Miracle-dz/UniFAR-FTUs huggingface dataset:
Adsorptive performance of MCM-41 towards hg(II) in water. Adsorption and desorption studies

MCM-41 mesoporous material was prepared under a standard conditions by using cetyltrimethylammonium bromide as a structure-directing agent and tetraethyl orthosilicate as silica source from the liquid phase at 80 °C. Powder X-ray diffraction and scanning electron microscopy used to characterize the product, showed that the MCM-41material had an average particle size of 110 nm. The synthesized material was used to investigate the effects of acidity, adsorption time, the concentration of Hg, adsorbent dosage and temperature on the adsorption of Hg. The results showed that the optimal adsorptive conditions were: pH 5.0, m(MCM-41):mHg2+ 6.67, temperature 20 °C and contact adsorption time 60 min, for the initial Hgconcentration of 1.0 mg/cm. The maximum adsorptive amount of Hg was 56.48 mg Hg/g MCM-41. The results of the desorptive effect of three desorption agents such as HCl, HNO3 and HAc showed that the best desorbent was HCl at the concentration of 0.10 mol/dm. The highest desorption efficiency was 77.21% for the desorption time 2 h.
A Computationally Efficient Hybrid Framework for Polarimetric Calibration of Quad‐Pol SAR Data

Polarimetric Synthetic Aperture Radar (PolSAR) calibration is an essential preprocessing step that must be performed to ensure that the data quality is adequate. This, in turn, helps to minimize the propagation of errors in any further data processing or information extraction. Crosstalk and channel imbalance are two major distortions found to be present in the uncalibrated polarimetric SAR data. The PolSAR calibration mainly aims to reduce these two distortions revealing the true scattering pattern of the targets. In this regard, Quegan's algorithm and Ainsworth algorithm are two widely used algorithms for the PolSAR calibration. In this study, the accuracy and efficiency of these two algorithms have been thoroughly compared using suitable metrics. It has been shown that the Ainsworth algorithm performs better than Quegan's in terms of accuracy at the cost of poor computational efficiency. The data quality metrics also highlight the better calibration accuracy of the Ainsworth algorithm. The issue of higher computational complexity has been effectively addressed by coupling both of these algorithms. Evidently, the computational cost has been reduced in the case of the proposed algorithm. The polarization orientation angle (POA) shift is another distortion caused by the topographic variations present in the target scene. Therefore, correction of POA shift has been incorporated in this research by coupling it with the PolSAR calibration. Subsequently, the improvement in the scattering has been observed. In essence, the proposed algorithm coupled with the correction of POA shift rectifies the major polarimetric distortions with adequate accuracy and computational efficiency.
Client–Agency Briefing: Using Paradox Theory to Overcome Challenges Associated with Client Resource Deployment

Abstract Agencies depend on client resources to fulfill their value proposition. An inadequate quantity or quality of client resources can negatively influence agency output. Advertising literature reports that agencies are frequently frustrated with deficiencies of information, expertise, and time. However, only a limited number of studies represent the client perspective on resourcing issues. Adopting a paradox perspective, this study explores the competing demands that confront clients when deploying resources during the briefing process. It advances strategies that simultaneously satisfy the requirements of agency and client, often with synergistic effect. Furthermore, the study proposes that these strategies trigger a complementary resource, increasing collaboration and trust, and positively influencing the quality of the client–agency relationship. In addition to contributing to theoretical understanding of resource allocation, the study recommends practical steps for improving the briefing process.
Assessing computational thinking through the lenses of functionality and computational fluency

ABSTRACT Background and Context: This study looks at computational thinking (CT) assessment of programming artifacts within the context of CT integrated with science education through computational modeling. Objective: The goal is to explore methodologies for assessment of student-constructed computational models through two lenses: functionality and conceptual fluency. Method: This study uses data from research with EcoMOD, a 3rd grade ecosystem science curriculum in which student pairs program computational models of a beaver building a dam. Snapshots of programs for 47 student pairs collected over time are assessed. Findings: A functionality-based rubric provided assessment of student task progress, but was less successful at correctly identifying CT gains in programs that were partially correct. A rubric for conceptual fluency identified development of fluency in CT concepts of sequencing, loops, and conditionals. Implications: This study contributes to the literature by exploring affordances of different rubric-based CT assessments of student programs.
A Comprehensive Review on Brushless Doubly-Fed Reluctance Machine

The Brushless Doubly-Fed Reluctance Machine (BDFRM) has been widely investigated in numerous research studies since it is brushless and cageless and there is no winding on the rotor of this emerging machine. This feature leads to several advantages for this machine in comparison with its induction counterpart, i.e., Brushless Doubly-Fed Induction Machine (BDFIM). Less maintenance, less power losses, and also more reliability are the major advantages of BDFRM compared to BDFIM. The design complexity of its reluctance rotor, as well as flux patterns for indirect connection between the two windings mounted on the stator including power winding and control winding, have restricted the development of this machine technology. In the literature, there is not a comprehensive review of the research studies related to BDFRM. In this paper, the previous research studies are reviewed from different points of view, such as operation, design, control, transient model, dynamic model, power factor, Maximum Power Point Tracking (MPPT), and losses. It is revealed that the BDFRM is still evolving since the theoretical results have shown that this machine operates efficiently if it is well-designed.
Studying of Cultural Properties of Pyricularia oryzae Cav. Strains in the South of Russia

This article presents the results of the development of a methodology for describing the main morphological and cultural traits of the Pyricularia oryzae Cav. strains widespread in the south of Russia. At the same time, the types of traits are identified and listed, which make it possible to unambiguously determine the uniqueness and variety of the pathogen. The relationships and patterns established using cluster and statistical analysis make it possible to identify the conditions for the development of a pathogen that determine its predominant forms. Thus, research shows that leaf forms of P. oryzae strains isolated from rice plants with leaf form of blast disease have an equally directional growth pattern of a colony with a felt structure, and strains isolated from neck-affected plant form often produce a zone of a colony with a clumpy structure. The classification of cultural traits will make it possible to obtain scientifically grounded and comparable data that can be used in the analysis of the interaction of P. oryzae strains with rice plants on various varieties and in various agro-technological conditions in order to improve and rationalize agricultural activities. The study opens up the possibility of using data in breeding, making it possible to identify forms of a pathogen that infect certain varieties.
Machine learning in patient flow: a review

This work is a review of the ways in which machine learning has been used in order to plan, improve or aid the problem of moving patients through healthcare services. We decompose the patient flow problem into four subcategories: prediction of demand on a healthcare institution, prediction of the demand and resource required to transfer patients from the emergency department to the hospital, prediction of potential resource required for the treatment and movement of inpatients and prediction of length-of-stay and discharge timing. We argue that there are benefits to both approaches of considering the healthcare institution as a whole as well as the patient by patient case and that ideally a combination of these would be best for improving patient flow through hospitals. We also argue that it is essential for there to be a shared dataset that will allow researchers to benchmark their algorithms on and thereby allow future researchers to build on that which has already been done. We conclude that machine learning for the improvement of patient flow is still a young field with very few papers tailor-making machine learning methods for the problem being considered. Future works should consider the need to transfer algorithms trained on a dataset to multiple hospitals and allowing for dynamic algorithms which will allow real-time decision-making to help clinical staff on the shop floor.
Fiber-matrix bond strength by pull-out tests on slag-based geopolymer with embedded glass and carbon fibers

Abstract The reinforcement efficiency on a composite depends on the effective transfer of the stress between matrix and fiber. This work presents an experimental and comparative study of fiber-matrix bond strength for fiber-matrix interface between glass fibers and carbon fibers added to the slag-based geopolymer matrix. This analysis was performed by pull-out test. A total of 18 tests have been conducted, three for each type of fiber at each embedded length of 10 mm, 20 mm and 30 mm. The critical embedded length and the maximum interfacial shear (bond) strength were analyzed, and SEM observations were carried out for the cross-section of each fiber to measure diameter and observe the interface. It was found that the greatest efficiency was obtained by reinforcing with the glass fibers, incorporated at 20 mm in the slag-based matrix.
Artificial-Delay Adaptive Control for Underactuated Euler–Lagrange Robotics

Artificial-delay control is a method in which state and input measurements collected at an immediate past time instant (i.e., artificially delayed) are used to compensate the uncertain dynamics affecting the system at the current time. This article formulates an artificial-delay control method with adaptive gains in the presence of nonlinear (Euler–Lagrange) underactuation. The appeal of studying Euler–Lagrange dynamics is to capture many robotics applications of practical interest, as demonstrated via stability and robustness analysis and via robotic ship and robotic aerial vehicle test cases.
Evaluation of native Trichoderma isolates for the management of sugarcane smut (Ustilago scitaminea) in sugar plantations of Ethiopia

Abstract Biological control of sugarcane smut (Ustilago scitaminea Syd.) using Trichoderma isolates is one of the untapped potentials in Ethiopian sugarcane plantations. To this end, this study was carried out with the objective to identify potential native Trichoderma isolates for the management of sugarcane smut. A total of 120 soil samples were collected from Kesem, Metehara, and Wonji sugarcane plantation fields. Consequently, 21 Trichoderma isolates were recovered from the samples, and in-vitro antagonist study of Trichoderma isolates against U. scitaminea was done using dual culture technique on fresh PDA. Among the native isolates, 12 of them revealed a pathogen suppression potential of >90% at 7 days after inoculation and most of them had higher pathogen suppression than the commercial isolate. However, five of the isolates were selected as potential candidates based on distinct suppression features, growth rate, and antagonistic performance. Identification of the five effective Trichoderma isolates (WI4-27, MKI2-k17, MI3-m33, WI12-25, and MKI3) was done based on macroscopic and microscopic characteristics. Thus, the isolates were identified as T. harzianum, T. asperellum, T. viride, T. viride, and T. harzianum, respectively. Accordingly, these isolates could be used as one component of integrated smut management practices once a study on compatibility with fungicides, in-vivo efficacy test against smut, and substrate selection for mass production of the isolates were commenced.
Learning Deep Cross-Modal Embedding Networks for Zero-Shot Remote Sensing Image Scene Classification

Due to its wide applications, remote sensing (RS) image scene classification has attracted increasing research interest. When each category has a sufficient number of labeled samples, RS image scene classification can be well addressed by deep learning. However, in the RS big data era, it is extremely difficult or even impossible to annotate RS scene samples for all the categories in one time as the RS scene classification often needs to be extended along with the emergence of new applications that inevitably involve a new class of RS images. Hence, the RS big data era fairly requires a zero-shot RS scene classification (ZSRSSC) paradigm in which the classification model learned from training RS scene categories obeys the inference ability to recognize the RS image scenes from unseen categories, in common with the humans’ evolutionary perception ability. Unfortunately, zero-shot classification is largely unexploited in the RS field. This article proposes a novel ZSRSSC method based on locality-preservation deep cross-modal embedding networks (LPDCMENs). The proposed LPDCMENs, which can fully assimilate the pairwise intramodal and intermodal supervision in an end-to-end manner, aim to alleviate the problem of class structure inconsistency between two hybrid spaces (i.e., the visual image space and the semantic space). To pursue a stable and generalization ability, which is highly desired for ZSRSSC, a set of explainable constraints is specially designed to optimize LPDCMENs. To fully verify the effectiveness of the proposed LPDCMENs, we collect a new large-scale RS scene data set, including the instance-level visual images and class-level semantic representations (RSSDIVCS), where the general and domain knowledge is exploited to construct the class-level semantic representations. Extensive experiments show that the proposed ZSRSSC method based on LPDCMENs can obviously outperform the state-of-the-art methods, and the domain knowledge further improves the performance of ZSRSSC compared with the general knowledge. The collected RSSDIVCS will be made publicly available along with this article.
Students’ conceptions of uncertainties in the context of climate change

Abstract The issue of uncertainty about climate change as transparently communicated by scientists is integral to climate research. Both concepts - the complexity and the uncertainty of projection data - make climate research an exciting field and confront the population with major challenges. The scientific consensus of anthropogenic climate change is described, but there are also uncertainties, which for example, result from incomplete knowledge, creating tension in the field of climate research. This study provides an empirical contribution to climate change education, which is a central topic in Education for Sustainable Development (ESD), and shows how uncertainties are perceived from the students’ perspective. Guided interviews were conducted with high school students using Mayring’s approach of deductive category formation. We found interesting results among students in the area of uncertainties in climate modeling. Uncertainties due to limited knowledge about climate change and anthropogenic factors were less mentioned by the students, but they appeared to have much knowledge about the limits of climate modeling.
Effects of TRX Suspension Training on Proprioception and Muscle Strength in Female Athletes with Functional Ankle Instability

Background: Functional ankle instability (FAI) is a common consequence of ankle sprain injury, especially in high-impact sports. Objectives: To investigate the effect of six weeks of suspension training with total resistance exercises (TRX) on proprioception and muscle strength in female athletes with FAI. Methods: Thirty female athletes with FAI (age: 21.9 ± 2.2 years, height: 169.3 ± 4.2 cm, and weight: 59.8 ± 6.1 kg) were randomly assigned to two equally numbered groups: TRX training and controls based on inclusion and exclusion criteria. In the pre-test, the anthropometric variables and also proprioception accuracy and muscle strength of subjects were evaluated by joint angle reset test (JART) and manual muscle testing (MMT) in both dorsiflexion and plantarflexion motions. The training group completed three sessions per week with progressive load for six weeks, and each training session lasted 15 - 20 minutes, whilst control subjects continued with their normal activity without special sport activities. After six weeks of training, all tests performed in the pre-test phase were also performed in the post-test phase. The t-test was used for statistic analysis (α ≤ 0.05). Results: The findings showed that TRX training significantly improved the proprioception accuracy (P ≤ 0.001) and muscle strength (P ≤ 0.001) in the training group rather than the control group. No statistically significant difference was found in any of the tests between pre-test and post-test for the control group (P > 0.05). Conclusions: Six weeks of TRX suspension training had a positive effect on strength and proprioception accuracy in female athletes with FAI.
Analysis of Factors Influencing Energy Efficiency Based on Spatial Quantile Autoregression: Evidence from the Panel Data in China

This research mainly studies the factors influencing the efficiency of energy utilization. Firstly, by calculating Moran’sI and local indicators of spatial association (LISA) of energy efficiency of regions in mainland China, we found that energy efficiency shows obvious spatial autocorrelation and spatial clustering phenomena. Secondly, we established the spatial quantile autoregression (SQAR) model, in which the energy efficiency is the response variable with seven influence factors. The seven factors include industrial structure, resource endowment, level of economic development etc. Based on the provincial panel data (1998–2016) of mainland China (data source: China Statistical Yearbook, Statistical Yearbook of provinces), the findings indicate that level of economic development and industrial structure have a significant role in promoting energy efficient. Resource endowment, government intervention and energy efficiency show a negative correlation. However, the negative effect of government intervention is weakened with the increase of energy efficiency. Lastly, we compare the results of SQAR with that of ordinary spatial autoregression (SAR). The empirical result shows that the SQAR model is superior to SAR model in influencing factors analysis of energy efficiency.
MComIoV: Secure and Energy-Efficient Message Communication Protocols for Internet of Vehicles

The Internet of Vehicles (IoV) offers an emerging paradigm that deals with interconnected vehicles interacting with the infrastructure, roadside units (RSUs), sensors, and mobile devices with a goal to sense, compute, store, and transmit vital information or data over a common channel while vehicles are moving. Secure and reliable communication and efficient on-device performance are thus crucial challenges in this paradigm, particularly in presence of limited computation resources. This paper presents a novel secure and energy-efficient message communication system, called MComIoV, using a one-way hash function and elliptic curve cryptography (ECC). We evaluate MComIoV through security proof and analysis against various attacks to verify its robustness. The proposed system is also implemented and tested on Raspberry Pi 3B+. Experimental results demonstrate the efficiency in computation time, storage cost, communication overhead, and energy consumption.
Real-Time Anti-Saturation Flow Optimization Algorithm of the Redundant Hydraulic Manipulator

As a typical single-pump multi-actuator system, the hydraulic manipulator faces the flow saturation problem when moving at a high speed to track a desired trajectory. To overcome this problem, this paper proposes a real-time anti-saturation flow optimization algorithm based on the gradient projection method. By projecting the gradient of the demand flow in the null space of the task Jacobians, this algorithm can reduce the flow demand while enforcing a global volumetric flow limit in real time. The model of a 7-degree-of-freedom (DOF) hydraulic redundant manipulator was established to carry out theoretical derivation and algorithm design. Then, the experimental verification was completed on the real manipulator platform. Experimental results show that this algorithm reduces average demand flow by 9.85% and average power consumption by 310.3 W under no saturation condition. When flow saturation occurs, the algorithm can increase the average endpoint velocity by 7.52% and reduce the maximum directional error by 71.73% with an average calculation time step of 3 ms. The average trajectory position error can also be reduced by 42.59% compared with the anti-saturation algorithm. Therefore, the proposed algorithm can achieve real-time optimization to reduce flow consumption and achieve anti-saturation in practical applications of redundant hydraulic manipulator.
Extraversion and Leadership Emergence: Differences in Virtual and Face-to-Face Teams

This study addresses calls for a better understanding of how team interaction mode (e.g. virtual versus face-to-face) moderates the relationship between member attributes and emergent team processes. We use Mullen’s model of salience to explain conflicting predictions and results about the effects of extraversion on leadership emergence in virtual and face-to-face teams. Participants were randomly assigned to 27 four-person teams that met three times, engaging in an iterative decision-making task. Assessments of each member’s leadership influence were taken after each meeting, and transcripts were content-coded. Results show that interaction mode has an indirect moderating effect on the relation between extraversion and leadership emergence, fully explained by salience. As such, this study explains an important difference in patterns of leadership emergence between virtual and face-to-face teams.
Isolation and identification entomopathogen fungi as candidate of bioinsecticide from flies and cockroaches’ (Insect vector’s disease)

Chemical insecticide has been used for many years to eradicate insects as disease vector, in which causing negative effects not only to those of target insects but also to the environment both in the short and long term. Therefore, it is necessary to determine an alternative biological control of this insect by using natural insecticide (bioinsecticide), such as using fungi as entomopathogen. As microorganism, fungi, contain of bioactive compound with their toxicity could kill the target insect from larvae to adults, called entomopathogen fungi. This entomopathogen fungi are presumably eco-friendly and able to produce toxic compound which can kill target insect such as cyclopeptida destruxin A, B, C, D and desmethyldestruxin B. Two of the fungi which known to be entomopathogenic are Metarhizium anisopliae and Beauveria bassiana. Yet, there should be more others, therefore it is necessary to explore and isolate any potential bio-insecticide of entomopatogen fungi for disease vectors such as from flies (Musca domestica) and cockroach (Periplaneta americana). This study found three isolated fungi from flies (L1, L2, L3). They were Geotrichum sp. , Penicillium sp. , and Aspergillus sp. Three isolated fungi from cockroach (K1, K2, K3) were also found, they were identified as Aspergillus sp. , except for K3 Penicillium sp.