Diverse Scales Research Articles

Combining impact goal and impact descriptor frameworks to elucidate the societal impacts of research: a pilot study

Universities, researchers and funders are increasingly asking how research contributes to positive changes in society and the environment, and seeking ways to document and describe impacts consistently across diverse disciplines and organisational scales. The societal impacts framework presented in this pilot study uses a combination of impact goal and impact descriptor frameworks to elucidate the societal impacts of research. The framework blends elements of assessment-driven and mission-driven reporting frameworks, and was administered online to volunteers from one interdisciplinary environmental research institute. The 12 projects in the pilot study addressed 15 of the 17 UN Sustainable Development Goals, and all 12 projects reported impacts in two or more of six impact descriptor categories. We also identified an impact subcategory dealing with changes to higher education practice. Combining two types of impacts frameworks – societal goals and descriptors of changes – allowed us to understand how the research projects contributed to broad societal goals, not just that they addressed the goals. Responses from study participants indicated a good fit between the framework and their research efforts. However, we found that the online reporting tool, in its current form, was not effective in eliciting full and accurate reports from all participants. We reflect upon how to improve data collection in the future, as well as on opportunities for additional tests of the framework in new contexts.

Food security and dietary diversity patterns amongst female handloom workers: a cross sectional study from Kerala, India

IntroductionThe onset of COVID-19 in December 2019 bought the entire world to a standstill and had a major impact on the social and economic sectors in India. One of the most affected groups in this pandemic has been the female handloom workers.PurposeThe purpose of this study was to assess food security and dietary diversity amongst female handloom workers in Kerala.MethodsAfter obtaining informed consent, data was collected from 140 respondents working in handloom cooperative societies across Ernakulum, Kerala. Data regarding the food security and dietary diversity status of female handloom workers were collected using the validated Household Food Insecurity Access Scale and the Individual Dietary Diversity Scale respectively. Descriptive statistics was used to calculate the mean, standard deviation, frequency, and percentage while the independent t-test and Chi-Square test were used to compare means and examine the relationship between continuous and categorical variables respectively.ResultsThe mean age of the respondents was reported to be 53.69 ± 8.39. Majority of the respondents (91.4%) had co-morbidities with the most prevalent one being occupational hazards (45.7%). 96.4% and 53.5% of respondents were found to be food insecure and have poor dietary diversity respectively. In unadjusted analyses, statistical relationships were found between several variables and food security status, like milk and milk products (p = 0.045), flesh meats (p = 0.007), and history of disease and limited variety of foods due to lack of resources (p = 0.003). A strong relationship was also established between receiving extra rations through the Pradhan Mantri Garib Kalyan Yojana and food security status (p = 0.002).ConclusionThe results revealed the bleak state of nutrition in the community. Further research into their nutritional knowledge, attitudes and practices needs to be carried out to recognize efficient variables for comparison from an Indian context.

Label-Free Techniques for Probing Biomolecular Condensates.

Biomolecular condensates play important roles in a wide array of fundamental biological processes, such as cellular compartmentalization, cellular regulation, and other biochemical reactions. Since their discovery and first observations, an extensive and expansive library of tools has been developed to investigate various aspects and properties, encompassing structural and compositional information, material properties, and their evolution throughout the life cycle from formation to eventual dissolution. This Review presents an overview of the expanded set of tools and methods that researchers use to probe the properties of biomolecular condensates across diverse scales of length, concentration, stiffness, and time. In particular, we review recent years' exciting development of label-free techniques and methodologies. We broadly organize the set of tools into 3 categories: (1) imaging-based techniques, such as transmitted-light microscopy (TLM) and Brillouin microscopy (BM), (2) force spectroscopy techniques, such as atomic force microscopy (AFM) and the optical tweezer (OT), and (3) microfluidic platforms and emerging technologies. We point out the tools' key opportunities, challenges, and future perspectives and analyze their correlative potential as well as compatibility with other techniques. Additionally, we review emerging techniques, namely, differential dynamic microscopy (DDM) and interferometric scattering microscopy (iSCAT), that have huge potential for future applications in studying biomolecular condensates. Finally, we highlight how some of these techniques can be translated for diagnostics and therapy purposes. We hope this Review serves as a useful guide for new researchers in this field and aids in advancing the development of new biophysical tools to study biomolecular condensates.

Antipersistent energy-current correlations in strong long-ranged Fermi-Pasta-Ulam-Tsingou-type models.

We study heat transfer in one-dimensional Fermi-Pasta-Ulam-Tsingou-type systems with long-range (LR) interactions. The strength of the LR interaction between two lattice sites decays as a power σ of the inverse of their distance. We focus on the strong LR regime (0≤σ≤1) and show that the thermal transport behaviors are remarkably nuanced. Specifically, we observe that the antipersistent (negative) energy current correlation in this regime is intricately dependent on σ, displaying a nonmonotonic variation. Notably, a significant qualitative change occurs at σ_{c}=0.5, where with respect to other σ values the correlation shows a minimum negative value. Furthermore, our findings also demonstrate that within the long-time range considered, these antipersistent correlations will eventually vanish for certain σ>0.5. The underlying mechanisms behind these intriguing phenomena are related to the crossover of two diverse space-time scaling properties of equilibrium heat correlations and the various scattering processes of phonons and discrete breathers.

Video frame interpolation via spatial multi‐scale modelling

AbstractVideo frame interpolation (VFI) is a technique that synthesises intermediate frames between adjacent original video frames to enhance the temporal super‐resolution of the video. However, existing methods usually rely on heavy model architectures with a large number of parameters. The authors introduce an efficient VFI network based on multiple lightweight convolutional units and a Local three‐scale encoding (LTSE) structure. In particular, the authors introduce a LTSE structure with two‐level attention cascades. This design is tailored to enhance the efficient capture of details and contextual information across diverse scales in images. Secondly, the authors introduce recurrent convolutional layers (RCL) and residual operations, designing the recurrent residual convolutional unit to optimise the LTSE structure. Additionally, a lightweight convolutional unit named separable recurrent residual convolutional unit is introduced to reduce the model parameters. Finally, the authors obtain the three‐scale decoding features from the decoder and warp them for a set of three‐scale pre‐warped maps. The authors fuse them into the synthesis network to generate high‐quality interpolated frames. The experimental results indicate that the proposed approach achieves superior performance with fewer model parameters.

Density data for Lake Erie benthic invertebrate assemblages from 1930 to 2019.

Benthic invertebrates are important trophic links in food webs and useful bioindicators of environmental conditions, but long-term benthic organism abundance data across broad geographic areas are rare and historic datasets are often not readily accessible. This dataset provides densities of benthic macroinvertebrates collected from 1930 to 2019 during surveys in Lake Erie, a Laurentian Great Lake. The surveys were funded by the governments of the United States and Canada to investigate the status and changes in the benthic community. From the total of 21 lake-wide and basin-wide benthic surveys conducted in Lake Erie from 1929 to 2019, we were able to acquire data for 17 surveys, including species-level data for 10 surveys and data by higher taxonomic groups for seven surveys. Our amassed Lake Erie dataset includes data from 11 surveys (including five with species-level data) conducted in the western basin in 1930-2019, seven surveys (six with species-level data) in the central basin, and eight surveys (seven with species-level data) in the eastern basin (1973-2019). This Lake Erie dataset represents the most extensive temporal dataset of benthic invertebrates available for any of the Laurentian Great Lakes. Benthic samples were collected using Ponar or Shipek bottom dredges and taxa densities were calculated as individuals per square meter using the area of the dredge. Density data are provided for taxa in the Annelida, Arthropoda, Mollusca, Cnidaria, Nemertea, and Platyhelminthes phyla. Current taxonomy was used for most groups but, in a few cases, older taxonomic names were used for consistency with historical data. Analysis of this dataset indicates that eutrophication, water quality improvement, and dreissenid introduction were the major drivers of changes in the benthic community in the western basin, while hypoxia was a major factor in the central basin, and dreissenid introduction was the most important driver in the eastern basin. Considering the rarity of high taxonomic resolution long-term benthic data for lake ecosystems, this dataset could be useful to explore broader aspects of ecological theory, including effects of eutrophication, hypoxia, invasive species, and other factors on community organization, phylogenetic and functional diversity, and spatial and temporal scales of variation in community structure. In addition, the dataset could be useful for studies on individual species, including abundance and distribution, species co-occurrence, and how the patterns of dominance and rarity change over space and time. Use of this dataset for academic or educational purposes is encouraged as long as this data paper is properly cited.

Development of a Scale to Measure Tolerance Toward Diverse Others1

AbstractOpportunities to interact with people from different cultures and languages have increased as society has become more globalized. Hence, it has become increasingly important for people living in today's international society to accept and try to build trusting relationships with those who may perceive the world differently. This study aimed to develop a scale to measure people's tolerant attitudes toward diverse others (hereafter, tolerance toward diverse others). Furthermore, after confirming the newly developed scale's reliability and validity, we used it to examine its associations with general trust and cultural interdependent self‐construal. Albeit exploratively, we then discuss the relationships among these variables. Our results showed that scores on the newly developed Tolerance of Diverse Others Scale were positively correlated with scores on the General Trust Scale and also with scores on tolerance‐related items in the World Values Survey. Furthermore, the harmony‐seeking aspect of cultural interdependent self‐construal was positively correlated with tolerance toward diverse others, while the rejection‐avoidance aspect was negatively correlated, although the positive correlation between harmony seeking and rejection avoidance was shown. We discuss what the results suggest about Japanese individuals' perceptions of diverse others.

DBFFT: Adversarial-robust dual-branch frequency domain feature fusion in vision transformers

Vision transformers (ViTs) have been successful in image recognition. However, it is difficult for ViTs to capture comprehensive information and resist adversarial perturbations by learning features from the spatial domain alone. Features with frequency domain information also play an important role in image classification and robustness improvement. In particular, the relative importance of spatial and frequency domain feature representations should vary depending on the encoding stage. Previous studies lack consideration of the flexible fusion of feature representations from different domains. To address this limitation, we propose a novel dual-branch adaptive frequency domain feature fusion architecture for Transformers with good classification ability and strong adversarial robustness, namely DBFFT. In each layer, we design two parallel Fourier transform and self-attention branches to learn hidden representations from the frequency domain and spatial domain, respectively. These are then adaptively weighted and fused according to their learned importance. Moreover, we further propose a dual-branch patch embedding fusion module. The module introduces different convolutional paths to extract input image features at different scales. The features are then embedded and combined into more informative tokens. Our DBFFT architecture can make full use of diverse domain and scale information, which benefits the image classification and enhances robustness against adversarial interference. Experimental results show that our DBFFT achieves promising performance and robustness in many image classification datasets and robustness benchmarks with favorable accuracy-complexity trade-offs.

Waves in elastic bars under axial constant velocity loading

Elastic bars serve as primary mechanical members, spanning from atomic to macroscopic scales. The development of theoretical frameworks for elastic bars is crucial for guiding a wide array of industrial and everyday applications. In this study, we comprehensively investigate wave propagation in elastic bars subjected to tension and compression under constant velocity loading conditions. First, axial waves were studied by analytically solving the axial wave equation with damping. Subsequently, we employed this theoretical framework in atomistic simulations to measure loading-rate effects and thus determine the conditions for distinguishing quasi-static and dynamic loading. Given the coupled compressive axial waves, deflection waves were analyzed to predict dynamic instability. Our findings revealed that a continuous increase in impact velocity may not necessarily result in a larger impact force. This study offers fresh insights into the mechanical theory on the dynamic behaviour of bars across diverse scales (atomic to macroscopic). These insights are invaluable for determining suitable loadings in atomistic simulations, and provide guidelines for engineering impact applications.

Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement

Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwater scenes. Therefore, we present the synergistic multi-scale detail refinement via intrinsic supervision (SMDR-IS) for enhancing underwater scene details, which contain multi-stages. The low-degradation stage from the original images furnishes the original stage with multi-scale details, achieved through feature propagation using the Adaptive Selective Intrinsic Supervised Feature (ASISF) module. By using intrinsic supervision, the ASISF module can precisely control and guide feature transmission across multi-degradation stages, enhancing multi-scale detail refinement and minimizing the interference from irrelevant information in the low-degradation stage. In multi-degradation encoder-decoder framework of SMDR-IS, we introduce the Bifocal Intrinsic-Context Attention Module (BICA). Based on the intrinsic supervision principles, BICA efficiently exploits multi-scale scene information in images. BICA directs higher-resolution spaces by tapping into the insights of lower-resolution ones, underscoring the pivotal role of spatial contextual relationships in underwater image restoration. Throughout training, the inclusion of a multi-degradation loss function can enhance the network, allowing it to adeptly extract information across diverse scales. When benchmarked against state-of-the-art methods, SMDR-IS consistently showcases superior performance. Our code is available at https://github.com/zhoujingchun03/SMDR-IS

Discriminative multi-scale adjacent feature for person re-identification

AbstractRecently, discriminative and robust identification information has played an increasingly critical role in Person Re-identification (Re-ID). It is a fact that the existing part-based methods demonstrate strong performance in the extraction of fine-grained features. However, their intensive partitions lead to semantic information ambiguity and background interference. Meanwhile, we observe that the body with different structural proportions. Hence, we assume that aggregation with the multi-scale adjacent features can effectively alleviate the above issues. In this paper, we propose a novel Discriminative Multi-scale Adjacent Feature (MSAF) learning framework to enrich semantic information and disregard background. In summary, we establish multi-scale interaction in two stages: the feature extraction stage and the feature aggregation stage. Firstly, a Multi-scale Feature Extraction (MFE) module is designed by combining CNN and Transformer structure to obtain the discriminative specific feature, as the basis for the feature aggregation stage. Secondly, a Jointly Part-based Feature Aggregation (JPFA) mechanism is revealed to implement adjacent feature aggregation with diverse scales. The JPFA contains Same-scale Feature Correlation (SFC) and Cross-scale Feature Correlation (CFC) sub-modules. Finally, to verify the effectiveness of the proposed method, extensive experiments are performed on the common datasets of Market-1501, CUHK03-NP, DukeMTMC, and MSMT17. The experimental results achieve better performance than many state-of-the-art methods.

Enhanced Water Quality Prediction in the Yellow River Basin: The Application of the HHO-LSTM Model

In the pivotal water resource region of the Yellow River Basin in China, precise prediction of water resources is essential for their effective and rational management. This study introduces a novel approach to water resource prediction by employing the Harris Hawks Optimization-Long Short-Term Memory (HHO-LSTM) model. This method overcomes the constraints faced by traditional techniques in processing time series data and various variable factors. It encompasses a comprehensive description of the multi-source hydrological data collection process within the Yellow River Basin, followed by meticulous data preprocessing. The data set for this study includes estimates of four critical water quality parameters, and the efficacy of the model is gauged through the mean squared error (MSE) and root mean squared error (RMSE) metrics. This facilitates the projection of future water quality trends in specific areas by leveraging historical water quality data. The HHO-LSTM model has demonstrated outstanding accuracy and robustness in predicting water quality across diverse temporal scales and water resource variables, marking a significant advancement in water resource management within the Yellow River Basin. This approach not only enhances current management strategies but also contributes valuable insights for ongoing water resource research and decision-making processes.

Exploring the nexus of innovation management, ultraviolet irradiation, and business scale: implications for sustainable fruit and vegetable preservation during the COVID-19 era

Innovation management stands as a pivotal driver in attaining enduring sustainability in livelihoods. However, its progress can face formidable obstacles, encompassing the complexities arising from human perceptions. This research delved into the realm of innovation management within the context of fruit and vegetable preservation, with a keen focus on the potential impact of the COVID-19 pandemic and the perceptual evaluations surrounding the viability of utilizing ultraviolet (UV) irradiation for this purpose. The perceived effectiveness of UV irradiation in fruit and vegetable preservation was found to be significantly influenced by factors, such as cost, safety, and limited understanding. The respondents' preference for preserved fruits and vegetables was most significantly influenced by the elimination of pathogenic microorganisms, as indicated by a mean score of 4.6. Notably, around 50.9% of the participants demonstrated a lack of understanding regarding the use of ultraviolet irradiation as a preservation technology. In addition, approximately 21.6% of the respondents expressed disinterest in adopting ultraviolet irradiation for preserving fruits and vegetables. In contrast, a smaller percentage of participants, merely 12.3%, showed a positive inclination towards employing ultraviolet irradiation in the preservation process. Interestingly, approximately 15.2% of respondents remained undecided and did not express a definitive preference concerning their willingness to utilize ultraviolet irradiation for preserving fruits and vegetables. Notably, the analysis has revealed a compelling trend, with a substantial portion of participants (63%) perceiving medium-sized enterprises involved in fruit and vegetable processing technologies as displaying notably low levels of motivation for innovation during the COVID-19 era. A similar viewpoint was shared by 51% of respondents regarding large-scale businesses. In stark contrast, the survey has illuminated a striking disparity, as small-scale businesses exhibited significantly heightened innovation motivation at 28%, surpassing the modest 10% observed in both medium-sized and large-scale counterparts. This pronounced variation in innovation motivation across diverse business scales highlights the multifaceted impact of the pandemic on their inclination towards embracing innovation. These findings offer invaluable insights into the intricacies governing innovation management within the fruit and vegetable preservation sector amidst the challenging COVID-19 period.

HF-YOLO: Advanced Pedestrian Detection Model with Feature Fusion and Imbalance Resolution

Pedestrian detection is crucial for various applications, including intelligent transportation and video surveillance systems. Although recent research has advanced pedestrian detection models like the YOLO series, they still face limitations in handling diverse pedestrian scales, leading to performance challenges. To address these issues, we propose HF-YOLO, an advanced pedestrian detection model. HF-YOLO tackles the complexities of pedestrian detection in complex scenes by addressing scale variations and occlusions among pedestrians. In the feature fusion stage, our algorithm leverages both shallow localization information and deep semantic information. This involves fusing P2 layer features and adding a high-resolution detection layer, significantly improving the detection of small-scale pedestrians and occluded instances. To enhance feature representation, HF-YOLO incorporates the HardSwish activation function, introducing more non-linear factors and strengthening the model’s ability to represent complex and discriminative features. Additionally, to address regression imbalance, a balance factor is introduced to the CIoU loss function. This modification effectively resolves the imbalance problem and enhances pedestrian localization accuracy. Experimental results demonstrate the effectiveness of our proposed algorithm. HF-YOLO achieves notable improvements, including a 3.52% increase in average precision, a 1.35% boost in accuracy, and a 4.83% enhancement in recall. Moreover, the algorithm maintains real-time performance with a detection time of 8.5ms, meeting the stringent requirements of real-time applications.

Mobilizing institutional capacities to adapt to climate change: local government collaboration networks for risk management in Mexico City

State-centered and hierarchical government strategies based on technocratic top-down approaches have proved inadequate to address multiple interacting and cross-scale risks. In contrast, multilevel governance (MLG) provides a form of governance by networks that operate both in a horizontal arena with neighboring local governments, civil society organizations, and local communities, and in vertical settings, with higher-level governmental authorities and international organizations. In this paper, we examined the role and experience of local governments in a multilevel governance system for risk management in Mexico City. We sought to understand what collaborative resources local governments have to address multiple risks in Mexico City? How do they mobilize these resources in specific actions to address a risk, and how do they interact with different levels on diverse organizational scales? Our results show that resources for vertical and horizontal collaboration were key for local governments for coordination to address multiple risks. We found that collaboration was more common between local governments and with the subnational government. Also, the most common actions focused on forecasting and prevention, as well as preparedness and relief of multiple risks. We found evidence of successful elements of multilevel governance and identified the need for further research on the engagement and representation for non-governmental organizations and private actors in networks of risk management. The results from this study provide a better understanding of the arrangement of collaborative resources underlying local government actions under a state of urgency and scarcity, where action cannot wait for institutional change.

Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method

Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% ( 0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks.

Global Coronal Magnetic Field Estimation Using Bayesian Inference

Estimating the magnetic field strength in the solar corona is crucial for understanding different physical processes happening over diverse spatiotemporal scales. However, the high temperatures and low density of the solar corona make this task challenging. The coronal magnetic field is too weak to produce a measurable splitting of the spectral lines using the Zeeman effect, and high temperature causes spectral lines to become weak and broad, making it difficult to detect the small Zeeman splitting. Coronal magneto-seismology, which combines the theoretical and observed properties of magnetohydrodynamic waves, can be used to infer the magnetic field strength of oscillating structures in the solar corona, which are otherwise difficult to estimate. In this work, we use the Doppler velocity and density data obtained from the Coronal Multichannel Polarimeter on 2016 October 14 to obtain the global map of the coronal magnetic field using Bayesian inference. Two priors are used for plasma density, viz Gaussian and uniform distributions. Bayesian inference provides us with the probability distribution for the magnetic field strength at each location from 1.05 to 1.35 R ⊙. A comparison between the magnetic field obtained using simple inversion and Bayesian inference is also drawn. We find that the values obtained using simple inversion do not always match the maximum posterior estimates obtained using Bayesian inference. We find that the inferred values follow a power-law function for the radial variation of the coronal magnetic field, with the power-law indices for simple and Bayesian inversion being similar.

Analysis of Plant Productivity, Farmer Income and Availability of Supporting Infrastructure on Soybean Agribusiness Institutional Performance in East Java

This research investigates the dynamics of soybean agribusiness in East Java through the lens of crop productivity, farmer income, supporting infrastructure, and institutional performance. A sample of 150 participants representing diverse scales of farming and geographic regions was surveyed to gather insights. Structural Equation Modeling with Partial Least Squares (SEM-PLS) was employed to analyze the relationships among variables. The study confirmed positive and significant associations between crop productivity and farmer income, supporting infrastructure and crop productivity, and supporting infrastructure and institutional performance. Discriminant validity was established, and model fit indices indicated the reliability of the proposed structural model. Hypothesis testing further validated the impact of the availability of supporting infrastructure, farmer income, and plant productivity on institutional performance. The findings offer practical insights for policymakers, farmers, and stakeholders to optimize soybean agribusiness in East Java.

Enhancing Mathematical Disposition in Geometry Instruction : The Role of E-Comics in Realistic Mathematics Education

<p dir="ltr"><span>Success in learning mathematics can be measured through five aspects called the five strands of mathematical proficiency. These five aspects need to be taught fairly, resulting in a change in students' mathematical attitudes where this aspect is still minimally looked at and researched. Therefore, the research aims to analyze changes in students' mathematical dispositions by reviewing the role of e-comics in Realistic Mathematics Education (RME). This context-based mathematics learning approach is assumed to provide meaningful learning for students. This mixed-methods research with a sequential explanatory research design was carried out on 30 elementary school students by combining quantitative data collection and analysis techniques with qualitative data support. The research showed that using e-comics in RME learning significantly improved students' mathematical dispositions with a large effect size (Cohen’s effect size = 0,98). In a qualitative review involving data triangulation, evidence was found that using e-comics in RME learning has a role in facilitating the development of each student indicator, as evidenced in the high students’ enthusiasm or response in learning both in observations, questionnaires, and documentation studies. In this way, it is hoped that this innovation can be researched and developed further on a more diverse scale and content. In addition, this research provides further opportunities for research and teacher training related to developing RME learning designs and e-comics in learning.</span></p><div><span><br /></span></div>

An Overview of Innovative Surface-Modification Routes for Pool Boiling Enhancement.

This overview intends to provide a comprehensive assessment of the novel fluids and the current techniques for surface modification for pool boiling enhancement. The surface modification at macro-, micro-, and nanoscales is assessed concerning the underlying fluid routing and capability to eliminate the incipient boiling hysteresis and ameliorate the pool boiling heat-transfer ability, particularly when employed together with self-rewetting fluids and nanofluids with enriched thermophysical properties. Considering the nanofluids, it is viable to take the profit of their high thermal conductivity and their specific heat simultaneously and to produce a film of deposited nanoparticles onto the heating surface, which possesses enhanced surface roughness and an increased density of nucleation sites. Whilst the diverse improvement scales are found to achieve distinct levels of success regarding the nucleate boiling heat-transfer capability enhancement, it is also shown that the micro-nanoscale boiling surface features are susceptible to blockage, leading to the degradation of the improvement with time. Furthermore, topics relating to the heat transfer thermal behavior, ease of manufacture, cost-effectiveness, reliability, and durability are reviewed whenever available and challenges and recommendations for further research are highlighted.