Miracle-dz/UniFAR-FTUs huggingface dataset:
An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.
Avaliação bio econômica de frangos de corte alimentados com farelo integral de raízes de mandioca suplementado com carboidrases exógena

O experimento foi conduzido no aviário da Fazenda São João, localizada no distrito de Santa Rita, município de Serra Talhada-PE. Objetivou-se avaliar o desempenho de frangos de corte alimentados com dietas contendo diferentes níveis de inclusão de farelo integral de raízes de mandioca (FIRM), suplementadas com complexo multienzimático assim como a sua avaliação econômica. Foram utilizados 450 pintos de um dia de idade, machos da linhagem Cobb, distribuídas em 25 parcelas experimentais, em um delineamento inteiramente casualizado (DIC) com cinco tratamentos, compostos por quatro níveis de inclusão de 25, 50, 75 e 100% de FIRM e uma dieta referência à base de milho e farelo de soja e cinco repetições por tratamento. O manejo adotado seguiu o preconizado no manual da linhagem Cobb. Foram mensuradas as variáveis de desempenho (ganho de peso médio diário, consumo de ração diário e conversão alimentar) e as variáveis econômicas receita bruta (RB), despesas (D), margem bruta (MB), e rentabilidade (REN). Os parâmetros de desempenho e de avaliação econômicas foram submetidos à análise de variância, sendo posteriormente realizadas análises de regressão e utilizado o teste Tukey com nível de significância a 5% de probabilidade (P<0,05). Foram observados melhora (P<0,05) no ganho de peso e conversão alimentar na fase de 36 a 42 dias e aumento (P<0,05) do ganho econômico. Concluiu-se que o FIRM suplementado com carboidrases exógenas pode ser incluído em dietas de frangos de corte industriais em até 100% produzindo melhora nos índices zootécnicos e aumento do ganho econômico em todo período experimental.
Iterative weighted active transfer learning hyperspectral image classification

Abstract. The hyperspectral image (HSI) has the characteristics of high resolution, a large amount of data, and a high correlation of bands. In the many HSI processing technologies, image classification is the most basic one. Supervised classification is the most effective and common classification method. However, to achieve the ideal classification effect, supervised classification needs a large number of labeled samples, which requires a lot of time and labor. To solve this problem, we combine active learning (AL) and transfer learning and propose an iterative weighted framework based on active transfer learning. First, we solve the optimal reconstruction matrix and projection matrix by minimizing the reconstruction error. Then, we project labeled samples from the source and target domains into the common subspace. In the iteration of the common subspace, the classifier performance will be better with the increase of iteration number. In each iteration, the weighted strategy is adopted to weigh the samples of the source domain. In this way, valuable source domain labeled samples will get a larger weight, so as to help the classification of target domain samples better. At the same time, AL is used to screen out a certain number of samples of the target domain for manual labeling, which is added to the labeled samples set. Experiments on three data sets demonstrate the effectiveness and reliability of the proposed method.
Machine learning using synthetic images for detecting dust emissions on construction sites

PurposeAutomated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.Design/methodology/approachThis study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.FindingsThis study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.Originality/valueThis study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.
Patterns of Paternal Involvement of Korean Fathers: A Person-Centered Approach

Given roles and expectations of father involvement in South Korea are in transition from traditional breadwinner to an involved caregiver to children, it is plausible that Korean fathers show diverse involvement behaviors in the contexts of work, family, and parenting. Using a person-centered approach, we explored if there were groupings of Korean fathers who could be identified from their involvement with their children. We also examined if those subgroup memberships were related to various factors in work, family, and parenting domains. With a sample of 212 married working fathers and the 12 items of involvement behaviors, we found four heterogeneous subgroups of people: low-involved, accessibility-focused, involved-but-less-accessible, and highly involved fathers. Significant differences among the four profiles were also found regarding various factors such as job stress, work and family conflict, work schedule, maternal employment, parenting satisfaction, and perceived level of involvement. Suggestions for future research, practitioners, and policymakers were discussed.
A Routing Query Algorithm Based on Time-Varying Relationship Group in Opportunistic Social Networks

With the fast development of IoT and 5G technologies, opportunity social networks composed of portable mobile devices have become a hot research topic in recent years. However, arbitrary node movement in opportunity networks and the absence of end-to-end pathways make node communication unstable. At the same time, the problem of ignoring human social preferences and relying on wrong message relay nodes lead to a low data transmission rate and high network overhead. Based on the above issues, we propose a time-varying relationship groups-based routing query algorithm for mobile opportunity networks (Time-varying Relationship Groups, TVRGs). Firstly, we construct the relationship groups based on the time-varying characteristics according to the intimacy between users. Secondly, we calculate the importance of nodes by their connectivity time and communication frequency. Finally, we find the suitable message relay nodes according to the similarity of node weights and their action trajectories and design the routing query algorithm accordingly. The simulation results show that the algorithm can vastly improve the message query success rate, effectively improve the data transmission efficiency, and reduce the average delay and system overhead compared with the existing routing algorithms.
The investigation of sensing mechanism of ethanol vapour in polymer-nanostructured carbon composite

Polymer-nanostructured carbon composites (PNCC) using three different polymers as composite matrix materials (polyvinylacetate (PVAc), polyethylene glycol (PEG) and ethylene-vinylacetate copolymer (EVA)) have been developed. High structure carbon black Printex XE2 (Degussa AG) was used as a composites filler. Ethanol vapour sensor-effect of composites was determined as a change of electrical resistance as the composite was held in ethanol vapour for 30 seconds. Reversibility of electrical resistance of PNCC, response stability and repeatability have been measured and compared. The electrical resistance response of EVA-nanostructured carbon composite (EVA-NCC) to ethanol vapour as a function of vinylacetate content in the copolymer has been evaluated. Promising ethanol vapour sensor-effect has been observed for PEG-NCC followed by PVAc-NCC and EVA-NCC.
Bird diversity, composition and response during COVID-19 in an urban landscape, Jamalpur, Bangladesh

The world has been encountering a COVID-19 pandemic since late 2019. The world’s people have also witnessed the free movement of wildlife, even in many urban areas in some countries during the imposed lockdown. We conducted research on the birds in the urban landscape of Jamalpur Sadar upazila from March 2020 to October 2020 during this pandemic situation. A total of 134 species of birds with 4338 individuals were recorded during the study period. The number of resident bird species was the maximum (115 species, 85.82%) compared to migrants. The highest number of birds was observed (120 species, 89.55%) and counted 2278 individual birds 52.51%) in the summer season. It was observed that the highest diversity of birds (89 species, 65.92%) was in March. The tree was the most (94 species, n = 2502) used microhabitat by birds in the study area. Among all birds, Pycnonotus cafer was the most abundant bird species with the highest relative abundance (6.11%). Some bird species (e.g. Metopidius indicus, Anastomus oscitans, Amaurornis phoenicurus, Streptopelia tranquebarica, Lonchura malacca) were frequently observed in the urban landscape of the study area during the pandemic. Proper management and awareness creation are essential for the conservation of the bird species in this area. Dhaka Univ. J. Biol. Sci. 30(2): 261-274, 2021 (July)
Physiological alterations and enzymatic evaluation of soybean cultivars under water deficit

Drought is one of the main abiotic factors limiting agricultural productivity, capable of having a major impact on the yield of most crops. The knowledge of the physiological and biochemical mechanisms that differentiate resistance and susceptibility to water deficit among soybean strains can be used in the generation of more tolerant cultivars. In this sense, the objective of this study was to characterize physiologically, two soybean cultivars with different patterns of tolerance to drought in the field, by determining photosynthetic rates, lipid peroxidation levels and antioxidant enzyme activity under three levels of water potential. Upon reaching the V4 development stage, the irrigation of the plants was suspended and three data collection were performed: full irrigation (control); moderate water deficit (Ψ = -1.5 ± 0.2 MPa) and severe deficit (Ψ = -3.0 MPa ± 0.2 MPa). Variations in perspiration rate, stomatal conductance, as well as decrease in photosynthetic rate were significant between the two cultivars, where the water potentials in cultivar BR 16 anticipated on average two days achieving the same water potentials in Embrapa 48 cultivar, thus presenting better efficiency in water use. In addition, the increased activity of enzymes and lipid peroxidation were more significant in the cultivar BR 16, demonstrating that this cultivar is less tolerant to drought than Embrapa 48 cultivar, corroborating to agronomic data previously found in the field.
Ciprofloxacin Hydrochloride Encapsulated into PLGA Nanoparticles for Drug Delivery Application: Fractional Factorial Design

Over several decades, poly (lactic-co-glycolic acid) (PLGA) have been widely used as Micro-and Nano-carriers of therapeutic agents for drug delivery applications. However, encapsulation process of therapeutic agents into PLGA Nanoparticles (NPs) necessitates a defined step to understand the effects and interactions of parameters involved in production process. In pharmaceutics formulations, compared to one factor at a time (OFAT) approach, statistical design of experiments (DOE) supersedes OFAT approach due to limited number of experiments required to investigate effects and interactions of a process parameters. The major objectives of the present study were to: 1) prepare and understand the effect of selected formulation parameters on particles size and drug recovery of PLGA NPs encapsulating Ciprofloxacin Hydrochloride (Cip-HCl) using a fractional factorial design (FFD) as a DOE approach; 2) understand the in-vitro release of Cip-HCl from PLGA NPs. Cip-HCl loaded PLGA were prepared by W1/O/W2 double emulsion solvent evaporation (DESE) method using poly-vinyl alcohol as a stabilizer. The Sizes of NPs were within 202 nm to 530 nm and percentage Cip-HCl recovered from dried NPs were within 1.7% w/w to 15.7% w/w. Increasing concentrations of PLGA and Cip-HCl was observed to increase NPs size. Increasing PVA concentration was observed to either reduce or increase NPs size. Increasing PLGA concentration was observed to increase the amount of Cip-HCl recovered. Within 1-24 hours, optimized formulations shows a controlled release of Cip-HCl from PLGA NPs.
8-Port Semi-Circular Arc MIMO Antenna with an Inverted L-Strip Loaded Connected Ground for UWB Applications

Multiple-input multiple-output (MIMO) antennas with four and eight elements having connected grounds are designed for ultra-wideband applications. Careful optimization of the lines connecting the grounds leads to reduced mutual coupling amongst the radiating patches. The proposed antenna has a modified substrate geometry and comprises a circular arc-shaped conductive element on the top with the modified ground plane geometry. Polarization diversity and isolation are achieved by replicating the elements orthogonally forming a plus shape antenna structure. The modified ground plane consists of an inverted L strip and semi ellipse slot over the partial ground that helps the antenna in achieving effective wide bandwidth spanning from (117.91%) 2.84–11 GHz. Both 4/8-port antenna achieves a size of 0.61 λ × 0.61 λ mm2 (lowest frequency) where 4-port antenna is printed on FR4 substrate. The 4-port UWB MIMO antenna attains wide impedance bandwidth, Omni-directional pattern, isolation >15 dB, ECC < 0.015, and average gain >4.5 dB making the MIMO antenna suitable for portable UWB applications. Four element antenna structure is further extended to 8-element configuration with the connected ground where the decent value of IBW, isolation, and ECC is achieved.
From Femtoseconds to Hours—Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays

X-ray photon correlation spectroscopy (XPCS) enables the study of sample dynamics between micrometer and atomic length scales. As a coherent scattering technique, it benefits from the increased brilliance of the next-generation synchrotron radiation and Free-Electron Laser (FEL) sources. In this article, we will introduce the XPCS concepts and review the latest developments of XPCS with special attention on the extension of accessible time scales to sub-μs and the application of XPCS at FELs. Furthermore, we will discuss future opportunities of XPCS and the related technique X-ray speckle visibility spectroscopy (XSVS) at new X-ray sources. Due to its particular signal-to-noise ratio, the time scales accessible by XPCS scale with the square of the coherent flux, allowing to dramatically extend its applications. This will soon enable studies over more than 18 orders of magnitude in time by XPCS and XSVS.
Long-Range Plasmon Field and Plasmoelectric Effect on Catalysis Revealed by Shell-Thickness-Tunable Pinhole-Free Au@SiO2 Core–Shell Nanoparticles: A Case Study of p-Nitrophenol Reduction

Plasmon-enhanced photocatalysis is an emerging hot field, but the underlying mechanisms are not very clear. The lack of fundamental understanding of the localized surface plasmon resonance (LSPR) induced electromagnetic field enhancement effects on catalysis is an obstacle to the development of general design rules for engineering efficient plasmonic photocatalysts. Herein, we reveal a long-range plasmon field and plasmoelectric effect on catalysis by using p-nitrophenol (also called 4-nitrophenol, 4-NP) as a model reactant and shell-thickness-tunable and pinhole-free Au@SiO2 core–shell nanoparticles (pf-Au@SiO2 NPs) as photocatalysts and sensing probes. The influence of long-range plasmon field and plasmoelectric effect on catalysis was evidenced by both the illumination-wavelength- and shell-thickness-dependent catalytic activities of pf-Au@SiO2 NPs. Impressively, catalytic 4-NP reduction occurs with AuNPs as catalysts, even if the AuNP surfaces were completely isolated and spaced by the insulating and .
The Multidimensional Religious Ideology scale

The Multidimensional Religious Ideology (MRI) scale is a new 43-item measure that quantifies conservative versus liberal aspects of religious ideology. The MRI focuses on recurring features of ideology rooted in innate moral instincts while capturing salient differences in the ideological profiles of distinct groups and individuals. The MRI highlights how religious ideology differs from political ideology while maintaining a robust grounding in the social psychology of ideology generally. Featuring three major dimensions (religious beliefs, religious practices, and religious morality) and eight subdimensions, the MRI is sensitive enough to generate novel insights into religious ideology across demographic groups and individual differences. The MRI is also summative, yielding a single quantitative measurement of left–right religious ideology with good scale and test–retest reliability. Analysis of 839 respondents across two studies confirmed the widespread assumption that religious ideology is a parallel construct to political ideology, emerging from similar foundations but following a distinct set of rules. The MRI shows the importance of conceptualizing ideology in ways that access the full spectrum of real-world ideological convictions—an important reminder, given the salience of religious factors for influencing ideology generally.
Evaluating the Impact of Planetary Boundary Layer, Land Surface Model, and Microphysics Parameterization Schemes on Cold Cloud Objects in Simulated GOES‐16 Brightness Temperatures

Infrared brightness temperatures (BTs) from the Geostationary Observing Environmental Satellite‐16 Advanced Baseline Imager are used to examine the ability of several microphysics and planetary boundary layer (PBL) schemes, as well as land surface models (LSM) and surface layers, to simulate upper‐level clouds. Six parameterization configurations were evaluated. Cloud objects are identified using the Method for Object‐Based Diagnostic Evaluation (MODE) and analyzed using the object‐based threat score, mean‐error distance, and pixel‐based metrics including the mean absolute error and mean bias error (MBE) for matched objects where the displacement between objects has been removed. Objects are identified using either a fixed BT threshold of 235 K or the 6.5th percentile of BTs for each model configuration. Analysis of the MODE‐identified cloud objects shows that, compared to a configuration with the Thompson microphysics scheme, Mellor‐Yamanda‐Nakanishi‐Niino (MYNN) PBL, Global Forecasting System (GFS) surface layer, and Noah LSM, the configuration employing the National Severe Storms Laboratory microphysics produced more cloud objects with higher BTs. Changing the PBL from MYNN to Shin‐Hong or Eddy‐Diffusivity Mass‐Flux also resulted in a slightly lower accuracy, though these changes result in configurations which more accurately reproduced the number of observation cloud objects and slightly reduced the high MBE. Changing the LSM from Noah to RUC reduces forecast accuracy by producing too many cloud objects with too low BTs. As the forecast hour increases, this accuracy reduction increases at a greater rate than occurred when changing the microphysics or PBL scheme and is further enhanced when using the MYNN surface layer rather than the GFS.
Variable universe fuzzy control of the lateral stability of forklift trucks based on roll grading

Based on the structural analysis and the roll mechanisms of counterbalanced forklift truck, a novel type of 3-link hydraulic supporting mechanism is designed as an actuator for forklift anti-rollover control. A rollover grading control strategy of the lateral stability of counterbalanced forklift truck is proposed, which is used to design a variable universe fuzzy controller. The variable universe fuzzy control of the first-order roll is utilized when the forklift is in the first-order roll, whereas the variable universe fuzzy control of the second-order is used when the forklift is in the second-order roll. A virtual prototype model of forklift is built in Automatic Dynamic Analysis of Mechanical Systems (ADAMS). The co-simulations and vehicle tests are implemented, the results indicate that the lateral stability and active security of the forklift could be improved effectively by using the variable universe fuzzy control strategy of the lateral stability of forklift truck based on roll grading.
Multigram Scale Enzymatic Synthesis of (R)‐1‐(4′‐Hydroxyphenyl)ethanol Using Vanillyl Alcohol Oxidase

The enantioselective oxyfunctionalisation of C−H bonds is a highly interesting reaction, as it provides access to chiral alcohols that are important pharmaceutical building blocks. However, it is hard to achieve using traditional methods. One way in which it can be achieved is through the action of oxidative enzymes. Although many reports of the oxyfunctionalisation capabilities of enzymes at an analytical scale have been published, reports on the use of enzymes to achieve oxyfunctionalisation on a synthetically relevant scale are fewer. Here, we describe the scale-up of the conversion of 4-ethylphenol to (R)-1-(4′-hydroxyphenyl)ethanol using the flavin-dependent enzyme vanillyl alcohol oxidase. The process was optimised by testing different reaction media and substrate and enzyme concentrations and by performing it under an oxygen atmosphere. Under optimised reaction conditions, 4.10 g (R)-1-(4′-hydroxyphenyl)ethanol at 97% ee was obtained from 10 g 4-ethylphenol (isolated yield 36%). These results highlight some of the challenges that can be encountered during scale-up of an enzymatic oxyfunctionalisation process to a synthetically relevant scale and will be of use for the development of enzymatic processes for the synthesis of industrially relevant compounds. (Figure presented.).