Search Results (1,426)

The Internet of Things has increasingly enabled advancing real-time environmental monitoring through the integration of Low-Power Wide-Area Networks. Among these technologies, LoRa (Long Range) has emerged as a prominent communication technology due to its combination of long-range communication, low energy consumption, and affordability, making it particularly suitable for remote and infrastructure-limited environments. Its adaptability is further enhanced through the use of open-source hardware, renewable energy sources, and intelligent algorithms. Despite LoRa’s limitations in bandwidth and data rate, recent innovations enabled increasingly data-intensive applications, including image transmission. This review critically examines recent advances in image transmission over LoRa networks, synthesising approaches across four interconnected strategies: image compression, packetisation and reliability, communication optimisation, and application-specific techniques. The analysis evaluates trade-offs among image size, transmission latency, energy consumption, coverage, and reconstructed image quality. These considerations are particularly relevant for environmental sensing applications, including water quality assessment, air pollution monitoring, wildlife tracking, and underground mining. This review synthesises recent advances in LoRa-based environmental and visual sensing and highlights persistent challenges, including duty-cycle restrictions, limited throughput, and energy constraints, that must be addressed for broader adoption in data-intensive sensing applications. By analysing current strategies and proposing future directions, including adaptive encoding, lightweight encryption, and energy-aware scheduling, the review demonstrates the potential of LoRa to play an increasingly important role in enabling sustainable, scalable, and accessible Internet of Things solutions across diverse environmental settings. Full article

Blockchain architectures increasingly rely on smart contracts as programmable execution components, yet the security challenges they introduce remain only partially addressed. Fuzz testing has emerged as one of the leading automated techniques for smart contract vulnerability discovery; however, a systematic treatment linking classical fuzzing concepts to the specific architectural constraints of smart contract execution environments has remained elusive. We conduct a large-scale systematic review of 258 publications collected from Scopus and Google Scholar. The review is structured around four research questions covering general fuzzing limitations, EVM execution constraints, cross-contract interaction challenges, and technique transferability. The contributions are fourfold. First, we develop a conceptual taxonomy establishing an explicit correspondence between classical and smart contract fuzzing. Second, we present an architectural taxonomy of representative fuzzers unified under a generalised waypoint architecture that exposes key feedback domains and composition gaps. Third, we provide a categorised technique review spanning coverage-guided, hybrid, and learning-based approaches. Fourth, we offer a reproducibility critique with a structured research roadmap. Coverage-guided greybox fuzzing emerges as the dominant paradigm (51.2% of the 121 SC fuzzer tools); learning-based approaches (machine learning, reinforcement learning, and LLMs) are a small but emerging class, with LLM-guided fuzzing the fastest-emerging subcategory by recency. Feedback-domain composition remains sparse: most surveyed fuzzers combine only a few of the eight identified feedback domains, and none approaches the full set, leaving several high-value multi-domain compositions unexplored. Open challenges include scalable stateful exploration, standardised benchmarks and oracles, and EVM-specific architectural optimisations. The complete categorised corpus, coding, and search strategy are openly available. Full article

Deep learning models for tabular data often behave unpredictably during training, and these internal dynamics directly influence the reliability of post hoc interpretability methods. Static techniques such as SHapley Additive exPlanations (SHAP) and Integrated Gradients offer useful but incomplete snapshots of model behaviour, while tabular models frequently undergo noise-driven fluctuations, structural reorganisations, and collapse phases that these methods cannot detect. This study introduces a temporally aligned, multimodal diagnostic framework that observes and interprets model behaviour as it evolves. The framework integrates fractal complexity, spectral energy, persistent homology, gradient-noise statistics, reflex responsiveness, attribution instability, and exploratory sonification into a single phase-aligned system. The framework is evaluated on Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) models across benchmark and real-world datasets using a reproducible protocol with consistent configurations and autocorrelation-robust analysis. An ablation study confirms the distinct diagnostic value of each modality, with reflex-aware metrics validated against optimiser behaviour and sonification assessed through event detection. The multimodal diagnostics reveal instability regimes, fragmentation events, attribution drift, and optimiser–noise coupling failures that static Explainable Artificial Intelligence (XAI) methods cannot detect. GRU models show repeated structural transitions, whereas MLPs exhibit simpler but noisier optimisation dynamics. These behaviour-aware, temporally aligned diagnostics provide a more reliable foundation for interpreting tabular deep learning models. Full article

The use of Artificial Intelligence (AI) and Machine Learning (ML) in cybersecurity, especially for creating Intrusion Detection Systems (IDSs), has become increasingly important. These systems are essential for detecting malicious behaviour, identifying network issues, and stopping cyberattacks in real time. Despite extensive research on various ML and Deep Learning (DL) models for IDS, the current literature remains incomplete. It has many different datasets, methods, and evaluation standards. As cyber threats become more advanced, it is crucial to conduct a thorough analysis of ML techniques for intrusion detection. The goal of this Systematic Literature Review (SLR) is to provide a full picture of the most recent academic articles on ML-based IDS. The study addresses important research questions about the most widely used algorithms, the types of attacks and network environments covered, the methodological problems that remain unsolved, and the new trends that should shape future research. Following the PRISMA framework, we conducted a systematic review of peer-reviewed articles published between January 2022 and May 2025. We searched IEEE Xplore, ACM Digital Library, and SpringerLink, yielding 22,558 initial records. After carefully applying strict inclusion criteria, 125 papers were selected for the final analysis. We created a standardised data extraction form (i.e., using MS Excel) to gather bibliographic details, research emphasis, methodological strategies, datasets, evaluation criteria, and recognised constraints. We employed thematic analysis to develop a clear taxonomy. We identified five main research themes in our analysis: (1) ensemble and hybrid learning pipelines focused on performance optimisation (30 papers), (2) context-specific IDS designs for Internet of Things (IoT), cloud, and Software-Defined Networking (SDN) environments (34 papers), (3) data-centric engineering that deals with class imbalance and feature selection (20 papers), (4) deep neural architectures for representation learning (31 papers), and (5) trustworthiness concerns like adversarial robustness, zero-day detection, and Explainable AI (XAI) (10 papers). Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Random Forests are the most commonly used algorithms, often combined. Nonetheless, significant deficiencies remain: about 2% of papers incorporate XAI, only 4% focus on adversarial robustness, and none validate their models in real-world production settings. Denial-of-Service (DoS) and Distributed DoS (DDoS) attacks are the most common types in the literature, whereas Web attacks, ransomware, and advanced persistent threats remain poorly studied. The number of publications grows at an average of 30.2% annually, but the field still relies on legacy benchmark datasets rather than operational validation. Full article

Despite the increasing adoption of soilless cultivation systems, little is known about how the gravel flow technique (GFT) influences postharvest quality, phytonutritional composition, and shelf life of cauliflower compared with conventional soil cultivation, and how these effects interact with cultivar genotype. Therefore, this study evaluated the influence of production system (soil cultivation vs. GFT), cultivar, and storage duration on the postharvest physiology, quality, and phytonutritional attributes of three cauliflower cultivars (‘Macerata’, ‘Sicilian Violet’, and ‘Snowball’) stored at 4 °C for 8 days. Significant production system × cultivar × storage interactions affected physiological, biochemical, and sensory characteristics. Soil-grown curds maintained higher O₂ and lower CO₂ concentrations, indicating lower respiration rates and slower deterioration than GFT-grown curds. ‘Snowball’ exhibited the highest weight loss and respiratory activity, whereas ‘Sicilian Violet’ showed the lowest weight loss. Soil cultivation enhanced the retention of phenolics, glucosinolates, pigments, and antioxidant activity. After 8 days, soil-grown ‘Sicilian Violet’ exhibited the highest total phenolic content (55.25 mg GAE g⁻¹ DW) and antioxidant capacity, while the highest glucosinolate concentration was recorded in soil-grown ‘Macerata’ (48.23 µmol g⁻¹ DW on day 6). ‘Macerata’ maintained the highest chlorophyll and β-carotene contents throughout storage, whereas ‘Sicilian Violet’ showed superior anthocyanin retention and antioxidant capacity. Although sensory quality declined during storage, soil-grown curds retained better quality and lower browning than GFT-grown curds. Overall, soil cultivation improved postharvest quality and nutraceutical value, highlighting the importance of integrating cultivar selection with production practices to optimise shelf life and nutritional quality. Full article

Proper municipal solid waste (MSW) management is vital for mitigating environmental degradation and protecting public health. Landfill site selection remains a complex spatial decision-making challenge, balancing ecological, social, and economic parameters. This study presents a comprehensive systematic review of 175 peer-reviewed articles published between 2016 and 2026, evaluating the evolution of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) frameworks. The findings indicate that road accessibility (93.7%), surface and groundwater protection (91.4%), slope gradients (84.6%), and settlement buffer zones (78.9%) represent the most critical and universally applied siting criteria. Digital Elevation Models (81.9%) and geological maps (57.3%) serve as foundational geospatial datasets. While the Analytic Hierarchy Process (AHP) remains the dominant weighting technique (63.41%), recent trends show an increasing adoption of hybrid multi-criteria models and optimisation algorithms. Geographically, research output is led by India, Iran, and Turkey, peaking significantly in 2025. Crucially, this review exposes prominent methodological shortcomings, notably a heavy reliance on subjective expert validation (73.8%), whereas quantitative validation, sensitivity analysis, and uncertainty assessment remain critically underutilised. In contrast to earlier reviews, this review offers a thorough and critical synthesis of GIS- and MCDA-based approaches to landfill site selection by carefully evaluating methodological advancements, examining the advantages, disadvantages, and limitations of current approaches, and incorporating statistical trends with a structured methodological framework. This approach highlights important research gaps and offers evidence-based suggestions for creating more transparent, reliable, and sustainable techniques for landfill site selection by selecting, screening, and including relevant articles. To foster sustainable urban planning, future research must prioritise standardised evaluation frameworks, rigorous uncertainty quantification, and the integration of artificial intelligence and machine learning with spatial modelling. Full article

Flue gas recirculation (FGR) is an effective technique for reducing thermal nitrogen oxide (NO_x) emissions of gas turbines. However, variations in inlet oxygen concentration significantly alter the internal combustion characteristics of staged swirl combustors and induce combustion instability. To clarify the coupling mechanism between oxygen content and combustion stability under diverse operating conditions, three-dimensional numerical simulations are performed on a staged swirl combustor. The effects of oxygen mass fraction ranging from 11% to 23%, together with multiple operating parameters including inlet temperature, inlet velocity and operating pressure, on flame morphology, velocity fluctuation, heat release fluctuation and pressure fluctuation, are systematically investigated. The results show that increasing the inlet temperature optimises the uniformity of heat release, compensates for the combustion inhibition under low-oxygen conditions, and effectively improves combustion stability. Oxygen content exhibits a non-monotonic regulatory effect on combustion pulsation characteristics. Appropriate reduction of oxygen content narrows the high-temperature reaction zone and suppresses pressure fluctuations, thereby improving combustion stability, whereas a moderate low-oxygen condition of 17% aggravates velocity fluctuations and deteriorates combustion stability. Although elevated oxygen content enhances the overall heat release intensity, it increases the amplitude and dominant frequency of heat release fluctuations, which triggers combustion instability. Furthermore, high inlet velocity and high operating pressure amplify the disturbance of low-oxygen environments on the flame field and further degrade combustion stability. This study clarifies the competitive and coupling relationships among oxygen concentration, operating parameters and combustion dynamic characteristics, providing a theoretical basis for the stability optimisation and low-oxygen combustion regulation of gas turbine combustors with flue gas recirculation. Full article

The practicality of post-quantum lattice-based schemes is crucial for their real-world applications. Integrating these schemes requires efficient implementations through hardware, software, and algorithmic optimisation to achieve the necessary speed and resource capability. This paper aims to improve arithmetic operations in lattice-based cryptography by accelerating the Number Theoretic Transform (NTT/INTT). It optimises the transform’s main bottleneck, the twiddle-factor modular multiplication within the butterfly unit, by replacing it with constant modular multiplication derived from Barrett reduction. We introduce two constant multipliers: the Constant Barrett and a proposed Truncated-Modulus-Size Constant Barrett (TMSCB) variant, which pre-computes each twiddle constant together with its reciprocal, eliminating Barrett’s data dependency and enabling area–time trade-offs. A comprehensive evaluation of the proposed constant modular multiplication is conducted against the classical Barrett multiplication, incorporating analytical complexity analysis and experimental quantitative analysis using FPGA hardware design. The proposed optimisation technique is deployed in the hardware design of the NTT/INTT for the ML-DSA and Falcon parameter sets with optimal use of the DSP cores. Performance comparisons with state-of-the-art implementations of ML-DSA NTT show 46.73% and 29.82% execution time improvements and 17% and 35.4% area resource reductions using single- and dual-butterfly units, respectively. On the Falcon, our design achieves an execution time improvement of at least 27.6%, with area savings across several butterfly configurations. These results validate the effectiveness of the Constant Barrett optimisation technique for accelerating the NTT/INTT, paving the way for more efficient implementations in other applications. Full article

Pre-treatment before drying is a crucial phase in food processing. This review highlights key traditional and innovative pre-treatment methods, focusing on their mechanisms of action at the cellular and tissue levels. This discourse examines innovative technologies with significant promise across various applications, as well as widely recognised methodologies. These include cold plasma, ultrasonication, pulsed electric fields, high-pressure processing, UV-C light, pulsed light, and coating techniques. It addresses biological components that act as barriers to mass transfer, thereby significantly influencing the efficiency of moisture evaporation. Understanding these interactions is crucial for optimising drying processes and enhancing the quality of dried food. Key pre-treatment parameters that affect outcomes are analysed and must be tailored to the specific characteristics of biological materials, which often require individualised adjustments. Each method is evaluated against goals like accelerated drying, microbiological purity, enzyme inactivation, and preservation of active components, emphasising the need for a targeted optimisation approach. The classification of biological materials into distinct categories has been proposed based on their structural and integumentary characteristics. The research outlines effective methodologies for each material type and pre-treatment purpose. This analysis also incorporates an economic perspective, considering the initial investment and operational costs of implementing the proposed methods. Full article

Objective: Anterior saphenous vein insufficiency is an underappreciated but clinically significant source of superficial venous reflux. Catheter-directed foam sclerotherapy represents a minimally invasive treatment approach, yet optimisation of sclerosant-to-endothelium contact remains a recognised technical challenge. The Sclerosafe system (VVT Medical, Kefar Sava, Israel)—a dual-lumen aspiration-assisted sclerotherapy device—was developed to address this limitation. The aim of this study was to evaluate the safety and efficacy of the Sclerosafe device in the treatment of anterior saphenous vein insufficiency. Methods: Retrospective analysis of prospectively collected data from 56 patients treated for isolated anterior saphenous vein insufficiency between 2020 and 2023. All procedures were performed in an outpatient setting under local anaesthesia (2 mL of 1% lignocaine at the puncture site) without tumescent infiltration. The Sclerosafe catheter was introduced directly, without an introducer sheath, through a single ultrasound-guided puncture of the ASV at the lowest point of reflux, and its tip was positioned 2 cm distal to the confluence with the common femoral vein. Treated segment length ranged from 8 to 17 cm (mean 10 cm) and vessel diameter from 4 to 12 mm (mean 6 mm). Polidocanol 2% foam (5 mL per session, Tessari technique) was administered and a standardised compression regimen was applied. Duplex ultrasound follow-up was conducted at 1, 2, 6, and 12 months; no venous severity, pain or quality-of-life score was recorded. Results: Technical success was achieved in all 56 cases. Complete occlusion of the treated segment was confirmed by duplex ultrasound at one month in 52 of 56 patients (92.9%). Occlusion rates at 6 months and 12 months were 92.5% (49/53 evaluable) and 90.4% (47/52 evaluable), with 3 and 4 cumulative patients lost to follow-up, respectively. Five occlusion failures were recorded in total: four primary failures at one month and one recanalisation between 6 and 12 months. Kaplan–Meier estimates of primary occlusion probability were 92.9% at 1 month (95% CI 86.1–99.6%), 92.9% at 6 months (no interval events), and 90.9% at 12 months (95% CI 83.3–98.5%). Minor adverse events included superficial skin pigmentation (8 patients, 14.3%), localised tenderness (6 patients, 10.7%), and transient bruising (11 patients, 19.6%); all resolved without specific intervention. No deep vein thrombosis, pulmonary embolism, or neurological complications were observed. Conclusions: Aspiration-assisted catheter-directed foam sclerotherapy using the Sclerosafe device demonstrated promising safety and anatomical efficacy for the treatment of anterior saphenous vein insufficiency, with a 12-month Kaplan–Meier occlusion estimate of 90.9%, without tumescent anaesthesia and in a fully outpatient setting. In the absence of a control group and clinical outcome measures, the technique cannot be presented as an established alternative to thermal or non-thermal ablation, but it may be a technically attractive option in patients with clinical CEAP class C2–C4 disease. Prospective comparative studies are warranted to confirm these findings. Full article

Gasification is a route for cattle manure resource utilisation and emission mitigation, but its mass-change mechanism under an O₂/H₂O atmosphere remains unclear. This work aims to reveal the staged apparent mass-gain behaviour and its underlying coupling mechanism during cattle manure thermochemical conversion under O₂/H₂O atmospheres, so as to provide support for biomass gasification process optimisation and industrial circulating fluidised-bed gasifier parameter regulation. Thermogravimetric (TG) experiments were conducted under five O₂:H₂O mass ratios (1:4, 1:2, 1:1, 2:1 and 4:1), set with reference to the typical gas–steam ratio of circulating fluidised beds, combined with four heating rates (5, 10, 15 and 20 °C min⁻¹), and the mass-change behaviour was interpreted using multiple characterisation techniques. The strongest peak appeared at 634 °C at an O₂:H₂O mass ratio of 1:2 and a heating rate of 10 °C min⁻¹, with a maximum mass-gain rate of 11.0% min⁻¹. The main mass-loss peaks occurred between 242 and 291 °C and were associated with organic-structure cracking and volatile release. The medium-temperature (297–457 °C) mass gain was attributed to oxidative adsorption on active char surfaces and transient oxygen-containing intermediates, whereas the high-temperature (521–864 °C) response was linked to ash mineral restructuring and char–mineral interfacial reactions. Full article

Background: Emergency aeromedical services, including helicopter emergency medical service (HEMS) and medical emergency evacuation (MEDEVAC), are critical to time-sensitive care. Dispatch decisions are complex and consequential, determining whether, which, and under what conditions to deploy aeromedical resources. This study reviews modelling approaches for emergency aeromedical dispatch. Methods: Following PRISMA, studies between 2003 and 2026 (June) were screened, yielding 42 studies. Models were classified as predictive and learning-based, sequential decision, and prescriptive optimisation-based, with solution techniques, operational applications, and policy contexts analysed. Results: Markov decision process and approximate dynamic programming models dominate the sequential decision literature, particularly in military MEDEVAC. Prescriptive models support resource allocation, base location, coverage planning, and dispatch optimisation, while predictive and AI/ML-based approaches remain limited but emerging. Key challenges include computational complexity, data uncertainty, policy fragmentation, and ethical concerns. Sequential models reflect dispatch’s dynamic, stochastic nature, where current deployments constrain future resource availability. Priority-aware policies outperform closest-unit rules, but limited real-world validation hinders adoption. Conclusions: This review maps methods and provides evidence-based guidance for researchers and dispatch organisations selecting decision support models. Future opportunities include AI-assisted dispatch, hybrid predictive–prescriptive modelling, real-time adaptive algorithms, sustainability-oriented optimisation, improved helicopter landing zone identification, and standardised ethical and regulatory frameworks. Full article

The increasing complexity of construction projects has made traditional planning methods inadequate for managing dynamic variables. In this context, the integration of building information modelling (BIM) and artificial intelligence (AI) has been increasingly investigated as a promising approach to improve estimation accuracy, decision-making, and sustainable project execution. This systematic literature review, conducted according to the PRISMA guidelines, analysed 47 articles on BIM-AI integration for construction cost and time planning, categorising them into three clusters: time-oriented, cost-oriented, and multi-objective planning. The reviewed studies indicate that BIM-AI workflows may improve planning efficiency, estimation accuracy, and resource allocation. A limited subset of studies directly incorporated energy consumption, carbon emissions, or lifecycle performance into the optimisation objectives. By contrast, broader benefits related to material waste, rework, equipment idle time, and safety were mainly inferred from improvements in scheduling and resource management rather than directly quantified across the reviewed studies. Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) emerged as the most frequently adopted and consistently reported techniques, while Revit was the most adopted BIM platform. Despite its potential, BIM-AI integration still faces challenges related to software interoperability, data quality, interdisciplinary coordination, and the limited integration of explicit sustainability indicators within optimisation models. Future research should focus on developing standardised and adaptable frameworks that jointly address cost, time, resource efficiency, environmental impact, and lifecycle performance across different construction contexts. Full article

In non-linear finite element analysis, the local structural response may exhibit significant sensitivity to spatial discretisation, even when the global structural response remains practically unchanged. Classical h-, p-, and r-adaptive procedures, as well as mesh optimisation and smoothing techniques, are primarily intended to improve mesh quality, solution accuracy, or numerical convergence, rather than to stabilise the local non-linear response while preserving the original computational model. This study presents a Selective Mesh Conditioning (SMC) procedure for selectively stabilising the local non-linear response through conditional nodal relocation without modifying the mesh topology, constitutive models, boundary conditions, contact definitions, or the adopted solution strategy. The procedure is activated by the Percent Error in Structural Energy Norm (SEPC), which identifies regions susceptible to discretisation-induced disturbances, whereas the corrective stage is performed exclusively for admissible nodes using centroid-based nodal relocation. The methodology was evaluated using a three-stage verification framework comprising qualification of the numerical model against four-point bending tests with digital image correlation (DIC) measurements, functional verification using a modified Scordelis–Lo benchmark, and a comparative assessment of the native mesh, classical r-adaptation, and the proposed SMC procedure. The results demonstrate that the proposed procedure selectively modifies discretisation-sensitive local stress and plastic-strain responses while preserving the global structural response. Owing to its non-intrusive implementation, the proposed procedure can be easily incorporated into existing finite element models as a practical tool for stabilising the local non-linear response. Full article

Background/Objectives: Antimicrobial resistance is a critical threat to global public health. Eugenol is a bioactive compound with broad-spectrum antimicrobial activity that has attracted increasing interest as a naturally derived antimicrobial agent with potential complementary applications to conventional antibiotics, but its clinical application is severely limited by its high volatility, low water solubility and thermo-oxidative instability. The main objective of this study was to develop eugenol-loaded microparticles using a ternary biopolymer matrix, to characterise their main physicochemical properties, and to evaluate their in vitro antimicrobial efficacy against clinically relevant bacterial reference strains. Methods: The microparticles were formulated from an emulsion of maltodextrin, gum arabic and soy lecithin, and encapsulated using a spray-drying technique. Product recovery, particle morphology assessed by scanning electron microscopy (SEM), particle size distribution determined by laser diffraction, and encapsulation efficiency quantified by GC-FID were analysed. Subsequently, antimicrobial activity was evaluated by comparing the microparticles with free eugenol using agar well diffusion and broth microdilution assays to determine the minimum inhibitory concentration (MIC) against eight bacterial strains. Results: The spray-drying process achieved a product recovery of 61.88% and an encapsulation efficiency of 52.45%. The resulting microparticles exhibited a smooth, spherical morphology with diameters of less than 20 µm. In microbiological assays, microencapsulation significantly reduced MIC values by 4- to 16-fold compared with free eugenol for susceptible strains. The formulation exhibited potent activity against most of the Gram-positive and Gram-negative pathogens tested, except for Pseudomonas aeruginosa, which remained resistant to both formulations. Conclusions: The encapsulation of eugenol in this optimised biopolymer matrix substantially improved its antimicrobial efficacy against the tested bacterial strains. These findings highlight the potential of spray-dried eugenol microparticles as a promising antimicrobial formulation and provide a basis for their further development for topical applications. Further studies are warranted to evaluate their pharmaceutical performance and antimicrobial mechanisms. Full article