Miracle-dz/UniFAR-FTUs huggingface dataset:
Valuing the invaluable: park visitors' perceived importance and willingness to pay for urban park trees in Pakistan

In complex urban systems, urban parks are rare hot spots of nature, responsible for delivering a range of ecosystem services. Trees are critically important components of urban parks that provide many benefits, and, at the same time, face challenges such as lack of funds, mismanagement, climate change, pests, and diseases. There is a growing need to increase the urban tree cover to sustain urban ecosystems. Successful policymaking requires engagement with all stakeholders, especially park users. An understanding of how people’s perceptions of the benefits and challenges faced by urban park trees is pivotal to making decisions that have long-standing support. We surveyed 521 park visitors in Multan, Pakistan, to assess their valuation of urban park trees, their understanding of the challenges faced by trees, and their willingness to pay (WTP) for increasing tree cover in urban parks. We found that people widely appreciated the ecosystem services provided by park trees. Provision of oxygen, shade, and clean air was considered the greatest benefits while cutting down, lack of space, and urbanization were perceived as the biggest challenges to trees. Respondents showed a WTP and believed in government–public cooperation for increasing tree cover. Income, age, and education were significant predictors of WTP. The study reflects findings in other development and geography contexts and highlights the need for broader dissemination of information on critical threats to park trees and underlines the potential for engaging locally to maximize effectively in the management of park trees.
Manganese determination in air, blood, and urine, using Mg(NO3)2 as modifier and in situ decontamination by graphite furnace atomic absorption spectrometry

A routine procedure was developed for the direct determination of manganese in air, whole blood, and urine, using Mg(NO 3 ) 2 as the modifier and its in situ decontamination by GFAAS. Maximum pyrolysis and atomization temperatures, linear range, sensitivity, and matrix interference were compared with and without using a modifier. We also studied the modifier cleaning temperature and concentration, sample dilution ratio, accuracy, and detection limits. The biological samples were diluted 1+3 with 0.1% (v/v) Triton® X-100 solution (whole blood) or 0.2% (v/v) HNO 3 (urine). Filters were dissolved in 2 mL conc. HNO 3 (1-2 h), taken to 50 mL with deionized H 2 O, and an aliquot was diluted 1+9 (or more, according to the filter concentration) with deionized H 2 O [or 0.2% (v/v) HNO 3 ]. The diluted biological samples were introduced onto the graphite tube platform already containing the in situ dried and decontaminated 0.2% (m/v) Mg(NO 3 ) 2 . The use of a chemical modifier was not necessary for the filter analysis. The proposed procedure involves pyrolysis and atomization temperatures of 1300°C and 2100°C, respectively, and a modifier cleaning temperature of 1900°C. In the filter analysis, 1200°C was found optimum for pyrolysis and 1900°C for atomization. The detection limits (σ=3, n=10) were 0.15 μg/L for blood, 0.30 μg/L for urine, and 0.06 μg/filter for the filter. Typical relative standard deviation values (n=3) were below 3%. With 0.2% (m/v) Mg(NO 3 ) 2 as the modifier, matrix effects were overcome and experimental values were statistically indistinguishable (Student's t test and 95% level of confidence) from the reference samples using the aqueous curve as the matrix-matched standards.
A New Optoelectronic Hybrid Network Based on Scheduling Optimization of Optical Links

The emergence of exascale computers will represent a milestone in high-performance computing (HPC). Optoelectronic interconnections and configurable switches will change the traditional supercomputer architecture. However, new hardware is not easily adapted to dynamic running conditions. Based on scheduling optimization of optical links, we propose a new optoelectronic hybrid network, the software-defined network accelerator (sDNA), for an exascale computer. Our scheduling optimization contains an optical interconnection method and an adaptive routing method. The main contribution of our work is an extended edge forwarding index (E-EFI) optical interconnection method based on slow-switching optical devices. The optical link connections are established by evaluating the traffic offloading revenue for each optical link candidate. To support optical interconnection, sDNA selects a suitable routing strategy according to the job-schedule information and prior HPC application knowledge. We tested sDNA in a network simulator and a prototype exascale computer system using both the US Department of Energy (DOE) application and real-world communication benchmarks. The verification results for traffic offloading reveal that our optical interconnection method not only offloads traffic from electrical links to optical links but also avoids the congestion inherent to electrical links. sDNA maintains a throughput of more than 80 percent bandwidth and reduces the communication delay by 10 percent in our real prototype system and simulator. Thus, sDNA is an ideal candidate for accelerating the communication performance of exascale computers.
Dynamic Security Assessment Method of Power System Based on Improved XGBoost

With the deployment of wide-area measurement systems (WAMS) equipment, dynamic security assessment (DSA) based on artificial intelligence (AI) has more and more advantages. For the power system, the number of transient stable cases is much larger than that of unstable cases, which leads to insufficient learning of unstable cases and affects the evaluation effect of the model. In addition, during the stability assessment process of the power system, the misjudgment cost of unstable case is much higher than that of stable case. Based on the above two points, we proposed a DSA method of power system based on improved XGBoost. The proposed method introduces the weight adjustment factor into the loss function to balance the quantity variance of two type cases and the difference of misjudgment cost, which greatly reduces the misjudgment rate of unstable samples. The test results on IEEE 16-machine 68-bus test system and IEEE 50-machine 145-bus test system show that the proposed method can have higher recognition of the unstable samples and can be applied to DSA.
Optimizing flame synthesis of carbon nanotubes: Experimental and modelling perspectives

Synthesis of carbon nanotubes in flames has become highly attractive due to its rapid, inexpensive, and simple method of production. The study of flame synthesis of carbon nanotubes revolves around the control of flame and catalyst parameters to increase the synthesis efficiency and to produce high quality nanotubes. The control parameters include flame temperature, concentration of carbon source species, catalyst type, equivalence ratio, and fuel type. Carbon nanotubes which are produced with rapid growth rate and possess high degree of purity and alignment are often desired. The present study reviews various optimization techniques from the advanced studies of chemical vapour deposition which are applicable for the synthesis of nanotubes in flames. The water-assisted and catalyst free synthesis are seen as possible candidates to improve the growth rate, alignment, and purity of the synthesized nanotubes. The state-of-the-art of the flame synthesis modelling at particle and flame scales are reviewed. Based on the thorough review of the recent experimental findings related to the catalytic growth of nanotube, possible refinement of the existing particle scale model is discussed. The possibility of two-way coupling between the two scales in computational fluid dynamics may be a major contribution towards the optimization of the flame synthesis.
Soil carbon flux research in the Asian region: Review and future perspectives

Soil respiration (Rs) is the largest flux of carbon dioxide (CO2) next to photosynthesis in terrestrial ecosystems. With the absorption of atmospheric methane (CH4), upland soils become a large CO2 source and CH4 sink. These soil carbon (C) fluxes are key factors in the mitigation and adaption of future climate change. The Asian region spans an extensive area from the northern boreal to tropical regions in Southeast Asia. As this region is characterised by highly diverse ecosystems, it is expected to experience the strong impact of ecosystem responses to global climate change. For the past two decades, researchers in the AsiaFlux community have meaningfully contributed to improve the current understanding of soil C dynamics, response of soil C fluxes to disturbances and climate change, and regional and global estimation based on model analysis. This review focuses on five important aspects: 1) the historical methodology for soil C flux measurement; 2) responses of soil C flux components to environmental factors; 3) soil C fluxes in typical ecosystems in Asia; 4) the influence of disturbance and climate change on soil C fluxes; and 5) model analysis and the estimation of soil C fluxes in research largely focused in Asia.
Software for design and analysis of multi-pass absorption cells

We present interactive calculative approach and relevant software for designing multi-pass optical cells. Our technique performs the analysis of these cells for any sets of concave spherical mirrors and finds the optimal parameters for both laser cavity and optical system that controls laser beam. In particular, we suggest an open-source, free-of-charge and user-friendly tool for simulating
Exploring Individual Differences of Public Speaking Anxiety in Real-Life and Virtual Presentations

Public speaking is a vital skill for making good impressions, effectively exchanging ideas, and influencing others. Yet, public speaking anxiety (PSA) ranks as a top social phobia. Recent advancements in wearable devices and ubiquitous virtual reality (VR) interfaces can help measure and mitigate PSA. This research quantifies PSA through bio-behavioral markers related to individuals’ physiological and acoustic characteristics. The effect of virtual reality (VR) training on alleviating PSA is measured through self-reported and bio-behavioral indices. Psychological (e.g., general trait anxiety, personality) and demographic (e.g., age, gender, highest education, native language) traits are examined as moderating factors between bio-behavioral indices and PSA, as well as moderating factors for measuring the VR effectiveness in mitigating PSA. These measures are also used as clustering criteria for stratifying participants in group-based models of PSA. Results indicate the significance of such traits to modeling PSA with the proposed group-based models yielding Spearman’s correlation of 0.55 (<inline-formula><tex-math notation="LaTeX">$p<0.05$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>05</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="chaspari-ieq1-3048299.gif"/></alternatives></inline-formula>) between the actual and predicted outcome. Results further demonstrate that systematic exposure to public speaking in VR can alleviate PSA in terms of both self-reported (<inline-formula><tex-math notation="LaTeX">$p<0.05$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>05</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="chaspari-ieq2-3048299.gif"/></alternatives></inline-formula>) and physiological (<inline-formula><tex-math notation="LaTeX">$p<0.05$</tex-math><alternatives><mml:math><mml:mrow><mml:mi>p</mml:mi><mml:mo><</mml:mo><mml:mn>0</mml:mn><mml:mo>.</mml:mo><mml:mn>05</mml:mn></mml:mrow></mml:math><inline-graphic xlink:href="chaspari-ieq3-3048299.gif"/></alternatives></inline-formula>) indices. Findings from this study will enable researchers to better understand antecedents and causes of PSA and lay the foundation for personalized adaptive feedback for PSA interventions.
Intervention through Physical Therapy in the Recovery of an Employee after Suffering a Tibial Fracture in an Accident at Work

— Fractures are the main cause of workers' licenses especially in urban centers. Based on the importance of fractures as a cause of the licensing of company employees as well as the importance of implementing regulatory legislation on safety in work environments and the importance of the functional recovery process through physiotherapy, our study proposed this study case. In our study we described how an employee that suffered a type of work-related accident was recovered through physical therapy after defining strategies or methods during thirty days of treatment. We also verify the effectiveness of the accident prevention standards assumed by the company. The physical therapy resources used were Transcutaneous Electrical Nerve Stimulation -TENS, lymphatic drainage, laser therapy, passive mobilization, joint mobilization, kinesiotherapy and stretching. The employee that was hit by an industrial battery of one ton and eight hundred kilos, fracturing the proximal portion of his tibia, presented partially functional recovery after treatment according to the data of the joint's angular amplitude, visual analogue scale of pain, gait evaluation and muscle strength. The study suggests that Physiotherapy has a fundamental role in the physical and functional recovery of workers that suffer accidents in the workplace, positively reinforcing their psychological condition, with social support.
Improving the efficiency of developing meta-subject scenarios in the Moscow Electronic School by means of educational analytics

One of the problems connected with the development of meta-subject skills in school students is the lack of publicly available educational materials with a focus on such skills. A possible solution is the formation of a collection of meta-subject materials stored in the library of learning scenarios of the Moscow Electronic School — now there are over 40,000 learning scenarios that have undergone moderation. This article presents the results of research that identified a cluster of teachers most inclined to create meta-subject scenarios and suggested recommendations for motivating teachers to create such scenarios. To achieve this purpose, a sample of authors of such scenarios published by the Moscow Electronic School were analyzed and clustered with the help of machine learning methods. As a result of this work, a gradient boosting algorithm was developed, which produced the best results. The clusters of users described as a result of the application of the algorithm followed five main behavior strategies in terms of the activity related to the creation of new scenarios. Teachers that are most likely to create meta-subject scenarios show interest in their colleagues’ scenarios not only in their subject but also in other academic disciplines taught at school, willingness to copy and customize them. To develop teachers’ readiness for the creation of meta-subject scenarios, it is recommended to conduct teacher training including their introduction to the best practices of developing such scenarios presented by the Moscow Electronic School. The research results are used in the development of a recommender system enabling easier search and navigation among the scenarios published by the Moscow Electronic School.
Identification of Maize Leaf Diseases based on Convolutional Neural Network

The identification and diagnosis of crop leaf disease is of great significance to improve the quality of crop cultivation. Compared with the traditional manual diagnosis method, the automatic identification of crop leaf disease based on computer vision technology has high efficiency and no subjective judgment error. But the traditional image processing technology is affected by different illumination conditions, cross shading. The algorithm’s robustness is affected. Because deep learning dose not need to set learning features manually, which greatly improves the recognition efficiency. In this paper, the two-channel Convolutional Neural Network was constructed based on VGG and ResNet. Taking the maize leaf diseases as research objects, the maize leaf disease data set has been constructed and preprocessed. And the structure and characteristics of AlexNet, VGG and ResNet are introduced respectively. By adjusting the parameters of the two-channel Convolutional Neural Network, the accuracy of identifying the maize leaf disease type in the validation set can reach 98.33%, while the VGG model can reach 93.33%. The classification results on three types of maize leaf diseases show that the two-channel Convolutional Neural Network has a better performance than the single AlexNet model. Three kinds of leaf disease data sets (big spot, gray leaf spot and rust ) are downloaded from the “kaggle”platform.
Plasticized Polymer Coatings for SH-SAW Sensors for High Sensitivity and Long-Term Monitoring of BTEX Analytes in Liquid Phase

PLASTICIZED POLYMER COATINGS FOR SH-SAW SENSORS FOR HIGH SENSITIVITY AND LONG-TERM MONITORING OF BTEX ANALYTES IN LIQUID PHASE PINTU ADHIKARI MARQUETTE UNIVERSITY, 2016 BTEX compounds (benzene, toluene, ethylbenzene, and xylene) are constituents of crude oil and hazardous to human health. Among them, benzene has the lowest maximum contaminant level for drinking water because of its carcinogenicity. Spills or leakage from underground storage tanks or hazardous waste sites can contaminate nearby groundwater with these volatile organic compounds. Therefore, it is very important to detect the presence of BTEX contamination as early as possible in order to start the remediation process and maintain a healthy environment. To develop an in-situ continuous monitoring sensor system, shear horizontal surface acoustic wave (SH-SAW) sensor devices are being investigated and have shown promising results with the use of suitable coatings for BTEX analyte sorption. However, commercially available polymers that can be used as suitable coatings for BTEX detection directly in the aqueous phase are limited in sensitivity and long-term stability. To improve the sensitivity of a suitable polymer, the addition of a plasticizer is a convenient means to lower the glass transition temperature and thus increase sensitivity. The best coatings for acoustic-wave chemical sensors will be those which are rubbery in the low-frequency range, resulting in good analyte sorption, but glassy at the operating frequency of the sensor device, resulting in low acoustic-wave attenuation. Plasticized polymer coatings allow adjustment of the shear modulus of the coating by varying the polymer-plasticizer mixing ratio; this enables the use of thicker coatings with larger analyte sorption capacity and, ultimately, higher sensitivity. This work investigates polymer-plasticizer blends as sensor coatings for detection of BTEX in water at low concentrations (parts per billion range). Two polymers and two plasticizers were studied. For each polymer-plasticizer combination, the influence of the mixing ratio of the blend on the sensor response was investigated. The sensitivity to benzene for each polymer-plasticizer blend was compared with commercially available polymers that had been used for BTEX detection in previous work. The highest sensitivity and lowest detection limit for benzene were found for a 1.25 μm-thick sensor coating of 17.5% diisooctyl azelate-polystyrene. This work demonstrates that by varying type of plasticizer, mixing ratio and coating thickness, the properties of the coating can be conveniently tailored for BTEX analyte sorption. Thus, the addition of plasticizers increases significantly the number of suitable coatings available for use with a single sensor or a sensor array.
Chemometric evaluation of binary mixtures of alginate and polysaccharide biopolymers as carriers for microencapsulation of green tea polyphenols

ABSTRACT In this study principal component analysis and artificial neural networks were used to evaluate the potential of using binary mixtures of sodium alginate and other polysaccharide biopolymers as the carriers for microencapsulation of green tea bioactive compounds. Using binary mixtures of alginate and adjunct biopolymers increased the particle size (from 722 to 1344 µm) and textural parameters of the microbeads. Chemometric techniques revealed the combination of biopolymers and their ratio as the main factors influencing the encapsulation performance. The combination of alginate with hydroxypropyl methylcellulose and locust bean gum enabled to retain the highest (-)-epigallocatechin gallate and caffeine contents, the highest total phenols encapsulation efficiency, and their most retarded release in water, confirming these as the best delivery systems of polyphenol-type active compounds and signifying their potent food applications.
Identification of Novel BRCA1 Germline Deleterious Variant Among a Tunisian Family

Inherited predisposition to breast and ovarian cancer are most frequently due to germline mutations in the main genes BRCA1 (OMIM# 113705) and BRCA2 (OMIM# 600185). These inactivating mutations, essentially frameshift and nonsense variation, occurs mainly across conserved regions. The aim of the present study is to report a novel germline BRCA1 mutation identified in a Tunisian family case with early onset of breast and ovarian cancer and to evaluate the genotype phenotype correlation. The proband had high-grade tumors, invasive unilateral ductal carcinoma developed at the age of 38 and a serous ovarian adenocarcinoma after a gap of twelve years. The molecular analysis revealed a novel heterozygous nonsense BRCA1 mutation NM_007294. 4: c.915T>A p.(C305*) in the proband and her daughter. This mutation leads to a truncated protein which pathogenicity was validated by bioinformatics tools. This variant is subject to nonsense-mediated mRNA decay. We also underlined the immunohistochemistry usefulness by lack of expression of BRCA1 protein in paraffin embedded breast tumor contrasting with normal tissue. Clinical and pathological data tend to be homogeneous and led to the conclusion that there is a genotype phenotype correlation in BRCA1, an element that must be taken into account in genetic counselling. Conclusively, we are the first to report this novel BRCA1 germline likely deleterious variant extending the molecular and clinical spectrum of BRCA1 pathogenic point mutations. Further in vitro functional experiments needs to be established. High-risk individuals carrying this BRCA1 mutation benefit from preventive measures to reduce morbidity.
A Personal Computer-Based On-Line Signal Validation System for Nuclear Power Plants

The safe operation and efficient control of a nuclear power plant requires reliable information about the state of the process. Therefore, the validity of sensors that measure the process variables is of great importance. Properly validated process signals are also beneficial from the standpoint of increased plant availability and reliability of operator actions. Signal validation is implemented by using various algorithms. The effectiveness of a few other techniques during steady-state and transient-operating conditions are investigated. These algorithms are implemented in a personal computer-based system, along with a fuzzy logic decision-making module. The effectiveness of the overall system is demonstrated using data from operating power plants.