Balancing Strategy Research Articles

Evolution mechanism of physical morphology of oxbow lake in the middle Yangtze River

Oxbow lakes, formed through river meandering cutoffs, are recognized as key components of river ecosystems, characterized by both fluvial and lacustrine features. The focus of previous research has been placed primarily on post-disconnection changes, leaving gaps in the understanding of their long-term morphological evolution. This gap is addressed through a comparative analysis of gate-controlled and naturally connected oxbow lakes over a 30-year period. In this study, Tian’ezhou Lake (gate-controlled) and Shangchewan Lake (naturally connected), located in the middle Yangtze River, are investigated. Key morphological parameters such as curvature, shoreline development index, and shrinkage rate were measured using Google Earth Engine (GEE). The influence of hydrological and meteorological factors on lake morphology was analyzed through Redundancy Analysis (RDA). Significant morphological changes were observed from 1990 to 2000. During this period, Tian’ezhou’s curvature was found to increase by 0.003/year, while Shangchewan’s curvature decreased by 0.011/year. After the construction of the Three Gorges Dam (TGD), a reduction in the rate of morphological evolution was detected, particularly between 2000 and 2010, when Tian’ezhou’s curvature decreased by 0.071/year and Shangchewan’s increased by 0.04/year. Post-2010, no significant changes in planar morphology were recorded in either lake. It was revealed by RDA that annual precipitation from 1990 to 1999 played a dominant role in influencing the morphological evolution of both lakes. From 2000 to 2020, water level fluctuations and hydrological connectivity emerged as the primary drivers. Gate control at Tian’ezhou Lake mitigated extreme water level fluctuations, contributing to a more stable environment and a slower rate of morphological evolution compared to Shangchewan. The findings underscore the importance of balanced water management strategies, integrating both natural and engineered hydrological interventions. Practical guidance is provided for ecological restoration and river management, with an emphasis on maintaining stable habitats and ensuring ecosystem resilience in response to hydrological changes.

Cloud migration and microservices optimization framework for large-scale enterprises

As large-scale enterprises increasingly adopt cloud computing, the need for a structured framework to guide seamless cloud migration and optimize microservices architecture becomes critical. This review presents a comprehensive designed to address the challenges and opportunities associated with transitioning to cloud environments and optimizing microservices for performance, scalability, and security. The review begins with an assessment of the enterprise’s current infrastructure, emphasizing a phased approach to cloud migration. It outlines key strategies such as lift-and-shift, re-platforming, and re-architecting, each tailored to specific organizational needs. The selection of the appropriate cloud model (public, private, or hybrid) and the use of multi-cloud strategies are discussed to ensure flexibility and risk mitigation during migration. For microservices optimization, focuses on designing services that are loosely coupled and highly scalable. Key elements include the use of service discovery, load balancing, API gateways, and database management strategies to improve the efficiency and reliability of microservices. Additionally, this introduces security best practices, including identity and access management (IAM), encryption protocols, and microservices-specific security measures such as Zero Trust Architecture. The review also highlights optimization strategies involving containerization (Docker) and orchestration (Kubernetes), alongside the integration of Continuous Integration/Continuous Delivery (CI/CD) pipelines for streamlined development and deployment. Performance monitoring, real-time logging, and proactive maintenance are emphasized for sustained efficiency and cost control in cloud environments. Case studies demonstrate the successful implementation of cloud migration and microservices optimization in large enterprises, showcasing both the benefits and common challenges. The review concludes by exploring future trends, such as serverless computing and AI-driven optimization, that will continue to shape cloud migration and microservices architecture.

Unraveling the paradox: the impact of one or more traditional risk factors on short-term mortality following acute coronary syndromes.

Abstract Background It has been recently suggested that individuals without exposure to any standardized modifiable risk factors may have an increased risk of all-cause mortality from acute coronary syndromes (ACS) compared with their counterparts with one or more risk factors, including diabetes. Purpose To estimate the individual contribution of the four traditional risk factors to the risk of adverse outcomes from first ACS. Methods We analyzed data from 70,953 Caucasian patients presenting with first ACS, but without prior coronary heart disease in the International Survey of Acute Coronary Syndromes (ISACS) Archives registry network from October 2005 to January 2021. The primary outcome was all-cause mortality within 30 days after hospital admission. We calculated sex-specific risk ratios (RRs) stratifying traditional risk factors, current smoking, diabetes, hypercholesterolemia and hypertension into mutually exclusive categories using a balancing strategy by inverse probability weighting. Results Compared with patients presenting without risk factors, those with diabetes had significantly higher 30-day mortality in both women (RR, 1.29; 95% CI, 1.06-1.57) and men (RR, 1.40; 95% CI, 1.16-1.69). By contrast, patients who were current smokers or presented with a history of hypercholesterolemia or hypertension had significantly lower 30-day mortality than patients with no risk factors with RRs ranging from 0.53; 95% CI 0.42–0.67 in women who were smokers to 0.46; 95% CI 0.37–0.59 in men with hypercholesterolemia. The impact of diabetes on mortality was obscured in patients who concomitantly presented with diabetes and one more traditional risk factor. The RRs in combination with current smoking were 1.39; 95% CI 0.92 -2.09 in women and 0.89; 95% CI 0.68 -1.17 in men; with hypercholesterolemia: 0.91; 95% CI 0.66 -1.25 in women and 0.75; 95% CI 0.53–1.06 in men; with hypertension: 1.14; 95% CI 0.99–1.32 in women and 1.12; 95% CI 0.96–1.31 in men. Conclusions Diabetes confers a significant independent excess mortality risk at 30 days following ACS. Dilution bias in this setting refers to the weakening of the observed effect of diabetes because of the inclusion of "one more" risk factor with lower risk profile. Potential misinterpretation of the relationship between diabetes and death from ACS may negatively affect healthcare organization

Corporate Social Responsibility and Financial Performance of Nawiri Sacco Society Limited in Embu County, Kenya

This study explored the relationship between Corporate Social Responsibility (CSR) and financial performance in the context of Nawiri Sacco Society Limited in Embu County, Kenya. Given the dynamic nature of CSR and the divergent results from global studies on its impact on financial performance, this research aimed to shed light on whether CSR activities contributed positively to Nawiri Sacco's financial outcomes. Specifically, it examined how stakeholder expectations, regulatory compliance, CSR-related costs and risks, and industry characteristics affected the Sacco’s financial performance. The research employed a regression model to evaluate these factors, with a notable emphasis on how stakeholder pressures and regulatory frameworks shaped CSR's financial implications. Data was collected from 85 executive members through structured questionnaires, achieving an impressive 88.9% response rate, which supported the reliability of the results.The findings indicated a significant relationship between CSR and financial performance, revealing mixed effects among the different CSR factors. For instance, stakeholder expectations showed a negative impact on financial performance, suggesting that while stakeholder engagement was critical, excessive focus could strain financial resources. Conversely, regulatory compliance, cost implications, and industry characteristics demonstrated a positive association, highlighting the potential of CSR to improve financial outcomes when effectively managed. These insights reinforced the need for a balanced CSR strategy that aligned with both social responsibilities and financial goals. This study contributed to existing literature by focusing on a Sacco's CSR practices, adding a unique perspective on how socially responsible initiatives could affect financial performance in cooperative institutions, particularly within the Kenyan context.

The Impact of Social Media on Mental Health of Young Adults: A Literature Review

This concept paper explores the complex relationship between social media usage and the mental health of young adults. The paper introduces the notion of social media and its significance in the lives of young adult, emphasizing the importance of studying its effects on their mental health and psychological well-being. Social media platforms have become an integral part of daily activities in the lives of young people. It has also become an invasive element in the lives of youth, shaping their social interactions, self-perception, and access to lots of information. Even though these platforms offer opportunities for social connection, creative expression, and educational engagement, they also present significant mental health challenges, including anxiety, depression, and low self-esteem, which are often driven by social comparison, cyberbullying, and the pressure to maintain an idealized online image. The paper investigates key areas of concern, including the rise of anxiety, depression, and low self-esteem linked to social comparison, cyberbullying, and the relentless pursuit of online validation. It also examines the addictive nature of social media, its impact on sleep, and the destruction of face-to-face social skills. By analysing existing literature and identifying research gaps, this paper aims to provide a comprehensive framework for understanding the two-edged nature of social media's influence on mental health. It advocates for the development of digital literacy programs, mental health support systems, and balanced usage strategies to help young people navigate the challenges posed by social media while maximizing its potential benefits.

A Balanced Mission Planning for Multiple Unmanned Underwater Vehicles in Complex Marine Environments

The collaboration of a multiple unmanned underwater vehicles (multi-UUVs) system has attracted widespread attention in recent years, as it can overcome the limitations of a single UUV and enhance mission completion efficiency. Oriented towards patrol and exploration missions with multiple waypoints, this paper proposes a balanced mission planning strategy, aiming to improve mission quality while reducing mission time for multi-UUVs. Firstly, due to the uneven performance of the two optimization objectives, a quick initialization screening method is employed specifically for mission quality to reduce the mission space. Secondly, to ensure mission load distribution and collaboration among multi-UUVs, and ease the difficulty in solving the issues of mission allocation and route planning, a balanced bi-level mission planning method based on regional segmentation is proposed. Finally, applicable weight evaluation criteria are utilized to evaluate the feasible solution set and determine the optimal solution. The efficacy of the balanced mission planning strategy is substantiated through comprehensive numerical simulations in a complex 2D marine environment.

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

The Ukraine invasion: Hierarchy, discipline and counterbalance

This article examines Russia’s 2022 military intervention in Ukraine through an eclectic theoretical framework that synthesises hierarchy theory, foreign-imposed regime change, and negative balancing strategy. We argue that Russia justified the attack as a means to protect its hierarchical objectives in the post-Soviet region against three perceived imminent threats: Ukraine’s independent trajectory, the growing alignment of certain post-Soviet states with the US-led hierarchy, and the expansion of the US-led Western hierarchy into the post-Soviet space. In response, Russia employed the hard face of its negative balancing strategy by launching an overt foreign-imposed regime change operation to replace Zelensky’s government with a pro-Moscow regime. From the outset, this operation has been tasked with two key aims: discipline and balance. The disciplinary objective has focused on ensuring Ukraine’s loyalty to Moscow and deterring other Western-leaning post-Soviet states from emulating Ukraine by sending a clear disciplinary message. The balance objective has sought to counter the negative and outbidding strategies of the US-led hierarchy, which Russia perceives as a neo-containment agenda aimed at undermining its regional influence.

Collaboration in Contradiction: Self-Adaptive Synergistic ROS Generation and Scavenge Balancing Strategies Used for the Infected Wounds Treatment.

The rational utilization of ROS is key to treating infected wounds. Exogenous ROS can destroy bacterial structures, quickly kill bacteria, and inhibit secondary infections. However, excess ROS at the wound will cause a secondary inflammatory response. Acute infections exacerbate this damage by increasing endogenous ROS, complicating the maintenance of ROS homeostasis. Therefore, regulating the balance of ROS production and scavenging in wounds has emerged as a promising strategy for wound treatment. Conventional ROS balancing platforms are mostly based on the " all for one" strategy of functional superposition and lack self-adaptability and integration. To subvert this conventional strategy, this study proposes a "one for all" self-adaptive integrated photodynamic therapy (PDT)-antioxidant model to actively regulate the ROS balance. A gelatin-hyaluronic acid hydrogel embedded with Se-modified cerium dioxide nanoparticles (Gel-HA-Se@CeO2 NPs) is designed for treating infected wounds. The Se@CeO2 NPs serve both as nanoenzymes and photosensitizers(PS). As nanoenzymes, they exhibit catalase and superoxide dismutase activities, converting hydrogen peroxide and superoxide anions into oxygen. As a PS, it synergizes with oxygen under NIR irradiation to rapidly produce singlet oxygen. Additionally, Se modification enhances the PDT effects by disrupting bacterial antioxidant systems. In vitro and in vivo experiments revealed that the ROS balance platform polarizes M1-type macrophages to M2-type macrophages, altering the wound microenvironment from proinflammatory to prohealing. RNA sequencing revealed that this hydrogel accelerated the reconstruction of the vascular network of the wound by activating the PI3K/AKT pathway and increasing VEGF secretion.This strategy is believed to be beneficial not only for infected wounds but also for treating other conditions that involve the regulation of reactive oxygen species, such as tumors and bacterial infections.

Optimized Resource Allocation for Sustainable Development in Beijing: Integrating Water, Land, Energy, and Carbon Nexus

Chinese megacities face significant challenges in reducing carbon emissions while balancing economic growth and social welfare. This study constructs an innovative multi-objective optimization model, the SD-NSGA-III model, integrated with a System Dynamics (SD) model and using the Non-dominated Sorting Genetic Algorithm III (NSGA-III) to optimize resource allocation in Beijing. The model targets environmental, economic, and social goals by establishing a water–land–energy–carbon (WLEC) nexus for analyzing resource allocation strategies and carbon reduction pathways under various constraints. Scenario simulations under the efficiency-oriented scenario indicated a potential reduction in energy carbon emissions of 81.4% by 2030. The fairness-oriented scenario revealed significant trade-offs between equity and emission reductions, emphasizing the need for balanced strategies. Introducing constraints on resources and economic growth significantly reduced median energy carbon emissions to 80 million tons by 2030. These findings demonstrate the effectiveness of the SD-NSGA-III model in providing actionable strategies for achieving carbon neutrality and sustainable development in cities.

Tree-based dual load balancing scheme in wireless mesh networks with multiple gateways

Tree-based dual load balancing scheme in wireless mesh networks with multiple gateways

State of Health Estimation of Lithium-Ion Battery Using Multi-Health Features Based on Savitzky–Golay Filter and Fitness-Distance Balance- and Lévy Roulette-Enhanced Coyote Optimization Algorithm-Optimized Long Short-Term Memory

Accurate and reliable state of health (SOH) estimation is extremely crucial for the safe and stable operation of lithium-ion batteries (LIBs). In this paper, a method based on Lévy roulette- and fitness-distance balance-enhanced coyote optimization algorithm-optimized long short-term memory (LRFDBCOA-LSTM) is employed for SOH estimation of LIB. Firstly, six health features are extracted from battery charging and discharging data, and Savitzky–Golay is used to filter the feature data to improve correlation between feature and SOH. Then, Lévy roulette and fitness-distance balance (FDB) strategies are used to improve the coyote optimization algorithm (COA), i.e., LRFDBCOA. Meanwhile, the improved algorithm is used to optimize the internal parameters of long short-term memory (LSTM) neural network. Finally, the effectiveness of the proposed model is comprehensively validated using five evaluation indicators based on battery data obtained under three different testing conditions. The experimental results manifest that after algorithm improvement and network parameter optimization, the performance of the model is significantly improved. In addition, the method has high estimation accuracy, strong generalization, and strong robustness for SOH estimation with a maximum R2 of 0.9896 and minimum R2 of no less than 0.9711.

The Impact of the Digital Transformation of Chinese Cultural Products on Marketing and Communication Strategies

In an era defined by globalization and rapid technological advancement, Chinas cultural industry is undergoing a significant transformation. This study investigates the profound effects of digital transformation on Chinas cultural industry, focusing on digital cultural innovation products such as digital art, virtual reality experiences, and online cultural creations. As globalization and technological advancements reshape cultural production, dissemination, and consumption, Chinas digital cultural sector has emerged as a significant global player. This research systematically reviews literature, industry reports, and case studies to analyze how Chinese digital cultural products are influencing marketing and communication strategies. The findings reveal that advancements in virtual reality, augmented reality, and artificial intelligence are leading to the creation of immersive and diverse cultural experiences, significantly enhancing consumer engagement and market reach. The study also highlights the transformative role of digital tools in marketing, including dynamic content creation and interactive experiences, while addressing challenges such as market saturation and the need for balanced engagement strategies. Future research should incorporate quantitative and qualitative data to further explore these dynamics.

Numerical simulation for subsidence control in CO2 storage and methane hydrate extraction

Gas hydrates are increasingly viewed as a promising alternative to traditional fossil fuels. However, their extraction process poses risks to structural integrity, potentially causing significant subsidence. In this study, we developed a Thermo-Hydro-Mechanical-Chemical (THMC) model to analyze the impact of gas hydrate extraction on seabed subsidence. Our investigation focused on the influence of bottom hole flowing pressure, initial hydrate concentration, gas saturation, permeability, porosity, and rock thermal conductivity on subsidence during gas hydrate extraction via depressurization.The results show that seabed subsidence is affected by various factors such as bottom hole flowing pressure, initial hydrate concentration, gas saturation, permeability, porosity, and rock thermal conductivity. It was noted that significant subsidence is associated with low initial hydrate concentration, high permeability, porosity, low gas saturation, low rock thermal conductivity, and a notable pressure drop of 79.31%.To address this issue, we propose a seabed subsidence mitigation strategy involving CO2 injection. This approach not only safeguards offshore infrastructure and coastal communities but also helps reduce CO2 emissions, aligning with global climate change mitigation efforts. In our model, CO2 injection occurs in the subsurface reservoir at the interface between the free water zone and hydrate-bearing formations. The CO2 hydrates formation process releases heat, which dissociates methane hydrates, allowing the methane to be replaced by CO2 molecules and move towards the production well.Our analysis reveals that increasing injection temperature and rate significantly reduces subsidence. Additionally, the range of investigated injection pressures, which included pressures equal to and more than double the initial reservoir pressure, showed inconsequential impacts on seabed subsidence.The effectiveness of subsidence reduction is significantly enhanced by injecting a CO2/N2 mixture compared to pure CO2 injection. The most substantial reduction in subsidence occurred when a mixture of CO2 and N2 in a 50/50 vol/vol ratio was injected at a high rate.These findings offer crucial insights for optimizing the efficiency and control of gas hydrate extraction methods. They emphasize the importance of employing balanced injection strategies to minimize environmental risks and ensure sustainable energy extraction.

Enhancing Basic Education Quality through the Implementation of the Regional Medium-Term Development Plan in Oil-Rich Areas

This study investigates the implementation of basic education quality improvement policies in Balikpapan City, a region known for its pivotal role in Indonesia’s oil industry, often referred to as an oil-rich area. The research aims to assess the effectiveness of the Regional Medium-Term Development Plan (2021-2026) in addressing educational quality, considering the city's economic dependence on oil. Adopting a qualitative approach, the study focuses on the Department of Education and Culture in Balikpapan, engaging key informants to evaluate the efficacy of policy implementation. The analysis framework encompasses an evaluation of policy standards, resources, inter-organisational communication, and the characteristics of the implementing agencies. The findings reveal that, while considerable progress has been made in enhancing educational facilities and teacher competencies, challenges persist due to the city’s heavy reliance on the oil sector and its impact on local resources. Furthermore, the study explores the relationship between these challenges and Sustainable Development Goal (SDG) 4 (quality education) by addressing disparities in resource allocation and the city's limited capacity for economic diversification. The research recommends the integration of more diverse data sources, continuous monitoring, and deeper analysis of the social and economic impacts to further improve educational outcomes. Balikpapan's economic context, as an oil-dependent city, significantly influences its ability to implement and sustain effective educational policies, highlighting the necessity for balanced development strategies aligned with broader SDGs.

Handling imbalanced data in intrusion detection using time weighted Adaboost support vector machine classifier and crossover boosted Dwarf Mongoose Optimization algorithm

Cybersecurity threats pose a serious challenge in the present day and age, and Intrusion Detection Systems (IDS) have emerged as an effective solution to counter these threats. In this paper, a novel IDS is proposed that captures data from the NSL-KDD dataset and are preprocessed. The Kernel Principal Component Analysis (KPCA) model extracts features presented in the data, and the Crossover Boosted Dwarf Mongoose Optimization (CDMO) algorithm selects the relevant features for classification. The CDMO algorithm offers the advantages of improving exploitation, providing optimal solutions, and balancing global exploitation and local search capabilities. The selected features are classified into five classes using the Time Weighted Adaboost Support Vector Machine (TWASVM) classifier. The TWASVM classifier effectively handles imbalanced data and delivers high-performance results. Experiments conducted on MATLAB R2019a and the proposed model achieved an higher accuracy of 98.6 % and less time complexity of 13 seconds. Comparative analysis demonstrated that the proposed IDS outperforms other state-of-the-art methods. The advantages of CDMO algorithm include improved exploitation, optimal solutions, and a balanced crossover strategy for global exploitation and local search capabilities. The advantages of the TWASVM classifier include the ability to handle imbalanced data and deliver high-performance results. Overall, the proposed IDS offer a novel solution to the challenges of intrusion detection in a rapidly evolving cybersecurity landscape.

Climate Change Effects in Stock Markets

Climate change significantly impacts global stock markets through both short-term extreme weather events and long-term climate shifts. This study examines the relationship between CO2 emissions and stock returns across 19 countries from 2001 to 2020 using regression analysis. The findings also reveal a positive correlation between CO2 emissions and stock returns, indicating that investors anticipate higher returns to offset climate-related risks. This suggests that higher CO2 emissions drive investor expectations for increased returns to compensate for potential economic losses. The study highlights the importance of balanced emission reduction strategies and promoting green finance to support sustainable economic growth. Additionally, it underscores the need to integrate climate risks into financial decision-making, offering valuable insights into the financial implications of climate change and guiding policymakers and investors in addressing these challenges. By providing empirical evidence on the link between CO2 emissions and stock returns, this research enriches existing knowledge and emphasizes the financial risks and opportunities presented by climate change. The study's results align with the carbon risk premium theory, which posits that investors demand higher returns from companies with higher carbon emissions. This study advocates for policies that balance economic growth with environmental sustainability, promoting green finance initiatives to facilitate the transition to a low-carbon economy and mitigate the adverse effects of climate change on financial markets.

Impact of collective knowledge on individual research competence: exploring the mediating influence of knowledge management processes

PurposeThe purpose of the current study is to investigate the impact of collective knowledge on individual research competence. Further, the study investigates the mediation effect of the knowledge management process (KMP).Design/methodology/approachThe cross-sectional study adopts a quantitative conclusive research design. The study collects data specifically from academicians of Uttarakhand higher education institutions (HEIs). The study uses random sampling because the list of faculty members is uploaded on the website of the university portal. The author directly sends the questionnaire to the email of the respondents and uses social media platforms. For the data analysis, the study used SMART-PLS 4.0.FindingsThe findings of the study reveal that KM infrastructure (KMI) and KM strategy directly impact KMP. The study also reveals that the KMP significantly impacts individual research competence. The current study also finds partial mediation support among KMI, KMP and individual research competence, and between KM strategy, KMP and individual research competence.Research limitations/implicationsThe current study uses a cross-sectional research design, future studies may include the longitudinal design to measure the effect of KM elements on individual research competence. The current study includes the data from the Uttarakhand region only, and it is difficult to generalize the results specifically to other states.Practical implicationsThe study emphasizes the importance of KM system development, a supportive KMI and a balanced KM strategy in HEIs.Originality/value Investigating collective knowledge’s impact on individual research competence, this study unveils mediation in KMP. Future research can broaden horizons by using the model.

BENN: Balanced Ensemble Neural Network for Handling Class Imbalance in Big Data

ABSTRACTClass imbalance is a critical challenge in big data analytics, often leading to biased predictive models. This imbalance can lead to biased models that perform well on the majority class but poorly on the minority class. Many machine learning models tend to be biased towards the majority class because they aim to minimise overall error, often leading to poor performance on the minority class. This paper presents the balanced ensemble neural network, a novel solution to effectively address class imbalance in big data. Balanced ensemble neural network combines the robust capabilities of neural networks with the power of ensemble learning, incorporating class balancing strategies to ensure fair representation of minority classes. The methodology involves integrating multiple neural networks, each trained on balanced subsets of data using techniques like Synthetic Minority Over‐sampling Technique and Random Undersampling. This integration aims to leverage the strengths of individual networks while reducing their inherent biases. Our extensive experiments across various datasets reveal that BENN achieves an AUC‐ROC score of 0.94, surpassing other models such as random forest (0.88), support vector (0.84) and single neural net (0.80). It was also observed that BENN's performance is better compared to traditional neural network models and standard ensemble methods in key metrics like accuracy, precision, recall, F1‐score and AUC‐ROC. The results specifically highlight BENN's effectiveness in accurately classifying instances of minority classes, a notable challenge in many existing models. These findings underscore BENN's potential as a substantial advancement in handling class imbalance within big data environments, offering a promising direction for future research and application in machine learning.

Fairer schooling for a better housing environment: Effect of abolishing school zones on the housing market

Access to high-quality public education is often closely linked to residential property ownership and location in many countries, contributing to rising housing costs in areas with better educational resources. This creates challenges in housing affordability and perpetuates inequality in education access. Our study explores the effectiveness of two policies aimed at reducing the association between property ownership and access to premium public education: the "six-year-one-position" policy, which limits school enrolment to one student per property every six years, and the "multi-dicing" policy, which uses a random allocation system for student placement. While this research focuses on Beijing, China, the implications of such policies are of broader relevance to cities worldwide facing similar issues. Using a spatial difference-in-difference approach, we find that eliminating school zoning can decrease housing prices by 2.5% in areas where these policies are implemented, with reductions of 4.4%–7.9% in districts previously associated with high-ranking schools. However, the announcement of these policies triggers short-term price spikes, with increases of 2.7%–4.7%, reflecting a panic-buying effect. Moreover, we observe a spillover effect, where demand shifts from regions that abolished school zones earlier to those that implemented the policy later, as revealed by a staggered difference-in-difference analysis. These findings offer insights for policymakers in other urban contexts where educational access is intertwined with housing markets, highlighting the need for balanced strategies to address both housing affordability and equitable education access.