Program Analysis Research Articles

Examining a 12-year experience within Kazakhstan’s national heart transplantation program

Kazakhstan has one of the lowest heart transplantation (HTx) rates globally, but there are no studies evaluating the outcomes of HTx. This study aimed to provide a comprehensive analysis of the national HTx program over a 12-year period (2012–2023). Survival analysis of the national HTx cohort was conducted using life tables, Kaplan‒Meier curves, and Cox regression methods. Time series analysis was applied to analyze historical trends in HTx per million population (pmp) and to make future projections until 2030. The number of patients awaiting HTx in Kazakhstan was evaluated with a regional breakdown. The pmp rates of HTx ranged from 0.06 to 1.08, with no discernible increasing trend. Survival analysis revealed a rapid decrease in the first year after HTx, reaching 77.0% at 379 days, with an overall survival rate of 58.1% at the end of the follow-up period. Among the various factors analyzed, recipient blood levels of creatinine and total bilirubin before surgery, as well as the presence of infection or sepsis and the use of ECMO after surgery, were found to be significant contributors to the survival of HTx patients. There is a need for public health action to improve the HTx programme.

Race Matters: The Long Walk to Decolonizing Development NGOs

Development NGOs have long been under scrutiny for their colonial legacy. Decision-making structures are predominantly white or European. There is little racial equity analysis of staff, programs, and campaigns. Poverty and development are understood as happening “there” in low-income countries. This article analyzes the UK-born NGO ActionAid and its steps toward decolonization. In the 2000s, country directors and international board members diversified beyond British nationals to give more decision-making power to African, Asian, and Latin-American countries. Other advances, yet recent, include an antiracism audit and the start of a large racial equity program. International campaigns are often critical toward the UK’s role in low-income countries or include UK domestic aspects requiring “development” (poverty, climate justice …). The paper shares practical measures to the sometimes-theoretical debate on decolonization and development. It suggests that this process is not in contradiction—but rather promotes—the mandate of addressing poverty where it is most severe worldwide.

Analysis of National Resident Matching Program for Radiology Fellowships - Factors Affecting Program Fill Rates

Analysis of National Resident Matching Program for Radiology Fellowships - Factors Affecting Program Fill Rates

Contemporary morbidity and mortality of open versus robotic cystectomy for bladder cancer: An analysis of the National Surgical Quality Improvement Program (NSQIP) procedure targeted cystectomy database

ObjectivesTo evaluate the association between surgical modality (RARC vs. ORC) and the risk of 30-day complications. Materials and MethodsWe utilized the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) Cystectomy-Targeted database from 2019 to 2021. The primary outcome was a composite of major complications including 30-day mortality, reoperation, cardiac events, and stroke. Secondary outcomes included individual major and cystectomy-specific complications. Propensity score matching (PSM) was employed to minimize inherent differences within our cohort. We performed logistic regression to assess the association between outcomes of interest and operative modality. ResultsWe found no difference between operative modality and the primary outcome, however, RARC was associated with a 70% lower risk of 30-day mortality (OR 0.30, 95% CI 0.13-0.70) and had favorable outcomes with respect to respiratory, deep venous thrombosis, wound complications, and length of stay. Limitations are related to residual confounding given the observational methodology. ConclusionsRARC was associated with reduced risk of multiple 30-day complications, including mortality, as well as organ system and cystectomy-specific outcomes. These data support the clinical benefit of increased adoption of RARC.

Association between self-efficacy and anxiety symptoms in adolescents: secondary analysis of a preventive program

Association between self-efficacy and anxiety symptoms in adolescents: secondary analysis of a preventive program

Putting Forward Sustainability as a Model for Journalism Education and Training

ABSTRACT African journalism practice presents unique opportunities and challenges that require journalists to be equipped with the necessary skills, knowledge, and values to engage in sustainable journalism. Training institutions play a critical role in ensuring that journalists are not only professionally-ready to execute their mandate but also that they can safeguard and promote ethical values in their everyday work. Some of these values include “truth telling, independence, objectivity, fairness, inclusivity and social justice” (Gade, Nduka, and Dastger 2017, 10). Africa, like other regions of the Global South, has several journalism training institutions that provide an opportunity to challenge “hegemonic epistemologies and ontologies of Western-centric journalism studies” (Mutsvairo et al. 2021, 993). In the context of this submission, the present study investigates the current state of sustainable journalism in Africa. We examined data based on a syllabi analysis of journalism programs in Kenya, South Africa and Ghana to appraise what role sustainable journalism education and training could play in Africa. Findings show that efforts are already in place across select learning and training institutions but also point to profound gaps in the curriculum, pedagogy and resources needed to prepare journalists for sustainable journalism.

Retrospective descriptive analysis of an urban pediatric collaborative care program

Retrospective descriptive analysis of an urban pediatric collaborative care program

Rideshare-based transportation services for surgical patients in an urban setting: A descriptive analysis of a quality initiative program

IntroductionTransportation access is a social determinant of health that hinders chronic care engagement for vulnerable populations. Few studies have examined how transportation interventions impact healthcare outcomes when implemented independently of other tailored interventions (i.e., multicomponent outreach programs). Data is further limited for rideshare initiatives. MethodsWe conducted a retrospective, descriptive analysis of a rideshare-based transportation program for pre- and post-operative patients as part of a quality improvement (QI) initiative at a San Francisco County hospital. We assessed the population benefiting from this rideshare program and compared outcomes between program participants and non-participants. Data obtained included sociodemographic information, effects of health care utilization (e.g., delays in the operating room (OR)), and hospital rideshare costs. ResultsOverall, $18,733 in funds were used to fully subsidize 1562 Lyft rides for patients traveling to and from the hospital. Inclusion criteria resulted in 4150 total patients, with 376 in the rideshare group and 3774 in the non-rideshare group. Rideshare group patients were more likely to be older, male, black/African American, less healthy per physical status classification ratings, and residing in neighborhoods with lower income. Non-rideshare group patients were more likely to be Hispanic/Latino and require interpreter use. The patients participating in the rideshare program had non-significant increases in the proportion of OR cases delayed (48.2% and 44.1%, respectively) and average time delayed in OR case start times (18.7 and 14.6 min, respectively) compared to the non-rideshare group. The average cost per Lyft ride to the first scheduled surgery of the day was $15.13. ConclusionsWhile rideshare programs may not decrease the rates or magnitude of OR delays, they can address transportation barriers for vulnerable communities, and likely at a minimal financial burden to the healthcare system. Future work is required to examine potential health benefits from transportation interventions, particularly those based in rideshare initiatives.

SOCIOLOGY OF SAFETY: PREVENTIVE MEASURES IN INSURANCE AS AN OBJECT OF ANALYSIS

Modern sociology actively examines issues related to occupational safety and preventive measures in the field of insurance, with a specific focus on safeguarding the well-being and health of workers. This article presents a sociological analysis of preventive programs in insurance, paying close attention to their sociological dimensions. The study investigates the experiences and future prospects of countries belonging to both the Organization for Economic Co-operation and Development and the Eurasian Economic Union that have implemented preventive measures within their social insurance systems. The article discusses the key components of preventive programs, their role in guaranteeing worker safety and health, and the effectiveness of investments made in preventive measures. Furthermore, it presents data regarding the allocation of resources for preventive measures in different countries and evaluates the return on investment in preventive programs. In conclusion, the article highlights the significance of further developing and enhancing preventive programs in insurance to ensure the safety and well-being of workers in their respective work environments.

Part 1: Improving Wildfire Occurrence Prediction for CONUS Using Deep Learning and Fire Weather Variables

Abstract The purpose of this research is to build an operational model for predicting wildfire occurrence for the contiguous United States (CONUS) in the 1-to-10-day range using the UNet3+ machine-learning model. This paper illustrates the range of model performance resulting from choices made in the modeling process, such as how labels are defined for the model, and how input variables are codified for the model. By combining the capabilities of the UNet3+ model with a neighborhood loss function, Fractions Skill Score (FSS), we can quantify model success by predictions made both in and around the location of the original fire occurrence label. The model is trained on weather, weather-derived fuel, and topography observational inputs and labels representing fire occurrence. Observational weather, weather-derived fuel, and topography data are sourced from the gridMET data set, a daily, CONUS-wide, high-spatial resolution data set of surface meteorological variables. Fire occurrence labels are sourced from the U.S. Department of Agriculture’s Fire Program Analysis Fire-Occurrence Database (FPA-FOD), which contains spatial wildfire occurrence data for CONUS, combining data sourced from the reporting systems of federal, state, and local organizations. By exploring the many aspects of the modeling process with the added context of model performance, this work builds understanding around the use of deep learning to predict fire occurrence in CONUS.

Static Program Analysis for IoT Risk Mitigation in Space-Air-Ground Integrated Networks

The space-air-ground integrated networks(SAGINs) are pivotal for modern communication and surveillance, with a growing number of connected devices. The proliferation of IoT devices within these networks introduces new risks due to potential erroneous synergistic interactions that could compromise system integrity and security. This paper addresses the challenges in coordination, synchronization, and security within SAGINs by introducing a novel static program analysis (SPA) technique using zero-knowledge (ZK) proofs. This approach ensures the detection of risky interactions without compromising sensitive source code, thus safeguarding intellectual property and privacy. The proposed method overcomes the incompatibility between SPA and ZK systems by developing an imperative programming language for SAGINs and a specialized abstract domain for interaction threats. The system translates network control algorithms into arithmetic circuits suitable for ZK analysis, maintaining high accuracy in detecting risks. Evaluations on real-world scenarios demonstrate the system's efficacy in identifying risky interactions with minimal computational overhead. This research presents the first ZK-based SPA scheme for SAGINs, enhancing security and confidentiality in network analysis while adhering to privacy regulations.

Newtonian Program Analysis of Probabilistic Programs

Due to their quantitative nature, probabilistic programs pose non-trivial challenges for designing compositional and efficient program analyses. Many analyses for probabilistic programs rely on iterative approximation. This article presents an interprocedural dataflow-analysis framework, called NPA-PMA, for designing and implementing (partially) non-iterative program analyses of probabilistic programs with unstructured control-flow, nondeterminism, and general recursion. NPA-PMA is based on Newtonian Program Analysis (NPA), a generalization of Newton's method to solve equation systems over semirings. The key challenge for developing NPA-PMA is to handle multiple kinds of confluences in both the algebraic structures that specify analyses and the equation systems that encode control flow: semirings support a single confluence operation, whereas NPA-PMA involves three confluence operations (conditional, probabilistic, and nondeterministic). Our work introduces ω-continuous pre-Markov algebras (ωPMAs) to factor out common parts of different analyses; adopts regular infinite-tree expressions to encode probabilistic programs with unstructured control-flow; and presents a linearization method that makes Newton's method applicable to the setting of regular-infinite-tree equations over ωPMAs. NPA-PMA allows analyses to supply a non-iterative strategy to solve linearized equations. Our experimental evaluation demonstrates that (i) NPA-PMA holds considerable promise for outperforming Kleene iteration, and (ii) provides great generality for designing program analyses.

A Pure Demand Operational Semantics with Applications to Program Analysis

This paper develops a novel minimal-state operational semantics for higher-order functional languages that uses only the call stack and a source program point or a lexical level as the complete state information: there is no environment, no substitution, no continuation, etc. We prove this form of operational semantics equivalent to standard presentations. We then show how this approach can open the door to potential new applications: we define a program analysis as a direct finitization of this operational semantics. The program analysis that naturally emerges has a number of novel and interesting properties compared to standard program analyses for higher-order programs: for example, it can infer recurrences and does not need value widening. We both give a formal definition of the analysis and describe our current implementation.

Learning Abstraction Selection for Bayesian Program Analysis

We propose a learning-based approach to select abstractions for Bayesian program analysis. Bayesian program analysis converts a program analysis into a Bayesian model by attaching probabilities to analysis rules. It computes probabilities of analysis results and can update them by learning from user feedback, test runs, and other information. Its abstraction heavily affects how well it learns from such information. There exists a long line of works in selecting abstractions for conventional program analysis but they are not effective for Bayesian program analysis. This is because they do not optimize for generalization ability. We propose a data-driven framework to solve this problem by learning from labeled programs. Starting from an abstraction, it decides how to change the abstraction based on analysis derivations. To be general, it considers graph properties of analysis derivations; to be effective, it considers the derivations before and after changing the abstraction. We demonstrate the effectiveness of our approach using a datarace analysis and a thread-escape analysis.

Evaluating the Effectiveness of Deep Learning Models for Foundational Program Analysis Tasks

While deep neural networks provide state-of-the-art solutions to a wide range of programming language tasks, their effectiveness in dealing with foundational program analysis tasks remains under explored. In this paper, we present an empirical study that evaluates four prominent models of code (i.e., CuBERT, CodeBERT, GGNN, and Graph Sandwiches) in two such foundational tasks: (1) alias prediction, in which models predict whether two pointers must alias, may alias or must not alias; and (2) equivalence prediction, in which models predict whether or not two programs are semantically equivalent. At the core of this study is CodeSem, a dataset built upon the source code of real-world flagship software (e.g., Linux Kernel, GCC, MySQL) and manually validated for the two prediction tasks. Results show that all models are accurate in both prediction tasks, especially CuBERT with an accuracy of 89% and 84% in alias prediction and equivalence prediction, respectively. We also conduct a comprehensive, in-depth analysis of the results of all models in both tasks, concluding that deep learning models are generally capable of performing foundational tasks in program analysis even though in specific cases their weaknesses are also evident. Our code and evaluation data are publicly available at https://github.com/CodeSemDataset/CodeSem.

Outcome Separation Logic: Local Reasoning for Correctness and Incorrectness with Computational Effects

Separation logic’s compositionality and local reasoning properties have led to significant advances in scalable static analysis. But program analysis has new challenges—many programs display computational effects and, orthogonally, static analyzers must handle incorrectness too. We present Outcome Separation Logic (OSL), a program logic that is sound for both correctness and incorrectness reasoning in programs with varying effects. OSL has a frame rule—just like separation logic—but uses different underlying assumptions that open up local reasoning to a larger class of properties than can be handled by any single existing logic. Building on this foundational theory, we also define symbolic execution algorithms that use bi-abduction to derive specifications for programs with effects. This involves a new tri-abduction procedure to analyze programs whose execution branches due to effects such as nondeterministic or probabilistic choice. This work furthers the compositionality promised by separation logic by opening up the possibility for greater reuse of analysis tools across two dimensions: bug-finding vs verification in programs with varying effects.

Mitigating Severe Hypoglycemia in Users of Advanced Diabetes Technologies: Impaired Awareness of Hypoglycemia and Unhelpful Hypoglycemia Beliefs as Targets for Interventions.

Objective: A subgroup analysis of the Hypoglycemia Awareness Restoration Programme for people with type 1 diabetes and problematic hypoglycemia persisting despite optimized care (HARPdoc) trial was conducted to explore the impact of Blood Glucose Awareness Training (BGAT, a hypoglycemia awareness training program) and the HARPdoc (a psychoeducation addressing unhelpful hypoglycemia beliefs) in reducing severe hypoglycemia (SH) in individuals using advanced diabetes technologies (ADTs). Methods: Data from trial participants who utilized ADTs, including continuous glucose monitors or automated insulin delivery systems, were extracted. Generalized linear mixed-effects models with Poisson distribution or linear mixed-effects models were used to evaluate SH incidence, and Gold questionnaire, Attitudes to Awareness of Hypoglycemia (A2A), Problem Areas in Diabetes (PAID), Hospital Anxiety and Depress Scale (HADS)-anxiety, and HADS-depression scores as measures of hypoglycemia awareness, unhelpful hypoglycemia beliefs, diabetes distress, and anxiety and depression symptoms, respectively. Results: In the 45 participants using ADTs, the BGAT and HARPdoc interventions both reduced SH incidence by more than 50% (P < 0.0001) and yielded improvements in hypoglycemia awareness (P < 0.05). HARPdoc outperformed BGAT in reducing SH at month 24 (P = 0.01). HARPdoc also mitigated unhelpful hypoglycemia beliefs (P < 0.0001), diabetes distress (P < 0.05), and anxiety symptoms (P < 0.05); BGAT demonstrated no significant impacts in these respects. Neither HARPdoc nor BGAT had significant effects on depression symptoms. Conclusion: Psychoeducation (BGAT and HARPdoc) was effective in reducing SH in people using ADTs. HARPdoc may also provide greater long-term SH reduction and improves psychological well-being in this patient group.

Quantitative Bounds on Resource Usage of Probabilistic Programs

Cost analysis, also known as resource usage analysis, is the task of finding bounds on the total cost of a program and is a well-studied problem in static analysis. In this work, we consider two classical quantitative problems in cost analysis for probabilistic programs. The first problem is to find a bound on the expected total cost of the program. This is a natural measure for the resource usage of the program and can also be directly applied to average-case runtime analysis. The second problem asks for a tail bound, i.e. ‍given a threshold t the goal is to find a probability bound p such that ℙ[total cost ≥ t ] ≤ p . Intuitively, given a threshold t on the resource, the problem is to find the likelihood that the total cost exceeds this threshold. First, for expectation bounds, a major obstacle in previous works on cost analysis is that they can handle only non-negative costs or bounded variable updates. In contrast, we provide a new variant of the standard notion of cost martingales, that allows us to find expectation bounds for a class of programs with general positive or negative costs and no restriction on the variable updates. More specifically, our approach is applicable as long as there is a lower bound on the total cost incurred along every path. Second, for tail bounds, all previous methods are limited to programs in which the expected total cost is finite. In contrast, we present a novel approach, based on a combination of our martingale-based method for expectation bounds with a quantitative safety analysis, to obtain a solution to the tail bound problem that is applicable even to programs with infinite expected cost. Specifically, this allows us to obtain runtime tail bounds for programs that do not terminate almost-surely. In summary, we provide a novel combination of martingale-based cost analysis and quantitative safety analysis that is able to find expectation and tail cost bounds for probabilistic programs, without the restrictions of non-negative costs, bounded updates, or finiteness of the expected total cost. Finally, we provide experimental results showcasing that our approach can solve instances that were beyond the reach of previous methods.

Robustness verification of k-nearest neighbors by abstract interpretation

AbstractWe study the certification of stability properties, such as robustness and individual fairness, of the k-nearest neighbor algorithm (kNN). Our approach leverages abstract interpretation, a well-established program analysis technique that has been proven successful in verifying several machine learning algorithms, notably, neural networks, decision trees, and support vector machines. In this work, we put forward an abstract interpretation-based framework for designing a sound approximate version of the kNN algorithm, which is instantiated to the interval and zonotope abstractions for approximating the range of numerical features. We show how this abstraction-based method can be used for stability, robustness, and individual fairness certification of kNN. Our certification technique has been implemented and experimentally evaluated on several benchmark datasets. These experimental results show that our tool can formally prove the stability of kNN classifiers in a precise and efficient way, thus expanding the range of machine learning models amenable to robustness certification.

Leadership education: a content analysis of postgraduate leadership education programs for health and human service leaders in Australian universities

PurposeA systematic investigation of postgraduate leadership programs for health and/or human services offered by Australian higher education institutions was undertaken.Design/methodology/approachQuantitative analysis identified the core characteristics of the programs. A thematic analysis of the course learning outcomes was conducted and six major themes of disciplinary leadership and management knowledge; research and analytical skills; professional practice; communication and collaboration; creativity and innovation; and system knowledge are shared in this study.FindingsThe authors conclude that Australian universities have taken an evidence-based approach to leadership education.Originality/valueMore work might need to be undertaken to ensure leadership theories are incorporated into learning outcomes.