Miracle-dz/UniFAR-FTUs huggingface dataset:
The Adoption of New Ecommerce Technologies in Romanian SME

Romanian companies are expanding in the online environment very fast due to high-speed Internet connections and availability of software programmers. Due to the increasing popularity of smartphones, many entrepreneurs have various opportunities to start or extend their online businesses. The increasing number of open APIs provided by local or global suppliers offer many possibilities for digitalizing a company and extending its activity locally or globally with less resources. Billing companies, shipping companies, advertising companies and other ecommerce related companies are developing business software to facilitate the communication between the most popular ecommerce platforms from the local and international market. The research identified which are the most important processes for ecommerce stores and the level of processes digitalization. For obtaining accurate conclusions our quantitative research was applied to ecommerce entrepreneurs from many niches in companies which are based in Romania. Our results can help to understand the current state of the Romanian ecommerce market and if entrepreneurs and managers would improve or invest in new technologies. Fast business digitalization can offer new opportunities for small and medium enterprises in order to extend locally or even globally.
Promotional Effect of Cerium and/or Zirconium Doping on Cu/ZSM-5 Catalysts for Selective Catalytic Reduction of NO by NH3

The cerium and/or zirconium-doped Cu/ZSM-5 catalysts (CuCexZr1−xOy/ZSM-5) were prepared by ion exchange and characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), temperature-programmed reduction by hydrogen (H2-TPR). Activities of the catalysts obtained on the selective catalytic reduction (SCR) of nitric oxide (NO) by ammonia were measured using temperature programmed reactions. Among all the catalysts tested, the CuCe0.75Zr0. 25Oy/ZSM-5 catalyst presented the highest catalytic activity for the removal of NO, corresponding to the broadest active window of 175–468 ◦C. The cerium and zirconium addition enhanced the activity of catalysts, and the cerium-rich catalysts exhibited more excellent SCR activities as compared to the zirconium-rich catalysts. XRD and TEM results indicated that zirconium additions improved the copper dispersion and prevented copper crystallization. According to XPS and H2-TPR analysis, copper species were enriched on the ZSM-5 grain surfaces, and part of the copper ions were incorporated into the zirconium and/or cerium lattice. The strong interaction between copper species and cerium/zirconium improved the redox abilities of catalysts. Furthermore, the introduction of zirconium abates N2O formation in the tested temperature range.
An expedient, chemoselective N-chloroacetylation of aminoalcohols under metal-free bio-compatible conditions

ABSTRACT We are reporting for the first time, efficient, highly chemoselective N-chloroacetylation of amino compounds (amino alcohols, amino acids) by chloroacetyl chloride, without compromising its high reactivity, to give chloroacetamides in phosphate buffer within 20 min. We have systematically studied the effects of buffers, metal salts and HCl scavengers to optimize the reaction conditions. We have carried out intermolecular competitive reactions and shown that anilines and aliphatic amines can be selectively N-chloroacylated in the presence of alcohols and phenols. The acylated products are obtained in high yields and can be easily isolated without chromatographic separation. This reaction represents the first example of a metal-free bio-compatible synthesis under neutral conditions. This method is eco-friendly, easily scalable and robust. We have further studied the intramolecular competitive reactions of aminoalcohols and prepared various N-chloroacetamides in very good yields. Finally, this protocol was conveniently extended to ceramides synthesis as well. GRAPHICAL ABSTRACT
TAIL DEPENDENCE OF OLS

This paper shows that if the errors in a multiple regression model are heavy-tailed, the ordinary least squares (OLS) estimators for the regression coefficients are tail-dependent. The tail dependence arises, because the OLS estimators are stochastic linear combinations of heavy-tailed random variables. Moreover, tail dependence also exists between the fitted sum of squares (FSS) and the residual sum of squares (RSS), because they are stochastic quadratic combinations of heavy-tailed random variables.
Business startup motives and growth expectations of micro and small-scale entrepreneurs in Southern Province of Sri Lanka: the mediating role of entrepreneurial orientation

New venture creations are vital for the economic growth of any country because of its contribution to employment generation, employment generation, GDP, innovations and value additions. Though a large number of new venture creations occur every year in Sri Lanka, research findings highlighted that a smaller percentage was able to survive. Hence, the number of business establishments cannot be considered as a meaningful indicator of economic growth of the country and the high failure rate is a crucial issue. Past studies revealed that business startup motives effect on the business growth and Entrepreneurial Orientation (EO) plays a crucial role in directing the business towards success. Studies are lacking about the impact of business startup motives on the success and survival of newly established ventures and the mediating role of the EO on the relationship between start-up motives and new venture success. Therefore, the present study focused to reveal whether significant relationship exists between business startup motives and growth expectations; growth expectations between the necessity driven (NDEs) and opportunity driven entrepreneurs (ODEs); and whether EO plays a mediating role on the relationship between business startup motives and growth expectations. Independent Sample T test and Structural Equation Modeling were applied to test the hypotheses. Findings revealed that significant difference exists between the NDEs and ODEs in terms of proactiveness, competitive aggressiveness, and autonomy and no significant difference exists in growth expectations between the NDEs and ODEs. Entrepreneurial Orientation partially mediates the relationship between business startup motives and growth expectations.
Spatial Feature-Based Prioritization for Transmission of Point Cloud Data in 3D-Image Sensor Networks

A sensor network that connects multiple 3D image sensor devices using light detection and ranging (LIDAR) is effective for collecting spatial information to detect potential risks associated with people’s movements. However, due to the large volume and high frame rate of point cloud data obtained by LIDAR devices, under the condition of strictly limited bandwidth, some part of the data can go missing when detection is performed from point cloud data, causing a degradation of the accuracy. As the communication traffic volume of other applications increases, the communication available bandwidth for spatial information applications becomes insufficient. This paper proposes a scheme of spatial feature-based prioritization for the transmission of point cloud data with strictly limited communication bandwidth. It estimates how much each piece of point cloud data contributes to the accuracy improvement of a machine learning task for spatial prediction and assigns a higher score to those with a larger contribution. These scores prioritize the control for the transmission of point cloud data from sensor devices to an edge server. Feature selection with machine learning is helpful for estimating data importance. To improve the estimation accuracy, the proposed scheme complements missing entries when performing feature selection. We developed an indoor experimental environment and found that the prediction accuracy was sufficiently maintained compared to two benchmark schemes, random and heuristic, with limited communication bandwidth. Comparison with the perturbation method, which is a conventional method of feature selection, demonstrated the effectiveness to complement missing values in the proposed scheme.
Diffusion Distributed Quantized State Estimation With Variable Bandwidth

In low-cost wireless sensor networks, the communication bandwidth between sensors may be variable due to power constraints on the sensors. Considering that quantization is an effective method to save communication bandwidth, a novel distributed adaptive quantization state estimation algorithm with variable bandwidth is proposed, where the quantization steps are time varying. Based on the minimum mean squared error criterion and diffusion strategy, the optimal local gain and neighborhood gain are designed to utilize quantized information for fusion estimation. These gains can be adjusted adaptively through local information fusion. Moreover, we also analyze the mean and mean-square performance of the proposed algorithm and find that the covariance is bounded under variable bandwidth. Finally, the effectiveness of the proposed algorithm is verified via numerical simulation.
The morbidity rate of acute stroke among adult population in both Moldova and India

Background: According to the WHO, over 15 million people worldwide suffer from stroke annually, 5.5 million die and 5 million become permanently disabled. According to the latest WHO data published in 2017, Stroke Deaths in the Republic of Moldova made up 15.87% of total deaths. Material and methods: A retrospective hospital-based study was conducted at the clinics of the Department of Emergency Medicine of Nicolae Testemitanu SUMPh and at the tertiary Care Hospital of Assam Medical College, the Republic of India, during January 01, 2019 – December 31, 2019. Two groups of patients were enrolled in the research, viz. lot 1 (80 patients) treated in the Institute of Emergency Medicine of the Republic of Moldova and lot 2 (80 patients) treated at the tertiary Hospital of Assam Medical College, India. The purpose of the study was to assess the major risk factors for developing ischemic stroke, as well as to evaluate the impediments in providing patients with modern treatment strategies among adult population. Results: The study group included 44 (55.0%) males and 36 (45.0%) females. 66 patients (83.0%) suffered an ischemic stroke and 14 patients (17.0%) had hemorrhagic stroke. The risk factor providing access to modern treatment strategies in acute stroke cases is taken in consideration. Conclusions: The high incidence of stroke suggests that primary prevention strategies used in the Republic of Moldova and the Republic of India are either not widely implemented or not sufficiently effective.
The Role of CTX-II , Dyslipidemia ,Vitamin D in Polycystic Ovary Syndrome

This study aimed to study the serum measurement of clinical value of type II collagen (CTX-II) in syndromeof polycystic ovary in women and indicter as osteoarthritis disease marker analyzed in future levels witha recent available immune enzyme-linked sorbent assay (ELISA) kit. For increasing sensitivity test, theprotocol was modified. Levels of CTX-II increased significantly as well total protein, while decrease vitaminD and difference in lipid profile were practical between the patients compared with healthy women’s. Theobtained results suggested the monitoring interest of the serum CTX-II for the OA development in patientsof polycystic ovary syndrome in women and the relevance of the analysis of multiple time point for thisbiomarker.
The Need for Permit Management within Biodiversity Collection Management Systems to Digitally Track Legal Compliance Documentation and Increase Transparency About Origins and Uses

A growing number of domestic and international legal issues are confronting biodiversity collections, which require immediate access to information documenting the legal aspects of specimen ownership and restrictions regarding use. The Nagoya Protocol, which entered into force in 2014, established a legal framework for access and benefit-sharing of genetic resources and has notable implications for collecting, researchers working with specimens, and biodiversity collections. Herein, we discuss how this international protocol mandates operating changes within US biodiversity collections. Given the new legal landscape, it is clear that digital solutions for tracking records at all stages of a specimen's life cycle are needed. We outline how the Harvard Museum of Comparative Zoology (MCZ) has made changes to its procedures and museum-wide database, MCZbase (an independent instance of the Arctos collections management system), linking legal compliance documentation to specimens and transactions (i.e., accessions, loans). We used permits, certificates, and agreements associated with MCZ specimens accessioned in 2018 as a means to assess a new module created to track compliance documentation, a controlled vocabulary categorizing these documents, and the automatic linkages established among documentation, specimens, and transactions. While the emphasis of this work was a single year test case, its successful implementation may be informative to policies and collection management systems at other institutions.
Reconstructing Missing Information of Remote Sensing Data Contaminated by Large and Thick Clouds Based on an Improved Multitemporal Dictionary Learning Method

The presence of clouds and cloud shadows has limited the applications of optical remote sensing data. Currently, most cloud removal methods are focused on reconstructing remote sensing data contaminated by small or thin clouds. This study proposes an improved method based on multitemporal dictionary learning to reconstruct missing information of remote sensing data contaminated by large and thick clouds. First, the contaminated target image is initialized using all available adjacent cloud-free reference images. Second, reconstructed images from each of the reference images are produced using dictionary learning and sparse representation methods. Then, weights are determined for the abovementioned reconstructed images based on their reconstruction errors over uncontaminated regions and are used to generate the preliminary reconstruction result. Finally, an error correction step for the contaminated regions is applied to the preliminary result, which is then combined with the original uncontaminated pixels to produce the final reconstruction result. The proposed method was evaluated on simulated clouds/cloud shadows based on remote sensing data with various sizes and land cover types. Visual and quantitative analyses of the reconstruction results show that the proposed method outperformed the generally used geostatistical neighborhood similar pixel interpolator (GNSPI) and nonnegative matrix factorization and error correction (S-NMF-EC) methods. Therefore, the results indicated that the proposed method was capable of accurately and effectively reconstructing data contaminated by large and thick clouds.
Computational explorations at the physics-chemistry-biology interface

and often in complex geometries, the exploration of the PCB interface raises a major challenge to both theory and experiment. Analytical methods are clearly of limited use towards nonlinear phenomena in complex geometries, while experimental approaches like in-cell NMR, single molecule techniques, and even neutron scattering, notwithstanding tremendous progress in the last decades, may still suffer accessibility constraints. Under such state of affairs, computer simulations offer a powerful and flexible alternative/complement to the canonical routes above.5
RLS-PSM: A Robust and Accurate Password Strength Meter Based on Reuse, Leet and Separation

Password strength meters (PSMs) are being widely used, but they often give conflicting, inaccurate and misleading feedback, which defeats their purpose. Except for fuzzyPSM, all PSMs assume passwords are newly constructed, which is not true in reality. FuzzyPSM considers password reuse, six major leet transformations and initial capitalization, and performs the best as evaluated by Golla and Dürmuth at ACM CCS’18. On the basis of fuzzyPSM, we propose a new PSM based on Reuse, Leet and Separation, namely RLS-PSM. First, we classify password reuse behaviors into capitalization and those that use special characters for leet or separation, and calculate the corresponding probabilities. Then, to balance efficiency and precision, we use Long Short-Term Memory to calculate the probabilities of alphanumeric strings. Besides, we propose to use benchmark passwords to show the relative strength of a password. Due to the varied impacts of different service types and diversified economic value of websites, we consider parameter settings of RLS-PSM under six different service types. Finally, we use the Monte Carlo method and weighted Spearman coefficient to measure and compare the robustness and accuracy of RLS-PSM, leading PSMs (including Markov-based PSM, PCFG-based PSM, fuzzyPSM, RNN, and Zxcvbn), and password cracking tools (including JtR and Hashcat). We find that the robustness of RLS-PSM is significantly higher than all counterparts when evaluating attempts > 104 (e.g., on average, Fraction of Successfully Evaluated passwords of RLS-PSM is 18.9% higher than fuzzyPSM). The accuracy of RLS-PSM is also better than other mainstream PSMs used for comparison in this paper, except for fuzzyPSM.
Correlation between memory characteristics and energy band bending resulted from composition distribution of trapping layer for charge trap memory

Charge trap memory devices with ZrxSi1–xO2 films as charge trapping layer consisting of nine [(ZrO2)m(SiO2)n(ZrO2)m(SiO2)n] units have been fabricated and investigated. The composition distribution was modulated by controlling the m and n values in each unit. It is observed that the variation of composition distribution can induce the energy band bending and additional potential barrier, which significantly improve the program/erase speed and data retention characteristics as well as extending the temperature insensitive range. When the effective potential barrier including classic potential barrier and additional potential barrier is 1.52 eV, the memory device exhibits a lower charge loss of 4.5% at 200 °C over a period of 104 s, a wider temperature insensitive range of 20 °C–111 °C, and a faster program time of 4.0 × 10−5 s achieving +6 V flat band voltage shift. The effective potential barrier values should be in the range of 1.52–1.36 eV, taking into consideration the trade-off between the retention and program/erase speed. The additional potential barrier can increase the electron tunneling distance in the directions of tunneling layer and blocking layer, giving rise to recapture process and temperature insensitive of retention characteristics. In addition, the additional potential barrier decreases the electron tunneling distance arriving trapping layer conduction band, improving the program speed. The results provide a reference to trapping layer composition distribution for future charge trap memory applications.
Partial Purification and Characterization of Polyphenol Oxidase from the Wild Edible Mushroom Lepiota Procera Using Three-Phase Partitioning

Abstract Polyphenol oxidase (PPO) from the wild edible mushroom Lepiota procera is partially purified and biochemically characterized using three-phase partitioning (TPP), which is an easily applied and effective method. This method includes ammonium sulfate saturation at different concentrations, t-butanol addition (1:1; 1:5), and adjustment of pH. Optimum purification parameters with 20% ammonium sulfate saturation and 1:1 t-butanol conditions led to the highest activity at bottom phase with 8.4 fold purification. Sodium dodecyl sulfate- polyacrylamide gel electrophoresis (SDS-PAGE) analysis showed the molecular weight of the purified enzyme to be 35 kDa. The partially purified PPO presented a high level of activity with L-DOPA (Michaelis-Menten constant, Km – 0.12 mM), followed by caffeic acid (0.27 mM) and 4-methylcatechol (0.46 mM) and it is classified as a catecholase type of PPO. The enzyme had peak activity at a temperature of 40 °C and a pH value of 7.0. These results demonstrate a new enzyme source and easy purification method, useful for various industrial applications.
Dramatically Enhanced Stability of Silver Passivated Dicalcium Nitride Electride: Ag-Ca2N

Electrides have received considerable attention due to their exotic properties. However, the high reactivity with oxygen and moisture immediately decomposed electrides in an ambient air environment. Here we passivated dicalcium nitride electride (Ca2N:e–) with silver via a wet chemical approach (Ag-Ca2N), significantly enhancing stability up to 17 min at room temperature in an ambient air environment. The Ca2N:e– was employed as a reducing agent due to the low work function (2.6 eV), high mobility, and high electron concentration facilitating electron transfer to Ag+ ion in an aprotic cosolvent. Moreover, the noble metal surface passivation (thickness: 55 nm) was achieved with negligible increase in work function of Ag-Ca2N (2.78 eV). The optimized molar ratio of AgNO3/Ca2N:e– was 0.5. The enhanced stability of Ag-Ca2N in organic reaction medium enabled successful aldol condensation reaction outside the glovebox, with a high α,β-unsaturated ketone yield of 75.4%, without involving environmentally harmful .
Design and Evaluation of a High-performance Key-value Storage for Industrial IoT Environments

In industrial IoT environments, sensors generate data for their detection targets and deliver the data to IoT gateways. Therefore, managing large amounts of real-time sensor data is an essential feature for IoT gateways, and key-value storage engines are widely used to manage these sensor data. However, key-value storage engines used in IoT gateways do not take into account the characteristics of sensor data generated in industrial IoT environments, and this limits the performance of key-value storage engines. In this paper, we optimize the key-value storage engine by utilizing the features of sensor data in industrial IoT environments. The proposed optimization technique is to analyze the key, which is the input of a key-value storage engine, for further indexing. This reduces excessive write amplification and improves performance. We implement our optimization scheme in LevelDB and use the workload of the TPCx-IoT benchmark to evaluate our proposed scheme. From experimental results we show that our proposed technique achieves up to 21 times better than the existing scheme, and this shows that the proposed technique can perform high-speed data ingestion in industrial IoT environments.