Threshold Setting Research Articles

Motivation for physical activity among university students residing in areas of military conflict in Ukraine

Background and Study Aim. Military conflicts significantly stress students in affected areas, impacting their physical well-being and motivational factors. This study aims to assess motivation levels for physical activity among university students in conflict-impacted regions of Ukraine. Materials and Methods. The study involved 196 students from three Ukrainian universities, two in the eastern region (Kharkiv - included in the list of territories of active military operations) and one in the western part (Ivano-Frankivsk - a territory that is under constant threat of shelling). A short-form 'Physical Activity Questionnaire' (PAQ) with seven Likert scale questions was used. Statistical analysis was performed using PyCharm CE and Python libraries. Metrics like mean, standard deviation, and Cronbach's alpha coefficient for reliability were calculated. Bartlett's test of sphericity and Principal Axis Factoring were employed, with a statistical significance threshold set at p<0.05. Results. The questionnaire's reliability was confirmed with a Cronbach's alpha coefficient of 0.872. Bartlett's test of sphericity indicated suitable data for factor analysis, with a Chi-Square value of 641.808 and a p-value < 0.05, affirming good construct validity. University students from the Eastern part of Ukraine show average motivation levels for physical activity of 25.0 and 27.193, respectively. University students from the Western part of Ukraine have the highest average motivation level at 29.234. This indicates stronger motivation for physical activity in the Western region. Additionally, male students showed higher average motivation scores than females. Conclusions. Students living in different regions of Ukraine have varying motivations for physical activity in the context of the military conflict. Students from the western part of Ukraine (an area subjected to periodic military shelling) show a higher interest in engaging in physical activities compared to students from the eastern part (a zone of active military operations).

Suitable-Matching Areas’ Selection Method Based on Multi-Level Saliency

Scene-matching navigation is one of the essential technologies for achieving precise navigation in satellite-denied environments. Selecting suitable-matching areas is crucial for planning trajectory and reducing yaw. Most traditional selection methods of suitable-matching areas use hierarchical screening based on multiple feature indicators. However, these methods rarely consider the interrelationship between different feature indicators and use the same set of screening thresholds for different categories of images, which has poor versatility and can easily cause mis-selection and omission. To solve this problem, a suitable-matching areas’ selection method based on multi-level saliency is proposed. The matching performance score is obtained by fusing several segmentation levels’ salient feature extraction results and performing weighted calculations with the sub-image edge density. Compared with the hierarchical screening methods, the matching performance of the candidate areas selected by our algorithm is at least 22.2% higher, and it also has a better matching ability in different scene categories. In addition, the number of missed and wrong selections is significantly reduced. The average matching accuracy of the top three areas selected by our method reached 0.8549, 0.7993, and 0.7803, respectively, under the verification of multiple matching algorithms. Experimental results show this paper’s suitable-matching areas’ selection method is more robust.

Acta Plane—A New Reference for Virtual Orientation of Cone Beam Computed Tomography Scans: A Pilot Study

Introduction: Cone Beam Computed Tomography (CBCT) scans should be digitally oriented using a reference plane before being evaluated for patient growth and treatment outcome. Current orientation planes are usually dependent on the Porion points. Using more reliable landmarks than the Porion, we introduce the Acta plane and investigate its reliability as an alternative reference plane for cases in which the Porion point is inaccessible. Methods: To test its reliability, a dental student and an orthodontist located sixteen skeletal and dental landmarks in sixteen CBCT scans before and after reorientation. Also, four measurements were calculated based on these landmarks’ coordinates. The reorientation of these CBCT scans with the Acta plane was performed by another dental student. The Intraclass Correlation Coefficient (ICC) was calculated before and after orientation, as well as the intra and inter-examiner. The data were tested for significance through the Shapiro–Wilk test with a threshold set at 0.05. Through the ICC variation of the points’ coordinates, this article also evaluated the reliability of this plane with the t-paired and exact-sign tests. Results: The identification of landmarks was more reliable in the reoriented CBCT images than in the original images, with a significant increase of 0.012 in the median of the ICC. Linear and angular measures showed no significant differences between groups. Conclusion: This plane is a reliable alternative for the orientation of CBCT images. The design of the Acta plane might allow the use of a smaller field of view and, consequently, a reduction in the radiation dose.

A hybrid domain-based watermarking for vector maps utilizing a complementary advantage of discrete fourier transform and singular value decomposition

Digital watermarking plays a crucial role in the copyright protecting of vector maps. Due to its solid theoretical foundation, Discrete Fourier transform (DFT) is frequently used in the construction of watermarking scheme for diverse electronic data. However, when applied to vector maps, DFT is particularly vulnerable to local changes in coordinate points, posing challenges in surviving coordinate point attacks and limiting its practicality. Furthermore, its resistance against geometric attacks is quite weak. To address these issues, we propose an algorithmic complementary strategy that enhances the performance of DFT-based vector map watermarking scheme. The proposed scheme is founded upon a hybrid transform domain consisting of DFT and singular value decomposition (SVD). The first step is to extract feature points using the Douglas Peucker algorithm, with the distance threshold set to a relative value to ensure synchronization of feature points throughout scaling. The feature points are then subjected to DFT to obtain magnitude coefficients, which are then further transformed using SVD. By combining the geometric invariance of magnitude coefficients with the singular vectors of SVD, an invariant with rotation, scaling, and translation invariance can be generated. For watermark embedding, the invariant serves as an embedding domain and are multiplied to minimize the impact on the host vector maps. The experimental results reveal that the proposed vector map watermarking has very low disturbance, substantially lower than the comparing algorithms, deriving in excellent invisibility. The scheme can extract watermark images with a normalized correlation value of 1 under a variety of attacks, including rotation, scaling, translation, vertex addition, simplification, and map cropping, and it can even extract identifiable copyright information under a 10% vertex deletion, demonstrating very comprehensive robustness.

Characteristics of Warm‐Season Mesoscale Convective Systems Over the Yangtze–Huaihe River Basin (YHR): Comparison Between Radar and Satellite

AbstractMesoscale convective systems (MCSs) are crucial in modifying the water cycle and frequently induce high‐impact weather events over eastern China. Radar and Climate Prediction Center (CPC)‐4 km satellite‐derived infrared cloud top temperature (Tb) data were used to thoroughly analyze the long‐term climatology of MCSs over eastern China, particularly in the Yangtze–Huaihe River Basin (YHR) in the warm season from 2013 to 2018. For the first time, we contrasted the effects of data set selection and threshold setting on research outcomes. The large‐scale environments of MCSs initiation were also investigated using the latest global reanalysis data ERA5. It is found that striction of thresholds, including duration, reflectivity/Tb, area, and linearity, would lead to a greater proportion of early‐morning MCSs. Satellite‐identified MCSs differed from radar‐derived ones, exhibiting afternoon diurnal peaks, faster movement speeds, longer travel distances, and expansive impact areas. The center of MCS and related precipitation shifted northward from Pre‐Meiyu to Post‐Meiyu seasons, contributing to up to 20% of total rainfall, with most MCSs moving along eastward trajectories. MCSs typically had the most substantial impact in the Meiyu season because of the most prolonged duration, largest convective core area, and strongest precipitation intensity. Warm‐season MCSs initiated ahead of midlevel troughs and were related to strong anomalous low‐level convergence and midlevel upward. The circulation anomalies were the strongest in the Pre‐Meiyu season among the three subseasons, with most moisture sourced from the southwest.

Experimental study and simulations of hydrogen cooling effectiveness for aviation PEM fuel cells

Proton exchange membrane fuel cells (PEMFCs) are seen as one possible future means of driving the change towards a zero-emission society. In a civil aircraft, fuel cell systems can have multiple potential benefits, such as reduced noise, lowered emissions and higher fuel economy compared to jet aircraft. For controlling the fuel cell temperature, thermal management systems are required which can be optimized for aircraft applications regarding their weight and reliability. In this work, a simplified and light-weight thermal management system relying on hydrogen cooling is presented and analysed. To investigate the feasibility, a test rig and a three-dimensional, singular channel model in ANSYS Fluent were designed. Fuel cell temperature could be maintained within the set threshold in the model and the test rig, thus showing that controlling the fuel cell temperature via the hydrogen reactant flow is a viable alternative thermal management system. Results from the model indicate that both the hydrogen mass flow and hydrogen inlet temperature should be used to control the fuel cell temperature. Furthermore, operating the fuel cell at medium to low current densities is favourable for hydrogen cooling. Future studies will explore alternate flow field designs to facilitate thermal management system relying on hydrogen.

Telemedicine network latency management system in 5G telesurgery: a feasibility and effectiveness study.

Network latency is the most important factor affecting the performance of telemedicine. The aim of the study is to assess the feasibility and efficacy of a novel network latency management system in 5G telesurgery. We conducted 20 telesurgery simulation trials (hitching rings to columns) and 15 remote adrenalectomy procedures in the 5G network environment. Telemedicine Network Latency Management System and the traditional "Ping command" method (gold standard) were used to monitor network latency during preoperative simulated telesurgery and formal telesurgery. We observed the working status of the Telemedicine Network Latency Management System and calculated the difference between the network latency data and packet loss rate detected by the two methods. In addition, due to the lower latency of the 5G network, we tested the alert function of the system using the 4G network with relatively high network latency. The Telemedicine Network Latency Management System showed no instability during telesurgery simulation trials and formal telesurgery. After 20 telesurgery simulation trials and 15 remote adrenalectomy procedures, the p-value for the difference between the network latency data monitored by the Telemedicine Network Latency Management System and the "Ping command" method was greater than 0.05 in each case. Meanwhile, the surgeons reported that the Telemedicine Network Latency Management System had a friendly interface and was easy to operate. Besides, when the network latency exceeded a set threshold, a rapid alarm sounded in the system. The Telemedicine Network Latency Management System was simple and easy to operate, and it was feasible and effective to use it to monitor network latency in telesurgery. The system had an intuitive and concise interface, and its alarm function increased the safety of telesurgery. The system's own multidimensional working ability and information storage capacity will be more suitable for telemedicine work.

Investigation of statistical methods used in prognostic prediction models for obstetric care: A 10 year-span cross-sectional study.

Obstetric care is a highly active area in the development and application of prognostic prediction models. The development and validation of these models often require the utilization of advanced statistical techniques. However, failure to adhere to rigorous methodological standards could greatly undermine the reliability and trustworthiness of the resultant models. Consequently, the aim of our study was to examine the current statistical practices employed in obstetric care and offer recommendations to enhance the utilization of statistical methods in the development of prognostic prediction models. We conducted a cross-sectional survey using a sample of studies developing or validating prognostic prediction models for obstetric care published in a 10-year span (2011-2020). A structured questionnaire was developed to investigate the statistical issues in five domains, including model derivation (predictor selection and algorithm development), model validation (internal and external), model performance, model presentation, and risk threshold setting. On the ground of survey results and existing guidelines, a list of recommendations for statistical methods in prognostic models was developed. A total of 112 eligible studies were included, with 107 reporting model development and five exclusively reporting external validation. During model development, 58.9% of the studies did not include any form of validation. Of these, 46.4% used stepwise regression in a crude manner for predictor selection, while two-thirds made decisions on retaining or dropping candidate predictors solely based on p-values. Additionally, 26.2% transformed continuous predictors into categorical variables, and 80.4% did not consider nonlinear relationships between predictors and outcomes. Surprisingly, 94.4% of the studies did not examine the correlation between predictors. Moreover, 47.1% of the studies did not compare population characteristics between the development and external validation datasets, and only one-fifth evaluated both discrimination and calibration. Furthermore, 53.6% of the studies did not clearly present the model, and less than half established a risk threshold to define risk categories. In light of these findings, 10 recommendations were formulated to promote the appropriate use of statistical methods. The use of statistical methods is not yet optimal. Ten recommendations were offered to assist the statistical methods of prognostic prediction models in obstetric care.

IoT Based Early Flood Detection System with Arduino and Ultrasonic Sensors in Flood-Prone Areas

IoT is one of the focuses of application development carried out by various developers today. The aim is to enable various devices and work independently to meet the various needs of their users. The flood early warning system is one of the much-needed IoT-based applications, enabling users to quickly obtain water level information in an area. This application can help people to be more aware of flood disasters, especially during the rainy season. This research develops a flood early warning system application by utilizing Arduino and ultrasonic sensors installed in flood-prone areas. The sensor is used to measure the water level at a time based on the distance from the water surface to the sensor. When the distance between the water surface and the sensor is less than or equal to the set threshold, the sensor will send data and alerts to the user via email. This research applies the IoT design and development method. In addition, this research also used the C and Python programming language for application prototypes and the MySQL database to store the data. the application in this study was tested using the blackbox method and the results showed that all application functions could run properly.

Some Considerations about the Incorporation of Dynamic Parameters in the Structural Health Monitoring Systems of Bridges

Currently, there is a growing concern about the conservation and maintenance of infrastructure. Within this context, bridges deserve special attention, given their technical complexity and strategic nature. To this end, modern technology provides the opportunity to implement systems for structural health monitoring (SHM), a field in which great advances have been made in recent years. In this sense, one of the fastest-growing lines of work in Civil Engineering is the early detection of incidents because of changes in the dynamic behaviour of structures. Throughout this paper, some of the most notable considerations that the authors have been appreciating in the latest structures studied are summarized. These may be of interest for the possible incorporation of dynamic parameters in SHM systems that could be implemented in other structures in the future. With this purpose, a review of the different issues that must be studied within the dynamic analysis of a structure is carried out, such as the structural typology, the type of instrumentation, the recorded accelerations, the analysis of the natural frequencies, the study of the modal damping ratio and the set of thresholds; issues that are also accompanied by examples observed in two real monitored structures.

A Robust Index Based on Phenological Features to Extract Sugarcane from Multisource Remote Sensing Data

Sugarcane is a major crop for sugar and biofuel production. Historically, mapping large sugarcane fields meticulously depended heavily on gathering comprehensive and representative training samples. This process was time-consuming and inefficient. Addressing this drawback, this study proposed a novel index, the Normalized Difference Vegetation Index (NDVI)-Based Sugarcane Index (NBSI). NBSI analyzed the temporal variation of sugarcane’s NDVI over a year. Leveraging the distinct growth phases of sugarcane (transplantation, tillering, rapid growth and maturity) four measurement methodologies, f(W1), f(W2), f(V) and f(D), were developed to characterize the features of the sugarcane growth period. Utilizing imagery from Landsat-8, Sentinel-2, and MODIS, this study employed the enhanced gap-filling (EGF) method to reconstruct NDVI time-series data for seven counties in Chongzuo, Guangxi Zhuang Autonomous Region, during 2021, subsequently testing NBSI’s ability to extract sugarcane. The results demonstrate the efficiency of NBSI with simple threshold settings: it was able to map sugarcane cultivation areas, exhibiting higher accuracy when compared to traditional classifiers like support vector machines (SVM) and random forests (RF), with an overall accuracy (OA) of 95.24% and a Kappa coefficient of 0.93, significantly surpassing RF (OA = 85.31%, Kappa = 0.84) and SVM (OA = 85.87%, Kappa = 0.86). This confirms the outstanding generalizability and robustness of the proposed method in Chongzuo. Therefore, the NBSI methodology, recognized for its flexibility and practicality, shows potential in enabling the extensive mapping of sugarcane cultivation. This heralds a new paradigm of thought in this field.

Dual Axis Solar Tracking of Solar Radiation for Agriculture usage

Energy is one of the important parts of our life. As there is decline in fossil fuels and increasing demand for energy an alternate energy source is required which is renewable energy source like solar, wind etc. So, we use solar panels which trap the energy from the sun and produce electricity, and this energy is used for agriculture purpose like to run water pumps and to meet other energy requirements in agriculture. Due to rotation of earth the stationery solar panel will receive energy only for smaller duration so to overcome this we use dual axis tracking system which rotates solar panel according to direction of sun and helps in producing more solar energy. Agriculture is one of the major contributing sectors to the economy of a country and it requires automation and advanced technology so that it helps farmers in producing more yield and better crops. So, in agriculture continuous monitoring of soil and water level is required so we can automate this which helps the farmers where the device continuously monitors and depending upon the moisture level of the soil the water pumps get on automatically and we can use this for different crops and set threshold depending upon the crop type. And we can also integrate this idea with IOT technology for improvements. By this we create sustainable energy indirectly producing sustainable environment.

Comparison of 0.5% Hyperbaric Bupivacaine with Midazolam Versus 0.5% Hyperbaric Bupivacaine Alone Intrathecally: Prospective Observational Study

Context: Neuraxial anesthesia is the preferred choice for infraumbilical surgeries due to its advantages. The incorporation of midazolam alongside local anesthetic drugs in spinal anesthesia has demonstrated positive outcomes. This study was undertaken to assess the effectiveness of midazolam in terms of analgesic and anesthetic efficacy, as well as potential adverse effects, in patients undergoing infraumbilical surgeries. Aims: To compare the analgesic and anaesthetic effect of mixture of midazolam - bupivacaine as compared to bupivacaine alone in patients undergoing infra-umbilical surgeries under spinal anaesthesia. Settings and Design: The present study is a prospective, observational study. Methods and Material: Fifty patients posted for elective infra-umbilical surgery were randomly divided into two groups of 25 each for intrathecal drug administration. (n=25). After administration of block, patients were assessed for analgesic and anesthetic effect of the drug. Statistical Analysis used: The study analyzed through the statistical programming software SPSS-22 and it involved the application of the student’s t-test, with a significance threshold set at a P value of <0.05. Results: Analgesic duration of patients in Midazolam Group was signiÞcantly longer compared to Bupivacaine Group for sensory block. More patients in the midazolam group were sedated and easily arousable. Conclusions: This study concludes that the addition of intrathecal preservative-free midazolam to hyperbaric bupivacaine improved intra - operative anaesthesia and prolonged duration of analgesia. Also, it was observed that there was a significant reduction in the consumption of analgesics during the postoperative period in patients undergoing infra-umbilical surgeries without causing any significant haemodynamic changes.

Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample

Spatial data sampling can improve the performance in geo-spatial prediction. However, measuring the reliability of polygon-based data in sampling process is still a challenge. In this study, a reliability-based sampling (RBS) method was proposed to deal with this question and it was applied in landslide susceptibility mapping. First, the prototype of landslide was extracted from landslide polygon data, then, the reliability of landslide samples and non-landslide samples is measured using the similarity in environmental factor between the candidate samples and the prototype. The mutual exclusion reliability threshold setting method is used to collect the landslide samples and non-landslide samples with reliability over certain threshold. A case study demonstrates that the RBS method is better than existing representative method (i.e. Landslide entity) in terms of Accuracy and AUC with different sample sizes. In summary, The RBS is an efficient method to improve the spatial pattern of samples can also be applied to in other geo-spatial predictions.

Relation of Iron Deficiency Anemia in Pre-Menopause Women with Calcium and Vitamin D Levels at Teaching Hospital

Background: Iron deficiency anemia (IDA) is a common condition influenced by various factors, such as age, symptom duration, and other biochemical markers. Premenopausal women often experience premenstrual syndrome (PMS), which is frequently associated with reduced blood levels of calcium and vitamin D. Given the worldwide public health concern of osteoporosis, understanding lifetime preventive measures is crucial. Methods: This study aimed to explore the relationship between IDA and various clinical characteristics in 161 participants. We evaluated parameters including age, symptom duration, and levels of calcium and vitamin D to identify potential associations with IDA. Data for the 161 patients were collected using a predesigned proforma, as approved by the ethics committee. The Chi-square test was employed to determine statistical significance, with a P-value threshold set at 0.05. Results: The average age of participants was 38.6 years, with the majority (58.4%) reporting symptoms for an average duration of 58.4 months. Among them, 54% had confirmed cases of IDA. However, there was no significant age-related correlation with IDA. Notably, a strong correlation was observed between IDA and low vitamin D levels, which were present in 79.8% of the patients. Conversely, no significant relationship was found between calcium levels and IDA. Conclusion: Current research underscores the complex interplay between IDA and various clinical factors. Low vitamin D levels appear to be strongly linked with IDA, whereas age and symptom duration are not reliable indicators of the condition. This emphasizes the importance of considering multiple factors in the diagnosis and treatment of IDA, particularly focusing on the potential role of vitamin D in its pathogenesis. Further prospective studies are needed to confirm and expand upon these findings, potentially leading to improved treatment and diagnostic strategies for patients with IDA.

Physico-chemical and Mineralogical Characterization of Coltan Ores from the Tanganyika Province in the Democratic Republic of Congo

The physico-chemical, mineralogical, and radioactive characterization of coltan ores has the ad-vantage of facilitating the prediction of the various hydrometallurgical methods to be implemented to pro-duce tantalum and niobium in their pure state, taking into account the content of their oxides and that of accompanying impurities. At the end of this investigation, we noted that the coltan ores collected from the four mining sites in Tanganyika province did not have the same mineralogy or the same chemical composition in terms of tantalum and niobium oxides and accompanying impurities. These ores contain a mineralogy that looks like either struëverite, euxènite, colombo-tantalite or tantalite. Moreover, the coltan from this province presented no risk of exposure to ionizing radiation, given the radioactivity values found by the dosimeter and whose average value in milli-Sieverts per year remained well below the threshold set in the regulations in force in DR Congo. We found an average value of 0.143 mSv/year versus 20 mSv/year featured in the law of March 29, 2005, and promulgated in the official gazette of country.

Dog owner mental health is associated with dog behavioural problems, dog care and dog-facilitated social interaction: a prospective cohort study

Despite numerous qualitative and cross-sectional studies investigating how dog-related factors may impact owners’ well-being, empirical studies to test these causal effects are lacking. This prospective cohort study examined the correlation and potential causal effect of 17 dog-related factors with six well-being outcomes (depression, anxiety, loneliness, suicidal ideation, hedonic well-being and eudaimonic well-being) in dog owners. Over a four-week period, 709 adult dog owners reported their weekly well-being and occurrence of each dog-related factor (e.g. how many times they ran with their dogs). A random intercept cross-lagged panel model (RI-CLPM) with significance threshold set at 0.001 was used. Six factors correlated with poorer owner well-being (i.e. aggressive dog behaviour, fearful dog behaviour, poor dog health, failure to provide for the dog, lack of control over the dog, and dog presence). Only ‘friendly conversation with others due to the dog’ correlated with better well-being. Purposeful reductions in the frequency of dog behavioural and health-related issues are likely to improve owner well-being, as well as greater consistency in dog care (i.e. provide for the dog) and more engagement in friendly dog-facilitated social interactions. No potential causal effects were significant. Further studies investigating causal relationships are essential to improve people’s well-being through dog ownership.

Long-term changes of rice yield loss estimated with AOT40 and M7 metrics using comprehensive ozone and rice cultivation data over South Korea

This study examines the change in rice yield due to ozone exposure in South Korea using extended air quality monitoring data from 2000 onwards. Notably, the maximum daily 8-h average O3 (MDA8O3) showed a substantial annual increase of 1 part per billion by volume (ppbv) from 1990 to 2021. AOT40 (accumulated dose of ozone over a threshold of 40 ppb) levels exceeded set thresholds in the early 2010s, and the M7 (mean 7-h ozone mixing ratio) index exhibited a parallel pattern, with a more pronounced increase than the AOT40 during the same period. Spatial variations of AOT40 and M7 metrics have been assessed annually across South Korea since 2000. Both metrics displayed spatial disparities, with higher values in western regions and lower values in the east. In particular, Dangjin and Seosan counties in Chungnam province experienced the greatest rice yield loss due to extensive rice cultivation area and high ozone exposure metrics. The quantified yield loss due to AOT40 increased from 127,000 in 2000 to 230,000 tonnes in 2021 with an increasing rate of 6500 tonnes per year. M7 indicated a rise in yield loss of 3500 tonnes per year, with yield losses growing from 32,000 in 2000 to 92,000 tonnes in 2021. Despite M7’s lower loss, it demonstrated a higher percentage increase of 188% over two decades, which was double AOT40’s 81%. While the decline in rice production was mainly linked to shrinking cultivation areas, its productivity was improved. Taking both factors into account, there was an unexplained 3% decrease in production over the same period. This discrepancy was close to the 2.5% rice yield loss attributed to the AOT40 metrics, suggesting that the majority of the additional 3% decline in production, surpassing improvements in productivity, could be attributed to the impacts of ozone exposure. We estimated the annual economic loss due to rice yield loss up to around 0.6 billion US dollars, corresponding to an annual rice production loss of 230,000 tonnes using AOT40. It is important to note that this value is expected to steadily worsen as ozone levels increase. This underscores the urgency of taking swift measures to reduce ozone levels, aiming not only to mitigate future economic losses but also to prevent potential health implications.

Heterogeneous decision-making dynamics of threshold-switching agents on complex networks.

In the classical two-player decision-making scenario, individuals may have different tendencies to take a certain action, given that there exists a sufficient number of neighbors adopting a particular option. This is ubiquitous in many real-life contexts including traffic congestion, crowd evacuation, and minimal vertex cover problem. Under best-response dynamics, we investigate the decision-making behaviors of heterogeneous agents on complex networks. Results of the networked games are twofold: for networks of uniform degree distribution (e.g., the lattice) and fraction of the strategy is of a linear function of the threshold setting. Moreover, the equilibrium analysis is provided and the relationship between the equilibrium dynamics and the change of the threshold value is given quantitatively. Next, if the games are played on networks with non-uniform degree distribution (e.g., random regular and scale-free networks), influence of the threshold-switching will be weakened. Robust experiments indicate that it is not the value of the average degree, but the degree distribution that influences how the strategy evolves affected by the threshold settings. Our result shows that the decision-making behaviors can be effectively manipulated by tuning the parameters in the utility function (i.e., thresholds) of some agents for more regular network structures.

Short‐term dynamics of drainage density based on a combination of channel flow state surveys and water level measurements

AbstractHeadwater streams often experience intermittent flow. Consequently, the flowing drainage network expands and contracts and the flowing drainage density (DD) varies over time. Monitoring the DD dynamics is essential to understand the processes controlling it. However, our knowledge of the event‐scale DD dynamics is limited because high spatial and temporal resolution data on the DD remain sparse. Therefore, our team monitored the DD dynamics and hydrologic variables in two 5‐ha headwater catchments in the Swiss pre‐Alps in the summer of 2021, through mapping surveys of the flow state and a wireless streamwater level sensor network. We combined the two data sources to calculate the DD at the event‐time scale. Our so‐called CEASE method assumes that flow in a channel reach occurs above a set of water level thresholds, and it determined the DDs with accuracies >94%. DD responses to events differed for the two catchments, despite their proximity and similar size. DD ranged from 2.7 to 32.2 km km−2 in the flatter catchment (average slope: 15°). For this catchment, the discharge‐DD relationship became steeper when DD exceeded 20 km km−2 and DD increased substantially with relatively small increases in discharge. For rainfall events during dry conditions, the discharge‐DD relationship showed counterclockwise hysteresis, likely due to initially high groundwater discharge from the area near the catchment outlet; once rainfall stopped, DD remained high during the streamflow recession due to rising groundwater levels throughout the catchment. For events during wet conditions, the discharge and DD responded synchronously. In the steeper catchment (average slope: 24°), the DD varied only from 7.8 to 14.6 km km−2 and there was no hysteresis or threshold behaviour in the discharge‐DD relationship, likely because multiple groundwater springs maintained streamflow throughout the network during the monitoring period. These results highlight the high variability in DD and its dynamics across small headwater catchments.