Miracle-dz/UniFAR-FTUs huggingface dataset:
Synchrophasing control based on tuned ADRC with applications in turboprop driven aircraft

Propeller synchrophasing control is an active noise control method which regulates the rotational speed of propellers to maintain their relative phase at an optimal setting to lower noise level. The effectiveness of propeller synchrophasing control is mainly dependent of the rotational speed control. A practical framework for the design and parameter tuning of an Active Disturbance Rejection Controller (ADRC) is proposed in order to reject unknown disturbances. Novel phase command logic is proposed to avoid unreasonable regulation of rotational speed. A filter based on an Extended State Observer (ESO) is used in the presence of measurement noise. Simulation results show that the proposed ADRC controller has a strong ability for disturbance rejection and its new phase control logic makes the system to behave more smoothly with a faster response to perturbation.
Analysis of the effect of Institutionalization of programmes in Execution of County Integrated Development Plans by County Governments in Kenya

County governments are expected to bring services closer to their citizens according to the Constitution of Kenya 2010. Different government agencies have reported many cases of low absorption of development funds, use of funds in areas not captured in County Integrated Development Plans (CIDP). This study sought to analyse the effect of institutionalization of programmes in execution of development plans by county governments in Kenya. Findings indicate that institutionalization effect in the execution of county integrated development plans had positive influence on execution of county integrated development plans. The beta coefficient of Institutionalization was 0.306 Organization culture and 0.255 Organization structure, which were statistically greater than zero, t-test statistic were 2.049 and 1.372 respectively which were greater than zero. This indicates that institutionalization has a positive influence on execution of county integrated development plans.
Modified Dual Output SEPIC Converter Integrated With Semi-Z-Source Inverter

Thispaper presents a combination of DC-DC Converter and a DC-AC Inverter. The DC-DC Converter employed is a modified dual output SEPIC converter with high static gain. The inverter employed is a low-cost SemiZ-Source inverter. The configuration employs magnetic coupling in the SEPIC converter which allows the increase of static gain. These semi-Z-source inverters employ the Z-source/quasi-Z-source network and only two active switches to achieve the same output voltage as the traditional voltage-fed full-bridge inverter does. The two active switches of the semi-Z-source inverter are controlled complementarily. The input dc source and the output ac voltage of the semi-Zsource inverter share the same ground, thus leading to less leakage ground current advantages over other non-doubly grounded inverters, such as voltage-fed full-bridge inverter. A revised nonlinear sinusoid pulse width modulation method for a semi-Z-source inverter is also proposed. By using this method, desired duty cycle can be generated to output the sinusoidal voltage. The major advantage of the proposed circuit is its reduced THD (less than 3%). A prototype is built; experimental results are provided to verify and demonstrate the special features of the proposed circuit.
Comparison of the aflatoxin B1 production pattern in fungal biomass and growth medium in toxigenic Aspergillus species of northern isolates of Iran

آفلاتوکسین از نقطه نظر اقتصادی از مهم­ترین انواع سموم قارچی در جهان است. سمی­ترین شکل این توکسین آفلاتوکسین B1بوده که یکی از معدود سموم قارچی است که سازمان غذا و داروی آمریکا بیشینه­ی مجاز برای آن تعیین نموده است. در مقایسه با سایر مایکوتوکسین­ها نیز مطالعات بیش­تری در خصوص بیولوژی و بیوسنتز آفلاتوکسین انجام شده است. در این مطالعه توانایی تولید سم آفلاتوکسین B1 در گونه­های مختلف آسپرژیلوس­های جداسازی شده از مناطق زراعی زیست محیطی و کارخانجات فرآوری مواد غذایی مورد بررسی واقع گردید. برای تشخیص سطح کمی آفلاتوکسین B1 از روش آنزیمی ایمونواسی رقابتی (الایزا) استفاده شد. همچنین الگوی تولید آفلاتوکسین B1 در محیط رشدو بیومس قارچی، در گونه­های توکسین­زا با یکدیگر مورد مقایسه واقع شدند. نتایج نشان دادند که همه­ی جدایه­های آسپرژیلوس، دارای توانایی تولید سم آفلاتوکسین B1 می­باشند، اگرچه بین میانگین سم تولید شده در محیط رشد و عصاره­ی سلول قارچی در گونه­های مختلف، اختلاف آماری معنی­داری وجود داشت. در گونه­های آسپرژیلوس فلاووس،آسپرژیلوس پارازیتیکوس،آسپرژیلوس سوژه،آسپرژیلوس ترئوس،آسپرژیلوس اوستیانوس،آسپرژیلوس کربوناریوس و آسپرژیلوس ونتی ای باقی ماندن سم در سلول قارچی بیش­تر از رهاسازی توکسین در محیط رشد قارچ بود. در سایر ایزوله­ها شامل آسپرژیلوس نایجر، آسپرژیلوس فتیدوس و آسپرژیلوس اکراسئوس میزان خروج سم از سلول قارچی بیش از باقیماندن آن در سلول اتفاق افتاد. نتایج نشان می­دهند که ایزوله­های توکسین­زای آسپرژیلوس می­توانند در تمام مناطق جغرافیایی مورد مطالعه در شمال ایران وجود داشته باشند. اگرچه رفتار گونه­ها در رهاسازی سم به محیط رشد با هم متفاوت می­باشند که به نظر می­رسد این تفاوت به دلیل تأثیرگذاری فاکتورهای مختلف بر مکانیزیم خروج سم به محیط خارج سلول قارچی است
Formations of Small Satellites to Realize Sensor Networks for Earth Observation

At the level of small satellites with a mass of a few kilograms all capabilities of self-organizing sensor networks for Earth observation can be realized. Photogrammetry methods enable sensor data fusion to generate 3-dimensional images of a target area. Here appropriate satellite position information in combination with attitude determination and control provides precision pointing accuracies to generate suitable input for subsequent data processing. This way, on small satellites efficient methods for distributed sensor systems in orbit can be implemented.
Making the Best Use of Review Summary for Sentiment Analysis

Sentiment analysis provides a useful overview of customer review contents. Many review websites allow a user to enter a summary in addition to a full review. Intuitively, summary information may give additional benefit for review sentiment analysis. In this paper, we conduct a study to exploit methods for better use of summary information. We start by finding out that the sentimental signal distribution of a review and that of its corresponding summary are in fact complementary to each other. We thus explore various architectures to better guide the interactions between the two and propose a hierarchically-refined review-centric attention model. Empirical results show that our review-centric model can make better use of user-written summaries for review sentiment analysis, and is also more effective compared to existing methods when the user summary is replaced with summary generated by an automatic summarization system.
Liquid–liquid phase separation and morphologies in organic particles consisting of α-pinene and β-caryophyllene ozonolysis products and mixtures with commercially available organic compounds

Abstract. Liquid–liquid phase separation (LLPS) in organic aerosol particles can impact several properties of atmospheric particulate matter, such as cloud condensation nuclei (CCN) properties, optical properties, and gas-to-particle partitioning. Yet, our understanding of LLPS in organic aerosols is far from complete. Here, we report on the LLPS of one-component and two-component organic particles consisting of α-pinene- and β-caryophyllene-derived ozonolysis products and commercially available organic compounds of relevance to atmospheric organic particles. In the experiments involving single-component organic particles, LLPS was observed in 8 out of 11 particle types studied. LLPS almost always occurred when the oxygen-to-carbon elemental ratio (O:C) was ≤0.44 but did not occur when O:C was >0.44. The phase separation occurred by spinodal decomposition as well as the nucleation and growth mechanism, and when LLPS occurred, two liquid phases coexisted up to ∼100 % relative humidity (RH). In the experiments involving two-component organic particles, LLPS was observed in 23 out of 25 particles types studied. LLPS almost always occurred when the average was O:C ≤0.67 but never occurred when the average O:C was >0.67. The phase separation occurred by spinodal decomposition as well as the nucleation and growth mechanism. When LLPS occurred, two liquid phases coexisted up to ∼100 % RH. These results provide further evidence that LLPS is likely a frequent occurrence in organic aerosol particles in the troposphere, even in the absence of inorganic salts.
Deconvolving multiplexed protease signatures with substrate reduction and activity clustering

Proteases are pleiotropic, promiscuous enzymes that degrade proteins and peptides, which drive important processes in health and disease. The ability to quantify the activity of protease signatures by sampling with Massively Multiplexed Activity (MMA) libraries will provide unparalleled biological information. Under such a framework, a designed library of peptide substrates is exposed to a cocktail of proteases, the cleavage velocity of each substrate is measured, and individual protease activity levels are inferred from the data. Previous studies have developed individual protease sensors, but multiplexed substrate cleavage data becomes difficult to interpret as the number of cross-cutting proteases increases. Computational methods for parsing this data to estimate individual protease activities primarily use an extensive compendium of all possible protease-substrate combinations, which require impractical amounts of training data when scaling up to MMA libraries. Here we provide a computational method for estimating protease activities efficiently by reducing the number of substrates and clustering proteases with similar cleavage activities into families. This method is scalable and will enable the future use of MMA libraries with applications spanning therapeutic and diagnostic biotechnology.
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices

We prove a Chernoff-type bound for sums of matrix-valued random variables sampled via a regular (aperiodic and irreducible) finite Markov chain. Specially, consider a random walk on a regular Markov chain and a Hermitian matrix-valued function on its state space. Our result gives exponentially decreasing bounds on the tail distributions of the extreme eigenvalues of the sample mean matrix. Our proof is based on the matrix expander (regular undirected graph) Chernoff bound [Garg et al. STOC ’18] and scalar Chernoff-Hoeffding bounds for Markov chains [Chung et al. STACS ’12]. Our matrix Chernoff bound for Markov chains can be applied to analyze the behavior of co-occurrence statistics for sequential data, which have been common and important data signals in machine learning. We show that given a regular Markov chain with \(n\) states and mixing time \(\tau\), we need a trajectory of length \(O(\tau (\log{n} + \log{\tau})/\epsilon^2)\) to achieve an estimator of the co-occurrence matrix with error bound \(\epsilon\). We conduct several experiments and the experimental results are consistent with the exponentially fast convergence rate from theoretical analysis. Our result gives the first bound on the convergence rate of the co-occurrence matrix and the first sample complexity analysis in graph representation learning.
Motivational effects on empathic choices

Empathy often feels automatic, but variations in empathic responding suggest that, at least some of the time, empathy is affected by one’s motivation to empathize in any particular circumstance. Here, we show that people can be motivated to engage in (or avoid) empathy-eliciting situations with strangers, and that these decisions are driven by subjective value-based estimations of the costs (e.g., cognitive effort) and benefits (e.g., social reward) inherent to empathizing. Across seven experiments (overall N = 1,348), and replicating previous work (Cameron et al., 2019), we found a robust empathy avoidance effect. We also find support for the hypothesis that individuals can be motivated to opt-in to situations requiring empathy that they would otherwise avoid. Participants were more likely to opt into empathy-eliciting situations if 1) they were incentivized monetarily for doing so (Experiments 1a and 1b), and 2) if a more familiar and liked empathy target was available (Experiments 2a and 2b). Framing empathy as explicitly related to one’s moral character and reputation did not motivate participants to engage in empathy (Experiment 3a and 3c), though these null results may be due to a weak manipulation. These findings suggest that empathy can be motivated in multiple ways, and is a process driven by context-specific value-based decision making.
CO2 Increase Experiments Using the CESM: Relationship to Climate Sensitivity and Comparison of CESM1 to CESM2

We examine the response of the Community Earth System Model Versions 1 and 2 (CESM1 and CESM2) to abrupt quadrupling of atmospheric CO2 concentrations (4xCO2) and to 1% annually increasing CO2 concentrations (1%CO2). Different estimates of equilibrium climate sensitivity (ECS) for CESM1 and CESM2 are presented. All estimates show that the sensitivity of CESM2 has increased by 1.5 K or more over that of CESM1. At the same time the transient climate response (TCR) of CESM1 and CESM2 derived from 1%CO2 experiments has not changed significantly—2.1 K in CESM1 and 2.0 K in CESM2. Increased initial forcing as well as stronger shortwave radiation feedbacks are responsible for the increase in ECS seen in CESM2. A decomposition of regional radiation feedbacks and their contribution to global feedbacks shows that the Southern Ocean plays a key role in the overall behavior of 4xCO2 experiments, accounting for about 50% of the total shortwave feedback in both CESM1 and CESM2. The Southern Ocean is also responsible for around half of the increase in shortwave feedback between CESM1 and CESM2, with a comparable contribution arising over tropical ocean. Experiments using a thermodynamic slab‐ocean model (SOM) yield estimates of ECS that are in remarkable agreement with those from fully coupled Earth system model (ESM) experiments for the same level of CO2 increase. Finally, we show that the similarity of TCR in CESM1 and CESM2 masks significant regional differences in warming that occur in the 1%CO2 experiments for each model.
Performance Differential between Private and State-owned Enterprises: An Analysis of Profitability and Solvency

ABSTRACT Motivated by the rise of state capitalism, the paper investigates the relationship between ownership identity and the performance of firms in terms of profitability and solvency. Using cross-sectional data covering over 25,000 firms worldwide and by employing various empirical methods, we find robust evidence that state-owned enterprises (SOEs) tend to be less profitable than private-owned enterprises. However, they appear to use debt for their financial need and are, thus, better leveraged. SOEs are also more labor-intensive and have higher labor costs. In addition, an improvement in institutional quality could benefit both SOEs and POEs. Thus, evidence from this study could be interpreted to mean that privatization could improve the performance of public firms; however, this process should come with several prior-privatization approaches. A study over a more extended period is needed before these results can be considered conclusive.
Self-Supervised Graph Transformer on Large-Scale Molecular Data

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery. Recent researches abstract molecules as graphs and employ Graph Neural Networks (GNNs) for molecular representation learning. Nevertheless, two issues impede the usage of GNNs in real scenarios: (1) insufficient labeled molecules for supervised training; (2) poor generalization capability to new-synthesized molecules. To address them both, we propose a novel framework, GROVER, which stands for Graph Representation frOm self-superVised mEssage passing tRansformer. With carefully designed self-supervised tasks in node-, edge- and graph-level, GROVER can learn rich structural and semantic information of molecules from enormous unlabelled molecular data. Rather, to encode such complex information, GROVER integrates Message Passing Networks into the Transformer-style architecture to deliver a class of more expressive encoders of molecules. The flexibility of GROVER allows it to be trained efficiently on large-scale molecular dataset without requiring any supervision, thus being immunized to the two issues mentioned above. We pre-train GROVER with 100 million parameters on 10 million unlabelled molecules -- the biggest GNN and the largest training dataset in molecular representation learning. We then leverage the pre-trained GROVER for molecular property prediction followed by task-specific fine-tuning, where we observe a huge improvement (more than 6% on average) from current state-of-the-art methods on 11 challenging benchmarks. The insights we gained are that well-designed self-supervision losses and largely-expressive pre-trained models enjoy the significant potential on performance boosting.
Malicious Requests Detection with Improved Bidirectional Long Short-term Memory Neural Networks

Detecting and intercepting malicious requests are one of the most widely used ways against attacks in the network security. Most existing detecting approaches, including matching blacklist characters and machine learning algorithms have all shown to be vulnerable to sophisticated attacks. To address the above issues, a more general and rigorous detection method is required. In this paper, we formulate the problem of detecting malicious requests as a temporal sequence classification problem, and propose a novel deep learning model namely Convolutional Neural Network-Bidirectional Long Short-term Memory-Convolutional Neural Network (CNN-BiLSTM-CNN). By connecting the shadow and deep feature maps of the convolutional layers, the malicious feature extracting ability is improved on more detailed functionality. Experimental results on HTTP dataset CSIC 2010 have demonstrated the effectiveness of the proposed method when compared with the state-of-the-arts.
Noisy Batch Active Learning with Deterministic Annealing.

We study the problem of training machine learning models incrementally with batches of samples annotated with noisy oracles. We select each batch of samples that are important and also diverse via clustering and importance sampling. More importantly, we incorporate model uncertainty into the sampling probability to compensate for poor estimation of the importance scores when the training data is too small to build a meaningful model. Experiments on benchmark image classification datasets (MNIST, SVHN, CIFAR10, and EMNIST) show improvement over existing active learning strategies. We introduce an extra denoising layer to deep networks to make active learning robust to label noises and show significant improvements.
Quantifying burning efficiency in megacities using the NO2∕CO ratio from the Tropospheric Monitoring Instrument (TROPOMI)

Abstract. This study investigates the use of co-located nitrogen dioxide (NO2) and carbon monoxide (CO) retrievals from the TROPOMI satellite to improve the quantification of burning efficiency and emission factors (EFs) over the megacities of Tehran, Mexico City, Cairo, Riyadh, Lahore, and Los Angeles. Efficient combustion is characterized by high NOx (NO+NO2) and low CO emissions, making the NO2∕CO ratio a useful proxy for combustion efficiency (CE). The local enhancement of CO and NO2 above megacities is well captured by TROPOMI at short averaging times compared with previous satellite missions. In this study, the upwind background and plume rotation methods are used to investigate the accuracy of satellite-derived ΔNO2∕ΔCO ratios. The column enhancement ratios derived using these two methods vary by 5 % to 20 % across the selected megacities. TROPOMI-derived column enhancement ratios are compared with emission ratios from the EDGAR v4.3. 2 (Emission Database for Global Atmospheric Research v4.3.2) and the MACCity (Monitoring Atmospheric Chemistry and Climate and CityZen) 2018 emission inventories. TROPOMI correlates strongly (r=0.85 and 0.7) with EDGAR and MACCity, showing the highest emission ratio for Riyadh and lowest emission ratio for Lahore. However, inventory-derived emission ratios are 60 % to 85 % higher than TROPOMI column enhancement ratios across the six megacities. The short lifetime of NO2 and the different vertical sensitivity of TROPOMI NO2 and CO explain most of this difference. We present a method to translate TROPOMI-retrieved column enhancement ratios into corresponding emission ratios, thereby accounting for these influences. Except for Los Angeles and Lahore, TROPOMI-derived emission ratios are close (within 10 % to 25 %) to MACCity values. For EDGAR, however, emission ratios are ∼65 % higher for Cairo and 35 % higher for Riyadh. For Los Angeles, EDGAR and MACCity are a factor of 2 and 3 higher than TROPOMI respectively. The air quality monitoring networks in Los Angeles and Mexico City are used to validate the use of TROPOMI. For Mexico City and Los Angeles, these measurements are consistent with TROPOMI-derived emission ratios, demonstrating the potential of TROPOMI with respect to monitoring burning efficiency.
Attosecond state-resolved carrier motion in quantum materials probed by soft x-ray XANES

Recent developments in attosecond technology led to tabletop X-ray spectroscopy in the soft X-ray range, thus uniting the element- and state-specificity of core-level x-ray absorption spectroscopy with the time resolution to follow electronic dynamics in real time. We describe recent work in attosecond technology and investigations into materials such as Si, SiO2, GaN, Al2O3, Ti, TiO2, enabled by the convergence of these two capabilities. We showcase the state-of-the-art on isolated attosecond soft x-ray pulses for x-ray absorption near edge spectroscopy (XANES) to observe the 3d-state dynamics of the semi-metal TiS2 with attosecond resolution at the Ti L-edge (460 eV). We describe how the element- and state-specificity at the transition metal L-edge of the quantum material allows to unambiguously identify how and where the optical field influences charge carriers. This precision elucidates that the Ti:3d conduction band states are efficiently photo-doped to a density of 1.9 x 10^21 cm^-3 and that the light-field induces coherent motion of intra-band carriers across 38% of the first Brillouin zone. Lastly, we describe the prospects with such unambiguous real-time observation of carrier dynamics in specific bonding or anti-bonding states and speculate that such capability will bring unprecedented opportunities towards an engineered approach for designer materials with pre-defined properties and efficiency. Examples are composites of semiconductors and insulators like Si, Ge, SiO2, GaN, BN, quantum materials like graphene, TMDCs, or high-Tc superconductors like NbN or LaBaCuO. Exiting are prospects to scrutinize canonical questions in multi-body physics such as whether the electrons or lattice trigger phase transitions.