Abstract: Traditional reliability engineering paradigms, originally designed to prevent physical component failures, are facing a fundamental crisis when applied to today's soft-ware-intensive and autonomous systems. In critical domains like aerospace, the dom-inant risks no longer stem from the aleatory uncertainty of hardware breakdowns, but from the deep epistemic uncertainty inherent in complex systematic interactions and non-deterministic algorithms. This paper reviews the historical evolution of reliability engineering, tracing the progression through the Statistical, Physics-of-Failure, and Prognostics eras. It argues that while these failure-centric frameworks perfected the management of predictable risks, they are structurally inadequate for the "unknown unknowns" of modern complexity. To address this methodological vacuum, this study advocates for an imperative shift towards a fourth paradigm: the Resilience Era. Grounded in the principles of Safety-II, this approach redefines the engineering objec-tive from simply minimizing failure rates to ensuring mission success and functional endurance under uncertainty. The paper introduces Uncertainty Control (UC) as the strategic successor to Uncertainty Quantification (UQ), proposing that safety must be architected through behavioral constraints rather than prediction alone. Finally, the paper proposes a new professional identity for the practitioner: the system resilience architect, tasked with designing adaptive architectures that ensure safety in an era of incomplete knowledge.

Abstract: Mechanical coffee dryers have been widely adopted to reduce weather dependence, improve yield, and stabilize product quality. However, their operation is still energy-intensive and often suboptimal in terms of controlling the temperature, airflow and moisture content of the grains. In parallel, digital twin (DT) technology has emerged to virtually replicate complex processes and enable model-based monitoring, optimization, and control. This article presents a systematic review based on PRISMA on mechanical coffee dryers and their modeling and control strategies and the current and emerging use of digital twins in drying processes, including agricultural and food products with technological analogies to coffee. The results show a large amount of research on mathematical modeling, energy evaluation, and quality evaluation of mechanical coffee drying. Rapidly growing but still predominantly conceptual literature on digital twins for food processing and drying. Finally, only a small convergence between the two fields, with no fully realized digital twin for mechanical coffee dryers having yet been reported. This review found key gaps in the detection, data infrastructure, and development of hybrid physical-informed AI models. Finally, lines of research are proposed for mechanical coffee dryers enabled with digital twins, aimed at energy efficiency, product traceability and quality assurance.

Abstract: Non-line-of-sight (NLOS) propagation poses a significant challenge to achieving high-accuracy ultra-wideband (UWB) indoor positioning. To address this issue, this study investigates solutions from two complementary perspectives: NLOS identification and error mitigation. First, an NLOS signal classification model is proposed based on multidimensional statistics of the channel impulse response (CIR). The model incorporates an attention mechanism and an improved snake optimization (ISO) algorithm, achieving significantly enhanced classification accuracy and robustness. Building on this foundation, a UKF–BiLSTM dual-directional mutual calibration framework is proposed to compensate for NLOS errors dynamically. The framework embeds the constant turn rate and velocity (CTRV) motion model within an unscented Kalman filter (UKF) to enhance trajectory modeling. It establishes a bidirectional correction loop with a bidirectional long short-term memory (BiLSTM) network. Through the synergy of physical constraints and data-driven learning, the framework adaptively suppresses NLOS errors. Experimental results demonstrate that the proposed classification model and positioning framework significantly outperform state-of-the-art methods, thereby providing a systematic solution for high-precision and robust UWB positioning in complex indoor environments.

Abstract: The growing demand for high-protein dairy products, driven by the expanding markets for infant formula and nutritional supplements, has led to a higher incorporation of milk protein ingredients like milk protein concentrate (MPC) and whey protein isolate (WPI) in dairy formulations. However, the effects of these protein additives on the thermal stability and sensory attributes of dairy products remain insufficiently studied. This research examines the influence of thermal processing (80 °C for 30 min) and protein fortification (MPC, WPI, and their combination) on the denaturation of whey proteins, the formation of volatile compounds, and the sensory characteristics of milk. Specifically, whole milk was fortified with MPC, WPI, and their combination at concentrations of 4% MPC, 4% WPI, and 2% MPC + 2% WPI, respectively, to evaluate the impact of different protein fortifications on these properties. Our findings reveal that heat treatment significantly promoted the denaturation of β-lactoglobulin and α-lactalbumin, with protein fortification playing a role in modulating these changes. Notably, lactoferrin exhibited matrix-dependent antioxidant behavior, meaning its antioxidant activity varied based on the protein composition and structure of the milk matrix, influencing its stability and function under different fortification conditions. Volatile profiling indicated that MPC enhanced the formation of sulfur-containing compounds and aldehydes, whereas WPI favored ketones and Maillard-derived volatiles. Sensory analysis revealed that heated WPI fortified samples exhibited stronger cooked and dairy fat aromas, while unfortified milk retained milky and grassy notes. Correlation analysis highlighted the mechanistic links between protein denaturation and lipid-derived compounds. These results emphasize that protein type and composition play crucial roles in flavor development. The strategic blending of MPC and WPI offers a practical approach to balancing volatile profiles and mitigating off-flavors, providing insights for the formulation of thermally stable, protein-fortified dairy products with optimized sensory quality.

Abstract: This study investigated the kinetics of aptamer-cardiac troponin I (cTnI) interaction to establish a new dynamic quantitative indicator for the rapid, highly sensitive detection of cTnI, a critical myocardial infarction biomarker. The goal was to overcome the limitations of conventional diagnosis based on saturated binding amounts, which takes excessive time for point-of-care testing (POCT). Cyclic voltammetry (CV) was performed on a gold electrode immobilized with double-stranded aptamers, and the interaction kinetics were rigorously analyzed across cTnI concentrations from 10 pg/mL to 90 pg/mL. The adsorption process, quantified by changes in charge amount, was found to follow a similar first-order interaction model. The most significant findings were the establishment of a robust power function (R2=0.9515) relating the cTnI concentration to the derived interaction rate constant. This high explanatory power confirms the predictable and quantitative relationship between concentration and reaction speed. In conclusion, the interaction rate constant is proposed as a novel dynamic indicator for predicting cTnI concentration, providing a crucial technological foundation for developing next-generation, high-speed, high- sensitivity aptamer-based biosensors essential for time-critical POCT applications.

Abstract: The importance of the infrastructure is associated with the value of the infrastructure, the greater the importance of infrastructure, the greater its value. The concept of the importance of road infrastructure can take on a different value instead of different points of view. For example, roads can be evaluated from an economic, social, political, and military, among others. In 2021, the Lazio Regional Road Authority (ASTRAL) requested assistance from the Research Center for Transport and Logistics (CTL) to develop a composite scoring index (Regional Index, Ri) that would rank the relative importance of ASTRAL–maintained roadway network. The Ri index is expressed numerically between values from 1 to 5 (with 5 representing the highest importance). It includes the following variables: Population density, AADT, road traffic crashes, accessibility to point of interest, maintenance cost, air emissions, and noise pollution. The methodology includes the following steps. First, the variables were selected on the basis of their reliability, measurability, coverage and relevance to the phenomenon to be measured. Then, the data collection and normalization of the variables on a scale of 1 to 5 were carried out. Subsequently, through a multicriteria analysis, the variables were weighted and added. Finally, a sensitivity analysis was performed to evaluate which variables had the most influence on the final output of the formula. The methodology proposed has been implemented on the Region Lazio roadway network in order to obtain the Ri of the road segments.

Abstract: This study numerically examined the anchorage mechanism of rebar hooks under varying straight development lengths, including high stress levels. A Three-Dimensional Rigid Body Spring Model (3D-RBSM) was used for the investigation, which the model has successfully reproduced the experimental pullout test stress–slip relationships and inner–outer strain distributions for the case of bonded hook part with and without a straight development length. The numerical model, which considered both hook and straight development length was able to output local concrete stresses and internal crack propagation enabling a clear interpretation of how straight development length influences the anchor-age mechanism. The results revealed that increasing straight development length increases stiffness, reduces rebar strains and concrete stresses in the hook region, promotes crack formation around the rebar surface and forms maximum tensile stresses closer to the top surface, ultimately resulting in earlier splitting failure at high rebar stress levels. A comparison of cases with and without hooks shows that combining the hook with straight development length improves stress distribution, delays crack propagation and increases anchorage by reducing tensile stress concentrations near the top surface and side faces. The findings offer insights to support rebar hook anchorage design and review of existing standards.

Abstract: Wire Arc Additive Manufacturing (WAAM) is a cost-effective method for fabricating large aluminum components; however, it tends to suffer from heat accumulation and coarse anisotropic microstructures, which can limit the part's performance and its mechanical properties. In this study, a wall is fabricated using a hybrid unified additive deformation manufacturing process (UAMFSP) method, which integrates friction stir processing (FSP) into WAAM, and is compared with a WAAM-only wall fabricated by Metal Inert Gas (MIG) deposition. Based on the outcomes, Infrared (IR) thermography revealed progressive heat buildup in WAAM-only MIG walls, with peak layer temperatures of about 870 to 1000 °C and occasional clipped peaks near the IR-camera limit (~1300 °C). In contrast, in the UAMFSP process, heat was redistributed through mechanical stirring, maintaining more uniform sub-solidus profiles below approximately 400 °C. Also, optical microscopy and quantitative image analysis showed that MIG walls developed coarse, dendritic grains with a mean grain area of about 314 µm², whereas the UAMFSP produced refined, equiaxed grains with a mean grain area of about 10.9 µm², which is approximately 1.5 orders of magnitude smaller. Mechanical performance assessment through microhardness measurement confirmed that the UAMFSP process can improve the hardness by 45.8% compared to the MIG process (75.8 ± 7.7 HV vs. 52.0 ± 1.3 HV; p = 0.0027). In summary, the outcomes of this study introduce the UAMFSP process as a robust method for addressing the thermal and microstructural limitations of WAAM and improving the performance of the fabricated part. By combining deposition with plastic deformation, UAMFSP enables the fabrication of aluminum parts with fine isotropic microstructures and improved strength. These findings provide a framework for further extending hybrid additive-deformation strategies to thicker builds, alternative alloys, and service-relevant mechanical evaluations.

Abstract: This paper presents a practical industrial hybrid control architecture that augments the widely deployed 49-rule Mamdani fuzzy supervisory PID controller with a lightweight online meta-tuner based on Soft Actor-Critic (SAC) reinforcement learning. While the inner 1 kHz fuzzy-PID loop remains fully deterministic and identical to the industrial baseline, a separate 10 Hz SAC agent autonomously adapts the three output scaling factors (α_Kp, α_Ki, α_Kd ∈ [0.5, 2.5]) of the fuzzy layer using an ONNX Runtime inference engine. The complete controller is implemented and experimentally validated on a real Siemens S7-1214C PLC (6ES7214-1AG40-0XB0) in a hardware-in-the-loop setup with a high-fidelity 5-DoF manipulator model incorporating measured friction, backlash, sensor noise, and payload variation (0–2.5 kg). Across four demanding scenarios (sinusoidal tracking, sudden payload jumps, sustained disturbances up to 0.76 Nm, and high-speed motions), the proposed method consistently achieves 46–52 % lower RMSE and 28–30 % reduced control energy compared to the fixed-scaling industrial baseline, while preserving strict real-time constraints (inner loop cycle time 0.68–0.89 ms, SAC inference < 0.6 ms). The full PLC program (SCL/FBD), HIL environment, and trained policies will be released open-source upon acceptance (DOI to be provided during revision).The full PLC program, HIL environment, and trained SAC policies will be released open-source as a preprint supplement.

Abstract: Web openings are created in reinforced concrete deep beams for various purposes. The CFRP strengthening technology is commonly employed to mitigate the adverse consequences of these openings. The impact of openings generated in areas of stirrupless or by arranging the stirrups at the bottom and top chords of the opening in a closed configuration has been examined in numerous studies. However, in reality, stirrup damage frequently occurs when openings are made due to the high number of stirrups employed in deep beams. In this study, three specimens tested in a previous experimental study were modeled via ABAQUS, and the results obtained were validated by comparing them with the experimental results. To create openings of varying sizes in the elements, the reinforcements were cut, and these beams were strengthened with CFRP laminates, followed by a parametric study. The findings indicated a 56% reduction in the load-carrying capacity of the unstrengthened beam (h = 500 mm) featuring a 300 mm diameter opening, alongside an 87% decrease in energy dissipation. Although the diameter of the opening, which was formed by cutting the stirrups, is less than one-third of the beam's height, the application of 1.8 mm thick laminates resulted in only limited improvement.

Abstract: Ensuring operational safety is a critical challenge for gantry cranes, particularly given the visual blind spots and complex dynamic conditions typical of industrial sites.Existing object detection methods often struggle to balance inference speed with detection accuracy,leading to missed detections of irregular obstacles or performance degradation in low-light environments.To address these issues,this paper proposes a high-performance real-time obstacle detection model based on an improved YOLOv5s architecture.First, an image preprocessing pipeline incorporating low-light enhancement and denoising is designed to mitigate environmental interference.Second, a parameter-free SimAM is integrated into the feature extraction network.Unlike traditional attention mechanisms,SimAM infers 3D attention weights directly from the feature map without adding extra parameters,thereby enhancing the model’s sensitivity to key obstacle features.Third,the EIoU loss function is introduced to replace the standard CIoU loss,optimizing the bounding box regression by explicitly minimizing the discrepancy in aspect ratios and center points.Experimental results on a self-constructed crane obstacle dataset demonstrate that the proposed method achieves a mean Average Precision of 95.2% with an inference speed of 20.1 ms.This performance significantly outperforms the original YOLOv5s and other state-of-the-art detectors,providing a robust and efficient solution for autonomous crane monitoring systems.

Abstract: The shift toward Industry 5.0 places human-centred and digitally integrated metrology at the core of modern manufacturing, particularly in the automotive sector, where portable Laser Line Triangulation (LLT) systems must combine accuracy with operator usability. This study addresses the challenge of operator-induced variability by evalu-ating how algorithmic strategies and mechanical support features jointly influence the performance of a portable LLT device derived from the G3F sensor. A comprehensive Measurement System Analysis was performed to compare three feature-extraction al-gorithms—GC, FIR, and Steger—and to assess the effect of a masking device designed to improve mechanical alignment during manual measurements. The results highlight distinct algorithm-dependent behaviours in terms of repeatability, reproducibility, and computational efficiency. More sophisticated algorithms demonstrate improved sensi-tivity and feature localisation under controlled conditions, whereas simpler gradi-ent-based strategies provide more stable performance and shorter processing times when measurement conditions deviate from the ideal. These differences indicate a trade-off between algorithmic complexity and operational robustness that is particu-larly relevant for portable, operator-assisted metrology. The presence of mechanical alignment aids was found to contribute to improved measurement consistency across all algorithms. Overall, the findings highlight the need for an integrated co-design of algorithms, calibration procedures, and ergonomic aids to enhance repeatability and support operator-friendly LLT systems aligned with Industry 5.0 principles.

Abstract: This study presents a fundamental validation of an AI-based impact analysis framework for wooden structures, aiming to support efficient and automated engineering judgment in seismic design. Focusing on a single-story residential building, the proposed method quantitatively evaluates the influence of individual seismic elements and their spatial lo-cations on structural response. Numerical time-history analyses were conducted using a detailed three-dimensional nonlinear model, and parametric variations of stiffness and strength were systematically generated using an orthogonal array. Machine learning models were then trained to capture the relationship between these parameters and seis-mic responses, and explainable artificial intelligence (XAI) techniques were applied to in-terpret parameter influence. The results demonstrated that wall elements oriented parallel to the target inter-story drift consistently exhibited dominant influence, which is consistent with structural engineer-ing knowledge. In addition, model comparison revealed that linear regression achieved high accuracy in the elastic response range, while Gradient Boosting outperformed other models under strong excitation conditions involving plastic behavior. This difference re-flects the transition from approximately linear to highly nonlinear structural response. These findings suggest that a hybrid modeling strategy combining interpretable linear models and flexible nonlinear models is effective for impact analysis. Overall, this fundamental study demonstrates that the proposed AI-based framework provides a transparent, rational, and time-efficient tool for seismic performance evalua-tion of wooden structures, bridging data-driven analysis and practical engineering deci-sion-making.

Abstract: Self-compacting concrete (SCC) offers significant advantages in construction due to its superior workability; however, optimizing SCC mixture design remains challenging because of complex nonlinear material interactions and increasing sustainability requirements. This study proposes an integrated, sustainability-oriented framework that combines machine learning (ML), SHapley Additive exPlanations (SHAP), and multi-objective optimization to improve SCC mixture design. A large and heterogeneous global dataset, compiled from 156 peer-reviewed studies and enhanced through a structured three-stage data augmentation strategy, was used to develop robust predictive models for key fresh-state properties. An optimized XGBoost model demonstrated high predictive performance, achieving coefficients of determination of R² = 0.835 for slump flow and R² = 0.828 for T50 time, with strong generalization to industrial SCC datasets. SHAP-based interpretability analysis identified the water-to-binder ratio and superplasticizer dosage as the dominant factors governing fresh-state behavior, providing physically meaningful insights into mixture performance. A cradle-to-gate life cycle assessment was integrated within a multi-objective genetic algorithm to simultaneously minimize embodied CO₂ emissions and material costs while satisfying workability constraints. The resulting Pareto-optimal mixtures achieved up to 3.9% reduction in embodied CO₂ emissions compared to conventional SCC designs without compromising performance. External validation using industrial data confirms the practical reliability and transferability of the proposed framework. Overall, this study presents an interpretable and scalable AI-driven approach for the sustainable optimization of SCC mixture design.

Abstract: The growing emphasis on environmental sustainability and the need for advanced manufacturing methods have accelerated progress in materials processing. Aluminum powder metallurgy (APM) is particularly promising due to aluminum’s low density, high strength-to-weight ratio, and the inherent benefits of the powder metallurgy (PM) process. However, the corrosion resistance of sintered aluminum components remains a significant concern. In this study, shot peening (SP) was employed as a surface modification technique to improve the corrosion behavior of Alumix 321 PM alloy. Sampleas of the as-sintered and shot peened Alumix 321 PM alloy, together with the wrought alloy counterpart AA6061, were characterized using non-contact optical profilometry, optical microscopy (OM), and scanning electron microscopy (SEM). Corrosion performance was evaluated in 3.5 wt.% NaCl solution using Tafel extrapolation (TE), cyclic polarization (CP), stair-step polarization (SSP), and electrochemical impedance spectroscopy (EIS). The results revealed that shot peening increased surface roughness and significantly reduced the corrosion rate from 0.079 mmpy to 0.004 mmpy for the unpeened and peened samples, respectively. While pitting was the dominant corrosion mechanism in the wrought alloy, the PM alloy exhibited a combination of pitting, crevice, and intergranular corrosion. These findings highlight the potential of SP in enhancing the durability of aluminum-based PM components, offering valuable insights for industrial applications.

Abstract: This study presents a drone-based method for assessing the condition of road markings from high-resolution imagery acquired by an unmanned aerial vehicle (UAV). A DJI Matrice 300 RTK equipped with a Zenmuse P1 camera is flown over urban road corridors to capture images with centimeter-level ground sampling distance. In contrast to common approaches that rely on vehicle-mounted or street-view cameras, using a UAV reduces survey time and deployment effort while still providing views that are suitable for marking. The flight altitude, overlap, and corridor pattern are chosen to limit occlusions from traffic and building shadows while preserving the resolution required for condition assessment.From these images, the method locates individual markings, assigns a class to each marking, and estimates its level of deterioration. Candidate markings are first detected with YOLOv9 on the UAV imagery. The detections are cropped and segmented, which refines marking boundaries and thin structures. The condition is then estimated at the pixel level by modeling gray-level statistics with kernel density estimation (KDE) and a two-component Gaussian mixture model (GMM) to separate intact and distressed material. Subsequently, we compute a per-instance damage ratio that summarizes the proportion of degraded pixels within each marking. All results are georeferenced to map coordinates using a 3D reference model, allowing visualization on base maps and integration into road asset inventories. Experiments on unseen urban areas report detection performance (precision, recall, mean average precision) and segmentation performance (intersection over union), and analyze the stability of the damage ratio and processing time. The findings indicate that the drone-based method can identify road markings, estimate their condition, and attach each record to geographic space in a way that is useful for inspection scheduling and maintenance planning.

Abstract:

Risk management is a critical process for achieving construction project objectives and supporting more sustainable project delivery. However, most existing research focuses on isolated aspects of risk, lacking an integrated approach that examines how risks evolve across the entire project life cycle. This study addresses this gap by identifying and assessing key risks affecting construction projects in the United Arab Emirates (UAE), with attention to how improved risk understanding can contribute to more resilient and sustainable project outcomes. Through a literature review, fifteen critical risks involving various stakeholders were identified. A questionnaire survey was conducted to evaluate the probability and impact of these risks on project cost. The study analyzes how these risks manifest across the project life cycle and affect different stakeholders. Using a coordinate system, it visualizes risk behavior across phases, offering a dynamic view of risk exposure. Findings show that the construction phase was the riskiest, followed by the handover, design, and feasibility phases. Additionally, delayed payments by owners emerged as the most significant risk, followed by poor contractor management. The study proposes a modified probability–impact matrix to account for multi-phase risks. These findings provide valuable insights for construction firms, helping improve stakeholder risk allocation, inform contract negotiations, and enhance project delivery in the UAE context while contributing to more efficient, responsible, and sustainable project management practices.

Abstract: The role of microneedles (MNs) in enhancing tissue permeability has long been established. Their capacity to serve as drug-delivery vehicles or biosensing platforms makes them ideal candidates for applications in which tissue serves as the primary pathway. Such potential can only be thoroughly validated through tissue-dependent tests. Although MNs are not limited to human tissues, humans remain the most relevant target group. This highlights the need to develop platforms that closely replicate the structure of human tissue for the intended applications. To date, many studies have addressed the limited availability of human samples, constrained by ethical concerns and other challenges, by using artificial, human-like tissue mimics. These models have been widely used to evaluate various aspects of MN performance, including penetrability, drug delivery, and biosensing. Despite limitations, artificial tissues provide a practical assessment tool in MN development. This review offers new insights into the role of synthetic tissue models in evaluating MN functionality. It discusses the underlying rationale for their use, highlights their flexibility and potential in MN application studies, addresses their limitations, and presents their future perspective. Finally, it highlights the need for standardized, scalable artificial tissue platforms to support the translational and commercial advancement of MN technologies

Abstract: With the rapid development of the automotive industry, autonomous driving has attracted growing research interest, among which path planning and trajectory tracking play a central role. To better understand the evolution, current status, and future directions of this field, this study conducts a comprehensive bibliometric analysis combined with latent Dirichlet allocation (LDA) topic modeling on publications related to autonomous vehicle path planning and trajectory tracking indexed in the Web of Science database. Multiple dimensions are examined, including publication trends, highly cited authors, leading institutions, research domains, and keyword co-occurrence patterns. The results reveal a sustained growth in research output, with trajectory planning, path optimization, trajectory tracking, and model predictive control emerging as dominant topics, alongside a notable rise in learning-based approaches. In particular, reinforcement learning and deep reinforcement learning have become increasingly prominent in complex decision-making and tracking control scenarios. The analysis further identifies core contributors and institutions, highlighting the leading roles of China and the United States in this research area. Overall, the findings provide a systematic overview of the knowledge structure and evolving research trends, offering valuable insights into key opportunities and challenges and supporting future research toward safer and more intelligent autonomous driving systems.

Abstract:

For many years, the hard coal mining industry has been searching for engineering solutions ensuring greater reliability of the machines operating in difficult underground conditions. The foregoing applies in particular to the scraper conveyors used in longwall systems, started up very frequently and exposed to variable dynamic loads, leading to accelerated wear of powertrain components. The authors of this study have developed a longwall scraper conveyor equipped with a torsionally flexible metal clutch of novel design. The article provides a description of a mathematical model of a conveyor featuring two centrally arranged chains along with a main (discharge) and auxiliary (return) drive, as well as results of the computer simulations performed for two variants of the drive system setup analysed: one with a typical flexible clutch and the other with the innovative torsionally flexible clutch. Analysis of these results has revealed that the solution proposed significantly reduces the amplitude of dynamic loads, which contributes to increased durability and reliability of conveyors under mining conditions.