaches to the development of methods and mechanisms for managing scientific and technological development of regions of the Russian Federation
Authors:   National Technology Initiative  Project Support Fund, Medvedev V.V.
Published: December 2023

The development of methods and mechanisms for managing the scientific and technological development of the regions of the Russian Federation is a critical task, especially in the context of large-scale anti-Russian sanctions against economic sectors. Russia has become the country with the most sanctions in the world. The restrictions affect the work of many companies in the Russian Federation and the import of not only a number of Western goods, but also technologies. In these conditions, at the federal level and in the constituent entities of the Russian Federation, it is important to recommend management mechanisms that will improve the quality, efficiency and effectiveness of management of scientific and technological development in the country. It is noteworthy that in May 2023, the Concept of Technological Development of Russia until 2030 was adopted. Thus, the current strategy for the country’s scientific and technological development has been updated. In this regard, the research and development carried out by the author can serve as a justification for the formation of a regional planning and forecast system for the integrated scientific and technological development of regions on a modern basis.
The Russian space complex as a multi-agent system with a high component of innovative activity
Authors:   Peoples' Friendship University  of Russia named after Patrice Lumumba, Zhdanov V.L.
Published: December 2023

Currently, the process of managing the space complex of the Russian Federation is undergoing significant changes due to the activation of private investors in the field of high-tech innovations. Аn economic and statistical analysis of the possibilities of ensuring the sustainable development of high-tech industries in the Russian Federation was carried out; their main indicators of innovation activity in the period from 2017 to 2021 were considered. Because of the analysis, it is concluded that public-private partnership in high-tech production allows maintaining a favorable investment climate in knowledge-intensive sectors of the economy, increasing the pace of economic development, as well as maintaining competitive positions in the market.