Miracle-dz/UniFAR-FTUs huggingface dataset:
Optical Flow Field Measurements of The Coolant In A Grinding Machine

Since the cooling mechanism of industrial grinding is not yet fully understood, a measurement of the coolant liquid flow field near the grinding wheel is desired. However, the curved surfaces of the liquid phase result in unpredictable light deflections and reflections, which impedes optical flow measurements. In order to obtain qualitative and quantitative information regarding the flow velocity field of the coolant liquid, shadowgraphy in combination with a pattern correlation technique as well as particle image velocimetry are applied in a grinding machine and studied with respect to their measurement capabilities. Particle image velocimetry enables flow measurements inside the laminar coolant jet, whereby the shadowgraph imaging velocimetry complements these measurements and is in particular suitable for spray-like flow regimes. As a result, optical flow field measurements of the coolant flow in a grinding machine are shown to be feasible, which is required to understand the flow mechanisms that affect the grinding cooling process.
Cancer and the Levels of Selection

Cancer is often seen as a case of multilevel selection, in which selfish cancer cells pursue short-term proliferation to the detriment of the collective. Thus cancer cells are described as `cheats', and an analogy is often drawn between the mechanisms by which organisms fight cancer and the mechanisms by which social groups enforce cooperation. Recently, Gardner ([2015a]) and Shpak and Lu ([2016]) have argued that cancer is not a true case of multilevel selection, that cancer cells should be not regarded as cheats, and that the analogy between anti-cancer adaptations and suppression mechanisms in social groups is misleading. Their arguments are powerful. However, by drawing on the (presumed) link between cancer and the evolution of multicellularity, the notion that cancer represents a form of selective conflict between cells and organisms can be salvaged.
Spatial Regression Modeling Approach for Assessing the Spatial Variation of Air Pollutants

Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM10, PM2.5), NO2, CO, and SO2 concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type; (ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM2.5 was affected more by the built environment. Concerning the neighborhoods’ main roads, the SO2 concentration was higher, that of PM2.5 was higher in neighborhoods with more bus stops, and those of CO and PM2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities.
Decision letter for "Improved forest management as a natural climate solution: A review"

Natural climate solutions (NCS), a set of land management, conservation and restoration practices aimed at mitigating climate change, have been introduced as cost-effective strategies to increase carbon (C) sequestration in terrestrial ecosystems. Improved forest management (IFM) has been identified as one NCS for working forests with substantial climate change mitigation potential. However, there is a disconnect between the policy and carbon markets context and the scientific evidence for verifiable C benefits. Further, forest soil C—the largest forest C pool—has largely been excluded from current forest management guidelines and has not been included in the IFM discourse. Herein, we assess the evidence for the potential of specific IFM practices to sequester C in live forest vegetation and store it in both live and dead organic matter, and forest soil.We review IFM approaches that can enhance forest C storage, and links to best management practices and silvicultural systems to offer guidance for practitioners and researchers in the Great Lakes region of the United States. Finally, we discuss the current challenges and opportunities in including soil C in forest Cmanagement guidelines and frameworks.
Enhanced response and improved selectivity for toxic gases with functionalized CNT thin film resistors

ABSTRACT High surface area of CNTs makes them an extremely sensitive detection element for a wide variety of analytes present in minute quantities of ppb and ppm levels. CNTs does not inherit selectivity for a particular gas/analyte, it can be introduced in CNT based gas sensor by means of functionalization, along with improvement in gas sensor's response. Effects of Carboxylated group and gold nano-particles were studied on the response of CNT sensors towards toxic gases such as NO2 and NH3. Gold nanoparticles have found to enhance the response for NO2 and NH3, while oxygenated functional have brought selectivity towards NH3.
An improvement to hybrid beamforming precoding scheme for mmWave massive MIMO systems based on channel matrix

The massive multiple-input multiple-output (MIMO) technology has been applied innew generation wireless systems due to growing demand for reliability and high datarate. Hybrid beamforming architectures in both receiver and transmitter, includinganalog and digital precoders, play a significant role in 5G communication networksand have recently attracted a lot of attention. In this paper, we propose a simple andeffective beamforming precoder approach for mmWave massive MIMO systems. Wefirst solve an optimization problem by a simplification subject, and in the second step,we use the covariance channel matrixfCk=Cov(Hk)andBk=HkHHkinstead of chan-nel matrixHk. Simulation results verify that the proposed scheme can enjoy a highersum rate and energy efficiency than previous methods such as spatially sparse method,analog method, and conventional hybrid method even with inaccurate Channel StateInformation (CSI). Percentage difference of the achievable rate ofCk=Cov(Hk)andBk=HkHHkschemes compared to conventional methods are 2.51% and 48.94%, re-spectively.
Variable Impedance Control for Bipedal Robot Standing Balance Based on Artificial Muscle Activation Model

The bipedal robot should be able to maintain standing balance even in the presence of disturbing forces. The control schemes of bipedal robot are conventionally developed based on system models or fixed torque-ankle states, which often lack robustness. In this paper, a variable impedance control based on artificial muscle activation is investigated for bipedal robotic standing balance to address this limitation. The robustness was improved by applying the artificial muscle activation model to adjust the impedance parameters. In particular, an ankle variable impedance model was used to obtain the antidisturbance torque which combined with the ankle dynamic torque to estimate the desired ankle torque for robotic standing balance. The simulation and prototype experimentation results demonstrate that the control method improves the robustness of bipedal robotic standing balance control.
Virtual to Real-World Transfer Learning: A Systematic Review

Machine learning has become an important research area in many domains and real-world applications. The prevailing assumption in traditional machine learning techniques, that training and testing data should be of the same domain, is a challenge. In the real world, gathering enough training data to create high-performance learning models is not easy. Sometimes data are not available, very expensive, or dangerous to collect. In this scenario, the concept of machine learning does not hold up to its potential. Transfer learning has recently gained much acclaim in the field of research as it has the capability to create high performance learners through virtual environments or by using data gathered from other domains. This systematic review defines (a) transfer learning; (b) discusses the recent research conducted; (c) the current status of transfer learning and finally, (d) discusses how transfer learning can bridge the gap between the virtual and the real.
Performance investigation of LAMBDA and bootstrapping methods for PPP narrow-lane ambiguity resolution

ABSTRACT Precise point positioning with ambiguity resolution (PPP-AR) is a powerful tool for geodetic and time-constrained applications that require high precision. The performance of PPP-AR highly depends on the reliability of the correct integer carrier-phase ambiguity estimation. In this study, the performance of narrow-lane ambiguity resolution of PPP using the Least-squares AMBiguity Decorrelation (LAMBDA) and bootstrapping methods is extensively investigated using real data from 55 IGS stations over one-month in 2020. Static PPP with 24-, 12-, 8-, 4-, 2-, 1- and ½-h sessions using two different cutoff angles (7° and 30°) was conducted with three PPP modes: i.e. ambiguity-float and two kinds of ambiguity-fixed PPP using the LAMBDA and bootstrapping methods for narrow-lane AR, respectively. The results show that the LAMBDA method can produce more reliable results for 2 hour and shorter observation sessions compared with the bootstrapping method using a 7° cutoff angle. For a 30° cutoff angle, the LAMBDA method outperforms the bootstrapping method for observation sessions of 4 h and less. For long observation times, the bootstrapping method produced much more accurate coordinates compared with the LAMBDA method without considering the wrong fixes cases. The results also show that occurrences of fixing the wrong integer ambiguities using the bootstrapping method are higher than that of the LAMBDA method.
Educational Data Mining versus Learning Analytics: A Review of Publications From 2015 to 2019

ABSTRACT To examine the similarities and differences between two closely related yet distinct fields – Educational Data Mining (EDM) and Learning Analytics (LA) – this study conducted a literature review of the empirical studies published in both fields. We synthesized 492 LA and 194 EDM articles published during 2015–2019. We compared the similarities and differences in research across the two fields by examining data analysis tools, common keywords, theories, and definitions listed. We found that most studies in both fields did not clearly identify a theoretical framework. For both fields, theories of self-regulated learning are most frequently used. We found, through keyword analysis, that both fields are closely related to each other as “learning analytics” is most frequently listed keyword for EDM and vice versa for LA. However, one notable difference relates to how LA studies listed social-related keywords whereas EDM studies listed keywords related to technical methods. The tools used for data analysis overlap largely but some of the LA studies listed tools for qualitative data analysis and social network analysis whereas EDM studies did not. Finally, the distinction of the two fields is defined differently by authors as some demarcate the differences whereas some address them interchangeably.
A parallel multi-block alternating direction method of multipliers for tensor completion

This paper proposes an algorithm for the tensor completion problem of estimating multi-linear data under the limitation of observation rate. Many tensor completion methods are based on nuclear norm minimization, they may fail to achieve the global solution for solving nuclear norm minimization in tensor completion problem with high missing ratio. To tackle this issue, an adaptive tensor completion method based on parallel multi-block alternating direction method of multipliers (ADMM) algorithm is proposed, it can derive the model from the initial estimate and compute the next estimate from the current solution. The parallel multi-block ADMM with global convergence is adopted to solve the dual problem, which greatly improves the processing power and reliability of the algorithm.
Research Progress and Prospects of Vehicle Driving Behavior Prediction

Autonomous driving technology is vital for intelligent transportation systems. Vehicle driving behavior prediction is the foundation and core of autonomous driving. A detailed review of the existing research on vehicle driving behavior prediction can improve the understanding of the current progress of research on autonomous driving and provide references for follow-up researchers. This paper primarily reviews and analyzes the control models of autonomous driving, prejudgment methods, on-road and intersection traffic decision-making, and shortcomings of the research about the prediction of individual intelligent vehicle driving behavior, the prediction on movements of vehicles connected via the Internet, and prediction of driving behavior in a mixed traffic environment. The deficiencies in the research on vehicle driving behavior prediction are as follows: (1) there are numerous limitations in the intelligent application scenarios of individual intelligent vehicles; (2) although the Internet of Vehicles is a significant developmental trend, the training and test datasets are not rich enough; and (3) as the research of mixed traffic flow is still in the initial stages, the comfort brought by autonomous driving in hybrid driving environments is not being considered. In addition to the above analyses and comments, the future research prospects of vehicle driving behavior prediction are discussed as well.
Application of Vertical Electrical Sounding for Groundwater Investigation in the Premises of the Sabaragamuwa University of Sri Lanka

Growing social, as well as development activities in the premises of Sabaragamuwa University of Sri Lanka (SUSL) and its surroundings, have reformed the natural landscape of the area. Consequently, water demand is increasing. Therefore, an understanding of the subsurface geology and their potential as beneficial aquifers help to overcome the impending water demand because surface water scares in the area. In view of assessing the groundwater potential of the area, an initial study was conducted using topographic and satellite maps followed by a geo-electrical resistivity survey, consisting of vertical electrical sounding (VES) and electrical profiling. Results revealed three subsurface layers in most places. The resistivity of the topsoil layer is ranging between about 50 to 2800 Ωm and the average thickness between 1 m to 15 m. The second layer is characterized by resistivity between about 16 to 9760 Ωm. The resistivity of the third layer ranges from about 2 to 5600 Ωm, and extending to a depth of more than 100 m. Most of the curves were identified as K type. The rest of the curves were H, Q, and A types. Out of 18 VES points, nine locations were identified as possible locations for groundwater abstraction. Groundwater could be located at a depth of between 25 to 60 m. Resistivity data indicate that the regolith mostly forms the aquifers in the area along with some weathered rock aquifers at depth. Most of the points close to or at valleys were found to be deep aquifers. Considering all the geological, structural, and morphological features, nine locations were identified as most convincing and could be recommended for test drilling.
3D printing of photopolymers

Three-dimensional (3D) printing is a process of fabricating 3D objects by adding successive layers of materials (e.g. polymers) on top of each other. Among various 3D printing approaches, the photochemical approach is extremely attractive as objects can be produced via photopolymerisation reactions of monomers/oligomers, which possesses environmental, economical, and production benefits. In this approach, photoinitiators and monomers/oligomers are two of the most important components of photopolymers for 3D printing. Specifically, photoinitiators absorb the irradiation light of a 3D printer and initiate the photopolymerisation reactions of monomers/oligomers layer-by-layer to produce the designed 3D objects, while monomers/oligomers can determine the final properties of the printed products. In this review, commercial photoinitiators and monomers/oligomers applicable for 3D printing are presented. Moreover, the newly developed photoinitiators and monomers/oligomers (from 2015 to 2017) for 3D printing of polymer-based materials with various properties and the application in 3D bio-printing are also demonstrated.
Exploring Curriculum Alignment through Syllabus Document Analysis: From National Language Policy to Local ELT Practice

Effective syllabus document design hinges on an alignment between learning objectives, teaching principles and curricular policy. Much of the work on syllabus design in the English Language Teaching (ELT) context focuses on the implementation of language teaching principles, such as those from the Communication Language Teaching (CLT) approach. Limited attention has been paid to this alignment, which is pivotal for ensuring the coherence and consistency between the intended outcomes, content selection and organisation, and assessment tasks. Employing qualitative document analysis (QDA), this study identifies the core syllabus elements and their alignment with current teaching approaches in twelve, localised ELT syllabuses. Utilising elements from Richards and Rodgers’ (2014) methods analysis and Nunan’s (1988) syllabus design, the analysis demonstrates that while CLT principles have been incorporated into the syllabuses, misalignments remain, such as those between intended objectives and assessment tasks. This paper offers an analytic framework to guide teachers in designing a more closely aligned syllabus and making visible its connections.
Evaluation and Management of Syncope in Pediatrics: A prospective Observational study at Sohag University Hospital

Background: Syncope is defined as temporary loss of consciousness and postural tone resulting from an abrupt transient decrease in cerebral blood flow. It is characterized by rapid onset, short duration and spontaneous complete recovery. Objective: Our study aimed to analyze the frequency of syncope due to cardiac, neurocardiogenic, neurologic, situational, psychiatric, and other causes and make a differential diagnosis of syncope types according to detailed medical history and further investigations with focus on cardiac cause as the most serious type of syncope. Patients and Methods: 125 pediatric patients were recruited from Arrhythmia Clinic in our Cardiology Unit, Sohag University Hospital. They presented with a primary complaint of syncope aged from 1year to 18 years with a mean age of 6.49 ± 3.77 years. Results: The most common diagnosis was neurocardiogenic syncope (n=76, 60.8%). Sixty-four cases (84.2%) had recurrent episodes. Epilepsy was found in Thirty-four cases (27.2%) and psychogenic pseudo-syncope affected one female child. There were fourteen cases (11.2%) with cardiac syncope: 8 patients with LQTS, one patient with CPVT, one patient with AVNRT, 2 patients with dilated cardiomyopathy, 1 patients with history of atrioventricular (AV) canal defect closure and one patient with cardiac mass. There was significant relationship between arrhythmia and cardiac syncope (p < 0.001). Exercise-induced syncopes were significantly associated with cardiac origins (p < 0.001). Positive ECG, Holter and echocardiographic findings were statistically significant for cardiac syncope (P < 0.001). Conclusion: Syncope is a common pediatric complaint. Most cases seen were resulting from benign causes, with only a small percentage because of serious medical conditions. In addition, most syncopal episodes in the pediatric population were diagnosed clinically or with minimally invasive testing, emphasizing the importance of a detailed history and physical examination.
Intelligent tourism system using prospective techniques and the Mactor methodology: a case study of Tunisian tourism

ABSTRACT This paper proposes a managerial intelligence system for tourism by identifying its future necessary conditions. We use the managerial approach and the joint decision-making system to introduce cooperation among the actors in the Tunisian tourism system. The Mactor methodology was used to simulate the convergences and divergences between these actors. The results indicate that the model helps to define the system’s future properties. The participation, without discrimination, of all tourism actors as a public–private partnership in the system’s design and management is essential. The actors diverge in terms of piloting and financing the system, but they converge in terms of cooperating and participating in the management of the system. When there is such participation in management, the actors cooperate by sharing strategic information. In a public–private partnership framework, the state can intervene in the information market as a partner of economic actors rather than as the holder of a market monopoly.
Cooperativity in Large Water ClustersLiquid Water, Ice and Clathrates

Long-range order in the form of water clusters is what gives liquid water, ice and gas clathrates their particular properties. Liquid water, made up of hydrogen-bonded, cross-linked H2O molecules, should boil nearly 200oC lower at approximately -100oC instead of at +100oC, based on its position as a Group VIA hydride together with H2S, H2Se and H2Te. The pres-ence of cooperative hydrogen bonding between the water monomers gives rise to liquid and solid phases with considerable long-range order which helps to explain many of their physical properties. Liquid water is thought to be composed of ’ﬂickering’ clusters of hexagonally coordinated molecules in which the O-H bonds and the two lone-pair electrons of the oxygen atom are arranged in a tetrahedrally symmetric fashion. Thus each water molecule in liquid water, and indeed in Ice Ih, is bound on average by four hydrogen bonds to neighbouring water molecules as a double hydrogen bond donor / double hydrogen bond acceptor. Because the O-H D . O A -H hydrogen bonds form extended networks of alternating donors and acceptors there is considerable cooperativity which enhances the stability of these networks and is the explanation for the unusual properties of water, such as its speciﬁc heat or boiling point. All the ice polymorphs consist of variations on the theme of tetracoordinated water molecules in the form of hexagons. On the other hand, the gas clathrates are composed of tetracoordinated water molecules hydrogen bonded to give structures formed of edge-sharing pentagons which are stable under moderate pressures and reduced temperatures, such as are found on the ocean ﬂoor. Deep-sea methane clathrates, produced naturally, are arguably the largest renewable en- ergy resource available if methods for the recovery of the methane gas without disturbing the equilibtrium of the oceans could be developed. This project is centred on understanding the electronic and quantum chemical interactions which stabilise these hydrogen-bonded water clusters with hexagonal (water and ice) or pentagonal (clathrates) motifs and which give rise to their particular physicochemical properties. Because it is necessary to study water clusters as large as (H2O)24 at a high level of quantum chemical theory, it is essential to use computational facilities such as those available on the IBMSC-Jump machine at NIC in J¨ulich.