Scientific Workflows

The Episteme Construction is a field manual for making claims auditable across domains. It introduces a contract-first calculus for generative collapse built around a conservation-style budget for log-integrity κ: Δκ = R·τ_R − (D_ω + D_C), closed on the same anchor with residual ≤ 10⁻¹². The book separates identities (equations, typed outcomes) from closures (thresholds, face policies) and narratives (interpretations), and it standardizes captions, manifests, and versioned “welds” so results remain legible under change.

Practically, the method instruments invariants ω, F=1−ω, S, C, τ_R, IC, κ, enforces typed outcomes (⊥oor, ∞rec), and requires pre/post tests on the same anchor with a secant weld that preserves κ-continuity. Portable vignettes show the discipline at work in physics (RCFT—working theory), collapse-aware AI, symbolic/behavioral systems, cryptographic codecs and key pipelines, and legal/institutional clauses. Each example reports a Budget Report row that closes the First-Law balance and carries a weld ID and manifest hash.

Appendices fix CSV headers, a byte-precise manifest schema and root-hash recipe, and caption keys for publication-grade audits. The build is single-source and reproducible (fixed package snapshot; America/Chicago timestamps). The intended reader is any scientist, engineer, or systems designer who needs verifiable, cross-domain evidence: freeze the contract, instrument invariants, close the κ-budget, and weld governed changes on the same anchor.

Keywords: generative collapse; auditability; invariants; First-Law budget; weld governance; ULRC; reproducibility; RCFT; portable audits.

In this paper, we introduce a new class of continuous functions as an application of $\Lambda$-generalized closed sets (namely $\Lambda_g$-closed set, $\Lambda$-g-closed set and $g \Lambda$-closed set) namely $\Lambda$-generalized continuous functions (namely $\Lambda g$-continuous, $\Lambda$-g-continuous and $g \Lambda$-continuous) and study their properties in topological space.

As herbaria move to digitize their collections, the question remains of how to efficiently digitize collections other than standard herbarium sheets, such as wood slide collections. Beginning in September 2018, the Harvard University Herbaria began a project to image and digitize the wood slides contained in the Bailey-Wetmore Wood Collection. The primary goal of this project was to produce images of the wood tissue that could be used for specimen-level research and to make them available on the internet for remote scholarship. A secondary goal was to establish best practices for digitizing and imaging a microscope slide collection of tissue sections. Due to the size of the wood slide collection (approximately 30,000 slides), a medical histology scanner and virtual microscopy software were used to image these slides. This article outlines the workflow used to create these images and compares the results with digital resources currently available for wood anatomy research. Prior to this project, the very little of the Bailey-Wetmore Wood Collection was cataloged digitally and none of it was imaged, which made access to this unique collection difficult. By imaging and digitizing 6605 slides in the collection, this project has demonstrated how other institutions can make similar slide collections available to the broader scientific community.

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Abstract. This paper presents a deployed semantic web application in the cultural domain: the semantic portal MuseumFinland. It is a demonstration of a community portal and a publication channel by which heterogeneous collection database contents of different museums can be published on the Semantic Web. By semantic web techniques, it is possible to make collections semantically interoperable and provide the museum visitors with intelligent content-based search and browsing services to the global collection base.

This document summarizes the contributions of the Electromagnetic $\gamma_vNN^*$ Transition Form Factors workshop participants that provide theoretical support of the excited baryon program at the 12 GeV energy upgrade at JLab. The main objectives of the workshop were (a) review the status of the $\gamma_vNN^*$ transition form factors extracted from the meson electroproduction data, (b) call for the theoretical interpretations of the extracted $N$-$N^*$ transition form factors, that enable access to the mechanisms responsible for the N* formation and to their emergence from QCD.

This paper presents the rst part of a comparative analysis between the Standard Model (SM) of fundamental interactions and the Tensor Model of Discrete Dynamics (TMDD), proposed as an ontologically grounded fundamental theory underlying the eective description provided by the SM. The fo-driver of both local and non-local interactions. Furthermore, time and space are shown to be emergent concepts, arising from discrete acts of consensus within the θ-network. This work lays the foundation for the second part of the study, in which the reverse discretization of the SM Lagrangian will be performed, and the parameters of TMDD will be quantitatively calibrated.

SHARP is a Linked Data approach for harmonizing cross-workflow provenance. In this demo, we demonstrate SHARP through a real-world omic experiment involving workflow traces generated by Taverna and Galaxy systems. SHARP starts by interlinking provenance traces generated by Galaxy and Taverna workflows and then harmonize the interlinked graphs thanks to OWL and PROV inference rules. The resulting provenance graph can be exploited for answering queries across Galaxy and Taverna workflow runs.

Les systèmes de workflows ont largement contribué à améliorer la reproductibilité des expériences scientifiques. Cependant, relativement peu de travaux ont porté sur la réutilisation des données produites au cours de l'exécution. Dans cet article, nous faisons l'hypothèse que ces données intermédiaires doivent être considérées comme des objets de premier ordre, qui doivent être conservés et publiés. Non seulement cela permettra d'économiser des ressources de calcul et de stockage, mais surtout cela facilitera et accélérera l'évaluation de nouvelles hypothèses. Pour aider les scientifiques à annoter ces données, nous exploitons plusieurs sources d'information : i) les informations de provenance capturées lors de l'exécution des workflows, et ii) les annotations de domaine qui sont fournies par des catalogues sémantiques d'outils, tels que Bio.Tools. Finalement, nous montrons, sur un scénario réel de bioinformatique, comment des graphes de provenance peuvent être transformés et résumés, à destination des utilisateurs et des machines.

HERBARIUM: CONCEPT AND DEFINITION The term herbarium, used in the strictest sense today, is simply a collection of dried specimens. Lawrence (1951) and others include in their definitions the arrangement of specimens in the sequence of an accepted classification and are available for reference or other scientific study. "Herbarium" used in its original sense, however, referred not to a collection of plants, but to a book about medicinal plants. Tournefort in about 1700 used the term as an equivalent to hortus siccus (Stearn, 1957), and this use was taken up by Linnaeus who also adopted it as a substitute for hortus siccus, hortus mortus, and others. It was largely through his influence that it superseded the former terms. The procedure of pressing and drying specimens for storage has been an amazingly successful one in terms of preservation of detail and specimen longevity, and the plants so preserved provide a concrete basis for past, present and future studies. In its more than four-hundred-year history the .herbarium has become an institution. Today one associates the term herbarium not only with preserved plant specimens but also with certain botanical activities. The herbarium is the basic reference source of the taxonomist and has become a center for research as well as teaching and public information. An herbarium, a special kind of museum, can also be regarded as a data bank with vast quantities of raw data. Each specimen has information content and therefore value which will, of course, vary depending on completeness of specimen and data and the source of the material. Each specimen has information about the vegetation of an area, a population, and the taxon to which it belongs (Rollins, 1965). The collection, therefore, represents a source of primary information about man's explorations and observations of the earth's vegetation, and document the results of much the past inquiry into the nature and relationships of plants. Herbarium specimens are now used for studies in the disci[plines which were probably never even dreamed of at the time early collections were made and herbaria organized. These studies include such fields as cytogeography, biochemical systematics, palynology, and genecology. II. HISTORICAL DEVELOPMENT The beginning of the herbarium as a collection of dried specimens affixed to paper for a lasting record is attributed to Luca Ghini (1490?-1556). According to Arber (1938) Ghini seems to have been the sole initiator of the art of herbarium-making and this art was disseminated over Europe by his students. Gherards Cibo, a pupil of Ghini, began collecting and preserving specimens as early as 1532 and his herbarium is extant today. John Falconer, an Englishman, is mentioned as possessing an herbarium in the writings of Lusitanus in 1553 and William Turner in 1569, and it is believed that he also learned of herbarium making either directly or indirectly from Ghini (Arber, 1938). Although the herbarium technique was a well-known botanical practice at the time of Linnaeus, he departed from the convention of the day (mounting specimens and binding them into volumes) by mounting his specimens on single sheets and

This paper seeks to delve into how shifting focus to AI-based predictive analytics. It has the potential to change the outlook in personalized medicine. Chronic diseases have become widespread and offer great challenges to the world's healthcare system. It is calling for potential approaches to diagnosis and treatment. It is a revolutionary concept that predictive analytical model underpinned by AI that helps healthcare risk forecast and introduce timely intercessions. The patient data analyzed using AI-based models, which can thereby generate specific information used to make much more accurate diagnosis and treatment plans. This research is an exemplar of big data. It aims at using algorithms in machine learning to process EHRs, genotype and phenotype data and lifestyle data among individuals in the world. The data are used for the educational process of building the predictive models, which assists in the early diagnosis of chronic diseases. It includes diabetes, cardiovascular diseases, cancer and others. The methods are used to supervised learning ranging from decision tree algorithms and support vector machines to neural networks where required. The real-time data analysis is included in the study for the purpose of real-time monitoring and risk analysis for proper healthcare preventative methodologies. The insights derived show that AI-advanced analytics of the chronic disease patients' data hold massive opportunities to redefine chronic illness treatment. AI for early detection of cases does not only lead to better care of patients but also helps lower health care expenditures since fewer intensive treatments are required. The proactive approach in connection with the individualization of treatment with the help of intelligent analytics is aimed at providing patient-tailored care.

This paper presents, for Frequent Query Discovery (FQD), an algorithm which employs a novel relation of equivalence in order to remove redundant queries in the output. An FQD algorithm returns a set of frequent queries from a data base of query transactions in Datalog formalism. A Datalog data base can represent complex structures, such as hyper graphs, and allows the use of background knowledge. Thus, it is useful in complex domains such as chemistry and bio-informatics. A conventional FQD algorithm, such as WARMR, checks the redundancy of the queries with a relation of equivalence based on the θ-subsumption, which results in discovering a large set of frequent queries. In this work, we reduce the set of frequent queries using another relation of equivalence based on relevance of a query with respect to a data base. The experiments with both real and artificial data sets show that our algorithm is faster than WARMR and the test of relevance can remove up to 92% of the frequent queries.

This paper presents, for Frequent Query Discovery (FQD), an algorithm which employs a novel relation of equivalence in order to remove redundant queries in the output. An FQD algorithm returns a set of frequent queries from a data base of query transactions in Datalog formalism. A Datalog data base can represent complex structures, such as hyper graphs, and allows the use of background knowledge. Thus, it is useful in complex domains such as chemistry and bio-informatics. A conventional FQD algorithm, such as WARMR, checks the redundancy of the queries with a relation of equivalence based on the θ-subsumption, which results in discovering a large set of frequent queries. In this work, we reduce the set of frequent queries using another relation of equivalence based on relevance of a query with respect to a data base. The experiments with both real and artificial data sets show that our algorithm is faster than WARMR and the test of relevance can remove up to 92% of the frequent queries.

Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure as a Service (IaaS) clouds. IaaS clouds are characterized by ondemand resource provisioning capabilities and a pay-per-use model. We discuss, develop, and assess novel algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account task granularity, uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions.

Large-scale applications expressed as scientific workflows are often grouped into ensembles of inter-related workflows. In this paper, we address a new and important problem concerning the efficient management of such ensembles under budget and deadline constraints on Infrastructure-as-a-Service (IaaS) clouds. We discuss, develop, and assess algorithms based on static and dynamic strategies for both task scheduling and resource provisioning. We perform the evaluation via simulation using a set of scientific workflow ensembles with a broad range of budget and deadline parameters, taking into account uncertainties in task runtime estimations, provisioning delays, and failures. We find that the key factor determining the performance of an algorithm is its ability to decide which workflows in an ensemble to admit or reject for execution. Our results show that an admission procedure based on workflow structure and estimates of task runtimes can significantly improve the quality of solutions.

Large computational problems may often be modelled using multiple scientific workflows with similar structure. These workflows can be grouped into ensembles, which may be executed on distributed platforms such as the Cloud. In this paper, we focus on the provisioning of resources for scientific workflow ensembles and address the problem of meeting energy constraints along with either budget or deadline constraints. We propose and evaluate two energy-aware algorithms that can be used for resource provisioning and task scheduling. Experimental evaluation is based on simulations using synthetic data based on parameters of real scientific workflow applications. The results show that our proposed algorithms can meet constraints and minimize energy consumption without compromising the number of completed workflows in an ensemble.

This paper presents a cost optimization model for scheduling scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous virtual machine instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a cloud object store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified using mathematical programming languages (AMPL and CMPL) and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications in a variety of domains. The data used for evaluation come from the synthetic workflows and from general purpose cloud benchmarks, as well as from the data measured in our own experiments wi...

This paper introduces a cost optimization model for scientific workflows on IaaS clouds such as Amazon EC2 or RackSpace. We assume multiple IaaS clouds with heterogeneous VM instances, with limited number of instances per cloud and hourly billing. Input and output data are stored on a Cloud Object Store such as Amazon S3. Applications are scientific workflows modeled as DAGs as in the Pegasus Workflow Management System. We assume that tasks in the workflows are grouped into levels of identical tasks. Our model is specified in AMPL modeling language and allows us to minimize the cost of workflow execution under deadline constraints. We present results obtained using our model and the benchmark workflows representing real scientific applications such as Montage, Epigenomics, LIGO. We indicate how this model can be used for scenarios that require resource planning for scientific workflows and their ensembles.

Motivation: We our efforts to work with HUBzero in a significant collaboration-oriented project, as well as our impressions of HUBzero after nearly a year of interaction with the platform. Our assessment is a mixed one: while the HUBzero platform does a good job of bringing users and modeling tools together, we found some noteworthy limitations of this recently open-sourced technology. For example: hubs are generally configured to deploy simulation tools of modest means with input and output data not readily shared across user accounts; by default, the security configuration of a HUBzero server is very open; hubs are inflexible in terms of their deployment requirements, and there is a need for more detailed documentation. In answer to the first (and most serious) of these issues, we have extended HUBzero to enable the use of more sophisticated modeling tools, such as openModeller Desktop, and to provide seamless access to external Network File System drive space.

We address the problem of task planning on multiple clouds formulated as a mixed integer nonlinear programming problem (MINLP). Its specification with AMPL modeling language allows us to apply solvers such as Bonmin and Cbc. Our model assumes multiple heterogeneous compute and storage cloud providers, such as Amazon, Rackspace, GoGrid, ElasticHosts and a private cloud, parameterized by costs and performance, including constraints on maximum number of resources at each cloud. The optimization objective is the total cost, under deadline constraint. We compute the relation between deadline and cost for a sample set of data-and compute-intensive tasks, representing bioinformatics experiments. Our results illustrate typical problems when making decisions on deployment planning on clouds and how they can be addressed using optimization techniques.

Artykuł stanowi prywatne wyznanie autora, który nie lubi przymusu wakacyjnych wyjazdów. Tekst jest częściowo żartobliwy, ale zawiera wiele prawdziwych stwierdzeń  dotyczących sposobów spędzania czasu na urlopie. Namawiam do jego przeczytania, chociaż jest on sprzed kilku lat. Ale w meritum nic się nie zmieniło...

Abstract. Mapping XML documents into relational database is a promising solution because relational databases are mature and scale very well and they have the advantages that in a relational database XML data and structured data can coexist making it possible to build application that involve both kinds of data with little extra effort. In this paper, we propose two algorithms (XRD, XRS) that maps XML documents into relational database. The algorithm steps are given in details to describe how to map the XML documents into relational ...

Cloud computing systems provide on demand access to computational resources for dedicated use. Grid computing allows users to share heterogeneous resources from multiple administrative domains applied to common tasks. In this paper we discuss the characteristics and requirements of a hybrid system composed of both grid and cloud technologies. We propose an infrastructure which is able to manage the execution of service workflows in such system. We show how the infrastructure can be expanded by acquiring computational resources on demand from the cloud during the workflow execution, and how it manages these resources and the workflow execution without user interference.

In this paper we present DFScala, a library for constructing and executing dataflow graphs in the Scala language. Through the use of Scala this library allows the programmer to construct coarse grained dataflow graphs that take advantage of functional semantics for the dataflow graph and both functional and imperative semantics within the dataflow nodes. This combination allows for very clean code which exhibits the properties of dataflow programs, but we believe is more accessible to imperative programmers. We first describe DFScala in detail, before using a number of benchmarks to evaluate both its scalability and its absolute performance relative to existing codes. DFScala has been constructed as part of the Teraflux project and is being used extensively as a basis for further research into dataflow programming.

Bioinformatics workflows require large amounts of resources and are commonly executed in clusters. Determining the adequate amount of resources for bioinformatics applications is a tricky matter, since the resource usage of a single application might vary substantially from one execution to the next. Resource management systems in clusters don't consider these variations and subsequent needs. As a result, the computing power offered by clusters is not harnessed properly, compromising both application performance and resource efficiency. To tackle these issues, we propose a History-Based Resource Manager for bioinformatics workflows applications running on clusters with heterogeneous nodes. The proposed resource manager features a prediction model that generates multiple performance predictions for each job under different combinations of cluster resources. Furthermore, the proposed resource manager includes a scheduling algorithm that considers the degree of multiprogramming of the nodes, scheduling combinations of applications for simultaneous same-node execution upon their compatibility. To test the proposed resource manager, we process two workloads formed by different amounts of workflows made up by common bioinformatics applications. Results prove that for the given cases, the proposed resource manager improves the performance obtained with SLURM, using First Come First Served policy. The proposal shows an average workflow makespan improvement range between 28 and 35%, averaging 32%, an average workflow efficiency improvement range between 75 and 83%, averaging 79%, and an average resource usage improvement range between 96 and 101%, averaging 99%. Furthermore, the pro-Funded by the Spanish Economy ministry. Project Number: TIN2014-53234-C2-1-R.

Although simulators provide approximate, faster and easier simulation of an application execution in Clouds, still many researchers argue that these results cannot be always generalized for complex application types, which consist of many dependencies among tasks and various scheduling possibilities, such as workflows. DynamicCloudSim, the extension of the well known CloudSim simulator, offers users the capability to simulate the Cloud heterogeneity by introducing noisiness in dozens parameters. Still, it is difficult, or sometimes even impossible to determine appropriate values for all these parameters because they are usually Cloud or application-dependent. In this paper, we propose a new model that simplifies the simulation setup for a workflow and reduces the bias between the behavior of simulated and real Cloud environments based on one parameter only, the Cloud noisiness. It represents the noise produced by the Cloud's interference including the application's (in our case a workflow) noisiness too. Another novelty in our model is that it does not use a normal distribution naively to create noised values, but shifts the mean value of the task execution time by the cloud noisiness and uses its deviation as a standard deviation. Besides our model reduces the complexity of DynamicCloudSim's heterogeneity model, evaluation conducted in Amazon EC2 shows that it is also more accurate, with better trueness (closeness to the real mean values) of up to 9.2% and precision (closeness to the real deviation) of up to 8.39 times.

Cloud computing provides a cheap and elastic platform for executing large scientific workflow applications, but it rises two challenges in prediction of makespan (total execution time): performance instability of Cloud instances and variant scheduling of dynamic schedulers. Estimating the makespan is necessary for IT managers in order to calculate the cost of execution, for which they can use Cloud simulators. However, the ideal simulated environment produces the same output for the same workflow schedule and input parameters and thus can not reproduce the Cloud variant behavior. In this paper, we define a model and a methodology to add a noise to the simulation in order to equalise its behavior with the Clouds' one. We propose several metrics to model a Cloud fluctuating behavior and then by injecting them within the simulator, it starts to behave as close as the real Cloud. Instead of using a normal distribution naively by using mean value and standard deviation of workflow tasks' runtime, we inject two noises in the tasks' runtime: noisiness of tasks within a workflow (defined as average runtime deviation) and noisiness provoked by the environment over the whole workflow (defined as average environmental deviation). In order to measure the quality of simulation by quantifying the relative difference between the simulated and measured values, we introduce the parameter inaccuracy. A series of experiments with different workflows and Cloud resources were conducted in order to evaluate our model and methodology. The results show that the inaccuracy of the makespan's mean value was reduced up to 59 times compared to naively using the normal distribution. Additionally, we analyse the impact of particular workflow and Cloud parameters, which shows that the Cloud performance instability is simulated more correctly for small instance type (inaccuracy of up to 11.5%), instead of medium (inaccuracy of up to 35%), regardless of the workflow. Since our approach requires collecting data by executing the workflow in the Cloud in order to learn its behavior, we conduct a comprehensive sensitivity analysis. We determine the minimum amount of data that needs to be collected or minimum number of test cases that needs to be repeated for each experiment in order to get less than 12% inaccuracy for our noising parameter. Additionally, in order to reduce the number of experiments and determine the dependency of our model against Cloud resource and workflow parameters, the conducted comprehensive sensitivity analysis shows that the correctness of our model is independent of workflow parallel section size. With our sensitivity analysis, we show that we can reduce the inaccuracy of the naive approach with only 40% of total number of executions per experiment in the learning phase. In our case, 20 executions per experiment instead of 50, and only half of all experiments, which means down to 20%, i.e. 120 test cases instead of 600.

The cloud is an eco-system in which virtual machine instances are starting and terminating asynchronously on user demand or automatically when the load is rapidly increased or decreased. Although this dynamical environment allows to rent computing or storage resources cheaper rather than buying them, still it does not guarantee the stable execution during a period of time as the traditional physical environment. This is emphasised even more for workflows execution, since they consist of many data and control dependencies, which cause the makespan to be instable when a workflow is being executed in different periods of time in cloud. In this paper we analyse several parameters of workflow and the cloud environment that are expected to impact the workflow execution instability and investigate the correlation between them. The cloud parameters include the number of instances and their type, as well as the correlation with the efficient or inefficient execution of workflow parallel sections. We conduct a series of experiments, repeating each experiment by 30 test cases in order to evaluate instability for different cloud and workflow parameters. The results show a neglectfully correlation between each pair of parameters, as well as the tasks and file transfers within the workflow. Oppose to the expectations, the distribution of the makespan per experiment does not always comply with the normal distribution, which is also not correlated to a particular cloud or workflow parameter.

Realistic, relevant, and reproducible experiments often need input traces collected from real-world environments. We focus in this work on traces of workflows-common in datacenters, clouds, and HPC infrastructures. We show that the state-of-the-art in using workflow-traces raises important issues: (1) the use of realistic traces is infrequent, and (2) the use of realistic, open-access traces even more so. Alleviating these issues, we introduce the Workflow Trace Archive (WTA), an open-access archive of workflow traces from diverse computing infrastructures and tooling to parse, validate, and analyze traces. The WTA includes >48 million workflows captured from >10 computing infrastructures, representing a broad diversity of trace domains and characteristics. To emphasize the importance of trace diversity, we characterize the WTA contents and analyze in simulation the impact of trace diversity on experiment results. Our results indicate significant differences in characteristics, properties, and workflow structures between workload sources, domains, and fields.

• A review of multiple QoS parameter workflow scheduling. • A new multiple QoS algorithm with quadratic complexity for workflow scheduling. • Similar performances of search-based algorithms in a small fraction of the time. • Results for randomly generated graphs as well as for real-world applications.

We introduce a new Manycore Workflow Runtime Environment (MWRE) to efficiently enact traditional scientific workflows on modern manycore computing architectures. In contrast to existing engines that enact workflows acting as external services, MWRE is compiler-based and translates workflows specified in the XMLbased Interoperable Workflow Intermediate Representation (IWIR) into an equivalent C++-based program. This program efficiently enacts the workflow as a stand-alone executable by means of a new callback mechanism that resolves dependencies, transfers data, and handles composite activities. Experimental results on a number of real-world workflows demonstrate that MWRE clearly outperforms existing Java-based workflow engines designed for distributed (Grid/Cloud) computing infrastructures in terms of enactment time, is generally better than an existing script-based engine for manycore architectures (Swift), and sometimes gets even close to an artificial baseline implementation of the workflows in the standard OpenMP language for shared memory systems.

Key factors causing irreproducibility of research include those related to inappropriate study design methodologies and statistical analysis1. In modern statistical practice irreproducibility could arise due to statistical (false discoveries, p-hacking, overuse/misuse of p-values, low power, poor experimental design) and computational (data, code & software management) issues2. These require understanding the processes and workflows practiced by an organization, and the development and use of metrics to quantify reproducibility. Within the Foundation of Discovery - Population Health Research, Center for Clinical and Translational Science, University of Utah, we are undertaking a project to streamline the study design and statistical analysis workflows and processes. As a first step we met with key stakeholders to understand the current practices by eliciting example statistical projects, and then developed process information models for different types of statistical needs using Luc...

All tools dedicated to system administration of local area network follow the same scheme. For each host they install an initial operating system and then update it using a package manager. The package managers run independently on the hosts, this makes dicult (even impossible) to create and maintain a stable environment in which multiple hosts coexist and cooperate. Furthermore, over time, the update process, that upgrades, adds or suppress software, results in instability of host systems. Finally, a package corresponds most often to a single software with a basic conguration which does not match to the network requirements. This article presents the YaKa framework which propose an orthogonal approach to this scheme in which the update feature almost disappears and is replaced by a very fast installation of a new clean system. All systems required in the network are generated all together by a compiler which has a complete view of the network. This general view allows to describe systems as a set of abstract services. A service groups one or several softwares and congures them for enabling a given functionality on one or several hosts.

All tools dedicated to system administration of local area network follow the same scheme. For each host they install an initial operating system and then update it using a package manager. The package managers run independently on the hosts, this makes dicult (even impossible) to create and maintain a stable environment in which multiple hosts coexist and cooperate. Furthermore, over time, the update process, that upgrades, adds or suppress software, results in instability of host systems. Finally, a package corre-sponds most often to a single software with a basic conguration which does not match to the network requirements. This article presents the YaKa framework which propose an orthogonal approach to this scheme in which the update feature almost disappears and is replaced by a very fast installation of a new clean system. All systems required in the network are generated all together by a compiler which has a complete view of the network. This general view allows to describe ...

The dative alternation the pirate will send the necklace to the princess (PP) the pirate will send the princess the necklace(DO)• dative verbs also display bias effects:• p (PP| send)> p (DO| send)• p (PP| show)< p (DO| show)• this cannot be reduced to a difference in meaning• the two constructions mean basically the same thing• controlling for semantic properties of the arguments, Bresnan et al (2007) still find e ects of verb bias

This research looks into the question of where and how Artificial Intelligence and Big Data can be usefully implemented into Affiliate Marketing. By consulting relevant literature and qualified experts, this work identifies 6 areas, where Artificial Intelligence can be beneficial. These areas were found to be Affiliate Recruitment, Affiliate Management, Product Data Feed Optimization, Tracking, Attribution and Forecasting. The implementation of Artificial Intelligence in these areas revealed 3 advantages to the Affiliate Marketing channel: Saving of time, support of decision-making, and incentivizing of publishers. While a more detailed study of this research topic would be necessary for validating the results, the findings show that the implementation of Artificial Intelligence technology can help a business gain competitive advantage.

Ein kurzer Leitfaden mit Strukturhilfe von Verena Glass Einleitung Der Anfang ist oft das Schwerste-besonders beim Schreiben. Viele Studierende wissen zwar, dass sie eine Bachelorarbeit schreiben müssen, aber nicht, wie sie anfangen sollen. In diesem Leitfaden findest du einen kompakten Überblick über die ersten Schritte: von der Themenfindung bis zum roten Faden. Das richtige Thema finden Nicht alles, was spannend klingt, eignet sich für eine wissenschaftliche Arbeit. Wähle ein Thema, das dich interessiert-und das gleichzeitig eingegrenzt und gut bearbeitbar ist. Ein guter Indikator: Du kannst deine Fragestellung in einem Satz erklären. Die Forschungsfrage klären Die Forschungsfrage ist das Herz deiner Arbeit. Sie entscheidet, wohin die Reise geht. Statt zu beschreiben, was es schon gibt, solltest du etwas untersuchen, vergleichen oder analysieren. Frage dich: Was genau will ich herausfinden-und warum ist das relevant? Gliederung als Gerüst Eine grobe Gliederung gibt dir Halt. Sie muss nicht perfekt sein-aber sie hilft, den Überblick zu behalten. Ein Klassiker: Einleitung, Theorie, Methode, Ergebnisse, Diskussion, Fazit. Passe das Gerüst an dein Thema an-und sei bereit, es im Schreibprozess weiterzuentwickeln. Literatur: Qualität statt Quantität Lieber fünf gute Quellen als zwanzig halbgare. Achte auf Fachliteratur, aktuelle Veröffentlichungen und zitierfähige Texte. Google Scholar, Bibliotheken oder Fachdatenbanken sind oft besser als der erste Treffer bei Google. Erste Sätze schreiben Fang einfach irgendwo an. Der perfekte Einstieg kommt oft später. Wichtig ist, ins Schreiben zu kommen: Notizen, Gedanken, kleine Abschnitte. Und wenn du dich festschreibst: Pausieren. Anders denken. Neu ansetzen. Fazit Der Anfang einer Bachelorarbeit ist kein Sprung ins Ungewisse-sondern ein Prozess. Mit einer klaren Fragestellung, einer guten Struktur und realistischen Schritten legst du die Basis für eine erfolgreiche Arbeit. Und wenn du dabei Hilfe brauchst: Frag ruhig. Niemand muss allein durch das akademische Dickicht.

Wer eine Hausarbeit schreibt, weiß: Der Weg von der Idee bis zur fertigen Abgabe kann holprig sein. Zwischen Gliederung, Literaturrecherche und Formatvorgaben schleichen sich schnell kleine Fehler ein, die am Ende wertvolle Punkte kosten-oder sogar den Eindruck mindern, den ein gut recherchierter Text verdient hätte. Dieser kurze Leitfaden zeigt typische Stolperfallen, die ich als Lektorin und Ghostwriterin immer wieder sehe-und wie man sie einfach vermeiden kann. Unklare Fragestellung Viele Arbeiten starten mit einem Thema, aber ohne echte Forschungsfrage. Das führt dazu, dass der rote Faden fehlt und der Text beliebig wirkt. Tipp: Stelle dir früh die Frage: Was genau will ich herausfinden, untersuchen oder zeigen? Zu wenig Struktur Ein häufiger Fehler: Die Arbeit springt zwischen Themen hin und her oder verliert sich in Details. Eine saubere Gliederung-auch als Arbeitsgrundlage-hilft, den Überblick zu behalten. Jeder Abschnitt sollte einem klaren Zweck dienen. Zitierchaos Uneinheitliche oder falsche Zitate gehören zu den Klassikern. Wichtig: Entscheide dich für einen Stil (z. B. APA, Fußnoten) und zieh ihn durch. Unbelegte Aussagen oder Copy-Paste ohne Quellenangabe können als Plagiat gewertet werden. Keine eigene Stimme Viele Arbeiten referieren nur Quellen, ohne eine eigene Haltung zu entwickeln. Auch in wissenschaftlichen Texten ist Reflexion gefragt: Was bedeutet das Ergebnis? Wo liegen Grenzen? Trau dich, in der Einleitung und im Fazit selbst zu denken. Formalia unterschätzt Schriftgröße, Zeilenabstand, Deckblatt, Seitenzahlen-all das wirkt nebensächlich, ist aber Teil der Bewertung. Prüfe die Vorgaben deiner Hochschule sorgfältig-und nimm sie ernst. Fazit Hausarbeiten sind kein Hexenwerk-aber auch kein Selbstläufer. Wer häufige Fehler kennt, kann sie vermeiden-und mehr Energie auf Inhalt, Argumentation und Stil legen.

The healthcare industry is undergoing a paradigm shift driven by the convergence of design thinking, artificial intelligence (AI), and emerging technologies. This paper explores how integrating human-centered design with advanced technological tools can revolutionize public health delivery, improve patient experiences, and enhance health equity. Drawing on case studies and current applications, it illustrates how this fusion offers scalable, ethical, and innovative solutions to persistent public health challenges.

This paper outlines a Java-based AI framework aimed at optimizing healthcare resource allocation under the principles of Responsible AI. Leveraging real-time data streams, the proposed approach employs constraint-satisfaction and reinforcement learning algorithms to dynamically distribute critical resources such as hospital beds, staff, and medical supplies. Emphasis is placed on integrating ethical guidelines, regulatory compliance (e.g., HIPAA), and bias detection within the algorithmic pipeline. Through simulation studies using historical patient flow data and emergency scenarios, the framework demonstrates scalability and adaptability. By providing actionable insights into how hospital systems can automate resource management while maintaining transparency and accountability, this research underscores the importance of responsible and efficient AI deployment in high-stakes healthcare environments.

This paper presents a unifying framework for applying data analytics in the finance and healthcare sectors, where large-scale datasets demand robust and domain-specific methods. Drawing on machine learning and statistical techniques, the study addresses challenges such as data heterogeneity, stringent privacy regulations, and real-time analytics requirements. By comparing supervised and unsupervised approaches, the framework demonstrates how to integrate model outputs into operational and strategic decision-making pipelines. Emphasis is placed on managing compliance, ensuring data security, and achieving interpretability of analytic results. The paper ultimately provides guidance for industry professionals and researchers seeking to harness advanced analytics for tasks like fraud detection, risk assessment, patient outcomes analysis, and resource allocation, thereby advancing data-driven decision-making in both fields.

This paper presents a no-code eXtended Reality (XR) workflow that allows non technical people to create XR experiences as part of equipment design review (EDR) processes. The demo showcases how the workflow can enable seamless visualization, interaction, and evaluation of complex CAD models in XR. By combining PiXYZ for CAD-to-mesh translation with Unity's XR Interaction Toolkit and Meta XR SDK, the system delivers immersive experiences that enhance spatial perception and reduce cognitive load for mechanical engineers. The demo also incorporates a stylus based approach for adding hand-drawn notes in real-time on top of imported CAD models. Attendees will witness the complete XR creation pipeline, from CAD import to a live Mixed Reality based design review, demonstrating efficiency, scalability, and potential to revolutionize XR-driven engineering workflows.

[1] Ilmu bukti (science) dapat mengacu pada dua hal, yaitu sebagai pengetahuan yang akurat (most likely to be correct) tentang dunia sejauh bukti-bukti yang ada dan sebagai proses dalam memperoleh pengetahuan yang akurat tentang dunia sejauh bukti-bukti yang ada; [2] Ilmu bukti memiliki tiga aspek: output, tujuan, dan proses. Output dari ilmu bukti adalah pengetahuan yang akurat (most likely to be correct) tentang dunia sejauh bukti-bukti yang ada. Tujuan dari ilmu bukti adalah memperoleh pengetahuan yang akurat tentang dunia sejauh bukti-bukti yang ada. Proses ilmu bukti adalah langkah-langkah yang terarah dalam menemukan pengetahuan yang akurat tentang dunia sejauh bukti-bukti yang ada; [3] Output ilmu bukti sebagai proses dapat dirincikan menjadi tiga hal, yaitu deskripsi tentang apa yang terjadi, penjelasan terkait mengapa sesuatu dapat terjadi, dan uraian mengenai bagaimana sesuatu dapat terjadi (baik yang terjadi di masa lalu, saat ini, maupun masa depan); Tujuan ilmu bukti dapat dijabarkan menjadi tiga, yaitu mendeskripsikan tentang apa yang terjadi, menjelaskan terkait mengapa sesuatu dapat terjadi, dan menguraikan mengenai bagaimana sesuatu dapat terjadi (baik yang terjadi di masa lalu, saat ini, maupun di masa depan); [4] Sebagai proses, ilmu bukti adalah langkah-langkah dalam menentukan suatu kesimpulan tentang dunia (berupa deskripsi tentang apa yang terjadi dan penjelasan mengenai mengapa serta bagaimana sesuatu dapat terjadi) di mana kesimpulan tersebut harus mengacu pada bukti-bukti empiris dan logika; [5] Bukti empiris adalah bukti yang dapat diperiksa, dialami, diindera, dan diukur. Bukti empiris dapat diperoleh melalui observasi/pengataman, pengalaman, dan pengujian/eksperimen. Dalam ilmu bukti, bukti empiris ini akan menjadi dasar untuk menentukan suatu kesimpulan; [6] Kata kunci dari output ilmu bukti ada tiga, yaitu apa, mengapa, dan bagaimana. Kata kunci dari tujuan ilmu bukti ada tiga, yaitu mendeskripsikan apa, menjelaskan mengapa, dan menguraikan bagaimana. Kata kunci dari proses ilmu bukti ada dua yaitu berdasarkan bukti empiris dan sesuai dengan logika; [7] Dalam proses ilmu bukti, bukti empiris dan penyimpulan logis (penyimpulan yang sesuai dengan logika) harus terpenuhi. Jika tidak, maka kesimpulan yang telah ditentukan (berupa deskripsi tentang apa yang terjadi dan penjelasan terkait mengapa serta bagaimana sesuatu dapat terjadi) dapat tidak akurat; [8] Alasan mengapa kita harus bernalar secara saintifik (berdasarkan ilmu bukti) salah satunya karena penalaran saintifik dapat mengantarkan kita pada pengetahuan yang akurat (most likely to be correct) tentang alam semesta ini sejauh bukti-bukti yang ada. [9] "Filsafat (philosophy) adalah studi khusus yang bertujuan/berupaya untuk menjawab keraguan-keraguan (doubts) terhadap suatu pengetahuan (knowledge), yang mana keraguan-keraguan ini muncul sebagai akibat dari adanya masalah-masalah (problems) tertentu, di mana masalah-masalah ini belum termasuk dalam lingkup area penyelidikan ilmu bukti khusus (special sciences). Dengan demikian, pengidentifikasian masalah (yang menyebabkan munculnya keraguan) dan keraguan (yang disebabkan oleh adanya suatu masalah) berkenaan dengan suatu pengetahuan merupakan langkah pertama dalam menentukan definisi dari filsafat sekaligus langkah pertama dalam studi kefilsafatan."; [10] Ilmu bukti (science) mengeneralisasi fakta² dari pengamatan (observation), pengalaman (experience), dan pengujian (experiment) ke dalam hukum, teori, atau pengetahuan saintifik yang dapat diuji kembali. Filsafat memeriksa validitas argumen dan menyelidiki keakuratan, asumsi dasar, lingkup, dan batasan dari ilmu bukti; [11] Ilmu bukti (science) tumbuh dari gagasan-gagasan yang berasal dari pemahaman umum (common sense); seperti benda dan sifat-sifatnya, ruang, waktu, dan sebab-akibat. Namun, seiring berkembangnya ilmu bukti, sebagian pemahaman umum terbukti tidak akurat dalam mendeskripsikan dan menjelaskan bagaimana dunia ini bekerja. Makanya fundamentalnya perlu di-rekonstruksi. Nah, ini tugas filsafat.

The integration of Machine Learning (ML) into healthcare systems is transforming the operational and clinical capabilities of hospitals, significantly impacting patient outcomes and the efficiency of hospital workflows. This article explores into the multifaceted role ML plays in analyzing large volumes of patient data, including clinical records, diagnostic imaging, and real-time monitoring data, to create predictive models that enhance healthcare delivery. By identifying patterns and correlations in patient data, ML algorithms can forecast patient needs, streamline resource allocation, and enable proactive responses to potential complications, ultimately facilitating timely interventions. Moreover, ML-driven decision support systems enhance diagnostic accuracy and enable personalized treatment plans tailored to each patient's unique medical profile, fostering more effective care pathways. The impact of ML extends beyond individual patient care to system-wide operational improvements. Predictive analytics assist in optimizing hospital workflows by minimizing bottlenecks, improving bed management, and ensuring the efficient utilization of medical staff and resources. The reduction in administrative overhead and operational delays contributes to a more responsive healthcare environment that prioritizes patient-centric care while maintaining high standards of efficiency and quality. Additionally, this paper explores case studies demonstrating how ML applications in hospitals contribute to accelerated diagnoses, better resource distribution, and overall improvements in hospital throughput. These advancements position ML as a cornerstone of a data-driven, intelligent healthcare system capable of adapting to the dynamic needs of patient care. Ultimately, smart health systems leveraging ML technology are creating a framework for a more proactive, precise, and sustainable approach to modern healthcare, fostering improved patient satisfaction and clinical outcomes.

The Workflow Scheduling directs in the area of workflow management systems. The efficient Scheduling in workflows helps the system to increase cost, which can be evaluation by considering various clustering methods, which is been studied in this paper. A number of parameters are there on the basis of which performance of cost is dependent and is evaluated and compared to find the efficient of workflow scheduling algorithm also. The key consideration in the paper determines the openness of various task clustering methods in cloud environment and based on detail experimentation results are drawn to evaluate the importance of methods suited with scheduling policy.

Reference managers, which have become a common tool in academic writing, seem to lend themselves almost naturally to historical research and to the management (and citation) of oftentimes vast collections of sources.
This paper discusses strenghts and weaknesses of working with the Zotero reference manager, especially when dealing with non-latin scripts, showcasing an example of the project "Open Arabic Periodical Editions" (Dr. Till Grallert).