Reviewerly/exHarmony huggingface dataset:
Beyond Trade-Off: An Optimized Binocular Stereo Vision Based Depth Estimation Algorithm for Designing Harvesting Robot in Orchards

Depth estimation is one of the bottleneck parts for harvesting robots to determine whether the operation of grasping or picking succeeds or not directly. This paper proposed a novel disparity completion method combined with bilateral filtering and pyramid fusion to improve the issues of incorrect outputs due to the missed or wrong matching when achieving 3D position from 2D images in open-world environments. Briefly, our proposed method has two significant advantages in general. Firstly, occlusion between leaves, branches, and fruits is a universal phenomenon in unstructured orchard environments, which results in the most depth estimation algorithms facing great challenges to obtain accurate outputs in these occluded regions. To alleviate these issues, unlike other research efforts that already exist, we optimized the semi-global matching algorithm to obtain high accuracy sparse values as an initial disparity map; then, an improved bilateral filtering algorithm is proposed to eliminate holes and discontinuous regions caused by occlusion to obtain precise and density disparity outputs. Secondly, due to taking the practical high-efficiency requirements of the automated harvesting robot in its working status into consideration, we attempted to merge multiple low-resolution bilateral filtering results through the pyramid fusion model which goes beyond the trade-off mechanism to improve the performance of both accuracy and time cost. Finally, a prototype harvesting robot was designed to conduct experiments at three kinds of different distances (0.6~0.75 m, 1~1.2 m, and 1.6~1.9 m). Experiment results showed that our proposed method achieved density disparity maps and eliminated holes and discontinuous defects in the disparity map effectively. The average absolute error of our proposed method is 3.2 mm, and the average relative error is 1.79%. In addition, the time cost is greatly reduced more than 90%. Comprehensive experimental results demonstrate that our proposed algorithm provides a potential possibility for designing harvesting.
Semi-Supervised Semantic Segmentation for Identification of Irrelevant Objects in a Waste Recycling Plant

In waste recycling plants, measuring the waste volume and weight at the beginning of the treatment process is key for a better management of resources. This task can be conducted by using orthophoto images, but it is necessary to remove from those images the objects, such as containers or trucks, that are not involved in the measurement process. This work proposes the application of deep learning for the semantic segmentation of those irrelevant objects. Several deep architectures are trained and compared, while three semi-supervised learning methods (PseudoLabeling, Distillation and Model Distillation) are proposed to take advantage of non-annotated images. In these experiments, the U-net++ architecture with an EfficientNetB3 backbone, trained with the set of labelled images, achieves the best overall multi Dice score of 91.23%. The application of semi-supervised learning methods further boosts the segmentation accuracy in a range between 1.31% and 2.59%, on average.
Renewable Power Generation via Animate Prime Movers by Using Automation Method

The project portion relates to the generation of power, specifically electricity, by animals, especially cattle. The project particularly relates to generating carts that can be used to produce power using animals. The device will be designed such that the energy of moving animals will be utilized to rotate the belt connected to the cart. DC generator would be attached to the belt to produce power which can either be stored in a battery or fed into the electrical system or can be used to power electrical equipment in a dairy operation. DC generator will give 12V in DC at 1450 rpm along with 300W power. There is an automation portion that will operate on a specific condition in the form of speaker command that will be an indication to the prime mover to move ahead. When this indication happens many times, we will stop the prime mover because this will be the maximum fatigue level of the prime mover. There are 3 different bulbs to show the different fatigue level. There is an inverter that will convert the DC into AC. There are both DC and AC loads. To constant the voltage level, there is a capacitor, connected to the DC generator.
Pengaruh Model Discovery Learning terhadap Keterampilan Menulis Teks Laporan Hasil Observasi

The fundamental background of this study is that researchers want to pilot the use of discovery learning models, whether discovery learning models can be an effective alternative in closing the weaknesses of models that have been used before or vice versa. This study was conducted with the aim of determining the effect of the discovery learning model on the text writing skills of observation reports of grade VII students of SMP Negeri 24 Padang. Thisstudy researchers used experimental research methods to reveal cause-and-effect relationships with hypotheses. This research uses a research design or design in the form of one group pretest-posttest design. The population in this study is grade VII students of SMP Negeri 24 Padang who are registered for 2022-2023 which amounts to 254. Based on data analysis and previous discussions, several results can be presented. First, the skill of writing the text of the observation report of grade VII students of SMP Negeri 24 Padang before using the discovery learning model obtained a number of 66.38 with a classification of "More than Enough". Second, the skill of writing the text of the observation report of grade VII students of SMP Negeri 24 Padang after using the discovery learning model obtained an average score of 87.99 with a classification of "Very Good". Third, there is a significant influence in the use of the discovery learning model on the ability to write text reports of observations of grade VII students of SMP Negeri 24 Padang. This is evidenced by the increase in the average score of writing the text of student observation reports before and after using the discovery learning model. Based on the results of the t-test, it was concluded that there was a significant influence in the use of the discovery learning model on the skill of writing the text of the observation report of grade VII students of SMP Negeri 24 Padang, because the significance value of the ≤ from the t-test table. Thus, accepted and rejected. So, there is a significant influence on the use of the discovery learning model in learning to write text reports of observations of grade VII students of SMP Negeri 24 Padang.
The Use of Virtual and Augmented Reality to aid the transformation of the Energy Business

Summary Augmented Reality (AR) and its close cousin Virtual Reality (VR) are a simulated experience that employs face/body tracking and near-eye displays to give the user either a fully immersive feel (VR) or a mixed real world-virtual world feel (AR). The latest hardware, for example from Meta, Magic Leap, Lumus and HTC are a few iterations away in terms of price, ergonomics, computing power and fidelity so that the non-tech savvy, everyday person can (i) afford and (ii) enjoy using them. It is somewhat less clear what pain points this technology is best suited to solve. The suggested consumer scenarios/use cases range from the vision of a full metaverse to some niche applications. The authors of this contribution are certain that for a multi-faceted company like OMV, the AR/VR technology will be important for certain business segments.
Prospects of Geothermal Field Development in Gandhar, Gujarat, India

Summary The aim of this study is to examine Gandhar region in Gujarat, India as a viable site for geothermal development. To determine whether Gandhar has the potential to be an exploitable geothermal hotspot, a thorough gravity and magnetic survey is carried out. The results of the gravity survey show that high-density formation is present, and the magnetic survey also revealed multiple magnetic anomalies. This suggests that there may be a geothermal zone in the Gandhar region’s geological background. The results of this analysis indicate that additional geothermal resource investigation and development are required in the region to properly exploit the geothermal resource of Gandhar.
Quantum annealer-assisted residual refraction statics estimation on the SEAM Arid model dataset

Summary Quantum annealing emerged as a very promising tool for solving combinatorial optimization problems. In particular, residual refraction statics estimation has been identified to be the first potential industrial use case of quantum annealing in geoscience. The currently available quantum annealers have still too small computing capabilities to effectively and independently estimate residual statics on problems of industrial scale. On the other hand, a quantum annealer-assisted hybrid solver, combining classical and quantum information processing could already be a viable tool for this purpose. Here we show the application of such a hybrid solver to the residual refraction statics estimation through stack-power maximization on a significant subset of the SEAM Arid model dataset, comprising of over two thousand gathers. We benchmark our approach against a dedicated deterministic classical solver, showing that in a fixed amount of time per gather, the quantum annealer-assisted solution can in many instances outperform the standard approach.
First arrival detection of seismological data from the Middle Magdalena Valley in Colombia using a cGAN

Summary In seismology, energy is produced through the interaction between geological layers. This energy is released when some of the layers fail, producing a wavefield that travels to the surface and is recorded by seismological stations as traces. These traces are used to determine the magnitude and location of an earthquake, and to create a tomographic image (TI) of the Earth’s interior. The TI allows for identification of the number of geological layers and their physical characteristics (e.g., background velocity) from the first break (FB) information. This work proposes a conditional Generative Adversarial Network (cGAN) to predict the FB in seismological data. The cGAN model is trained on the STanford EArthquake Dataset, STEAD ( Mousavi et al. 2019 ), which shares similarities with data from the Middle Magdalena Valley (MMV) in Colombia. The MMV is a region of geophysical interest due to the presence of oil and its proximity to the Bucaramanga Seismic Nest (BSN). After training and validation, the cGAN predicts FB on real earthquakes from the MMV. Overall, the proposed cGAN shows potential for improving seismic analysis in regions like the MMV, where traditional seismic methods may face challenges due to complex geological structures.
Exploration Advisory Tool: AI and NLP in the Geosciences

Summary Wintershall Dea and IBM co-developed the Exploration Advisory Tool (EAT), a domain-specific cognitive search platform, to overcome the challenge of timely evaluating large unstructured data. EAT composes of a Natural Language Processing (NLP) pipeline that is trained to ingest a large number of prioritized document types by extracting texts, images and tables from these documents. Within text paragraphs, pre-defined geological concepts are identified using Named Entity Recognition (NER). Geological concepts such as ‘Seal’ or ‘Volumes’, which can have a broader meaning, are recognized within the geological context using transformers, rules, and dictionaries. Structuring of the information is done by means of a knowledge graph, where the nodes are formed by the geological concepts and the edges are defined by relationships between these concepts. The high-performance knowledge graph is used to search and explore the documents via the EAT user interface enhanced by a GIS-type mapping. EAT was developed within the DevOps and MLOps frameworks. The team has been working agile employing collaborative and incremental delivery using a more or less automated Continuous Integration and Continuous Delivery (CI/CD) pipeline on a multi-cloud and hybrid environment.
An unsupervised approach for geoscientific data similarity analysis: well logs and thin sections applications

Summary Finding analogous data is a crucial and time-consuming task in geological interpretation. This paper proposes a generic framework that uses unsupervised learning to create latent representations of well data useful for similarity clustering and information retrieval tasks. We test the framework with two datasets (petrographic thin sections and well log intervals), and we show how the framework performs in two tasks: well analog recommendation and clustering. Applying this technique to data retrieval tasks can speed up the search for geologically analog data.
Industry First Quantum-Based Seismic Attribute

Summary With the growing size of seismic data, high performance computing is critical in seismic processing tasks. In this paper, we leverage quantum computing principles to break beyond classical computing limitations and develop the industry first quantum-based seismic attribute computation algorithm. The experimental results from our algorithm are comparable in quality to classical Sobel-filter based similarity (or coherence) attribute. The algorithm was run on a quantum simulator, an exponential speed up is expected on a real quantum hardware by virtue of the superposition characteristics of quantum bits (qubits). This approach has the potential to change how we do seismic attribute computation in the future, reducing cost and time significantly.
Geochemical Data Characterization and Spatialization for Petroleum System Analysis–An Example from Offshore North Gabon Basin, Africa

Summary Spatialization of multiple geochemical variables is a strong and well-established method to investigate, analyze source potential variation, hydrocarbon fluid types, and source organofacies through time and space in a petroleum system. We conducted regional synthesis of available rock and fluid geochemical data that clearly demonstrates the presence of multiple petroleum systems, including the pre-salt lacustrine petroleum system within the North Gabon Basin. In our findings, the Post Salt Petroleum System of offshore North Gabon Basin of Africa is characterized by 4 key source stratigraphic intervals: Albian, Ceno-Turonian and Coniacian-Santonian and Upper-Cretaceous. We could correlate these source events with global anoxic events that favored the production and preservation of organic matter with excellent generation potential. Through the spatialization of different fluid families along with other geological knowledge, we could demarcate the area where fluid from the pre-salt interval with organofacies class C could charge post-salt reservoirs through possible salt windows. We further extrapolated the spatial distribution trends for generating one component of a source CRS map. We averaged the qualified TOC and HI data for a considered age interval for petroleum system model, subsequently. The work summarily helps to recognize and focus on the desired play and petroleum system elements for further evaluation.
Future Energy: Imaging hidden lithium-rich brines with satellite imagery.

Summary The salars of the Lithium Triangle in South America contain approximately 55% of the world’s lithium resources ( Cabello, 2021 ; Schulz et al., 2017 ). The source, transportation and concentration of the lithium-brines involves a complex mineral system that is dependent upon climate, weathering, basin closure, surface drainage, hydrothermal and groundwater systems, lithium-bearing rock distribution and geological structural control. A suite of satellite platforms, geological datasets, advanced data analytics and automated workflows has enabled the delineation and targeting of potential lithium-rich salars and paleosalars buried beneath recent sediments and volcanics.
Game of Glances

The beginning, development and consolidation of the youth research field in Latin America are shown in the text. The journey begins with the moment when pioneers stopped to think about the youth of the continent. But the text looks at the trigger that 1985 represented, as the International Year of Youth and the subsequent reports made by regional agencies like ECLAC to propose the elements, themes and problems that Latin American youth was facing. National particularities acquired by the research field from the most relevant countries are reviewed, where youth subjects’ heterogeneity is installed and some lines of flight are underlined, in terms of youth practices caused by young people’s own agency outside the agenda of topics in the construction of knowledge. The text concludes with an outline of the main lines that are currently being generated as new or reinforced inquiry processes of the field. Bibliographical references that support the historical journey as a minimal source to understand this process are added.
Assessing the Role of Environmental Factors in the Transmission of Infectious Diseases in Communal Spaces

Communal spaces provide different facilities for users while they are the primary place for the spread of diseases, especially respiratory. Transmission is possible through human behaviors, the way they communicate with each other and breathe in an environment by airborne pathogenic particles. Experts from various fields have gained valuable experience and achievements regarding how to prevent these diseases by means of environmental factors. Due to the spread of the corona virus in the past years, environmental planners and designers seriously considered the need to review the design and use of spatial components. This study provides a framework for decision making and design of communal spaces based on how environmental components can be effective in preventing the spread of respiratory diseases such as coronavirus and influenza. The research method used in this article is logical reasoning combined with ANP method and focus group discussion. According to the results of this research, indoor air quality plays the most crucial role in preventing the transmission of viruses (contagious respiratory diseases) based on expert groups.
How big data analytics is used in forensic accounting and auditing

In this study, we investigate the application of big data analytics in forensic accounting and auditing. Big data technology mediates the connection between awareness and plans to use for fraud detection. We made an effort to assess how big data might be used in forensic accounting and auditing practices. Design/methodology This research is based on systematic literature review, and it provides a thorough understanding of the existing literature on big data research in forensic accounting and auditing. The research was gathered through peer-reviewed literature using current (post-2014) articles. Results/findings The model for corporate auditing has changed. Managers at the organization are proactively identifying and resolving internal abnormalities and discrepancies prior to an external audit using conventional audit techniques. Known white-collar crimes at several publicly traded corporations have led to a high demand for forensic accountants. Big data analytics is a key tool for spotting fraud patterns. Students should be encouraged to develop skills in data generation, sharing, analytics, mining, reporting, and storage because big data analytics is essential to business education. Practical implications and conclusions The study's conclusions can be applied to the forensic accounting industry to assist in the development of Big Data fraud-fighting technology. Big data analytics and forensic accounting should be covered in accounting curricula. By offering different perspectives on big data and its practical implications in accounting practice, this research contributes to the body of published knowledge. As more companies use big data, the need to update the accounting curriculum is highlighted because success now requires a distinct set of skills. Future research should involve interviewing and surveying accountants, auditors, and other members of the financial services industry, which will be difficult but necessary.
Analysis of Whole Blood Prothrombin Time/International Normalized Ratio Using Image Processing

Research in Medical & Engineering Sciences Analysis of Whole Blood Prothrombin Time/International Normalized Ratio Using Image Processing Sarath S Nair1, Rakhi MR1, Lakshmi Sadanandan2 and Harikrishnan S3 and Anugya Bhatt1* 1Division of Thrombosis research, Sree Chitra Tirunal Institute for Medical Sciences and Technology, India 2Extracorporeal Division, Sree Chitra Tirunal Institute for Medical Sciences and Technology, India 3Department of Cardiology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, India *Corresponding author: Dr. Anugya Bhatt, Division of Thrombosis research, Biomedical Technology Wing, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India Submission: January 18, 2021Published: March 19, 2021 DOI: 10.31031/RMES.2021.09.000711 ISSN: 2576-8816Volume9 Issue3
Students' Teaching Experiences Of Kampus Mengajar Program In Teaching Literacy and Numeracy At Elementary Schools In Cirebon

Nadiem Makarim founded the kampus mengajar program in 2021, which entails students from various study programs going straight to schools in 3T location. This study aimed to explore students experiences through kampus mengajar program in conducting teaching literacy and numeracy in the 2021–2022 academic year. Five students from the Faculty of Teacher Training and Education at Universitas Muhammadiyah Cirebon participated in this qualitative research study using a narrative inquiry design as its methodology. The narrative frame and interview findings were utilized as the source of the data in this study to examine how students experienced teaching literacy and numeracy while actively participating in the kampus mengajar program as well as the difficulties they encountered. The stages of this narrative research included identifying the phenomena to be studied, deciding participants who would allow researcher to study the phenomena, gathering information (experiences) from participants, retelling information (experiences) from participants, and writing narratives based on information (experiences) from participants. The results revealed that teachers at the target schools greatly benefited from the Kampus Mengajar program's implementation. Nevertheless, aside from that students faced the challenges in teaching literacy and numeracy. The lack of student motivation, the amount of students who still struggle with reading and counting, the lack of learning materials that assist literacy and numeracy, and the restricted use of technology by teachers are a few of the factors.
Workshop Pengembangan Video Pembelajaran Bagi Guru MI Muhammadiyah Program Khusus Tegalampel, Klaten

The purpose of this service activity are (1) Providing knowledge to participants regarding the creation of teaching materials using learning media based on information technology and the internet. (2) Provide skills in making learning videos based on information technology and the internet through the Zoom Meeting application. The method used in this training is a combination of lectures, questions and answers, discussions, and hands-on practice. Given the pandemic period, the implementation is carried out according to health protocols so that participants maintain distance. The training material is divided into two, namely how to make interesting learning materials and how to record video learning materials through Zoom Meetings. Thisservice activity can support the learning process, it can also increase the credibility of the school. The implementation of the training was highly appreciated by the school. They hope to be able to practice what has been obtained in this training and still want to be given assistance