Multiple Programs Research Articles

Confidence-based interactable neural-symbolic visual question answering

Visual question answering (VQA) task demands proficiency in processing multi-modal information, and the ability to reason effectively using the information. One promising method for this task is neural-symbolic (NS) learning, which leverages the strengths of both neural network (NN) learning and symbolic reasoning to achieve efficient VQA. However, current NS approaches do not account for the uncertain nature of NN learning and can only provide a single answer to a question without any indication of its confidence, thereby limiting their ability to handle incorrect reasoning. To address this limitation, we propose a confidence-based neural-symbolic (CBNS) approach, which evaluates the confidence of the NN inferences based on uncertainty quantification and makes confidence-based reasoning. The proposed approach comprises three main components: (1) a probabilistic question parser that generates multiple program candidates, each with a corresponding confidence evaluation; (2) a probabilistic scene perception module that provides object-based scene representation and confidence evaluations for each attribute of objects in an image; and (3) a confidence-based program executor that provides answers with confidence evaluations throughout the inference process by leveraging the confidence evaluations of the scene representation and programs. Additionally, we present a data augmentation method to improve the training efficiency of NS learning. The proposed approach allows user interactions and feedback on the weak links based on confidence evaluations. Experiments on CLEVR and GQA datasets demonstrate that the proposed approach was effective in identifying the correctness of predictions and led to a promising performance improvement with a significantly reduced computation cost.

Visual Attention Patterns in Finding Source Code Defects

Existing research used visual effort metrics to determine the visual attention patterns of participants with varying skill levels while finding source code defects. While most of the findings of these studies agree on the results for fixation count metrics, there are differences in the results for fixation duration metrics. Therefore, there is a need for further investigations on the use of visual effort metrics to determine the difference in the visual effort of experts and novices between multiple programs. Thus, we aimed to identify the factors affecting the varying results on fixation duration metrics and validate the results on fixation count metrics. We used visual effort metrics to identify the visual attention patterns of high and low-performing students engaged in defect-finding tasks on multiple programs. We performed statistical tests on the total fixation count, fixation counts on the error lines, total fixation duration, and fixation duration on the error lines to determine the difference in the visual attention patterns between the groups. Among the fixation metrics used, only the total fixation duration metric revealed a significant difference between the high and low-performing students across all programs. High-performing students spent less time on simple programs with simple error types but spent more time on complex programs with logical and semantic error types. In contrast, low-performing students focused more attention on easy programs with one or more syntax errors compared to high-performing students. The results of this study could shed some light on the contrasting findings of previous studies regarding fixation duration. These findings suggest that visual attention patterns of high and low-performing students may vary on multiple programs. The amount of visual effort exerted by the group depends upon the program’s complexity, location of errors in the source code, type of errors injected, and the number of lines of code. This implies that the time spent finding the errors may be associated with the characteristics of the programs and the location and type of injected errors. Therefore, researchers must provide detailed information on these characteristics when describing differences in visual effort metrics between subjects engaged in bug-finding tasks.

Distributed Representation for Assembly Code

In recent years, the number of similar software products with many common parts has been increasing due to the reuse and plagiarism of source code in the software development process. Pattern matching, which is an existing method for detecting similarity, cannot detect the similarities between these software products and other programs. It is necessary, for example, to detect similarities based on commonalities in both functionality and control structures. At the same time, detailed software analysis requires manual reverse engineering. Therefore, technologies that automatically identify similarities among the arge amounts of code present in software products in advance can reduce these oads. In this paper, we propose a representation earning model to extract feature expressions from assembly code obtained by statically analyzing such code to determine the similarity between software products. We use assembly code to eliminate the dependence on the existence of source code or differences in development anguage. The proposed approach makes use of Asm2Vec, an existing method, that is capable of generating a vector representation that captures the semantics of assembly code. The proposed method also incorporates information on the program control structure. The control structure can be represented by graph data. Thus, we use graph embedding, a graph vector representation method, to generate a representation vector that reflects both the semantics and the control structure of the assembly code. In our experiments, we generated expression vectors from multiple programs and used clustering to verify the accuracy of the approach in classifying similar programs into the same cluster. The proposed method outperforms existing methods that only consider semantics in both accuracy and execution time.

Evaluating the Performance of Multiple Sequence Alignment Programs with Application to Genotyping SARS-CoV-2 in the Saudi Population

This study explores the accuracy and efficiency of multiple sequence alignment (MSA) programs, focusing on ClustalΩ, MAFFT, and MUSCLE in the context of genotyping SARS-CoV-2 for the Saudi population. Our results indicate that MAFFT outperforms the others, making it an ideal choice for large-scale genomic analyses. The comparative performance of MSAs assembled using MergeAlign demonstrates that MAFFT and MUSCLE consistently exhibit higher accuracy than ClustalΩ in both reference-based and consensus-based approaches. The evaluation of genotyping effectiveness reveals that the addition of a reference sequence, such as the SARS-CoV-2 Wuhan-Hu-1 isolate, does not significantly affect the alignment process, suggesting that using consensus sequences derived from individual MSA alignments may yield comparable genotyping outcomes. Investigating single-nucleotide polymorphisms (SNPs) and mutations highlights distinctive features of MSA programs. ClustalΩ and MAFFT show similar counts, while MUSCLE displays the highest SNP count. High-frequency SNP analysis identifies MAFFT as the most accurate MSA program, emphasizing its reliability. Comparisons between Saudi and global SARS-CoV-2 populations underscore regional genetic variations. Saudis exhibit consistently higher frequencies of high-frequency SNPs, attributed to genetic similarity within the population. Transmission dynamics analysis reveals a higher frequency of co-mutations in the Saudi dataset, suggesting shared evolutionary patterns. These findings emphasize the importance of considering regional diversity in genetic analyses.

Abstract 226: Canadian Experience Facilitating a Hands‐on Neurointerventional Workshop for Neurology Trainees

Introduction Neurointerventional radiology is a growing field that has seen innumerable advances in recent years. Unlike the US, which has a sizeable presence of interventional neurologists[1], Canada has disproportionately fewer neurologists in the field of neurointervention. One of the barriers to neurology trainees entering the field of neurointerventional radiology is a lack of exposure during their training. To address this barrier, we conducted the first hands‐on neurointervention workshop focusing on neurology trainees during a Canadian national conference. Though this workshop was open to trainees from neurology, neuroradiology, and neurosurgery, it was conducted in association with the Canadian Stroke Consortium and therefore marketed towards neurology trainees. This pilot hands‐on workshop was the first of its kind in Canada targeting neurology trainees. Methods A 2‐hour workshop was conducted in June, 2023 during a Canadian national conference. The workshop included 3 stations: basic equipment orientation, overview of endovascular techniques for ischemic stroke, and an intracranial aneurysm treatment model treatment station. Participants had an opportunity to rotate through each station at least once. Each station was facilitated by a neurointerventionalist or fellow who demonstrated the techniques and devices to participants. Participants practiced handling wires and catheters, deploying stent‐retrievers, and deploying coils in an aneurysm model, among other essential neurointerventional skills. Participants were asked to voluntarily provide feedback via electronic survey. An electronic survey was emailed to participants, on a voluntary basis; responses were anonymized. Survey results were collected and analyzed using Google Forms. Results Twelve workshop participants consented to participate in the survey and seven completed the entire survey. Respondents were from multiple Canadian neurology residency programs, medical schools, and vascular neurology fellowship programs. Prior to the workshop, most respondents (71.4%) had little‐to‐no knowledge about interventional techniques. Only one respondent answered that their training program provides hands‐on neurointervention experience. Most respondents found the station on endovascular techniques for ischemic stroke to be the most helpful. Prior to the workshop, many respondents had not considered pursuing neurointerventional radiology training. At least one participant responded that they would more favorably consider pursuing neurointerventional radiology following the workshop. Conclusion Increasing neurology trainee exposure to neurointerventional radiology is essential for expanding the number of neurologists entering this field. Events at national and international conferences that provide hands‐on experience are one way to increase exposure. This workshop provided practical experience, particularly in endovascular therapy techniques. All participants had little to no exposure to neurointervention and all favorably rated the workshop. Based on this pilot project, further efforts are needed to incorporate neurointerventional radiology training into Canadian neurology training programs.

An Architecture for a Tri-Programming Model-Based Parallel Hybrid Testing Tool

As the development of high-performance computing (HPC) is growing, exascale computing is on the horizon. Therefore, it is imperative to develop parallel systems, such as graphics processing units (GPUs) and programming models, that can effectively utilise the powerful processing resources of exascale computing. A tri-level programming model comprising message passing interface (MPI), compute unified device architecture (CUDA), and open multi-processing (OpenMP) models may significantly enhance the parallelism, performance, productivity, and programmability of the heterogeneous architecture. However, the use of multiple programming models often leads to unexpected errors and behaviours during run-time. It is also difficult to detect such errors in high-level parallel programming languages. Therefore, this present study proposes a parallel hybrid testing tool that employs both static and dynamic testing techniques to address this issue. The proposed tool was designed to identify the run-time errors of C++ and MPI + OpenMP + CUDA systems by analysing the source code during run-time, thereby optimising the testing process and ensuring comprehensive error detection. The proposed tool was able to identify and categorise the run-time errors of tri-level programming models. This highlights the need for a parallel testing tool that is specifically designed for tri-level MPI + OpenMP + CUDA programming models. As contemporary parallel testing tools cannot, at present, be used to test software applications produced using tri-level MPI + OpenMP + CUDA programming models, this present study proposes the architecture of a parallel testing tool to detect run-time errors in tri-level MPI + OpenMP + CUDA programming models.

Towards end-to-end deep RNN based networks to precisely regress of the lettuce plant height by single perspective sparse 3D point cloud

Nowadays, 3D point cloud is supposed to be the most direct and effective data form for studying plant morphology structure. However, automatic and high-throughput acquisition of accurate individual plant height traits from 3D point cloud remains an urgent challenging problem. Summarizing the related research results in recent years, the factors limiting its application mainly come from these aspects: (1) Many existing methods require spatial auxiliary information such as ground control points (GCP), digital terrain models (DTM) and digital surface models (DSM) to obtain accurate plant height; (2) For 3D point cloud data in different environments, specialized modeling and careful parameter fine-tuning are usually required; (3) Sometimes, the point cloud processing involves the combined utilization of multiple programming languages and software, which is difficult for system integration. Focusing on these challenges, firstly, we proposed a novel end-to-end deep Recurrent Neural Network (RNN) based regression network framework called DRN, which consists of three parts: point cloud feature extraction network, deep RNN and regression network. The convolution operations-based point cloud feature extraction network is function as filtering noise, outliers and redundant information; The deep RNN network with long and short-term memory (LSTM) ability is used to learn the relationships between the feature points on the high-dimensional feature sequence separated by a certain distance; regression network is used to regress the output from deep RNN to plant height value. Experiments results on the 3rd Greenhouse Growing Challenge datasets show that DRN can directly regress the plant height of a single plant effectively without manual operations and the participation of spatial auxiliary information with an R2 of 0.948 and a relative root mean square error (RRMSE) of 10.06% in four different varieties of lettuce at different growth period. After studying the influence of the weights of the x, y, z coordinate of the input 3D point cloud on the regression result, then, we design a Dimension Attention (DA) module at the front end of the feature extraction network to learning the characteristic coordinate weight for every input point cloud sample. The DRN network with a DA module is called D-DRN, experiment results indicate D-DRN tend to achieve better result (R2 =0.960 ; RRMSE=8.680%) than DRN. Considering the end-to-end-based DRN and D-DRN network capable of ease of integration and their considerable prediction accuracy on public datasets, we believe they has a certain complementary effect on the existing study methods of obtaining plant morphological structure phenotype by point cloud data.

How will ecosystem carbon sequestration contribute to the reduction of regional carbon emissions in the future? analysis based on the MOP-PLUS model framework

Carbon neutralization of land use and land cover (LULC) has become an important way for countries to cope with future climate change. Existing studies focus on the quantification and analysis of historical and current carbon storage and emissions but lack an understanding of future LULC carbon storage and emissions, which limits the practical guiding value of research findings for regional dynamic management and scientific decision-making. In this study, the temporal and spatial distribution patterns of carbon dynamics in different ecosystems, such as forest, cropland and grassland, in the West Liao River Basin (WLRB) since 1990 were quantitatively assessed. Grey multiple objective programming (G-MOP) and patch‑generating land use simulation (PLUS) models were used to predict the LULC distribution, carbon emissions and carbon storage under the sustainable development scenario (SDS) in the WLRB in 2030 and 2060, and a comparative analysis was performed with other development scenarios. The overall area of grassland and forest ecosystems showed an upwards trend from 1990 to 2020, while the area of farmland ecosystems slightly decreased. In the western part of the WLRB, net ecosystem production (NEP) showed high aggregation, and low NEP showed an aggregation effect in the urban area. Under the economic development scenario (EDS) in 2030 and 2060, cropland and impervious layers will be further expanded, carbon emissions will be the highest, and the carbon storage amount will be the lowest. Under the ecological protection scenario (EPS), the water area and forest area increased significantly, carbon emissions were the lowest, and carbon storage was the highest. Under the SDS scenario, the areas of cropland, forest, impervious layer and water body are moderate, and the carbon budget is better than that under the natural development scenario (NDS). Under these three scenarios, the barren area is lower than that of the natural development scenario. Considering the economic benefit, ecological benefit and carbon budget, the SDS scenario has the best performance. This study provides a new perspective for discussing the future carbon budget status of small-scale watershed areas and combines the goals of “sustainable development” and “dual carbon”. It provides a data-supported basis for discussing the diversified ecological compensation mechanism of the WLRB from the perspective of carbon neutrality as well as insights for the formulation of regional LULC optimization management policies.

A Combinatorial Regulatory Platform Determines Expression of RNA Polymerase III Subunit RPC7α (POLR3G) in Cancer.

RNA polymerase III (Pol III) subunit RPC7α, which is encoded by POLR3G in humans, has been linked to both tumor growth and metastasis. Accordantly, high POLR3G expression is a negative prognostic factor in multiple cancer subtypes. To date, the mechanisms underlying POLR3G upregulation have remained poorly defined. We performed a large-scale genomic survey of mRNA and chromatin signatures to predict drivers of POLR3G expression in cancer. Our survey uncovers positive determinants of POLR3G expression, including a gene-internal super-enhancer bound with multiple transcription factors (TFs) that promote POLR3G expression, as well as negative determinants that include gene-internal DNA methylation, retinoic-acid induced differentiation, and MXD4-mediated disruption of POLR3G expression. We show that novel TFs identified in our survey, including ZNF131 and ZNF207, functionally enhance POLR3G expression, whereas MXD4 likely obstructs MYC-driven expression of POLR3G and other growth-related genes. Integration of chromatin architecture and gene regulatory signatures identifies additional factors, including histone demethylase KDM5B, as likely influencers of POLR3G gene activity. Taken together, our findings support a model in which POLR3G expression is determined with multiple factors and dynamic regulatory programs, expanding our understanding of the circuitry underlying POLR3G upregulation and downstream consequences in cancer.

Patterns of Sustainability Capacity Among Organizations That Deliver the National Diabetes Prevention Program: A Latent Profile Analysis.

Since the launch of the National Diabetes Prevention Program (DPP) in 2010, more than 3,000 organizations have registered with the Centers for Disease and Control and Prevention to deliver the program; today, however, only approximately 2,000 organizations are registered, indicating challenges with sustainability. We used the Program Sustainability Assessment Tool (PSAT) to explore patterns of sustainability capacity among National DPP delivery organizations. We used data from a cross-sectional online survey conducted in August and September 2021 of staff members (N = 440) at National DPP delivery organizations. We conducted a latent profile analysis to identify latent subpopulations on the basis of respondent PSAT domain scores. Regression analyses were used to estimate associations between derived latent classes, PSAT scores, and respondent characteristics. The 4-class model included 4 groups of capacity for program sustainability, ranging from low to high: low (class 1) with 8.0% of the sample, medium-low (class 2) with 22.0%, medium-high (class 3) with 41.6%, and high (class 4) with 28.4%. Program evaluation (mean score = 5.1 [SD = 1.4]) and adaptation (mean score = 5.3 [SD = 1.3]) were the domains with the highest scores, while funding stability (mean score = 4.0 [SD = 1.6]) and Partnerships (mean score = 4.0 [SD = 1.7]) had the lowest scores. In our sample of National DPP delivery organizations, most reported relatively high capacity for program sustainability, and key indicators associated with sustainability capacity were virtual delivery, location of delivery, funding sources, and organization type. Similar to sustainability capacity findings from other PSAT studies, our study found that funding stability and partnerships are areas to strengthen. This insight is useful in sustainability planning at organizational and national levels across multiple programs.

Characterizing Antarctic fish assemblages using eDNA obtained from marine sponge bycatch specimens

International conservation goals have been set to mitigate Southern Ocean ecosystem deterioration, with multiple monitoring programs evaluating progress towards those goals. The scale of continuous monitoring through visual observations, however, is challenged by the remoteness of the area and logistical constraints. Given the ecological and economic importance of the Southern Ocean, it is imperative that additional biological monitoring approaches are explored. Recently, marine sponges, which are frequently caught and discarded in Southern Ocean fisheries, have been shown to naturally accumulate environmental DNA (eDNA). Here, we compare fish eDNA signals from marine sponge bycatch specimens to fish catch records for nine locations on the continental shelf (523.5–709 m) and 17 from the continental slope (887.5–1611.5 m) within the Ross Sea, Antarctica. We recorded a total of 20 fishes, with 12 fishes reported as catch, 18 observed by eDNA, and ten detected by both methods. While sampling location was the largest contributor to the variation observed in the dataset, eDNA obtained significantly higher species richness and displayed a significantly different species composition compared to fish catch records. Overall, eDNA read count correlated more strongly with fish abundance over biomass. Species composition correlated on a regional scale between methods, however eDNA signal strength was a low predictor of catch numbers at the species level. Our results highlight the potential of sponge eDNA monitoring in the Southern Ocean by detecting a larger fraction of the fish community compared to catch recordings, thereby increasing our knowledge of this understudied ecosystem and, ultimately, aiding conservation efforts.

Impact of Green Servant Leadership in Pakistani Small and Medium Enterprises: Bridging Pro-Environmental Behaviour through Environmental Passion and Climate for Green Creativity

Small and medium enterprises (SMEs) are crucial for any economy to grow and succeed, as evidenced in the Pakistani context, where SMEs contribute 30% of the country’s GDP. The objective of this study is to link green servant leadership (GSL) to the pro-environmental behaviour (PEB) of employees, particularly in SMEs, which very few studies have investigated. Building on social learning theory (SLT), this study developed and tested the conceptual framework that examines the impact of GSL on PEB and the mediating role of environmental passion (EP) and climate for green creativity (CFGC) among employees of SMEs. Data were collected from 460 middle-line managers, and a structural equation modelling (SEM) technique was applied to test hypotheses. The main findings revealed that GSL is impacted by the PEB, while EP and CFGC mediated these relations. The study findings demonstrated that a GSL with strong practices and values towards the environment could have a significant impact on employees’ PEB. This study fills the research gaps in different ways: First, by identifying the role of GSL on the PEB of employees. Second, by examining the dual-mediation mechanism of EP and CFGC between GSL and PEB. Third, this study is focused on the economic context of a developing country. The study offers guidelines for establishing PEB in SMEs. Multiple training programmes and effective planning procedures can achieve this milestone. The administration of SMEs should also give special consideration to pro-environmental issues in its hiring and recruitment practices for managers and leaders.

Blended Learning Program Based on Multiple Representations: Needs Analysis to Stimulate Complex Problem Solving and Reduce Learning Loss

Various complex problems arise along with the increasingly massive development of science and technology in the 21st century. Special skills are needed to survive and adapt to a complex environment and demand various problems to be solved. This study aimed to determine the need for developing multiple representations-based blended learning programs to stimulate complex problem-solving (CPS) and reduce learning loss. This study uses a qualitative descriptive method to provide an overview of the implementation of CPS in high schools and the learning loss during lessons during the Covid-19 pandemic. This research was conducted in July 2022. The data source was obtained using a non-test technique in the form of an open questionnaire. The subjects in this study were 13 physics teachers and 25 students spread across 4th provinces in Indonesia, including Lampung, DIY, Central Java and West Java. The data analysis technique is the Milles Hubberman analysis technique carried out continuously until the data is saturated. Data analysis involves three steps, data reduction, data presentation, and concluding/verification. Study result show that CPS has yet to be fully implemented in physics learning in high schools. Learning during the Covid-19 pandemic was ineffective and maximal, which resulted in most students experiencing setbacks in learning and a decrease in academic progress (learning loss). Therefore, developing a blended learning program based on multiple representations is necessary to stimulate complex problem-solving and reduce learning loss

Unlocking the Potential of Programming Education: Enhancing Conceptual Understanding and Student Engagement with Sphero SPRK Robot

This research investigates the use of the 'Sphero SPRK+' robot as a programming assistant to enhance conceptual understanding and engagement in programming education. The research addresses the challenges of teaching programming and underlines the significance of students understanding programming structures, semantics, syntax and planning. The Sphero robot offers multiple programming options, including graphic blocks, a pictorial command line, and text commands, catering to diverse learning styles. The approach of the study is based on a cognitive learning model, with the Sphero robot providing immediate feedback throughout practising. The experiment incorporates two groups: one using Blocks and the other using JavaScript code with Sphero's assistance. Results suggest that using Sphero SPRK robot enhances programming learning outcomes, particularly for novice learners. Participants express satisfaction with the engaging and practical learning experience, favouring the ease of Blocks for learning programming logic. The study provides significance for educational settings and emphasises the value of robotics and block-based programming in promoting algorithmic thinking and computational skills.

YAEL: Your Advanced Electrode Localizer.

Intracranial electroencephalography (iEEG) provides a unique opportunity to record and stimulate neuronal populations in the human brain. A key step in neuroscience inference from iEEG is localizing the electrodes relative to individual subject anatomy and identified regions in brain atlases. We describe a new software tool, Your Advanced Electrode Localizer (YAEL), that provides an integrated solution for every step of the electrode localization process. YAEL is compatible with all common data formats to provide an easy-to-use, drop-in replacement for problematic existing workflows that require users to grapple with multiple programs and interfaces. YAEL's automatic extrapolation and interpolation functions speed localization, especially important in patients with many implanted stereotactic (sEEG) electrode shafts. The graphical user interface is presented in a web browser for broad compatibility and includes an interactive 3D viewer for easier localization of nearby sEEG contacts. After localization is complete, users may enter or import data into YAEL's 3D viewer to create publication-ready visualizations of electrodes and brain anatomy, including identified brain areas from atlases; the response to experimental tasks measured with iEEG; and clinical measures such as epileptiform activity or the results of electrical stimulation mapping. YAEL is free and open source and does not depend on any commercial software. Installation instructions for Mac, Windows, and Linux are available at https://yael.wiki.

The Athletic Training Student as a Core Member of an Interprofessional Student-Led Clinic: An Educational Technique

Context Athletic trainers (ATs) collaborate with other professionals to improve the health and well-being of their patients. To prepare ATs to care for individuals, communities, and populations, it is important for athletic training students to experience interprofessional (IP) education, social determinants of health (SDOH), and varying health conditions, in a variety of practice settings. Objective To describe an educational technique that provides athletic training students with an IP experience at a student-led clinic. Background Athletic trainers care for clients/patients who have limited resources for health care and personal needs. To care for these individuals, ATs must have collaborative practices that engage them with parents/guardians, school-based social workers, and nurses through a variety of settings, including free/low-cost clinics and social services. A purposeful educational strategy that provides direct clinical experiences using IP education and IP practice to address SDOH in multiple settings is important in preparing future health care providers. Student-led IP clinics provide community-based teaching and learning to prepare students for clinical practice. Description First-year graduate-level athletic training students enrolled in clinical courses participated in this educational technique. A clinical experience provided students the unique opportunity to learn and apply IP practice with students and faculty in multiple academic programs (undergraduate and graduate nursing, undergraduate public health, undergraduate and graduate social work) while caring for underserved individuals and communities in the rural Midwest region. Clinical Advantages This educational strategy positively impacts the community, students, faculty, academic programs, and the university. Participation in community-based IP student-led clinics prepares athletic training students to care for a variety of individuals, populations, and health conditions through a collaborative approach. This approach also addresses gaps in health care delivery, particularly among underserved groups with varying SDOH, while introducing students to practice settings they may not have considered previously. Conclusions Incorporating the athletic training student into an IP student-led clinic provides unique learning opportunities for students to care for underserved individuals, populations, and communities, preparing them to provide whole-person care as clinicians.

GAST: A generic AST representation for language-independent source code analysis

Organizations use various programming languages to develop their systems. These aim to take advantage of the most appropriate features of each language for a given domain and require programmers to command different languages and also to face the growing complexity of software development and maintenance. So, they need tools to help them analyze programs to identify relationships between their internal elements, uncover patterns, and calculate quality metrics. However, most tools have limited support for parsing multiple programming languages and high acquisition costs. Therefore, there is a need for new methods to analyze code written in multiple programming languages. This article describes the design of a method to automatically transform the syntax of various programming languages into a universal language with a generic syntax. The function of the generic language is to encapsulate the specificities of each specific language, so that the analysis of programs is facilitated in a single programming syntax and not in multiple syntaxes. The advantage of this approach is that only one analysis engine is required, not multiple code analyzers, to study the programs.

Model for Working Capital Management of Micro, Small and Medium Enterprises in Indonesia by Using Multiple Objective Stochastic Programming

Micro, small and medium enterprises (MSME) have a big influence for the economy in Indonesia. But almost of this company don’t have an optimal working capital management because less knowledge and limitation of financial not only from the owner but also financial credit from bank. Working capital management plays an important role in the success or failure of a company its business activities. To achieve optimal use of working capital management managers the company must accurately control the trade-off between profitability and liquidity. The purpose of this research is to develop a model of working capital management where the sales are under uncertainty and the company has a limit financial. This study was tested using multiple linear regression analysis to see the correlation between profitability and liquidity with x1, x2, x3, x4, and x5 then find optimal solution by stochastic multi objective programming because we consider that sales is in under certainty. The main variables used in the analysis of return on assets to measure profitability, receivable turnover to measure liquidity, inventory turnover, accounts payable days, cash conversion. The results showed that account payable, inventory turnover, and cash turn over have a positive correlation for liquidity but inventory turnover and cash turnover have negative correlation to profitability.

From Two Dimensions to Multidimensions: A Mechanistic Model to Support Deliberate CPD Development, Coordination, and Evaluation.

The effectiveness of continuing professional development as an intervention to improve health professional behavior and patient health is variable and contentious. To clarify the causal relationships underlying program outcomes and facilitate a necessary shift from outcomes-only-based approaches to outcome-based and theory-based approaches in program development and evaluation, we developed a model of mechanisms mapped to relevant outcomes. Mechanisms identified in a prior realist synthesis of opioid agonist therapy continuing professional development programs were iteratively tested and refined using purposive and opportunistic sampling and realist approaches against two systematic reviews of programs in analgesic prescribing and palliative care. Further testing involved practical application within programs in sustainable health care and pain management. Ninety reports on 75 programs and practical application to multiple additional programs informed the final model consisting of five distinct mechanisms: motivation transformation, expert influence, confidence development, self-efficacy facilitation, and community of practice expansion. The mechanisms and related analysis emphasize that continuing professional development is heterogeneous, complex, and context dependent. Shifting toward outcome-based and theory-based approaches facilitates further conceptual shifts at intraprogram and interprogram and interintervention levels toward more deliberate program development and evaluation, increased program complementarity and subsequent collaboration. It clarifies opportunities for intercalation of continuing professional development with other intervention sciences. The model presents a resource for practitioners, researchers, and policymakers to advance continuing professional development planning, coordination, and evaluation.

A single neuron in C. elegans orchestrates multiple motor outputs through parallel modes of transmission

Animals generate a wide range of highly coordinated motor outputs, which allows them to execute purposeful behaviors. Individual neurons in the circuits that generate behaviors have a remarkable capacity for flexibility as they exhibit multiple axonal projections, transmitter systems, and modes of neural activity. How these multi-functional properties of neurons enable the generation of adaptive behaviors remains unknown. Here, we show that the HSN neuron in C.elegans evokes multiple motor programs over different timescales to enable a suite of behavioral changes during egg laying. Using HSN activity perturbations and invivo calcium imaging, we show that HSN acutely increases egg laying and locomotion while also biasing the animals toward low-speed dwelling behavior over minutes. The acute effects of HSN on egg laying and high-speed locomotion are mediated by separate sets of HSN transmitters and different HSN axonal compartments. The long-lasting effects on dwelling are mediated in part by HSN release of serotonin, which is taken up and re-released by NSM, another serotonergic neuron class that directly evokes dwelling. Our results show how the multi-functional properties of a single neuron allow it to induce a coordinated suite of behaviors and also reveal that neurons can borrow serotonin from one another to control behavior.