Search Results (245)

Background: The ability of synthetic plastics to persist in the environment and the accumulation of microplastics has intensified the need to explore biological mechanisms capable of interacting with, and possibly degrading, polymeric materials. Microbial enzymes that have extensive catalytic flexibility represent promising candidates in this context. Aim: This study set out to examine the molecular interaction patterns and dynamical stability of Pseudomonas aeruginosa elastase (LasB) with representative structural fragments of typical synthetic plastics to assess the suitability of the enzyme to polymer-derived substrates. Methods: The crystallographic structure of LasB (PDB ID: 1EZM) was retrieved from the Protein Data Bank and pre-prepared with the help of AutoDock4.2.6 Tools. Those polymer-derived ligands that were associated with the major industrial plastics such as polyamide (PA), polyvinyl chloride (PVC), polycarbonate (PC), poly-ethylene terephthalate (PET), polymethyl methacrylate (PMMA), and polyurethane (PUR) were retrieved in the PubChem database and geometrically optimized with the help of the MMFF94 force field. AutoDock Vina, with a specific grid box around the catalytic pocket, including Zn²⁺ ion, was used to perform molecular docking simulations. PyMOL and BIOVIA Discovery Studio software were used to analyze binding conformations, interaction residues and types of intermolecular contacts. Phosphoramidon, a known metalloprotease inhibitor, served as a positive control to confirm the docking protocol. Additional assessment of the structural stability and conformational behavior of the enzyme–ligand complexes was conducted by molecular dynamics (MD) simulations with the Desmond engine and explicit solvent model in a 50 ns trajectory using the OPLS4 force field. RMSD, RMSF, radius of gyration, hydrogen bonding analysis and solvent accessibility parameters were used to measure structural stability. Results: The docking experiment showed varying binding affinities with the test polymers. Polycarbonate (−5.774 kcal/mol) and polyurethane (−5.707 kcal/mol) had the highest in-teractions with the LasB catalytic pocket, polyamide (−5.277 kcal/mol) and PET (−4.483 kcal/mol) followed PMMA and PVC, which had weaker affinities. The following were the important residues involved in interaction networks: Glu141, His140, Val137, Arg198, Tyr114, and Trp115 that were implicated in interaction networks with hydrophobic interactions, π-cation interactions and van der Waals forces that were the major stabilization forces. MD simulations had stabilized complexes, and RMSD values were found to be within acceptable ranges of stability, and ligand-specific changes (around 1.0-3.2 A), which is also in line with stable protein-ligand systems. Phosphoramidon used as a positive control had an RMSD of 1.205 A which is within this stability range. PCA determined various ligand-bound conformational states of LasB with PA in com-pact state, PC and PVC in intermediate states and PUR, PMMA and PET in ex-panded conformations, indicating structur-al stability and adaptability of the binding pocket. Conclusion: These findings show that LasB has a structurally flexible catalytic pocket that can accommodate a wide range of polymer-derived ligands. These results offer an insight into the recognition of enzymes with polymers at the molecular level and also indicate that LasB might help in the interaction of microorganisms with synthetic plastics in environmental systems. Full article

The increasing reliance of Internet of Things (IoT) applications on low-power wide-area network technologies, particularly Long Range Wide Area Network (LoRaWAN), has amplified the need for security monitoring approaches that go beyond attack-specific signatures and generic traffic anomalies. Existing solutions are often tailored to individual threat scenarios or rely on statistical indicators, which limits their ability to systematically capture protocol-level misuse in an interpretable manner. This paper addresses this gap by proposing a protocol-aware validation methodology based on a Digital Twin abstraction of LoRaWAN communication behavior. The Over-The-Air Activation (OTAA) procedure is modeled as a finite-state machine that encodes expected message sequences, timing constraints, and specification-driven state transitions. Observed network events are continuously evaluated against this formal state model, enabling the identification of protocol-level deviations indicative of anomalous or non-conformant behavior. Illustrative examples include replay behavior, timing inconsistencies, and integrity-related anomalies, although the framework is not limited to predefined attack categories. The results demonstrate that state machine-based Digital Twin provides a structured and extensible foundation for protocol-aware security validation and Security Operation Center (SOC)-oriented telemetry enrichment. In this sense, the presented approach represents a concrete step toward protocol-aware intrusion detection for LoRaWAN networks by establishing a state-synchronized semantic validation layer upon which higher-level detection mechanisms can be built. Full article

Background/Objectives: Sepsis is a leading cause of morbidity and mortality due to its rapid progression and variability in care delivery. While existing predictive models estimate sepsis risk using clinical variables, they typically rely on static attributes and overlook temporal, behavioral, and process-related characteristics of care pathways. In particular, deviations from recommended protocols and process inefficiencies are rarely incorporated into early deterioration prediction. This study proposes a Conformance-Aware Predictive Process Monitoring (CAPPM) framework to enable early detection of sepsis deterioration using incomplete care pathways. Methods: The proposed framework integrates process mining with predictive modeling. Using the publicly available Sepsis Cases Event Log, we first discovered the reference care pathway and generated prefix-level representations of ongoing cases. Temporal and behavioral features were engineered alongside alignment-based and declarative conformance metrics to quantify pathway deviations. These features were used to train and evaluate multiple supervised learning models, including Adaptive Boosting and Gradient Boosting. Predictive performance was assessed using the area under the receiver operating characteristic curve (AUROC). Results: Incorporating conformance and pathway-based features improved predictive performance compared to models relying solely on traditional attributes. Adaptive Boosting and Gradient Boosting achieved the strongest results, with AUROC values of 0.744 and 0.731, respectively, demonstrating enhanced early detection ability. Conclusions: The findings indicate that early deviations in care pathways and temporal progression patterns provide meaningful predictive signals for sepsis deterioration. Integrating process mining with machine learning offers a promising approach for time-critical clinical decision support and proactive intervention. Full article

Organic mixed ionic–electronic conductors (OMIECs) are pivotal for bioelectronics; however, the microscopic origins of doping-dependent charge transport remain elusive. In this study, we established a multi-scale computational framework to quantify the distinct intramolecular electronic coupling ($H_{ab}$) distributions in systems with 25% and 75% doping levels. Our protocol employs automated quantum chemical calculations to exhaustively identify intrinsic local minima, ensuring thermodynamically stable initial conformations. Subsequent Molecular Dynamics (MD) simulations characterize the equilibration timescales and counter-ion dispersion behaviors. The simulation results reveal that the 75% doped system exhibits significantly stronger counter-ion confinement and a distinct electrostatic landscape compared to the 25% system. Finally, hybrid QM/MM calculations integrated with Constrained Density Functional Theory (CDFT) were utilized to evaluate $H_{ab}$ within these specific environments. The computed coupling distributions show a clear correlation with local electrostatic fluctuations induced by differing counter-ion arrangements. These findings indicate that doping-induced environmental disorder is a critical factor modulating intramolecular transport efficiency, providing a theoretical basis for optimizing OMIEC performance through electrostatic engineering. Full article

Eating disorders are multifactorial mental health conditions that predominantly affect adolescent girls and young women and constitute a major public health concern due to their severe and often chronic impact on physical, psychological, and psychosocial functioning. Although existing research suggests that gender-related constructs and traditional gender roles may be associated with the development and expression of eating disorders, empirical evidence using validated measures remains limited. Accordingly, the present study examines health-related variables from a gender-sensitive perspective in a clinical sample of women diagnosed with an eating disorder. Forty women aged 14 to 50 years completed an assessment protocol including measures of gender norms, eating disorder symptoms, mental health, and self-perceived overall health. Results indicated that poorer mental health and self-perceived overall health were significantly associated with higher levels of eating disorder symptomatology. In an exploratory hierarchical regression analysis, overall conformity to traditional feminine gender norms was associated with eating disorder symptomatology after accounting for health-related variables. Exploratory analyses of individual gender norm dimensions indicated that only a small number of associations remained statistically significant after applying a false discovery rate correction. In sum, within the limitations of a modest and heterogeneous clinical sample, the findings suggest that conformity to traditional feminine gender norms is associated with less favorable health indicators and greater eating disorder symptomatology among women with EDs. These results underscore the potential value of incorporating gender-informed perspectives into future research and clinical reflection, while highlighting the need for replication in larger and longitudinally designed studies. Full article

Understanding interactions between cations and DNA is essential for elucidating the structural dynamics of this fundamental biomolecule. While B-DNA is well known to dominate in long genomic DNA under physiological ionic conditions, its stability in very short DNA fragments—particularly in dilute solutions and in crude oligonucleotide preparations—has remained largely unexplored. Previous spectroscopic studies have primarily focused on long DNA, highly purified oligonucleotides, or high-salt environments, where collective polyion effects dominate. In contrast, the present results demonstrate that even in the absence of chain overlap and under low-salt conditions, Mg²⁺ ions efficiently stabilize the B-form by screening phosphate–phosphate electrostatic repulsion at the intrachain level. The ability to induce an A-to-B transition in crude, ultra-short DNA fragments highlights the fundamental role of divalent counterions in governing DNA conformation and establishes a lower bound for the length scale at which B-DNA can be stabilized. These findings are particularly relevant for dilute biological systems, fragmented DNA samples, and analytical protocols where short DNA fragments and low ionic strength are unavoidable. Full article

Modern Autonomous Vehicles generate large volumes of heterogeneous in-vehicle data, making cybersecurity a critical challenge as adversarial attacks become increasingly adaptive, stealthy, and multi-protocol. Traditional intrusion detection systems often fail under these conditions because of their limited contextual understanding, poor robustness to distribution shifts, and insufficient regulatory transparency. This study introduces LLM-Guardian, a hierarchical intrusion detection framework with decision-making mechanisms that integrates Large Language Models (LLMs) with classical statistical detection theory, optimal transport drift analysis, graph neural networks, and formal uncertainty quantification. LLM-Guardian uses semantic anomaly scoring, conformal prediction for distribution-free confidence calibration, adaptive cumulative sum (CUSUM) sequential testing for low-latency detection, and topology-aware GNN reasoning designed to identify coordinated attacks across CAN, Ethernet, and V2X interfaces. In this work, the framework is empirically evaluated on four heterogeneous CAN-bus datasets, while the Ethernet and V2X components are instantiated at the architectural level and left as directions for future multi-protocol experimentation. Full article

This perspective provides a multidisciplinary assessment of the use of lithium-ion batteries from electric vehicles (EVs) for second-life applications, motivated by the need to improve resource efficiency, reduce environmental impacts, and support a circular battery economy. Second-life deployment requires the integrated consideration of technical performance, legal compliance, and economic viability. The analysis combines a technical evaluation of battery aging mechanisms, operational load effects, and qualification strategies with a legal assessment of the EU Batteries Regulation (EU) 2023/1542 and an economic analysis of market potential and business models (BM). From a technical perspective, the limitations of State of Health (SOH) as a standalone indicator are demonstrated, highlighting the need for multiple health indicators and degradation-aware qualification. A scalable two-step qualification approach, combining qualitative inspection with a standardized quantitative measurement protocol, is discussed. From a legal perspective, regulatory requirements and barriers related to repurposing, waste classification, and conformity assessment are analyzed. From an economic perspective, business model patterns and market dynamics are evaluated, identifying Automated Guided Vehicles (AGVs) and industrial Energy Storage Systems (ESSs) for renewable firming as particularly promising applications. The paper concludes with recommendations for action and key research needs to enable safe, economically viable, and legally compliant second-life deployment. Full article

The Transport Layer Security (TLS) protocol is widely used nowadays to create secure communications over TCP/IP networks. Its purpose is to ensure confidentiality, authentication, and data integrity for messages exchanged between two endpoints. In order to facilitate its integration into widely used applications, the protocol is typically implemented through libraries, such as OpenSSL, BoringSSL, LibreSSL, WolfSSL, NSS, or mbedTLS. These libraries encompass functions that execute the specialized TLS handshake required for channel establishment, as well as the construction and processing of TLS records, and the procedures for closing the secure channel. However, these software libraries may contain vulnerabilities or errors that could potentially jeopardize the security of the TLS channel. To identify flaws or deviations from established standards within the implemented TLS code, a specialized tool known as TLS-Anvil can be utilized. This tool also verifies the compliance of TLS libraries with the specifications outlined in the Request for Comments documents published by the IETF. TLS-Anvil conducts numerous tests with a client/server configuration utilizing a specified TLS library and subsequently generates a report that details the number of successful tests. In this work, we exploit the results obtained from a selected subset of TLS-Anvil tests to generate rules used for anomaly detection in Suricata, a well-known signature-based Intrusion Detection System. During the tests, TLS-Anvil generates .pcap capture files that report all the messages exchanged. Such files can be subsequently analyzed with Wireshark, allowing for a detailed examination of the messages exchanged during the tests and a thorough understanding of their structure on a byte-by-byte basis. Through the analysis of the TLS handshake messages produced during testing, we develop customized Suricata rules aimed at detecting TLS anomalies that result from flawed implementations within the intercepted traffic. Furthermore, we describe the specific test environment established for the purpose of deriving and validating certain Suricata rules intended to identify anomalies in nodes utilizing a version of the OpenSSL library that does not conform to the TLS specification. The rules that delineate TLS deviations or potential attacks may subsequently be integrated into a threat detection platform supporting Suricata. This integration will enhance the capability to identify TLS anomalies arising from code that fails to adhere to the established specifications. Full article

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming. Full article

Coastal shipping plays a critical role in meeting maritime decarbonization targets under the International Maritime Organization’s (IMO) Carbon Intensity Indicator (CII) and the European Union Emissions Trading System (EU ETS); however, operators currently lack robust tools to forecast route-specific carbon intensity and evaluate the causal benefits of fuel switching. This study developed a distribution-free causal forecasting framework for voyage-level Carbon Dioxide (CO₂) intensity using an enriched panel of 1440 real-world voyages across four Nigerian coastal routes (2022–2024). We employed a physics-informed monotonic Light Gradient Boosting Machine (LightGBM) model trained under a strict leave-one-route-out (LORO) protocol, integrated with split-conformal prediction for uncertainty quantification and Causal Forests for estimating heterogeneous treatment effects. The model predicted emission intensity on completely unseen corridors with a Mean Absolute Error (MAE) of 40.7 kg CO₂/nm, while 90% conformal prediction intervals achieved 100% empirical coverage. While the global average effect of switching from heavy fuel oil to diesel was negligible (≈−0.07 kg CO₂/nm), Causal Forests revealed significant heterogeneity, with effects ranging from −74 g to +29 g CO₂/nm depending on route conditions. Economically, targeted diesel use becomes viable only when carbon prices exceed ~100 USD/tCO₂. These findings demonstrate that effective coastal decarbonization requires moving beyond static baselines to uncertainty-aware planning and targeted, route-specific fuel strategies rather than uniform fleet-wide policies. Full article

Preservation of spermatogonial cells is of critical importance for male patients undergoing gonadotoxic therapies. Testicular organoids generated by 3D polymeric scaffolds filled with decellularized extracellular matrix (dECM) have the potential to promote stem cell growth. We propose a protocol to produce dECM from porcine prepubertal tunica albuginea for use in polymeric scaffolds. Spectroscopic analysis, molecular biology techniques, and histo-morphological assessment were used to evaluate the morphology and mechano-chemistry of the dECM at each phase of the process. The results obtained from this study demonstrate that the protocol can produce a high-purity product without causing significant alterations to protein conformation. The dECM obtained was then employed in the creation of a 3D scaffold for the cultivation of testis organoids. This was achieved by utilizing a mixture of alginate (A) and chitosan (C), which are natural polymers with a high degree of biocompatibility, that have extensive application in the field of biomedicine. Scaffold characterization demonstrated that the presence of dECM affects the scaffold’s mechanical properties by tuning structural reorganization and reducing hygroscopicity. The cell viability assay demonstrates that the A/C scaffolds are non-cytotoxic after a pre-phase of immersion in the medium. Full article

Forests play a strategic role in global sustainability, and restoration is essential to meet ESG targets. Seedling quality strongly influences reforestation success, but standardized evaluation protocols are often lacking. This study aimed to identify and prioritize critical technical parameters of forest seedlings and determine the highest-priority factor affecting field performance. A total of 100 seedlings of Handroanthus impetiginosus and Sparattosperma leucanthum were evaluated using Quality Function Deployment (QFD), considering reforestation as the client to translate field performance requirements into nursery-level technical parameters. Seedling characteristics were compared to standards based on the literature and nursery best practices. QFD analysis revealed that stem thickness and integrity, absence of borers, well-developed and firm roots, and complete and healthy leaves were the most critical attributes. Hardiness, combining structural robustness, disease resistance, and vigor, emerged as the central factor. Observed non-conformities included disease (15%), stem bifurcations (10%), and substrate deficiencies (12%). These results demonstrate that QFD is an effective tool for systematically identifying and prioritizing seedling attributes. The study provides a structured approach for nursery evaluation and quality control, supporting informed decision-making to enhance the success of forest restoration projects. Full article

Wearable healthcare is shifting from passive tracking to active, closed-loop care by integrating polymeric three-dimensional (3D)-printed microneedle arrays (MNAs) with soft electronics and wireless modules. This review surveys the design, materials, and the manufacturing routes that enable skin-conformal MNA wearables for minimally invasive access to the interstitial fluid and precise but localized drug delivery. Looking ahead, the converging advances in multimaterial printing, nano/biofunctional coatings, and artificial intelligence (AI)-driven control are promising “wearable clinics” that can personalize monitoring and therapy in real time, thus accelerating the translation of MNA-integrated wearables from laboratory prototypes to clinically robust, patient-centric systems. Overall, this review identifies a clear transition from proof-of-concept MNA devices toward integrated, wearable, and closed-loop therapeutic platforms. Key challenges remain in scalable manufacturing, drug dose limitations, long-term stability, and regulatory translation. Addressing these gaps through advances in hollow MNA architectures, system integration, and standardized evaluation protocols is expected to accelerate clinical adoption. However, the realization of closed-loop wearable MNA-based systems remains constrained by challenges related to power consumption, real-time data latency, and the need for robust clinical validation. Full article

Amphiphilic dendrimers represent a promising class of nanoscale building blocks for functional materials, yet their conformational behavior, solvation, and interfacial activity remain incompletely understood. In this work, we employ atomistic molecular dynamics simulations to investigate G2–G4 carbosilane dendrimers functionalized with ethylene glycol terminal groups of two lengths—R1 (one ethylene glycol unit) and R3 (three units)—in water, toluene, and at fluid interfaces (water–toluene and water–air). Both types of dendrimers adopt compact, nearly spherical conformations in water but swell significantly (~83% in volume for G4) in toluene, a good solvent for the hydrophobic core. At the water–toluene interface, the dendrimers remain fully solvated in the toluene phase and show no surface activity. In contrast, at the water–air interface, they adsorb and adopt a mildly anisotropic, biconvex conformation, with a modest deformation. The total number of hydrogen bonds is reduced by ~50% compared to bulk water. Notably, the R3 dendrimers form more hydrogen bonds overall due to their higher oxygen content, which may contribute to the enhanced stability of their monolayers observed experimentally. These results demonstrate how dendrimer generation as well as terminal group length and hydrophilicity finely tune dendrimer conformation, hydration, and interfacial behavior, which are key factors for applications in nanocarriers, interfacial engineering, and self-assembled materials. The validated simulation protocol provides a robust foundation for future studies of multi-dendrimer systems and monolayer formation. Full article