Dynamic Environmental Factors Research Articles

MetaRange: A framework to build mechanistic range models

Abstract Mechanistic or process‐based models offer great insights into the range dynamics of species facing non‐equilibrium conditions, such as climate and land‐use changes or invasive species. Their consideration of underlying mechanisms relaxes the species‐environment equilibrium assumed by correlative approaches, while also generating conservation‐relevant indicators, such as range‐wide abundance time series and migration rates if demographically explicit. However, the computational complexity of mechanistic models limits their development and applicability to large spatiotemporal extents. We present the R package “metaRange”: a modular framework to build population‐based and metabolically constrained range models. We also provide a catalogue of biological functions to calculate niche‐based suitability, metabolic scaling, population dynamics, biotic interactions and kernel‐based dispersal, which may include directed movement. The framework's modularity enables the user to combine, extend, or replace these functions, making it possible to customize the model to the ecology of the study system. The package supports an unlimited number of static or dynamic environmental factors as input, including climate and land use. As examples, we include one single‐species application to predict the range dynamics of the European wildcat (Felis silvestris) in Germany, and one theoretical study in which we simulated 100 virtual species in three scenarios: without competition, with competition, and with competition under a generalist‐specialist trade‐off. Due to the population‐level, the package can execute such extensive simulation experiments on regular end‐user hardware in a short amount of time. We provide detailed technical documentation, both for the individual functions in the package as well as instructions on how to set up different types of model structures and experimental designs. The metaRange framework enables process‐based simulations of range dynamic of multiple interacting species on a high resolution and low computational demand. We believe that it allows for theoretical insights and hypotheses testing about future range dynamics of real‐world species, which may better support conservation policies targeting biodiversity loss mitigation.

Overlooked role of long capping time and environmental factors in the plateau lake for impairing lanthanum-modified-bentonite's immobilization to phosphate

Lanthanum-modified-bentonite(LMB) has been applied for eutrophication management as a phosphate(P)-binding agent in many lakes. However, re-eutrophication took place several years or decades later after the first practice of capping due to dynamic environmental factors in the plateau lake. Here, we investigated the effect of long-term capping and integrated environmental factors in the plateau lake including alkalinity, organic matter, disturbance and photodegradation to the LMB immobilization. Long-term LMB immobilization exhibited C accumulation(82.3%), La depletion(53.5%) and lager size effect in the sediment particle, indicating the breakage of La-O-P bonds and the formation of La-O-C bonds over immobilization time. Additionally, pH(8–10) in the plateau lake could enhance the P desorption and decrease P adsorption through electrostatic repulsion enhancement with the zeta potential reduction(7.2 mV). Further disturbance experiment indicated a significant releasing trend of active P and DGT-labile P from the solid phase, pore water to the overlying water after disturbances due to resuspended releasing, particle size and amorphous Fe, Mn and Al's redistribution. Moreover, 31P NMR and EPR results indicated photodegradation after disturbance converted diester phosphate into orthophosphate with long-term LMB immobilization via the oxidation of ·OH in the sediment of the plateau lake. Therefore, management issues for Xingyun Lake may apply to other plateau lakes with low external P input, intermediate depth and intense disturbance.

Occupational Risk Management for a Sustainable Workplace Using Simulation

Theoretical background: The article delves into the realm of occupational risk management within the context of supply chain management and enterprise logistics. It emphasizes the importance of ensuring safety and sustainable working conditions in today’s dynamic business environment, where supply chains are becoming increasingly globalized and complex. Purpose of the article: The primary objective of the article is to introduce a novel approach to occupational risk management that leverages advanced digital technologies and simulations. It aims to address the existing research gap by developing a method that considers dynamic environmental factors and individual worker needs. By analyzing the synergy of various hazards, monitoring employee workload, and employing simulations, the article seeks to enhance the accuracy of occupational risk assessment and facilitate more effective corrective actions. Research methods: The article employs a mixed-method research approach, drawing upon both theoretical analysis and practical application. It begins with a thorough literature review to establish the theoretical framework and identify research gaps. Subsequently, it discusses the proposed solution – a method for managing occupational risks using simulation – by presenting its theoretical description and implementation process. The research methodology involves the development and application of this method to a selected order picking process, followed by simulation to minimize occupational risks. The authors also utilize software tools to simulate work processes and assess health risks to workers. Main findings: The main findings of the research suggest that occupational risk assessment using simulation has a significant impact on improving work processes in terms of sustainable workplaces. By introducing elements of labor humanization and redesigning work processes, the study demonstrates a reduction in the sources of occupational risk. The innovative approach to risk assessment, based on recursion across different organizational resources, facilitates the identification of improvement action projects. Additionally, the study emphasizes the importance of digitalization and Industry 4.0 technologies in monitoring working conditions, identifying risks, and implementing preventive measures. Overall, the research contributes to shaping new standards for safe and sustainable work environments, thereby enhancing both safety and efficiency within organizations.

The Universal Neighborhood Effect Averaging in Mobility-Dependent Environmental Exposures.

The neighborhood effect averaging problem (NEAP) is a fundamental statistical phenomenon in mobility-dependent environmental exposures. It suggests that individual environmental exposures tend toward the average exposure in the study area when considering human mobility. However, the universality of the NEAP across various environmental exposures and the mechanisms underlying its occurrence remain unclear. Here, using a large human mobility data set of more than 27 000 individuals in the Chicago Metropolitan Area, we provide robust evidence of the existence of the NEAP in a range of individual environmental exposures, including green spaces, air pollution, healthy food environments, transit accessibility, and crime rates. We also unveil the social and spatial disparities in the NEAP's influence on individual environmental exposure estimates. To further reveal the mechanisms behind the NEAP, we perform multiscenario analyses based on environmental variation and human mobility simulations. The results reveal that the NEAP is a statistical phenomenon of regression to the mean (RTM) under the constraints of spatial autocorrelation in environmental data. Increasing travel distances and out-of-home durations can intensify and promote the NEAP's impact, particularly for highly dynamic environmental factors like air pollution. These findings illuminate the complex interplay between human mobility and environmental factors, guiding more effective public health interventions.

Deep structural insights into the origin of the Heyuan ms 4.5 revealed by deep seismic sounding profiles in southeast China

This study presents an interpretation of a deep seismic sounding (DSS) profile that carried out along the Cathaysia Block in southeast china, aiming to explore the crustal velocity structure. Data used in the survey were obtained from three controlled-source explosions conducted along the 320 km long Lianping-Heyuan-Shanwei profile. The modeling was based on ray tracing, using the extrapolation of seismic wave arrival times with the help of travel times predicted from a one-dimensional velocity model. The average velocity structure of the middle crust is 6.0–6.4 km/s, while a low velocity anomaly of approximately 0.1–0.2 km/s in the vicinity of the Heyuan-Shaowu fault zone. The resulting 2D velocity model indicates that steeply dipping low-velocity zones that correlate with the projection of two major fault zones. These zones, together with a flat LVZ at a depth of 12 km, define a triangular region that correlates with numerous hypocenters. This tectonic setting is favorable for the accumulation and release of strain in high-velocity media within the triangular region. The unique triangular structure in the upper crust provides necessary shallow medium conditions for seismic activity. This indicates that increased seismicity within this area is partially attributed to heightened stress within higher-velocity material. The triangular annular low-velocity body, situated in the upper crust, is influenced by dynamic environmental factors caused by deep thermal disturbances. The deep-seated fault serves as a conduit for the historical migration of thermal material, likely contributing to the seismogenic conditions for earthquakes in Heyuan’s region through deep-seated thermal disturbances. These findings provide a novel geophysical reference model for the regional seismicity near the Xinfengjang reservoir and significantly contribute to understanding the causal relationship between tectonic setting and seismicity. In comparison with previous studies, our research is dedicated to investigating the causes of shallow earthquakes in the region and exploring the relationship between deep and shallow structures.

Stochastic LPV MPC-based path following control for bevel-tip flexible needle with probabilistic constraints

This paper addresses the path-tracking problem for flexible needle control systems using a stochastic linear parameter varying (LPV) and model predictive control (MPC) strategy. Flexible needles operating in dynamic environments with non-uniform tissue density often deviate from ideal assumptions, resulting in non-standard models. The bicycle kinematics model for flexible needle motion control is transformed into an LPV model, improving accuracy and enabling more efficient control. The proposed stochastic LPV MPC approach aims to mitigate uncertainties arising from modelling errors and dynamic environmental factors, ensuring accurate trajectory tracking for the flexible needle. The sample and removal method is utilized to reformulate the probabilistic-constrained optimization problem for implementation. The contributions of this work lie in the application of stochastic LPV MPC to address the trajectory tracking problem in the presence of uncertainties. The simulation results illustrate the superior robustness of the stochastic LPV MPC approach, as evidenced by significantly smaller tracking errors across various scenarios.

Evaluation of psychometric characteristics of employee motivation in the workplace

Researchers constantly look for the ideal motivating factors in various countries, industries, and sectors, due to its multilateral dimension, which includes employees, managers, organizational policies and practices, and the way that the very dynamic external environmental factors influence them. Self-Determination Theory of motivation proposed by Edward Deci and Richard Ryan, embraces a holistic approach to human motivation which started by identifying several motivational styles and has been implemented in different fields of workplace motivation and management This paper aims to emphasize the importance of examining the psychometric characteristics of the employee motivation scale in the workplace, relying on some bifactor model ancillary indices. The degree of motivation was measured through the Multidimensional Work Motivation Scale (MWMS), based on Self-Determination Theory (SDT). The data were collected from a self-report questionnaire addressed to 658 employees from various businesses and institutions in the Korça region, in Albania. The ancillary indices, derived from the standardized loadings of a confirmatory bifactor model, provide a more comprehensive overview of the psychometric characteristics of the measurement instrument. The study showed that treating the MWMS as unidimensional within a multidimensional construct may cause only a small bias. However, its interpretation seems questionable. On the other hand, the added value of some subscale scores should be considered and can be statistically appropriate for interpretation, helping applied researchers. This study contributes to the field by highlighting the necessity of scrutinizing the psychometric properties of the employee motivation scale and demonstrates the utility of employing bifactor model ancillary indices for a more nuanced understanding. For the first time in Albania, we have a measurement instrument related to the motivation of employees in the workplace, which enjoys good psychometric characteristics.

Real-time pavement temperature prediction through ensemble machine learning

Pavement temperature is crucial for assessing asphalt pavement's performance and structural integrity due to its viscoelastic behavior. Currently, asphalt temperature prediction includes analytical, numerical, and statistical methods and relies on field investigation with intensive labor and time to hinder routine practice. Machine learning (ML) offers a promising solution for reliable and accurate real-time predictions by considering various dynamic environmental factors. This research employs four ensemble ML algorithms: eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Random Forest (RF), and Adaptive Boosting (AdaBoost) to predict pavement temperature at different depths. XGB consistently outperformed other ensemble models, boasting the highest average R2 value (97.92%) and lower MAE and RMSE than LGBM, RF, and AdaBoost. XGB also significantly outperformed other commonly used models (linear regression, SVR, ANN, LSTM, and GRU) with an average reduction in errors of 35.29%, 63.75%, 42.75%, and an increase in R2 by 4.39% for MAE, MSE, RMSE, and R2, respectively. The Shapley additive explanation (SHAP) was applied to interpret the underlying influencing factors and their interactions, and revealed atmospheric temperature as the most impactful feature, contributing over 40.58% among the significant features.Furthermore, the predictive performance of XGB was enhanced by incorporating Principal Component Analysis to reduce computational consumption with average reductions of 8.98%, 16.59%, 9.01%, and 7.89% in MAE, MSE, MAPE, and RMSE. XGB with PCA emerges as the most effective and efficient model for predicting pavement temperatures at various depths to facilitate timely decision-making for preventive maintenance and rehabilitation and prevent future catastrophic failures.

Outdoor Radon Dose Rate in Canada's Arctic amid Climate Change.

Decades of radiation monitoring data were analyzed to estimate outdoor Radon Dose Rates (RnDRs) and evaluate climate change impacts in Canada's Arctic Regions (Resolute and Yellowknife). This study shows that the RnDR involves dynamic sources and complex environmental factors and processes. Its seasonality and long-term trends are significantly impacted by temperatures and soil-and-above water contents. From 2005 to 2022, Yellowknife's RnDR increased by +0.35 ± 0.06 nGy/h per decade, with the fastest increases occurring in cold months (October to March). The rise is largely attributable to water condition changes over time in these months, which also caused enhanced soil gas emissions and likely higher indoor radon concentrations. In Resolute, the RnDR increased between 2013 and 2022 at +0.62 ± 0.19 nGy/h (or 16% relatively) per decade in summer months, with a positive temperature relationship of +0.12 nGy/h per °C. This work also demonstrates the relevance of local climate and terrain features (e.g., typical active layer depth, precipitation amount/pattern, and ground vegetation cover) in researching climate change implications. Such research can also benefit from using supporting monitoring data, which prove effective and scientifically significant. From the perspective of external exposure to outdoor radon, the observed climate change effects pose a low health risk.

Unleashing the Potential of a Hybrid 3D Hydrodynamic Monte Carlo Risk Model for Maritime Structures’ Design in the Imminent Climate Change Era

Submarine pipelines have become integral for transporting resources and drinking water across large bodies. Therefore, ensuring the stability and reliability of these submarine pipelines is crucial. Incorporating climate change impacts into the design of marine structures is paramount to assure their lifetime safety and serviceability. Deterministic design methods may not fully consider the uncertainties and risks related to climate change compared to risk-based design models. The latter approach considers the future risks and uncertainties linked to climate and environmental changes, thus ensuring infrastructure sustainability. This study pioneers a Hybrid 3D Hydrodynamic Monte Carlo Simulation (HMCS) Model to improve the reliability-based design of submarine pipelines, incorporating the effects of climate change. Current design approaches may follow deterministic methods, which may not systematically account for climate change’s comprehensive uncertainties and risks. Similarly, traditional design codes often follow a deterministic approach, lacking in the comprehensive integration of dynamic environmental factors such as wind, waves, currents, and geotechnical conditions, and may not adequately handle the uncertainties, including the long-term effects of climate change. Nowadays, most countries are developing new design codes to modify the risk levels for climate change’s effects, such as sea-level rises, changes in precipitation, or changes in the frequency/intensity of winds/storms/waves in coastal and marine designs. Our model may help these efforts by integrating a comprehensive risk-based approach, utilizing a 3D hydrodynamic model to correlate diverse environmental factors through Monte Carlo Simulations (MCS). The hybrid model can promise the sustainability of marine infrastructure by adapting to future environmental changes and uncertainties. Including such advanced methodologies in the design, codes are encouraged to reinforce the resilience of maritime structures in the climate change era. The present design codes should inevitably be reviewed according to climate change effects, and the hybrid risk-based design model proposed in this research should be included in codes to ensure the reliability of maritime structures. The HMCS model represents a significant advancement over existing risk models by incorporating comprehensive environmental factors, utilizing advanced simulation techniques, and explicitly addressing the impacts of climate change. This innovative approach ensures the development of more resilient and sustainable maritime infrastructure capable of withstanding future environmental uncertainties.

Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization

BackgroundDental resin-based composites are widely recognized for their aesthetic appeal and adhesive properties, which make them integral to modern restorative dentistry. Despite their advantages, adhesion and biomechanical performance challenges persist, necessitating innovative strategies for improvement. This study addressed the challenges associated with adhesion and biomechanical properties in dental resin-based composites by employing molecular docking and dynamics simulation.MethodsMolecular docking assesses the binding energies and provides valuable insights into the interactions between monomers, fillers, and coupling agents. This investigation prioritizes SiO2 and TRIS, considering their consistent influence. Molecular dynamics simulations, executed with the Forcite module and COMPASS II force field, extend the analysis to the mechanical properties of dental composite complexes. The simulations encompassed energy minimization, controlled NVT and NPT ensemble simulations, and equilibration stages. Notably, the molecular dynamics simulations spanned a duration of 50 ns.ResultsSiO2 and TRIS consistently emerged as influential components, showcasing their versatility in promoting solid interactions. A correlation matrix underscores the significant roles of van der Waals and desolvation energies in determining the overall binding energy. Molecular dynamics simulations provide in-depth insights into the mechanical properties of dental composite complexes. HEMA-SiO2-TRIS excelled in stiffness, BisGMA-SiO2-TRIS prevailed in terms of flexural strength, and EBPADMA-SiO2-TRIS offered a balanced combination of mechanical properties.ConclusionThese findings provide valuable insights into optimizing dental composites tailored to diverse clinical requirements. While EBPADMA-SiO2-TRIS demonstrates distinct strengths, this study emphasizes the need for further research. Future investigations should validate the computational findings experimentally and assess the material's response to dynamic environmental factors.

A Comparison of the Functioning and Disability Levels of Children With Hemiplegic and Diplegic Cerebral Palsy Based on ICF-CY Components.

We compared children with hemiplegic and diplegic cerebral palsy (CP) using the conceptual framework of the International Classification of Functioning, Disability and Health: Child and Youth version (ICF-CY). We enrolled 42 children with CP aged 5 - 13 years old (M age = 9.57, SD = 2.8 years). We assessed their trunk control and dynamic balance with the Trunk Control Measurement Scale (TCMS) and the Timed Up and Go test (TUG), and we used ABILHAND-Kids and Assessment of Life Habits (Life-H) to assess their manual ability and participation with activities of daily living. We administered the European Child Environment Questionnaire (ECEQ) to identify relevant environmental factors. We employed structural equation modeling (SEM) to identify specific factors contributing to potential differences between these CP groups. Children with hemiplegic CP demonstrated significantly better outcomes in terms of trunk control, dynamic balance, and environmental factors compared to those with diplegic CP (p < .05). In contrast, children with diplegic CP demonstrated superior outcomes regarding manual ability, compared to those with hemiplegic CP (p < .001). In our structural equation models, trunk control strongly predicted both dynamic balance (0.75) and environmental factors (0.74). Moreover, the relationships between trunk control and participation in daily and social activities were 0.54 and 0.47, respectively. Impaired trunk control and dynamic balance were significant contributors to increased activity restrictions and environmental barriers in children with diplegic CP. This suggests that improving disability and functioning in children with diplegic CP requires a focus on trunk control training and dynamic balance exercises.

Extension of the gamma concept to handle changing environment during shelf-life. Applied to an example with Listeria monocytogenes in salads with changing pH.

Prediction of microbial growth is the main topic within the area of predictive microbiology. A commonly used framework is the so-called gamma concept which is to view the bacterial growth rate as a product of a bacteria- and medium specific maximum instantaneous factor, and several environmental factors. Traditionally environmental factors are assumed to be constant, leading to exponential growth. In the current study, we expand the gamma concept to include changing dynamic environmental factors, exemplified by pH, and show how this might affect estimates for Listeria monocytogenes growth rates and thereby predicted shelf-life. The study shows how substantial environmental changes might lead to large biases for the estimated growth and shelf-life, if not accounted for in the model. Finally, the study shed some light on how accuracy for the growth rate estimates might be improved by allocating more weight to observations prone to favorable than unfavorable environmental conditions.

How to Integrate Strategy and Culture for Transformative Change

Purpose: This paper demonstrates that strategy and culture are important collaborators in the successful design and implementation of transformative change for sustainable corporate transformation and ethical institutional development.&#x0D; Materials and Methods: The researcher explores select, classic, contemporary and current scholarly literature on the theory of change. The research reviews theory articulated by Kurt Lewin on change management, systems theory, theory U and complexity in relation to their efficacy in facilitating organization change in global and local contexts. The study draws from ubuntu philosophy and the African business experience to propose a conceptual framework for the integration of strategy and culture to establish transformative change. &#x0D; Findings: The study finds a distinction in the context in which the theory of change is applied at the global and local levels. Business leaders need to use different approaches to change initiatives in the two contexts. The study also shows why culture has the power to frustrate transformative change and the implementation of new strategy. Subsequently, the paper proposes that sensitivities to culture should be built into strategy design in order to mitigate its inertial power during strategy implementation. The study submits that global change theory presumes the stability of the macro-operating environments in which strategy is developed. It also assumes the existence of supportive institutional culture, predictable environmental forces and the availability of well-developed macroeconomic infrastructure to underwrite desirable change initiatives. Conventional models suggest that the only intervening agency to establish change is the design and implementation of a new strategy. However, continental business microenvironments are made up of unstable, diverse, complex and volatile microeconomic disparities and cultural undercurrents that resist the implementation of change. In other words, the outcomes of strategy implementation in microenvironments, do not always mirror the predictive designs of global change theory. The study suggests that while global contexts may also benefit from the proposed model, culture should be integrated into the design and implementation of corporate strategy, in local contexts, to facilitate sustainable change. &#x0D; Implications to Theory, Practice and Policy: This paper recommends that; 1) Business and institutional leaders should carefully evaluate the strategic initiatives and development models they adopt and establish whether they indeed have the capacity to create and sustain transformative change within their operating environment, 2) Because proposed change always invites a response from culture, businesses must put in place mitigating measures to ensure that undue cultural inertia does not hinder ethical institutional advance, 3) Business leaders must remain alive to the fact that strategists, however brilliant, are not in control of the environmental forces in the ecosystem they seek to navigate. Hence the need to take pre-emptive measures, design strategic alternatives and employ dynamic approaches to successfully implement strategic plans, 4) Successful strategy implementation depends on the supportive confluence of multiple, complex and dynamic environmental factors beyond the designers of a good strategic plan. Thus, business leaders need to protect good strategy from being disoriented by fluctuating environmental change, 5) The perceived conflict of culture and strategy can be resolved by incorporating the ends of both in the design of crafting transformative change, 6) Ubuntu provides a powerful ethical lens to evaluate the acceptability, suitability and sustainability of strategic business initiatives in the continental business experience.

Open Dataset for Testing of Visual SLAM Algorithms under Different Weather Conditions

Existing datasets for testing SLAM algorithms in outdoor environments are not suitable for assessing the influence of weather conditions on localization accuracy. Obtaining a suitable dataset from the real world is difficult due to the long data collection period and the inability to exclude dynamic environmental factors. Artificially generated datasets make it possible to bypass the described limitations, but up to date, researchers have not identified testing SLAM algorithms under different weather conditions as a stand-alone task, despite the fact that it is one of the main aspects of the difference between outdoor and indoor environments. This work presents a new open dataset that consists of 36 sequences of robot movement in an urban environment or rough terrain, in the form of images from a stereo camera and the ground truth position of the robot, collected at a frequency of 30 Hz. Movement within one area occurs along a fixed route; the sequences are distinguished only by whether conditions, which can make it possible to correctly assess the influence of weather phenomena on the accuracy of localization.

Machine learning-based techniques for land subsidence simulation in an urban area

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Differential Game-Based Deep Reinforcement Learning in Underwater Target Hunting Task.

To meet requirements for real-time trajectory scheduling and distributed coordination, underwater target hunting task is challenging in terms of turbulent ocean environments and dynamic adversarial environment. Despite the existing research in game-based target hunting area, few approaches have considered dynamic environmental factors, such as sea currents, winds, and communication delay. In this article, we focus on a target hunting system consisted of multiple unmanned underwater vehicles (UUVs) and a target with high maneuverability. Besides, differential game theory is leveraged to analyze adversarial behaviors between hunters and the escapee. However, it is intractable that UUVs have to deploy an adaptive scheme to guarantee the consistency and avoid the escape of the target without collision. Therefore, we conceive the Hamiltonian function with Leibniz's formula to obtain feedback control policies. In addition, it proves that the target hunting system is asymptotically stable in the mean, and the system can satisfy Nash equilibrium relying on the proposed control policies. Furthermore, we design a modified multiagent reinforcement learning (MARL) to facilitate the underwater target hunting task under the constraints of energetic flows and acoustic propagation delay. Simulation results show that the proposed scheme is superior to the typical MARL algorithm in terms of reward and success rate.

An improved Approach for Sustainable Risk Management Practices in Modern Businesses

The complexity of the business environment, which is shaped by dynamic economic, environmental, and social factors, makes it more important for companies to develop sustainable risk management practices. This research proposes a novel approach that combines traditional methods with modern machine learning techniques in response to the complex challenges faced by contemporary businesses in balancing risk and sustainability. Our study uses a public bank loan default dataset as a case study to address missing data systematically through robust imputation mechanisms and transform categorical variables using feature encoding. Spearman correlation analysis helps us understand complex variable relationships and guides subsequent feature selection. The decision tree classifier, a powerful machine learning algorithm known for its interpretability, is applied to identify key factors contributing to risk assessment. The hierarchical structure of the decision tree not only reveals important variables but also provides an explicit representation of the decision-making process. ROC curve analysis shows how well our predictive model can differentiate potential loan defaults.

Genetic diversity analysis of indigenous pomegranate (Punica granatum L.) germplasm lines

Pomegranate (Punica granatum L.) is an ancient edible fruit crop well recognized for its immense nutraceutical and medicinal properties. Precise morphological characterization of wild, exotic and commercial (cultivar) varieties and understanding the molecular variability in perennial crops like pomegranate is essential for developing varietal DNA fingerprints, identifying true-to-type of genotypes, to avoid duplication and overcome the problem of clonal admixture. It has become a challenge to identify varieties based solely on observable phenotypic traits due to dynamic environmental factors that influence physical traits and increasing number of varieties. Hence, the implementation of stable molecular markers is the best complementary biotechnological tool, which fulfil the above requirements. In our first objective, we identified 24 polymorphic SSR markers out of 40. These SSRs screened on 13 different varieties of pomegranate. The PIC value ranged from 0.13 to 0.37 (mean 0.25) and heterozygosity percent ranged from 0.14% to 0.50% (mean 0.35%). Highest PIC value and heterozygosity observed in PgSSR78 marker. Similarity coefficient matrix showed the genetic distance between the twenty-four varieties, ranged from 0.58 to 0.99. Maximum similarity was observed between Alah and Khandari. Minimum similarity was found between IC318743 and 1185 followed by Maha and 1185. PgSSR16, PgSSR23, PgSSR49, PgSSR60, PgSSR32, PgSSR40, PgSSR78 markers were found with high PIC value and heterozygosity and these primers could be useful for pomegranate cultivar identification. From this study, microsatellite markers have proven to be very useful marker for developing varietal identification marker at early growth stage due to its high polymorphism, abundance, multi-allelic and co-dominant inheritance

Environmental memory boosts group formation of clueless individuals

The formation of groups of interacting individuals improves performance and fitness in many decentralised systems, from micro-organisms to social insects, from robotic swarms to artificial intelligence algorithms. Often, group formation and high-level coordination in these systems emerge from individuals with limited information-processing capabilities implementing low-level rules of communication to signal to each other. Here, we show that, even in a community of clueless individuals incapable of processing information and communicating, a dynamic environment can coordinate group formation by transiently storing memory of the earlier passage of individuals. Our results identify a new mechanism of indirect coordination via shared memory that is primarily promoted and reinforced by dynamic environmental factors, thus overshadowing the need for any form of explicit signalling between individuals. We expect this pathway to group formation to be relevant for understanding and controlling self-organisation and collective decision making in both living and artificial active matter in real-life environments.