Relative Performance Research Articles

Differences in Player Performance and Longevity After Achilles Tendon Rupture Between Professional Basketball Players in the NBA and WNBA.

Prior studies in the National Basketball Association (NBA) and Women's National Basketball Association (WNBA) reported worse player performance after Achilles tendon rupture (ATR). The purpose of this study was to compare time to return to play (RTP) and performance after ATR between NBA and WNBA athletes. It was hypothesized that there would be no relative difference between the NBA and WNBA players. Cohort study; Level of evidence, 3. ATRs in the NBA between 1987 and 2017 and WNBA between 2006 and 2017 were identified through a rigorous online search of articles. Included athletes had no prior leg injuries and had played ≥3 seasons before and after ATR. Sex, age, position, body mass index, height, years of experience, time to RTP, and player efficiency rating (PER) were recorded. Relative performance was measured by matching injured athletes to uninjured controls in the same league in a 1:2 ratio. Relative differences were compared between leagues, with adjustment for baseline features. Multiple regression analysis was employed to identify variables correlating with RTP and PER. Included were 102 professional basketball players, of whom 34 sustained ATR (21 male, 13 female). Sex/league correlated with differences in RTP (P < .001). There was a significant difference between the WNBA and NBA in PER when comparing 1 year pre- and 1 year postinjury (1.49 ± 0.25 vs 3.87 ± 0.43, respectively; mean ± SD P < .001). Compared with intraleague controls, the relative difference in PER postinjury was 0.81 ± 0.11 (WNBA) and 3.9 ± 0.89 (NBA) (P < .001). Multiple regression analysis indicated that when controlling for years of experience, player position, and age, NBA players took 126 days longer than WNBA players to RTP (P < .001) and NBA players had 9.96 times increased odds of taking >200 days to RTP compared with WNBA players (P = .006). Sex/league was a significant predictor of RTP after ATR. When compared with their respective controls, NBA players saw a greater decrease in postinjury performance than WNBA players. NBA players took longer to RTP than WNBA players. ATRs appear to more negatively affect NBA players than WNBA players.

The Dunning‒Kruger effect in resident predicted and actual performance on the American Board of Emergency Medicine in-training examination.

The Dunning-Kruger effect (DKE) is a cognitive bias wherein individuals who are unskilled overestimate their abilities, while those who are skilled tend to underestimate their capabilities. The purpose of this investigation is to determine if the DKE exists among American Board of Emergency Medicine (ABEM) in-training examination (ITE) participants. This is a prospective, cross-sectional survey of residents in Accreditation Council for Graduate Medical Education (ACGME)-accredited emergency medicine (EM) residency programs. All residents who took the 2022 ABEM ITE were eligible for inclusion. Residents from international programs, residents in combined training programs, and those who did not complete the voluntary post-ITE survey were excluded. Half of the residents taking the ITE were asked to predict their self-assessment of performance (percent correct), and the other half were asked to predict their performance relative to peers at the same level of training (quintile estimate). Pearson's correlation (r) was used for parametric interval data comparisons and a Spearman's coefficient (ρ) was determined for quintile-to-quintile comparisons. A total of 7568 of 8918 (84.9%) residents completed their assigned survey question. A total of 3694 residents completed self-assessment (mean predicted percentage correct 67.4% and actual 74.6%), with a strong positive correlation (Pearson's r 0.58, p<0.001). There was also a strong positive correlation (Spearman's ρ 0.53, p<0.001) for the 3874 residents who predicted their performance compared to peers. Of these, 8.5% of residents in the first (lowest) quintile and 15.7% of residents in the fifth (highest) quintile correctly predicted their performance compared to peers. EM residents demonstrated accurate self-assessment of their performance on the ABEM ITE; however, the DKE was present when comparing their self-assessments to their peers. Lower-performing residents tended to overestimate their performance, with the most significant DKE observed among the lowest-performing residents. The highest-performing residents tended to underestimate their relative performance.

Cognitive, Affective, and Normative Drivers of Pro-Environmental Intentions Among Urban Forest Visitors – The IPMA Approach

This study investigates the cognitive, affective, and normative drivers of pro-environmental intentions among urban forest visitors, applying the Importance-Performance Map Analysis (IPMA) to identify key dimensions influencing sustainable behaviors. Drawing from existing literature, the research examines how environmental awareness, connectedness to nature, environmental empathy, place attachment, social norms, and moral norms shape pro-environmental intentions. Data were collected from 550 respondents in Bangkok using a self-administered online survey. Structural equation modeling was used to evaluate the relationships among these constructs, and IPMA was employed to assess their relative importance and performance. Results revealed that affective components, particularly place attachment and connectedness to nature, and normative factors like social norms, have a significant impact on pro-environmental intentions. Meanwhile, environmental awareness and moral norms showed limited influence. The findings highlight areas for improvement and provide practical implications for enhancing sustainable behaviors in urban forests.

Local adaptation, plasticity, and evolved resistance to hypoxic cold stress in high-altitude deer mice

A fundamental question in evolutionary biology concerns the relative contributions of phenotypic plasticity vs. local adaptation (genotypic specialization) in enabling wide-ranging species to inhabit diverse environmental conditions. Here, we conduct a long-term hypoxia acclimation experiment to assess the relative roles of local adaptation and plasticity in enabling highland and lowland deer mice (Peromyscus maniculatus) to sustain aerobic thermogenesis at progressively increasing elevations. We assessed the relative physiological performance capacities of highland and lowland natives as they were exposed to progressive, stepwise increases in hypoxia, simulating the gradual ascent from sea level to an elevation of 6,000 m. The final elevation of 6,000 m far exceeds the highest attainable elevations within the species' range, and therefore tests the animals' ability to tolerate levels of hypoxia that surpass the prevailing conditions within their current distributional limits. Our results demonstrate that highland natives exhibit superior thermogenic capacities at the most severe levels of hypoxia, suggesting that the species' broad fundamental niche and its ability to inhabit such a broad range of elevational zones is attributable to genetically based local adaptation, including evolved changes in plasticity. Transcriptomic and physiological measurements identify evolved changes in the acclimation response to hypoxia that contribute to the enhanced thermogenic capacity of highland natives.

Rapid and environmentally friendly reconstruction of airport runways under combined laser and liquid energy fields: Ushering a new era of low-carbon and efficient rubber removal in the aviation industry

When the aircraft takes off or lands, the tread rubber melts and adheres to the airport runway and its embedded ground lights, forming black pavement rubber marks, affecting the smoothness of the pavement, reducing the lighting degree and friction coefficient of the pavement, and directly affecting the safety of the aircraft take-off and landing. There are many shortcomings in the standard rubber removal methods. The multi-energy field composite laser cleaning technology comprehensively utilizes the laser field and auxiliary hydrodynamic field. It leverages the high-efficiency advantages of laser processing and avoids the shortcomings of its thermal energy surplus, achieving efficient, accurate, and green material removal. Based on this, this article presents for the first time the composite energy-field laser cleaning process mechanism and related performance of rubber mud layer from the surface of cement/asphalt runway and embedded grounding light cover (aluminum alloy)/light window (tempered glass). Pulse laser single energy-field cleaning is suitable for removing rubber marks from cement runways, grounding lamp covers, and light window surfaces. The cleaning mechanism is divided into two types based on the substrate: ablation and thermal stress peeling. Composite energy-field laser cleaning is suitable for removing rubber marks from asphalt runway surfaces, and the phase explosion of the liquid film is the cleaning mechanism. Under appropriate process parameters, laser cleaning can improve the runway's skid resistance, enhance the grounding light cover's corrosion resistance, and increase the grounding light window's transparency. This study can provide a reference for the laser cleaning behavior of different materials and solve the problem of rubber removal on international civil aviation airport pavement, promoting the construction of "safe, green, and smart" airports.

An artificial neural network visible mathematical model for predicting slug liquid holdup in low to high viscosity multiphase flow for horizontal to vertical pipes

AbstractSlug liquid holdup (SLH) is a critical requirement for accurate pressure drop prediction during multiphase pipe flows and by extension optimal gas lift design and production optimization in wellbores. Existing empirical correlations provide inaccurate predictions because they were developed with regression analysis and data measured for limited ranges of flow conditions. Existing SLH machine learning models provide accurate predictions but are published without any equations making their use by other researchers difficult. The only existing ML model published with actual equations cannot be considered optimum because it was selected by considering artificial neural network (ANN) structures with only one hidden layer. In this study, an ANN-based model for SLH prediction with actual equations is presented. A total of 2699 data points randomly divided into 70%, 15%, and 15% for training, validation, and testing was used in constructing 71 different network structures with 1, 2, and 3 hidden layers respectively. Sensitivity analysis revealed that the optimum network structure has 3 hidden layers with 20, 5, and 15 neurons in the first, second, and third hidden layers, respectively. The optimum network structure was translated into actual equations with the aid of the weights, biases, and activation functions. Trend analysis revealed that this study’s model reproduced the expected effects of inputs on SLH. Test against measured data revealed that this study’s model is in agreement with measured data with coefficient of determinations of 0.9791, 0.9727, 0.9756, and 0.9776 for training, testing, validation, and entire datasets, respectively. Comparative study revealed that this study’s model outperformed existing models with a relative performance factor of 0.002. The present model is presented with visible mathematical equations making its implementation by any user easy and without the need for any ML framework. Unlike existing ANN-based models developed with one hidden layered ANN structures, the present model was developed by considering ANN structures with one, two, and three hidden layers, respectively.

Annual relative performance degradation in photovoltaic solar plants

This study aims to assess annual relative performance degradation σ to be used in the computations of the Levelised Cost of Energy (LCOE) of new plants based on the standard year for weather and solar resource. The assessment is based on the experimental data of power generation, weather, and resource for different years, and simulations of power generation by using the National Renewable Energy Laboratory (NREL) System Advisor Model (SAM) software. The raw data of power generation for 53 different plants spanning about one decade show a σ assessed at less than 0.29%. This makes it reasonable to assume in SAM σ=0% to σ=0.29%. This is less than the default σ=0.5% currently used in SAM based on limited outdated data. The updated performance degradation factor diminished to σ=0-0.29% reduces the computed LCOE for typical new projects in the United States from 2.86 to 2.74-2.80 ¢/kWh. Correction of the σ trends for interannual variability of weather and resource is unnecessary, providing enough years of operation are covered, given the inaccuracies in the model and the supporting data, the mitigation by management of the performance changes, and the complex phenomena correlated to the change of weather and irradiance affecting the power output in different directions. The reduced σ is the result of a significant product improvement over the last decades, especially for large power plants, compared to the plant which provided data for the prior correlation of performance degradation, and much better management and maintenance of the plants.

Assessing performance of water supply operation of water companies in China: A union dynamic multi-activity network data envelopment analysis

Urban growth and the increasing urban water demand highlight the importance of performance analysis in the water supply industry. Both water supply companies (WSCs) and water supply and sewage companies (WSSCs) produce and deliver potable water for city dwellers. Water authorities use benchmarks to monitor the two types of companies’ operations. The internal operation process and heterogeneities of observations were not considered for relative performance evaluation by conventional DEA models. To overcome these limitations, this study proposes a union dynamic multi-activity network data envelopment analysis (DMANDEA) model to extend static network DEA to a more general case for dynamic efficiency assessment of the water supply operations of water companies. For the first time, the model allows for the decomposition of the water supply operation into water production (WP) and water consumption (WC) substages and performance comparisons for water companies by considering their heterogeneity simultaneously. Specifically, by using a non-radial directional distance function (DDF), the inefficiencies for substages and the entire system can be both assessed dynamically. This model is applied to a sample of 246 water companies in China in 2016–2018. The overall, period, and substages’ group efficiencies, meta efficiency, and technology gap ratio (TGR) for 179 WSCs and 67 WSSCs are all provided. The empirical results reveal that: (1) the average group efficiencies of WSCs and WSSCs are 0.4567 and 0.6611, respectively. The meta efficiencies of WSCs and WSSCs are 0.4523 and 0.4703, respectively. The TGR of WSCs (0.994) is greater than that of WSSCs (0.7067). (2) WSSCs perform better than WSCs do, which suggests that economies of scope do exist in WSSCs. However, WSCs possess the best technology for water supply operations because of their high TGR. (3) The average meta efficiency for all the observed water companies (0.4572) is not optimistic, which correlates more with inefficiency in the WP stage. The causes stem from the natural monopoly, lack of competition, and relatively lower water tariffs. The Chinese water supply industry requires diversification of ownership and operation modes, and gradual increases in water tariffs. (4) Water companies in the eastern region outperform those in other regions. Regional cooperation, such as technology sharing, the transfer of managerial experience, and information exchange, needs to be emphasized.

Using Empirical Performance Data to Source Bluebunch and Snake River Wheatgrass Plant Materials to Restoration Sites in the Eastern Great Basin, USA.

To infer adaptation of plant material, restoration practitioners often consider only surrogate geographic or climatic information. However, empirical biomass data could assist in deciding what material to use where. To test this approach, we transplanted seven bluebunch wheatgrass (BBWG; Pseudoroegneria spicata) and five Snake River wheatgrass (SRWG; Elymus wawawaiensis) populations to three sites ranging from low to high precipitation (LPPT, MPPT, and HPPT). We measured establishment-year (2011) biomass at all sites and 2012-16 biomass at MPPT and HPPT. When data were standardized by site, P-7 and Anatone produced the mostBBWG biomass across sites and Wahluke the least in both 2011 and 2012-16, while E-58X produced the most SRWG biomass and Secar and E-49X the least in 2011 and 2012-16, respectively. Among BBWG populations in 2011, relative performance of P-7 (G6 generation) and Goldar increased and Whitmar decreased at wetter sites, while Columbia was stable (high) and Wahluke was stable (low) over sites. Among SRWG populations in 2011, Secar, Secar78, and E-58X increased at drier sites and Discovery at wetter sites. However, once established, populations of both species were much more similar for trend. In 2012-16, trend somewhat increased for five BBWG populations from MPPT to HPPT, was stable for Wahluke, but declined for Columbia, while all five SRWG populations declined at HPPT. These results suggest that, once established, BBWG is mostly stable across sites, while SRWG is less adapted to wetter sites. In 2012-16, BBWG populations originating at drier (or wetter) sites mostly performed relatively better at MPPT (or HPPT), suggesting adaptation to site. However, in the establishment year (2011), this relationship did not hold, suggesting seedling vigor and immature growth rate play a stronger role than precipitation at the site of origin.

Brand loyalty, managerial delegation and position choices: Bertrand versus Cournot competition

In a mature product market, brands compete aggressively with each other using a marketing mix that includes position, pricing, and managerial delegation to win loyal customers and gain a larger market share. We examine the effects of brand loyalty on the endogenous choices of managerial compensation contracts and optimal brand positions in an uncovered market model. We analytically compare 18 symmetric and asymmetric mutual delegation cases associated with three delegation schemes—no delegation, sales delegation and relative performance (RP) delegation—in the Bertrand and Cournot competition modes. In the case of symmetric mutual contracts, under Bertrand competition, a price commitment effect resulting from the delegation decision weakens the competition, inducing higher prices and profits for both brands regardless of the type of delegation scheme. Under Cournot competition, delegation decisions can be viewed as signals to promote competition, intensifying the degree of competition. To acquire more loyal customers and higher profits, mutual RP delegation is the dominant choice under Bertrand competition, whereas mutual profit maximization is the dominant choice under Cournot competition. Furthermore, the social welfare is the highest when both brands optimally choose the RP delegation under Bertrand or optimally choose no delegation under Cournot; however, consumer surpluses are the lowest in both optimal cases.

Time-Dependent Aftershock Probabilistic Forecasting and Ensemble Model Implementation: Insights from the 2021 Ms 7.4 Maduo Earthquake

Abstract On 22 May 2021, an Ms 7.4 earthquake with a focal depth of 17 km struck the Maduo region of Qinghai province, breaking a 3.8-year quiescence of strong earthquakes (magnitude &amp;gt;7.0) in mainland China. This event has increased stress on the Maqin–Maqu segment of the Kunlunshan fault, heightening the potential for a large earthquake in the region. In this study, we employed the epidemic-type aftershock sequence (ETAS) model and Reasenberg–Jones (R–J) model to fit the aftershock sequence following the mainshock, analyzing the temporal response and its ability to influence future generations. Concurrently, ensemble models were explored to leverage the strengths of both the ETAS and R–J models. Short-term forecasts of the probability and occurrence rate of aftershock events with varying magnitudes were conducted for the next three days. Then statistical methods, that is, receiver-operating characteristic diagrams and information gain, were used to evaluate the unconditional and relative performance. Our findings include that the ETAS model indicates a high decay rate with many aftershocks triggered by previous events, whereas the R–J model shows a normal decay rate with a higher proportion of strong aftershocks. The ETAS and R–J models perform better than random guesses for different aftershock magnitudes, but the ETAS model is somewhat affected by the problem of missing small earthquakes for a short period of time after the mainshock. The ensemble model that chooses the minimum strategy shows promise, especially in the early period, suggesting a reasonable and cautious decision approach should be chosen during the unstable stage. This study highlights the importance of short-term aftershock probability estimation for seismic research and decision-making. In the absence of more accurate models, current analytical approaches within the operational earthquake forecasting framework remain valuable. Continuous testing, feedback, and refinement of forecasting models, along with the development of ensemble models, are essential for enhancing seismic risk assessment and mitigation strategies.

Machine learning solutions for adaptive traffic signal control: A review of image-based approaches

This paper gives an overview and performance evaluation of various machine learning models implemented in management of urban traffic congestions, more specifically, adaptive traffic signal control systems. It considers a review of deep learning algorithms including R- CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO v4, and YOLOv8, with regard to their efficiencies for vehicle detection and traffic prediction under varying scenarios. Certain traffic conditions, camera placements, and environmental factors—related performance for each of the models are discussed. The major performance in most of the scenarios was depicted by the YOLO v4. However, at the same time, YOLOv8 has shown potential to do much better than YOLO v4 on image processing and the resultant accuracy. It also proposes a new algorithm for traffic light timing, whose efficacy is tested using the SUMO simulation platform. While results have shown improvements in urban traffic management, a review underlines that such is in deep need of extensive real-world testing. Future directions should include views from varied angles and weather conditions, and the detection of emergency vehicles, probably with specialized datasets.

Bacillus amyloliquefaciens CU33 Fermented Feather–Soybean Meal Product Improves the Crude Protein Digestibility, Diarrhea Status, and Growth Performance of Goat Kids

This study investigates the effects of replacing fish meal with fermented feather meal–soybean meal product (FFSMP) from Bacillus amyloliquefaciens CU33 in the starter on growth performance, relative health performance, and digestibility of Alpine goat kids. In trial 1, thirty-two Alpine goat kids (male) were randomly assigned to dietary supplementation of 2% feather meal–soybean meal mix (FSM), 2% fish meal, or replacing fish meal with 50% or 100% FFSMP (FFSMP-50 or FFSMP-100) in starter (n = 8). In trial 2, sixteen goat kids were selected after trial 1 and used in this digestion trial which began from 12 weeks old. The treatments were the same as in trial 1 (n = 4). In trial 1, the growth performance of the FFSMP groups was better than the FSM group at 0 to 10 weeks (p &lt; 0.05). The fecal consistency index of the FFSMP-100 group was better than the FSM group at 0 to 5 weeks and 5 to 10 weeks. In trial 2, the crude protein (CP) digestibility of the FSM group decreased (p &lt; 0.05). In conclusion, dietary supplementation with 2% FFSMP in goat kids’ diets can improve the growth performance, the CP digestibility, and diarrhea status, and it can completely replace the fish meal in starter diets.

Should you buy gold stocks or paper gold?

We analyze which type of gold investment is better for investors by studying the determinants of returns on gold mining stocks (gold stocks) and gold exchange-traded commodities (gold ETCs) over the period from March 2012 to February 2024. We find that gold ETCs outperform gold stocks in both returns and risk, and respond significantly more positively to economic policy uncertainty (EPU), offering investors more favorable safe-haven properties. Gold stocks are also more sensitive to oil price increases than are gold ETCs, and their relative price performance is negatively related to both oil price changes and Gold VIX. We conclude that investing in gold stocks is less suitable for investors who buy gold as a safe-haven asset.

Multi-agent system-based framework for an intelligent management of competency building

To measure the effectiveness of learning activities, intensive research works have focused on the process of competency building through the identification of learning stages as well as the setup of related key performance indictors to measure the attainment of specific learning objectives. To organize the learning activities as per the background and skills of each learner, individual learning styles have been identified and measured by several researchers. Despite their importance in personalizing the learning activities, these styles are difficult to implement for large groups of learners. They have also been rarely correlated with each specific learning stage. New approaches are, therefore, needed to intelligently coordinate all the learning activities while self-adapting to the ongoing progress of learning as well as to the specific requirements and backgrounds of learners. To address these issues, we propose in this paper a new framework for an intelligent management of the competency building process during learning. Our framework is based on a recursive spiral Assess-Predict-Oversee-Transit model that is orchestrated by a multi-agent system. This system is particularly responsible of enabling smart transitions between learning stages. It is also responsible of assessing and predicting the process of competency building of the learner and, then, making the right decisions about the learning progress, accordingly. Results of our solution were demonstrated via an Augmented Reality app that we created using the Unity3D engine to train learners on Air Conditioner maintenance.

Subjective performance assessment protocol for visual explanations-based face verification explainability

The integration of Face Verification (FV) systems into multiple critical moments of daily life has become increasingly prevalent, raising concerns regarding the transparency and reliability of these systems. Consequently, there is a growing need for FV explainability tools to provide insights into the behavior of these systems. FV explainability tools that generate visual explanations, e.g., saliency maps, heatmaps, contour-based visualization maps, and face segmentation maps, show promise in enhancing FV transparency by highlighting the contributions of different face regions to the FV decision-making process. However, evaluating the performance of such explainability tools remains challenging due to the lack of standardized assessment metrics and protocols. In this context, this paper proposes a subjective performance assessment protocol for evaluating the explainability performance of visual explanation-based FV explainability tools through pairwise comparisons of their explanation outputs. The proposed protocol encompasses a set of key specifications designed to efficiently collect the subjects’ preferences and estimate explainability performance scores, facilitating the relative assessment of the explainability tools. This protocol aims to address the current gap in evaluating the effectiveness of visual explanation-based FV explainability tools, providing a structured approach for assessing their performance and comparing with alternative tools. The proposed protocol is exercised and validated through an experiment conducted using two distinct heatmap-based FV explainability tools, notably FV-RISE and CorrRISE, taken as examples of visual explanation-based explainability tools, considering the various types of FV decisions, i.e., True Acceptance (TA), False Acceptance (FA), True Rejection (TR), and False Rejection (FR). A group of subjects with variety in age, gender, and ethnicity was tasked to express their preferences regarding the heatmap-based explanations generated by the two selected explainability tools. The subject preferences were collected and statistically processed to derive quantifiable scores, expressing the relative explainability performance of the assessed tools. The experimental results revealed that both assessed explainability tools exhibit comparable explainability performance for FA, TR, and FR decisions with CorrRISE performing slightly better than FV-RISE for TA decisions.

Assessing generalizability of Deep Reinforcement Learning algorithms for Automated Vulnerability Assessment and Penetration Testing

Modern cybersecurity best practices and standards require continuous Vulnerability Assessment (VA) and Penetration Test (PT). These activities are human- and time-expensive. The research community is trying to propose autonomous or semi-autonomous solutions based on Deep Reinforcement Learning (DRL) agents, but current proposals require further investigations. We observe that related literature reports performance tests of the proposed agents against a limited subset of the hosts used to train the models, thus raising questions on their applicability in realistic scenarios. The main contribution of this paper is to fill this gap by investigating the generalization capabilities of existing DRL agents to extend their VAPT operations to hosts that were not used in the training phase. To this purpose, we define a novel VAPT environment through which we devise multiple evaluation scenarios. While evidencing the limited capabilities of shallow RL approaches, we consider three state-of-the-art deep RL agents, namely Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Advantage Actor–Critic (A2C), and use them as bases for VAPT operations. The results show that the algorithm using A2C DRL agent outperforms the others because it is more adaptable to unknown hosts and converges faster. Our methodology can guide future researchers and practitioners in designing a new generation of semi-autonomous VAPT tools that are suitable for real-world contexts.

Marshes to mangroves: Residential surveys reveal perceived wetland trade-offs for ecosystem services

Coastal landscapes are rapidly changing due to both climate change and the decisions of waterfront landowners. For instance, the climate-driven encroachment of woody mangrove species into grassy marshland areas is predicted to impact coastal ecosystems, with consequences for the ecosystem services these landscapes provide to people. However, there is a dearth of knowledge concerning coastal resident perceptions of the effects of mangrove expansion on wetlands and their ecosystem services, which may impact residents’ behavior around shorelines and landscape-level patterns. We surveyed waterfront residents in the northern Gulf of Mexico (USA) to understand perceptions of the relative performance of marshes and mangroves to deliver fisheries ecosystem services. Residential-scale shoreline condition and preference, recreational fishing activity, geography, and demographics were evaluated as potential predictors of resident perceptions through non-parametric comparisons across groups and ordered logit modeling. Significant predictors included area of residence, marsh shoreline condition, marsh shoreline preference, fishing frequency, and household income. Florida residents (where mangroves are most prevalent) and frequent recreational fishing participants exhibited stronger preference for mangroves. Unexpectedly, residents with marsh currently present on their shoreline also perceived that mangroves were better at delivering fisheries ecosystem services than marshes. Considering the important role that coastal residents play in shoreline management decisions, these results demonstrate how coastal resident attitudes may drive or mediate climate-driven processes in ways that are not evident by examining environmental conditions alone. Understanding social-ecological shifts due to climate change will be important to inform effective landscape management that promotes resilience in coastal ecosystems and societies.

A two-layer stochastic game approach to reinsurance contracting and competition

We propose a two-layer stochastic game model to study reinsurance contracting and competition in a market with one insurer and two competing reinsurers. The insurer negotiates with both reinsurers simultaneously for proportional reinsurance contracts that are priced using the variance premium principle. The reinsurance contracting between the insurer and each reinsurer is modeled as a Stackelberg game. The two reinsurers compete for business from the insurer and optimize the so-called relative performance, instead of their own surplus, and their competition is settled by a noncooperative Nash game. We obtain a sufficient and necessary condition, related to the competition degrees of the two reinsurers, for the existence of an equilibrium. We show that the equilibrium, if exists, is unique, and the equilibrium strategy of each player is constant, fully characterized in semiclosed form. Furthermore, we obtain interesting sensitivity results for the equilibrium strategies through both analytical and numerical studies.

Real-time prediction of the week-ahead flood index using hybrid deep learning algorithms with synoptic climate mode indices

This paper aims to propose a hybrid deep learning (DL) model that combines a convolutional neural network (CNN) with a bi-directional long-short term memory (BiLSTM) for week-ahead prediction of daily flood index (IF) for Bangladesh. The neighbourhood component analysis (NCA) is assigned for significant feature selection with synoptic-scale climatic indicators. The results successfully reveal that the hybrid CNN-BiLSTM model outperforms the respective benchmark models based on forecasting capability, as supported by a minimal mean absolute error and high-efficiency metrics. With respect to IF prediction, the hybrid CNN-BiLSTM model shows over 98% of the prediction errors were less than 0.015, resulting in a low relative error and superiority performance against the benchmark models in this study. The adaptability and potential utility of the suggested model may be helpful in subsequent flood monitoring and may also be beneficial to policymakers at the federal and state levels.