Mitigation Techniques Research Articles

World Climate Research Programme lighthouse activity: an assessment of major research gaps in solar radiation modification research

It is increasingly evident that maintaining global warming at levels below those agreed in the legally binding international treaty on climate change. i.e., the Paris Agreement, is going to be extremely challenging using conventional mitigation techniques. While future scenarios of climate change frequently include extensive use of terrestrial and marine carbon dioxide removal in the second part of the 21st century, it is unproven that these techniques can be scaled-up to reach the scale required to significantly reduce concentrations of atmospheric carbon dioxide and significant uncertainties and detrimental side-effects exist. These issues have led to increasing interest in so-called “Solar Radiation Modification” whereby the global mean temperature of the Earth is reduced by either blocking a small fraction of sunlight from reaching it or by increasing the Earth’s albedo to reflect a small proportion of incident sunlight back out to space. Here we systematically identify key research gaps associated with the two most prominent Solar Radiation Modification techniques, i.e., Stratospheric Aerosol Injection (SAI) and Marine Cloud Brightening (MCB). We provide an assessment of the research gaps associated with other less prominent SRM techniques. We assert that transparency and inclusivity in SRM research is essential in providing objective and impartial research findings to each and every stakeholder in an equitable way.

On the Application of a Sparse Data Observers (SDOs) Outlier Detection Algorithm to Mitigate Poisoning Attacks in UltraWideBand (UWB) Line-of-Sight (LOS)/Non-Line-of-Sight (NLOS) Classification

The classification of the wireless propagation channel between Line-of-Sight (LOS) or Non-Line-of-Sight (NLOS) is useful in the operation of wireless communication systems. The research community has increasingly investigated the application of machine learning (ML) to LOS/NLOS classification and this paper is part of this trend, but not all the different aspects of ML have been analyzed. In the general ML domain, poisoning and adversarial attacks and the related mitigation techniques are an active area of research. Such attacks aim to hamper the ML classification process by poisoning the data set. Mitigation techniques are designed to counter this threat using different approaches. Poisoning attacks in LOS/NLOS classification have not received significant attention by the wireless communication community and this paper aims to address this gap by proposing the application of a specific mitigation technique based on outlier detection algorithms. The rationale is that poisoned samples can be identified as outliers from legitimate samples. In particular, the study described in this paper proposes a recent outlier detection algorithm, which has low computing complexity: the sparse data observers (SDOs) algorithm. The study proposes a comprehensive analysis of both conventional and novel types of attacks and related mitigation techniques based on outlier detection algorithms for UltraWideBand (UWB) channel classification. The proposed techniques are applied to two data sets: the public eWINE data set with seven different UWB LOS/NLOS different environments and a radar data set with the LOS/NLOS condition. The results show that the SDO algorithm outperforms other outlier detection algorithms for attack detection like the isolation forest (iForest) algorithm and the one-class support vector machine (OCSVM) in most of the scenarios and attacks, and it is quite competitive in the task of increasing the UWB LOS/NLOS classification accuracy through sanitation in comparison to the poisoned model.

Fluoride in Groundwater of Chhattisgarh: A Comprehensive Review of Health Impacts on Humans, Mitigation Strategies and Sustainable Solutions

Fluorosis is a slowly progressing and often neglected disease that poses great health challenges by gradually affecting the human body. Although fluoride toxicity is theoretically reversible, such slow progression leaves with few choices of cure for these side effects, making prevention crucial. Inequitable access to safe sources of drinking water has worsened the prevalence of dental fluorosis, especially in regions where the concentration of fluoride in water is high. This review discusses the association between fluoride exposure, dental fluorosis, and skeletal fluorosis, bringing together an elaborate study of the negative effects that fluoride has on the human body. Skeletal fluorosis is one of the conditions that have received much attention lately. It remains the central theme in this study, which provides information about the latest discoveries made on this condition, detailing its prevalence, health effects, and the course it takes. It also reviews the latest developments in the prevention of fluorosis: new and cheap fluoride removal technologies and mitigation techniques. The conclusions are meant to stimulate further studies on the treatments of fluorosis and on ways to provide clean drinking water inexpensively and sustainably. Fluoride contamination of water is driven by geochemical reactions and natural as well as anthropogenic activities. Although fluoride is necessary for health, quantities over 1.5 mg L⁻¹ are toxic, and prolonged exposure through water has severe health effects, including skeletal fluorosis and oxidative damage to vital organs such as kidneys, the brain, thyroid gland, and liver. The review shows the need of prevention and intervention while shedding light on the multifaceted impacts from fluoride toxicity. By focusing on fluoride pollution, the article aims at unravelling the burden on human health and global water security in safeguarding human lives.

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.

Analysis and Optimization of DC-DC Converters Through Sensitivity to Parametric Variations

The optimization of DC-DC converters is crucial for enhancing their performance and efficiency in various applications. This study focuses on the sensitivity analysis of DC-DC converters to parametric variations, which plays a key role in designing robust and efficient systems. The methodology involves developing a simulation model that describes the behavior of converters under different conditions and analyzing the effects of parameter variations through simulation tools. Sensitivity analysis of DC-DC converters involves understanding the sources of harmonics, modeling the converter, analyzing the harmonic content, and implementing mitigation techniques. By combining theoretical analysis with practical design modifications, engineers can optimize DC-DC converters for improved performance, efficiency, and compliance with electromagnetic compatibility standards. Examples of harmonic analysis of the main types of DC-DC converters—Buck, Boost, and Buck-Boost—are discussed in the manuscript. Based on a study of the influence of harmonics in the operating modes, ratios have been derived to be applied during design. In this respect, the research presented is useful for designers and for use in power electronics education.

Localized sensing data-driven efficacy evaluation of heat stress mitigation techniques in ‘Honeycrisp’ apple cultivar

Localized sensing data-driven efficacy evaluation of heat stress mitigation techniques in ‘Honeycrisp’ apple cultivar

Comprehensive Approaches for Detecting and Mitigating Distributed Denial of Service (DDoS) Attacks in Modern Network Environments

Distributed Denial of Service (DDoS) attacks remain one of the most significant threats to the security and availability of online services. These attacks exploit multiple systems, typically compromised devices, to flood a target server with excessive traffic, causing service disruption and resource depletion. Over the years, DDoS attacks have evolved in both scale and complexity, posing new challenges to cybersecurity professionals. This paper provides an in-depth analysis of DDoS attacks, categorizing various types, exploring their impact on both businesses and infrastructure, and reviewing the latest detection and mitigation techniques. We focus on the intersection of machine learning, network traffic analysis, and cloud-based solutions as advanced strategies to counteract these persistent threats. Additionally, we explore case studies highlighting the real-world applications of these methods. The paper concludes by proposing future research directions and the role of emerging technologies such as AI and blockchain in strengthening DDoS defenses.

Deep Learning Approaches for DDoS Attack Detection in Communication Networks and IoT: A Comprehensive Review

The increasing adoption of transformative technologies, such as the Internet of Things (IoT), has brought convenience and optimization to various domains. However, it has also introduced new challenges, including the vulnerability to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. DDoS attacks have shown an alarming rise in frequency and potency, making it crucial to devise efficient mechanisms to prevent such attacks and safeguard communication networks. IoT networks, with their numerous interconnected devices and limited resources, are particularly susceptible to DDoS attacks. Traditional rule-based approaches have proven insufficient to cope with the dynamic nature of modern attacks, leading to the emergence of deep learning-based detection and mitigation techniques. Deep learning models, supported by real-world datasets, off er promising results with detection rates exceeding 98%. This study explores various deep learning architectures, focusing on their success in DDoS attack detection, particularly in IoT networks. It also addresses the challenges associated with such networks and highlights potential areas for future research.

Mitigating errors in analog quantum simulation by Hamiltonian reshaping or Hamiltonian rescaling

Simulating quantum many-body systems is crucial for advancing physics but poses substantial challenges for classical computers. Quantum simulations overcome these limitations, with analog simulators offering unique advantages over digital methods, such as lower systematic errors and reduced circuit depth, making them efficient for studying complex quantum phenomena. However, unlike their digital counterparts, analog quantum simulations face significant limitations due to the absence of effective error mitigation techniques. This work introduces two novel error mitigation strategies—Hamiltonian reshaping and Hamiltonian rescaling—in analog quantum simulation for tasks like eigen-energy evaluation. Hamiltonian reshaping uses random unitary transformations to generate new Hamiltonians with identical eigenvalues but varied eigenstates, allowing error reduction through averaging. Hamiltonian rescaling mitigates errors by comparing eigenvalue estimates from energy-scaled Hamiltonians. Numerical calculations validate both methods, demonstrating their significant practical effectiveness in enhancing the accuracy and reliability of analog quantum simulators.

Digital quantum simulation of cosmological particle creation with IBM quantum computers

We use digital quantum computing to simulate the creation of particles in a dynamic spacetime. We consider a system consisting of a minimally coupled massive quantum scalar field in a spacetime undergoing homogeneous and isotropic expansion, transitioning from one stationary state to another through a brief inflationary period. We simulate two vibration modes, positive and negative for a given field momentum, by devising a quantum circuit that implements the time evolution. With this circuit, we study the number of particles created after the universe expands at a given rate, both by simulating the circuit and by actual experimental implementation on IBM quantum computers, consisting of hundreds of quantum gates. We find that state-of-the-art error mitigation techniques are useful to improve the estimation of the number of particles and the fidelity of the state.

Adaptive neural network for quantum error mitigation

Quantum computing holds transformative promise, but its realization is hindered by the inherent susceptibility of quantum computers to errors. Quantum error mitigation has proved to be an enabling way to reduce computational error in present noisy intermediate scale quantum computers. This research introduces an innovative approach to quantum error mitigation by leveraging machine learning, specifically employing adaptive neural networks. With experiment and simulations done on 127-qubit IBM superconducting quantum computer, we were able to develop and train a neural network architecture to dynamically adjust output expectation values based on error characteristics. The model leverages a prior classifier module outcome on simulated quantum circuits with errors, and the antecedent neural network regression module adapts its parameters and response to each error characteristics. Results demonstrate the adaptive neural network’s efficacy in mitigating errors across diverse quantum circuits and noise models, showcasing its potential to surpass traditional error mitigation techniques with an accuracy of 99% using the fully adaptive neural network for quantum error mitigation. This work presents a significant application of classical machine learning methods towards enhancing the robustness and reliability of quantum computations, providing a pathway for the practical realization of quantum computing technologies.

PegasosQSVM: A Quantum Machine Learning Approach for Accurate Fake News Detection

ABSTRACT The rapid spread of fake news on social media poses a significant threat to modern societies. Traditional machine learning approaches have limitations in handling the ever-increasing volume and complexity of data. This research explores quantum machine learning for fake news classification by proposing Pegasos Quantum Support Vector Machines, a novel algorithm combining Pegasos Support Vector Machines with quantum kernels, and advanced data encoding. Through experimentation on the IBM Qasm Simulator, Pegasos Quantum Support Vector Machines scored 90.67% in accuracy. This study is primarily focused on local simulation, where the proposed algorithm scored as high as 95.63%, with 95.44% precision, 99.52% recall, and 96.76% f1-score. The achieved results outperform other machine learning methods on the BUZZFEED dataset, including Quantum Neural Networks and Quantum K-Nearest Neighbors. Its successful implementation paves the way for further refinement of quantum machine learning techniques in fake news classification. The PegasosQSVM algorithm encounters, however, some implementation issues on real world Quantum Processing Units(QPU). Noisy Intermediate-Scale Quantum era QPU are prone to noise effects that affect the computations negatively, and by extension, the results of quantum machine learning algorithms. Further implementation on real QPU and use of error mitigation techniques, are needed for optimal results on quantum hardware.

Analyzing and Mitigating Attacks in IoT Smart Home Using a Threat Modeling Approach-Based STRIDE

The Internet of Things (IoT) is a network of interconnected devices that enables data exchange. It is widely used in areas such as healthcare, aviation, agriculture, energy, and home automation. Despite its rapid growth and the massive adoption of connected devices, IoT presents significant security risks. Traditional threat modeling approaches are insufficient to address these risks. Architecture-based modeling is recommended, as it considers the entire system and helps in understanding potential threats. Threat modeling is a systematic technique used to identify and evaluate potential threats that could compromise the security of a system. The main objective is to understand the vulnerabilities of a system in order to design appropriate security measures to mitigate them. This paper aims to analyze and mitigate specific IoT smart home threats using the STRIDE threat modeling framework, which systematically identifies potential vulnerabilities at the development level. By applying STRIDE, which stands for Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege, we focused on addressing key security threats, including denial of service (DoS), phishing, and man-in-the-middle (MitM) attacks. Our findings demonstrate that the proposed mitigation strategies are effective in countering these threats, providing a robust security layer for IoT smart homes. Through this study, we highlight the importance of architecture-based threat modeling to enhance security within the IoT ecosystem and offer practical solutions that strengthen IoT smart home resilience. The outcomes of the STRIDE-based analysis and the effectiveness of the mitigation techniques are detailed, offering empirical evidence to support our approach.

Deep learning techniques for atmospheric turbulence removal: a review

Atmospheric turbulence significantly complicates the interpretation and analysis of images by distorting them, making it hard to classify and track objects within a scene using traditional methods. This distortion arises from unpredictable, spatially varying disturbances, challenging the effectiveness of standard model-based techniques. These methods often become impractical due to their complexity and high memory demands, further complicating the task of restoring scenes affected by atmospheric turbulence. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares performances of a range of state-of-the-art deep neural networks, including Transformers, SWIN and MAMBA, when used to mitigate spatio-temporal image distortions. Furthermore, this review presents: a list of available datasets; applicable metrics for evaluation of mitigation methods; an exhaustive list of state-of-the-art and historical mitigation methods. Finally, a critical statistical analysis of a range of example models is included. This review provides a roadmap of how datasets and metrics together with currently used and newly developed deep learning methods could be used to develop the next generation of turbulence mitigation techniques.

Improving and Protecting the Privacy of Data Security Using Blake2 Algorithm

Several characteristics of data open up many new avenues for exploitation, and as such, new privacy and security models are required to address these emerging challenges. The magnitude of datasets generated make it near impossible for data managers to capture all the contextual semantics relevant to a unit of data, and this causes novel difficulties in privacy and security of these data systems using BLAKE2 Algorithm. At the micro-level, a datum in any software system transitions between several phases, from inception to deletion; this is known as the data life cycle, which provides a concrete guide for tracking the many states of a unit of data. The usefulness of such a model only increases with the complexity of the system that it represents, so it is useful to examine privacy and security from this perspective, as it provides a precise framework for discussion. This paper provides practical privacy and security recommendations for every step of the data life cycle, examining prominent infrastructures and their features that relate to their data management policies using BLAKE2 Algorithm with 98.97% secure and reliable all the time. BLAKE2 is a cryptographic hash function faster than MD5, SHA-1, SHA-2, and SHA-256. The most glaring issue with SHA is that its data storage system is completely unencrypted by default. Like MD5, SHA1, and many encryption algorithms were designed to be run within a trusted environment. In the case of unencrypted data, this means that malicious parties that have physical or virtual access to the file system can extract information as they please. Fortunately, BLAKE2 is largely reliant on the client code for encryption of any sensitive information before it saves to the database. Some mitigation techniques for this scheme include implementing proper file system permissions and file system level encryption.

Aircraft resilience to GNSS jamming and impact on ADS-B quality indicators

Abstract The presented research investigates the impact of interference on the performance of aircraft Global Navigation Satellite System (GNSS) receivers with a specific focus on the behaviour of Automatic Dependent Surveillance-Broadcast (ADS-B) position quality indicators. Several experiments were performed with different aircraft types, such as Airbus, Boeing, Beechcraft King Air B350 or Tecnam, and using various intensities of GNSS jamming. The behaviour of various quality indicators, such as the Navigation Integrity Category, Navigation Accuracy Category, Source Integrity Level and System Design Assurance transmitted in different types of ADS-B messages, is analysed. We investigate not only situations where the quality indicators drop to zero, but also the complete evolution of the changes in the indicators as a function of the increasing power of the jamming signal. Based on the analysis of changes in the ADS-B quality indicators, the estimation of the most likely interference signal power required to discontinue the tracking of an already acquired GPS L1 Coarse/Acquisition signal is made. Additionally, the interference signal power to prevent re-acquisition is also estimated. The findings improve the understanding of interference effects and can support the development of robust interference mitigation techniques in aviation applications.

Comprehensive Review of Bearing Currents in Electrical Machines: Mechanisms, Impacts, and Mitigation Techniques

Comprehensive Review of Bearing Currents in Electrical Machines: Mechanisms, Impacts, and Mitigation Techniques

Self-consistent Quantum Linear Response with a Polarizable Embedding Environment.

Quantum computing presents a promising avenue for solving complex problems, particularly in quantum chemistry, where it could accelerate the computation of molecular properties and excited states. This work focuses on computing excitation energies with hybrid quantum-classical algorithms for near-term quantum devices, combining the quantum linear response (qLR) method with a polarizable embedding (PE) environment. We employ the self-consistent operator manifold of quantum linear response (q-sc-LR) on top of a unitary coupled cluster (UCC) wave function in combination with a Davidson solver. The latter removes the need to construct the entire electronic Hessian, improving computational efficiency when going toward larger molecules. We introduce a new superposition-state-based technique to compute Hessian-vector products and show that this approach is more resilient toward noise than our earlier gradient-based approach. We demonstrate the performance of the PE-UCCSD model on systems such as butadiene and para-nitroaniline in water and find that PE-UCCSD delivers comparable accuracy to classical PE-CCSD methods on such simple closed-shell systems. We also explore the challenges posed by hardware noise and propose simple error mitigation techniques to maintain accurate results on noisy quantum computers.

Multi-hazard Risk Unveiled: Pioneering Techniques for Comprehensive Risk Analysis and Mitigation

Multi-hazard Risk Unveiled: Pioneering Techniques for Comprehensive Risk Analysis and Mitigation

Potential Approaches and Techniques for Long-term Management of the Human-Elephant Conflict in Western Serengeti, Tanzania

Human-elephant conflict is a ubiquitous feature between Ikorongo-Grumeti Game Reserves (IGGRs) management and the adjacent communities of Bunda and Serengeti Districts, in the Mara Region. This study aimed at analyzing the conflict and then coming up with potential techniques for effective mitigation of the conflict in the area. Data collection involved direct observations, key informant interviews and household surveys using questionnaires. The analysis was done using SPSS computer software. Results revealed and recommended several non-conventional mitigation measures namely the construction of trench (95.3%), electric fencing (92.7%), establishment of buffer zone management units (BZMUs) (92.7%) and geo-fencing system (92.3%). Also were wireless sensing networks (WSN) (85.3%), translocation of problem elephants (11.7%), and evacuation of people adjoining wildlife area boundaries (22%) as HEC prevention and mitigation measures with long-term impacts. Generally, no single solution was considered effective as different approaches must be integrated to address the problem proactively. Hence, it is recommended that community involvement in decision-making and policy formulation be emphasized to effectively implement proposed mitigation measures