General Programming Research Articles

Evaluation of General Entrepreneurship Programme Curriculum in Tertiary Institutions in Ogun State, Nigeria

Entrepreneurship programme has recently been discussed as the only solution to the unemployment ravaging the country. Using epistemological approach of the quantitative case study of the descriptive research design, this study assessed the general entrepreneurship programme curriculum in tertiary institutions in Ogun State. This was with a view to providing information on the extent of implementation of the entrepreneurship programme curriculum in Tertiary institutions in Ogun State, Nigeria. Directors of three higher institutions of learning (one University, one Polytechnic and one College of Education) were purposively selected based on compliance with the requirements for entrepreneurship programme. The results showed that the entrepreneurship programme curriculum in tertiary institutions in Ogun State was not effectively implemented. Also, it was discovered that only 37.4% of the courses had written curriculum. In addition, it was discovered that only 35.4% written curriculum are available for the entrepreneurship programme courses. Based on the findings of the study, it was therefore recommended that more skill acquisition courses should be embraced as stipulated by the federal government and experts should be employed to design written curriculum for entrepreneurship courses in tertiary institutions in Ogun state among others.

Adaptive Two-Stage Stochastic Programming with an Analysis on Capacity Expansion Planning Problem

Problem definition: Multistage stochastic programming is a well-established framework for sequential decision making under uncertainty by seeking policies that can be dynamically adjusted as uncertainty is realized. Often, for example, because of contractual constraints, such flexible policies are not desirable, and the decision maker may need to commit to a set of actions for a certain number of periods. Two-stage stochastic programming might be better suited to such settings, where first-stage decisions do not adapt to the uncertainty realized. In this paper, we propose a novel alternative approach, named as adaptive two-stage stochastic programming, where each component of the decision policy requiring limited flexibility has its own revision point, a period prior to which the decisions are determined at the beginning of the planning until this revision point, and after which they are revised for adjusting to the uncertainty realized thus far until the end of the planning. We then analyze this approach over the capacity expansion planning problem, that may require limited flexibility over expansion decisions. Methodology/results: We provide a generic mixed-integer programming formulation for the adaptive two-stage stochastic programming problem with finite support, in particular, for scenario trees, and show that this problem is NP-hard in general. Next, we focus on the capacity expansion planning problem and derive bounds on the value of adaptive two-stage programming in comparison with the two-stage and multistage approaches in terms of revision points. We propose several heuristic solution algorithms based on this bound analysis. These algorithms either provide approximation guarantees or computational advantages in solving the resulting adaptive two-stage stochastic problem. Managerial implications: We provide insights on the choice of the revision times based on our analytical analysis. We further present an extensive computational study on a generation capacity expansion planning problem with different generation resources including renewable energy. We demonstrate the value of adopting adaptive two-stage approach against the existing policies under limited flexibility and highlight the efficiency of the proposed heuristics along with practical implications on the studied problem. Funding: This work was supported by the National Science Foundation [Grant 1633196] and the Office of Naval Research [Grant N00014-18-1-2075]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.0157 .

Children with disabilities and inclusive education in general schools: pricipals’ attitudes

Abstract Background As inclusive education is being implemented nationwide in Lithuania, various attitudes towards education of children with disabilities in general schools are observed. We aimed to assess the attitudes of heads of general schools towards the provision of education to children with disabilities in general education schools of Lithuania. Methods Heads of all schools in the Lithuanian capital and its surrounding municipalities implementing general education programmes were invited to take part in this study. 115 persons agreed to participate and completed the questionnaire in spring of 2024. The distribution of the respondents by their answers to the questions about the attitudes towards the provision of inclusive education was compared according to gender, age, education level, work experience, school location (municipality), language of education, type of school, subordination, number of pupils, teachers, teaching assistants and pupils with disabilities. Results The most frequently indicated barriers for inclusive education provision were the lack of specialists, compensatory measures, methodological tools, adaptation of the physical environment, and high number of pupils (87.8%, 84.3%, 78.8% and 76.6% respectively). Attitudes of the respondents were similar in most of the cases when compared according to socio-demographic factors (p more than 0.05). Negative attitudes towards the possibilities for provision of inclusive education were more common among respondents from schools with fewer pupils and higher relative number of teachers, positive attitudes - among those from schools located in municipalities with lower number of residents and schools with more teaching assistants (p less than 0.05). Conclusions While attitudes of heads of general schools towards the provision of inclusive education for children with disabilities are similar in most of the cases, negative attitudes are more common among those from schools with fewer pupils and more teachers. Key messages • While attitudes of heads of general schools towards the inclusive education are similar in most of the cases, negative attitudes are more common among those from smaller schools. • Higher number of teaching assistants and smaller municipalities were associated with positive attitudes towards inclusive education of children with disabilies in general schools.

Code and Data Repository for On Discrete Subproblems in Integer Optimal Control with Total Variation Regularization in Two Dimensions

The goal of this repository to be able to trace and reproduce the results and plots in the referenced article. For the results in this article, we used the general purpose integer programming solver Gurobi version 10.0.0. In addition, you need to have the python packages pandas, numpy, scipy, and pybind11 installed to be able to build the code for computing the weights of the cutting planes from 5.1.1.

A comparison of general surgery training programmes across 11 countries: improving understanding of the experience level of international medical graduates in the UK.

Within the past five years there has been a significant increase in the number of international medical graduates (IMGs) joining the United Kingdom's (UK) workforce. Having mentors and supervisors who understand the needs of IMGs and clinical and cultural differences in the workplace can benefit in the transition to working in a new country. Improving knowledge of and understanding differences between general surgical training programmes and grades across different countries could therefore aid in the support of IMGs within the UK. Data on general surgical training programmes of the top ten countries for the primary medical qualifications of IMGs in the UK were collected to provide comparison with the UK training programme. The following countries were included: UK, India, Pakistan, Nigeria, Egypt, Ireland, Sudan, Sri Lanka, Romania, Iraq and South Africa. Training programme lengths ranged from 3 to 10 years. Only some training programmes provide additional training and qualification in sub-specialisation in general surgery. Other differences included a requirement for internship/non specialist training prior to training, differences in lengths of time spent in other surgical specialties and a requirement for research. Understanding the training programmes of other countries may help UK surgeons to understand the prior experience of IMGs and enable them to provide better training and support.

Combined exercise Intervention protocol validation in sedentary workers health risk with Nonspecific Chronic low back pain People

Background: Most chronic low back pain is nonspecific as the cause of the pain is unknown. Hence exercise protocol focussing on one area of fitness is not appropriate. A general exercise programme that combines muscular strength, flexibility and aerobic fitness would benefit the rehabilitation of Nonspecific Chronic Low Back Pain and its health risk. The present study aimed to validate the content of the combined exercise intervention protocol. Methods: The development and expert judgement stages are part of the content validation process. The development of the exercise protocol was based on a comprehensive literature review. The protocol was created in a step-by-step progressive manner. Exercises for flexibility (20 minutes), stabilisation and strengthening (20 minutes), and aerobic (20 minutes) make up the three main parts of the exercise protocol. 12 independent professionals with a minimum of 10 years of experience were invited. Ten of the twelve specialists consented to take part in the study. In India, all of the professionals were engaged in orthopaedic and musculoskeletal physical therapy. There are two professors, three associate professors, and five senior physiotherapists among the experts. Responses were gathered by mail. During the validation phase, The Exercise protocol was graded by the experts on relevance and ease of performance using a 5-point scale. After collecting the responses content validity index and kappa values were calculated. Results: 37 out of 39 items in the combined exercise intervention protocol showed good agreement. Treadmill walking with 60-75% and 80% and above target heart rate showed a kappa value of 0.66 for relevance and 0.50 for ease of performance. Conclusions: The designed exercise protocol exhibited good content validity.

Endocrine surgery fellowship is necessary for competent endocrine surgical practice: perspectives from Australia and New Zealand.

Currently pathways to practice in endocrine surgery vary based on location and surgical training programme. International data highlights the impact of surgeon volume on outcomes, and the importance of understanding the learning curve in developing minimum training competencies. This study aims to explore how surgeons obtain competence in endocrine surgery in Australia and New Zealand, and perceptions around competence and scope of practice. A web-based survey was distributed to fellows practicing endocrine surgery. Participants were invited to complete a semi-structured interview to explore key themes around competence. Thematic analysis was performed. Responses from 87 surgeons, with 30% practicing primarily in a regional or rural area, showed 94% emphasized post-fellowship training to be competent in endocrine surgery. Median primary operator procedural volume learning curves were 50 thyroid, 30 parathyroid and 20 laparoscopic adrenalectomy procedures. Semi-structured interviews with 12 participants identified four major themes: (1) learning opportunities during general surgical education and training programmes alone are insufficient for consultant-level competence; (2) the importance of sufficient training to develop clinical decision-making, insight and judgement to appropriately select patients in the management of endocrine disease; (3) expected standards of clinical and technical performance are independent of practice location or context; (4) the importance of multi-disciplinary teams for complex cases including advanced cancers. Practicing endocrine surgeons acknowledge formal fellowship training is required to achieve competence across technical and non-technical domains. The definition of competence and expectations regarding technical outcomes are independent of practice location or context.

Concurrent Data Structures Made Easy

Design of an efficient thread-safe concurrent data structure is a balancing act between its implementation complexity and performance. Lock-based concurrent data structures, which are relatively easy to derive from their sequential counterparts and to prove thread-safe, suffer from poor throughput under even light multi-threaded workload. At the same time, lock-free concurrent structures allow for high throughput, but are notoriously difficult to get right and require careful reasoning to formally establish their correctness. In this work, we explore a solution to this conundrum based on a relatively old idea of batch parallelism---an approach for designing high-throughput concurrent data structures via a simple insight: efficiently processing a batch of a priori known operations in parallel is easier than optimising performance for a stream of arbitrary asynchronous requests. Alas, batch-parallel structures have not seen wide practical adoption due to (i) the inconvenience of having to structure multi-threaded programs to explicitly group operations and (ii) the lack of a systematic methodology to implement batch-parallel structures as simply as lock-based ones. We present OBatcher---a Multicore OCaml library that streamlines the design, implementation, and usage of batch-parallel structures. OBatcher solves the first challenge (how to use) by suggesting a new lightweight implicit batching design pattern that is built on top of generic asynchronous programming mechanisms. The second challenge (how to implement) is addressed by identifying a family of strategies for converting common sequential structures into the corresponding efficient batch-parallel versions, and by providing a library of functors that embody those strategies. We showcase OBatcher with a diverse set of benchmarks ranging from Red-Black and AVL trees to van Emde Boas trees, skip lists, and a thread-safe implementation of a Datalog solver. Our evaluation of all the implementations on large asynchronous workloads shows that (a) they consistently outperform the corresponding coarse-grained lock-based implementations---the only ones available in OCaml to date, and that (b) their throughput scales reasonably with the number of processors.

A policy analysis of sleep-related legislation for Canadian licensed childcare facilities

BackgroundNational legislative guidelines for sleep and rest are lacking in the Canadian licensed childcare sector. No review of Canadian legislation for licensed childcare facilities has focused on sleep. This paper provides a review of the Canadian provincial and territorial legislative landscape, regarding sleep, rest, and naps in licensed childcare centers.MethodsChildcare statutes and regulations for each province and territory were identified and downloaded on a particular date. Statutes and regulations were reviewed focusing on sections articulating licensed childcare facility mandates governing sleep, rest, naps, and sleep equipment. An excel file was used to facilitate systematic data retrieval and comparisons across provinces and territories. Two authors developed and discussed themes that summarized data from the documents.ResultsNo statutes indicated recommendations for sleep, rest, or naps. Only one regulation defined rest (Alberta). Our analysis of regulations identified four themes representing sleep, rest, and naps: programming (general programming, daily programming); space (dedicated space, amount of space, age-specific space); equipment (developmental appropriateness, acceptable sleep equipment, age-specific equipment); and safety (staffing during sleep/rest, sleep position, sleep monitoring, sleep equipment safety, prohibited practices). In Canada, minimal regulatory consistency is evident in required sleep programming, space, acceptability of sleep equipment, and sleep safety considerations. Most jurisdictions’ regulations indicated necessity for developmentally appropriate rest or sleep areas and equipment, in particular for infants, but there was minimal consistency in defining infant age groups.ConclusionsAlthough we identified themes related to sleep across regulations, childcare regulations differ in their definitions of infants and specifications for children’s sleep and rest in licensed Canadian childcare facilities. Without adequate definitions in legislative components of appropriate sleep duration linked to children’s developmental stages, childcare facilities lack guidance to support healthy sleep for children in their care. Future research can examine translation of healthy sleep guidelines into government legislation and mandates for sleep, rest, and naps among young children in licensed childcare.

An outcome space algorithm for solving general linear multiplicative programming

The following article presents and corroborates an outcome space branch-and-bound algorithm for solving the general linear multiplicative programming problem (GLMPP). In this new algorithm, GLMPP is transformed into its equivalent problem by introducing auxiliary variables. To compute the tight upper bound for the optimal value of GLMPP, the linear relaxation programming problem is assembled by using bound and hull linear approximations. Furthermore, the global convergence and computational complexity of the algorithm are demonstrated. Finally, the soundness and advantage of the proposed algorithm are validated by solving a demonstrative linear multiplicative problem.

Three Ellipsoids for External Approximation of the Half-Ball

Introduction. Ellipsoids that approximate the half-ball in Rn (n≥2) and their volume is smaller than the volume of the ball can be used to construct algorithms for solving the problem of minimization of a convex (smooth or non-smooth) function, the problem of minimization of a convex function on a sphere, a general problem convex programming, saddle point problems of convex-concave functions and others. The speed of convergence of such algorithms will be determined by the ratio of the volume of the approximating ellipsoid to the volume of the ball. The purpose of the work is to describe properties of three ellipsoids for approximation of a n-dimensional half-ball, the volume reduction factor of which depends only on n - dimension of space. The first is the well-known ellipsoid of minimum volume, which is used in the classical Yudin-Nemirovsky-Shor ellipsoid method. It provides a minimum volume reduction factor and can be used if n≥2. If n=1, then the classical ellipsoid method replaces the dichotomy method. The other two ellipsoids are close to the minimum volume ellipsoid and at large n guarantee volume reduction coefficients close to the minimum. The results. It is shown that when n=1 the first approximate ellipsoid provides a coefficient of reduction of the segment length equal to 2–√2 ≈ 0.5858. The second approximate ellipsoid is new and the volume reduction factor for it is slightly larger than the factor for the first approximate ellipsoid. If n=1, then it provides a coefficient of reduction of the segment length equal to (√5–1)/2 ≈ 0.6180. For three ellipsoids, comparative results are given in terms of volume reduction coefficients (n=1/20) and the number of iterations for solving problems with relative accuracy 1e-10 (n=1/30). Conclusions. A new ellipsoid has been constructed to approximate the n-dimensional half-ball, which is close in volume to the minimal ellipsoid. The volume reduction factor of the proposed ellipsoid is approximated by an asymptotic formula 1–1/(2n)+1/n², which differs little from the formula 1–1/(2n) for an ellipsoid of minimum volume.

A generic stochastic network-based formulation for production optimization of district heating systems

AbstractDistrict heating (DH) systems are an important component in the EU strategy to reach the emission goals, since they allow an efficient supply of heat while using the advantages of sector coupling between different energy carriers such as power, heat, gas and biomass. Most DH systems use several different types of units to produce heat for hundreds or thousands of households (e.g. natural gas-fired boilers, electric boilers, biomass-fired units, waste heat from industry, solar thermal units). Furthermore, combined heat and power units units are often included to use the synergy effects of excess heat from electricity production. To address the challenge of providing optimization tools for a vast variety of different system configurations, we propose a generic mixed-integer linear programming formulation for the operational production optimization in DH systems. The model is based on a network structure that can represent different system setups while the underlying model formulation stays the same. Therefore, the model can be used for most DH systems although they might use different combinations of technologies in different system layouts. The mathematical formulation is based on stochastic programming to account for the uncertainty of energy prices and production from non-dispatchable units such as waste heat and solar heat. Furthermore, the model is easily adaptable to different application cases in DH systems such as operational planning, bidding to electricity markets and long-term evaluation. We present results from three real cases in Denmark with different requirements.

Digital Staff Support Interventions for the Psychological Wellbeing of Healthcare Professionals: A Systematic Review

AbstractHealthcare professionals experience stressful events which impact on overall wellbeing. The rise of digital interventions marks an opportunity to support healthcare professionals in their psychological wellbeing. This systematic review aims to assess the feasibility, acceptability and effectiveness of digital interventions for healthcare professionals in mental and physical healthcare to improve psychological wellbeing (PROSPERO CRD42023394966). Four electronic databases (Embase, MEDLINE, PsycINFO and Web of Science) were searched, from inception, until 10 June 2024. The review was carried out according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Effective Public Health Practice Project (EPHPP) tool was used to assess the methodological quality of studies. Thirty-five studies were included (N = 5364, published 2004–2024), including 13 randomised controlled trials. The majority of the studies were carried out in Europe and North America (mostly UK and USA). Digital interventions varied across format (e.g. web based, virtual reality based) and content type (e.g. biofeedback training). The most frequently used format for digital interventions were smartphone apps with the most common digital intervention type being general stress management programmes. Thirty-three studies indicated significant changes in at least one wellbeing measure, such as depression, anxiety, stress, resilience or compassion. On the EPHPP, 4 studies were rated strong, 14 were rated as moderate and 17 were rated as weak. Evidence suggests that digital interventions are a feasible, acceptable and effective approach to support wellbeing in healthcare professionals, although there are significant methodological limitations. Further research would benefit from stronger methodological designs, maximising participant engagement, and greater, more diverse sample sizes.

Probabilistic unifying relations for modelling epistemic and aleatoric uncertainty: Semantics and automated reasoning with theorem proving

Probabilistic programming combines general computer programming, statistical inference, and formal semantics to help systems make decisions when facing uncertainty. Probabilistic programs are ubiquitous, including having a significant impact on machine intelligence. While many probabilistic algorithms have been used in practice in different domains, their automated verification based on formal semantics is still a relatively new research area. In the last two decades, it has attracted much interest. Many challenges, however, remain. The work presented in this paper, probabilistic unifying relations (ProbURel), takes a step towards our vision to tackle these challenges.Our work is based on Hehner's predicative probabilistic programming, but there are several obstacles to the broader adoption of his work. Our contributions here include (1) the formalisation of its syntax and semantics by introducing an Iverson bracket notation to separate relations from arithmetic; (2) the formalisation of relations using Unifying Theories of Programming (UTP) and probabilities outside the brackets using summation over the topological space of the real numbers; (3) the constructive semantics for probabilistic loops using Kleene's fixed-point theorem; (4) the enrichment of its semantics from distributions to subdistributions and superdistributions to deal with the constructive semantics; (5) the unique fixed-point theorem to simplify the reasoning about probabilistic loops; and (6) the mechanisation of our theory in Isabelle/UTP, an implementation of UTP in Isabelle/HOL, for automated reasoning using theorem proving.We demonstrate our work with six examples, including problems in robot localisation, classification in machine learning, and the termination of probabilistic loops.

Effect of Seed and Fertilizer Subsidies on the Technical Efficiency of Rice Farmers in Senegal

The aim of this study was to analyze the effect of seed and fertilizer subsidies (NPK and urea) on the technical efficiency of rice farmers in Senegal. Using data from the Annual Agricultural Survey (EAA) of the Directorate of Analysis, Forecasting and Agricultural Statistics of Senegal (DAPSA), the results of Stochastic Frontier Analysis (SFA) revealed that rice farmers in Senegal have on average a technical efficiency level of 0.545. This suggests that they could increase their current production by 45.5% while using the same level of inputs. Estimation of an SFA model for technical inefficiency revealed that seed and urea subsidies have a significant effect on reducing technical inefficiency. A farmer using subsidized seed saw a reduction in technical inefficiency level by 10.5% and using urea was associated with a 5.1% decrease in inefficiency. In contrast, the model showed no association between the use of subsidized NPK or the use of herbicides and technical inefficiency. And use of organic fertilizer was estimated to worsen technical inefficiency by 4.4% (perhaps reflecting greater reliance on lower-cost inputs among less productive farm households in Senegal). With regard to socio-demographic factors, the results further revealed older respondents experienced more severe technical inefficiency, and that women on average were 9.6% more inefficient than men. These barriers to improved efficiency among older farmers and women suggest targeted supports may be necessary alongside general subsidy programming.

Investigation of Markovian Process in Server Break-down

In day to day life goods produced at a number of places are consumed at different locations and then the problem faced by producer or dealer is to transfer goods from numerous places of origin to various places of consumption such that cost of transportation, time required, damages to the product etc. are optimum. This is called classical transportation problem. In this study we have studied transportation problem solve by Markov Decision Process. Introduction to the operations research is made along with over view of optimization techniques of single objective transportation problem. For literature review in the subject of multi objective fractional as well as linear multi objective transportation problems. In basic concepts of Markov Decision Process are given for further usage in the thesis work. We have stated algorithms for solving different types of multi objective programming problems. In entire work we have used basically linear membership functions, hyperbolic membership function and exponential membership function. These membership functions are Markov Decision Process. In case of linear membership function weighted arithmetic mean, weighted quadratic mean, geometric mean is used to find compromise solution. Almost everywhere we have found that the compromise solutions are fairly close to optimal solution when the problem is solved as single objective function. We have solution procedure for multi objective linear transportation is given using Markov Decision Process. Introduction to fractional programming is made and solutions of multi objective linear fractional programming problems are discussed. We have solutions of multi objective linear fractional transportation problem. In this topic fractional objective functions are approximated as linear functions using Taylor’s theorem and then applied method to solve the multi objective linear transportation problem discussed earlier. We focus on solution to fractional transportation problem with Markov Process coefficients. To multi objective fractional transportation problem with goal programming approach. We deal with general integer programming problem along with fractional transportation problems. The main result of this thesis is a characterization of increasing Marko processes satisfying certain conditions. At the end bibliography and research papers published by authors are given. Keywords: Markov Process, Transportation, Queen analyses Server system.

Weight-adaptive augmented Lagrange multiplier sequential convex programming for nonlinear trajectory optimization

Sequential convex programming has become a prominent research focus in aerospace trajectory optimization due to its high computational efficiency. One critical challenge in sequential convex programming is the infeasibility of subproblems, which significantly impacts its stability. To address this issue, certain constraints must be relaxed using relaxation variables, which are then incorporated into the objective function as a penalty term. However, the choice of penalty weight greatly influences the optimality and convergence of sequential convex programming. Therefore, this paper proposes a weight-adaptive augmented Lagrange multiplier sequential convex programming algorithm to tackle the challenge. First, this paper analyzes the difficulty of determining an appropriate penalty weight from the perspective of its penalty effect. Based on this analysis, an augmented penalty term is introduced, and the update rule for the augmented Lagrange multiplier is formulated using the Karush-Kuhn-Tucker conditions. Subsequently, a method with adaptive weight adjustment is designed based on the change in the penalty term to further improve the algorithm's solving speed. Finally, the proposed method is validated through numerical simulations in a Mars-powered landing problem and a small glide vehicle reentry problem. The results show that the proposed method is 20.59 % and 25.16 % faster compared to the general sequential convex programming algorithm, respectively. Furthermore, the proposed method is demonstrated to be robust to the initial choice of penalty weight.

Sparse Integer Programming Is Fixed-Parameter Tractable

We study the general integer programming problem where the number of variables n is a variable part of the input. We consider two natural parameters of the constraint matrix A: its numeric measure a and its sparsity measure d. We present an algorithm for solving integer programming in time [Formula: see text], where g is some computable function of the parameters a and d, and L is the binary encoding length of the input. In particular, integer programming is fixed-parameter tractable parameterized by a and d, and is solvable in polynomial time for every fixed a and d. Our results also extend to nonlinear separable convex objective functions. Funding: F. Eisenbrand, C. Hunkenschröder, and K.-M. Klein were supported by the Swiss National Science Foundation (SNSF) within the project “Convexity, geometry of numbers, and the complexity of integer programming” [Grant 163071]. A. Levin and S. Onn are partially supported by the Israel Science Foundation [Grant 308/18]. A. Levin is also partially supported by the Israel Science Foundation [Grant 1467/22]. S. Onn is also partially supported by the Dresner Chair at the Technion. M. Koutecký is partially supported by Charles University project UNCE 24/SCI/008, and by the project 22-22997S of the Grantová Agentura České Republiky (GA ČR).

Epistemic practices in conceptions of computer science

ABSTRACT Background and context We address the question of what computer science students take the discipline to be. How students conceive the discipline can influence whether a student pursues computer science, what particular area within computer science they focus on and whether they persist in the discipline. In this paper, we examine the epistemic practices – how knowledge is created and warranted – of students and teacher as a way to understand what computer science is, as practiced by the participants. Objective The goal of this study is to provide an account for how the students and teacher make and legitimize knowledge in the classroom and computer lab. Method In this case study, we use a combination of ethnographic and multimodal methods. The data collected include audio and video recordings and researcher field notes of classroom and lab sessions. The analysis of the data considers speech, marks on the blackboard and embodied actions including gesture and gaze orientation. Findings In both of these learning environments, CS knowledge in the form of general principles and programming practice are discussed by students and the instructor. However, the way in which such knowledge is warranted in these two settings is different. In the classroom, the instructor is the authority that warrants knowledge claims, whereas in the computer lab, the computer plays a key authority role to determine “what works”. Implications This study provides insight into how instructors can design learning environments based on ways they intend knowledge to be created and warranted.

Bi-affine scaling iterative method for convex quadratic programming with bound constraints

To solve general convex quadratic programming problems with bound constraints, this paper proposes a new interior point iterative method that is easy to be implemented. The method exhibits a simple and sufficiently smooth search direction, and possesses the characteristics of affine scaling. Under the limited optimal stepsize rule, starting from an arbitrary interior point, any accumulation point of the generated sequence is an optimal solution of the corresponding problem. Furthermore, due to the absence of introducing dual variables and solving equations, the proposed method is more suitable for solving large-scale problems. Preliminary numerical results indicate that the new method has advantages in terms of both efficiency and accuracy.