Variable Sampling Research Articles

P-1813. Improving Adherence to an Antibiotic Pre-Authorization Protocol at a Community Academic Medical Center

Abstract Background Preauthorization is integral to antimicrobial stewardship and is associated with decreased utilization of restricted antimicrobials, lower cost, and reduced development of antimicrobial resistance. Implementation of such a program is resource-intensive and dependent on the skill of the approving provider. The aim of this quality improvement project was to update the preauthorization program at a community hospital and increase protocol adherence from 23.8% to 85% by September 1st, 2023.Figure 1:Adherence to the Preauthorization Process presented as a P-chart, representing the rate of the desired outcome with variable sample size over time. Statistical significance identified through mean-line shift, based on 8 consecutive data points above or below a previous mean line. Methods A community academic medical center developed a workgroup of infectious diseases (ID) pharmacists and physicians to optimize the existing antibiotic preauthorization policy. Baseline data were collected over a one month period, based on policy requirements for approval prior to implementation of improvement initiatives. Updates implemented an ID pharmacist into the approval process, streamlined request communication, and standardized documentation of approval. Requests were considered adherent to protocol if the process metrics of appropriate use were met. This included following the off-hours pathway appropriately and completion of standardized documentation prior to order verification. Balancing metrics included duration of restricted antimicrobial utilization. Data were extracted through a query of electronic medical records with manual chart review of patients who received at least one dose of a restricted antimicrobial. Data were analyzed through descriptive statistics pursuant to the Institute of Healthcare Improvement’s Model for Improvement or Wilcoxon Rank-Sum, where appropriate. Results Between September 2022 and September 2023, 701 restricted therapies were ordered. Of these, 410 were not associated with an infectious diseases consult. Mean protocol adherence increased from 23.8% (5/18) to 86.2% (307/356) [Fig. 1]. Median duration of restricted antimicrobial therapy decreased from 4.0 days to 3.0 days (p=0.0025). Conclusion The implemented updates to the preauthorization program markedly increased protocol adherence and decreased duration of restricted antimicrobial therapy. This work demonstrates the feasibility and quality impact of implementing antimicrobial stewardship initiatives in a community hospital setting. Disclosures All Authors: No reported disclosures

Refractive epitrochoids sampling for light field measurement using wedge prisms.

Conventional light field measurement using a micro-lens array suffers from grid-like artifacts in the refocused image because the sub-aperture images are sampled at linear and equal intervals like a grid pattern. In this study, we propose a method for measuring light field using a pair of wedge prisms and a method for calibrating the sub-aperture position. The epitrochoids trajectory of rays generated by rotating prisms yields unequal intervals and variable sampling of the sub-aperture position. We designed and calibrated the optical system, and verified the effectiveness of the proposed method on refocused images using the measured light field.

Beyond Information Distortion: Imaging Variable-Length Time Series Data for Classification

Time series data are prevalent in diverse fields such as manufacturing and sensor-based human activity recognition. In real-world applications, these data are often collected with variable sample lengths, which can pose challenges for classification models that typically require fixed-length inputs. Existing approaches either employ models designed to handle variable input sizes or standardize sample lengths before applying models; however, we contend that these approaches may compromise data integrity and ultimately reduce model performance. To address this issue, we propose Time series Into Pixels (TIP), an intuitive yet strong method that maps each time series data point into a pixel in 2D representation, where the vertical axis represents time steps and the horizontal axis captures the value at each timestamp. To evaluate our representation without relying on a powerful vision model as a backbone, we employ a straightforward LeNet-like 2D CNN model. Through extensive evaluations against 10 baseline models across 11 real-world benchmarks, TIP achieves 2–5% higher accuracy and 10–25% higher macro average precision. We also demonstrate that TIP performs comparably on complex multivariate data, with ablation studies underscoring the potential hazard of length normalization techniques in variable-length scenarios. We believe this method provides a significant advancement for handling variable-length time series data in real-world applications. The code is publicly available.

Comparing innovative AI algorithms to assess echocardiographic videos for clinical modelling.

We conduct a comparative study that employs the use of multiple dynamic deep learning algorithms to develop predictive models with video-based echocardiographic images using sample size determination as a key variable to assess optimal performance metrics. Our study compares performance of 3D convolutional neural networks, video vision transformers, and hybrid convolutional neural networks and Long Short-Term Memory models within both supervised and semi-supervised domains using variable sample sizes. For supervised learning, the ResNet3D model achieved the lowest Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) across all training set sizes (200, 400, and 800-video datasets), with the best performance observed on the 800-video training set (MAE: 7.409, RMSE: 10.216). In the semi-supervised setting, both ResNet3D and ResNet+LSTM models benefited from the inclusion of unlabelled data, particularly at larger dataset sizes. Because semi-supervised models use both labelled and unlabelled data sets, our findings are significant in showing that performance of certain predictive models using mixtures of unlabelled and labelled data is comparable to those using only labelled data with similar sample sizes, thus obviating the need for large sample sizes of labelled data.

Analytical investigation of residual stress in samples with variable cross-sectional surfaces in the laser powder bed fusion process

One of the primary challenges associated with the laser powder bed fusion process is the presence of residual stress. These residual stresses arise from the significant thermal gradients induced by the rapid heating and cooling of the material, which are inherent to the laser powder bed fusion process. Such stresses can contribute to crack formation, distortion, and, ultimately, failure of the component. Consequently, effective control of residual stress is of paramount importance. In this study, an analytical approach was employed to establish the relationship between the distribution of residual stresses within samples exhibiting two variable cross-sectional geometries. Utilizing the principles of force and moment equilibrium, the distribution of residual stresses in each layer of the sample was determined. To validate the analytical method, experimental measurements of residual stresses in both the surface layer and the interlayer region of Inconel 625 samples were conducted. Additionally, the impact of hatch distance on the residual stress within the experimental samples was examined. Residual stress measurements were performed using a non-destructive X-ray diffraction technique. The results from the experimental analysis indicate that an increase in hatch distance correlates with a reduction in residual stress. Furthermore, a comparison between the residual stress at the center of the surface layer and that at the interface of the variable cross-sectional sample reveals that the residual stress at the interface is approximately halved.

Optimum design of an efficient variables sampling system for validating process yield with Six-Sigma quality requirement and creation of a cloud-computing too

Optimum design of an efficient variables sampling system for validating process yield with Six-Sigma quality requirement and creation of a cloud-computing too

An Effective Approach for Space-Borne ISAR Receiving Echoes With Variable Sampling Start Based on IMM Method

An Effective Approach for Space-Borne ISAR Receiving Echoes With Variable Sampling Start Based on IMM Method

Getting back on track after treatment of cancer: A qualitative interview study of cancer survivors' experiences.

An increasing number of people resumes life after cancer treatment. Although the (long-term) side-effects of cancer and its treatment can be significant, less is known about the impact on cancer survivors' participation in daily life. The aim of this study was to explore the common experiences of cancer survivors in resuming life after treatment. A semi-structured interview study was conducted among 22 cancer survivors having a favorable prognosis after treatment. Purposive sampling was used to select a variable sample in terms of diagnoses (e.g. breast cancer, colorectal cancer, melanoma), age (18-77 years), and time after diagnosis (0-7 years). Interviews were audio-taped, transcribed verbatim and analyzed in a thorough thematic analysis. Four main themes were identified. First, cancer survivors reported an emotional fallout episode to often follow treatment, which was characterized by a loss of direction and control due to discontinuation of medical care, decreased support from healthcare professionals and their social network, and an emotional set back. Second, survivors experienced challenges with getting back on track such as the impact of impaired physical and cognitive functions, and challenges and uncertainty related to work and finances. Third, in coping with changes and regaining trust they tried to find a balance between dealing with patient stigma and fear of recurrence on the one hand, and maintaining a positive mindset on the other hand. Fourth, the disease often led to re-evaluating values in life, health and work, which included realizing and accepting change and having a more conscious outlook on life. In resuming life after treatment, cancer survivors experience several challenges and changes in values in different life domains that extend beyond the specific diagnosis. To improve supportive care, healthcare professionals should focus on the (changed) individual needs and values of survivors in the domains considered relevant to them.

DIFFERENCES IN STRENGTH AND SPEED TESTS AMONG STUDENTS FROM 5TH TO 8TH GRADE

The aim of the research is to determine the differences in certain strength and speed tests among students from the 5th to 8th grade. The sample consisted of four age groups of students (n=60) from the "Vuk Karadžić" primary school in Sočanica. The sample of variables for strength testing included: Standing long jump (MSUD); Vertical jump (MVJ); Throwing a 1 kg medicine ball from a supine position (MBML); Push-ups to failure (MSKL); Sit-ups to failure (MPT); Squats to failure (MCUC); and Dominant hand grip strength (MDIN). The sample of variables for speed testing included: Hand tapping (MTR); Foot tapping (MTN); 20 m sprint from a standing start (MT20); 30 m sprint from a standing start (MT30); and 50 m sprint from a standing start (MT50). All statistical analyses were conducted using IBM SPSS Statistics software (Version 25). The data obtained from the tests were processed using descriptive analysis, normality tests, and one-way analysis of variance (ANOVA). To further determine the differences between the analyzed age categories, Bonferroni post hoc analysis was applied. No statistically significant difference was found in the test for throwing a 1 kg medicine ball from a supine position (MBML, p > 0.05), while statistically significant differences were found in all other strength tests (MSUD, MVJ, MSKL, MPT, MCUC, and MDIN, p < 0.05). Statistically significant differences were also found in all speed tests (MTR, MTN, MT20, MT30, and MT50, p < 0.05) among students from the 5th to 8th grade.

Deep learning-based metabolomics data study of prostate cancer

As a heterogeneous disease, prostate cancer (PCa) exhibits diverse clinical and biological features, which pose significant challenges for early diagnosis and treatment. Metabolomics offers promising new approaches for early diagnosis, treatment, and prognosis of PCa. However, metabolomics data are characterized by high dimensionality, noise, variability, and small sample sizes, presenting substantial challenges for classification. Despite the wide range of applications of deep learning methods, the use of deep learning in metabolomics research has not been extensively explored. In this study, we propose a hybrid model, TransConvNet, which combines transformer and convolutional neural networks for the classification of prostate cancer metabolomics data. We introduce a 1D convolution layer for the inputs to the dot-product attention mechanism, enabling the interaction of both local and global information. Additionally, a gating mechanism is incorporated to dynamically adjust the attention weights. The features extracted by multi-head attention are further refined through 1D convolution, and a residual network is introduced to alleviate the gradient vanishing problem in the convolutional layers. We conducted comparative experiments with seven other machine learning algorithms. Through five-fold cross-validation, TransConvNet achieved an accuracy of 81.03% and an AUC of 0.89, significantly outperforming the other algorithms. Additionally, we validated TransConvNet's generalization ability through experiments on the lung cancer dataset, with the results demonstrating its robustness and adaptability to different metabolomics datasets. We also proposed the MI-RF (Mutual Information-based random forest) model, which effectively identified key biomarkers associated with prostate cancer by leveraging comprehensive feature weight coefficients. In contrast, traditional methods identified only a limited number of biomarkers. In summary, these results highlight the potential of TransConvNet and MI-RF in both classification tasks and biomarker discovery, providing valuable insights for the clinical application of prostate cancer diagnosis.

A Martingale-Free Introduction to Conditional Gaussian Nonlinear Systems.

The conditional Gaussian nonlinear system (CGNS) is a broad class of nonlinear stochastic dynamical systems. Given the trajectories for a subset of state variables, the remaining follow a Gaussian distribution. Despite the conditionally linear structure, the CGNS exhibits strong nonlinearity, thus capturing many non-Gaussian characteristics observed in nature through its joint and marginal distributions. Desirably, it enjoys closed analytic formulae for the time evolution of its conditional Gaussian statistics, which facilitate the study of data assimilation and other related topics. In this paper, we develop a martingale-free approach to improve the understanding of CGNSs. This methodology provides a tractable approach to proving the time evolution of the conditional statistics by deriving results through time discretization schemes, with the continuous-time regime obtained via a formal limiting process as the discretization time-step vanishes. This discretized approach further allows for developing analytic formulae for optimal posterior sampling of unobserved state variables with correlated noise. These tools are particularly valuable for studying extreme events and intermittency and apply to high-dimensional systems. Moreover, the approach improves the understanding of different sampling methods in characterizing uncertainty. The effectiveness of the framework is demonstrated through a physics-constrained, triad-interaction climate model with cubic nonlinearity and state-dependent cross-interacting noise.

Lane Detection Based on ECBAM_ASPP Model.

With the growing prominence of autonomous driving, the demand for accurate and efficient lane detection has increased significantly. Beyond ensuring accuracy, achieving high detection speed is crucial to maintaining real-time performance, stability, and safety. To address this challenge, this study proposes the ECBAM_ASPP model, which integrates the Efficient Convolutional Block Attention Module (ECBAM) with the Atrous Spatial Pyramid Pooling (ASPP) module. Building on traditional attention mechanisms, the ECBAM module employs dynamic convolution kernels to eliminate dimensionality reduction, enhancing the efficiency of feature channel learning and local interactions while preserving computational efficiency. The ECBAM_ASPP model incorporates the ECBAM attention mechanism into the feature extraction network, effectively directing the network to focus on salient features while suppressing irrelevant ones. Additionally, through variable sampling of the input, the model achieves multi-scale feature extraction, enabling it to capture richer lane-related feature information. Experimental results on the TuSimple and CULane datasets demonstrate that the ECBAM_ASPP model significantly improves real-time performance while maintaining high detection accuracy. Compared with baseline methods, the proposed model delivers superior overall performance, showcasing greater robustness and practicality.

Assessing the economic-statistical performance of variable acceptance sampling plans based on loss function

Assessing the economic-statistical performance of variable acceptance sampling plans based on loss function

Designing host-associated microbiomes using the consumer/resource model.

Generative models are extremely popular in modern biology. They have been used to model the variation of protein sequences, entire genomes, and RNA sequencing profiles. Importantly, generative models have been used to extrapolate and interpolate to unobserved regimes of data to design biological systems with desired properties. For example, there has been a boom in machine-learning models aiding in the design of proteins with user-specified structures or functions. Host-associated microbiomes play important roles in animal health and disease, as well as the productivity and environmental footprint of livestock species. However, there are no generative models of host-associated microbiomes. One chief reason is that off-the-shelf machine-learning models are data hungry, and microbiome studies usually deal with large variability and small sample sizes. Moreover, microbiome compositions are heavily context dependent, with characteristics of the host and the abiotic environment leading to distinct patterns in host-microbiome associations. Consequently, off-the-shelf generative modeling has not been successfully applied to microbiomes.To address these challenges, we develop a generative model for host-associated microbiomes derived from the consumer/resource (C/R) framework. This derivation allows us to fit the model to readily available cross-sectional microbiome profile data. Using data from three animal hosts, we show that this mechanistic generative model has several salient features: the model identifies a latent space that represents variables that determine the growth and, therefore, relative abundances of microbial species. Probabilistic modeling of variation in this latent space allows us to generate realistic in silico microbial communities. The model can assign probabilities to microbiomes, thereby allowing us to discriminate between dissimilar ecosystems. Importantly, the model predictively captures host-associated microbiomes and the corresponding hosts' phenotypes, enabling the design of microbial communities associated with user-specified host characteristics.

Theoretical and Experimental Investigation of Thermal Conductivity of Unsaturated Soils Amended with a Sustainable Biochar

This study investigates the thermal conductivity of unsaturated kaolin soil amended with biochar to promote sustainable geotechnical engineering. Biochar from agricultural waste offers the dual benefits of carbon sequestration and sustainable waste management. Experimental measurements were conducted for kaolin soil with 0% (control) and 10% biochar under varying moisture contents. Peach pit biochar increased thermal conductivity by 2–3% at 30–40% saturation and 40–50% at higher saturation as compared to the bare soil. Reed biochar decreased thermal conductivity by 1–2% at lower saturation but increased it by 55–60% at higher saturation. Applewood biochar increased thermal conductivity by 35–50% at moderate saturation, decreased beyond 50% water content, and had minimal variation at lower saturation. Further, the existing empirical models (such as Kersten and the Johansen model, Wiener’s model, and Mickley’s model) for predicting the thermal conductivity of materials were validated using the measured results of biochar-amended soils. Adding 10% biochar reduces thermal conductivity by 34.8%, and the Haigh model (2012) fits best with high accuracy and lower RMSE values than models such as Kersten and Johansen, which appears to be less reliable in case of biochar-amended soils. With an addition of biochar, the R2 values of the models decreased from a range of 0.8 to 0.9 to a range of 0.4–0.6, indicating the need for better model adaptation. Wiener bounds accurately predicted thermal conductivity at low saturation levels but varied greatly at higher ones. The most variable sample was peach pit biochar, highlighting the need to refine predictive models for material-specific differences. These findings provide a foundation for developing improved predictive models and integrating biochar into sustainable geotechnical and geothermal systems.

Enhancing Diagnostic Precision and Clinical Outcomes With FNAC in Parotid Gland Masses.

Parotid gland masses encompassing both neoplastic and non-neoplastic conditions present diagnostic challenges. Fine-needle aspiration cytology (FNAC) is commonly used to differentiate between benign and malignant lesions but has limitations such as variable sensitivity and potential sampling errors. This study assesses FNAC's accuracy for parotid gland lesions, evaluates the Milan Salivary Gland Cytopathology Reporting System (MSRSGC), and compares FNAC results with histopathological findings. This retrospective study at our medical center analyzed data from 2321 patients who underwent parotid gland FNAC followed by surgical resection over 20 years. Patients were included if they had both cytological analysis and surgery between January 2004 and July 2024. Surgical procedures varied based on mass characteristics and FNAC results, and all samples were assessed using MSRSGC. Statistical analysis compared FNAC results with histopathology findings, calculating sensitivity, specificity, and accuracy. This study included 352 patients who underwent both FNAC and surgical resection. FNAC had an overall accuracy of 93.9%, with a sensitivity of 78.3% and specificity of 91.5%. The risk of malignancy was 30% for Milan Category I (nondiagnostic), 6.3% for Category II (non-neoplastic), 0% for Category III (Atypia of Undetermined Significance), 2.3% for Category IVa (benign neoplasm), 5.8% for Category IVb (neoplasm of uncertain malignant potential), 58.8% for Category V (suspicious for malignancy), and 95% for Category VI (malignant). The false negative rate was 2.4%, while the false positive rate was 3.4%. The malignancy risk increased with age, and malignant masses were larger and more likely to cause facial paralysis. FNAC is a key diagnostic tool for parotid gland masses, offering high specificity and accuracy despite some limitations. Patients with young ages and inappropriate histories should be assessed more carefully. MSRSGC is a useful system to show appropriate risk of malignancy.

Calibration Weights Estimation in Stratified Random Sampling for Multiple Skewed Variables

Calibration Weights Estimation in Stratified Random Sampling for Multiple Skewed Variables

Impact of random variations in the sampling period on the stability of embedded control systems

Abstract Control of embedded systems is a key task in many industrial and technological applications, and their performance and stability are critically dependent on the ability to effectively implement control algorithms within limited computational and time resources. One of the main challenges in this area is the need to process data with a random variable that comes to processing randomly and with variable periodicity. This variability in the sampling period can cause a decrease in the quality of the control, which threatens the stability of the entire system. The article examines the limits to which this variability can be tolerated without losing the stability of the controlled system. The study includes an analysis of systems dynamics with a variable sampling period and determining the necessary period of system discretization. It is also essential to consider the impact of the random component of the sampling period on the system’s transient response. The proposal is based on determining the permissible limits of the period change based on analysis and simulation. The result of this research provides recommendations for the design and implementation of control systems that can maintain high-quality regulation even with unpredictable changes in sampling periodicity. The present article also opens space for further research in the field of control of systems with variable periodicity, especially regarding their practical implementation in real embedded devices.

A 'how-to' guide for estimating animal diel activity using hierarchical models.

Animal diel activity patterns can aid understanding of (a) how species behaviourally adapt to anthropogenic and natural disturbances, (b) mechanisms of species co-existence through temporal partitioning, and (c) community or ecosystem effects of diel activity shifts. Activity patterns often vary spatially, a feature ignored by the kernel density estimators (KDEs) currently used for estimating diel activity. Ignoring this source of heterogeneity may lead to biased estimates of uncertainty and misleading conclusions regarding the drivers of diel activity. Thus, there is a need for more flexible statistical approaches for estimating activity patterns and testing hypotheses regarding their biotic and abiotic drivers. We illustrate how trigonometric terms and cyclic cubic splines combined with hierarchical models can provide a valuable alternative to KDEs. Like KDEs, these models accommodate circular data, but they can also account for site-to-site and other sources of variability, correlation amongst repeated measures, and variable sampling effort. They can also more readily quantify and test hypotheses related to the effects of covariates on activity patterns. Through empirical case studies, we illustrate how hierarchical models can quantify changes in activity levels due to seasonality and in response to biotic and abiotic factors (e.g. anthropogenic stressors and co-occurrence). We also describe frequentist and Bayesian approaches for quantifying site-specific (conditional) and population-averaged (marginal) activity patterns. We provide guidelines and tutorials with detailed step-by-step instructions for fitting and interpreting hierarchical models applied to time-stamped data, such as those recorded by camera traps and audio recorders. We conclude that this approach offers a viable, flexible, and effective alternative to KDEs when modelling animal activity patterns.

Physical and occupational therapy service delivery models for populations identified as hard-to-reach: A scoping review.

The delivery of rehabilitation services for hard-to-reach populations (e.g., refugees) is highly complex. There is a need for evidence-based approaches to deliver physiotherapy (PT) or occupational therapy (OT) services to this underserved group. The purpose of this scoping review was to identify PT and OT service delivery models that have been implemented, for populations typically identified as hard-to-reach and their associated health outcomes. Articles were eligible if they described PT and/or OT services for hard-to-reach populations. There were no restrictions on study design. Six electronic databases (AMED, CINAHL, MEDLINE, EMBASE, Healthstar, and PsycINFO) were searched from January 2000 to June 2023. Articles were screened in duplicate by two independent reviewers, and conflicts were resolved by consensus. Twenty-one articles with variable sample sizes (min, max n = 3 to 237) were included and detailed PT and/or OT services for immigrants/migrants, refugees, hard-to-reach veterans, people experiencing homelessness, lower incomes, trauma/torture, and those living in rehabilitation-deficient areas. Common rehabilitation needs (e.g., clinician to client connectivity), barriers (e.g., high transportation costs) and facilitators (e.g., encouragement) were identified among the various populations, mainly due to intersecting identities such as those who are both traumatized and refugees. Unique factors pertaining to the PT and OT services were also identified in some groups, including access to child and family services for people experiencing homelessness. Despite common and individual needs, barriers, and facilitators in hard-to-reach groups in the literature, there is a need for studies with larger sample sizes, rigorous methodology and a conscious effort to publish the results of interventions to generate stronger recommendations for practice.