Miracle-dz/UniFAR-FTUs huggingface dataset:
Mapping photovoltaic performance with nanoscale resolution

Photo‐conductive AFM spectroscopy (‘pcAFMs’) is proposed as a high‐resolution approach for investigating nanostructured photovoltaics, uniquely providing nanoscale maps of photovoltaic (PV) performance parameters such as the short circuit current, open circuit voltage, maximum power, or fill factor. The method is demonstrated with a stack of 21 images acquired during in situ illumination of micropatterned polycrystalline CdTe/CdS, providing more than 42 000 I/V curves spatially separated by ~5 nm. For these CdTe/CdS microcells, the calculated photoconduction ranges from 0 to 700 picoSiemens (pS) upon illumination with ~1.6 suns, depending on location and biasing conditions. Mean short circuit currents of 2 pA, maximum powers of 0.5 pW, and fill factors of 30% are determined. The mean voltage at which the detected photocurrent is zero is determined to be 0.7 V. Significantly, enhancements and reductions in these more commonly macroscopic PV performance metrics are observed to correlate with certain grains and grain boundaries, and are confirmed to be independent of topography. These results demonstrate the benefits of nanoscale resolved PV functional measurements, reiterate the importance of microstructural control down to the nanoscale for 'PV devices, and provide a widely applicable new approach for directly investigating PV materials. Copyright © 2015 John Wiley & Sons, Ltd.
In-Silico Study on the Interaction of Saffron Ligands and Beta-Lactoglobulin by Molecular Dynamics and Molecular Docking Approach

ABSTRACT Safranal, crocetin, and dimethylcrocetin are secondary metabolites found in saffron and have a wide range of biological activities. An investigation of their interaction with a transport protein, such as β-lactoglobulin (β-lg), at the atomic level could be a valuable factor in controlling their transport to biological sites. The interaction of these ligands and β-lg as a transport protein was investigated using molecular docking and molecular dynamics (MD) simulation methods. The molecular docking results showed that safranal and crocetin bind on the surface of β-lg. However, dimethylcrocetin binds in the internal cavity of β-lg. The β-lg affinity for binding saffron ligands decreases in the following order: crocetin > dimethylcrocetin > safranal. The analysis of MD simulation trajectories showed that the β-lg and β-lg–ligand complexes became stable at approximately 3000 ps and that there was little conformational change in the β-lg–safranal and β-lg–dimethylcrocetin complexes over a 10-ns timescale. In addition, the profiles of atomic fluctuations showed the rigidity of the ligand binding site during the simulation time.
Embedding nuclear physics rules and fuel chemistry limits in control algorithms for a fuel-efficient nuclear reactor

In a nuclear reactor, the energy which is stored in the fuel is proportional to the amount of fissile material. The consumption of that amount, after a set burn-up, should and must result in delivering a set energy, barring leakages (losses). The loss of energy due to an inefficient control algorithm, after a set burn-up, is a problem that no one has yet addressed. Nuclear fuel chemists and metallurgists may have a given amount of fuel with a set energy content, but control system engineers, treating a nuclear reactor as a conventional PID (Proportional Integral Derivative) control system, use lots of control energy to stabilise the nuclear reactor power and also thereby consume 'unnecessary' fuel energy. The aim of this paper is to draw attention to basic neutronic behaviour of a nuclear reactor and to control it in its natural physics instead of treating it as a conventional PID.
Green Diesel: A Second Generation Biofuel

Environmentally-conscious design of processes and products is increasingly viewed as an important strategy in the sustainable development of new refining and chemical processes. This paper discusses a new process technology developed by UOP and Eni S.p.A; the UOP/Eni EcofiningTM process to produce green diesel from vegetable oil. This novel process utilizes catalytic saturation, hydrodeoxygenation, decarboxylation and hydroisomerization reactions to produce an isoparaffin-rich diesel fuel from renewable feedstock containing triglycerides and fatty acids. The resultant biofuel product has a high cetane value, a lower gravity, good cold flow properties and excellent storage stability. Green diesel is completely compatible for blending with the standard mix of petroleum-derived diesel fuels, thus providing significant value to the refiner. The process for producing green diesel operates at mild operating conditions and integrates well within existing petroleum refineries. In contrast to fatty acid methyl esters, where fuel properties depend on feed origin and process configuration, green diesel product is independent of feed origin and the fully deoxygenated biofuel is readily blended with conventional diesel fuel. A life cycle assessment (LCA) of this promising new biofuel production technology has been undertaken to quantify the intrinsic benefits of green diesel production over the current practice of converting various forms of lipids to fatty acid methyl esters. This paper will describe the technology, discuss the results of the LCA study and summarize the advantages this new technology can offer over other processing routes.
Charge-Transfer-Induced Isomerization of DCNQI on Cu(100)

This article reports on the temperature-controlled irreversible transition between the two isomeric forms of the strong electron acceptor dicyano-pquinonediimine (DCNQI) on the Cu(100) surface. A combination of experiment (time-resolved, variable-temperature scanning tunneling microscopy, STM) and theory (density functional theory, DFT) shows that the isomerization barrier is lower than in the gas phase or solution due to the fact that charge transfer from the substrate modifies the bond configuration of the molecule, aromatizing the quinoid ring of DCNQI and enabling a more free rotation of the cyano groups with respect to the molecular axis.
Rainforest litter quality and chemical controls on leaf decomposition with near-infrared spectrometry

Plant-litter chemical quality is an important driver of many ecosystem processes, however, what actually constitutes high- or low-quality litter (chemical potential for fast and slow decomposition, respectively) is often interpreted by the indices available. Here, near-infrared spectroscopy (NIRS) was used to explore leaf-litter chemical quality and the controls on decomposition in the tropical rainforest region of north Queensland Australia. Leaf-litter samples from litterfall collections and litterbag studies were used. NIRS was used to calibrate the chemical compositions of the material (N, P, C, Mg, Ca, acid detergent fiber, acid detergent lignin, α-cellulose, and total phenolics) from a smaller sample set covering the spectral range in the full set of samples. Calibrations were compared for both separate (local) and combined models, for litterbags, and litterfall. Coefficients of determination (r2) in the local models ranged from 0.88 (litterbag Mg) to 0.99 (litterfall N), with residual prediction deviation ratios > 3 for all constituents except Mg (≈ 2.5). Mass loss in the litterbags was strongly related to the NIR spectra, with model r2's of 0.75 (in situ leaves) and 0.76 (common control leaf). In situ decomposability was determined from modeling the initial NIR spectra prior to decomposition with litterbag exponential-decay rates (model r2 of 0.81, n = 85 initial samples). A best subset model including litter-quality, climate, and soil variables predicted decay better than the NIR decomposability model (r2 = 0.87). For litter quality alone the NIR model predicted decay rate better than all of the best predictive litter–chemical quality indices. The decomposability model was used to predict in situ decomposability in the litterfall samples. The chemical variables explaining NIR decomposability for litterfall were initial P, C, and phenolics (linear model r2 = 0.80, n = 2471). NIRS is a holistic technique that is just as, if not more accurate, than litter–chemical quality indices, when predicting decomposition and decomposability, shown here in a regional field study.
Environmental and health risk assessment of an oil contaminated site in the Mexican tropical southeast

The aim of the present study was the environmental and health risk assessment of the oil contaminated soil in the Mexican tropic. The case is based on the total petroleum hydrocarbons (TPH) and polycyclic aromatic (PAH) soil contamination resulting from the activities of the oil industry over 70 years. The results of this study were estimated using CalTOX, a multimedia exposure model. The aromatic fraction constituted by PAH of two, three, four and five rings presented concentrations from 0.005 to 3.84 mg/kg. The BaP was the chemical of major concern due to its high carcinogen potential. The health risk assessment with CalTOX showed that risk mainly came from two exposure pathways: dermal uptake and ingestion. Only one zone was classified in the high risk level and rest of the zones did not exceed the acceptable health risk value (>10-E6) without requiring remediation actions. The environmental compartment that required main attention was the surface soil. [Received: September 22, 2009; Accepted: December 8, 2009]
A Class of Exactly Solvable Schrödinger Equations

An algebraic approach to solving a class of one-particle Schrodinger equations is presented. As an example, quasi-exact solutions of the eigenvalue problem of a Hamiltonian describing two interacting particles confined in a parabolic well are obtained. This example constitutes a unification and a generalization of several models known in the literature, as the ones of Taut ( Phys. Rev. A 1993 , 48 , 3561) and of Samanta and Ghosh ( Phys. Rev. A 1990 , 42 , 1178). Two confined particles interacting by Coulomb forces and the nuclear motion of a diatomic molecule are discussed as practical implementations.
Optimal control of multi-photon dissociation and ionization processes in small NamKn clusters

Closed loop optimization of fs pulse shapes is investigated on the model systems consisting of small mixed alkali clusters. By controlling the photo-excited wave packet dynamics the multi-photon fragmentation and ionization of the NamKn clusters are optimized. The resultant optimized pulse shapes carry fingerprints of the chosen dynamical pathways of the involved molecules. The dependence of fragmentation with cluster size is additionally investigated by gradually shifting the cluster size distribution towards larger masses. The resulting modification of the optimized pulse forms offers new insights into competing fragmentation channels during the control process. Characteristic motions of the involved wave packets are proposed to explain the optimized dynamic pathways. In particular, the steering of the wave packet in the trimer Na2K by the optimized pulse can be described in terms of avoiding the predissociative curve crossing and thereby achieving an effective and selective fragmentation.
A Cluster Center Initialization Method using Hyperspace-based Multi-level Thresholding (HMLT): Application to Color Ancient Document Image Denoising

Many iterative supervised clustering algorithms such as K-means and its derivatives depend closely on the initial cluster center positions. In order to overcome the convergence problems inherent in the clustering algorithms (i.e., local optimum), and subsequently to avoid a drop in clustering performance, many researchers continue to propose novel efficient methods able to determine automatically the optimal cluster centers. Therefore, in this paper, we propose a simple and efficient cluster center initialization method, called hyperspace-based multi-level thresholding (HMLT). The proposed HMLT method is based on using a novel multi-level thresholding approach on the multi-dimensional representation of color images (called hyperspace). In order to show the high performance of the HMLT method, experiments have been conducted using a recent clustering method, called the hyperkernel-based intuitionistic fuzzy c-means (HKIFCM), and after initializing the cluster center positions randomly and by means of the HMLT method. The HKIFCM clustering method that its performance tightly depends on the cluster center initialization, is applied for color ancient document image denoising (i.e., separate noise from text and background). Qualitative and quantitative assessments of results are deduced from a number of ancient document images collected from two different datasets.
Rapid and Accurate Thin Film Thickness Extraction via UV-Vis and Machine Learning

Thin-film processes are ubiquitous in photovoltaics research and are increasingly incorporated into high-throughput experimentation (HTE) equipment. However, HTE is limited by the slowest steps, and accurate thickness measurements have emerged as a bottleneck. This study demonstrates rapid yet accurate thin-film thickness extraction by leveraging machine learning (ML) in combination with non-destructive optical measurements (UV-Vis). We achieve 86.9% accuracy of thickness prediction within 10-percentage-error bounds on simulated data.
ANNETTE: Accurate Neural Network Execution Time Estimation With Stacked Models

With new accelerator hardware for Deep Neural Networks (DNNs), the computing power for Artificial Intelligence (AI) applications has increased rapidly. However, as DNN algorithms become more complex and optimized for specific applications, latency requirements remain challenging, and it is critical to find the optimal points in the design space. To decouple the architectural search from the target hardware, we propose a time estimation framework that allows for modeling the inference latency of DNNs on hardware accelerators based on mapping and layer-wise estimation models. The proposed methodology extracts a set of models from micro-kernel and multi-layer benchmarks and generates a stacked model for mapping and network execution time estimation. We compare estimation accuracy and fidelity of the generated mixed models, statistical models with the roofline model, and a refined roofline model for evaluation. We test the mixed models on the ZCU102 SoC board with Xilinx Deep Neural Network Development Kit (DNNDK) and Intel Neural Compute Stick 2 (NCS2) on a set of 12 state-of-the-art neural networks. It shows an average estimation error of 3.47% for the DNNDK and 7.44% for the NCS2, outperforming the statistical and analytical layer models for almost all selected networks. For a randomly selected subset of 34 networks of the NASBench dataset, the mixed model reaches fidelity of 0.988 in Spearman’s $\rho $ rank correlation coefficient metric.
Airline Passenger Forecasting using ARIMA and Artificial Neural Networks Approaches

Demand uncertainty has been increasing as a result of the rising trend of using airplanes as a transportation mode option in Indonesia over the years. This condition results in the need for the ability to accommodate the rise for airline companies to withstand within the industry. The forecast accuracy highly determines strategy formulation. Thus, accurate forecasting models are crucially needed. In this study, neural network is proposed to create the best-fitted model to predict future values as a non-traditional method that has already been tested to result in accurate predictions. As a comparison with the traditional model, Autoregressive Integrated Moving Average (ARIMA) model is applied. This study used monthly passenger data from Indonesian airlines, focused on Jakarta-Yogyakarta (CGK-JOG) and Jakarta-Singapore (CGK-SIN) routes, which are the representatives of the most profitable route for both domestic and international flight. Mean Absolute Percentage Error (MAPE) of both methods were then compared and forecasted future demand for the next 12 months were calculated. In both routes, neural network produced better value than ARIMA with MAPE of 1.29 for the CGK-JOG route and 1.66 for the CGK-SIN route.
LiDAR - Stereo Camera Fusion for Accurate Depth Estimation

Dense 3D reconstruction of the surrounding environment is one the fundamental way of perception for Advanced Driver-Assistance Systems (ADAS). In this field, accurate 3D modeling finds applications in many areas like obstacle detection, object tracking, and remote driving. This task can be performed with different sensors like cameras, LiDARs, and radars. Each one presents some advantages and disadvantages based on the precision of the depth, the sensor cost, and the accuracy in adverse weather conditions. For this reason, many researchers have explored the fusion of multiple sources to overcome each sensor limit and provide an accurate representation of the vehicle’s surroundings. This paper proposes a novel post-processing method for accurate depth estimation, based on a patch-wise depth correction approach, to fuse data from LiDAR and stereo camera. This solution allows for accurate edges and object boundaries preservation in multiple challenging scenarios.
STATCOM with Active Filter Using STATCOM as Active Filter, Improving Power Quality and reducing Harmonics

Todays grids are changing fast and operators are facing new challenges. The increasing number of inverters and renewable power generation necessitate new and innovate solutions to operate the grids within the required power quality parameters. Traditional equipment for power quality optimization is not flexible enough to handle the fast-changing grid conditions. The implementation of active harmonic filtering is a solution to maintain the quality of power transmission today and into the future. This paper is about the design and real time testing process of the Active Filter Control of Siemens Static Synchronous Compensator (STATCOM) located at American Electric Power's (AEP) Falfurrias station.
Root cause prediction based on bug reports

This paper proposes a supervised machine learning approach for predicting the root cause of a given bug report. Knowing the root cause of a bug can help developers in the debugging process—either directly or indirectly by choosing proper tool support for the debugging task. We mined 54755 closed bug reports from the issue trackers of 103 GitHub projects and applied a set of heuristics to create a benchmark consisting of 10459 reports. A subset was manually classified into three groups (semantic, memory, and concurrency) based on the bugs’ root causes. Since the types of root cause are not equally distributed, a combination of keyword search and random selection was applied. Our data set for the machine learning approach consists of 369 bug reports (122 concurrency, 121 memory, and 126 semantic bugs). The bug reports are used as input to a natural language processing algorithm. We evaluated the performance of several classifiers for predicting the root causes for the given bug reports. Linear Support Vector machines achieved the highest mean precision (0.74) and recall (0.72) scores. The created bug data set and classification are publicly available.
Pixel Sampling for Style Preserving Face Pose Editing

The existing auto-encoder based face pose editing methods primarily focus on modeling the identity preserving ability during pose synthesis, but are less able to preserve the image style properly, which refers to the color, brightness, saturation, etc. In this paper, we take advantage of the well-known frontal/profile optical illusion and present a novel two-stage approach to solve the aforementioned dilemma, where the task of face pose manipulation is cast into face inpainting. By selectively sampling pixels from the input face and slightly adjust their relative locations with the proposed “Pixel Attention Sampling” module, the face editing result faithfully keeps the identity information as well as the image style unchanged. By leveraging high-dimensional embedding at the inpainting stage, finer details are generated. Further, with the 3D facial landmarks as guidance, our method is able to manipulate face pose in three degrees of freedom, i.e., yaw, pitch, and roll, resulting in more flexible face pose editing than merely controlling the yaw angle as usually achieved by the current state-of-the-art. Both the qualitative and quantitative evaluations validate the superiority of the proposed approach.
Gaussian Mixture Model based Convolutional Sparse Coding for Radar Heartbeat Detection

Accurate detection of heartbeat through radar has many potential applications in, e.g., security and health. However, it is generally challenging to obtain clear heart-beat signature, due to its weak signal and relatively large interference caused by, e.g., body and respiration movement. In this paper, we propose an advanced algorithm based on convolutional sparse coding (CSC) and Gaussian mixture model (GMM) for suppressing the interference and extracting clear heartbeat signals. In this study, heartbeat signals are modelled by CSC and recovered by exploiting the sparsity of the signal. GMM is introduced to model the unknown noise, which could be a mixture from multiple noise/interference sources. The parameters of GMM , dictionary and codes are computed via the expectation maximization (EM) algorithm. To achieve faster processing, convolution computing is proposed to be processed in the frequency domain. The proposed method is tested and validated by simulation and experiments. The results show that our proposed algorithm can accurately extract the heartbeat components.
Applying Modular Decomposition in Simulink

Modular decomposition is widely used in software engineering to support design, testing and maintenance of software intensive systems. Model-Based Development (MBD) is a paradigm for developing complex software systems using graphical approaches, with MathWorks’ Simulink being a popular choice. How to develop modular Simulink models with stable interfaces, that facilitate understanding and testing, and achieve low coupling and high cohesion, is relatively understudied. This paper applies a new modular decomposition approach to Simulink case studies from the aerospace and nuclear domains. We evaluate how well it supports information hiding, and its impact on coupling and cohesion, interface complexity, cyclomatic complexity, testability, and performance.
Performance of Lossy P-LDPC Codes over GF(2)

This paper presents a low complexity lossy compression algorithm with protograph LDPC codes (Lossy P-LDPC Codes) over GF(2), for binary symmetric sources. The proposed scheme takes a shuffled reinforced belief propagation (SRBP) algorithm for encoding, which achieves asymptotically the theoretical rate-distortion function. Additionally, the proposed coding algorithm converges fast and performs very close to the counterparts using ultra sparse LDPC codes over GF(256). Simulation results also reveal that the code structures need to be further optimized, in order to adapt to different source statistics.
Similarity Calculation of Executable Using Intel Pin Instrumentation Framework

With the increase in the number of open-source and commercial code in the market, copyright and license infringement cases are on the rise. The lack of availability of source code makes identification a difficult task as existing techniques heavily rely on the source code. We propose two similarity measurement methods using the instruction log and the call-trace of each executable using Intel Pin tool. A Software Plagiarism Detector(SPD) was developed using the Intel Pin instrumentation tool and we have tested this approach on different small executable single-threaded and multi-threaded files. The results portray the validity of this method. We also talk about the possibility to expand this method for bigger software.