Set Of Values Research Articles

The Value of Emergency Care Data Set (ECDS) Presentation Codes for Predicting Mortality and Inpatient Admission.

Early identification of patients at higher risk of death and hospital admission is an important problem in Emergency Departments (ED). Most triage scales were developed before current electronic healthcare records were developed.The implementation of a national Emergency Care Data Set (ECDS) allows for the standardised recording of presenting complaints and the use of Electronic Patient Records (EPR) offers the potential for automated triage. The mortality risk and need for hospital admission associated with the different presenting complaints in a standardised national data set has not been previously reported. This study aimed to quantify the risks of death and hospitalisation from presenting complaints. This would be valuable in developing automated triage tools and decision support software. We conducted an observational retrospective cohort study on patients who visited a single ED in 2021. The presenting complaints related to subsequent attendances were excluded. This patient list was then manually matched with a routinely collected list of deaths. All deaths that occurred within 30 days of attendance were included. Data was collected from 84,999 patients, of which 1,159 people died within 30 days of attendance.The mortality rate was the highest in cardiac arrest [32(78.1%)], cardiac arrest due to trauma [2(50%)] and respiratory arrest [3(50%)]. Drowsy [17(12%)], hypothermia [3(13%)] and cyanosis [1(10%)] were also high-risk categories. Chest pain [34(0.6%)] was not a high-risk presenting complaint. The initial presenting complaint in ECDS may be usefulto identify people at higher and lower risk of death. This information is useful for building automated triage models.

Research on the Identification Method of Maize Seed Origin Using NIR Spectroscopy and GAF-VGGNet

The origin of seeds is a crucial environmental factor that significantly impacts crop production. Accurate identification of seed origin holds immense importance for ensuring traceability in the seed industry. Currently, traditional methods used for identifying the origin of maize seeds involve mineral element analysis and isotope fingerprinting, which are laborious, destructive, time-consuming, and suffer from various limitations. In this experiment, near-infrared spectroscopy was employed to collect 1360 maize seeds belonging to 12 different varieties from 8 distinct origins. Spectral information within the range of 11,550–3950 cm−1 was analyzed while eliminating multiple interferences through first-order derivative combined with standard normal transform (SNV). The processed one-dimensional spectral data were then transformed into three-dimensional spectral maps using Gram’s Angle Field (GAF) to be used as input values along with the VGG-19 network model. Additionally, a convolution layer with a step size of 1 × 1 and the padding value set at 1 was added, while pooling layers had a step size of 2 × 2. A batch size of 48 and learning rate set at 10−8 were utilized while incorporating the Dropout mechanism to prevent model overfitting. This resulted in the construction of the GAF-VGG network model which successfully decoded the output into accurate place-of-origin labels for maize seed detection. The findings suggest that the GAF-VGG network model exhibits significantly superior performance compared to both the original data and the PCA-based origin identification model in terms of accuracy, recall, specificity, and precision (96.81%, 97.23%, 95.35%, and 95.12%, respectively). The GAF-VGGNet model effectively captures the NIR features of different origins of maize seeds without requiring feature wavelength extraction, thereby reducing training time and enhancing accuracy in identifying maize seed origin. Moreover, it simplifies near-infrared (NIR) spectral modeling complexity and presents a novel approach to maize seed origin identification and traceability analysis.

Capitalism as “the best of all possible worlds”: a marxist critique of Boaventura de Sousa Santos' postmodern political thought

The general aim of this work is to critically analyse certain conceptions and political positions of Boaventura de Sousa Santos on the problems of Western modernity and postmodernity. For the Portuguese sociologist, Western modernity is going through an unprecedented crisis of civilisation that can be identified through three fundamental dimensions: the epistemological dimension (the problem of Western scientific knowledge), the theoretical dimension (the problem of the Marxist theory of social emancipation) and the political dimension (the problems of the liberal social contract, social fascism and the disunity of left-wing forces). Postmodernity, however, represents the possibilities and opportunities for overcoming that crisis in view of the epistemological (the ecology of knowledge), theoretical (the new emancipatory political culture) and political (high-intensity democracy) renewals that are underway. The main hypothesis to be presented is that Boaventura Santos' political conceptions and positions – in parallel with his limited understanding of Marxism, the main interlocutor and target of his often mistaken criticism – correspond to the postmodern ideology, which can be characterised, according to Marxism, by epistemological scepticism, contempt for history and political defeatism. In this vein, the theoretical framework adopted here necessarily comes from Marxism, understood, according to Michael Löwy, as a social worldview, that is, an organic, articulated and structured set of values, representations, ideas and cognitive orientations about man in his relationship with nature, society and history, internally unified by a determined perspective and a certain socially conditioned point of view.

Tractable Binary Contests

Much of the work on multi-agent contests is focused on determining the equilibrium behavior of contestants. This capability is essential for the principal for choosing the optimal parameters for the contest (e.g., prize amount). As it turns out, many contests exhibit not one, but many possible equilibria, hence precluding contest design optimization and contestants’ behavior prediction. In this article, we examine a variation of the classic contest that alleviates this problem by having contestants make the decisions sequentially rather than in parallel. We study this model in the setting of a binary contest , wherein contestants only choose whether or not to participate, while their performance level is exogenously set. We show that by switching to the sequential mechanism not only does there emerge a unique equilibrium behavior, but also that the principal can design this behavior to be as good, and, at times, better, than any pure-strategy equilibrium of the parallel setting (assuming the principal’s profit is either the maximum performance or the sum of performances). We also show that in the sequential setting, the optimal prize, which is inherently a continuous parameter, can be effectively computed and reduced to a set of discrete values to be evaluated. The theoretical analysis is complemented by comprehensive experiments with people over Amazon Mechanical Turk. Here, we find that the modified mechanism offers great benefit for the principal in terms of an increased over-participation in the contest (compared to theoretical expectations). The effect on the principal average profit, however, depends on its goal in the contest—when benefiting from the maximum performance the modified mechanism results in increased average profit, while when benefiting from the sum of performances, it is preferred to stay with the original (parallel) contest.

Performance of EOM-CCSD(T)(a)*-Based Quartic Force Fields in Computing Fundamental, Anharmonic Vibrational Frequencies of Molecular Electronically Excited States with Application to the Ã1A″ State of :CCH2 (Vinylidene).

Highly accurate anharmonic vibrational frequencies of electronically excited states are not as easily computed as their ground electronic state counterparts, but recently developed approximate triple excited state methods may be changing that. One emerging excited state method is equation of motion coupled cluster theory at the singles and doubles level with perturbative triples computed via the (a)* formalism, EOMEE-CCSD(T)(a)*. One of the most employed means for the ready computation of vibrational anharmonic frequencies for ground electronic states is second-order vibrational perturbation theory (VPT2), a theory based on quartic force fields (QFFs),fourth-order Taylor series expansions of the potential portion of the internuclear Watson Hamiltonian. The combination of these two is herein benchmarked for its performance for use as a means of computing rovibrational spectra of electronically excited states. Specifically, the EOMEE-CCSD(T)(a)* approach employing a complete basis set extrapolation along with core electron inclusion and relativity (the so-called "CcCR" approach) defining the QFF produces anharmonic fundamental vibrational frequencies within 2.83%, on the average, of reported gas-phase experimentally assigned values for the test set including the states of HCF, HCCl, HSiF, HNO, and HPO. However, some states have exceptional accuracy in the fundamentals, most notably for ν2 of HCCl in which the CcCR QFF value is within 1.8 cm-1 at 927.9 cm-1 (or 0.2%) of the experiment. Additionally, this approach produces rotational constants to, on the absolute average, within 0.41% of available experimental data, showcasing notable accuracy in the computation of rovibronic spectral data. Furthermore, utilizing a hybrid approach composed of harmonic CcCR force constants along with a set of simple EOMEE-CCSD(T)(a)*/aug-cc-pVQZ QFF cubic and quartic force constants is faster than using pure CcCR and better represents those modes that suffer from numerical instability in the anharmonic portion of the QFF, implying that this so-called "CcCR + QZ" QFF approach may be the best for future applications. Finally, complete, rovibrational spectral data are provided for :CCH2, a molecule of potential astrochemical interest, in order to aid in its potential future experimental rovibronic characterization.

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology.

When machining high-speed steels (HSS) with micro-wire electrical discharge machining (micro-WEDM), high surface quality is achieved as standard. The value of the roughness parameter Ra is less than 0.2 μm. However, the problem is the performance of the electroerosion process (MRR), which is low. This problem is related to the mechanical and physical properties of the HSS in combination with the setting of the main technological parameters (MTP). The proposed solution to eliminate this problem relies on the selection of proper procedures for the determination of optimization criteria in relation to Ra and MTP, with the inclusion of properties of the machined material. The solution consisted in the identification of four significant physical (ρ, κ) and mechanical (Rm, HRC) indicators of HSS properties, on the basis of which a suitable combination of the process output parameters Ra and MRR can be determined through established mathematical regression models using simulation and optimization. In the next step, the proper values of the MTP output process parameter settings, which correspond to the optimized output parameters Ra and MRR during machining of HSS by micro-WEDM technology, were then obtained by the same approach.

The third Ukraine: A case of civic nationalism

To some extent, the current Russian-Ukrainian may be described as a conflict between two visions of nation, respectively, ethnic and civic models. Putin believes that a language defines a nation. In his understanding, since many Ukrainians are Russian speakers, they are Russians. His perception of Ukraine is anachronistic. He has failed to notice Ukraine's radical transformation since it gained independence. The current Ukrainian identity has a strong civic component. Its core is represented by a new urban middle class with a new set of values. Sustainability of this identity largely depends on longevity and results of the war.

A Systematic Overview of Fuzzy Extended Entity Relationship (Fuzzy EER) Model Using Practical Examples

Most real-world data are vague and imprecise, so traditional relational databases cannot represent, integrate, and manage them effectively. In this research, we gave an overview of the application of fuzzy logic to the conceptual modeling facet of database design, which is an enhancement of the Extended Entity Relationship (EER) model. This is done to give a clearer picture of how imprecise and vague data are represented and managed, especially in multiple-criteria decision-making database systems. To achieve this, an overview of fuzzy attributes, fuzzy values in the attributes, notation for fuzzy representations, fuzzy degree in each value of an attribute, fuzzy degree in a set of values of different attributes, fuzzy degree with its own meaning and fuzzy degree to the model was carried out with practical examples.

Traceable Attribute-Based Encryption Scheme Using BIM Collaborative Design

BIM collaborative design involves numerous participants from various specialties who create and share vast amounts of design data at different design stages to ensure the efficient transmission of design data between these specialties. It is imperative for the BIM collaborative design platform to guarantee the security of design data and effectively trace any instances of malicious leakage or tampering. Therefore, this paper proposes a traceable ciphertext-policy attribute-based encryption scheme (TCP-ABE) that formulates a dynamic data access control mechanism based on different participants and effectively tracks malicious users in the event of risks such as tampering, theft, and unauthorized access to BIM data. In this scheme, the user’s identity information is embedded into their private key as a key component, ensuring that only users who satisfy the access policy can decrypt it. The compromised private key allows for tracing of the user’s identity. Additionally, Linear Secret Sharing Scheme (LSSS) is employed as the access structure with the user’s attribute set divided into an attribute name set and an attribute value set to protect privacy by concealing the latter within the access policy. Furthermore, the scheme integrates blockchain with cloud storage as a trusted third-party storage mechanism to ensure data integrity. Finally, the TCP-ABE scheme is comprehensively evaluated by comparing its strengths and weaknesses with other algorithms. This evaluation includes a theoretical analysis of functional and computational time overhead aspects, as well as an experimental analysis of initialization time, data encryption time, and data decryption time. The scheme exhibits excellent performance across all stages and encompasses the most comprehensive functionalities, as demonstrated by the comparative analysis and experimental results.

Psychometric performance of EQ-5D-5L and SF-6Dv2 in patients with lymphoma in China.

To assess and compare the measurement properties of EQ-5D-5L and SF-6Dv2 among lymphoma patients in China. A face-to-face survey of Chinese lymphoma patients was conducted at baseline (all types) and follow-up (diffuse large B-cell). EQ-5D-5L and SF-6Dv2 health utility scores (HUSs) were calculated using the respective Chinese value sets. Ceiling effect was assessed by calculating the percentage of respondents reporting the optimal health state. Convergent validity of EQ-5D-5L and SF-6Dv2 was assessed using the Spearman rank correlation coefficient (r) with QLQ-C30 as a calibration standard. Known-groups validity of the two HUSs was evaluated by comparing their scores of patients with different conditions; and their sensitivity was further assessed in the known-groups using relative efficiency (RE). Test-retest reliability and responsiveness was tested using ICC and standardized response mean (SRM), respectively. Altogether 200 patients were enrolled at baseline and 78 were followed up. No ceiling effect was found for SF-6Dv2 compared to 24.5% for EQ-5D-5L. Correlation between the two HUSs and with QLQ-C30 score was strong (r > 0.5). Each dimension of EQ-5D-5L and SF-6Dv2 had moderate or greater correlations with similar dimensions of QLQ-C30 (r > 0.35). Both EQ-5D-5L and SF-6Dv2 could only a minority known-groups, and the latter may have better sensitivity. EQ-5D-5L had better test-retest reliability (ICC = 0.939); while both of them were responsive to patients with worsened and improved clinical status. EQ-5D-5L and SF-6Dv2 were found to have good convergent validity and responsiveness, while EQ-5D-5L had better test-retest reliability and higher ceiling effect. Not enough evidence indicates which of the two measures has better known-group validity and sensitivity.

Technology and Discipline: Examining the Use of Apps for Classroom Behavior Management

Background/Context: In thousands of classrooms throughout the United States and internationally, behavior management apps have become an integral part of schools’ discipline machineries. Such apps are designed to help teachers enforce rules, especially when it comes to rewards and punishments within school token economies. To understand how these apps are beginning to shape and reshape schooling, research is needed that sheds light on the values and beliefs underpinning their use. Purpose: The purpose of this study is to describe and analyze three schools’ discipline practices involving behavior management apps. Our two research questions focused on the paradigms underpinning schools' discipline models and on the influence of these paradigms on schools’ app uses. Our investigation is informed by the scholarship on classroom management, as well as key concepts from Foucault’s (1977) Discipline and Punish. Research Design: This qualitative, comparative case study drew on data gathered at three urban schools. In total, 34 interviews were conducted with campus administrators and teachers. Within-case portraits described paradigms and practices at each school, and cross-case analyses addressed patterns across the schools. Findings: Schools’ behavior app uses were underpinned by differing school discipline paradigms. Although all three schools saw behavior apps as fostering orderly classroom environments, schools’ practices were underpinned by differing sets of values and beliefs (e.g., accountability/social control; neoliberalism; positivity and fun). In this way, the look and feel of app practices, and, ultimately, schools’ overarching discipline systems, also varied. Conclusions: This study examines a widespread, yet understudied, educational technology. By problematizing the values and beliefs underpinning teachers’ behavior app practices, the present study invites scholars and practitioners to question how rewards, punishments, and relationships manifest in schools. In doing so, it highlights opportunities to join with students, parents, and other stakeholders in broader conversations about schooling and discipline.

Health-related quality of life among adults living with chronic non-communicable diseases in the Ho Municipality of Ghana: a health facility-based cross-sectional study

BackgroundMorbidity and mortality rates from chronic non-communicable diseases (CNCDs) are increasing globally. In Ghana, CNCDs account for 43% of all deaths. We examined the Health-Related Quality of Life (HRQoL) and associated factors among adults living with CNCDs in the Ho Municipality.MethodsThis was a health facility-based descriptive cross-sectional study among 432 adults living with cancer, diabetes, chronic kidney disease (CKD), stroke, and hypertension in the Ho Municipality of Ghana. The study adopted the EQ-5D-5L instrument and the Ugandan value set to compute respondents’ HRQoL index. Quantile regression models were used in analysing the data with STATA v17.0 at 95% Confidence Intervals, and statistical significance set at p < 0.05.Results63.7% of our respondents reported having a problem across the five dimensions of the EQ-5D-5L. The most problems were reported in the dimensions “Anxiety/Depression” (94.4%) and “Pain/Discomfort” (91.4%). Divorced/separated respondents (aOR=-0.52, 95% CI=-0.71, -0.33) and those living with comorbidities (aOR=-0.95, 95% CI=-0.15, -0.04,) were less likely to report high index for HRQoL. However, respondents diagnosed with CKD (aOR = 0.26, 95% CI = 0.10, 0.42), diabetes (aOR = 0.28, 95% CI = 0.11, 0.45), hypertension (aOR = 0.35, 95% CI = 0.19, 0.50) and stroke (aOR = 0.26, 95% CI = 0.11, 0.40) were more likely to report higher index than those diagnosed with cancer.ConclusionOur study revealed elevated proportions of reported problems in the “Anxiety/Depression” and “Pain/Discomfort” dimensions, indicating noteworthy concerns in these areas of HRQoL. The prevalent issues reported across HRQoL dimensions are cause for concern, posing potential exacerbation of health conditions. We advocate for collaborative efforts from the Ministry of Health, Ghana Health Service, and relevant stakeholders to scrutinize and implement interventions targeting social and psychological factors. These efforts should specifically address contributors to diminished health-related quality of life, particularly among less educated, divorced, and comorbid individuals.

Heat exchanger control: Using deep-Q network

To achieve stable operation of district heating systems and improve the performance of heat exchange station control systems, this paper explores the application of intelligent control algorithms for controlling district heating systems. Initially, a heat exchanger neural network model is established. Secondly, a mass regulation controller based on a deep reinforcement learning algorithm is designed to accurately track the desired outlet temperature of the secondary side set point. Finally, the performance of the controller in terms of tracking response and disturbance suppression is verified using an example of a district heating system. The experimental results demonstrate that the controller exhibits minimal overshoot, with the outlet temperature of the secondary side deviating by less than 1 % when the controller setting value changes. Additionally, during disturbance suppression response, the deviation of the outlet temperature of the secondary side remains within ±2 °C. Compared to other controllers, the designed controller exhibits superior dynamic performance across various operating points and under different disturbances. Additionally, it outperforms traditional control methods in terms of stability and disturbance rejection capabilities.

(212) The Questionable Clinical Utility of Sperm Morphology: Rate of Isolated Teratospermia in a Population of Fertile Men

Abstract Introduction Over the last several decades, sperm morphology has represented a standard component of semen analysis. However, its clinical utility is not well-defined, despite continual efforts by the WHO to address critiques of semen morphology criteria through increasingly strict guidelines. Issues with intra- and inter-laboratory technical variability, in addition to the subjective nature of the morphology assessment, have been thoroughly discussed in existing literature. Furthermore, current literature has highlighted the low predictive value of sperm morphology abnormalities in assisted fertility treatment (ART) and natural pregnancy outcomes. As of 2020, the American Society for Reproductive Medicine (ASRM) guidelines have noted that morphology alone is not highly predictive or diagnostic of infertility. However, past survey studies have demonstrated the pervasive belief among physicians of the value of sperm morphology, potentially leading to counseling patients towards more expensive ART methods. These concerns call into question the emphasis placed on sperm morphology assessment and its value in the clinical setting. Objective To better understand the impact of sperm morphology on fertility, we assessed sperm morphology in a population of known fertile men. Methods Healthy men over 18 years of age were recruited to provide one semen sample prior to vasectomy from March 2020 through November 2022. Inclusion criteria for the study was as follows: (1) patients were required to have biological children under the age of 5 years old, with no history of difficulty achieving pregnancy, and (2) had undergone no prior fertility procedures. Sperm morphology was analyzed using Papanicolaou stain with Kruger strict criteria by two technicians in an Andrology specialty laboratory per the WHO 5th edition manual. Results There were 83 men who participated in the study, 68 of whom (81.9%) had a complete sperm morphology assessment available. On average, participants were 37 years of age and had a mean BMI of 28 (22.8, 33.2). Of those with a smoking history available (n=61), 38 (62.3%) were non-smokers, 7 (11.5%) were former smokers, and 16 (26.2%) were current smokers. Patients had an average of 3 children with a mean youngest child age of 11.5 months old. There were 38 patients (55.9%) with 3% or lower normal sperm morphology, including two patients who had 0% normal morphology (Table 1). The most common morphologic abnormalities were head shape defects (n=59, 84.3%), followed by coiled tails (n=14, 20.3). Men 3% or lower normal sperm morphology were four times as likely to have both head and tail abnormalities than men with 4% or greater normal sperm morphology (n=8 vs n=2, respectively); half of these men also had lower than normal motility, whereas none of the men with 4% or greater normal sperm morphology had lower than normal motility. Conclusions More than half of fertile male patients (65.6%) had lower than normal sperm morphology in our study. The results of our study further question the clinical relevance of sperm morphology on fertility outcomes and if the current approach in assessing morphology is too strict. Disclosure No.

A control algorithm to follow the limitations of an electric vehicle motor using the Tm4 Sumo drive as an example

A control algorithm to follow the limitations of an electric vehicle motor using the Tm4 Sumo drive as an example

Identifying the system of value factors of green consumer choice

To justify the production of green products, it is necessary to anticipate the choice of consumers toward products with different environmental friendliness. Therefore, it is necessary to understand the factors that determine this choice, particularly value factors. The study is based on the idea that a consumer is stimulated to eco-consumption by a set of values, not excluding individualist values; and the influence of values is mediated by motives. The purpose of the study is to develop the theoretical foundations of constructing a three-level system of value factors of green consumption on the example of office paper consumption. As a result, the study formed a system of factors containing preferences, motives, and values of office paper consumers. According to the proposed approach, for each respondent, quantitative characteristics of the elements of the system of factors were determined through a survey, which made it possible to identify correlation relationships between the elements. A pilot study was used to test the proposed approach to constructing a value factors system. The results of the pilot survey showed a positive connection between eco-friendliness of consumer preferences – through motives – with such values as “Nature,” “Self-development,” “Country success,” and “Social power.” This finding provides additional justification for the assumption that individuals are driven to green consumption not only by socially oriented values but also by individualistic values. Identifying the value factors of green consumption will allow predicting consumer behavior and influencing it through targeted marketing offers.

The Measurement Properties of the EQ Health and Well-being and EQ Health and Well-Being Short Form in Italian Population: A Comparative Study With EQ-5D-5L

ObjectivesThe EQ Health and Well-being, EQ-HWB (25-item) and the EQ-HWB-S (9-item), are new generic measures of health and well-being. The purpose of this study was to examine the measurement properties of the EQ-HWB and EQ-HWB-S measures in relation to the EQ-5D-5L among the Italian general population. MethodsA cross-sectional survey was conducted from October 2020 to February 2021, followed by secondary analysis of collected data from Italian adults. This analysis included response pattern distributions, correlation strength, and known-group comparison. Known-group comparison was assessed using effect sizes (ES) across health conditions, caregiver status, and social care usage. The EQ-HWB-S index-based score was based on the UK pilot value set, whereas the Italian value set was used for scoring the EQ-5D-5L index. ResultsOut of the 1182 participants, 461 reported having a chronic condition, 185 identified as caregivers, and 42 were social care users. EQ-HWB items (7.5%) showed fewer ceiling effects than EQ-5D-5L items (34.7%). Strong correlations (rs > 0.5) were found between overlapping EQ-HWB and EQ-5D-5L items. EQ-HWB-S and EQ-5D-5L index scores demonstrated similar discrimination based on symptomatic chronic conditions (ES d = 0.68 vs d = 0.71), but EQ-HWB-S had slightly higher ES for social care users (ES d = 0.84 vs d = 0.74). ConclusionsInitial evidence supports the validity of EQ-HWB/EQ-HWB-S because outcome measures in the Italian population. EQ-HWB-S performed comparably to EQ-5D-5L among patients and was better in differentiating social care users. A slight decrease in discriminative properties for caregivers was observed when transitioning from EQ-HWB to EQ-HWB-S.

Using machine learning to find exact analytic solutions to analytically posed physics problems

We investigate the use of machine learning for solving analytic problems in theoretical physics. In particular, symbolic regression (SR) is making rapid progress in recent years as a tool to fit data using functions whose overall form is not known in advance. Assuming that we have a mathematical problem that is posed analytically, e.g. through equations, but allows easy numerical evaluation of the solution for any given set of input variable values, one can generate data numerically and then use SR to identify the closed-form function that describes the data, assuming that such a function exists. In addition to providing a concise way to represent the solution of the problem, such an obtained function can play a key role in providing insight and allow us to find an intuitive explanation for the studied phenomenon. We use a state-of-the-art SR package to demonstrate how an exact solution can be found and make an attempt at solving an unsolved physics problem. We use the Landau-Zener problem and a few of its generalizations as examples to motivate our approach and illustrate how the calculations become increasingly complicated with increasing problem difficulty. Our results highlight the capabilities and limitations of the presently available SR packages, and they point to possible modifications of these packages to make them better suited for the purpose of finding exact solutions as opposed to good approximations. Our results also demonstrate the potential for machine learning to tackle analytically posed problems in theoretical physics.

Experimental and ANN Analysis of Shearing Rate Effects on Coarse Sand Crushing

The present study analyzes laboratory experiments on how shearing rate affects the shear strength and crushability of natural coarse sand, employing artificial neural network (ANN) analysis. This study tested three different coarse sands obtained from the crushing of natural rocks: Black Virgin Tuff, weathered Zeolitic Tuff, and calcareous limestone. The behavior of crushed sand specimens with consistent grading, which passed through sieve #4 and were retained on sieve #8, was analyzed using a direct shear box. The specimens were subjected to varied normal loads and shearing speeds to examine their behavior at different relative densities. The test results were analyzed using ANN to investigate the significance of shearing rates on shearing strength parameters, specifically internal mobilized peak friction, the constant volume (residual) internal friction angle, and the consequence of shearing rate on the particle's breakage index. The selected normal (Gaussian) rate significantly affected both the shear strength parameters and breakage. The loading rate increased both shear strength parameters and particle breakage. Therefore, it's highly recommended to maintain secure sets of shear strength values and comprehensive test data for assessing parameters at typical strain rates, prioritizing using slower rates whenever possible. Doi: 10.28991/CEJ-2024-010-03-011 Full Text: PDF

A Predictive Algorithm for Discriminating Myeloid Malignancies and Leukemoid Reactions

BackgroundAdults presenting with a neutrophil-predominant leukocytosis (white cell count >50,000/μL) often necessitate urgent medical management. These patients are diagnosed with either acute presentations of chronic myeloid malignancies or leukemoid reactions, yet accurate models to distinguish between these entities do not exist. We used demographic and lab data to build a machine learning model capable of discriminating between these diagnoses. MethodsThe medical record at a tertiary care medical center was queried to identify adults with instances of white counts greater than 50,000/μL and >50% neutrophils from 2000 to 2021. For each patient, a full set of demographic and lab values were extracted at the time of their first presentation with a white count >50,000/μL. We generated a series of models in which the parameters most predictive of myeloid malignancies were identified, and a supervised machine learning approach was applied to the dataset. ResultsOur best model—using a support vector machine algorithm—produced a sensitivity of 96% and a specificity of 95.9% (area under the curve = 0.982) for identifying myeloid malignancies. We also identified a clinically meaningful and significant disparity in outcomes based on diagnosis—a 6-fold increase in 12-month mortality in those diagnosed with leukemoid reactions. ConclusionsThese findings need to be validated but fill an unmet need for timely and accurate diagnosis in the setting of profound, neutrophil-predominant leukocytosis and support the use of predictive models as a means to improve patient outcomes.