Scaling Issues Research Articles

Contagion and control: the role of epidemic discourse in shaping domestic violence as a nascent policy issue

ABSTRACT Domestic violence is widely recognised as a global issue and a fundamental human rights violation. Internationally, it is often addressed through human rights frameworks that emphasise victim-centered interventions. In the United States, a distinct policy trajectory focused on criminalisation has developed. Drawing from untapped sources, including 1977–1978 congressional debates, this study reveals the emergence of an “epidemic discourse” on domestic violence. This discourse, guided by the epidemic metaphor, depicted domestic violence as a contagious social problem transmitted by children mimicking spousal violence. The metaphor gained traction in Congressional debates by aligning with statistics, magnifying the issue's scale. However, it inadvertently shifted attention from victim support to preventive and punitive strategies aimed at isolating perpetrators while maintaining, through the notion of risk factors, individual responsibility for a woman having become a victim. Notably, the U.S. has since exported many of its punitive policies globally, influencing other nations’ approaches to domestic violence. The findings underscore the subtle yet significant role metaphors can play in policymaking and in shaping public perceptions of emerging social problems. This analysis, with its focus on unsuccessful legislative bills and debates, enhances our understanding of discursive processes that shape social problem construction, leading to unforeseen dynamics within policymaking.

Wearable System Integrating Dual Piezoresistive and Photoplethysmography Sensors for Simultaneous Pulse Wave Monitoring.

Wearable, flexible piezoresistive pressure sensors have garnered substantial interest due to their diverse applications in fields such as electronic skin, robotic limbs, and cardiovascular monitoring. Among these applications, arterial full pulse waveform monitoring stands out as a critical area of research. The emergence of piezoresistive pressure sensors as a prominent tool for capturing pulse waveforms has led to extensive investigations. However, the lack of a linear response while achieving high sensitivity, limited compactness of signal collection systems, and scaling issues are a few potential causes of the gap between research advances and technology market readiness. Here, we present a scalable dual piezoresistive sensor that uses two complementary resistance-changing mechanisms to balance the trade-off between linear response and high sensitivity. This synergic design enables excellent sensitivity of 8.4 kPa-1 and near linear pressure response. It boasts a fast response time of 95/145 ms for loading and unloading at a pressure of 10.1 kPa. The durability tests, encompassing nearly 5000 two-stage compression cycles, validate the reliable performance of the sensor, even after prolonged use. Leveraging these performance metrics, we developed a high-resolution and compact signal collection device capable of accurately detecting the three distinct peaks associated with the full pulse waveform, including the systolic and reflected diastolic peaks. Moreover, the signal acquisition system incorporates a photoplethysmography sensor, which, when paired with the dual piezoresistive sensor, offers new insights into localized vascular health monitoring. The unique feature of our approach is its ability to perform localized vascular monitoring using multiple sensors placed on different veins. The simultaneous detection of forward- and backward-going pulse waves at the body extremities allows for a comprehensive evaluation of localized vascular health, which is not achievable with a single sensor. Finally, the comparison of wrist pulse waveforms between the compact signal collection device and a standard sourcemeter (such as the Keithley 2460) confirmed that the wearable sensing system is on par with conventional sourcemeters in terms of capturing the typical details of the full pulse waveform (i.e., systolic peak, dicrotic notch, and diastolic peak). This validation underscores the reliability and effectiveness of the developed wearable sensing system for practical and continuous pulse waveform monitoring.

Protection of water resources from agricultural pressures: Embracing different knowledge domains in governance approaches

AbstractThe remediation of nitrate and pesticide pollution from agriculture in drinking water resources has manifested itself as a complex and multifaceted challenge in Europe and in other continents. Addressing agricultural pollution in water resources requires cross‐sectoral approaches. The EU Water Framework Directive aims to build bridges among these sectors, but the often sectoral implementation by Member States prevents its potential from being fully explored. This study aims to contribute to the body of interdisciplinary knowledge on the driving forces towards water quality improvement from agricultural pollution by case study research in five European countries in an interdisciplinary setting. The cases have shown that the added value of voluntary practices is considerable for creating shared ambitions but limited for actual water quality improvement. Implementation of strategies should be supported by practical guidance and monitoring of outcomes that enables compliance testing and refines simulation models for the formulation of follow‐up actions. Dynamic interactions among the knowledge domains, for example, social‐economic context, the legal framework, and the state of the water system, help to identify necessary actions at the different stages of the policy cycle. Especially in the implementation, monitoring, evaluation, and adaptation stage of the policy cycle, there is a need for further studies in order to improve effectiveness, for example on the role of monitoring and evaluation, licensing, and the issue of scale in cross‐sectoral approaches.

Uncovering hidden alignments in two-dimensional point fields

The problem of mapping hidden alignments of points in data sets of two-dimensional points is of significant interest in many geoscience disciplines. In this paper, we revisit this issue and provide a new algorithm, insights, and results. The statistical significance of alignments is assessed by using percentile confidence intervals estimated by a Monte Carlo procedure in which important issues, such as the shape of the geometric support and the possible non-homogeneity of the point density (i.e., clustering effects), have been considered. The procedure is not limited to the simplest case of occurrence and the chance of triads (alignments of three points in a plane) but has been extended to k-ads with k arbitrarily large. The important issue of scale, when searching for point alignments, has also been taken into account. Case studies using synthetic and real data sets are provided to illustrate the methodology and the claims.

Digital watermarking algorithm for QR code based on hybrid PSOGWO optimization

Abstract With the rapid development of digital media, QR codes have become widely used as an important medium for information transmission. However, this also presents serious security challenges, such as frequent image infringement and inevitable compression and scaling issues during data transfer. These challenges directly threaten the effectiveness and integrity of image information, and effective protection measures must be taken. Under this background, digital watermarking technology comes into being as an innovative method to enhance security. This paper presents an innovative Hybrid Particle Swarm Optimization and Gray Wolf optimization (HPG) algorithm, which combines the advantages of PSO and overcomes the limitations of traditional GWO algorithm. The algorithm is further improved by using Bernoulli chaotic mapping and nonlinear convergence factor. The experiments focus on the global and local search balance of the algorithm and its ability to resist repeated scaling attacks. The results show that the nonlinear convergence factor of HPG algorithm slows down the iteration speed at the initial stage, strengthens the global search, and avoids falling into the local optimal. In the later stage, the iteration is accelerated to improve the local search speed and convergence efficiency, thus optimizing the performance of the algorithm and ensuring the safety and quality of image information. In addition, the algorithm has better performance in balancing imperceptibility and robustness of digital watermarking, and its peak signal-to-noise ratio (PSNR) is higher. Even with many scaling iterations, the normalized correlation (NC) value is still close to 1. Compared with the existing typical algorithms, HPG algorithm is more robust to repeated scaling attacks.

Evidence-Based Programs in France: Insights and Strategies for Implementation Support

Abstract Issue In France, both regional and national health institutions increasingly support and fund the implementation of evidence-based programs (EBP), promoted at the national level by the French Public Health agency. Description of the problem practitioners who implement EBP face several operational questions: scaling issues, implementation in specific and varying contexts, integration and adjustment to daily regional practices. Results/effects In 2023, the French Society for Public Health initiated a collaborative effort to create a platform for discussion among practitioners, researchers, and decision-makers involved in EBP implementation. The first step was to assess EPB implementation experiences, using 3 types of data: an analysis of 6 documented EBP (capitalization works), an online questionnaire answered by 29 stakeholders, and four focus groups with funders, researchers, practitioners, and institutions. Data analysis was based on thematic content analysis and structural data analysis. Lessons Different ways to implement EBP were identified through a typology. The results highlight favorable conditions and levers for EPB implementation in France: • Facilitate access and adoption of EBP by concerned stakeholders, including through clear communication and knowledge sharing; • Strengthen and value support for implementation support (i.e., providing necessary resources and continuous monitoring); • A ‘favorable environment’ is also required (i.e. regional context acceptability and information on program themes or topics); • Breadthen perspectives and promote mutual learning through sharing and comparing implementation experiences across regions; • Value transferability and adaptation studies, which are key to enable EBP adaptations to account for context specificities while maintaining effectiveness. The platform’s next steps are to develop deployment tools and structure an advocacy strategy to mobilize stakeholders around the issue. Key messages • Achieving wide-scale implementation of EBP requires to tackle key implementation issues such as scaling, context adaptation, etc. and to give support to implementing stakeholders. • A collaborative assessment helped identify levers and barriers to EBP implementation in France. Involvement in EBP fosters an evidence-based mindset among practitioners.

Investigation of Scaling and Materials' Performance in Simulated Geothermal Brine.

Geothermal energy generation faces challenges in efficiency, partly due to restrictions on reinjection temperatures caused by scaling issues. Therefore, developing strategies to prevent scaling is critical. This study aims to simulate the scaling tendencies and corrosion effects of geothermal fluids on various construction materials used in scaling reactor/retention tank systems. A range of materials, including carbon steel, austenitic stainless steel, duplex stainless steel, two proprietary two-part epoxy coatings, and thermally sprayed aluminium (TSA), were tested in a simulated geothermal brine. Experiments were conducted in a laboratory vessel designed to replicate the wall shear stress conditions expected in a scaling reactor. The tests revealed varying scaling tendencies among the materials, with minimal corrosion observed. The dominant scale formed was calcium carbonate, consistent with geochemical modelling. The findings suggest that despite the high operating temperatures, the risk of corrosion remains low due to the brine's low chloride content, while the wettability of materials after immersion may serve as a useful indicator for selecting those that promote scaling.

Laboratory-Scale Implementation of Standardized Reconstituted Geothermal Water for Electrochemical Investigations of Carbon Steel Corrosion

Currently, the demand for heat production by geothermal energy is increasingly strong amid the controversy surrounding non-renewable forms of energy. In France, the Dogger aquifer in the Paris Basin (DAPB) produces saline geothermal waters (GWs) that are hot (70–85 °C), anaerobic, and slightly acidic (pH 6.1–6.4), and are characterized mainly by the presence of Cl−, SO42−, CO2/HCO3−, and H2S/HS. These GWs are corrosive, while the well casings used are carbon steel. GWs have been continuously treated since the 1990s by corrosion inhibitors at the bottom of production wells to reduce water–steel interactions and scaling issues. Electrochemical experiments to optimize inhibitors were carried out on site, protected from the ambient atmosphere, with actual geothermal water, using water tapping at the wellhead. Currently, carbon steel corrosion/scaling, corrosion inhibition phenomenology, and kinetics evaluation remain important challenges. These issues are, of course, linked to the durability of installations. The novelty of our work consists of our validation of a modus operandi that properly reproduces, at the laboratory scale, operating conditions similar to those encountered on the types of geothermal installations. Particular attention was paid to characterizing waters and gases from 13 production wellheads that were modelled with PhreeqC® Version 3 hydrogeochemical software and the Thermoddem thermodynamic database for implementing standardized reconstituted geothermal water (SRGW), a well-balanced water representative of the major elements and dissolved gases of actual DAPB geothermal waters. The developed electrochemical setup enabled us to analyze corrosion mechanisms such as those observed on site and to investigate corrosion inhibition using petrosourced and biosourced inhibitors. The modus operandi constitutes a reference for further investigations, at the laboratory scale, of corrosion inhibition. These investigations may include screening and optimizing the formulas of petrosourced and biosourced inhibitors for use in DAPB waters.

A Multi-Scale Evaluation Model of Sustainable Development Goal 11.7 for Problem-Solving at Different Levels

Sustainable Development Goal 11.7 (SDG 11.7) aims to promote the improvement of urban public spaces. However, the localization process of SDG 11.7 mainly relies on a bottom-up problem-solving approach, which fails to fully encompass the connotation of SDG 11.7. Additionally, existing evaluations primarily focus on a single scale, neglecting the impact of scale issues. These limitations can lead to imbalanced development or misallocation of responsibilities when guiding governments at different levels in promoting the sustainable development of public spaces. Therefore, this article introduces a multi-scale assessment model of SDG 11.7. It employs a top-down problem-solving approach to construct a sustainable development indicator framework, setting appropriate sustainable development indicators for various levels of government based on the connotation of SDG 11.7, and generates city-scale results by integrating three scales: apartment complexes, street blocks, and counties. Testing this model in Xi’an, China, revealed that it adequately captures four key aspects of SDG 11.7—safety, inclusiveness, accessibility, and greenness—through 11 indicators. The evaluation outcomes at the apartment complex, street block, and county levels effectively guide future development directions for various levels of government. Ultimately, the synthesis of these scales reveals the spatial pattern of SDG 11.7 at the city scale and identifies focal areas for development. Overall, this exploratory model demonstrates high accuracy and robustness, providing a comprehensive understanding of the essence of SDG 11.7. It also alleviates challenges posed by scale issues, offering decision support for monitoring SDG 11.7 across different levels of government in Chinese cities and promoting the process of sustainable development.

Reconstructing the Ocean State Using Argo Data and a Data-Driven Method

Abstract Over the past two decades, Argo profiles have provided unprecedented insight into the global patterns of space and time variability of ocean temperature and salinity, significantly reducing associated uncertainties. However, analyzing such assessments during the pre-Argo period remains challenging due to the scarcity of observations in many regions. From the Argo period, a set of dominant three-dimensional patterns can be estimated using Empirical Orthogonal Function (EOF) analysis, which helps to fill in observational gaps. From the associated principal components, temporal fluctuations can be observed, aiming to build a catalog of possible ocean state trajectories. To map pre-Argo observations, EOFs are used in a data assimilation framework that uses this catalog to feed an analog prediction and provide reanalysis. In this study, a new data-driven interpolation method called RedAnDA (Reduced-space Analog Data Assimilation) was tested in the tropical Pacific Ocean. RedAnDA was first validated through an Observing System Simulation Experiment (OSSE) approach, using synthetic observations extracted from a model simulation. It was subsequently applied to a real historical dataset and compared with other available reanalysis products. Overall, the reconstructed temperature field showed variability consistent with the OSSE true field and other reanalysis products in the real data application. Further improvements are needed to optimally estimate uncertainty, but RedAnDA already combines valuable information about state predictability, observational sampling, and unresolved scale issues.

Strengthening network DDOS attack detection in heterogeneous IoT environment with federated XAI learning approach

Due to the rising use of the Internet of Things (IoT), the connectivity of networks increases the risk of Distributed Denial of Service (DDoS) attacks. Decentralized systems commonly used in centralized security systems fail to adequately prevent potential cyber threats in IoT because of the issues of privacy and scaling. The method proposed in this study seeks to remedy these facts by employing Explainable Artificial Intelligence (XAI) together with Federated Deep Neural Networks (FDNNs) to detect and prevent DDoS attacks. Our approach is thus to use federated learning models that are to be trained on distributed and dissimilar sources of data without compromising on the privacy aspect. FDNNs were trained over three rounds with information from three client gadgets incorporating pre-processed datasets of various types of DDoS attacks. Additionally, for feature selection, we integrated XGBoost with SHapley Additive exPlanations (SHAP) to improve model interpretability. The proposed solution can be considered to be quite robust, privacy-preserving, and highly scalable for the detection of DDoS attacks on the IoT network. The results shown on the server side indicate that this approach accurately detects 99.78% of DDoS attacks with a precision rate as high as 99.80%, recall rate (detection rate) going up to 99.74% and F1 score reaching 99.76%. They emphasize that FL-based IDSs are strong enough to cope with cybersecurity challenges in IoT, thus offering hope for securing modern network infrastructures against ever-growing cyber threats.

At the opinion crossroads: Kuibyshev in the days of the Great Patriotic War (1941–1943)

The article considers Kuibyshev city (Samara nowadays) in the wartime (1941–1943), the features of its life activity and development. The solving of the large-scale tasks raised in connection with the unprecedented evacuation of the government departments, diplomatic body, people’s commissariats, factories and population to the city deeply changed the city life. Kuibyshev was the regionally governed city, but it performed many specific functions typical for the soviet capital city of Moscow. The circumstance gave occasion to call the city of Kuibyshev unofficially «alternate capital». The research task is to analyze the various aspects of the Kuibyshev city life in the wartime: to show the different interpretations of its status perception by the historians in 1941–1943; to specify the data concerning the specified All-Union people’s commissariats being on evacuation in the city of Kuibyshev and its quantitative composition; to raise the question of the final date of the acceptance of the «special objects» No. 1 (Stalin’s bunker) and No. 2 (for General Staff of the Red Army) of the Building of Metrostroi (Soviet subway construction company); to qualify the questions about the zoning of the city in the wartime; to cite new data about Bezymyanka district of the prewar time using comparative-historical, analytical and retrospective methods. The article introduces a new document material of the central archives of the Russian Federation into scientific discourse. It is proved that Kuibyshev was neither «alternate» nor «military» capital. It tasted the peak of its «capital city status» during 2–3 months while solving many issues of the All-Union scale and performing capital functions. The conclusion that the acceptance of the city services in the analyzed period was the rating of the city of Kuibyshev to the cities of republican subordination on 21st August 1943 is drawn.

A novel multicriteria web‐based decision support system to enhance resource allocation in energy companies: a portfolio selection with c‐optimal PROMETHEE

AbstractIn an uncertain world, decision support systems (DSSs) are essential for effective portfolio management. Electricity companies often face challenges in allocating limited resources among competing projects, influenced by sustainability and regulatory requirements. To address these challenges, multicriteria approaches have been developed, focusing on complex issues involving outranking methods. This paper introduces a methodological advance with a novel algorithm that enhances the c‐optimal PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) approach, resolving scaling issues inherent in the PROMETHEE V method. Our key contribution is a refined procedure for efficiently identifying and comparing c‐optimal solutions while preserving the method's axiomatic integrity, ensuring the selection of the “true best” portfolio. Additionally, we present a web‐based DSS designed to implement this methodology, featuring interactive visualization and sensitivity analysis tools. Freely available online, both the algorithm and DSS were validated through a case study with a Brazilian electricity company, demonstrating their practical benefits and applicability across various business contexts.

Contraband classification method for X-ray security images considering sample imbalance

X-ray security image contraband classification is widely used to assist in maintaining aviation and transportation security. This paper suggests an end-to-end X-ray security inspection image classification method that takes sample imbalance into account in order to address the issues of different scales of contraband in X-ray images, challenging samples, and unbalanced positive and negative samples inherent in passenger baggage security inspection. The feature fusion module is used to enhance the model's ability to express picture edge and texture features while the multi-scale feature extraction network is used to capture the features of numerous sorts of illegal goods with various scales. Based on the cost-sensitive idea, the loss function is designed to solve the problem of dataset imbalance, and improve the classification accuracy of difficult samples. experimental results of the subset constructed on the public dataset SIXray show that the proposed method improves the mean AP index by compared with the current optimal end-to-end classification model, especially for hard-to-classify samples such as scissors, the AP index has a significant improvement effect.

Resolving Scaling Issues in Self‐Assembled Monolayer‐Based Perovskite Solar Modules via Additive Engineering

Resolving Scaling Issues in Self‐Assembled Monolayer‐Based Perovskite Solar Modules via Additive Engineering

Exploring the decision-making process in model development: focus on the Arctic snowpack

Abstract. The Arctic poses many challenges for Earth system and snow physics models, which are commonly unable to simulate crucial Arctic snowpack processes,such as vapour gradients and rain-on-snow-induced ice layers. These limitations raise concerns about the current understanding of Arctic warming and its impact on biodiversity, livelihoods, permafrost, and the global carbon budget. Recognizing that models are shaped by human choices, 18 Arctic researchers were interviewed to delve into the decision-making process behind model construction. Although data availability, issues of scale, internal model consistency, and historical and numerical model legacies were cited as obstacles to developing an Arctic snowpack model, no opinion was unanimous. Divergences were not merely scientific disagreements about the Arctic snowpack but reflected the broader research context. Inadequate and insufficient resources, partly driven by short-term priorities dominating research landscapes, impeded progress. Nevertheless, modellers were found to be both adaptable to shifting strategic research priorities – an adaptability demonstrated by the fact that interdisciplinary collaborations were the key motivation for model development – and anchored in the past. This anchoring and non-epistemic values led to diverging opinions about whether existing models were “good enough” and whether investing time and effort to build a new model was a useful strategy when addressing pressing research challenges. Moving forward, we recommend that both stakeholders and modellers be involved in future snow model intercomparison projects in order to drive developments that address snow model limitations currently impeding progress in various disciplines. We also argue for more transparency about the contextual factors that shape research decisions. Otherwise, the reality of our scientific process will remain hidden, limiting the changes necessary to our research practice.

Exploring thermal effects of advanced backside power delivery network beyond 3 nm node

Backside Power Delivery Networks (BSPDNs) address scaling issues by relocating power, boosting efficiency and density. However, thermal effects pose challenges. In this work, a comprehensive thermal analysis of BSPDN is performed to elaborate the key modulation factors and possible optimization approaches, where the specific backside metal layers are constructed to investigate the impacts of operating conditions, via distribution and materials on thermal effects. To improve the simulation efficiency, the effective thermal conductivity is employed to simplify the Nanosheet (NSH) FET based front-end-of-line (FEOL) and other layers at the 3 nm technology node. Results show that non-uniform via distribution in BSPDN causes temperature fluctuations, but augmenting Backside Via counts effectively mitigates local peak temperature increases from higher power. For BSPDN, backside cooling solutions outperform frontside in efficiency, particularly at high via densities. Using high thermal conductivity inter-metal dielectric (IMD) materials significantly reduces global temperature rise and fluctuations from non-uniform vias in BSPDN, enhancing PDN design flexibility.

Sediment Source Partitioning and Budgeting Over Historical Timescales in a Glacierized, Mountain Catchment

AbstractManaging and living with geohazards is especially challenging in mountain landscapes and requires an understanding of catchment‐scale sediment dynamics and internal system functioning. While sediment budgeting is a valuable framework, challenges remain including partitioning sediment yield by source and grain size and addressing scale issues. This study advances our understanding of bed material dynamics in glacierized mountain catchments by applying a range of complementary techniques to measure sediment transfers in the Fitzsimmons Creek watershed. First, we measured the historical bed material yield using field surveys and historical air photo analysis, revealing an average specific sediment yield of 210 Mg km−2 yr−1, that varied by a factor of 17 over the 76‐year record. Hydro‐meteorological and historical analyses suggest that gravel extraction had the largest impact over the past three decades, while an extreme landslide and flood event produced the highest recorded sediment yield. Second, we constructed a detailed sediment budget along the river system using high‐resolution, multi‐temporal lidar and geomorphic mapping data. Sediment source partitioning indicates that landslide, active channel, and floodplain sources each contributed about one‐third of the total sediment supply. Net degradation occurred along the valley bottom upstream of the fan‐delta, resulting in steadily increasing downstream sediment yield. This trend is punctuated by chronic landsliding near the outlet, driven by postglacial incision through glaciogenic sediments at a hanging valley step. Contemporary glacial and proglacial sources were not measured directly but surprisingly contributed minimally. These findings provide insight into the sediment dynamics of glacierized mountain catchments and their potential controls.

Innovative temperature-responsive membrane with an elastic interface for biofouling mitigation in industrial circulating cooling water treatment

To address the issues of scaling caused by heat and water evaporation in regard to circulating cooling water (CCW), TFC membrane filtration systems have been increasingly considered for terminal treatment processes because of their excellent separation performance. However, membrane biofouling phenomenon significantly hinders the widespread utilization of TFC membranes. In this study, to harness the thermal phenomenon of CCW and establish a stable and durable multifunctional antibiofouling layer, temperature-responsive Pnipam and the spectral antibacterial agent Ag were organically incorporated into commercially available TFC membranes. Biological experimental findings demonstrated that above the lower critical solution temperature (LCST), the contraction of Pnipam molecular chains facilitated the inactivation of bacteria by the antibacterial agent, resulting in an impressive sterilization efficiency of up to 99 %. XDLVO analysis revealed that below the LCST, the establishment of a hydration layer on the functional interface resulted in the creation of elevated energy barriers, effectively impeding bacterial adhesion to the membrane surface. Consequently, a high bacterial release rate of 98.4 % was achieved on the low-temperature surface. The alterations in the functional membrane surface conformation induced by temperature variations further amplified the separation between the pollutants and the membrane, creating an enhanced "elastic interface." This efficient and straightforward cleaning procedure mitigated the formation of irreversible fouling without compromising the integrity of the membrane surface. This study presents a deliberately engineered thermoresponsive antibiofouling membrane interface to address the issue of membrane fouling in membrane-based CCW treatment systems while shedding new light on the mechanisms of "inactivation" and "defense."

Insights into gypsum and silica scaling behaviors of dense Janus membranes in membrane distillation: The effect of varied surface properties

Dense Janus membranes (JMs) have emerged as potential candidates to address the tough scaling issue in hypersaline wastewater treatments for membrane distillation (MD). However, behaviors and mechanisms of various scalants on dense JMs have not been systematically investigated. Herein, we designed three dense JMs to simultaneously compare their anti-scaling properties against gypsum and silica in MD and focus on the critical role of varied surface property in scaling control. Results showed that dense JMs with highly positive gypsum-membrane interfacial energy and with strong electrical repulsion against silica species were more effective in resisting gypsum and silica, respectively. Scaled membrane characterization, quartz crystal microbalance monitoring, interfacial energy analysis and AFM force measurement revealed the distinct anti-scaling mechanisms. Highly hydrophilic surfaces without specific interactions with scaling ions provided robust thermodynamic barriers and less kinetically affected local supersaturations, preventing gypsum nucleation and growth. In contrast, positively charged surfaces favorably attracted negatively charged silica species with high reactivity, promoting subsequent polymerization with silicic acid that kinetically dominated silica scaling. Moreover, coexisting oversaturated gypsum and silica influenced the scaling behavior of each other, and surface properties of the preferentially formed scaled layer might also matter in scaling process. This study shall provide useful insights into the anti-scaling mechanisms of dense JMs in MD and highlight the potential of dense surface design as an effective scaling control strategy to pave the way for potential MD applications.