Typical Class Research Articles

The Effect of High Impact Teaching Practices on Student Comprehension

Despite a large number of published studies on high-impact teaching practices (HIP), there is little consistency in design or measurable outcomes and few quantitative studies, which leads to a lack of information on how specific HIPs improve learning outcomes. Therefore, the research questions for this study are a) does the same HIP have similar results across sections and, b) does a HIP in one section result in a different learning outcome than in another section that does not have a HIP? The hypothesis for the first question is there is no difference in student comprehension between sections when all sections have the same HIP. The hypothesis for the second question is there will be a difference in student comprehension between sections, with students exhibiting greater comprehension in the section with the HIP. For several years we have been using a variety of HIPs in our classes without measuring their impact. This is a preliminary study designed to measure the impact of HIPs on two different sections of Human Anatomy and Physiology II, taught by the same professor. The design of the study is as follows: [Formula: see text] We will analyze our data for two attributes, average and variability of pre-tests and post-tests. For the first attribute, we will study mean distribution. T-test analysis of the mean will be used to measure differences between sections for the pre-test and each post-test. Similarly, T-test analysis of the means will be used within each section to analyze differences between the pre-test and each post-test. For the second attribute, we will use F-test analysis of variance. Tests of normality of grade distribution for three assignments or tests in two sections of a typical biology class were conducted. Our analyses showed that all skewness and kurtosis values were between -2 to 2, and the median and mean were very close. Thus, it is reasonable to assume that the data are from normally distributed populations. Therefore, the approach we have outlined to measure HIP impact is appropriate for our classes. The results from this preliminary study will be used to identify specific HIPs that result in higher learning outcomes and will assist us in refining our pedagogical approach. Funding was provided by Lincoln University's Offce of Title III; Award #P031E200019-HBCU Title III, Part F Program. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Influence of spatial variability of soil shear-wave velocity considering intralayer correlation on seismic response of engineering site

Shear-wave velocity uncertainty significantly impacts seismic response analysis at engineering sites. Previous studies focused on the correlations of shear-wave velocities within soil layers. However, the influence of the vertical spatial variability of shear-wave velocity within individual soil layers was not considered. This study selected a typical Site Class II engineering site to investigate this influence based on the equivalent linear seismic response analysis method of layered soil deposits. Existing field test data on shear-wave velocity were used to discretize the shear-wave velocity within soil layers into a one-dimensional (1D) random field through covariance matrix decomposition and local average process theory. Monte Carlo simulations generated random shear-wave velocity profiles with different vertical correlation distances. Three artificial records with different earthquake return periods were used as input motions at the engineering bedrock for the 1D free-field model. Considering soil shear-wave velocity uncertainty, the peak shear strain increased by 19–27% with the input ground motion amplitude. Moreover, the site peak shear strain variability increased by 25–34% with the vertical correlation distance. The proposed 1D random field model effectively simulated the interlayer correlation and spatial variability of shear-wave velocity within soil layers, demonstrating its significance in seismic response analysis.

Significant improvement in surface hardness of CrMnFeCoNi high entropy alloy via nanosecond pulse laser grain refinement

The CrMnFeCoNi alloy with a stable single face-centered cubic (FCC) phase, representing a typical class of high-entropy alloys (HEAs), has garnered considerable attention owing to their exceptional mechanical properties. However, the inherent low hardness has impeded their utilization in structural or contact components. This study attempted to improve the surface hardness of CrMnFeCoNi HEA via grain refinement realized by nanosecond pulse laser irradiation. The effects of laser parameters on the surface microstructures, chemical composition, and mechanical properties were investigated, and the mechanism for grain refinement was analyzed. The experimental results showed that the hardness of the laser-irradiated surface was significantly improved without altering the chemical composition. This study provides a simple but effective method to refine the grain of HEAs and improve their surface hardness.

Thermoviscoelastic response of skin tissue via fractional Pennes bioheat and fractional viscoelasticity

In this paper, the thermoviscoelastic response of skin tissue, as a typical class of viscoelastic biological tissue, subject to thermal shock is studied based on the fractional Kelvin-Voigt model and fractional Pennes bioheat conduction. A time-fractional biothermoviscoelastic governing equation is derived and initial-boundary value conditions are given for monolayer skin tissue of finite thickness. The temperature change and thermal stress of viscoelastic skin tissue are obtained by the Laplace transform and its inversion using matlab software. The influences of viscosity coefficient, relaxation times, fractional-order viscosity/heat parameter, and blood perfusion rate of skin tissue on the temperature distribution and the elastic displacement are analyzed. The numerical results show that fractional-order viscosity does not affect temperature, whereas fractional-order heat flow does. Moreover, fractional-order heat flow affects the sensitivity of blood perfusion rate to the thermodynamic response.

Designing an algorithm for predicting plane ticket prices using feedforward neural network modeling

PurposeThis research aims to determine the ideal fare for various aircraft itineraries by modeling prices using a neural network method. Dynamic pricing has been studied from the airline’s point of view, with a focus on demand forecasting and price differentiation. Early demand forecasting on a specific route can assist an airline in strategically planning flights and determining optimal pricing strategies.Design/methodology/approachA feedforward neural network was employed in the current study. Two hidden layers, consisting of 18 and 12 neurons, were incorporated to enhance the network’s capabilities. The activation function employed for these layers was tanh. Additionally, it was considered that the output layer’s functions were linear. The neural network inputs considered in this study were flight path, month of flight, flight date (week/day), flight time, aircraft type (Boeing, Airbus, other), and flight class (economy, business). The neural network output, on the other hand, was the ticket price. The dataset comprises 16,585 records, specifically flight data for Iranian airlines for 2022.FindingsThe findings indicate that the model achieved a high level of accuracy in approximating the actual data. Additionally, it demonstrated the ability to predict the optimal ticket price for various flight routes with minimal error.Practical implicationsBased on the significant alignment observed between the actual data and the tested data utilizing the algorithmic model, airlines can proactively anticipate ticket prices across all routes, optimizing the revenue generated by each flight. The neural network algorithm utilized in this study offers a valuable opportunity for companies to enhance their decision-making processes. By leveraging the algorithm’s features, companies can analyze past data effectively and predict future prices. This enables them to make informed and timely decisions based on reliable information.Originality/valueThe present study represents a pioneering research endeavor that investigates using a neural network algorithm to predict the most suitable pricing for various flight routes. This study aims to provide valuable insights into dynamic pricing for marketing researchers and practitioners.

Seismic Hazard Microzonation Map for the Central Plain of Thailand

A study was conducted to investigate the seismic hazard microzonation of the central plain of Thailand, which is situated in an area with a thick quaternary basin. Unconsolidated sediments can lead to amplification of earthquake ground shaking at fundamental frequency and can cause a significant increase in damage to buildings. The research yielded significant results, including the development of a fundamental frequency map through the analysis of HVSR for 149 microtremor measurement sites. Subsequently, a Vs30 map was derived utilizing HVSR inversion techniques, and a soil classification map was constructed based on the NEHRP classification. The upper central plain along the Yom River and the Nan River had a low fundamental frequency of 0.3-0.5 Hz and low Vs30, which can be classified as soil type Class E. In the southern areas of Ayutthaya, Pathum Thani, and central Bangkok, an extremely low Vs30 of less than 100 m/s was observed, indicating soil class F or special soft soil. A comprehensive investigation was conducted on probabilistic seismic hazard analysis by considering the Vs30 sites condition for PGA, SA0.2s, and SA1.0s with a 2475-year return period. The northern region of the upper central plain and the western side of the central basin exhibit relatively high seismic hazards. Furthermore, the site effect significantly amplifies ground motion at the 1.0 second period, surpassing the earthquake-resistant design standard for buildings in Thailand by more than 5 times, particularly in the central region of the lower central plain.

Planning for heterogeneous teams of robots with temporal logic, capability, and resource constraints

This paper presents a comprehensive approach for planning for teams of heterogeneous robots with different capabilities and the transportation of resources. We use Capability Temporal Logic (CaTL), a formal language that helps express tasks involving robots with multiple capabilities with spatial, temporal, and logical constraints. We extend CaTL to also capture resource constraints, where resources can be divisible and indivisible, for instance, sand and bricks, respectively. Robots transport resources using various storage types, such as uniform (shared storage among resources) and compartmental (individual storage per resource). Robots’ resource transportation capacity is defined based on resource type and robot class. Robot and resource dynamics and the CaTL mission are jointly encoded in a Mixed Integer Linear Programming (MILP), which maximizes disjoint robot and resource robustness while minimizing spurious movement of both. We propose a multi-robustness approach for Multi-Class Signal Temporal Logic (mcSTL), allowing for generalized quantitative semantics across multiple predicate classes. Thus, we compute availability robustness scores for robots and resources separately. Finally, we conduct multiple experiments demonstrating functionality and time performance by varying resources and storage types.

Selection of optimal intensity measures for seismic performance evaluation of underground utility tunnel and internal pipeline system

Seismic intensity measures (IMs), which are closely related to both earthquake hazards and structural responses, play a critical role in the performance-based earthquake engineering (PBEE) framework. This paper aims to investigate and define the most representative IMs for probabilistic seismic demand model (PSDM) for the longitudinal utility tunnel and internal pipeline system. Similar to the requirements by the Chinese Code for seismic design of urban rail transit structures, the utility tunnel in this study is assumed being buried in four typical engineering site classes for the analyses. Soil-tunnel-pipeline interactions are simulated using a double-beam system resting on a nonlinear foundation. Soil-tunnel interaction, tunnel-pipeline interaction and joint connections are modeled as nonlinear springs with different mechanical properties. A series of 17 pairs of outcrop ground-motion records and 27 commonly used IMs are selected in the analyses. Engineering demand parameter (EDP) is defined based on the maximum joint opening and the maximum strain of the utility tunnel and internal pipeline, respectively. The selected IMs for predicting the seismic responses of tunnel-pipeline systems are tested using different criteria, i.e., correction, efficiency, practicality, and proficiency. The final decision-making procedure employs a fuzzy multi-criteria decision method. The numerical results indicate that: (1) The velocity-related IMs are the most representative IMs based on the four criteria for structures buried in four sites of different conditions; (2) The optimal seismic IMs vary with different criteria, site classes and structure types. According to the fuzzy multi-criteria decision approach, optimal IMs for the tunnel-pipeline system are PGV, PGV, EPV and HI for site classes I – IV, respectively. The proposed optimal IMs can be utilized to construct seismic fragility curves of utility tunnel, internal pipeline and tunnel-pipeline systems in four different site classes.

Square-integrable representations and the coadjoint action of solvable Lie groups

Abstract We characterize the square-integrable representations of (connected, simply connected) solvable Lie groups in terms of the generalized orbits of the coadjoint action. We prove that the normal representations corresponding, via the Pukánszky correspondence, to open coadjoint orbits are type I, not necessarily square-integrable representations. We show that the quasi-equivalence classes of type I square-integrable representations are in bijection with the simply connected open coadjoint orbits, and the existence of an open coadjoint orbit guarantees the existence of a compact open subset of the space of primitive ideals of the group. When the nilradical has codimension 1, we prove that the isolated points of the primitive ideal space are always of type I. This is not always true for codimension greater than 2, as shown by specific examples of solvable Lie groups that have dense, but not locally closed, coadjoint orbits.

Distribution, seasonal variation and ecological risk assessment of phthalates in the Yitong River, a typical urban watercourse located in Northeast China

Phthalates (PAEs) are a typical class of endocrine disruptors (EEDs). As one of the most commonly used plasticizers, they have received widespread attention due to their wide application in various countries and high detection rates in various environmental media. To be able to clarify the contamination status of PAEs pollutants in a typical northern cold-temperate urban river, 30 water samples from Yitong River in Changchun City, northern China were collected, during the 2023 dry season (March), normal season (May) and wet season (July). Using these samples, a total of 16 target PAEs are investigated. The resulting total PAEs concentrations are: dry season 408 to 1494 ng/L, wet season 491 to 1299 ng/L, and normal season 341 to 780 ng/L. The average concentration of the 16 PAEs over the three seasons is 773 ng/L. Di-2-ethylhexyl phthalate (DEHP) and Dibutyl phthalate (DBP) have the highest concentrations, ranging from 12 to 403 ng/L and 28–680 ng/L respectively. The ecological risks within the Yitong River Basin are evaluated based on the degree of PAEs contamination. DBP and DEHP pose higher risk assessment levels for algae, crustaceans and fish than the other target PAEs. The accurate determination of PAEs provided baseline data on PAEs for the management of the Yitong River, which is of great significance for the prediction of ecological risk assessment and the development of corresponding control measures, supported further research on PAEs in the cold-temperate zone aquatic environments, and shed light on the seasonal variations of PAEs in the Northeast region in the future. Moreover, considering the bioaccumulation and persistence of PAEs, it is necessary to continue to pay attention to the pollution status of cold-temperate zones rivers and the changes in ecological risks in the future.

Kantorovich-Stancu type (α,λ,s) - Bernstein operators and their approximation properties

ABSTRACT In this study, we establish a new class of Kantorovich-Stancu type α , λ , s − Bernstein operators via an adaptation of Bézier bases which are formulated with the inclusion of the shape parameters λ ∈ − 1 , 1 , α ∈ 0 , 1 , and a positive parameter s . First, we present a uniform convergence result for these operators and, subsequently, examine the convergence properties by utilizing the weighted B -statistical convergence notion. Furthermore, we estimate the rate of the weighted B -statistical convergence of these operators. We conclude our work by providing a numerical example with explanatory graphs to show their approximation behaviours.

Lipid regulation of the glucagon receptor family.

The glucagon receptor family are typical class B1 G protein-coupled receptors (GPCRs) with important roles in metabolism, including the control of pancreas, brain, and liver function. As proteins with seven transmembrane domains, GPCRs are intimately in contact with lipid bilayers and therefore can be putatively regulated by interactions with their lipidic components, including cholesterol, sphingolipids, and other lipid species. Additionally, these receptors, as well as the agonists they bind to, can undergo lipid modifications, which can influence their binding capacity and/or elicit modified or biased signalling profiles. While the effect of lipids, and in particular cholesterol, has been widely studied for other GPCR classes, information about their role in regulating the glucagon receptor family is only beginning to emerge. Here we summarise our current knowledge on the effects of cholesterol modulation of glucagon receptor family signalling and trafficking profiles, as well as existing evidence for specific lipid-receptor binding and indirect effects of lipids via lipid modification of cognate agonists. Finally, we discuss the different methodologies that can be employed to study lipid-receptor interactions and summarise the importance of this area of investigation to increase our understanding of the biology of this family of metabolically relevant receptors.

Stability properties of ultraholomorphic classes of Roumieu-type defined by weight matrices

We characterize several stability properties, such as inverse or composition closedness, for ultraholomorphic function classes of Roumieu type defined in terms of a weight matrix. In this way we transfer and extend known results from J. Siddiqi and M. Ider, from the weight sequence setting and in sectors not wider than a half-plane, to the weight matrix framework and for sectors in the Riemann surface of the logarithm with arbitrary opening. The key argument rests on the construction, under suitable hypotheses, of characteristic functions in these classes for unrestricted sectors. As a by-product, we obtain new stability results when the growth control in these classes is expressed in terms of a weight sequence, or of a weight function in the sense of Braun–Meise–Taylor.

Graph Bipartization and Via Minimization for Intersection Graphs

The graph bipartization problem, arising from via minimization in VLSI design and related areas, consists in finding a vertex subset [Formula: see text] of graph [Formula: see text] such that the induced subgraph [Formula: see text] is bipartite and [Formula: see text] is maximized. The problem has been proved to be NP-hard even for planar graphs and cubic graphs. On the other hand, the study of polynomial-time algorithms for typical graph classes is significant in both theoretical and applied aspects. This paper focuses on several intersection graph classes, such as line graphs, circular-arc graphs, and directed path graphs. For the line graphs, we show the NP-hardness results in general and present the polynomial-time algorithms for special cases. For circular-arc graphs and directed path graphs, we propose algorithms that improve on the previously known ones.

Becoming epistemically active in online reading: Facilitating elementary school students’ multimodal multiple document reading via sourcing organizers

In the AI era, it has become crucial to evaluate information found on the Internet critically. This research aimed to investigate the impact of a sourcing organizer on sixth graders' online multimodal and multiple document reading (MMDR) abilities, focusing on aspects such as source-content link and text integration in relation to reading on the Internet. Cognitive and affective factors associated with MMDR were examined. The study involved 52 sixth-graders (55.77% males) from two typical elementary school classes in the northern region of Taiwan. Two intact classes were randomly assigned to either the experimental or control group with the quasi-experimental design. The experimental group (n = 26) received a pre-outlined sourcing organizer, guiding them to record the article title, author, publication date, website name, and major assertions from six assigned multimodal texts. In contrast, the control group (n = 26) received a regular organizer, prompting them to summarize the main ideas from the same six assigned multimodal texts. The study's findings indicated that employing sourcing organizers positively impacted students' performance in text integration. However, it was observed that both groups, regardless of whether they used regular organizers or sourcing organizers, experienced benefits in terms of source-content links. Furthermore, reading ability emerged as the sole significant predictor for source-content links, whereas both reading ability and the use of sourcing organizers predicted text integration. The implications of these findings were discussed to provide insights into instructional strategies to develop online MMDR competencies in elementary students.

Use of Spatial Data in Mapping Landslide Prone Areas in Luhurjaya and Surrounding Areas, Lebak Regency, Banten Province

Landslides are a natural process that occurs in a natural landscape on earth which can cause various environmental damage and can cause loss of life. An increase in the risk of landslides can be caused by uncontrolled land conversion due to the increasing population so that land development is necessary. The research was conducted in Luhurjaya and surrounding areas, Cipanas District, Lebak Regency, Banten with an area of 9 x 9 km. The spatial integration method was used to map landslide-prone zones using a scoring and weighting method, using five parameters, namely slope, rainfall, lineament density, rock type, and NDVI. Those five parameters are then overlaid to form a landslide susceptibility map. As a result of analyzing the research area based on these five parameters, it was found that this area has five slope classes, one rainfall class, five lineament density classes, three rock type classes, and four NDVI classes. After all the datas are scored and weighed, it was found that this research area can be classified to three zones, namely low landslide susceptibility zone, medium landslide susceptibility zone, and high landslide susceptibility zone. From the results of this analysis, disaster mitigation should be needed that can affect the community in the Lebak Regency area, Banten Province, namely in the form of providing provisions in the form of knowledge to the community regarding disaster management which is useful for minimizing the risk of the consequences of landslides

Deep learning bulk spacetime from boundary optical conductivity

We employ a deep learning method to deduce the bulk spacetime from boundary optical conductivity. We apply the neural ordinary differential equation technique, tailored for continuous functions such as the metric, to the typical class of holographic condensed matter models featuring broken translations: linear-axion models. We successfully extract the bulk metric from the boundary holographic optical conductivity. Furthermore, as an example for real material, we use experimental optical conductivity of UPd2Al3, a representative of heavy fermion metals in strongly correlated electron systems, and construct the corresponding bulk metric. To our knowledge, our work is the first illustration of deep learning bulk spacetime from boundary holographic or experimental conductivity data.

Analysis and design of voltage‐source parallel resonant class E frequency multiplier

AbstractIn this paper, the analysis and design process of the voltage‐source parallel resonant class E frequency multiplier with a 50% duty ratio is proposed. The proposed circuit adopts two series resonant filters for reducing the interference of harmonics to the output waveform and achieves zero‐voltage switching (ZVS) and zero‐voltage derivation switching (ZVDS) conditions. The design equations for the proposed circuit are given in detail. The new type class E frequency multiplier can operate at lower transistor voltage stress, higher operating frequency, and larger output power. Meanwhile, the volume and weight of the measured circuit are greatly decreased compared to the conventional class E frequency multiplier. To demonstrate the analysis process, the circuit operating at 1 MHz is fabricated and measured. Compared to the traditional class E frequency multiplier, the transistor peak voltage is reduced by 47.9%, the diameter of the resonant inductor core is decreased by 56.2%, and the measured efficiency can reach 95.1%. The theoretical and simulated results are all agreed with the experimental results.

Memorization versus conceptual practice with number combinations: their effects on second graders with different types of mathematical learning difficulties

ABSTRACT The study set out to explore different mathematical difficulties among 877 second-grade children and to test the effect of memorization versus conceptual practices with number combinations. It used a latent profile analysis of baseline measurements of digit writing speed, number combination fluency, multidigit calculation, and number sense skills to identify six latent classes: three mathematical learning difficulty classes, two typical classes, and one high-achieving class. The memorization practice produced superior improvement for all classes of students except the high-achievers class. These results suggest that memorization practice with basic number combinations should not be considered poor teaching practice. It is important for teachers to incorporate practices with number combinations that focus on speed and memorization, even for children who struggle with mathematics.

RrA, an enzyme from Rhodospirillum rubrum, is a prototype of a new family of short-chain L-asparaginases.

L-Asparaginases (ASNases) catalyze the hydrolysis of L-Asn to L-Asp and ammonia. Members of the ASNase family are used as drugs in the treatment of leukemia, as well as in the food industry. The protomers of bacterial ASNases typically contain 300-400 amino acids (typical class 1 ASNases). In contrast, the chain of ASNase from Rhodospirillum rubrum, reported here and referred to as RrA, consists of only 172 amino acid residues. RrA is homologous to the N-terminal domain of typical bacterial class 1 ASNases and exhibits millimolar affinity for L-Asn. In this study, we demonstrate that RrA belongs to a unique family of cytoplasmic, short-chain ASNases (scASNases). These proteins occupy a distinct region in the sequence space, separate from the regions typically assigned to class 1 ASNases. The scASNases are present in approximately 7% of eubacterial species, spanning diverse bacterial lineages. They seem to be significantly enriched in species that encode for more than one class 1 ASNase. Here, we report biochemical, biophysical, and structural properties of RrA, a member of scASNases family. Crystal structures of the wild-type RrA, both with and without bound L-Asp, as well as structures of several RrA mutants, reveal topologically unique tetramers. Moreover, the active site of one protomer is complemented by two residues (Tyr21 and Asn26) from another protomer. Upon closer inspection, these findings clearly outline scASNases as a stand-alone subfamily of ASNases that can catalyze the hydrolysis of L-Asn to L-Asp despite the lack of the C-terminal domain that is present in all ASNases described structurally to date.