Miracle-dz/UniFAR-FTUs huggingface dataset:
Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

The HyspIRI Aquatic Studies Group (HASG) has developed a conceptual list of data products for the HyspIRI mission to support aquatic remote sensing of coastal and inland waters. These data products were based on mission capabilities, characteristics, and expected performance. The topic of coastal and inland water remote sensing is very broad. Thus, this report focuses on aquatic data products to keep the scope of this document manageable. The HyspIRI mission requirements already include the global production of surface reflectance and temperature. Atmospheric correction and surface temperature algorithms, which are critical to aquatic remote sensing, are covered in other mission documents. Hence, these algorithms and their products were not evaluated in this report. In addition, terrestrial products (e.g., land use land cover, dune vegetation, and beach replenishment) were not considered. It is recognized that coastal studies are inherently interdisciplinary across aquatic and terrestrial disciplines. However, products supporting the latter are expected to already be evaluated by other components of the mission. The coastal and inland water data products that were identified by the HASG, covered six major environmental and ecological areas for scientific research and applications: wetlands, shoreline processes, the water surface, the water column, bathymetry and benthic cover types. Accordingly, each candidate product was evaluated for feasibility based on the HyspIRI mission characteristics and whether it was unique and relevant to the HyspIRI science objectives.
In vitro and in silico effects of the polyisoprenylated benzophenones guttiferone K and oblongifolin C on P-glycoprotein function

The polyisoprenylated benzophenones, guttiferone K and oblongifolin C, were found in several Garcinia plants (family Clusiaceae) and exhibited remarkable antitumor activity. However, their effect on P-glycoprotein (P-gp) function has never been investigated. This study aimed to assess the effect of guttiferone K and oblongifolin C on P-gp function in Caco-2 cells using the calcein-AM uptake assay to measure the accumulation of the fluorescent calcein, a substrate of P-gp, within the cells under pre/co-treatment condition. Verapamil, a well-known P-gp substrate/ inhibitor, was employed as a positive control. Both compounds could inhibit P-gp function, causing increased cellular accumulation of calcein. Their roles as both P-gp substrates and P-gp inhibitors were predicted from their chemical structures through SwissADME and admetSAR programs. Furthermore, the effect of these two benzophenones on the ATP-binding region at nucleotide-binding domain 1 (NBD1) of P-gp was investigated by a molecular docking study. They could bind more favorably than verapamil to the ATP-binding region within NBD1 of P-gp. They are also bound with ATP-binding region within NBD1 of P-gp, suggesting possible inhibition of ATP hydrolysis. Therefore, guttiferone K and oblongifolin C in Garcinia extracts or fruits have the potential to inhibit P-gp function and might increase the risks of herb–drug interaction when used or consumed with drugs that are P-gp substrates.
Fault Prediction and Recovery Using Machine Learning Techniques and the HTM Algorithm in Vehicular Network Environment

The amount of data available to vehicles has become very large in the vehicular networks’ environment. Failures that mislead real-time data from vehicle sensors and other devices have become massive, and the need for automated techniques that can analyze data to detect malicious sources has become paramount. The application of machine learning techniques in the environment of vehicular ad hoc networks (VANET) is very promising and is beginning to show results in terms of applications designed and articles published. These techniques are increasingly accessible and used intensively, as many researchers are working to detect anomalous data. However, there is no universal, effective technique so far that can detect all abnormal data and then recover it. This work is an effort in that direction. We propose a smart model that uses multiple machine-learning classification methods. Our contribution also relates to a study of the attributes of interest for the algorithm used during the detection phase, namely the hierarchical temporal memory algorithm (HTM). The packets exchanged by the vehicle are grouped in instant description windows. These windows are then analyzed to extract a set of attributes. These are linked to the properties of network traffic such as flow or latency. They are subject to the process of detecting anomalies and intrusions carried out thanks to the algorithm with HTM. We propose the performance of fault detection and recovery at the level of the fog layer. The obtained simulation results demonstrate the efficiency of the learning methods and HTM for the detection of defects and errors in the IoV.
Effectiveness of Multidirectional Stepping Training on Balance among Geriatric Population

Background: In order to perform an activity that requires an upright posture, balance is a complex process that involves receiving and integrating the sensory inputs as well as planning and carrying out movements. Falls are frequent among elderly people. An example of home based aerobic activity is multidirectional stepping training.Purpose: To find out the effectiveness of multidirectional stepping training on balance among geriatric population.Materials and Methods: From November 2022 to April 2023, 200 participants were recruited from the Sai Chai Physio Centre, with only 82 participants selected based on inclusion and exclusion criteria.The subjects were separated into two groups: Group A (n=41) and Group B (n=41). Group A received multidirectional stepping training, whereas Group B received conventional exercise.Both the interventions were given for four weeks, three times a week for 40 minutes.Results: The findings indicated that both the groups showed statistically significant improvement after the interventions in scores of fall efficacy scale and Balance outcome measure for elderly rehabilitation(p<0.005). Multidirectional stepping training showed statistically significant improvement in both the measures.Conclusion: When compared to conventional exercise, multidirectional stepping training is more effective at improving balance.
Tourism Development Strategy in Mundeh Kangin Village Sub District Selemadeg Barat, District Tabanan Bali

Mundeh Kangin Village has natural resource potential that can be developed into agrotourism. The MundehKangin Village farmer group transformed the village into a durian village. The unique nature and culture attracttourists to visit the village. In improving the quality of tourism, of course there needs to be an effective strategyso that the tourism ecosystem in Mundeh Kangin Village can become a leading tourism destination in TabananRegency, Bali. The focus and aim of this research is to formulate a development strategy for Mundeh KanginVillage. Using the SWOT analysis method (Strength, Weakness, Opportunities, Threats) in analyzing thestrategic environment in the management of Tourism Villages. This research uses a qualitative research typeusing sources from Mundeh Kangin Village, namely the village head (perbekel), the Tourism Awareness Group(POKDARWIS) and farmer groups. Based on the IFE and EFE matrix tables, the total weighted scores are 2.75and 3.12. This shows that agrotourism in Mundeh Kangin Village is relatively strong in overcoming weaknessesand taking advantage of existing opportunities to overcome threats. The internal strategic factor in MundehKangin Village which is the main strength is that it is one of the horticultural commodities which has reachednational and international markets and the garden products in the form of local fruits such as durian, salak,buni, chocolate, coffee, coconut, porang grow abundantly in Mundeh Kangin Village 0.64, while the mainweakness is capital and infrastructure which is still lacking with a total value of 0.33. The external strategicfactor in the development of Mundeh Kangin village which is the main opportunity is the lifestyle of the peoplewho want to return to nature with a value of 0.57, while the main threat faced is the unpredictable climate witha value of 0.30. The research results show that the strategy that can be used by Mundeh Kangin Village is aGeneric expansion strategy. Meanwhile, Strategy variations are, concentric diversification, market penetration,market and product development and backward integration.Keywords: SWOT analysis, development, tourist villages
Silver and Copper Complexes with Ibuprofen and Caffeine—Preparation and Evaluation of Their Selected Biological Effects

Several organometallic complexes based on more than twenty different metals have already been approved for medical applications. The aim of the presented research was to obtain complexes of silver and copper with the non-steroidal anti-inflammatory drugs ibuprofen and xanthine alkaloid caffeine and evaluate selected aspects of their bioactivity and biosafety in terms of their future possible applications. The obtained complexes were characterized by Fourier-transform infrared spectroscopy, thermogravimetry, UV-VIS spectroscopy, conductometry, elemental analysis, and bioassays. Cytotoxicity for normal human cells of the CCD-Co18 cell line was evaluated by determining the IC50 value, with metabolic and morphology assessments. It was observed that complexes containing ibuprofen and caffeine exhibited lower toxicity than those with ibuprofen only. Complexes with copper showed lower toxicity towards healthy human fibroblasts compared to silver-based compounds, with an IC50 above 140 μg mL−1. However, in the silver complexes, the presence of caffeine increased the potency of COX-2 inhibition. Antimicrobial effects against different Gram-positive and Gram-negative bacterial strains were evaluated by MIC determination with values less than 20 μg mL−1.
The Impact Of Climate Change And Bioenergy On Nutrition

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Aptamers: A Cutting-Edge Approach for Gram-Negative Bacterial Pathogen Identification

Early and accurate diagnoses of pathogenic microorganisms is essential to correctly identify diseases, treating infections, and tracking disease outbreaks associated with microbial infections, to develop precautionary measures that allow a fast and effective response in epidemics and pandemics, thus improving public health. Aptamers are a class of synthetic nucleic acid molecules with the potential to be used for medical purposes, since they can be directed towards any target molecule. Currently, the use of aptamers has increased because they are a useful tool in the detection of specific targets. We present a brief review of the use of aptamers to detect and identify bacteria or even some toxins with clinical importance. This work describes the advances in the technology of aptamers, with the purpose of providing knowledge to develop new aptamers for diagnoses and treatment of different diseases caused by infectious microorganisms.
Study of inner scale ratio of rock and soil material based on numerical tests of particle flow code

Inner scale ratio is a ratio between minimum crystal structure of material and its specimen size.Rock and soil material,being a type of different sizes mineral particles assembler under microscopic conditions,contains different scale raw faults.These size features of material structure will affect accuracy of rock and soil material characteristics test results.That is to say there exists inner scale ratio in rock and soil materials like that of metal material.So virtual uniaxial compression test done by inner embedded fish language of particle flow code is used to study inner scale ratio of geotechnical material.Twelve numerical specimens with twelve types of grain size such as 0.05,0.1,0.2,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8 and 2.0 mm are designed and tested.According to failure shape and stress-strain curve of these specimens,effect of inner scale ratio on test results is analyzed.It is found that the computing time increases dramatically and computing efficiency decreases sharply if grain size less than 0.4 mm;and that test results tend to stability and accuracy if inner scale ratio less than 0.01,so as to prove existence of inner scale ratio in rock and soil material.Size effect in geotechnical material is reflection of inner scale ratio.
Simulasi Furnitur Ruang dengan Augmented Reality Menggunakan Marker Based Tracking

AR menjadi sarana inovatif untuk memfasilitasi pengguna dalam mendesain dan menyesuaikan tata letak furnitur ruang secara virtual sebelum implementasi fisik. Penelitian ini fokus pada pengembangan sistem simulasi furnitur ruang berbasis Augmented Reality (AR) menggunakan metode Marker-Based Tracking dengan menerapkan metodologi penelitian Multimedia Development Life Cycle (MDLC) sebagai kerangka kerja untuk memandu proses pengembangan yang terstruktur, mulai dari perencanaan hingga implementasi. Langkah-langkah penelitian melibatkan identifikasi kebutuhan pengguna, perancangan konsep AR, pengembangan prototipe, implementasi, dan evaluasi. Metode marker-based tracking digunakan untuk meningkatkan akurasi penempatan objek virtual dalam lingkungan fisik. Kontribusi diharapkan dapat meningkatkan kualitas pengalaman pengguna dalam desain interior, memungkinkan mereka untuk berinteraksi dengan furnitur ruang secara lebih dinamis dan efektif
Integrative care: acupuncture based neuromodulation therapy for diabetes and heart failure

The relationship between heart failure and diabetes is intricate and bidirectional. Individuals with diabetes face an elevated risk of developing heart failure due to factors like insulin resistance, chronic inflammation, and metabolic irregularities. Elevated blood sugar levels can harm blood vessels and nerves, culminating in the buildup of fatty deposits in arteries, atherosclerosis, and hypertension, which significantly contribute to heart failure. Furthermore, diabetes can adversely impact the structure and function of the heart muscle, impairing its pumping capacity. Conversely, heart failure can also contribute to the onset of diabetes by disrupting the body’s metabolic processes and amplifying insulin resistance. The complex interaction between these conditions mandates a comprehensive approach to managing individuals with both diabetes and heart failure, underscoring the importance of addressing both aspects for enhanced patient outcomes. Although existing pharmacological treatments are limited and frequently associated with undesirable side effects, acupuncture has established itself as a traditional practice with a legacy. It remains a supplementary option for treating cardiovascular diseases. Heart failure and diabetes are both heavily associated with chronic upregulation of the sympathetic nervous system, which has been identified as a pivotal factor in the progression of disease. Mechanistic interplays such as the attenuation of central nitric oxide signaling may interfere with the production or availability of nitric oxide in key areas of the central nervous system, including the brainstem and hypothalamus. This review will delve into the current understanding of acupuncture on the autonomic nervous system and offer insights into its potential role in the future treatment landscape for diabetes and heart failure.
Development and Evaluation of Gold Standard Dataset for Sentiment Analysis of Tweets

Pre-labeled data is typically required for supervised machine learning. A limited number of object classes in the majority of open access and pre-annotated datasets make them unsuitable for certain tasks, even though they are readily available for training machine learning algorithms. For custom models, previously available pre-annotated data is typically insufficient, so gathering and preparing training data is necessary for the majority of real-world applications. The quantity and quality of annotations clearly trade-off with one another. Either more annotated data can be produced or better data quality can be guaranteed by allocating time and resources. Development of the gold standard by annotating textual information is an essential part of the “Text Analytics” domain in the field of “Natural Language Processing-NLP”. In “Text Analytics”, annotation can be done by adopting a manual, semi-automatic or automatic approach. In the case of the manual approach, annotators often work with partial parts of the corpus, and the results are generalized by automated text classification which may affect the final classification results. Annotations reliability and suitability of assigned labels are particularly important in the NLP applications related to opinion mining or sentiment analysis. In this research study, we have evaluated the significance of the annotation process on a novel dataset that contained multiple languages (English, Roman Urdu), a free text dataset that was extracted from Twitter. This unique dataset contained multiple languages which makes this annotation process essential for researching this data. Using this multi-language dataset, we examine the inter-annotator agreement in multiclass and multi-label sentiment annotation. To scrutinize the reliability of this research work, several annotation agreement metrics, statistical analysis, and Machine Learning methods have been considered to evaluate the accuracy of resulting annotations. It was observed that the annotation process is significant and a complex step that is essential for the proper implementation of Natural Language processing tasks for text analytics in machine learning. During this research, different gaps were identified and resolved which can impact the overall reliability of the annotation process which are reported in this paper. We conclude that while inaccurate annotations worsen the results, the impact is minimal, at least when using text data. The advantages of the larger annotated data set (obtained by employing subpar auto-annotation techniques) surpass the degradation resulting from the use of annotated data.
Simplifying the evidential condition on asking polar questions

In classic accounts of polar question semantics, positive polar questions like "Did Mo sing? ", low negation questions like "Did Mo not sing? ", and high negation questions like "Didn't Mo sing? " all denote the same set of answers: {that Mo sang, that Mo didn’t sing}. At the same time, it is well known that these three question types have different distributions. In particular, they have different requirements with respect to contextual evidence for the answers, the Evidential Condition on polar questions. Despite widespread discussion of this fact, no universally accepted explanation has emerged. In this paper, I make the novel argument that high negation questions do not have an Evidential Condition, and so only the conditions for positive and low negation questions need to be explained. I then argue that an explanation can be given based on general principles of markedness and information structure, even while maintaining a classic {p, not-p} semantics for both positive and low negation questions. I discuss ramifications for polar question semantics.
AN EFFICIENT ALGORITHM FOR SORTING A SET OF ELEMENTS WITH PARENT-CHILD RELATIONSHIPS

Một hệ thống trong thế giới thực có thể có một bảng cơ sở dữ liệu gồm các bản ghi có mối quan hệ cha-con. Các bản ghi có thể không được chèn vào bảng theo thứ tự cây gia đình. Bài báo này đề xuất một thuật toán hiệu quả để giải bài toán sắp xếp danh sách các phần tử có quan hệ cha-con tương ứng với tất cả các bản ghi trong bảng cơ sở dữ liệu. Giả sử rằng tất cả các bản ghi được tải vào bộ nhớ dưới dạng danh sách các phần tử. Đầu tiên, thuật toán tạo một danh sách S với phần tử đầu tiên lưu trữ các đại diện của các phần tử gốc và mỗi phần tử khác lưu trữ tập đại diện của các con của một phần tử trong danh sách đầu vào. Danh sách S tạo thành một tập các cây của các phần tử đầu vào. Sau đó, mỗi phần tử đầu vào được thêm vào danh sách được sắp xếp đầu ra bằng cách thực hiện tìm kiếm theo chiều sâu trên mỗi cây. Thuật toán cần thời gian O(nlog(n)) và không gian O(n) để giải bài toán trong đó n là số phần tử. Thuật toán mất khoảng 154 (giây) để sắp xếp 2.441. 405 phần tử, sử dụng máy tính xách tay có bộ xử lý Intel(R) Core(TM) i7-4510U CPU @ 2,00GHz 2,60 GHz và RAM 8 GB.
Diabetic Retinopathy Blood Vessel Detection Using CNN and RNN Techniques

This research aims to detect diabetic retinopathy using Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). The main objective is to compare these two methods in detecting the condition. Based on the study’s result after training 10 times on each method, the accuracy results were 92% for the CNN method and 50% for the RNN method. These results show, this study with the dataset used, the CNN method is much more effective in detecting diabetic retinopathy than the RNN method. The CNN method is better due to its ability to extract spatial features from images, which is important in image classification tasks.
Investigation of the Potential Targets and Mechanism Actions of Berberine in the Treatment of Pulpitis Based on Bioinformatics Analysis

Berberine, an active compound extracted from the Chinese herb, was reported to have an antibacterial effect and an anti-inflammatory effect and promote osteogenic differentiation of human dental pulp stem cells. However, the underlying therapeutic mechanism of berberine in pulpitis is still unknown. Here, bioinformatics analysis was performed to investigate the potential mechanism of berberine against pulpitis. First, we identified the collective targets of berberine and pulpitis from several databases using a Venny online tool. The pattern of interaction between berberine and the targeted protein was visualized by molecular docking. Moreover, we performed GSEA, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to obtain potential pathways. Cell experiments were performed to validate the bioinformatics analysis results. Prostaglandin-endoperoxide synthase 2 (PTGS2) was identified as the crucial antipulpitis target of berberine via the CytoHubba plugin. Docking analysis indicated that berberine could fit in the binding pocket of the PTGS2 protein. Additionally, berberine exerted its therapeutic effects against pulpitis via multiple pathways. Overall, berberine possessed therapeutic effects against pulpitis through the inactivation of toll-like receptor and NOD-like receptor signaling pathways. The present research proposes a novel approach to explore the therapeutic mechanism of natural products.
Climate change challenges and adaptations at farm-level: case studies from Asia and Africa.

This book emphasis the role of farm level adaptation as a key in developmental pathways that are challenged by climate risks in the semi-arid tropics of Asia and Africa. It throws light on key issues that arise in farm level impacts, adaptation and vulnerability to climate change and discusses Q2 methodological approaches undertaken in study domains of Asia and Africa. The book systematically describes the perceptions, aspirations as elicited/voiced by the farmers and identifies determinants of adaptation decisions. Chapters identify constraints and opportunities that are translated into indicative intervention recommendations towards climate resilient farm households in the semi-arid tropics of Asia and Africa. Furthermore, it discusses with evidences that contributes to the development of livelihood strategy for poor farmers in Asia (Bangladesh, India, Sri Lanka, Thailand, Vietnam and China) and Africa (Burkina Faso, Niger, Kenya and Ghana).