Strong Components Research Articles

Investigating the Role of Digital Learning in Enhancing Educational Values: Online Socialization and Its Effect on Peer Learning, Collaborative Skills and Knowledge Construction

This study aimed to examine the advantages of online socializing and digital learning for the growth of interpersonal, peer learning and collaborative skills. The impact of these three skills on knowledge construction was evaluated. The Salmon five step e-moderation model has been used to explore the many phases of learning in a digital environment. This study examined the first stage of the e-moderation model, which is the respondents' internet accessibility, and the stage of knowledge construction in digital learning. The respondents of this study were 402 undergraduate students from four universities who have completed four semesters of online study. A stratified cluster sampling technique was employed in choosing the samples for this study. The instrument for data collection from the respondents was a 40-item questionnaire. Google Forms was utilized as the medium for data gathering. The main findings show that online learning involves a lot of interpersonal, peer learning, and collaborative activities, which are correlated to knowledge construction. The three abilities of peer learning, collaborative skills, and knowledge construction, as indicators of educational values, are positively correlated. The results suggest that a carefully designed online learning session that promotes group activities can deliver beneficial educational values. The common online socializing and information-sharing activities seen on social network sites (SNS), such as Facebook, Telegram, Twitter, etc., have strong educational components that should be included in digital learning, which is the future of education.

Covalent organic framework membranes prepared via mixed linker modulated assembly for hydrogen peroxide enrichment

Hydrogen peroxide (H2O2) enrichment is a significant separation process that catches tremendous attention in energy and environment fields. Membrane technology is envisioned as a promising candidate for H2O2 enrichment owing to its cost-effective and environmental-friendly features. However, it is challenging to develop membrane materials tolerant to rigorous operations of H2O2 enrichment. Here, we explored covalent organic frameworks (COFs) as membrane materials for H2O2 enrichment. The all-organic and periodical assembled nature endowed COFs with excellent chemical robustness, which exhibited excellent performance for H2O2 enrichment. A novel mixed linker modulated assembly method was developed to modulate the interfacial polymerization for COF membrane. Hydrazine hydrate (Hz) and p-phenylenediamine (Pa) were chosen as mixed diamine linkers to synthesize the COF layer composed of TpPa and TpHz clusters. By varying the relative proportion of the mixed linkers, the pore size of COF membranes was tuned from 1.70 nm to 0.50 nm, which exhibited more pronounced size-selective ability for the separation of water and H2O2. Moreover, the water capture ability of membranes was also fortified by introducing hydrophilic linkers during interfacial polymerization process. As a result, the COF membranes showed a total flux of 38.35 kg m−2 h−1, H2O/H2O2 separation factor of 23 and superior long-term operation performance up to 7 days, manifesting the great potential in H2O2 enrichment. Our study may open a novel route for engineering membranes toward molecular separations involving strong oxidative components.

Multivariate time-varying complex signal processing framework and its application in rotating machinery rotor-bearing system

Affected by multi-field coupling factors, the vibration response of rotating machinery similar to the hydro-generator unit often exhibits strong time-varying frequency components, which makes rotor fault detection more challenging. The fusion analysis of the vibration signals of multiple bearing sections of the rotor has been proved to be a very effective method for rotor vibration fault diagnosis. However, how to more accurately and synchronously extract the instantaneous features of rotor non-stationary vibration signals associated with multiple sections has been unresolved. To this end, a framework for multivariate time-varying complex signal decomposition of the rotor-bearing system (RBS) is proposed, namely multivariate complex nonlinear chirp mode decomposition. First, the decomposition of multivariate time-varying complex signals is realized by two-stage processing. Second, instantaneous orbit features (IOFs) are obtained through the proposed framework. Finally, a three-dimensional instantaneous orbit map reflecting the time-varying process is constructed through the IOFs. The framework not only realizes the decomposition of the multi-channel time-varying complex signals of the rotor but also simultaneously extracts the instantaneous features of the multi-channel signals. In addition, it also realizes the description of the instantaneous vibration state of the RBS in the non-stationary process (such as startup and shutdown). Simulation experiments show that the framework is superior to other multi-channel signal processing methods in processing time-varying complex signals. The results based on field-measured signals show that the framework can guide the real-time analysis of the signals generated by rotating machinery, which improves the intuition of condition monitoring.

Performance Optimisation of Single-Phase Variable Reluctance Shaded-Pole Motor

Shaded pole induction motors’ theoretical analysis is quite difficult because of the presence of strong harmonic components and the elliptical rotating field. The change of air gap reluctance is affecting the air gap flux distribution and thus the performance of the motor. In the study, the effect of the change in the SPIM’s stator, rotor and squirrel cage on the performance of the motor is analysed by the Finite Element Method. The flux distributions and torque values of the closed-interpoles type and the stator with different interpoles clearances are obtained. The effects of single and double cage structures in the rotor part and the effects of copper and aluminium cage materials in the squirrel cage were analysed. According to the analyses, it was determined that the single cage motor with aluminium cage material exhibited the best torque performance with a torque value of 132.78 mNm.

Need of Strong Biostatistics Component in Medical Colleges in India: Time to Accept the Overdue Reality

Statistics may be defined as “Methods to plan a study scientifically, collect quality data, analyze appropriately, interpret analytical results correctly and derive implications accurately.” To be more specific, all methods of gaining knowledge are essential statistics. Statistics principles remain the same regardless of the area in which it is used. However, it is differentiated by area of its use, like biostatistics, while it is used in medical sciences, business statistics in business research, and agricultural statistics in agricultural sciences. A host of literature is available on various aspects of statistics, including its varying applications, uses and misuses. The present write-up aims to briefly emphasize the need of strong biostatistics components in medical colleges in India.

Material flow and microstructure evolution on the flow path of friction stir lap welded aluminium alloy

Vertical material flow and microstructure evolution were investigated by the material tracing technology during friction stir lap welding of 6061-T6 aluminium alloys from a new perspective: various angles of keyhole obtained by emergency stop technique. First, lamellar grains were transformed into equiaxed subgrains, and texture was transitioned from {110}//shear direction fibre texture to strong components when materials flowed from the advancing side to the leading side. Then subgrains grew into recrystallised grains at the retreating side. After that the downward material flow at the trailing side induced plastic deformation, resulting in many subgrains with strong texture components. Finally, grains grew up and retained with strong components in the stir zone.

Strong energy component is more important than spectral selectivity in modeling responses of midbrain auditory neurons to wide-band environmental sounds

Modeling central auditory neurons in response to complex sounds not only helps understanding neural processing of speech signals but can also provide insights for biomimetics in neuro-engineering. While modeling responses of midbrain auditory neurons to synthetic tones is rather good, modeling those to environmental sounds is less satisfactory. Environmental sounds typically contain a wide range of frequency components, often with strong and transient energy. These stimulus features have not been examined in the conventional approach of auditory modeling centered on spectral selectivity. To this end, we firstly compared responses to an environmental sound of auditory midbrain neurons across 3 subpopulations of neurons with frequency selectivity in the low, middle and high ranges; secondly, we manipulated the sound energy, both in power and in spectrum, and compared across these subpopulations how their modeled responses were affected. The environmental sound was recorded when a rat was drinking from a feeding bottle (called the ‘drinking sound’). The sound spectrum was divided into 20 non-overlapping frequency bands (from 0 to 20 kHz, at 1 kHz width) and presented to an artificial neural model built on a committee machine with parallel spectral inputs to simulate the known tonotopic organization of the auditory system. The model was trained to predict empirical response probability profiles of neurons to the repeated sounds. Results showed that model performance depended more on the strong energy components than on the spectral selectivity. Findings were interpreted to reflect general sensitivity to rapidly changing sound intensities at the auditory midbrain and in the cortex.

Ballistic Resistance of Steel with the Structure of a Natural Ferrite-Martensitic Composite

Introduction. For steel armor materials, it is important to reduce the thickness and, consequently, the metal consumption of the protective structure. The previously developed class of materials with the structure of a natural ferrite-martensite composite (NFMC) has characteristics that favor the inhibition of crack development under impact loads and has prospects as an armor material.Problem Statement. The purpose of this work is to evaluate the possibility of using NFMC materials for armor protection devices based on the results of ballistic resistance tests under a high-power concentrated impact (a projectile flying at a hyper-high speed).Theoretical Part. The study of ballistic resistance was carried out on samples of steel 14G2 processed according to various modes. The samples had the shape of plates and a square grid on the surface. Simulation tests of the impact of heat-strengthened dowels from a powder-actuated tool and firing of military small arms at the testing site from an SVD sniper rifle and an AK-74 assault rifle with machine loading ammunition were carried out. The results of simulation tests showed a clear advantage of steel with the NFMC structure. The comparison of the results of firing with military small arms has showed that the ballistic resistance of steel with the NFMC structure depends on the ratio of the volume fractions of ferrite and martensite, which cause different thicknesses of the ductile and strong components of the composite. The highest resistance was observed for a sample with an NFMC structure processed according to the regime: quenching 730°C and tempering 180°C.Conclusions. Steels treated for the NFMC structure can provide effective protection for military personnel with a lower material consumption of armor protection devices, which is due to a special method of braking destruction during a high-power local impact. Thus, the practical application of the developed class of natural composite materials seems promising for obtaining an armor plate with a thinner thickness, which helps to reduce the weight of combat vehicles, increase their mobility and reduce fuel consumption.

Nonlinear-Lamb-wave-based plastic damage detection assisted by topologically designed metamaterial filters

Owing to their high sensitivity to material microstructural defects, nonlinear guided waves (NGWs) show great promise for the early detection of incipient damages, conducive to numerous structural health monitoring (SHM) applications. In an SHM system, however, inevitable non-damage-related nonlinear sources exist, which may overwhelm the damage-induced nonlinear components and in turn jeopardize the practical efficacy of the detection methodology. Therefore, these deceptive nonlinear interferences need to be mitigated or eliminated as much as practically possible. By embracing the concept of metamaterials, a wave filtering device, referred to as a meta-filter (MF), is developed in this paper. The MF is constructed through mounting periodic stubs on the surface of the structure under inspection to condition the probing signals under the second harmonic Lamb-wave-based SHM paradigm. Through topological optimization, the MF enables ultra-wide stop bands to eliminate the second harmonic Lamb waves of the probing waves while preserving their strong fundamental wave components. The band structure and the underlying mechanism, alongside the MF-enabled wave properties, are numerically investigated. Upon tactically introducing deceptive nonlinear sources such as the nonlinear adhesive bonding layers in a piezoelectric transducer-activated SHM system, the performance of the MF is examined from the SHM perspective, which is finally validated experimentally using a metal specimen containing local plasticized incipient damage. Results demonstrate that the designed MF allows for flexible selection of the excitation frequency on one hand, and entails significant enhancement of the detection ability of the NGW-based SHM system on the other hand owing to the customized band features arising from the topological optimization.

3D printing of glass fiber reinforced acrylonitrile butadiene styrene and investigation of tensile, flexural, warpage and roughness properties

AbstractThree‐dimensional (3D) printing or additive manufacturing (AM) enables to fabricate, complex parts in low quantities without expensive molds or tools in short time. While several 3D printing methods are available, printing with the method of fused deposition modeling (FDM) is particularly widespread because of the simplicity and potential applicability. The applications of FDM to obtain strong components used in aerospace and automotive industries are widely being explored. This study investigates mechanical properties of 3D printed parts with composite filaments made by acrylonitrile butadiene styrene (ABS)/short glass fibers (SGFs). At first, composite filaments with varying GF contents 5, 10, and 15 wt% were manufactured. In addition, maleic anhydride grafted ABS (ABS‐g‐MA) as coupling agent, nozzle temperature and raster angle orientation were considered as other parameters. Results showed that with 15 wt% of GF, means of tensile and flexural strengths were increased 33% and 36%, respectively. Meanwhile, tensile and flexural moduli were improved 57% and 62%. Among the different raster angle, 0° had the best mechanical properties. Maximum ultimate tensile strength was obtained 61 MPa. Warpage and surface roughness were measured in this study. Generally, nozzle temperature and coupling agent did not have any significant effects on all the results in this study.

Tailoring the texture and mechanical properties of 3% Y2O3p/ZGK200 composites fabricated by unidirectional and cross rolling followed by annealing

3% Y2O3p/ZGK200 composites were subjected to unidirectional rolling (UR) and cross rolling (CR) at 400 °C and 350 °C followed by annealing at 300 °C for 1 h. The microstructure, texture and mechanical properties of rolled and annealed composites were systematically studied. The rolled composites exhibited a heterogeneous microstructure, consisting of deformed grains elongated along rolling direction (RD) and Y2O3 particles bands distributed along RD. After annealing, static recrystallization (SRX) occurred and most deformed grains transformed into equiaxed grains. A non-basal texture with two strong T-texture components was obtained after UR while a non-basal elliptical/circle texture with circle multi-peaks was obtained after CR, indicating that rolling path had great influences on texture of the composites. After annealing process, R-texture component disappeared or weakened, as results, a non-basal texture with double peaks tilting from normal direction (ND) to transverse direction (TD) and a more random non-basal texture with circle multi-peaks were obtained for UR and CR composites, respectively. The yield strength of rolled composites after UR showed obvious anisotropy along RD and TD while a low anisotropic yield strength was obtained after CR. Some Y2O3 particles broke during rolling. The fracture of the composites was attributed to the existence of Y2O3 clusters and interfacial debonding between particles and matrix during tension, as a result, the ductility was not as superior as matrix alloy.

Constraints on the magnetic field structure in accreting compact objects from aperiodic variability

ABSTRACTWe investigate the aperiodic variability for a relatively large sample of accreting neutron stars and intermediate polars, focusing on the properties of the characteristic break commonly observed in power spectra of accreting objects. In particular, we investigate the relation of the break frequency and the magnetic field strength, both of which are connected to the size of the magnetosphere. We find that for the majority of objects in our sample the measured break frequency values indeed agree with estimated inner radii of the accretion disc, which allows to use observed break frequencies to independently assess the magnetic field strength and structure in accreting compact objects. As a special case, we focus on Hercules X-1 which is a persistent, medium-luminosity X-ray pulsar accreting from its low-mass companion. In the literature, it has been suggested that the complex pulse profiles, the spin-up behaviour and the luminosity-correlation of the cyclotron energy seen in Her X-1 can be explained with a complex magnetic field structure of the neutron star. Here, we connect the measured break frequency to the magnetospheric radius and show that the magnetic field strength derived assuming a dipole configuration is nearly an order of magnitude smaller than the magnetic field strength corresponding to the cyclotron energy. Accordingly, this discrepancy can be explained with the magnetic field having strong multipole components. The multipolar structure would also increase the accreting area on the neutron star surface, explaining why the critical luminosity for accretion column formation is puzzlingly high in this source.

Characterization and parametric optimization of additive manufacturing process for enhancing mechanical properties

Additive manufacturing (AM), also known as 3D printing, is a cutting-edge industrial production technique that enables the creation of lighter, stronger components and systems. Fused deposition modeling (FDM) is a popular AM process for creating prototypes and functional components out of common engineering polymers. The mechanical characteristics of printed items are dramatically altered as a result of various process factors. As a result, it is critical to examine the impact of printing settings on the quality of the printed item. In terms of flexural strength, this study presents an experimental examination into the quality analysis of parameters on printed components utilizing FDM. By adjusting process factors such as layer height, raster width, raster angle, and orientation angle, the experiment was carried out utilizing Taguchi's L18 mixed orthogonal array approach. The UNITEK-94100 universal testing equipment was used to evaluate the flexural strength of Acrylonitrile butadiene styrene (ABS) specimens that had been conditioned as per ASTM D790 standard. The impacts of parameters on experimental results were examined and optimized using the hybrid genetic algorithm with response surface methods, response surface approach, and Taguchi method. When the optimal solutions of each technique were studied, the response surface approach and Taguchi methods were determined to be less promising than the genetic algorithm method.

Short-Term Prediction of Building Sub-Item Energy Consumption Based on the CEEMDAN-BiLSTM Method

In order to improve the accuracy of the short-term prediction of building energy consumption, this study proposes a short-term prediction model of building energy consumption based on the CEEMDAN-BiLSTM method. In this study, the energy consumption data of an office building in 2019 are selected as a sample, and CEEMDAN is used to decompose the energy consumption data into multiple components, and the strong correlation components are selected and sent to the BiLSTM network. The final energy consumption prediction results are obtained by superimposing the prediction results of each sub-component, and five models are built simultaneously to compare the errors with the proposed models. The results showed that the weather type has a great influence on the accuracy of energy consumption prediction. When the weather fluctuates greatly, the prediction error of energy consumption by a single prediction model is large. When the weather suddenly changes, the EMD-LSTM model has a big error in the prediction of air conditioning energy consumption. After CEEMDAN decomposition of energy consumption data, more detailed components can be extracted, which makes the BiLSTM prediction algorithm more accurate. Compared with the CEEMDAN-LSTM model, the CEEMDAN-BiLSTM model reduces eRMSE, eMAPE, and eTIC by 4.1%, 9.441, and 1.3%, respectively. The proposed model can effectively improve the accuracy of short-term prediction of building energy consumption.

Viscoelastic simulations using the closed-form Adaptive Length Scale (ALS-C) model

In this paper we employ the closed-form of the Adaptive Length Scale Model (ALS-C) [Ghosh et al., “A new model for dilute polymer solutions in flows with strong extensional components”, J. Rheol. 46, 1057–1089 (2002)] and we investigate its characteristics and potential to more accurately capture pressure-drop in contraction flows of viscoelastic fluids. The ALS-C model was originally derived based on purely homogeneous elongational flows in order to model coil-stretch hysteresis. However, in its originally proposed form we reveal a number of numerical issues which have not been analysed previously and are reported here considering both standard rheological flows, simple channel flows and complex flows within a 4:1 contraction. We demonstrate a new approach for evaluating the instantaneous change in the adaptive length scale as a result of instantaneous changes in the flow field, overcoming the need to employ other root-finding approaches. Guidelines are provided for the correct use of the employed local Weissenberg number and a modified approach is considered for the evolution equation of the actual extensibility, allowing its efficient use in complex numerical simulations. We illustrate that a suitable combination of the model parameters can produce behaviours that are found experimentally in viscoelastic fluids and we find that pressure-drop enhancements in flows within 4:1 contractions observed experimentally are achievable.

Multiscale Weighted Morphological Network Based Feature Learning of Vibration Signals for Machinery Fault Diagnosis

Vibration signals are widely utilized for machinery fault diagnosis. However, the fault-related components (i.e., impulse) in vibration signals are often buried by strong background noises due to complex working conditions. Thus, it is challenging to directly extract discriminate features from vibration signals. In this article, a novel deep neural network (DNN), multiscale weighted morphological network (MWMNet), is proposed to extract impulses from vibration signals. First, a novel morphological layer is smoothly embedded in DNN as a signal processing layer to extract impulses and filter out the noise. Second, multiple branches with different structure element (SE) scales are employed to respectively extract impulses. An adaptive weighted fusion is utilized to enhance the scales that provide strong impulsive components. Third, a morphological operator, called average-hat transform, is adopted in MWMNet, and both positive and negative impulses can be extracted from vibration signals. The effectiveness of MWMNet is validated by the experiments of gearbox fault diagnosis and bearing fault diagnosis. The experimental results show that MWMNet can learn the fault-related features and filter out noise from vibration signals well. The comparison results illustrate that MWMNet has a better fault diagnosis performance than those DNNs, e.g., one-dimensional convolutional neural network, densely connected convolutional network, and residual network.

Social Media and the Internet of Things for Emergency and Disaster Medicine Management.

Social Media and the Internet of Things are nowadays full and strong components of day-to-day life worldwide. Both allow communicating with others 24 hours a day, 7 days a week without distance limitations. During the last decade, on-site citizens have shared disaster-related first reports on social media. Official institutions are using the same framework for delivering up-to-date and follow-up directives. Moreover, monitoring health risks, patients, and systems behavior in real-time over the Internet-of-Things allows detecting different levels of anomalies that might lead to critical events that need to be managed as an emergency. Emergency and disaster medicines deal with broad and complex medical, surgical, mental health, epidemiological, managerial, and communicational issues. Social Media platforms and the Internet of Things are technologies that increase cyber-physical interactions between individuals, machines, and their environment. The generated data over time are massive and are supporting the emergency or disaster mitigation process. This chapter deals with, in the first section, the social media platforms, and the Internet of Things. Then, at a second one, the concepts of emergency, disaster medicine and management are discussed. In the following two sections, we discuss applications and usages of social media and IoT technologies for improving the management (preparedness, response, recovery, mitigation) of emergencies and disasters as fundamental keys and pillars for efficiently handling the managerial information flow in emergency and disaster contexts.

Mnemic Images in the Early Works of Sigmund Freud

Freud was interested in the problem of memory from the time of his very early works. The processes taking place in memory, imaging, remembering and forgetting images focused his attention and were one of the pillars of shaping his conception of the unconscious and mind as “the storehouse of total memory,” which in one of his works he compared to “the Eternal City of Rome” (Culture and Its Discontents), which accumulate images-memories gathered throughout life. Shifts, changes, deformations, strong emotional components determine the specificity of many processes of remembering and forgetting, a specific “coding” of information in the form of visual, acoustic and other images. Nowadays, there are indications of Freud’s views in various dimensions of scientific research on memory and the consciousness of the importance of these approaches in our culture and its discovery of the role of autobiographical memory.

Integrative climate leadership in multi-level policy packages for urban mobility - A study of governance systems in two Nordic urban regions

This paper involves a comparative study of policymaking related to urban transport in the Gothenburg and Oslo regions. It sets out to show how strong climate leadership relates to the integration of policy measures and governance systems. Methodologically, we apply document analysis and qualitative interviews with key informants. Our clearest finding is the importance of the city's contextual governance framework for integrative climate leadership. The strong components of the leadership in Oslo—involving both horizontal and upward coordination—rely on a well-developed governance framework with operationalised goals, strategies and steering tools. This enables them to utilise multiple types of climate leadership. This contrasts with the lower degree of integration and defensiveness in Gothenburg, in terms of willingness to use effective but politically sensitive policy measures.

Gamblification: risks of digital gambling games to adolescents

Gamblification is the insertion of gambling mechanics into digital entertainment activities, including video games. Although the distinction between gambling, game playing, and consumer purchasing has traditionally been clear cut, diverse and new digital products increasingly blur these lines. Many products have strong gambling components that might not be perceived as such by adolescents—eg, virtual casinos and computer games with randomised rewards. Is such gamblification exposing a generation of adolescents to heightened risks of harm?