Two isotropic pitches were prepared by air blowing and nitrogen distillation method using ethylene tar (ET) as a raw material. And correspondent carbon fibers were obtained through conventional melt spinning, stabilization and carbonization. The structures and properties of resultant pitches and fibers were characterized and their differences were discussed in this work. The results showed that introduction of the oxygen by air blowing method could quickly increase the yield and softening point of pitch. Moreover, the air blown pitch (ABP) composed of linear methylene chains of aromatic molecules while the nitrogen distilled pitch (NDP) mainly contained polycondensed aromatic rings, which was due to the oxygen containing functional groups existed in ABP could impede order stack of pitch molecules and form methylene bridge structure, instead of aromatic condensed structure like NDP. Meanwhile, the spinnability of ABP was not decreased even containing 2.31 wt% oxygen. In contrast, ABP had narrower molecular weight distribution, which contributed to better stabilization properties and higher tensile strength of carbon fiber. The tensile strength of carbon fibers from ABP was reached to 860 MPa with fiber diameter of about 10 μm, which was higher than that of NDP-derived carbon fibers of 640 MPa.

Being able to forecast events is of great importance in many fields, from brain behavior to earthquakes or stock markets. Because each dynamical system has intrinsic features, different statistical tools have to be used for each system. Here we study the time series of the output intensity of a fiber laser with an ordinal patterns analysis, and we look for temporal correlations in order to statistically forecast the most intense events. We set two thresholds, a low one and a high one, to distinguish between low intensity versus high intensity events. We find that when the time series is performing events below the low threshold it shows some preferred temporal patterns before performing events above a high threshold.

For development and successful application of any material, a clear understanding of their mechanical behavior is one of the most important things, but when it comes to nanofibers networks it become a challenge due to, their high porosity, many scales in their structure, and characteristics non-linear. Therefore, an experimental methodology in conjunction with a theoretical model that can fully consider their characteristics is still needed. In this work we proposed a model that incorporates the propagation of the elastic waves in two-phase media to determine the effective elastic modulus of electrospun membranes of PLA/gelatin given the mechanical properties of nanofibers, shape, distribution and concentration. The model was verified via laser ultrasonic testing. It was found that the values predicted for the effective modulus by the model were higher than the values obtained from experimental results. One explanation is due to the experimental density. As a result, the P-Wave velocity from the model best fit to experimental results and it has the same behavior, decrees as the concentration of gelatin in the solution. These results indicate the model and experimental methodology can assist in the dressing of nanofibers networks and electrospun materials.

A novel 8-channel demux device based on multicore photonic crystal fiber (PCF) structures that operate at C-band range (1530-1565nm) has been demonstrated. The PCF demux design is based on replacing some air-holes areas with lithium niobate and silicon nitride materials over the PCF axis alongside with the appropriate optimizations of the PCF structure. The beam propagation method (BPM) combined with Matlab codes were used to modeled the demux device and to optimized the geometrical parameters of the PCF structure. Simulation results show that 8-channel can be demultiplexing after light propagation of 5 cm with large bandwidth (4.03-4.69nm) and crosstalk ((-16.88)-(-15.93) dB). Thus, the proposed device has a great potential to be integrated in dense wavelength division multiplexing (DWDM) technology for increasing performances in networking systems.

In this article, bio-composites derived from starch-glycerol biodegradable matrix reinforced with jute fibers have been fabricated using the wet hand lay-up and compression moulding techniques. Samples having different weight percentages of jute fiber in the starch matrix have been analysed. The fibers surface was chemically treated by alkaline sodium hydroxide for improving the interphase bonding between fiber and matrix. Tensile test for the composites were done and the sample with highest tensile strength was selected for further tests that included water absorption, scanning electron microscopy and thermal analysis. It has been concluded that the ultimate tensile strength was found to be maximum for the composition of 15% fiber by weight composite as 7.547 MPa without epoxy coating and 10.43 MPa with epoxy coating. The major disadvantage of bio-composite is its high water absorption property, which in this study has been inhibited by the epoxy resin layer. Herein, the results of various tests done disclose a noteworthy improvement in the overall properties of bio-composite, in comparison to the neat biodegradable starch matrix.

This review examines the progressive separation of South America and Africa, which began approximately 145 million years ago during the Early Cretaceous and completed 100 million years ago. The rifting process created the South Atlantic Ocean and profoundly influenced vertebrate evolution on both continents. Initially connected as part of Gondwana, the gradual separation led to vicariant speciation and distinct evolutionary trajectories for previously continuous vertebrate populations. The separation particularly affected terrestrial vertebrates, resulting in parallel evolution of similar ecological niches on both continents, while creating opportunities for endemic lineages to develop. This process explains many modern distribution patterns and evolutionary relationships between South American and African vertebrate taxa, including parallel radiations in groups such as titanosaur dinosaurs during the Cretaceous and later parallel developments in mammals during the Cenozoic. The isolation also contributed to unique characteristics in each continent's modern vertebrate assemblages, such as the distinctive nature of South American primates and African ungulates.

The serious impact of Alternaria alternata on jujube black spot disease has seriously affected the quality and yield of jujube, constraining the sustainable development of the jujube industry. The purpose of this study was to isolate and screen highly effective biocontrol strains of jujube black spot disease from jujube rhizosphere soil. Thirty-three soil samples were collected from 4 regions in southern Xinjiang. The strains with antagonistic effects were isolated and screened by the dilution spread method and plate confrontation method and identified by morphological, physiological, and biochemical characteristics, as well as 16S rDNA, gyrB, and rpoB gene sequences. Indoor and field efficacy experiments were conducted to determine their biocontrol effect. A total of 110 strains with antibacterial activity were selected, and one strain, Bacillus velezensis 26-8, with a stable antagonistic effect was further tested. Biological characteristic experiments showed that strain 26-8 could grow at NaCl concentrations of 0.5%-10% and pH 4.0-9.0. The biocontrol experiment results showed that Bacillus velezensis 26-8 could achieve an 89.83% control effect against black spot disease. In conclusion, strain 26-8 has good salt and alkali tolerance, exerts a good control effect on jujube black spot disease, and is worthy of further study.

Ramsey theory enables re-shaping of the basic ideas of the quantum mechanics. Quantum observables, represented by linear Hermitian operators are seen as the vertices of the graph. Relation of commutation define coloring of the edges linking the vertices: if the operators commute, they are connected with the red link; if they do not commute they are connected with the green link. Thus, a bi-colored, complete, Ramsey graph emerges. According to the Ramsey theorem, complete, bi-colored graph built of six vertices, will inevitably contain at least one monochromatic triangle; in other words, the Ramsey number R(3,3)=6. In our interpretation, this triangle represents the triad of observables, which could or, alternatively, could not be established simultaneously in a given quantum system. The Ramsey approach to the quantum mechanics is illustrated with the numerous examples, including the motion of a particle in a centrally symmetrical field.

Tailored Skincare and Wellness Solutions for HIV-Positive Individuals: Addressing Skin Conditions, Joint Pains, and Memory Loss. Research Topic This research aims to explore tailored skincare and wellness solutions for HIV-positive individuals, specifically focusing on addressing skin conditions, joint pains, and memory loss. The research question revolves around developing innovative and effective medical approaches to improve the quality of life for this population. Research Importance The topic is crucial as it addresses the unique challenges faced by HIV-positive individuals, highlighting the need for tailored healthcare solutions. By understanding and addressing the physical symptoms associated with HIV, such as skin conditions, joint pains, and memory loss, the research contributes to enhancing the overall well-being and quality of life of individuals living with HIV. Research Method The research encompasses a comprehensive analysis of existing literature, including studies on dermatological problems, musculoskeletal issues, and cognitive impairments in HIV-positive individuals. Additionally, the development of a proposed skincare and serum line, RadientResilience, is based on a careful consideration of pharmacological effects and the unique skin conditions of this population. Research Findings The research findings emphasize the prevalence of dermatological conditions as a significant marker for HIV in affected populations. Furthermore, the study highlights the importance of thorough clinical assessment and the potential use of specific compounds and ingredients to alleviate joint discomfort in HIV-positive individuals. Research Conclusions The interpretations and conclusions drawn from the research underscore the need for tailored medical approaches to address the unique healthcare needs of HIV-positive individuals. The proposed skincare and serum line, RadientResilience, represents an innovative and empowering solution providing effective care for this targeted audience. Research Implications The research has implications for the development of specialized skincare and wellness products tailored to address the specific needs of HIV-positive individuals. Potential challenges in developing and implementing tailored skincare and wellness solutions for HIV-positive people include stigma and discrimination, as well as logistical and financial constraints. As Bouabida, Chaves, and Anane (2023) noted, “Stigma and discrimination are major barriers to HIV care engagement and adherence” (Challenges and barriers to HIV care engagement and care cascade: viewpoint, para. 3). Additionally, the authors highlight that “logistical and financial constraints can limit access to necessary healthcare services” (para. 5). Logistical and financial constraints can limit access to necessary healthcare services for individuals living with HIV by creating barriers such as transportation challenges, limited availability of healthcare professionals, and financial strain related to medical expenses.

In this work, we explore Conjecture 1 proposed in the newly released pioneering paper “Volatility transformers: an optimal transport-inspired approach to arbitrage-free shaping of implied volatility surfaces” by Zetocha Valer, which posits that the “One-X” property is both a necessary and sufficient condition for convex order between non-negative continuous strictly increasing distributions with the same mean. We provide a counterexample demonstrating that the conjecture, as originally stated, does not hold. By examining stochastic orders, particularly the increasing convex order, and the “One-X” property, we propose an enhanced version of the conjecture which is shown to be valid. Furthermore, we discuss the implications of these findings in the context of equity implied volatility fitting and the construction of implied volatility surfaces, highlighting the challenges posed by real-market conditions. In the end, we propose several applications in implied volatility surface construction or simulation.

Heat exchangers play a vital role in numerous sectors like power generation and automotive manufacturing. Enhancing the energy efficiency of these exchangers stands as a significant hurdle. Utilizing nanofluids emerges as an optimal solution to augment heat transfer rates, leveraging their superior thermal conductivity while addressing economic constraints linked with traditional heat exchanger technologies. The motivation for this study is to consider the comprehensive synergistic features of MWCNTs, SiO₂, and hybrid MWCNTs–SiO₂ EG-based nanofluids in plate heat exchangers. This was achieved by simulating a plate heat exchanger using CFD (Fluent and Gambit softwares) considering the comparison of two SiO₂, MWCNTs, and their hybrid forms at different mass concentrations. The Reynolds-Averaged Navier-Stokes (RANS) method was employed to model turbulent flows, and the nanoparticles were compared in both single-phase and two-phase states, with their properties derived from experimental studies. Moreover, Nusselt number, convective heat transfer coefficient, and pressure drop variations were computed at different Reynolds numbers. The findings indicated an increase in the Nusselt number with the incorporation of the hybrid nanofluid, achieving a maximum enhancement of 11.19% in comparison to EG at a Reynolds number of 240 and a concentration of 0.86 wt.%. Noticeably, the pressure drop remained negligible at lower Reynolds numbers, exhibiting marginal variations up to 0.3% at higher Reynolds numbers. At a concentration of 0.86 wt.% and a Reynolds number of 480, the introduction of MWCNTs, hybrid, and SiO₂ nano-additives resulted in respective increases in the heat transfer coefficient by 21.02%, 10.76%, and 2.16% and corresponding alterations in pressure drop of 0.11%, 0.04%, and 0.02%.

Artificial intelligence (AI) driven language models have seen a rapid rise in development, deployment, and adoption over the last few years. This surge has sparked many discussions about their societal and political impact, including political bias. Bias is a crucial topic in the context of large models due to its far-reaching consequences on technology, politics, and society. It significantly influences public perception, decision-making, political discourse, and AI policy governance and ethics. This study investigates political bias through a comparative analysis of four prominent AI models: ChatGPT-4, Perplexity, Google Gemini, and Claude.Through a comprehensive analysis by systematically and categorically evaluating their responses to politically and ideologically charged tests and prompts, utilizing the Pew Research Center’s Political Typology Quiz, the Political Compass assessment, and ISideWith political party quiz, this study identifies significant ideological leanings and the nature of political bias within these models. The findings revealed that ChatGPT-4 and Claude exhibit a liberal bias, Perplexity is more conservative, while Google Gemini adopts more centrist stances. The presence of such biases underscores the critical need for transparency in AI development and the incorporation of diverse training datasets, regular audits, and user education to mitigate these biases. This analysis also advocates for more robust practices and comprehensive frameworks to assess and reduce political bias in AI, ensuring these technologies contribute positively to society and support informed, balanced, and inclusive public discourse, which will point towards neutrality.The results of this study add to the ongoing discourse about the ethical implications and development of AI models, highlighting the critical need to build trust and integrity in AI models. Additionally, future research directions have been outlined to explore and address the complex issue of bias in AI.

Grass pea (Lathyrus sativus L.) is a protein-rich crop that is resilient to various abiotic stresses, including drought. However, it is not cultivated widely for human consumption due to the neurotoxin β-N-oxalyl-L-α, β-diaminopropionic acid (β-ODAP), and its association with neurolathyrism. Though some varieties with low β-ODAP have been developed through classical breeding, the β-ODAP content is increasing due to genotype x environment interactions. This review covers grass pea nutritional quality, β-ODAP biosynthesis, mechanism of paralysis, traditional ways to reduce β-ODAP, candidate genes for boosting sulfur-containing amino acids, and the potential and targets of gene editing to reduce β-ODAP content. Recently, two key enzymes (β-ODAP synthase and β-cyanoalanine synthase) have been identified in the biosynthetic pathway of β-ODAP. We proposed four strategies through which the genes encoding these enzymes can be targeted and suppressed using CRISPR/Cas9 gene editing. Compared to its homology in Medicago truncatula, the grass pea β-ODAP synthase gene sequence and β-cyanoalanine synthase showed 62.9% and 95% similarity, respectively. The β-ODAP synthase converts the final intermediate L-DAPA into toxic β-ODAP whist β-cyanoalanine synthase converts O-Acetylserine into β-isoxazolin-5-on-2-yl-alanine. Since grass pea is low in methionine and cysteine amino acids, improvement of these amino acids is also needed to boost its protein content.

Present experimental study assesses the mechanical properties of glass/carbon/glass hybrid composite laminates after exposure to moisture and elevated temperatures for extended periods. The top and bottom layers of the hybrid laminates are glass fiber reinforced and the middle layer is carbon fiber reinforced with the matrix specified as a polymer material. The hybrid laminates were manufactured using the resin transfer molding method and their compressive and tensile properties were determined using a tensile testing machine. The material parameters such as the storage modulus, the loss modulus, and the damping factors of the laminates were identified using dynamic mechanical analysis as a function of the temperature and the vibration frequency. The experimental results on compressive and tensile properties revealed slight variations when the hybrid laminates were kept at low temperatures for extended periods. This might occur due to the increasing molecular cross-linking of the polymer network. As the temperature increased, compressive, tensile, storage modules, loss modulus, and damping factors decreased. This might occur due to the increasing mobility of the binder material. Particularly, the highest stiffness parameters were obtained on -80°C/GCG (Glass/Carbon/Glass) laminates due to the presence of beta transition on the glassy region. The relationships between the glass transitions and the targeted frequencies were characterized. The values of the glass transition shift towards higher temperatures as the frequency increases. This might occur due to a reduction in the gaps between the crosslinking of the epoxy network when the frequency increases. The accuracy of the storage modulus results was compared with the empirical models. The model based on the Arrhenius law provided the closest correlation. Meanwhile, another model was observed that was not accurate enough to predict when gamma and beta relaxations occur under a glassy state.

This paper presents a comparative study of fundamental similarity functions for Siamese networks in semantic textual similarity (STS) tasks. We evaluate various similarity functions using the STS Benchmark dataset, analyzing their performance and stability. Additionally, we introduce a multi-objective approach for optimal threshold selection. Our findings provide insights into the effectiveness of different similarity functions and offer a straightforward method for threshold selection optimization, contributing to the advancement of Siamese network architectures in STS applications.

Human papillomavirus (HPV), a DNA virus, causes one the most common sexually transmitted infections (STI), spreading through direct sexual contact to the anogenital and oropharyngeal regions. Although most infected individuals may be asymptomatic and naturally undergo viral clearance, in some individuals HPV causes carcinogenesis of the infected epithelia. HPV now accounts for over 70% of oropharyngeal carcinomas (OPC). More than 90% of these OPC are associated with HPV 16, one of the 14 high-risk subtypes. In recent years, HPV positive (HPV+) OPC has been one of the most rapidly rising cancers, by new cancer incidence rates, especially among men. The HPV driven OPCs present some unique infection driven cellular and molecular features, constituting distinct preventive, therapeutic and prognostic strategies, in contrast to HPV negative (HPV-) OPC. In this manuscript, we drive through the role of HPV, from a sexually transmitted infection to malignancy transformation landscape, while parsing through the significant immune response environment. In this comprehensive review we collate key literature around transmission and epidemiology of HPV, while addressing the current state of clinical management, diagnosis, prevention and screening strategies, that can help shed light on this critical area.

Dark pigmentation can be observed in various parts of the plant, ranging from foliage to petals to berries. The molecular basis of black pigmentation appears to vary among species, with anthocyanins playing a significant role, although specific anthocyanin types and their mechanisms differ. These findings suggest that the development of phenotypes is species-specific or varies between larger taxonomic groups; this is further supported by the polyphyletic nature of dark pigmentation. Additionally, several different regulatory mechanisms have been described for the occurrence of dark pigmentation. Firstly, the repression or knockout of the competing flavone biosynthesis has been shown to lead to darker pigmentation while another mechanism is based on the activation and upregulation of the anthocyanin biosynthesis genes in the presence of MYB transcription factors. Potential ecological functions of dark pigmentation were identified as the protection of the photosynthesis apparatus, camouflage against herbivores, and the attraction of pollinators. Promising industrial applications include microbial factories for the production of natural food colourants, induction of novel phenotypes for the ornamental plant industry and lastly the increase of anthocyanins within agriculturally relevant crops.

Inducer of C-Repeat Binding Factor (CBF) expression (ICE), a well-known MYC-type bHLH transcription factor, plays a crucial role in the ICE-CBF-cold regulated (COR) cold signaling pathway. Brassica rapa (B. rapa), an important oilseed and vegetable crop, exhibits strong chilling and freezing tolerance. However, the exact molecular mechanisms by which ICE1 functions in B. rapa remain unclear. Here, 41 ICE1 homologous genes were identified from six widely cultivated Brassica species using a bioinformatics approach. These genes exhibit high conservation but reveal evolutionary complexity between diploid and allotetraploid species. Low-temperature stress induced ICE1 homolog expression, with patterns differing between strong and weak cold-tolerant varieties. Two novel ICE1 paralogs, BrICE1 and BrICE2, were cloned from Longyou 6. They positively regulate cold tolerance in B. rapa via a CBF-dependent pathway, contributing to reactive oxygen species (ROS) scavenfging and osmotic adjustment. Immunoblot analysis revealed that low temperatures induce BrICE1 and BrICE2 degradation via the 26S-proteasome pathway. In summary, ICE1 exhibits complex evolutionary relationships in Brassica species. BrICE1 and BrICE2 positively regulate cold tolerance via the CBF-dependent pathway and ROS scavenging mechanism and are also responsible for balancing the development and cold defense of B. rapa.

In the global carbon cycle, atmospheric carbon emissions, both ‘natural’ and anthropogenic, are balanced by carbon uptake (i.e. sequestration) that mostly occurs via photosynthesis, plus a much smaller proportion via geological processes. Since the formation of the Earth about 4.54 billion years ago the ratio between emitted and sequestered carbon has varied considerably with atmospheric CO₂ levels ranging from 100,000 ppm to a mere 100 ppm. Over this time, a huge amount of carbon has been sequestered due to photosynthesis and essentially removed from the cycle, being buried as fossil deposits of coal, oil and gas. Relatively low atmospheric CO₂ levels were the norm for the past 10 million years, and during the past million years they averaged about 220 ppm. More recently, the Holocene epoch, starting ~11,700 years ago, has been a period of unusual climatic stability with relatively warm, moist conditions and low atmospheric CO₂ levels of between 260 and 280 ppm. During the Holocene, stable conditions facilitated a social revolution with domestication of crops and livestock leading to urbanisation and development of complex technologies. As part of the latter process, immense quantities of sequestered fossil carbon have recently been used as energy sources, resulting in a particularly rapid increase in CO₂ emissions after 1950 CE to the current value of 424 ppm, with further rises to >800 ppm predicted by 2100. This is already perturbing the previously stable Holocene climate and threatening future food production and social stability. Today, the global carbon cycle has been shifted such that carbon sequestration is no longer keeping up with recent anthropogenic emissions. In order to address this imbalance it is important to understand the roles of high-capacity carbon sequestration systems, such as natural forests and tropical croplands, and to devise strategies to facilitate net CO₂ uptake.

Infrared thermography is a contactless imaging technique, useful to measure surface temperature at distance which has been widely applied in animal production. The possible applications within the pig husbandry include sow heat detection, reduction of the incidence of neonatal mortality, recognition of febrile state caused by pathogens, study of metabolic efficiency, evaluation of stress caused by heat, by constraints, or by aggressive interaction and meat quality prediction. Even if this technique could be easily influenced by environmental factors, the continuous improvement in the procedures and the decrease in the cost of the equipment will allow in the next future new and more applications in swine farming.

The energy transition is a challenge that affects all sectors, from transport to civil structures and industry. With reference to the industrial sector, the greatest need is the decarbonisation of industries energy-intensive, such as producers of iron and steel, aluminium, cement, petrochemicals, paper, etc. In particular, the steel industry is a hard to abate high-emissivity industry which due to chemical processes and high thermal demands (often above 500 °C) contributed to 7.4% of global CO₂ in the year 2021. This paper has explored the potential applications of hydrogen in the hard-to-abate sectors, highlighting its versatility and significance in decarbonization efforts. It provides an overview of hydrogen production methods, emphasizing the importance of sustainable and green hydrogen production. It reviews hydrogen production methods, storage, and potential in blended combustion. In the final part of the article, some considerations are introduced on the possible role of hydrogen in the steel sector (both as a supplementary fuel and as a reducing agent in innovative processes), showing how although there is potential for its inclusion as an element for the decarbonization, it is necessary to evaluate the energy balances of the various processes and the methods of hydrogen production very carefully, in order not to risk obtaining a marginal effect.

This paper examines the transformative journey of Artificial Intelligence (AI) agents from copilots to integral coworkers within diverse sectors. Beginning with a foundational overview, we delve into the historical evolution of AI agents, highlighting key technological milestones and the shift from simple automated tools to sophisticated decision-making systems capable of learning and adapting over time. We explore the capabilities of AI agents, including problem-solving, predictive analytics, and automation, and their applications across healthcare, business, and education, showcasing their role in diagnostics, customer service, and personalized learning, among other areas. We also address the integration of AI agents in the workplace, presenting strategies for collaboration between humans and AI, and outline the challenges and ethical considerations that accompany the development and deployment of AI technologies. A comprehensive tutorial provides insight into the development process of AI agents, emphasizing goal definition, data handling, algorithm selection, and ethical guidelines. We also consider the pathway towards Artificial General Intelligence (AGI) and the societal and ethical implications of widespread AI integration, proposing strategies for equitable and ethical AI use.

Based on our recent thermodynamic formulation of the Friedmann equation, we demon- strate how the age of the universe must be approximately 14.6 billion years. This is about 800 million years older than predicted by the Λ-CDM model and therefore greatly alleviates the early galaxy formation problem.

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

Friction stir welding (FSW) is a solid state welding process for joining metallic alloys and dissimilar metals. It has emerged as an alternative technology for welding high strength alloys that are difficult to join with conventional techniques. FSW is used to produce products with better mechanical properties. Now a days aluminum alloys have been widely used in several industrial applications such as aerospace, marine, automobile and commercial applications due to their light weight structure, better mechanical properties and high corrosion resistance.In this work, three welding parameters such as spindle speed, welding speed and plunge depth were considered for friction stir welding. FSW was carried out between dissimilar aluminum alloy plates (AA6061 and AA 8011) to produce butt joint.A Box-Behnken design with three factors and three levels was chosen to minimize the number of experimental conditions .The tensile strength and hardness of the welded regions were evaluated. The optimum level settings and the major parameters that influence the joint strength were obtained by Box-Behnken design.

This study explores the potential of natural compounds, including Daidzin, Luteolin, and Silibinin, as inhibitors of human monoamine oxidase B (MAO-B) for the treatment of Parkinson's disease. Docking simulations were performed using Autodock Vina with Pyrx program to evaluate the binding affinities of these compounds with MAO-B. Results indicate that Daidzin, Luteolin, and Silibinin exhibit promising binding affinities with MAO-B, with Daidzin demonstrating the highest affinity. Toxicity parameters were further predicted using the PkCSM Server, confirming the low toxicity of these compounds. Overall, the findings suggest that Daidzin, Luteolin, and Silibinin have the potential to be effective and safe treatments for Parkinson's disease through their inhibition of MAO-B. Further experimental validation is warranted to support their therapeutic efficacy in clinical settings.

“All diseases originate in the gut.” Hippocrates (400 BC) A healthy gut microbiome via the gut-brain-axis (GBA) elevates heart rate variability (HRV), a general measure of health and well-being. A dysbiotic gut microbiome, low in biodiversity and butyrate producers, alters tryptophan metabolism with release of proinflammatory cytokines, predominantly TNF-α, IL-6, and IL-1β. These also characterize chronic inflammation, oxidative stress, and a multitude of diseases, all exhibiting low HRV. Gut dysbiosis upregulates IFN-γ and with it IDO (indoleamine 2,3 dioxygenase). Tryptophan pivots from serotonin synthesis to that of IDO induced kynurenine, increasing the kynurenine to tryptophan ratio (KTR). An elevated KTR is positively linked to neurodegenerative and autoimmune diseases and negatively linked to HRV. Elevated IDO activity is not only enzymatic but also an intracellular signal transducer potentiated by TGF-β. This cytokine is the primary determinant of the TME. Also proposed is the gut-lung dysbiosis concept and consequent degradation of ACE2 (richest in lungs and gut). Leaky gut induced autoantibodies related to G-protein coupled receptors (GPCRs) in combination with increased Ang II further potentiate oxidative stress. Aldosterone and paroxysmal orthostatic tachycardia syndrome (POTS) paradoxes are highlighted in the context of GPCR and gut dysbiosis, and the role of Candida is explored. The triple play of a prebiotic (d-mannose), probiotic (bifidobacteria and lactobacilli), and postbiotic (butyrate) might improve intestinal barrier integrity, oppose entry of GPCR antigens, suppress the inflammatory cytokine triad, balance IFN-γ and TGF-β, suppress oxidative stress, depress KTR, elevate HRV, and extend lifespan and its quality.

With continued scaling of semiconductor devices , the value of predictive analysis through 1 accurate quantum transport modeling is exponentially increasing. Quantum transmitting Boundary 2 method (QTBM) has been a well-known method of choice to model quantum transport in simple 1-D 3 devices. Here, we present an extension of the QTBM to the 2-D domain and apply it for more realistic 4 devices for accurate performance analysis.

The necessity for effective intrusion detection systems (IDS) in big data environments has grown critical due to the rising prevalence of big data systems and the rising amount of security threats. Applying conventional intrusion detection methods to the enormous and intricate data produced by big data ecosystems presents difficulties. Deep learning, has proven to be exceptionally adept at deciphering complex, large-scale data. This study proposes a Deep Learning-based Intrusion Detection System (IDS) created specifically for the Big Data environment. Traditional intrusion detection systems struggle to efficiently identify and prevent cyber threats due to the ever-increasing volume and complexity of data in current networks. We suggest an IDS that makes use of Deep Learning (CNN, LSTM, GAN, etc.), to address this problem. These cutting-edge neural network topologies give the system the ability to process and analyze massive amounts of data for precise intrusion detection. Data collection, data preprocessing, feature engineering, DL model training, intrusion detection, warning generation, and reaction are some of the crucial parts of the suggested IDS. The data collection module mines the Big Data environment for network traffic, system logs, and security event information. To make sure the data is suitable for analysis, it is preprocessed. To improve the detection abilities of the DL models, important features are extracted from the data using feature engineering approaches.

There is a great need to develop research-based models for supporting collaboration between educational institutions. Collaboration models are needed, for example, to assist the transition from lower educational level to higher education or from higher education to work life. In this research, collaboration was conducted through a joint upper-secondary school and university course that addressed global challenges by using a project-based learning approach. There is a shortage of research exploring students’ perceptions of collaboration. Therefore, the main purpose of this qualitative case study is to explore the relevance of the joint course for students and university students. The research was guided by the following research question: What kind of relevance did the students experience in the joint project-based learning course? The data was gathered using questionnaires, interviews, and reflective study reports. The data was analyzed via theory-based content analysis where research-oriented relevance model was used as the analysis framework. The analysis framework enabled categorizing experienced relevance on individual, vocational and societal dimensions. Altogether, the analysis produced 7 relevance categories for upper-secondary students and 9 for university students. According to the analysis, upper-secondary school students felt that the course offered the most on an individual relevance dimension whereas university students experienced the vocational relevance dimension most diverse. As the main conclusion, this research produced new insights on the experienced relevance of upper-secondary level and higher education cooperation from the students’ point of view. The acquired knowledge can be useful for everyone developing new cross-institutional cooperation models.

This cross-sectional study investigates the impact of Virtual Private Networks (VPNs) on Nigerians during the 2021 Twitter ban, prompted by the Nigerian government's suspension of Twitter operations following the deletion of a tweet by the President. The study aims to analyze the frequency and awareness of VPN and Twitter usage among Nigerians during the ban, addressing specific objectives. Employing a survey research method, data was collected from 400 respondents through online and offline questionnaires. Descriptive statistical tools were used for data analysis, revealing significant findings. Results indicate a substantial decrease in Twitter usage during the ban, with IPVanish emerging as the preferred VPN brand. The study also highlights an increase in VPN awareness post-ban, emphasizing the need for educational initiatives. The demographic analysis reveals a dominant age group of 21 to 25 among social media users. Overall, the research underscores the complex interplay between social media dynamics, VPN adoption, and demographic factors, emphasizing the importance of nuanced regulatory approaches and ongoing user education in Nigeria's online landscape.

This study employs molecular docking experiments to investigate the binding interactions of Hypericin with key proteins implicated in Alzheimer's disease (AD). Hypericin demonstrated a significant binding energy of -10.1 kcal/mol with ABAD (3-hydroxyacyl-CoA dehydrogenase type II). The analysis revealed multiple binding interactions, including hydrogen bonds with arginine 6, serine 7, and glutamic acid 140, as well as hydrophobic bonds involving leucine 11, aspartic acid 142, and aspartic acid 86. These findings illuminate the intricate molecular interactions between Hypericin and ABAD, suggesting its potential as a lead compound for further therapeutic development in the context of Alzheimer's disease. Additionally, a parallel docking investigation was conducted for Beta-secretase 1 (BACE1), a protein pivotal in the generation of beta-amyloid peptides—a hallmark of Alzheimer's disease. Inhibiting BACE1 activity is considered a promising therapeutic strategy for AD. Computational analyses suggest that Hypericin may serve as a potential inhibitor of BACE1. Molecular Docking results reveal a robust binding affinity between Hypericin and BACE1, supported by a significant binding energy score of -10.3 kcal/mol. Notably, both ABAD and BACE1 exhibit high binding affinity with Hypericin. This theoretical exploration provides an initial understanding of the intricate molecular pathways involved in Alzheimer's disease and proposes Hypericin as a potential therapeutic compound for mitigating its progression.

This study aimed to overcome the difficulty of conducting the horizontal-well cavity leaching test in the field and to investigate the long-term stability of the horizontal-well salt-cavity natural gas storage. The simulation test design is combined with the similarity theory to study the cavity expansion characteristics and the influence law of cavity leaching parameters. Through the design of a rubber hose connection, an integrated closed test system for multi-stage horizontal-well cavity leaching and brine drainage was built. The test system also realises the repeatable backward movement of the injection well during the test. A similarity simulation of the test design was carried out, and the test platform was constructed to carry out multi-stage horizontal-well leaching tests with a nitrogen cushion. In the horizontal-well leaching tests with a nitrogen cushion, the influence laws of the well spacing, flow rate and liquid level position on cavity expansion were investigated. Based on the morphological characteristics of the horizontal-well cavity, a numerical model of the horizontal-well salt cavity was developed, which reflects the real cavity morphology leached in the test. The long-term stability of the horizontal-well salt-cavity natural gas storage under different internal pressures was investigated through numerical simulation.

Energy communities are key actors in the energy transition since they optimally interconnect renewable energy capacities with the consumers. Despite versatile objectives, they usually aim at improving the self-consumption of renewable electricity within low voltage grids to maximize revenues. In addition, energy communities are an excellent opportunity to supply renewable electricity to regional and national energy systems. However, effective price signals have to be designed to coordinate the needs of the energy infrastructure with the interests of these local stakeholders. The aim of this paper is to demonstrate the integration of energy communities at the national level with a bottom-up approach. District energy systems with a building scale resolution are modelled in a mixed integer linear programming problem. The Dantzig-Wolfe decomposition is applied to reduce the computational time. The methodology lies within the framework of renewable energy hub, characterized by a high share of photovoltaic capacities. Both investments into equipment and its operation are considered. The model is applied on a set of five typical districts and weather locations representative of the Swiss building stock. The extrapolation to the national scale revealed a heterogeneous photovoltaic potential throughout the country. Present electricity tariffs promote a maximal investment into photovoltaic panels in every region, reaching an installed capacity of 67.2 GW and generating 80 TWh per year. Since the national PV capacity needed is forecast at 15.4 GW peak, coordinated investments between local and national actors are needed to prevent dispensable expenses. An uncoordinated design is expected to increase the total costs for residential energy systems by 43.2% and curtails 48.2% of local renewable electricity.

Physical properties like shape, volume and size affect the dynamics of biological systems. Along these lines, we focus on the topological properties of biological fluids and their biochemical and physiological outcomes. We take as a paradigmatic example the salivary fluid and describe how its topological features may affect the physiopathology of the oral cavity. Topological approaches assess the general properties of saliva, ignoring small-scale physical details such as density, flow rate, stiffness, viscosity. Specifically, the mucin aggregates scattered in the salivary fluid can be tackled in terms of topological holes, i.e., vortical clusters that modify the direction, flow, impulse, local rate-of-change and velocity of saliva. While the current methodological approaches are inclined to remove the effects of impurities assessing systems as homogeneous structures, we argue that the occurrence of mucins breaks up the salivary fluid’s homogeneity, leading to unexpected biophysical modifications. We suggest that every collected salivary sample is not reliable for accurate clinical and experimental investigation, since it displays highly local as well as variable chemical, physical and biological features, not reflecting the current physiological state of the oral cavity. Therefore, the assessment of a single salivary sample is not fully reproducible and cannot provide information about the biophysical, enzymatic and microbiological content of the whole saliva. In sum, the very topological features of the saliva - such as volume, shape, antipodal cells, vortex area, whirling fluid mass, segmentation, discretization, triangulation, node numbering - produce unnoticed biological consequences and network connectivity features with intriguing operational implications.

A paddle wheel aerator is developed in this research, which includes a wireless sensor network for measuring the water quality in the aquaculture of marine shrimp ponds. It can also move to the desired position to fill oxygen and measure water quality. A basic requirement is good standard water quality to prevent shrimp from epidemics and improve production. The water quality paddle wheel aerator applies a microcontroller and sensors to measure eight parameters of water quality. These water quality parameters are observed on the web application via an IoT module. The movement system of the water-quality paddle wheel aerator consists of a LiDAR, GPS, and remote RF signal. Each item of data is recorded immediately on the cloud server while the water-quality paddle wheel moves in the marine shrimp farm. The controlled paddle wheel aerator is harnessed automatically to enhance precisely the spatial monitoring resolution of the measurement system installed, which is needless for a multiple measurement system with high cost of investment. Also, farmers can access real data through the Line application. Hence, they are able to plan and control a good environment for aquaculture, preventing the occurrence of various epidemics and decomposing organic matter in the pond.

Visible light communication (VLC) can offer the advantages of license and electromagnetic interference (EMI) free wireless transmission. As optical signal does not interference with the radio-frequency (RF) signal, VLC can be used to augment RF wireless communication to provide extra communication capacity while without degrading the performance of both signals. In order to achieve high performance VLC transmission, optical alignment between the optical transmit (Tx) and receiver (Rx) is very critical to enhance the received signal-to-noise ratio (SNR). Optical beam-steering at the Tx can be utilized to ensure narrow optical beam can reach the Rx; however, complicated and active tracking are required. Lenses or compound parabolic concentrators (CPCs) can be install in front of the Rx for focusing to enhance the SNR. However, these will limit the Rx field-of-view (FOV) and making the VLC transmission more subjected to misalignment issue. Hence, many creative optical antennas have been proposed and demonstrated using special optical materials as well as special Rx to enhance the FOV of VLC systems. However, they have their limitations, such as data rates and FOVs. In this work, we put forward and demonstrate a bi-direction free-space VLC system supporting multiple moveable Rxs using a light-diffusing optical fiber (LDOF). The downlink (DL) signal is launched from an head-end or central office (CO) far away to the LDOF at the client side via a free-space transmission. When the DL signal is launched to the LDOF, which acts as an optical antenna to re-transmit the DL signal to different moveable Rxs. The uplink (UL) signal is sent via the LDOF towards the CO. In a proof-of-concept demonstration, the LDOF is 100 cm long and the free space VLC transmission between the CO and the LDOF is 100 cm. 210 Mbit/s DL and 850 Mbit/s UL transmissions, meeting the pre-forward-error-correction bit error rate (pre-FEC BER = 3.8 × 10−3) threshold are achieved.

Direct measurement of strain within muscle is important for understanding muscle function in health and disease. Current technology (kinematics, dynamometry, electromyography) provides limited ability to measure strain within muscle. Regional fiber orientation and length are related with active/passive strain within muscle. Currently, ultrasound imaging provides the only non-invasive means of observing regional fiber orientation within muscle during dynamic tasks. Previous attempts to automatically estimate fiber orientation from ultrasound are not adequate, often requiring manual region selection, feature engineering, providing low-resolution estimations (one angle per muscle), and deep muscles are often not attempted. Here, we propose deconvolutional neural networks (DCNN) for estimating fiber orientation at the pixel-level. Dynamic ultrasound images sequences of the calf muscles were acquired (25 Hz) from 8 healthy volunteers (4 male, ages: 25–36, median 30). A combination of expert annotation and interpolation/extrapolation provided labels of regional fiber orientation for each image. We then trained DCNNs both with and without dropout using leave one out cross-validation. Our results demonstrated robust estimation of regional fiber orientation with approximately 3° error, which was an improvement on previous methods. The methods presented here provide new potential to study muscle in disease and health.

The poor impact resistance of carbon fiber reinforced composites limits their application in broader fields. In this study, a toughened epoxy resin (E2) modified with toughening agents and a micron-sized toughening particle (TP) were developed. Standard epoxy resin (E1), toughened epoxy resin (E2), and TP-toughened E2 resin (E3) were prepared into prepregs with SYT55-12K carbon fiber and ultimately fabricated into carbon fiber reinforced composites. The mechanical properties of the resins and the composites, particularly the compression properties, were characterized. The failure morphology of the composites and the distribution of toughening particles were observed using scanning electron microscopy (SEM), ultrasonic C-scan testing, and metallographic microscopy. It was found that the toughening agents improved the bending strength of the resin by approximately 12%, while the incorporation of TP enhanced the impact performance of the resin by 122%. Further characterization of the mechanical properties of the carbon fiber reinforced composites revealed that, with the addition of toughening agents, the resin increased the 0° bending strength of the composites by 29.9% and the 0° compressive strength by 34.3%. Additionally, significant enhancements were observed in interlaminar shear strength and open-hole compressive strength. After the addition of TP, the compressive strength of EC3 compared to EC2 increased by 74.3% after impact, while the mode I interlaminar fracture toughness increased by 34.8% and the mode II interlaminar fracture toughness improved by 67.9%. C-scan analysis indicated that the damage area of EC3 decreased by 60.7% compared to EC2, demonstrating that the particle toughening significantly enhanced the impact resistance of the composites. The compression failure of the TP-toughened EC3 exhibited typical characteristics of kink band failure, and the micron-sized particles were able to significantly balance and absorb loads. The findings of this study indicate that resin toughening can significantly improve the 0° compressive strength of carbon fiber reinforced composites, and that the use of micron-sized toughening particles can markedly enhance the post-impact compressive strength of the composites through interlaminar toughening. This research provides insights for improving the compressive and impact resistance performance of carbon fiber reinforced composites, with implications for industrial application and guidance for further industrialization trials.

The complex structure of the plant cell wall makes it difficult to use the biomass produced by biosynthesis. For this reason, the search for new strains of microorganisms capable of efficiently degrading fiber is a topic of interest. For these reasons, the present study aimed to evaluate both the microbiological and enzymatic characteristics of the fungus Curvularia kusanoi L7strain. For this, its growth in different culture media was evaluated. Wheat straw mineralization was evaluated by gas chromatography assisted by infrared spectroscopy. The production of endo- and exoglucanase, laccase and peroxidase enzymes in submerged solid fermentation of wheat and sugarcane bagasse were characterized. The strain efficiently mineralized raw wheat straw, showing a significant decrease in signals associated with cellulose, hemicellulose and lignin in the infrared spectra. High enzyme productions were achieved in submerged solid fermentation of both substrates, highlighting the high production of endoglucanases in sugarcane bagasse (2.87 IU/mL) and laccases in wheat (1.64 IU/mL). It is concluded that C. kusanoi L7 is an ascomycete with a versatile enzyme production that allows it to exhaustively degrade complex fibers such as raw wheat straw and sugar cane bagasse, making it a microorganism with great potential in the bioconversion of plant biomass.

The field of biosensors has found great use for surface plasmon resonance (SPR), which has attracted attention for its multiple applications. With the goal of early cancer detection at the level of a single living cell, this research numerically investigates a very sensitive D-shaped fiber-optic biosensor based on surface plasmon resonance (SPR). The titanium oxide (TiO2) is coated on gold (Au), which is utilized as a thin-film plasmonic material in the suggested biosensor structure. The guiding of light in the fiber is modeled using the finite element method (FEM) when six different types of healthy human cells and their malignant counterparts are exposed to its sensitive region. Refractive index (RI) differences between cancer cells and healthy cells are found through the analysis of the optical spectra and their amplitudes. This enables us to identify small changes in the optical characteristics of cells that could be a sign of pathogenic alterations. In this work, we concentrated on three forms of cancer: skin cancer (basal cell) and breast cancer (MDA-MB-231 and MCF-7). The results show which parameters are optimal to ensure that the sensor performs well when used to detect the three types of cancer under study. When the suggested sensor’s performance is compared to SPR sensors that have previously been published in the literature, it becomes clear that our sensor offers great promise for early cancer detection.

SIBO is a pathology of the small intestine and may predispose individuals to a range of nutritional deficiencies. Very little is known whether specific subtypes of SIBO, such as hydrogen-dominant (H+), methane-dominant (M+), or hydrogen/methane –dominant (H+/M+), impact nutritional status and dietary intake in SIBO patients. The aim of this study was to investigate possible correlations between biochemical parameters, dietary nutrient intake, and distinct SIBO subtypes. This observational study included 67 patients who were newly diagnosed with SIBO. Biochemical parameters and diet were studied utilizing laboratory tests and food records, respectively. The H+/M+ group was associated with low serum vitamin D (p<0.001), low serum ferritin (p=0.001) and low fiber intake (p=0.001). The M+ group was correlated with high serum folic acid (p=0.002) and low intakes of fiber (p=0.001) and lactose (p=0.002). The H+ group was associated with low lactose intake (p=0.027). These results suggest that the subtype of SIBO may have varying effects on dietary intake, leading to a range of deficiencies in the body. Conversely, specific dietary patterns may predispose to the development of a SIBO subtype. The assessment of nutritional status and diet, along with diagnosis of SIBO substype, are believed to be key components of SIBO therapy.

Knowing the mechanical properties of fibrous composite materials used in aeronautical industry from the design phase is a primary objective for designers. There are many methods for determining them, but they are laborious and require a long calculation time. And some of the methods are very approximate, giving only upper and lower bounds for these values. Experimental measurements also consume time and others resources. A method for sufficiently accurate and quickly obtained estimation of the engineering constants of the homogenized material is presented in the work. FEM is used to determine the natural frequencies of a standard bar, for which there are sufficiently precise classical methods for expressing these constants according to the properties of the homogenized material. In this way, these sought constants can be determined. In the work, Young's modulus will be determined for such a material, using the relationships that provide the proper pulsations for the longitudinal vibrations. Within the adopted model, transverse and torsional vibrations will be eliminated by blocking the nodes on the surface of the bars. In this way, more longitudinal self-pulsations can be obtained, so the precision in calculating the Young's modulus will increase.

A novel simplifying Volterra structure algorithm is proposed for intensity modulation direct detection (IM-DD) optical fiber short distance communication system by using the decision feedback equalization algorithm (DFE). Based on this algorithm, the signal damage for four-level pulse amplitude modulation signal (PAM-4) is compensated, which is caused by device bandwidth limitation and dispersion during transmission. Experiments have been carried out using a 25GHz Electro-absorption Modulated Laser (EML), showing that PAM-4 signals can transmit over 10km in standard single-mode fiber (SSMF). The 112Gbps and 128Gbps signals can reach the error rate threshold of KP4-FEC (BER=2*10-4) and HD-FEC (BER=3.8*10-3), respectively. The simplified principle and process of proposed Volterra-based equalization algorithm are presented. Experimental results show that the algorithm complexity is greatly reduced by 75%, which provides an effective theoretical support for the commercial application of this algorithm.

Carbon fiber insole (CFI), which is lightweight and stiff to reduce energy loss and help wearers perform better in sports, has recently been introduced. However, there are scarce reports on the effects of CFI on sports performance, muscle fatigue and wearing comfort. This study investigat-ed the short-term effects of CFI on sports performance, lower extremity muscle activity, and sub-jective comfort. Thirty young healthy males performed various sports tasks and treadmill runs with wearable sensors under two experimental insole conditions (benchmark insole as a baseline, CFI). The results showed that compared to the benchmark insole, CFI significantly improved sports performance in terms of power generation and agility. However, it activated more of the Tibialis Anterior and Gastrocnemius Medialis muscles and was perceived stiffer, and less com-fortable. These findings suggested that CFI can improve sports performance, but it could cause more lower extremity muscle fatigue and subjective discomfort.

As use of composite materials increases, the search for suitable automated processes gains relevance to guarantee production quality by ensuring uniformity of the process, minimizing the amount of generated scrap and reducing time and energy consumption. Limitations on production by traditional means such as hand lay-up, vacuum bagging and in-autoclave methods, tend not to be as efficient when the size and shape complexity of the part being produced increases, motivating the search for alternative processes such as the Automated Tape Laying (ATL). This work aims to describe the process of modelling and simulating a composite ATL with in situ consolidation by characterizing the machine elements, using the finite differences method in conjunction with energy balances, in order to create a digital twin of the process for further control design. The modelling approach implemented is able to follow the process dynamics when changes to the heating element are imposed as well as to predict the composite material temperature response, making it suitable to work as a digital twin of a production process using an ATL machine.

The booming wearable market and recent advances in material science has led to the rapid development of the various wearable sensors, actuators, and devices that can be worn, embedded in fabric or accessories, or tattoos directly onto the skin. Wearable actuators, a subcategory of wearable technology, have attracted enormous interest from researchers in various disciplines and many wearable actuators and devices have been developed in the past few decades to assist and improve people's everyday lives. In this paper, we review the actuation mechanisms, structures, applications, and limitations of recently developed wearable actuators including pneumatic and hydraulic actuators, shape memory alloys and polymers, thermal and hygroscopic materials, dielectric elastomers, ionic and conducting polymers, piezoelectric actuators, electromagnetic actuators, liquid crystal elastomers, etc. Examples of the recent applications such as wearable soft robots, haptic devices, and personal thermal regulation textiles are highlighted. Finally, we point out the current bottleneck and suggest the prospective future research directions for wearable actuators.

Lignocellulosic fibers are widely applied as composite reinforcement due to their properties. The thermal degradation behavior determines the maximum temperature in which the fiber can be applied without significant mass loss. It is possible to determine these temperatures using Thermogravimetric Analysis (TG). In particular, when curves are obtained at different heating rates, kinetic parameters can be determined and more detailed characteristics of the material are obtained. However, every curve obtained at a distinct heating rate demands material, cost, and time. Methods to predict thermogravimetric curves can be very useful in the materials science field and in this sense mathematical approaches are powerful tools if well employed. For this reason, in the present study, curaua TG curves were obtained at three different heating rates (5, 10, 20, and 40 °C.min-1) and Vyazovkin kinetic parameters were obtained. After, the experimental curves were fitted using an artificial neural network (ANN) approach followed by a Surface Response Methodology (SRM). Curves at any heating rate between the minimum and maximum experimental heating rates were obtained with high reliability. Finally, Vyazovkin kinetic parameters were tested again with the new curves showing similar kinetic parameters from the experimental ones. In conclusion, due to the capability to learn from the own data, ANN combined with SRM seems to be an excellent alternative to predict TG curves that do not test experimentally, opening the range of applications.

A new covered yarn system is proposed in this manuscript by controlling the tension of the spandex elastic yarn drawing.. By analyzing the relationship between the draw ratio and yarn tension, it has been verified that the new tension controlled drawing system is feasible and results in yarns with superior quality and process stability.

The damage caused by global warming is rapidly increasing, and its adverse effects become more evident with each passing day. Although it is known that the use of alternative binder materials in concrete would decrease this negative effect, reluctance to new composites continues. Waste use plays a vital role in sustainability studies. In this study, pure cement paste was prepared and enriched with carbon fiber. This study investigated the wide range of volume fraction of carbon fiber in cement-based composites. Two different industrial wastes, marble dust, and bottom ash were chosen and mixed with cement and four different (0.3%, 0.75%, 1.5%, and 2.5%) carbon fiber volume fractions. Based on physical, mechanical, and durability tests at 7, 28, and 56-days of curing, the composites were resistant to sulfate and seawater attack. The 0.75% carbon fiber addition seems to be an optimum volume percentage beyond which both physical and mechanical properties were adversely affected. The composites with 0.75% carbon fiber have reached 48.4 MPa and 47.2 MPa at 56-days of curing for marble dust and bottom ash mixture groups, respectively.

We propose a 10-Gb/s 64-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes Reinforcement Learning (RL) SARSA based decision method to indicate an effective decision which mitigates nonlinearity. By utilizing RL-SARSA algorithm, the results demonstrate that significant reduction can be obtained in terms of bit error rate.

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

Expansive soil shows dual swell-shrink which is not suitable for the construction. Several mitigating techniques exist to counteract the problem promulgate by expansive clayey soils. This paper explored the penitential mecho-chemical reinforcement of expansive clayey soil to mitigate the effect of upward swelling pressure and heave. The polypropylene fiber is randomly distributed in the soil for mechanical stabilization, and the industrial residual silica fume is used as a chemical stabilizer. The experimental analysis is made in three phases which involved the tests on mechanical reinforced expansive soil using randomly distributed polypropylene fibers with different percentages (0.25%, 0.50%, and 1.00%), and 12mm length. The second phase of experiments carried out on chemical stabilized expansive soil with different percentages (2%, 4% and 8%) of silica and next phase of the experimental focused in the combination of mecho-chemical stabilization of the expansive soil with different combination of silica (i.e., 2%, 4% and 8%) and polypropylene fibers (i.e., 0.25%, 0.50% and 1.00%). Maximum dry density (MDD), optimum moisture content (OMC), liquid limit (LL), plastic limit (PL), plastic index (PI) grain size, and constant volume swelling pressure test were performed on unreinforced and reinforced expansive soil to investigate the effect of polypropylene fiber and silica fume on the engineering properties of expansive clayey soil. The experimental results illustrate that the inclusion of polypropylene fiber has a significant effect on the upward swelling pressure and expansion property of expansive soil. The reduction in the upward swelling pressure and expansion is a function of fiber content. These results also indicated that the use of silica fume caused a reduction in upward swelling potential, and its effect was considerably more than the influence of fiber.

An ethanol vapor sensor based on a microfiber with quantum-dots (QDs) gel coating is proposed and demonstrated. The QDs gel was made from UV glue as the gel matrix and the CdSe/ZnS QDs with a concentration of 1mg/mL. The drawing and coating process were conducted by using a simple and low-cost home-made system. The bending, ethanol sensing, temperature response and time response tests were carried out, alternatively. The experimental results show that the fabricated sensor has a high sensitivity of -3.3%/ppm, a really low temperature cross-sensitivity of 0.17 ppm/℃ and a fast response time of 1.1s. The robust structure with ease of fabrication and excellent sensing performance render it a promising platform for real ethanol sensing application.

Acoustic emission (AE) has proven to be a very useful technique for determining damage in ceramic matrix composites (CMCs). CMCs rely on various cracking mechanisms which enable non-linear stress-strain behavior with ultimate failure of the composite due to fiber failure. Since these damage mechanisms are all micro-fracture mechanisms, they emit stress waves ideal for AE monitoring. These are typically plate waves since for most specimens or applications one dimension is significantly smaller than the wavelength of the sound waves emitted. By utilizing the information of the sound waveforms captured on multiple channels from individual events, the location and identity of the sources can often be elucidated. The keys to the technique are the use of wide-band frequency sensors, digitization of the waveforms (modal AE), strategic placement of sensors to sort the data and acquire important contents of the waveforms pertinent for identification, and familiarity with the material as to the damage mechanisms occurring at prescribed points of the stress history. The AE information informs the damage progression in a unique way which adds to the understanding of the process of failure for these composites. The AE methodology was applied to composites tested in fatigue at different frequencies where identification of when and where AE occurred coupled with waveform analysis leads to source identification and failure progression.

In this paper, a geometric body-centered model to simulate the periodic structure of unidirectional fibrous composites is presented. To this end, three prescribed configurations are introduced to predict in a deterministic manner the arrangement of internal and neighboring fibers inside the matrix. Thus, three different representative volume elements (RVEs) are established. Furthermore, the concept of the interphase has been taken into account, stating that each individual fiber is encircled by a thin layer of variable thermomechanical properties. Next, these three unit cells are transformed in a unified manner to a coaxial multilayer cylinder model. This advanced model includes the influence of fiber contiguity in parallel with the interphase concept on the thermomechanical properties of the overall material. Then, by the use of this model, the authors propose explicit expressions to evaluate the longitudinal and transverse thermal conductivity of this type of composite. The theoretical predictions were compared with experimental results, as well as with theoretical values yielded by some reliable formulae derived from other workers, and a reasonable agreement was found.

The interest in random fiber lasers (RFLs), first demonstrated one decade ago, is still growing and their basic characteristics have been studied by several authors. RFLs are open systems that present instabilities in the intensity fluctuations due to the energy exchange among their non-orthogonal quasi-modes. In this work, we present a review of the recent investigations on the output characteristics of a continuous-wave erbium-doped RFL, with emphasis on the statistical behavior of the emitted intensity fluctuations. A progression from the Gaussian to Lévy and back to the Gaussian statistical regime was observed by increasing the excitation laser power from below to above the RFL threshold. By analyzing the RFL output intensity fluctuations, the probability density function of emission intensities was determined, and its correspondence with the experimental results was identified, enabling a clear demonstration of the analogy between the RFL phenomenon and the spin-glass phase transition. A replica-symmetry-breaking phase above the RFL threshold was characterized and the glassy behavior of the emitted light was established. We also discuss perspectives for future investigations on RFL systems.

A single mode waveguide grating coupler based on multiple Si-SiO₂ pairs onto Si substrate has been designed. Numerical analysis has been carried out to calculate optimum thickness of the layers of Si-SiO₂ that ensures the constructive interference between reflected waves and actual guided wave for high coupling efficiency. Based on the results, an optimal design is developed and modeled by using a 2-D finite difference time domain (2-D FDTD) simulator that dictates a coupling efficiency of as much as 78% (-1.07 dB) at the wavelength of 1550 nm, and a 1-dB bandwidth of 77 nm. The numerical method will be useful to calculate the optimum thicknesses of the layers for any reflector based grating coupler of different materials.

This article examines the integration of artificial intelligence (AI) and robotic process automation (RPA) in financial accounting and management, underscoring their role in driving the digital transformation of corporate finance. It discusses the shortcomings of traditional financial processes and highlights the potential of AI and RPA technologies to enhance efficiency, accuracy, and cost-effectiveness. The paper also explores the limitations of RPA, such as its challenges in processing unstructured data and handling complex decision-making scenarios. Looking forward, it considers the future trends in AI and RPA, emphasizing the benefits of cloud technology in scaling automated systems and addressing associated challenges.

Understanding the factors that contribute to slope failures, such as soil saturation, is essential for mitigating rainfall-induced landslides. Cost-effective capacitive soil moisture sensors have the po-tential to be widely implemented across multiple sites for landslide early warning systems. How-ever, these sensors need to be calibrated for specific applications to ensure high accuracy in readings. In this study, a soil-specific calibration was performed in a laboratory setting to integrate the soil moisture sensor with an automatic monitoring system using the Internet of Things (IoT). This re-search aims to evaluate a low-cost soil moisture sensor (SKU:SEN0193) and develop calibration equations for the purpose of slope model experiment under artificial rainfall condition using silica sand. The results indicate that a polynomial function is the best fit, with a coefficient of determi-nation (R²) ranging from 0.92 to 0.98 and a root mean square error (RMSE) ranging from 1.17 to 2.67. The calibration equation was validated through slope model experiments, with soil samples taken from the model after the experiment finished. Overall, the moisture content readings from the sensors showed approximately a 10% deviation from the actual moisture content. The findings suggest that the cost-effective capacitive soil moisture sensors has the potential to be used for the development of landslide early warning system.

Nanobubbles, gas-filled entities that possess a size of less than , have notably emerged as critical components within various domains such as environmental science and medicine, primarily due to their remarkable stability and distinctive diffusion characteristics that differentiate them from larger bubbles. This comprehensive study meticulously explores the diffusion rates and stability of hydrogen and oxygen when encapsulated in oxyhydrogen nanobubbles, specifically within aqueous environments, utilizing the Chapman-Enskog theory, which is a well-established framework for understanding gas diffusion processes, alongside a modified Arrhenius equation to effectively evaluate activation volume and stability parameters. The findings from this investigation reveal that the diffusion processes of both gasses experience a significant reduction in rate due to the influence of high internal pressure, as well as the molecular interactions occurring with water, whereby hydrogen is observed to exhibit a marginally accelerated diffusion rate in comparison to oxygen. It is crucial to understand that elements like surface charges, zeta potential, and the idea of activation volume significantly influence the enduring stability of nanobubbles, particularly in specific environmental scenarios that could boost their longevity. These significant findings pose a challenge to classical diffusion models, thereby suggesting a promising potential for the sustained application of nanobubbles in various fields such as water treatment processes and therapeutic medical interventions. Future research endeavors should prioritize the refinement of theoretical models while also aiming to experimentally validate the mechanisms of stability that have been observed across a range of diverse applications to further substantiate these findings.

There are many features of homeostasis worth investigating such as how it works (information pathways, set point processing), where its command and control processors are located in the cell, the language used and how homeostasis interacts with all effectors (all aspects of an organism's functional maintenance and development such as cell differentiation). All are necessary and valid inquiries, but this paper examines the implications of ignoring it as a factor of disease causation. Alternatively when properly understood the homeostasis profile becomes a rational explanation for any individual’s range of health; where some areas are very resistant to disease (resilient) and other areas of homeostasis are dysfunctional leading to minor, moderate or very susceptible tendencies to disease (a lack of resilience in homeostasis). An understanding of this phenomenon, its interactions with gene expression (epigenetics) is necessary in order to evaluate the solutions for acute and chronic diseases. By this method diseases can be defined more precisely as specific susceptibilities in homeostasis, leading to a method to restore specific homeostasis functions.

The rapid acceleration of artificial intelligence (AI) has sparked extensive debate about its impact on the safety and security of society. While AI has shown trans- formative potential, particularly in areas such as healthcare and independent structures where it complements productivity and minimizes human error, accu- racy and protection. However, the misuse of artificial intelligence poses real risks, along with threats such as deep counterfeiting, computer cyber-attacks and the manipulation of social behavior through disinformation. These risks vary from privacy and cyber security breaches to moral dilemmas around liability. The unchecked improvement of AI is expected to threaten the social order, so the relationship between innovation and governance is essential so that AI-related threats do not overshadow its benefits. This article offers a detailed assessment and review of the literature that studies the potential dangers that AI poses to society. The cumulative effects underscore the urgent need for comprehensive regulatory frameworks to mitigate these threats.

The rapid emergence of Decentralized Finance (DeFi), driven by blockchain technology and smart contracts, is transforming the landscape of the financial industry by enabling peer-to-peer financial services without traditional intermediaries. This paper examines the impact of DeFi on traditional banking systems, focusing on the opportunities, challenges, and future directions of this revolutionary financial model. We explore how DeFi disrupts traditional financial intermediation by offering faster, cheaper, and more transparent financial services, potentially reducing the dominance of conventional banks. The study addresses critical issues such as regulatory challenges, including compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements, and the importance of consumer protection in a decentralized environment. We also investigate collaboration opportunities between traditional banks and DeFi platforms, which could lead to innovative financial products and services. To provide a comprehensive analysis, the paper employs a mixed-method approach, combining a review of recent literature, empirical data, case studies, and expert interviews. The findings highlight significant gaps in current regulatory frameworks and emphasize the need for a balanced approach that fosters innovation while ensuring financial stability and consumer safety. This study concludes by offering recommendations for policymakers, financial institutions, and DeFi developers to harness the potential of DeFi while addressing its inherent risks, paving the way for the sustainable growth of decentralized finance in the broader financial ecosystem.

In this preliminary investigation, a concise molecular docking study using the Pyrx program screened thousands of compounds against the active site of HIV-1 reverse transcriptase. The Virtual Screening library was employed to assess binding energetics scores, focusing on identifying candidates with high Vina Scores. Eight compounds, including Tetrophan, Imatinib, Amentoflavone, Alprazolam, Bilobetin, Bafetinib, Nilotinib, and Ginketin, emerged as promising candidates with significant binding energies.Recognizing compounds that strongly bind to HIV-1 reverse transcriptase holds potential for antiretroviral drug development in the fight against HIV infection. Subsequently, the study evaluated the toxicity properties of these candidates using pKCSM. Amentoflavone, a natural biflavonoid found in Ginkgo biloba and Nilotinib, a tyrosine kinase inhibitor, demonstrated lower overall toxicity profiles compared to other candidates. Further toxicity analysis using Pro-Tox-II revealed Amentoflavone's higher predictive LD50, lower toxicity class, and reduced probabilities of hepatotoxicity, carcinogenicity, mutagenicity, and moderate immunotoxicity compared to Nilotinib. While Nilotinib, a pharmaceutical drug, exhibited higher probabilities of adverse effects in these categories. In summary, Amentoflavone and Nilotinib present as promising candidates with lower toxicity concerns. Amentoflavone, as a natural substance, showcases a more favorable toxicity profile than Nilotinib, offering valuable insights for potential therapeutic applications.

Traditional urban design often overlooks the synchronisation of human and ecological connectivities, typically favouring corridors for ecological continuity. Our study challenges this convention by introducing a computational design approach, Meta-Connectivity, leveraging the deep generative models performing cross-domain translation to integrate human-wildlife landscape connectivity in urban morphology amidst the planetary urbanisation. Utilising chained Pix2Pix models, our research illustrates a novel meta-connectivity design reasoning framework, combining landscape connectivity modelling with conditional reasoning based on deep generative models. This framework enables the adjustment of both human and wildlife landscape connectivities based on their correlative patterns in one single design process, guiding the re-materialisation of urban landscapes without the need for explicit prior ecological or urban data. Our empirical study in East London demonstrated the framework's efficacy in suggesting wildlife connectivity adjustments based on human connectivity metrics. The results demonstrate the feasibility of creating an innovative urban form in which the land cover guided by the connectivity gradients replaces the corridors based on simple geometries. This research thus presents a methodology shift in urban design, proposing a symbiotic approach to integrating disparate yet interrelated landscape connectivities within urban contexts.

This paper deals with the design and kinematic analysis of a novel mechanism for the elbow joint of an upper-limb exoskeleton, with the aim of helping operators, in terms of effort and physical resistance, in carrying out heavy operations. In particular, the proposed eight-bar el-bow joint exoskeleton mechanism consists of a motorized Watt I six-bar linkage and a suitable RP dyad, which connects mechanically the external parts of the human arm with the corre-sponding forearm by hook and loop velcro and thus, helping their closing relative motion for lifting objects during repetitive and heavy operations. This relative motion is not a pure rotation and thus, the upper part of the exoskeleton is fastened to the arm, while the lower part is not rigidly connected to the forearm, but through a prismatic pair which allows both rotation and sliding along the forearm axis. Instead, the human arm is sketched by means of a crossed four-bar linkage, which coupler link is considered as attached to the glyph of the prismatic pair, that is fastened to the forearm. Therefore, the kinematic analysis of the whole ten-bar mecha-nism, which is obtained by joining the Watt I six-bar linkage and the RP dyad to the crossed four-bar linkage, is formulated to investigate the main kinematic performance and for design purposes. The proposed algorithm has given several numerical and graphical results. Finally, a double-parallelogram linkage, as particular case of the Watt I six-bar linkage, was considered in combination with the RP dyad and the crossed four-bar linkage, by giving a first mechanical de-sign and a 3D printed prototype.

The periodontal ligament (PDL) is a complex connective tissue that connects the tooth root to the dental alveolar bone and plays crucial mechanical roles. PDL also exhibits regenerative roles and regulatory functions to maintain per-iodontium integrity and homeostasis. While PDL exposure to oral microbial pathogens is common, virtually nothing is known regarding viral infections of PDL. In particular, human herpes simplex virus type 1 (HSV-1) persistently infects the oral cavity through infections of the oral epithelium, connective tis-sue and neurons. While the oral spread of HSV-1 is generally asymptomatic, this virus has also been implicated in various oral pathologies. In this study, using a primary cell model derived from PDL (PDL-cells), and whole surgical fragments of PDL, we provide evidence supporting the efficient infection of PDL by HSV-1 and the promotions of cytopathic effects. Infection of PDL by HSV-1 was also associated with an acute innate inflammatory response, as il-lustrated by the production of the antiviral interferons and pro-inflammatory cytokines. Furthermore, this inflammatory response to HSV-1 was exacerbated in the presence of bacterial-derived products, such as peptidoglycans. This work therefore highlights the ability of HSV-1 to infect mesenchymal cells from PDL, suggesting that PDL may serve as a viral reservoir for the perio-dontal spread of HSV-1. Moreover, this raises questions about HSV-1 oral pathogenesis, as HSV-1-associated cytopathic and inflammatory effects may contribute to profound alterations of PDL integrity and functioning.

Epithelial barrier damage plays a central role in the development and maintenance of allergic inflammation. The rise in airways epithelial barrier permeability alters the tissue homeostasis and allows the allergen and other external agents penetration. Different factors contribute to barrier impairment, such as eosinophilic infiltrate and allergens proteases action: the eosinophilic cationic proteins and the allergens' proteolytic activity have a central role in contributing to epithelial damage. In the airways, the allergens proteases degrade the epithelial junctional proteins allowing the allergen penetration and the uptake by dendritic cells. The increase in allergen-immune system interactions induce the release of the alarmins and the activation of type 2 inflammatory pathways causing, or worsening, the main symptoms at skin, bowel and respiratory levels. We aim to highlight the molecular mechanisms underlying allergenic proteases-induced epithelial barrier damage and the role of immune response in allergic asthma onset, maintenance and progression. We further will explore clinical and radiological potential biomarkers of airways remodeling in allergic asthma patients.

Avian influenza viruses (AIVs) are highly contagious respiratory viruses of birds, lead-ing to significant morbidity and mortality globally and causing substantial economic losses to the poultry industry. Since their first isolation in 2013, the Asian-origin H5N1 highly pathogenic avian influenza viruses (HPAI) of clade 2.3.4.4b have undergone unprecedented evolution and re-assortment of internal gene segments and have spread to Asia, Europe, Africa, and America, caus-ing outbreaks in all the poultry categories. Novel epidemiological and pathobiological character-istics, distinct from other clades, are specific of clade 2.3.4.4b viruses. Wild waterfowl, the natu-ral reservoir of AIVs, are frequently found infected with clade 2.3.4.4b viruses, which can also cause high morbidity and mortality in these birds. The sustained clade 2.3.4.4b virus circulation in waterfowl has also led to virus infection in other wild bird species, with implications for the conservation of endangered species. Furthermore, clade 2.3.4.4b viruses have been isolated in var-ious wild and domestic mammals worldwide, and critical mutations related to virus adaptation to mammalian species have been identified, raising concerns about virus spillover to humans. The main clinical signs, and anatomopathological findings associated to clade 2.3.4.4b virus infection in birds and non-human mammals are hereby summarized.

: Microplastics and nanoplastics (MNPs) are becoming an increasingly severe global problem due to their widespread distribution and complex impact on living organisms. Apart from their environmental impact, the effects of MNPs on living organisms have also continued to attract attention. The harmful impact of MNPs has been extensively documented in marine invertebrates and larger marine vertebrates like fish. However, the research on the toxicity of these particles on mammals is still limited, and their possible effects on humans are poorly understood. Considering that MNPs are commonly found in food or food packaging, humans are primarily exposed to them through ingestion. It would be valuable to investigate the potential harmful effects of these particles on gut health. This review focuses on recent research exploring the toxicological impacts of micro- and nanoplastics on the gut, as observed in human cell lines and mammalian models. Available data from various studies indicate that the accumulation of MNPs in mammalian models and human cells may result in adverse consequences, in terms of epithelial toxicity, immune toxicity, and disruption of gut microbiota. The paper also discusses the current research limitations and prospects in this field, aiming to provide a scientific basis and reference for further studies on the toxic mechanisms of micro- and nanoplastics.

This paper is dedicated to examining dynamic dark energy models in the context of observational constraints and their implications for the universe's future. By integrating theoretical models with various observational data, we aim to provide a comprehensive view of how dynamic dark energy influences cosmic evolution and the universe's fate. This study tests these models against the latest observational evidence, including the Cosmic Microwave Background, supernovae, and large-scale structure surveys, to refine our understanding of the universe's accelerating expansion and the mysterious nature of dark energy.

In this preliminary investigation, a concise molecular docking study using the Pyrx program screened thousands of compounds against the active site of HIV-1 reverse transcriptase. The Virtual Screening library was employed to assess binding energetics scores, focusing on identifying candidates with high Vina Scores. Eight compounds, including Tetrophan, Imatinib, Amentoflavone, Alprazolam, Bilobetin, Bafetinib, Nilotinib, and Ginketin, emerged as promising candidates with significant binding energies.Recognizing compounds that strongly bind to HIV-1 reverse transcriptase holds potential for antiretroviral drug development in the fight against HIV infection. Subsequently, the study evaluated the toxicity properties of these candidates using pKCSM. Amentoflavone, a natural biflavonoid found in Ginkgo biloba and Nilotinib, a tyrosine kinase inhibitor, demonstrated lower overall toxicity profiles compared to other candidates. Further toxicity analysis using Pro-Tox-II revealed Amentoflavone's higher predictive LD50, lower toxicity class, and reduced probabilities of hepatotoxicity, carcinogenicity, mutagenicity, and moderate immunotoxicity compared to Nilotinib. While Nilotinib, a pharmaceutical drug, exhibited higher probabilities of adverse effects in these categories. In summary, Amentoflavone and Nilotinib present as promising candidates with lower toxicity concerns. Amentoflavone, as a natural substance, showcases a more favorable toxicity profile than Nilotinib, offering valuable insights for potential therapeutic applications.

In the paper we show that the general relativity in recent Einstein-Palatini formulation is equivalent to a gauge field. We begin with a bit of information of the Einstein-Palatini formulation and derive Einstein field equations from it. In the next section, we consider general relativity with a positive cosmological constant in terms of the corrected curvature. We show that in terms of the corrected curvature general relativity takes the form typical for a gauge field. Finally, we give a geometrical interpretation of the corrected curvature.

A typical halogen-free flame-retardant (HFFR) formulation for electric cables may contain polymers, various additives, and fire-retardant fillers. In this study, composites are prepared by mixing natural magnesium hydroxide (n-MDH) with linear low-density polyethylene (LLDPE) and a few types of ethylene-octene copolymers (C₈-POE). Depending on the content of LLDPE and C₈-POE, we obtained composites with different crystallinity that affected the final mechanical properties. The nucleation effect of the n-MDH and the variations in crystallinity caused by the blending of C₈-POE/LLDPE/n-MDH were investigated. In the C₈-POE/LLDPE blend, we found a decrease in the crystallization temperature of LLPDE compared to pure LLDPE and an increase in the crystallization temperature of C₈-POE compared to pure C₈-POE. On the contrary, the addition of n-MDH led to an increase in the crystallization temperature of LLDPE. As expected, the increase in the crystallinity of the polyolefin matrix of composites led to higher elastic modulus, higher tensile strength, and lower elongation at break. Overall, these results show how to obtain the required mechanical features for halogen-free flame-retardant compounds for electric cable applications depending on the quantities of the two miscible components in the final blend.

This work presents results of property researches of fabricated samples of silica few-mode optical fiber (FMF) with induced chirality under twisting 10 and 66 revolutions per meter, core diameter 11 µm, typical “telecommunication” cladding diameter 125 µm and improved height of quasi-step refractive index profile. Proposed FMF supports 4 guided modes over “C”-band. We represent results of computed characteristics, as well as experimentally measured spectral responses of laser-excited optical signals, including researches and analysis of few-mode effects, occurring after fiber Bragg grating writing.

Sleep deprivation is an increasing problem in the modern age. While molecular theories on the mechanism of sleep are still developing, sleep’s importance for cognitive function and physical health has been well reported. In the past 2 decades, major advances in the genetics of sleep have been made. Among families of natural short sleepers averaging around 4-6 hours nightly, mutations on 4 genes (DEC2, ADRB1, NPSR1, and GRM1) have been associated with significantly shorter sleep without observed cognitive or health detriments. Studies in mice have further established causality of these mutations as well as finding increased resistance to neurodegeneration. Yet, despite these promising results, there is a current lack of studies investigating potential chronic risks of sleep deprivation, particularly physical and physiological effects that may not be apparent. For circadian repressor element DEC2, circadian dysregulation risks should be evaluated further. For adrenergic receptor ADRB1, cardiovascular and heart failure risks would be of interest. For NPSR1, asthma and other immune responses, as well as mood disorders are relevant. For the currently least studied G-protein receptor GRM1, its altered regulation of the extracellular signal-regulated kinase (ERK) pathway could result in developmental and proliferation effects. While these familial natural short sleep genes hold potential for our understanding of sleep and therapeutic development, it is critical not to overdraw conclusions. By investigating the possible downstream effects, one can be aware of the potential risks of natural short-sleep genetics, better understand the mechanisms of sleep, and advance research in reducing the harms of sleep deprivation.

Valles Marineris (VM), the most prominent example of a Martian Valley Network, has been extensively studied for over 50 years, yet no detailed examination of the principal azimuths of the system exists. To address this, two methods are presented to precisely calculate the principal azimuths, a Bezier Spline analysis, and a GIS technique. The medial axis of the main canyon of VM was determined analytically from cubic polynomial splines fitted to 93 coordinate points along both north and south edges of the canyon. These splines were optimized, and medial axis points were calculated through numerical techniques that ensured orthogonality between the tangents of each spline and their connecting normal lines. 1,000 medial axis points were extracted, and various regression models constructed, including fitting to sinusoidal and cubic polynomial curves, achieving accuracies with R² values of 0.98 and 0.99, respectively. Principal azimuths were obtained using the sinusoidal equation with the slope of the tangent at any point x simply determined by the derivative of the curve’s equation. This analytic approach was cross-validated by a GIS method (using QGIS software), where a vector medial axis was obtained which produced principal azimuths that agreed with values from the analytic study with a correlation coefficient of 1.00, and a p value of 6.43e-65. The findings demonstrate that an azimuthal framework can be rigorously constructed as a potential standard reference in VM geoscience, replacing less precise and ambiguous compass bearings with the accurate azimuths necessary for high-resolution spatial analysis for future investigations.

This report provides a comprehensive analysis of portfolio risk management using Value at Risk (VaR) for a banking sector investment portfolio. We evaluate a portfolio composed of SBI, HDFC, Axis Bank, and ICICI Bank, using historical data from July 1, 2023, to June 30, 2024. The report covers the calculation of VaR, portfolio variance, and standard deviation, highlighting the impact of different weightage adjustments on the portfolio’s risk profile. Two scenarios—one with increased weightage in SBI and another with increased weightage in HDFC—demonstrate how changes in asset allocation affect potential losses. The findings emphasize the importance of strategic asset allocation for both speculators and hedgers. Speculators are advised to focus on high volatility and market trends, while hedgers should prioritize stability and risk management. This analysis aids investors in making informed decisions to balance risk and return in their banking sector investments.

Costa Rica, widely acknowledged for its environmental leadership, is now facing a significant challenge in managing waste, a situation that could potentially compromise both its environmental and public health achievements. This analysis delves into the waste management issues in Costa Rica, particularly in urban areas such as San José, responsible for nearly half of the country's waste. The study identifies several pressing concerns, including the heavy burden on landfill capacities, a recycling rate of just 9.6%, and substantial regional differences in waste management practices.To address these challenges, the research employs a combined methodology, utilizing Linear Regression (LR) and AutoRegressive Integrated Moving Average (ARIMA) models to project future trends in CO2e emissions and waste minimization from 2024 to 2050. This method underscores the vital need for public involvement and the introduction of innovative strategies in waste management. The research also introduces a Waste Management Innovation Index to compare Costa Rica’s practices with those of leading nations like Germany and South Korea, identifying key areas for improvement.Furthermore, the study incorporates insights from stakeholders, revealing the intricate balance of cultural, economic, and regulatory factors that shape waste management policies. Despite efforts by the government, such as the Environmental Health Route and the National Circular Economy Strategy, results show limited progress, particularly in rural regions. The study highlights the urgent need for better infrastructure, targeted public awareness campaigns, and the use of advanced technologies like Artificial Intelligence (AI) and blockchain to enhance sustainable waste management practices. The research provides strategic recommendations to help Costa Rica improve its waste management systems in line with international standards, ensuring long-term environmental sustainability and protection of public health.

This paper discusses the application of artificial intelligence in imaging omics, especially in cancer research. Imaging omics enables detailed analysis of spatial and temporal heterogeneity of tumours through high-throughput extraction of quantitative features from medical images such as MRI, PET, and CT. This paper focuses on applying PARKS systems to automate the recognition, segmentation, and extraction of image features, significantly enhancing the capabilities of clinical decision support systems (CDSS). The future direction is to establish a robust network infrastructure for radiology Medication-led Health care (RLHC) to facilitate the development and application of personalised treatment protocols, and to improve diagnostic accuracy, prognosis assessment, and treatment recommendations by uploading quantitative image features to a shared database and comparing them with historical images.

The analysis of aggregate data has received increasing attention in the statistical discipline over the past 20 years. Much of this attention has been focused on estimating the cells frequencies of a 2×2 contingency table given only the marginal totals; the analyses proposed have been received with missed reviews. More recently, the focus has shifted toward analysing the overall association structure rather than on the estimation of the cell frequencies. This article provides some insight int how informative the aggregate data of a single 2×2 contingency table is for assessing the association. The information contained in the margins of the table is quantified using the squared deviation of the expected cell frequencies over the possible range of values, where the row and column totals are known and fixed. The new measure we discuss to quantify this association is referred to as the aggregate informative index (AII) and is shown to be the standardized area under the squared deviation curve.

Complexity science studies physical phenomena that cannot be explained by the mere analysis of the single units of a system but requires to account for their interactions. A feature of complexity in connected systems is the emergence of mesoscale patterns in a geometric space, such as groupings in bird flocks. These patterns are formed by groups of points that tend to separate from each other, creating mesoscale structures. When multidimensional data or complex networks are embedded in a geometric space, some mesoscale patterns can appear respectively as clusters or communities, and their geometric separability is a feature according to which the performance of an algorithm for network embedding can be evaluated. Here, we introduce a framework for the definition and measure of the geometric separability (linear and nonlinear) of mesoscale patterns by solving the travelling salesman problem (TSP), and we offer experimental evidence on embedding and visualization of multidimensional data or complex networks, which are generated artificially or are derived from real complex systems. For the first time in literature the TSP’s solution is used to define a criterion of nonlinear separability of points in a geometric space, hence redefining the separability problem in terms of the travelling salesman problem is an innovation which impacts both computer science and complexity theory.

Instagram is a popular social media platform among youth, with over a billion active users worldwide. The platform allows users to share photos, videos, and stories and connect with friends and followers. Instagram has become a significant part of youth culture, with many young people spending a considerable amount of time on the app. According to Enterprise (2012:02), Instagram is a community shared among one member with other members from all over the world. This study examines the impact of Instagram on the physical and mental well-being of Indian youth. With the rapid proliferation of social media platforms in India, Instagram has emerged as a prominent influence on the lives and perceptions of young individuals. Studies have shown that using Instagram among youth can have positive and negative effects. On the positive side, Instagram can be a way for young people to express themselves, connect with others, and discover new interests. However, excessive use of the platform can also lead to negative outcomes, such as social comparison, anxiety, and low self-esteem. Instagram addiction among youth is a growing concern. Some young people may feel compelled to check their accounts frequently and may experience negative emotions if they are unable to do so. In extreme cases, excessive use of the platform can interfere with daily activities and relationships, leading to a negative impact on mental health. Parents and educators can play a role in helping young people manage their Instagram use. This can include setting limits on screen time, encouraging healthy online habits, and promoting offline activities and social interaction. Additionally, mental health professionals can offer support to young people who are struggling with addiction or other negative effects of Instagram use. This study explores the prevalence of Instagram dependency among Indian youth.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

Strong-aroma Baijiu is a traditional Chinese spirit distilled from grains that undergo solid-state fermentation. At the end of each fermentation cycle, a significant quantity of Huangshui accumulates at the bottom of the fermentation pit, constituting one of the primary by-products in the brewing process of strong-flavor liquor. In this report, the chemical component analysis of Huangshui obtained from a strong-flavor liquor factory revealed a diverse array of organic acids, alcohols, esters, starch, reducing sugars, amino acids, and other compounds present in Huangshui. Additionally, two specialized solid-state fermentation models were developed to investigate the formation process of Huangshui. Our findings demonstrate a positive correlation between the yield of Huangshui and the initial moisture content of fermented grains. It is concluded that Huangshui is the percolate produced during the solid-state brewing process of strong-flavor liquor, with its water content originating from the initial moisture of fermented grains.

Background/Objectives: Although the role of F-18 FDG PET/CT imaging is well established in oncology, its diagnostic value in routine monitoring for recurrent colorectal cancer (CRC) is still controversial. The aim was to evaluate the diagnostic value of FDG PET/CT in detecting recurrent CRC in correlation with CEA, CA 19-9 levels, and conventional imaging modalities (CIM). Methods: Between 2009 and 2023, we retrospectively studied 134 CRC patients referred for FDG PET/CT imaging on the suspicion of recurrence based on elevated CEA and/or CA 19-9, and/or equivocal radiological findings. According to the Institutional Tumor Board CRC protocol, after initial treatment which was dependent on TNM stage (neoadjuvant therapy, primary resection or adjuvant treatment), patients underwent a standard 5-year surveillance including CEA and CA 19-9 measurements, CIM, and colonoscopy every 6 months. The statistics including univariate and multivariable analyses were conducted using the IBM SPSS 20.0 statistical software. P-values <0.05 were considered statistically significant. Results: Recurrent CRC was confirmed in 54/134 (40.3%) patients with elevated tumor markers. FDG PET/CT shows high diagnostic performance in detecting recurrent CRC with sensitivity, specificity, PPV, NPV, and accuracy of 94.4%, 82.5%, 78.5%, 95.7%, and 87.3%, respectively. The CEA shows a high sensitivity of 98.1%, but both low specificity and accuracy of 15% and 48.5%, respectively. The sensitivity, specificity, and accuracy, for CA 19-9 and CIM for the correct diagnosis in CRC patients were: 44.4%, 67.5%, 58.2%, and 51.9%, 98.8%, 79.9%, respectively. The AUC for FDG PET/CT, elevated CEA levels, CIM, and elevated CA 19-9 levels was 0.885 (95% CI: 0.824-0.946; p<0.001), 0.844 (95% CI: 0.772-0.916; p<0.001), 0.753 (95% CI: 0.612-0.844; p<0.001); and 0.547 (95% CI: 0.442-0.652; p=0.358). Univariate analysis shows that both FDG PET/CT- and CIM-positive results were highly associated with CRC recurrence (p<0.001 and p<0.001, respectively), while gender, mucinous tumor type, presence of initial lymph node metastasis (N+), and presence of initial distant metastasis (M+) have no significance (p=0.211, p=0.158, p=0.583, and p=0.201, respectively). Our multivariate analysis shows that independent predictors for CRC recurrence are: positive FDG PET/CT scans (p<0.001), positive CIM results (p=0.001) and elevated CA19-9 levels (p=0.023). Although CA 19-9 was not detected as a statistically significant predictor in the univariate analysis (p=0.358), in a multivariate analysis it was recognized as a significant predicting factor in detecting CRC recurrence (p=0.023). Conclusions: FDG PET/CT shows a high diagnostic efficacy in CRC recurrence detection, in correlation with CEA levels, CA 19-9 levels, and CIM. This imaging modality should be routinely integrated into the postoperative follow-op in patients with elevated tumor markers.

A recent study revealed a persistent presence of the PP-Spike recombinant protein in 50% of individuals who received ModRNA injections, contrasting with unvaccinated controls, whether SARSCoV-2 infected or not, up to 187 days post-injection. The implications of these findings are discussed in the context of PP-Spike recombinant protein-induced hyperimmune/inflammatory reactions and thrombotic vasculitis as mechanisms to elucidate the serious side effects, long-term organ complications, and excess deaths observed after administering Covid ModRNA products. These results underscore the urgent need for a comprehensive reassessment of the risk-benefit profile of ModRNA injections, including an immediate halt in their use for any person, and a renewed emphasis on understanding and addressing their long-term effects on human health

Aim: the main aim of this study is to compare the onset of effectiveness of mental nerve block with lingual infiltration and inferior alveolar nerve block on patients visiting dental hospital for extraction of mandibular anterior teeth. Materials and methods: The study was set in a clinical setting. This was a randomized controlled double-blind cross study, where 25 subjects received two treatments at an interval of at least 1 week in a within a dental hospital.Inferior alveolar nerve block and lingual nerve block injection is given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered for the right side. Mental nerve block along with lingual infiltration of the corresponding tooth was given with 2mL lignocaine 2% with 1:2,00,000 epinephrine was administered on the left side.Visual analogue scale (VAS) was used in this study, which consists of a scale with range of 0-10 pain severity on which 0 indicated absence of pain and 10 the worst pain possible. The patients were asked to give scores corresponding to his/her pain, after IANB, mental nerve block and lingual infiltration and these scores were recorde.Results: From the results we can see that patients gave scores between 3-4, stating that they had moderate pain during extraction after administration of IANB (54%) (graph 2) and VAS scores of 0-1 were given by patients, which is suggestive of no pain during extraction after administration of Mental nerve block and lingual nerve infiltration (62%)Conclusion: Within the limits of the study we can conclude that mental nerve block along with lingual infiltration is an effective way to completely and quickly anaesthetise the central incisor to the 1st premolar region.

Flavin-dependent monooxygenases (FMOs) have made substantial influence as catalysts for monooxygenation reactions, impacting diverse field such as drug metabolism, environmental studies, and natural product synthesis. Their application in biocatalysis boasts several advantages over conventional chemical catalysis, such as heightened selectivity, safety, sustainability, and eco-friendliness. In the realm of biomedicine, FMOs are pivotal in antibiotic research, modulating natural products, antimicrobial agents, and pathways involved in drug synthesis. They're also underscored as potential pharmaceutical targets, pivotal in disease progression and viable for therapeutic intervention. Additionally, FMOs play a substantial role in environmental science, especially in pesticide processing and plant vitality. Their involvement in biosynthesizing compounds like polyethers, tropolones, and ω-hydroxy fatty acids, with remarkable regio- and stereoselectivity, renders them indispensable in drug discovery and development. As our comprehension of FMOs' catalytic mechanisms and structures advances, coupled with cutting-edge biotechnologies like computational design and directed evolution, FMOs are poised to occupy an increasingly significant role in both scientific exploration and industrial applications.

Hund's rule of maximum multiplicity is a powerful empirical tool for determining the electron population of electronic shells. It was recently discovered that it is also the rule of minimum Shannon entropy. This study extends these findings to the Aufbau rule and discusses them from the perspective of emergent dimensionality, providing a monotonically increasing entropy function. We observed that some elements that violate the Aufbau rule have the same entropies in the actual and Aufbau electron configurations. On the other hand, lower entropies of all actual elements' configurations are associated with higher or equal numbers of unpaired electrons in these configurations compared to the Aufbau configurations. The only exception to this rule is palladium.

In this work, numerical and experimental implementation of a state observer applied to an Erbium-Doped Fiber Laser (EDFL) has been developed. The state observer is designed through the mathematical model of the EDFL to estimate the non-measurable variable; however, in numerical estimation, the state variables can be measurable given the mathematical model. Only the laser intensity variable was experimentally measured. The state observer estimated the population inversion through the obtained experimental laser intensity time series fitted with their numerical laser intensity using the mean square error (MSE) tool. A bifurcation diagram of the population inversion time series local maximum was built from the state observer. The state space of the experimental laser intensity versus observed population inversion was built.

Outlined is a proposal designed to culminate in the foundry fabrication of arrays of singly addressable quantum dot sources deterministically emitting single pairs of energy-time entangled photons at C-band wavelengths; each pair having negligible spin-orbit fine structure splitting; each pair being channeled into single mode pig-tail optical fibers. Entangled photons carry quantum state information among distributed quantum servers via I/O ports having two functions; the unconditionally secure distribution of decryption keys to decrypt publicly distributed, encrypted classical bit streams as input to generate corresponding qubit excitations, and to convert a stream of quantum nondemolition measurements of qubit states into a classical bit stream. Outlined are key steps necessary to fabricate arrays of on-demand quantum dot sources of entangled photon pairs, principal among are 1) foundry fabrication of arrays of isolated quantum dots, 2) generation of localized sub-surface shear strain in a semiconductor stack, 3) a cryogenic anvil cell, 4) channeling entangled photons into single mode optical fibers, 5) unconditionally secure decryption key distribution over the fiber network, 6) resonant excitation of a Josephson tunnel junction qubits from classical bits, and 7) conversion of a quantum nondemolition measurements of a qubit states into a classical bit.

This study explores the impact of carbon fiber length and content on the rheological properties and performance of coatings used in lost foam casting. The investigation encompassed fiber lengths of 1 mm, 3 mm, and 6 mm, and fiber contents of 0.2%, 0.5%, and 0.8%. The effects on coating viscosity, shear stress, coating weight, and surface morphology were meticulously evaluated. The results demonstrate that incorporating carbon fibers significantly enhances coating viscosity and shear stress compared to fiber-free coatings, with more pronounced effects observed at higher fiber contents and longer fiber lengths. Nevertheless, excessive fiber content and length can lead to agglomeration, negatively impacting coating uniformity. Optimal fiber length and content were identified, striking a balance between improved rheological properties and coating performance. These findings provide critical insights for the development and industrial application of high-performance coatings in lost foam casting.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

This study introduces a Fiber Specklegram Sensor (FSS) design for temperature measurement utilizing a single-multi-single mode optical fiber configuration. FSS technology offers unique advantages for potential development into portable sensing systems. This investigation explores an FSS design compatible with miniaturization by optimizing the interrogation system based on the relationship between speckle grain size and filtering fiber area. Temperature variations are detected through fluctuations in optical power at the sensor output, offering a simple and reduced interrogation scheme. The performance of the FSS is assessed through post-calibration measurements compared to a commercial Fiber Bragg Grating (FBG) sensor, demonstrating comparable results. These findings pave the way for compact and portable fiber optic temperature sensing systems leveraging FSS advantages while maintaining compatibility with established fiber optic measurements.

Optical backbone networks constitute the fundamental infrastructure employed today by network operators to deliver services to users. As network capacity is a key factor influencing optical network performance, it is important to understand how topological and physical properties impact its behavior and to have the capability to estimate its value. In this context, we propose here a method to evaluate the network capacity that relies on the optical reach to account for physical layer aspects, in conjunction with constrained routing techniques for traffic routing. As this type of routing can lead to traffic blocking, particularly due to the limitation on the number of wavelengths per fiber, we also propose a fiber assignment algorithm designed to deal with this problem. We apply this method to a set of randomly generated networks using a modified Waxman model, and for a network with 60 nodes, in a scenario without blocking, we obtain capacities of about 2.5 Pbit/s for a symbol rate of 64 Gbaud and about 5 Pbit/s for a symbol rate of 128 Gbaud. Remarkably, this duplication in the total network capacity is achieved by an increase in the total fiber length of only about 51%.

This study aimed to use biomineralization technology to improve the bond strength of fiber-reinforced lightweight aggregate concrete (LWAC) after exposure to temperatures of 300 °C and 500 °C. The specimens of a control group (Group A) and two experimental groups (Group B and Group C) were prepared. These specimens healed themselves in different ways after exposure to high temperatures and pull-out tests. The self-healing method of Group A involved placing the specimen into an incubator. The self-healing method of Group B was the same as that of Group A. The self-healing method of Group C involved immersing the specimen in a mixed solution of urea and calcium acetate for two days, then taking it out and placing it in an incubator for two days. A cycle of four days was undertaken until the desired age was reached. After the specimens were healed to the planned age, the first and secondary pull-out tests and repair compound identification were carried out. After exposure to a temperature of 300 °C and self-healing for 90 days, compared with the specimens in Group A, the relative bond strength ratios of the secondary pull-out tests of the specimens in Group C increased by 20.3%. This indicates that biomineralization can effectively improve the bond strength of LWAC after high-temperature damage.

During carbonation process, the presence of water is an important parameter to accelerate this phenomenon. The carbon dioxide (CO₂) has been used as curing process production of fiber cement boards keeping the dimensional stability of cellulose fibers in cementitious matrix and promoting the CO₂ capture of environment. In addition, magnesium oxysulfate (MOS) cement has been used as another alternative binder to reduce the amount of CO₂ in the nature. The aim of this scientific paper is analysis the effect of rehydration method on the physical-mechanical properties of CO2-cured magnesium-based fiber cement boards. These boards were produced by Hatschek process simulation. The physical-mechanical performance and microstructural characteristics of MOS boards before and after carbonation were investigated by: water absorption (WA), apparent porosity (AP), and bulk density (BD); four-point bending test; X-ray diffraction (XRD); thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). According to the results, it was possible observe which the accelerated carbonation process resulted in improvements in the mechanical properties of the boards. The boards carbonated after 48 hours showed higher Modulus of Rupture values. The rehydration process of the composites before carbonation led to enhancements in the boards pre-cured for 48 and 72 hours, demonstrating that carbonation occurred more effectively after the supply of water through rehydration. The improvements in mechanical properties were associated with the formation of hydration products, which preferentially formed in the pores and voids of the fiber-cement. The presence of these carbonation products altered the physical properties of the materials, increasing the density of the boards and reducing the void volume. The decomposition of the formed carbonates was confirmed by thermogravimetric analysis, which indicated that the rehydration process favored the carbonation of the materials

For a fiber optic gyroscope, thermal deformation of the fiber coil can introduce additional ther-mal-induced phase errors, commonly referred to as thermal errors. Thermal error compensation techniques are effective means of addressing this issue. The principle behind these techniques involves real-time sensing of thermal errors and correcting them within the output signal. Since it is challenging to directly separate thermal errors from the output signal of the fiber optic gyro-scope, it is necessary to predict thermal errors based on temperature. To establish a mathematical model between temperature and thermal errors, this paper measured synchronized data of phase errors and angular velocity for the fiber coil under different temperature conditions and aimed to model it using data-driven methods. Due to the difficulty of conducting tests and the limited number of data samples, an algorithm called TD-model modeling is proposed to address the issue of overfitting, which can reduce the model's generalization ability. First, a theoretical analysis of the phase errors caused by thermal deformation of the fiber coil is performed. Subsequently, the critical parameters, such as the thermal expansion coefficient, are determined, and a theoretical model is established. Finally, the theoretical analysis model is incorporated as a regularization term and combined with the test data to jointly participate in the regression of model coefficients. Through experimental comparative analysis, it is shown that, relative to ordinary regression models, the TD-model effectively mitigates overfitting caused by the limited number of samples, leading to a 58% improvement in predictive accuracy.