Evaluation of E Layer Dominated Ionosphere Events Using COSMIC/FORMOSAT-3 and CHAMP Ionospheric Radio Occultation Data

At certain geographic locations, especially in the polar regions, the ionization of the ionospheric E layer can dominate over that of the F2 layer. The associated electron density profiles show their ionization maximum at E layer heights between 80 and 150 km above the Earth’s surface. This phenomenon is called the “E layer dominated ionosphere” (ELDI). In this paper we systematically investigate the characteristics of ELDI occurrences at high latitudes, focusing on their spatial and temporal variations. In our study, we use ionospheric GPS radio occultation data obtained from the COSMIC/FORMOSAT-3 (Constellation Observing System for Meteorology, Ionosphere, and Climate/Formosa Satellite Mission 3) and CHAMP (Challenging Minisatellite Payload) satellite missions. The entire dataset comprises the long period from 2001 to 2018, covering the previous and present solar cycles. This allows us to study the variation of the ELDI in different ways. In addition to the geospatial distribution, we also examine the temporal variation of ELDI events, focusing on the diurnal, the seasonal, and the solar cycle dependent variation. Furthermore, we investigate the spatiotemporal dependency of the ELDI on geomagnetic storms

Understanding social interpersonal interaction via synchronization templates of facial events

Automatic facial expression analysis in inter-personal communication is challenging. Not only because conversation partners' facial expressions mutually influence each other, but also because no correct interpretation of facial expressions is possible without taking social context into account. In this paper, we propose a probabilistic framework to model interactional synchronization between conversation partners based on their facial expressions. Interactional synchronization manifests temporal dynamics of conversation partners' mutual influence. In particular, the model allows us to discover a set of common and unique facial synchronization templates directly from natural interpersonal interaction without recourse to any predefined labeling schemes. The facial synchronization templates represent periodical facial event coordinations shared by multiple conversation pairs in a specific social context. We test our model on two different dyadic conversations of negotiation and job-interview. Based on the discovered facial event coordination, we are able to predict their conversation outcomes with higher accuracy than HMMs and GMMs

Can mangroves help combat sea level rise through sediment accretion and accumulation?

Mangroves have substantial roles to induce sedimentation in the vulnerable coastal regions, which subsequently helps to combat climate change induced impacts like sea level rise. Although Sarawak has numerous pristine estuarine mangroves, studies on the roles of these mangroves in regards to sediment deposition are scanty. Therefore, this study was carried out to determine the sediment accretion and accumulation pattern of pristine Sibuti mangrove using tiles and sediment traps from January to December 2013. Monthly average accretion and accumulation rate of sediments of this mangrove were 0.55 mm and 0.08 g cm-2, respectively. A total of 6.56 mm and 0.93 g cm-2 sediments were accreted and accumulated annually. Significantly positive correlation (r=0.794) was found for the monthly accretion of sediments with accumulation. Accretion and accumulation of sediments were also positively correlated with rainfall. Comparatively higher rate of accretion and accumulation of sediments were estimated in the months of wet season when the rainfall and tidal inundation duration were high. Erosion was found higher in the months of dry season when the rainfall was low. Seasonal variations were not found for sediment accretion as well as accumulation in the study area. The findings of the study suggest that the roles of this forest in regards to sediment accretion through retention is compatible with the predicted annual rate of sea level rise of 1.8 to 5.9 mm within 21st century by IPCC

Access to safe drining water and availability of environmental sanitation facilities among Dukem town households in Ethiopia

The objective of this study was to assess the accessibility of water and environmental sanitation amongst households of Dukem town in Ethiopia. This was a cross-sectional study conducted among 391 households. Almost all the households had access to improved sources of drinking water. Majority of the households had access to water within a distance of up to 200 metres or less and had access to water within a time of 30 minutes or less. More than two-thirds of households had improved toilets (flush/pour-flush toilet, ventilated improved pit (VIP) latrine and traditional pit latrine). It is important to make water available by supplying with private or yard tap connections for underserved population and improved basic sanitation by promoting Total Sanitation Approach which aims to achieve universal access and use of toilets and the elimination of open defecation in the communities.NoneHealth Studie

BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP

Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing (NLP) for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various tasks, mostly in English, they have not yet undergone thorough evaluation in under-resourced languages such as Bengali (Bangla). In this paper, we evaluate the performance of LLMs for the low-resourced Bangla language. We select various important and diverse Bangla NLP tasks, such as abstractive summarization, question answering, paraphrasing, natural language inference, text classification, and sentiment analysis for zero-shot evaluation with ChatGPT, LLaMA-2, and Claude-2 and compare the performance with state-of-the-art fine-tuned models. Our experimental results demonstrate an inferior performance of LLMs for different Bangla NLP tasks, calling for further effort to develop better understanding of LLMs in low-resource languages like Bangla.Comment: First two authors contributed equall

Assay Type Detection Using Advanced Machine Learning Algorithms

The colourimetric analysis has been used in diversified fields for years. This paper provides a unique overview of colourimetric tests from the perspective of computer vision by describing different aspects of a colourimetric test in the context of image processing, followed by an investigation into the development of a colorimetric assay type detection system using advanced machine learning algorithms. To the best of our knowledge, this is the first attempt to define colourimetric assay types from the eyes of a machine and perform any colorimetric test using deep learning. This investigation utilizes the state-of-the-art pre-trained models of Convolutional Neural Network (CNN) to perform the assay type detection of an enzyme-linked immunosorbent assay (ELISA) and lateral flow assay (LFA). The ELISA dataset contains images of both positive and negative samples, prepared for the plasmonic ELISA based TB-antigen specific antibody detection. The LFA dataset contains images of the universal pH indicator paper of eight pH levels. It is noted that the pre-trained models offered 100% accurate visual recognition for the assay type detection. Such detection can assist novice users to initiate a colorimetric test using his/her personal digital devices. The assay type detection can also aid in calibrating an image-based colorimetric classification

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively

Clustering and Classification of a Qualitative Colorimetric Test

In this paper, we present machine learning based detection methods for a qualitative colorimetric test. Such an automatic system on mobile platform can emancipate the test result from the color perception of individuals and its subjectivity of interpretation, which can help millions of populations to access colorimetric test results for healthcare, allergen detection, forensic analysis, environmental monitoring and agricultural decision on point-of-care platforms. The case of plasmonic enzyme-linked immunosorbent assay (ELISA) based tuberculosis disease is utilized as a model experiment. Both supervised and unsupervised machine learning techniques are employed for the binary classification based on color moments. Using 10-fold cross validation, the ensemble bagged tree and k-nearest neighbors algorithm achieved 96.1% and 97.6% accuracy, respectively. The use of multi-layer perceptron with Bayesian regularization backpropagation provided 99.2% accuracy. Such high accuracy system can be trained off-line and deployed to mobile devices to produce an automatic colourimetric diagnostic decision anytime anywhere