Decision Time Research Articles

A Method for Assessing the Level of Nonspecific Protection of the Human Body by Indicators Characterizing the Processes of Adaptation

The purpose of the research is to develop a method for assessing the level of nonspecific protection of the human body according to indicators characterizing the adaptation processes, the use of which in the decisive rules of forecasting and diagnosis of diseases ensures an improvement in the quality of decisions made.Methods. Numerous sources have a fairly detailed description of the protective mechanisms of the human body, implemented by non-specific and specific protection systems, which are closely related to such concepts as: adaptive potential, adaptive reserve, functional state and functional reserve. The paper investigates the use of indicators characterizing the adaptive potential and adaptive reserves for quantifying the nonspecific level of protection of the body from the effects of heterogeneous external factors. The methodology of synthesis of hybrid fuzzy decision rules is used as a basic mathematical apparatus.Results. The paper shows that to quantify the level of protection of the body, it is advisable to use an indicator of the activity of regulatory systems, adaptive potential, an indicator characterizing the immune-endocrine component of adaptation and an indicator characterizing the neuropsychic component of adaptation. For this system of indicators, a method has been developed to assess the level of protection based on basic indicators characterizing adaptation processes at the system-wide level, characterized by taking into account components characterizing various mechanisms for the formation of adaptive potential and adaptive reserve, taking into account the specifics of the data structure and ways of processing them for the best results in terms of quality and time of decisions. Fuzzy functions of the level of protection according to the selected system of indicators with an integral assessment of the level of protection of the body are obtained.Conclusion. The paper shows that in order to improve the quality of forecasting and diagnosis of socially significant and occupational diseases, it is advisable to use indicators of the level of protection of the body determined by adaptive mechanisms. It is established that the quality of decision–making using the proposed models increases by 10-15%, depending on the type of tasks being solved, compared with models that do not use indicators of the level of protection of the body.

P975 Monitoring Inflammatory Bowel Disease (IBD) by intestinal ultrasound decreases time to treatment change and time to remission in comparison to conventional management: Analysis of patients with IBD on multiple IBD therapies

Abstract Background Intestinal ultrasound (IUS) is a non-invasive, repeatable, and accurate disease monitoring modality that can provide real-time assessment of inflammatory bowel disease (IBD). We previously demonstrated that IUS shortens time to treatment change and time to remission in patients receiving upadacitinib and monitored by IUS compared to conventional management (ACG 2023). Here, we aim to assess the impact of IUS on management and outcomes of our pts with IBD receiving any IBD therapy. Methods We performed a retrospective analysis of patients who completed induction therapy (per individual drug label) but have active disease as measured by SCCAI >2 or HBI >4 between October 2021 and October 2023. We assessed for the time to treatment change (time from a positive clinical index to the time of a medical decision made), and time to clinical remission (time from a positive clinical index to the time of normalization (SCCAI ≤2, HBI ≤4 of the clinical index) and compared those between patients monitored by IUS and those managed with a conventional approach. Patients were matched by their provider and disease type. Active disease by faecal calprotectin (FCP) (FCP >150 mcg/g) and endoscopy (Mayo endoscopic score (MES) >0) were assessed ± a month before a positive clinical index was collected. An abnormal IUS was considered bowel wall thickness (BWT) >3 mm in the colon or terminal ileum and any hyperemia by colour Doppler signal (CDS) (modified Limberg score >0). Chi square analysis was performed, with p<0.10 significance. Results 54 patients (63 encounters) were included in this analysis (33 in the IUS cohort, 30 in the conventional management cohort) from 3 different providers. At inclusion, 9 (27%) pts in the IUS group and 10 (33.3%) in the conventional group had elevated FCP, and 5 (15.2%) pts in the IUS group and 29 (96%) had endoscopic disease. In the IUS group, the average time-to-treatment change was 2.2 (±1.2) days compared to conventional management 25.6 (±14.5), p=0.050. A total of 44 patients achieved clinical remission (22 IUS; 22 conventional management). The average time to remission in the IUS group was 128.1 days (±22.3) vs 230.6 (±38.2) days in the conventional management group, p=0.035). Drug class did not affect time to treatment change or time to remission (Table 1). The most significant reason for delay in the conventional group compared to the IUS group was awaiting endoscopy and FCP results. Conclusion Using IUS to assess disease activity in IBD is associated with earlier treatment changes and shorter time to remission compared to conventional approaches to disease monitoring, independent of therapy choice. Ongoing dissemination and incorporation of IUS in the management of IBD are warranted.

A Multilobulated Intramural Fibroid in Pregnancy: A Clinical Case Report

Leiomyoma is the most common benign tumor of the female reproductive tract. It is prevalent among women in the age group 30 to 50 years. Other than placental estrogens and progesterone, a range of endocrine and paracrine factors have their impact on fibroid blood supply, growth rate, and risk of degeneration during the gestational and puerperal periods. The current case report is about a 30-year-old primigravida with pregnancy and a large fibroid presenting with complications such as malpresentation and pressure symptoms, ending up in a cesarean section at 36 weeks delivering a live baby with a birth weight of 2.5 kgs and a large fibroid weighing 6.4 kg. During the pregnancy, there was adequate fetal development, without major maternal complications. There were four crucial issues: tailored pregnancy surveillance, the decision of time and route of delivery, postpartum hemorrhage management, the decision to perform an interval myomectomy, and the management of decreasing blood loss perioperatively.

Autonomous imaging scheduling networks of small celestial bodies flyby based on deep reinforcement learning

AbstractDuring the flyby mission of small celestial bodies in deep space, it is hard for spacecraft to take photos at proper positions only rely on ground-based scheduling, due to the long communication delay and environment uncertainties. Aimed at imaging properly, an autonomous imaging policy generated by the scheduling networks that based on deep reinforcement learning is proposed in this paper. A novel reward function with relative distance variation in consideration is designed to guide the scheduling networks to obtain higher reward. A new part is introduced to the reward function to improve the performance of the networks. The robustness and adaptability of the proposed networks are verified in simulation with different imaging missions. Compared with the results of genetic algorithm (GA), Deep Q-network (DQN) and proximal policy optimization (PPO), the reward obtained by the trained scheduling networks is higher than DQN and PPO in most imaging missions and is equivalent to that of GA but, the decision time of the proposed networks after training is about six orders of magnitude less than that of GA, with less than 1e−4 s. The simulation and analysis results indicate that the proposed scheduling networks have great potential in further onboard application.

An adaptive neuro‐fuzzy inference system optimized by genetic algorithm for brain tumour detection in magnetic resonance images

AbstractAn adaptive neuro‐fuzzy inference system is presented which is optimized by a genetic algorithm to classify normal and abnormal brain tumours. The classifier is fast and simple, named genetic algorithm‐adaptive neuro‐fuzzy inference system, and the determined learning rules minimize its error and improve its accuracy. The presented system follows five steps including preprocessing, morphological operation, feature extraction, feature selection, and classification. Morphological operators segment the abnormal regions and calculate the tumour area. The statistical features and the grey‐level co‐occurrence matrix are employed for feature extraction. Magnetic resonance images are considered and 12 statistical features are extracted, then the genetic algorithm‐based selection technique helps to select features and reduce the extracted features and improves the accuracy and decision time. So, the high dimensionality and the computational complexity of the adaptive neuro‐fuzzy inference system are reduced, and the classifier decides more efficiently. The input data are the figshare brain tumour dataset with 670 abnormal and 670 normal magnetic resonance images, and the classifier requires 10.788 s for classification. The efficient performance of the genetic algorithm‐adaptive neuro‐fuzzy inference system is confirmed by the accuracy of 99.85%, sensitivity of 99.7%, specificity of 100%, precision of 100%, and mean square error of 0.0027.

Hierarchical Pruning of Deep Ensembles with Focal Diversity

Deep neural network ensembles combine the wisdom of multiple deep neural networks to improve the generalizability and robustness over individual networks. It has gained increasing popularity to study and apply deep ensemble techniques in the deep learning community. Some mission-critical applications utilize a large number of deep neural networks to form deep ensembles to achieve desired accuracy and resilience, which introduces high time and space costs for ensemble execution. However, it still remains a critical challenge whether a small subset of the entire deep ensemble can achieve the same or better generalizability and how to effectively identify these small deep ensembles for improving the space and time efficiency of ensemble execution. This article presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks. Our hierarchical ensemble pruning approach (HQ) leverages three novel ensemble pruning techniques. First, we show that the focal ensemble diversity metrics can accurately capture the complementary capacity of the member networks of an ensemble team, which can guide ensemble pruning. Second, we design a focal ensemble diversity based hierarchical pruning approach, which will iteratively find high quality deep ensembles with low cost and high accuracy. Third, we develop a focal diversity consensus method to integrate multiple focal diversity metrics to refine ensemble pruning results, where smaller deep ensembles can be effectively identified to offer high accuracy, high robustness and high ensemble execution efficiency. Evaluated using popular benchmark datasets, we demonstrate that the proposed hierarchical ensemble pruning approach can effectively identify high quality deep ensembles with better classification generalizability while being more time and space efficient in ensemble decision making. We have released the source codes on GitHub at https://github.com/git-disl/HQ-Ensemble .

Overview of organic, vegan, and plant-based diets, family farming and healthy food consumption in Brazil: concrete attributes, instrumental values, and perceived psychological consequences of consumption

This paper draws an overview of healthy food consumption, such as organic, vegetarian, and plant-based diets, from family farming and those related to food restrictions. Also, it proposes to explain how the concrete attributes, instrumental values, and perceived psychological consequences of consumption are associated as motivations for consuming these products. The Means-End Theory was used as the base theory for this research, which addresses those tangible attributes (means) that are connected to intangible consequences and personal values (ends). In the Marketing and Consumer Behavior field, the theory was introduced and postulated that there is a hierarchically organized relationship that connects product values, psychological consequences, and personal values, providing an understanding of the personal importance to the consumer at the time of purchase decision making. Survey research was conducted with Brazilians who are of legal age. A total of 1,085 valid questionnaires were obtained (44% were non-consumers of any of the products under analysis (n=478), and 56% were consumers (n=607) – who had their behavior analyzed in the study. The results support the hypotheses, being that: (1) the ease of purchase increases the frequency of consumption of healthy products; (2) The perception of concrete attributes increases the psychological consequences of healthy consumption; (3) the perception of instrumental values increases the perceived psychological consequences of healthy consumption; (4), and finally, it is possible to infer that the sample has a healthy food consumption behavior, but it is not a frequent behavior.

Rural Trauma Team Development Course Positively Impacts its Desired Objectives.

The Rural Trauma Team Development Course (RTTDC) is designed to help rural hospitals better organize and manage trauma patients with limited resources. Although RTTDC is well-established, limited literature exists regarding improvement in the overall objectives for which the course was designed. The aim of this study was to analyze the goals of RTTDC, hypothesizing improvements in course objectives after course completion. This was a prospective, observational study from 2015 through 2021. All hospitals completing the RTTDC led by our Level 1, academic trauma hospital were included. Our institutional database was queried for individual patient data. Cohorts were delineated before and after RTTDC was provided to the rural hospital. Basic demographics were obtained. Outcomes of interest included: Emergency Department (ED) dwell time, decision time to transfer, number of total images/computed tomography scans obtained, and mortality. Chi square and non-parametric median test were used. Significance was set at P < .05. Sixteen rural hospitals were included with a total of 472 patients transferred (240 before and 232 after). Patient demographics were similar before and after RTTDC. ED dwell time was significantly reduced by 64min (P = .003) and decision to transfer time was cut by 62min (P = .004) after RTTDC. Mean total radiographic images and CT scans were significantly reduced (P < .001 and P = .002, respectively) after RTTDC. Mortality was unaffected by RTTDC completion (P = .941). The RTTDC demonstrates decreased ED dwell time, decision time to transfer, and number of radiographic images obtained prior to transfer. More rural hospitals should be offered this course.

Accounting for the evolution of sedentarism on minimum dietary energy requirements

Estimates of food-insecure populations are biased upwards, lacking adjustment for global increases in sedentary behavior in recent decades. We construct a household model to account for sedentary choices during leisure and work time decisions. The model rationalizes increasing sedentary behavior from the household by accounting for increasing returns to cognitive human capital vs physical capital, alongside increased productivity of more sedentary activities both at work and at home. The household model informs an empirical model applied to our unique international pseudo-panel data on sitting time, proxy-ing for sedentarism. We econometrically estimate a transfer function linking sedentarism to widely available covariates and make out-of-sample predictions. It is applicable to most countries. The estimated sedentary time can be used to adjust the physical activity level reflected in the minimum dietary energy requirement used to determine a cutoff for food insecurity.

Automated, real-time material detection during ultrashort pulsed laser machining using laser-induced breakdown spectroscopy, for process tuning, end-pointing, and segmentation.

The rapid, high-resolution material processing offered by ultrashort pulsed lasers enables a wide range of micro and nanomachining applications in a variety of disciplines. Complex laser processing jobs conducted on composite samples, require an awareness of the material type that is interacting with laser both for adjustment of the lasering process and for endpointing. This calls for real-time detection of the materials. Several methods such as X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and energy dispersive X-Ray spectroscopy (EDS) can be used for material characterization. However, these methods often need interruption of the machining process to transfer the sample to another instrument for inspection. Such interruption significantly increases the required time and effort for the machining task, acting as a prohibitive factor for many laser machining applications. Laser induced breakdown spectroscopy (LIBS) is a powerful technique that can be used for material characterization, by analyzing a signal that is generated upon the interaction of laser with matter, and thus, it can be considered as a strong candidate for developing an in-situ characterization method. In this work, we propose a method that uses LIBS in a feedback loop system for real time detection and decision making for adjustment of the lasering process on-the-fly. Further, use of LIBS for automated material segmentation, in the 3D image resulting from consecutive lasering and imaging steps, is showcased.

Implementing trades of the National Football League Draft on blockchain smart contracts

PurposeDemonstrate proof-of-concept for conducting NFL Draft trades on a blockchain network using smart contracts.Design/methodology/approachUsing Ethereum smart contracts, the authors model several types of draft trades between teams. An example scenario is used to demonstrate contract interaction and draft results.FindingsThe authors show the feasibility of conducting draft-day trades using smart contracts. The entire negotiation process, including side deals, can be conducted digitally.Research limitations/implicationsFurther work is required to incorporate the full-scale depth required to integrate the draft trading process into a decentralized user platform and experience.Practical implicationsCutting time for the trade negotiation process buys decision time for team decision-makers. Gains are also made with accuracy and cost.Social implicationsFull-scale adoption may find resistance due to the level of fan involvement; the draft has evolved into an interactive experience for both fans and teams.Originality/valueThis research demonstrates the new application of smart contracts in the inter-section of sports management and blockchain technology.

Real-Time sanitary sewer blockage detection system using IoT

Sewer blockages and overflows have significant economic and environmental repercussions on communities. Thus, it is crucial to detect and remove sewer blockages prior to the occurrence of overflows. With the improvement in mobile networks and the development of high-quality and low-power sensors and loggers, wastewater network operators can now adopt monitoring devices enabled by the “Internet of Things” (IoT) technology for real-time monitoring. To this end, this paper studies the current state-of-the-art in sewer blockage management and introduces a novel methodology to monitor sanitary sewer blockages to prevent sanitary sewer overflows (SSO) with the least human interaction. The proposed system incorporates low-power level sensors and 4G telemetry for real-time monitoring of the manhole's sewage level to detect sewer blockages. The blockage detection methodology encompasses a set of decision rules and time series analysis to identify blockage events. The final blockage detection model offers a versatile capability to be implemented on sanitary sewer networks of different types with minimum computational costs.

A short decision time for transcatheter embolization can better associate mortality in patients with pelvic fracture: a retrospective study.

Early use of hemostasis strategies, transcatheter arterial embolization (TAE) is critical in cases of pelvic injury because of the risk of hemorrhagic shock and other fatal injuries. We investigated the influence of delays in TAE administration on mortality. Patients admitted to the Advanced Critical Care Center at Gifu University with pelvic injury between January 2008 and December 2019, and who underwent acute TAE, were retrospectively enrolled. The time from when the doctor decided to administer TAE to the start of TAE (needling time) was defined as "decision-TAE time." We included 158 patients, of whom 23 patients died. The median decision-TAE time was 59.5 min. Kaplan-Meier curves for overall survival were compared between patients with decision-TAE time above and below the median cutoff value; survival was significantly better for patients with values below the median cutoff value (p = 0.020). Multivariable Cox proportional hazards regression analysis revealed that the longer the decision-TAE time, the higher the risk of mortality (p = 0.031). TAE duration modified the association between decision-TAE time and overall survival (p = 0.109), as shorter TAE duration (procedure time) was associated with the best survival rate (p for interaction = 0.109). Decision-TAE time may play a key role in establishing resuscitation procedures in patients with pelvic fracture, and efforts to shorten this time should be pursued.

ІНВЕСТИЦІЙНА ДІЯЛЬНІСТЬ УКРАЇНИ У ВОЄННИЙ ПЕРІОД

The article aims to examine the impact of military conflicts on the investment climate and dynamics of investment activity in Ukraine. It analyzes key factors influencing investors' decisions in times of armed conflict, such as political and economic instability, security risks, changes in the legal environment, and the impact of international sanctions. The study also explores measures taken by the state to attract and support investments during wartime. Using data analysis, investment dynamics in Ukraine, and comparative methods, the author seeks to identify the key factors affecting Ukraine's investment activity during periods of war and proposes strategies for preserving and attracting investments in challenging conditions of military conflicts. The article explores the dynamics of investment activity in Ukraine during periods of military conflict. It examines the impact of the conflict on the investment climate, investor behavior, and the overall economy. Today, the Ukrainian economy is experiencing unprecedented upheaval in its entire history. The full-scale military aggression by the occupying state has struck at every level of our country's economic system. Production of key goods, especially those forming the backbone of Ukraine's export potential, is dwindling. Additionally, ports are nearly closed, regional transportation, logistics, social, marketing, and engineering infrastructure are being destroyed. Due to the brain drain abroad and partial relocation to the western part of the country, hundreds of thousands, and even millions, of people are temporarily displaced from active economic life. Additionally, it discusses the strategies employed by the government and other stakeholders to mitigate the adverse effects of the conflict on investment and foster international integration. The Ukrainian economy and businesses are gradually recovering after nearly coming to a standstill in the initial days of hostile attacks. They are seeking new opportunities for development in extreme conditions and military tactics. Partially, this recovery involves resuming entrepreneurial activities in territories liberated from occupation, partially relocating production to safer regions, and partially a natural process of adaptation.

Pricing and Timing Decisions in Promotion Channels

When planning for online promotion festivals, retailers must consider both pricing and timing decisions to determine an appropriate promotion strategy. In this article, we first consider <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retailer-promotion channel</i> where the retailer sells a new product on his marketing channel in normal and promotion sales periods. A greater number of early consumers can reduce late consumers' risk perception for product performance and increase their utilities via word-of-mouth. Consumers arriving in the promotion sales period strategically time purchases to maximize their utilities. Our analysis shows that when word-of-mouth is strong and consumers are patient to wait for price markdown, the retailer increases the price from that of the normal sales period and then provides price discounts. Otherwise, the retailer sets a high initial retail price to earlier pursue high marginal profit. We then consider <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">platform-promotion channel</i> where the retailer sells the product on the platform. Our analysis suggests that three additional factors in platform-promotion channel, commission rate, subsidy level, and market expansion, have distinct impacts on the equilibrium pricing and timing decisions. Interestingly, we find that the retailer's profit can be nonmonotonic in the subsidy level and a higher subsidy level does not necessarily induce the retailer to engage in platform-promotion channel.

From Decision in Risk to Decision in Time (and Return)

From Decision in Risk to Decision in Time (and Return)

Analysing expert advice on political decisions in times of crisis

This article analyses the relationship between expert knowledge and political decision-making during the COVID-19 pandemic in Denmark, Greece and the United States. Due to the swift spread of the COVID-19 virus across the globe, the management of the pandemic required urgent government responses. Our empirical findings enabled us to identify a new typology of different types of advice, from naked power to technocracy, including in-between modes of networking and interactions. We also demonstrate how systemic and governance characteristics affect expert-politics responses in the three countries. The findings demonstrate similar responses in Greece and Denmark, by contrast with the United States. We conclude by proposing a new research agenda for expert-politician relations during crises, which focuses on the role of experts in public sensemaking, which is particularly important in an era of recurring and overlapping crises.

Decision time: illuminating performance in India’s district courts

Decision time: illuminating performance in India’s district courts

End-of-Life Care and Use of Hospital Resources in Radiotherapy-Treated Cancer Patients with Brain Metastases: A Single-Institution Retrospective Study.

The aim of this single-institution retrospective study of patients treated with radiotherapy for brain metastases (BM) was to evaluate the timing of the palliative care (PC) decision, the use of health care services, i.e., emergency department (ED) visits and hospitalizations, and the implementation of radiotherapy at the end of life (EOL). Data on all cancer patients with BM treated in Finland at the Vaasa Central Hospital Radiotherapy Department between March 2011 and November 2020 were retrospectively reviewed. The follow-up period lasted until November 2021. Altogether, 91 patients (54 men, mean age 67 years [range 23-91 years]) were analyzed. Data on timing of PC decision, visits to the PC outpatient unit, and ED and hospitalization periods were collected retrospectively from patients' records. The median overall survival from diagnosis of BM was 3.7 months (range 1-62 months) and, after radiotherapy, 2 months (0-61 months). Thirty-two percent of the patients received radiotherapy in the last month of life. During the last 30 days of life, 44 patients (48%) visited the ED and 38 (42%) were hospitalized. Patients with an early PC decision (>30 days before death) had fewer hospitalizations (22% vs. 53%; p = 0.005) and died less often during the hospitalization period (9% vs. 27%; p = 0.047) at EOL. No significant difference was found in ED visits (41% vs. 53%; p = 0.28). For a large proportion of patients with BM, the prognosis is very poor. It is important to identify these patients and abstain from radiotherapy at EOL to reducing inappropriate health care utilization.

Foreign Experience in Digitalization of Preventive Public Financial Control (on the Example of USA, China, Canada, India and Australia)

The article discusses the issues of implementing preventive financial control using modern digital technologies. The up-to-date level of informatization of the government agencies activity and local governments allows moving to a qualitatively new level of interaction between regulatory authorities and the environment under control. In this case the verification and analysis of reporting is performed not only after the fact of fulfillment but also in a preventative manner. This allows a quick respond to emerging risk situations, prevention of erroneous actions, and management decisions in a real time. When solving the problem of digitalization of state financial control procedures, the emphasis and priorities can be placed differently, both in the selection of automated processes and in the selection of technological solutions. The experience of other countries represents a valuable source of knowledge in this area. Therefore, the aim of the article is to study the best practices in digitalization of state financial control and to develop the recommendations for implementing preventive digital state financial control in Russia. This article presents the analysis of the experience of the USA, China, Canada, India, and Australia. The main projects are considered that were implemented in these countries to improve the financial control by implementing digital solutions. The features and areas of application of cloud computing, technologies of data collection, processing and analyzing the Big Data, as well as systems based on artificial intelligence in the field of public financial control are presented. As a result, recommendations have been formulated for adapting the best practices of these countries for the implementation of preventive digital control in the financial and budgetary sphere in our country. A further elaborated investigation of organization of the collection and processing of data on the object under control, coming not only from its corporate sources and government information systems but also from publicly available Internet resources, is considered as a relevant issue.