Mitigation Approaches Research Articles

Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm

In network topology, the sinkhole attacks ensue while a malicious node deceives it’s adjust mode in the routing traffic and it results a predominant interruptions like amplified latency, depletion of energy with conceded data reliability. Moreover a decentralized feature of MANETs are specifically exposed to treats and emphasize the necessity of distinct solutions. In this study, Federated Learning is utilized to improve the security and privacy through empowering nodes to train the model deprived of distribution complex data. Then, every node gathers information about the local routing and contributes towards an inclusive model, which captures behaviour of the entire network when conserving its specific privacy. Further, the Hierarchical Deep Belief Network Convolutional Neural Network (HDBNCNN) algorithm has analysed the accumulated data in detecting the anomalies revealing the sinkhole activity centred on learning routing patterns. Besides, detection, the model can implement mitigation approaches, which includes redirecting traffic from conceded nodes and informing the routing tables to reinforce flexibility from imminent attacks. The proposed study aims to enhance the reliability and efficiency of MANETs in attack situations when minimizing overhead from malicious traffic management. By combining progressive ML methods through decentralized network structure, hence the study expressively provides a secure communication in MANETs and contributes a scalable and effective results through evaluating its performance metrics.

The Quay Walls of Amsterdam, Netherlands: An Approach for Collapse Risk Mitigation at the Municipal Scale Based on Multisource Monitoring and Surveying Data

The Quay Walls of Amsterdam, Netherlands: An Approach for Collapse Risk Mitigation at the Municipal Scale Based on Multisource Monitoring and Surveying Data

Influence of calcium carbonate on ferrihydrite bio-transformation and associated arsenic mobilization/redistribution.

The sulfate-reducing bacteria (SRB)-induced ferrihydrite transformation is an important cause for arsenic (As) contamination in the aquifer near mining area. Calcium carbonate (CaCO3) is widespread and has the potential of regulating As fate directly or indirectly. However, the influence of CaCO3 on ferrihydrite transformation and the associated As mobilization/redistribution in SRB-containing environments remains unclear. Therefore, in this research, batch experiments coupled with a series geochemical, spectroscopic, and microscopic technologies were conducted collectively to address the research gap above. The results suggested that under low CaCO3 loading conditions, the reductive transformation of ferrihydrite to Fe-S minerals resulted in a significant release of adsorbed As into the solution and As(V) reduction to more mobile and toxic As(III). Inside the cell of SRB, there existed S and As zone (no Fe), where As(V) might be reduced by S2- without interference of Fe(III). Although the high CaCO3 loadings exerted little effect on Fe(III) reduction, they promoted ferrihydrite transformation into dufrenite and vivianite, which sequestered As effectively via structural incorporation process, retarding As mobilization. Besides, the coprecipitation of As with dufrenite and vivianite enhanced the stability of solid phase As. Overall, the high CaCO3 loadings altered the mineralogical transformation of ferrihydrite and the associated As biogeochemistry significantly in SRB-containing environments. Regulating ferrihydrite transformation and the associated As fate via CaCO3 addition is an effective approach for mitigation of As mobilization in the aquifer near mining area.

Comprehensive Assessment, Intelligent Prediction, and Precise Mitigation Strategies for Greenhouse Gas Emissions in Full-Scale Wastewater Treatment Plants.

Wastewater treatment plants (WWTPs) are major contributors to global anthropogenic greenhouse gas (GHG) emissions, with China ranks among the leading emitters. In the context of China's "dual-carbon" journey, precision quantification and predictive forecasting of GHG fluxes, particularly methane (CH4) and nitrous oxide (N2O)-are crucial for developing advanced mitigation strategies of WWTPs. To accurately assess GHG emissions, this study firstly introduced customized emission factors (EFs) to precisely evaluate the GHG emissions of a full - scale A2O - based WWTP in Beijing. This approach addressed the overestimation of emissions when using the IPCC's standard EFs. Additionally, the study proposed machine learning (ML) techniques to predict GHG fluxes based on routine wastewater quality parameters. Specifically, Long Short-Term Memory (LSTM) and Random Forest (RF) models showed the strong performance in predicting CH4 and N2O emissions, respectively. Moreover, our findings revealed distinct spatiotemporal patterns of GHG emission: CH4 emissions peak during the summer solstice, while N2O emissions rise during the winter months. For the first time, this study identified the nitrification biofilter in the advanced treatment unit as a significant direct source of N2O emissions. Eventhough, indirect CO2 emissions account for a dominant 57%-90% of the total GHG emissions. Scenario analyses revealed a strategic mitigation approach. Energy conservation emerged as the most effective measure, capable of reducing emissions by 23.41%, followed by heat recovery, which could cut emissions by 10.15%. In practical applications, improving energy efficiency is of utmost importance in real - world mitigation strategies. This highlights the significance of integrated approaches for achieving the sustainable development of WWTPs in the "dual - carbon" background.

Odour prevention strategies in wastewater treatment and composting plants: A review.

Odour emissions from wastewater treatment plants (WWTPs) and composting plants (CPs) have become a critical environmental and public health issue due to the release of complex mixtures of volatile organic compounds, volatile inorganic compounds, and volatile sulphur compounds. These emissions do not only affect ambient air quality but also contribute to nuisance complaints and potential health risks in the nearby communities. This paper provides a comprehensive review of current odour prevention strategies employed in WWTPs and CPs, focusing on both the underlying mechanisms of odour generation and the efficacy of state-of-the-art mitigation techniques. Malodours mitigation approaches including physical, chemical, and biological methods such as the addition of chemical agents, the use of microbial inoculants, the application of adsorbents and bulking agents and the modifications of operational parameters are explored and their performance critically evaluated. By integrating cost-effective odour control strategies into plant design and operational practices, WWTPs and composting facilities can achieve substantial reductions in odour emissions and compliance with stringent environmental regulations, while enhancing relationships with neighbouring communities. Finally, this review underscores the importance of a holistic and multi-disciplinary approach to odour management, combining scientific innovation with practical engineering solutions.

A Compact Stepped Frequency Continuous Waveform Through-Wall Radar System Based on Dual-Channel Software-Defined Radio

Software-defined radio (SDR) has high flexibility and low cost. It conforms to the miniaturization, lightweight, and digitization trends in through-wall radar systems. Stepped frequency continuous waveform (SFCW) is commonly used in through-wall radar, which has high resolution and strong anti-interference ability. This article develops an SFCW through-wall radar system based on a dual-channel SDR platform. Without changing hardware structure and complicated accessories, a phase compensation method of solving the phase incoherence problem in a low-cost dual-channel SDR platform is proposed. In addition, this article proposes a wall clutter mitigation approach by means of singular value decomposition (SVD) and principal component analysis (PCA) framework for through-wall applications. This approach can process the wall clutter and noise ‌efficiently,‌ and then extract the target subspace to obtain location information. The experimental results indicate that the proposed windowing-based SVD-PCA approach is effective for the developed radar system, which can ensure the accuracy of through-wall detection. It is also superior to the traditional methods in terms of the image quality of range profiles or signal-to-noise ratio (SNR).

Enhancing salt stress tolerance in Carthamus tinctorius L. through selenium soil treatment: anatomical, biochemical, and physiological insights

Selenium (Se) plays a crucial role in ameliorating the negative impact of abiotic stress. The present study was performed to elucidate the efficacy of soil treatment of Se in reducing salt-induced stress in Carthamus tinctorius L. In this study, three different levels of Na2SeO4 (0, 0.01, and 0.02 g kg− 1) and four levels of NaCl (0, 0.5, 1.5, and 2.5 g kg− 1) were applied. The findings revealed that while NaCl decreased seed germination parameters, growth characteristics, K+ content, relative water content (RWC), and photosynthetic pigments, it increased Na+ content, soluble carbohydrates, H2O2 content, and malondialdehyde (MDA) level. The application of Se showed a positive effect on seed germination and growth characteristics under salinity conditions, which is linked to alterations in anatomical, biochemical, and physiological factors. Anatomical studies showed that treatment with Se led to increased stem diameter, cortical parenchyma thickness, and pith diameter under salinity stress. However, variations in the thickness of the xylem and phloem did not reach statistical significance. The application of Se (0.02 g kg− 1) raised Na+ content (7.65%), K+ content (29.24%), RWC (15%), Chl a (17%), Chl b (21.73%), Chl a + b (16.9%), Car (4.22%), and soluble carbohydrates (11%) in plants subjected to NaCl (2.5 g kg− 1) stress. Furthermore, it decreased H2O2 (25.65%) and MDA (11.9%) in the shoots. The findings of the current study advocate the application of the Se-soil treating technique as an approach for salt stress mitigation in crops grown in saline conditions.

Chemistry of Persistent Organic Pollutants (POPs): Distribution, Environmental Impact and Mitigation Approaches

Chemistry of Persistent Organic Pollutants (POPs): Distribution, Environmental Impact and Mitigation Approaches

Penicillin Allergy Management in India and Sri Lanka: Current Challenges.

Data regarding Penicillin allergy labels (PALs) from India and Sri Lanka are sparse. Emerging data suggests that the proportion of patients declaring an unverified PAL in secondary care in India and Sri Lanka (1%-4%) is lesser than that reported in High Income Countries (15%-20%). However, even this relatively small percentage translates into a large absolute number, as this part of the world accounts for approximately 25% of the global population. There is a huge unmet need for allergy specialists in India and Sri Lanka. Penicillin allergy management is further compromised by unavailability of skin test reagents, lack of formal training in drug allergy, pre-emptive, non-standardised and unregulated skin testing by untrained operators and a weak health service framework. This has an adverse impact on antimicrobial stewardship, particularly in the management of rheumatic fever, rheumatic heart disease, bacterial endocarditis, syphilis and other sexually transmitted infections. This narrative review highlights the burden of PALs in India and Sri Lanka, as well as gaps in the published literature. It describes current challenges and a pragmatic, cautious and staged bespoke mitigation approach to improve and standardise antimicrobial stewardship in accordance with the World Health Organisation AWaRe guidance.

Risk Mitigation Approach to Cyber Threat using AI-Driven Models for the Evolving Threat Landscape

Risk Mitigation Approach to Cyber Threat using AI-Driven Models for the Evolving Threat Landscape

Intimate Partner Violence Against Women: Strengthen Social Support Network Mechanism as a Mitigation Approach

Intimate Partner Violence Against Women: Strengthen Social Support Network Mechanism as a Mitigation Approach

Nonlinear sub-structured pseudo-dynamic testing: application to progressive collapse on reinforced concrete frames

In the past decades, progressive collapse (PC) has been of growing importance. Better understanding this phenomenon is mandatory to propose ad hoc mitigation and design approaches. Many experimental studies are carried out on sub-assemblies in quasi-static conditions due to setup and cost of full-scale dynamic tests. Numerical simulations describe dynamic responses of whole structures, although result interpretation can prove tricky due to models’ complexity. Recommendations account for dynamic effects, combining static analyses with dynamic amplification factors; however, this cannot be applied to all types of PC scenarios. Thus, a new testing method is proposed: sub-structured pseudo-dynamic experiments are adapted to the PC framework. Sub-structuring couples an experimentally tested critical member with a dynamic finite element model of the whole structure. The latter simulates inertial and viscous forces, while the static contribution of the critical member is tested in quasi-static conditions. This article considers a column removal scenario on a reinforced concrete frame. The structure is modelled using multi-fibre beam finite elements. The tested member consists of the lower floor’s beams, subjected to compressive arch and tensile catenary actions. Pushover and PsD tests are presented, and highlight the interest of the method in providing additional information on the dynamic response.

Thrombotic Microangiopathy Associated with Systemic Adeno-Associated Virus Gene Transfer: Review of Reported Cases.

Complement-mediated thrombotic microangiopathy (TMA) in the form of atypical hemolytic uremic syndrome (aHUS) has emerged as an immune complication of systemic adeno-associated virus (AAV) gene transfer that was unforeseen based on nonclinical studies. Understanding this phenomenon in the clinical setting has been limited by incomplete data and a lack of uniform diagnostic and reporting criteria. While apparently rare based on available information, AAV-associated TMA/aHUS can pose a substantial risk to patients including one published fatality. Reported cases were originally limited to pediatric Duchenne muscular dystrophy patients receiving micro- or mini-dystrophin transgenes via AAV9 but have subsequently been reported in both pediatric and adult patients across a range of disorders, transgenes, promoters, and AAV capsid types. This article provides an introduction to the complement system, TMA and aHUS, and anticomplement therapies, then presents clinical reviews of AAV-associated TMA/aHUS cases that have been reported publicly. Finally, exploration of risk factors and current and future mitigation approaches are discussed.

Algorithmic bias: sexualized violence against women in GPT-3 models

Abstract This study explores the occurrence and implications of sexualized violence against women in text completion tasks performed by GPT-3 models. The study began as an exploratory investigation into gender inequalities within GPT-3 models to discover what GPT3 models generate in text completion tasks when prompted with basic gendered prompts. For example, "Men can…", "Women can…". Using an adapted version of Safia Noble’s prompt scheme, we generated a dataset of 9 million tokens from GPT-3 models, then conducted topic analysis using BERTopic, VADER for sentiment analysis, and Detoxify for toxicity detection. The results were unexpected. Topic analysis revealed sexualized violence ranked as the top topic for prompts featuring’women’. Women were often depicted in the context of sexualized violence and victimization, in stark contrast to the portrayal of men as superheroes, or in contexts involving urination and alcohol. For example, a randomly selected output generated by the prompt. "Why are women so", generated "Why are women so arrogant about being raped?". Our findings stress the critical need for ethical considerations in large language model (LLM) development to address and mitigate the generation of text containing sexualized violence against women. We discuss the implications of amplifying and normalizing sexualized violence against women in content generated by LLMs. Our work builds on previous research examining gender bias in LLMs, with a specific focus on the manifestation of sexualized violence against women in LLM outputs, an area that has received little attention. We discuss the mitigation approaches such as content filtering and moderation; user safety and trauma-informed responses; legal and ethical considerations; avoiding misinformation; reflecting societal changes; and global discourse and action. With the overall aim to contribute to the understanding of such biases, their impact on survivors and wider society, and offer insights to guide the development of more equitable and ethical AI systems.

Mitigating Regression Faults Induced by Feature Evolution in Deep Learning Systems

Deep learning (DL) systems have been widely utilized across various domains. However, the evolution of DL systems can result in regression faults. In addition to the evolution of DL systems through the incorporation of new data, feature evolution, such as the addition of new features, is also common and can introduce regression faults. In this work, we first investigate the underlying factors that are correlated with regression faults in feature evolution scenarios, i.e., redundancy and contribution shift. Based on our investigation, we propose a novel mitigation approach called FeaProtect, which aims to minimize the impact of these two factors. To evaluate the performance of FeaProtect, we conducted an extensive study comparing it with state-of-the-art approaches. The results show that FeaProtect outperforms the in-processing baseline approaches, with an average improvement of 50.6% \(\sim\) 56.4% in terms of regression fault mitigation. We also show that FeaProtect can further enhance the effectiveness of mitigating regression faults by integrating with state-of-the-art post-processing approaches.

Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review.

Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals' personal or protected attributes. This study aimed to describe the attempts, strategies, and methods used to mitigate bias in AI models within primary health care, to identify the diverse groups or protected attributes considered, and to evaluate the results of these approaches on both bias reduction and AI model performance. We conducted a scoping review following Joanna Briggs Institute (JBI) guidelines, searching Medline (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), and Web of Science databases for studies published between January 1, 2017, and November 15, 2022. Pairs of reviewers independently screened titles and abstracts, applied selection criteria, and performed full-text screening. Discrepancies regarding study inclusion were resolved by consensus. Following reporting standards for AI in health care, we extracted data on study objectives, model features, targeted diverse groups, mitigation strategies used, and results. Using the mixed methods appraisal tool, we appraised the quality of the studies. After removing 585 duplicates, we screened 1018 titles and abstracts. From the remaining 189 full-text articles, we included 17 studies. The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as "male versus female" in 10 of the studies. We categorized bias mitigation approaches into four clusters: (1) modifying existing AI models or datasets, (2) sourcing data from electronic health records, (3) developing tools with a "human-in-the-loop" approach, and (4) identifying ethical principles for informed decision-making. Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. Other methods aimed at enhancing model fairness included group recalibration and the application of the equalized odds metric. However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations. The results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm's preprocessing stage. Further empirical studies that include a broader range of groups, such as Indigenous peoples in Canada, are needed to validate and expand upon these findings. OSF Registry osf.io/9ngz5/; https://osf.io/9ngz5/. RR2-10.2196/46684.

Leveraging Multifaceted Polyphenol Interactions: An Approach for Hair Loss Mitigation

AbstractHair loss affects millions globally, creating a demand for safer, more effective treatments. Traditional treatments like minoxidil often have limited efficacy and side effects. This study explores tannic acid, a polyphenol, as a promising alternative, serving as a multifaceted adhesion mediator that binds to keratin on the hair surface and serves as a reservoir for functional molecules‐salicylic acid (SCA), niacinamide (N), and dexpanthenol (DAL)‐collectively termed SCANDAL. Tannic acid forms a stable coating on hair through hydrogen bonding, while its unbound phenolic sites sequester SCANDAL molecules, enabling their controlled release upon exposure to moisture. This gradual release delivers active molecules directly to hair follicles, enhancing hair health and mitigating hair loss. In a seven‐day study with 12 human participants, an average reduction of 56.2 ± 18.2% in the number of shed hairs is observed compared to their baseline counts. The presence of tannic acid on the hair surface is confirmed via X‐ray photoelectron spectroscopy (XPS), and the formation of tannic acid/SCANDAL complexes is visualized using transmission electron microscopy (TEM). This approach demonstrates that hair keratin surfaces can serve as large‐area platforms for controlled release of therapeutic agents, addressing various challenges in hair and scalp health.

Adaptive Q-Learning-Based Radio Resource Management Optimization in 5G and Beyond Heterogeneous-Homogeneous Networks: A Comprehensive Review

This paper reviews advanced radio resource management (RRM) optimization techniques in 5G and beyond heterogeneous-heterogeneous networks (Het-HetNets). Key innovations include fairness-aware models for mmWave 5G, machine learning (ML)-driven traffic management, and game-theoretic approaches for interference mitigation in Massive MIMO systems. Blockchain technology emerges as a promising tool for secure spectrum sharing, while deep learning enhances handover management and resource allocation. Hybrid frameworks, such as deep reinforcement learning and non-orthogonal multiple access, address energy efficiency and quality of service (QoS) challenges for IoT, autonomous vehicles, and smart cities. Despite these advancements, challenges like scalability, computational complexity, and data privacy persist. Q-learning-based adaptive RRM frameworks demonstrate potential for optimizing energy and spectral efficiency by addressing dynamic network conditions. The integration of ML with blockchain enables secure and decentralized RRM. Critical research gaps identified include scalability, real-time deployment, and interference management in ultra-dense networks. This review highlights the importance of scalable, efficient, and adaptive solutions to advance the telecommunications system.

Vessel strike encounter risk model informs mortality risk for endangered North Atlantic right whales along the United States east coast

Vessel strikes are a critical threat to endangered North Atlantic right whales (Eubalaena glacialis), significantly contributing to their elevated mortality. Accurate estimates of these mortality rates are essential for developing effective management strategies to aid in the species’ recovery. This study enhances existing vessel strike models by incorporating detailed regional data on vessel traffic characteristics as well as whale distribution and behavior. Our model assesses the spatial and temporal variability in vessel strike risk along the U.S. east coast apportioned into three vessel length classes (26–65 feet, 65–350 feet, > 350 feet). By including regional right whale depth distributions and parameterizing potential whale avoidance based on factors such as descent rate, bottom depth, and vessel speed and size, the model provides a refined estimation of mortality risk. We also address the underrepresentation of smaller vessel activity via a correction factor, offering a more accurate annual mortality risk estimate for each vessel size class. These findings highlight that vessels > 350 feet in length pose the greatest risk to right whales. Simulations of reduced vessel speeds indicate that speed measures can mitigate mortality rates; however, residual risk remains even at speeds of 10 knots or less suggesting limitations to this mitigation approach.

Dilemma Zone Identification and Mitigation Approach at Signalized Intersections under Mixed Traffic Conditions Using UAV Data

Dilemma Zone Identification and Mitigation Approach at Signalized Intersections under Mixed Traffic Conditions Using UAV Data