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Due to the increasing size of s (s), their design and optimization phases (i.e., ) grow increasingly complex. At design time, a large design space needs to be explored to find an implementation that fulfills all specifications and then optimizes metrics like energy, area, delay, reliability, etc. At run time, a large configuration space needs to be searched to find the best set of parameters (e.g., voltage/frequency) to further optimize the system. Both spaces are infeasible for exhaustive search typically leading to heuristic optimization algorithms that find some trade-off between design quality and computational overhead. ML can build powerful models that have successfully been employed in related domains. In this survey, we categorize how () may be used and is used for design-time and run-time optimization and exploration strategies of s. A meta-study of published techniques unveils areas in that are well-explored and underexplored with, as well as trends in the employed algorithms. We present a comprehensive categorization and summary of the state of the art on for. Finally, we summarize remaining challenges and promising open research directions.

In this study, an inkjet bioprinting-based high-throughput screening (HTS) system was designed and applied for the first time to a catecholpyrimidine-based small molecule library to find hit compounds that inhibit c-Jun NH2-terminal kinase1 (JNK1). JNK1 kinase, inactivated MAPKAPK2, and specific fluorescent peptides along with bioink were printed on parchment paper under optimized printing conditions that did not allow rapid evaporation of printed media based on Triton-X and glycerol. Subsequently, different small compounds were printed and tested against JNK1 kinase to evaluate their degree of phosphorylation inhibition. After printing and incubation, fluorescence intensities from the phosphorylated/nonphosphorylated peptide were acquired for the % phosphorylation analysis. The IM50 (inhibitory mole 50) value was determined as 1.55 × 10–15 mol for the hit compound, 22. Thus, this work demonstrated that inkjet bioprinting-based HTS can potentially be adopted for the drug discovery process using small molecule libraries, and cost-effective HTS can be expected to be established based on its low nano- to picoliter printing volume.

(1) Background: The Sustainable Development Goals (SDGs) are a set of objectives and sub-targets that aim to promote a more comprehensive system for sustainability, peace, and social justice. The SDGs propose a more holistic approach to the evolution of society, enlarging the responsibilities and roles for their implementation, therefore, it is worthwhile asking who will be in charge of the processes to develop adequate strategies. (2) Methods: This is a positional paper based on a literature review about the two main topics of this analysis: hybrid organizations and the SDGs’ governance. (3) Results: The hybrids combine diverse institutional logics within a unique organizational structure as a central tool for SDG implementation. The scientific literature explains how this organizational form is capable of mitigating possible tensions and trade-offs stemming from its heterogeneity and how hybrid structures can prove to be a useful solution for SDG implementation at the micro-level. (4) Conclusion: Although hybrid organizations can be a valuable aid to sustainable development, the absence of clear-cut coordination and responsibility structures at the macro-level could jeopardize efforts at the micro-level. In light of this hypothesis, this paper argues that hybrid organizations cannot be left in isolation in this mission, especially as the SDGs’ rationality is based on a new, holistic vision of development, which exposes a risk of implausibility surrounding macro-level political forces.

To rigorously measure the implementation of evidence-based interventions, implementation science requires measures that have evidence of reliability and validity across different contexts and populations. Measures that can detect change over time and impact on outcomes of interest are most useful to implementers. Moreover, measures that fit the practical needs of implementers could be used to guide implementation outside of the research context. To address this need, our team developed a rating scale for implementation science measures that considers their psychometric and pragmatic properties and the evidence available. The Psychometric and Pragmatic Evidence Rating Scale (PAPERS) can be used in systematic reviews of measures, in measure development, and to select measures. PAPERS may move the field toward measures that inform robust research evaluations and practical implementation efforts.

Hybrid microfluidic systems that are composed of multiple different types of substrates have been recognized as a versatile and superior platform, which can draw benefits from different substrates while avoiding their limitations. This review article introduces the recent innovations of different types of low-cost hybrid microfluidic devices, particularly focusing on cost-effective polymer- and paper-based hybrid microfluidic devices. In this article, the fabrication of these hybrid microfluidic devices is briefly described and summarized. We then highlight various hybrid microfluidic systems, including polydimethylsiloxane (PDMS)-based, thermoplastic-based, paper/polymer hybrid systems, as well as other emerging hybrid systems (such as thread-based). The special benefits of using these hybrid systems have been summarized accordingly. A broad range of biological and biomedical applications using these hybrid microfluidic devices are discussed in detail, including nucleic acid analysis, protein analysis, cellular analysis, 3D cell culture, organ-on-a-chip, and tissue engineering. The perspective trends of hybrid microfluidic systems involving the improvement of fabrication techniques and broader applications are also discussed at the end of the review.

Background: Prognostic models combine several prognostic factors to provide an estimate of the likelihood (or risk) of future events in individual patients, conditional on their prognostic factor values. A fundamental part of evaluating prognostic models is undertaking studies to determine whether their predictive performance, such as calibration and discrimination, is reproduced across settings. Systematic reviews and meta-analyses of studies evaluating prognostic models’ performance are a necessary step for selection of models for clinical practice and for testing the underlying assumption that their use will improve outcomes, including patient's reassurance and optimal future planning. Methods: In this paper, we highlight key concepts in evaluating the certainty of evidence regarding the calibration of prognostic models. Results and Conclusion: Four concepts are key to evaluating the certainty of evidence on prognostic models’ performance regarding calibration. The first concept is that the inference regarding calibration may take one of two forms: deciding whether one is rating certainty that a model's performance is satisfactory or, instead, unsatisfactory, in either case defining the threshold for satisfactory (or unsatisfactory) model performance. Second, inconsistency is the critical GRADE domain to deciding whether we are rating certainty in the model performance being satisfactory or unsatisfactory. Third, depending on whether one is rating certainty in satisfactory or unsatisfactory performance, different patterns of inconsistency of results across studies will inform ratings of certainty of evidence. Fourth, exploring the distribution of point estimates of observed to expected ratio across individual studies, and its determinants, will bear on the need for and direction of future research. © 2021

In over fve years, Bornmann, Stefaner, de Moya Anegon, and Mutz (2014b) and Bornmann, Stefaner, de Moya Anegón, and Mutz (2014c, 2015) have published several releases of the www.excellencemapping.net tool revealing (clusters of) excellent institutions worldwide based on citation data. With the new release, a completely revised tool has been published. It is not only based on citation data (bibliometrics), but also Mendeley data (altmetrics). Thus, the institutional impact measurement of the tool has been expanded by focusing on additional status groups besides researchers such as students and librarians. Furthermore, the visualization of the data has been completely updated by improving the operability for the user and including new features such as institutional profle pages. In this paper, we describe the datasets for the current excellencemapping.net tool and the indicators applied. Furthermore, the underlying statistics for the tool and the use of the web application are explained.

VLDB is a premier annual international forum for data management, and it targets database researchers, users, vendors, and application developers. Each year the conference covers current issues in data management and database systems, which remain key technological cornerstones of emerging applications of the twenty-first century. VLDB 2013 took place at the picturesque town of Riva del Garda in Italy. We received a total of 559 research paper submissions, and of these 127 submissions were accepted

Urbanization is accelerating due to economic and societal development. The accurate identification of urban functional zones is significant for urban structure optimization, urban planning, and resource allocation. This paper reviews the scholarly literature on urban functional zone identification. Based on the retrieval results of databases, we analyzed the overview and current status. The identification methods and classification schemes are summarized from the existing research. The following results were obtained: (1) point of interest (POI) data are widely used for functional zone identification; (2) the block is the most common unit for functional zone identification; (3) cluster analysis is the main approach for urban functional zone identification; (4) most of the classification schemes are based on the dominant land use and characteristics of data sources. We predict future trends of urban functional zone identification based on the reviewed literature. Our findings are expected to be valuable for urban studies.

Social bookmarking and publication sharing systems are essential tools for web resource discovery. The performance and capabilities of search results from research paper bookmarking system are vital. Many researchers use social bookmarking for searching papers related to their topics of interest. This paper proposes a combination of similarity based indexing “tag title and abstract” and static ranking to improve search results. In this particular study, the year of the published paper and type of research paper publication are combined with similarity ranking called (HybridRank). Different weighting scores are employed. The retrieval performance of these weighted combination rankings are evaluated using mean values of NDCG. The results suggest that HybridRank and similarity rank with weight 75∶25 has the highest NDCG scores. From the preliminary result of experiment, the combination ranking technique provide more relevant research paper search results. Furthermore the chosen heuristic ranking can improve the efficiency of research paper searching on social bookmarking websites.

International audience; Advanced internet and multimedia set-top boxes are today massively based on 4*4 MIMO systems, with multi-sub-bands standards (Wifi bands, LTE, Bluetooth,…). Multiple antennas spatially distributed on the plastic casing become therefore a great challenge for cost and performances motivations, thus promoting flexible technology and substrate, for assembling subsystems , under compactness considerations, as well as electrical performances improvement. In this paper, we discuss about the characterisation of Multi-band antennas designed on an ultra-low cost material (a cellulosic substrate, i.e. a paper) with design challenges in terms of radiation efficiency maximization and wideband properties under compactness constraints. Simulation and measurements are proposed to validate the designs.

International audience; As part of the Historic Photographic Paper Classification Challenge, a multitude of approaches to quantifying paper texture similarity have been developed. These approaches have yielded encouraging results when applied to very controlled datasets containing photomicrographs of familiar specimens. In this paper, we report on the k-nearest neighbors classification performance of two multiscale analysis-based texture similarity approaches when applied to a much larger reference collection of silver gelatin photographic papers. The clusters for this data set were derived from a visual sorting experiment conducted by art conservators and paper experts later extended through crowd-sourcing. The results show that these texture similarity approaches, when combined with a simple k-nearest neighbors classification algorithm, yield workable performances with accuracy of up to 69%. We discuss this outcome in the context of available data and the cross-validation procedure used, then provide suggestions for improvement.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.

In this research, a security paper based on frequency selective structure technologies was designed and fabricated using selective wave reflection characteristics to prevent the offline leakage of confidential documents. Document leakage detection systems using security papers detect security papers using transceiving antenna gates. For the application of such systems, the structure must be designed with excellent reflection performance and stability at the angle of incidence. For this purpose, a loop and patch-type frequency selective structure based on a four-legged element structure was designed to have X-band frequency reflection characteristics. This design was based on optimized variables and was realized through the screen printing method using silver ink on A4 paper. It was verified that both the design and simulation results matched well. To verify its actual applicability, a detector module operable at 10 GHz was manufactured to observe both the security paper detection range in relation to distance with a signal strength of &minus, 10 dBm and the detection area in relation to the number of times that the security paper had been folded.

The age of a relatively old document is one of the pending issues to be resolved in the field of forensic documentary examination. Although nowadays there are a variety of analytical methodologies focused in the analysis of inks for dating documents, the paper analysis has attained little attention. This work aims to develop two complementary approaches for estimating the age of documents based on paper analysis employing the pyrolysis technique coupled to gas chromatography with detection by mass spectrometry (Py-GC/MS): (i) a direct approach using the pyrolytic fingerprints and multivariate regression with artificially aged samples, and (ii) an indirect approach based on the identification of compounds characteristic of the document period. The direct approach has successfully allowed the age estimation of relatively old documents under police custody (up to 30 years of age) and the determination of a relation between the natural and the accelerated aging of paper under the used conditions. This approach is applicable to papers that have the same (or similar) composition and have been stored under comparable storage conditions. Additionally, the indirect approach is presented as an interesting perspective to ratify valuable information of the document age.

We use origami to create a compact, scalable three-dimensional (3-D) biobattery stack that delivers on-demand energy to the portable biosensors. Folding allows a two-dimensional (2-D) paper sheet possessing predefined functional components to form nine 3-D microbial fuel cells (MFCs), and connect them serially within a small and single unit (5.6 cm × 5.6 cm). We load the biocatalyst Pseudomonas aeruginosa PAO1 in predefined areas that form the MFCs, and freeze-dry them for long-term storage. The biobattery stack generates a maximum power and current of 20 μW and 25 μA, respectively, via microbial metabolism when the freeze-dried cells are rehydrated with readily available wastewater. This work establishes an innovative strategy to revolutionize the fabrication, storage, operation, and application of paper-based MFCs, which could potentially make energy available even in resource-limited settings.

Cloud applications expose - beside service endpoints - also potential or actual vulnerabilities. And attackers have several advantages on their side. They can select the weapons, the point of time and the point of attack. Very often cloud application security engineering efforts focus to harden the fortress walls but seldom assume that attacks may be successful. So, cloud applications rely on their defensive walls but seldom attack intruders actively. Biological systems are different. They accept that defensive "walls" can be breached at several layers and therefore make use of an active and adaptive defense system to attack potential intruders - an immune system. This position paper proposes such an immune system inspired approach to ensure that even undetected intruders can be purged out of cloud applications. This makes it much harder for intruders to maintain a presence on victim systems. Evaluation experiments with popular cloud service infrastructures (Amazon Web Services, Google Compute Engine, Azure and OpenStack) showed that this could minimize the undetected acting period of intruders down to minutes., Preprint of CLOSER 2018 position paper

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

© 2018, the Authors. Fast-growing scientific papers bring the problem of rapidly and accurately finding a list of reference papers for a given manuscript. Reference paper recommendation is an essential technology to overcome this obstacle. In this paper, we study the problem of personalized query-focused astronomy reference paper recommendation and propose a heterogeneous information network embedding based recommendation approach. In particular, we deem query researchers, query text, papers and authors of the papers as vertices and construct a heterogeneous information network based on these vertices. Then we propose a heterogeneous information network embedding (HINE) approach, which simultaneously captures intra-relationships among homogeneous vertices, inter-relationships among heterogeneous vertices and correlations between vertices and text contents, to model different types of vertices as vector formats in a unified vector space. The relevance of the query, the papers and the authors of the papers are then measured by the distributed representations. Finally, the papers which have high relevance scores are presented to the researcher as recommendation list. The effectiveness of the proposed HINE based recommendation approach is demonstrated by the recommendation evaluation conducted on the IOP astronomy journal database.

In this letter, a chipless radio frequency identification (chipless-RFID) system with erasable/programmable 40-bit tags inkjet printed on paper substrates, where tag reading proceeds sequentially through near-field coupling, is presented for the first time. The tags consist of a linear chain of identical split ring resonators (SRRs) printed at predefined and equidistant positions on a paper substrate, and each resonant element provides a bit of information. Tag programming is achieved by cutting certain resonant elements, providing the logic state “0” to the corresponding bit. Conversely, tags can be erased (all bits set to “1”) by short circuiting those previously cut resonant elements through inkjet. An important feature of the proposed system is the fact that tag reading is possible either with the SRR chain faced up or faced down (with regard to the reader). To this end, two pairs of header bits (resonators), with different sequences, have been added at the beginning and at the end of the tag identification chain. Moreover, tag data storage capacity (number of bits) is only limited by the space occupied by the linear chain. The implementation of tags on paper substrates demonstrates the potential of the proposed chipless-RFID system in secure paper applications, where the necessary proximity between the reader and the tag, inherent to near-field reading, is not an issue.

Recommendation techniques in scientific paper recommender systems (SPRS) have been generally evaluated in an offline setting, without much user involvement. Nonetheless, user relevance of recommended papers is equally important as system relevance. In this paper, we present a scientific paper recommender system (SPRS) prototype which was subject to both offline and user evaluations. The lessons learnt from the evaluation studies are described. In addition, the challenges and open questions for multi-method evaluation in SPRS are presented. NRF (Natl Research Foundation, S’pore) Accepted version

Blockchain technology has become almost as famous for incidents involving security breaches as for its innovative potential. We shed light on the prevalence and nature of these incidents through a database structured using the STIX format. Apart from OPSEC-related incidents, we find that the nature of many incidents is specific to blockchain technology. Two categories stand out: smart contracts, and techno-economic protocol incentives. For smart contracts, we propose to use recent advances in software testing to find flaws before deployment. For protocols, we propose the PRESTO framework that allows us to compare different protocols within a five-dimensional framework., 8 pages, 1 figure, accepted for presentation as a regular paper at IEEE Blockchain 2018 (Halifax, Canada)

Modelling can be applied to all aspects of healthcare systems but one area in particular, clinical pathways, are of great interest currently due to many flow-oriented issues that are well-documented in the media. These pathways typically describe sequences of sorting and treatment activities such as surgical procedures or the care process for managing injuries, for example bone fractures. Previous efforts in using modelling languages have not been promoted as being highly generalizable nor have emphasised the inclusion of constraints defining rules for particular treatment activities. In this paper, we propose a workflow for building flexible models for healthcare systems by exploiting the combination of UML, OCL, and SMT solving. This paper serves as an exposition of an idea that can be developed into a more complete framework that could be used to create workflow models for improving the efficiency and safety of more complex clinical activities. A good application would be to tackle the prevalent problems of emergency departments and some of the challenges in this respect are discussed.

The decision to implement an orders-based versus an encounters-based imaging workflow poses various implications to image capture and storage. The impacts include workflows before and after an imaging procedure, electronic health record build, technical infrastructure, analytics, resulting, and revenue. Orders-based workflows tend to favor some imaging specialties while others require an encounters-based approach. The intent of this HIMSS-SIIM white paper is to offer lessons learned from early adopting institutions to physician champions and informatics leadership developing strategic planning and operational rollouts for specialties capturing clinical multimedia.

The arrival of Cyber-physical systems provided space for a new emerging field of research oriented on this type of embedded systems. The aim of this paper is to provide a better understanding of this integrative research field focused on the concept, architecture and challenges in deployment of such systems within the concept of Industry 4.0. Cyber-physical systems represent an emerging area of research that attracts the interest of researchers around the world, because, in the field of design and development of future systems, they are expected to play a major role.

With the stringent regulations from various academic bodies making it desirable for a post graduate resident to have submitted an original article prior to appearance for their exams or those working in teaching institutions to have published articles to claim credit for applying for promotions and private practitioners to increase their visibility on the academic front, paper, everyone is on a publishing spree. In this article, we attempt to elucidate the processes involved in publication; approaching it in a systematic and practical manner, incorporating some tips and tricks. This collection of pearls is aimed to serve as a beginner's guide to scientific writing and publications. The pearls cover assorted topics like- benefits of publications, collecting resources, various tools available and technical processes related to how a manuscript is processed.

Allocating environmental impacts within life cycle assessment (LCA) is a recognised methodological problem. Different allocation methods have been developed to fulfil the various goals of LCA studies. At present, there is neither a consensus regarding the method to be used nor a one-fits-all method for dealing with recycling in LCA. The paper discusses some of the main allocation procedures practised in the pulp and paper industry, namely those covered by ISO/TS 14067:2018, GHG Protocol/PAS 2050, and the Product Environmental Footprint (PEF). All in all, the allocation method described in the ISO/TS 14067:2018 standard was considered the most appropriate to account for open-loop recycling of paper products. However, the method neither considers the number of subsequent uses of recycled fibres, nor the recycled fibre age. Yet, both of them are considered especially important in the open-loop recycling of paper products. In order to account for these aspects, the present study proposes an advanced, step-wise approach to handle allocation in LCAs accounting for the open-loop recycling of paper products. The advanced approach is based on the fibre mass flow model used to calculate the mean fibre age and the mean number of uses of paper products within the European context. The approach was validated with the data from the pulp and paper industry, particularly examining the newsprint paper grade with different contents of recycled fibre and fibre age. The paper, however, introduces all the parameters required to perform allocation based on the proposed method for other paper grades. The categpory of global warming potential was considered in the study as an illustrative example. yet the method could be applied to other impact assessment categories. The results calculated using the proposed allocation approach were compared to those obtained with the allocation methods described in the GHG Protocol/PAS 2050 and the PEF. The proposed allocation method can be used to guide further development of available standards and the product category rules, which will contribute to increased harmonisation and consistency of LCA studies in the pulp and paper sector, as well as to the enhanced practical implementation of LCA in industry.

The present article aims at presenting several points-commonly not realized by potential TOEFL test takers-that may be utilized to take the paper-and-pencil TOEFL. To these ends, several topics will be presented covering mainly the test format and the scoring scheme. It is under these topics that several technical strategies are discussed with the perspectives on successful paper-based TOEFL test taking.

This investigation proposes a systematic framework based on the Fault Tree analysis, for the energy and exergy optimization of a paper machine. In the first step, energy and exergy analysis was performed on the dryer section and its related steam and condensate system, to assess the existing performance of paper machine. In the next step, checklists were developed for the identification of key safety and operational issues related to the steam and condensate system. Then, a fault tree analysis was applied to make out and resolve the root causes. Corrective actions were taken in order to rectify various problems. As a result, energy and exergy efficiencies of the system increased by 22% and 14 % respectively. Moreover, an improvement in steam and condensate recovery system was observed.

From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models’ accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper’s text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.

For this Special Issue “2020 Feature Papers by JDB’ Editorial Board Members,” we present a collection of studies, including original research papers, and review articles by our distinguished editorial board members that focus on advances in understanding multicellular organisms’ growth, differentiation, and remodeling [...]

With the help of Student Information System (SIS), student information can be stored and maintenance can be done. It is useful for educational institutions to maintain the records of students. The storage and management of information regarding a students’ academics is crucial in any institution. This system can be useful for all types of details, academic reports, institutional details, curriculum, and other resource as well. Student Information System software is very essential nowadays for all the educational institutions to store the information of students. For storing the student data, the institutions are spending a lot of money on paper, files, and other stationary stuffs including pens/pencil etc or some institutions are purchasing expensive systems to do this stuff. But here we are just creating software for the similar work it can save a lot of expenditure of an institution and also it will save use of papers which is ultimately beneficial for our environment also. This system is very useful to sort and search student records because sorting and searching in file system is time consuming also difficult to conduct manually. This software was developed in such a way that it remains to be user centric and easily understandable and usable by the user. The software is fully functional and tested over several testing techniques available and the software is functioning smoothly.

Structural proof theory is praised for being a symbolic approach to reasoning and proofs, in which one can define schemas for reasoning steps and manipulate proofs as a mathematical structure. For this to be possible, proof systems must be designed as a set of rules such that proofs using those rules are correct by construction. Therefore, one must consider all ways these rules can interact and prove that they satisfy certain properties which makes them "well-behaved". This is called the meta-theory of a proof system. Meta-theory proofs typically involve many cases on structures with lots of symbols. The majority of cases are usually quite similar, and when a proof fails, it might be because of a sub-case on a very specific configuration of rules. Developing these proofs by hand is tedious and error-prone, and their combinatorial nature suggests they could be automated. There are various approaches on how to automate, either partially or completely, meta-theory proofs. In this paper, I will present some techniques that I have been involved in for facilitating meta-theory reasoning., In Proceedings LFMTP 2021, arXiv:2107.07376

Various data sharing platforms are being developed to enhance the sharing of cohort data by addressing the fragmented state of data storage and access systems. However, policy challenges in several domains remain unresolved. The euCanSHare workshop was organized to identify and discuss these challenges and to set the future research agenda. Concerns over the multiplicity and long-term sustainability of platforms, lack of resources, access of commercial parties to medical data, credit and recognition mechanisms in academia and the organization of data access committees are outlined. Within these areas, solutions need to be devised to ensure an optimal functioning of platforms.

Attention to the environment and its problems has undergone unprecedented growth in recent years [...]

To support nanocrystal device development, we have been working on a computational framework to utilize information in research papers on nanocrystal devices. We developed an annotated corpus called “ NaDev” (Nanocrystal Device Development) for this purpose. We also proposed an automatic information extraction system called “NaDevEx” (Nanocrystal Device Automatic Information Extraction Framework). NaDevEx aims at extracting information from research papers on nanocrystal devices using the NaDev corpus and machine-learning techniques. However, the characteristics of NaDevEx were not examined in detail. In this paper, we conduct system evaluation experiments for NaDevEx using the NaDev corpus. We discuss three main issues: system performance, compared with human annotators; the effect of paper type (synthesis or characterization) on system performance; and the effects of domain knowledge features (e.g., a chemical named entity recognition system and list of names of physical quantities) on system performance. We found that overall system performance was 89% in precision and 69% in recall. If we consider identification of terms that intersect with correct terms for the same information category as the correct identification, i.e., loose agreement (in many cases, we can find that appropriate head nouns such as temperature or pressure loosely match between two terms), the overall performance is 95% in precision and 74% in recall. The system performance is almost comparable with results of human annotators for information categories with rich domain knowledge information (source material). However, for other information categories, given the relatively large number of terms that exist only in one paper, recall of individual information categories is not high (39–73%); however, precision is better (75–97%). The average performance for synthesis papers is better than that for characterization papers because of the lack of training examples for characterization papers. Based on these results, we discuss future research plans for improving the performance of the system.

Nowadays, forecasting applications are receiving unprecedent attention thanks to their capability to improve the decision-making processes by providing useful indications [...]

This study aims to determine the effectiveness of project based learning model by using reused paper on neuron system lesson to students learning outcomes in SMA Negeri 1 Pemalang. This study used pretest-posttest control group design. The samples that were used are the third mathematics and scince major of eleventh-graders students (control class) and eleventh-graders of the fourth mathematics and science major (experimental class). The samples were taken by cluster random sampling technique. The results show that the learning outcomes in the experimental class are better than control class. The average of students’ N-gain score in the experimental class was significantly different with control class and it was higher than control class (0,72 > 0,58), the average of experimental’s class psychomotor learning outcomes was classified in the very good category, the experimental’s class affective learning outcomes (environmental care attitude) was significantly different with control class and it was greater than the control class (82> 63). This result is supported by students' and teachers' positive responses of project based learning model by using reused paper. Based on this stufy results, it is concluded that the implementation of project based learning model by using reused paper was effectively optimizing students learning outcomes on neuron system material.

Automatic data processing represents the future for the development of any system, especially in scientific research. In this paper, we describe one of the automatic classification methods applied to scientific research as a supervised learning task. Throughout the process, we identify the main features that are used as keys to play a significant role in terms of predicting the new rank under the supervised learning setup. First, we propose an overview of the work that has been realized in ranking scientific research papers. Second, we evaluate and compare some of state-of-the-art for the classification by supervised learning, semi-supervised learning and non-supervised learning. During the preliminary tests, we have obtained good results for performance on realistic corpus then we have compared performance metrics, such as NDCG, MAP, GMAP, F-Measure, Precision and Recall in order to define the influential features in our work. Keywords: Scientific research, Ranking scientific research papers, Data mining, Supervised learning, Multilayer perceptron algorithm

We present a computational framework for stability analysis of systems of coupled linear Partial-Differential Equations (PDEs). The class of PDE systems considered in this paper includes parabolic, elliptic and hyperbolic systems with Dirichlet, Neuman and mixed boundary conditions. The results in this paper apply to systems with a single spatial variable. We exploit a new concept of state for PDE systems which allows us to include the boundary conditions directly in the dynamics of the PDE. The resulting algorithms are implemented in Matlab, tested on several motivating and illustrative examples, and the codes have been posted online. Numerical testing indicates the approach has little or no conservatism for a large class of systems and can analyze systems of up to 20 coupled PDEs.

Most existing studies and public datasets for handwritten Chinese text recognition are based on the regular documents with clean and blank background, lacking research reports for handwritten text recognition on challenging areas such as educational documents and financial bills. In this paper, we focus on examination paper text recognition and construct a challenging dataset named examination paper text (SCUT-EPT) dataset, which contains 50 000 text line images (40 000 for training and 10 000 for testing) selected from the examination papers of 2 986 volunteers. The proposed SCUT-EPT dataset presents numerous novel challenges, including character erasure, text line supplement, character/phrase switching, noised background, nonuniform word size, and unbalanced text length. In our experiments, the current advanced text recognition methods, such as convolutional recurrent neural network (CRNN) exhibits poor performance on the proposed SCUT-EPT dataset, proving the challenge and significance of the dataset. Nevertheless, through visualizing and error analysis, we observe that humans can avoid vast majority of the error predictions, which reveal the limitations and drawbacks of the current methods for handwritten Chinese text recognition (HCTR). Finally, three popular sequence transcription methods, connectionist temporal classification (CTC), attention mechanism, and cascaded attention-CTC are investigated for HCTR problem. It is interesting to observe that although the attention mechanism has been proved to be very effective in English scene text recognition, its performance is far inferior to the CTC method in the case of HCTR with large-scale character set.

Outbreaks of infectious diseases can occur after natural disasters as vital services are disrupted and populations move into evacuation centres. National notifiable disease surveillance may be inadequate in these situations because of resource-consuming disease confirmation or system interruptions. Although syndromic surveillance has been used as an alternative in post-disaster situations, no systematic evaluations of it have been published. We evaluated the ad hoc paper-based syndromic surveillance system implemented in evacuation centres in Ibaraki prefecture after the 2011 Great East Japan Earthquake and Tsunami. We assessed the simplicity, acceptability, data quality, timeliness and portability of this system and reviewed its usefulness. We concluded that the system was simple, acceptable, portable and useful. The documentation and monitoring of disease events and trends were useful for developing interventions in evacuation centres and have since been used to improve post-disaster infectious disease and surveillance knowledge in Japan. We believe timeliness was a challenge due to the chain of data transmission and communication passing through an intermediary. Future implementations of this system could consider a more direct chain of data transmission and communication from collectors to analysers. Too few key informant interviewees and the inability to obtain original paper-based data from evacuation centres limited our findings; we conducted this evaluation four years after the response occurred. Future evaluations should be completed closer to when operations cease. The usefulness of the system suggests adopting it in future disasters. A simple, plain-language manual should be developed to improve future employment.