Technical Debt (TD) is a successful metaphor in conveying the consequences of software inefficiencies and their elimination to both technical and non-technical stakeholders, primarily due to its monetary nature. The identification and quantification of TD rely heavily on the use of a small handful of sophisticated tools that check for violations of certain predefined rules, usually through static analysis. Different tools result in divergent TD estimates calling into question the reliability of findings derived by a single tool. To alleviate this issue we use 18 metrics pertaining to source code, repository activity, issue tracking, refactorings, duplication and commenting rates of each class as features for statistical and Machine Learning models, so as to classify them as High-TD or not. As a benchmark we exploit 18,857 classes obtained from 25 Java projects, whose high levels of TD has been confirmed by three leading tools. The findings indicate that it is feasible to identify TD issues with sufficient accuracy and reasonable effort: a subset of superior classifiers achieved an F 2-measure score of approximately 0.79 with an associated Module Inspection ratio of approximately 0.10. Based on the results a tool prototype for automatically assessing the TD of Java projects has been implemented.
In this chapter, the construction of an efficient method for a fast and complete scanning of a given area is presented. An additional aspect is the insertion of FitzHugh–Nagumo chaotic system in order to create random-like motions. Then, a modulo operator was used in the production of the necessary motion commands. For further improvement of the produced results, it is decided to use the feature of memory, which will be helpful to avoid visiting same areas. Numerical simulations have been performed for evaluating the new method. It is shown that for both cases of robot’s moving in four and eight directions, the new method has presented an increased coverage rate. Moreover, it managed to reduce multiple visits in same areas in comparison to memory-free method.
The quest toward the development of green analytical methods includes redefining sample preparation and analytical extraction in a green perspective. This chapter provides an overview of sample preparation methodologies and how they comply with the principles of Green Analytical Chemistry and Green Sample Preparation. Metrics to evaluate the greenness of analytical methods are discussed and applied to various sample preparation techniques.
In this paper, a new chaotic memristor system is developed based on a well-known nonlinear circuit. By replacing the nonlinear positive conductance of Shinriki's circuit with a memristor emulator, the new simple chaotic circuit can reduce the number of active elements and reduce power consumption. An analysis of equilibria and stability, dissipative properties, Lyapunov exponents, Kaplan-Yorke dimensions, and bifurcation diagrams is presented as well as numerical simulation of the new memristor chaotic system. As a final step, the electronic circuit is designed using Multisim software, which is complemented by a physical realization to demonstrate the feasibility of the chaotic system.
Backround Astrovirus, Norovirus and Sapovirus exhibit a wide distribution in swine pig herds worldwide. However, the association of porcine Astrovirus (PAstV), porcine Norovirus (PoNoV) and porcine Sapovirus (PoSaV) with disease in pigs remains uncertain. In this study, we investigated the prevalence of PAstV, PoNoV and PoSaV in Greek pig farms using both conventional RT-PCR and SYBR-Green Real-time RT-PCR in an effort to compare the sensitivity of the two methods. We examined 1400 stool samples of asymptomatic pigs originating from 28 swine farms throughout Greece in pools of five. Results PAstV was detected in all 28 swine farms examined, with an overall prevalence of 267/280 positive pools (95.4%). Porcine Caliciviruses prevalence was found at 36 and 57 out of the 280 examined samples, by the conventional and SYBR-Green Real time RT-PCR, respectively. Sequencing and phylogenetic analysis of the positive samples revealed that the detected PAstV sequences are clustered within PAstV1, 3 and 4 lineages, with PAstV3 being the predominant haplotype (91.2%). Interestingly, sequencing of the Calicivirus positive samples demonstrated the presence of non-target viruses, i.e. Sapovirus, Kobuvirus and Sapelovirus sequences and one sequence highly similar to bat Astrovirus, while no Norovirus sequence was detected. Conclusions The high prevalence of PAstV in Greek pig farms poses a necessity for further investigation of the pathogenicity of this virus and its inclusion in surveillance programs in case that it proves to be important. To our knowledge, this is the first epidemiological study of these viruses in pig farms in Greece.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
Background Mucopolysaccharidoses (MPS) are a group of lysosomal storage disorders caused by defects in genes coding for different lysosomal enzymes which degrade glycosaminoglycans. Impaired lysosomal degradation causes cell dysfunction leading to progressive multiorgan involvement, disabling consequences and poor life expectancy. Enzyme replacement therapy (ERT) is now available for most MPS types, offering beneficial effects on disease progression and improving quality of life of patients. The landscape of MPS in Europe is not completely described and studies on availability of treatment show that ERT is not adequately implemented, particularly in Southern and Eastern Europe. In this study we performed a survey analysis in main specialist centers in Southern and Eastern European countries, to outline the picture of disease management in the region and understand ERT implementation. Since the considerable number of MPS IVA patients in the region, particularly adults, the study mainly focused on MPS IVA management and treatment. Results 19 experts from 14 Southern and Eastern European countries in total responded to the survey. Results outlined a picture of MPS management in the region, with a high number of MPS patients managed in the centers and a high level of care. MPS II was the most prevalent followed by MPS IVA, with a particular high number of adult patients. The study particularly focused on management and treatment of MPS IVA patients. Adherence to current European Guidelines for follow-up of MPS IVA patients is generally adequate, although some important assessments are reported as difficult due to the lack of MPS skilled specialists. Availability of ERT in Southern and Eastern European countries is generally in line with other European regions, even though regulatory, organizational and reimbursement constrains are demanding. Conclusions The landscape of MPS in Southern and Eastern European countries is generally comparable to that of other European regions, regarding epidemiology, treatment accessibility and follow up difficulties. However, issues limiting ERT availability and reimbursement should be simplified, to start treatment as early as possible and make it available for more patients. Besides, educational programs dedicated to specialists should be implemented, particularly for pediatricians, clinical geneticists, surgeons, anesthesiologists and neurologists.
This work describes a novel end-to-end data ingestion and runtime processing pipeline, which is a core part of a technical solution aiming to monitor frailty indices of patients during and after treatment and improve their quality of life. The focus of this work is on the technical architectural details and the functionalities provided, which have been developed in a manner that are extensible, scalable and fault-tolerant by design. Extensibility refers to both data sources and the exact specification of analysis techniques. Our platform can combine data not only from multiple sensor types but also from electronic health records. Also, the analysis component can process the patient data both individually and in combination with other patients, while exploiting both cloud and edge resources. We have shown concrete examples of advanced analytics and evaluated the scalability of the system, which has been fully prototyped.
The Laser Interferometer Space Antenna (LISA) has the potential to reveal wonders about the fundamental theory of nature at play in the extreme gravity regime, where the gravitational interaction is both strong and dynamical. In this white paper, the Fundamental Physics Working Group of the LISA Consortium summarizes the current topics in fundamental physics where LISA observations of gravitational waves can be expected to provide key input. We provide the briefest of reviews to then delineate avenues for future research directions and to discuss connections between this working group, other working groups and the consortium work package teams. These connections must be developed for LISA to live up to its science potential in these areas.
The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism , consisting of a semantic descriptor that relies on Natural Language Processing (NLP) algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study.
This paper investigates the application of Line Surge Arresters (LSAs) to 150 kV double-circuit overhead lines through ATP-EMTP simulations. The 150 kV lines under study are the backbone of the autonomous electrical system of Rhodes island in southeastern Aegean Sea, Greece. Hence, lightning-related insulation flashovers causing outages of these lines may lead to a blackout of this isolated system. In this work, both Non-Gapped Line Arresters (NGLAs) and Externally Gapped Line Arresters (EGLAs) are evaluated. Two installation configurations with 3 and 6 LSAs per tower are considered protecting one circuit and both circuits of the line, respectively. Simulations were performed for both lightning strikes to phase conductors and tower; these may cause shielding failure flashover and backflashover, respectively. The conducted current through the LSAs and the voltage at their terminals are computed, enabling the estimation of the energy stressing the LSAs. The effects of the lightning current time to half value, the phase angle of the operating voltage, and the power frequency ground resistance of towers are assessed. The required LSA energy absorption capability is determined. The lightning performance of a critical overhead line of the Rhodes 150 kV system is estimated and its improvement due to LSA installation is quantified.
Loess is a kind of world-recognized problematic soil and, as a consequence, it is a major hazard in geotechnical engineering. This study examines the small to medium strain dynamic properties of Lanzhou intact and recompacted loess through a set of resonant column (RC) tests. The influence of soil structure on the small-strain shear modulus G0, the variations of G/G0, and damping ratio DT(%) with shear strain γ were analyzed and discussed. The test results show that the structure has an important influence on the dynamic properties of dry loess at small and medium strain, whereas, with the increase of moisture, its effect weakens. It is proved that a normalized correlation between G/G0 and γ/γ0.7 is practically identical for undisturbed and recompacted loess in Lanzhou, irrespective of soil structure, which may be useful in practical applications.
This paper presents a Day-Ahead Local Flexibility Market in the Distribution Network cleared using a novel Successive Linear Programming algorithm for Optimal Power Flow in the Distribution Network. The proposed algorithm is shown to be robust and produce high quality, AC feasible solutions with satisfactory solution speed. The efficiency of the proposed algorithm is validated via its application in various distribution networks and under different loading scenarios. The proposed Flexibility Market encompasses flexibility products for upward and downward active energy as well as service products for the provision of reactive power. The efficiency of the proposed Flexibility Market in solving operational issues of the distribution network is demonstrated via a case-study.
The advent of distributed renewable energy sources (DRESs) has led to a series of technical issues affecting the secure and reliable operation of active distribution networks. Among them, under-/overvoltages, current overload, and voltage unbalance can be considered as the most important problems limiting the increase of DRES penetration. In this paper, a new control architecture is proposed to overcome these issues using the reactive power of DRESs and the active/reactive power of distributed battery energy storage systems (DBESSs). Its distinct feature is the implementation in the symmetrical components domain, allowing the efficient decoupling between under-/overvoltage and voltage unbalance mitigation techniques. Furthermore, a central controller is introduced to improve the system performance in terms of reduced network losses and effective DBESS utilization by coordinating the response of DRESs and DBESSs. The validity of the proposed control strategy is evaluated by performing time-domain and time-series simulations on the IEEE European LV test feeder.
Transport infrastructure is the backbone of the economy and society, while at the same time is exposed to multiple hazards. Previous natural disasters, including earthquakes, had a significant impact on transport networks with severe consequences for the users and supply chain. In this context, the resilience assessment of critical assets such as tunnels is of paramount importance for increasing safety and maintaining their functionality in seismic-prone areas. This study presents a practical resilience assessment framework for tunnels subjected to earthquakes. The proposed framework combines fragility and restoration functions, for assessing the robustness of tunnels exposed to different seismic scenarios, and the rapidity of the recovery considering different damage levels. The framework is applied to circular tunnels in alluvial deposits. A life-cycle resilience index is estimated, and the effects of soil conditions, tunnel burial depths, construction quality, and aging of the tunnel lining, on the resilience quantifications are examined and assessed. This effort contributes to the resilience-based design and management of tunnels and underground transport networks, and hence, facilitates decision-making and efficient allocation of resources by consultants, operators, and stakeholders.
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