Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of the Global Pandemic on China’s One Belt, One Road Outward Foreign Direct Investment in the Association Southeast Asian Nations (ASEAN) Region

This article explores the potential economic impact of the COVID-19 crisis on China’s outward foreign direct investment (OFDI), especially that involving Chinese state-owned enterprises (SOEs) in the Belt-Road countries. It also aims to understand the attitude of relevant stakeholders toward the performance of these SOEs in the Association of Southeast Asian Nations (ASEAN) region in relation to the current global health crisis. Applying the annual panel data regression from 2005–2020, we find statistically significant outcomes for the business environment in terms of Chinese OFDIs initiated in ASEAN countries. The first empirical results show that the global health pandemic can impair Chinese OFDIs. However, the strong annual growth and the connection between China and the ASEAN could eliminate the risks of the crisis that is impacting the Chinese OFDI in the region. This study conveys clear and useful suggestions for both governments and investors during these extremely turbulent and challenging times. The most important of these arises from our study’s conclusion that the two-way investments between China and the ASEAN under the One Belt One Road (OBOR) plan will remain strong and will become stronger. The support of the OBOR initiative is undiminished because most of the investment projects, such as energy and infrastructure, remain important to the ASEAN countries.
Firms’ Innovation Trajectories and Inventors’ Productivity in the Context of Acquisitions

Studying acquisitions in the US pharmaceutical industry, we first question how the alignment of acquirers’ innovation trajectories (as path-dependent or path-breaking change through the acquisition of targets with similar or distant innovations) with their own inventors’ pre-acquisition knowledge (as specialized or diversified) may influence these inventors’ post-acquisition productivity. We examine two characteristics of productivity – namely whether acquirers’ future innovations are indicative of knowledge deepening or broadening, as well as their impact. Second, we focus on both the acquirers’ and targets’ human capital (i.e., inventors’) knowledge resources, and study characteristics of who the knowledge is being acquired from at the target firm and who it is being absorbed or integrated by at the acquirer, and further how this alignment may also impact the acquirer’s inventors’ productivity.
Recession-Specific Recoveries: L's, U's and Everything in Between

We relax the assumption that recessions are all alike and propose a new model of output growth that allows for recession-specific recoveries. Output growth is modelled as the weighted average of Markov-switching processes that temporarily alter the level of real GDP (U-shaped) and those with permanent effects (L-shaped), where the recession-specific weight is endogenously estimated. Only the 1969-70 and 2007-09 recessions are characterized exclusively as U and L, respectively. The other 85% of U.S. recessions reflect a weighted combination of the two shapes. Consequently, models that imply only one possible path for a given recession may be insufficient to fully characterize the behavior of output during recessionary periods. With respect to fitting output growth, our model outperforms those that generate either U- or L-shaped recoveries and the model-implied paths closely track the level of actual U.S. real GDP during recessions and recoveries.
Simplifying Business Group Structures: Value and Governance Implications for Korean Chaebols

We examine Korean business groups' transitions from circular-shareholding to (relatively simple) pyramidal-shareholding structures during 2011-2018. The transition did not impact chaebol families' degrees of control or incentive conflicts in firms belonging to circular-shareholdings ("loops"), yet these firms’ values declined, particularly when they have higher incentive conflicts. Non-loop group firms experienced a value increase (decline) if they had little (significant) agency issues that were difficult to identify under opaque circular-shareholding structures. Firms in transitioning chaebols also experienced an increase in earnings informativeness. Our findings suggest ownership transparency enables investors to more clearly identify agency issues and managerial incentives across group firms.
Synthesis and characterization of mixed matrix membranes incorporated with hydrous manganese oxide nanoparticles for highly concentrated oily solution treatment

In this work, ultrafiltration (UF) mixed matrix membranes (MMMs) incorporated with hydrophilic hydrous manganese oxide (HMO) nanoparticles were synthesized and used for the treatment process of a highly concentrated oily solution. The MMMs were fabricated via a phase inversion process and were characterized with respect to physiochemical properties and filtration performances. The results showed that the MMMs demonstrated higher water flux and better oil rejection rates compared to the control membrane (without HMO incorporation) when tested at three different oily solutions with oil concentration in the range of 5000–15? 000? ppm. The membrane incorporated with the highest amount of HMO (labelled as MMM-2) in particular showed the best results even when subjected to 15? 000? ppm oily solution filtration, recording water flux of 32.2 L/m2? ·? h? ·? bar and oil rejection of 82? %. The enhanced performance can be attributed to improved surface hydrophilicity coupled with better structural integrity and higher porosity that contributed to lower degree of oil molecules deposition and greater water transport rate. It was also reported that with the use of a feed spacer during the filtration process, the degree of flux deterioration of MMMs could be further reduced without compromising rejection. The findings of this work indicated the potential of MMMs for the treatment of highly concentrated oily effluent produced from industries, by producing permeate of high quality at reasonably good water permeability.
Modeling chemistry and flow in catalytic fixed-bed reactors with detailed geometry

Industrial catalytic fixed-bed reactors are made of steel, operate at high temperatures and pressures and contain hazardous chemicals. What happens inside remains hidden. Due to the limited information elucidated from the conventional measurement techniques aiming to resolve the gradients of species and temperature along the reactor (typically only in and out information are available), engineers have to rely on time consuming and costly trial and error method in reactor design and optimization as well as reactor model development. In order to design and develop reactors with higher accuracy, to trouble-shoot and optimize reactor in a cost-effective manner, a reliable, predictive and informative reactor model is desired. Three-dimensional computational fluid dynamics (CFD) models with detailedly resolved bed geometry are promising modeling approach for achieving this goal. In this work, CFD models were developed and applied to simulate three catalytic fixed-bed reactors with different types of packing configuration and reactions. Based on the packing geometry, different approaches were taken to create the simulation domains. A unit cell containing 1/4 of a single Pt wire was drawn to simulate the catalytically assisted combustion of methane on a Pt gauze. The reconstruction of X-ray microtomographic scans was used as input to generate the modeling domain to simulate CO oxidation on a Pt coated α-Al2O3 foam monolith. Computer generated random packing by discrete element method (DEM) was applied to simulate and optimize a pilot-scale fixed-bed reactor for n-butane oxidation to maleic anhydride under industrial relevant operational conditions. A spatially resolved profile measurement technique was applied in this work. This sampling technique provides high resolution concentration and temperature profiles inside the reactor, providing the possibility to critically validate the simulation results, and to calibrate parameters required in the model with high confidence. This work demonstrates that detailed reactor modeling and profile measurements are useful tools for achieving in-depth understanding of the interactions between flow, heat and mass transfer and catalytic reactions in fixed-bed reactors. Furthermore, with the comprehensive knowledge gained from both methods, a knowledge-based reactor optimization approach can be put forward.
Fermentation characteristics and nutritive value of corn silage intercropped with soybean under different crop combination ratios

Corn silage is an important feed for intensive ruminant production. Combining corn with legumes for silage is a feasible strategy to improve crude protein (CP) concentration in corn silage. This study was conducted to determine silage nutritive quality and fermentation profiles of corn grown in mixture with soybean at different crop combination ratio. In this experiment, corn-soybean combinations of 75:25, 50:50 and 25:75 in addition to monocrops of corn and soybean were evaluated. The crop combination ratio had significant effects on nutritive quality and fermentation characteristics of silage. Silage quality in terms of CP (75:25 ratio 12.23%, 50:50 ratio 12.88% and 25:75 ratio 13.65%) was improved by intercropping compared with corn sole crop (9.91% CP). Increase ratio of soybean resulted in an increase in lactic acid and pH of the mixed silage. Sole soybean gave significantly higher lactic acid (3.57%) and pH of silage (4.33) compared to all other treatments but dry matter concentration was significantly higher in corn monocrop silage (34.34%) than other treatments. The propionic acid and butyric acid content of soybean monocrop and intercrops silage were in all cases higher than sole corn silage.
Modeling Optical Properties of Combustion Soot emitted in the Troposphere

This work concerns the modeling, at the molecular level, of the interaction between carbonaceous particles of nanometric size and the electromagnetic radiation. The goal is to improve our understanding of the optical properties of soot particles, to better quantify the influence of soot on the atmosphere and on climate change. The study of the interaction between radiation and fresh soot particles was carried out using the point dipole interaction method; it has been shown that: i) the mass absorption coefficient (MAC) of these soot nanoparticles may significantly depend on their atomistic details, especially between 200 and 350 nm; ii) the MAC depends on whether the heart of the carbonaceous particle is occupied or not by graphite planes; iii) an analytical model is not suitable for calculating the MAC of carbonaceous nanoparticles having structural defects. In addition, quantum chemical methods have been used to characterize the ageing of soot. The results obtained are i) NO, Cl, and HCl are physisorbed on a perfect carbonaceous surface whereas on a defective surface, these species are chemisorbed and lead to a modification of the surface; ii) on a carbonaceous surface, the presence of adsorbed Cl atoms leads to a strong trapping of the surrounding water molecules. This may be related to the highly hydrophilic nature of soot emitted during fires in industrial environments. Finally, the PDI method was applied to calculate the polarizability of PAHs to help at interpreting the absorption spectra of carbonaceous grains in the interstellar medium, including molecules for which no data was currently available.
Multi-Particulate Drug Delivery Systems of Fenofibric Acid: Optimization of Formulation Using Statistical Experimental Design

The objective of the present research work was to develop a multi-particulate modified release system of Fenofibric acid using Wurster (Bottom spray fluid bed coating) process. Impact of various formulation variables was assessed by using statistical interpretation such as ANOVA. A 3 3 (three factor, three level) face centered central composite design was employed to study the effect of independent variables (concentration of ER Polymer, plasticizer and pore former), on dependent variables (drug release at 2.5 th h & 6 th h). Optimization of the formulation variables was done by fitting experimental data to the software program (Design Expert). The design space for formulation variables was developed. Fabricated pellets were characterized for various physico-chemical parameters. In vitro release data of the optimized formulation was fitted into various kinetic equations. The optimized formulation showed a desired drug release at both 2.5 th h and 6 th h as 17.5 ± 2.28% & 87.1 ± 0.75% respectively. The drug release from the capsules followed first order kinetics and controlled by non fickian transport. The information acquired in this study recommends that the multi-particulates of Fenofibric acid can be effectively intended to give a delayed release of Fenofibric acid and thus enhanced bioavailability.
Density of States Calculation for Indium-Arsenide Zincblende Based on Density Functional Theory

Abstract Ab initio study of the density of states on InAs zincblende phase is calculated using the linearized augmented plane wave method based on density functional theory, implemented with WIEN2k codes. These calculations used the local density approximation for the exchange and correlation potential. The result shows that InAs is the n-type smiconductor (donor) which has direct band gap of 0.388eV which is in agreement with experimental result. There is an internal gap between upper valence band and lower valence band. The result indicates a strong hybridization between arsenide atom and 5p state of indium atom, which belongs to the InAs at upper valence band.
Removal of metal ions from electrochemical decontamination solution using organic acids

ABSTRACT Applicability of the organic acids and cyclodextrin (CD) for the removal of Fe, Co and Ni from the spent electro-decontamination solution was investigated. Oxalic acid showed the highest removal efficiency: 90% for 0.89 M Fe and 95% for 0.0089 M Co and Ni, respectively. The metal–oxalate precipitates were characterized by differential scanning calorimetry/thermogravimetry analysis (DSC/TGA), Fourier transform infrared (FTIR) and X-ray fluorescence (XRF). After thermal decomposition at >300°C, the metal–oxalate precipitates were transformed into metal oxides (Fe2O3, FeO, CoO and NiO) and pure metals (Co and Ni). The results imply that organic acids have a high potential for the removal of heavy metals from electro-decontamination solutions.
Crystal structure of paliperidone palmitate (INVEGA SUSTENNA®), C39H57FN4O4

The crystal structure of paliperidone palmitate has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional techniques. Paliperidone palmitate crystallizes in space group P21/c (#14) with a = 34.415 40(35), b = 10.093 49(7), c = 10.904 92(9) Å, β = 94.3917(9)°, V = 3776.94(6) Å3, and Z = 4. The conformation of the paliperidone fragment differs from that of the parent compound. The palmitate chain exhibits a slight twist close to the ester group. Several C–H⋅⋅⋅O hydrogen bonds contribute to the crystal packing, which is dominated by van der Waals interactions. The powder pattern is included in the Powder Diffraction File™ as entry 00-066-1614.
Evolution of anisotropic distributions of weakly charged heavy ions downstream collisionless quasiperpendicular shocks

Hybrid simulations of quasiperpendicular collisionless shocks (QRCS) in multicomponent plasmas have revealed that weakly charged ions can keep anisotropic nonequilibrium distributions at relatively long distances downstream the shock front. However, analytic considerations suggest that such distributions lead to growth of the Alfven ion cyclotron (AIC) instability, which in turn governs isotropisation. Hence, we have conducted targeted hybrid simulations of QRCS with increased accuracy, which appeared to agree with the analytical predictions and suggest that the nonequilibrium distributions eventually relax due to the AIC instability. On another hand, downstream electromagnetic instabilities generated by anisotropic particle distributions may affect the energy balance, therefore influencing macroscopic shock parameters, such as the compression ratio. Simulations of QRCS with parameters close to those of the heliospheric termination shock measured by the Voyager mission, showed that an admixture of just 0.7% (by mass) of oxygen decreases the compression ratio by 3-4%. Thus, one might conclude that along with the effect of the pickup ions, the multi-species composition of the interplanetary plasma can add to the observed decrease of the shock compression.
Green Synthesis of Gold Nanoparticles Using Barberry and Saffron Extracts

: Recently, synthesis of gold nanoparticles (AuNPs) is the subject of a lot of studies due to various applications in medicine, agriculture, and industry. The development of non-toxic and safe methods such as green chemistry to produce AuNPs is obviously recommended. In the current study, 2 antioxidant rich herbs of barberry and saffron stigma were applied as reducing agents for the 1-pot synthesis of size-controlled AuNPs. These AuNPs were characterized through ultraviolet-visible spectrophotometry (UV-vis), transmission electron microscopy (TEM), and X-ray diffraction (XRD) techniques. Optimal temperature and pH for the biosynthesis of AuNPs conjugated with herbs were 50°C and 7.5, respectively. XRD and TEM results authorized AuNPs production in 5 - 15 nm for barberry and 5 - 10 nm for saffron and confirmed the stability, sphericity, and uniformity for both the nanoparticles.
Sharks in the Dark: Quantifying Latency Arbitrage

Using proprietary order book data with participant-level message tra c and matching engine time stamps, we investigate stale reference pricing in dark pools. We document a substantial amount of stale trading which im- poses large adverse selection on passive dark pool participants. We show that HFTs almost never provide dark liquidity, instead frequently consuming dark liquidity, in particular in order to take advantage of stale reference prices. Fi- nally, we examine several market design interventions to mitigate stale trades, showing that only mechanisms to protect passive dark liquidity, such as ran- dom uncrossings, are e ective at ensuring accurate reference prices.
Discriminative Attribution from Counterfactuals

We present a method for neural network interpretability by combining feature attribution with counterfactual explanations to generate attribution maps that highlight the most discriminative features between pairs of classes. We show that this method can be used to quantitatively evaluate the performance of feature attribution methods in an objective manner, thus preventing potential observer bias. We evaluate the proposed method on three diverse datasets, including a challenging artificial dataset and real-world biological data. We show quantitatively and qualitatively that the highlighted features are substantially more discriminative than those extracted using conventional attribution methods and argue that this type of explanation is better suited for understanding fine grained class differences as learned by a deep neural network.
On The Faithfulness of the extension of Lawrence-Krammer representation of the group of conjugating automorphisms C_3

Let Cn be the group of conjugating automorphisms. We study the representation ρ of Cn, an extension of Lawrence-Krammer representation of the braid group Bn, defined by Valerij G. Bardakov. As Bardakov proved that the representation ρ is unfaithful for n ≥ 5, the cases n = 3, 4 remain open. In our work, we make attempts towards the faithfulness of ρ in the case n = 3.
Extensible Logging and Empirical Attainment Function for IOHexperimenter

In order to allow for large-scale, landscape-aware, per-instance algorithm selection, a benchmarking platform software is key. IOHexperimenter provides a large set of synthetic problems, a logging system, and a fast implementation. In this work, we refactor IOHexperimenter's logging system, in order to make it more extensible and modular. Using this new system, we implement a new logger, which aims at computing performance metrics of an algorithm across a benchmark. The logger computes the most generic view on an anytime stochastic heuristic performances, in the form of the Empirical Attainment Function (EAF). We also provide some common statistics on the EAF and its discrete counterpart, the Empirical Attainment Histogram. Our work has eventually been merged in the IOHexperimenter codebase.
Generating texts under constraint through discriminator-guided MCTS

Large pre-trained language models (LM) based on Transformers allow to generate very plausible long texts. In this paper, we explore how this generation can be further controlled to satisfy certain constraints (eg. being non-toxic, positive or negative, convey certain emotions, etc.) without fine-tuning the LM. Precisely, we formalize constrained generation as a tree exploration process guided by a discriminator according to how well the associated sequence re-spects the constraint. Using a discriminator to guide this generation, rather than fine-tuning the LM, in addition to be easier and cheaper to train, allows to apply the constraint more finely and dynamically. We propose several original methods to search this generation tree, notably the Monte Carlo Tree Search (MCTS) which provides theoretical guarantees on the search efficiency, but also simpler methods based on re-ranking a pool of diverse sequences using the discriminator scores. We evaluate these methods on two types of constraints and languages: review polarity and emotion control in French and English. We show that MCTS achieves state-of-the-art results in constrained generation, without having to tune the language model, in both tasks and languages. We also demonstrate that our other proposed methods based on re-ranking can be really effective when diversity among the generated propositions is encouraged.
Spin-Valley Coupling Anisotropy and Noise in CMOS Quantum Dots

Cameron Spence, ∗ Bruna Cardoso Paz, Bernhard Klemt, Emmanuel Chanrion, David J. Niegemann, Baptiste Jadot, Vivien Thiney, Benoit Bertrand, Heimanu Niebojewski, Pierre-André Mortemousque, Xavier Jehl, Romain Maurand, Silvano De Franceschi, Maud Vinet, Franck Balestro, Christopher Bäuerle, Yann-Michel Niquet, Tristan Meunier, and Matias Urdampilleta † Univ. Grenoble Alpes, CNRS, Grenoble INP, Institut Néel, 38402 Grenoble, France CEA, LETI, Minatec Campus, F-38054 Grenoble, France Univ. Grenoble Alpes, CEA, IRIG, 38000 Grenoble, France (Dated: September 29, 2021)
Touchtone leakage attacks via smartphone sensors: mitigation without hardware modification

Smartphone motion sensors provide a concealed mechanism for eavesdropping on acoustic information, like touchtones, emitted by a device. Eavesdropping on touchtones exposes credit card information, banking pins, and social security card numbers to malicious 3rd party apps requiring only motion sensor data. This paper's primary contribution is an analysis rooted in physics and signal processing theory of several eavesdropping mitigations, which could be implemented in a smartphone update. We verify our analysis imperially to show how previously suggested mitigations, i.e. a low-pass filter, can undesirably reduce the motion sensor data to all applications by 83% but only reduce an advanced adversary's accuracy by less than one percent. Other designs, i.e. anti-aliasing filters, can fully preserve the motion sensor data to support benign application functionality while reducing attack accuracy by 50.1%. We intend for this analysis to motivate the need for deployable mitigations against acoustic leakage on smartphone motion sensors, including but not limited to touchtones, while also providing a basis for future mitigations to improve upon.