Mitigation Techniques Research Articles

Realization of higher-order topological lattices on a quantum computer

Programmable quantum simulators may one day outperform classical computers at certain tasks. But at present, the range of viable applications with noisy intermediate-scale quantum (NISQ) devices remains limited by gate errors and the number of high-quality qubits. Here, we develop an approach that places digital NISQ hardware as a versatile platform for simulating multi-dimensional condensed matter systems. Our method encodes a high-dimensional lattice in terms of many-body interactions on a reduced-dimension model, thereby taking full advantage of the exponentially large Hilbert space of the host quantum system. With circuit optimization and error mitigation techniques, we measured on IBM superconducting quantum processors the topological state dynamics and protected mid-gap spectra of higher-order topological lattices, in up to four dimensions, with high accuracy. Our projected resource requirements scale favorably with system size and lattice dimensionality compared to classical computation, suggesting a possible route to useful quantum advantage in the longer term.

Spectral redundancy for calibrating interferometers and suppressing the foreground wedge in 21 cm cosmology

ABSTRACT Observations of 21 cm line from neutral hydrogen promise to be an exciting new probe of astrophysics and cosmology during the Cosmic Dawn and through the Epoch of Reionization (EoR) to when dark energy accelerates the expansion of our Universe. At each of these epochs, separating bright foregrounds from the cosmological signal is a primary challenge that requires exquisite calibration. In this paper, we present a new calibration method called nucal that extends redundant-baseline calibration, allowing spectral variation in antenna responses to be solved for by using correlations between visibilities measuring the same angular Fourier modes at different frequencies. By modelling the chromaticity of the beam-weighted sky with a tunable set of discrete prolate spheroidal sequences, we develop a calibration loop that optimizes for spectrally smooth calibrated visibilities. Crucially, this technique does not require explicit models of the sky or the primary beam. With simulations that incorporate realistic source and beam chromaticity, we show that this method solves for unsmooth bandpass features, exposes narrow-band interference systematics, and suppresses smooth-spectrum foregrounds below the level of 21 cm reionization models, even within much of the so-called wedge region where current foreground mitigation techniques struggle. We show that this foreground subtraction can be performed with minimal cosmological signal loss for certain well-sampled angular Fourier modes, making spectral-redundant calibration a promising technique for current and next-generation 21 cm intensity mapping experiments.

Shoreline barriers may amplify coastal groundwater hazards with sea-level rise

Subsurface barriers have been proposed to protect coastal aquifers from sea-level rise induced seawater intrusion, but the potential for groundwater emergence near subsurface barriers remains unknown. Here, we investigated how emergence changes groundwater flow conditions and influences the protective performance of subsurface barriers with sea-level rise. We tested the subterranean consequences of sea-level rise for cutoff walls and subsurface dams with cross-shore groundwater flow and salt transport models, investigating how barrier design, aquifer properties, and hydrological conditions control the potential for emergence, groundwater partitioning at the barrier, and seawater intrusion with sea-level rise. We find that most subsurface infrastructure cannot prevent seawater intrusion and emergence simultaneously. Subsurface dams spanning more than half of the aquifer thickness created emergence hazards and subsequent groundwater partitioning for all scenarios tested. Cutoff walls were less effective at reducing seawater intrusion for all opening sizes but could reduce the emergence potential compared to similarly sized subsurface dams. Our results demonstrate the challenging trade-offs in mitigating the coastal groundwater hazards of seawater intrusion and emergence with sea-level rise, where groundwater flooding inland of protective infrastructure would require combinations of subsurface impoundments and other mitigation techniques, such as pumping or drains.

Resilience building interventions to manage climate induced livestock poverty and deaths in Southern Zimbabwe

Livestock production in Mwenezi District (MD) has become more susceptible to effects of climatic variability and change which undermines developmental gains from livestock value chain. Climate change impacts are increasing the intensity of livestock poverty and deaths across all forms of livestock. The study examined livestock production interventions implemented to reduce livestock poverty deaths in MD. Descriptive research design which utilizes both qualitative and quantitative paradigms was used to accurately and systematically describe the phenomenon. A household survey questionnaire was administered to sampled households in MD wards 4 and 10 while interviews were conducted with purposively selected key informants from the department Agriculture Technical and Extension Services (AGRITEX), Veterinary Services Department, Rural District Council, CARE and District Development Coordinator's Office. Various interventions including construction of improved livestock structures, availing of safe drinking water for livestock, livestock feed formulation have been initiated to reduce and contain livestock poverty deaths. The study recommends that development actors should initiate more interventions to boost livestock production sector since it is the most suitable intervention in relation to climate and weather conditions experienced in MD. The research offers insights to address the complex interplay between climate change, livestock farming, poverty and resilience building. Results inform future researchers while guiding policy makers working in similar contexts on how best to support vulnerable communities. Findings lessen the burden to achieve Sustainable Development Goals namely no poverty, zero hunger amongst others. The study enlightened farmers on how to implement drought mitigation and adaptation techniques during drought periods.

A Comprehensive Survey of Social Engineering Attacks: Taxonomy of Attacks, Prevention, and Mitigation Strategies

Social engineering attacks pose a significant threat to individuals, organizations, and governments globally. These attacks leverage human psychology, manipulating individuals into divulging sensitive information or undertaking actions that jeopardize security. The various forms of social engineering attacks can lead to dire consequences for the targeted victims. However, with preventive and mitigation strategies such as security awareness training, policy formulation, and technical controls, the risks associated with social engineering attacks can be diminished. This paper offers a comprehensive overview of social engineering attacks, delving into their diverse types and methodologies. It further discusses various strategies to counteract and alleviate the threats posed by these attacks. The study introduces a taxonomy of social engineering attacks, categorizing them based on the operator, prevention measures, and mitigation techniques. This thorough analysis underscores the current challenges in the domain and suggests potential future trajectories in the field.

Artificial intelligence-powered intelligent reflecting surface systems countering adversarial attacks in machine learning

With the increase in the computation power of devices wireless communication has started adopting machine learning (ML) techniques. Intelligent reflecting surface (IRS) is a programmable device that can be used to control electromagnetic wave propagation by changing the electric and magnetic values of its surface. State-of-the-art ML especially on deep learning (DL)-based IRS-enhanced communication is an emerging topic. Yet while integrating IRS with other emerging technologies possibilities of adversarial data creaping is high. Threats to security, their mitigation, and complexes for AI-powered applications in next generation networks are continuously emerging. In this work the ability of an IRS enhanced wireless network in future-generation networks to prevent adversarial machinelearning attacks is studied. The artificial intelligence (AI) model is used to minimize the susceptibility of attacks using defense distillation mitigation technique. The outcome shows that the defensive distillation technique (DDT) increases the strength and performance by around 22% of the AI method under an adversarial attack.

Unveiling pollutants in Sonipat district, Haryana: Exploring seasonal, spatial and meteorological patterns

Air pollution is a serious environmental problem that aggravates climate change. Sonipat district, is a fast-urbanizing area with serious air pollution issues brought on by a variety of industry, busy traffic, and agricultural methods. Since in-situ measurements of air pollution are not feasible everywhere on Earth's surface, satellite data sets are crucial for determining trends in air pollution. This study uses several data sets, such as Sentinel-5P air pollution data, CHIRPS rainfall data, and GPWv4 population density data to investigate the spatial and temporal fluctuations of air pollution with reference to meteorological and other important socioeconomic factors. Pollutants include Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), and Absorbing Aerosol Index (AAI) are the main focus of the analysis during the winter, summer, and post-monsoon seasons. Findings revealed that the district's southeast portion has greater concentrations of CO and NO2, which are likely due to the intense traffic and industrial activity along National Highway 44. While O3 and SO2 exhibit seasonal fluctuations correlated with temperature and sunlight, AAI levels peak in the October in post-monsoon season as a result of regional wind patterns and agricultural burning. Rainfall during the September in post-monsoon season lowers the concentrations of pollutants because of retreating monsoon. Pollutant dispersion is greatly impacted by wind direction and speed. The study highlights the significance of weather-related elements in influencing air pollution levels and emphasizes the necessity for efficient air quality management and pollution mitigation techniques.

Optimal harmonic mitigation technique for high-power IM drives

Optimal harmonic mitigation technique for high-power IM drives

Temporal trends in indoor bioaerosols: implications for dental healthcare environments

ABSTRACT Antibiotic resistance, a significant public health hazard, is predicted to cause 10 million deaths worldwide by 2050. The study aimed to identify culturable bioaerosols in the indoor air of dental units in Lahore and assess their antibiotic resistance. Air samples were collected from 10 dental unit locations at different distances, with average concentrations of fungi and bacteria falling within intermediate ranges, per the Global Index of Microbial Contamination (GIMC/m3) index. The study found higher antibiotic-resistant strains in hospital dental units, particularly during winter. The most vigorous strain, S.aureus-NAJIH18, exhibited 70% resistance to ceftazidime. The research highlights the importance of quantifying microbial pollutants for evaluating their source and complexity. It suggests proactive mitigation techniques, such as focused cleaning and air filtration, to improve indoor air quality can mitigate the spread of antibiotic-resistant strains. These insights offer hope in combating the growing public health threat of antibiotic resistance.

Global Warming, its Causes, Environmental Effects, and Solution to Avoid this Phenomenon: Review

This review aims to identify the causes that contribute to global warming and offers the most likely cause, according to the published evidence. Additionally, this page provides various mitigation strategies for the global warming issue. The most practical economic and technical solution to the global warming problem is the simultaneous adoption of mitigation and adaptation techniques. Global warming is a major consequence of human activities caused by the excessive use of old fuels as an energy- source, which has caused increased quantities of “greenhouse gases” (GHGs). The atmosphere contains gases that increase the Earth's average surface temperature, such as carbon dioxide, methane, nitrous oxide, and water vapor. Global warming phenomena have become a global problem with negative effects on various parts of the environment due to the glaciers melting, overflowing carbon dioxide (CO2), and too much warming of aquatic bodies, among others. Generality people are still unaware of global warming and do not see it as a main problem in the coming years.

Computed tomography as distortion mitigation method for selective laser sintering mass production

Most additive manufacturing (AM) technologies use heat to fuse materials together to create the manufactured part. The heat used in the AM process distorts the parts. Powder bed–based 3D printers can print multiple parts in their build chamber. The distortion is not uniform across the different locations of the build volume. Parts printed in different locations will have different thermal histories and therefore different distortions. In some cases, the achievable accuracy of the parts is insufficient due to the distortion. Subtractive processes such as milling, turning, and grinding make it difficult or impossible to improve part accuracy. For AM to produce more accurate parts, a distortion reduction method must be implemented. To take advantage of the ability to print multiple parts in a powder-based polymer 3D printing process in one build unit, a distortion mitigation technique must be applied to all the parts being printed simultaneously in the build chamber. The performance of the distortion mitigation method can be evaluated by measuring the dimensional accuracy of the uncompensated and compensated parts. Uncompensated 3D printing uses the nominal 3D model, which is the normal use of the 3D printers. Compensated 3D printing uses a distorted 3D model that is used for the printing. The 3D model is compensated with the reversed distortion data obtained from uncompensated manufacturing. X-ray computed tomography (XCT) is the chosen measurement method to extract the point cloud for the dimensional measurements. Unlike optical 3D scanners and coordinate measuring machines (CMM), the XCT is able to measure undercut and internal surfaces. The nominal difference % is improved by 18% by using compensation for the 3D models in the case of distances between two parallel planes. The standard deviation of the measured values was also improved. The distortion reduction method studied can significantly reduce the calibration errors of the 3D printer build chamber. When the tolerances of the parts are close to the limit of the 3D printer, this method can reduce the number of rejected parts. The XCT measurement of the parts is costly, so this method can be cost effective for high value parts or large production volumes.

Flood Susceptibility Mapping for Kedah State, Malaysia: Geographics Information System-Based Machine Learning Approach

ABSTRACT Background: The world economy is significantly impacted by floods. Identifying flood risk is essential to flood mitigation techniques. Aim: The primary goal of this study is to create a geographic information system (GIS)-based flood susceptibility map for the study area. Methods: Ten flood-influencing factors from a geospatial database were taken into account when mapping the flood-prone areas. Every element demonstrated a robust relationship with the probability of flooding. Results: The highest contributing elements for the flood disaster in the study region were drainage density, distance, and the curvature. Flood susceptibility models’ performance was validated using standard statistical measures and AUC. The ROC curves demonstrated that all ensemble models had good performance on the validation data sets (AUC = >0.97) with high accuracy scores of 0.80. Based on the flood susceptibility maps, most of the northwest regions of the study area are more likely to flood because of low land areas, areas with a lower gradient slope, linear and concave shape curvature, high drainage density with high rainfall, more “water bodies,” “crops land,” and “built areas,” abundance on sea and surface water, and Quaternary types of soil feature and so on. The very high flood susceptibility class accounts for 18.2% of the study area, according to the RF-embedding model, whereas the high, moderate, low, and very low susceptibility classes were found at about 20.0%, 24.6%, 24.3%, and 12.9%, respectively. Conclusion: In comparison with other commonly used applied approaches, this research presents a novel modeling approach for flood susceptibility that integrates machine learning and geospatial data. It has been found to be stronger and more efficient, highly accurate, has good prediction performance, and is less biased. Overall, our research into machine learning-based solutions points in a positive path technologically and can serve as a reference manual for future research and applications for academic specialists and decision-makers.

Automated evaluation of environmental coupling for Advanced LIGO gravitational wave detections

The extreme sensitivity required for direct observation of gravitational waves by the Advanced LIGO detectors means that environmental noise is increasingly likely to contaminate Advanced LIGO gravitational wave signals if left unaddressed. Consequently, environmental monitoring efforts have been undertaken and novel noise mitigation techniques have been developed which have reduced environmental coupling and made it possible to analyze environmental artifacts with potential to affect the 90 gravitational wave events detected from 2015–2020 by the Advanced LIGO detectors. So far, there is no evidence for environmental contamination in gravitational wave detections. However, automated, rapid ways to monitor and assess the degree of environmental coupling between gravitational wave detectors and their surroundings are needed as the rate of detections continues to increase. We introduce a computational tool, PEMcheck, for quantifying the degree of environmental coupling present in gravitational wave signals using data from the extant collection of environmental monitoring sensors at each detector. We study its performance when applied to 79 gravitational waves detected in LIGO’s third observing run and test its performance in the case of extreme environmental contamination of gravitational wave data. We find that PEMcheck’s automated analysis identifies only a small number of gravitational waves that merit further study by environmental noise experts due to possible contamination, a substantial improvement over the manual vetting that occurred for every gravitational wave candidate in the first two observing runs. Building on a first attempt at automating environmental coupling assessments used in the third observing run, this tool represents an improvement in accuracy and interpretability of coupling assessments, reducing the time needed to validate gravitational wave candidates. With the validation provided herein; PEMcheck will play a critical role in event validation during LIGO’s fourth observing run as an integral part of the data quality report produced for each gravitational wave candidate.

Phantoms for Quantitative Body MRI: a review and discussion of the phantom value.

In this paper, we review the value of phantoms for body MRI in the context of their uses for quantitative MRI methods research, clinical trials, and clinical imaging. Certain uses of phantoms are common throughout the body MRI community, including measuring bias, assessing reproducibility, and training. In addition to these uses, phantoms in body MRI methods research are used for novel methods development and the design of motion compensation and mitigation techniques. For clinical trials, phantoms are an essential part of quality management strategies, facilitating the conduct of ethically sound, reliable, and regulatorily compliant clinical research of both novel MRI methods and therapeutic agents. In the clinic, phantoms are used for development of protocols, mitigation of cost, quality control, and radiotherapy. We briefly review phantoms developed for quantitative body MRI, and finally, we review open questions regarding the most effective use of a phantom for body MRI.

Optimizing Surgical Outcomes and the Role of Preventive Surgery: A Scoping Review.

Plastic and reconstructive surgeons are often presented with reconstructive challenges as a sequela of complications in high-risk surgical patients, ranging from exposure of hardware, lymphedema, and chronic pain after amputation. These complications can result in significant morbidity, recovery time, resource utilization, and cost. Given the prevalence of surgical complications managed by plastic and reconstructive surgeons, developing novel preventative techniques to mitigate surgical risk is paramount. Herein, we aim to understand efforts supporting the nascent field of Preventive Surgery, including (1) enhanced risk stratification, (2) advancements in postoperative care. Through an emphasis on four surgical cohorts who may benefit from preventive surgery, two of which are at high risk of morbidity from wound-related complications (patients undergoing sternotomy and spine procedures) and two at high risk of other morbidities, including lymphedema and neuropathic pain, we aim to provide a comprehensive and improved understanding of preventive surgery. Additionally, the role of risk analysis for these procedures and the relationship between microsurgery and prophylaxis is emphasized. (1) medical optimization and prehabilitation, (2) surgical mitigation techniques. Reconstructive surgeons are ideally placed to lead efforts in the creation and validation of accurate risk assessment tools and to support algorithmic approaches to surgical risk mitigation. Through a paradigm shift, including universal promotion of the concept of "Preventive Surgery," major improvements in surgical outcomes may be achieved.

IMTIBOT: An Intelligent Mitigation Technique for IoT Botnets

The tremendous growth of the Internet of Things (IoT) has gained a lot of attention in the global market. The massive deployment of IoT is also inherent in various security vulnerabilities, which become easy targets for hackers. IoT botnets are one type of critical malware that degrades the performance of the IoT network and is difficult to detect by end-users. Although there are several traditional IoT botnet mitigation techniques such as access control, data encryption, and secured device configuration, these traditional mitigation techniques are difficult to apply due to normal traffic behavior, similar packet transmission, and the repetitive nature of IoT network traffic. Motivated by botnet obfuscation, this article proposes an intelligent mitigation technique for IoT botnets, named IMTIBoT. Using this technique, we harnessed the stacking of ensemble classifiers to build an intelligent system. This stacking classifier technique was tested using an experimental testbed of IoT nodes and sensors. This system achieved an accuracy of 0.984, with low latency.

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them crucial in pharmacoproteomic studies. While many drugs target specific GPCR states to exert their effects-thereby regulating the protein's activity-unraveling the activation pathway remains challenging due to the multitude of intermediate transformations occurring throughout this process, and intrinsically influencing the dynamics of the receptors. In this context, computational modeling, particularly molecular dynamics (MD) simulations, may offer valuable insights into the dynamics and energetics of GPCR transformations, especially when combined with machine learning (ML) methods and techniques for achieving model interpretability for knowledge generation. The current study builds upon previous work in which the layer relevance propagation (LRP) technique was employed to interpret the predictions in a multi-class classification problem concerning the conformational states of the β2-adrenergic (β2AR) receptor from MD simulations. Here, we address the challenges posed by class imbalance and extend previous analyses by evaluating the robustness and stability of deep learning (DL)-based predictions under different imbalance mitigation techniques. By meticulously evaluating explainability and imbalance strategies, we aim to produce reliable and robust insights.

Integrated Assessment of Heavy Metal Contamination in Urban Road Dust: Implications for Human Health and Ecosystem Sustainability in Abeokuta, Nigeria

This research aims to comprehensively investigate the heavy metal contamination in road dust within the rapidly expanding urban area of Abeokuta, Nigeria. Focusing on potentially hazardous metals such as Cr, Co, As, Ni, Cu, Zn, Cd, Fe, and Pb, the study employs multivariate statistical techniques to identify sources, assess ecological and health hazards, and determine metal concentrations. Through the analysis of twenty road dust samples, variations in metal concentrations are observed, leading to the application of the geo-accumulation index, contamination factor, contamination degree, and pollution load index for contamination assessment. The results reveal varying degrees of contamination, with notable concentrations of Pb and Ni, indicating a deteriorating urban environment. Ecological risk assessments underscore potential threats to local flora and wildlife, while health risk evaluations emphasize urgent corrective actions needed, particularly for children exposed to heightened carcinogenic and non-carcinogenic hazards. Factor analysis and correlation studies unveil common anthropogenic origins for several metals, shedding light on the need for pollution mitigation techniques in the face of rapid urbanization and industrialization. The research contributes valuable insights to environmental and public health evaluations in the Abeokuta region, emphasizing the importance of informed decision-making for policymakers and the public.

A comprehensive review of time skew background calibration and mitigation techniques in high‐speed time‐interleaved analog‐to‐digital converters

A comprehensive review of time skew background calibration and mitigation techniques in high‐speed time‐interleaved analog‐to‐digital converters

Mitigating Errors on Superconducting Quantum Processors Through Fuzzy Clustering

AbstractQuantum utility is severely limited in superconducting quantum hardware until now by the modest number of qubits and the relatively high level of control and readout errors, due to the intentional coupling with the external environment required for manipulation and readout of the qubit states. Practical applications in the Noisy Intermediate Scale Quantum (NISQ) era rely on Quantum Error Mitigation (QEM) techniques, which are able to improve the accuracy of the expectation values of quantum observables by implementing classical post‐processing analysis from an ensemble of repeated noisy quantum circuit runs. In this work, a recent QEM technique that uses Fuzzy C‐Means (FCM) clustering to specifically identify measurement error patterns is focused. For the first time, a proof‐of‐principle validation of the technique on a two‐qubit register, obtained as a subset of a real NISQ five‐qubit superconducting quantum processor based on transmon qubits is reported. It is demonstrated that the FCM‐based QEM technique allows for reasonable improvement of the expectation values of single‐ and two‐qubit gates‐based quantum circuits, without necessarily invoking state‐of‐the‐art coherence, gate, and readout fidelities.