Miracle-dz/UniFAR-FTUs huggingface dataset:
Non-Compete Agreements and Labor Allocation Across Product Markets

I analyze the effect of non-compete agreements (NCAs) on career trajectories of 600,000 inventors in the US. NCAs limit the choice set of inventors as they are less able to move to competitors. Inventors bypass their NCAs by moving to a new employer in a more distant product market. I identify causal effects using staggered changes in NCA enforceability across US states. Inventors are 25% more likely to move to another industry after higher NCA enforceability. Inventors move to new employers who are less likely to rely on NCAs and patent in unfamiliar technologies. There is a lower quality match between inventors and new employers. Consistent with this evidence, affected inventors are subsequently less productive. Regulation in the form of non-compete agreements put a constraint in the industry choice set of inventors, which leads to some detrimental reallocation of human capital in the economy.
The robustness of model-free adaptive control with disturbance suppression

For a class of single-input single-output(SISO) nonlinear discrete-time systems, the measurement disturbance impacts to a model-free adaptive control are considered. The relationship between the output error and the measurement disturbance are given, and the influence of the measurement disturbance is also analyzed. Then, an improved model-free adaptive control algorithm with a filter is proposed, which suppresses the measurement disturbance effectively. The analysis is supported by simulations.
Polycyclic Aromatic Hydrocarbon (PAH) Contents of Four Species of Smoked Fish from Different Sites in Senegal

Polycyclic aromatic hydrocarbons (PAHs) are compounds resulting from any incomplete combustion process. These are pollutants that have proven toxicity due to their carcinogenic nature and can contaminate food during traditional smoking methods. Their highly toxic effect on human health requires monitoring of their levels in food products and the development of appropriate analytical methods for their determination. Thus, this study was conducted to assess the level of PAHs contamination of four (4) species of smoked fish (Arius heudelotii, Sardinella aurita, Ethmalosa fimbriata, and Sardinella maderensis) which were sampled in seventeen (17) localities in Senegal. The compounds targeted in this study were benzo(a)pyrene (B(a)P), benzo(a)anthracene (B(a)A), benzo(b)fluoranthene (B(b)F), and chrysene (Chr). The QuEChERS method was used for the extraction of PAHs, and their contents were quantified by gas chromatography (GC) coupled with mass spectroscopy (MS). The validation method was performed in accordance with the French standard NF V03-110 (2010). Satisfactory linearity (R2 > 0.999), LOD (0.05–0.09 μg/kg), LOQ (0.19–0.24 μg/kg), and precision (1.33–3.13%) of the four PAHs were obtained. The results of analysis in the 17 localities showed that all samples are contaminated by the four (4) PAHs with great variability of the contents between the different species and their origin. The B(a)P and ∑4PAHS contents in the samples ranged from 1.7 to 33 µg/kg and from 4.8 to 1082.3 µg/kg, respectively. Twelve (12) samples showed high levels of B(a)P, ranging from 2.2 to 33 µg/kg, thus exceeding the maximum authorized level (2 µg/kg). Fourteen (14) samples showed an overall ∑4PAHS content varying from 14.8 to 1082.3 µg/kg, which is above the maximum authorized limit (12 µg/kg). The principal component analysis showed that sardinella (Sardinella aurita and Sardinella maderensis) have very low levels of B(a)P, B(b)F, B(a)A, and Chr contents. However, high ∑4PAHS contents characterize smoked fish of the Kong species (Arius heudelotii), from Cap Skiring, Diogne, Boudody, and Diaobé, and of the Cobo species (Ethmalosa fimbriata) from Djiffer. Thus, based on the authorized limits for PAHs in smoked fish, it appears that smoked fish of the sardinella species are less carcinogenic for human consumption.
Characterisation of errors of a regional model of the Bay of Biscay in response to wind uncertainties: a first step toward a data assimilation system suitable for coastal sea domains

This study is aimed at exploring the errors of a regional model of the Bay of Biscay, a regional zoom of the IBI configuration of the ocean model NEMO, with the ultimate objective of guiding the choice and implementation of a data assimilation system in that region. An ensemble experiment was carried out by randomly perturbing winds along a base of EOFs with the aim to mimic a potential source of error in the model forecasts. A characterisation was attempted with proxy forecast errors by using statistical moments of order 1 to 4. The temporal variability of model correction patterns in a hypothetical data assimilation system was also illustrated. Significant departures from linear/Gaussian response were found, as well as well-marked non-stationarities in the error patterns. Within the limits of the experimental protocol, this could be technically applicable to other coastal areas as the study illustrates the likely limits of stationary/Gaussian data assimilation approaches in the Bay of Biscay.
Alexander duality for parametrized homology

This paper extends Alexander duality to the setting of parametrized homology. Let X with be a compact subset of R^n x R (n \geq 2) satisfying certain conditions, let Y be its complement, and let p be the projection onto the second factor. Both X and Y are parametrized spaces with respect to the projection. The parametrized homology is a variant of zigzag persistent homology that measures how the homology of the level sets of the space changes as we vary the parameter. We show that if (X, p|_X) has a well-defined parametrized homology, then the pair (Y, p|_Y) has a well-defined reduced parametrized homology. We also establish a relationship between the parametrized homology of (X, p|_X) and the reduced parametrized homology of (Y, p|_Y).
FOXD1 promotes chemotherapy resistance by enhancing cell stemness in colorectal cancer through β‑catenin nuclear localization

Forkhead box D1 (FOXD1) serves a critical role in colorectal cancer (CRC). FOXD1 expression is an independent prognostic factor in patients with CRC; however, the molecular mechanism and signaling pathway of FOXD1 that regulates cell stemness and chemoresistance has not been fully characterized. The aim of the present study was to further validate the effect of FOXD1 on the proliferation and migration of CRC cells, and to delve into the possible potential of FOXD1 in the clinical treatment of CRC. The effect of FOXD1 on cell proliferation was assessed using Cell Counting Kit 8 (CCK-8) and colony formation assays. The effect of FOXD1 on cell migration was assessed by wound-healing and Transwell assays. The effect of FOXD1 on cell stemness was assessed by spheroid formation in vitro and limiting dilution assays in vivo. The expression of stemness associated proteins, leucine rich repeat containing G protein-coupled receptor 5 (LGR5), OCT4, Sox2 and Nanog, and epithelial-mesenchymal transition associated proteins, E-cadherin, N-cadherin and vimentin, were detected by western blotting. Proteins interrelationships were assessed by a co-immunoprecipitation assay. Oxaliplatin resistance was assessed using CCK-8 and apoptosis assays in vitro, and using a tumor xenograft model in vivo. By constructing FOXD1 overexpression and knockdown stably transfected strains of colon cancer cells, it was revealed that the overexpression of FOXD1 increased CRC cell stemness and chemoresistance. By contrast, knockdown of FOXD1 produced the opposite effects. These phenomena were caused by the direct interaction between FOXD1 and β-catenin, thus promoting its nuclear translocation and the activation of downstream target genes, such as LGR5 and Sox2. Notably, inhibition of this pathway with a specific β-catenin inhibitor (XAV-939) could impair the effects induced by the overexpression of FOXD1. In summary, these results indicated that FOXD1 may promote cell stemness and the chemoresistance of CRC by binding directly to β-catenin and enhancing β-catenin nuclear localization; therefore, it may be considered a potential clinical target.
In Situ Lunar Regolith Analysis by Laser-Based Mass Spectrometry

We are developing laser-based mass spectrometry (LIMS) for the in situ investigation of the chemical and mineralogical composition of the lunar regolith. The current development of our LIMS instrument is for an application on a robotic mission within the Artemis CLPS program of NASA. The CLPS lander will be placed in the south polar region. The LIMS system consists of a time-of-flight mass analyzer (TOF-MS), a laser system (LSS) providing nano-second laser pulses focused to um spots on the sample surface, electronics (ELU) for operating the LIMS system, and a sample handling system (SHS). The TOF-MS, LSS, and ELU are according to our established design presented earlier. The SHS is specially designed for the CLPS lander to collect regolith grains from the lunar surface in the vicinity of the lander. The SHS design foresees rotating steel brushes that free regolith grains from the surface into ballistic trajectories. A conveyor belt collects these grains, which is electrically biased to improve its collection efficiency. Adjusting the speed of the brushes and the voltage on the conveyor belt allow to optimize the collection efficiency of the grains. The conveyor belt transports the grains to the entrance of the mass analyzer where grain by grain analysis will be performed. The main scientific objective for the LIMS instrument is the geochemical analysis of the lunar regolith, by the analysis of individual regolith grains and assessing their mineralogical diversity. In addition, this investigation will also address technical aspects of sampling a planetary surface at or near a landed spacecraft, i.e., the effect the plume of the retrorockets has on the regolith underneath the lander. Of particular interest is the chemical contamination of the surface by the spent fuel, and the amount of removal grains by the gas drag.
Genetic-Based Virtual Machines Consolidation Strategy With Efficient Energy Consumption in Cloud Environment

In cloud computing environments, virtualization is used to share physical machine (PM) resources among multiple users by creating virtual machines (VMs). Running the PM consumes a large amount of energy. Additionally, the PM will be overloaded when the demand for resources exceeds the PM capacity. This overload on the PM leads to violations of Service Level Agreements (SLAs). Dynamic VM consolidation techniques use live migration of VMs to optimize resource utilization and minimize energy consumption. However, excessive migration of VMs impacts negatively the application performance due to the incurred overhead at the runtime. This paper presents a modified genetic-based VM consolidation (MGVMC) strategy that aims to replace VMs in an online manner taking into account energy consumption, SLA violations, and the number of VM migrations. The MGVMC strategy utilizes the genetic algorithm to migrate VMs to the appropriate PM in a way that minimizes the number of over-utilized and under-utilized physical machines (PMs) as low as possible. The performance of the MGVMC strategy was evaluated using the CloudSim Plus framework with a large number of VMs and workload traces from the PlanetLab platform. The experimental results revealed that the MGVMC strategy achieved a significant improvement in energy consumption, SLA violations, and the number of VM migrations compared to other recent approaches. These results demonstrate the effectiveness of the MGVMC strategy in optimizing VM consolidation in the cloud environment.
Adaptive Modulation and Coding Based on Multi-Agent Reinforcement Learning for Power Emergency Communications

Power emergency communications are critical to rescue work when some disasters happen. For the sake of alleviating the shortage of spectrum resources and maintaining system connections, an adaptive modulation and coding scheme is studied in this paper. For the target system, the principle of cognitive radio networks (CRNs) is involved and the users in power emergency communications are modelled as primary users (PUs) and secondary users (SUs) according to their communication requirements. A maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm in deep reinforcement learning is proposed to train the system and achieve an optimal policy, in which different users can access the system with varying modulation and coding schemes. The simulation results show that the proposed ME-MAAC algorithm outperforms the Deep Q-Network (DQN) algorithm in accordance with efficiency and performance. The proposed adaptive modulation and coding (AMC) scheme can improve system connection rate and spectrum efficiency, that is, the users in power emergency communications can obtain more communications with limited power and spectrum resources. This paper provides an useful guidance for the design of practical power emergency communications.
Forecasting Value-at-Risk Using Deep Neural Network Quantile Regression

In this article, we use a deep quantile estimator, based on neural networks and their universal approximation property to examine a non-linear association between the conditional quantiles of a dependent variable and predictors. This methodology is versatile and allows both the use of different penalty functions, as well as high dimensional covariates. We present a Monte Carlo exercise where we examine the finite sample properties of the deep quantile estimator and show that it delivers good finite sample performance. We use the deep quantile estimator to forecast value-at-risk and find significant gains over linear quantile regression alternatives and other models, which are supported by various testing schemes. Further, we consider also an alternative architecture that allows the use of mixed frequency data in neural networks. This article also contributes to the interpretability of neural network output by making comparisons between the commonly used Shapley Additive Explanation values and an alternative method based on partial derivatives.
Predictive Control of a Synchronous Generator Network Using Neural Networks

It appears that the current global upswing is in tandem with a strong push toward electrical energy as an industrialization strategy. Power plants are an essential aspect of any modern infrastructure, and this project is the best option for backup and industrial generators. In this paper, we provide a novel approach based on neural network technology long-short-term-memory (LSTM), and particle swarm optimization (PSO) methods to address this issue. Predictive control of a synchronous generator system is the focus of this research. In the first phase, we tried a few different approaches to solving the issue, including a support vector machine, a random forest, and a decision tree. Then, we realized that the results we had been getting weren’t cutting it, so we set out to create a new LSTM-based technique. Based on the results of the traditional LSTM, a new study applies the LSTM-based PSO algorithm to solve the issue. The findings obtained are compared, and it is shown that the suggested LSTM-based PSO is superior to the previous investigations.
Conceptual Design of an Automated Irrigation and Fixation System for Long-Term Crop Care of Cherry Tomatoes for Applications in Microgravity Machines

Currently, colonizing space is the next step for humanity. However, it has many challenges to be completed before reaching this goal. Thus, multiple fields of study have been developed to solve some of the problems to live out on Earth such as a source of renewable food. For this reason, the Center for Space Emerging Technologies (C-SET) has been working on projects to contribute to the research of this task with this project, which is an automatized cultivation system to study the effect of simulated microgravity in plants with the highest precision reducing human intervention or error. In addition, many parts of this system can be easily made by current technologies like 3D printers or CNC machines. This project presents a conceptual design of the system focusing on water storage, selective irrigation, and improvement of the simulation (fixing system), besides the parts designed are the simplest to give good maintenance. This system is pretended to be applied to cherry tomatoes to analyze the correct functionating of the machine in simulated microgravity at the same time that biological assays will be done to the sample as a part of a complete project. In conclusion, if favorable results were achieved; consequently, the next step of this project is to reduce the human intervention with camera sensors to record the times of growth, flowering, and fruit ripening, at the same time, developing and improving the method to refill the water container.
CCDF measurement with Multi Impairment of Wi-Fi 6 performance using MATLAB Simulink

The current 802.11ac WiFi standard is built upon and improved by WiFi 6, commonly referred to as “AX WiFi” or “802.11ax WiFiIn response to the increasing number of gadgets in the globe, WiFi 6 was first developed. If you have numerous smart home devices, VR equipment, or a lot of other gadgets, WiFi 6 employs a 160 MHz Channel to offer a channel that larger and providing by 1024-QAM to more data with one packed, increasing efficiency. In this paper 802.11 ax design and implemented in MATLAB Simulink where CCDF measurement (Complementary Cumulative Distribution Function measure the probability that a signal's instantaneous power may exceed its target level or average power) with multi-impairment performance illustrated using 1024-QAM to offer a packed signal with increasing data and a channel with160 MHz, 1024 QAM examined with AWGN and Rayliegh fading channel in term of bit error rate.
Planning and Execution of Juno Radio Occultation Experiments at Jupiter

Since entering orbit around Jupiter in July 2016, the Juno spacecraft has made unprecedented strides in understanding Jupiter's atmosphere, magnetosphere, and interior structure. In June 2021, Juno completed its Prime Mission and entered the Extended Mission phase to continue exploring the Jovian system. Juno is equipped with an X-band telecommunications link and a Ka-band radio science link which allow precision measurements of the Doppler shift between the spacecraft and NASA's Deep Space Network with the primary purpose of determining Jupiter's gravitational field. In Juno's Extended Mission, the scope will expand beyond gravity field studies. Radio occultations to measure the ionosphere properties of Ganymede and Europa were executed in June 2021 and September 2022, respectively. Radio occultations of Jupiter's ionosphere and atmosphere are planned later in the mission, beginning in 2023. Although the radio science instrument was originally designed to measure the gravity field, the instrument is fully capable of making measurements for radio occultations. Challenges in collecting radio occultation data include Juno's pointing capability due to spin-stabilization, lack of an onboard highly stable frequency reference, and compensation from refraction of Jupiter's atmosphere on the radio frequency. This paper describes how the Juno Ganymede and Europa radio occultation experiments were planned and executed, along with the necessary preparation work required to plan and execute radio occultations of Jupiter's intense ionosphere and dense atmosphere.
Progressive Web Application for Plant Disease Detection using CNN

A large portion of India's population relies primarily on agriculture for their livelihood. Farmers suffer a considerable amount of loss due to the innumerable diseases affecting their plants. Detection of such plant diseases with the human eye often yields inaccurate results. Furthermore, to correctly identify the disease, the individual assessing the plant should be an expert in the respective field. The diagnosis of plant illness is a visual task and thus, many computer vision techniques have been used previously for tackling it. Recently, convolutional Neural Networks have shown excellent results in many computer vision tasks. This study develops an application for plant disease classification by comparing the results obtained by training two convolutional neural networks, one from scratch and one by the transfer learning method. Both achieved a validation accuracy of 86 percent and 96 percent, respectively. The system was developed in the form of a web application for both mobile and web devices using the model, which is capable of functioning without any network requirements.
Assessment of 30 Years of Randomized Controlled Trials in The American Journal of Sports Medicine: 1990-2020

Background: Randomized controlled trials (RCTs) stand atop the evidence-based hierarchy of study designs for their ability to arrive at results with the lowest risk of bias. Even for RCTs, however, critical appraisal is essential before applying results to clinical practice. Purpose: To analyze the quality of reporting of RCTs published in The American Journal of Sports Medicine (AJSM) from 1990 to 2020 and to identify trends over time and areas of improvement for future trials. Study Design: Systematic review; Level of evidence, 1. Methods: We queried the AJSM database for RCTs published between January 1990 and December 2020. Data pertaining to study characteristics were recorded. Quality assessments were conducted using the Detsky quality-of-reporting index and the modified Cochrane risk-of-bias (mROB) tool. Univariate and multivariable models were generated to establish factors with associations to study quality. The Fragility Index was calculated for eligible studies. Results: A total of 277 RCTs were identified with a median sample size of 70 patients. A total of 19 RCTs were published between 1990 and 2000 (t1); 82 RCTs between 2001 and 2010 (t2); and 176 RCTs between 2011 and 2020 (t3). From t1 to t3, significant increases were observed in the overall mean-transformed Detsky score (from 68.2% ± 9.8% to 87.4% ± 10.2%, respectively; P < .001) and mROB score (from 4.7 ± 1.6 to 6.9 ± 1.6, respectively; P < .001). Multivariable regression analysis revealed that trials with follow-up periods of <5 years clearly stated primary outcomes, and a focus on the elbow, shoulder, or knee were associated with higher mean-transformed Detsky and mROB scores. The median Fragility Index was 2 (interquartile range, 0-5) for trials with statistically significant. Studies with small sample sizes (<100 patients) were more likely to have low Fragility Index scores and less likely to have a statistically significant finding in any outcome. Conclusion: The quantity and quality of published RCTs published in AJSM increased over the past 3 decades. However, single-center trials with small sample sizes were prone to fragile results.