Security Challenges Research Articles

Evaluation of teff production in Ethiopia using water footprint analysis for food security

Achieving Sustainable Development goal (SDG)2; Zero Hunger by 2030 in Africa requires reconsidering the challenges of food security in relation to several factors including agricultural practices and water availability. Teff crop plays a significant role in Ethiopia but the yield is low due to rain-fed production practices. The water footprint (WF) concept provides a useful perspective on the dependency of crops on precipitation, revealing the need for irrigation. So, WF analysis of teff production can help farmers to increase the yield and maintain water efficiency. In this study, the green and blue water footprints of teff production in Ethiopia were estimated for 2019/2020 season using the CROPWAT 8.0 and CLIMWAT 2.0 models. The results show that WFgreen is dominant with a value of 1170 m3 ton-1 in Tigray region to 1481 m3 ton-1 in SNNPR region. On the other hand, the WFblue varied significantly from 264 m3 ton-1 in Amhara to 1022 m3 ton-1 in Tigray, respectively, indicating the need for irrigation since water requirement is much higher than the effective precipitation. The economic water productivity of teff was found to be 0.68 USD m-3, which is higher than other crops such as maize. Given the potential impact of climate change and droughts, this study suggests increasing water allocation to teff production and implementing appropriate irrigation practices at a national level. Integrating water footprint analysis into river basin-level water allocation plans would be beneficial for sustainable water resource management and food security.

Navigating the three circles: the politics of Italy’s foreign policy under the Meloni government

ABSTRACT In 2024, the Italian government responded to external crises – the ongoing war in Ukraine and the conflicts in Gaza, Lebanon, and beyond – while also proactively addressing what is increasingly seen as both a source of security challenges and strategic opportunities: the African continent. On the first two fronts, the Government maintained strong support for Ukraine but stopped short of measures such as authorizing Kyiv to use European-supplied weapons on Russian territory. Regarding the war in Gaza, Italy aligned with the EU’s mainstream position, advocating a ceasefire and providing humanitarian aid. Consistent with previous centre-right governments, Giorgia Meloni’s has maintained a firmly Atlanticist foreign policy while adopting a largely pragmatic approach towards Europe. In contrast, its Africa policy – building on an evolving Italian strategy that dates back at least to the Renzi government – reflects a broader ambition for autonomy, strategic depth and international status. The launch of the Mattei Plan, which is based on the principle of equal partnerships with African governments, exemplifies this approach. While it may appear as a move towards the re-nationalization of foreign policy, it remains aligned with US, European and African regional interests, particularly in the wake of the withdrawal of France and the EU from the Sahel.

Factors Influenced of Health Status of Under Five Children in Wadajir District, Mogadishu-Somalia

ABSTRACT Background; Somalia is committed achieving both Millennium development goal 4 and 5 MDG and Sustainable development goal 3 SDG. However, Somalia owing to the country’s political instability, security challenges, and inadequate financial support to the health system, Somalia did not achieve any of the MDG goals, and progresses on the SDGs will be a big challenge. The objective of the study was to determine the prevalence of certain diseases in under five children with health conditions that are common during childhood in Wadajir district Mogadishu-Somalia (Underweight, Diarrhea and Malaria), and this Study was conducted between January, and April 2021.Methods; The research design was a cross-sectional study, characterized by a one-time assessment using a non-random, convenience sampling method. In addition, the study was using the primary data as to ensure the validity and reliability. As well as the sample size calculation was using a single proportion population formula. followed by structured questionnaire and SPSS 19 version, observed the data on descriptive and inferential statistic upon data modules to determine in adjusted the objectives. Results; the total of 378 under five children was recruited to conduct the study at Wadajir health facility in Wadajir district, Mogadishu, Somalia. This Study reported the underweight malnourished children (12.7%) measured anthropometric measurement refers (<-2 SD weight for age and below 5th BMI), while children with malaria referred in past 24 hours’ fever and chills with blood test used by RDTs (34.1%), and last children with diarrhea disease was determined by clinical features based on last three days of present diarrhea (45%). Conclusion; The study was concluded low income status was the major factor associated with health status of under-five children addressing many health conditions that related with different aspects related on poverty that reflects with impact, and experienced with certain conditions, due to high unemployment, food insecurity, poor water quality, improper hygiene practices and loss of sanitation system management and others circumstances that marginalize the child’s health and growth, overall meaning low healthcare accessibility, poor water quality, early marriage, home birthing and bottle feeding was attributed the diseases.

GHEFL: Grouping Based on Homomorphic Encryption Validates Federated Learning

Federated learning is a powerful tool for securing participants’ private data due to its ability to make data “available but not visible”. In recent years, federated learning has been enhanced by the emergence of multi-weight aggregation protocols, which minimize the impact of erroneous parameters, and verifiable protocols, which prevent server misbehavior. However, it still faces significant security and performance challenges. Malicious participants may infer the private data of others or carry out poisoning attacks to compromise the model’s correctness. Similarly, malicious servers may return incorrect aggregation results, undermining the model’s convergence. Furthermore, substantial communication overhead caused by interactions between participants or between participants and servers hinders the development of federated learning. In response to this, this paper proposes GHEFL, a group-based, verifiable, federated learning method based on homomorphic encryption that aims to prevent servers from maliciously stealing participant privacy data or performing malicious aggregation. While ensuring the usability of the aggregated model, it strives to minimize the workload on the server as much as possible. Finally, we experimentally evaluate the performance of GHEFL.

Privacy and security of smart irrigation data using IOTA distributed ledger

ABSTRACT IoT-enabled smart agriculture enhances farming with real-time data insights but faces security and scalability challenges. This study integrates IOTA’s distributed ledger technology into smart irrigation systems, leveraging its Tangle architecture for fee-less, lightweight, and scalable transactions. The system ensures tamper-proof data integrity and real-time decision-making, crucial for precision agriculture. Experimental results show efficient data transmission (336 bytes) with processing times of 0.7–1.3 seconds and rapid data retrieval (1×10⁻⁶– 4×10⁻⁶ seconds). A comparative analysis highlights IOTA’s superiority over traditional blockchain systems, demonstrating its robustness, scalability, and efficiency for secure, real-time IoT-based smart farming.

Remote sensing and simulation: A novel approach to rice yield estimation in the Cauvery delta

This study explores the use of the C-band Synthetic Aperture Radar (SAR) dataset from Sentinel-1A for crop area delineation and its integration with the DSSAT crop simulation model for spatial yield estimation for rice crop in the Cauvery Delta region of Tamil Nadu, India. With the global population increasing at a rapid rate, precision agriculture is critical for addressing food security challenges. The near-real-time monitoring capabilities of remote sensing techniques, especially microwave datasets made all season crop monitoring possible. The DSSAT model has demonstrated its capability to simulate yields under varying climatic and management scenarios. This approach offers timely and scalable solutions for monitoring crop health and forecasting yields, which are critical for mitigating the impact of climate change on agriculture. The estimated rice area using the Parameterized classification technique was 118104 ha in the Thanjavur district and 102138 ha in the Thiruvarur district, with an accuracy of 86 %. Upon validation against crop-cutting experiments, the DSSAT approach achieved average RMSE values of 440?kg?ha-1 and 450?kg?ha-1, along with yield agreements of 90 % and 89 % in Thanjavur and Thiruvarur respectively. These quantitative results highlight the enhanced precision of the integrated remote sensing and simulation framework for rice yield estimation, offering a robust tool for precision agriculture and improved decision-making under variable climatic conditions.

X-rays and gamma rays do not differ in their effectiveness for sterilizing pupae and adults of the mosquito Aedes aegypti(Diptera:Culicidae).

The sterile insect technique (SIT) is increasingly used as an alternative or supplemental tool in the integrated mosquito management toolbox to protect human health worldwide. SIT programs targeting mosquitoes such as Aedes aegypti (L.) have generally used high-activity isotopes of 60Cobalt or 137Caesium to sterilize males, however, these gamma irradiators pose substantial security challenges and are becoming more difficult and expensive to obtain and maintain. One practical alternative is using commercially available low-energy X-ray irradiators. In this study, we compared the efficacy of a low-energy X-ray irradiator and a traditional gamma irradiator for achieving male sterility in both male pupae and adults of the mosquito Ae. aegypti Linn. We found that both irradiators performed equivalently with 99% sterility achieved at ~50 Gy when male pupae were irradiated and ~70 Gy when male adults were irradiated. There were no differences in lifespan or male mating competitiveness at the sterilizing dose between the 2 irradiation platforms. Taken together, our results suggest that with careful dosimetry X-ray irradiators can effectively be used as an alternative to gamma irradiators in SIT programs targeting Ae. aegypti.

Addressing security vulnerabilities in autonomous vehicles through resilient frameworks and robust cyber defense systems

The rapid advancement of autonomous vehicle (AV) technology has introduced significant security vulnerabilities that pose risks to passenger safety, system integrity, and public trust. This paper investigates the critical security challenges facing AVs, including sensor spoofing, GPS jamming, adversarial machine learning attacks, and communication network breaches. Through an extensive review of recent cyber threats, the study highlights the increasing sophistication of cyber-attacks targeting AV ecosystems and the potential consequences of security breaches. To address these vulnerabilities, the paper explores the implementation of resilient frameworks and robust cyber defense systems. Specifically, it examines the role of artificial intelligence-driven anomaly detection, blockchain-based secure communication, encryption techniques, and intrusion detection and prevention systems in mitigating security risks. Additionally, it evaluates the effectiveness of cybersecurity-by-design approaches, emphasizing proactive security measures in AV development.Key findings indicate that a multi-layered security approach integrating real-time threat detection, automated response mechanisms, and continuous system monitoring is essential for enhancing AV resilience. Furthermore, collaboration between industry stakeholders, policymakers, and cybersecurity experts is crucial in developing standardized security protocols and regulatory frameworks. The study underscores the necessity of adopting an adaptive and resilient security architecture to safeguard AVs against evolving cyber threats. By implementing robust cyber defense mechanisms and fostering a security-conscious AV ecosystem, the risks associated with autonomous vehicle operations can be significantly reduced, ensuring safer and more reliable transportation systems. Keywords: Autonomous Vehicles, Cybersecurity, Security Vulnerabilities, Resilient Frameworks, Cyber Defense Mechanisms, Machine Learning Security, AI-driven Automation, Vehicle-to-Vehicle Communication.

A new DDoS attack detection model based on improved stacked autoencoder and gated recurrent unit for software defined network

Abstract With the widespread adoption of Software Defined Networking (SDN), detecting Distributed Denial of Service (DDoS) attacks has become an urgent challenge in SDN maintenance and Security. Given the diversity of DDoS attack types, we face significant challenges. This paper proposes a model called ARSAE-QGRU, which is based on integrating attention mechanisms and residual connections within a stacked autoencoder for DDoS attack detection. By introducing attention mechanisms and residual connections into the stacked autoencoder (SAE), the model effectively conveys more valuable information and facilitates gradient propagation, allowing it to learn low-dimensional representations better. It also combines the learned low-dimensional representations with traffic features to generate data for DDoS attack training. Furthermore, incorporating Gated Recurrent Unit aids in a more in-depth understanding of the temporal characteristics of traffic data, resulting in improved detection accuracy. This model demonstrates outstanding performance on the CICDDoS2019 and CICIDS2017 datasets, achieving accuracy rates of 97.2% and 97.9%, respectively. Moreover, when applied to datasets in SDN environments, it reaches an even higher accuracy rate of 99.8%. This research provides a reliable solution for high-dimensional data processing and DDoS attack detection within SDN, addressing the urgent challenges in these domains.

Human Immune System Inspired Security for Federated Learning-Empowered Internet of Things

The emergence of the Internet of Things (IoT) has revolutionized service automation, enabling the development of smart applications. However, the vast interconnectivity of IoT devices not only produces large volumes of data but also creates multiple potential attack surfaces. While Machine Learning (ML) offers insights from IoT data, inherent data privacy and security challenges hinder its effective utilization. Federated Learning (FL) offers privacy-preserving ML for distributed edge devices. Nevertheless, the susceptibility to attacks poses a threat to the integrity of IoT data impacting ML for IoT services and applications. To tackle this challenge and identify IoT devices compromised by attacks like label-flipped data, this paper introduces an innovative defense mechanism modeled after the human immune system. Analogous to ‘B’ cells, which detect viruses within the human body, the Reinforcement Learning (RL) agent identifies malicious IoT nodes that participate in federated learning enabled IoT. Subsequently, the FL server, similar to ‘T’ cells in Human immune systems eliminate/destroy infected cells, quarantines/discards the malicious IoT nodes (that are FL clients) and their reported parameters. Like ‘B’ cells and ‘T’ cells work together to defend the human body against infections and diseases, RL agent and FL server work together to defend/secure FL enabled IoT from compromised/malicious IoT devices. Specifically, with the help of Deep Reinforcement Learning (DRL), the RL agent continually monitors model updates from participating IoT nodes during training phase to find malicious nodes and then to isolate or remove those malicious nodes (i.e., parameters) at FL server while aggregating parameters for the global model. The effectiveness of the proposed approach is demonstrated through experiments, where RL agent detects malicious/compromised IoT nodes and FL server discards the parameters from such malicious/compromised IoT nodes. We evaluate our proposed approach using numerical results obtained from experiments where we observe that our approach outperforms the existing state-of-the-art approaches in terms of detection rate, error and accuracy for enhancing IoT security in FL enabled IoT.

Security Architectural Approaches and Risk Assessment Methods for Blockchain Systems: A Review and Future Directions

Amid the widespread use of blockchain technology, the escalating frequency of cyber attacks exploiting its inherent security challenges underscores the critical necessity for a robust and adaptable security risk assessment approach. The distinctive attributes and intricate internal structure of blockchain not only attract malicious actors but also elevate the risk of ill-informed architectural design decisions, potentially introducing security vulnerabilities. This study addresses this imperative by conducting a systematic literature review, classifying publications that elucidate secure architectural design approaches, and categorising those that delineate methods for assessing security risks associated with blockchain and smart contracts. The findings reveal four prevalent approaches supporting secure architectural design—decision models, taxonomies, design patterns, and guidelines—alongside contributions in blockchain risk assessment encompassing risk identification, analysis, and evaluation methods. Furthermore, the study identifies unresolved architectural design challenges and proposes future research directions in this evolving landscape.

A risk and conformity assessment framework to ensure security and resilience of healthcare systems and medical supply chain

In recent years, the healthcare sector has undergone a significant digital transformation, driven by the rise of the Internet of Medical Things and the exponential use of connected medical devices in healthcare service delivery. This transformation offers numerous benefits, including enhanced patient data collection, processing, and informed treatment decisions. Despite these advantages, digital adoption brings several security challenges that pose considerable risks to overall healthcare service delivery. Additionally, connected medical devices must comply with sector-specific regulatory requirements to ensure trustworthiness and facilitate their broader adoption in the healthcare sector. There is, therefore, a pressing need to understand and manage these risks and compliance issues to secure and strengthen the resilience of healthcare systems. This work addresses these needs by introducing a novel Risk and Conformity Assessment Framework and Certification Scheme, implemented within an agile Information Security Management System context to enhance the security and resilience of healthcare systems. The framework leverages Artificial Intelligence (AI) in risk management practices, improving security assessments, risk prediction, security control implementation, and continuous monitoring. AI algorithms analyze large data volumes from various sources, enabling efficient processing and the identification of potential risk patterns. Additionally, AI-driven automation tools ensure consistent deployment of security controls, while continuous AI monitoring detects abnormal activities and enables rapid response to security incidents. The proposed Cybersecurity Certification Scheme incorporates AI-based security assessments into the certification process, facilitating efficient conformity assurance. This scheme also promotes a collaborative approach with relevant regulatory bodies to achieve compliance. While this work introduces a conceptual framework, its implementation and potential refinements remain subjects for future research. Further studies are necessary to validate its effectiveness, enhance its components, and evaluate its practical application in real-world healthcare environments.

RPFL: A Reliable and Privacy-Preserving Framework for Federated Learning-Based IoT Malware Detection

The proliferation of Internet of Things (IoT) devices and their vulnerability to malware infections pose critical security challenges in IoT networks and multi-access edge computing (MEC). Traditional federated learning-based IoT malware detection (FL-IMD) methods face limitations in privacy, reliability, and client authentication, necessitating innovative solutions. This study proposes a reliable and privacy-preserving federated learning framework (RPFL) that integrates elliptic curve digital signature algorithm (ECDSA), homomorphic encryption and blockchain technology to enhance privacy, reliability, and client verification in FL-IMD. To address challenges with fully homomorphic encryption (FHE), particularly its reliance on an external aggregator, we introduce two smart contract-based schemes: one to incentivize client participation and another to mitigate aggregator failures. Experimental results on the N-BaIoT dataset show that RPFL achieves IoT malware detection accuracy comparable to state-of-the-art methods, while significantly enhancing reliability and privacy in the aggregation process. Furthermore, our blockchain integration outperforms the prominent blockchain-based FL framework, BCFL, by reducing communication costs and latency. These findings highlight the potential of RPFL to advance privacy-preserving, reliable, and secure FL-based IMD in IoT networks and MEC environments.

Identification and role of CmLhcb2.1 in regulating low-light stress resistance in Chinese chestnut (Castanea mollissima)

Chinese chestnut (Castanea mollissima) is a significant woody food plant that has garnered increasing attention due to its potential role in addressing food security challenges. However, low yield remains a critical issue facing the Chinese chestnut industry. One contributing factor to this low yield is insufficient light, particularly since Chinese chestnuts predominantly grow in mountainous regions. Therefore, the present study aims to investigate the intrinsic mechanisms underlying chestnut resistance to light stress, identify and validate genes associated with low light stress tolerance, and provide a foundation for targeted breeding of chestnut varieties that can withstand light stress. Studies have demonstrated that the light-harvesting chlorophyll a/b (Lhca/b) proteins play key roles in regulating the adaptation of plants to low-light stress. However, there have been no reports on the role of the Lhca/b gene family in the chestnut under light stress. We initially identified 17 CmLhca/b gene members across the chestnut genome and constructed a phylogenetic tree that divided them into five subgroups: the Lhca, the Lhcb, the CP24, the CP26, and the CP29 groups. CmLhcb2.1 and CmLhcb2.2 were grouped on the same branch with GhLhcb2.3 of upland cotton that involved in chlorophyll synthesis.The chestnut leaves exhibited phenotypic and transcriptomic differences under low and normal light conditions. By the 10th day of shading treatment, the leaves showed signs of damage, with the extent of damage intensifying as shading intensity increased. Additionally, the leaf color darkened due to the gradual increase in chlorophyll b content, which was correlated with increased shading intensity. The gene CmLhcb2.1 was upregulated across all shading intensities. Specifically, quantitative reverse transcription PCR (qRT-PCR) confirmed the upregulation of CmLhcb2.1 in chestnut under low-light stress. Overexpression studies in tobacco indicated that CmLhcb2.1 enhances chestnut resistance to low-light stress by promoting chlorophyll b synthesis. Finally, yeast one-hybrid and dual-luciferase reporter assays confirmed that the transcription factor CmGLK positively regulated CmLhcb2.1. These findings lay a theoretical foundation for exploring how CmLhcb2.1 regulates chestnut resistance to low-light stress.

Enhancing cybersecurity through script development using machine and deep learning for advanced threat mitigation

This scholarly paper explores the utilization of Machine Learning (ML) and Deep Learning (DL) methodologies to enhance the cybersecurity aspects of script development. Given the increasing panorama of threats in contemporary software creation, cybersecurity has ascended to a critical realm of concern. Traditional security measures frequently prove inadequate in countering complex breaches. However, ML and DL present promising solutions by facilitating automated and intelligent scrutiny of security-centric tasks. In this investigation, we leverage the Fashion MNIST dataset, deploying a Convolutional Neural Network (CNN) model to underscore the efficacy of ML and DL in elevating cybersecurity. The trajectory of script development encompasses stages like data preprocessing, model training, and assessment through metrics such as accuracy and loss. Our empirical findings convincingly demonstrate that the proposed methodology yields significant enhancements in cybersecurity benchmarks, thereby validating the potential of ML and DL techniques in reinforcing software security. Furthermore, we explore practical implications and delineate the application of ML/DL integration within real software development scenarios. Through the adept amalgamation of ML and DL techniques in script development, developers can augment the robustness of their software systems against various cybersecurity threats. This paper enriches the growing body of cybersecurity research while providing invaluable insights to practitioners striving to bolster their software resilience against the ever-evolving landscape of security challenges.

Emerging Technologies for Precision Crop Management Towards Agriculture 5.0: A Comprehensive Overview

Agriculture 5.0 (Ag5.0) represents a groundbreaking shift in agricultural practices, addressing the global food security challenge by integrating cutting-edge technologies such as artificial intelligence (AI), machine learning (ML), robotics, and big data analytics. To adopt the transition to Ag5.0, this paper comprehensively reviews the role of AI, machine learning (ML) and other emerging technologies to overcome current and future crop management challenges. Crop management has progressed significantly from early agricultural methods to the advanced capabilities of Ag5.0, marking a notable leap in precision agriculture. Emerging technologies such as collaborative robots, 6G, digital twins, the Internet of Things (IoT), blockchain, cloud computing, and quantum technologies are central to this evolution. The paper also highlights how machine learning and modern agricultural tools are improving the way we perceive, analyze, and manage crop growth. Additionally, it explores real-world case studies showcasing the application of machine learning and deep learning in crop monitoring. Innovations in smart sensors, AI-based robotics, and advanced communication systems are driving the next phase of agricultural digitalization and decision-making. The paper addresses the opportunities and challenges that come with adopting Ag5.0, emphasizing the transformative potential of these technologies in improving agricultural productivity and tackling global food security issues. Finally, as Agriculture 5.0 is the future of agriculture, we highlight future trends and research needs such as multidisciplinary approaches, regional adaptation, and advancements in AI and robotics. Ag5.0 represents a paradigm shift towards precision crop management, fostering sustainable, data-driven farming systems that optimize productivity while minimizing environmental impact.

Spinach (Spinacia oleracea L.) Growth Model in Indoor Controlled Environment Using Agriculture 4.0

Global trends in health, climate, and population growth drive the demand for high-nutrient plants like spinach, which thrive under controlled conditions with minimal resources. Despite technological advances in agriculture, current systems often rely on traditional methods and need robust computational models for precise plant growth forecasting. Optimizing vegetable growth using advanced agricultural and computational techniques, addressing challenges in food security, and obtaining efficient resource utilization within urban agriculture systems are open problems for humanity. Considering the above, this paper presents an enclosed agriculture system for growth and modeling spinach of the Viroflay (Spinacia oleracea L.) species. It encompasses a methodology combining data science, machine learning, and mathematical modeling. The growth system was built using LED lighting, automated irrigation, temperature control with fans, and sensors to monitor environmental variables. Data were collected over 60 days, recording temperature, humidity, substrate moisture, and light spectra information. The experimental results demonstrate the effectiveness of polynomial regression models in predicting spinach growth patterns. The best-fitting polynomial models for leaf length achieved a minimum Mean Squared Error (MSE) of 0.158, while the highest MSE observed was 1.2153, highlighting variability across different leaf pairs. Leaf width models exhibited improved predictability, with MSE values ranging from 0.0741 to 0.822. Similarly, leaf stem length models showed high accuracy, with the lowest MSE recorded at 0.0312 and the highest at 0.3907.

Security Sharing Method of University Ideological and Political Education Resources Based on Internet of Things

The existing data security technology and privacy protection mechanism are not perfect, and it is difficult to effectively deal with the complex security challenges in the Internet of Things (IoTs) environment. Therefore, a method of security sharing of ideological and political education resources in universities based on the IoTs is proposed. In combination with IoTs technology and blockchain technology, we design data request access for educational resources, introduce dual encryption algorithm to encrypt ideological and political resources of online colleges and universities, establish ideological and political teaching database and cloud computing resource sharing platform, build data security sharing model based on master–slave blockchain structure, introduce slave chain access strategy and based on data encryption and decryption processing and sharing transmission methods. Introduce the idea of integration of production and education to realize the sharing. The experimental results show that the design method can process more than 700 concurrent requests per second, the fastest transmission speed is 23.39 MB/s, the lowest packet loss rate is only 0.5% and the highest data encryption strength can reach about 0.88.

Bias Configurable Se0.25Te0.75/Si Photodiode Array for Multi‐Channel Encrypted Communication

AbstractThe transmission of image data in the Internet of Things (IoT) faces growing challenges in security and privacy protection. Traditional optical encryption methods, which typically rely on single‐ or dual‐channel signal processing, are insufficient for large‐scale, high‐throughput data transmission, necessitating advanced solutions. Here, the study presents a multi‐channel encryption strategy based on bias‐selective configuration, realized through an on‐chip integrated multi‐channel pixel‐level image encryption system using a Se0.25Te0.75/Si photodiode array as the encryption hardware. By calibrating the silent‐state bias as the encryption key, the system transcends the constraints of dual‐channel approaches, enabling scalable encryption for n ≥ 3 channels. This method is demonstrated in the high‐security transmission of both static and dynamic images. Notably, the three‐channel system achieves a remarkable entropy increase of 1.3 and a correlation coefficient of 0.069, providing a significant enhancement in encryption strength over traditional single or dual‐channel systems. With the expansion of the detector array size and the number of optical channels, the encryption level can be further amplified. This work introduces a transformative paradigm for optical communication encryption, with profound implications for fields such as autonomous driving, IoT, and other security‐critical applications.

Engineering nitrogen and carbon fixation for next-generation plants.

Engineering nitrogen and carbon fixation for next-generation plants.