Miracle-dz/UniFAR-FTUs huggingface dataset:
Responses of Phyllosphere Microbiome to Ozone Stress: Abundance, Community Compositions and Functions

Ozone is a typical hazardous pollutant in Earth’s lower atmosphere, but the phyllosphere and its microbiome are promising for air pollution remediation. Despite research to explore the efficiency and mechanism of ozone phylloremediation, the response and role of the phyllosphere microbiome remains untouched. In this study, we exposed Euonymus japonicus to different ozone levels and revealed microbial successions and roles of the phyllosphere microbiome during the exposure. The low-level exposure (156 ± 20 ppb) induced limited response compared to other environmental factors. Fungi failed to sustain the community richness and diversity, despite the stable ITS concentration, while bacteria witnessed an abundance loss. We subsequently elevated the exposure level to 5000~10,000 ppb, which considerably deteriorated the bacterial and fungal diversity. Our results identified extremely tolerant species, including bacterial genera (Curtobacterium, Marmoricola, and Microbacterium) and fungal genera (Cladosporium and Alternaria). Compositional differences suggested that most core fungal taxa were related to plant diseases and biocontrol, and ozone exposure might intensify such antagonism, thus possibly influencing plant health and ozone remediation. This assumption was further evidenced in the functional predictions via a pathogen predominance. This study shed light on microbial responses to ozone exposure in the phyllosphere and enlightened the augmentation of ozone phylloremediation through the microbial role.
Reflecting on the Success of Open Data

Despite the high level of interest in open data, little research has evaluated how municipal government evaluates the success of their open data programs. This research presents results from interviews with eight Canadian municipal governments that point to two approaches to evaluation: internal and external. Internal evaluation looks for use within the data generating government, and for support from management and council. External evaluation tracks use by external entities, including citizens, private sector, or other government agencies. Three findings of this work provide guidance for the development of open data evaluation metrics. First, approaches to tracking can be both passive, via web metrics, and active, via outreach activities to users. Second, value of open data must be broadly defined, and extend beyond economic valuations. Lastly, internal support from management or council and the contributions of many organization employees towards the production of open data are important forms of self-evaluation of open data programs.
Study on AC fault ride-through of a MMC back-to-back HVDC system

The Back-to-Back HVDC transmission system (BtB) based on modular multilevel converter (MMC), can realize the asynchronous (frequency or phase is not synchronized) interconnection of two regional power grids. , and it can effectively solve the power grid security and stability problems caused by power transfer. However, the rectifier and inverter equipment of BtB are directly connected by smoothing reactors, making the electrical connection between devices closer. Therefore, fault interaction would be transmitted between the AC systems and the DC side of BtB. The paper first introduces the mathematical model and conventional control strategies of the system. Next, the transient characteristics of the AC and DC system under the asymmetric AC fault are analysed emphatically. Finally, the negative-sequence voltage feedforward compensator and DC voltage limiter are designed to improve the AC fault ride-through capability of the system. Finally, the effectiveness of proposed fault ride-through strategies is verified through PSCAD/EMTDC.
Weakly-supervised Consumption Intent Detection in Microblogs ⋆

Nowadays, more and more people are using microblogs to discuss various topics, sometimes revealing their potential consumption intent (CI). A consumption intent is a desire or hope for something to purchase. This paper proposes a weakly-supervised approach to detect those user posts containing consumption intent in microblogs. First, we collect a large number of posts containing a few designated hashtags and regard them as containing CI (positive instances). Posts without such hashtags are considered as negative instances. Then, we train a statistical classification model using the training data constructed as above and design three kinds of post-dependent features. We evaluate the model on 7,902 manually annotated posts from microblogs and show that the proposed method is effective, even outperforms the supervised method.
Adaptive LFC incorporating modified virtual rotor to regulate frequency and tie-line power flow in multi-area microgrids

This research investigates a new coordination strategy for both isolated single-area and interconnected multi-area microgrids (MGs) using a modified virtual rotor-based derivative technique supported with Jaya optimizer based on balloon effect modulation (BE). Accordingly, the main concept of BE is to assist the classic Jaya to be more sensitive and trackable in the event of disturbances, as well as to provide optimum integral gain value on the secondary frequency controller adaptively for both suggested MGs. The proposed modified virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs considering full participation of the inverter-based energy storage systems. The proposed virtual rotor mechanism is consisting of virtual inertia and virtual damping that are added as a tertiary controller within proposed MGs to emulate the reduction in system inertia and the enhanced damping properties. Several nonlinearities were proposed in this work such as a dead band of governor, generation rate constraints, and communication time-delay are considered within the dynamic model of the suggested MGs. In addition, the proposed design of multi-area MGs takes the interval time-varying communication delays into account for stability conditions. In this study, A comparative study using unimodal (i.e., Sphere) and multimodal (i.e., Rastrigin) benchmark test functions are conducted to validate the proposed direct adaptive Jaya-based BE. Furthermore, Wilcoxon’s rank-signed non-parametric statistical test using a pairwise comparison was performed at a 5 % risk level to judge whether the proposed algorithm output varies from those of the other algorithms in a statistically significant manner. Thence, the superiority and effectiveness of the proposed method have also been verified against a variety of other metaheuristics optimization techniques, including classic electro-search, particle swarm, multi-objective seagull, and Jaya optimizers. In addition, an operative performance is assessed against the conventional integral controller, coefficient diagram method, and classic Jaya with/without virtual inertia. The final findings emphasize the superiority of the proposed direct adaptive Jaya-based BE supported by a modified virtual rotor and state better performance and stability compared to existing controllers.
Performance Evaluation of LMS and CM Algorithms for Beamforming

In this paper, we compare the performances of the least mean square (LMS) and constant modulus (CM) algorithms for beamforming. Our interest in these algorithms finds its origins in their reliability as a source-receiver pair. In addition, their use brings a great frequency of diversity even to respond quickly to the increasing spectral demand. The results suggest that the greater the number of elements in the antenna, the better the directivity for both LMS and CM. We also note that a judicious choice of the control parameter mu leads to a better speed of convergence for the two algorithms. Let us note, however, that LMS is more efficient. Our simulations show that in an environment affected by white Gaussian noise, LMS is more robust than CM. This confirms the theoretical result due to the fact that LMS uses a sequence for learning. Performance analyses of the two techniques are simulated in the MATLAB environment.
Oleoylethanolamide Alleviates Hepatic Ischemia-Reperfusion Injury via Inhibiting Endoplasmic Reticulum Stress-Associated Apoptosis

Liver ischemia/reperfusion (I/R) injury is a primary complication in major liver surgery. Our previous study about proteome profiling has revealed that the PPAR signaling cascade was significantly upregulated during liver ischemia/reperfusion. To elucidate the potential mechanisms of PPARα involved in I/R injury, we used oleoylethanolamide (OEA), the peroxisome proliferator-activated receptor alpha (PPARα) agonist, in this study. We demonstrated a protective role of OEA on liver I/R injury by using a mouse model of partial warm ischemia-reperfusion and hypoxia-reoxygenation model of hepatocytes. These effects were caused by ameliorating liver damage, decreasing the level of serum ALT and AST, and reducing the apoptosis of hepatocytes. Furthermore, a mechanistic study revealed that OEA regulated endoplasmic reticulum (ER) stress by activating PPARα, thereby reducing ER stress-associated apoptosis to attenuate liver I/R injury. Briefly, these data first proposed that OEA-mediated PPARα activation could be an effective therapy against hepatic ischemia/reperfusion injury.
Aggregate Flexibility Capacity of TCLs With Cycling Constraints

Thermostatically Controlled Loads (TCLs), e.g., an air conditioner, typically maintain their temperature within a preset range using on/off actuation. These types of loads are inherently flexible: many different power consumption trajectories exist that can keep the temperature within range. Grid operators need tools for quantifying demand flexibility of a collection of TCLs in order to use them as a resource. However, computationally tractable characterization of flexibility capacity obtained so far has considered temperature constraints alone, ignoring their cycling/lock-out constraints: the length of time a TCL must stay in “on” stage before switching to “off,” or vice versa. In this work, we present a tractable characterization of the flexibility capacity of a collection of TCLs that incorporates not only temperature but also cycling and total energy consumption constraints. Unlike prior attempts at capacity characterizations incorporating cycling constraints, our results are independent of the algorithm used to coordinate the TCLs. The characterization leads to a set of convex constraints. A grid operator can use this characterization to compute a power consumption trajectory for an ensemble of TCLs that comes closest to what the operator needs to maintain demand-supply balance. Numerical results are provided to showcase the effectiveness of the proposed characterization.
Robust Iterative Learning Control for Pneumatic Muscle With Uncertainties and State Constraints

In this article, we propose a new iterative learning control (ILC) scheme for trajectory tracking of pneumatic muscle (PM) actuators with state constraints. A PM model is constructed in three-element form with both parametric and nonparametric uncertainties, while full state constraints are considered for enhancing operational safety. To ensure that system states are within the predefined bounds, the barrier Lyapunov function (BLF) is used in the analysis, which reaches infinity when some of its arguments approach limits. The proposed ILC incorporates the BLF with the composite energy function (CEF) approach and ensures the boundedness of CEF in the closed-loop, thus, assuring that those limits are not transgressed. Through rigorous analysis, we show that under the proposed ILC scheme, uniform convergences of PM state tracking errors are guaranteed. Simulation studies and experimental validations are conducted to illustrate the efficacy of the proposed scheme. Experimental results show that the proposed ILC satisfies the state constraint requirements and the tracking error is less than 2.5% of the desired trajectory.
Identification of Potential Poly (ADP-Ribose) Polymerase-1 Inhibitors Derived from Rauwolfia serpentina: Possible Implication in Cancer Therapy

Poly (ADP-ribose) polymerase-1 (PARP-1) has been recognized as a prospective target for the development of novel cancer therapeutics. Several PARP-1 inhibitors are currently being considered for anticancer drug development and clinical investigation. Lately, natural compounds seem to be excellent alternative drug candidates for cancer treatment. Rauwolfia serpentina is a medicinal plant traditionally used in Indian subcontinents to treat various diseases. This study has been designed to identify the bioactive compounds derived from R. serpentina for possible binding and inhibition of PARP-1 using the molecular docking approach. Thirteen compounds were found to interact with the target with a binding affinity greater than the value of −9.0 kcal/mol. After screening the physicochemical properties, only 5 ligands (ajmalicine, yohimbine, isorauhimbine, rauwolscine, and 1,2-dihydrovomilenine) were found to obey all the parameters of Lipinski's rule of five, showed maximum drug-likeness, and possess no significant toxicity. These ligands displayed strong interactions with target PARP-1 via several hydrogen bonds and hydrophobic interactions. Therefore, these identified compounds derived from R. serpentina can be considered for drug development against cancer-targeting PARP-1.
“So we start from zero”: Lessons and reflections from online preschool during the COVID-19 pandemic

In the world of teacher education, the ability of teachers to adapt is associated with effective teaching. In the midst of a worldwide pandemic, however, teachers’ adaptability was put to the ultimate test. With this in mind, this article explores the journey of two preschool teachers as they embarked on the sudden move from face-to-face to online instruction alongside school administrators, parents, and young children during the COVID-19 pandemic. Using a tripartite model of adaptability, the authors examine the cognitive, emotional and behavioral adaptations employed by the focal teachers, as they supported the learning process in a multilingual and multicultural early childhood program under unprecedented teaching conditions. The article highlights the lessons learned during this traumatic time and the innovative ways educators partnered with families to develop new means of collaboration. The paper ends by calling attention to the socioeconomic line of division that exists among various populations both locally and globally, which shapes the educational landscape in significant ways.
Improving Inference Time of Deep Learning Model with Partial Skip of ReLU-fused Matrix Multiplication Operations

Deep learning has been expanding its application, while large-scale models tend to perform well. However, as such a model inevitably requires a vast amount of resources and computations, lengthy inference time is a crucial, but essential, consequence that needs to be optimized for the efficient utilization of deep learning. To achieve the goal, we aim at fusing the Rectified Linear Unit and matrix multiplication in the inference process, which we may reduce the total amount of computation by predicting the sign bit of output value. We propose four methods of prediction and statistically choose an optimal method for reducing inference time with low accuracy loss.
Experimental and theoretical review on covalent coupling and elemental doping of carbon nanomaterials for environmental photocatalysis

Abstract Carbon nanomaterials (CNMs) are outstanding photocatalysts for tackling environmental issues, however, their pristine forms possess poor dispersibility and limited stability under photocatalytic conditions. The impediments of using CNMs in environmental applications can be challenged by covalent coupling and elemental doping. With these caveats in mind, we address our perspectives from chemistry and engineering concepts to benefit from CNMs in water decontamination. Compiled data from density functional theory (DFT) and molecular dynamics simulation support that the changes in the physicochemical properties can enhance the durability and functionality of CNMs in environmental remediation. This review critically analyzes experimental and theoretical approaches related to the functionalization of CNMs by covalent coupling and elemental doping for environmental photocatalysis.
An Integration Algorithm for Bistatic Radar Weak Target Detection

The bistatic radar weak target detection problem is considered in this paper. An effective way to detect weak target is the long time integration. However, range migration (RM) will occur due to the high speed. Without knowing the target motion parameters, a long time integration algorithm for bistatic radar is proposed in this paper. Firstly, the algorithm utilizes second-order keystone transform (SKT) to remove range curvature. Then the quadratic phase term is compensated by the estimated acceleration. After that, SKT is used once more and the Doppler ambiguity phase term compensation is performed. At last, the target energy is integrated via FT. Simulations are provided to show the validity of the proposed algorithm in the end.
Student Performance Prediction in Mathematics Course Based on the Random Forest and Simulated Annealing

Educational data mining is becoming a more and more popular research field in recent years, mainly with the help of cross research conducted by various disciplines, so as to solve various difficult problems in the teaching and education process. In this paper, we proposed a hybrid approach for student performance prediction. We collected the dataset, including 15 characteristics of students from three categories (individual basic information, individual education information, and individual behavior information). Based on the random forest (RF) and simulated annealing (SA) algorithms, we binary encode the relevant parameters (number of features, tree size, and tree decision weights) as the target variables for algorithm optimization, use the out-of-bag error as the optimization objective function, and then propose the IRFC (improved random forest classifier) algorithm in this paper. Compared with other mainstream improved random forest algorithms, the research results demonstrate that the proposed algorithm in this paper has higher generalization ability and smaller OOB error. This study provides a methodological reference for the prediction of student achievement and also makes a marginal contribution to student management work.
Prescription drug misuse among adults in Canada: a scoping review

ABSTRACT Background Generally, substance misuse is associated with young people, however there is a growing concern on the rates of prescription drug misuse among older adults. This review aims to assess the extent of the prescription drug misuse problem among older adult Canadians. Methods A scoping review was carried out following the PRISMA guidelines. PubMed, PsycINFO, MEDLINE and Google scholar databases were used to carry out the search. Retrieved articles were assessed for relevance against the inclusion and exclusion criteria. Results were reviewed to identify major findings. Results 840 abstracts were reviewed, and 13 articles were included in the scoping review. Key findings are as follows: (1) misuse of prescription drugs is more prevalent among users who used other substances, (2) prescription misuse is positively associated with cannabis use, (3) adults exposed to prescription drugs at hospitals are likely to misuse prescription drugs in the future, (4) prescription drug misuse negatively influences the continuation of medication-assisted treatment, (5) reasons for non-medical prescription drug use include sleep, pain, stress, and nausea. Conclusion Prescription drug misuse is a problem among adults, but little attention is paid to older adults. To better understand this problem and create age-specific interventions more research in the area of prescription drug misuse among older adults and motives for misuse needs to be thoroughly studied.
Estimating Personal Consumption Rates for Husbands and Wives: A Comparison of Income-Strata and Microdata Models

Various studies estimate personal consumption expenditures from Consumer Expenditure (CEX) data. Early analysis relied on CEX data that was aggregated across expenditure categories and income groupings, while later studies used microdata containing disaggregated expenditures across individual items for each surveyed household. These newer studies allocated expenditures for each item to relevant household members, but analyzed results aggregated across income strata. We extend the microdata analysis by examining personal consumption expenditures at the household level. Our results indicate that at all but the lowest incomes, personal consumption rates generated using microdata-level observations differ only slightly from existing models using income-strata data. Forensic experts accounting for personal consumption may, thus, feel comfortable using either method of analysis.
The influence of minimalist and maximalist footwear on the kinetics and kinematics of running

Minimalist footwear has been the subject of much attention in footwear research. More recently, maximalist footwear, which feature an oversized midsole have been developed. The aim of the current investigation was to compare the kinetics and kinematics of running in minimalist, maximalist and conventional footwear. Twelve male runners ran over an embedded force platform at 4.0 m s−1 in the three footwear conditions. Lower limb kinematics were collected using an eight-camera motion capture system, and tibial accelerations were obtained using an accelerometer. Differences in kinetics and kinematics between footwear were examined using one-way repeated measures ANOVA. The results showed that both instantaneous loading rate and peak tibial acceleration were significantly larger in the minimalist (305.58 BW/s and 9.54 g) compared to the maximalist (141.67 BW/s and 7.99 g) and conventional (127.30 BW/s and 6.73 g) footwear. In addition, peak tibial internal rotation was significantly larger in the minimalist (9.64°) compared to conventional (6.88°) footwear. These findings indicate that minimalist footwear may place runners at increased risk from chronic injury. Further work is, nonetheless, required using runners who habitually utilize these footwear before these findings can be truly substantiated.
Building Global Infrastructure for Diversity and Inclusion in Engineering Education

Dr. Reed coordinates campus-wide initiatives designed to enhance and support faculty diversity at UMBC. Working collaboratively with the Leadership Team in the Office of the Provost, the Deans, and Faculty Leaders from the Executive Committee on the Recruitment, Retention, and Advancement of URM Faculty, the ADVANCE Executive Committee, the STRIDE Committee, and the faculty leaders of UMBC’s Community-Based Faculty Groups, Dr. Reed develops, implements, and evaluates programmatic interventions designed to recruit, retain and advance diverse faculty at UMBC. Dr. Reed also routinely disseminates best practices learned from UMBC’s diversity initiatives at national and international venues. Dr. Reed is on the advisory board for the Mid-Atlantic Higher Education Recruitment Consortium.