Abstract: CAD/CAM hybrid ceramic materials have been increasingly used in restorative dentistry due to their ability to combine ceramic strength with the handling advantages of composite resins. The present study focused on how surface treatment protocols and commonly used immersion solutions affect the color stability and surface roughness of these materials. For this purpose, 256 specimens were fabricated from Vita Enamic, Lava Ultimate, Cerasmart, and Shofu Block HC. Following surface treatment using either mechanical polishing or Optiglaze, the specimens were immersed in coffee, red wine, cola, or distilled water for 14 days. Color difference (ΔE₀₀) and surface roughness (Ra) were measured at baseline and after 7 and 14 days. Data were analyzed using three-way repeated measures ANOVA (p < 0.05). Polymer matrix composition and surface treatment significantly influenced color stability and surface roughness (p < 0.05). Coffee and red wine caused the greatest discoloration, particularly in Bis-GMA- and TEGDMA-containing materials, while Cerasmart demonstrated the highest color stability. Although Optiglaze reduced surface roughness, it was associated with increased color change over time. These results emphasize the role of polymer composition and surface treatment in the esthetic performance of hybrid ceramic CAD/CAM materials.

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: Dysphagia, or difficulty swallowing, is a significant issue that impacts 10% to 33% of the elderly population and can lead to serious complications such as aspiration, malnutrition, and weight loss. To overcome these obstacles, there is a critical need for comprehensive rheological data and detailed information on food texture, specifically designed to align with local eating habits and cooking methods. This study aims to develop tables of rheological properties for foods commonly consumed by older adults in Portugal. Additionally, it will assess the impact of water quality on these properties during the cooking process. Based on this data, we will develop texture-modified diets that meet the nutritional needs of elderly dysphagic patients, ensuring they are safe, palatable, and practical for everyday care settings.

Abstract:

Lexicographic Preference Trees (LP-Trees) offer a compact and expressive framework for modeling complex decision-making scenarios. However, efficiently measuring similarity between complete or partial structures remains a challenge. This study introduces PLPSim, a novel metric for quantifying alignment between Partial Lexicographic Preference Trees (PLP-Trees), and develops three coalition formation algorithms—HRECS1, HRECS2, and HRECS3—that leverage PLPSim to group agents with similar preferences. We further propose ContractLex and PriceLex protocols (comprising five lexicographic protocols CLF, CFB, CFW, CFA, CFP), along with a new evaluation metric, F@LeX, designed to assess satisfaction under lexicographic preferences. To illustrate the framework, we generate a synthetic dataset (PLPGen) contextualized in a hybrid renewable energy market, where consumer PLP-Trees are matched with supplier tariffs to optimize coalition outcomes. Experimental results, evaluated using Normalized Discounted Cumulative Gain (nDCG), Davies–Bouldin dispersion, and F@LeX, show that PLPSim-based coalitions outperform baseline approaches. Notably, the combination HRECS3 + CFP yields the highest consumer satisfaction, while HRECS3 + CFB achieves balanced satisfaction for both consumers and suppliers. Although electricity tariffs and renewable energy contracts—both static and dynamic—serve as the motivating example, the proposed framework generalizes to broader multiagent systems, offering a foundation for preference-driven coalition formation, adaptive policy design, and sustainable market optimization.

Abstract: We present an improved algorithm based on the POlarization LIdar PHOtometer Networking (POLIPHON) method to retrieve cloud condensation nuclei (CCN) concentration profiles from spaceborne lidar observations. Our previous paper, which was the first study to demonstrate the feasibility of using measurements from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) to retrieve CCN, is revisited. Our results focus on the Evaluation of CALIPSO’s Aerosol Classification scheme over Eastern Mediterranean (ACEMED) research campaign that took place over Thessaloniki, Greece, in September 2011. We compare our results with our earlier retrievals, discussing the critical changes that have been made and the importance of using the proper conversions factors. We also demonstrate the use of conversion factors acquired based on CALIPSO aerosol typing for CCN retrievals. The analysis highlights the strong influence of smoke on CCN concentrations and shows that the assumed aging state of the smoke can significantly alter the retrieval outcome.

Abstract:

Ground-nesting shorebirds face growing pressure from recreational activities in coastal urban areas. We monitored the breeding success of Kentish Plover (Charadrius alexandrinus) and Little Ringed Plover (Charadrius dubius) over six consecutive years (2020–2025) at the Promenade of Sablettes, a heavily visited waterfront in Algiers, Algeria. We combined field surveys with multi-sensor remote sensing analysis using Sentinel-1, Sentinel-2, and Dynamic World data to quantify habitat change. A total of 105 nests were recorded across both species. Breeding success reached 70% during the COVID-19 lockdown period (2020–2021), when human visitation dropped sharply. In contrast, complete reproductive failure occurred in 2022 and 2023, coinciding with resumed tourism and unplanned construction activities. Remote sensing revealed that 80–85% of the study area experienced severe habitat degradation between 2020 and 2025, while suitable refuge zones shrank to less than 10% of the total surface. Fledged chicks consistently moved toward a less disturbed vegetated zone, highlighting its functional importance for brood survival. Our results show that human disturbance, rather than intrinsic habitat quality, is the main factor limiting breeding success at this site. When disturbance was reduced during the pandemic, the habitat proved fully functional for both species. These findings suggest that simple management measures such as seasonal access restrictions and symbolic fencing during the April–July breeding period could restore breeding conditions without major habitat engineering. This study provides one of the first integrations of long-term field breeding data with landscape-scale remote sensing to document the effects of the anthropause and subsequent recovery on urban shorebird populations.

Abstract: In the theory of mixed-type equations, there are many works in bounded domains with smooth boundaries bounded by a normal curve for first and second-kind mixed-type equations. In this paper, for a second-kind mixed-type equation in an unbounded domain whose elliptic part is a horizontal half-strip, a Bitsadze-Samarskii type problem is investigated. The uniqueness of the solution is proved using the extremum principle, and the existence of the solution is proved by the Green’s function method and the integral equations method. When constructing the Green’s function, the properties of Bessel functions of the second kind with imaginary argument and the properties of the Gauss hypergeometric function are widely used. Visualization of the solution to the Bitsadze-Samarskii type problem is performed, confirming its correctness from both mathematical and physical points of view.

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: This study addresses the challenge of long-term dependency modeling in agent behavior planning for long-horizon tasks and proposes a memory-driven agent planning framework. The method introduces hierarchical memory encoding and dynamic memory retrieval structures, enabling the agent to selectively retain and effectively utilize historical information across multiple time scales, thereby maintaining policy stability and goal consistency in complex dynamic environments. The core idea is to construct an interaction mechanism between short-term and long-term memory, where attention-guided retrieval integrates historical experience with current perception to support continuous planning and decision optimization in long-term tasks. The proposed framework consists of four key modules: perception input, memory encoding, state updating, and behavior generation, forming an end-to-end task-driven learning process. Experimental evaluations based on success rate, average planning steps, memory consistency score, and policy stability demonstrate that the proposed algorithm achieves superior performance in long-term task scenarios, effectively reducing planning redundancy and improving strategy coherence and task efficiency. The results confirm that the memory-driven mechanism provides a novel theoretical foundation and algorithmic framework for developing long-term task agents, establishing a solid basis for adaptive decision-making and continuous planning in complex environments.

Abstract: Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for tackling complex tasks that exceed individual agent capabilities. However, existing approaches often suffer from coordination inefficiencies, a lack of trust mechanisms, and suboptimal role assignment strategies. This paper presents a novel trust-aware coordination framework that enhances multi-agent collaboration through dynamic role assignment and context sharing. Our framework introduces a multi-dimensional trust evaluation mechanism that continuously assesses agent reliability based on performance history, interaction quality, and behavioral consistency. The coordinator leverages these trust scores to dynamically assign roles and orchestrate agent interactions while maintaining a shared context repository for transparent information exchange. We evaluate our framework across eight diverse task scenarios with varying complexity levels, demonstrating significant improvements over baseline approaches. Experimental results show that our trust-aware framework achieves a 87.4% task success rate, reducing execution time by 36.3% compared to non-trust-based methods, while maintaining 43.2% lower communication overhead. The framework's ability to adapt agent roles based on evolving trust scores enables more efficient resource utilization and robust fault tolerance in dynamic multi-agent environments.

Abstract: Cisplatin and other chemotherapy agents are critical for cancer treatment but pose a significant ecotoxicological risk following excretion into the environment. This study examined the efficacy of Cucurbita maxima (pumpkin) seed extract, a natural antioxidant source, in alleviating cisplatin-induced nephrotoxicity in an albino rat model. Forty rats were divided into four groups: a negative control, a positive control (cisplatin only), and two treatment groups receiving cisplatin alongside pumpkin seed extract at 300 mg/kg or 600 mg/kg body weight. Nephrotoxicity was assessed via serum biomarkers (urea, creatinine, total protein, albumin, and electrolytes) and histopathological examination. Phytochemical analysis verified the existence of flavonoids, phenolic compounds, and additional antioxidants, with the extract exhibiting a significant (92%) DPPH radical scavenging activity. Cisplatin administration significantly (p<0.05) elevated urea and creatinine levels and induced severe tubular necrosis and leukocytic infiltration. Co-treatment with the pumpkin seed extract, particularly at 600 mg/kg, markedly attenuated these effects, significantly restoring renal function markers and preserving histological architecture. The findings demonstrate that Cucurbita maxima seed extract possesses significant renoprotective properties, primarily attributed to its potent antioxidant constituents. This research highlights the value of plant-derived bioactive compounds as potential natural adjuvants to reduce the toxic side effects of pharmaceutical contaminants, offering insights relevant to both biomedical science and environmental toxicology.

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: This paper addresses the challenges in traditional enterprise performance analysis, including complex multi-source data structures, ambiguous indicator correlations, and poor decision interpretability. It proposes an enterprise performance optimization decision model that integrates knowledge graphs with causal inference. The model constructs a multi-entity and multi-relation knowledge graph to semantically integrate heterogeneous information from financial, market, and operational dimensions, enabling high-level representation of structured relationships among enterprise features. It further incorporates causal structure learning and inference mechanisms to identify key performance drivers and estimate intervention effects, revealing the true causal pathways among variables. In the optimization layer, the model combines knowledge representation with causal relationships to establish an interpretable decision objective function, ensuring that predictions possess both numerical accuracy and causal consistency with logical traceability. Experiments conducted on public enterprise datasets demonstrate that the proposed method outperforms mainstream deep learning and sequence modeling approaches in terms of error control and generalization performance, showing higher robustness and stability. Sensitivity analysis further confirms that the model maintains strong adaptability and consistent performance under different embedding dimensions, noise levels, and optimization strategies. This study provides a novel methodological framework and theoretical foundation for building interpretable and intervention-oriented intelligent decision systems, offering significant implications for data-driven performance evaluation and decision optimization.

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: This study presents two-dimensional numerical simulations of acoustic wave scattering involving a simplified human body model placed inside an enclosed cabin. The simulations utilise the µ-diff backscattering algorithm in MATLAB, which is suitable for model-ling frequency-domain interactions with multiple scatterers under penetrable boundary conditions. The body is represented as a cluster of penetrable, tangent circular cylinders with acoustic properties mimicking muscle, fat, bone, and clothing layers. Hidden PVC cylinders are embedded to simulate concealed objects. Several configurations were examined, varying the number of PVC inclusions (two to four), the frequency range, and the presence of an absorbing cabin wall. Sound pressure level (SPL) distributions around the body and at a 1-meter distance were analysed. Polar plots reveal distinct differences between the baseline body model and those incorporating PVC inclusions. The most pronounced effects occur near 160 Hz when an absorbing wall is present within the acoustic enclosure. The presence of an absorbing wall modifies wave behaviour, producing enhanced directional attenuation. The results demonstrate how object composition, spatial arrangement, and enclosure geometry influence acoustic backscattered fields. These findings highlight the potential of wave-based numerical modelling for detecting concealed items on the human body in confined acoustic environments, supporting the development of non-invasive security screening technologies. This work presents the first study addressing the 2D simulation of multiple acoustic waves scattering by a human body model within an acoustically enclosed environment for detecting hidden items on the human body.

Abstract: This research aims to analyze the influence of digital literacy, government policies, and infrastructure on coffee productivity through technology adoption in Kerinci Regency. A random sampling method was employed, and the sample size was determined using the Slovin formula, resulting in 95 respondents. Both primary and secondary data sources were utilized. Data were collected through observations, interviews, and questionnaires, and analyzed using a multiple linear regression model with SPSS 16.0 for Windows. The findings reveal that digital literacy, government policy, and infrastructure each have a significant impact on coffee productivity. Moreover, these three variables collectively exert a significant simultaneous effect on coffee productivity.

Abstract: The rapid evolution of technology characteristics has significantly influenced various sectors including fashion in which technology-enabled platforms have increasingly been utilized to enhance personalization and consumer engagement. This study investigates the effect of these characteristics on consumer behavior within fashion curation platforms. Integrating the task-technology fit and the unified theory of acceptance and use of technology models, this study examines key constructs using structural equation modeling. Data were collected via a week-long survey of 300 Korean consumers using fashion curation platforms. The findings reveal that technology characteristics exert a significant influence on task-technology fit and effort expectancy. Additionally, hedonic motivation, social influence, and facilitating conditions were pivotal in shaping behavioral intention. The novelty of this work lies in extending the integrated-model framework to a fashion curation context to offer a more nuanced understanding. Moreover, the findings provide practical insights for optimizing technology-enabled fashion platforms to boost user adoption and engagement.

Abstract: In this article we show that the polynomial \( t^2(4x - n)^2 - 2ntx \) does not always admits a perfect square with \( n\geq 2 \) and \( (x,t)\in \mathbb{(N^*)^2} \). We prove this when \( n=3 \) and we show by contradiction that one of x or t (in the expression \( t^2(4x - 3)^2 - 6tx \)) isn't an integer.

Abstract: Objectives: Acute obstructive pyelonephritis (AOP) is a urological emergency that combines bacterial infection with upper urinary tract obstruction. This retrospective study focuses on the microbial etiology and causes of obstruction, clinical manifesta-tions, antibacterial therapy, drainage type, and outcomes in patients diagnosed with AOP at a tertiary urology center between 1 January 2020 and 30 December 2024. Methods: One hundred patients with a mean age of 61.30 years were included in this retrospective study, which examines demographic data, comorbidities, clinical fea-tures, pathogens involved, antimicrobial regimens, and hospital outcomes. Results: Urolithiasis was the most frequent cause of obstruction (62%), followed by ureteral stenosis (14%) and tumors (11%). AOPs were mainly produced by Escherichia coli (58%), followed by Klebsiella spp. (21%); 18% of all identified bacteria were ESBL-producing Gram-negative bacilli, and 29% were MDR bacteria. The most used IV antibiotics were fluoroquinolones (52%), followed by cephalosporins (19%) and carbapenems (18%). Carbapenems were administered to all patients with AOP caused by ESBL-producing pathogens and to 62% of those with MDR bacteria. The duration of antibiotic therapy was individualized based on clinical response. Switch to oral administration was made after 4.3 ± 1.5 days, and the antibiotic treatment lasted 10.8 ± 3.2 days. Conclusions: The present study results support the integration of evidence-based guidelines with regional patterns of bacterial susceptibility to optimize therapeutic approaches and reduce severe outcomes in patients with AOP, most of whom have multiple comorbidities.

Abstract: Sustainability is now central to corporate legitimacy, yet its implementation remains uneven—particularly in emerging and fragile institutional contexts marked by weak enforcement, shifting stakeholder expectations, and fragmented governance. Although research acknowledges that senior executives shape sustainability outcomes, it often relies on structural or demographic proxies and overlooks how leaders actually interpret and address these demands. This conceptual, hypothesis-generating paper develops Executive Sustainability Cognition (ESC) as cognitive governance: the capability through which C-suite leaders select, frame, prioritise, and embed sustainability imperatives when formal institutional guidance is weak or ambiguous. Integrating Upper Echelons Theory, Institutional Theory, Stakeholder Theory, Strategic Leadership Theory, and sensemaking research, the paper proposes a four-stage ESC process: (1) attention and interpretation of sustainability cues, (2) framing and prioritisation of competing imperatives, (3) construction of sustainability vision, and (4) translation into structures, incentives, and culture. Eight testable hypotheses specify how ESC mediates between external pressures and organizational responses, and how institutional fragility, stakeholder fragmentation, and organisational learning orientation moderate these effects to produce symbolic versus substantive outcomes. By framing executive cognition as a substitute governance mechanism in fragile contexts, the paper offers a context-sensitive framework to guide research and improve sustainability practices in emerging and weak-governance markets.