Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Lignin Aromatic Structure in Wood Fractions Based on IR Spectroscopy

In our previous studies, a method of quantitative analysis of lignin structure by IR spectroscopy with direct use of wood meal was developed which can be apply to a wide range of wood species. Lignin structure deposition in different fractions in the same wood sample also has characteristic diversity and differences. In this study, quantitative analysis of lignin structure by IR spectroscopy was used to determine whether it can be applied to different fractions in the same wood sample. Finely milled beech wood was fractionated by stepwise extraction using aqueous dioxane and followed by subsequent dimethyl sulfoxide (DMSO)/LiCl solvents with increasing LiCl concentrations. The results showed that fractions of the peak area ratio of log(1595/1509) fit a linear relationship with different wood species very well. Therefore, log(1595/1509) could be used not only with different wood species but also with different fractions from the same wood. For 1509/1460, log(1275/1220), 1130/1030 and 835/(835+815), different fractions deviated from the original relationship obtained from different wood species.
Nanocomposite of Ellagic Acid with Multi-Walled Carbon Nanotubes for the Simultaneous Voltammetric Detection of Six Biomolecules

In this proof-of-concept study, a highly sensitive electrochemical sensor using a graphite paste electrode modified with ellagic acid and multi-walled carbon nanotubes (MGPE/MWCNTs-EA) was developed for the simultaneous determination of six biomolecules: ascorbic acid (AA), dopamine (DA), uric acid (UA), tryptophan (Trp), xanthine (XA), and caffeine (CA). Differential pulse voltammetry (DPV) was performed at a potential range from 0.1–1.2 V vs. Ag/AgCl in phosphate electrolyte (pH 2.0). The modified GPE enabled the simultaneous determination of biomolecules under investigation in human urine and blood serum samples with detection limits ranging from 11–91 nM with recoveries of 94.0–106.0%. The electrochemical performance of the modified GPE for the analytes was stable and reproducible and checked with standard high performance liquid chromatography technique. The data suggested that the modified GPE provided a promising platform for routine quantitative determination of the biomolecules under investigation in quality control studies of real samples collected from food and pharmaceutical products.
An exploratory MALDI-TOF MS library based on SARAMIS superspectra for rapid identification of Aspergillus section Nigri

Matrix-assisted laser desorption ionization–time-of-flight mass spectrometry (MALDI-TOF MS) is a broadly used technique for identification and typing of microorganisms. However, its application to filamentous fungi has been delayed. The objective of this study was to establish a data library for rapid identification of the genus Aspergillus sect. Nigri using MALDI-TOF MS. With respect to sample preparation, we compared the utility of using mature mycelia, including conidial structures, to accumulate a wider range of proteins versus the conventional method relying on young hyphae. Mass spectral datasets obtained for 61 strains of 17 species were subjected to cluster analysis and compared with a phylogenetic tree based on calmodulin gene sequences. Specific and frequent mass spectral peaks corresponding to each phylogenetic group were selected (superspectra for the SARAMIS system). Fifteen superspectra representing nine species were ultimately created. The percentage of correct identification for 217 spectra was improved from 36.41% to 86.64% using the revised library. Additionally, 2.76% of the spectra were assigned to candidates that comprised several related species, including the correct species.
Fostering Team Creativity Through Team-Focused Inclusion: The Role of Leader Harvesting the Benefits of Diversity and Cultivating Value-In-Diversity Beliefs

This article advances prior theory on inclusive leadership to better understand how leaders foster team creativity through members’ experience that their uniqueness belongs within the team (i.e., team-derived inclusion). We argue that leaders can instigate such sense of inclusion in their team by engaging in two behaviors: stimulating all members of the team to fully express their unique viewpoints and perspectives (harvesting the benefits of diversity) and facilitating beliefs about the value of differences in the team (cultivating value-in-diversity beliefs). In Study 1 (n = 491 employees), we validated newly developed scales measuring these two leader behaviors. Using a sample of 38 teams within one organization (Study 2), we showed that harvesting the benefits of diversity, without also cultivating value-in-diversity beliefs, has a negative effect on team-derived inclusion and indirectly team creativity. In Study 3, we demonstrated based on 93 teams from multiple organizations, while ruling out several alternative explanations, that harvesting the benefits of diversity positively relates to team-derived inclusion and indirectly team creativity, if leaders also cultivated value-in-diversity beliefs. Our model and findings across studies are the first to shed light on inclusive leadership as double-edged sword in that leaders may need to complement harvesting with cultivating to prevent negative effects and elicit positive effects on inclusion and, eventually, team creativity.
Biosynthesis of highly stabilized silver nanoparticles by Rhizopus stolonifer and their Anti-fungal efficacy

Highly stabilized monodispersed silver nanoparticles (AgNPs) were synthesized by Rhizopus stolonifer and the antifungal efficacy of silver nanoparticles (AgNPs) against Candida sp. were studied. Characterization of biosynthesized nanosilver was made by TEM-EDS and AFM. Minimum Inhibitory Concentration (MIC) of biosynthesized AgNPs, Amphotericin B, and Fluconazole have been studied on pathogenic fungi and the changes on membrane reactions have been elucidated by Scanning Electron Microscopy (SEM). The present study indicates AgNPs has considerable antifungal activity comparison with other antifungal drugs Nanosilver showed potent activity against pathogenic fungi. The results showed nano-Ag exerted activity on the mycelia. Thus, the present study indicates nano-Ag may have effective antifungal activity, deserves further investigation for clinical applications.
A Case Study of an Industrial Power Plant under Cyberattack: Simulation and Analysis

For critical infrastructures, technological developments regarding real-time data transmission and processing improve the system’s operability and reliability. However, vulnerabilities are introduced in the case of implementing new remote access methods or where redundancy is low. At the national level, most critical infrastructures are connected, and, therefore, achieving a level of security and resilience is based on identifying a multitude of risks. In this respect, the reduction of risk to acceptable levels directly affects the quality of citizens’ lives and decreases losses in the industry. This study starts from the threats to power systems, namely cyberattacks, which are much more dangerous, although less visible, to operators, and almost invisible to the public or the media. From this point of view, it was proved that the most vulnerable parts of the power system were human–machine interfaces, electrical equipment, Surveillance, Control, and Data Acquisition (SCADA) systems. This paper’s main achievements include the simulation of cyberattacks on existing electrical equipment from a petrochemical plant (case study), which consists of modifying the remote data transmitted by the SCADA system. Two locations were submitted to simulated cyberattacks that were considered critical for the overall plant operation. Furthermore, the changes that occur following each fault resulting from the cyberattack and the influence of the electrical parameter changes upon the process flow were analyzed. Furthermore, by using Electrical Power System Analysis Software—ETAP—the changes that occur following each fault due to the cyberattack and the influence of the electrical parameter changes upon the process flow were analyzed. By considering the two malfunction events, the resilience assessment of the system was analyzed. In the second case, only partial resilience action, up to 40%, restored the operability of the industrial power plant.
Analysis and Design of a Fully-Integrated High-Power Differential CMOS T/R Switch and Power Amplifier Using Multi-Section Impedance Transformation Technique

In this paper, a new topology for a high-power single-pole-double-throw (SPDT) antenna switch is presented, and its loss mechanisms are fully analyzed. The differential architecture is employed in the proposed switch implementation to prevent unwanted channel formations of OFF-state Rx switch transistors by relieving the voltage swing over the Rx switch devices. In addition to that, the load impedance seen by the Tx switch is stepped down to reduce the voltage swing even more, allowing the antenna switch to handle a high-power signal without distortions. To drop the switch operating impedance, two matching networks are required at the input and the output of the Tx switch, respectively, and they are carefully implemented considering the integration issue of the front-end circuitries. From the loss analysis of the whole signal path, an optimum switch operating impedance is decided in view of a trade-off between power handling capability and insertion loss of the antenna switch. The insertion loss of the proposed design is compared to the conventional design with electromagnetic (EM) simulated transformer and inductors. The proposed antenna switch is implemented in a standard 0.18 µm CMOS process, and all switch devices adopt the deep n-well structure. The measured performance of the proposed transmitter front-end chain shows a 1 dB compression point (P1dB) of 32.1 dBm with 38.3% power-added efficiency (PAE) at 1.9 GHz.
A Simulation-Based Multi-Objective Optimization Framework for the Production Planning in Energy Supply Chains

The work presents a simulation-based Multi-Objective Optimization (MOO) framework for efficient production planning in Energy Supply Chains (ESCs). An Agent-based Model (ABM) that is more comprehensive than others adopted in the literature is developed to simulate the agent’s uncertain behaviors and the transaction processes stochastically occurring in dynamically changing ESC structures. These are important realistic characteristics that are rarely considered. The simulation is embedded into a Non-dominated Sorting Genetic Algorithm (NSGA-II)-based optimization scheme to identify the Pareto solutions for which the ESC total profit is maximized and the disequilibrium among its agent’s profits is minimized, while uncertainty is accounted for by Monte Carlo (MC) sampling. An oil and gas ESC model with five layers is considered to show the proposed framework and its capability of enabling efficient management of the ESC sustained production while considering the agent’s uncertain interactions and the dynamically changing structure.
Engineering of films of disperse red 19 based polyurethanes containing poly(ethylene glycol) or polydimethylsiloxane segments with controlled morphology

Abstract In this work, we report the synthesis of polyurethane derivatives from Disperse Red 19 and hexamethylene diisocyanate as well as polyurethanes containing poly(ethylene glycol) and polydimethylsiloxane segments. Polyurethanes were incorporated as films on glass substrates by spin-coating. The polyurethane containing poly(ethylene glycol) fragments formed the best quality films with a height value of 206 nm, and roughness of 25.6 nm. Films of polyurethane containing polydimethylsiloxane segments exhibit quasi-spherical agglomerates on the surface with an approximate size of 0.6 μm. The polyurethane obtained from Disperse Red 19 and hexamethylene diisocyanate formed rose-like agglomerates with a size of 10 μm.
Tax Revenue of Nepal: Assessing the Impact on GDP

This study examines the economic impact of tax revenue and economic growth of Nepal, from 2000 to 2018. In order to attain the objectives, relevant time-series secondary data were collected from the Economic Survey published by Ministry of Finance, Quarterly Economic Bulletin published by Nepal Rastra Bank, Revenue Department and previous works done by scholars. The collected data has been analyzed by using linear regression model. The results display that tax revenue and total revenue has a positive significant effect on GDP. The trends of tax revenue has signified the positive trends but not sufficient to meet the government expenditure of Nepal.
A Multi-View SVM Approach for Seizure Detection from Single Channel EEG Signals

Seizures are the part of the epilepsy that occurs in central nervous system which leads to abnormal brain activity. Electroencephalogram (EEG) signal recordings are mostly used in epileptic seizure detection process. Detection of seizures is a crucial part for further treatment of patients. This paper proposes a multi-view SVM model for seizure detection using the single channel EEG signals. In this experiment, two views of the EEG data have been extracted, (1) the time domain features using Independent Component Analysis (ICA) and (2) power spectral densities are obtained in the frequency domain. Extracted features have been fed to multi-view SVM classification model. In this study, a single channel EEG dataset is used for seizure detection. Performance estimation parameters namely Accuracy, Sensitivity, Specificity, F1-score, and AUC value have been estimated for evaluating the proposed model. The model classified seizure and non-seizure over the sets A vs E and B vs E with an accuracy greater than 99% using k-fold cross validation. The classification accuracy obtained by multi-view SVM is better by 1–4% than single view SVM using the same features. Furthermore, the proposed model is also compared with existing single view SVM models. It is observed that the multi view SVM model performed significantly better, compare to a single view SVM model over the same features.
Psychometric origins of depression

This article examines the historical construction of depression over about a hundred years, employing the social life of methods as an explanatory framework. Specifically, it considers how emerging methodologies in the measurement of psychological constructs contributed to changes in epistemological approaches to mental illness and created the conditions of possibility for major shifts in the construction of depression. While depression was once seen as a feature of psychotic personality, measurement technologies made it possible for it to be reconstructed as changeable and treatable. Different types of scaling techniques (Likert versus dichotomous scales) enabled the separation of depressive personality from reactive depression, paving the way for measuring the severity and intensity of emotions. Techniques to test sensitivity to change provided a means of demonstrating the efficacy of new psychoactive drug treatments. Later, more advanced techniques of precision scaling enabled the management of a new measurement problem, clinician unreliability, associated with the growing number of professionals involved in mental health care. Through statistical management of unreliability, the construct of depression has dramatically reduced over this period from hundreds of questionnaire items to potentially just two. Exploring the history of depression through this lens produces an alternative narrative to those that have emerged as a result of medicalisation and the actions of individuals and pressure groups.
Pyramid Ensemble Convolutional Neural Network for Virtual Computed Tomography Image Prediction in a Selective Laser Melting Process

Additive manufacturing (AM) is a type of advanced manufacturing process that enables fast prototyping to realize personalized products in complex shapes. However, quality defects existed in AM products can directly lead to significant failures (e.g., cracking caused by voids) in practice. Thus, various inspection techniques have been investigated to evaluate the quality of AM products, where X-ray computed tomography (CT) serves as one of the most accurate techniques to detect geometric defects (e.g., voids inside an AM product). Taking a selective laser melting (SLM) process as an example, voids can be detected by investigating CT images after the fabrication of products with limited disturbance from noises. However, limited by the sensor size and scanning speed issue, CT is difficult to be used for online (i.e., layer-wise) voids detection, monitoring, and process control to mitigate the defects. As an alternative, optical cameras can provide layer-wise images to support online voids detection. The intricate texture of the layer-wise image restricts the accuracy of void detection in AM products. Therefore, we propose a new method called pyramid ensemble convolutional neural network (PECNN) to efficiently detect voids and predict the texture of CT images using layer-wise optical images. The proposed PECNN can efficiently extract informative features based on the ensemble of the multiscale feature-maps (i.e., image pyramid) from optical images. Unlike deterministic ensemble strategies, this ensemble strategy is optimized by training a neural network in a data-driven manner to learn the fine-grained information from the extracted feature-maps. The merits of the proposed method are illustrated by both simulations and a real case study in a SLM process.
Gender Role Ideologies, Marital Satisfaction, and Eudaimonic Well-Being in Evangelical Women

Recent research has demonstrated that egalitarian gender role ideologies are associated with higher marital satisfaction and higher psychological well-being. However, there has been little research investigating whether gender role ideologies in Evangelical populations follow these same trends. The current study examined how gender role ideologies in an Evangelical population are related to marital satisfaction and eudaimonic well-being, as well as the role of locus of control in these relationships. Three hundred sixty-three Evangelical Christian women were administered a survey containing measures of gender role ideology, marital satisfaction, eudaimonic well-being, and locus of control. No bivariate relationship was found between marital satisfaction and gender role ideologies. However, locus of control significantly moderated this relationship, such that the relationship was only significant for women with a more external locus of control. Specifically, externalizing women evidenced a significant positive relationship between egalitarian gender role ideologies and marital satisfaction, whereas the relationship was not significant for internalizing women. Moreover, egalitarian gender role ideologies were found to be positively related to eudaimonic well-being. The results of this research expand the current body of literature surrounding gender role ideologies within religious populations, specifically regarding outcomes that are associated with holding specific gender role ideologies.
The Development and Testing of a Digital ITER-Type Mock-Up Based on Virtual Reality Technology

Abstract ITER is an experimental tokamak device that has many collaborators from different countries and aims to produce energy from nuclear fusion inside a reactor with magnetic confinement. Corresponding scientific data and structure visualization are important factors in connecting qualitative information from human intuition and quantitative content of physical-based datasets. Therefore, building an integrated platform with the interaction of scientific data visualization is inevitable for engineers and researchers in geographically distributed groups to effectively promote consistent design work, and it is also a key part in research and training processes for operators and students to better understand the ITER structure with analysis results. In this context, a digital ITER-type mock-up has been developed at the Institute of Modern Physics, Chinese Academy of Sciences based on an immersive virtual reality platform that is designed using client/server hybrid heterogeneous cloud architecture with many user-friendly and flexible features. Detailed ITER-type models have been developed and optimized in low-poly style to increase rendering speed, and scientific data in the scalar field have been represented by means of the view-dependent ray-casting method with topological texture images, which can fulfill real-time roaming and inspecting tasks for research purposes and provide an efficient and effective tool for fusion technology education at the University of Chinese Academy of Sciences.
Mechanical degradation of graphite/PVDF composite electrodes: A model-experimental study

Author(s): Takahashi, K; Higa, K; Mair, S; Chintapalli, M; Balsara, N; Srinivasan, V | Abstract: © The Author(s) 2015. Mechanical failure modes of a graphite/polyvinylidene difluoride (PVDF) composite electrode for lithium-ion batteries were investigated by combining realistic stress-stain tests and mathematical model predictions. Samples of PVDF mixed with conductive additive were prepared in a similar way to graphite electrodes and tested while submerged in electrolyte solution. Young's modulus and tensile strength values of wet samples were found to be approximately one-fifth and one-half of those measured for dry samples. Simulations of graphite particles surrounded by binder layers given the measured material property values suggest that the particles are unlikely to experience mechanical damage during cycling, but that the fate of the surrounding composite of PVDF and conductive additive depends completely upon the conditions under which its mechanical properties were obtained. Simulations using realistic property values produced results that were consistent with earlier experimental observations.
The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing

Model predictive control (MPC) is widely used for microgrids or unit commitment due to its ability to respect the forecasts of loads and generation of renewable energies. However, while there are lots of approaches to accounting for uncertainties in these forecasts, their impact is rarely analyzed systematically. Here, we use a simplified linear state space model of a commercial building including a photovoltaic (PV) plant and real-world data from a 30 day period in 2020. PV predictions are derived from weather forecasts and industry peak pricing is assumed. The effect of prediction accuracy on the resulting cost is evaluated by multiple simulations with different prediction errors and initial conditions. Analysis shows a mainly linear correlation, while the exact shape depends on the treatment of predictions at the current time step. Furthermore, despite a time horizon of 24h, only the prediction accuracy of the first 75min was relevant for the presented setting.
Intrapersonal Behavioral Coordination and Expressive Accuracy During First Impressions

What factors influence how accurately we express our personalities? Here, we investigated the role of targets’ nonverbal expressivity or the intrapersonal coordination between head and body movements. To do so, using a novel movement quantification method, we examined whether variability in a person’s behavioral coordination was related to how accurately their personality was perceived by naive observers. Targets who exhibited greater variability in intrapersonal behavior coordination, indicating more expressive behavior, were perceived more accurately on high observability personality items, such as how energetic and helpful they are. Moreover, these associations held controlling for other indicators of overall movement, self- and perceiver-rated extroversion, as well as how engaging and likable targets were perceived to be. This provides preliminary evidence that variability in intrapersonal behavioral coordination may be a unique behavioral indicator of expressive accuracy, although further research that replicates these findings and examines the causal associations is needed.