Miracle-dz/UniFAR-FTUs huggingface dataset:
Starting Track Event Selection 2 . 1 Incoming Muon

IceCube analyses which look for an astrophysical neutrino signal in the southern sky face a large background of atmospheric muons and neutrinos created by cosmic ray air showers. By selecting starting events in the southern sky, atmospheric muons and neutrinos with accompanying muons are rejected, producing a sample with high astrophysical neutrino purity at lower energies than northern sky samples. Our new selection method looks for muon tracks from a neutrino interaction with a vertex contained inside the detector volume by using the good pointing resolution of the track morphology to create an event specific veto region in the detector to reject entering tracks. This starting track event selection has a high astrophysical neutrino purity above 10 TeV at declinations less than -30◦ which makes it ideal for use as a southern sky realtime neutrino alert stream. We will discuss neutrino point source searches using this event selection and look at the advantages of the starting track alert stream for multimessenger astrophysics.
Quality and Nutritional Value of Functional Strawberry Marmalade Enriched with Chia Seed (Salvia hispanica L.)

The aim of the study was to develop and characterize marmalade having functional food ingredients such as omega-3 fatty acid, dietary fiber, protein, and antioxidants with the addition of chia seed (Salvia hispanica L.). During the development of marmalade formulations, sweetener type and chia content in the strawberry marmalade were decided by two-step sensory analysis. In the first step, four different formulas were prepared separately by using sorbitol, isomalt, commercial Stevia™ powder, and isomalt together with sorbitol. The control formula was prepared with sucrose (refined commercial sugar). In the first part of the study, sensorial parameters showed good acceptability for sorbitol. Thereafter, in the second step, marmalades were prepared with 2.5% and 5% (by weight) chia seed including sorbitol. According to sensory panels, sorbitol had the highest acceptance level and the chia seed content was chosen to be used as 5% in the formulations. Chia seed and sorbitol addition increased the phenolic content by 15.45% and the dietary fiber content by 168% and decreased the caloric value by 48% compared to the control prepared with sucrose and without chia seed. The final product had 1.5% omega-3 fatty acid and could be declared as “omega-3 source” in the label. The viscosity of chia-added marmalade was found to be slightly higher than the viscosity of control, even though there was no distinct difference between the two samples. The gel-like character was more dominant in chia-added strawberry marmalade compared to the control. The addition of chia at 5% may contribute to the crosslinking without formation of a gel structure.
Efficient Runtime Capture of Multiworkflow Data Using Provenance

Computational Science and Engineering (CSE) projects are typically developed by multidisciplinary teams. Despite being part of the same project, each team manages its own workflows, using specific execution environments and data processing tools. Analyzing the data processed by all workflows globally is a core task in a CSE project. However, this analysis is hard because the data generated by these workflows are not integrated. In addition, since these workflows may take a long time to execute, data analysis needs to be done at runtime to reduce cost and time of the CSE project. A typical solution in scientific data analysis is to capture and relate the data in a provenance database while the workflows run, thus allowing for data analysis at runtime. However, the main problem is that such data capture competes with the running workflows, adding significant overhead to their execution. To mitigate this problem, we introduce in this paper a system called ProvLake, which adopts design principles for providing efficient distributed data capture from the workflows. While capturing the data, ProvLake logically integrates and ingests them into a provenance database ready for analyses at runtime. We validated ProvLake in a real use case in the O&G industry encompassing four workflows that process 5 TB datasets for a deep learning classifier. Compared with Komadu, the closest solution that meets our goals, our approach enables runtime multiworkflow data analysis with much smaller overhead, such as 0.1%.
Thrombotic microangiopathy with Atypical Posterior Reversible Encephalopathy Syndrome ( PRES ) after kidney transplantation . A case report

Neurological manifestation of thrombotic microangiopathy (TMA) after renal transplantation is rare and dif cult to be distinguished from other conditions. A 51-year-old female with ESRD of unknown etiology received deceased donor kidney transplantation from a 57-year-old donor. The immunosuppression included basiliximab induction, tacrolimus, mycophenolate mofetil (MMF) and prednisolone. She had delayed graft function requiring hemodialysis for 5 weeks. Kidney biopsy at 6th week showed acute tubular necrosis without evidence of rejection. Two months later she presented with alteration of mental status, psychomotor retardation, slow speech followed by generalized tonic-clonic seizure. Serum electrolyte was normal. The platelet count was 48,000 /ml and peripheral blood smear shows microangiopathic hemolytic anemia (MAHA) pattern. Tacrolimus level was 6.0 ng/ml. MRI brain showed multifocal symmetric hypersignal Fluid-Attenuated Inversion Recovery (FLAIR) lesion at bilateral medial thalami and subcortical area compatible with atypical posterior reversible encephalopathy syndrome (PRES). Serum creatinine rose to 6.49 mg/ dL and blood urea nitrogen was 65 mg/dL. She was diagnosed as TTP/HUS and treated with plasmapheresis. Repeated kidney biopsy showed TMA. Several weeks after treatments, the neurological symptoms was improved but requiring regular hemodialysis. We conclude that PRES can be a manifestation of TMA in kidney transplant recipient.    63
ersistent land-use legacies increase small-scale diversity and trengthen vegetation – soil relationships on an unmanaged eathland

Land-use legacies are recognized determinants of vegetation dynamics and plant community assembly. The duration of these egacies and how they influence the structure of vegetation communities developing naturally in nutrient-poor ecosystems is ot well understood. Here, we focus on the effects of increased nutrient availability from previous agricultural practices on ultiple vegetation properties in a heathland where agriculture and domestic grazing ceased near 1870 and 1895, respectively. e compared diversity, compositional and functional properties of the vegetation responses to land-use legacies in the soil etween areas with different agricultural histories (previously cultivated vs. uncultivated). Diversity measures were found to be igher in the previously cultivated soils. -diversity was mainly driven by changes in species relative cover and increased with ncreasing nutrient availability in the cultivated area. Furthermore, functional traits related to nutrient acquisition (SLA and eaf Nitrogen content) and the changes in vegetation composition were directly linked to soil properties only in the previously ultivated part of the heathland. Together these results revealed a shift to a deterministic control of the plant community, here increased nutrient availability leads to stronger associations between soil and vegetation properties. This suggests that as utrients become available, niche differentiation and competitive interactions become the predominant underlying mechanisms tructuring the community. Our study shows that land-use legacies of moderate intensity can alter the assembly mechanisms nd diversity patterns in unmanaged vegetation that can be maintained after more than a century since cessation of agricultural
SYNTHESIS AND ANTIBACTERIAL ACTIVITY OF SOME NOVEL N , NDI-OXALAMIDE DERIVATIVES

Novel N,N-di-oxalamide derivatives 1-6 were synthesized and characterized by the spectroscopic techniques. These derivatives showed good antibacterial activities against Gram-negative organism Escherichia coli and Gram-positive organisms Staphylococcus aureus and Bacillus subtilus. However, compounds 3 and 4 showed potent antibacterial activity against Gram-negative organism Escherichia coliand Gram-positive organism Staphylococcus aureus. In addition to that, we compared their antibacterial profiles with previously synthesized N,N-di-oxalamide derivatives 7-9. The new compounds showed superior antibacterial activities compared to moderate activities for 7-9.
PREDICTION OF PHASE BEHAVIOR OF MIXED REFRIGERANTS HFC 125-HFC 134 a BY USING RADIAL BASIS FUNCTION ARTIFICIAL NEURAL NETWORK

A Normalized Radial Basis Function (NRBF) neural network has been presented for accurate prediction of the vaporAccording to the network’s training, validation and testing results, a network with sixteen hidden nodes is selected as the best architecture. The presented model is very accurate over wide ranges of experimental pressure and temperature values. The predicted VLE of the refrigerant mixture is accurate enough to be employed in the design of refrigeration cycles.
Proteomic Differences between Azole-Susceptible and -Resistant Aspergillus fumigatus Strains

Background: Azole-resistance is increasingly reported in Aspergillus fumigatus infections. It remains challenging to rapidly assess antifungal susceptibility to initiate the appropriate therapy. The aim of this study was to map the proteomic differences of azole-susceptible and -resistant strains. Methods: Proteomic studies were performed with ultra-performance liquid chromatography tandem mass-spectrometry (UPLC-MS/MS). Results: UPLC-MS/MS detected 7899 peptides, of which 1792 peptides had a significantly different abundance (p A. fumigatus resistance. Conclusions: UPLC-MS/MS provided proof-of-concept that the proteome of azole-resistant A. fumigatus is diverse enough to serve as a diagnostic tool.
A Bidirectional LSTM for Estimating Dynamic Human Velocities from a Single IMU

The main challenge in estimating human velocity from noisy Inertial Measurement Units (IMUs) are the errors that accumulate by integrating noisy accelerometer signals over a long time. Known approaches that work on step length estimation are optimized for a specific application, sensor position, and movement type, require an exhaustive (manual) parameter tuning, and can thus not be applied to other movement types or to a broader range of applications. Moreover, varying dynamics (as they are present for instance in sports applications) cause abrupt and unpredictable changes in step frequency or step length and hence result in erroneous velocity estimates.We use machine learning (ML) and deep learning (DL) to estimate a human’s velocity. Our approach is robust to varying motion states and orientation changes in dynamic situations. On data from a single un-calibrated IMU, our novel recurrent model not only outperforms the state-of-the-art on instantaneous velocity (≤0.10 m/s) and on traveled distance (≤29 m/km). It can also generalize to different and varying rates of motion and provides accurate and precise velocity estimates.
Low-Density Lipoprotein Concentration is an Independent Predictor for Oxidised LDL in Patients with Familial Hypercholesterolaemia

Familial hypercholesterolaemia (FH) is an autosomal dominant genetic disorder characterised by severe hypercholesterolaemia leading to premature coronary artery disease (CAD). Oxidised low-density lipoprotein (ox-LDL), F2-isoprostanes (ISP) and malondialdehyde (MDA) are established oxidative stress biomarker, but the status of oxidative stress in FH is not well studied. The aim of this study is to investigate oxidative stress status among FH patients and normocholesterolaemic control (NC) subjects. Ninety-eight FH patients and 100 (age, gender and BMI matched) NC subjects were recruited in series of health screening programmes across the country. Fasting blood samples were analysed for serum ox-LDL, ISP and MDA. Ox-LDL, ISP and MDA concentrations were higher in FH groups compared to NC (mean±SEM: 63.0±6.5 vs 25.5±1.2 (U/l), p<0.001); 749.7±74.0 vs 354.2±18.1 pg/ml, p<0.0001; 342.4±46.0 vs 162.7±13.5 nmol/g, p<0.0001). Ox-LDL showed correlation with glucose (p<0.05), TC (p<0.001), LDL-c (p<0.001) and HDL-c (p<0.01) in all subjects. LDL-c was associated positively with ox-LDL concentration (p<0.001). LDL-c was an independent predictor for ox-LDL concentration (p<0.001) after adjustment for the various confounding factors. In conclusion, FH patients have higher oxidative stress status which contributes to the greater risk of developing atherosclerosis and its related complications. LDL concentration is an independent determinant of ox-LDL, suggesting that both the proatherogenic quantity and quality of LDL coexist in FH which enhance the risk of premature CAD.
Applicability Of Activity Based Costing By Manufacturing Firms In Zaria Local Government Area Of Kaduna State , Nigeria

The thrust of this study is to examine the level of application of Activity Based Costing (ABC) in the manufacturing companies in Zaria local Government Area of Kaduna state, Nigeria. As business environment is fast changing, accountants, managers, entrepreneurs and other stakeholders are to invent new ideas and strategies capable of drastically eliminating unnecessary expenses which invariably enhance the performance of their firms, increase their profitability and ensure their going concern status. Five manufacturing companies were conveniently sampled and copies of questionnaire were administered to the twenty five (25) randomly selected staff of these companies. Correlation co-efficient and T-test were used in testing the formulated hypothesis. The study reveals that none of the companies was applying ABC in apportioning its overhead cost to cost centers and cost units; they go by the traditional method of costing which was still obtainable in the sampled companies and as a result most of them are at the verge of collapsing, because they are not operating at full capacity. The study recommends that there is the need for general awareness campaign on the benefits of ABC and how it is being practically applied in advanced countries with a view to resuscitating and enhancing the financial performance of ailing companies in the country.
Development of a big data bank for PV monitoring data, analysis and simulation in COST Action ‘PEARL PV’

COST Action entitled PEARL PV aims at analyzing data of monitored PV systems installed all over Europe to quantitatively evaluate the long-term performance and reliability of these PV systems. For this purpose, a data bank is being implemented that can contain vast amounts of data, which will enable systematic performance analyses in combination with simulations. This paper presents the development process of this data bank.
Influence of Angle’s classification and condylotrack distance on sagittal condylar inclination in a group of Thais

Objectives: The objectives of this study were to investigate the sagittal condylar inclination (SCI) in a Thai ethnic group and to evaluate the influence of Angle’s classifications, sides and condylotrack distance on the SCI. Materials and Methods: Seventy Thai participants, ages ranging between 20-42 years old, were allocated into 2 groups according to Angle’s classification, i.e., Class I (35 persons) and Class II (35 persons). The mandibular movements from a minimum to the maximum opening and excursion of the mouth were recorded using the computerized axiograph. Three SCI values at 1, 2 and 3 mm of protrusive condylar path from the hinge axis (condylotrack distance) were obtained from the graphic record of the mandibular movement. The statistical analysis of SCI according to the Angle’s classification was performed using the independent samples t-test at α=. 05. The difference between right and left sides were analyzed using a dependent samples t-test. The statistical analysis for the condylotrack distance was performed by a repeated measured ANOVA at α=. 05. Results: The mean SCI of 70 Thai people was 44.7±8.8 degrees independent of any factors. There was no statistically significant difference between the SCI values obtained from Angle’s classification I and II. Furthermore, no statistically significant differences were observed for the left and right sides. For a condylotrack distance parameter, the mean SCI at 1 mm, 2 mm and 3 mm were 45.7±10.6, 44.8±8.6 and 43.7±7.9 degrees respectively. The SCI at 3 mm condylotrack distance were significantly lower than those of 1 mm and 2 mm condylotrack distance. Conclusions: SCI values were statistically different related to the condylotrack distance. There was no significant difference in SCI between Angle’s classification I and II group and between left and right side.
An Investment model for the Smart Grid

The paper discusses next-generation energy efficiency optimization enabled by incorporating smart grid technology into the current electric grid system and exchanging real-time power information between the supplier and the consumer. In this paper an investment model is introduced for development of the smart power grid management system, that focuses on developing new services by activating demand response market system, power transactions, investment allocation etc. through a real-time pricing. Key-Words: Power pricing, reactive power, investment model, prosumer
Pedotransfer Functions for Soil Resistance to Penetration of a Soil under Subsoiling

Strategies to simplify obtaining the mechanical resistance to penetration required for the pedotransfer functions are a viable alternative. The objective of this study was to develop pedotransfer functions for estimating soil resistance to penetration (RMSP) in irrigated soils under subsoiling. The study was conducted in an area with Conilon coffee (Coffea canephora Pierre), irrigated and subjected to subsoiling in the row crop planting. Deformed samples were collected for textural and structural attributes for the undisturbed soil analysis. These samples were collected on the line (P1) and leading (P2) culture, in the 0.00 to 0.70 m layer. The determination was performed using the RMSP impact penetrometer on line and leading to a depth of 0.70 m, also collecting soil samples to determine the water content of the soil. The variable that best correlated positively with clay RMSP was on the line. The RMSP negatively correlated with soil micro porosity, which had the highest correlation with RMSP, higher than the line; the other texture attributes were highly correlated. The coarse sand fraction correlated negatively with RMSP and top line. Statistical indicators showed the pedotransfer function for the RMSP underestimates and overestimates the line between rows. The leading indicators produced better results. The resistance to penetration can be estimated from the coarse sand and fine sand for line culture and the water content of the soil and total pore volume for leading culture.
WORKFORCE LEARNING IN SMALL TO MEDIUM ENTERPRISES

This short article considers if there are skills and knowledge that can be transferred from academia to SMEs (small to medium enterprises). It points out some of the fundamental difficulties at the most basic level, our understanding of differing organisational size and industry requirements and the corresponding contextual importance, and suggests ways in which more academic engagement with SMEs that may be mutually beneficial may be achieved. Thus the paper contributes to the lack of research into management and leadership in practice noted by a number of authors (Stogdill, 1974; Bryman, 1992; Gronn & Ribbins, 1996; Yukl, 2005). It also introduces a management classification system in an attempt to help to identify a manager’s training requirements. Workforce Learning in SMEs 3
A New Density-based Spatial Clustering Algorithm for Extracting Attractive Local Regions in Georeferenced Documents

Nowadays, with the increasing attention being paid to social media, a huge number of georeferenced documents, which include location information, are posted on social media sites via the Internet. People have been transmitting and collecting information through these georeferenced documents. Georeferenced documents are usually related to not only personal topics but also local topics and events. Therefore, extracting “attractive” local regions associated with local topics from georeferenced documents is one of the most important challenges in different application domains. In this paper, a novel spatial clustering algorithm, called the (ǫ, σ)-densitybased spatial clustering algorithm, for extracting “attractive” local regions in georeferenced documents is proposed. We defined a new type of spatial cluster called an (ǫ, σ)-densitybased spatial cluster. The proposed clustering algorithm can recognize not only semantically-separated but also spatiallyseparated spatial clusters. To evaluate our proposed clustering algorithm, geo-tagged tweets posted on the Twitter site are used. The experimental results show that the (ǫ, σ)-densitybased spatial clustering algorithm can extract “attractive” local regions as (ǫ, σ)-density-based spatial clusters.
Optimization of Performance and Security by Division and Replication of Data in Cloud

1. Abstract Cloud computing is a third party administrative control; here our data are outsourced so it gives rise to security concerns. Data compromise occurs due to attacks or within the nodes itself. High security measures needed to protect the data. In this paper, we propose Optimization of Performance and security by Division and Replication of Data in Cloud for excellent performance and security that collectively approaches the security and performance issues. In this methodology, we divide a file into fragments, and replicate the fragmented data over the cloud nodes.
Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy. IMPACT FACTOR 2.676