Mitigation Mechanisms Research Articles

Mechanisms for sulfadiazine effective mitigation in biochar-amended soil: From antibiotic resistance to soil microbial community composition and function.

Mechanisms for sulfadiazine effective mitigation in biochar-amended soil: From antibiotic resistance to soil microbial community composition and function.

The mitigation efficacy and mechanism for H2S and CH4 emission from wastewater by adding bacterium-fungus mixture

The mitigation efficacy and mechanism for H2S and CH4 emission from wastewater by adding bacterium-fungus mixture

Synergetic shrinkage mitigation and anti-cracking mechanism of SAP and expansive agent in alkali-activated slag/fly ash-based concrete

Synergetic shrinkage mitigation and anti-cracking mechanism of SAP and expansive agent in alkali-activated slag/fly ash-based concrete

Divergent mitigation mechanisms of soil antibiotic resistance genes by biochar from different agricultural wastes.

Divergent mitigation mechanisms of soil antibiotic resistance genes by biochar from different agricultural wastes.

In-Network DDoS Mitigation Mechanism for Vehicle Road Cooperation Network With Victim-Centric Approach

In-Network DDoS Mitigation Mechanism for Vehicle Road Cooperation Network With Victim-Centric Approach

Mitigation mechanism of α-LA on AFB1-induced hepatocyte injury in snakehead fish (Channa argus): A potential substance

Mitigation mechanism of α-LA on AFB1-induced hepatocyte injury in snakehead fish (Channa argus): A potential substance

Proline mitigates antibiotic resistance evolution induced by ciprofloxacin at environmental concentrations.

Proline mitigates antibiotic resistance evolution induced by ciprofloxacin at environmental concentrations.

Collaborative DDoS defense for SDN-based AIoT with autoencoder-enhanced federated learning

Collaborative DDoS defense for SDN-based AIoT with autoencoder-enhanced federated learning

Multi-Attack Identification and Mitigation mechanism based on multi-agent collaboration in Vehicular Named Data Networking

Multi-Attack Identification and Mitigation mechanism based on multi-agent collaboration in Vehicular Named Data Networking

Perlindungan Hukum Bagi Bank yang Terikat Perjanjian Kerja Sama Penyediaan Fasilitas KPR dengan Skema Perjanjian Buy Back Guarantee Ketika Terjadi Kredit Macet

In banking practice, cooperation agreements between banks and developers for providing Kredit Pemilikan Rumah (“KPR”) facilities often incorporate the buy back guarantee scheme as a form of security against the risk of debtor default. This scheme obligates the developer to repurchase the property as collateral in case of a loan default. The buy back guarantee scheme in KPR is essential for banks as a risk mitigation mechanism to ensure the recovery of disbursed funds, reduce the potential for non-performing loans, and provide legal certainty and protection against losses caused by debtor default. Therefore, it is necessary to analyze the legal protection available to banks bound by cooperation agreements for the provision of KPR facilities under the buy back guarantee scheme, taking into account the legal position of the banks and developers, as well as examining legal certainty and dispute resolution mechanisms applicable in cases of loan default.

Legal aspects in managing non-performing loans in unsecured KUR micro at Bank Syariah Indonesia

The kafalah scheme serves as a risk mitigation mechanism widely applied in Islamic financing, particularly for unsecured Micro People’s Business Credit (KUR (Kredit Usaha Rakyat) Micro). This scheme involves guarantee institutions that assume responsibility for financing risks, reducing the financial burden on Islamic banks and facilitating access to credit for Micro, Small, and Medium Enterprises (MSMEs). Despite its advantages, challenges remain, particularly in terms of kafalah fees and delays in claim processing, which can impact its overall effectiveness. This study aims to analyze the role of guarantee institutions, the claim process, subrogation rights, and their implications for the financial stability of Islamic banks. Using a descriptive qualitative approach, the study finds that guarantee institutions play a strategic role by covering up to 70 % of financing risks, allowing Islamic banks to extend financing to a broader range of MSMEs. The subrogation rights ensure that debtors remain accountable for their obligations, preserving financial discipline within the system. Furthermore, the findings indicate that the kafalah scheme significantly reduces credit risk for Islamic banks while promoting financial inclusion. However, the study also highlights several challenges, including the complexity of the claim process, administrative inefficiencies, and potential financial burdens on banks due to delays in reimbursements. These factors necessitate a more efficient regulatory framework to enhance the scheme’s effectiveness. This research concludes that the kafalah scheme aligns with Maqasid al-Shariah, emphasizing fairness, financial justice, and asset protection, making it a critical tool for sustainable Islamic financing and inclusive economic growth. The study provides policy recommendations to improve the efficiency and accessibility of Islamic banking in addressing MSME financing needs while maintaining financial stability.

Hybrid Machine Learning Framework for Anomaly Detection in 5G Networks

The rapid adoption of 5G networks has transformed the communication landscape, offering unprecedented speed, capacity, and connectivity for diverse applications such as IoT, autonomous vehicles, and critical infrastructure. However, this evolution also introduces vulnerabilities that can compromise network performance, security, and reliability. Anomaly detection, the process of identifying irregular patterns or deviations in network traffic, has emerged as a critical mechanism to ensure the resilience of 5G networks. It enables proactive identification of issues such as latency spikes, packet losses, Denial of Service (DoS) attacks, and other disruptions that could significantly degrade network quality. This research focuses on collecting and analysing 5G network traffic to detect and classify various anomalies. By leveraging advanced data collection techniques, we ensure comprehensive traffic coverage from diverse scenarios, including simulated and real-world environments. A systematic approach is used to reprocess the data, extract pertinent characteristics, then use cutting-edge machine learning techniques to identify anomalies.These models are tailored to address the particular difficulties presented by 5G networks, such as fast data flows, massive device connectivity, and dynamic network conditions. Key findings reveal the prevalence of specific anomalies such as throughput degradation, signalling storms, and malicious traffic patterns. The proposed framework achieves high accuracy in detecting these anomalies, demonstrating its potential for enhancing network reliability and security. Moreover, the findings underline the importance of integrating anomaly detection systems into 5G network management for real-time monitoring and automated mitigation.This work highlights the significance of anomaly detection in sustaining the performance and security of 5G networks. Future research could focus on real-time implementation and the integration of self-healing mechanisms to further enhance network robustness. Highlights Revolutionizing Connectivity: Explores the significance of anomaly detection in ensuring the reliability of high-speed 5G networks. Comprehensive Anomalies Dataset: Collection and analysis of diverse 5G network anomalies, including latency spikes, packet loss, and signalling storms. Dynamic Network Monitoring: Addresses challenges posed by the dynamic and high-speed nature of 5G data flows. Feature Extraction: Highlights the role of advanced preprocessing techniques to identify patterns and detect anomalies effectively. Security Enhancement: Detects cyber threats like Denial of Service (DoS) attacks and malicious traffic patterns in 5G networks. Scalability: Designed to accommodate the massive device connectivity and data volumes characteristics of 5G systems. Network Reliability: Proposes a proactive approach to mitigating network performance degradation caused by anomalies. Precision Detection: Achieves high detection accuracy using metrics such as precision, recall, and F1 scores. Signalling Storm Analysis: Identifies and mitigates issues caused by excessive signalling requests, a unique challenge in 5G. Automated Mitigation: Suggests integration of detection systems with automated mitigation mechanisms for faster response times. Impactful Insights: Discusses key findings on the impact of anomalies on network performance and security. Future-Proof Design: Advocates for integrating self-healing mechanisms and real-time anomaly detection into 5G network management systems. Pioneering Framework: Establishes a foundational approach for researchers and practitioners in anomaly detection for next generation networks.

Mitigation effect and mechanism of different pretreatment methods on reverse osmosis membrane fouling in coal chemical wastewater treatment

Mitigation effect and mechanism of different pretreatment methods on reverse osmosis membrane fouling in coal chemical wastewater treatment

AI-Powered Risk Assessment Models: Transforming Credit Scoring and Default Prediction

This paper examines the transformative impact of Artificial Intelligence (AI) on credit scoring, focusing on AI-driven risk assessment models for credit evaluation and default prediction. The research delves into how machine learning, real-time data processing, and alternative data sources can combine to provide more accurate predictions of an individual's creditworthiness. The study also addresses critical challenges such as bias, fairness, and the interpretability of AI models, particularly regarding the opaque nature of "black box" AI algorithms. These concerns raise significant questions about transparency and ethical compliance. A primary ethical issue in AI credit scoring is the risk of discrimination, underscoring the importance of robust bias detection and mitigation mechanisms. The paper advocates for transparent and explainable AI models, coupled with strong data governance frameworks. It also emphasizes the need for compliance with legal frameworks such as the General Data Protection Regulation (GDPR) and other relevant laws governing AI applications. In exploring real-world applications, the paper highlights how AI can promote financial inclusion, enhance risk management and decision-making, and enable faster and more equitable credit evaluations. However, it also addresses challenges such as balancing accuracy with fairness, ensuring data privacy, and improving the interpretability of AI systems. Emerging trends in the credit scoring industry, such as Explainable AI (XAI), Natural Language Processing (NLP), and blockchain integration, are also discussed as potential drivers of innovation. The study anticipates that regulatory frameworks will need to evolve to address the challenges posed by AI in credit risk assessment. At the same time, it emphasizes the importance of maintaining ethical standards while fostering innovation in this critical area. Keywords: AI-Driven Credit Scoring, Machine Learning Credit Analysis, Predictive Analytics Creditworthiness, Algorithmic Risk Assessment

Novel Strategies for Designing Climate‐Smart Crops to Ensure Sustainable Agriculture and Future Food Security

ABSTRACTTo fulfil food and nutritional demand for nine billion people by the mid‐21st century, global food production must increase by 60% regardless of challenges such as environmental pollution, water scarcity and land degradation. Climate change exacerbates the frequency and intensity of biotic and abiotic stresses, which, in turn, severely compromise global crop yields, jeopardize food supply, deteriorate sustainable development goals for achieving global food safety, and limit sustainable climate‐smart crop production. Current food production and consumption practices negatively influence the environment, posing a major threat to the global ecosystem and human health. Addressing these critical issues to achieve sustainable agriculture necessitates designing future crops employing cutting‐edge breeding strategies for enhanced productivity with minimal environmental footprints. This endeavour requires a comprehensive understanding of plant stress adaptation, signalling pathways and mitigation mechanisms. In this review, we first explain the diverse impacts of ongoing climate change events on crop production. Subsequently, we outline various strategies to tackle climate change, including agronomic practices, and advanced technologies for understanding the physiological and molecular mechanisms of plant stress tolerance. We also discuss breeding and engineering crops with superior stress tolerance and disease resistance and nurturing healthy microbial partnerships between plants and soil to ensure food and nutrition security for current and future populations amidst mounting environmental challenges.

Evolving contractual obligations in construction law: Implications of regulatory changes on project delivery

The landscape of construction law is undergoing significant transformation due to evolving contractual obligations driven by regulatory changes. The increasing complexity of construction projects, coupled with heightened regulatory oversight, necessitates a deeper understanding of how legal frameworks impact project delivery. Regulatory reforms in areas such as environmental compliance, worker safety, dispute resolution, and contract standardization are reshaping risk allocation and contractual relationships among project stakeholders. This study explores the implications of evolving contractual obligations on construction project delivery, focusing on how legislative changes affect contract formation, execution, and dispute resolution. The research examines key regulatory shifts, including stricter liability clauses, enhanced contractor responsibilities, and new mechanisms for risk mitigation. The role of digitalization and emerging technologies in contract management is also analyzed, particularly regarding smart contracts and Building Information Modeling (BIM)-integrated legal frameworks. A comparative analysis of global construction regulations reveals that jurisdictions adopting proactive legal reforms experience improved contractual clarity, reduced disputes, and enhanced project efficiency. However, compliance with evolving legal standards introduces challenges, such as increased administrative burdens and legal uncertainties. The study highlights the need for adaptive contract management strategies, emphasizing collaboration, alternative dispute resolution, and legal technology integration to mitigate risks associated with regulatory shifts. By examining recent case studies and legislative updates, this research provides insights into how construction firms, legal professionals, and policymakers can navigate the evolving regulatory landscape. Understanding the implications of these contractual changes is essential for ensuring legal compliance, optimizing risk allocation, and enhancing the overall efficiency of construction project delivery in an increasingly regulated environment.

Strategies of Environmental Adaptation in the Haloarchaeal Genera Haloarcula and Natrinema.

Haloarchaea, a group of extremophilic archaea, thrive in hypersaline environments characterized not only by high salinity but also by other extreme conditions, such as intense UV radiation, high osmotic pressure, heavy metal contamination, oxidative stress, and fluctuating temperatures. This study investigates the environmental adaptation strategies of species of two genera, Haloarcula and Natrinema, the second and third largest haloarchaeal genera, respectively, after Halorubrum. Comparative genomic analyses were conducted on 48 species from both genera to elucidate their genomic diversity, metabolic potential, and stress-tolerance mechanisms. The genomes revealed diverse metabolic pathways, including rhodopsin-mediated phototrophy, nitrogen assimilation, and thiamine biosynthesis, which support their survival and adaptation to extreme conditions. The analysis identified mechanisms for oxidative stress mitigation, DNA repair, "salt-in" and "salt-out" osmoregulatory strategies, adaptations to temperature shifts and heavy metal exposure, and immune defense. Experimental validation of four representative species, Haloarcula terrestris S1AR25-5AT, Haloarcula saliterrae S1CR25-12T, Haloarcula onubensis S3CR25-11T, and Natrinema salsiterrestre S1CR25-10T, isolated from the heavy-metal-rich hypersaline soils in the Odiel Saltmarshes (Huelva, Spain), demonstrated their tolerance, especially to arsenic, corroborating genomic predictions. This study advances our understanding of the resilience of haloarchaea under poly-extreme conditions and underscores their ecological significance and promise for biotechnological applications, such as the bioremediation of heavy-metal-polluted environments and the production of valuable biomolecules.

Adaptive Jamming Mitigation for Clustered Energy-Efficient LoRa-BLE Hybrid Wireless Sensor Networks.

Wireless sensor networks (WSNs) are fundamental for modern IoT applications, yet they remain highly vulnerable to jamming attacks, which significantly degrade communication reliability and energy efficiency. This paper proposes a novel adaptive cluster-based jamming mitigation algorithm designed for heterogeneous WSNs that integrate LoRa and Bluetooth Low Energy (BLE) technologies. The proposed strategy dynamically switches between communication protocols, optimizes energy consumption, and reduces retransmissions under interference conditions by leveraging real-time network topology adjustments and adaptive transmission power control. Through extensive experimental validation, we demonstrate that our mitigation mechanism reduces energy consumption by up to 38% and lowers packet retransmission rates by 47% compared to single-protocol networks under jamming conditions. Additionally, our results indicate that the hybrid LoRa-BLE approach outperforms standalone LoRa and BLE configurations in terms of network resilience, adaptability, and sustained data transmission under attack scenarios. This work advances the state-of-the-art by introducing a multi-protocol interference-resilient communication strategy, paving the way for more robust, energy-efficient, and secure WSN deployments in smart cities, industrial IoT, and critical infrastructure monitoring.

A review on sources and distribution of polycyclic aromatic hydrocarbons (PAHs) in wetland ecosystem: focusing on plant-biomonitoring and phytoremediation.

In the contemporary era, rapid global urbanization, coupled with intense industrial development, has led to a continuous influx of carcinogenic pollutants like PAHs, into the ecosystems. Owing to their long-range transportation potential, PAHs have driven their way from a regional scale to a global platform and become readily available in air, water, sediment, and biota of the most ecologically diverse ecosystems, like wetlands. The wetland ecosystems, due to their susceptibility to anthropogenic activities, face a heightened vulnerability to anthropogenic PAHs pollution. This PAHs pollution load adversely influences the unique biodiversity of wetlands. Hence, it is a pertinent to implement immediate and continuous monitoring programs to assess the present and ongoing PAHs pollution status. In this context, the use of plants for biomonitoring emerges as a potential alternative tool to the traditional monitoring process which also offers simultaneous mitigation mechanism and provides sustainability through detoxification. Therefore, sources and distribution of PAHs in wetland sediment and water are discussed in this review work to highlight the major sources of PAHs pollution and their distribution which would aid in proper strategic planning for phytoremediation the present study focuses on phytoremediation studies of wetland PAHs reported so far emphasizing its potential as a sustainable solution for addressing and mitigating PAHs pollution in wetlands. Various phytoremediation mechanisms are pointed out case to case to understand the plants' potential in bioremediation technique.

The mitigation mechanism of morin on PM2.5-induced apoptosis by inhibiting mitochondrial damage via the ROS/ERK signaling pathway in human HaCaT keratinocytes

ABSTRACT Particulate matter 2.5 (PM2.5) can penetrate the human skin, causing inflammatory disease, senescence, and carcinogenesis. This study assessed the effects of morin on PM2.5-induced apoptosis of HaCaT human keratinocytes. According to our results, morin at concentrations up to 12 µg/mL did not exhibit any harmful effects on cells and effectively reduced the generation of reactive oxygen species induced by PM2.5. Furthermore, morin significantly inhibited PM2.5-induced macromolecular damage, mitochondrial damage, and expression of pro-apoptotic proteins. Moreover, the cytoprotective effects of morin correlated with the activation of the transcription factor nuclear factor erythroid 2-related factor 2 and heme oxygenase-1 expression. The results of our experiments using a mitogen-activated protein kinase kinase inhibitor indicated that morin protected cells from apoptosis by the extracellular signal-regulated kinase pathway. Morin mitigated skin damage caused by PM2.5 via attenuation of oxidative stress, mitochondrial dysfunction, and apoptosis.