nils-herrmann/clean_scirepeval huggingface dataset:
Real time anomaly detection for very short term load forecasting
Year: 2018

Although the recent load information is critical to very short term load forecasting (VSTLF) power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real time anomaly detection in most recent load information used by VSTLF. This paper proposes a model based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state of the art method used by a winning team of the Global Energy Forecasting Competition 2014. Finally, a general anomaly detection framework is proposed for the future research.
Predicting the artificial immunity induced by RUTI(r) vaccine against tuberculosis using universal immune system simulator (UISS)
Venue: BMC Bioinformatics
Year: 2019

Tuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world's population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multi drug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project "In Silico Trial for Tuberculosis Vaccine Development" (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare. We present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e. the artificial immunity induced by RUTI(r) vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb) Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI(r) and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUTI(r) vaccine administration. The application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis.
To Do or Not to Do a PhD? Insight and Guidance from a Public Health PhD Graduate
Year: 2021

This section is a summary of your thesis. It should give a background of the study, the objectives set, the basic methods, the important results and their associated conclusions under the different headings of: Background,    The word count for the thesis abstract is much more than that for an abstract of a research manuscript; however, every institution has its own guidelines. No citations are included in the abstract. In summary, the abstract should address the following questions: 'What was done? 'Why was it done? 'What questions were set to be answered? 'How was it done? 'What was learnt? and 'Why does it matter?  Introduction or Background (Check with the University's Guidelines for the Appropriate Heading Title) This sets the scene for your thesis by providing a critical literature review of the topic you are investigating, e.g. type 2 diabetes. The introduction chapter needs to motivate and lure the reader into continuing to read your thesis by describing the problem at hand and the niche present in the scientific world that your thesis will address. It is paramount that the aim and objectives of the thesis are kept in mind while you are doing the literature review in order to stay on track and not deviate from the topic. All citations should originate from the original data source and not utilise other researchers' reviews and interpretations of the original research. Since this chapter is usually laid out during the initial phases of your PhD, it is important to keep updating the chapter with any new concepts, findings, studies and guidelines that may get published along your PhD journey. Personally, I suggest that you start this chapter early on. Most of the literature review is required as part of your PhD proposal, so once you already have laid the foundations, write it down in the form of an introduction chapter for your thesis. Having at least one chapter completed (even if you would need to update it along the way) means one less thing on your agenda when you reach the thesis writing stage. It is recommended that the methodology you followed for your literature review is noted down; The Structure of a Thesis
System and method for cleaning single layer curtain wall glass of high rise outer standing face by utilizing ultrasonic waves
Year: 2013

Utilizing ultrasonic cleaning system and method for a single layer glass curtain wall facade buildings, including the inlet pipe, a single layer glass curtain wall, the inlet valve, the metal frame and an ultrasonic device, an ultrasonic device comprising an ultrasonic transducer, an ultrasonic generator, ultrasonic power, ultrasonic FM knob, into the drainage pipe with a slit opening, the inner side of the ultrasonic transducer via a couplant monolayer bonded glass curtain wall, ultrasound FM tuning range of the knob into the atomizing frequency and cleaning frequency, cleaning frequency in the range of 25kHz 130kHz, atomizing frequency range of 1.7MHz 2.4MHz, single glass wall by ultrasonic cleaning and atomization of the water film buildings facades. The present invention may be a single layer curtain wall glass facade for buildings in the cleaning chamber, to solve the safety problems of doing high glass curtain wall hanging rinse the monolayer present, easy to use, high cleaning efficiency, low cost cleaning.
A Comparison of Defense and Plaintiff Expert Witnesses in Orthopaedic Surgery Malpractice Litigation
Venue: The Journal of bone and joint surgery. American volume
Year: 2018

According to the American Academy of Orthopaedic Surgeons (AAOS) Standards of Professionalism, the responsible testimony of expert witnesses in orthopaedic surgery malpractice lawsuits is important to the public interest. However, these expert witnesses are recruited and compensated without established standards, and their testimony can potentially sway court opinion, with substantial consequences. The objective of this study was to characterize defense and plaintiff expert orthopaedic surgeon witnesses in orthopaedic surgery malpractice litigation.  Utilizing the WestlawNext legal database, defense and plaintiff expert witnesses involved in orthopaedic surgery malpractice lawsuits from 2013 to 2017 were identified. Each witness's subspecialty, mean years of experience, involvement in academic or private practice, fellowship training, and scholarly impact, as measured by the Hirsch index (h index) were determined through a query of professional profiles, the Scopus database, and a PubMed search. Statistical comparisons were made for each parameter among defense and plaintiff expert witnesses.  Between 2013 and 2017, 306 expert medical witnesses for orthopaedic cases were identified; 174 (56.9% testified on behalf of the plaintiff, and 132 (43.1% testified on behalf of the defense. Orthopaedic surgeons who identified themselves as general orthopaedists comprised the largest share of expert witnesses on both the plaintiff (n 61) and defense (n 25) sides. The plaintiff witnesses averaged 36 years of experience versus 31 years for the defense witnesses (p 0.001) 26% of the plaintiff witnesses held an academic position versus 43% of the defense witnesses (p 0.013) Defense witnesses exhibited a higher proportion of fellowship training in comparison to plaintiff expert witnesses (80.5% versus 64.5% respectively, p 0.003) The h index for the plaintiff group was 6.6 versus 9.1 for the defense group (p 0.04) Two witnesses testified for both the plaintiff and defense sides.  Defense expert witnesses held higher rates of academic appointments and exhibited greater scholarly impact than their plaintiff counterparts, with both sides averaging >30 years of experience. These data collectively show that there are differences in characteristics between plaintiff and defense witnesses. Additional study is needed to illuminate the etiology of these differences.
The Impact of Prison Based Therapeutic Community Programs on Motivation for Treatment
Year: 2019

The Impact of Prison Based Therapeutic Community Programs on Motivation for Treatment by Jessica Tabor M.A, Centenary College, 2011 B.A, Bloomsburg University of Pennsylvania, 2007 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy General Psychology Walden University August 2019  The sentencing and use of mandated treatment policies throughout the country have heightened the number of inmates incarcerated for drug related offenses. The purpose of this quasi experimental, archival, correlational descriptive study was to determine whether motivation changes during therapeutic community (TC) treatment among a group of incarcerated adult male offenders, as measured by differences in pre and postThe sentencing and use of mandated treatment policies throughout the country have heightened the number of inmates incarcerated for drug related offenses. The purpose of this quasi experimental, archival, correlational descriptive study was to determine whether motivation changes during therapeutic community (TC) treatment among a group of incarcerated adult male offenders, as measured by differences in pre and post levels of motivation. The hypothesis that was tested was that there would be significant differences between levels of motivation as measured upon entry and discharge from treatment. The theoretical framework that guided the study was the stages of change theory. Data were collected from archived pre and post treatment Texas Christian University, Treatment Motivation scale (MOT) scores. The target population comprised adult males, who were incarcerated in the state of New Jersey between January 1, 2014 and December 31, 2016, and had completed a TC program. A paired sample t test was completed, which indicated that there was a significant difference between levels of motivation from admission to discharge in the TC program. Discharge MOT scores for motivation were determined to be higher than admission scores, which answered the research question regarding levels of motivation change during a TC program. The study findings lend support to the utility of TC programs in changing offender behavior, thereby making inmates more productive members of society and strengthening public safety. The Impact of Prison Based Therapeutic Community Programs on Motivation for Treatment by Jessica Tabor MA, Centenary College, 2011 BA, Bloomsburg University of Pennsylvania, 2007 Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy General Psychology Walden University August 2019  This study is dedicated to my loving and supportive father, mother, boyfriend, sisters, and son Brayden. Thank you for always standing by my side and believing in me. Acknowledgements Thank you Dr. David Rentler and Dr. Jana L. Price Sharps for all of your encouragement, direction and patience throughout this process.
Metal Organic Framework (MOF) Hybrid as a Tandem Catalyst for Enhanced Therapy against Hypoxic Tumor Cells.
Venue: Angewandte Chemie
Year: 2019

Encapsulation of active biomolecules and/or nanoparticles in metal organic frameworks (MOFs) remains a great challenge in biomedical applications. In this work, through a stepwise in situ growth method, a black phosphorus quantum dot (BQ) and catalase were precisely encapsulated into the inner and outer layers of MOFs, respectively. The integrated MOF system as a tandem catalyst could convert H2 O2 into O2 in MOF stabilized catalase outer layer, and then O2 was directly injected into MOF sensitized BQ inner, leading to high quantum yield of singlet oxygen. Upon internalization, the photodynamic therapy efficiency of the MOF system was 8.7 fold greater than that without catalase, showing an enhanced therapeutic effect against hypoxic tumor cells. Furthermore, by coupling with photothermal therapy of BQs, photodynamic thermal synergistic therapy was realized both in vitro and in vivo.
Observations of the shape and group dynamics of rogue waves
Year: 2017

Surface elevation records from two locations in the northeast Pacific are used to examine rogue waves and the relationship to wave groups. Three hundred individual rogue waves with heights greater than 2.2 times the significant wave height are found in analyzing >2 x 106 wave groups. In contrast to recent nonlinear modeling results, we do not find that rogue waves occur at the front of wave groups. There is a tendency for steep waves to occur at the front of a group, but these are not the largest waves of the group and do not meet the rogue wave criterion. Rogue waves are most commonly located in the center of the group, but their height ratio to the neighboring crest is greater than in the average wave group. Assessing group dynamics in terms of spectral width suggests that random superposition of nonlinear waves is sufficient to explain the observations of individual rogue waves.
Recent development in artificial neural network based distributed fiber optic sensors
Venue: 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP)
Year: 2020

Distributed fiber optic sensors are promising technique for measuring strain, temperature and vibration over tens of kilometres by utilizing the backscattered Rayleigh, Raman and Brillouin signals. Recently, the use of an artificial neural network (ANN) has been adopted into the distributed fiber sensors for advanced data analytics, fast data processing time, high sensing accuracy and event classification. In this paper, the recent developments of ANN based distributed fiber sensors and their operating principles are reviewed. Moreover, the performance of ANN is compared with the conventional signal processing algorithms. The future perspective view that can be extended further research development has also been discussed.
Rethinking Visual Relationships for High level Image Understanding
Venue: ArXiv
Year: 2019

Relationships, as the bond of isolated entities in images, reflect the interaction between objects and lead to a semantic understanding of scenes. Suffering from visually irrelevant relationships in current scene graph datasets, the utilization of relationships for semantic tasks is difficult. The datasets widely used in scene graph generation tasks are splitted from Visual Genome by label frequency, which even can be well solved by statistical counting. To encourage further development in relationships, we propose a novel method to mine more valuable relationships by automatically filtering out visually irrelevant relationships. Then, we construct a new scene graph dataset named Visually Relevant Relationships Dataset (VrR VG) from Visual Genome. We evaluate several existing methods in scene graph generation in our dataset. The results show the performances degrade significantly compared to the previous dataset and the frequency analysis do not work on our dataset anymore. Moreover, we propose a method to learn feature representations of instances, attributes, and visual relationships jointly from images, then we apply the learned features to image captioning and visual question answering respectively. The improvements on the both tasks demonstrate the efficiency of the features with relation information and the richer semantic information provided in our dataset.
Application of Metamodel Assisted Multiple Gradient Descent Algorithm (MGDA) to Air Cooling Duct Shape Optimization
Year: 2012

MGDA stands for Multiple Gradient Descent Algorithm, which was introduced in a previous report. MGDA was tested on several analytical test cases and also compared with a well known Evolution Strategy algorithm, Pareto Archived Evolution Strategy (PAES) Using MGDA in a multi objective optimization problem requires the evaluation of a substantial number of points with regard to criteria, and their gradients. In industrial test cases, in which computing the objective functions is CPU demanding, a variant of the method was to be found. Here, a metamodel assisted MGDA is proposed and tested. The MGDA is assisted by a Kriging surrogate model construction. A first database is computed as an Latin Hypercube Sampling (LHS) distribution in the admissible design space, which is problem dependent. Then, MGDA leads each database point to a non dominated set of the surrogate model. In this way, each function computation is made on the surrogate model at a negligible computational cost.
Industry 4.0: A Special Section in IEEE Access
Venue: IEEE Access
Year: 2017

Industry 4.0 can be said to be the current trend of automation and data exchange in manufacturing technologies. Originally, the term "Industrie 4.0" is from a project in the high tech strategy of the German government, which hope to promote the computerization of manufacturing. Usually involves terms like cyber physical systems, Internet of things, amd cloud computing. For now, Industry 4.0 becomes an emerging buzzword that is gaining significant interest among all stakeholders of the global industry related R&D market from academia to international companies. It is a new business model attracting much interest, yet the definitions are not very matured and is an amazing melting pot of disruptive technologies. No doubt, to maximize the impact of Industry 4.0, researchers from different fields and industry have to work together applying the new technologies in practice. On the top of the wave, it is timely to analyze the cross section who can benefit from the novel achievements of Industry 4.0.
Whose 'West Africa' The regional dynamics of peace and security
Year: 2017

The articles of this Special Issue show how theoretical perspectives, normative frames, discursive strategies and conceptual issues shape, and are shaped by, intervention practices and the dynamics of peace and security in West Africa. As they construe and construct understandings of West African conflicts, they impact and justify conflict management practices. Put another way, how one defines the region called West Africa is not disassociated from one's understanding of peace and security. The concepts are mutually constitutive. The increasing significance awarded to 'radicalisation' and 'terrorism' said to be coming from the Sahel suggests how practices of security do not simply respond to 'threats' somehow, somewhere, in 'West Africa' As the focus on international intervention moves north toward the Sahel, it transforms the meaning and formation of West African states and the West African region as it connects them to the extra regional dynamics from the 'Sahel' and 'North Africa'
A visually secure image encryption scheme based on 2D compressive sensing and integer wavelet transform embedding
Year: 2021

Using a certain image encryption algorithm to encrypt plain image into visually meaningless noise image is a common way to protect the security of image data. Nevertheless, meaningless noisy image will cause more attention during the transmission process and thus results in lots of attacks. Here, we propose a visually secure image encryption scheme based on two dimensional compressive sensing (2DCS) and integer wavelet transform (IWT) embedding. During the encryption process, the confusion and diffusion of plain image are realized through random scrambling and grayscale transformation, then a pre encrypted image is obtained, and then the pre encrypted image is re encrypted into a secret image via 2DCS. During the embedding process, the carrier image is converted into a coefficient matrix through IWT, then embedding technology is used to embed the secret image into the coefficient matrix to obtain the modified coefficient matrix, and then it is transformed into the spatial domain through inverse IWT to achieve the final meaningful cipher image. The application of 2DCS can ensure data security and increase the speed of the decryption algorithm, and the IWT embedding can achieve visual security without loss of information. The effectiveness, data security, visual security and real time performance of the scheme have been tested in simulation results and comprehensive performance analysis.
GORFLM: Globally Optimal Robust Fitting for Linear Model
Venue: Signal Process. Image Commun.
Year: 2020

Fitting a model to data contaminated by noise and outliers is a common task in computer vision, and it is often solved by maximizing inlier set. Most existing methods cannot guarantee global optimality, due to the two techniques widely utilized in inlier maximization: randomized sampling to generate candidate models and a hard threshold on residuals to classify inliers and outliers. In this paper, we propose a deterministic globally optimal linear model fitting method, in which we use the negative Gaussian function as a soft loss function over the residual and formulate model fitting as minimizing the sum of the Gaussian functions. We derive a convex quadratic function as the lower bound function of the objective function so that it can be globally minimized by a branch and bound algorithm. Experiments showed that the proposed method outperformed the state of the art methods on several typical CV problems, especially when there are multiple models with different noise levels and large number of data points.
Biofuel cells Activation of micro and macro electronic devices.
Venue: Bioelectrochemistry
Year: 2018

The article represents a short conceptual overview of biofuel cell applications, rather than their design and operation. Special attention is given to interfacing enzyme based biofuel cells with power consuming microelectronic devices. Importance of electronic management of the power extracted from biological sources is emphasized. In addition to several briefly explained examples collected from recent publications, one system demonstrating powering of a standard glucometer with an implantable or wearable biofuel cell is described in details. The opinion on the biofuel cell applications and limitations represents the personal vision of the authors and might be not fully in accordance with the opinions of other experts.
What do young and brilliant minds think about Development, Environment and Foresight?
Year: 2016

Albert Einstein once said, "No problem can be solved from the same level of consciousness that created it." to honour his quote, we have decided to dedicate this issue to our young colleagues, PhD candidates and students, as we strongly believe that there is potential in their work and their ideas. Therefore, you will find three papers in this issue, written by brilliant young minds in the areas of development, environment and foresight.