Miracle-dz/UniFAR-FTUs huggingface dataset:
APLIKASI SISTEM MONITORING PERTUMBUHAN TANAMAN BERBASIS WEB MENGGUNAKAN MACHINE VISION Application of Web-based Monitoring System for Plant Growing by Using Machine Vision

Nowadays, demand for integrating between information technology (IT) and development of agricultural system isin order to increase the productivity, efficiency and profitability in term of precision agriculture. This matter occurred due to some problems in the field, such as; unintensively monitoring activities for plant during the growing period. One of the alternative solutions to overcome the problem was introducing the machine vision technology in the farming system. The research is actually as a basic research that aims using technology of digital image processing and software of computation (mathematics) to support a function of real-time monitoring system for plant growing. The research mechanism was started from digital image processing by using an image segmentation method that can identify between the main object (plant) and others (soil, weed). Image processing algorithm used excess color method and color normalization to identify plants, to calculate crop area. Otsu method was used to convert it to binary images. The next was to calculate and analyze a percentage of the plant growing, from after planting until harvesting time. The analyzed data were stored as MySQL database format in the web server. Final output of the research was the web based monitoring instruments for plant growing that can be accessed through intranet (local area network) as well as internet technology. From the software testing, monitoring with a machine vision system has a success rate reached 70 % for identifying plants. ABSTRAK Tuntutan integrasi teknologi sistem informasi dan sistem pertanian saat ini dimaksudkan guna mendukung efisiensi,produktivitas dan profitabiltas pertanian. Hal tersebut didorong oleh timbulnya permasalahan di lapangan terkait dengan belum optimalnya produktivitas tanaman yang diakibatkan antara lain, kurang intensifnya pemantauan (monitoring) tanaman pada masa pertumbuhan. Salah satu alternatif solusi untuk memperbaiki permasalahan tersebut dengan mengaplikasikan teknologi machine vision. Penelitian yang dilakukan merupakan penelitian dasar yang bertujuan memanfaatkan teknologi pengolahan citra digital dan perangkat lunak komputasi untuk mendukung fungsi monitoring pertumbuhan tanaman secara real-time. Mekanisme penelitian dimulai dengan tahap pengolahan citra digital yang menggunakan metode segmentasi untuk mengenali objek tanaman dengan objek lainnya. Algoritma pengolahan citra menggunakan metode kelebihan hijau dan normalisasi warna, sedangkan untuk menghitung luas tanaman digunakan metode Otsu dengan mengubah ke citra biner. Tahap berikutnya menghitung prosentase pertumbuhan tanaman selama proses budidaya sampai dengan panen. Data hasil pencitraan disimpan dalam basisdata MySql. Hasil akhir dari pengolahan data ditampilkan sebagai informasi pertumbuhan tanaman yang ditampilkan di website. Dari hasil pengujian, sistem monitoring dengan machine vision ini memiliki tingkat keberhasilan mencapai 70 % dalam mengenali tanaman.
On the Sensitivity and Stability of Model Interpretations in NLP

Recent years have witnessed the emergence of a variety of post-hoc interpretations that aim to uncover how natural language processing (NLP) models make predictions. Despite the surge of new interpretation methods, it remains an open problem how to define and quantitatively measure the faithfulness of interpretations, i.e., to what extent interpretations reflect the reasoning process by a model. We propose two new criteria, sensitivity and stability, that provide complementary notions of faithfulness to the existed removal-based criteria. Our results show that the conclusion for how faithful interpretations are could vary substantially based on different notions. Motivated by the desiderata of sensitivity and stability, we introduce a new class of interpretation methods that adopt techniques from adversarial robustness. Empirical results show that our proposed methods are effective under the new criteria and overcome limitations of gradient-based methods on removal-based criteria. Besides text classification, we also apply interpretation methods and metrics to dependency parsing. Our results shed light on understanding the diverse set of interpretations.
Contextual dimensions of moral injury: An interdisciplinary review

ABSTRACT The concept of moral injury, referring to the psychological impact of having one’s moral expectations and beliefs violated, is gaining a firm place in research on military trauma. Yet, although moral injury has the recognized potential to extend the understanding of trauma beyond the individualizing and pathologizing focus of the clinical realm, most studies nevertheless focus on clinical assessment, diagnosis and treatment. This review aims to contribute to a better understanding of contextual dimensions of moral injury. To this end, it complements current theory on moral injury with a systematic review of literature relevant to contextual factors in moral injury. It draws together insights from psychology, philosophy, theology and social sciences into spiritual/existential, organizational, political and societal dimensions of moral injury. Thus an interdisciplinary theoretical foundation is created for context-sensitive research and interventions.
The Impact of Advertising Regulation on Industry - The Cigarette Advertising Ban of 1971

This paper develops and estimates a dynamic oligopoly model of advertising in the cigarette industry. With this estimated model, I evaluate the impact of the 1971 TV/Radio advertising ban on the cigarette industry. A puzzling fact about this ban is that, while industry advertising spending decreased sharply immediately following its passage, spending then recovered and actually exceeded its pre-ban level within five years. While simple static models cannot account for such a turn of events, the rich dynamic model developed in this paper can. This paper exploits previously confidential micro data, now made public through tobacco litigation. In addition, the paper uses a new concept of Oblivious Equilibrium to handle intractable state space and accelerate equilibrium computation.
ScandEval: A Benchmark for Scandinavian Natural Language Understanding

This paper introduces a Scandinavian bench-001 marking platform, ScandEval , which can 002 benchmark any pretrained or ﬁnetuned model 003 on 29 datasets in Danish, Norwegian, Swedish, 004 Icelandic and Faroese, two of which are new. 005 We develop and release a Python package and 006 Command-Line Interface (CLI), scandeval , 007 which can benchmark any model that has been 008 uploaded to the HuggingFace Hub, with re-009 producible results. Using this package, we 010 benchmark over 60 Scandinavian or multilin-011 gual models and present the results of these in 012 an interactive online leaderboard 1 . The bench-013 marking results shows that the investment in 014 language technology in Norway and Sweden 015 has led to language models that outperform 016 multilingual models such as XLM-RoBERTa 017 and LaBSE. We release the source code for 018 both the package and leaderboard 2 3 . 019
ANTIMONY STRIKE ON HUMAN HEALTH BY INAPT DISPOSAL OF E-WASTE

Antimony strike on human health were reported in surface soil of Inapt disposal of e-waste sites. E-waste is a popular informal name for electronic devices nearing the end of their useful life. E-waste consisting discarded electrical and electronic appliances like Television, Computers, Telephones, Electrical switches, Mobiles, Semi-conductors, Flame retardants, circuit board. The Government policies are forced to the ‘reuse’ procedure to save the human health and environment and controls the money flow and management the e-waste. But certain product cannot be reused hence it is needful to adopt advanced technology and formal techniques for recycle and dispose of discarded electrical and electronic products with high precautions. The inapt disposal or recycle process of e-waste leach Antimony as one of the toxic elements among other heavy metal such as Chromium, Lithium, Mercury, Beryllium, Lead, Cadmium, Cobalt etc. The ultra trace level of Antimony have been elulid with a aqua regia and measured by Atomic Absorption Spectrometer. The obtained result of Antimony concentration in soil were compare with EPA and WHO. The average concentration of Antimony in soil samples from the disposal sites of e-waste were found to be very near to threshold limits. This study stimulate the toxic effect of Antimony on human health by Non-strategic disposal of Informal Electronic and Electrical appliances.
Improving the fluid loss and rheological characteristics of Oil-based drilling fluid by using Copper Oxide nanoparticles

* Corresponding Author Email: mkhavarpoor@yahoo.com How to cite this article Esmaiel Naderi M. , Khavarpour M. , Fazaeli R. , Ghadi A. Improving the fluid loss and rheological characteristics of Oil-based drilling fluid by using Copper Oxide nanoparticles. Int. J. Nano Dimens. , 2022; 13(2): 205-213. Abstract A successful drilling operation depends strongly on a useful drilling fluid system. Using nanoparticles (NP)s to formulate intelligent drilling fluids gives them a wide range of optimal properties under different operating conditions and resolves any operational problems. In this study, in order to provide an effective solution for improving the rheological and high pressure/high temperature (HPHT) filtration properties of the oilbased drilling fluid (OBDF), copper oxide (CuO) NPs were synthesized with dandelion morphology. CuO dandelions were characterized by using XRD, FTIR, SEM, and zeta potential measurements. A long time stabilized nanofluid (NF) was prepared and evaluated. The OBDF samples consisting of various amounts of NF ranging from 1 to 11% (V/V) were prepared. Then, the fluid loss and rheological properties of OBDF were examined. The results showed that the OBDF containing 7% (V/V) NF was appropriate to improve the rheological properties such as yield point (YP), apparent viscosity (AV), and gel strength (GS). In addition, the minimum HPHT filtration value of 2 ml was acquired for the drilling fluid containing 9% (V/V) NF. In conclusion, CuO NPs demonstrated a positive effect on the performance of the OBDF system.
Step-Indexed Logical Relations for Nondeterministic and Probabilistic Choice

Developing denotational models for higher-order languages that combine probabilistic and nondeterministic choice is known to be very challenging. In this paper, we propose an alternative approach based on operational techniques. We study a higher-order language combining parametric polymorphism, recursive types, discrete probabilistic choice and countable nondeterminism. We define probabilistic generalizations of may-and must-termination as the optimal and pessimal probabilities of termination. Then we define step-indexed logical relations and show that they are sound and complete with respect to the induced contextual preorders. For may-equivalence we use step-indexing over the natural numbers whereas for must-equivalence we index over the countable ordinals. We then show than the probabilities of may-and must-termination coincide with the maximal and minimal probabilities of termination under all schedulers. Finally we derive the equational theory induced by contextual equivalence and show that it validates the combination of the algebraic theories for probabilistic and nondeterministic choice and the distributive property between them.
ARTchives: a Linked Open Data native catalogue of art historians’ archives

Art historians’ personal archives include a variety of sources documenting creators’ work, opinions, and methodologies. Such a wealth of information is fundamental to trace the trajectories of art history through the lenses of historiographical research. However, the potential of such collections is still unveiled, and performing cross-collection research is not possible via online catalogues. The ARTchives project aims at crowdsourcing curated information on notable art historians’ archives and providing scholars with a centralised access point to this heritage. In this paper we present the agile cataloguing process developed to support ARTchives contributors. ARTchives is based on a Linked Open Data native cataloguing system that leverages Semantic Web technologies and Natural Language Processing to facilitate data entry, editorial process, and data quality. 1
Different Techniques of Sentimental Analysis using Deep Learning

- World Wide Web like social media forums, review sites and blogs that generate a lot of data the type of users views, feelings, ideas and arguments about various social events, products, products, and politics. Emotions of users exposed to the web has a huge influence on it students, product retailers and politicians. Unstructured the type of data from social media is required for analysis and is well organized and for this purpose, emotional analysis has been required the saw important attention. Emotional analysis is called the textual structure used to distinguish the expressed attitude or emotions in different ways such as negative, positive, favoble, wrong, thumbs up, thumbs down, etc. This is a challenge emotion analysis lacks sufficient label data in the field Indigenous Languages (NLP) Processing. And to solve this problem, Emotional analysis and in-depthlearning strategies have been are integrated because in-depth learning models work for them the ability to read automatically. This Review Paper highlights the latest lessons on the implementation of in-depth learning models such as deep neural networks, convolutional neural networks as well many more to solve various emotional analysis problems such as emotional isolation, problems of different languages, text as well as visual analysis and product review analysis, etc.
Effect of Two Different Rehabilitation Approaches on Pulmonary Functional Tests, Neuromuscular Functions and Quality of Life in Juvenile Myasthenia Gravis: A Randomized Controlled Trial Study

Background and Objectives: Children with juvenile myasthenia gravis have a variety of symptoms, ranging from isolated intermittent ocular complaints to overall muscle weakness with or without respiratory insufficiency. This study aimed to investigate the efficacy of a specialized physical therapy with or without partial body weight supported treadmill training on pulmonary functional tests, neuromuscular functions, and quality of life. Materials and Methods: Thirty children, ranging in age from 13 to 16 years, were distributed randomly into two study groups (A or B). Both groups underwent a designed physical therapy program. In addition, group A underwent the partial body weight supported treadmill training. The treatment was conducted three times a week for 12 weeks successively. Pulmonary functional tests (FVC, FEV1, PEFR, and MVV), neuromuscular function tests (compound motor action potential, isometric muscle force of biceps brachii and rectus femoris, balance, walking endurance, and fatigue), and quality of life were measured before and after 12 successive weeks. Results: A significant improvement in all investigated variables were recorded in both groups in favor of group A. Conclusions: Both a specialized physical therapy and partial body weight supported treadmill training are effective in terms of enhancing pulmonary functional tests, neuromuscular functions, and quality of life. Partial body weight supported treadmill training is an excellent adjunctive to the physical therapy program.
Discriminative Part CNN for Pedestrian Detection

SUMMARY Pedestrian detection is a signiﬁcant task in computer vision. In recent years, it is widely used in applications such as intelligent surveillance systems and automated driving systems. Although it has been exhaustively studied in the last decade, the occlusion handling issue still remains unsolved. One convincing idea is to ﬁrst detect human body parts, and then utilize the parts information to estimate the pedestrians’ existence. Many parts-based pedestrian detection approaches have been proposed based on this idea. However, in most of these approaches, the low-quality parts mining and the clumsy part detector combination is a bottle-neck that limits the detection performance. To eliminate the bottleneck, we propose Discriminative Part CNN (DP-CNN). Our approach has two main contributions: (1) We propose a high-quality body parts mining method based on both convolutional layer features and body part subclasses. The mined part clusters are not only discriminative but also representative, and can help to construct powerful pedestrian detectors. (2) We propose a novel method to combine multiple part detectors. We convert the part detectors to a middle layer of a CNN and optimize the whole detection pipeline by ﬁne-tuning that CNN. In experiments, it shows astonishing e ﬀ ectiveness of optimization and robustness of occlusion handling.
The Impact of Radiation Dose to Heart Substructures on Major Coronary Events and Patient Survival after Chemoradiation Therapy for Esophageal Cancer

Simple Summary Whether it is necessary to evaluate the radiation exposure of cardiac substructures when making radiotherapy plans is one of the current research hotspots. In this cohort study of 355 patients with esophageal cancer, the radiation dose to key coronary substructures such as the left anterior descending artery V30Gy and mean left main coronary artery was closely associated with major coronary events and overall patient survival, and showed better predictive value than the mean heart dose or heart V30Gy recommended by current guidelines. Our findings suggest that, in addition to the whole heart, key coronary substructures should be contoured as organs at risk during radiotherapy plan optimization. Abstract Background: There is a paucity of data regarding the association between radiation exposure of heart substructures and the incidence of major coronary events (MCEs) in patients with esophageal cancer (ESOC) undergoing chemoradiation therapy. We studied radiation dosimetric determinants of MCE risk and measured their impact on patient prognosis using a cohort of ESOC patients treated at a single institution. Methods: Between March 2005 and October 2015, 355 ESOC patients treated with concurrent chemoradiotherapy were identified from a prospectively maintained and institutional-regulatory-board-approved clinical database. Dose-distribution parameters of the whole heart, the atria, the ventricles, the left main coronary artery, and three main coronary arteries were extracted for analysis. Results: Within a median follow-up time of 67 months, 14 patients experienced MCEs at a median of 16 months. The incidence of MCEs was significantly associated with the left anterior descending coronary artery (LAD) receiving ≥30 Gy (V30Gy) (p = 0.048). Patients receiving LAD V30Gy ≥ 10% of volume experienced a higher incidence of MCEs versus the LAD V30Gy < 10% group (p = 0.044). The relative rate of death increased with the left main coronary artery (LMA) mean dose (Gy) (p = 0.002). Furthermore, a mutual promotion effect of hyperlipidemia and RT on MCEs was observed. Conclusion: Radiation dose to coronary substructures is associated with MCEs and overall survival in patients with ESOC. In this study, the doses to these substructures appeared to be better predictors of toxicity outcomes than mean heart dose (MHD) or whole-heart V30Gy. These findings have implications for reducing coronary events through radiation therapy planning.
Next-Generation Molecular Discovery: From Bottom-Up In Vivo and In Vitro Approaches to In Silico Top-Down Approaches for Therapeutics Neogenesis

Protein and drug engineering comprises a major part of the medical and research industries, and yet approaches to discovering and understanding therapeutic molecular interactions in biological systems rely on trial and error. The general approach to molecular discovery involves screening large libraries of compounds, proteins, or antibodies, or in vivo antibody generation, which could be considered “bottom-up” approaches to therapeutic discovery. In these bottom-up approaches, a minimal amount is known about the therapeutics at the start of the process, but through meticulous and exhaustive laboratory work, the molecule is characterised in detail. In contrast, the advent of “big data” and access to extensive online databases and machine learning technologies offers promising new avenues to understanding molecular interactions. Artificial intelligence (AI) now has the potential to predict protein structure at an unprecedented accuracy using only the genetic sequence. This predictive approach to characterising molecular structure—when accompanied by high-quality experimental data for model training—has the capacity to invert the process of molecular discovery and characterisation. The process has potential to be transformed into a top-down approach, where new molecules can be designed directly based on the structure of a target and the desired function, rather than performing screening of large libraries of molecular variants. This paper will provide a brief evaluation of bottom-up approaches to discovering and characterising biological molecules and will discuss recent advances towards developing top-down approaches and the prospects of this.
Research and Development Trends of Underwater Optical Wireless Communication Technologies

In recent years, the research and development of high-speed and long-distance underwater optical wireless communication technology using visible light have been encouraged for undersea resource development and shallow-water leisure applications. This paper introduces the fundamentals of underwater optical wireless communication technology, technical issues, recent R&D trends, and the authors’ recent research outcomes.
Performance of High-Layer-Thickness Ti6Al4V Fabricated by Electron Beam Powder Bed Fusion under Different Accelerating Voltage Values

The electron beam powder bed fusion (EB-PBF) process is typically carried out using a layer thickness between 50 and 100 μm with the accelerating voltage of 60 kV for the electron beam. This configuration ensures forming accuracy but limits building efficiency. The augmentation of the accelerating voltage enlarges the molten pool due to the rise in penetrability, suggesting that a higher layer thickness can be used. Therefore, the effects of layer thickness and accelerating voltage were investigated simultaneously in this study to explore the feasibility of efficiency improvement. Ti6Al4V was fabricated by EB-PBF using layer thicknesses of 200 and 300 μm. Two accelerating voltage values of 60 and 90 kV were used to study their effects under expanded layer thickness. The results reveal that dense parts with the ultimate tensile strength higher than 950 MPa and elongation higher than 9.5% could be fabricated even if the layer thickness reached 300 μm, resulting in a building rate of up to 30 mm3/s. The expansion of the layer thickness could decrease the minimum bulk energy density needed to fabricate dense parts and increase the α platelet thickness, which improved the energy efficiency. However, expanding layer thickness had a significant negative effect on surface roughness, but it could be improved by applying augmented accelerating voltage.