Miracle-dz/UniFAR-FTUs huggingface dataset:
A new soft coral genus (Coelenterata: Octocorallia) from Palau

Elbeenus lauramartinae, a new genus and species of the soft coral family Alcyoniidae from deep ( 159 m) water off Palau is described. The lobed colonies bear a resemblance to some species of Sirwlaria or Klyxum, but unlike these genera the coenenchymal sclerites consist of large interior spindles only. Colonies have very large polyps armed with large collaret and points sclerites that are characteristic of some species of the Nephtheidae and Nidaliidae.
Design Methodology for Bidirectional Resonant Converter With Dual Resonant Frequencies for Wide Voltage Range

This article presents the design methodology for a bidirectional CLLC resonant converter with dual resonant frequencies for charging a wide range of battery voltages. The proposed design method improves the battery-charging voltage range by operating at two resonant frequencies with constant current and constant voltage regardless of load variations. Furthermore, the proposed design method improves the conversion efficiency and simplifies the controller because it operates at fixed resonant frequencies. To validate the proposed design method, a 3.6-kW experimental prototype is configured. Experimental results show that compared with the conventional method, the proposed design method improves the efficiency by approximately 3.13% under light load conditions, and the measured maximum efficiency in the charging and discharging modes is 98.04% and 98.05%, respectively.
Diabetes mellitus prediction using machine learning within the scope of a generic framework

Artificial intelligence (AI) based automated disease prediction has recently taken a significant place in the field of health informatics. However, due to unavailability of real time large scale medical data, the dynamic learning of prediction models remains principally subsided. This paper, therefore proposes a dynamic predictive modelling framework for chronic diseases prediction in real-time. The framework premise suggests creation of a centralized patient-indexed medical database to dynamically train machine learning (ML) models and predict risk levels of chronic diseases in real time. In this study, comprehensive empirical evaluations to train seven state-of-the-art ML models for diabetes risk prediction are performed in context of phase 2 of the suggested framework. The selected optimal model can then be dynamically applied to predict diabetes in phase 3 of the framework. Various metrics such as accuracy, precision, Recall, F1-score and receiver operating characteristic (ROC) curve are employed for evaluating performances of the trained models. Parameter tunings using different type of kernels, different number of neighbors and estimators are rigorously performed in order to create a suggestive literature for healthcare prediction ecosystem. Comparative analysis indicates high prediction accuracies on diabetes test data records for neural network and support vector machine (SVM) models as compared to other applied models.
Lapsation Risk: Takaful Operators In Malaysia

Insurance serves as a tool for risk management. Takaful is an Islamic insurance where the concepts of mutual cooperation (ta’awun) and donation (tabarru’) are used as the main pillars. Apart from the usual market and credit risk, lapsation risk is one of the main risks faced by a life insurer. It is important to determine factors that may influence policy lapsation as it could affect the solvency and the profitability of insurance or a Takaful operator. Therefore, the relationship between surrender rates and economic variables (Unemployment Rate, Real Interest Rate, Growth Domestic Product (GDP), Inflation - Annual % change in Consumer Price Index and Stock Market Price Index (%) ) were examined for Takaful operator ordinary life and unit investment link products. Out of sample forecast was performed for 2019 to 2020 surrender rates for Takaful operator ordinary life and unit investment link products. Panel data model was applied for both purposes as the method is viable to overcome small sample size issue. The empirical findings revealed that the economic variable displayed a different impact on the surrender rate for both products. Both ordinary life and investment linked products exhibited a significant relationship between unemployment rate and surrender rate; in which higher unemployment rate led to increased surrender rate. Stock also had a statistically significant impact on the surrender rate of both products. Meanwhile, interest rate had an insignificant link with the surrender rate of both products. This outcome is in agreement with several past studies that reported no real relationship between interest rate and surrender rate; thus disregarding the infamous interest rate hypothesis. Lastly, Gross Domestic Product (GDP) displayed an insignificant correlation with the surrender rate of both products. By using Pool OLS model, the trend for ordinary life product decreased in 2020 for all Takaful operators. As for investment linked products, a decreasing trend was noted from 2019 to 2020; the lowest and the highest forecast rates in 2019 were ascribed to Takaful Ikhlas and Takaful Malaysia, respectively.
Reliable Eye-Blinking Detection With Millimeter-Wave Radar Glasses

One of the most felt and serious problems of our society involves the capability to enable the communication of people affected by neurodegenerative pathologies. Indeed, the inability to communicate as a consequence of the patient paralysis is one of the main detrimental effects of such diseases. Since the head is the last affected part of the body, an accepted solution to enable the communication considers the detection of intentional eye blinking to be interpreted as messages or commands. In this article, a millimeter-wave (mm-wave) Doppler radar is devoted to this purpose with advantages in terms of size, computational cost, privacy concerns, and immunity to different light conditions, compared with competing optical image-based technologies. Displacement and micro-Doppler signature are measured with a 120-GHz radar to accurately recognize intentional motions. After analyzing the performance dependence on different radar positions, the radar has been integrated into a glass frame. This allowed us to mitigate the effects of random body motion as a consequence of the movements due to both the physiological activity and external factors. This contribution aims to demonstrate the radar’s effectiveness as assistive systems to enable the patient’s communication.
MERRC: A Memristor-Enabled Reconfigurable Low-Power Reservoir Computing Architecture at the Edge

The massive growth in the Internet of Things (IoT) has led to an increase in demand for devices receiving and transmitting data to and from the cloud during operations. Edge computing has been developed in an attempt to bring computations in proximity to the devices to overcome latency and cost overhead. IoT applications heavily reliant on machine learning (ML) tasks such as image or voice recognition can benefit from edge devices that facilitate real-time operations without costly data transmission back and forth from memory. In this work, we develop MERRC, a memristor-enabled reconfigurable architecture that incorporates processing-in-memory and reservoir computing to carry out ML tasks of image classification at the edge. This design uses a novel masking circuit to allow for image segmentation, which is combined with a delay-based reservoir to form a recurrent neural network. We further implement the final stage of classification using a fabricated memristor crossbar. Our hardware measurement results on the MNIST dataset of the delay-based reservoir with memristor crossbar arrays provide a recognition accuracy of 98%. On a more complex image classification dataset of CIFAR-10, MERRC shows a high accuracy of 88%, further highlighting the edge computing capabilities of the architecture.
A Deep Insight into the Diversity of Microfungal Communities in Arctic and Antarctic Lakes

We assessed fungal diversity in water and sediment samples obtained from five Arctic lakes in Ny-Ålesund (Svalbard Islands, High Arctic) and five Antarctic lakes on Livingston and Deception Islands (South Shetland Islands), using DNA metabarcoding. A total of 1,639,074 fungal DNA reads were detected and assigned to 5980 ASVs amplicon sequence variants (ASVs), with only 102 (1.7%) that were shared between the two Polar regions. For Arctic lakes, unknown fungal taxa dominated the sequence assemblages, suggesting the dominance of possibly undescribed fungi. The phylum Chytridiomycota was the most represented in the majority of Arctic and Antarctic samples, followed by Rozellomycota, Ascomycota, Basidiomycota, and the less frequent Monoblepharomycota, Aphelidiomycota, Mortierellomycota, Mucoromycota, and Neocallimastigomycota. At the genus level, the most abundant genera included psychrotolerant and cosmopolitan cold-adapted fungi including Alternaria, Cladosporium, Cadophora, Ulvella (Ascomycota), Leucosporidium, Vishniacozyma (Basidiomycota), and Betamyces (Chytridiomycota). The assemblages displayed high diversity and richness. The assigned diversity was composed mainly of taxa recognized as saprophytic fungi, followed by pathogenic and symbiotic fungi.
Water vapour in the tropical upper troposphere and lower stratosphere measured by the Microwave Limb Sounder on UARS

This thesis exploits two water vapour products from the Microwave Limb Sounder (MLS), an instrument on the Upper Atmosphere Research Satellite, to investigate the spatial distribution and temporal variation of water vapour in the tropical region of the upper troposphere and lower stratosphere. Measurements are centred on 215 hPa in the upper troposphere and at 68 hPa and 100 hPa in the lower stratosphere. MLS provides almost global measurements with good vertical resolution, and offers many advantages over other measurement techniques. Focusing on the tropical region, time-series analysis techniques have been employed to investigate the variability of upper tropospheric water vapour on different timescales. The construction of longitude-time sections has allowed the annual cycle to be investigated in more detail than previously. The amplitude is found to vary greatly with longitude and to be most pronounced over the landmasses of South America and Africa. The oceans are found to have a moderating effect on the annual cycle but secondary maxima occur in regions influenced by the Asian monsoon, and over Indonesia and the central Pacific. An intraseasonal cycle with a period of 30-85 days is evident over the western Pacific at latitudes from 10°N to 20°S. This is associated with eastward moving moist features apparent in longitude-time sections. Through comparison with meteorological fields from the European Centre for Medium Range Weather Forecasting's re-analysis project, the intraseasonal cycle in water vapour is identified with the convective anomalies associated with the Madden-Julian Oscillation. Seasonal averages of water vapour at 68 hPa show an expected zonal structure, but some departures from zonal symmetry are observed. In December-February, dry areas are found in the region of the 'stratospheric fountain'. Enhanced moisture is noted in the Asian monsoon region in June-August. The effects of the monsoon and the stratospheric fountain reach higher in the stratosphere than has previously been observed with MLS. Sequences of water vapour fields show that the stratospheric fountain region is the place where the dry phase of the annual cycle first begins.
The Determinant of Job Performance for Laborers in Manufacturing Companies in Jakarta

This study aims to answer the determinants of job performance based on workplace spirituality, workplace climate, and compensation. Spiritual workplaces are represented by engaging work and a sense of community. By conducting multiple regression tests on respondents from manufacturing companies in Jakarta, it can be concluded that a sense of community and compensation determine the increase in the job performance of workers. The method used is descriptive and verification tests with the classical assumption test. To increase the sense of community, companies can emphasize the formation of workers' sense of belonging to an organization through informal activities such as outings and 'small talks' with employees so that they feel part of the company. Keywords: Job Performance; Workplace Spirituality; Workplace Climate; Compensation; Engaging Work; Sense of Community
Mega‐Tidal and Surface Flooding Controls on Coastal Groundwater and Saltwater Intrusion Within Agricultural Dikelands

Climate change will increase sea levels, driving saltwater into coastal aquifers and impacting coastal communities and land use viability. Coastal aquifers are also impacted by tides that control groundwater‐ocean interactions and maintain an “upper saline plume” (USP) of brackish groundwater. Coastal dikes are designed to limit the surface impacts of high‐amplitude tides, but, due to ongoing sea‐level rise (SLR), low‐lying dikelands and underlying aquifers are becoming increasingly vulnerable to flooding from high tides and storm surges. This study combines field observations with numerical modeling to investigate ocean‐aquifer mixing and future saltwater intrusion dynamics in a mega‐tidal (tidal range >8 m) dikeland along the Bay of Fundy in Atlantic Canada. Field data revealed strong connectivity between the ocean and coastal aquifer, as evidenced by pronounced tidal oscillations in deeper groundwater heads and an order of magnitude intra‐tidal change in subsurface electrical resistivity. Numerical model results indicate that SLR and surges will force the migration of the USP landward, amplifying salinization of freshwater resources. Simulated storm surges can overtop the dike, contaminating agricultural soils. The presence of dikes decreased salinization under low surge scenarios, but increased salinization under larger overtopping scenarios due to landward ponding of seawater behind the dike. Mega‐tidal conditions maintain a large USP and impact aquifer freshening rates. Results highlight the vulnerability of terrestrial soil landscapes and freshwater resources to climate change and suggest that the subsurface impacts of dike management decisions should be considered in addition to protection measures associated with surface saltwater intrusion processes.
Organic sulfur biogeochemistry: Recent advances and future research directions

The biogeochemistry of sulfur is of widespread interest in the earth science community due to its impact on many different biogeochemical processes. Organic sulfur is of particular interest due to its impact on petroleum formation and refi ning and its relationship to microbial sedimentary processes, organic carbon accumulation, and the overall integrity of paleoenvironmental proxy records. This paper reviews many of the advances in organic sulfur biogeochemical research spanning the past ~15 years. These advances include (1) an improved mechanistic understanding of why sulfur-rich organic deposits form petroleum products earlier during diagenesis than sulfur-poor deposits, (2) constraints on the timing and pathways of organic sulfur formation as well as the forms of organic sulfur present in the environment, and (3) recognition of the impacts of organic matter sulfurization on organic carbon preservation at bulk and molecular scales and the implications of this enhanced preservation for paleoenvironmental studies.
Online identification of burn-through and weld deviation in sheet lap MIG welding based on YOLOv5

Online monitoring of the welding process is very important to the development of the modern manufacturing industry. However, monitoring welding defects in real-time and with high accuracy is challenging because welding is inherently a dynamic, non-linear process. In this paper, the forming defects generated during the melt Inert-gas welding of sheet steel as the object of research. We propose a multi-task simultaneous monitoring system for welding defects based on the YOLOv5 model, which can achieve highly accurate simultaneous detection of burn-through and weld deviation defects. Firstly, a passive vision system has been designed that can filter out strong arc light interference. In combination with a self-made camera bracket with flexible adjustment and repeatable positioning, clear images of the molten pool can be acquired. Compared to common methods that require a laser for weld deviation detection, all monitoring tasks can be performed with just one charge coupled device camera, significantly reducing the cost of deploying the system. Secondly, to obtain experimental data closer to the industrial field environment. We simulated different degrees of local and continuous burn-through by changing three parameters: welding current, plate thickness, and lap width. Four different deviations: left, right, and tilt were designed to obtain rich weld deviation image data. Finally, based on the above data, we compared several aspects such as detection speed, recognition accuracy, and loss function. The YOLOv5s model with the smallest model parameters was finally selected as the base model. The results show that our proposed multi-task simultaneous monitoring system achieves an average accuracy of 98.84% for the identification of four molten pool states. Burn-through defect and weld deviation detection with more than 50 frames per second can be achieved in the online detection state. This paper can provide some guidance for the online monitoring of the welding manufacturing process.
Hyperspectral Object Detection Using Bioinspired Jellyfish Search Optimizer With Deep Learning

Hyperspectral images (HSI) provide a rich source of data for remote sensing applications, offering extensive spectral data about the Earth’s surface. Object detection in HSI remains a challenging process with various application areas in environmental monitoring, agriculture, and geospatial analysis. The development of deep learning (DL) models for HSI object detection paves the way for new opportunities in advanced remote sensing analysis. DL models enable the automated and reliable detection of target objects. Particularly, convolutional neural networks (CNNs) can handle the high-dimensional nature of hyperspectral data and efficiently learn complex relationships among spectral patterns and object classes. This results in improved detection performance and reduces the need for manual feature engineering. Therefore, this study presents a Hyperspectral Object Detection using Bioinspired Jellyfish Search Optimizer with Deep Learning (HSOD-JSODL) technique for Enhanced Remote Sensing Analysis. The aim of the HSOD-JSODL method lies in the effectual recognition of interested objects in the HSI using the DL model. To achieve this, the HSOD-JSODL technique employs EfficientDet object detector to recognize various kinds of objects in the HSI. EfficientDet is a recently developed object detector which integrates efficiency via a compound scaling approach and efficient network design. For the classification of detected objects, the HSOD-JSODL technique uses a deep belief network (DBN) classifier model. To improve the object classification results of the DBN model, the JSO algorithm is applied as a hyperparameter optimizer. The simulation analysis of the HSOD-JSODL technique is examined on the HSI dataset, and the outcomes are examined under various measures. The simulation values portrayed the betterment of the HSOD-JSODL technique over compared methods.
Secure and Efficient Encryption Scheme Based on Bilinear Mapping

— With the increasing uses of internet technologies in daily life, vulnerability of personal data/information is also increasing. Performing secure communication over the channel which is insecure has always been a problem because of speedy development of various technologies. Encryption scheme provides secrecy to data by enabling only authorized user to access it. In the proposed paper, we present an encryption algorithm designed for data security based on bilinear mapping and prove it secure by providing its security theoretical proof against adaptive chosen cipher-text attack. With the help of a lemma, we have shown that no polynomially bounded adversary has non-negligible advantage in the challenging game. We also give the comparative analysis of the proposed scheme in terms of security and performance with Deng et al., 2020 and Jiang et al., 2021 schemes and prove that proposed algorithm is more efficient and secure than others existing in literature against adaptive chosen cipher-text attack.
Correction of Skeletal Class II Malocclusion in a Growing Child Using Standard Twin Block: A Case Report

Mandibular defiiency in a Class II malocclusion is a frequently encountered condition that can be managed with myofunctional appliance in a growing child. An 11-year-old patient presented with a complaint of forwardly placed upper front teeth. The patient had a convex facial profie, acute naso-labial angle, deep mento-labial sulcus with lip trap. Clinically the patient showed positive VTO. The clinical fidings and cephalometric analysis indicated skeletal Class II jaw bases owing to prognathic maxilla and retrognathic mandible with Angle’s Class II division 1 malocclusion. CVMIshowed that the patient was in his growth phase. The treatment was done with a standard twin block appliance for 9 months followed by fied orthodontic treatment. The post functional phase showed correction of skeletal class II jaw base and marked improvement in the facial profie from convex to straight. It was later followed by fixed orthodontic treatment in the second phase upon completion of which bonded lingual retainer and wrap around retainers were given in the upper and lower arch. The total duration of the treatment was 23 months.
Pretoria Radiocarbon Dates I

In 1969 radiocarbon dating facilities were established at the National Physical Research Laboratory of the C.S. I.R. in Pretoria (25° 43′ S Lat, 28° 21′ E Long; alt 1500 m). The counters are situated in an underground room built of selected concrete and covered by ca. 12 m earth. In this room, the nucleonic component of cosmic radiation is practically absent and the meson flux is reduced by a factor of 3.5 as compared to the surface at sea level in Groningen, Netherlands. A neutron monitor which registers 30 cpm on the surface, counts ca. 0.1 cpm in the underground room.