Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Artificial Neural Networks for Maximal Power Point Tracking

In this paper, a hybrid controller of maximum power point tracking of photovoltaic systems based on the artificial neuron network has been proposed and studied. The data needed for model generation are obtained from the series of measurements. The training of neural networks requires two modes: the off-line mode to get optimal structure, activation function and learning algorithm of neural networks and in an online way these optimal neural networks are used in the PV system. The hybrid model is made up of two neural networks; the first network has two inputs and two outputs; the solar irradiation and the ambient temperature are the inputs; the outputs are the references voltage and current at the maximal power point. The second network has two inputs and one output; the inputs use the outputs of the first network, and the output will be the periodic cycle which controls the DC/DC converter. The performance of the method is analyzed under the different conditions of climatic variation using the MATLAB/Simulink simulation tool. A we compared our proposed method to the perturbation and observation approach, in terms of tracking accuracy, steady state ripple and response time.
Magnesium Sulfate Effect on Fetal Umbilical Artery and Middle Cerebral Artery Doppler Indicies in Women with Severe Preeclampsia and Eclampsia

Objective: To study MgSO4 effect on fetal MCA and UA blood flow changes using Doppler ultrasound in cases of severe PET and eclampsia. Materials and Methods: A total of 40 patients with severe PET admitted to El-Shatby Maternity University Hospital, Alexandria (Egypt) were examined before and after administration of MgSO4 using Doppler study to measure fetal MCA and UA blood flow changes. Results: After administration of MgSO4, the mean RI of UA, PI of UA showed a statistically significant decrease (P The C/U (cerebroumblical) ratio increased after the treatment (P Conclusions: Infusion of MgSO4 significantly decreases the fetal RI, PI, SDR umbilical and increases the fetal RI, PI, SDR MCA and increases cerebroumblical ratio indices obtained by Doppler examinations.
Türkiye’de Küreselleşme ve Enerji Tüketimi Arasındaki İlişki: Frekans Dağılımı Nedensellik Analizi

Kuresellesme sureci olumlu ve olumsuz yonleriyle gecmisten bugune tartisilan konularin basinda gelmektedir. Ozellikle teknolojik gelismeler ve dunya ticaretindeki gelismeler ile birlikte ulkeler arasindaki sinirlar ortadan kalkmistir. Ulkelerin en temel hedefi toplumsal refahi artirmaktir. Ekonomik buyume hedefini gerceklestirebilmek icin enerji girdisinin temini cok onemlidir. Enerji kaynaklarinin fosil enerjiden yenilenebilir enerjiye donusumu, surdurulebilir enerji arzi acisindan onemli bir gerekliliktir. Ulkeler arasindaki ekonomik, sosyal ve politik iliskilerin gelismis olmasi, enerji tuketiminin fosil enerjiden yenilenebilir kaynaklara donusumu ve seyri acisindan kritik bir oneme sahiptir. Bu calismanin amaci, Turkiye’de ekonomik, sosyal ve politik kuresellesme ile enerji tuketimi iliskisini ampirik olarak arastirmaktir. Soz konusu arastirma icin 1970-2017 arasi doneme ait yillik verilerle zaman serisi yontemi kullanilmistir. Elde edilen ampirik bulgular, degiskenler arasinda uzun donemli iliskinin oldugunu gostermektedir. Degiskenlere ait uzun donem katsayilari ise istatistiki olarak anlamsizdir. Yapilan nedensellik testlerinde ise Hacker ve Hatemi-J testinde herhangi bir nedensellik yokken, Breitung ve Candelon (2006) frekans dagilimi testinde uzun, orta ve kisa vadede nedensellik iliskileri tespit edilmistir. Sosyal ve politik kuresellesme ve ekonomik buyume ile enerji tuketimi arasinda iliski oldugu sonucuna ulasilmistir.
The Co-Evolution of Trust and Coordination in Global Software Development Teams-An Extensible Evolutionary Game Theory Model

Trust is important for effective coordination in global software development teams. However, the co-evolution of trust and coordination is often neglected. To ﬁll the gap, we develop an evolutionary game theory model. Using the Behavior-Preference-Constraint (BPC) model and Adaptive Play, the model challenges the traditional view of trust as a static “resource” for coordination and proposes an alternative view that trust dynamically restricts people’s action choices in interacting with other team members. Through analyzing the model, we describe how trust and coordination co-evolve in the progress of interactions among team members. We propose three propositions summarizing the long-term characteristics of coordination and trust in the process. For example, the co-existence of low trust and high trust can be a stable state in the long run, which explains why low trust can always exist even when all team members strictly prefer effective coordination.
Contractual Dimensions and Buyer-Supplier Perceived Risk: the Moderating Role of Information Technology Integration

The contract is widely conceived as an effective approach to decrease risk perception and resolve disputes. However, previous literature on the link between contracts and risk perception is contradictory, wherein both positive and negative findings exist. This study provides a novel insight about the mechanism of contractual dimensions and considers the boundary conditions of information technology (IT) integration. Data collected from 225 retailers of a manufacturer support most hypotheses. Specifically, contractual complexity has a positive influence on relational risk, and contractual recurrence has a negative influence on both performance and relational risk. This study further reveals the positive moderating effect of IT integration on the influence of contractual complexity on relational risk and the negative effect of IT integration on the influence of contractual recurrence on relational risk and performance risk. Implications and limitations of the study are discussed.
A Hybrid Compositional Approach to Optimal Mission Planning for Multi-rotor UAVs using Metric Temporal Logic

This paper investigates a hybrid compositional approach to optimal mission planning for multi-rotor Unmanned Aerial Vehicles (UAVs). We consider a time critical search and rescue scenario with two quadrotors in a constrained environment. Metric Temporal Logic (MTL) is used to formally describe the task specifications. In order to capture the various modes of UAV operation, we utilize a hybrid model for the system with linearized dynamics around different operating points. We divide the mission into several sub-tasks by exploiting the invariant nature of various task specifications i.e., the mutual independence of safety and timing constraints along the way, and the different modes (i,e., dynamics) of the robot. For each sub-task, we translate the MTL formulae into linear constraints, and solve the associated optimal control problem for desired path using a Mixed Integer Linear Program (MILP) solver. The complete path is constructed by the composition of individual optimal sub-paths. We show that the resulting trajectory satisfies the task specifications, and the proposed approach leads to significant reduction in computational complexity of the problem, making it possible to implement in real-time.
Development of Simple-To-Use Predictive Models to Determine Thermal Properties of Fe2O3/Water-Ethylene Glycol Nanofluid

Thermophysical properties of nanofluids play a key role in their heat transfer capability and can be significantly affected by several factors, such as temperature and concentration of nanoparticles. Developing practical and simple-to-use predictive models to accurately determine these properties can be advantageous when numerous dependent variables are involved in controlling the thermal behavior of nanofluids. Artificial neural networks are reliable approaches which recently have gained increasing prominence and are widely used in different applications for predicting and modeling various systems. In the present study, two novel approaches, Genetic Algorithm-Least Square Support Vector Machine (GA-LSSVM) and Particle Swarm Optimization- artificial neural networks (PSO-ANN), are applied to model the thermal conductivity and dynamic viscosity of Fe2O3/EG-water by considering concentration, temperature, and the mass ratio of EG/water as the input variables. Obtained results from the models indicate that GA-LSSVM approach is more accurate in predicting the thermophysical properties. The maximum relative deviation by applying GA-LSSVM was found to be approximately ±5% for the thermal conductivity and dynamic viscosity of the nanofluid. In addition, it was observed that the mass ratio of EG/water has the most significant impact on these properties.
Bayesian 3D Reconstruction of Complex Scenes from Single-Photon Lidar Data

Light detection and ranging (Lidar) data can be used to capture the depth and intensity profile of a 3D scene. This modality relies on constructing, for each pixel, a histogram of time delays between emitted light pulses and detected photon arrivals. In a general setting, more than one surface can be observed in a single pixel. The problem of estimating the number of surfaces, their reflectivity, and position becomes very challenging in the low-photon regime (which equates to short acquisition times) or relatively high background levels (i.e., strong ambient illumination). This paper presents a new approach to 3D reconstruction using single-photon, single-wavelength Lidar data, which is capable of identifying multiple surfaces in each pixel. Adopting a Bayesian approach, the 3D structure to be recovered is modelled as a marked point process, and reversible jump Markov chain Monte Carlo (RJ-MCMC) moves are proposed to sample the posterior distribution of interest. In order to promote spatial correlation between points belonging to the same surface, we propose a prior that combines an area interaction process and a Strauss process. New RJ-MCMC dilation and erosion updates are presented to achieve an efficient exploration of the configuration space. To further reduce the computational load, we adopt a multiresolution approach, processing the data from a coarse to the finest scale. The experiments performed with synthetic and real data show that the algorithm obtains better reconstructions than other recently published optimization algorithms for lower execution times.
Dispersion relations of periodic quantum graphs associated with Archimedean tilings (I)

There are 11 kinds of Archimedean tilings for the plane in total. Applying the Floquet–Bloch theory, we derive the dispersion relations of the periodic quantum graphs associated with a number of Archimedean tilings, namely the triangular tiling (36), the elongated triangular tiling , the trihexagonal tiling and the truncated square tiling (4,82). The derivation makes use of characteristic functions, with the help of the symbolic software Mathematica. The resulting dispersion relations are surprisingly simple and symmetric. They show that in each case the spectrum is composed of point spectrum and an absolutely continuous spectrum. We further analyzed the structure of absolutely continuous spectra. Our work is motivated by the studies on the periodic quantum graphs associated with hexagonal tiling in Kuchment and Post (2007 Commun. Math. Phys. 275 805–26) and Korotyaev and Lobanov (2007 Ann. Henri Poincaré 8 1151–76).
Towards 3D Human Shape Recovery Under Clothing

We present a learning-based scheme for robustly and accurately estimating clothing fitness as well as the human shape on clothed 3D human scans. Our approach maps the clothed human geometry to a geometry image that we call clothed-GI. To align clothed-GI under different clothing, we extend the parametric human model and employ skeleton detection and warping for reliable alignment. For each pixel on the clothed-GI, we extract a feature vector including color/texture, position, normal, etc. and train a modified conditional GAN network for per-pixel fitness prediction using a comprehensive 3D clothing. Our technique significantly improves the accuracy of human shape prediction, especially under loose and fitted clothing. We further demonstrate using our results for human/clothing segmentation and virtual clothes fitting at a high visual realism.
Empirical identification of skills gaps between chief information officer supply and demand: a resource-based view using machine learning

PurposeThe role of emerging digital technologies is of growing strategic importance as it provides significant competitive advantage to organisations. The chief information officer (CIO) plays a pivotal role in facilitating the process of digital transformation. Whilst demand continues to increase, the supply of suitably qualified applicants is lacking, with many companies forced to choose information technology (IT) or marketing specialists instead. This research seeks to analyse the organisational capabilities required and the level of fit within the industry between CIO requirements and appointments via the resource-based view.Design/methodology/approachJob postings and CIO curriculum vitae were collected and analysed through the lens of organisational capability theory using the machine learning method of Latent Dirichlet Allocation (LDA). FindingsThis research identifies gaps between the capabilities demanded by organisations and supplied by CIOs. In particular, soft, general, non-specific capabilities are over-supplied, while rarer specific skills, qualifications and experience are under-supplied.Practical implicationsThe research is useful for practitioners (e.g. potential CIO candidates) to understand current market requirements and for companies aiming to develop internal training that meet present and future skill gaps. It also could be useful for professional organisations (e.g. CIO Forum) to validate the need to develop mentoring schemes that help meet such high demand and relative undersupply of qualified CIOs.Originality/valueBy applying LDA, the paper provides a new research method and process for identifying competence requirements and gaps as well as ascertaining job fit. This approach may be helpful to other domains of research in the process of identifying specific competences required by organisations for particular roles as well as to understand the level of fit between such requirements and a potential pool of applicants. Further, the study provides unique insight into the current supply and demand for the role of CIO through the lens of resource-based view (RBV). This provides a contribution to the stream of information systems (IS) research focused on understanding CIO archetypes and how individual capabilities provide value to companies.
ENUMERATING NECKLACES WITH TRANSITIONS

Abstract Necklaces are the equivalence classes of words under the action of the cyclic group. Let a transition in a word be any change between two adjacent letters modulo the word’s length. We present a closed-form solution for the enumeration of necklaces in n beads, k colours and t transitions. We show that our result provides a more general solution to the problem of counting alternating (proper) colourings of the vertices of a regular n-gon.
Reconstruction of Mimetic Gravity in a Non-Singular Bouncing Universe from Quantum Gravity

We illustrate a general reconstruction procedure for mimetic gravity. Focusing on a bouncing cosmological background, we derive general properties that must be satisfied by the function f(□ϕ) implementing the limiting curvature hypothesis. We show how relevant physical information can be extracted from power-law expansions of f in different regimes, corresponding e.g., to the very early universe or to late times. Our results are then applied to two specific models reproducing the cosmological background dynamics obtained in group field theory and in loop quantum cosmology, and we discuss the possibility of using this framework as providing an effective field theory description of quantum gravity. We study the evolution of anisotropies near the bounce, and discuss instabilities of scalar perturbations. Furthermore, we provide two equivalent formulations of mimetic gravity: one in terms of an effective fluid with exotic properties, the other featuring two distinct time-varying gravitational “constants” in the cosmological equations.
Meta-Learning Acquisition Functions for Bayesian Optimization

Many practical applications of machine learning, such as tuning hyperparameters or process settings, rely on data-efficient black-box function optimization. Readily available algorithms are typically designed to be universal optimizers and, thus, often suboptimal for specific tasks. We propose a method to meta-learn customized optimizers within the well-established framework of Bayesian optimization (BO), allowing our algorithm to utilize the proven generalization capabilities of Gaussian processes. Indeed, only the acquisition function (AF) is replaced by a learned neural network, while retaining the standard BO optimization loop. The approach is evaluated on several simulated tasks, two hyperparameter search problems, as well as a sim-to-real transfer experiment. The results show that the learned optimizers (1) perform better than or on-par with classical AFs on general function classes, (2) automatically identify structural properties of a function class on available source tasks or cheap simulations and (3) are able to transfer this knowledge to new problems, thereby significantly outperforming existing hand-engineered AFs.
An unbiased approach to compressed sensing

In compressed sensing a sparse vector is approximately retrieved from an under-determined equation system Ax = b. Exact retrieval would mean solving a large combinatorial problem which is well known to be NP-hard. For b of the form Ax 0 + ϵ, where x 0 is the ground truth and ϵ is noise, the ‘oracle solution’ is the one you get if you a priori know the support of x 0, and is the best solution one could hope for. We provide a non-convex functional whose global minimum is the oracle solution, with the property that any other local minimizer necessarily has high cardinality. We provide estimates of the type ‖x̂−x0‖2⩽C‖ϵ‖2 with constants C that are significantly lower than for competing methods or theorems, and our theory relies on soft assumptions on the matrix A, in comparison with standard results in the field. The framework also allows to incorporate a priori information on the cardinality of the sought vector. In this case we show that despite being non-convex, our cost functional has no spurious local minima and the global minima is again the oracle solution, thereby providing the first method which is guaranteed to find this point for reasonable levels of noise, without resorting to combinatorial methods.
SN 2017czd: A Rapidly Evolving Supernova from a Weak Explosion of a Type IIb Supernova Progenitor

We present optical and near-infrared observations of the rapidly evolving supernova (SN) 2017czd that shows hydrogen features. The optical light curves exhibit a short plateau phase (∼13 days in the R-band) followed by a rapid decline by 4.5 mag ∼20 days after the plateau. The decline rate is larger than those of any standard SNe, and close to those of rapidly evolving transients. The peak absolute magnitude is −16.8 mag in the V band, which is within the observed range for SNe IIP and rapidly evolving transients. The spectra of SN 2017czd clearly show the hydrogen features and resemble those of SNe IIP at first. The Hα line, however, does not evolve much with time, and it becomes similar to those in SNe IIb at the decline phase. We calculate the synthetic light curves using a SN IIb progenitor that has 16 at the zero-age main sequence and evolves into a binary system. The model with a low explosion energy (5 × 1050 erg) and a low 56Ni mass ( ) can reproduce the short plateau phase, as well as the sudden drop of the light curve, as observed in SN 2017czd. We conclude that SN 2017czd might be the first weak explosion identified from a SN IIb progenitor. We suggest that some rapidly evolving transients can be explained by such a weak progenitor explosion with a barely hydrogen-rich envelope.
The Dynamics of Quasi-Stationary Atmospheric Rivers and Their Implications for Monsoon Onset

A global Hovmöller diagram of column water vapor (CWV) at 30°N from daily ERA-Interim reanalysis data shows seasonally migrating North Pacific/Atlantic quasi-stationary atmospheric rivers (QSARs) located in the Eastern Pacific/Atlantic in winter and propagate to the Western Pacific/Atlantic in summer. Simplified general circulation model (GCM) experiments produce QSAR-like features if the boundary conditions include (1) the sea surface temperature contrast from the tropical warm pool-cold tongue and (2) topographic contrast similar to the Tibetan plateau. Simulated QSARs form downstream of topographic contrast during winter and coincide with it in summer. Two models of baroclinic instability demonstrate that QSARs coincide with the location where the most unstable mode phase speed equals that of the upper-level zonal winds. A consistent interpretation is that the waves become quasi-stationary at this location and break. The location of quasistationarity migrates from the Eastern Pacific/Atlantic in the winter, when upper-level winds are strong and extended over the basin, to the Western Pacific/Atlantic when winds are weak and contracted. Low-level wind convergence and moist static energy coincide with QSARs, and since the former two are essential ingredients to monsoon formation, this implies an important role for QSARs in monsoon onset. This connection opens a new window into the dynamics of subtropical monsoon extensions.
Trophic niche changes associated with the eradication of invasive mammals in an insular lizard: an assessment using isotopes

Abstract Invasive species are a major threat to island biodiversity, and their eradications have substantially contributed to the conservation of island endemics. However, the consequences of eradications on the trophic ecology of native taxa are largely unexplored. Here, we used the eradication of invasive black rats Rattus rattus and European rabbits Oryctolagus cuniculus from the Berlenga Island, in the western coast of Portugal, as a whole-ecosystem experiment to investigate the effects of the eradication of invasive mammals on the trophic niche and body dimensions of the island-restricted Berlenga wall lizard Podarcis carbonelli berlengensis over a 2-year period. Our results suggest an expansion of the isotopic niche and an intensification of the sexual dimorphism of the lizard following mammal eradication. Additionally, we found considerable variability in isotopic niche across the island and detected evidence of sex-specific and season-modulated nutritional requirements of this threatened reptile. Our findings support that the eradication of 2 of the planet’s most problematic invasive vertebrates led to changes in the lizard trophic niche and sexual dimorphism in just 2 years. This suggests that the ecological pressures—for example, prey availability and habitat structure—to which lizards are exposed have substantially changed post-eradication. Our study emphasizes the scientific value of island eradications as experiments to address a wide range of ecological questions and adds to the increasing body of evidence supporting substantial conservation gains associated with these restoration interventions.