Miracle-dz/UniFAR-FTUs huggingface dataset:
Solid Fluorescence pH Sensors Based on 1,8-Naphthalimide Copolymers Synthesized by UV Curing

Novel water-swollen photo-crosslinked membranes were obtained by copolymerization of the N-vinylpyrrolidone, butyl acrylate and ethyl methacrylate monomers functionalized with naphthalimide groups, as pH sensitive fluorescence probes. For that purpose, two monomers with pending naphthalimide groups anchored to ethyl methacrylate through alkyl chains with different length, were previously synthesized. The membranes were characterized using different techniques. The pH dependence of absorbance and the corresponding quenching of fluorescence were investigated and related to the structure of naphthalimide substituents linked to the membrane. The new solid sensors exhibited sensitive fluorescence changes at pH < 3, and lower time response was determined for membranes where the sensing group was linked through longer alkyl chain to the polymer matrix. The membranes were solid, thermally stable and easily handled to be applied as sensor materials, and showed a reversible behavior, which is an important feature for further fabrication of an economical on-site tool for the acidity detection in aqueous environments.
Remote data monitoring system for heating supply based on GPRS network

In order to realize the remote the real-time on-line monitoring of the measured data as well as the concentrative control and management of heat consumption,a communication scheme for heating supply monitoring system was presented based on GPRS network technology to meet the requirement of monitoring system in both real-time and economy.The system consists of SIEMENS GPRS communication module MC55,monitoring center server,and heat measuring terminal based on a low-power mixed signal microcontroller MSP430FW427.The serial communication RS485 was used between the measuring terminal and communication module,which could provide a transparent data transmission channel and realize real-time data exchange between remote nodes and monitoring center.The experimental results show that the data transmission rate of the system is about 60kbit/s.The system has such advantages as high reliability and data transmission rate.
Nitrogen Enriched Organic Fertilizer (NEO) and Its Effect on Ryegrass Yield and Soil Fauna Feeding Activity under Controlled Conditions

This study aimed to investigate the effects of a new nitrogen-enriched organic-based fertilizer (NEO) on Italian ryegrass (Lolium multiflorum Lam.) yield and soil fauna feeding activity. Nitrogen is transformed from the air to manure by a plasma process. At the farm level, NEO could improve self-sufficiency and sustainability. The work was carried out under controlled conditions in two pot trials. Five fertilization regimes were used: no fertilizer, different amounts of mineral fertilizer, three NEO types, organic fertilizer (untreated manure), and organic fertilizer + different amounts of N in mineral fertilizer, including 14 treatments in trial one and 11 treatments in trial two. Besides evaluating dry matter yields, we utilized the Bait-lamina test system to assess the feeding activity of soil fauna. The results indicated a clear positive impact of nitrogen (N) on ryegrass yield where all fertilizers increased the yield in correspondence with their N availability regardless of the fertilizer type; whereas the yield was highest with mineral fertilizer up to our maximum level of 235 kg N ha−1 in trial one and 175 kg N ha−1 in trial two. The NEO fertilizers yielded in the same range as mineral fertilizers. The same clear pattern was not observed for soil fauna feeding activity. Instead, a tendency was observed where no fertilization tends to give the highest feeding activity. We saw no correlation between the yield and the soil fauna feeding activity. The feeding activity was highest in depth below 5 cm from the soil surface. Feeding activity also increased over time after fertilization. The NEO fertilizers had no more adverse effects on soil fauna feeding activity than other fertilizers. Other factors than fertilization alone are determining the soil fauna feeding activity.
Impact of low-dose intrathecal morphine on orthopedic surgery: a protocol of a systematic review and meta-analysis of randomized controlled trials

Introduction: Patients undergoing orthopedic surgery usually suffer considerably from peri-operative pain and intrathecal morphine (ITM) has recently been used as an effective analgesic method. The intrathecal morphine dose achieving optimal analgesia for orthopedic surgery while minimizing side effects has not yet been determined. There is currently a lack of literature synthesis on the safety and effects of low-dose ITM on orthopedic surgery. Methods and analysis: A thorough literature search will be performed on multiple electronic databases and trial registries until January 11th 2022, and reference lists will be examined. Two independent reviewers will select studies, extract data and assess the risk of bias and quality of the included studies. We will select randomized controlled trials comparing low-dose ITM (no more than 100ug) with high-dose ITM (no less than 200ug) or placebo treatment or other comparatives. We will assess the risk of bias using the Cochrane risk of bias tool and Jadad score will be applied to assess the quality of each included trial. We will also use Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) to determine the confidence in effect estimates. Publication bias will be evaluated by visually inspecting funnel plots and Egger regression asymmetry test in estimations of more than 10 trials. Standard meta-analysis will be performed using R package meta. Ethics and dissemination: The results of this systematic review and meta-analysis will be disseminated through peer-reviewed journal and related conferences. The data used in this meta-analysis will not contain individual patient data and ethical approval is therefore not required.
Building STEM Inquiry-Based Teaching Proposal Through Collaborations Between Schools and Research Centres: Students’ and Teachers’ Perceptions

This study presents an inquiry-based teaching approach using a rich methodology involving STEM (science-technology-engineering and mathematics) projects, developed using a cooperative way to work between schools and research centres. We describe the teaching pattern scenario with students, teachers, and scientists. We also illustrate the learning process, conducted through two inquiry-based problems in Biochemistry, the mutagenesis process and the transgenesis effects caused by inoculation of bacteria. The teaching sequence, as well as the scientific knowledge and students’ competencies involved, are shown. We then analyse the students' and teachers' perceptions in this scenario regarding the development of students' STEM learning through their inquiry skills promoted and concerning predictors to vocational careers involved (students’ identities as potential scientists, scientific attitudes, social implications towards science or students´ agency). Finally, we add some conclusions and contributions to teaching STEM education, related to factors of the design teaching scenario that bring connections with the interests and motivations of students, such as the relevance of the projects to evoke understanding of processes or causal relationships of content or the teacher's professionalisation supported by a proper allocation between scientists and teachers.
No leeway to enhance carbon sequestration and stock capacity via changes to forest management

Forest management interventions can act as value-based agents to remove CO2 from the atmosphere and slow anthropogenic climate change and thus might play a strategic role in the framework of the EU forestry-based mitigation strategy. To what extent diversified management actions could lead to quantitatively important changes in carbon sequestration potential and stocking capacity at the tree level remains to be thoroughly assessed. To that end, we used a state-of-the-science bio-geochemically based forest growth model to assess effects of multiple alternative forest management scenarios on plant net primary productivity (NPP) and potential carbon woody stocks (pCWS) under differing scenarios of climate change. The experiments indicated that the capacity of trees to assimilate and store atmospheric CO2 in recalcitrant standing woody tissue is already being attained as its optimum under business-as-usual forest management conditions regardless of the different climate change scenarios investigated. Nevertheless, on the long-term and under increasing atmospheric CO2 concentration and warming, managed forests show both higher productivity and a larger pool of stored carbon than unmanaged ones as long as forest thinning and tree harvesting are of moderate intensity.
Nonlinear Dynamic Characteristic Analysis of Conditional Jump Duration

Based on the five-minute tick market data of the Shanghai Composite Index from 2015 to 2019, the time duration of the jumps is realized by extracting the dynamic behavior model of the asset price to achieve the jumps, the dynamic process of the jump changes is described, and the broad ACD (1,1) model is established for the time duration sequence, which achieves a good fit for the dynamic behavior of the jump time duration. The results show that there is also aggregation effect in the time duration of the jumps, which shows that the intermittent phenomenon of the jump in asset prices opens up new ideas for further studying the characteristics and movement laws of asset price jump behavior from the perspective of jumping intensity.
mTOR Signaling and Potential Therapeutic Targeting in Meningioma

Meningiomas are the most frequent primary tumors arising in the central nervous system. They typically follow a benign course, with an excellent prognosis for grade I lesions through surgical intervention. Although radiotherapy is a good option for recurrent, progressive, or inoperable tumors, alternative treatments are very limited. mTOR is a protein complex with increasing therapeutical potential as a target in cancer. The current understanding of the mTOR pathway heavily involves it in the development of meningioma. Its activation is strongly dependent on PI3K/Akt signaling and the merlin protein. Both factors are commonly defective in meningioma cells, which indicates their likely function in tumor growth. Furthermore, regarding molecular tumorigenesis, the kinase activity of the mTORC1 complex inhibits many components of the autophagosome, such as the ULK1 or Beclin complexes. mTOR contributes to redox homeostasis, a vital component of neoplasia. Recent clinical trials have investigated novel chemotherapeutic agents for mTOR inhibition, showing promising results in resistant or recurrent meningiomas.
Different Markov chains modulate visual stimuli processing in a Go-Go experiment in 2D, 3D, and augmented reality

The introduction of Augmented Reality (AR) has attracted several developments, although the people’s experience of AR has not been clearly studied or contrasted with the human experience in 2D and 3D environments. Here, the directional task was applied in 2D, 3D, and AR using simplified stimulus in video games to determine whether there is a difference in human answer reaction time prediction using context stimulus. Testing of the directional task adapted was also done. Research question: Are the main differences between 2D, 3D, and AR able to be predicted using Markov chains? Methods: A computer was fitted with a digital acquisition card in order to record, test and validate the reaction time (RT) of participants attached to the arranged RT for the theory of Markov chain probability. A Markov chain analysis was performed on the participants’ data. Subsequently, the way certain factors influenced participants RT amongst the three tasks time on the accuracy of the participants was sought in the three tasks (environments) were statistically tested using ANOVA. Results: Markov chains of order 1 and 2 successfully reproduced the average reaction time by participants in 3D and AR tasks, having only 2D tasks with the variance predicted with the current state. Moreover, a clear explanation of delayed RT in every environment was done. Mood and coffee did not show significant differences in RTs on a simplified videogame. Gender differences were found in 3D, where endogenous directional goals are in 3D, but no gender differences appeared in AR where exogenous AR buttons can explain the larger RT that compensate for the gender difference. Our results suggest that unconscious preparation of selective choices is not restricted to current motor preparation. Instead, decisions in different environments and gender evolve from the dynamics of preceding cognitive activity can fit and improve neurocomputational models.
Probe into the Cell-phone Radar Based on the Opportunity Illuminators and its Key Techniques

The cell-phone passive radar has become one important developing direction of the passive radar since it has great military civilian values and lots of advantages such as anti-jamming, resisting anti-radiation missile,anti-low-altitude-surprise,anti-stealth,agility,portability and low price etc. This paper reviews the progress course of passive radar briefly,presents the notion of cell phone passive radar and analyzes the relative key techniques. This work is very enlightening to the research development of cell phone passive radar.
SUPPORT VECTOR MACHINES BASED APPROACH FOR FAULT DIAGNOSIS OF VALVES IN RECIPROCATING PUMPS

A Support Vector Machines-based approach is presented for fault diagnosis of valves in reciprocating pumps. According to the approach, the input vectors of Support Vector Machines consisted of wavelet packet transform coefficients extracted from the collected vibration signals of valves as the time-frequency characteristics. And these vectors were inputted into a multi-class classifier composed of many Support Vector Machines to identify multiple fault modes of valves. The experimental diagnosis results show that, the approach can not only correctly diagnose single faulty valve but also precisely identify multiple faulty valves. Furthermore, comparing with the traditional artificial neural networks, the approach is more efficient, robust and adaptive, which indicates the potential of the SVMs techniques in machinery fault diagnosis.
Homeostatic-inspired controller algorithm for a hybrid-driven autonomous underwater glider

The autonomous hybrid-driven underwater glider presented in this thesis combines the concept of a buoyancy-driven underwater glider and a conventional autonomous underwater vehicle. It is classified as a new kind of autonomous underwater glider. The existing buoyancy-driven gliders have proven to be powerful tools in oceanographic applications. This is because they are inexpensive, high-endurance and energy-efficient. However, they still have weaknesses in terms of speed and manoeuvrability due to the under-actuated system; relatively slow; limited propulsion forces; and limited external control surfaces. Furthermore, it is difficult to control the glider because of the high nonlinearity and complexity of the glider dynamics, coupled with the underwater environments and disturbances. Thus, the main objective of this research is to design and develop a controller algorithm that is able to make the glider adaptive despite facing these constraints. A robust and reliable homeostatic-inspired controller system has been designed for this purpose. The controller is able to adapt efficiently to the dynamically changing conditions and is able to compensate the disturbance from water currents. The controller algorithm has been designed based on the human innate control mechanism by integrating three artificial systems: the artificial neural network (ANN), artificial endocrine system (AES), and artificial immune system (AIS). According to simulation results of control methods benchmarking, the homeostatic controller was able to achieve the desired pitch angle at the fastest settling time, which was 12.5 seconds faster than the xxix model predictive control (MPC); 9 seconds faster than the linear-quadratic regulator (LQR); 6.5 seconds faster than the neural network (NN) controller; and 3.75 seconds faster than the neuroendocrine controller. In addition, the homeostatic controller was able to optimise the ballast mass and distance of the sliding mass in order to achieve the desired pitch angle by shortening the sliding mass distance up to 53.7% and reducing the ballast mass up to 17.7% when compared with the LQR and MPC. Overall, the homeostatic controller has achieved the best performance compared with the LQR, MPC, NN and neuroendocrine controllers. Furthermore, the validation analyses between the simulation and experimental results have shown that the homeostatic control system produces very satisfactory performance, with the homeostatic controller able to achieve the desired angle.
Thermal Design for High Precision DC Resistance Divider

The heat for a DC resistance divider hinders long-term reliability,and is a key factor in measurement accuracy,especially in the higher working current.This paper combined experimental and simulation methods,found one kind of solution,designed one kind of DC resistance divider structure which take the high thermal conductivity solid material as the dielectric,carried on the temperature field calculation and the temperature measurement test,and provided the foundation for the development DC electronic voltage transformers product.
CHARMM-GUI Multicomponent Assembler for Modeling and Simulation of Complex Multicomponent Systems

Atomic-scale molecular modeling and simulation are powerful tools for computational biology. However, constructing models with large, densely packed molecules, non-water solvents, or with combinations of multiple biomembranes, polymers, and nanomaterials remains challenging and requires significant time and expertise. Furthermore, existing tools do not support such assemblies under the periodic boundary conditions (PBC) necessary for molecular simulation. Here, we describe Multicomponent Assembler in CHARMM-GUI that automates complex molecular assembly and simulation input preparation under the PBC. We demonstrate its versatility by preparing 6 challenging systems with varying density of large components: (1) solvated proteins, (2) solvated proteins with a pre-equilibrated membrane, (3) solvated proteins with a sheet-like nanomaterial, (4) solvated proteins with a sheet-like polymer, (5) a mixed membrane-nanomaterial system, and (6) a sheet-like polymer with gaseous solvent. Multicomponent Assembler is expected to be a unique cyberinfrastructure to facilitate innovative studies of complex interactions between small (organic and inorganic) molecules, biomacromolecules, polymers, and nanomaterials.
Estimation of Vitamin C and the Fructose Levels in Some Medicinal Plants and their Effects on Iron Bioavailability in Rats

Iron deficiency is considered as a common problem facing the general world population. In the current research, experiments were conducted to evaluate the effects of aqueous extracts of Acacia senegal (Gum Arabic, GA), Spinacia oleracea (spinach), Zea mays (corn), and Capsicum annuum (red chili pepper) on iron and ferritin levels in rats. Vitamin C and fructose levels were first estimated in these plants by using High-Performance Liquid Chromatography (HPLC). The results showed that the GA extract contained the highest level of fructose (853 mg.L-1), followed by red chili pepper (635 mg.L-1), corn (521 mg.L-1), and spinach (271 mg.L-1) extracts. Also, the results of vitamin C estimation showed levels of 3.376, 0.645, 0.579, and 0.347 mg/ml in the extracts of spinach, red chili pepper, corn, and GA, respectively. Next, thirty male albino rats (age 8-12 weeks, weight 120-200 g) were divided randomly into six groups, each kept in a separate polypropylene rat cage. Treatments of rats with various plant extracts were conducted following a CRD complete random design, with five replicates for each treatment. The results of lipid profile test revealed that GA treatment caused the highest reduction in the serum levels of TC, TG, LDL, VLDL, and HDL in the treated rats (76.25, 55.25,23, 15.25, and 38 mg/dl , respectively), as compared to the other plant extracts. Evidence from this study suggests that the higher serum level of fructose in the group of rats treated with all plant extracts enhances the process of ferritin formation, possibly via increasing the level of bioavailable iron.
A Gas Chromatography Mass Spectrometry-Based Method for the Quantification of Short Chain Fatty Acids

Short Chain Fatty Acids (SCFAs) are produced by the gut microbiota and are present in varying concentrations in the intestinal lumen, in feces but also in the circulatory system. By interacting with different cell types in the body, they have a great impact on host metabolism and their exact quantification is indispensable. Here, we present a derivatization-free method for the gas chromatography mass spectrometry (GC-MS) based quantification of SCFAs in plasma, feces, cecum, liver and adipose tissue. SCFAs were extracted using ethanol and concentrated by alkaline vacuum centrifugation. To allow volatility for separation by GC, samples were acidified with succinic acid. Analytes were detected in selected ion monitoring (SIM) mode and quantified using deuterated internal standards and external calibration curves. Method validation rendered excellent linearity (R2 > 0.99 for most analytes), good recovery rates (95–117%), and good reproducibility (RSD: 1–4.5%). Matrix effects were ruled out in plasma, feces, cecum, liver and fat tissues where most abundant SCFAs were detected and accurately quantified. Finally, applicability of the method was assessed using samples derived from conventionally raised versus germ-free mice or mice treated with antibiotics. Altogether, a reliable, fast, derivatization-free GC-MS method for the quantification of SCFAs in different biological matrices was developed allowing for the study of the (patho)physiological role of SCFAs in metabolic health.
VaR prediction for GARCH (1,1) model with normal and student-t error distribution

This study aims at determining the estimated parameters of the GARCH (1.1) model establishing the prediction of the VaR value, and defining the accuracy of the VaR prediction. In this study, the error in the GARCH (1,1) model uses a normal distribution and student-t distribution. The research method focuses on parameter calculation and the prediction of VaR value within two aspects regarding analytic and numeric aspects. Analytically, the prediction of the VaR value and the accuracy of the prediction of VaR value through the VaR coverage opportunity. It isn't easy to estimate the parameters for the GARCH (1.1) model analytically. Thus, the parameters are estimated numerically using the Quansi Newton optimization method. Prediction of VaR value and VaR coverage probability will be simulated numerically by using stock return data of IBM, INDF.JK and GSPC. The results show that the GARCH (1.1) model can model stock returns for IBM, INDF.JK and GSPC. There is no significant difference between the GARCH (1,1) model with a normally distributed error and GARCH (1,1) with a student-t distribution error in determining the prediction of VaR values. The numerical simulation results show that the VaR value prediction using the GARCH (1,1) model with a normally distributed error is more accurate than the student-t-distributed error.