Search Results (110,180)

The Václav vein (Březové Hory deposit, Příbram ore area, Czech Republic) is a base–metal vein containing minor Cu-Zn-Pb-Ag-Sb sulfidic mineralization in a usually hematitized gangue. A detailed mineralogical study using an electron microprobe revealed a complicated multistage evolution of the vein. Early siderite and Fe-rich dolomite were strongly replaced by assemblages of hematite+rhodochrosite and hematite+kutnohorite/Mn-rich dolomite, respectively. In addition, siderite also experienced strong silicification. These changes were associated with the dissolution of associated sulfides (sphalerite, galena). The following portion of the vein contains low-Mn dolomite and calcite gangue with Zn-rich chlorite, wittichenite, tetrahedrite-group minerals, chalcopyrite, bornite, and djurleite, again showing common replacement textures in case of sulfides. The latest stage was characterized by the input of Ag and Hg, giving rise to Ag-Cu sulfides, native silver (partly Hg-rich), balkanite, and (meta)cinnabar. We explain the formation of hematite-bearing oxidized assemblages at the expense of pre-existing “normal” Příbram mineralization due to repeated episodic infiltration of oxygenated surface waters during the vein evolution. Episodic mixing of ore fluids with surface waters was suggested from previous stable isotope and fluid inclusion studies in the Příbram ore area. Our mineralogical study thus strengthens this genetic scenario, illustrates the dynamics of fluid movement during the evolution of a distinct ore vein structure, and shows that the low content of ore minerals cannot be necessarily a primary feature of a vein. Full article

In modern industrial applications, production quality, system performance, process reliability, and safety have received considerable attention. This article proposes a dynamic event-triggered attack estimator for Markovian jump stochastic systems susceptible to actuator deception attacks. Utilizing the developed estimator, the presented attack-tolerant control strategy can tolerate the effects of such attacks and ensure the mean-square convergence of the overall closed-loop system. A dynamic event-triggered mechanism is implemented on the sensor side to optimize communication efficiency. To address the potential threat of deception attacks, a plug-and-play (PnP) secure monitoring and control architecture is introduced. This architecture facilitates the seamless integration of the designed attack-tolerant controller with the nominal feedback controller, thereby enhancing system security without requiring significant modifications to the existing control structure. The practicality and effectiveness of the proposed approaches are demonstrated through experimental results on a switched boost converter circuit. Full article

During the process of liquefied petroleum gas (LPG) exploitation, various sulfide-containing gases are produced, which significantly bring about corrosion attacks to copper equipment and facilities. Investigations on the effects of sulfides, hydrogen sulfide (H₂S), carbonyl sulfide (COS), and ethanethiol (C₂H₆S) on copper corrosion and synergistic mechanisms are of great significance for LPG production. This paper studied the synergistic corrosion effects of mixed sulfide-containing gases in LPG on copper plates, including the influence of H₂S + COS, H₂S + C₂H₆S, as well as H₂S + COS + C₂H₆S. The results showed that there exists an apparent synergistic effect between different sulfide-containing gases, which decreased the critical point of corrosion and enhanced the severity of copper corrosion. SEM observation on corrosion products with the addition of different sulfide-containing gases demonstrated that the microstructures of corrosion products are significantly different, which reveals different corrosion mechanisms. By characterizing the corrosion products on copper surfaces, corresponding corrosion mechanisms were proposed. Individual H₂S reacts with copper directly as chemical corrosion. The presence of water leads to the dissolution of H₂S into water film at the copper surface and results in electrochemical corrosion in nature. COS tends to decompose into acidic gas H₂S and CO₂, which accelerates the electrochemical corrosion at the copper surface. C₂H₆S can react with copper directly as chemical corrosion. A mixture of different sulfur-containing gases enhanced the corrosion attack by synergistic effect. Full article

Research has been conducted on medium-voltage direct current (MVDC) to address the limited transmission capacity of existing AC power transmission lines and to achieve efficient integration of renewable energy sources. Another method to increase the transmission capacity is to raise the maximum allowable temperature of the power cable. The maximum allowable temperature for cross-linked polyethylene (XLPE) in commercial power cables is 90 °C. Polypropylene (PP) is considered as an alternative material. PP has a maximum allowable temperature of 110 °C and possesses thermoplastic properties, making it environmentally friendly. However, PP may not ensure uniformity of the insulation layer depending on the extrusion process, including cooling conditions. This study aimed to determine the applicability of MVDC cables by assessing the uniformity of the insulation layer of extruded cables, considering the cooling conditions of PP in specimens. For the cooling conditions, ambient air, cooling press, and water cooling were evaluated for DC breakdown strength. Furthermore, the uniformity of the breakdown strength of the insulation layer, which was divided into sections such as conductor and sheath, was evaluated for commercial PP, XLPE, and the developed PP cables. This study aims to provide a comprehensive analysis of the DC BD strength of PP under various cooling conditions and emphasize the importance of uniformity in extruded cable sections. Full article

Background: Alzheimer’s disease is a slowly progressing neurodegenerative illness and the most common form of dementia. This pathology leads to an increase in cognitive decline and is responsible, in patients, for several difficulties in performing various activities of daily living, such as oral hygiene. Several experimental studies have shown that oral health in patients with Alzheimer’s disease worsens in direct proportion to the progression of the disease due to the appearance of gingivitis and periodontitis. Methods: This clinical literature review aims to evaluate a possible correlation between periodontal disease and Alzheimer’s disease, trying to understand if the periopathogens can contribute to the onset or the progression of Alzheimer’s disease (AD). The study was conducted on the database PubMed (MEDLINE) of full-text systematic reviews in English on humans and animals that were published in the last five years, from 2018 to 2023. This returned 50 publications, which, once the eligibility criteria were applied, resulted in the 10 publications examined in this review. The selected articles were organized through the construction of tables, analyzed, and compared through Judith Garrard’s Matrix method to arrive at the review results. Results: Infection by periopathogens can increase the risk of developing Alzheimer’s disease, but also the onset of the latter can make it more difficult to maintain proper oral hygiene, favoring the onset of periodontal disease: it is possible to affirm the existence of a correlation between periodontitis and AD. It was found that patients exposed to chronic periodontitis have a greater risk of developing a cognitive decline or AD and that oral pathogens can be responsible for neuropathologies and increasing systemic inflammation. Conclusions: Periodontitis and periodontal pathogens represent a real risk factor for the onset or worsening of AD; however, the pathogenetic mechanism is still not completely clear. Full article

Background: Myopathies encompass a wide range of diseases with diverse etiologies, courses, and prognoses, and can be either genetic or acquired in nature. One of the rare causes of acquired myopathies in children is hyperthyroidism. Ocular manifestations of hyperthyroidism include proptosis (exophthalmos) and widening of the palpebral fissure. Conversely, ptosis may indicate co-existing myasthenia or primary or secondary myopathy. Methods: This study presents a case of a 2-year-old child exhibiting both ocular disorders—each in one eye—along with features of proximal myopathy associated with undiagnosed thyrotoxicosis. Results: To our knowledge, this unique presentation of thyrotoxicosis in a young child has not been previously reported. After appropriate treatment for thyrotoxicosis, the child’s ocular and muscular symptoms showed improvement. Conclusions: Given that thyroid disorders can be a rare cause of both myopathy and ocular disorders in children, it is recommended that any child presenting with such symptoms undergo thyroid function screening tests. Full article

This study proposes a novel framework for determining variables’ weights in transportation assets condition indices calculations using statistical and machine learning techniques. The methodology leverages subjective ratings alongside objective measurements to derive data-driven weights. The motivation for this study lies in addressing the limitations of existing expert-based weighting methods for condition indices, which often lack transparency and consistency; this research aims to provide a data-driven framework that enhances accuracy and reliability in infrastructure asset management. A case study was performed as a proof of concept of the proposed framework by applying the framework to obtain data-driven weights for pavement condition index (PCI) calculations using data for the city of West Des Moines, Iowa. Random forest models performed effectively in modeling the relationship between the overall condition index (OCI) and the objective measures and provided feature importance scores that were converted into weights. The data-driven weights showed strong correlation with existing expert-based weights, validating their accuracy while capturing contextual variations between pavement types. The results indicate that the proposed framework achieved high model accuracy, demonstrated by R-squared values of 0.83 and 0.91 for rigid and composite pavements, respectively. Additionally, the data-driven weights showed strong correlations (R-squared values of 0.85 and 0.98) with existing expert-based weights, validating their effectiveness. This advanceIRIment offers transportation agencies an enhanced tool for prioritizing maintenance and resource allocation, ultimately leading to improved infrastructure longevity. Additionally, this approach shows promise for application across various transportation assets based on the yielded results. Full article

The existence or nonexistence of a complex structure on a differential manifold is a central problem in differential geometry. In particular, this problem on ${\mathrm{S}}^6$ was a long-standing unsolved problem, and differential geometry is an important tool. Recently, G. Clemente found a necessary and sufficient condition for almost-complex structures on a general differential manifold to be complex structures by using a covariant exterior derivative in three articles. However, in two of them, G. Clemente used a stronger condition instead of the published one. From there, G. Clemente proved the nonexistence of the complex structure on ${\mathrm{S}}^6$. We study the related differential operators and give some examples of nilmanifolds. And we prove that the earlier condition is too strong for an almost complex structure to be integrable. In another word, we clarify the situation of this problem. Full article

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45$°$ sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel. Full article

This study addresses the critical need for improved sustainability reporting in the construction sector, focusing on the integration of Life Cycle Sustainability Assessment (LCSA) indicators to enhance reporting quality and promote standardization. The increasing regulatory pressure from the European Commission, particularly in sustainability reporting, has intensified the demand for corporate transparency. Despite these efforts, many companies still face challenges in implementing robust sustainability performance measures. This research employs a systematic literature review alongside the case studies of three leading German construction companies to critically assess the current reporting practices and explore the integration potential of LCSA indicators. The findings highlight a significant gap between the existing sustainability disclosures and LCSA indicators, with only 7–19% of the assessed indicators being integrated into the current reporting practices. Although some consistency in reporting themes and qualitative disclosures is evident, the misalignment with LCSA indicators underscores the need for further integration of standardized, life cycle-based metrics. This study concludes that collaborative efforts among companies, policymakers, and LCSA researchers are required to bridge this gap, ensuring the adoption of the existing, scientifically robust indicators that enhance the precision, comparability, and transparency of sustainability reporting in the construction sector. Full article

Remote sensing image (RSI) scene classification aims to identify semantic categories in RSI using neural networks. However, high-performance deep neural networks typically demand substantial storage and computational resources, making practical deployment challenging. Knowledge distillation has emerged as an effective technique for developing compact models that maintain high classification accuracy in RSI tasks. Existing knowledge distillation methods often overlook the high inter-class similarity in RSI scenes, leading to low-confidence soft labels from the teacher model, which can mislead the student model. Conversely, overly confident soft labels may discard valuable non-target information. Additionally, the significant intra-class variability in RSI contributes to instability in the model’s decision boundaries. To address these challenges, we propose an efficient method called instance-level scaling and dynamic margin-alignment knowledge distillation (ISDM) for RSI scene classification. To balance the target and non-target class influence, we apply an entropy regularization loss to scale the teacher model’s target class at the instance level. Moreover, we introduce dynamic margin alignment between the student and teacher models to improve the student’s discriminative capability. By optimizing soft labels and enhancing the student’s ability to distinguish between classes, our method reduces the effects of inter-class similarity and intra-class variability. Experimental results on three public RSI scene classification datasets (AID, UCMerced, and NWPU-RESISC) demonstrate that our method achieves state-of-the-art performance across all teacher–student pairs with lower computational costs. Additionally, we validate the generalization of our approach on general datasets, including CIFAR-100 and ImageNet-1k. Full article

Ensuring the absence of vulnerabilities or flaws in smart contracts before their deployment is crucial for the smooth progress of subsequent work. Existing detection methods heavily rely on expert rules, resulting in low robustness and accuracy. Therefore, we propose EDSCVD, an enhanced deep learning vulnerability detection model based on dual-channel networks. Firstly, the contract fragments are preprocessed by BERT into the required word embeddings. Next, we utilized adversarial training FGM to the word embeddings to generate perturbations, thereby producing symmetric adversarial samples and enhancing the robustness of the model. Then, the dual-channel model combining BiLSTM and CNN is utilized for feature training to obtain more comprehensive and symmetric information on temporal and local contract features.Finally, the combined output features are passed through a classifier to classify and detect contract vulnerabilities. Experimental results show that our EDSCVD exhibits excellent detection performance in the detection of classical reentrancy vulnerabilities, timestamp dependencies, and integer overflow vulnerabilities. Full article

The frequency and severity of extreme hydroclimatic events in humid southeastern China have increased in the past half century, which is a serious concern. In this research, we used wood samples from 134 trees growing in the southeast coastal region of China (SECC) to reconstruct the Standardized Precipitation Evapotranspiration Index (SPEI) for the last 173 years (1843–2015 CE). Our reconstruction explained 41.6% of the variance contained in the November SPEI at a 7 month scale for the period 1957–2015. 17 extremely wet and 16 extremely dry events, 8 dry and 9 wet periods have been identified since 1843, and the most severe drought, coinciding with historical records, occurred in 1869 and 1870. The reconstruction reveals. Although the results reveal a modest upward trend in the SPEI and a predominance of extreme wet events over droughts throughout the period, the 20th century accounted for nine of the summers classified as extremely dry. Strong agreement between the current reconstruction and existing hydroclimatic reconstructions in southeastern China implied that our reconstruction exhibited high reliability. The composite anomalies of circulation during the period from May to November (MJJASON) indicate that the temporal variability in the SPEI reconstruction might be modulated by the local Hadley cell. These findings underscore the effectiveness of tree-ring-derived indices for reconstructing hydroclimatic trends in China’s humid regions and enhance our understanding of these changes within a long-term framework. Full article

Errors affect the actual meshing process of gears, alter the actual wear pattern of the tooth profile, and may even impact the overall service life of machinery. While existing research predominantly focuses on individual errors or a narrow set of factors, this study explores the combined effects of multiple errors on tooth profile wear. A comprehensive gear wear prediction model was developed, integrating the slice method, lumped mass method, Hertz contact model, and Archard’s wear theory. This model accounts for initial tooth surface topography, thermal deformation, dynamic effects, and wear, establishing strong correlations between gear wear prediction and key factors such as tooth surface morphology, temperature, and vibration. Experimental validation demonstrated the model’s high accuracy, with relatively small deviations from the observed wear. Initial profile errors (IPEs) at different positions along the tooth width result in varying relative sliding distances, leading to differences in wear depth despite a consistent overall trend. Notably, large IPEs at the dedendum and addendum can influence wear progression, either accelerating or decelerating the wear process over time. Full article

Microorganisms that exist in the seawater form microbial biofilms on materials used in marine construction, especially on metal surfaces submerged in seawater, where they form biofilms and cause severe corrosion. Biofilms are mainly composed of bacteria and their secreted polymeric substances. In order to understand how biofilms promote metal corrosion, planktonic and biofilm cells of Desulfovibrio bizertensis SY-1 (D. bizertensis) from Q235 steel were collected and analyzed as to their intracellular proteome and extracellular polymeric substances (EPS). The intracellular proteome analysis showed that the cellular proteins were strongly regulated in biofilm cells compared to planktonic cells, e.g., along with flagellar proteins, signaling-related proteins were significantly increased, whereas energy production and conversion proteins and DNA replication proteins were significantly regulated. The up-and-down regulation of proteins revealed that biofilm formation by bacteria on metal surfaces is affected by flagellar and signaling proteins. A significant decrease in DNA replication proteins indicated that DNA is no longer replicated and transcribed in mature biofilms, thus reducing energy consumption. Quantitative analysis and lectin staining of the biofilm on the metal’s surface revealed that the bacteria secreted a substantial amount of EPS when they began to attach to the surface, and proteins dominated the main components of EPS. Further, the infrared analysis showed that the secondary structure of the proteins in the EPS of the biofilm was mainly dominated by β-sheet and 3-turn helix, which may help to enhance the adhesion of EPS. The functional groups of EPS analyzed using XPS showed that the C element of EPS in the biofilm mainly existed in the form of combinations with N. Furthermore, the hydroxyl structure in the EPS extracted from the biofilm had a stronger hydrogen bonding effect, which could maintain the stability of the EPS structure and biofilm. The study results revealed that D. bizertensis regulates the metabolic pathways and their secreted EPS structure to affect biofilm formation and cause metal corrosion, which has a certain reference significance for the study of the microbially influenced corrosion (MIC) mechanism. Full article