Miracle-dz/UniFAR-FTUs huggingface dataset:
The first report on oribatid mites in tundra belts of the Lovozersky Mountains on the Kola Peninsula, Russia

The fauna of oribatid mites (Acari, Oribatida) in the mountain tundra on the Kola Peninsula (Russia) is still poorly studied. The main aim of this research is to obtain initial data on the fauna and abundance of oribatid mites of the Lovozersky Mountains. We investigated five plots, including three lichen tundra plots, one dwarf shrub tundra plot and one wetted tundra plot in one of the largest mountain massifs of the Kola Peninsula — the Lovozersky Mountains. Multidimensional scaling and discriminant function analysis were used to identify some trends in the fauna and abundance of species on the explored plots. The abundance of oribatid mites in the lichen tundra reached 23,680 ind. /m2. In the dwarf shrub tundra, this value is over three times higher — up to 81,160 ind. /m2, which is comparable with the oribatid mite abundance of forest habitats. The minimum values were discovered in wetted tundra (6,200 ind./m2). A total of 71 species, 38 genera, and 24 families of oribatid mites were found during the course of this study. Eight species of Oribatida have been added to the fauna of the Kola Peninsula. A significant difference was observed in the structure of oribatid faunas of the explored habitats. The occurrence in samples and relative abundance of Mycobates sarekensis, Oribatula amblyptera, Tectocepheus velatus and Carabodes labyrinthicus increased in lichen tundra compared to dwarf shrub tundra. Species of Suctobelbidae and Oppiidae were higher in occurrence and relative abundance in dwarf shrub tundra compared to lichen tundra. The occurrence and relative abundance of Trimalaconothrus angulatus, T. maior and Platynothrus peltifer prevailed in wetted tundra as compared to automorphic tundra habitats. The family Suctobelbidae was represented by a large number of species, while the family Ceratozetidae showed a small number of species in the discovered fauna. According to the literature, these features of the fauna are unusual for tundra. A low diversity of Ceratozetidae, a high diversity of Suctobelbidae and high abundance of oribatid mites in dwarf shrub tundra may be explained by the ambivalent nature of the oribatid community in the explored region, which combines boreal and arctic features due to marine climate.
01-Rameez Khalid

INTRODUCTION Rameez Khalid1* Zulfiquar Ali Shaikh1 Nasir Afghan1 The need for effective safety systems in preventing accidents has long been recognized as a necessary part of doing business. The positive relationship between safety programs and profits has already been accepted (Cox & Cox, 1996; Gunningham & Johnstone, 1999). The primary objective of any organization is to stay in business, and without having safety culture at workplace, it is strenuous for it to be competitive in the market (Clarke, Probst, Guldenmund, & Passmore, 2015; Kelloway, Nielsen, & Dimoff, 2017; Morrow, Koves, & Barnes, 2014; Öz, Özkan, & Lajunen, 2013; Reinhold & Tint, 2007). Ironically, according to International Labor Organization (ILO), approx. 2.34 million workers die at the workplace, and 317 million develop workplace injuries every year globally. However, the European Agency for Safety and Health at Work (EU-OSHA) emphasized that with proper OHS policy implementation, businesses can grow faster with satisfied employees (Ramos, Arezes & Afonso 2014). For managing the interface between system and people, an effective and efficient safety management system is essential (Casey, Griffin, Flatau Harrison, & Neal, 2017). Herbert. 01 July-December 2019 JISR-MSSE Number 2 Volume 17 10.31384/jisrmsse/2019.17. 2.1
Designing and Valuating System on Dependability Analysis of Cluster-Based Multiprocessor System

Analysis of dependability is a significant stage in structuring and examining the safety of protection systems and computer systems. The introduction of virtual machines and multiprocessors leads to increasing the faults of the system, particularly for the failures that are software- induced, affecting the overall dependability. Also, it is different for the successful operation of the safety system at any dynamic stage, since there is a tremendous distinction in the rate of failure among the failures that are induced by the software and the hardware. Thus this paper presents a review or different dependability analysis techniques employed in multiprocessor systems.
A Survey Of Secure And Deduplication Frameworks For Cloud Based Applications

-In the contemporary era, data is growing rapidly and it is assuming characteristics of big data. Such data is outsourced to cloud infrastructure to have benefits like availability, scalability and affordability. However, there are issues related to storing duplicates copies of data. When data is duplicated, it causes storage overhead. Deduplication is the process of identifying and elimination of redundant copies of data in cloud. It allows one instance of data to be saved permanently and its duplicate copies will not have actual data but a reference to the same copy of saved data. It is widely used in cloud computing for backup technology to improve efficiency. Data compression and deduplication are two important techniques used by cloud service providers (CSPs) to optimize utilization of space in storage media. Data deduplication may take place at file level or block level. Deduplication may be made at source or the target end. Source deduplication consumes more processing power and it becomes difficult to handle it with existing resources. The target deduplication takes place in the backup system, probably in cloud storage which will be easier to get deployed. In many cloud based applications, there is need for performance optimization with secure deduplication. In this paper, a survey of different deduplication techniques is made to provide useful insights.
A Novel Direct Manipulation Technique for Motion-editing using a Timeline-based Interface

Recently, motion data is becoming increasingly available for creating computer animation. Motion capture is one of the systems that can generate such motion data. However, it is not suitable to capture a lot of motion due to the cost of motion capture technique, and the diculty of its postprocessing. This paper presents a timeline-based motion-editing system that enables users to perform motion-editing tasks easily and quickly. A motion sequence is summarized and displayed in the 3D environment as a set of editable icons. Users can edit the motion data by performing a sequence of operations on a single key frame or over an interval. The recorded sequence is then propagated automatically to a set of target key frames or intervals, which can be either user dened or system dened. In addition, we provide a simple interaction method for manipulating the duration of specic intervals in the motion data. Methods for combining and synchronizing two dierent motions are also provided in this system. In contrast with the previous work that allows only temporal editing, the proposed system provides editing functions for both geometry and temporal editing. We describe a user study that demonstrated the eciency of the proposed system.
WYSIWIB: A declarative approach to finding API protocols and bugs in Linux code

Eliminating OS bugs is essential to ensuring the reliability of infrastructures ranging from embedded systems to servers. Several tools based on static analysis have been proposed for finding bugs in OS code. They have, however, emphasized scalability over usability, making it difficult to focus the tools on specific kinds of bugs and to relate the results to patterns in the source code. We propose a declarative approach to bug finding in Linux OS code using a control-flow based program search engine. Our approach is WYSIWIB (What You See Is Where It Bugs), since the programmer expresses specifications for bug finding using a syntax close to that of ordinary C code. The key advantage of our approach is that search specifications can be easily tailored, to eliminate false positives or catch more bugs. We present three case studies that have allowed us to find hundreds of potential bugs.
Plant community response following the removal of the invasive Lupinus arboreus in a coastal dune system

The removal of invasive species is common in restoration projects, yet the long‐term effects of pest management programs are seldom assessed. We present results of a long‐term program to remove the invasive species Lupinus arboreus (lupin) from sand dunes in New Zealand. We evaluate the response of plant communities to lupin removal, by comparing total plant cover, the cover of non‐native and native plant species, and species richness between sand dune sites where lupin removal has occurred, not occurred, and where lupin has never been present. Neither lupin presence nor removal had a significant impact on the foredune environment. Following removal, total and other non‐native plant cover remained higher, and the cover of several native sand dune species remained lower compared with uninvaded sites in the deflation and backdune environments. These changes can be attributed to persistent effects associated with the invasion of lupin, but have also developed in response to lupin removal. The results of this study have implications for restoration projects in sand dunes. Pest management alone is unlikely to be sufficient to restore plant communities. Given the difficulties in restoring plant communities once an invasive species has established, managers should prioritize actions to prevent the spread of invasive species into uninvaded areas of sand dunes. Finally, the response to lupin invasion and removal differed between dune habitats. This highlights the importance of tailoring a pest management program to restoration goals by, for example, prioritizing areas in which the impacts of the invading species are greatest.
Review Of Improving Healthcare Services Through Human Activity Recognition

: Healthcare industry is the one which renders valuable services to humans. With technological innovations, the quality of healthcare services is increased substantially. With the advancements in image processing and deep learning techniques, new avenues in healthcare domain are being witnessed. Human actions with respect to healthcare services exhibit different viewpoints that can help in understanding the status of health of patients under observation and elders under constant care. Traditional approaches for monitoring human actions are prone to errors and time consuming which lead to deteriorated health conditions and delay in seeking healthcare services. With an automated process driven by technology, humans who are in need can be monitored and observed remotely. This sort of research comes under Human Action Recognition (HAR). It is essential to capture spatio temporal features in order to capture live information pertaining to people under observation. Machine learning with its advancements like deep learning is part of Artificial Intelligence (AI) which provides actionable knowledge by processing human actions in real time. This approach has plenty of advantages including saving time, effort, life and money. Due to its indispensable need, in this paper, we study the state of the art and provide useful insights and research gaps if any in order to improve the research further in the area of HAR.
Pattern And Determinants Of Living Arrangements Of Female Older Adults In Assam

Population ageing is one of the increasing concerns in the recent time. This is because it has involved many consequences especially in the type of living arrangements. In Assam the share elderly population is 7.6 percent in 2016 and which is expected to rise up to 11 percent in 2026. The continuous growing number of aged has a serious implication in the type living arrangements. In this study we have investigated the different socioeconomic factors that influenced the living alone for female elderly in Assam using NSSO 60 th round. The result of binomial logistic regression reveals that, age, number of living sons, number of dependent and marital status negatively influenced living alone. On the contrary, state of economic dependence, relation to the head and education positively influenced the living alone of female elderly. Finally, the study concludes with suitable policy prescriptions.
Studies on a Neutral Exopolysaccharide of Lactobacillus fermentum TDS030603

A neutral exopolysaccharide produced in large amounts by Lactobacillus fermentum TDS030603 was purified from culture supernatants and its structure determined by monosaccharide analysis, methylation analysis and H-NMR spectroscopy. Optimum conditions for the production of this exopolysaccharide by the above bacterial strain, when cultured using cheese whey medium or MRS broth, were also investigated. The exopolysaccharide was found to be composed of D-galactose and D-glucose in the molar ratio of 1:2.5 and to consist of one major as well as one minor saccharide, both of which contained a tetrasaccharide repeating unit consisting of two α and two β anomers. The major saccharide contained non-reducing glucopyranose, O3 substituted glucopyranose, O-6 substituted galactopyranose and O-2,3 disubstituted glucopyranose. The minor saccharide consisted of non-reducing galactopyranose, O-3 substituted glucopyranose, O-6 substituted galactopyranose and O-2,3 disubstituted glucopyranose. The results suggest that these exopolysaccharides are novel saccharides that differ from previously reported products of Lactobacillus fermentum strains. Their presence may contribute to the ropy properties of the medium.
Multivariate Time Serious Traffic Prediction Using Long Short Term Memory Network

: Short term traffic prediction is essential in the modern intelligent transportation systems. Numerous algorithms were developed for time serious traffic prediction. Long short term memory network (LSTM) is a time serious prediction model which is able to integrate multiple variables such as flow, weather and precipitation. Most of the researches were carried out using the main source of data such as total flow or average speed to predict the vehicle congestion. Weather and rainfall are other factors which are gradually increase the traffic congestion in the crowded city. In this paper, LSTM network is proposed for multivariate analysis based traffic flow prediction. Traffic data contains noise and missing values due to device failures and communication problems. Missing data has been imputed and noise are removed using stacked denoise autoencoder (SDAE). Results are compared with LSTM based univariate analysis and convolutional neural network (CNN) based multivariate analysis. According to the results of LSTM based Multivariate (flow, weather, precipitation) approach without missing value reduces the RMSE error rate to 15.01 to predict the future congestion of a road.
Using a DBN to integrate sparse classification and GMM-based ASR

The performance of an HMM-based speech recognizer using MFCCs as input is known to degrade dramatically in noisy conditions. Recently, an exemplar-based noise robust ASR ap­ proach, called sparse classification (SC), was introduced. While very successfully at lower SNRs, the performance at high SNRs suffered when compared to HMM-based systems. In this work, we propose to use a Dynamic Bayesian Network (DBN) to im­ plement an HMM-model that uses both MFCCs and phone pre­ dictions extracted from the SC system as input. By doing ex­ periments on the AURORA-2 connected digit recognition task, we show that our approach successfully combines the strengths of both systems, resulting in competitive recognition accuracies at both high and low SNRs.
ASSOCIATION OF INR QUALITY WITH COMPLICATIONS AMONG PATIENTS ON WARFARIN THERAPY IN HOSPITAL SULTAN ISMAIL PETRA, KELANTAN STATE

Background: Poor International Normalised Ratio (INR) quality of warfarin (time in therapeutic range (TTR) <70%) can lead to increased risk of complications (bleeding/thromboembolism). However, the association of INR quality (TTR) with warfarin complications in Hospital Sultan Ismail Petra (HSIP) is not well established. This study aimed to investigate the INR quality using TTR and its association with complications among patients on warfarin therapy in HSIP. Materials and Methods: A total of 117 patients were recruited from INR Clinic and were retrospectively traced from the medical records office. TTR was computed using Rosendaal method and categorized into two groups (<70% or ≥ 70%). Clinical complications assessed were bleeding or thromboembolism (TE), defined based on hospitalization. Patients were followed from warfarin initiation to the first occurrence of complication. Chi-square test was used to evaluate the association of TTR with complications. Result: Mean TTR was 55% with 57.3% of the total patients had TTR <70%. During observation, 12 complications (5 bleedings, 7 thromboembolisms) occurred in 10% of the patients, and those with TTR <70% were at higher risk. Patients with TTR <70% had a higher risk for any bleeding (RR: 2.99; 95% CI: 0.34 – 25.90) but a lower risk for any thromboembolism (RR: 0.56; 95% CI: 0.13 – 2.39). However, no statistically significant association was observed between TTR and overall complications (p = 0.94). Conclusion: Bleeding and thromboembolism complications were associated with warfarin therapy, independently of INR control (TTR). Future study with a larger sample size is warranted to further validate this study’s finding. Keywords: warfarin, TTR, bleeding, thromboembolism, complications
A Comprehensive Review on Heart Disease Prediction Using Data Mining and Machine Learning Techniques

Heart disease is one of the major causes of life complicacies and subsequently leading to death. The heart disease diagnosis and treatment are very complex, especially in the developing countries, due to the rare availability of efficient diagnostic tools and shortage of medical professionals and other resources which affect proper prediction and treatment of patients. Inadequate preventive measures, lack of experienced or unskilled medical professionals in the field are the leading contributing factors. Although, large proportion of heart diseases is preventable but they continue to rise mainly because preventive measures are inadequate. In today’s digital world, several clinical decision support systems on heart disease prediction have been developed by different scholars to simplify and ensure efficient diagnosis. This paper investigates the state of the art of various clinical decision support systems for heart disease prediction, proposed by various researchers using data mining and machine learning techniques. Classification algorithms such as the Naive Bayes (NB), Decision Tree (DT), and Artificial Neural Network (ANN) have been widely employed to predict heart diseases, where various accuracies were obtained. Hence, only a marginal success is achieved in the creation of such predictive models for heart disease patients therefore, there is need for more complex models that incorporate multiple geographically diverse data sources to increase the accuracy of predicting the early onset of the disease.
Using sentinel prey to assess predation pressure from terrestrial predators in water-filled tree holes

Tree-related microhabitats are important for forest biodiversity. Water-filled tree holes are one such microhabitat and can be abundant in temperate forests. The arthropod community in this microhabitat not only contribute to forest biodiversity but also provides food for terrestrial predators such as arthropods, small mammals and birds. The extent of the threat of attack from terrestrial predators on insect larvae in this microhabitat, however, is poorly known. To measure predation in this microhabitat, we produced fake prey resembling insect larvae using white plasticine and exposed them at the aquatic-terrestrial habitat interface. We recorded: (1) which predators attacked the fake larvae, (2) the predation probability on the fake larvae after two days and after two weeks and (3) whether predation probability on fake larvae differed between managed and unmanaged forest zones in one of the last primeval beech forests, the Uholka division of the Carpathian Biosphere Reserve in the Ukrainian Carpathians. By addressing these questions, we aimed to quantify the predation pressure of terrestrial predators on insect larvae in tree-holes. The probability that a fake larva in a tree hole was attacked by predators ranged between 25-58% (95% CI) after two days and between 76-96% (95% CI) after two weeks. Overall, the highest attack rates were recorded for small mammals, followed by arthropods and birds. Arthropods took longer to detect potential prey items than small mammals and birds, and they were the only group that showed significant differences in attack rates between forest zones (unmanaged > managed). This study revealed that sentinel prey might be a suitable method for measuring the predation pressure from terrestrial predators on insect larvae in water-filled tree holes.
Impact Of Servant Leadership And Ethical Climate At Higher Educational Institutions And Industries

The Servant Leadership, ethical climate is highly effective in an organizational sense. A questionnaire to measure servant leadership, ethical climate based on survey is very much need of the hour. In this regard, surveys were conducted in Institutions and industries, questionnaire of 23 items, 5 dimension and 36 items, 9 dimensions. In the final study based 302 and 217 survey samples. Reliability of questionnaire has been established by computing Cronbach’s alpha at higher educational institutions and Industries. A comprehensive questionnaire has been studied Impact of the servant leadership and ethical climate in higher educational institutes and industries in south western parts of India taking in to account the responses from latest survey
Cloud computing: A comparison of adoption issues between UK and sub-Saharan Africa SMEs

Cloud computing can provide remote access to computing resources via the internet. This paper analyses and compares the adoption of cloud computing by small and medium-sized enterprises (SMEs) in the United Kingdom (UK) and Sub-Saharan Africa (SS-Africa). A cloud adoption model theoretically grounded in the Technological, Organisational, and Environmental (TOE) framework is used. Findings from this study show that there are several exciters and inhibitors to cloud computing adoption decisions in both UK and SS-Africa that are similar or dissimilar. For instance, findings from this paper show that the issue of security, privacy and trust which is an uncertainty that users highly depend on can be viewed as influencing cloud adoption in both places whereas other issues like fear of data loss; another uncertainty, can be viewed as inhibiting cloud adoption in the UK and exciting or enabling it in SS-Africa. Furthermore, the findings show that these exciters and inhibitors are perceived by the users to interact associatively with each other to influence adoption. These findings will be of interest to policy makers and entrepreneurs interested in developing IS/ICT infrastructure.