Journal Articles, Multidisciplinary Digital Publishing Institute (MDPI)

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Integrated Fatigue Evaluation of As-Built WAAM Steel Through Experimental Testing and Finite Element Simulation
Gothivarekar, Sanjay; Brains, Steven; Raeymaekers, Bart; Talemi, Reza (MDPI, 2025-10-11)
Additive Manufacturing (AM) has attracted considerable interest over the past three decades, driven by growing industrial demand. Among metal AM techniques, Wire and Arc Additive Manufacturing (WAAM), a Directed Energy Deposition (DED) variant, has emerged as a prominent method for producing large-scale components with high deposition rates and cost efficiency. However, WAAM parts typically exhibit rough surface profiles, which can induce stress concentrations and promote fatigue crack initiation under cyclic loading. This study presents an integrated experimental and numerical investigation into the fatigue performance of as-built WAAM steel. Fatigue specimens extracted from a WAAM-fabricated wall were tested under cyclic loading, followed by fractography to assess the influence of surface irregularities and subsurface defects on fatigue behaviour. Surface topography analysis identified critical stress-concentration regions and key surface roughness parameters. Additionally, 3D scanning was used to reconstruct the specimen topography, enabling detailed 2D and 3D finite element (FE) modelling to analyze stress distribution along the as-built surface and predict fatigue life. A Smith-Watson-Topper (SWT) critical plane-based approach was applied for multiaxial fatigue life estimation. The results reveal a good correlation between experimental fatigue data and numerically predicted results, validating the proposed combined methodology for assessing durability of as-built WAAM components.
Spatial–Temporal Patterns of Methane Emissions from Livestock in Xinjiang During 2000–2020
Xu, Qixiao; Li, Yumeng; You, Yongfa; Zhang, Lei; Zhang, Haoyu; Zhang, Zeyu; Yao, Yuanzhi; Huang, Ye (MDPI, 2025-10-11)
Livestock represent a significant source of methane (CH₄) emissions, particularly in pastoral regions. However, in Xinjiang—a pivotal pastoral region of China—the spatiotemporal patterns of livestock CH₄ emissions remain poorly characterized, constraining regional mitigation actions. Here, a detailed CH₄ emissions inventory for livestock in Xinjiang spanning the period 2000–2020 is compiled. Eight livestock categories were covered, gridded livestock maps were developed, and the dynamic emission factors were built by using the IPCC 2019 Tier 2 approaches. Results indicate that the CH₄ emissions increased from ~0.7 Tg in 2000 to ~0.9 Tg in 2020, a 28.5% increase over the past twenty years. Beef cattle contributed the most to the emission increase (59.6% of total increase), followed by dairy cattle (35.7%), sheep (13.9%), and pigs (4.3%). High-emission hotspots were consistently located in the Ili River Valley, Bortala, and the northwestern margins of the Tarim Basin. Temporal trend analysis revealed increasing emission intensities in these regions, reflecting the influence of policy shifts, rangeland dynamics, and evolving livestock production systems. The high-resolution map of CH₄ emissions from livestock and their temporal trends provides key insights into CH₄ mitigation, with enteric fermentation showing greater potential for emission reduction. This study offers the first long-term, high-resolution CH₄ emission inventory for Xinjiang, providing essential spatial insights to inform targeted mitigation strategies and enhance sustainable livestock management in arid and semi-arid ecosystems.
Decentralized Cycle-Free Game-Theoretic Adaptive Traffic Signal Control: Model Enhancement and Testing on Isolated Signalized Intersections
Shafik, Amr K.; Rakha, Hesham A. (MDPI, 2025-10-14)
This research enhances and evaluates the performance of a Decentralized Nash Bargaining (DNB) adaptive traffic signal controller that operates a flexible National Electrical Manufacturers Association (NEMA) phasing and timing scheme responding dynamically to fluctuating traffic demands. The DNB controller is enhanced to (1) use traffic density estimates instead of queues to optimize signal timings; (2) to consider the eight-phase two-ring NEMA controller configuration within the game-theoretic approach; and (3) to consider dynamically adaptable control time steps. The enhanced DNB controller is benchmarked against (1) a fixed-time traffic signal control using the state-of-practice Webster’s method and an emerging Laguna-Du-Rakha (LDR) method for computing the optimum cycle length; (2) a state-of-the-practice actuated traffic signal control; and (3) a state-of-the-art reinforcement learning (RL) traffic signal controller presented in the literature. The controller is tested on two isolated signalized intersections, demonstrating enhanced overall intersection performance compared to the baseline pretimed and actuated controllers at various demand levels, and offers better performance than a previously developed RL controller. Specifically, the DNB controller results in a decrease in the average vehicle delay and queue size by up to 54% and 63%, respectively, compared to Webster’s state-of-the-practice pretimed control. Unlike the RL controller, the DNB controller requires no pre-training while adapting to fluctuating traffic conditions, thereby providing a flexible framework for reducing traffic congestion at signalized intersections. As such, this research contributes to the development of smarter and more responsive urban traffic control systems.
Parameter Identification of Soil Material Model for Soil Compaction Under Tire Loading: Laboratory vs. In-Situ Cone Penetrometer Test Data
Shokanbi, Akeem; Jasoliya, Dhruvin; Untaroiu, Costin (MDPI, 2025-10-15)
Accurate numerical simulations of soil-tire interactions are essential for optimizing agricultural machinery to minimize soil compaction and enhance crop yield. This study developed and compared two approaches for identifying and validating parameters of a LS-Dyna soil model. The laboratory-based approach derives parameters from triaxial, consolidation, and cone penetrometer tests (CPT), while the optimization-based method refines them using in-situ CPT data via LS-OPT to better capture field variability. Simulations employing Multi-Material Arbitrary Lagrangian–Eulerian (MM-ALE), Smoothed Particle Hydrodynamics (SPH), and Hybrid-SPH methods demonstrate that Hybrid-SPH achieves the optimal balance of accuracy (2% error post-optimization) and efficiency (14-h runtime vs. 22 h for SPH). Optimized parameters improve soil–tire interaction predictions, including net traction and tire sinkage across slip ratios from −10% to 30% (e.g., sinkage of 12.5 mm vs. 11.1 mm experimental at 30% slip, with overall mean-absolute percentage error (MAPE) reduced to 3.5% for sinkage and 4.2% for traction) and rut profiles, outperforming lab-derived values. This framework highlights the value of field-calibrated optimization for sustainable agriculture, offering a cost-effective alternative to field trials for designing low-compaction equipment and reducing yield losses from soil degradation. While sandy loam soil at 0.4% moisture content was used in this study, future extensions to different soil types with varied moisture are recommended.
Agentic Actions and Agentic Perspectives Among Fellowship-Funded Engineering Doctoral Students
Denton, Maya; Chasen, Ariel; Fleming, Gabriella Coloyan; Borrego, Maura; Knight, David (MDPI, 2025-10-15)
In the US and Europe, institutions, foundations and governments invest significant financial resources in doctoral fellowships. Unlike other graduate funding mechanisms, fellowships are typically not tied to specific projects or job responsibilities and thus may afford more agency to students. We examined how fellowship funding contributes to or undermines agency of doctoral student recipients. We interviewed 23 US engineering doctoral students primarily funded on a fellowship for at least one semester. We qualitatively analyzed the interviews, using inductive and deductive methods of coding. Participants described increased flexibility with their projects, advisor, and personal life; additional access to physical resources, people and networks, and research experiences; and feelings of internal validation and external recognition from fellowship awards. Contexts of advising, timing of fellowship, source of fellowship, financial circumstances, and fellowship structure influenced their experiences. Agentic perspectives and actions included choice of advisor and research projects, switching advisors if necessary, completing internships and visiting other labs, and enjoying a higher standard of living. Advisor support is a necessity for students funded on fellowships. Multi-year fellowships from external sources, in comparison to internal sources, more often supported agency. We make recommendations for institutions to structure and administer fellowships to better support students.
Towards an End-to-End Digital Framework for Precision Crop Disease Diagnosis and Management Based on Emerging Sensing and Computing Technologies: State over Past Decade and Prospects
Nkwocha, Chijioke Leonard; Chandel, Abhilash Kumar (MDPI, 2025-10-16)
Early detection and diagnosis of plant diseases is critical for ensuring global food security and sustainable agricultural practices. This review comprehensively examines latest advancements in crop disease risk prediction, onset detection through imaging techniques, machine learning (ML), deep learning (DL), and edge computing technologies. Traditional disease detection methods, which rely on visual inspections, are time-consuming, and often inaccurate. While chemical analyses are accurate, they can be time consuming and leave less flexibility to promptly implement remedial actions. In contrast, modern techniques such as hyperspectral and multispectral imaging, thermal imaging, and fluorescence imaging, among others can provide non-invasive and highly accurate solutions for identifying plant diseases at early stages. The integration of ML and DL models, including convolutional neural networks (CNNs) and transfer learning, has significantly improved disease classification and severity assessment. Furthermore, edge computing and the Internet of Things (IoT) facilitate real-time disease monitoring by processing and communicating data directly in/from the field, reducing latency and reliance on in-house as well as centralized cloud computing. Despite these advancements, challenges remain in terms of multimodal dataset standardization, integration of individual technologies of sensing, data processing, communication, and decision-making to provide a complete end-to-end solution for practical implementations. In addition, robustness of such technologies in varying field conditions, and affordability has also not been reviewed. To this end, this review paper focuses on broad areas of sensing, computing, and communication systems to outline the transformative potential of end-to-end solutions for effective implementations towards crop disease management in modern agricultural systems. Foundation of this review also highlights critical potential for integrating AI-driven disease detection and predictive models capable of analyzing multimodal data of environmental factors such as temperature and humidity, as well as visible-range and thermal imagery information for early disease diagnosis and timely management. Future research should focus on developing autonomous end-to-end disease monitoring systems that incorporate these technologies, fostering comprehensive precision agriculture and sustainable crop production.
Primary Uterine Inertia (PUI) in Dogs Is Associated with Impaired Placental Availability of Factors Involved in the Parturition Cascade
Steiner, Marianne; Schuler, Gerhard; Frehner, Bianca L.; Reichler, Iris M.; Goericke-Pesch, Sandra; Balogh, Orsolya; Tavares Pereira, Miguel; Kowalewski, Mariusz P. (MDPI, 2025-10-20)
The canine parturition cascade involves decreased placental progesterone (P4) signaling mediated through its nuclear receptor PGR in decidual cells, leading to increased trophoblast production of PGF2α that promotes luteolysis, placentolysis, and myometrial contractility. A local role for glucocorticoids in initiating parturition through increased placental availability of cortisol and glucocorticoid receptor (GR/NR3C1), possibly affecting P4-PGR signaling, has been suggested. Primary uterine inertia (PUI) is a major cause of canine dystocia, but its pathophysiology remains unclear. Here, we hypothesized that dysregulated placental signaling could contribute to PUI. The availability of parturition cascade-related factors was assessed in placentae of dogs with PUI and during physiological prepartum luteolysis (LUT). Compared with LUT, PUI had no significant changes in prostaglandin-related factors PTGS2, PTGES, and HPGD (p > 0.05), but had lower PGF2α synthase PGFS/AKR1C3 (p < 0.001), and higher PGT abundance (p < 0.001). PUI had increased PGR transcript and protein levels (p < 0.001), but the same number of decidual cells (p > 0.05). GR/NR3C1 availability was reduced in PUI (p < 0.05), along with decreased placental cortisol-to-cortisone conversion. Our findings suggest that PUI could be associated with disturbances of the parturition cascade, possibly due to inadequate P4-PGR and glucocorticoid signaling in the placenta.
Probabilistic Models for Military Kill Chains
Adams, Stephen; Kyer, Alex; Lee, Brian; Sobien, Dan; Freeman, Laura; Werner, Jeremy (MDPI, 2025-10-20)
Military kill chains are the sequence of events, tasks, or functions that must occur to successfully accomplish a mission. As the Department of Defense moves towards Combined Joint All-Domain Command and Control, which will require the coordination of multiple networked assets with the ability to share data and information, kill chains must evolve into kill webs with multiple paths to achieve a successful mission outcome. Mathematical frameworks for kill webs provide the basis for addressing the complexity of this system-of-systems analysis. A mathematical framework for kill chains and kill webs would provide a military decision maker a structure for assessing several key aspects to mission planning including the probability of success, alternative chains, and parts of the chain that are likely to fail. However, to the best of our knowledge, a generalized and flexible mathematical formulation for kill chains in military operations does not exist. This study proposes four probabilistic models for kill chains that can later be adapted to kill webs. For each of the proposed models, events in the kill chain are modeled as Bernoulli random variables. This extensible modeling scaffold allows flexibility in constructing the probability of success for each event and is compatible with Monte Carlo simulations and hierarchical Bayesian formulations. The probabilistic models can be used to calculate the probability of a successful kill chain and to perform uncertainty quantification. The models are demonstrated on the Find–Fix–Track–Target–Engage–Assess kill chain. In addition to the mathematical framework, the MIMIK (Mission Illustration and Modeling Interface for Kill webs) software package has been developed and publicly released to support the design and analysis of kill webs.
Immune Evasion by the NSs Protein of Rift Valley Fever Virus: A Viral Houdini Act
Petraccione, Kaylee; Omichinski, James G.; Kehn-Hall, Kylene (MDPI, 2025-10-21)
Rift Valley fever virus (RVFV) is a negative-sense arbovirus that causes several severe diseases, including hemorrhagic fever in ruminants and humans. There are currently no FDA-approved vaccines or therapeutics for RVFV. The viral nonstructural protein NSs acts like a molecular Harry Houdini, the world-famous escape artist, to help the virus evade the host’s innate immune response and serves as the main virulence factor of RVFV. In this review, we discuss the molecular mechanisms by which NSs interacts with multiple factors to modulate host processes, evade the host immune response, and facilitate viral replication. The impact of NSs mutations that cause viral attenuation is also discussed. Understanding the molecular mechanisms by which NSs evades the host innate immune response is crucial for developing novel therapeutics and vaccines targeting RVFV.
Discovery of Cryptic Mussel Biodiversity in the Genera Pleurobema and Pleuronaia Using Molecular Phylogenetics and Morphology, with Descriptions of a New Species and a Previously Synonymized Species
Schilling, Daniel E.; Jones, Jess W.; Hallerman, Eric M.; Phipps, Andrew T.; Dinkins, Gerald R. (MDPI, 2025-10-21)
Freshwater mussels in the genera Fusconaia, Pleurobema, and Pleuronaia are similar in their external shell morphology, which has made the identification and classification of species within these genera difficult and led to many taxonomic revisions. Large samples (N = 464) of select mussel species in these genera were collected from 2012 through 2014, primarily in the upper Tennessee River basin of Tennessee and Virginia, USA. Mitochondrial ND1 and nuclear ITS1 DNA sequences were analyzed to assess phylogenetic relationships among taxa. Ten species were verified as phylogenetically distinct at ND1, two of which were cryptic and previously unrecognized species. Described herein as Pleurobema parmaleei and Pleuronaia estabrookianus, each species clade was diverged at this gene region by ~3.0% from the respective closest congener. The nuclear ITS1 gene region’s nucleotide-site insertion/deletion (indel) patterns were analyzed as single mutational events rather than as fifth character states or missing data. Most species, including these two, were phylogenetically distinct at the ITS1 region when incorporating indels into analyses, but some estimated interspecific pairwise distances were lower than corresponding intraspecific estimates. Among morphological traits assessed for each species, differences in foot color and gravidity characteristics illustrated differences between phylogenetically recognized species and their closest congeners. Due to the limited known geographical distributions of these two cryptic species, each may require protection under the U.S. Endangered Species Act. While this study collected large sample sizes for each species, many streams in the basin remain unsampled and could potentially contain populations of these species or additional cryptic species.
No-Signaling in Steepest Entropy Ascent: A Nonlinear, Non-Local, Non-Equilibrium Quantum Dynamics of Composite Systems Strongly Compatible with the Second Law
Ray, Rohit Kishan; Beretta, Gian Paolo (MDPI, 2025-09-28)
Lindbladian formalism models open quantum systems using a ‘bottom-up’ approach, deriving linear dynamics from system–environment interactions. We present a ‘top-down’ approach starting with phenomenological constraints, focusing on a system’s structure, subsystems’ interactions, and environmental effects and often using a non-equilibrium variational principle designed to enforce strict thermodynamic consistency. However, incorporating the second law’s requirement—that Gibbs states are the sole stable equilibria—necessitates nonlinear dynamics, challenging no-signaling principles in composite systems. We reintroduce ‘local perception operators’ and show that they allow to model signaling-free non-local effects. Using the steepest-entropy-ascent variational principle as an example, we demonstrate the validity of the ‘top-down’ approach for integrating quantum mechanics and thermodynamics in phenomenological models, with potential applications in quantum computing and resource theories.
“We’re Controversial by Our Mere Existence”: Navigating the U.S. Sociopolitical Context as TQ-Center(ed) Diversity Workers
Kannan, Kalyani; Oliveira, Kristopher; Feldman, Steven; Catalano, D. Chase J.; Duran, Antonio; Pryor, Jonathan T. (MDPI, 2025-09-29)
In the face of escalating sociopolitical hostility toward diversity, equity, and inclusion (DEI) efforts, trans and queer (TQ) center(ed) diversity workers in higher education are navigating increasingly precarious professional landscapes. This study explores the lived experiences of TQ-center(ed) diversity workers through a general qualitative design informed by participatory action research (PAR). Drawing on the concept of “burn through,” critiquing the role of institutions in the exhaustion of practitioners, and the theory of tempered radicalism, describing the fine line diversity workers must navigate to advocate for change within oppressive systems, we examine how these practitioners persist amid institutional neglect, emotional labor, and political antagonism. Findings from interviews with eight participants reveal three central themes: the systemic nature of burn through, the protective power of community, and the multifaceted role of liberation in TQ-center(ed) diversity work. Participants described both the toll and the transformative potential of their roles, highlighting community as a critical site of resistance and renewal. This study contributes to the growing literature on TQ advocacy in higher education and underscores the need for institutional accountability and collective care in sustaining liberatory futures.
Membrane Composition Modulates Vp54 Binding: A Combined Experimental and Computational Study
Guo, Wenhan; Dong, Rui; Okedigba, Ayoyinka O.; Sanchez, Jason E.; Agarkova, Irina V.; Abisamra, Elea-Maria; Jelinsky, Andrew; Riekhof, Wayne; Noor, Laila; Dunigan, David D.; Van Etten, James L.; Capelluto, Daniel G. S.; Xiao, Chuan; Li, Lin (MDPI, 2025-10-03)
The recruitment of peripheral membrane proteins is tightly regulated by membrane lipid composition and local electrostatic microenvironments. Our experimental observations revealed that Vp54, a viral matrix protein, exhibited preferential binding to lipid bilayers enriched in anionic lipids such as phosphatidylglycerol (PG) and phosphatidylserine (PS), compared to neutral phosphatidylcholine/phosphatidylethanolamine liposomes, and this occurred in a curvature-dependent manner. To elucidate the molecular basis of this selective interaction, we performed a series of computational analyses including helical wheel projection, electrostatic potential calculations, electric field lines simulations, and electrostatic force analysis. Our results showed that the membrane-proximal region of Vp54 adopted an amphipathic α-helical structure with a positively charged interface. In membranes containing PG or PS, electrostatic potentials at the interface were significantly more negative, enhancing attraction with Vp54. Field line and force analyses further confirmed that both the presence and spatial clustering of anionic lipids intensify membrane–Vp54 electrostatic interactions. These computational findings align with experimental binding data, jointly demonstrating that membrane lipid composition and organization critically modulate Vp54 recruitment. Together, our findings highlight the importance of electrostatic complementarity and membrane heterogeneity in peripheral protein targeting and provide a framework applicable to broader classes of membrane-binding proteins.
Practical Application of Passive Air-Coupled Ultrasonic Acoustic Sensors for Wheel Crack Detection
Shaju, Aashish; Kumar, Nikhil; Mantovani, Giovanni; Southward, Steve; Ahmadian, Mehdi (MDPI, 2025-10-03)
Undetected cracks in railroad wheels pose significant safety and economic risks, while current inspection methods are limited by cost, coverage, or contact requirements. This study explores the use of passive, air-coupled ultrasonic acoustic (UA) sensors for detecting wheel damage on stationary or moving wheels. Two controlled datasets of wheelsets, one with clear damage and another with early, service-induced defects, were tested using hammer impacts. An automated system identified high-energy bursts and extracted features in both time and frequency domains, such as decay rate, spectral centroid, and entropy. The results demonstrate the effectiveness of UAE (ultrasonic acoustic emission) techniques through Kernel Density Estimation (KDE) visualization, hypothesis testing with effect sizes, and Receiver Operating Characteristic (ROC) analysis. The decay rate consistently proved to be the most effective discriminator, achieving near-perfect classification of severely damaged wheels and maintaining meaningful separation for early defects. Spectral features provided additional information but were less decisive. The frequency spectrum characteristics were effective across both axial and radial sensor orientations, with ultrasonic frequencies (20–80 kHz) offering higher spectral fidelity than sonic frequencies (1–20 kHz). This work establishes a validated “ground-truth” signature essential for developing a practical wayside detection system. The findings guide a targeted engineering approach to physically isolate this known signature from ambient noise and develop advanced models for reliable in-motion detection.
Deep Approaches to Learning, Student Satisfaction, and Employability in STEM
Kapania, Madhu; Savla, Jyoti S.; Skaggs, Gary (MDPI, 2025-08-29)
This study examines the link between deep approaches to learning (DAL) and undergraduate senior students’ employability skills and perceived satisfaction in STEM fields in the United States. DAL, comprising higher-order (HO) and reflective/integrated (RI) learning constructs, enhances the understanding of real-world applications and promotes reflective thinking about individual ideas in broader contexts. HO activities focus on analyzing, synthesizing, and applying new information in practical scenarios such as internships, classroom discussions, and presentations. RI activities involve integrating existing knowledge with new ideas. The efficacy of DAL in improving student outcomes including employability and satisfaction skills was investigated using Structural Equation Modeling (SEM), which included a Confirmatory Factor Analysis (CFA) to measure observed variables associated with the four latent factors (HO, RI, student satisfaction, and employability skills), followed by structural analysis to explore the relationship between these latent factors. Data from 14,292 senior students surveyed by the National Study of Student Engagement (NSSE) in 2018 were analyzed. The results indicated a significant positive effect of DAL on students’ satisfaction and perceived employability skills, underscoring its importance in higher education for STEM students. These findings can guide higher education institutions (HEIs) in focusing on DAL activities for meaningful learning outcomes and enhanced critical thinking.
Establishing Human and Canine Xenograft Murine Osteosarcoma Models for Application of Focused Ultrasound Ablation
Hay, Alayna N.; Simon, Alex; Ruger, Lauren N.; Gannon, Jessica; Coutermarsh-Ott, Sheryl; Vickers, Elliana R.; Eward, William; Neufeld, Nathan J.; Vlaisavljevich, Eli; Tuohy, Joanne (MDPI, 2025-08-30)
Background: Osteosarcoma (OS) is the most commonly occurring type of bone cancer in both humans and canines. The survival outcomes for OS patients have not improved significantly in decades. A novel and innovative treatment option that is currently under investigation for OS in the veterinary field is the focused ultrasound ablation modality, histotripsy. Histotripsy is a non-thermal, non-invasive, non-ionizing ablation modality that destroys tissue through generation of acoustic cavitation. Objective: In the current study, we sought to investigate the utility of an orthotropic OS xenograft murine model for characterization of chronic ablative and clinical outcomes post-histotripsy ablation. Method: Given the high comparative relevance of canine to human OS, histotripsy was delivered to orthotopic OS tumors in both human and canine xenograft murine models. Results: Histotripsy improved limb function in tumor-bearing mice compared to untreated tumor bearing mice. The results of this study demonstrated the utility of the orthotopic OS xenograft murine model for histotripsy-based preclinical studies. Conclusions: The current study is the first published investigation for the use of an orthotopic xenograft murine model for the development of histotripsy ablation for OS. The developmental process of the model, technical limitations, and future directions are discussed.
Pavement Friction Prediction Based Upon Multi-View Fractal and the XGBoost Framework
Peng, Yi; Kai, Jialiang; Yu, Xinyi; Zhang, Zhengqi; Li, Qiang Joshua; Yang, Guangwei; Kong, Lingyun (MDPI, 2025-09-02)
The anti-slip performance of road surfaces directly affects traffic safety, yet existing evaluation methods based on texture features often suffer from limited interpretability and low accuracy. To overcome these limitations, a portable 3D laser surface analyzer was used to acquire road texture data, while a dynamic friction coefficient tester provided friction measurements. A multi-view fractal dimension index was developed to comprehensively describe the complexity of texture across spatial, cross-sectional, and depth dimensions. Combined with road surface temperature, this index was integrated into an XGBoost-based prediction model to evaluate friction at driving speeds of 10 km/h and 70 km/h. Comparative analysis with linear regression, decision tree, support vector machine, random forest, and backpropagation (BP) neural network models confirmed the superior predictive performance of the proposed approach. The model achieved backpropagation (R²) values of 0.80 and 0.82, with root mean square errors (RMSEs) of 0.05 and 0.04, respectively. Feature importance analysis indicated that fractal characteristics from multiple texture perspectives, together with temperature, significantly influence anti-slip performance. The results demonstrate the feasibility of using non-contact texture-based methods to replace traditional contact-based friction testing. Compared with traditional statistical indices and alternative machine learning algorithms, the proposed model achieved improvements in R² (up to 0.82) and reduced RMSE (as low as 0.04). This study provides a robust indicator system and predictive model to advance road surface safety assessment technologies.
Advancing Bioresource Utilization to Incentivize a Sustainable Bioeconomy: A Systematic Review and Proposal of the Enhanced Bioresource Utilization Index
Ugwu, Collins O.; Berry, Michael D.; Winans, Kiara S. (MDPI, 2025-09-03)
Over 15 billion tonnes year⁻¹ of biomass is used globally, yet 14% is downcycled for energy, forfeiting billions in potential revenue for higher-value products. Robust metrics that couple cascading use with cradle-to-gate greenhouse gas (GHG) emissions and economic value are essential for identifying superior biomass pathways. The aim of this review is to systematically map biomass utilization indicators published between 2010 and 2025; compare their treatment regarding circularity, climate, and economic value; and introduce the enhanced Bioresource Utilization Index (eBUI). A PRISMA-aligned search of Scopus and Web of Science yielded 80,808 records, of which 33 met the eligibility criteria. Each indicator was scored on cascading, data intensity, and environmental and economic integration, as well as computational complexity and sector scope. The Material Circularity Indicator, Biomass Utilization Efficiency, the Biomass Utilization Factor, and legacy BUI satisfied no more than two criteria simultaneously, and none directly linked mass flows to both GHG emissions and net revenue. The eBUI concept integrates mass balance, lifecycle carbon intensity, and value coefficients into a single 0–1 score. An open-access calculator and data quality checklist accompany the metric, enabling policymakers and industry to prioritize biomass pathways that are circular, climate-smart, and economically attractive.
Students’ Perceptions of Generative AI Image Tools in Design Education: Insights from Architectural Education
Huh, Michelle Boyoung; Miri, Marjan; Tracy, Torrey (MDPI, 2025-09-05)
The rapid emergence of generative artificial intelligence (GenAI) has sparked growing interest across educational disciplines, reshaping how knowledge is produced, represented, and assessed. While recent research has increasingly explored the implications of text-based tools such as ChatGPT in education, far less attention has been paid to image-based GenAI tools—despite their particular relevance to fields grounded in visual communication and creative exploration, such as architecture and design. These disciplines raise distinct pedagogical and ethical questions, given their reliance on iteration, authorship, and visual representation as core elements of learning and practice. This exploratory study investigates how architecture and interior architecture students perceive the use of AI-generated images, focusing on ethical responsibility, educational relevance, and career implications. To ensure participants had sufficient exposure to visual GenAI tools, we conducted a series of workshops before surveying 42 students familiar with image generation processes. Findings indicate strong enthusiasm for GenAI image tools, which students viewed as supportive during early-stage design processes and beneficial to their creativity and potential future professional competitiveness. Participants regarded AI use as ethically acceptable when accompanied by transparent acknowledgment. However, acceptance declined in later design stages, where originality and critical judgment were perceived as more central. While limited in scope, this exploratory study foregrounds student voices to offer preliminary insights into evolving conversations about AI in creative education and to inform future reflection on developing ethically and pedagogically responsive curricula across the design disciplines.
Hardware Validation for Semi-Coherent Transmission Security
Fletcher, Michael; McGinthy, Jason; Michaels, Alan J. (MDPI, 2025-09-05)
The rapid growth of Internet-connected devices integrating into our everyday lives has no end in sight. As more devices and sensor networks are manufactured, security tends to be a low priority. However, the security of these devices is critical, and many current research topics are looking at the composition of simpler techniques to increase overall security in these low-power commercial devices. Transmission security (TRANSEC) methods are one option for physical-layer security and are a critical area of research with the increasing reliance on the Internet of Things (IoT); most such devices use standard low-power Time-division multiple access (TDMA) or frequency-division multiple access (FDMA) protocols susceptible to reverse engineering. This paper provides a hardware validation of previously proposed techniques for the intentional injection of noise into the phase mapping process of a spread spectrum signal used within a receiver-assigned code division multiple access (RA-CDMA) framework, which decreases an eavesdropper’s ability to directly observe the true phase and reverse engineer the associated PRNG output or key and thus the spreading sequence, even at high SNRs. This technique trades a conscious reduction in signal correlation processing for enhanced obfuscation, with a slight hardware resource utilization increase of less than 2% of Adaptive Logic Modules (ALMs), solidifying this work as a low-power technique. This paper presents the candidate method, quantifies the expected performance impact, and incorporates a hardware-based validation on field-programmable gate array (FPGA) platforms using arbitrary-phase phase-shift keying (PSK)-based spread spectrum signals.

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