Efficient Mechanism Research Articles

MIS Solutions During Natural Disaster Management: A Review On Responsiveness, Coordination, And Resource Allocation

This systematic review explores the impact of Management Information Systems (MIS) on enhancing disaster management by optimizing decision-making, resource allocation, and inter-agency communication. As the frequency and severity of natural disasters increase globally, there is a pressing need for more efficient disaster response mechanisms. This study systematically reviewed a total of 160 peer-reviewed articles published between 2010 and 2024, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a comprehensive and transparent review process. The findings reveal that MIS technologies, such as decision support systems (DSS), geographic information systems (GIS), and predictive analytics, play a crucial role in improving the speed and accuracy of emergency responses. Specifically, DSS and predictive models were found to enhance situational awareness and optimize resource deployment, reducing response times by up to 30%. Additionally, GIS tools significantly improved spatial data analysis, enabling better-targeted relief efforts. However, challenges related to data interoperability, cybersecurity, and the integration of advanced technologies remain significant barriers to fully leveraging MIS in disaster management. Addressing these challenges through investments in infrastructure, standardized protocols, and specialized training will be essential for maximizing the potential of MIS in future disaster response efforts. The review concludes that the strategic use of MIS is vital for building more resilient and responsive disaster management systems, ultimately reducing the socio-economic impacts of emergencies.

Sustainable digital rent: a transformative framework for value dynamics in the digital age

IntroductionThis paper introduces the concept of Sustainable Digital Rent (SDR), highlighting the shift from traditional economic rent based on tangible assets to rent derived from digital platforms. At the heart of this shift is the “value state,” a dynamic balance between constructive expectations and destructive information. As digital platforms generate increasing amounts of information, expectations are increasingly met and shared more efficiently with all users, leading to a reduction in individual and general motivational, emotional, and cognitive engagement. These platforms, now essential to modern life, facilitate online activities that reduce as well physical engagement and natural interactions, thereby impacting cognitive function and physical health. By extracting rent directly, digital platform operators limit the benefits users could gain to support their mental and physical well-being.MethodsThis paper empirically defines and estimates SDR using the collective estimates of price, cost, and income (PCI) as practiced in North American real estate appraisal, demonstrated through abstract art rent. Our approach provides a new perspective on valuing intangible assets, such as knowledge, by showing the shift from expectation to information, governed by the value state in cognitive evaluations. Emphasizing interdisciplinary relevance, the method underscores the need for an efficient mechanism to redistribute SDR benefits to digital platform users, supporting fair and equitable digital development.Results and discussionThe results show that digital rent is driven primarily by cognitive and informational content, demonstrating the need for redistribution mechanisms to address the growing inequality on digital platforms. The use of abstract art as a case study provides a convenient and illustrative way to explore how intangible assets, like digital rents, can be evaluated and redistributed. SDR offers insights into how digital rents can be captured and redistributed equitably, ensuring that platform users and creators benefit from the knowledge economy’s growth. The findings underscore the relevance of measuring SDR to guide policy recommendations aimed at reducing digital monopolization and promoting sustainable digital development.

The Impact of Land-Use Carbon Efficiency on Ecological Resilience—The Moderating Role of Heterogeneous Environmental Regulations

China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems.

The Distributed Xin’anjiang Model Incorporating the Analytic Solution of the Storage Capacity Under Unsteady-State Conditions

Developing a functional linkage between hydrological variables and easily accessible terrain and soil information is a novel concept for distributed hydrological models. This approach aims to address limitations imposed by data scarcity and high computational demands. The model hypothesizes that the relationship between the evaporation flux and the absolute value of the matric potential follows a power exponential pattern. Analytic solutions for the groundwater depth, the evaporation capacity, and the storage capacity are derived with respect to the topographic index, considering the relationship between the groundwater depth and the topographic index and the influence of setting off. Subsequently, a distributed Xin’anjiang Model using the analytic solution of the storage capacity under unsteady-state conditions is constructed. This new model is employed to simulate soil moisture and discharge in the Tarrawarra Watershed. The simulation results for soil moisture and discharge are compared with those from the Storage Capacity Model and the DHSVM. Additionally, the computational speeds of all three models are compared. The findings indicate that the simulation accuracy of the new model for soil moisture and discharge surpasses that of the Storage Capacity Model and the DHSVM. Meanwhile, the computational speed of the new model is significantly faster than the DHSVM and slightly slower than the Storage Capacity Model. It offers a balance between computational efficiency, predictive accuracy, and physical mechanism representation. The data requirements of the new model are minimal and easy to procure, and it requires less computational effort. Moreover, it accurately captures the spatial and temporal dynamics of soil moisture and the discharge process of the watershed.

Quantifying farmers' Livelihood Efficiency, Regional Indicators and Its Influencing Factors: Implementing an Improved Sustainable Livelihood Framework

ABSTRACTResearch on sustainable livelihoods tend to emphasis on objective and macroregional aspects. However, there is space to expand research on the farmers' livelihoods in the context of poverty alleviation and rural revitalization development. This paper uses a field survey on farmers' livelihood efficiency, influencing factors and mechanisms of farmers' livelihood efficiency in the Qinba mountainous areas of southern Shaanxi China. Based on DEA and Tobit approaches, the authors investigate farmer livelihood efficiency and analyze the disparities according to the farm households' socio‐demographics descriptions. Findings of this study show that, livelihood efficiency is a new entry point of sustainable livelihoods for farmers, and currently, the overall livelihood efficiency of farmers in the study area is low, which needs to be further improved. The results also show that the willingness of environmental protection activities, village busses and government financial expenditure have a significant positive impact on the livelihood efficiency, while the abandonment has a significant negative impact on the livelihood efficiency of farmers. These factors have significant differences in the impact of farmers' technical efficiency and scale efficiency. The results further indicate that, utilization and conversion rate of the existing livelihood capital of farmers is low. Livelihood diversification should be stimulated to enhance “livelihood export” and its operation ability of livelihood capital accumulation. The study findings also propose that, there is a need of reform of dual paths to improve the efficiency of farmers' livelihood, and promote farmers' livelihood development with “wide path” and “wide choice.”

Relationship between tooth macrowear and jaw morphofunctional traits in representative hypercarnivores

The mammalian adult dentition is a non-renewable resource. Tooth attrition and disease must be accommodated by individuals using behavioral, physiological, and/or musculoskeletal shifts to minimize impact on masticatory performance. From a biomechanical perspective, the musculoskeletal system becomes less efficient at producing bite force for a given amount of muscle input force over an individual’s life, because tooth-food contact area increases as cusps wear. In this study we ask the question: does mandibular biomechanical performance show evidence of compensation with increasing tooth wear? We use representative taxa of three carnivoran ecomorphologies (meat specialist, scavenger, bone cracker) as a study system to compare morphofunctional data on tooth macrowear, jaw depth, bite mechanical efficiency, and jaw stress during biting. No significant shifts in adult mandibular corpus dimensions occurs in the sampled taxa as canine and carnassial teeth wear. In bone cracking spotted hyenas carnassial biting mechanical efficiency increases significantly with increasing tooth wear, with no significant change in mandibular stress. Analyses of the fossil carnivore Hyaenodon suggests an increase in canine biting efficiency with increased tooth wear, but this may reflect interspecific variation or phylogenetic contingency rather than a life history shift. Overall, these findings indicate that scavenging hyaenids and felid meat specialists do not exhibit morphofunctional compensation for the decreased mechanical capability of worn and dull teeth. Behavioral modifications, rather than musculoskeletal adjustments, may instead play a major role in maintaining food acquisition and processing capabilities for individuals surviving into advanced ontogenetic age and tooth wear. These observations highlight the mammalian masticatory system as having a dynamic performance profile through its useful lifespan, and encourage a more nuanced understanding of past and present carnivore guilds by considering wear-dependent performance changes as a possible source of selection.

Investigating the e-learning choice under the learners’ perspective using demand driven learning model: insights from Vietnam

Over the past four decades, e-learning has experienced rapid global growth, revolutionizing higher education. In Vietnam, universities have increasingly turned their attention and resources toward e-learning development, adapting to specific contexts and needs. This study investigates the drivers of e-learning choice within the framework of the demand-driven learning model (DDLM), with a primary focus on three core factors: quality content, delivery, and service. Employing a quantitative approach and the PLS-SEM technique, this research uncovers significant findings. The results of the study highlight the pivotal role of quality content, which exerts the most potent influence on e-learning choice, evidenced by a robust path coefficient of 0.400. Service and delivery, with direct path coefficients of 0.183 and 0.173, respectively, also play substantial roles in shaping e-learning decisions. Moreover, mediate role of delivery in the the e-learning choice model has been confirmed. Quality content leads to delivery, and delivery, in turn, leads to e-learning choice. Similar pathway has been found with service. A higher level of service increases delivery, which positively impacts on e-learning choice. These findings hold critical implications for the formulation of e-learning development policies in Vietnam’s higher education institutions. Universities should prioritize the continuous development of high-quality, engaging, and up-to-date educational content that aligns with industry needs and student interests. Additionally, emphasis should be placed on providing a supportive e-learning experience, characterized by responsive customer service, accessible technical support, and efficient issue resolution mechanisms. Moreover, universities should consider the implementation of user-friendly and interactive content delivery platforms and methods that actively engage learners. In essence, this research serves as a guide for universities in Vietnam, enabling them to enhance their e-learning offerings by ensuring the content quality, support services, and delivery methods, ultimately fostering a dynamic and accessible learning environment that meets the evolving needs of their students and the demands of the modern educational landscape.

Optimization of Tensile Strength and Cost-Effectiveness of Polyethylene Terephthalate Glycol in Fused Deposition Modeling Using the Taguchi Method and Analysis of Variance

This paper investigates the optimization of tensile strength, tensile strength per unit weight, and tensile strength per unit time of polyethylene terephthalate glycol (PETG) material in fused deposition modeling (FDM) technology using the Taguchi method and analysis of variance (ANOVA). Unlike previous studies that typically focused on optimizing a single mechanical property, our research offers a multi-dimensional evaluation by simultaneously optimizing three critical quality characteristics: tensile strength, tensile strength per unit weight, and tensile strength per unit time. This comprehensive approach provides a broader perspective on both the mechanical performance and production efficiency, contributing new insights into the optimization of PETG in FDM. The Taguchi method (L16 45) was designed and executed, with the layer height, infill density, print temperature, print speed, and infill line direction as the control factors. Sixteen tensile tests were conducted, and ANOVA was employed to identify the main influencing factors for each quality characteristic. For the tensile strength, the infill density was found to have the greatest impact (48.45%), while the print temperature had the least impact (0.78%). The optimal parameter combination reduced the quality loss to 31.28% and standard deviation to 55.93%. For tensile strength per unit weight, the infill line direction had the greatest impact (87.22%), whereas the print temperature had the least impact (0.77%). The optimal parameter combination reduced the quality loss to 54.09% and standard deviation to 73.54%. Regarding the tensile strength per unit time, the layer height had the greatest impact (82.12%), while the print temperature had the least impact (0.08%). The optimal parameter combination reduced the quality loss to 10.81% and standard deviation to 32.87%.

Skin Disease Analysis and Prediction using Machine Learning

A number of patients all over the world suffer from skin diseases; detection in the early stages is very important for curing such disease. For diagnosing skin conditions however, several factors make this very difficult, these include the multitude of skin diseases, their similar symptoms and dependence on expert dermatological evaluation. In this case, adopting machine learning (ML) is quite a suitable approach as it can help improve the accuracy of diagnosis and its availability. Therefore, the focus of the project is on the developing of a machine learning based skin disease detection system.The problem in consideration is the high demand for efficient and effective mechanisms that can be used in skin disease identification/ detection, whereby such tools should also be able to be used in low income areas. To tackle this, we use a machine learning model which classifies different forms of skin diseases through the use of dermatoscopic images.

Further experimental analysis of undershot water wheels towards the development of a prototype model

ABSTRACT The current research aims to analyse the effect of the number and shape of the blades and the curvature of the flume bottom on the performance curves of undershot water wheels, based on experimental tests conducted in a fully instrumented laboratory facility. Six wheels are tested: four wheels with plane blades (16, 24, 36, 48) and two with 24 curved blades for two flume bottom configurations. Torque, mechanical power and mechanical efficiency performance curves are determined for several rotational speed and flow rate values. Results demonstrate that the maximum efficiency is achieved for the 36-plane blade wheel, the curved flume bottom reduces water losses under the wheel and increases efficiency, and the blades’ shape strongly influences the wheel efficiency. Non-dimensional performance curves are provided to generalise the results. This research provides relevant contributions towards the development of a low-cost energy recovery solution to be applied in water infrastructures.

Wildfire Identification Based on an Improved MobileNetV3-Small Model

In this paper, an improved MobileNetV3-Small algorithm model is proposed for the problem of poor real-time wildfire identification based on convolutional neural networks (CNNs). Firstly, a wildfire dataset is constructed and subsequently expanded through image enhancement techniques. Secondly, an efficient channel attention mechanism (ECA) is utilised instead of the Squeeze-and-Excitation (SE) module within the MobileNetV3-Small model to enhance the model’s identification speed. Lastly, a support vector machine (SVM) is employed to replace the classification layer of the MobileNetV3-Small model, with principal component analysis (PCA) applied before the SVM to reduce the dimensionality of the features, thereby enhancing the SVM’s identification efficiency. The experimental results demonstrate that the improved model achieves an accuracy of 98.75% and an average frame rate of 93. Compared to the initial model, the mean frame rate has been elevated by 7.23. The wildfire identification model designed in this paper improves the speed of identification while maintaining accuracy, thereby advancing the development and application of CNNs in the field of wildfire monitoring.

Triplet supercurrents in lateral Josephson junctions with a half-metallic ferromagnet

In the area of superconducting spintronics, spin-triplet supercurrents in half-metallic ferromagnets (HMFs) could yield dissipationless spin transport over large distances and high current density. Promising among the HMFs is the perovskite oxide La0.7Sr0.3MnO3 (LSMO), and recent studies in combination with the high-Tc superconductor YBa2Cu3O7, or the conventional superconductor NbTi, showed long-range effects. Here we focus on two issues that as yet have received less attention: the value of the critical current in the HMF in the limit of a very small electrode distance (20 nm) and the nature of the spin-triplet generator. We use lateral junctions shaped as a bar, square, and disk, and find high supercurrent densities of the order of 1011 A/m2, pointing to an efficient triplet generation mechanism. This is surprising in the sense that no magnetic inhomogeneity is purposely built in, as is done in conventional metal triplet junctions. Furthermore, from the magnetic field dependence of the critical current interference patterns, we find a uniform supercurrent distribution in bar-shaped devices, but one more constricted to the rim in disk devices, which is an expected consequence of the geometry. We also analyze the temperature dependence of the critical current and find the quadratic dependence that was predicted in the limit of small junction lengths. From studying the NbTi/LSMO interface with scanning electron transmission microscopy, we conclude that the magnetic inhomogeneity required for triplet generation resides in the LSMO layer adjacent to the interface. Published by the American Physical Society 2024

Parametric influences on flow, heat transfer, and nutrient transport in the knee joint cavity, a numerical approach

A novel aspect of this study is the investigation of the impact of temperature-dependent Arrhenius reactions and metabolic heat generation on the overall heat transfer within a knee joint cavity. This is a unique contribution where the interaction between thermal effects and metabolic activity is shown to influence the transport of nutrients and the removal of waste products. For the first time, the study also aims to examine the influence of key non-dimensional numbers on the characteristics of mixed convection flow, providing new insights into the dominant physical mechanisms at play. An innovative feature of the model is the incorporation of a non-linear reaction term to address the balance between protein synthesis and degradation. By integrating both thermal and biochemical dynamics, this approach offers a comprehensive analysis of protein aggregation in the knee joint, with potential implications for understanding and treating joint diseases, particularly those related to the health of synovial fluid and cartilage, such as osteoarthritis. The findings have the potential to inform the development of improved therapeutic strategies for managing these conditions. In the context of knee joint cavity dynamics, a low Cauchy number Ca plays a crucial role in ensuring steady and laminar flow, which reduces inertia and ensures that the continuity equation is satisfied. This laminar nature of the flow is essential for maintaining stable synovial fluid movement within the cavity, which aids in the smooth transport of nutrients and waste products. Additionally, higher Peclet number Pe values indicate an increased dominance of convective transport over diffusion, thereby enhancing the supply of essential nutrients to cartilage and bone, while also facilitating the removal of waste products and inflammatory mediators. This efficient convective transport mechanism is vital for the overall health of the joint and for mitigating inflammation. Furthermore, the Zeldovich number β significantly influences the temperature sensitivity of the Arrhenius reaction term within the cavity, where higher values reflect increased reactivity to temperature changes. This temperature dependence impacts metabolic heat generation and protein dynamics, further influencing the biochemical processes that occur within the joint. Collectively, these non-dimensional parameters highlight key aspects of synovial fluid dynamics, nutrient transport, and biochemical reactions that are essential to the functioning and health of the knee joint.

Nanocomposites in Tire Development – Benefits, Challenges, and the Role of Carbon Nanotubes and Graphene

In the rapidly evolving automotive industry, enhancing vehicle performance and sustainability through innovative materials is paramount. Nanocomposite materials, incorporating nanoparticles such as carbon nanotubes (CNTs), graphene, and silica, have emerged as key enablers in redefining tire technology with improved durability, efficiency, and environmental impact. This review discusses the integration of these nanocomposites into tire manufacturing, emphasizing their role in enhancing mechanical properties, wear resistance, and rolling efficiency while reducing environmental footprints. Challenges such as dispersion uniformity within the rubber matrix and economic considerations due to high material costs are examined, alongside future research directions aimed at scalability and cost reduction. The paper predicts a significant increase in the adoption of nanocomposite tires, driven by technological advances and the shift toward sustainable manufacturing practices. By detailing the synergistic effects of nanocomposites, this review highlights their potential to revolutionize tire performance and contribute to the next generation of green automotive technologies.

Sensitive Self-Driven Single-Component Organic Photodetector Based on Vapor-Deposited Small Molecules.

Typically, organic solar cells (OSCs) and photodetectors (OPDs) comprise an electron donating and accepting material to facilitate efficient charge carrier generation. This approach has proven successful in achieving high-performance devices but has several drawbacks for upscaling and stability. This study presents a fully vacuum-deposited single-component OPD, employing the neat oligothiophene derivative DCV2-5T in the photoactive layer. Free charge carriers are generated with an internal quantum efficiency of 20 % at zero bias. By optimizing the device structure, a very low dark current of 3.4 · 10-11Acm-2 at -0.1V is achieved, comparable to the dark current of state-of-the-art bulk heterojunction OPDs. This optimization results in specific detectivities of 1· 1013Jones (based on noise measurements), accompanied by a fast photoresponse (f-3dB = 200kHz) and a broad linear dynamic range (>150dB). Ultrafast transient absorption spectroscopy unveils that charge carriers are already formed at very short time scales (<1ps). The surprisingly efficient bulk charge generation mechanism is attributed to a strong electronic coupling of the molecular exciton and charge transfer states. This work demonstrates the very high performance of single-component OPDs and proves that this novel device design is a successful strategy for highly efficient, morphological stable and easily manufacturable devices.

Large-Kernel Central Block Masked Convolution and Channel Attention-Based Reconstruction Network for Anomaly Detection of High-Resolution Hyperspectral Imagery

In recent years, the rapid advancement of drone technology has led to an increasing use of drones equipped with hyperspectral sensors for ground imaging. Hyperspectral data captured via drones offer significantly higher spatial resolution, but this also introduces more complex background details and larger target scales in high-resolution hyperspectral imagery (HRHSI), posing substantial challenges for hyperspectral anomaly detection (HAD). Mainstream reconstruction-based deep learning methods predominantly emphasize spatial local information in hyperspectral images (HSIs), relying on small spatial neighborhoods for reconstruction. As a result, large anomalous targets and background details are often well reconstructed, leading to poor anomaly detection performance, as these targets are not sufficiently distinguished from the background. To address these limitations, we propose a novel HAD network for HRHSI based on large-kernel central block masked convolution and channel attention, termed LKCMCA. Specifically, we first employ the pixel-shuffle technique to reduce the size of anomalous targets without losing image information. Next, we design a large-kernel central block masked convolution to make the network pay more attention to the surrounding background information, enabling better fusion of the information between adjacent bands. This, coupled with an efficient channel attention mechanism, allows the network to capture deeper spectral features, enhancing the reconstruction of the background while suppressing anomalous targets. Furthermore, we introduce an adaptive loss function by down-weighting anomalous pixels based on the mean absolute error. This loss function is specifically designed to suppress the reconstruction of potentially anomalous pixels during network training, allowing our model to be considered an excellent background reconstruction network. By leveraging reconstruction error, the model effectively highlights anomalous targets. Meanwhile, we produced four benchmark datasets specifically for HAD tasks using existing HRHSI data, addressing the current shortage of HRHSI datasets in the HAD field. Extensive experiments demonstrate that our LKCMCA method achieves superior detection performance, outperforming ten state-of-the-art HAD methods on all datasets.

Automation in Agriculture: A Robotic Approach to Weed Control for Greenhouse Cucumber Cultivation

The increasing global population has heightened the demand for efficient food production, leading to the adoption of compact cultivation methods such as greenhouses. However, weed growth within these controlled environments significantly challenges crop productivity. The application of robots can be a useful and economic choice. This study presents an innovative, purely mechanical system for weed control in greenhouses, specifically designed to operate autonomously on a monorail. The machine stops in the distance between the two main plant rows (cucumber) and its arm goes into the gap to deploy the weeds by rotating its blades. This is continued repeatedly. The cutting is by the rotational speed of the blade. Variable-speed motions of the blade were at 3500, 2500 and 1500 rpm for 3 types of moulinex, triangular, and circular blades. The movement speed of the arm was 10 and 30 rpm and the forward movement speed of the machine was 30, 40, 50, 60, 80 and 120 rpm. The results showed that different blades, blade speeds and engine speed affect significantly (p &lt;0.05) the percentage of weeds being cut. Although the interactions of these factors have no significant effect on percentage; the average percentages by the blades have significant differences. Although the interactions between these factors were not statistically significant, the comparison of means revealed that at lower blade speeds, the blade type had a pronounced effect on weed-cutting efficiency. As blade speed increased, differences in blade performance diminished. The most effective combination was achieved using Moulinex blades at 3500 rpm with a 10-rpm arm speed, resulting in the highest percentage of weeds cut. These findings suggest that optimizing blade type and speed can significantly enhance the mechanical weed control efficiency in greenhouse environments.

Exploring the potential of induced dipole-dominated electrorheological effect: advancing ER elastomers

Abstract Prior research has predominantly focused on traditional electrorheological (ER) effects while overlooking the transformative potential of induced dipoles in enhancing the overall performance of ER materials. In this study, we introduced a novel type of ER elastomer called induced dipole-dominated ER elastomer (ID-ERE). Through high-energy ball milling (HEBM) of the filler particles, the oxygen vacancies were produced within the particles that acted as local charge centers. In the presence of an external electric field (E), these oxygen vacancies induced the dipoles with significant dipole moments, thus amplifying the local electric field EL within the particle gaps. The powerful interactions of these dipoles significantly improved the overall performance of elastomer; the phenomenon referred to as the ID-ER effect. The viscoelastic results showed that ID-EREs have high field-induced storage modulus (G’ = 395.7 kPa), a significant increment in storage modulus (ΔG’ = 270.5 kPa) and high relative ER effect (ΔG’/G0 = 217.2%) at 3 kV mm−1. Additionally, after testing ID-EREs viscoelastic properties, it was discovered that excessive powder content leads to a decline in the elastomer’s performance. The results showed that ID-ERE’s viscoelastic, mechanical, dielectric, and overall efficiency is finer than the control ER elastomer (C-ERE) having unmilled TiO2 particles. Besides, the preparation method is straightforward, easily replicated, scalable, and cost effective. Thus, these ID-EREs should be a new generation of elastomer with the potential to be used in various automotive, robotics, construction, and electroactive actuators industries.

Information, Entanglement, and Emergent Social Norms: Searching for ‘Normal’

Social norms are often regarded as informal rules or strategies. Previously, we have proposed that norms are better understood as information. Social norms represent a behavioral adaptation that identifies and curates the information required to create and maintain a predictable environment. Here, we further demonstrate that social norms act as the leading edge of individual and collective search and optimization processes. These processes provide efficient and effective evolutionary mechanisms for constant adjustment and adaptation to an environment. We show that social norms constitute the forefront of our ability to ingest and process information, and are responsible for the conditions under which social and collective cognition are possible. This new model of social information processing provides not only insight into how humans adapt and evolve to diverse environments, but also provides concrete definitions of human sociality and its distinctions from that of other social animals. Our social constructs and behaviors are not separate from the physical world we inhabit, but are instead the primary means by which we exist within it.

Adaptation of Object Detection Algorithms for Road Indicator Lights in Complex Scenes

In the realm of autonomous driving, practical driving scenarios are fraught with numerous complexities, including inclement weather conditions, nighttime blurriness, and ambient light sources that significantly hinder drivers’ ability to discern road indicators. Furthermore, the dynamic nature of road indicators, which are constantly evolving, poses additional challenges for computer vision-based detection systems. To address these issues, this paper introduces a road indicator light detection model, leveraging the advanced capabilities of YOLOv8. We have ingeniously integrated the robust backbone of YOLOv8 with four distinct attention mechanism modules—Convolutional Block Attention Module (CBAM), Efficient Channel Attention (ECA), Shuffle Attention (SA), and Global Attention Mechanism (GAM)—to significantly enhance the model performance in capturing nuanced features of road indicators and boosting the accuracy of detecting minute objects. Additionally, we employ the Asymptotic Feature Pyramid Network (AFPN) strategy, which optimizes the fusion of features across different scales, ensuring not only an enhanced performance but also maintaining real-time capability. These innovative attention modules empower the model by recalibrating the significance of both channel and spatial dimensions within the feature maps, enabling it to hone in on the most pertinent object characteristics. To tackle the challenges posed by samples rich in small, occluded, background-similar objects, and those that are inherently difficult to recognize, we have incorporated the Focaler-IOU loss function. This loss function deftly reduces the contribution of easily detectable samples to the overall loss, thereby intensifying the focus on challenging samples. This strategic balancing of hard-to-detect versus easy-to-detect samples effectively elevates the model’s detection performance. Experimental evaluations conducted on both a public traffic signal dataset and a proprietary headlight dataset have yielded impressive results, with both mAP50 and mAP50:95 metrics experiencing significant improvements exceeding two percentage points. Notably, the enhancements observed in the headlight dataset are particularly profound, signifying a significant step forward toward realizing safer and more reliable assisted driving technologies.