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The use of confidence intervals (CIs) for making a statistical inference is gaining popularity in research communities. To evaluate college statistics instructors' readiness to teach CIs, this study explores their attitudes toward teaching CIs in elementary statistics courses, and toward using CIs in inferential statistics. Data were collected with a survey that classifies instructors' attitudes on the basis of three previously established pedagogical components: affective, cognitive, and behavioral. Based on the survey responses from 270 participants, we created three profiles (subgroups) via latent profile analysis, and identified each profile's pattern of attitudes toward CIs and common characteristics of the instructors that fit each profile. In addition, we compared the profiles across groupings created by six variables: gender, academic background, statistics teaching experience, subject preference, degree level, and desire to improve teaching. The results of the latent profile analysis support three profiles within the population of statistics instructors, and the results of the comparative analysis of teacher characteristics indicate that the six variables are moderate to strong predictors of the grouping of the sample into three profiles.

Background: Tibiofibula fractures occur across all age groups, and postoperative complications are frequent. An accurate and rapid classification methodology for these fractures could significantly assist physicians. Clinically, tibiofibula fractures occur at various locations, and the fracture types are not evenly distributed. Methods: This paper presents a deep learning model for the interpretable multi-label classification of tibiofibula fractures in two-dimensional (2D) CT scan images, addressing the challenges posed by a limited sample size and an uneven distribution of fracture types. We retrospectively collected 2494 2D CT images from 168 patients with tibia or fibula fractures. The types of fractures identified in the CT scan images were classified according to the AO/OTA fracture classification. A deep learning model was developed to classify composite fractures in 2D CT images, providing visual interpretation for each identified class. The visual interpretation was given with the saliency maps constructed by the Grad-CAM++ method. The deep learning model was trained using data augmentation techniques to address class imbalance and the limited dataset size. Results: Our experiments demonstrated that the proposed model achieved a mean average precision (mAP) of 95.71%. Conclusions: The saliency map-based visual interpretation enables the verification of whether the model provides reliable decision-making for classification. [ABSTRACT FROM AUTHOR]

Research indicates multi-section coordination improves the academic performance of students in STEM education. This paper describes the process of coordination in Precalculus, Calculus 1, and Calculus 2 courses undertaken by a large department that grew from the merger of two institutions through a pilot program and a project grant. Components introduced in the project courses are documented, including collaborative problem-solving sessions, student learning assistants, Q&A sessions, and additional technology resources. Preliminary data is provided on the impacts of the initiative on student success. The study findings provide a template for coordination, faculty buy-in, and increased student engagement at similar institutions undergoing consolidations or implementing initiatives in core courses. Finally, this work provides proof-of-concept for coordination at a large minority-serving institution.

Research demonstrates a disjuncture between the practices encouraged by teacher education programs and what teachers actually do in the classroom. It also informs us that the cognitive and social characteristics of individual teachers such as their attitudes, beliefs and knowledge contribute to their classroom practices. This qualitative study investigates how the teacher identity of mathematics teachers -- the person's sense of who he/she is as a mathematics teacher -- is related to the disjuncture between encouraged and actual classroom practices. Specifically, the study looks into how mathematics teachers form their teaching practices in the social context of their classroom interactions, and tries to understand the nature of the discomfort that teachers sometimes experience in the process of shaping their classroom role and teaching practices. The study takes a dialogical approach to identity, seeing the self as something that an individual develops through interactions between his or her core "substantial self" and context-dependent "situational selves." The qualitative data were collected from four in-service high school teachers in the United States. The study sheds light on the variability of the process of shaping teaching practices; it discusses factors in this variability, and explores how teachers develop and settle into their practices through negotiation between the substantial self and situational selves in the classroom context.

The latest convolutional neural network (CNN) models for object detection include complex layered connections to process inference data. Each layer utilizes different types of kernel modes, so the hardware needs to support all kernel modes at an optimized speed. In this paper, we propose a high-speed and optimized CNN accelerator with flexible diagonal cyclic arrays (FDCA) that supports the acceleration of CNN networks with various kernel sizes and significantly reduces the time required for inference processing. The accelerator uses four FDCAs to simultaneously calculate 16 input channels and 8 output channels. Each FDCA features a 4 × 8 systolic array that contains a 3 × 3 processing element (PE) array and is designed to handle the most commonly used kernel sizes. To evaluate the proposed CNN accelerator, we mapped the widely used YOLOv5 CNN model and evaluated the performance of its implementation on the Zynq UltraScale+ MPSoC ZCU102 FPGA. The design consumes 249,357 logic cells, 2304 DSP blocks, and only 567 KB BRAM. In our evaluation, the YOLOv5n model achieves an accuracy of 43.1% (mAP@0.5). A prototype accelerator has been implemented using Samsung's 14 nm CMOS technology. It achieves 1.075 TOPS, a peak performance with a 400 MHz clock frequency. [ABSTRACT FROM AUTHOR]

This study addresses an integrated batching and scheduling problem for a single-machine flexible machining cell in which each pallet can load multiple parts, i.e. multi-fixturing pallets, and part processing times can be changed with different processing costs, i.e. controllable processing times. The batching sub-problem is to select the set of parts to be produced in each period of a planning horizon and the resulting scheduling sub-problem is to determine the set of parts to be loaded on each multi-fixturing pallet, the part processing times and the pallet input/processing sequences for the parts selected in each period. A bi-criterion objective is considered that minimises the total tardiness and the total processing cost simultaneously. A solution approach is proposed that consists of three phases from the first to the last period: (a) generating the whole schedule over the planning horizon; (b) selecting the parts to be produced during the current period using the scheduling information; and (c) determining the final schedule for the selected parts. Simulation experiments were done on a number of test instances and the results are reported. [ABSTRACT FROM AUTHOR]

Selective disassembly sequencing is the problem of determining the sequence of disassembly operations to extract one or more target components of a product. This study considers the problem with random operation times in the parallel disassembly environment in which one or more components can be removed at the same time by a single disassembly operation. After representing all possible disassembly sequences using the extended process graph, a stochastic integer programming model is developed for the objective of minimising the sum of disassembly and penalty costs, where the disassembly costs consist of sequence-dependent set-up and operation costs and the penalty cost is the expectation of the costs incurred when the total disassembly time exceeds a threshold value. A sample average approximation-based solution algorithm is proposed that incorporates an optimal algorithm to solve the sample average approximating problem under a given set of scenarios for disassembly operation times. The algorithm is illustrated with a hand-light case and a large-sized random instance, and the results are reported. [ABSTRACT FROM AUTHOR]

This study considers selective disassembly sequencing in parallel disassembly environment in which two or more components can be removed by a single disassembly operation. The problem is to determine the sequence of disassembly operations to extract multiple target components while satisfying the precedence relations among disassembly operations. The objective is to minimise the sum of sequence-dependent set-up and operation costs. An integer programming model is developed after representing all possible disassembly sequences using an extended process graph, and then an optimal branch and bound algorithm is proposed that incorporates the methods to obtain the lower and upper bounds as well as a dominance property to reduce the search space. To show the performance of the algorithm, computational experiments are done on various random instances, and the results are reported. In particular, it is shown from the test results that the optimal algorithm requires much shorter computation times than a competitive commercial software package. Finally, a case is reported to illustrate the extended process graph and the solution algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notions of the spherical p-hyponormality and the spherical log-hyponormality for commuting pairs of operators. We first prove that for a commuting pair T = (T 1 , T 2) of operators, if T is jointly hyponormal, then T is spherically p-hyponormal for 0 < p ≤ 1. Second, we show that for 0 < p , q ≤ 1 and q ≤ p , a spherically p-hyponormal operator is spherically q-hyponormal. Third, we completely characterize the spherically p-hyponormal 2-variable weighted shifts. Fourth, we show that if T is Taylor invertible and spherically p-hyponormal, then T is spherically log-hyponormal. Moreover, it is possible to create a striking example regarding spherically log-hyponormal but not p-hyponormal. Furthermore, we show that the spherical log-hyponormality of T with a norm condition implies the spherical log-hyponormality for T ˆ , where T ˆ is the spherical Aluthge transform of T . Finally, in the case of 2-variable weighted shifts, we show that the spherical Aluthge transform does not preserve joint hyponormality, in sharp contrast with the 1-variable case. [ABSTRACT FROM AUTHOR]

Disassembly process planning is an act of preparing detailed operation instructions for disassembling used or an end-of-life (EOL) product to recover or dispose of its constituent parts or subassemblies. The main decisions are: (a) disassembly level; (b) disassembly sequence; and (c) EOL options such as reuse, remanufacturing, recycling, incineration, landfill, etc. This study deals with the three decision variables simultaneously in the parallel disassembly environment for the objective of maximising the profit. Unlike previous studies, we consider practical constraints, i.e., reuse probability and environmental impacts of parts or subassemblies, sequence-dependent setup costs, regulation on recovery rate, and incineration capacity. To represent and solve the problem, we develop an extended AND/OR graph, and then suggest a two-phase algorithm. To show the effectiveness of the proposed algorithm, two case studies have been carried out on an automatic pencil and a telephone. Also, test results on other general product structures are reported. [ABSTRACT FROM PUBLISHER]

This study considers the problem of determining heterogeneous vehicle routes in each period of a given planning horizon while satisfying service combinations, customer demands and vehicle capacities. The objective is to minimize the sum of vehicle operation costs and carbon emission trading cost/benefit, where the trading cost is incurred to purchase the carbon emission right if the total emission exceeds an upper limit in each period, while the trading benefit can be obtained by selling the right in each period, otherwise. A mixed integer programming model is developed to formulate the problem mathematically. Then, a tabu search algorithm is proposed that incorporates the characteristics of the heterogeneous and the period vehicle routing problems while considering the amount of carbon emission in each period. Computational experiments were done on modified benchmark instances and additional random instances, and the results show that the multi-period approach outperforms the existing single-period one in overall average. In particular, the test results show that the multi-period approach can reduce carbon emission more significantly than the single-period one without sacrificing the total cost. [ABSTRACT FROM AUTHOR]