Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced photosynthetic efficiency for increased carbon assimilation and woody biomass production in hybrid poplar INRA 717-1B4

Increasing CO2 levels in the atmosphere and the resulting negative impacts on climate change have compelled global efforts to achieve carbon neutrality or negativity. Most such efforts focus on carbon sequestration through chemical or physical approaches. We aim to harness the power of synthetic biology to enhance plants’ natural ability to draw down and sequester carbon, thereby positively affecting climate change. Past decades of scientific progress have shed light on strategies to overcome the intrinsic limitations of carbon drawdown and fixation through photosynthesis, particularly in row crops in hopes of improving agricultural productivity for food security. Incorporating a photorespiration bypass in C3 plants has shown promising results of increased biomass and grain yield. Despite their globally dominant role in atmospheric carbon flux, the drawdown rates of most trees are currently limited by their C3 photosynthetic metabolism, and efforts to improve the photosynthetic capacity of trees, such as by reducing energy loss in photorespiration, are currently lacking. Here, we selected a photorespiration bypass pathway and tested its effectiveness on photosynthetic enhancement in hybrid poplar INRA717-IB4. The design includes a RNAi strategy to reduce the transportation of the photorespiration byproduct, glycolate, out of chloroplast and a shunt pathway to metabolize the retained glycolate back to CO2 for fixation through the Calvin-Benson cycle. Molecular and physiological data collected from two repeated growth experiments indicates that transgenic plants expressing genes in the photorespiration bypass pathway have increased photosynthetic efficiency, leading to faster plant growth and elevated biomass production. One lead transgenic event accumulated 53% more above-ground dry biomass over a five month growth period in a controlled environment. Pilot projects with photosynthesis-enhanced trees in the field are in progress. Our results provide a proof-of-concept for engineering trees to help combat climate change.
Experimental Study and Numerical Analysis of the Seismic Performance of Reactive Power Concrete Box Pier

Taking a reactive power concrete(RPC) box pier as the object of study,3 specimens with reasonable cross-section size and structure conformation were firstly designed.The effect of the direction angle of horizontal loading on the seismic performance of RPC box pier was experimentally studied through applying constant axial force and horizontal cyclic load.The ductility,hysteresis curve,skeleton curve,load degradation curve and stiffness degradation curve of specimen were studied,and the displacement ductility factor and energy loss factor of specimens were obtained.Non-linear numerical analysis program describing the complete process of specimen under one-way loading was compiled and verified by experimental results.Numerical and experimental results show that RPC box pier presents good seismic performance,and the direction angle of horizontal loading is a key factor in seismic performance of box pier,and the component subjected to oblique force presents weaker seismic performance than that of component subjected to axial force.
A Quasi-Natural Experiment: Product Market Competition and Stock Price Crash Risk

ABSTRACT This paper uses the sample companies in China, taking the promulgation and implementation of the <<Catalogue for the Guidance of Industries for Foreign Investment>> as an external event shock, and studies the impact of product market competition on stock price crash risk. The result shows that product market competition significantly reduces stock price crash risk, which is quite different from the conclusion under the American economic environment. Further research finds that high management agency problems and opacity of enterprise information may be the impact channel of product market competition to reduce stock price crash risk.
Influencing Factors of Smart Government Information Security: Experience from China

: Based on information technology, smart government processes information data to help improve the efficiency of government operations. Information security has become the key to the transformation of government wisdom and improvement of government service efficiency and transparency. To explore the influencing factors of government information security, 27 influencing factors from 6 dimensions of personnel, facilities, information, personnel management, system, and environment were extracted. The decision-making trial and evaluation laboratory (DEMATEL)-interpretative structural modeling (ISM) composite model was used to determine the importance of each factor, the mechanism between the influencing factors was analyzed, and a smart government information security interpretation structure model was constructed. Results show that policies and regulations are the root factors affecting the information security of the smart government. The international environment is a deep-layer influencing factor on government information security. Infrastructure, moral training, and other factors are middle-layer influencing factors that are influenced by the superior factors. Psychological factors, platform construction, and others are surface-layer influencing factors that play a connecting role. Security awareness, behavior security, moral level, and others are directly influencing factors and determine the level of smart government information security. Results of this study also show the feasibility of DEMATEL-ISM model in identifying the relevant factors and analyzing their influencing mechanism on smart government information security. A novel modeling method and its analysis approach are provided for the government to improve the level of information security.
Mobile Robot Localization Using Map Based on Cadastral Data for Autonomous Navigation

For autonomous navigation, localization in an environment is a fundamental capability. In this regard, it is necessary for robots to have environmental maps. The environmental maps are, in general, built beforehand through SLAM. On the other hand, the authors have focused on cadastral data including road and building information. In this paper, we propose a localization method using an environmental map based on the cadastral data. The robot is assumed to be equipped with 3D LiDAR. However, the cadastral data does not include natural objects, such as trees. Moreover, the position of a building in the cadastral data is sometimes different from its actual position in the environment. For data association between the sensor measurements and environmental map, we describe point cloud processing. Furthermore, we use an RGB-D camera to obtain a dense point cloud. In the navigation experiments, we show that the robot is able to move towards the destination autonomously through the localization in the map.
Ageing in the labour market: a spatial VAR approach

ABSTRACT This study examines the effects of ageing on per capita labour income, consumption and wealth of the working population in Italian provinces. Starting from the overlapping generations (OLG) model of perpetual youth, we estimate a spatial vector autoregressive (VAR) model to investigate how ageing affects the model’s dynamics. We find that ageing positively impacts per capita labour income and wealth, consequently having an adverse effect on consumption (over-accumulation). A simple exercise shows that over-accumulation is beneficial to the Italian social security system.
Stabilization of Trafﬁc Flow Based on the Multiple Information of Preceding Cars

To enhance the stability of trafﬁc ﬂow, a new car-following model is proposed by taking into account the support of intelligent transportation system (ITS) information, which includes both the headway and the velocity difference of multiple preceding cars. The new model is based on the Optimal Velocity (OV) model and its extended models. The stability condition of the model is obtained by using the linear stability theory. Through nonlinear analysis, the modiﬁed Korteweg-de Vries equation is constructed and solved, and the trafﬁc ﬂow is classiﬁed into three types, i.e. stable, metastable, and unstable. The jam phase can thus be described by the kink-antikink soliton solution for the mKdV equation. The numerical simulation results show that compared with previous models considering only one of the ITS information, the proposed model can suppress trafﬁc jams more efﬁciently when both headway and velocity difference of arbitrary preceding cars are taken into account. The results of numerical simulation coincide with the theoretical ones.
Puncture ’Em All: Updatable Encryption with No-Directional Key Updates and Expiring Ciphertexts

Updatable encryption (UE) allows to periodically rotate encryption keys without the need to decrypt and re-encrypt already encrypted data. This is achieved by means of an update token that allows to perform the ciphertext update via any semi-trusted party. Unfortunately, apart from a recent UE construction from indistinguishability obfuscation (Nishimaki, ePrint’21), in all existing constructions the update token provides additional functionality and at least allows to downgrade keys. Such a leakage is undesirable and leads to rather involved and complex security models. A recent UE model due to Jiang (Asi-acrypt’20), extending the model of Boyd et al. (Crypto’20), explicitly considers these di-rectionality and leakage issues, and left open the construction of UE schemes where keys cannot be transformed via a token in any direction (aka UE schemes with “no-directional” key updates).
Autonomous Time Series Data Processing on Historical and Real-Time Settings

—Heterogeneous sensor networks, including water distribution systems and trafﬁc monitoring systems, produce abundant time series data for monitoring network dynamics and detecting events of interest. Nevertheless, errors and failures in the calibration, data storage or acquisition can occur on some of the sensors installed in those systems, producing missing and/or anomalous values. This work proposes a computational system, referred as AutoMTS, for the fully autonomous cleaning of multivariate time series data using strict quality criteria assessed against ground truth extracted from the targeted series data. The proposed methodology is parameter-free as it relies on robust principles for the assessment, hyperparameterization and selection of methods. AutoMTS coherently supports an extensive set state-of-the-art methods for (multivariate) time series imputation and outlier detection-and-treatment, considering both point and segment/serial occurrences. A comprehensive evaluation of AutoMTS is accomplished using heterogeneous sensors from two water distribution systems with varying sampling rates, water consumption patterns, and inconsistencies. Results conﬁrm the relevance of the proposed AutoMTS system.
Validation of E-Learning Software in Introductory Physics

: The advent of Education 4.0 leads to implicit application of technology especially in higher learning institutions. The use of E-learning platforms becomes prominent, and different studies proved its effectiveness. The study aims to validate an e-learning software in teaching introductory Physics, specifically it seeks to evaluate an e-learning modules in physics, assess and enhance the system as well as appraise the performance of the e-learning software based on the selected criteria of ISO 9126.The study is anchored in the e-learning model which emphasized the analysis, design and evolution of the software and is conducted as a research process. The study is a product and tool research as well as model research which focused on developing instructional system design model for science teachers. Through Purposive sampling 30 IT students, five science instructors and seven IT experts were used as participants respectively. The appraisal of the e-learning software was based on the selected criteria on international organization for standardization / international electro technical commission (ISO/IEC) 9126. The data were collected using weighted mean and Likert scale and interpreted using it. The program was successfully evaluated with a broad average of 4.54, which is translated as excellent based on the given scale and on ISO -9126 software evaluation selected criteria. This shows that the software developed conforms to the standard in question and can be used for instruction.
Taxing Labor Income in an Economy with High Employment Informality

This paper develops a static general equilibrium model of occupational choice with heterogeneity in both labor and entrepreneurial skills that generates high levels of employment informality. The model uses a detailed structure of personal income taxes (PITs) and subsidies to formal workers to capture the labor wedges present in many countries. These features enable the model to assess how changes in PITs and subsidies affect labor market outcomes and the government’s fiscal accounts. The model is calibrated for Mexico, which, like many developing countries, has high levels of labor informality. The model’s simulations shed light on the impact of a series of reforms to PITs and subsidy schemes aimed at increasing labor formality among low-income workers. The results suggest that adjusting the current structure of the formal employment subsidy combined with PIT exemptions for low-income workers could reduce informality while marginally improving the government’s fiscal balance.
Missed Opportunities: Measuring the Untapped TLS Support in the Industrial Internet of Things

The ongoing trend to move industrial appliances from previously isolated networks to the Internet requires fundamental changes in security to uphold secure and safe operation. Consequently, to ensure end-to-end secure communication and authentication, (i) traditional industrial protocols, e.g., Modbus, are retrofitted with TLS support, and (ii) modern protocols, e.g., MQTT, are directly designed to use TLS. To understand whether these changes indeed lead to secure Industrial Internet of Things deployments, i.e., using TLS-based protocols, which are configured according to security best practices, we perform an Internet-wide security assessment of ten industrial protocols covering the complete IPv4 address space. Our results show that both, retrofitted existing protocols and newly developed secure alternatives, are barely noticeable in the wild. While we find that new protocols have a higher TLS adoption rate than traditional protocols (7.2% vs. 0.4%), the overall adoption of TLS is comparably low (6.5% of hosts). Thus, most industrial deployments (934,736 hosts) are insecurely connected to the Internet. Furthermore, we identify that 42% of hosts with TLS support (26,665 hosts) show security deficits, e.g., missing access control. Finally, we show that support in configuring systems securely, e.g., via configuration templates, is promising to strengthen security.
Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment

-In the current world, user experience in various platforms matters a lot for different organizations. But providing a better experience can be challenging if the multimedia content on online platforms is having different kinds of distortions which impact the overall experience of the user. There can be various reasons behind distortions such as compression or minimal lighting condition while taking photos. In this work, a deep CNN-based Non-Screen Content and Screen Content NR-IQA framework is proposed which solves this issue in a more effective way. The framework is known as DNSSCIQ. Two different architectures are proposed based upon the input image type whether the input is a screen content or non-screen content image. This work attempts to solve this by evaluating the quality of such images
Quantitative Radiomic Features From Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis

Objectives: Pancreatic cancer (PC) is the 3rd leading cause of cancer deaths. We aimed to detect early changes on computed tomography (CT) images associated with pancreatic ductal adenocarcinoma (PDAC) based on quantitative imaging features (QIF). Methods: Adults 18+ years of age diagnosed with PDAC in 2008-2018 were identified. Their CT scans 3 months-3 years prior to the diagnosis date were matched to up to two scans of controls. Pancreas was automatically segmented using a previously developed algorithm. 111 QIF were extracted. The dataset was randomly split for training/validation. Neighborhood and principal component analyses were applied to select the most important features. Conditional support vector machine was used to develop prediction algorithms. The computer labels were compared with manually reviewed CT images 2-3 years prior to the index date in 19 cases and 19 controls. Results: 227 scans from cases (stages: 35% I-II, 44% III-IV, 21% unknown) and 554 matched scans of healthy controls were included (average age 71 years; 51% females). In the validation dataset, accuracy measures were 94%-95%, and area under the curve (AUC) measures were 0.98-0.99. Sensitivity, specificity, positive predictive value, and negative predictive values were in the ranges of 88-91%, 96-98%, 91-95%, and 94-96%. QIF on CT examinations within 2-3 years prior to index date also had very high predictive accuracy (accuracy 95-98%; AUC 0.99-1.00). The QIF-based algorithm outperformed manual re-review of images for determination of PDAC-risk. Conclusions: QIF can accurately predict PDAC on CT imaging and represent promising biomarkers for early detection of pancreatic cancer.
Radiographic Imaging of Power Injectable Medical Access Ports as a Supplemental Identification Tool

Purpose: Our aims were to describe characteristic radiographic features of two power injectable medical access ports (MAPs) on various imaging modalities for rapid and precise identification; and to demonstrate the value of this approach in identifying other types of MAPs via 'pictorial atlas'. Methods: We analyzed two commonly seen MAPs at our clinical center, Smart Port(R) CT-Injectable Port and PowerPort(R) M.R. I. (R) Implantable Port. Photographs of these two MAPs were retrospectively compared with identity-verified MAPs seen on chest X-ray (CXR), computed tomography (CT) and dual energy subtraction radiography (DESR) images from routine patient encounters at our clinical center. Visualized radiographic features were used for MAP differentiation and identification. Results: Based on selected patient case examples for these two MAPs, physical characteristics seen on imaging were used for MAP identification. These properties included port body and chamber shape; location and number of suture holes; and radiopaque and radiolucent features. Each imaging modality provided a unique set of radiographic features and highlighted specific components of each MAP for rapid and precise identification. CXR offered better visualization of unique MAP features compared to CT. Conclusions: Radiographic imaging can serve as a tool for medical staff to quickly identify MAPs. Hospital-specific 'pictorial atlases' can be developed to display MAPs along with their associated distinctive radiographic and physical features for rapid and precise identification. This may be useful for large referral centers that see a wide array of MAPs by mitigating complications associated with MAP misidentification and usage, thus improving patient care.
Clinical activity of MAPK targeted therapies in patients with non-V600 BRAF mutant tumors

Purpose: Non-V600 mutations comprise approximately 35% of all BRAF mutations in cancer. Many of these mutations have been identified as oncogenic drivers in a wide array of cancer types and can be classified into three Classes according to molecular characteristics. Consensus treatment strategies for Class 2 and 3 BRAF mutations have not yet been established. Methods: We performed a systematic review and meta-analysis of individual patient data to assess treatment outcomes with FDA-approved mitogen activated protein kinase pathway (MAPK) targeted therapy according to BRAF Class, cancer type and MAPK targeted therapy type. A search was conducted on literature from 2010-2021. Individual patient data was collected and analyzed from published reports of patients with cancer harboring Class 2 or 3 BRAF mutations and who received MAPK targeted therapy with available treatment response data. Co-primary outcomes were response rate (RR) and progression-free survival (PFS). Results: 18167 studies were screened, identifying 80 studies with 238 patients that met inclusion criteria. This included 167 patients with Class 2 and 71 patients with Class 3 BRAF mutations. Overall, 77 patients achieved a treatment response. In both univariate and multivariable analyses, RR and PFS were higher among patients with Class 2 compared to Class 3 mutations, findings that remain when analyses are restricted to patients with melanoma or lung primary cancers. MEK +/- BRAF inhibitors demonstrated greater clinical activity in Class 2 compared to Class 3 BRAF mutant tumors than BRAF or EGFR inhibitors. Conclusions: This meta-analysis suggests that MAPK targeted therapies have clinical activity in some Class 2 and 3 BRAF mutant cancers. BRAF Class may dictate responsiveness to current and emerging treatment strategies, particularly in metastatic melanoma and lung cancers. Together, this analysis provides clinical validation of predictions made based on a mutation classification system established in the preclinical literature. Further evaluation with prospective clinical trials is needed for this population.