Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts of Irrigation Managements on Soil CO2 Emission and Soil CH4 Uptake of Winter Wheat Field in the North China Plain

The North China Plain is an important irrigated agricultural area in China. However, the effects of irrigation management on carbon emission are not well documented in this region. Due to the uneven seasonal distribution of rainfall, irrigation is mainly concentrated in the winter wheat growing season in the North China Plain. In this study, we estimated CO2 emission and soil CH4 uptake from winter wheat fields with different irrigation methods and scheduling treatments using the static chamber-gas chromatography method from April to May 2017 and 2018. Treatments included three irrigation methods (surface drip, sprinkler, and border) and three irrigation scheduling levels that initiated as soon as the soil moisture drained to 50%, 60%, and 70% of the field capacity for a 0–100 cm soil profile were tested. The results showed that both the irrigation methods and scheduling significantly influenced (p < 0.05) the cumulative CO2 and CH4 emission, grain yield, global warming potential (GWP), GWP Intensity (GWPI), GWPI per unit irrigation applied, and water use efficiency (WUE). Compared to 60% and 70% FC, 50% FC irrigation scheduling de-creased accumulated CH4 uptake 26.8–30.3% and 17.8–25.4%, and reduced accumulated CO2 emissions 7.0–15.3% and 12.6–19.4%, respectively. Conversely, 50% FC reduced GWP 6.5–13.3% and 12.5–19.4% and lower grain yield 10.4–19.7% and 8.5–16.6% compared to 60% and 70% FC irrigation scheduling in 2017 and 2018, respectively. Compared to sprinkler irrigation and border irrigation, drip irrigation at 60% FC increased the accumulated CH4 uptake 11.3–12.1% and 1.9–5.5%, while reduced the accumulated CO2 emissions from 7.5–8.8% and 10.1–12.1% in 2017 and 2018, respectively. Moreover, drip irrigation at 60% FC increased grain yield 5.2–7.5% and 6.3–6.8%, WUE 0.9–5.4% and 5.7–7.4%, and lowered GWP 8.0–9.8% and 10.1–12.0% compared to sprinkler and border irrigation in 2017 and 2018, respectively. The interaction of irrigation scheduling and irrigation methods significantly impacted accumulated CH4 uptake, cumulative CO2 amount, and GWP in 2018 only while grain yield and WUE in the entire study. Overall, drip irrigation at 60% FC is the optimal choice in terms of higher grain yield, WUE, and mitigating GWP and GWPI from winter wheat fields in North China Plain.
An Improved Sparsity Adaptive Matching Pursuit Algorithm and Its Application in Shock Wave Testing

In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.
From ambidextrous learning to digital creativity: An integrative theoretical framework

Digital creativity (DC) stands for employee's generation of useful and fresh ideas through the use of digital technologies, which is one of the prominent consequences of effective digital technology use. Drawing insights from the tripartite view of technology use (ie, technology, individual and task elements) and the social role lens, our study proposes and tests an integrative theoretical framework to understand how female and male employees progress from ambidextrous learning in digital technology use to DC. We first interviewed five frontline employees and then surveyed 221 employees that were different from the interview sample from eight organisations. All participants utilised a similar version of internet‐of‐things (IoT) in their daily work. We find that (a) exploitative use has a stronger influence on DC for women than for men, while explorative use displays a higher impact on DC for men than for women, and (b) technology digital affordance (TDA), digital knowledge (DK), and task variety (TV) exhibit significant influences on both exploitative and explorative uses to varying extents. The post‐hoc analysis reveals that exploitative use mediates the influences of TDA and DK on DC only for women; explorative use mediates the impacts of TDA and TV on DC only for men. Our study advances the understandings of the downstream impact of technology use in the digital context.
Towards a Hybrid Deep Learning Model for Anomalous Activities Detection in Internet of Things Networks

The tremendous number of Internet of Things (IoT) applications, with their ubiquity, has provided us with unprecedented productivity and simplified our daily life. At the same time, the insecurity of these technologies ensures that our daily lives are surrounded by vulnerable computers, allowing for the launch of multiple attacks via large-scale botnets through the IoT. These attacks have been successful in achieving their heinous objectives. A strong identification strategy is essential to keep devices secured. This paper proposes and implements a model for anomaly-based intrusion detection in IoT networks that uses a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect and classify binary and multiclass IoT network data. The proposed model is validated using the BoT-IoT, IoT Network Intrusion, MQTT-IoT-IDS2020, and IoT-23 intrusion detection datasets. Our proposed binary and multiclass classification model achieved an exceptionally high level of accuracy, precision, recall, and F1 score.
Why I persist while others leave? Investigating the path from passion to persistence in entrepreneurship

ABSTRACT Although research affirms that entrepreneurial passion (EP) is an important predictor of entrepreneurial persistence, much less is known of the underlying processes through which EP leads to entrepreneurial persistence. Drawing upon the broaden-and-build theory of positive emotions, this study uncovers the mediating effects of entrepreneurial self-efficacy (ESE) and the moderating role of proactive personality between the relationship of EP and entrepreneurial persistence. The results of survey data from 215 small firms in China demonstrate that ESE mediates the link between EP and entrepreneurial persistence. The proactive personality of an entrepreneur also strengthens the direct relationship between EP and ESE and the indirect relationship between EP and entrepreneurial persistence via ESE. The findings contribute to the literature on entrepreneurs’ proactive personalities, ESE and entrepreneurial persistence.
The relationship between mindfulness and individual adaptability in dynamic work contexts

Purpose: Individual adaptability has been proposed as a source of adaptive performance. This is an increasingly important performance dimension in dynamic contexts. Mindfulness has been demonstrated to improve dimensions of performance and well-being in the workplace, but the underlying mechanisms of this relationship are not well understood. Addressing this gap, the study hypothesised a link between mindfulness and individual adaptability in dynamic work contexts. Design/methodology/approach: One hundred and ninety-eight individuals in dynamic work contexts completed a self-rating survey that measured mindfulness and a multifactor measure of individual adaptability. These data were then analysed to test the hypotheses developed. Findings/results: A significant positive relationship was found between mindfulness and five dimensions of adaptability (work-stress adaptability, uncertainty adaptability, crisis adaptability, creative problem-solving adaptability and learning adaptability). Interpersonal and cultural adaptability were not found to correlate with mindfulness. Practical implications: This study demonstrates that mindfulness is not simply a stress management skill but is correlated with key aspects of adaptability such as learning and problem-solving. The findings suggest it may be possible to enhance individual adaptability through Mindfulness-Based Interventions and thus support adaptive performance. Originality/value: This study is original in examining the relationship between mindfulness and individual adaptability in the workplace. This study highlights how different methods of operationalising mindfulness can lead to different conclusions. It points to the value of broader measures of mindfulness that capture attitudinal dimensions. In addition, as few studies on mindfulness in African contexts have been performed, this study broadens the research contexts in which mindfulness is understood.
Integrating Literacy and Science Instruction in Kindergarten: Results From the Efficacy Study of Zoology One

Abstract This study examines the efficacy, cost, and implementation of an integrated science and literacy curriculum for kindergarten. The study was conducted in a large urban district and included 1,589 students in 71 classrooms in 21 schools. The research includes a multi-site cluster-randomized controlled trial and mixed-methods cost and implementation studies. Analysis revealed significant impacts on comprehension, letter-naming fluency, and motivation to read. No main impacts were observed on decoding, word identification, or writing; however, exploratory analysis revealed that students whose teachers implemented the treatment with fidelity performed statistically significantly better in writing and decoding. The cost to produce the observed effects was estimated at $480 per student, two-thirds of which was borne by the school. Despite this cost, treatment classrooms achieved savings by using an average of three fewer instructional programs than control classrooms. Teachers reported positive effects from the integrated curriculum on student engagement, learning, and behavior.
Superior pitch identification ability is associated with better mental rotation performance

Musicians with absolute pitch ability show better spatial ability, but little is known about whether non-musicians’ pitch identification (PI) ability is associated with their spatial ability. In the present study, a PI test, two mental rotation (MR) tests (Three-Dimensional Mental Rotation [3DMR] and Spatial Relationship [SR]), and eight executive function (EF) tests were administered to a large sample (N = 525) of Chinese college students who were non-music majors. A subsample with superior PI ability (n = 42) was identified and compared with a sample with average PI ability matched in age and IQ (n = 42). Two-way analysis of covariance (ANCOVA) (PI ability x musical training, with gender as a covariate due to a minor difference in matching by gender) revealed that superior PI ability was associated with better performance on MR and various EF tests. Musical training was associated with performance on select visual and EF tests (i.e., visual perception, working memory, and executive attention control), but not with MR performance. Additional ANCOVA showed that PI ability was significantly associated with 3DMR, but not SR, even after scores on EF tests were included as covariates. These results indicate a unique association between PI ability and 3DMR, which helps shed light on the cognitive mechanisms related to PI ability.
Gallic acid mediated oxidation of pentachlorophenol by the Fenton reaction under mild oxidative conditions

BACKGROUND: The present study demonstrates the decomposition of pentachlorophenol (PCP) by the Fenton reaction in the presence of gallic acid (GA). Unlike other studies, catalytic Fe²⁺ concentrations were used with respect to PCP in an effort to evaluate an environmentally benign Fenton system, studying the effect of phenolic‐type compounds widely found in the environment. The effect of operating variables was evaluated. RESULTS: At initial reaction times or at low H₂O₂ and Fe concentrations, GA enhanced PCP removal. At high H₂O₂ and Fe concentrations GA enhanced PCP removal only at initial reaction times. GA was the determinant factor controlling activity acting both as a chelating agent and reducer of Fe³⁺. Electron paramagnetic resonance (EPR) spectroscopy was applied to shed light on mechanistic aspects, verifying the interaction of GA with Fe ions. GA accelerated significantly the Fe³⁺/Fe²⁺ redox cycling. This enhanced the formation of hydroxyl radicals (HO·) as confirmed by in situ EPR spin trap experiments. A reaction mechanism was proposed including the redox cycle of GA quinoide forms. CONCLUSION: The operational variables of a GA modified Fenton system are provided and mechanistic steps are discussed including PCP degradation, GA/Fe interaction, Fe³⁺/Fe²⁺ redox cycling and HO· formation. GA concentration controls the catalytic degradation of PCP. © 2017 Society of Chemical Industry
Neural Network-Based Model for Classification of Faults During Operation of a Robotic Manipulator

The importance of error detection is high, especially in modern manufacturing processes where assembly lines operate without direct supervision. Stopping the faulty operation in time can prevent damage to the assembly line. Public dataset is used, containing 15 classes, 2 types of faultless operation and 13 types of faults, with 463 force and torsion datapoints. Four different methods are used: Multilayer Perceptron (MLP) selected due to high classification performance, Support Vector Machines (SVM) commonly used for a low number of datapoints, Convolutional Neural Network (CNN) known for high performance in classification with matrix inputs and Siamese Neural Network (SNN) novel method with high performance in small datasets. Two classification tasks are performed-error detection and classification. Grid search is used for hyperparameter variation and F1 score as a metric, with a 10 fold cross-validation. Authors propose a hybrid system consisting of SNN for detection and CNN for fault classification.
Calibration of CFD grid for simulating the impeller clearance flow and axial hydraulic force of centrifugal pump

The axial hydraulic force of centrifugal pump is an important parameter affecting pump performance. The force mainly includes the force inside impeller and in clearance. Due to the special structural characteristics of the clearance, the influence of grid discretization method on the calculation of axial force in the clearance is not fully understood. Therefore, based on the Reynolds-averaged method with shear stress transport turbulence model, an orthogonal experiment was designed to compare the correlation coefficient of velocity and pressure distribution between shear stress transport model and large eddy simulation models. A more suitable grid discretization strategy was found by artificial neural network for grid calibration. When the strategy is applied to the entire centrifugal pump, the prediction of axial force has high accuracy. The range analysis shows that the grid node number in the wall-wall direction has the greatest impact on velocity distribution. When the mesh parameters are in a certain range, it can compromise between the simulation accuracy and computational resource. The Reynolds-averaged model based simulation is proved accurate in capturing the complex velocity and pressure field inside clearance. The entire pump model is also used for the verification after the calibration of grid. The typical axial force law can be found under different flow rate conditions. This study provides a significant guidance in determining the grid scheme for accurate prediction of centrifugal pump’s axial force. It makes the computational fluid dynamics simulation feasible in the initial design of centrifugal pump which specifically considers the axial force problem.
Place-based industrial strategy and economic trajectory: advancing agency-based approaches

ABSTRACT Agency-based approaches represent a fundamental advance in how researchers and policymakers can address questions of place-based industrial strategy, including issues of governance, leadership, new technology and regional assets. However, these approaches can be advanced further by recognizing the centrality of discourse in regional change. This paper does this by synthesizing two conceptual frameworks: Grillitsch and Sotarauta’s trinity of change agency and Moulaert et al.’s framework of Agency Structure Institutions Discourse (ASID). Deploying two Australian case studies to shed light on drivers of change at the local scale, this paper demonstrates that discourse is a necessary component of transformative regional processes. Furthermore, it contends that successful transformation is presupposed by the extent to which local discourse overlaps with local opportunity spaces and forms of agency. Successful place-based industrial strategies need to mobilize these multiple elements of regional change in order to maximize their potential for success.
Multi-Objective Optimum Design and Maintenance of Safety Systems: An In-Depth Comparison Study Including Encoding and Scheduling Aspects with NSGA-II

Maximising profit is an important target for industries in a competitive world and it is possible to achieve this by improving the system availability. Engineers have employed many techniques to improve systems availability, such as adding redundant devices or scheduling maintenance strategies. However, the idea of using such techniques simultaneously has not received enough attention. The authors of the present paper recently studied the simultaneous optimisation of system design and maintenance strategy in order to achieve both maximum availability and minimum cost: the Non-dominated Sorting Genetic Algorithm II (NSGA-II) was coupled with Discrete Event Simulation in a real encoding environment in order to achieve a set of non-dominated solutions. In this work, that study is extended and a thorough exploration using the above-mentioned Multi-objective Evolutionary Algorithm is developed using an industrial case study, paying attention to the possible impact on solutions as a result of different encodings, parameter configurations and chromosome lengths, which affect the accuracy levels when scheduling preventive maintenance. Non-significant differences were observed in the experimental results, which raises interesting conclusions regarding flexibility in the preventive maintenance strategy.
Identification of the Optimum Environments for the High Yield and Quality Traits of Lentil Genotypes Evaluated in Multi-Location Trials

Lentil is a versatile and profitable pulse crop with high nutritional food and feed values. The objectives of the study were to determine suitable locations for high yield and quality in terms of production and/or breeding, and to identify promising genotypes. For this reason, five lentil genotypes were evaluated in a multi-location network consisting of ten diverse sites for two consecutive growing seasons, for seed yield (SY), other agronomic traits, crude protein (CP), cooking time (CT) and crude protein yield (CPY). A significant diversification and specialization of the locations was identified with regards to SY, CP, CT and CPY. Different locations showed optimal values for each trait. Locations E4 and E3, followed by E10, were “ideal” for SY; locations E1, E3 and E7 were ideal for high CP; and the “ideal” locations for CT were E3 and E5, followed by E2. Therefore, the scope of the cultivation determined the optimum locations for lentil cultivation. The GGE-biplot analysis revealed different discriminating abilities and representativeness among the locations for the identification of the most productive and stable genotypes. Location E3 (Orestiada, Region of Thrace) was recognized as being optimal for lentil breeding, as it was the “ideal” or close to “ideal” for the selection of superior genotypes for SY, CP, CT and CPY. Adaptable genotypes (cv. Dimitra, Samos) showed a high SY along with excellent values for CP, CT and CPY, and are suggested either for cultivation in many regions or to be exploited in breeding programs.
Biodiversity and Phylogenetic Relationship of Total Hydrocarbon Degrading Genes in Selected Bacteria Species

Hydrocarbons which forms the bulk of soil and water pollutants in the Niger Delta region of Nigeria differs in their susceptibility to microbial attack and degradation. Considering the importance of hydrocarbons in the economy of any nation, a complete knowledge of the hydrocarbons degrading capabilities of associated microbial species becomes increasingly important and indispensable. The research was aimed at assessing the biodiversity and phylogenetic relationship of hydrocarbon degrading genes in selected bacteria species. Sequences of nucleotides and amino acids of hydrocarbons degrading genes in 12 species of bacteria such as Pseudomonas aeruginosa I & II, P. stutgeni, Thalassosqir spp. I & II, Alcavorox spp. , Arthrobacter spp. , Martellela spp. , P. taenensis, Aneuribacillus species, Rhodococcus spp. and Uncultured bacteria, were retrieved from the National Center for Biotechnology Information (NCBI) and analysed for their variability in physicochemical properties, percentage identity and similarity, G-C content, secondary and tertiary structures, their biodiversity and their phylogenetic relationship using MEGA 6 soft-wares, GOR IV, Phyre, Genscan and SIB Protparam. The analysis showed great genetic diversity and polymorphism in the hydrocarbon degrading genes present in the bacteria species. The phylogenetic tree showed that the genes in most species had undergone mutation. Martella spp had the highest probability or chance of mutating with E-value of 7e-129. Only two of the bacteria species had low instability index indicating that majority of the bacteria species contains or possessed the unstable nature of the hydrocarbon degrading genes. The biodiversity of bacterial species and their genetic distance was ascertained.