Miracle-dz/UniFAR-FTUs huggingface dataset:
Grid-constrained optimal predictive power dispatch in large multi-level power systems with renewable energy sources, and storage devices

This paper presents a novel approach for the predictive power dispatch of a large number of power system units that can be diverse in their power as well as power ramp ratings and can be dispersed both in distribution grids and transmission networks. In particular, the involved unit portfolio consists of Renewable Energy sources (RES), conventional generation sources, flexible loads, and storage devices, which are modeled using the Power Nodes modeling framework. Unlike aggregation methods which do not consider the position of a power system unit in the grid topology, our centralized optimal power dispatch strategy with a combined AC Optimal Power Flow (AC-OPF) explicitly accounts for grid constraints. The multi-period dispatch problem induced by the energy storage devices is solved by a predictive power dispatch scheme based on Model Predictive Control (MPC). We further propose a distributed three-stage optimization process that hierarchically divides the original dispatch problem into independent sub-problems according to the grid structure which are solvable in parallel. As a result, we obtain a significant complexity reduction with respect to the original centralized dispatch optimization problem, such that we can calculate the power dispatch of a large number of units with reasonable computational effort. Finally, based on benchmark grids of different sizes, we demonstrate the improved performance of the here proposed distributed approach over the original centralized dispatch optimization approach and show that the simulation time is reduced for large systems as compared with the centralized approach.
Tracking the evolution of police training and education in Poland: linear developments and exciting prospects

ABSTRACT An important issue for police services across the world is the training and education of police personnel. Both training and education have often been linked to the professionalization of policing. An overview of current developments in police training and education in Poland is provided by a discussion and delineation of learning pathways for Polish police officers and the relevant pieces of legislation that have influenced these pathways. This contributes significant knowledge to international literature by being the first repository of information about Polish police training and education. The historical context of police and police training in Poland is introduced first, followed by an overview of the two-tier system of training. Finally, developments of police training in the Polish context are critically assessed to demonstrate the slow, yet undeniable, approach toward professionalisation of police officers in Poland.
Scalable plasma simulation with ELMFIRE using efficient data structures in MPI process communication .

We describe the parallel full-f gyrokinetic particle-in-cell plasma simulation code ELMFIRE and the issue of solving a electrostatic potential from particle data distributed across several MPI (Message Passing Interface) processes. The potential is solved through a linear system with a strongly sparse matrix and ELMFIRE stores data of the estimated nonzero diagonals of the whole matrix in every MPI process, with low performance. We present three alternative more memory efficient structures for gathering the data while keeping only a local part of the matrix. We also demonstrate that these alternative structures improve scalability, thus enabling more MPI processes and a finer time and space scale than before without sacrificing performance.
Fluid Vesicles in Shear Flow.

The shape dynamics of fluid vesicles is governed by the coupling of the flow within the two-dimensional membrane to the hydrodynamics of the surrounding bulk fluid. We present a numerical scheme which is capable of solving this flow problem for arbitrarily shaped vesicles using the Oseen tensor formalism. For the particular problem of simple shear flow, stationary shapes are found for a large range of parameters. The dependence of the orientation of the vesicle and the membrane velocity on shear rate and vesicle volume can be understood from a simplified model.
Comparison of a web-based food record tool and a food-frequency questionnaire and objective validation using the doubly labelled water technique in a Swedish middle-aged population

Abstract Two web-based dietary assessment tools have been developed for use in large-scale studies: the Riksmaten method (4-d food record) and MiniMeal-Q (food-frequency method). The aim of the present study was to examine the ability of these methods to capture energy intake against objectively measured total energy expenditure (TEE) with the doubly labelled water technique (TEEDLW), and to compare reported energy and macronutrient intake. This study was conducted within the pilot study of the Swedish CArdioPulmonary bioImage Study (SCAPIS), which included 1111 randomly selected men and women aged 50–64 years from the Gothenburg general population. Of these, 200 were enrolled in the SCAPIS diet substudy. TEEDLW was measured in a subsample (n 40). Compared with TEEDLW, both methods underestimated energy intake: −2·5 (sd 2·9) MJ with the Riksmaten method; −2·3 (sd 3·6) MJ with MiniMeal-Q. Mean reporting accuracy was 80 and 82 %, respectively. The correlation between reported energy intake and TEEDLW was r 0·4 for the Riksmaten method (P < 0·05) and r 0·28 (non-significant) for MiniMeal-Q. Women reported similar average intake of energy and macronutrients in both methods whereas men reported higher intakes with the Riksmaten method. Energy-adjusted correlations ranged from 0·14 (polyunsaturated fat) to 0·77 (alcohol). Bland–Altman plots showed acceptable agreement for energy and energy-adjusted protein and carbohydrate intake, whereas the agreement for fat intake was poorer. According to energy intake data, both methods displayed similar precision on energy intake reporting. However, MiniMeal-Q was less successful in ranking individuals than the Riksmaten method. The development of methods to achieve limited under-reporting is a major challenge for future research.
Nonlinear transformation of tensor factorization for collaborative filtering

In this paper, an extension of tensor factorization based on nonlinear transformation is proposed to formulate collaborative filtering using spatiotemporal information. The nonlinear function we adopted for the transformation has only one tuning parameter, but it will give us greater flexibility to model observed data more precisely. We experimentally investigated the effectiveness of our method using an artificial dataset. Keywords—Collaborative filtering; nonlinear transformation; tensor factorization; model selection; spatiotemporal information
An empirical study of specification by example in a software engineering tool

Meta-CASE tools offer CASE tool specialisation by enabling a designer to specify a tool which is then generated automatically. Constraints are often used in such meta-CASE tools for governing the syntax and semantics of model elements and the values of their attributes. However, the constraint definition process is complex, time-consuming and error-prone. This paper presents an empirical study of the use of Specification by Example (SBE), based on the well-known notion of Programming by Example (PBE), as a user-computer interactive technique for such constraint specification. Two constraint specification techniques have been implemented in a meta-CASE tool a wizard that represents a conventional form-filling technique and an SBE technique that depends on the user providing one or more examples and the system inferring a list of possible intended constraints. The empirical study compared the wizard and SBE with respect to constraint definition correctness, task completion time, and user satisfaction. Two common modelling diagrams have been used, a State Transition Diagram and a Use Case Diagram. Results suggest that SBE is superior to the wizard in terms of measured criteria described above. Observations on the interaction of users with the system and opinions of participants are also presented.
NetCycle: Collective Evolution Inference in Heterogeneous Information Networks

Collective inference has attracted considerable attention in the last decade, where the response variables within a group of instances are correlated and should be inferred collectively, instead of independently. Previous works on collective inference mainly focus on exploiting the autocorrelation among instances in a static network during the inference process. There are also approaches on time series prediction, which mainly exploit the autocorrelation within an instance at different time points during the inference process. However, in many real-world applications, the response variables of related instances can co-evolve over time and their evolutions are not following a static correlation across time, but are following an internal life cycle. In this paper, we study the problem of collective evolution inference, where the goal is to predict the values of the response variables for a group of related instances at the end of their life cycles. This problem is extremely important for various applications, e.g., predicting fund-raising results in crowd-funding and predicting gene-expression levels in bioinformatics. This problem is also highly challenging because different instances in the network can co-evolve over time and they can be at different stages of their life cycles and thus have different evolving patterns. Moreover, the instances in collective evolution inference problems are usually connected through heterogeneous information networks, which involve complex relationships among the instances interconnected by multiple types of links. We propose an approach, called NetCycle, by incorporating information from both the correlation among related instances and their life cycles. We compared our approach with existing methods of collective inference and time series analysis on two real-world networks. The results demonstrate that our proposed approach can improve the inference performance by considering the autocorrelation through networks and the life cycles of the instances.
The optimal operation of energy storage in a wind power curtailment scheme

Generator curtailment allows Distribution Network Operators to increase the maximum capacity of distributed renewable generation connections to their networks, but curtailment means lost revenue for generators. Energy Storage Systems (ESS) can mitigate curtailment by time-shifting generation away from congested periods and can combine this with other tasks. This paper develops a linear-programming optimization to maximize the revenue generated by an ESS connected to a wind farm in a curtailment scheme. The storage is used for curtailment reduction and price-arbitrage in an external market. A case study is developed and the optimization applied for storage devices with a range of efficiencies and penetrations. The effect of storage efficiency on revenue is shown to be stronger in price arbitrage than in generation-curtailment. An economic analysis is carried out for a Sodium Sulphur battery store and it is clear that, at current costs, more valuable revenue streams are required to achieve economic viability.
Automatic Evaluation of Computer Generated Text : Final Report on the TextEval Project

The report describes computational and psychological experiments carried out under grant number GR/H29179 from the Science and Engineering Research Council of Great Britain. These would not have been possible without the gracious assistance of Ian Mason of Heriot Watt University, Edinburgh. We aimed to assess the usefulness of a new technique for the evaluation of translation quality, comparing human rankings with automatic measures. The basis of our approach is the use of a standard set and the adoption of a statistical view of translation quality. This approach has the ability to provide evaluations which avoid dependence on any particular theory of translation, which are therefore potentially more objective than previous techniques.
Agent fitness functions for evolving coordinated sensor networks

Distributed sensor networks are an attractive area for research in agent systems. This is due primarily to the level of information available in applications where sensing technology has improved dramatically. These include energy systems and area coverage where it is desirable for sensor networks to have the ability to self-organize and be robust to changes in network structure. The challenges presented when investigating distributed sensor networks for such applications include the need for small sensor packages that are still capable of making good decisions to cover areas where multiple types of information may be present. For example in energy systems, singular areas in power plants may produce several types of valuable information, such as temperature, pressure, or chemical indicators. The approach of the work presented in this paper provides agent fitness functions for use with a neuro-evolutionary algorithm to address some of these challenges. In particular, we show that for self-organization and robustness to network changes, it is more advantageous to evolve individual policies, rather than a shared policy that all sensor units utilize. Further, we show that using a difference objective approach to the decomposition of system-level fitness functions provides a better target for evolving these individual policies. This is because the difference evaluation for fitness provides a cleaner signal, while maintaining vital information from the system level that implicitly promotes coordination among individual sensor units in the network.
Prevalence of depressive symptoms in school aged children with type 1 diabetes - a questionnaire study.

UNLABELLED AIM : Current studies show that diabetic patients are at greater risk of developing psychiatric disorders than general population. The aim of this study was to evaluate the frequency of depressive symptoms in school-aged children with type 1 diabetes (T1D). METHODS The study involved 477 children with T1D, with mean age of 13.1 ± 2.7 years and mean diabetes duration of 5.0 ± 3.5 years, treated for at least one year. Patients were asked to fill out the Polish version of the Children's Depression Inventory (CDI) and Quality of Life Questionnaire. Demographic data such as height, weight, diabetes duration, daily insulin dose (TDD) was also collected. RESULTS 17% (81/477) of all participants presented depressive symptoms (CDI . 13 scores), 20.9% of them were children . 12 years of age, and 8.1% of them were children at the age of . 12 years, p = 0.0005. Participants with CDI scores of 13 or higher were older (p = 0.002), had higher BMI (p = 0.029), TDD (p = 0.026) and lower quality of life (p < 0.0001) in comparison with children who scored < 13. There was no difference in glycated hemoglobin (HbA1c) values between groups with and without depressive symptoms (p = 0.249). However, there is a correlation between HbA1c value and CDI score (r = 0.16; p = 0.0002). CONCLUSIONS Depressive symptoms were observed in 1 out of 12 T1D children in a primary school and in 1 out of 5 teenagers. Depressive symptoms may affect metabolic control and quality of life. Therefore, early detection and treatment of depressive symptoms in T1D school children is needed.
Faster speed , better spatial resolution lead 3 T benefits New pulse sequences and strategies for cardiac imaging and spectroscopy must be developed

The FDA has approved 3T MR scanners for whole-body, including cardiovascular, imaging, and multinuclear spectroscopy at this field strength is pending approval. The three main vendors of clinical MR scanners-GE, Philips, and Siemens-all have clinical 3T whole-body platforms available. While 3T platforms have demonstrated a clear advantage over 1.5T and lower fields in many neuroimaging and neurospectroscopy applications, pulse sequences and strategies for cardiac imaging and spectroscopy at the higher field strength are under active development. Merely transporting sequences that were effective at lower fields will not be sufficient. Rather, pulseand gradient-sequence design and implementation must take into account the characteristics and physics of the higher field. Signal to noise improves at higher field, due to the increased polarization of the nuclear spins present in the magnetic field, and the transverse relaxation rates are increased. Unfortunately, motion, flow, and susceptibility effects also increase; these problems are intrinsic to cardiac imaging and spectroscopy. The interactions of the radio-frequency wavelengths that are required and the impact of the magnetohydrodynamic effects on electrocardiogram triggering, which has not been important at 1.5T, may need to be addressed. Standing waves due to body dielectric resonances pose new challenges for the design of receiver coils. And increased T1 relaxation times of blood, myocardium, and epicardial fat must be considered with black blood sequences or in fat-saturation schemes. Despite the difficulties inherent in imaging at 3T, there are significant benefits. The higher field (greater than or equal to3T) scanners will improve a number of areas related to clinical cardiac studies: spatial resolution, fast imaging speed, myocardial tagging techniques, perfusion imaging, development of cardiac blood oxygen level-dependent (BOLD) imaging, MR-guided catheter placement, and cardiac spectroscopy.
In vitro and in vivo secondary structure probing of the thrS leader in Bacillus subtilis.

The Bacillus subtilis thrS gene is a member of the T-box gene family in Gram-positive organisms whose expression is regulated by a tRNA-mediated transcriptional antitermination mechanism involving a direct tRNA:mRNA interaction. The complex leader sequences of these genes share only short stretches of primary sequence homology, but a common secondary structure has been proposed by comparing the leaders of many genes of this family. The proposed mechanism forthe tRNA:mRNA interaction depends heavily on the secondary structure model, but is so far only supported by genetic evidence. We have studied the structure of the B.subtilis thrS leader in solution, in protection experiments using both chemical and enzymatic probes. The thrS leader structure was also probed in vivo using dimethylsulphate and the in vitro and in vivo data are in good accordance. We have organized the thrS leader into three major domains comprising six separate stem-loops. All but one of the short sequences conserved in this gene family are present in loop structures. The ACC specifier codon proposed to interact with the tRNAThrGGUisoacceptor is present in a bulge and probably exists in a stacking conformation. The proposed antiterminator structure is not visible in transcripts containing the terminator, but was probed using a transcript with the 3'-half of the terminator deleted and its folding appears consistent with the regulatory model. The leader sequences, and in particular the specifier domains, of the other genes of this family can be folded similarly to the experimentally solved thrS structure.
The Effect of Social Screening on Bond Mutual Fund Performance

This study measures the financial impact of screening for environmental, social and governance criteria on corporate bond portfolios. Specifically, the risk-adjusted financial performance of 103 socially responsible bond funds in the US and the Eurozone is compared with a matched sample of conventional funds. During the period 2001–2014, socially responsible bond funds outperform by one-half of one percent annually. An evaluation of fund holdings and a performance-attribution analysis suggest that this outperformance is directly related to the mitigation of ESG risks, which is achieved by the exclusion of corporate bond issuers with poor corporate social responsibility activities. A separation of crisis and non-crisis periods further indicates that the outperformance is especially likely to occur during recessions or bear market periods. We confirm this crisis-related return effect from a sample of socially screened bond indices. Moreover, our results are robust to alternative definitions of sustainability, survivorship bias, fund characteristics and stable in the US and Eurozone sub-samples.
Code reuse for improving software productivity and quality in AOP

Aspect oriented programming languages support the principle of modularity and abstraction by allowing explicit separation between aspects and system core concerns. However, using these tools does not necessarily imply loose coupling as aspect definitions may maintain explicit visibility over application components. In this paper we look at the problem of the binding of aspect definitions to application code and propose a framework that deploys reflection in order to decrease the level of coupling between them, thereby increasing the level of reusability of aspect definitions.
Chasing the Clouds Without Getting Drenched: A Call for Fair Practices in Cloud Computing Services

This article is one of the first to provide a comprehensive analysis of the dangers of privacy and security in the context of cloud computing. In order to engage both an academic and practical audience, this article promotes core privacy principles and strategic business directions in cloud adoptions with the goal of fostering a consensus in legislative, regulatory, and international Internet privacy policy. The article opens by providing a detailed and technical description of various cloud computing models that have been put into practice. The inherent risks of cloud computing are then examined, and the current factors setting the stage for the perfect storm to erupt if uniform regulatory privacy action does not take place.
Antibiotic Susceptibility Profiles of Mycoplasma Hominis and Ureaplasma Urealyticum Isolated During a Population-Based Study Concerning Women Infertility in Northeast Romania

The study was carried out on 1068 infertile women under initial evaluation. For Mycoplasma hominis, the highest resistance rates were registered for ciprofloxacin (72.22%), followed by macrolides and ofloxacin. For Ureaplasma urealyticum, the ciprofloxacin resistance was also high (51.72%), while the resistance rates to other tested antibiotics were significantly lower.
GLRT-based detection-estimation of Gaussian signals in under-sampled training conditions

A likelihood ratio test has recently been developed for detection-estimation in under-sampled scenarios where the number of training data T is less than the number of antenna elements M. This test can be applied in a GLRT detection-estimation framework to many problems not satisfactorily addressed by conventional techniques. In particular, we consider under-sampled MUSIC “performance breakdown” phenomenon for independent sources, and use the under-sampled likelihood ratio to detect the presence of MUSIC outliers.
Cooperative Tracking with Mobile Robots and Networked Embedded Sensors

We study the target tracking problem using multiple, environment-embedded, stationary sensors and mobile robots. The stationary sensors and robots maintain region-based density estimates which are used to guide the robots to parts of the environment where unobserved targets may be present. Experiments in simulation show that the region-based approach works better than a ‘naive’ target following approach when the number of targets in the environment is high. We present real-robot experiments which support the results from the simulation study.