Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Learning in Virtual Reality with Learning on a 2D Screen Using Electrostatics Activities

Virtual Reality (VR) has the potential to overcome natural constraints and present things that would not be visible in the physical world. This makes the medium of VR a powerful tool for learning that allows users to become highly immersed in complex topics. In this paper, we compare a VR learning environment with a traditional 2D learning environment. To investigate the differences between VR and 2D learning environments, we designed two activities that help learners gain an intuitive understanding of concepts from electricity and magnetism. We performed an experiment comparing the learning that took place using these two variant. Although our quantitative learning measures did not reveal a significant difference between 2D and VR, VR was perceived by learners to have advantages. We did find significant quantitative differences in learners’ completion times. We share findings, based on the quantitative and qualitative feedback received, about what makes VR environments beneficial for learning about complex spatial topics, and propose corresponding design guidelines.
Signalling through Src family kinase isoforms is not redundant in models of thrombo‐inflammatory vascular disease

The Src family kinases (SFK) are a group of signalling molecules with important regulatory functions in inflammation and haemostasis. Leucocytes and platelets express multiple isoforms of the SFKs. Previous studies used broad‐spectrum pharmacological inhibitors, or murine models deficient in multiple SFK isoforms, to demonstrate the functional consequences of deficiencies in SFK signalling. Here, we hypothesized that individual SFK operate in a non‐redundant fashion in the thrombo‐inflammatory recruitment of monocyte during atherosclerosis. Using in vitro adhesion assays and single SFK knockout mice crossed with the ApoE−/− model of atherosclerosis, we find that SFK signalling regulates platelet‐dependent recruitment of monocytes. However, loss of a single SFK, Fgr or Lyn, reduced platelet‐mediated monocyte recruitment in vitro. This translated into a significant reduction in the burden of atherosclerotic disease in Fgr−/−/ApoE−/− or Lyn−/−/ApoE−/− animals. SFK signalling is not redundant in thrombo‐inflammatory vascular disease and individual SFK may represent targets for therapeutic intervention.
Harnessing Marine Biocatalytic Reservoirs for Green Chemistry Applications through Metagenomic Technologies

In a demanding commercial world, large-scale chemical processes have been widely utilised to satisfy consumer related needs. Chemical industries are key to promoting economic growth and meeting the requirements of a sustainable industrialised society. The market need for diverse commodities produced by the chemical industry is rapidly expanding globally. Accompanying this demand is an increased threat to the environment and to human health, due to waste produced by increased industrial production. This increased demand has underscored the necessity to increase reaction efficiencies, in order to reduce costs and increase profits. The discovery of novel biocatalysts is a key method aimed at combating these difficulties. Metagenomic technology, as a tool for uncovering novel biocatalysts, has great potential and applicability and has already delivered many successful achievements. In this review we discuss, recent developments and achievements in the field of biocatalysis. We highlight how green chemistry principles through the application of biocatalysis, can be successfully promoted and implemented in various industrial sectors. In addition, we demonstrate how two novel lipases/esterases were mined from the marine environment by metagenomic analysis. Collectively these improvements can result in increased efficiency, decreased energy consumption, reduced waste and cost savings for the chemical industry.
Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions

As time progresses with vast development of information technology, a large number of industries are more dependent on network connections for sensitive business trading and security matters. Communications and networks are highly vulnerable to threats because of increase in hacking. Personnel, governments, and armed classified networks are more exposed to difficulties, so the need of the hour is to install safety measures for network to prevent illegal modification, damage, or leakage of serious information. Intrusion detection, an important entity towards network security, has the ability to observe network activity as well as detect intrusions/attacks. This study highlights the developing research about the application of machine learning and data mining in Internet security. We provide background, enthusiasm, discussion of challenges, and recommendations for the application of ML/DM in the field of intrusion detection.
Load Balancing in Cloud Computing Using Meta-Heuristic Algorithm

Cloud computing, also known as “country as you go”, is used to turn any computer into a dematerialized architecture in which users can access different services. In addition to the daily evolution of stakeholders’ number and beneficiaries, the imbalance between the virtual machines of data centers in a cloud environment impacts the performance as it decreases the hardware resources and the software’s profitability. Our axis of research is the load balancing between a data center’s virtual machines. It is used for reducing the degree of load imbalance between those machines in order to solve the problems caused by this technological evolution and ensure a greater quality of service. Our article focuses on two main phases: the pre-classification of tasks, according to the requested resources; and the classification of tasks into levels (‘odd levels’ or ‘even levels’) in ascending order based on the meta-heuristic “Bat-algorithm”. The task allocation is based on levels provided by the bat-algorithm and through our mathematical functions, and we will divide our system into a number of virtual machines with nearly equal performance. Otherwise, we suggest different classes of virtual machines, but the condition is that each class should contain machines with similar characteristics compared to the existing binary search scheme.
Medical Image Segmentation of Blood Vessels Based on Clifford Algebra and Voronoi Diagram

First, a region growing algorithm based on Clifford algebra is put forward to segment blood vessels of abdominal aorta. Compared with the traditional region growing method, the proposed algorithm is easy to operate and good at robustness. Furthermore, it is available to segment both normal and abnormal blood vessels of abdominal aorta: the seed point can keep on growing even when there are some isolated points in region of blood vessels. The key point of this algorithm is that: the gray values of eight neighborhood points are composed as unit feature vectors of current seed points, and then Clifford algebra vector product representation will be applied. Second, for the purpose to segment thinner global abdominal blood vessels rapidly, a novel segmentation algorithm of blood vessels based on 2D Voronoi diagram is put forward as an attempt. Experiments demonstrate that it is potential to segment thinner global abdominal blood vessels fast. In the future, this algorithm is potential to segment blood vessels in abdominal organs.
The Triarchic Psychopathy Measure: An Examination in a Lithuanian Inmate Sample

The goal of the current investigation was to evaluate the Lithuanian translation of the Triarchic Psychopathy Measure (TriPM; Patrick, 2010) as well as more broadly extend the construct validity of this psychopathy measurement model to forensic and correctional criteria. Two samples derived from male Lithuanian correctional facilities were merged for this study. The TriPM Meanness and Disinhibition scales evinced acceptable internal reliability coefficients, whereas that for the TriPM Boldness scale was in the questionable range. All three TriPM scales were generally associated with external criteria reflecting psychopathy, aggression, and transdiagnostic clinical and personality constructs in ways consistent with the extant literature, though TriPM Boldness evinced surprisingly small negative correlations with measures of negative emotionality and fear. Furthermore, TriPM Disinhibition was associated with the largest correlations with two risk assessment measures. Implications of these findings in the context of the broader literature are discussed.
An empirical investigation of the relationship between e-government development and the digital economy: the case of Asian countries

Purpose This paper aims to investigate and comprehend the nature of the relationship between e-government development and the digital economy. Design/methodology/approach A multidimensional research paradigm is developed on the basis of the technology adoption model and Fountain’s technology enactment theory. The model is empirically examined using a regional study of 20 Asian countries. Findings A positive two-way relationship between e-government development and the digital economy has been indicated by the findings. Moreover, along with social, economic, political, technological and demographic factors, certain national cultural characteristics have significant effects on the digital economy and e-government development. Research limitations/implications One of the key limitations of the study is that it is based on publicly available secondary data. Therefore, some degree of caution should be kept in mind when making generalisations about the findings of this study. Originality/value The contribution of this study is that it provides a more accurate and comprehensive understanding of the dynamic association between e-government development and the digital economy by providing aid to policymakers in understanding the nature of dynamic relationships between the digital economy, government organisations and citizens’ adoption of technologies.
DROP SIZE MEASUREMENT TECHNIQUES APPLIED TO GASOLINE SPRAYS

Combustion processes involved in internal combustion engines greatly depend on the characteristics of the spray. This study is focused on the comparison of three different techniques used for spray drop sizing. Laser diffraction (LDG), phase Doppler anemometry (PDA) and Image Analysis (IMA) have been used to characterize gasoline sprays produced by gasoline injectors of a direct injection type. Whereas studies comparing drop sizing techniques found in the literature are mainly dealing with steady flows, attention is paid here to the unsteady nature of the spray. The differences in the measurement volumes of the different techniques are also considered. As these diagnostics do not measure exactly the same kind of distribution, both PDA and IMA measurements were converted to spatial-averaged volume-weighted drop size distributions to be compared to LDG. The Sauter Mean Diameter D32 and the mean diameter D43 are used to characterize the spray for different time and position of the measurement volume in the spray. The comparison between the three techniques shows a good agreement. Greatest mean diameters are found on the leading edge of the spray and on the side of the spray facing stagnant air, so where the conditions are favourable to a fast evaporation of the smallest droplets. Velocity measurements done by PDA and drop shape morphology characterization are also discussed. Paper ID ILASS08-7-7
Cognitive impairment in hodgkin lymphoma survivors

Cancer‐related cognitive impairment (CRCI) is commonly reported post‐chemotherapy in adults with solid tumours. Hodgkin lymphoma (HL) mostly affects young adults. Data regarding CRCI in HL survivors (HLS) are scarce. The current study aimed to objectively assess CRCI incidence and characteristics in HLS. HLS, who completed first‐line (chemotherapy ± radiation) therapy and remained in complete remission for 6 months to 5 years from therapy end, were evaluated. Age‐ and education‐matched healthy individuals served as controls (n = 14). Test results were compared to population norms and healthy controls. Study participants completed self‐reported questionnaires evaluating fatigue, depression, anxiety, quality of life and cognitive function. Subjects underwent neurocognitive evaluation, assessing processing speed, memory, attention, executive functions and intelligence domains. The present study included 51 HLS with a median age of 28 years, mean education of 14·5 ± 2·5 years. Complaints related to cognitive deterioration and fatigue were significantly more severe and frequent in HLS compared to healthy controls. Objective neurocognitive evaluation demonstrated that 30% of HLS were impaired in ≥2 cognitive domains. In conclusion, the present study demonstrates that fatigue and cognitive impairment, predominantly in executive functions and memory, constitute frequent and alarming findings in HLS. These adverse effects can persist and exert an impact on all aspects of life.
Assessing Student Learning Outcomes in the Introductory Accounting Information Systems Course Using the AICPA's Core Competency Framework

This paper describes a unique learning outcome process for the assessment of an undergraduate course in AIS. Learning expectations targeted for assessment are the “functional competencies” promulgated in the AICPA's Core Competency Framework for Entry into the Accounting Profession (Framework) (1999). This paper first frames the importance of assessment for motivating faculty to foster “continuous improvement” in student learning, as well as demonstrating accountability to public officials, accreditation bodies, and university administrators. The paper then focuses on a specific example (case study) of AIS course assessment. Evidence of student learning is observed, albeit imperfectly, through triangulation of multiple direct measurements, supplemented by indirect measures such as student self‐assessments. Results indicate that students became more competent in certain functional competencies and also reflect that the expected emphasis on certain competencies to be gained in the AIS course required modifi.
Chinese Text Project - A Dynamic Digital Library of Pre-modern Chinese

This article presents technical approaches and innovations in digital library design developed during the design and implementation of the Chinese Text Project, a widely-used, large-scale full-text digital library of premodern Chinese writing. By leveraging a combination of domain-optimized Optical Character Recognition, a purpose-designed crowdsourcing system, and an Application Programming Interface (API), this project simultaneously provides a sustainable transcription system, search interface and reading environment, as well as an extensible platform for transcribing and working with premodern Chinese textual materials. By means of the API, intentionally loosely integrated text mining tools are used to extend the platform, while also being reusable independently with materials from other sources and in other languages.
Accounting for Uncertainty

The recent crisis of confidence in the accounting industry has raised issues about the need for reform. In one area the timing and technology are right today for significantly different accounting rules—particularly rules that govern the treatment of uncertainty in financial statements. Discussed here are some basic misconceptions about uncertainty that are actually sanctioned by current accounting practice. A variety of analytical tools are available to improve representation of uncertainty for firms. The authors address some of the legal consequences of relying on accepted but flawed accounting standards, and point to a possible path to reform of accounting practice.
Model Checking Knowledge And Commitments In Multi-Agent Systems Using Actors And UPPAAL

This paper proposes a method for modelling and analysis of knowledge and commitments in multi-agent systems. The approach is based on an actors model and its reduction onto UPPAAL. A key factor of the approach is the possibility of exploiting the same UPPAAL model for exhaustive verification or, when state explosion problems forbid model checking, for quantitative evaluation of system properties through statistical model checking. The article describes the method, shows its application to modelling the NetBill protocol, proposes a translation into the terms of the timed automata language of UPPAAL and demonstrates the analysis of the NetBill protocol together with some experimental results.
Diversity of piRNA expression patterns during the ontogeny of the German cockroach

The Piwi-interacting RNA (piRNA) system is an evolutionarily conserved mechanism involved in the control of transposable elements and maintenance of genomic stability, especially in germ line cells and in early embryo stages. However, relevant particularities, both in mechanism and function, exist across species among metazoans and even within the insect class. As a member of the scarcely studied hemimetabolan group, Blattella germanica can be a suitable reference model to study insect evolution. We present the results of a stringent process of identification and study of expressed piRNAs for B. germanica across 11 developmental stages, ranging from unfertilized egg to nymphs and adult female. Our results confirm the dual origin of piRNA in this species, with a majority of them being generated from the primary pathway, and a smaller but highly expressed set of sequences participating in the secondary ("ping-pong") reamplification pathway. An intriguing partial complementarity in expression is observed between the piRNA of the two biogenesis pathways, with those generated in the secondary pathway being quite restricted to early embryo stages. In addition, many piRNAs are exclusively expressed in late embryo and nymphal stages. These observations point at piRNA functions beyond the role of transposon control in early embryogenesis. Our work supports the view of a more complex scenario, with different sets of piRNAs acting in different times and having a range of functions wider than previously thought.
Fully-Dynamic Bin Packing with Little Repacking

We study the classic bin packing problem in a fully-dynamic setting, where new items can arrive and old items may depart. We want algorithms with low asymptotic competitive ratio while repacking items sparingly between updates. Formally, each item i has a movement cost c_i >= 0, and we want to use alpha * OPT bins and incur a movement cost gamma * c_i, either in the worst case, or in an amortized sense, for alpha, gamma as small as possible. We call gamma the recourse of the algorithm. This is motivated by cloud storage applications, where fully-dynamic bin packing models the problem of data backup to minimize the number of disks used, as well as communication incurred in moving file backups between disks. Since the set of files changes over time, we could recompute a solution periodically from scratch, but this would give a high number of disk rewrites, incurring a high energy cost and possible wear and tear of the disks. In this work, we present optimal tradeoffs between number of bins used and number of items repacked, as well as natural extensions of the latter measure.
Signal Words and Signal Icons in Application Control and Information Technology Exception Messages - Hazard Matching and Habituation Effects

Information technology (IT) users encounter signal words (e.g., “Warning”) and signal icons (e.g., an exclamation point) in “exception messages. ” The first of two experiments reported in this paper examines the “arousal strength” associated with signal words and icons that commonly appear in exception messages. An elicitation exercise was completed by 316 participants, in which each viewed exception messages containing combinations of signal words and icons and provided their perception as to the severity of a computer problem communicated. The results allow “hazard matching”; whereby, the severity of hazard implied by the exception message can be matched to the level of the hazard. The second experiment reports a strong habituation effect in that users exhibit decreased attention to an exception message after repeated exposure, with a corresponding decrease in compliance. The effect was also found to be mitigated by increasing the arousal strength of the exception message.
A Novel Mobility Similarity Measurement Method to Increase the Performance of Community-based Video Delivery in VANETs

—The mobility of mobile nodes is a distinctly important inﬂuence factor for video sharing performance, user quality of experience and trafﬁc load remission of core networks in vehicular ad hoc networks (VANETs). In this paper, we propose a novel mobility similarity measurement method to increase the performance of community-based video delivery in VANETs (MSMM). In order to accurately represent movement trajectories of vehicles, MSMM calculates the relative location between vehicles or between vehicles and road side units (RSU) to reﬁne the geographical location of vehicles. MSMM investigates the continuous variation of reﬁned vehicle location to estimate the subjection relationship between vehicles and roads and designs a line-segment-based representation method for movement trajectories of vehicles according to the subjection relationship. We discuss the inﬂuence factors for travel path selection of vehicles, such as trafﬁc capacity of roads and stay time and number of vehicles on roads. MSMM analyzes the historical movement trajectories of vehicles to estimate trafﬁc capacities of roads in terms of variation of stay time and number of vehicles on roads and calculate arrival and departure rate of vehicles on roads. MSMM further extracts the movement patterns of vehicles from the historical movement trajectories of vehicles and designs a recognition method of movement patterns of vehicles in terms of current movement trajectories of vehicles. MSMM designs a similarity estimation method of movement behaviors between vehicles, which enables the video requesters to select the video providers which have similar movement behaviors and implement high-efﬁciency video sharing. We use MSMM to replace the similarity estimation method of node mobility in our previous work PMCV and construct a new video sharing solution (called “M-PMCV”) in VANET. Simulation results show how M-PMCV achieves much better performance in comparison with the original solution PMCV.
Evidence of linkage to chromosome 5p13.2‐q11.1 in a large inbred family with genetic generalized epilepsy

The clinical genetics of genetic generalized epilepsy suggests complex inheritance; large pedigrees, with multiple affected individuals, are rare exceptions. We studied a large consanguineous family from Turkey where extensive electroclinical phenotyping revealed a familial phenotype most closely resembling juvenile myoclonic epilepsy. For a subject to be considered affected (n = 14), a diagnostic electroencephalogram was required. Seizure onset ranged between 6 and 19 years (mean = 12 years). Thirteen of 14 experienced myoclonic jerks; in 11, this was associated with eyelid blinking, and in 10 it was interspersed with absences. Generalized tonic–clonic seizures were seen in 11. One individual had generalized tonic–clonic seizures alone. Electroencephalograms demonstrated generalized polyspike and wave discharges that were not associated with photoparoxysmal response. Intellect was normal. Nineteen family members were subsequently chosen for nonparametric multipoint linkage analyses, which identified a 39.5 Mb region on chromosome 5 (P < 0.0001). Iterative analysis, including discovery of a subtly affected individual, narrowed the critical region to 15.4 Mb and possibly to 5.5 Mb. Homozygous versus heterozygous state of the refined 5p13.2‐q11.1 haplotype was not associated with phenotypic severity or onset age, suggesting that one versus two pathogenic variants may result in similar phenotypes. Whole exome sequencing (n = 3) failed to detect any rare, protein‐coding variants within the highly significant linkage region that includes HCN1 as a promising candidate.