Mitigation Methods Research Articles

KrishiRaksha – A Disease and Pest Control App

Agriculture remains at the forefront of human civilization, driving economies and providing sustenance. However, challenges like plant diseases and pests significantly reduce productivity, causing economic losses and threatening food security. Traditional mitigation methods, including manual inspections and chemical treatments, are often inefficient, costly, and unsustainable. Leveraging advancements in artificial intelligence (AI), this paper presents Krishi Raksha, an AI-powered application powered by Gemini AI, offering real-time diagnostics and actionable insights for farmers. By employing advanced image processing, real-time cloud-based analytics, and intuitive design, Krishi Raksha bridges the technological gap, empowering even those with minimal digital literacy. This comprehensive solution not only boosts productivity but also aligns with sustainable agricultural practices, marking a transformative step in smart farming. Through detailed analysis, this study explores the methodologies, results, and broader impacts of Krishi Raksha in combating agricultural challenges effectively. Key Words - Pests, Artificial Intelligence, Gemini AI, Real Time Diagnosis, Image Analysis, Sustainable Farming, Crop Loss Mitigation, Actionable Insights.

Assessing Impacts of Offshore Wind Development: An Analysis of the Minimization of Economic Exposure of the Scallop Fishery Through the Regulatory Process

ABSTRACTOffshore wind energy has expanded as a source of clean energy in the United States since the first US offshore wind farm began operations off the coast of Rhode Island in 2016. The emergence of offshore wind has increased the need to manage ocean use across multiple stakeholder groups, a difficult and contentious process. We use 15 years of scallop (Placopecten magellanicus) fishery data to describe how offshore wind may expose one of the most valuable commercial fisheries in the United States to economic risks. Our analysis shows that the current configuration of approved offshore wind lease areas off the northeastern coast of the United States is expected to result in relatively small economic exposure for the scallop fishery. We also illustrate how the measured development process, which includes ample opportunity for stakeholder input, has mitigated exposure through minimization or avoidance by characterizing the change in impacted activity through two case studies. We find moderate to strong levels of exposure mitigation across our three scallop fleet métiers within the Central Atlantic (CA) region. In contrast, exposure mitigation was more variable in the New York Bight (NYB) region suggesting mitigation methods in the NYB are not as effective for the scallop fishery as the CA. The open development process that allowed for early stakeholder engagement has largely mitigated the potential for economic risk of offshore wind on the scallop industry by approving the siting of offshore wind development in less utilized or less productive scalloping areas.

A Method for Mitigating NLOS Error for UWB Based on CNN-Stacked-LSTM Model

Abstract To address the shortcomings of traditional Long Short-Term Memory (LSTM) network in Non-Line-of-Sight (NLOS) mitigation, such as the large amount of training data required and the lengthy training times, in order to enhance the model's ability to process spatial features and multi-level features, this paper proposes an NLOS mitigation method based on Stacked Long Short-Term Memory (Stacked-LSTM) network and Convolutional Neural Network (CNN). This method combines CNN and Stacked-LSTM models to efficiently extract spatial and higher-level temporal features from the Channel Impulse Response (CIR) signal, reducing the input dimension and improving the performance of the model. The constructed CNN-Stacked-LSTM model is used to mitigate NLOS errors and reduce the impact of NLOS in the original ranging data. In the model performance validation experiment, the accuracy of the CNN-Stacked-LSTM model was improved by 4%-14% compared with the CNN-LSTM, Transformer, Attention-LSTM and LSTM models, and the training time was reduced by 0.07h compared with the traditional LSTM model. The experimental results in the two actual Ultra-WideBand (UWB) environments show that compared with the other four models, the RMSE value of the CNN-Stacked-LSTM model proposed in this paper is reduced by 19.55%-58.96% and 8.64%-45.52%, respectively. It has the best mitigation effect on NLOS and the highest positioning accuracy.

Treatment of Acne With a 1726 nm Laser, Air Cooling, and Real-Time Temperature Monitoring, Software-Assisted Power Adjustment to Achieve a Temperature Endpoint With Selective Sebaceous Gland Photothermolysis.

This work highlights the methods used to develop a multi-pulse 1726 nm laser system combined with bulk air-cooling for selective sebaceous gland (SG) photothermolysis using thermal imaging and software algorithms. This approach enables treating to a desired tissue temperature and depth to provide a safe, effective, reproducible, and durable treatment of acne. We designed and built a 1726 nm laser system with a 40 W maximum power output, a highly controlled air-cooling device, and a thermal camera in the handpiece, which permits real-time temperature monitoring of the epidermis. IRB-approved safety and efficacy trials demonstrated SG damage at depth, resulting in safe, efficacious, and durable clinical outcomes. Bioheat transfer and light transport modeling confirmed that the pulsing protocols could produce therapeutic temperatures at various SG depths, while protecting the epidermis and dermis with bulk air-cooling. Similarly, we employed clinical observations and photothermal modeling to identify pain mitigation opportunities while maintaining therapeutic efficacy. Biopsies were subsequently taken for histological evaluation. Clinical and histological data, confirmed with modeling, demonstrated that multi-pulse laser delivery with bulk air-cooling selectively increased SG temperature compared to surrounding dermis and at depths unachievable by a single pulse. Subjects showed an average 71% ILC reduction at 3 months posttreatment. We identified two different pulsing protocols with similar selective photothermolysis (SP) of the SG with very different pain responses. Thus, changing the pulsing protocols allowed for pain mitigation and eliminated the need for injectable anesthetic. Histology confirmed the selective damaging of the SG at depth and the preservation of the surrounding dermis and the epidermis. The multi-pulse 1726 nm laser with bulk air-cooling, thermal monitoring, treat-to-temperature (and depth) control, and a unique pulsing protocol, is capable of selectively damaging SGs at depth without damage to the surrounding dermis or the epidermis. The system offers two different protocols that were developed with different levels of discomfort allowing for two different methods for pain mitigation (injectable vs. topical anesthesia).

Cross-Technology Interference-Aware Rate Adaptation in Time-Triggered Wireless Local Area Networks

The proliferation of IoT using heterogeneous wireless technologies within the unlicensed spectrum has intensified cross-technology interference (CTI) in wireless local area networks (WLANs). As WLANs increasingly adopt time-triggered transmission methods to support real-time services, this interference affects throughput, packet loss, and latency. This paper presents a CTI-aware rate adaptation framework designed to mitigate interference in WLANs without direct coordination with heterogeneous wireless devices. The framework includes a CTI identification model and CTI-aware rate selection algorithms. Leveraging short-time Fourier transform, the identification model captures the time–frequency–power characteristics of CTI signals, enabling the estimation of the average power of various heterogeneous wireless technologies employed by interfering devices. The rate selection algorithms predict CTI occurrence times and adjust the transmission rate accordingly, enhancing the performance of existing explicit and implicit interference mitigation methods. Experimental results demonstrated that the lightweight CTI identification model accurately estimated the average power of each type with an error margin of ±1.414 dBm, achieving this in under 1 ms on the target hardware. Additionally, applying the proposed framework to explicit interference mitigation enhanced goodput by 20.67%, reduced packet error rate by 2.38%, and decreased the probability of packets exceeding 1 ms latency by 0.932% compared to conventional methods.

Collaborating to Address Heavy Metals in Fresh Produce Supply—A Case Study of Cadmium in Spinach and Carrots Grown in Arizona and California

Providing consumers with safe, nutritious food is a common goal shared by the produce industry, the public health community, government agencies, and academia. To this end, these stakeholders have adopted a collaborative approach to address the challenge of improving the safety of food commonly eaten by infants and children. On 24 to 26 October 2023, Western Growers, in collaboration with federal and state government agencies, organized a pilot workshop in Salinas, CA, focused on cadmium in carrots and spinach grown in Arizona and California. Following a day of touring carrot and leafy green fields, workshop attendees participated in 2 days of panel discussions and presentations on cadmium toxicology and health effects, the nutritional benefits of carrots and spinach, Arizona’s and California’s carrot and spinach production practices, current research on plant uptake of cadmium, and mitigation methods and strategies. Panel discussions focused on obstacles that hinder adoption of methods to reduce cadmium, industry-regulator trust and communication, confidential data sharing, and industry-academia collaborative research. This article is a summary of workshop proceedings highlighting the stakeholders’ expectations and roles as perceived by the attendees and essential support needed for continuous improvement going forward.

Origin Analysis and Mitigation Method of Voltage Oscillation Occurred Inside the Winding of Medium-Voltage Medium-Frequency Transformer

Origin Analysis and Mitigation Method of Voltage Oscillation Occurred Inside the Winding of Medium-Voltage Medium-Frequency Transformer

Dust mitigation methods and multi-criteria decision-making cleaning strategies for photovoltaic systems: Advances, challenges, and future directions

Dust mitigation methods and multi-criteria decision-making cleaning strategies for photovoltaic systems: Advances, challenges, and future directions

A two-stage mitigation method for optical turbulence-induced errors in vision-based structural displacement measurement

Vision-based structural displacement measurement techniques have been widely applied. However, the visual sensors used for remote monitoring of structures in high-temperature weather are easily affected by optical turbulence, which introduces errors in displacement measurement. Therefore, this paper proposes a two-stage optical turbulence-induced error alleviation method. In the first stage, the steerable pyramid method is used to decompose the monitoring video and perform temporal filtering on the phase, which can significantly attenuate the motion and distortion caused by optical turbulence in the video. In the second stage, a feature point matching method considering the weighted distance is used to track the multi-point displacement in the reconstructed video to improve the robustness of feature point tracking, and the results are spatially filtered to improve measurement accuracy. The effectiveness of the proposed method has been verified through laboratory experiments and on-site testing.

Optimization of microwave components using machine learning and rapid sensitivity analysis

Recent years have witnessed a tremendous popularity growth of optimization methods in high-frequency electronics, including microwave design. With the increasing complexity of passive microwave components, meticulous tuning of their geometry parameters has become imperative to fulfill demands imposed by the diverse application areas. More and more often, achieving the best possible performance requires global optimization. Unfortunately, global search is an intricate undertaking. To begin with, reliable assessment of microwave components involves electromagnetic (EM) analysis entailing significant CPU expenses. On the other hand, the most widely used nature-inspired algorithms require large numbers of system simulations to yield a satisfactory design. The associated costs are impractically high if not prohibitive. The use of available mitigation methods, primarily surrogate-based approaches, is impeded by dimensionality-related problems and the complexity in microwave circuit characteristics. This research introduces a procedure for expedited globalized parameter adjustment of microwave passives. The search process is embedded in a surrogate-assisted machine learning framework that operates in a dimensionality-restricted domain, spanned by the parameter space directions being of importance in terms of their effects on the circuit characteristic variability. These directions are established using a fast global sensitivity analysis procedure developed for this purpose. Domain confinement reduces the cost of surrogate model establishment and improves its predictive power. The global optimization phase is complemented by local tuning. Verification experiments demonstrate the remarkable efficacy of the presented approach and its advantages over the benchmark methods that include machine learning in full-dimensionality space and population-based metaheuristics.

Inulin biopolymer as a novel material for sustainable soil stabilization

The development of new urban areas necessitates building on increasingly scarce land, often overlaid on weak soil layers. Furthermore, climate change has exacerbated the extent of global arid lands, making it imperative to find sustainable soil stabilization and erosion mitigation methods. Thus, scientists have strived to find a plant-based biopolymer that favors several agricultural waste sources and provides high strength and durability for sustainable soil stabilization. This contribution is one of the first studies assessing the feasibility of using inulin to stabilize soil and mitigate erosion. Inulin has several agricultural waste sources, making it a sustainable alternative to traditional additives. Soil samples susceptible to wind erosion were collected from a dust-prone area in southwest Iran and treated with inulin at 0%, 0.5%, 1%, and 2% by weight. Their mechanical strength was evaluated using unconfined compressive strength tests and a penetrometer. In addition, wind tunnel tests (at 16 m/s) were performed to investigate inulin’s wind erosion mitigation potential. The durability of treated samples was evaluated after ten wetting–drying cycles to assess the effect of environmental stressors. The results indicated a 40-fold increase in the unconfined compressive strength (up to 8 MPa) of the samples treated with 2% inulin and only 0.22% weight loss after ten wetting–drying cycles. SEM images revealed the formation of biopolymer-induced particle-to-particle bonds. Moreover, Raman spectroscopy indicated molecular (hydrogen) bonding of the biopolymer hydrogel-soil particles facilitated by the hydroxyl groups of inulin. The deterioration in stiffness and strength of treated samples was less noticeable after 3rd dry–wet cycle, indicating the durability of the samples. The durability of samples against wet–dry cycles was attributed to molecular bonding of soil-biopolymer hydrogel, as revealed by FTIR analysis.

Bias Mitigation Methods: Applicability, Legality, and Recommendations for Development

As algorithmic decision-making systems (ADMS) are increasingly deployed across various sectors, the importance of research on fairness in Artificial Intelligence (AI) continues to grow. In this paper we highlight a number of significant practical limitations and regulatory compliance issues associated with the application of existing bias mitigation methods to ADMS. We present an example of an algorithmic system used in recruitment to illustrate these limitations. Our analysis of existing methods indicates a pressing need for a change in the approach to the development of new methods. In order to address the limitations, we provide recommendations for key factors to consider in the development of new bias mitigation methods that aim to be effective in real-world scenarios and comply with legal requirements in the European Union, United Kingdom and United States, such as non-discrimination, data protection and sector-specific regulations. Further, we suggest a checklist relating to these recommendations that should be included with the development of new bias mitigation methods.

Corrosion Performance of Steel Bar Embedded in Seawater Mixed Mortar with Batching Plant Waste

Reinforcing steel deterioration is complicated by corrosion. Reinforcing steel corrosion can weaken a structure. Corrosion cannot be eliminated; however, it can be reduced to increase building service life. The objective of the research it to demonstrate the effect of coating method as corrosion prevention and the cover depth to the corrosion performance of steel bar embedded in seawater mixed mortar. This study examines the corrosion rate of steel reinforcement in a 15 x 15 x 15 cm mortar cube made by using seawater as mixing water and containing Portland Pozzolan Cement (PPC) as a binder material. This study also experiences numerous corrosion mitigation methods using wet, dry, and dry-wet cycle exposure methods. The reinforcement and mortar surface were protected with anti-corrosive paint. Additionally, specimens without protective measures were also fabricated for comparison. Two reinforcing steels were attached in the two different cover depths, 3 cm and 5 cm. This study used sand and batching plant byproducts as fine aggregate. Study found a hierarchy of corrosion-causing exposures. The dry-wet cycle was the most corrosive, followed by wet and dry. Steel coating prevents corrosion better than surface coating. However, both methods outperformed the uncoated method in corrosion resistance. The mortar cover was 5 cm thick, compared to 3 cm expected. A combination of mortar with fine sand aggregate outperformed dry mortar made from batching plant leftovers. The investigation of corrosion potential through the utilization of the half-cell potential technique reveals that the outcomes obtained from test specimens using the steel coating prevention approach exhibit a higher degree of positivity in comparison to the prevention method including surface coating. The unprotected approach exhibits outcomes that lean towards being more unfavorable compared to the steel coating prevention method and the surface coating prevention method. The findings indicate that the performance of reinforcing steel embedded within a 3 cm mortar cover depth is often worse when compared to reinforcing steel situated inside a 5 cm mortar cover depth.

Elevating the TMI threshold by incorporating QCW pumping in a high-power CW fiber oscillator

Transverse mode instability (TMI) significantly limits the power scaling of ytterbium-doped fiber lasers. In this Letter, what we believe to be a novel TMI mitigation strategy is proposed and demonstrated in a bidirectional output fiber laser. On the basis of the continuous wave (CW) pump, integrating a quasi-continuous wave (QCW) pump can effectively improve the TMI threshold of the system. In the experiment, a QCW pump with a repetition rate of 1 kHz and a duty cycle of 10% was injected into a CW-pumped fiber laser oscillator, which successfully broke the power limit and alleviated the beam quality degradation. Moreover, when applying a pump modulation depth of approximately 68%, the TMI threshold was notably increased from 624 W in the free-running condition to 1093 W at end B, which is a 469 W (75%) improvement. At this time, the beam quality factor M2 at end B is approximately 1.48, and the stimulated Raman scattering suppression ratio is about 28 dB. Compared to conventional TMI mitigation methods, this approach provides active control in the time domain of fiber lasers, without compromising the optical structures, thereby effectively mitigating TMI and offering broad application prospects.

A Gaussian Process-Enhanced Non-Linear Function and Bayesian Convolution–Bayesian Long Term Short Memory Based Ultra-Wideband Range Error Mitigation Method for Line of Sight and Non-Line of Sight Scenarios

Relative positioning accuracy between two devices is dependent on the precise range measurements. Ultra-wideband (UWB) technology is one of the popular and widely used technologies to achieve centimeter-level accuracy in range measurement. Nevertheless, harsh indoor environments, multipath issues, reflections, and bias due to antenna delay degrade the range measurement performance in line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. This article proposes an efficient and robust method to mitigate range measurement error in LOS and NLOS conditions by combining the latest artificial intelligence technology. A GP-enhanced non-linear function is proposed to mitigate the range bias in LOS scenarios. Moreover, NLOS identification based on the sliding window and Bayesian Conv-BLSTM method is utilized to mitigate range error due to the non-line-of-sight conditions. A novel spatial–temporal attention module is proposed to improve the performance of the proposed model. The epistemic and aleatoric uncertainty estimation method is also introduced to determine the robustness of the proposed model for environment variance. Furthermore, moving average and min-max removing methods are utilized to minimize the standard deviation in the range measurements in both scenarios. Extensive experimentation with different settings and configurations has proven the effectiveness of our methodology and demonstrated the feasibility of our robust UWB range error mitigation for LOS and NLOS scenarios.

Progress in the Stability of Small Molecule Acceptor-Based Organic Solar Cells.

Significant advancements in power conversion efficiency have been achieved in organic solar cells with small molecule acceptors. However, stability remains a primary challenge, impeding their widespread adoption in renewable energy applications. This review summarizes the degradation of different layers within the device structure in organic solar cells under varying conditions, including light, heat, moisture, and oxygen. For the photoactive layers, the chemical degradation pathways of polymer donors and small molecule acceptors are examined in detail, alongside the morphological stability of the bulk heterojunction structure, which plays a crucial role in device performance. The degradation mechanisms of commonly used anode and cathode interlayers and electrodes are addressed, as these layers significantly influence overall device efficiency and stability. Mitigation methods for the identified degradation mechanisms are provided in each section to offer practical insights for improving device longevity. Finally, an outlook presents the remaining challenges in achieving long-term stability, emphasizing research directions that require further investigation to enhance the reliability and performance of organic solar cells in real-world applications.

Climate intervention research in the World Climate Research Programme: a perspective

The 2023 World Climate Research Programme (WCRP) Open Science Conference underscored the critical need for increased climate change mitigation and adaptation efforts, along with enhanced climate knowledge and decision-making systems. This Perspective discusses climate intervention (CI) within WCRP’s research framework, emphasizing three main approaches: terrestrial carbon dioxide removal (CDR), marine CDR, and solar radiation modification (SRM). As global anthropogenic greenhouse gas emissions continue to rise, CI strategies are increasingly recognized as potentially critical supplements to traditional mitigation methods. We call for WCRP to take a leadership role in CI research, highlighting the need for inclusivity and collaboration, especially with researchers from the Global South, to establish a firm scientific foundation for an equitable and comprehensive assessment of the benefits and risks of CI approaches relative to the risks of anthropogenic climate change.

The mechanism of cryptocurrency adoption as a means of payment in business

This article examines the theoretical foundations and conceptual specifics of the mechanism for adopting cryptocurrencies as a payment method in business. It discusses the definition and key features of this mechanism, along with summarizing essential recommendations for the use of cryptocurrencies and crypto assets in business payments. The cryptocurrency adoption framework is analyzed through the organizational–economic mechanism, aiming to achieve practical objectives such as cost reduction, improved access to cross–border payments, and transaction security with crypto assets. The article stresses the importance of risk analysis related to cryptocurrency use and the development of risk mitigation methods through proactive management and the application of appropriate tools. The conclusions highlight the feasibility and benefits of using an organizational–economic mechanism to incorporate crypto assets into business payment processes, ensuring efficiency and adaptability of crypto assets or other alternative payment instruments. The study’s novelty lies in its contribution to the development of a mechanism for integrating cryptocurrencies into business operations, while its theoretical value is in the synthesis and clarification of key issues related to cryptocurrency acceptance in business. The proposed concept and vision of the organizational–economic mechanism to enhance the efficiency of cryptocurrency use in business payments offer valuable insights for future research and practical implementation.

Connecting the ruminant microbiome to climate change: insights from current ecological and evolutionary concepts.

Ruminant livestock provide meat, milk, wool, and other products required for human subsistence. Within the digestive tract of ruminant animals, the rumen houses a complex and diverse microbial ecosystem. These microbes generate many of the nutrients that are needed by the host animal for maintenance and production. However, enteric methane (CH4) is also produced during the final stage of anaerobic digestion. Growing public concern for global climate change has driven the agriculture sector to enhance its investigation into CH4 mitigation. Many CH4 mitigation methods have been explored, with varying outcomes. With the advent of new sequencing technologies, the host-microbe interactions that mediate fermentation processes have been examined to enhance ruminant enteric CH4 mitigation strategies. In this review, we describe current knowledge of the factors driving ruminant microbial assembly, how this relates to functionality, and how CH4 mitigation approaches influence ecological and evolutionary gradients. Through the current literature, we elucidated that many ecological and evolutionary properties are working in tandem in the assembly of ruminant microbes and in the functionality of these microbes in methanogenesis. Additionally, we provide a conceptual framework for future research wherein ecological and evolutionary dynamics account for CH4 mitigation in ruminant microbial composition. Thus, preparation of future research should incorporate this framework to address the roles ecology and evolution have in anthropogenic climate change.

Interference mitigation method for LEO satellites with optimal dynamic switching array elements in co‐frequency beams

AbstractSatellite communication has rapidly advanced, from navigation to broadband Internet and direct mobile connectivity. Driven by low Earth orbit satellites, these networks are merging with terrestrial mobile networks, forming integrated space–air–ground systems. However, the growing scale of satellite constellations has intensified intra‐system interference, particularly among ground cells utilizing co‐frequency beams. A mitigation method based on switching array elements is proposed, optimizing their state by considering beamwidth and maximum sidelobe level. Simulations show that the proposed algorithm effectively reduces beam coverage widening and sidelobes, mitigating the interference between co‐frequency beams.