Reasonable Tool Research Articles

P-971. Chogoria Rounds: A Novel Clinical Reasoning Tool for Residents

Abstract Background Teaching clinical reasoning requires a clinician being able to demonstrate their thought process when approaching a case, and a large volume of case exposure during residency to gain a basis of understanding. Training programs have observed improved application of clinical reasoning skills by internal medicine (IM) residents when a case-based reasoning curriculum is introduced. We present a novel educational tool for IM residents involving a series of case presentations (Chogoria Rounds, CR) from a physician partner in Kenya to general IM and Infectious Disease attendings, along with IM residents at different stages of training. Methods Over an 18-month period, 30 cases were presented virtually, de novo, allowing attendings to discuss their clinical assessment of these cases in real time with the residents in attendance. A survey with Likert scale response (1 strongly disagree, 5 strongly agree) was distributed to residents to assess CR as an effective clinical reasoning teaching tool Results Of the 30 presented cases, 11 were related to tuberculosis (pulmonary, miliary, meningitis), 10 to other infections (severe malaria, leishmaniasis) and other interesting cases such as African night shade ingestion. Thirteen residents were surveyed with 10 respondents. All 10 strongly agreed or agreed that CR was a useful clinical reasoning teaching tool (50% strongly agreed, 50% agreed), and 90% felt it impacted patient care. 80% reported CR drove them to read more about a particular disease process that was discussed. 50% of residents did not feel CR impacted their practice regarding high value care, but 90% felt it generated meaningful discussion surrounding resource utilization. All residents expressed interest in participating in a global medicine experience in the future, and 90% became more interested in global medicine. Conclusion CR showed to be an effective clinical teaching tool for IM residents by giving them insight into clinical reasoning from expert attendings as well as rapid fire case conferences to provide increased volume of exposure.This virtual case conference model can easily be replicated for the benefit of residents and other learners at the US-based institution while at the same time benefiting those at the partnering international institution. Disclosures All Authors: No reported disclosures

Bluebell: An Alliance of Relational Lifting and Independence for Probabilistic Reasoning

We present Bluebell, a program logic for reasoning about probabilistic programs where unary and relational styles of reasoning come together to create new reasoning tools. Unary-style reasoning is very expressive and is powered by foundational mechanisms to reason about probabilistic behavior like independence and conditioning . The relational style of reasoning, on the other hand, naturally shines when the properties of interest compare the behavior of similar programs (e.g. when proving differential privacy) managing to avoid having to characterize the output distributions of the individual programs. So far, the two styles of reasoning have largely remained separate in the many program logics designed for the deductive verification of probabilistic programs. In Bluebell, we unify these styles of reasoning through the introduction of a new modality called “joint conditioning” that can encode and illuminate the rich interaction between conditional independence and relational liftings ; the two powerhouses from the two styles of reasoning.

Relaxed Memory Concurrency Re-executed

Defining a formal model for concurrency in programming languages that addresses conflicting requirements from programmers, compilers, and architectures has been a long-standing research question. It is widely believed that traditional axiomatic per-execution models that reason about individual executions do not suffice to address these conflicting requirements. Consequently, several multi-execution models were proposed that reason about multiple executions together. Although multi-execution models were major breakthroughs in satisfying several desired properties, these models are complicated, challenging to adapt to existing language specifications given in per-execution style, and they are typically not friendly to automated reasoning tools. In response, we propose a re-execution-based memory model (XMM). Debunking the beliefs around per-execution and multi-execution models, XMM is (almost) a per-execution model. XMM reasons about individual executions, but unlike traditional per-execution models, it relates executions by a re-execution principle. As such, the memory consistency axioms and the out-of-order re-execution mechanics are orthogonal in XMM, allowing to use it as a semantic framework parameterized by a given axiomatic memory model. We instantiated the XMM framework for the RC20 language model, and proved that the resulting model XC20 provides DRF guarantees and allows standard hardware mappings and compiler optimizations. Noteworthy, XC20 is the first model of its kind that also supports thread sequentialization optimization. Moreover, XC20 is also amenable to automated reasoning. To demonstrate this, we developed a sound model checker XMC and evaluated it on several concurrency benchmarks.

Prognostic Prediction Model for Endovascular Treatment of Acute Anterior Circulation Ischemic Stroke Based on RAPID Software

To construct a universally applicable nomogram using computed tomography perfusion (CTP) parameters based on Rapid processing of PerfusIon and Diffusion (RAPID) software for evaluating the prognosis of acute anterior circulation large vessel occlusion (LVO) treated by endovascular therapy. A total of 130 patients were divided into two groups: a group with a good prognosis [Rankin Rating Scale (mRS) score ≤ 2 points] and a group with a poor prognosis (mRS score >2 points). The least absolute shrinkage and selection operator (LASSO) logistic regression method was used to screen the predictive factors, and a nomogram chart prediction model was established. There were 52 patients in the good prognosis group and 78 patients in the poor prognosis group. Logistic regression analysis showed that age, baseline National Institute of Health Stroke Scale score, and volumes of cerebral blood flow (VCBF)<30% were independent influencing factors for the prognosis of patients [OR=1.065, 95% CI: 1.007-1.126; OR=1.097, 95% CI: 1.008-1.193; OR=1.107, 95% CI: 1.024-1.197; P all<0.05]. Our nomogram model showed good discrimination and calibration. The ROC curve analysis showed that the area under the curve of the prediction model was 0.809 (95% CI: 0.733-0.885, P<0.001), and the calibration curve was close to the ideal curve. Our nomogram can be used as a simple and reasonable tool to predict the prognosis of acute anterior circulation LVO treated by endovascular therapy. The CTP parameter VCBF<30% obtained by RAPID software was an important predictive factor in this model.

Integrating symbolic reasoning into neural generative models for design generation

Design generation requires tight integration of neural and symbolic reasoning, as good design must meet explicit user needs and honor implicit rules for aesthetics, utility, and convenience. Current automated design tools driven by neural networks produce appealing designs, but cannot satisfy user specifications and utility requirements. Symbolic reasoning tools, such as constraint programming, cannot perceive low-level visual information in images or capture subtle aspects such as aesthetics. We introduce the Spatial Reasoning Integrated Generator (SPRING) for design generation. SPRING embeds a neural and symbolic integrated spatial reasoning module inside the deep generative network. The spatial reasoning module samples the set of locations of objects to be generated from a backtrack-free distribution. This distribution modifies the implicit preference distribution, which is learned by a recurrent neural network to capture utility and aesthetics. The sampling from the backtrack-free distribution is accomplished by a symbolic reasoning approach, SampleSearch, which zeros out the probability of sampling spatial locations violating explicit user specifications. Embedding symbolic reasoning into neural generation guarantees that the output of SPRING satisfies user requirements. Furthermore, SPRING offers interpretability, allowing users to visualize and diagnose the generation process through the bounding boxes. SPRING is also adept at managing novel user specifications not encountered during its training, thanks to its proficiency in zero-shot constraint transfer. Quantitative evaluations and a human study reveal that SPRING outperforms baseline generative models, excelling in delivering high design quality and better meeting user specifications.

Paths for the development of financial intermediation in industry 5.0: Opportunities for developing economies (On the example of former USSR republics)

Currently, there is a unique situation in the global economy, industrial eras coexist together, there is interaction and transformation of financial systems simultaneously within the framework of Industry 4.0 and Industry 5.0. New, digital resources are entering the economy, intellectual capital is becoming virtual, artificial intelligence is increasingly finding its application in the structure of financial support. Financial intermediation in developing countries is also subject to global trends, the active development of new instruments for developing economies is especially important. The aim of the study is to identify effective ways to develop financial intermediation in Industry 5.0 for the economies of developing countries. Based on the results of the study on the development of financial institutions mediation revealed a problem related to the lack of reasonable tools that could be used to improving the efficiency of the financial intermediaries market, proposed the main directions of such a process: mobilization of savings, distribution financial assets, payment system, risk management and control over market agents involved in financial operations.

Evaluating a novel clinical reasoning tool for physical therapy students: A pilot study.

This paper explores the use of the Anticipate-Plan-Pause Clinical Reasoning (APP CR) Tool to enhance clinical reasoning (CR) in Doctor of Physical Therapy (DPT) students. Current pedagogical methods often inadequately integrate CR abilities in a manner that is both engaging and effective. Drawing on several theoretical frameworks, the APP CR Tool was integrated into an interactive module for DPT students, aiming to improve CR abilities while reducing cognitive load and stress during high-stakes simulations. A randomised controlled trial was conducted with nine DPT students, divided into a control group (n = 5) and an intervention group (n = 4). Both groups used an interactive module with case-based scenarios, but only the intervention group used the APP CR Tool. Improvements in CR abilities and reduced stress and cognitive load were observed in both groups. However, statistical analysis revealed no significant differences between the groups. Despite this, a trend toward greater improvements in CR abilities was noted in the intervention group (p = 0.084). Further research is needed to validate these findings and refine the tool. The APP CR Tool shows potential for enhancing CR in DPT students, although its current application may require refinement to achieve statistically significant results. Educators should consider iterative improvements to the tool and its curriculum integration to better support CR development.

1 + 1 > 2? The synergistic effect of carbon emissions reduction policies: empirical evidence from China

ABSTRACT The low-carbon city pilot policy (LCCPP) and the carbon emissions trading pilot policy (CETPP) are important measures for China to reduce its carbon emissions. However, the existing literature typically focuses narrowly on the effect of a single policy tool, leaving it unclear whether the LCCPP and CETPP are in conflict or complementary. Therefore, we investigated the synergistic effect of the LCCPP and CETPP in China using the difference-in-differences (DID) method. Compared with the 5.4% reduction of the LCCPP and the 5.1% reduction of the CETPP, the dual pilot policy (DPP) can improve the carbon emissions reduction effect to 11.9%. However, the effect of the DPP takes time to become apparent owing to a policy lag. The DPP primarily achieves its effect by promoting technological progress and optimizing energy allocation. In addition, the carbon emissions reduction effect of the DPP is more evident in large, high-ranking, and resource-based cities. Our work can help policymakers fully consider the coordination between future climate policies and design reasonable mixed policy tools.

Prospective secondary mathematics teachers’ use of inquiry-based teaching principles as conceptual tools when modifying mathematical tasks

The objective of this study was to investigate how prospective secondary mathematics teachers apply inquiry-based teaching principles to modify tasks that support students' engagement in specific mathematical practices. The research employed the theory of goal-oriented decision-making to describe and explain the use of inquiry-based teaching principles as a conceptual tool by these prospective teachers. The study involved two cohorts, comprising 43 prospective teachers (20 in one cohort and 23 in the other) enrolled in a Secondary Education Teaching program. Data were collected from written reports documenting the implementation of two professional tasks, where participants modified textbook assignments to promote exploratory teaching. An inductive analysis was conducted in two phases. The findings revealed that prospective teachers consistently applied inquiry-based teaching principles when they set specific mathematical practices as student learning objectives, such as analyzing particular cases, identifying patterns and relationships, and formulating conjectures and generalizations. However, when these mathematical practices were not established as learning objectives, teachers struggled to apply inquiry-based teaching principles consistently during task modification. These results suggest that inquiry-based teaching principles are an effective conceptual tool for prospective teachers' instructional reasoning. Nonetheless, for consistent application, it is crucial to establish a coherent network of logical connections between the conceptual tool and the intended learning objectives.

Evaluation of the Smart Logistics Based on the SLDI Model: Evidence from China

Smart logistics (SL) reflects the digital transformation of the logistics industry, which is key for economic development. Most evaluations are based on the application of technology in SL, and few studies have evaluated SL from a comprehensive perspective. The paper builds the SL development index (SLDI) model from five dimensions based on the driving force, pressure, state, impact, and response (DPSIR) model and identifies the indicator weight by the entropy weight technique. The paper employs the ETDK method, a combined quantitative approach that incorporates entropy weight (E), the technique for order preference by similarity to an ideal solution (TOPSIS) (T), the Dagum Gini coefficient (D), and Kernel density estimation (K), to calculate the closeness degree, analyze spatial-temporal differentiation, and explain the distribution characteristics using data from China spanning 2013 to 2021. The findings show that (1) The SL evaluation is multidimensional and cannot be evaluated only based on technical indicators. A comprehensive evaluation indicator system is necessary. (2) A combined quantitative approach can measure SL development from multiple perspectives and get a clearer picture of the characteristics and regional differences of SL. (3) Influenced by economic development, infrastructure, regional clusters, location, talent, etc., the overall SL development is improving yearly, but SL development in different regions is unbalanced and has different distribution characteristics. The SLDI model developed in this paper will provide a more scientific and reasonable tool for comprehensively evaluating SL. The findings are helpful in proposing suggestions and optimization approaches for subsequent research on SL evaluation and development.

Entangled Mathematics as a Tool of Reasoning in the Mid-Twentieth-Century UK Electricity Industry

In the mid-twentieth century, the identity of those who oversaw the UK electricity grid tentatively and slowly began to shift from those who joined the electricity industry directly from secondary school to a university-educated elite with a higher level of technical education. At the same time, electricity infrastructure became increasingly centralised, leading to the creation of a national grid in 1938, meaning that control of electricity became concentrated in the hands of an ever-smaller group and increasing the stakes in debates over strategy. By using the case studies of power engineer Paul Schiller and mathematician Maurice Davies, this paper traces how the boundaries of what mathematics could or could not be used for were renegotiated between factions within the UK electricity industry. Schiller unsuccessfully attempted to use mathematics, including the authority of academic mathematicians, to reinforce controversial arguments about the strategic direction of the industry in the 1940s. Such arguments failed to resonate with a class of, as Schiller put it, “practical men” that still held considerable influence. Davies on the other hand was employed by the industry in the 1950s to improve efficiency, and was able to implement lasting measures in this effort by using the weather to forecast electricity demand, and successfully defended the usefulness of his work in the 1970s against colleagues who felt that forecasting could be done better with mathematical models without weather inputs. The contrast between these two stories shows how the influence of mathematical thinking was contingent upon existing power dynamics, and reinforce how the history of mathematics should be socially embedded.

APPROACH TO CREATION OF GEOLOGICAL AND RESERVOIR SIMULATION MODEL AND METHODS FOR HISTORY MATCHING OF RESERVOIR ENERGY STATE

Under conditions of complex geological structure and substantial share of hard-to-recover oil reserves, reservoir simulation is a key tool in reservoir management. Direct problems solved by reservoir simulation include forecasting of the production performance, control and management of reservoir development, and localization of residual oil reserves. Inverse problem solution is useful for updating the knowledge of geological structure, reservoir quality, and historical production data by means of history matching. The quality of history matching is fundamental to ensure reliable future forecasts and localization of residual oil zones. The main approaches to appropriate history matching rely on analysis and adjustment of well logging and geological parameters, as well as checking and correction of errors, inadequate historical data, application of physics-based and reasonable approaches and tools. The prerequisite for adequate history matching and predictive ability of the resultant reservoir simulation model is the compliance of simulated and actual energy state of the reservoir. In this paper, the initial stage of reservoir simulation is performed, the approach to history matching of reservoir energy state aimed at improving the quality of further predictions is described as exemplified by one of the production areas of Russian oil field. The main causes of poor history match of formation and bottomhole pressures are revealed. These causes are grouped as follows: — geological causes/reservoir quality, which include permeability field distribution, reservoir connectivity, and determination of rock and fluid compressibility;— technical causes (during reservoir simulation), that is model limitations in case of larger areas, the need to use sector models, consideration of buffer region;— technological causes, including inadequate historical data or lack of data, detection of behind-the-casing flows and production casing leaks in wellbore, etc. For each group of factors that have a substantial impact on reservoir pressure, recommendations and methods of energy state history matching on geological and reservoir simulation models are provided, examples are given.

Assessing the diagnostic accuracy of postnatal clinical scoring methods and foot length measurement for estimating gestational age and birthweight of newborns in low- and middle-income countries: a systematic review and meta-analysis

BackgroundThis study aimed to update systematic reviews and meta-analyses on the diagnostic accuracy of postnatal clinical scoring (PCS) methods and foot length (FL) measurement for assessing gestational age (GA) and...

Buckling prediction based on joint opening and safe temperature estimation in jointed plain concrete pavements

Concrete pavement buckling is a significant distress that disrupts traffic flow and results in costly repairs, issues that are increasingly exacerbated by global warming. Addressing these issues, this study develops a systematic framework for assessing jointed plain concrete pavement (JPCP) by integrating temperature and moisture gradients, pavement structures, and material properties into mechanical-based models for predicting buckling potential. Specifically, a two-stage analysis is developed to analyze buckling development based on the slab deformation and the slab-base interface, including joint closure assessment and upheaval buckling prediction. Through the established model, parametric studies are conducted to quantitatively identify key factors influencing buckling. Moreover, the developed models are applied to pavement sections in various climate regions and validated through comparative analysis with field data from the Long-Term Pavement Performance (LTPP) database to confirm model accuracy. Additionally, a case study using pavement buckling data from Wisconsin between 2014 and 2020 demonstrates the model's practical application. As the results, it is found that temperature increase, the percentage of incompressible materials, and the coefficient of thermal expansion are critical for joint opening changes, while the coefficient of thermal expansion, elastic modulus, and slab thickness are crucial to post-buckling performance. Regression equations are provided to estimate safe temperature increases as a recommended buckling temperature limit. Furthermore, the predicted joint opening shows reasonable alignment with joint gauge measurement in the field recordings from the LTPP database. Finally, the predicted buckling events are reasonably compared with field recordings for a Wisconsin pavement section from 2014 to 2020. In summary, by considering pavement structural parameters, material properties, and weather conditions, this comprehensive model provides a reasonable tool for predicting and mitigating JPCP buckling in different climate conditions.

Effect of structure tool on nano-cutting surface integrity of single crystal γ-TiAl alloy via atomic simulation

To investigate the impact of microgroove geometry at the tool's flank face on the surface integrity of the workpiece, molecular dynamics simulation is utilized to establish a model of cutting single crystal γ-TiAl alloy using various structured nanodiamond tools. The effect of groove shape on the surface integrity of single crystal γ-TiAl alloys was investigated by analyzing mechanical properties, workpiece surface quality, and subsurface damage during the cutting process. The results show that non-structured tool cuts γ-TiAl alloy with the highest cutting force, and the V-groove tool cuts with the lowest friction coefficient, which is favorable for the removal of single crystal γ-TiAl alloy. Statistical atomic phase transitions during cutting confirmed that structured tools reduce subsurface damage, especially V-grooved tool reduce the generation of defective atoms. The circular arc grooved tool effectively reduces the subsurface temperature of the single crystal γ-TiAl alloy workpiece by reducing the friction at the tool chip interface due to lower normal cutting force. In addition, the circular arc grooved tool cutting has stronger atomic flow and surface recovery ability compared with other tools, and smaller surface roughness can be obtained. Therefore, selecting a reasonable structured tool can effectively improve the machinability of single crystal γ-TiAl alloy.

A Continuity in Logical Development: Domain-General Disjunctive Inference by Toddlers.

Children grow up surrounded by opportunities to learn (the language of their community, the movements of their body, other people's preferences and mental lives, games, social norms, etc.). Here, we find that toddlers (N = 36; age range 2.3-3.2 years) rely on a logical reasoning strategy, Disjunctive Inference (i.e., A OR B, A is ruled out, THEREFORE, B), across a variety of situations, all before they have any formal education or extensive experience with words for expressing logical meanings. In learning new words, learning new facts about a person, and finding the winner of a race, toddlers systematically consider and reject competitors before deciding who must be the winner. This suggests that toddlers may have a general-purpose logical reasoning tool that they can use in any situation.

The need for research targeting the link between occupational carcinogens and hepatopancreatobiliary cancers in Africa: A systematic review

IntroductionHepatopancreatobiliary (HPB) cancers encompassing malignancies of the liver, pancreas, gall bladder, and bile ducts pose a significant health burden in Africa. While the association of certain occupational carcinogens in cancer is well established globally, their potential role in HPB cancers remains understudied, especially in an African context. AimThis systematic review delves into the association between occupational carcinogens and HPB cancer in Africa. It examines the current state of research on occupational carcinogens and HPB cancers in Africa, identifying key challenges and knowledge gaps. MethodsThis systematic review examined publications (published between 01 January 2012 and 31 May 2023) that highlight occupational carcinogens and HBP cancers in Africa. The search was conducted on electronic databases namely PubMed, Web of Science, and Africa Wide Information. ResultDue to the lack of information on the association between occupational carcinogens and HPB cancers in Africa, as a result of the paucity of published studies, only four articles were included in this study. Hepatocellular carcinoma (HCC) was the predominant cancer associated with the occupational carcinogen, aflatoxin. Agricultural workers, especially those involved in the production and processing of maize and peanuts, appear to be the most exposed to aflatoxin. ConclusionDespite the sample size limitations due to the paucity of research studies on occupational carcinogens and HPB cancers in Africa, this study provides a reasonable tool for subsequent epidemiological studies. There is a need for more research on the association of occupational carcinogens and HPB cancers in Africa, especially with the growing industrialization.

Uncertainty of microseismic sources identification and probabilistic location in underground excavation

Microseismic (MS) source location is an integral component of MS technology and essential to understanding the rock failure mechanism and avoiding potential geological hazards in underground rock excavation. However, accurate location remains challenging owing to the complex geological conditions and unknown rock failure mechanisms. In this study, a novel location framework was developed to locate the MS source positions and their uncertainties based on probabilistic programming. Probabilistic programming was utilized to determine the coordinates of the MS source and its variation using the Markov Chain Monte Carlo (MCMC) method based on the waveform equation. A classical benchmark problem was utilized to verify and illustrate the developed framework. The developed framework can not only locate the position of the MS source but also determine its variation due to the uncertainty during the monitoring and excavation. The located MS source is in agreement with the actual positions. The results show that the developed framework is a scientific, accurate, reasonable, and promising tool for the location of MS sources. Then, the developed framework was applied to locate the position of the blasting in a practical mine. This further proved that the developed framework could locate the MS source, providing an excellent uncertainty analysis tool for underground rock excavation.

Ceftobiprole quantification in human serum by HPLC-UV to implement routine TDM in clinical practice

Background and aimsCeftobiprole is a recent 5th generation parenteral cephalosporin with antibacterial activity against a large range Gram+ and Gram− bacteria. Therapeutic drug monitoring (TDM) is an essential tool for maintaining plasma concentrations of antibiotics above the MIC by the end of the dosing interval, thus preventing the resistant strain diffusion. TDM is already recommended for other cephalosporins, and it is a reasonable tool contributing to the safety and efficacy of these drugs. During the treatment of patients in real-life, a number of pharmacokinetic (PK) changes not normally seen in healthy volunteers can occur which can impair the pharmacokinetic/pharmacodynamic target attainment.We aimed to develop simple and rapid HPLC-UV method for determination of ceftobiprole in human serum to implement TDM in clinical practice and support PKs and pharmacokinetic/pharmacodynamic (PK/PD) studies. Materials and methodsSamples preparation of calibration standards, QC, and anonymous patients serum samples was performed by protein precipitation by adding 0.01 ml of sulphosalicylic acid at 30 % to 0.1 ml of each sample. Then samples were vortexed and the centrifuged at 12,000 rpm for 10 min at 4 °C. Fifty microlitres of clear supernatant were diluted 1:1 with mobile phase and transferred into HPLC autosampler held at 8 °C. Chromatographic separation was carried out in a gradient mode at 35 °C on an ultra-Biphenyl column using a Thermo Scientific chromatographic system with a Diode array. Data management was performed with Chromeleon 7.4 software. ResultsThe HPLC-UV method proved to be linear over wide concentration ranges (0.5–50.0 mg/L) and was accurate and reproducible in the absence of matrix effects, allowing for robust, specific, and rapid quantification of ceftobiprole from a low amount of serum (0.1 mL). The mean steady state Ctrough and Cend values measured in the anonymous patients’ samples were 6.26 ± 3.81 mg/L and 22.56 ± 15.69 mg/L, respectively. ConclusionsWe report a broadened simple and fast HPLC with UV detection method for quantification of ceftobiprole in human serum to implement ceftobiprole TDM as clinical routine, and support future (PK/PD) studies in special patients’ population.

Visualization or Mathematization? The London School of Economics and “Diagrammatic Economics” in the 1930s

Although the subject of numerous contributions in the history of economics, the use of theoretical diagrams by economists is still quite misunderstood, as this practice is often characterized as a basic form of mathematization, soon replaced by more rigorous techniques of demonstration or exhibition. The present article attempts to challenge this view, focusing on a period of time, the 1930s, during which “diagrammatic economics” and other techniques coexisted, and on a specific community, that of students and young researchers from the London School of Economics with a keen interest in using diagrams as tools for economic reasoning. The cosmopolitan nature of research and education at the LSE, with its insistence on reading a vast amount of literature and attending seminars by a revolving cast of local and foreign lecturers, offered a favorable environment for a form of visual analysis that helped clarify concepts and resolve definitional issues, at a time when interest in various economic traditions had not yet given way to attempts to systematize and unify economic knowledge and its standards of production.