Search Results (7,855)

Weapon systems have transitioned from hardware-centered designs to software-driven platforms, introducing new cybersecurity risks, including software manipulation and cyberattacks. To address these challenges, this study proposes an improved manufacturing readiness level assessment (MRLA) method that integrates cybersecurity capabilities into the evaluation process to address the gaps in hardware-focused practices in South Korea. Based on the MITRE adversarial tactics, techniques, and common knowledge, and the defensive cybersecurity framework, this study identified security requirements, assessed vulnerabilities, and constructed exploratory testing scenarios using defense trees. These methods evaluate system resilience, the effectiveness of security controls, and response capabilities under diverse attack scenarios. The proposed MRLA approach incorporates cyberattacks and defense scenarios that may occur in operational environments. This approach was validated through a case study involving unmanned vehicle systems, where the modified MRLA successfully identified and mitigated critical cybersecurity threats. Consequently, the target operational mode summary/mission profile of a weapon system can be revised based on practical considerations, enhancing the cybersecurity assessments and thereby improving the operational readiness of weapon systems through scenario-based, realistic evaluation frameworks. The findings of this study demonstrate the practical utility of incorporating cybersecurity evaluations into MRLA, contributing to more resilient defense systems. Full article

This study examines the evolution of agricultural raw material exports in seven emerging economies of Central and Eastern Europe (Romania, Poland, Slovakia, Croatia, Bulgaria, the Czech Republic, and Hungary) from 1995 to 2023 and provides forecasts for 2024–2026 using ARIMA models. The results indicate a general downward trend in the share of agricultural raw material exports within total exports, reflecting ongoing economic modernization and a structural shift toward higher value-added products and industrial sectors. Romania, Poland, and Hungary remain as significant players in the cereals market, while Slovakia and the Czech Republic show the most pronounced transitions toward non-agricultural industries. Croatia, however, follows an atypical trajectory, maintaining a relatively high share of agricultural exports. Statistical tests (Dickey–Fuller) confirm the non-stationarity of the initial series, necessitating differencing for ARIMA modeling. Correlation analyses reveal a synchronized regional dynamic, with strong links among Poland, Slovakia, the Czech Republic, and Bulgaria. Forecasts suggest continued decline or stabilization at low levels for most countries: Romania (0.45% in 2026), Poland (0.93%), Slovakia (0.62%), Bulgaria (0.51%), the Czech Republic (0.95%), and Hungary (0.53%), while Croatia is an exception, with a projected moderate increase to 4.19% in 2026. Although the share of raw agricultural exports is decreasing, the findings confirm that agriculture remains a strategic sector for food security and regional trade. The study recommends investments in processing, technological modernization, and export market diversification to strengthen the competitiveness and resilience of the agricultural sector in the context of global economic transformations. Full article

Background: Undergraduate nursing students (UNSs) often enter clinical training just as they are still mastering the emotional labor of the profession. In Nepal, where teaching hierarchies discourage upward dialogue and hospitals routinely struggle with overcrowding, supply shortages, and outward nurse migration, these learners confront a distinct, under-documented burden of psychological distress. Objective: This study examines how UNSs interpret, negotiate, and cope with the mental health challenges that arise at the intersection of cultural deference, resource scarcity, and migration-fueled uncertainty. Methods: A qualitative design employing reflexive thematic analysis (RTA), guided by the Reflexive Thematic Analysis Reporting Guidelines (RTARG), was used. Fifteen second-, third-, and fourth-year Bachelor of Science in Nursing students at a major urban tertiary institution in Nepal were purposively recruited via on-campus digital flyers and brief in-class announcements that directed students (by QR code) to a secure sign-up form. Participants then completed semi-structured interviews; audio files were transcribed verbatim and iteratively analyzed through an inductive, reflexive coding process to ensure methodological rigor. Results: Four themes portray a continuum from silenced struggle to systemic constraint. First, Shrouded Voices, Quiet Connections captures how students confide only in trusted peers, fearing that formal disclosure could be perceived as weakness or incompetence. Second, Performing Resilience: Masking Authentic Struggles describes the institutional narratives of “strong nurses” that drive students to suppress anxiety, adopting scripted positivity to satisfy assessment expectations. Third, Power, Hierarchy, and the Weight of Tradition reveals that strict authority gradients inhibit questions in classrooms and clinical placements, leaving stress unvoiced and unaddressed. Finally, Overshadowed by Systemic Realities shows how chronic understaffing, equipment shortages, and patient poverty compel students to prioritize patients’ hardships, normalizing self-neglect. Conclusions: Psychological distress among Nepalese UNSs is not an individual failing but a product of structural silence and resource poverty. Educators and policymakers must move beyond resilience-only rhetoric toward concrete reforms that dismantle punitive hierarchies, create confidential support avenues, and embed collaborative pedagogy. Institutional accountability—through regulated workloads, faculty-endorsed wellbeing forums, and systematic mentoring—can shift mental health care from a private struggle to a shared professional responsibility. Multi-site studies across low- and middle-income countries are now essential for testing such system-level interventions and building a globally resilient, compassionate nursing workforce. Full article

The increasing awareness of the need for sustainable solutions to secure future energy supplies has spurred the search for innovative approaches. Energo-Efekt Sp. z o.o. has prepared a project for the green transformation of the energy system at a producer of spirits through the rectification of raw alcohol. An installation was conceptualised to develop the system to convert energy from biomass fuels into electricity and heat. The innovation of the installation is the use of an expander—a Heliex system which is the twin-screw turbine generator converting energy in the form of wet steam into electrical power integrated with pressure-reducing valve. This system captures all or part of the available steam flow and reduces the steam pressure, not only delivering steam at the same, lower pressure but also generating rotary energy that can be used to produce electricity with the power output range of 160 to 600 kWe. Currently, the company utilises natural gas as a fuel source and acquires electricity from the external grid. Implementing the system could reduce the carbon footprint associated with the production of vodka at the plant by 97%, to 102 t CO₂ annually. This reduction would account for approximately 21% of the total carbon footprint of the entire alcohol production process. The system could also be applied to other low-power systems that produce < 250 kW, making it a viable option for use in distributed energy networks, and can be used as a model solution for other distillery plants. The transformation project dedicated to Polmos Żyrardów involves a comprehensive change in both the energy source and its management. The fossil fuels used until now are being replaced with a renewable energy source in the form of biomass. The steam and electricity cogeneration system meets the rectification process’s energy demand and can supply the central heating node. Heat recovery exchangers recuperate heat from the boiler room exhaust gases and the rectification cooling process. Potentially, all of these changes lead to the company’s energy self-sufficiency and reduce its overall environmental impact with almost zero CO₂ emissions. Full article

Intelligent sensors with embedded AI are key to modern cyber-physical systems. They find applications in industrial automation, medical diagnostics and healthcare, smart cities, and autonomous systems. Despite their significant potential, they face several hardware challenges related to computing power, energy consumption, communication capabilities, and security, which limit their effectiveness. This article analyzes factors influencing the production and deployment of AI sensors. The key limitations are energy efficiency, computing power, scalability, and integration of AI sensors in real-time conditions. Among the main problems are the high requirements for data processing, the limitations of traditional microprocessors, and the balance between performance and energy consumption. To meet these challenges, the article presents several practical and innovative approaches, including the development of specialized microprocessors and optimized architectures for “edge computing,” which promise radical reductions in latency and power consumption. Through a synthesis of current research and practical examples, the article emphasizes the need for intermediate hardware–software solutions and standardization for mass deployment of AI sensors. Full article

High-resolution (HR) remote sensing images contain rich, sensitive information regarding the distribution of geospatial objects and natural resources. With the widespread application of HR remote sensing images, there is an urgent need to protect the data security of HR remote sensing images during transmission and sharing. Existing encryption approaches typically employ a global encryption strategy, overlooking the varying texture complexity across different sub-regions in HR remote sensing images. This oversight results in low efficiency and flexibility for encrypting large-scale remote sensing image data. To address these limitations, this paper presents a texture-adaptive hierarchical encryption method that combines region-specific security levels. The method first decomposes remote sensing images into grid-based sub-blocks and classifies them into three texture complexity types (i.e., simple, medium, and complex) through gradient and frequency metrics. Then, chaotic systems of different dimensions are adaptively adopted to encrypt the sub-blocks according to their texture complexity. A more complex chaotic system encrypts a sub-block with a more complex texture to ensure security while reducing computational complexity. The experimental results on publicly available high-resolution remote sensing datasets demonstrate that the proposed method achieves adequate information concealment while maintaining an optimal balance between encryption security and computational efficiency. The proposed method is more competitive in encrypting large-scale HR remote sensing data compared to conventional approaches, and it shows significant potential for the secure sharing and processing of HR remote sensing images in the big data era. Full article

Ensuring food security is a challenge for humans. Rice grain yield and quality must urgently be increased to overcome this challenge. MicroRNA (miRNA) is an important regulatory module in plant development and stress responses. Grain yield and quality are pleiotropic traits that employ cooperative genetic factors, including miRNA and its regulatory mechanisms. This review provides an overview of plant miRNAs and the composition and development process of rice grains. It also summarizes the research progress in miRNA regulation for agronomically important rice grain traits, providing a basis for further identifying miRNAs related to rice grain development and elucidating their regulatory mechanisms. Full article

Industrial Wireless Sensor Networks (IWSNs) play a critical role in Industry 4.0 environments, enabling real-time monitoring and control of industrial processes. However, existing lightweight authentication protocols for IWSNs remain vulnerable to sophisticated security attacks because of inadequate initial authentication phases. This study presents a security analysis of Gope et al.’s PUF-based authentication protocol for IWSNs and identifies critical vulnerabilities that enable man-in-the-middle (MITM) and denial-of-service (DoS) attacks. We demonstrate that Gope et al.’s protocol is susceptible to MITM attacks during both authentication and Secure Periodical Data Collection (SPDC), allowing adversaries to derive session keys and compromise communication confidentiality. Our analysis reveals that the sensor registration phase of the protocol lacks proper authentication mechanisms, enabling attackers to perform unauthorized PUF queries and subsequently mount successful attacks. To address these vulnerabilities, we propose an enhanced authentication scheme that introduces a sensor-initiated registration process. In our improved protocol, sensor nodes generate and control PUF challenges rather than passively responding to gateway requests. This modification prevents unauthorized PUF queries while preserving the lightweight characteristics essential for resource-constrained IWSN deployments. Security analysis demonstrates that our enhanced scheme effectively mitigates the identified MITM and DoS attacks without introducing significant computational or communication overhead. The proposed modifications maintain compatibility with the existing IWSN infrastructure while strengthening the overall security posture. Comparative analysis shows that our solution addresses the security weaknesses of the original protocol while preserving its practical advantages for industrial use. The enhanced protocol provides a practical and secure solution for real-time data access in IWSNs, making it suitable for deployment in mission-critical industrial environments where both security and efficiency are paramount. Full article

Despite significant advancements in sensor technologies, real-time monitoring and prediction of fermentation dynamics remain challenging due to the complexity and nonlinearity of environmental variables. This study presents an integrated framework that combines deep learning techniques with blockchain-enabled data logging to enhance the reliability and transparency of fermentation monitoring. A Long Short-Term Memory (LSTM)-based Fermentation Process Prediction Model (FPPM) was developed to predict Fermentation Percent (FP) and cumulative Fermentation Quantification (FQ) using multivariate time-series data obtained from modular sensor units (PBSU, GBSU, and FQSU). Fermentation conditions were systematically varied under controlled environments, and all data were securely transmitted to a Fermentation–Blockchain–Cloud System (FBCS) to ensure data integrity and traceability. The LSTM models trained on AAG1–3 datasets demonstrated high predictive accuracy, with coefficients of determination (R²) between 0.8547 and 0.9437, and the estimated FQ values showed strong concordance with actual measurements. These results underscore the feasibility of integrating AI-driven prediction models with decentralized data infrastructure for robust and scalable bioprocess control. Full article

Background/Objectives: Food insecurity (FI) is a well-defined factor in pediatric health outcomes and has been associated with lower diet quality. While poor diet quality has been linked to the rising prevalence of inflammatory bowel disease (IBD), little is known about the impact of FI on pediatric IBD. This pilot study explores the feasibility and potential impact of FI on dietary intake and clinical outcomes in children with newly diagnosed IBD. Methods: This pilot study included newly diagnosed IBD patients aged 5 to 18. FI screening was completed using the USDA 6-item and AAP 2-item screeners at diagnosis and 6 months. Dietary intake was classified according to their degree of processing (NOVA classification). Clinical data, anthropometrics, and healthcare utilization were collected over 6 months. Results: Among 20 patients, FI was identified in 40% of families. Food-insecure patients had significantly lower weight and BMI z-scores at diagnosis compared to food-secure peers (p = 0.002 and p = 0.0013, respectively). Food-insecure patients consumed more ultra-processed foods (UPFs, 70.6% vs. 66.7%, p = 0.473). However, most patients consumed diets high in ultra-processed foods. FI status was dynamic over the study period. Hospitalizations were more frequent among food-insecure patients. Conclusions: FI is common in pediatric IBD and associated with poorer nutritional status. FI was associated with higher consumption of UPFs, although diet quality was poor among most patients. Future studies should validate these findings in large cohorts and evaluate longitudinal interventions. Full article

Food security among college students is an increasing concern, with potential implications for their health, academic performance, and future well-being. This study investigated food security within a college community in Mexico, focusing on food availability, access (both economic and physical), and consumption patterns. A mixed-methods approach was employed at Ciudad Universitaria, BUAP, Mexico, between 2023 and 2024. Stratified random sampling was used, resulting in a final sample of 606 students. Data were collected through structured questionnaires covering sociodemographic characteristics and eating habits, the ELCSA, structured cafeteria observations, semi-structured interviews with key informants, and three focus groups. Statistical analysis was performed using chi-square tests (p < 0.05). Post hoc analysis with Bonferroni adjustment confirmed that origin (p = 0.0017), mode of transportation (p = 2.31 × 10⁻⁵) and private vehicles (p = 1.77 × 10⁻⁵) were the key determinants. Although the environment offered a variety of options, processed and ultra-processed products dominated the food choices. A total of 95.9% of students purchased food on campus, yet only 21.8% reported engaging in healthy eating habits. Focus groups revealed that students’ food choices were influenced by availability, access, and perceptions of affordability and convenience. These findings highlight the urgent need for targeted interventions to improve food security and promote healthier dietary practices within the college setting. Full article

This work presents an end-to-end encryption–decryption framework for securing electromagnetic signals processed through a nanoantenna. The system integrates amplitude normalization, uniform quantization, and Reed–Solomon forward error correction with key establishment via ECDH and bitwise XOR encryption. Two signal types were evaluated: a synthetic Gaussian pulse and a synthetic voice waveform, representing low- and high-entropy data, respectively. For the Gaussian signal, reconstruction achieved an RMSE = 11.42, MAE = 0.86, PSNR = 26.97 dB, and Pearson’s correlation coefficient = 0.8887. The voice signal exhibited elevated error metrics, with an RMSE = 15.13, MAE = 2.52, PSNR = 24.54 dB, and Pearson correlation = 0.8062, yet maintained adequate fidelity. Entropy analysis indicated minimal changes between the original signal and the reconstructed signal. Furthermore, avalanche testing confirmed strong key sensitivity, with single-bit changes in the key altering approximately 50% of the ciphertext bits. The findings indicate that the proposed pipeline ensures high reconstruction quality with lightweight encryption, rendering it suitable for environments with limited computational resources. Full article

Industrial Control Systems (ICSs) are fundamental to critical infrastructure, yet they face increasing cybersecurity threats, particularly data integrity attacks like replay and data forgery attacks. Traditional IT-centric security measures are often inadequate for the Operational Technology (OT) environment due to stringent real-time and reliability requirements. This paper proposes an endogenous security defense mechanism based on the Luenberger observer and residual analysis. By embedding a mathematical model of the physical process into the control system, this approach enables real-time state estimation and anomaly detection. We model the ICS using a linear state-space representation and design a Luenberger observer to generate a residual signal, which is the difference between the actual sensor measurements and the observer’s predictions. Under normal conditions, this residual is minimal, but it deviates significantly during a replay attack. We formalize the system model, observer design, and attack detection algorithm. The effectiveness of the proposed method is validated through a simulation of an ICS under a replay attack. The results demonstrate that the residual-based approach can detect the attack promptly and effectively, providing a lightweight yet robust solution for enhancing ICS security. Full article

As IoT (internet of things) devices grow in prominence, safeguarding them from cyberattacks is becoming a pressing challenge. To bootstrap IoT security, device identification or authentication is crucial for establishing trusted connections among devices without prior trust. In this regard, radio frequency fingerprinting (RFF) is gaining attention because it is more efficient and requires fewer computational resources compared to resource-intensive cryptographic methods, such as digital signatures. RFF works by identifying unique manufacturing defects in the radio circuitry of IoT devices by analyzing over-the-air signals that embed these imperfections, allowing for the identification of the transmitting hardware. Recent studies on RFF often leverage advanced classification models, including classical machine learning techniques such as K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), as well as modern deep learning architectures like Convolutional Neural Network (CNN). In particular, CNNs are well-suited as they use multidimensional mapping to detect and extract reliable fingerprints during the learning process. However, a significant limitation of these approaches is that they require large datasets and necessitate retraining when new devices not included in the initial training set are added. This retraining can cause service interruptions and is costly, especially in large-scale IoT networks. In this paper, we propose a novel solution to this problem: RFF using Siamese networks, which eliminates the need for retraining and allows for seamless authentication in IoT deployments. The proposed Siamese network is trained using in-phase and quadrature (I/Q) samples from 10 different Software-Defined Radios (SDRs). Additionally, we present a new algorithm, the Similarity-Based Embedding Classification (SBEC) for RFF. We present experimental results that demonstrate that the Siamese network effectively distinguishes between malicious and trusted devices with a remarkable 98% identification accuracy. Full article

Invasive weeds pose a growing threat to global biodiversity, ecosystem stability, and agricultural productivity with significant ecological and economic consequences. In Iran, the rapid spread of invasive species such as Boreava orientalis, Azolla spp., Ibicella lutea, Physalis divaricata, Picnomon acarna, Cynanchum acutum, Vicia hyrcanica, Eichhornia crassipes, and Ambrosia psilostachya has severely affected native ecosystems, disrupted ecological processes, and threatened food security. These species exhibit aggressive traits such as rapid maturity, high reproductive rates, seed dormancy, and allelopathy that enable them to outcompete native species and successfully invade and dominate delicate habitats. Despite their documented impacts, a critical gap remains in understanding their biology, ecology, and management, particularly in understudied regions like Iran. This review synthesizes current knowledge on major invasive weeds affecting Iranian agroecosystems, with a focus on their ecological impacts and the urgent need for sustainable management strategies. It presents an integrated framework that combines ecological, biological, and management perspectives to address invasiveness, particularly in highly adaptable species like B. orientalis and A. psilostachya. This review highlights the critical role of interdisciplinary collaboration, advanced technology, and community involvement in developing effective strategies. It offers practical guidance for researchers, policymakers, and agricultural stakeholders, serving as a model for managing invasive species in other vulnerable regions. Ultimately, it supports global efforts to safeguard biodiversity, improve crop productivity, and strengthen ecological resilience against the growing threat of invasive species. Full article