Management Decisions Research Articles

Navigating the Waves of Critical Care Echocardiography: Unveiling its Role, Advantages, and Pitfalls in the Cardiac Intensive Care Unit.

Critical Care Echocardiography (CCE) is now established as an important tool in the intensive care unit (ICU). This paper aims to examine the expanding role of cardiovascular ultrasound in the ICU, focusing on its applications, benefits, and challenges, while highlighting recent advancements shaping the future of critical care echocardiography. Non-invasive echocardiographic measurement of hemodynamic parameters including stroke volume, cardiac output, left ventricular filling pressures, and pulmonary pressures have been well-validated against invasive measurements. Myocardial perfusion can also be evaluated using ultrasound enhancing agent techniques to further risk-stratify patients with chest pain. Echocardiography enables clinicians to visualize cardiac anatomy and physiology directly at the bedside, providing immediate feedback in rapidly changing clinical situations. Assessment of stroke volume, cardiac output, and left ventricular filling pressures can be readily measured at the bedside and correspond with clinical outcomes including mortality. Measurement of central venous pressure and pulmonary pressures may guide clinical decisions in fluid management and mechanical ventilation strategies. Lastly, myocardial perfusion imaging can supplement the 2D echocardiographic evaluation to further risk-stratify patients presenting with chest pain.

Predicting work ability impairment in post COVID-19 patients: a machine learning model based on clinical parameters.

The Post COVID-19 condition (PCC) is a complex disease affecting health and everyday functioning. This is well reflected by a patient's inability to work (ITW). In this study, we aimed to investigate factors associated with ITW (1) and to design a machine learning-based model for predicting ITW (2) twelve months after baseline. We selected patients from the post COVID care study (PCC-study) with data on their ability to work. To identify factors associated with ITW, we compared PCC patients with and without ITW. For constructing a predictive model, we selected nine clinical parameters: hospitalization during the acute SARS-CoV-2 infection, WHO severity of acute infection, presence of somatic comorbidities, presence of psychiatric comorbidities, age, height, weight, Karnofsky index, and symptoms. The model was trained to predict ITW twelve months after baseline using TensorFlow Decision Forests. Its performance was investigated using cross-validation and an independent testing dataset. In total, 259 PCC patients were included in this analysis. We observed that ITW was associated with dyslipidemia, worse patient reported outcomes (FSS, WHOQOL-BREF, PHQ-9), a higher rate of preexisting psychiatric conditions, and a more extensive medical work-up. The predictive model exhibited a mean AUC of 0.83 (95% CI: 0.78; 0.88) in the 10-fold cross-validation. In the testing dataset, the AUC was 0.76 (95% CI: 0.58; 0.93). In conclusion, we identified several factors associated with ITW. The predictive model performed very well. It could guide management decisions and help setting mid- to long-term treatment goals by aiding the identification of patients at risk of extended ITW.

Locally Specific Genome-Wide Signatures of Adaptation to Environmental Variation at High Resolution in an Alpine Plant.

Microevolutionary processes shape adaptive responses to heterogeneous environments, where these effects vary both among and within species. However, it remains largely unknown to which degree signatures of adaptation to environmental drivers can be detected based on the choice of spatial scale and genomic marker. We studied signatures of local adaptation across two levels of spatial extents, investigating complementary types of genomic variants-single-nucleotide polymorphisms (SNPs) and polymorphic transposable elements (TEs)-in populations of the alpine model plant species Arabis alpina . We coupled environmental factors, derived from remote sensed digital elevation models (DEMs) at very high resolution (0.5 m), with whole-genome sequencing data of 304 individuals across four populations. By comparing putatively adaptive loci detected between each local population versus a regional assessment including all populations simultaneously, we demonstrate that responses of A. alpina to similar amounts of abiotic variation are largely governed by local evolutionary processes. Furthermore, we find minimally overlapping signatures of local adaptation between SNPs and polymorphic TEs. Notably, functional annotations of candidate genes for adaptation revealed several symbiosis-related genes associated with the abiotic factors studied, which could represent selective pressures from biotic agents. Our results highlight the importance of considering different spatial extents and types of genomic polymorphisms when searching for signatures of adaptation to environmental variation. Such insights provide key information on microevolutionary processes and could guide management decisions to mitigate negative impacts of climate change on alpine plant populations.

Increasing Cattle Herd Size or Density in C3 Grass–Legume Meadows Reduces Dietary Quality

Animal density is a grazing management decision implemented by managers that may reduce animal diet quality. Over three years, we collected herd-level cattle samples with varying animal numbers and densities across C3 grass–legume meadows near Powell, WY, USA. Paddocks ranged in size from 3 to 72 ha, cattle groups ranged from 80 to 370 animals, animal units (AUs; defined as heifers = 0.8, cow–calf pairs = 1.3, and bulls = 1.6) ranged from 52 to 248.6, animal density ranged from 1.5 to 30.3 animals/ha, and AU density ranged from 0.9 to 19.8 AUs/ha. Near-infrared reflectance spectroscopy of fecal samples (f.NIRS) was used to estimate crude protein (CP), digestible organic matter (DOM), and the DOM:CP ratio. Structural equation modeling (SEM) that mediated time (i.e., Day of Year) rendered all animal number or density variables as significant predictors for CP and DOM (negative estimates) and for DOM:CP (positive estimate), indicating that congregating animals in larger groups and/or greater densities, even in meadows with high-quality forage species, may reduce post-ingestive diet quality estimates by −1.2% and −2.0 for CP and DOM for every +100 animals or for every +10 animals per hectare. Livestock managers could potentially override this negative effect by adjusting when animals are congregated at higher densities.

Customer Relationship Management System with Decision Support

This paper studied customer relationship management system with decision support. At present, the fitness studio management system has accumulated a large amount of customer data, but the studio still has problems in customer management such as inaccurate customer target positioning and imperfect customer relationship management functions. It is still unable to conduct intelligent analysis, resulting in a decline in the overall management ability of customer groups. As such, customer turnover is high, and customer loyalty is reduced. To solve such problems, this research constructed a customer relationship management system with decision support, which can be used to identify customers with high studio and high platform loyalty, and with repeated purchase of GT fitness studio. With the purpose of helping the studio achieve accurate customer relationship management and business decision management, this study built an intelligent customer cluster analysis system through K-means clustering algorithm, which can automatically cluster customers into four (4) categories, indicate the characteristics of consumer behavior under each category, and screen out high-quality fitness customers with large consumption amount, high consumption frequency, high platform evaluation, high loyalty, and heavy consumption. At the same time, it also screens out general customers. Moreover, the intelligent group display of the clustered customers is visualized in the form of reports, and the visual display of each group is also carried out, so that decision makers can conveniently implement accurate group management of different groups of customers.

Multi-environment field trials for wheat yield, stability and breeding progress in Germany

Multi-environmental trials (MET) with temporal and spatial variance are crucial for understanding genotype-environment-management (GxExM) interactions in crops. Here, we present a MET dataset for winter wheat in Germany. The dataset encompasses MET spanning six years (2015–2020), six locations and nine crop management scenarios (consisting of combinations for three treatments, unbalanced in each location and year) comparing 228 cultivars released between 1963 and 2016, amounting to a total of 526,751 data points covering 24 traits. Beside grain yield, ten agronomic traits, four baking quality traits, plant height, heading date, maturity date and six fungal disease infection indices are included. Additionally, we provide management records, including fertilizer use, plant protection measures, irrigation, and weather data. We demonstrate how this dataset can address four agronomic questions related to GxExM interactions. Further potential applications of the dataset include empirical analyses, genomic and enviromic analyses for breeding targets, or development of decision-supporting models for agricultural management and policy decisions.

Assessing the Role of Data in Operational Decisions and Risk Planning for Water Permit Holders

ABSTRACTWater data are evolving rapidly, driven by the expansion of diverse data platforms and the implementation of new policy frameworks. Despite advancements, the utilization of water data by water users remains uncertain. We surveyed 173 water permit holders across New York State to understand what water data are collected and for what purpose across geography, sector, and withdrawal volume. This study reveals that water data primarily serve regulatory purposes and play little role in permittees' operational, sustainability, or risk management decisions. To unlock the full potential of water data for users, we recommend educational initiatives such as training programs for data‐driven assessments, alongside technological advancements like developing web data portals that support risk and impact assessments. These recommendations aim to foster collaboration for navigating the complexities of stressors and achieving a more informed and adaptable approach to water usage.

Analysis of Trends in the Global Energy Market from the Standpoint of Ensuring Economic Security of the Russian Federation

The energy sector has traditionally been one of the key components of the economic security of the Russian Federation. The importance of the energy sector for the country’s economic security is evidenced by the tasks for its provision, defined in the Strategy for Economic Security of the Russian Federation for the period up to 2030. At the same time, the growth of variability of the parameters of the external environment, determined by the continuing complexity of the geopolitical situation in the world, requires continuous updating of the main trends in the development of the world energy market based on an analysis of the factors determining them in the interests of developing a timely and adequate response to the ongoing changes in the strategy for the development of domestic energy, which predetermined the choice of the research topic, which has undoubted relevance. The purpose of the presented studies is to analyze the factors determining the main trends in the development of the world energy market in the interests of ensuring the economic security of the Russian Federation and the effective development of the domestic energy sector by developing rational strategic decisions based on current and forecast assessments. The scientific novelty of the obtained results lies in identifying a set of factors that determine the main trends in the development of the global energy market, demonstrating that the restraining factors in the development of innovative (alternative) energy sources are the overestimation of their capabilities (in terms of quality and price), as well as the overestimation of the prospects for implementing the green energy policy in combination with measures to stimulate its development, on the one hand, and the underestimation of the impact on the availability of traditional energy sources as a result of global warming, on the other hand. The practical signifi cance of the obtained results lies in expanding the possibilities of analyzing the infl uence of various factors on the main trends in the development of the global energy market on the problems of ensuring the economic security of the Russian Federation, allowing, taking into account the presented forecast estimates, to develop rational management decisions within the framework of adjustments to the technologies for implementing the Energy Strategy of the Russian Federation.

Assessment of Stability of the Banking System with Dl-Model Rf and the Hurwitz Matrix in the Conditions of Turbulent Economy

The article considers theoretical issues of assessing the sustainability of the banking system using the DL-model "Random Forest" and the "Hurwitz" matrix in a turbulent economy. Noting the main aspects that determine the relevance of the study, two points should be noted: firstly, that in modern conditions artificial intelligence systems are very often used in scientific research, and secondly, new approaches that allow filling the existing gaps in theoretical issues regarding the aspects of assessing the financial stability of the banking system in a turbulent economy remain in demand. The scientific novelty lies in the fact that the study puts forward and proves the hypothesis that with the help of the artifi cial intelligence system DL-model "Random Forest" it is possible to obtain an accurate forecast of the net profi t of the banking system, which can be used to assess the sustainability of the banking system according to the Hurwitz criterion. The practical significance lies in the fact that the results obtained in the course of the study can be recommended for implementation in practice to provide support for management decisions in developing a development strategy for the Russian banking system. The accuracy of the obtained DL model forecast is characterized by the mean error of the forecast (MAE). The best decision tree is used in the generated DL model. The Random Forest DL model was generated using optimal hyperparameter settings.

Invading and range-expanding pink salmon inform management actions for marine species on the move

Abstract Species redistributions, whether resulting from invasions or range expansions, pose similar challenges for conservation and management. Redistribution impacts on host ecosystems and species combine with those from climate change, which are already acute at northern latitudes. Using pink salmon Oncorhynchus gorbuscha, which are native to the Pacific Ocean, we employ knowledge exchange to inform decision-making in non-native marine areas: they are expanding their range to the Arctic Ocean and are invasive in the Atlantic Ocean. The predicted future marine distribution of pink salmon focuses effort on where and when pink salmon are present and informs on potential interactions with native species. Management actions taken in the Atlantic Ocean to reduce invasive pink salmon are resource-intensive, but removed salmon could be a food resource. Addressing identified gaps regarding the invasion potential of pink salmon, interactions among pink salmon and other species, and current mitigation efforts would support forward-thinking management decisions given predictions of continued environmental change. We also highlight steps that can be taken immediately to coordinate actions and better inform responses. Managed for production in the Pacific and as an invasion in the Atlantic, pink salmon provide a tangible approach to informed decision-making through collaboration for marine species on the move.

Экономическое развитие сельских агломераций: анализ факторов, способствующих экономическому росту в сельских регионах

In the period from 1993 to the present day, agriculture in Russia was characterized by unstable and even depressive development, which was accompanied by a lack of rural development, which occurred against the background of ongoing negative trends in both the social and economic spheres. These trends manifested themselves in a decrease in the number of agricultural enterprises, which led to a decrease in the quality and standard of living of the rural population, deterioration of the social and transport infrastructure, optimization of municipalities in terms of their enlargement. The highlighted consequences as a result of the influence of negative factors require drastic measures to prevent further collapse of rural areas, which in turn required the activation of management decisions regarding the formation of rural agglomerations and the identification of factors ensuring their sustainable economic development. Objective: The complexity and versatility of the topic under study is reflected in the goal set – to analyze the factors determining the economic growth of rural agglomerations. Methods: in achieving the goal, scientific methods were used: analytical, methods of theoretical and empirical knowledge; economic and statistical methods; methods of analysis and synthesis. Results: having studied the scientific views of modern authors on the identification of factors and analyzed their impact on the level of development of agglomerations, the article reveals the key ones and assesses the impact of these factors on the level of economic growth of rural agglomerations. Conclusion: the implementation of a positive synergistic effect of the impact of factors will allow achieving economic growth of agglomerations in rural areas.

Forecast modeling of the dynamics of main resources and construction of a simulation system for managing the activities of the regional energy system of the Samara region

Regional energy systems are complex dynamic systems that operate in conditions of constant changes in the external environment. In this regard, the task of developing complex management decisions and conducting a systemic analysis of the efficiency of its activities arises. To solve this problem, it is necessary to improve the mathematical models of the dynamics of capital, labor and fuel resources, since they have the greatest impact on the energy output of the energy system. The paper analyzes existing methods of mathematical description of the dynamics of basic resources and their shortcomings. The paper considers covariance-stationary models of time series in the form of difference equations with a deterministic polynomial trend. The paper presents the results of mathematical modeling of the dynamics of capital, labor and fuel resources based on statistical data on the activities of the energy system of the Samara Region, published in the annual reports of regional ministries and energy companies, and conducts a statistical analysis of them. Based on a comparative analysis of mathematical models, the most effective models of the dynamics of capital, labor and fuel resources with the best predictive qualities were selected. Also, in the work, a single-loop simulation model of the energy system management system was constructed by forming investments for the renewal of capital resources and the structure of the decision support system (DSS) was developed, allowing the formation of mathematically sound management decisions.

Comparison of Single and Ensemble Regression Model Workflows for Estimating Basal Area by Tree Size Class in Pine Forests of Southeastern U.S

Quantifying basal area in terms of diameter classes is important for informing forest management decisions. It is commonly derived from stand diameter distributions using field measurements, LiDAR, and a distribution function. This study compares alternative methods for directly estimating basal area in three tree diameter classes that are relevant to timber operations and wildlife habitat planning in southern United States pine forests. Specifically, linear modeling, ensemble linear modeling (ELM) and ensemble general additive modeling (EGAM) were compared. The results showed that the EGAM method provided the highest r-squared values and the lowest RMSE, and the ELM method provided good interpretability and 30 times faster processing than the EGAM method. Both ensemble methods produced a spatially explicit standard error estimate output without additional steps, unlike the single linear model. In general, the estimation results of this study were comparable or improved over prior studies’ estimates of basal area by tree diameter class.

Public Consultations As A Participatory Component Of The Project Management Cycle In The Activities Of Local Government Authorities

The article provides a scientific justification for the need to use public consultations as a participatory component in the implementation of the project management cycle in the activities of local government authorities. The main advantages of applying the project approach in the process of decision-making by local government authorities are identified, namely: systematization of the process (forming a structured basis for planning, analysis, and evaluation of results; efficient and timely execution of tasks; optimization and rational use of financial, economic, technological, and human resources), transparency of implementation (increasing citizens' trust in local government officials, conducting objective monitoring, analysis, and evaluation of their activities; providing the public with a clear understanding of goal achievement, resource use, and results), and compliance with regulatory acts (ensuring compliance with normative requirements; ensuring lawful activities of implementers; successful implementation of legislative initiatives). The article systematizes and generalizes the description of the characteristics of the stages (phases) of the project management life cycle in the activities of local government authorities, considering the participatory component of public consultations, specifically at the initiation stage — defining the problem, formulating goals and tasks, creating a stakeholder group from among the residents of territorial communities; at the planning stage — forecasting the impact of internal and external factors considering public opinion, particularly from various local government entities; at the implementation stage — using modern team management methods and tools for digital participation; at the monitoring, control, and evaluation stage (of intermediate and final results) — collecting and processing information through public consultations (providing feedback with the population, organizations, enterprises, stakeholders) to determine the level of satisfaction with the results of project implementation; at the completion stage — identifying mistake and shortcomings based on public opinion surveys. It is proven that the use of public consultations at all stages of the project management cycle in the activities of local government authorities contributes to the timely correction of plans and the adaptation of community and territorial development strategies according to changes in the external and internal environment, thereby increasing the effectiveness of management decisions.

Assessment of the efficiency of using financial resources at JSC Yakutsk Poultry Farm

The current state of industrial enterprises largely depends on internal and external conditions, tasks and management methods, and the state of financial resources. In the conditions of uncertainty of the economic environment, rapidly changing pricing policy, and, as a consequence, the need to diversify, enterprises are required to rationalize management finances. Being the main link in the financial system, commercial organizations participate in the formation of primary incomes of the state, strengthen macroeconomic parameters, promote employment of the population, which also has a favorable effect on the investment climate in the country. Financial management of commercial organizations is implemented through working capital management, financial diagnostics and control, etc., which should ensure the stability of enterprises in various industries and areas. The main task that contributes to the effective operation of organizations is the identification and use of reserves to increase the volume of their own financial resources (profit, formation of depreciation charges). The use of various tools that contribute to the rationalization of expenses, costs, optimization of cash flow management allows you to identify reserves for profit growth. This article examines the role of financial resources in creating a solid financial base for enterprises, which is consistent with the relevance of the chosen research topic. The purpose of the study is to validate management decisions based on the analysis of financial resources using the example of JSC Yakutsk Poultry Farm. The methodological basis of the study is based on generally accepted methods, including comparative, trend, deductive, the method of calculating profitability indicators, financial coefficients. As well as the taxonomic method and the sum of places method, which are rarely used in research practice. The information basis of the study is scientific papers, statistical data, periodicals, electronic resources, financial statements and other regulatory documents of JSC Yakutsk Poultry Farm

Long‐Term Mobility of a Harvested, Rocky‐Reef Gastropod

ABSTRACTStocks of Turbo militaris (Turbinidae) are under increasing harvesting pressure, but management is currently hampered by data deficiency. Management decisions for rocky‐reef gastropod fisheries should consider long‐term species mobility, yet this is often poorly understood. Therefore, mark‐recapture was used to evaluate annual displacement and upshore‐to‐downshore movement of T. militaris in Eastern Australia. Tags were glued onto 676 snails, their positions georeferenced and recapture surveys were conducted after 6 and 12 months. Overall, 25% of tagged snails were recaptured. Snails moved an average of 20–21 m year−1 at subtropical sites and 34–44 m year−1 at temperate sites. Movement was non‐directional, limited in upshore‐to‐downshore mixing and unrelated to animal size. T. militaris is neither sedentary nor site‐attached, and small and large snails move similarly. Our findings suggest a limited capacity for adults to repopulate other tidal zones, thus recreational harvesting could impact intertidal snails.

Z-score evaluation and inferences for management of aortopathy patients.

Echocardiographic Z-score models play a crucial role in defining cardiac pathology in paediatric patients. There are multiple models that practitioners utilize in the United States without guiding principles to standardize their use. Discrepant interpretations can occur depending on the model chosen, even if standardized Z-score cutoffs are applied. In this study, a survey was developed to assess pediatric cardiologists' use of Z-score systems when evaluating and treating patients with isolated bicuspid aortic valve. The majority of respondents reported using Z-score cutoffs to evaluate the degree of aortic root and ascending aorta dilation. For the aortic root, mild, moderate, and severe dilation averaged at 2.13 (SD = 0.32), 3.59 (SD = 0.49), and 5.11 (SD = 0.84), respectively. Similar cutoffs were reported for determining ascending aorta dilation. A large proportion of respondents primarily used the Boston system (36%, 18/50) or Boston and Pediatric Heart Network systems together (36%, 18/50). There were also differences in management decisions, such as implementing competitive sports restrictions, based on Z-scores and the 2015 Task Force 7 Bethesda exercise guidelines. These survey results demonstrate variability that exists among paediatric cardiologists in their use of Z-scores for describing aortic root and ascending aorta dilation in patients with isolated bicuspid aortic valve and suggests the need for implementation of national guidelines for Z-score usage.

Examining Deep Learning Pixel-Based Classification Algorithms for Mapping Weed Canopy Cover in Wheat Production Using Drone Data

Deep learning models offer valuable insights by leveraging large datasets, enabling precise and strategic decision-making essential for modern agriculture. Despite their potential, limited research has focused on the performance of pixel-based deep learning algorithms for detecting and mapping weed canopy cover. This study aims to evaluate the effectiveness of three neural network architectures—U-Net, DeepLabV3 (DLV3), and pyramid scene parsing network (PSPNet)—in mapping weed canopy cover in winter wheat. Drone data collected at the jointing and booting growth stages of winter wheat were used for the analysis. A supervised deep learning pixel classification methodology was adopted, and the models were tested on broadleaved weed species, winter wheat, and other weed species. The results show that PSPNet outperformed both U-Net and DLV3 in classification performance, with PSPNet achieving the highest overall mapping accuracy of 80%, followed by U-Net at 75% and DLV3 at 56.5%. These findings highlight the potential of pixel-based deep learning algorithms to enhance weed canopy mapping, enabling farmers to make more informed, site-specific weed management decisions, ultimately improving production and promoting sustainable agricultural practices.

Science and technical priorities for private sector action to address biodiversity loss.

Target 15 of the Kunming-Montreal Global Biodiversity Framework recognizes the importance of the private sector monitoring, assessing and disclosing biodiversity-related risks, dependencies and impacts. Many businesses and financial institutions are progressing with science-based assessments, targets and disclosures and integrating into strategy, risk management and capital allocation decisions. Developments will continue in response to investor expectations, emerging corporate sustainability reporting regulations in Europe, China and elsewhere and evolving global sustainability reporting standards. Voluntary action is also being encouraged by the disclosure recommendations of the Taskforce on Nature-related Financial Disclosures and the target-setting methods of the Science Based Targets Network. Based on experience supporting the private sector in practice, we identify four critical science and technical advances needed to enable business action at scale and to redirect finance globally to halt and reverse biodiversity loss. First, consensus on indicators and metrics for measuring changes in the state of nature and provision of ecosystem services. Second, access to global, regularly updated, location-specific and consistent nature data. Third, standardized and consistent accounting systems that structure data, support risk management and create accountability at corporate, ecosystem and national levels. Fourth, integrated risk assessment approaches to help corporates, financial institutions, central banks and supervisors to assess nature-related risks.This article is part of the discussion meeting issue 'Bending the curve towards nature recovery: building on Georgina Mace's legacy for a biodiverse future'.

Standardising lipid testing and reporting in the United Kingdom; a joint statement by HEART UK and The Association for Laboratory Medicine.

Atherosclerotic cardiovascular disease remains a major cause of premature death in the UK. Lipid testing is a key tool used to assess cardiovascular risk and guide clinical management decisions. There are currently no national guidelines to provide evidence-based recommendations on lipid testing and reporting for UK laboratories and clinicians. Here we present consensus guidance, following a review of published evidence by a multidisciplinary group of UK experts across a range of laboratory and clinical services. Recommendations include: the composition of a standard lipid profile; indications for, and composition of, an enhanced lipid profile including apolipoprotein B and lipoprotein (a); use of the Sampson-NIH calculation for LDL-c estimation; and guidance on when to flag abnormal results. This consensus guidance on lipid testing and reporting in the UK has been endorsed by HEART UK and The Association for Laboratory Medicine.