Interval Values Research Articles

Load peak intensity in pre-season training sessions and official competitions in professional soccer

This single case study compares load peak intensity (LPI) in pre-season training sessions and official matches in professional soccer. This aims to answer the question whether the pre-season training sessions of one selected team adequately prepared for the peak demands of the season. Therefore, spatiotemporal data of 8 professional male soccer players was collected using a radio-based local positioning system in training (KINEXON) and a video-based tracking system in Bundesliga competitions (TRACAB). Load peaks were determined based on the distance covered (DC) during Acceleration, Deceleration and Sprinting phases. For each parameter, our algorithm quantifies the LPI by using the maximum rolling average value for time intervals of 1, 3, and 5 min within a training session or a match. The results on team level indicate that LPI for Acceleration was significantly higher in pre-season training sessions compared to matches (for 3-min interval: DCTR = 22.59 m ± 3.06 m, DCMA = 18.38 m ± 3.08 m, d = 1.97, p < 0.01). In contrast, LPI regarding Deceleration was significantly lower in pre-season training compared to matches (for 3-min interval: DCTR = 20.8 m ± 2.65 m, DCMA = 23.15 m ± 3.17 m, d = −1.29, p < 0.01). This suggests that exercises focused more on acceleration situations and less on deceleration situations. However, analysis on an individual level shows a considerably high variability of load peak intensity between players during both periods. We conclude that individual load monitoring is an essential component of training management. Practitioners are especially advised to carefully examine the worst-case scenarios of Deceleration during pre-season to avoid falling short on load requirements and adequately prepare players for match demands.

Dynamic classification and attention mechanism-based bidirectional long short-term memory network for daily runoff prediction in Aksu River basin, Northwest China.

Inland river runoff variability is pivotal for maintaining regional ecological stability. Daily flow forecasting in arid regions is crucial in understanding water body ecological processes and promoting healthy river ecology. Precise daily runoff forecasting serves as a cornerstone for ecological evaluation, management, and decision-making. With the advancement of artificial intelligence technology, data-driven models have exhibited promising capabilities in runoff prediction. Nevertheless, the arbitrary selection of boundaries between different flow patterns without considering temporal changes across seasons limits the accuracy of runoff simulation. This paper proposed an integrated modeling approach encompassing a dynamic classification method, an attention mechanism, and a bidirectional long short-term memory network (CA-BiLSTM) to enhance flow prediction performance while accommodating diverse flow patterns. The classification boundary was determined by the dynamic change interval value of relevant hydrological variables, facilitating a more comprehensive exploration of the relationships and information within hydrological data. The performance of the CA-BiLSTM model was compared against a traditional machine learning model lacking data classification, utilizing data from the West Bridge station of the Aksu River Basin (ARB). The results indicate that the CA-BiLSTM model outperforms traditional LSTM and BiLSTM models across all seasons. The CA-BiLSTM model demonstrates superior performance in arid zones. Compared to the single LSTM model, CA-BiLSTM exhibits reductions of 42.99%, 36.89%, and 49.73% in MAE, RMSE, and MAPE, respectively, while enhancing R2 and KGE by 10.47% and 11.76%. The proposed hybrid model effectively reduces runoff prediction uncertainty, offering valuable insights for water resource management in arid zones.

P-1275. Quasi-experimental Approaches for Estimating Epidemiological Efficacy of Non-randomised Field Trials: Applications to Wolbachia Interventions

Abstract Background Wolbachia symbiosis in Aedes aegypti is an emerging biocontrol measure against dengue. However, assessing its real-world efficacy is challenging due to the non-randomised, field-based nature of most intervention studies. This research re-evaluates the spatial-temporal impact of Wolbachia interventions on dengue incidence using a large battery of quasi-experimental methods and assesses each method’s validity. Aggregated intervention efficacies by event time for each study site Aggregated intervention efficacies for (a) Selangor, (b) Niterói, and (c) Singapore based on event time. Each efficacy assessment method is represented by a specific color: red for Pre-post, yellow for RDD (Regression Discontinuity Design), green for 2x2 DiD (Difference-in-Differences), blue for SCM (Synthetic Control Method), and purple for cSCM (count Synthetic Control Method). The intervention efficacy (IE) reported in the original paper is depicted on the right side of each diagram, represented by the colour black. The dot represents the IE point estimate for each method, while the line indicates the corresponding confidence intervals. Extreme confidence interval values and point estimates are denoted by arrows, with their respective values displayed next to the arrow. Methods A systematic search for Wolbachia intervention data was conducted via PUBMED. Efficacy was reassessed using commonly-used quasi-experimental approaches with extensive robustness checks, including geospatial placebo tests and a simulation study. Intervention efficacies across multiple study sites were computed using high-resolution aggregations to examine heterogeneities across sites and study periods. We further designed a stochastic simulation framework to assess the methods’ ability to estimate intervention efficacies (IE). Aggregate intervention efficacy (IE) estimates (%) of Wolbachia releases on total dengue incidence rates across all intervention sitesTable 2:Aggregate intervention efficacy (IE) estimates (%) of Wolbachia releases on total dengue incidence rates across all intervention sites. The estimates are reported for each method and are accompanied by the corresponding confidence intervals (in parentheses). The IE estimate reported in the respective original papers, highlighted in blue, is provided for comparison. Numbers in parenthesis represent lower and upper bounds for 95% confidence intervals, estimated using the bootstrapping procedure. # indicates that the estimate passed in-time placebo checks while * indicates that it passed in-space placebo check. Bolded figures represent significant IE estimates which also passed in-space and in-time placebo checks. Results Wolbachia interventions in Singapore, Malaysia, and Brazil significantly decreased dengue incidence, with reductions ranging from 48.17% to 69.19%. IEs varied with location and duration. Selangor showed increasing efficacy over time, while Niterói exhibited initial success with subsequent decline, hinting at operational challenges. Singapore's strategy was highly effective despite partial saturation. Simulations identified Synthetic Control Methods (SCM) and its variant, count Synthetic Control Method (cSCM), as superior in precision, with the smallest percentage errors in efficacy estimation. These methods also demonstrated robustness in placebo tests. Conclusion Wolbachia interventions exhibit consistent protective effects against dengue. SCM and cSCM provided the most precise and robust estimates of IEs, validated across simulated and real-world settings. Disclosures All Authors: No reported disclosures

Neonatal Thyroid-Stimulating Hormone Reference Intervals in Multi-Ethnics Population.

(1) Background: This study is designed to establish thyroid-stimulating hormone (TSH) reference intervals tailored to different neonatal age groups and Indonesian local populations. (2) Methods: Dried blood spot neonatal TSH values, from 1 January 2022 to 31 December 2023, were used to establish the neonatal TSH reference intervals partitioned by sex, gestational age, and ethnic group at different neonatal ages. (3) Results: A significant difference in the reference intervals value was observed in sex, gestational ages, and parental ethnicity groups in different neonatal age subgroups (p < 0.05). Male reference intervals were significantly higher than those of females at all neonatal ages. Late and post-term gestational age categories reference intervals were higher than early and full-term. Among the ethnic groups, Madurese had a higher upper limit TSH reference interval. (4) Conclusions: Our neonatal TSH reference intervals were needed to provide a reference adapted to the local population of Indonesia.

Prevalence of neutropenia in the U.S. among reproductive-aged women: a population-based analysis of NHANES 2013–2020

BackgroundInfertility is one of the prominent public health concerns nationwide. Neutrophils, despite their established significance as vital players in both inflammatory and immune processes, have been studied scarcely in terms of their effect on female infertility. The present study aimed to determine the prevalence of neutropenia among women of reproductive age in the U.S. to contribute valuable insights to the broader context of reproductive health.MethodsThe present study was designed as a cross-sectional investigation. The data of 5,250 female participants aged 18–45 years were obtained from the National Health and Nutrition Examination Survey (NHANES) conducted between the years 2013 and 2020. The representativeness of the population was ensured by conducting statistical assessments based on NHANES weights. A logistic regression model was established to assess the hematologic parameters across the distinct populations stratified according to age, ethnicity, smoking status, and infertility. Multivariate logistic regression was performed next, and weighted odds ratios along with the 95% confidence interval values were calculated, which assisted in predicting the prevalence of neutropenia among the female participants.ResultsThe data of a total of 5,250 female participants, representing a multiracial population of 51.17 million in the United States, were analyzed in the present study. Meanwhile, the estimated neutropenia incidence was 7.09% (95% CI: 6.16−8.01%), which indicated a prevalence among approximately 36.2 million U.S. citizens. In comparison to white subjects, black subjects exhibited a significantly lower average leukocyte count, with a mean difference (MD) of 1.16 × 109/L (P < 0.001), along with a lower neutrophil count (MD: 1.09 × 109/L; P < 0.001). It is noteworthy that a substantial decrease was noted in the distribution graphs of both neutrophil and leukocyte counts among the black subjects. Moreover, compared to non-smokers in the racial populations, including white, Mexican American, and black people, the smokers exhibited significantly elevated mean leukocyte count and mean neutrophil count. The logistic regression analysis indicated an elevated risk of neutropenia among black individuals and females with infertility.ConclusionsNeutropenia appears to have a higher prevalence in the general population compared to that acknowledged previously. The findings of the present study indicated association between neutropenia and infertility. This highlighted the importance of directing increased attention toward neutropenia in the context of both research and clinical practice.

Interval-valued optimal control strategies for vaccine distribution and infection awareness investment in preservation technology

Purpose This study aims to explore the global threat of diseases that affect people, such as diarrheal, Hepatitis B, Rotavirus, Measles diseases, emphasizing the integration of disease vaccines into immunization programs globally as recommended by the World Health Organization, resulting in significant case reductions. Design/methodology/approach Notably, it stresses the necessity of raising awareness about diseases and vaccines through promotional efforts alongside effective inventory management because of vaccine perishability, highlighting preservation techniques and cold storage. Addressing environmental concerns, including carbon emissions from vaccine deterioration, the study proposes green technology investments aligned with Sustainable Development Goals to mitigate these impacts. Additionally, advanced optimization algorithms, including ant colony, modified flower pollination, cuckoo search and particle swarm optimization algorithms, are used to optimize pricing, preservation strategies, green investments and replenishment schedules. The research also uses the concept of interval values to enhance the robustness of the optimization framework. Through numerical experiments, the study demonstrates the effectiveness of this dynamic investment approach, providing empirical validation. Findings Furthermore, sensitivity analysis on critical parameters yields valuable insights for decision-makers, underscoring the importance of dynamically managing vaccine inventory. The study offers practical solutions and managerial insights that can inform policy decisions and strategic planning in disease response efforts. Originality/value This study concludes by emphasizing how creative green technology approaches can help decision-makers manage the social and environmental effects of vaccine inventories in the health care of people.

Hierarchical-based self-triggered control for linear systems with external disturbances

This paper introduces a hierarchical-based robust self-triggered control mechanism for linear systems with disturbances, with the goal of achieving anti-interference and computational resource saving. To address the potential external interference in the system, the H∞ performance index is introduced, and the self-triggered controller is developed based on this index to guarantee the system’s desired anti-interference capabilities. Furthermore, a hierarchical framework is employed to facilitate collaborative optimization of triggering mechanisms and control strategies. Within this framework, the triggering interval values and control input values are computed sequentially using a cyclic iteration approach. By upholding the H∞ performance standard and incorporating a hierarchical optimization framework, the system’s sampling frequency is notably diminished, anti-interference performance is safeguarded, and the convergence speed of the state trajectory is accelerated. The efficacy of the proposed method is validated through two illustrative examples.

Tectonic implications of raised Quaternary relative sea‐level indicators along the NE border of the Campania Plain (southern Italy)

AbstractTectonically raised paleoshorelines have been recently identified along the southern fault scarps of the Mt. Fellino and Roccarainola horst blocks, which are part of the northeastern border of the Campania Plain coastal basin (southern Apennines, Italy). Such horst blocks are bounded to the south by the Polvica Fault, a roughly E‐W trending normal fault. The sequence of uplifted paleoshorelines has been studied in detail by integrating geomorphological, structural and stratigraphical analyses to assess the Quaternary uplift of the Mt. Fellino and Roccarainola horst blocks. Yet, the staircase of paleoshorelines is still not chronologically well constrained.Aimed at constraining the uplift history of Mt. Fellino and Roccarainola horst blocks and the rate of activity of the Polvica fault, in this study, we integrate former knowledge on paleoshorelines with a geomorphological analysis to map erosional terraces, that we interpret as remnants of shore platforms. We apply the synchronous correlation method, driven by new and a former 230Th/234U dating of calcite veins cutting marine sands, to infer the age of the paleoshorelines and terraces. Based on the synchronous correlation, the mapped paleoshorelines and terraces are correlated with sea‐level peaks of the late Early to Late Pleistocene. In particular, the paleoshorelines along the Mt. Fellino ridge are correlated with the Marine Isotope Stage (MIS) 7e and 9c or 11, while the oldest terrace is correlated with the sea‐level peak of 980 ka. Using inferred paleoshorelines ages, we estimate the uplift rate of the Polvica Fault footwall. The uplift rate varies from c. 0.2 mm/yr close to the western fault tip up to c. 0.5–0.6 mm/yr in the East, in the Roccarainola block. We combine surface evidence with subsurface data from a shallow well to constrain the vertical throw of the Polvica Fault. A mean fault throw rate of c. 0.4 mm/yr in the last c. 1 Ma is estimated for the central part of the PF. Assuming that the Polvica Fault is still active, we estimate the maximum expected earthquake by means of empirical relationship and obtain a Mw ~ 6.2 value and recurrence interval value of c. 1,200 yr. Historical seismicity activity of the PF has not been acknowledged to date. However, our results raise the crucial question of an in‐depth assessment of the seismic hazard for the densely populated Campania Plain.

What Is Grazing Time? Insights from the Acoustic Signature of Goat Jaw Activity in Wooded Landscapes.

Acoustic monitoring facilitates the detailed study of herbivore grazing by generating a timeline of sound bursts associated with jaw movements (JMs) that perform bite or chew actions. The unclassified stream of JM events was used here in an observational study to explore the notion of "grazing time". Working with shepherded goat herds in a wooded landscape, a horn-based acoustic sensor with a vibration-type microphone was deployed on a volunteer animal along each of 12 foraging routes. The software-generated timeline of unclassified JMs contained a total of 334,582 events. After excluding rumination bouts, minutely JM rates showed a broad, non-normal distribution, with an overall mean of 61 JM min-1. The frequency distribution of inter-JM interval values scaled logarithmically, with a peak in the region of 0.43 s representing a baseline interval that generates the unconstrained, more-or-less regular, rhythm of jaw movement (≈140 JM min-1). This rhythm was punctuated by interruptions, for which duration scaled logarithmically, and which were primarily related to the search phase of the intake process. The empirical time accumulation curve shows the contribution of the inter-JM interval to the total foraging time and provides a penetrating profile of how the animal interacted with the foraging environment. The sum total of time along a foraging route spent at a near-potential JM rate was only ≈1 h, whereas sub-potential rates containing intervals as long as ≈30 s accounted for the bulk of the foraging route. The dimensionless behavioral grazing intensity was defined as the product of the number of ingestive JMs performed and the baseline interval, divided by the duration of the foraging route (excluding rumination). Values were mostly <0.5 for the foraging routes examined. This has implications for how animal presence should be translated to grazing pressure and for how long animals need to forage to meet their nutritional requirements.

Prospective Evaluation of the Prevalence and Laboratory Findings in Adult Cats with Low Thyroxine and Increased Thyrotropin Concentration

Adult-onset spontaneous feline hypothyroidism (SH) is considered rare, but its prevalence is unknown. This study aimed to screen laboratory submissions for cats with laboratory suspected SH (LSSH) and to identify laboratory abnormalities associated with LSSH. Submissions to a commercial laboratory in Germany between January 2022 and April 2023 were prospectively screened for cats aged 3–12 years with low total thyroxine (TT4); in these cats, thyrotropin (TSH) was measured. Iatrogenic hypothyroidism was excluded by contacting submitting veterinarians. Creatinine, triglyceride and cholesterol concentration and red blood cell count (RBC) of cats with LSSH (low TT4, TSH &gt; 0.53 ng/mL; i.e., [one sided TSH reference change value (76%) of TSH upper reference interval (RI)] + [TSH upper RI] = 0.53) were compared to euthyroid cats (TT4 within RI) and cats with suspected non-thyroidal illness (SNTIS) (low TT4, normal TSH (&lt;0.3 ng/mL; upper RI)) by non-parametric tests. p &lt; 0.05 was significant. In total, 31,572 submissions of cats were included, 25,169 (79.7%) were euthyroid, 3818 (12.1%) had SNTIS and 61 (0.2%) had LSSH. Cats with LSSH had higher creatinine (p = 0.002) and lower RBC count (p &lt; 0.0001) than euthyroid cats as well as higher creatinine (p = 0.00035) than cats with SNTIS. Azotaemia (creatinine &gt; 140 µmol/L) and anaemia (RBC &lt; 7.2 × 1012/L) were present in 28/49 (57%) and 19/44 (43%) cats with LSSH, respectively. There was no difference between LSSH and SNTIS for the remaining parameters. In cats with low TT4, azotaemia and anaemia might indicate the presence of SH and reinforce the need for TSH testing. Hypercholesterolaemia and hypertriglyceridaemia are not indicators of SH.

The Evaluation Method of the Power Supply Capability of an Active Distribution Network Considering Demand Response

The accurate quantification of power supply capability (PSC) is crucial for the planning and operation of active distribution networks. To address the issue of PSC quantification, this paper presents a PSC evaluation method of active distribution networks while considering demand response (DR). Firstly, an incentive-based DR model is introduced, followed by the development of a PSC evaluation model that incorporates DR. This model completely describes the PSC of active distribution networks while considering N-1 security constraints. Secondly, a PSC evaluation algorithm based on uniform state-space sampling is presented, enabling the quantification of the complete PSC of active distribution networks with DR. Then, the influence of the load reduction coefficient in DR on the PSC is studied. It is found that the maximum PSC increases with the load reduction coefficient initially and then stabilizes. Furthermore, measures such as appropriately increasing the load reduction coefficient and expanding the bottleneck component capacity are proposed to enhance the PSC. Finally, the effectiveness of the proposed PSC evaluation method is verified by two active distribution networks, CASE1 and CASE2. The proposed method visualizes the complete PSC of active distribution networks considering DR as a curve and quantifies it as an interval value. For CASE1, the complete PSC is quantified within the range of [4.0, 8.4] MVA, and for CASE2, it is quantified within the range of [17.0, 33.0] MVA. The proposed measures effectively enhance the PSC, facilitating the efficient, safe, and low-carbon operation of smart distribution networks.

The Reference Interval Values of Biochemical Parameters in Iranian Kurdish Adults: Results from Non-communicable Diseases of Ravansar Cohort

The Reference Interval Values of Biochemical Parameters in Iranian Kurdish Adults: Results from Non-communicable Diseases of Ravansar Cohort

Revised bounds on local cosmic strings from NANOGrav observations

In a recent paper, the NANOGrav collaboration studied new physics explanations of the observed pulsar timing residuals consistent with a stochastic gravitational wave background (SGWB) [1], including cosmic strings in the Nambu-Goto (NG) approximation. Analysing one of current models for the loop distribution, it was found that the cosmic string model is disfavored compared to other sources, for example, super massive black hole binaries (SMBHBs). When both SMBHB and cosmic string models are included in the analysis, an upper bound on a string tension Gμ≲ 10-10 was derived. However, the analysis did not accommodate results from cosmic string simulations in an underlying field theory, which indicate that at most a small fraction of string loops survive long enough to emit GW. Following and extending our previous study [2], we suppose that a fraction f NG of string loops follow NG dynamics and emit only GWs, and study the three different models of the loop distribution discussed in the LIGO-Virgo-KAGRA (LVK) collaboration analyses.We re-analyse the NANOGrav 15yrs data with our signal models by using the NANOGrav ENTERPRISE analysis code via the wrapper PTArcade. We find that loop distributions similar to LVK Model B and C yield higher Bayes factor than Model A analysed in the NANOGrav paper, as they can more easily accommodate a blue-tilted spectrum of the observed amplitude. Furthermore, because of the degeneracy of Gμ and f NG in determining the signal amplitude, our posterior distribution extends to higher values of Gμ, and in some cases the uppermost value of credible intervals is close to the Cosmic Microwave Background limit Gμ≲ 10-7.Hence, in addition to the pulsar timing array data, further information about the fraction of long-lived loops in a cosmic string network is required to constrain the string tension.

Changes in Shoulder and Lumbar Injury Incidence in Swimmers After Physical Examination and Exercise Programs.

Previous injury and physical examination data collected by a physiotherapist were reviewed to investigate the causes of injury among competitive swimmers. Each swimmer received an injury improvement exercise program based on their injury history and physical examination findings. The purpose of this study was to identify trends in the number of shoulder and lumbar injuries and discern the effectiveness of exercise programs in swimmers. The authors hypothesize that these efforts would decrease injury rates. Observational Cohort study. Thirty-one male high school and college student swimmers underwent a physical examination. Previous injury was considered as pain that lasted for > three weeks or that caused the participant to stop practicing. A questionnaire was administered to all swimmers at the beginning of the study and one year later. The questionnaire assessed the number of injuries sustained and the daily practice hours over the previous year. The injury rate at the first examination and one-year followup was calculated as the number of injury incidents divided by the total number of player hours (1 h of practice is equivalent to 1) for one year, multiplied by 1,000 to obtain the injury rate per 1,000 hours (1000 player hours: 1,000 ph). Shoulder and hip exercise programs were prescribed based on measurement results and previous research. The numbers of injuries seen in the group were 12 shoulder and six lumbar at the first examination, and two shoulder and one lumbar at the one-year followup. The 1,000 ph (95% confidence interval) values were 0.32 (0.13-0.50) and 0.16 (0.03-0.29) at the first examination and 0.05 (0.00-0.12) and 0.03 (0.00-0.07) at the one-year followup for the shoulder and lumbar areas, respectively. The results indicates that exercise programs prescribed after a physical examination may reduce the incidence of injuries in male swimmers over the course of a year. Level 3.

Measuring multidimensional inequality: a new proposal based on the Fourier transform

Inequality measures are quantitative measures that take values in the unit interval, with a zero value characterizing perfect equality. Although originally proposed to measure economic inequalities, they can be applied to several other situations, in which one is interested in the mutual variability between a set of observations, rather than in their deviations from the mean. While unidimensional measures of inequality, such as the Gini index, are widely known and employed, multidimensional measures, such as Lorenz Zonoids, are difficult to interpret and computationally expensive and, for these reasons, are not much well known. To overcome the problem, in this paper, we propose a new scaling invariant multidimensional inequality index, based on the Fourier transform, which exhibits a number of interesting properties, and whose application to the multidimensional case is rather straightforward to calculate and interpret.

(Invited) A Random Forest–Based Artificial Intelligence Model Decoupling Ion Concentrations from Effluent Conductivity in Capacitive Deionization: Implications and Future Directions

Effluent conductivity is a widely used parameter in evaluating the performance of desalination processes, such as capacitive deionization (CDI) and pseudocapacitive deionization (BDI), due to its simplicity in measurement. However, the conversion of effluent conductivity to ion concentration is often inaccurate, necessitating a more reliable method for real-time monitoring of effluent ion concentrations. To address this issue, a random forest (RF)-based artificial intelligence (AI) model was developed, demonstrating excellent prediction accuracy for both CDI (R2 = 0.86) and BDI (R2 = 0.95) data for effluent conductivity. Furthermore, the model successfully predicted the concentration of individual ions (Na⁺ and Cl⁻) from the conductivity values, with an even higher accuracy than that of the effluent conductivity prediction. The impact of sampling interval on the model's performance was also investigated for both conductivity and ion concentrations. The results showed no significant difference in the model's error value for sampling intervals up to <80 s. These findings highlight the potential of the RF model as a valuable tool for predicting ion concentrations in CDI/BDI processes, which can serve as key indicators in assessing desalination performance.By providing a more reliable method for predicting ion concentrations, the RF model can contribute to the optimization and automation of CDI and BDI systems, ultimately leading to more efficient and cost-effective desalination technologies. The ability to decouple ion concentrations from effluent conductivity can lead to more accurate monitoring and control of desalination systems, enabling operators to make informed decisions and optimize process performance. Future directions for this research could include the expansion of the RF model to incorporate additional input variables, such as pH, temperature, and flow rate, to further enhance its predictive capabilities, and improving robustness of the model under conditions more closely resembling industrial settings. Such implications and future directions of the research will be discussed in further details in the presentation.

Existence of optimal strategies in bimatrix game and applications

This paper delves into interval-valued bimatrix games, where precise payoffs remain elusive, but lower and upper bounds on payoffs can be determined. The study explores several key questions in this context. Firstly, it addresses the issue of the existence of a universally applicable equilibrium across all instances of interval values. The paper establishes a fundamental equivalence by demonstrating that this property hinges on the solvability of a specific system of interval linear inequalities. Secondly, the research endeavors to characterize the comprehensive set of weak and strong equilibrium using a system of interval linear inequalities. The findings in this paper shed light on the complexities and intricacies of interval-valued bimatrix games, offering valuable insights into their equilibrium properties and computational aspects. Through illustrative examples, we underscore the practical utility of these approaches and compare them with previously developed state-of-the-art methods, demonstrating their ability to generate conservative solutions in the face of interval uncertainty. The findings of this research not only offer valuable insights into the equilibrium properties and computational aspects of interval-valued bimatrix games but extend their practical implications. In particular, the paper delves into real-life applications, exemplifying the significance of these findings for crude oil trading decision-making.

A risk-based stochastic energy-water-carbon nexus analytical model to support provincial multi-system synergistic management – A case study of Shanxi, China

The synergistic management of energy-water‑carbon (EWC) nexus systems is crucial for achieving the sustainable development goals (SDGs). Therefore, a non-deterministic interval chance-constrained fractional optimization model for EWC nexus system (ICCF-EWC) management has been developed in this study. This model is capable of handling uncertain parameters represented as stochastic probability distributions and interval values, providing an effective approach to addressing dual-objective optimization problems. Meanwhile, this model is expected to investigate the effect of water scarcity/carbon abatement pressure on the overall system, and potential synergistic abatement effects. A case study of Shanxi Province shows that over the next 30 years, the cumulative installed capacity for clean and renewable energy will exceed 65 %. The dominance of coal-fired electricity will be considerably diminished, with wind power overtaking coal/gas-fired power by around 30 %. Moreover, water scarcity and carbon mitigation pressure would promote the development of electricity conversion mode to clean energy rather than the large-scale carbon capture and storage (CCS) technology upgradation of thermal power. The results can help support low-carbon transition of power systems at a province-level in China and financial incentives related policy making to advance water conservation and carbon emission mitigation. The developed model can also be adapted to other energy resource-dependent regional power systems.

Computer Modelling of Logistic Regression for Binary Classification

This article discusses the practical aspects of applying logistic regression for binary data classification. Logistic regression determines the probability of an object belonging to one of two classes. This probability is calculated with the help of a sigmoid function, the argument of which is a linear convolution of the feature vector of the object with the weighting coefficients obtained during the minimization of the logarithmic loss function. Predicted class labels are determined by comparing the calculated probability with a given threshold value. The logistic regression study was performed using the computer simulation method. For this, a software complex was developed, the work of which reproduces the main stages of logistic regression: preparation of input data, training, testing with determination of quality metrics of binary classification, application of the logistic regression method for data classification in practice. The paper examines the effect of overlapping and imbalance of classes in the input data set on the efficiency of binary classification. The overlapping of classes is modeled by the formation of input data based on two shifted relative to each other density functions of the normal distribution of random variables. Class imbalance is simulated by the probability of switching between these features. It is shown that when the distance between the mathematical expectations of the density functions of the normal distribution decreases or when the dispersion of random variables increases, the overlapping of relevant classes increases, which leads to an increase in the number of objects that the classifier can assign to one or another class. Approaching the probability of switching between the distribution functions of random variables to the extreme values of the unit interval leads to an increase in class imbalance, which is manifested in an increase in the number of elements of the input data set labeled with the label of the same class. It has been experimentally confirmed that the AUC ROC metric, popular in binary classification problems, is dependent on the degree of class overlap and relatively resistant to class imbalance.

Predictive value of Tpeak-Tend interval of supine and orthostatic electrocardiogram in the diagnosis between psychogenic pseudosyncope and vasovagal syncope in children

Tpeak-Tend interval (Tp-Te) is an indicator of transmural dispersion of repolarization, which is often related to ventricular arrhythmias. The relationship between Tp-Te and vasovagal syncope (VVS) has been reported in previous studies, but very little is known about Tp-Te in differentiation between psychogenic pseudosyncope (PPS) and VVS in children. We aim to discuss the predictive value of Tp-Te of supine and orthostatic electrocardiogram (ECG) in the diagnosis between PPS and VVS in children. The 31 children with PPS and 40 children with VVS from July 2014 to November 2023 were enrolled as the study group. Meanwhile, 30 healthy children who underwent a physical examination in the same hospital were selected as the control group. Tp-Te and QT interval in 12-lead supine and orthostatic ECG were measured. (1) Supine ECG: ① The Tp-Te/QT in PPS group was significantly lower than that in control group in II lead (P < 0.05). The Tp-Te/QTc in PPS group was significantly lower than that in control group in II and aVR lead (P < 0.05). There was no significant difference in Tp-Te between PPS group and control group (P > 0.05). ② The Tp-Te in VVS group was significantly higher than that in control group in aVR, aVF, and V4 lead (P < 0.05). The Tp-Te/QT in VVS group was significantly higher than that in control group in aVF lead (P < 0.05). The Tp-Te/QTc in VVS group was significantly higher than that in control group in aVL, aVF and V1 lead (P < 0.05). ③ The Tp-Te, Tp-Te/QT in PPS group were significantly lower than those in VVS group in some leads (P < 0.05). The Tp-Te/QTc in PPS group was significantly lower than that in VVS group in some leads. (2) Orthostatic ECG: ① The Tp-Te in PPS group was significantly higher than that in control group in V1, V2 lead (P < 0.05). The Tp-Te/QT, Tp-Te/QTc in PPS group were significantly higher than those in control group in V2 lead (P < 0.05). ② The Tp-Te and Tp-Te/QTc in V4 lead, Tp-Te/QT in V1, V4 lead in VVS group were significantly higher than those in control group (P < 0.05). ③ The Tp-Te in PPS group was significantly lower than that in VVS group in some leads (P < 0.05). The Tp-Te/QT in PPS group was significantly lower than that in VVS group in some leads (P < 0.05). The Tp-Te/QTc in PPS group was significantly lower than that in VVS group in some leads. (3) The receiver-operating characteristic (ROC) curve analysis: Tp-Te of supine ECG and orthostatic ECG in some leads had a predictive value on diagnosis between PPS and VVS (P < 0.05). Tp-Te, Tp-Te/QT, and Tp-Te/QTc of supine and orthostatic ECG in some leads have a certain predictive value in the diagnosis between PPS and VVS in children.