DNA polymerases replicate cell genomes and/or participate in the maintenance of genome integrity. DNA polymerases sharing high sequence homology with E. coli DNA polymerase I (pol I), have been grouped in Family A. Pol I participates in Okazaki fragment maturation and in bacterial genome repair. Since its discovery in 1956, pol I has been extensively studied, primarily to get deeper insights into the mechanism of DNA replication. As research on DNA polymerases advances, many novel functions of this group of polymerases are uncovered. For example, human DNA polymerase θ (a Family A DNA pol) has been shown to synthesize DNA using RNA as a template, a function typically attributed to retroviral reverse transcriptase. A heightened interest in drug discovery against pol θ has emerged due to its roles in cancer. Likewise, Pol I family enzymes also appear attractive drug-development targets against microbial infections. Development of antimalarial compounds targeting apicoplast apPOL, an ortholog of Pol I, further extends the druggability of this family of enzymes. Here, we summarize reported drug-development efforts against Family A polymerases, and future perspective regarding these enzymes as antibiotic targets. Current techniques such artificial intelligence can be used to facilitate the goal of new drugs.

Chronic rhinosinusitis is a common inflammatory disease of paranasal sinuses, which causes rhinorrhea, nasal congestion and hyposmia. The genetic predisposition or the exposure to irritants can sustain the inflammatory response and the development of nasal polyposis. Nasal polyps are benign and teardrop-shaped growths that project in the nasal cavities and originate from the ethmoid sinuses. This inflammatory process is associated with high expression of IL-5 cytokine and infiltration of eosinophils. Humanized monoclonal antibodies targeting IL-5 or its receptor, represent a therapeutic strategy in the treatment of nasal polyposis in combination with corticosteroids. The molecular pathogenesis of nasal polyps in CRS patients is associated to the epithelial-mesenchymal transition (EMT), a process in which epithelial cells lose their typical phenotype acquiring a mesenchymal phenotype. TGFβ/SMAD, ERK, and Wnt/β-catenin pathways are altered in EMT during the nasal tissue remodeling. miRNA and inhibitor molecules targeting these altered signaling pathways are able to interfere with EMT; which could lead to alternative therapies. Nasal polyps are an alternative source of mesenchymal stem cells which can be easily isolated from surgical biopsies. A molecular understanding of the biology of PO-MSCs will contribute to delineating inflammatory process underlying the development of nasal polyps.

Reactions of quinones with compounds containing an amino group can produce a wide variety of addition or substitution products that depend on reactivity of both quinone and amino derivative. 6,7-Dichloropyrido[1,2-a]benzimidazole-8,9-diones undergo selective nucleophilic substitution reaction with different benzohydrazides and α-hydroxy-p-quinone imine derivatives stabilized by strong intramolecular hydrogen bond were isolated. Synthesized compounds represent a combination of several structural motifs: benzimidazole core fused with α-hydroxy-p-quinone imine which contains a benzamido fragment. The protonation/deprotonation processes were investigated in a solution using UV-Vis spectroscopy and 1H NMR titration experiment. X-ray crystallography analysis revealed a set of weak non-covalent interactions such as intra- and intermolecular hydrogen bonds and π-π stacking. Additionally, the redox behavior of 6,7-dichloropyrido[1,2-a]benzimidazole-8,9-dione and its p-imino derivative was investigated in acidic and neutral environment using cyclic voltammetry measurements. Cathode material based on 6,7-dichloropyrido[1,2-a]benzimidazole-8,9-dione could act as potential effective active electrode in aqueous electrolyte batteries, however further optimization is required.

This paper studies the maximum reliability of multi-hop relay UAV, in which UAV provides wireless services for remote users without direct communication link on the ground as a coded cooperative relay. In this paper, the analytical expressions of total power loss and total bit error rate are derived as reliability measures. Firstly, based on the environmental statistical parameters, a LOS probability model is proposed. Then the problem of minimizing the bit error rate of static and mobile UAVs is studied. The goal is to minimize the total bit error rate by jointly optimizing the height, elevation, power and path loss, and subject to the maximum allowable path loss con-straints, transmission power allocation constraints and UAV height and elevation constraints. At the same time the total path loss is minimized to achieve maximum ground communication cov-erage. However, the formulated joint optimization problem is non-convex and generally difficult to solve. Therefore, we decomposed the problem into two subproblems and proposed an effective joint optimization iteration algorithm. Finally, the simulation results are given, and the analysis shows that the optimal height of different reliability measures is slightly different, and using the mobility of UAVs can improve the reliability of communication performance.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

The treatment of the bulky Rind-based dibromosilanes, (Rind)2SiBr2 (2) [Rind = 1,1,7,7-tetra-R1-3,3,5,5-tetra-R2-s-hydrindacen-4-yl: EMind (a: R1 = Et, R2 = Me) and Eind (b: R1 = R2 = Et)], with two equivalents of tBuLi in Et2O at low temperatures resulted in the formation of blue solutions derived from the diarylsilylenes, (Rind)2Si: (3). Upon warming the solutions above −20 °C, the blue color gradually faded, accompanying the decomposition of 3 and yielding the cyclic hydrosilanes (4) via intramolecular C−H bond insertion at the Si(II) center. The molecular structures of the bulky Eind-based 3b and 4b were confirmed by X-ray crystallography. Thus, at −20 °C, blue crystals were formed (Crystal-A), which were identified as mixed crystals of 3b and 4b. Additionally, colorless crystals of 4b as a singular component were isolated (Crystal-B), whose structure was also determined by an X-ray diffraction analysis. Although the isolation of 3 was difficult due to their thermally-labile nature, their structural characteristics and electronic properties were discussed based on the experimental findings complemented by computational results. We also examined the hydrolysis of 3b to afford the silanol, (Eind)2SiH(OH) (5b).

Railway has been used as one of the most crucial means of transportation in public mobility and economic development. For efficiently operating railways, the electrification system in railway infrastructure, which supplies electric power to trains, is essential facilities for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are recognized key objects in the electrification system. In general, the layout of railway electrification system shows a strong regularity of spatial relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds is converted into a set of line segments where the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of CRF model. As the short-range pairwise contextual term, Potts model is applied to enforce a local smoothness in short-range graph. While, long-range pairwise potentials are designed to enhance spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by Naïve Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables which represent co-occurrence rate of spatial relations in horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify detailed railway elements, representing the average recall of 97.66% and the average precision of 97.07% for all classes.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Attribute reduction is a core technique in the rough set domain and an important step in data preprocessing. Researchers have proposed numerous innovative methods to enhance the capability of attribute reduction, such as the emergence of multi-granularity rough set models, which can effectively process distributed and multi-granularity data. However, these innovative methods still have many shortcomings, such as how to deal with complex constraints and how to perform multi-angle effectiveness evaluations. Based on the multi-granularity model, this study proposes a new method of attribute reduction, namely using multi-granularity neighborhood information gain ratio as the measurement criterion. This method combines both supervised and unsupervised perspectives, and by integrating multi-granularity technology with neighborhood rough set theory, constructs a model that can adapt to multi-level data features. This model can select the optimal granularity level or attribute set according to the requirements. Finally, the proposed method is experimented with on 15 UCI datasets against six other attribute reduction algorithms. The experimental outcomes, upon analysis and comparative review, substantiate the reliability and consistency of the attribute reduction approach introduced in this study.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

As the adoption of renewable energy sources grows, ensuring a stable power balance across various time frames has become a central challenge for modern power systems. In line with the "dual carbon" objectives and the seamless integration of renewable energy sources, harnessing the advantages of various energy storage resources and coordinating the operation of long-term and short-term storage have become pivotal directions for future energy storage deployment. To address the complexities arising from the coupling of different time scales in optimizing energy storage capacity, this paper proposes a method for energy storage planning that accounts for power imbalance risks across multiple time scales. Initially, the Seasonal and Trend decomposition using the Loess (STL) decomposition method is utilized to temporally decouple actual operational data. Subsequently, power balance computations are performed based on the obtained data at various time scales to optimize the allocation of different types of energy storage capacities and assess the associated imbalance risks. Finally, the effectiveness of the proposed approach is validated through hourly applications using real-world data from a province in China over recent years.

Metabolic and bariatric surgery (MBS) is the most effective long-term treatment for Class III obesity. Reduced dietary intake is considered a behavioral driver of post-surgical weight loss, but limited data have examined this association. Therefore, this study examined prospective, longitudinal relationships between dietary intake and weight loss over 24 months following Roux-en-Y Gastric Bypass and Sleeve Gastrectomy. Relationships between weight loss and dietary intake were examined using a validated 24-hour dietary recall method. Associations between total energy/macronutrient intake and weight loss outcomes were assessed at 12-, 18-, and 24-months following MBS, defining patients as “responders” and “suboptimal responders” according to the Reinhold criteria of 50% excess weight loss. Consistent with previous literature, 12-month responders and suboptimal responders showed significant associations between weight loss and dietary intake. Unlike previous work, this study revealed that many of these associations are no longer significant 24 months post-MBS, despite consistent weight loss trends. This study suggests a short-term signal between these dietary factors and weight loss outcomes 12 months post-MBS, consistent with previous observations. However, this signal does not persist beyond 12 months. These results are essential for interpreting and designing clinical studies measuring long-term post-surgical weight loss outcomes.

Using a sample of 103 surviving and non-surviving global bond funds over a long sample period of 1998 to 2022, we perform a comprehensive analysis of the funds under our modified Sharpe’s (1992) style analysis model. We find that dynamic style analysis is useful in explaining time-series and cross-sectional and in predicting future returns of global bond funds. The dynamic style analysis also allows us to examine asset allocation over time. The results show that, during 2001 to 2014, funds shift their allocations from intermediate-term bonds to world corporate bonds and from world corporate bonds to emerging market bonds in search of higher returns at the price of credit quality.

Multi-rotor drones, a kind of unmanned equipment which is widely used in military, commercial consumption and other fields, has been developed very rapidly in recent years. However, their short flight time has hindered the expansion of their application range. This can be addressed by utilizing hydrogen fuel cells, which exhibit high energy density, strong adaptability to ambient temperature, and no pollution emissions, as the power source. Accordingly, the application of hydrogen fuel cells as the power source in multi-rotor drones is a promising technology that has attracted significant research attention. This paper summarizes the development process of hydrogen fuel cell mul-ti-rotor drones and analyzes the key obstacles that need to be addressed for the further development of hydrogen fuel cell multi-rotor drones, including structural lightweight, hydrogen storage methods, energy management strategies. Additionally, prospects for the future development of hydrogen fuel cell multi-rotor drones are presented.

The influence of short term water absorption on the mechanical properties of halloysite nanotubes-multi layer graphene reinforced polyester hybrid nanocomposites has been investigated. The addition of nano-fillers significantly increased the flexural strength; tensile strength and impact strength in dry and wet conditions. After short term water exposure; the maximum microhardness; tensile; flexural and impact toughness values were observed at 0.1 wt% MLG. The microhardness increased up to 50.3%; tensile strength increased up to 40% and flexural strength increased up to 44%. Compared to dry samples; the fracture toughness and surface roughness of all types of produced nanocomposites were increased that may be attributed to plasticization effect. Scanning electron microscopy revealed that the main failure mechanism is caused by the weakening of nano-filler-matrix interface induced by water absorption. It was further observed that synergistic effects were not effective at concentration of 0.1 wt% to produce considerable improvement in mechanical properties of produced hybrid nanocomposites.

A bacterium known as Akkermansia muciniphila (AkkermansiaA. muciniphila) was detected in human feces in 2004. It is defined as a gram-negative, anaerobic, and mucin-degrading microbe belonging to the Verrucomicrobia species. Although the majority of A. muciniphila is found in the human intestinal tract playing a role in the gut microbiome, there are also smaller levels detected in breast milk and oral cavities as well as in the pancreas and appendix.Evidence suggests that having A. muciniphila present in the gut microbiome has positive effects on human health. As a result, A. muciniphila has been shown to have health benefits when taken as a dietary supplementation where it is considered a promising next-generation probiotic. However, the large-scale production of A. muciniphila remains a challenge due to its need for a stable environment. While culturing A. muciniphila in a mucin-based medium is one option, other strategies that use plate-based mediums have also been explored. Currently, both live and pasteurized forms of A. muciniphila are available on the market and shown to have promising outcomes in animal models. Additionally, researchers are investigating various prebiotics to increase the abundance of A. muciniphila in the human gut. This article presents a detailed summary of the phenotypic characteristics of A. muciniphila and its associations with various human health condition illnesses. Finally, the paper explores A. muciniphila's potential as an effective next-generation probiotic.

With the rising cost of animal feed protein, finding affordable and effective substitutes is crucial. Walnut kernel cake, a polyphenols, fiber, protein and fat-rich byproduct of walnut oil extraction, has been underexplored as a potential protein replacement in pig feed. In this study, we found that feeding large Diqing Tibetan pigs walnut kernel cake promoted adipose deposition and improved pork quality during pig growth. Transcriptome analysis revealed the upregulation of genes ANGPTL8, CCNP, ETV4, and TRIB3, associated with adipose deposition. Pathway analysis highlighted enrichment in adipose-deposition-related pathways, including PPAR, Insulin, PI3K-Akt, Wnt, and MAPK signaling. Further analysis identified DEGs (differentially expressed genes) positively correlated with adipose-related traits, such as PER2 and PTGES. Single-cell transcriptome data pointed to the specific expression of CD248 and PTGES in adipocyte progenitor/stem cells (APSCs), pivotal for adipocyte differentiation and adipose deposition regulation. This study demonstrates walnut kernel cake's potential to substitute soybean cake in pig feed, providing high-quality protein and promoting adipose deposition. It offers insights into feed protein replacement, human functional food, fat metabolism, and related diseases, with marker genes and pathways supporting pig breeding and pork quality improvement.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

Recent years have witnessed significant advances in reinforcement learning (RL), which has registered remarkable success in solving various sequential decision-making problems in machine learning. Most of the successful RL applications, e.g., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively long history, and has recently re-emerged due to advances in single-agent RL techniques. Although, typically, the communication protocol between agents is manually specified and not altered during training, recently, some papers have shown signs of trying to emerge a communication between agents on the one hand and, on the other hand, to understand what is exchanged between agents. So, there is a growing body of literature on this topic which includes qualitative and quantitative studies and the ones that apply mixed methods. This study presents the scoping review of the methodological strategies undertaken in a total of 16 research articles. The results present the critical appraisal of quantitative methods in terms of validity and reliability and for qualitative methods considering four trustworthiness factors. In the end, relevant insights are further explored with implications and reflections on how they can benefit one's research in the field.

Underwater visual detection technology plays a pivotal role in fields such as marine exploration. With the increasing demand for underwater monitoring, the quest for efficient and reliable methods for underwater target recognition has become particularly significant. To address this requirement, this study developed an innovative underwater object detection architecture based on YOLOv8, named YOLOv8-MU, aimed at significantly enhancing detection accuracy.By integrating the LarK module proposed in UniRepLKNet to optimize the backbone network, YOLOv8-MU aims to achieve a larger receptive field without increasing the model’s depth. Further, this research introduces C2fSTR, an innovative method that combines Swin Transformer with the C2f module. Additionally, we have incorporated the SPPFCSPC_EMA module, which combines Cross-Stage Partial Fast Spatial Pyramid Pooling (SPPFCSPC) with attention mechanisms, significantly improving the detection accuracy and robustness of various biological targets. Moreover, by introducing a fusion block based on DAMO-YOLO into the neck of the model, we further enhanced the model’s capability in multi-scale feature extraction. Finally, the adoption of the MPDIoU loss function, designed around vertex distance, effectively tackles the challenges of localization accuracy and boundary clarity in underwater organism detection. Experimental results on the URPC2019 dataset demonstrate that the YOLOv8-MU model achieved an [email protected] of 78.4%, marking improvements of 5.6%, 1.1%, and 4.0% over YOLOv5s, YOLOv7, and YOLOv8n respectively, indicating the leading performance (SOTA) of this method. On the other hand, further evaluation on the URPC2020 dataset confirmed the generalization capability of the YOLOv8-MU architecture, with its [email protected] reaching 80.4%, surpassing various models including YOLOv5x and YOLOv8n, showcasing the wide applicability of our proposed improved model architecture.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Coronary microvascular dysfunction (CMD) is a clinical entity linked with various risk factors that significantly affect cardiac morbidity and mortality. Hypertension, one of the most important, causes both functional and structural alterations in the microvasculature, promoting the occurrence and progression of microvascular angina. Endothelial dysfunction and capillary rarefaction play the most significant role in the development of CMD among patients with hypertension. CMD is also related to several hypertension-related morphological and functional changes in the myocardium in the subclinical and early clinical stages, including left ventricular hypertrophy, interstitial myocardial fibrosis, and diastolic dysfunction. This indicates the fact that CMD, especially if associated with hypertension, is a subclinical marker of end-organ damage and heart failure, particularly the one with preserved ejection fraction. This is why it is important to search for microvascular angina in every patient with hypertension and chest pain not associated with obstructive coronary artery disease. Several highly sensitive and specific non-invasive and invasive diagnostic modalities have been developed to evaluate the presence and severity of CMD, but also to investigate additional complications that can affect further prognosis, especially among those with hypertensive heart disease. Although various pharmacological and interventional treatments demonstrated certain clinical effects, integrated diagnostic and therapeutic algorithms are necessary to reduce the burden of this emerging condition.

The current study explores the problem of illiquidity in the corporate bond market and examines a broad set of liquidity proxies through empirical investigation. Purpose: The purpose of this paper is to give solution to the global issue of illiquidity in the corporate bond market. The problem is identified by many researchers and this paper attempts to find out the viable solution of the "fungibility route" as an alternative to the "liquidity route". Design/ Methodology: An Analysis of a sample size of 2,34,772 trade data of corporate bonds and a sample size of 2,00,607 trade data of G-securities is done to identify the problem. Findings / Solution proposed: A mathematical model based on credit risk differential and sensitivity differential is proposed to find out the fair value at which an illiquid bond can be swapped by a liquid bond. To arrive at the fair value of the illiquid bond, we have calculated risk adjusted discount rate using modified duration and credit risk differential. With our solution, it is possible to enhance the liquidity of the bond not directly but through fungibility route. Originality: the research is first of its kind in which solution is given to the global problem of corporate bond illiquidity. To execute the trade in the fungibility route, proposed parties involved are the Fungibility platform, the initiator, and the bidder. The derived fair value would facilitate trade and serve as an exit strategy for illiquid bond holders worldwide.

The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

By this contribution we would like to briefly highlight the problems in conceiving the “Hydrogen Bond” (HB) as a real short-range, directional, electrostatic, attractive interaction and to reframe its nature through the non-approximated view of condensed matter offered by a Quantum Electro-Dynamic (QED) perspective. We focus our attention on water, as the paramount case to show the effectiveness of this 40-years’ old theoretical background, which depicts water as a two-fluid system (where one of the two phases is coherent). The HB emerge to be the result of the electromagnetic field gradient in the coherent phase of water, whose vacuum level is lower than the non-coherent (gas-like) fraction. Thus, the HB can be properly looked at, i.e. no more as a “dipolar force” among molecules, but as the phenomenological effect of their collective thermodynamic tendency to occupy a lower ground-state, compatibly with temperature and pressure. This perspective allows to account for many “anomalous” behaviours of water and to understand why the calculated energy associated to HB should change when considering two molecules (water-dimer), or the liquid state, or the several kinds of ice. The emergence of a condensed, liquid, phase at room temperature is indeed the consequence of the boson condensation as described in the framework of spontaneous symmetry breakings (SSB). The switch from a still semi-classical Quantum Mechanical (QM) in the 1st quantization to a Quantum Field Theory (QFT) view embedded in the 2nd quantization is advocated for a more realistic and authentic description of water, condensed matter and living systems.

The paper is an investigation on the impact of financial markets on the volatility of green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) of the Global Bloomberg Barclays MSCI Green Bond Index, for both the non and pandemic periods. For these purpose, after observing the dynamic joint correlations between all the variables through a DCC-GARCH, we adopt GARCH(1,1) and EGARCH(1,1) models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two EGARCH models is positive: so the “green” credit risk volatility is more sensitive to positive shocks than negative ones. With reference to the conditional mean, we note that if during the non pandemic time only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context.

In recent years, polyureas with dynamic hindered urea bonds (HUBs), as a class of promising biomedical polymers, have attracted attention as a benefit of their controlled hydrolytic property. The effect of the chemical structures on the properties of polyureas and their assemblies was rarely reported. In this study, four kinds of polyureas with different chemical groups have been synthesized, and the polyurea from cyclohexyl diisocyanate and tert-butyl diamine showed the fastest hydrolytic rate. The amphiphilic polyurea composed of hydrophobic cyclohexyl-tert-butyl polyurea and hydrophilic poly(ethylene glycol) was synthesized for controlled delivery of antitumor drug paclitaxel (PTX). The PTX-loaded PEGylated polyurea micelle more effectively entered into the murine breast cancer 4T1 cells and inhibited the corresponding tumor growth in vitro and in vivo. Therefore, the PEGylated polyurea with adjustable degradation might be a promising polymer matrix for drug delivery.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

Genetic engineering has become an essential element in developing climate-resilient crops and environmental sustainable solutions to respond to the increasing need for global food security. Genome editing using CRISPR/Cas [Clustered regulatory interspaced short palindromic repeat (CRISPR)-associated protein (Cas)] technology is being applied to a variety of organisms, including plants. This technique has become popular because of its high specificity, effectiveness, and low production cost. Therefore, this technology has the potential to revolutionize agriculture and contribute to global food security. Over the past few years, increasing efforts have been seen in its application in developing higher-yielding, nutrition rich, disease resistant, stress-tolerant “crops” fruits and vegetables. Cas proteins such as Cas9, Cas12, Cas13, and Cas14, among others, have distinct architectures and have been used to create new genetic tools that improve features that are important for agriculture. The versatility of Cas has accelerated genomic analysis and facilitated the use of CRISPR/Cas to manipulate and alter nucleic acid sequences in cells of different organisms. This review provides the evolution of CRISPR technology exploring its mechanisms and contrasting it with traditional breeding and transgenic approaches to improve different stress tolerance. We have also discussed the CRISPR/Cas system and explored three Cas proteins that are currently known to exist: Cas12, Cas13, and Cas14 and their potential to generate foreign DNA free or non-transgenic crops that could be easily regulated for commercialization in most countries.

This paper presents an innovative multi-agent, data-driven reinforcement learning (RL) approach to develop and utilize the thermal equivalent network model that represents the motor's thermal dynamics. A multi-agent reinforcement learning is designed and trained to adjust the model parameters using data from several motor driving cycles. To ensure the incoming driving cycle matches the historical data before employing the pre-trained RL agents, offline statistical analysis and clustering techniques are developed and used. Numerical simulations highlighted the RL agent's ability to develop strategies that effectively address the variability of driving cycles. The proposed RL framework showed its capability to accurately reflect the motor's thermal behavior under various driving conditions.

Keywords: pelvic ultrasound, follicle number per ovary, FNPO, ovarian volume, PCOM, upper normal limits, referral population

This paper explores the application of machine learning (ML) algorithms in predicting trends in the fixed bond market, where traditional analytical methods have proven inadequate. Focusing on various ML techniques such as supervised and unsupervised learning, neural networks, and deep learning, the study evaluates their effectiveness in forecasting market movements. It details a series of experiments in which different ML models are rigorously trained and tested against historical bond market data. The findings reveal that models employing time series analysis and advanced deep learning show marked potential in accurately predicting bond market trends. Additionally, the paper delves into the challenges and limitations inherent in these ML approaches, including data requirements and the risk of model overfitting. Finally, it proposes directions for future research, emphasizing the integration of ML into broader financial market analysis.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

Probiotic feed additives have attracted considerable research interest in recent years because the effectiveness of probiotics can differ across microbial strains and the supplemented macroorganisms. The present study was conducted on 16 lambs divided into two groups (C - control and E - experimental). The examined lambs were aged 11 days at the beginning of the experiment and 40 days at the end of the experiment. The diet of group E lambs was supplemented with a multi-strain probiotic formulation (Lactobacillus plantarum AMT14, Lactobacillus plantarum AMT4, Lactobacillus rhamnosus AMT15, and Bifidobacterium animalis AMT30), whereas group C lambs did not receive the probiotic additive. At the beginning of the experiment (day 0) and on experimental days 15 and 30, blood was sampled from the jugular vein to determine and compare: phagocytic activity (PHAGOTEST) and oxidative metabolism (PHAGOBURST) of peripheral blood granulocytes and monocytes by flow cytometry. An analysis of the phagocytic activity of granulocytes and monocytes revealed significantly higher levels of phagocytic activity (expressed as the percentage of phagocytic cells and mean fluorescence intensity) in lambs administered the multi-strain probiotic formulation than in the control group. The probiotic feed additive also exerted a positive effect on the oxidative metabolism of both granulocytes and monocytes (expressed as the percentage of oxidative metabolism and mean fluorescence intensity) after stimulation with Escherichia coli bacteria and with PMA (4-phorbol-12-β-myristate-13-acetate).

This article aims to solve the problem of robust supervision of a reverse osmosis desalination system (RO-DS) with two models, external model and bond graph model. The structure of an industrial system from the point of view of the external model operates according to several modes of operation (degraded and normal). For this external model, we cannot locate the faults since we are talking about a global operation of the system. The possible solution for the development of research was to use the multidisciplinary model called bond graph. This model by its graphic nature and using a unified language makes it possible to model the industrial system element by element from where it helps the user not only to detect the faults but also to locate them when they appear in the system. The results suggest that the use of the bond graph model for alarm 02 is a reverse osmosis error (RO1); these phenomena are readable on the bond graph model and can be quantified by equations. The equation of the model of this reverse osmosis (RO1) is found in the residual equations (r5, r6 and r7), so that these residues will be sensitive to this rupture. The possible solution during research development was to use the bond graph model to supplement information with physical knowledge and to locate faults. This article also describes the operating safety (by minimizing false alarms and non-detections as well as delays in fault detection) of the desalination system by using the bond graph model described in Linear Fractional Transformation (LFT) form for enable it to manage the robust supervision of a desalination system. An approach based on (LFT-BG) is developed to monitor tank leakage (Cu) and reverse osmosis (RO1 and RO2) faults or valve level (V1 and V2) closures and membrane clogging reverse osmosis (Rm1 and Rm2) that can occur in the reverse osmosis desalination system (RO-DS).

The bond index is an indicator of the grindability of the material and is widely used in the preparation of mineral raw materials and the cement industry. Paper offers new, abbreviated and simplified procedure to determine Bond work index that relies on first-order kinetics law and can be performed with any number of grinding cycles, depending on the desired accuracy of the required data. The parameters G and P80 of each grinding cycle are multiplied by the newly founded coefficients giDT and piDT to obtain values approximately equal to these parameters of the last grinding cycle when the equilibrium state is reached in the standard test. The paper presents comparative results obtained by standard Bond and new shortened procedure on individual samples of andesite, limestone, copper-ore and smelter slag and on composite samples of andesite from limestone and copper-ore with smelter slag in different mass ratios. As the number of grinding cycles increases, the precision of the shortened procedure increases, and the mean square error decrease 3.59%, 2.61% and 1.74% for two, three and four grinding cycles.

The essence of cross-modal generation and retrieval modeling for image-text interactions lies in semantic consistency. Among these, cross-modal representation alignment serves as the foundational requirement for achieving cross-modal semantic consistency. The crux of our proposed method entails the collaborative training of an image captioning model(referred to as the ’captioner’) and a pair of contrastive encoders for image-text matching(referred to as the ’concoder’). This synergistic approach yields concurrent enhancements in concoder’s performance, captioner’s proficiency, and cross-modal semantic coherence. We coin the proposed method as ‘ReCap’. To begin, we initialize the concoder through knowledge distillation from a pre-trained model. During the training process, we use the output of the initialized concoder as input to a residual attention network, jointly training it with the captioner to achieve semantic consistency. Subsequently, we iteratively update the concoder during the image-text momentum encoding phase using the residual attention network, creating a closed-loop for semantic consistency resolution. Upon completion of training, the concoder and captioner modules can be used independently to perform tasks related to cross-modal retrieval and cross-modal generation of text and images. In order to substantiate the efficacy of our proposed method, we initially conducted empirical experiments encompassing image-text retrieval and image captioning tasks, employing a widely recognized benchmark dataset, MSCOCO. Subsequently, we assessed the fine-grained cross-modal alignment performance of the concoder through an image classification task on the iNaturalist 2018 dataset. The achieved performance metrics notably surpassed those achieved by several incumbent state-of-the-art models, thus validating the proficiency of our method. Finally, motivated by the practical requirements of handling the nature conservation image data, we performed caption annotation for the iNaturalist 2018 dataset. Subsequently, we trained the ReCap on this annotated data. Experimental results demonstrate that our proposed method can maintain semantic consistency between cross-modal retrieval and image caption generation for species with similar visual features but distinct semantics. This achievement signifies a meaningful exploration in the field of cross-modal semantic consistency representation, holding significant practical value in the domain of biological research.

Biochar is a carbonaceous material derived from the thermochemical conversion of biomass under oxygen-lean conditions. Its properties render it many applications in agriculture, environmental remediation, and catalysis. Commonly produced by pyrolysis under a nitrogen atmosphere, the biochar composition is influenced mainly by residence time, heating rate, and process temperature. The existing literature primarily assesses biochar properties, such as ultimate, proximate, calorific, porosity, and combustibility, generated under various thermo-conversion conditions. However, there is a notable lack of sufficient investigation and attention given to the practical application of biochar. This article seeks to bridge that gap by enhancing the comprehension of the necessary experimental assessment for specific biochar applications and proposing process optimization methods using Multi-Criteria Decision Analysis (MCDA). By delving into these aspects, the study offers a concise overview of biochar production and presents a comprehensive panorama based on articles and patents. Additionally, the research explores the physicochemical characterization techniques associated with biochar and its various applications in conjunction with MCDA. This comprehensive approach aims to uncover valuable insights that could further advance the practical and sustainable utilization of biochar in a wide range of fields.

Introduction: maxillary hypoplasia and mandibular asymmetry may be corrected with orthognathic surgery at the completion of growth. Many patients require segmental Le Fort I osteotomies to obtain the most stable results. Unfortunately, if Invisalign is chosen for pre-surgical preparation, ClinCheck © software does not allow for prediction when these complex surgeries are planned. The objective of this paper was to show the potential of aligners beyond its software in most complex cases like multiple-piece maxillary osteotomies in cleft and non-cleft patients. Method: Thirteen patients who underwent pre-surgical treatment with clear aligners-Invisalign (study group) were matched in terms of diagnosis, surgical procedure, and orthodontic complexity with thirteen patients treated using fixed appliances (control group). A technique based on simple superimposition was devised to enhance the software for creating a suitable occlusion for segmental pre-surgical treatments. Virtual curves following the lower arch were employed to guide the correct pre-surgical positions of the upper teeth. The amount of impressions required in both groups to achieve satisfactory pre-surgical alignment of the segmented arches was compared. Results: one or no refinement phase were needed in the Invisalign group to reach an acceptable pre-surgical occlusion while the amount of pre-surgical impressions taken in order to reach adequate coordination with fixed appliance treatment was slightly higher. The average overall treatment duration did not show a statistically significant variation between the groups. Conclusions: it appears that clear aligner could serve as an effective treatment for individuals with skeletal discrepancies, including cleft and craniofacial anomalies, necessitating segmental Le Fort I osteotomies when aided by the suggested superimposition approach during digital pre-surgical movement simulations. More sophisticated softwares will certainly be implemented in the future.

The Circular Bioeconomy (CBE) combines the concepts of bioeconomy and circular economy. As an alternative concept to the current fossil-based, linear economy, it de-scribes an economy based on the efficient valorization of biomass. It is regional in nature and aims to improve sustainability. An analysis of the transition process, by identifying its success criteria and assessing its impacts through the modelling of technology-specific scenarios is necessary to ensure that CBE concepts are sustainable. However, a compre-hensive consideration of regional influences on both is lacking. Based on extensive literature research and an expert survey, we (i) present a compre-hensive catalog of CBE success criteria and discuss their region-specific character and (ii) develop a methodology based on evaluation matrices that enable to match CBE technolo-gies with regions. The matrices support the evaluation of technological and regional characteristics influencing the successful CBE implementation. The results show that the success criteria "biomass resources", "technological", and "social" are perceived as highly important, and that most of the success criteria are both region- and technology-specific, highlighting the relevance of developing matrices to match them. We describe such matrices indicatively for the two broadest and most im-portant success criteria clusters “social acceptance” and “biomass supply chain”. With this, we substantiate the regional nature of CBE and raise the awareness on the importance of considering regional conditions in CBE transition processes. Furthermore, we provide practical guidance on how regional conditions can be reflected in the selection of technologies, e.g. in regional CBE technology scenarios.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. Maintaining water quality is crucial for environmental well-being. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, and evaluate associated health risks pertaining to nitrate and flouride in AlHassa region, KSA. The physiochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021-4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate Health Quotient (HQ) values indicated chronic human health risks ranging from low to severe, with newborns being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The Hazard Index (HI) yielded moderate to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the Total Hazard Index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources and vulnerable human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

The prognostic abilities of artificial intelligence and neuroscience in forensics and the criminal justice system stand as a reformatory paradigm for understanding any criminal conduct. While the use of artificial intelligence has been labeled transformational data analytical capabilities, neural predictive approaches also enable an intricate understanding of culpability and criminal propensities. The literature on the complex nature of neuroprediction and artificial intelligence, its ethical deliberations and its usability in curving recidivism are analyzed. This theoretical review elucidates the complex interplay, nuptial relationships and convergence of these relationships in the quest for justice. The consequences of not protecting individual rights in the criminal justice system are surveyed using grounded theory. The degree of acceptability and dependability of AI-generated evidence in legal proceedings are also reviewed. All these topics are yet to be contemplated under one roof to offer an argumentative view. The author expects to prompt readers and new commers to embrace more sociolegal and technological research before incorporating such research in the Indian Judiciary. The review focuses on the question of whether to blame such technology inclusion wholly or rather to prioritize the acquisition of bias-free pretrained datasets and processing models.

In this study, the influence of graphene oxide nanoparticles on the bond-slip behavior of fiber and fly ash based geopolymer paste was examined. Geopolymer paste incorporating graphene oxide nanoparticles solution was cast in half briquetted specimens and embedded with a fiber. Three types of fibers were used: steel, polypropylene, and basalt. The pullout test was performed at two distinct speeds: 1 mm/second and 3 mm/second. Results showed that the addition of graphene oxide increased the compressive strength of geopolymer by about 7%. The bond-slip responses of fibers embedded in geopolymer mixed with graphene oxide exhibited higher peak stress and toughness as compared to those embedded in normal geopolymer. Each fiber type also showed different mode of failure. Both steel and polypropylene fibers showed full bond-slip responses due to their high ductility. Basalt fiber, on the other hand, because of its brittleness, failed by fiber fracture mode which showed no-slip in pull out responses. Both bond strength and toughness were found to be rate sensitive. The sensitivity was higher in graphene oxide/geopolymer than in conventional geopolymer.

By using the Black-Derman-Toy (BDT) model, we predict the future trend of the riskless rate, and then we build an equation that relates the market price of zero-coupon bonds and the theoretical price of zero-coupon bonds calculated using a binomial option pricing model. Based on this, we can find the implied daily return μ, implied natural upturn probability p, and implied daily volatility σ with respect to different time-to-maturity values of zero-coupon bonds. With these results, we can give some suggestions to bond traders.

Carbon fiber reinforced polymer (CFRP) has been used widely in strengthening of steel structures. Steel/CFRP systems subjected to elevated temperatures is realistic in a summer climate event in many countries where the temperature in steel may approach 50 °C or even higher, which will lead to the degradation of the bond performance between CFRP and steel. Therefore, predicting the bond behavior of the CFRP/steel system under elevated temperature is critical. This paper investigated the mechanical performance of CFRP/steel adhesively-bonded double strap joints at elevated temperatures. Thirty CFRP-steel double strap joints were tested to failure under temperatures between 10 °C and 90 °C. It was found that the joint failure mode changed from adherend failure to debonding failure as the temperature approached T_g. In addition, the ultimate load and joint stiffness decreased significantly at temperatures near to and greater than T_g. Based on the experimental results, a model is proposed to predict the bond stress of the CFRP/steel at different temperatures.

Public restrooms are often a hub of microbial contamination, and the examination of bacterial contamination in these facilities can serve as an important indicator of the transmission of infectious diseases. This study was conducted to determine the prevalence of bacterial contamination in public restrooms based on the economic class of the building. Samples were collected from various spots in 32 restrooms found in 10 shopping malls, classifying them into two categories: upper-end restrooms and lower-end restrooms. The findings showed that the level of contamination was higher in the lower-end restrooms, with the seat being the most contaminated area. The most dominant Gram-positive bacteria were coagulase-negative staphylococci species, making up 86% of the identified Gram-positive isolates. The most dominant Gram-negative bacteria identified were Klebsiella pneumoniae and Pseudomonas aeruginosa. The antibiotic sensitivity test results revealed the presence of multi-drug resistant bacteria among the Gram-positive and negative isolates, including Staphylococcus haemolyticus, Staphylococcus kloosi, Acinetobacter baumanii, and Pseudomonas aeruginosa. In conclusion, the study underscores the significance of monitoring bacterial contamination in public restrooms and the need for measures to reduce the spread of infectious diseases. Further research is crucial to gain a complete understanding of the bacterial contamination in public restrooms and their resistance patterns, to ensure the safety and health of the public. The implementation of improved cleaning practices and hands-free designs in restrooms can help reduce the risk of cross-contamination and prevent the spread of diseases.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

This study explores optimizing Synthetic Aperture Radar (SAR) satellite constellation scheduling for multi-imaging missions in densely targeted areas using an in-house developed Modified Dynamic Programming (MDP) algorithm. By employing Mixed-Integer Linear Programming (MILP) to define the mission planning problem, the research aims to maximize observation of high-value targets within restricted planning horizons. Numerical simulations, covering a wide range of target numbers and satellite configurations, reveal the MDP algorithm’s superior mission allocation efficiency, enhanced success rates, and reduced revisit times, compared to the Greedy algorithm. The findings underscore the MDP algorithm’s improved operational efficiency and planning robustness for complex imaging tasks, demonstrating significant advancements over traditional approaches.

Enzyme immobilization is known to enhance polypeptide resistance towards environmental changes, particularly in terms of their stability. The Y509E mutant of β-xylosidase from Geobacil-lus stearothermophilus (XynB2Y509E) (which also bears xylanase activity) has been immobilized in chitosan spheres through either entrapment or covalent bond formation methods. In the immo-bilization by entrapment, besides observing a total loss of xylanase activity, it was found that chitosan concentration affects the process efficiency. Additionally, the maturation time of the spheres and the alkaline pH used to precipitate the spheres affect the β-xylosidase activity of the enzyme. In covalent bond immobilization, it was found that the pH during the modification with glutaraldehyde, as well as the subsequent protein binding, were key factors in preserving the dual enzymatic activity. The activity expressed after immobilization by covalent bonding was 23% higher compared to the activity expressed following entrapment immobilization, with values of 122.3 and 99.4 U. g-1, respectively. Both biocatalysts showed increased thermal and pH stability, along with an improved storage capacity, as they retained 40% and 88% of their activi-ty after being stored at 4 C for two months. Moreover, XynB2Y509E immobilized by covalent binding also exhibited outstanding reusability, retaining a 92% of activity after ten cycles of re-use.

Berberis vulgaris L. (Berberidaceae) is a shrub that has been widely used in European folk medicine as an anti-inflammatory and antimicrobial agent. The purpose of our study was to elucidate the mechanisms of antiproliferative action of the plant’s methanolic root extract (BVR) against colon cancer cells. Cytotoxicity and pro-apoptotic activity were determined in human colon adenocarcinoma cell lines (LS180 and HT-29) and control colon epithelial CCD841 CoN cells. According to the MTT assay, after 48 h of cell exposure, the IC50 values were as follows: 4.3; 46.1 and 50.2 µg/mL for LS180, HT-29, and CCD841 CoN cells, respectively, showing the greater sensitivity of cancer cells to BVR. The Cell Death Detection ELISAPLUS kit demonstrated that BVR induced programmed cell death only against HT-29 cells. RT-qPCR with relative quantification method showed significant changes in the expression of genes related to apoptosis in both colon cancer cell lines. The genes BCL2L1 (126.84-366.66%), BCL2L2 (277.08-330.12%), CASP3 (180.36-261.36%) and CASP9 (158.08-213.46%) had significantly elevated expression, while BCL2 (23.08-52.06%) had reduced expression. Furthermore, in a panel of antioxidant tests, BVR showed positive effects (63.93±0.01, 122.92±0.01, and 220.29±0.02 mg Trolox equivalents (TE)/g in the DPPH•, ABTS•+ and ORAC assays, respectively). In the lipoxygenase (LOX) inhibition test, BVR revealed 62,60±0.87% of enzyme inhibition. The chemical composition of BVR was determined using UHPLC-UV-CAD-MS/MS analysis and confirmed the presence of several known alkaloids, including berberine, as well as other alkaloids and two derivatives of hydroxycinnamic acid (ferulic and sinapic acid hexosides). This is the first such extensive assessment of the biological activity of BVR. The results are very promising and encourage for the use of BVR as a comprehensive chemopreventive agent (anti-inflammatory, antioxidant, and pro-apoptotic) in colorectal cancer.

In the past few decades, N-heterocyclic carbenes (NHCs) open the new field of organocatalysis in synthetic organic chemistry. This review highlights the dramatic progress in the NHC-catalyzed C−O bond formation based on the activation of aldehyde C(sp2)−H bonds. The oxidative and redox transformations for the synthesis of various molecules with structural diversity and complexity are summarized. Furthermore, new methods and strategies for NHC catalysis are emerging continuously; thus, cooperative catalysis with Brønsted acid, hydrogen-bonding catalyst, transition-metal catalyst, and photocatalyst is also described.

Abstract: Safety issues related to the electrification of more electric aircraft (MEA) need to be addressed because of the increasing complexity of aircraft electrical power systems and the growing number of safety-critical sub-systems that need to be powered. Managing the energy storage systems and the flexibility in the load-side plays an important role in preserving the system’s safety when facing an energy shortage. This paper presents a system-level centralized operation management strategy based on model predictive control (MPC) for MEA to schedule battery systems and exploit flexibility in the demand-side while satisfying time-varying operational requirements. The proposed online control strategy aims to maintain energy storage (ES) and prolong the battery life cycle, while minimizing load shedding, with fewer switching activities to improve devices lifetime and to avoid unnecessary transients. Using a mixed-integer linear programming (MILP) formulation, different objective functions are proposed to realize the control targets, with soft constraints improving the robustness of the model. Besides, an evaluation framework is proposed to analyze the effects of various objective functions and the prediction horizon on system performance, which provides the designers and users of MEA and other complex systems with new insights into operation management problem formulation.

The UHPC grouted bellow is an innovative technology for connecting assembled buildings, where the anchorage performance of the rebar and UHPC filled in bellows plays a critical role in determining the overall connection effectiveness. To ascertain a dependable anchorage length and establish a bond-slip relationship between the rebar and UHPC within the bellow, we conducted tests on 8 groups of 16 specimens of metal corrugated pipes filled with UHPC and rebar under tension. The tests considered varying parameters such as the aperture ratio D/d and anchorage length L. By analyzing the failure modes, load versus deflection curves, and steel strain data, we gained valuable insights into the influence of the aperture ratio and anchorage length on the anchorage performance. Furthermore, we calculated the experimental curve of the bond-slip relationship between the rebar and the UHPC interface grouted in the bellows and derived a bond-slip model through fitting. These findings serve as a crucial foundation for the force analysis of assembled structures connected using UHPC grouted bellows.

Three ternary mixtures composed by choline chloride (ChCl), ethylene glycol (EG) and a second hydrogen bond donor (HBD) as ethanol (A), 2-propanol (B), and glycerol (C) were studied in terms of composition related to the band gap energy (BGE). A Design of Experiments (DoE) approach, and in particular a Simple Lattice three-components design, was employed for determining the variation of the BGE upon the composition of each system. UV-VIS analysis and subsequent Tauc plot methodology provided the data requested from the DoE and multivariate statistical analysis revealed a drop of the BGE in correspondence to specific binary compositions for systems A and B. In particular, a BGE of 3.85 eV was registered for the mixtures ChCl/EtOH (1:1), and ChCl/2-propanol (1:1), which represents one of the lowest values ever observed for these systems.

In order to clarify the nature of the halogen bond (XB), we considered the prototype noble gas – dihalogen molecule (Ng-X2) systems, focusing on the nature, range and strength of the interaction. We exploited data gained from molecular beam scattering experiments with the measure of interference effects to obtain a suitable formulation of the interaction potential, with the support of high-level ab initio calculations, and charge displacement analysis. The essential interaction components involved in the Ng-X2 adducts have been characterized, pointing at their critical balance in the definition of the XB. Particular emphasis is devoted to the energy stability of the orientational Ng-X2 isomers, the barrier for the X2 hindered rotation, and the influence of the X2 electronic state. The present integrated study returns reliable force fields for molecular dynamics simulations in Ng-X2 complexes that can be extended to systems with increasing complexity and whose properties depend on the selective formation of XB.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

This paper presents experimental identification and vibration suppression of a flexible manipulator with non-collocated piezoelectric actuators and strain sensors using optimal multi-poles placement control. To precisely identify the system model, a reduced order transfer function with relocated zeros is proposed, and a first-order inertia element is added to the model to compensate the non-collocation. Comparisons show the identified model match closely with the experimental results both in the time and frequency domains, and a fit of 97.2% is achieved. Based on the identified model, a full-state multi-poles placement controller is designed, and the optimal locations of the closed loop poles are determined. The feasibility of the proposed controller is validated by simulations. Moreover, the controller is tested for different locations of the closed loop poles, and an excellent performance of the optimal locations of the closed loop poles is shown. Finally, the effectiveness of the proposed controller is demonstrated by experiments. Results show that the vibrations of the expected modes are significantly diminished. Besides, vibrations of the higher modes are also slightly suppressed. Accordingly, multi-mode vibrations of the manipulator are well attenuated, and the tip displacement converges quickly with the proposed method.

Increased traffic in combination with growing environmental impacts, have led to accelerated degradation of the built infrastructure. In reinforced concrete structures, the corrosion of steel reinforcement is the predominant cause of deterioration. Thus, over the last years the use of glass fiber reinforced polymer (GFRP) composites as internal reinforcement bars (rebars) for concrete structures was evaluated, and has been proved to be a viable alternative to traditional steel reinforcement mainly due to its tensile strength and non-corrosive nature. However, thus far, the GFRP rebar market is diverse and manufacturers around the world produce GFRP rebar types with different surface enhancement to improve the bond to concrete characteristics. In this study, the bond performance of three dissimilar GFRP rebar types (sand coated, helically grooved and with surface lugs) was evaluated over time in seawater environments, with a focus on the bond strength. Accordingly, specimens were expose to seawater in circulating chambers, at three different temperatures (23 °C, 40 °C and 60 °C) for multiple time periods (60 and 120 days). To evaluate the bond performance, pullout tests were conducted according to ASTM D7913 [1]. The results showed that the bond strength varies with surface enhancement features. However, the bond strength didn’t vary significantly with exposure time and temperature for all three evaluated rebar types.

The structural performance of reinforced concrete relies heavily on the bond between reinforcement and concrete. In nonlinear finite element analyses, bond is either modelled by merged, also called perfect bond, or coincident with slip, also called bond-slip, approaches. Here, the performance of these two approaches for the modelling of failure of reinforced concrete was investigated using a damage-plasticity constitutive model in LS-DYNA. Firstly, the influence of element size on the response of tension-stiffening analyses with the two modelling approaches was investigated. Then, the results of the two approaches were compared for plain and fibre reinforced tension stiffening and a drop weight impact test. It was shown that only the coincident with slip approach provided mesh insensitive results. However, both approaches were capable of reproducing the overall response of the experiments in the form of load and displacements satisfactorily for the meshes used.

A Staudinger reaction on solid phase between an electronodeficit organic azide such as sulfonyl azide, and the phosphite triester formed upon phosphoramidite coupling is a convenient method for chemical modification of oligonucleotides at the internucleotidic phosphate position. In this work, 4-carboxybenzenesulfonyl azide either with a free carboxy group or in the form of an activated ester such as pentafluorophenyl, 4-nitrophenyl, or pentafluorobenzyl ester were used to introduce a carboxylic acid function to the terminal or internal internucleotidic phosphate of an oligonucleotide via Staudinger reaction. A subsequent treatment with excess primary alkyl amine followed by usual work-up, after prior activation with a suitable peptide coupling agent such as HBTU/HOBt in the case of a free carboxyl, afforded amide-linked oligonucleotide conjugates in good yields including multiple conjugations of up to the exhaustive modification at each phosphate position for a weakly activated pentafluorobenzyl ester, whereas more strongly activated and, thus, more reactive aryl esters provided only single conjugations at the 5′-end. The conjugates synthesized include those with di- and polyamines that introduce a positively charged side-chain to potentially assist intracellular delivery of the oligonucleotide.

Conclusion: When applied to the air-abraded Filtek™ Bulk Fill Flowable Restorative composite surface, 10-MDP-containing adhesives perform better than 10-MDP-free adhesives. The binding strength was unaffected by the 10-MDP-containing adhesives' chemical makeup. The bond strength endurance of adhesives containing 10-MDP decreases with age. Keywords: keyword 1; keyword 2; keywords: low-viscosity bulk fill composite, universal adhesives, self-etch adhesive, 10-MDP, aging, bond strength.

The eutrophication of open water bodies, especially in coastal water area, has received intensive public focuses in recent years. Biological treatment of organic pollutants in industrial and domestic wastewater requires microbial flora to participate. The environmental impact of these microbes, particularly in regard to the spread of antibiotic resistance genes and pathogenicity in coastal water, remains largely unknown. We initiate studies to examine antibiotic resistance genes (ARGs) in microbial flora and study the mode of antibiotic spread in the coastal waters of Bohai Bay, North China by using high performance liquid chromatography-mass spectrum analysis and quantitative polymerase chain reaction. The metabolic features of bacteria related to the presence of ARGs in the coastal area showed the enrichment of Pseudomonadale species with triple resistance properties (ampicillin, kanamycin and gentamycin) at the estuary site locating adjacent to a coastal sewage treatment plant where both type I and type II polyketide synthase genes and b-lactam type antibiotics are detected. Pseudomonadale order is phylogenetically belonged to Proteobacteria Phylum, Gamma-proteobacteria Class and contains Pseudomonadaceae family and Pseudomonas genus. QPCR analysis suggests that the percent occupation of Pseudomonadale species to total Proteobacteria in the examined stations is close to several sampling stations close to sewage treatment plants, where consistent release of organic wastes occurred.

Face verification for unrestricted faces in the wild is a challenging task. This paper proposes a method based on two deep convolutional neural networks(CNN) for face verification. In this work, we explore to use identification signal to supervise one CNN and the combination of semi-verification and identification to train the other one. In order to estimate semi-verification loss at a low computation cost, a circle, which is composed of all faces, is used for selecting face pairs from pairwise samples. In the process of face normalization, we propose to use different landmarks of faces to solve the problems caused by poses. And the final face representation is formed by the concatenating feature of each deep CNN after PCA reduction. What's more, each feature is a combination of multi-scale representations through making use of auxiliary classifiers. For the final verification, we only adopt the face representation of one region and one resolution of a face jointing Joint Bayesian classifier. Experiments show that our method can extract effective face representation with a small training dataset and our algorithm achieves 99.71% verification accuracy on LFW dataset.

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.

Hydrogen-bond (H-bond) energies in 310-helices of short alanine peptides were systematically examined by precise DFT calculations with the negative fragmentation approach (NFA), a modified method based on the molecular tailoring approach. The contribution of each H-bond was evaluated in detail from the 310-helical conformation of total energies (whole helical model; WH3-10 model), and the results were compared with the property of H-bond in a-helix from our previous study. The H-bond energies of the WH3-10 model exhibited tendencies different from those exhibited by the -helix, in that they depended on the helical position of the relevant H-bond pair. H-bond pairs adjacent to the terminal H-bond pairs were observed to be strongly destabilized. The analysis of electronic structures indicated that structural characteristics cause the destabilization of the H-bond in 310-helices. We also found that the longer the helix length, the more stable the H-bond in the terminal pairs of the WH3-10 model, suggesting the action of H-bond cooperativity.

Phenol is added to acetophenone (methyl phenyl ketone) and to six of its halogenated derivatives in a supersonic jet expansion to determine the hydrogen bonding preference of the cold and isolated 1:1 complexes by linear infrared spectroscopy. Halogenation is found to have a pronounced effect on the docking site in this intermolecular ketone balance experiment. The spectra unambiguously decide between competing variants of phenyl group stacking due to their differences in hydrogen bond strength. Structures where the phenyl group interaction strongly distorts the hydrogen bond are more difficult to quantify in the experiment. For unsubstituted acetophenone, phenol clearly prefers the methyl side despite a predicted sub-kJ/mol advantage which is nearly independent of zero point vibrational energy, turning this complex into a challenging benchmark system for electronic structure methods which include long range dispersion interactions in some way.

Multilayered metallic sheets fabricated by hot metal forming processes are attractive structural components in the aerospace and automobile industry. The strength of the mechanical bond between the different layers is critical to the applicability of such components in load bearing applications. While it is well understood that the process conditions such as temperature, deformation and rate of deformation influence the bond strength, a clear functional relationship between these conditions and the bond strength has yet to be established. The objective of the current study is to investigate the joining of Aluminum-6061 alloy specimens by hot compression bonding. Hot compression tests of beam specimen pairs were conducted at different temperatures (300-500℃), with different strains and strain rates (10-2-10-1 [1sec]). Coupled thermo-mechanical finite element models were utilized to compute the time dependent thermo-mechanical fields that develop along the specimen interface. The computed values were used to map the spatial distribution of a scalar parameter J which is hypothesized to govern bond formation. Mechanical tensile tests were used to determine the bond strength and expose the bonded surfaces. It is demonstrated that the computed J distribution can be correlated well with SEM observations of the debonded surface. It is also shown that the actual bonded area can be predicted from the computations for a certain range of critical J values.

A mild, efficient, and photocatalytic synthesis of 6-phosphorylated phenanthridines via tandem radical addition/cyclization/aromatization of 2-isocyanobiphenyls with diarylphosphine oxides is reported. The method features operational simplicity, metal-free conditions, using low-cost rose Bengal as catalyst and sustainable air as terminal oxidant at room temperature, and providing the desired products in moderate to good yields.

Prereactive complexes in noncovalent organocatalysis are sensitive to the relative chirality of the binding partners and to hydrogen bond isomerism. Both effects are present when a transiently chiral alcohol docks on a chiral α-hydroxy ester, turning such 1:1 complexes into elementary, non-reactive model systems for chirality induction in the gas phase. With the help of linear infrared and Raman spectroscopy in supersonic jet expansions, conformational preferences are investigated for benzyl alcohol in combination with methyl lactate, also exploring p-chlorination of the alcohol and the achiral homolog methyl glycolate to identify potential London dispersion and chirality effects on the energy sequence. Three of the four combinations prefer barrierless complexation via the hydroxy group of the ester (association). In contrast, the lightest complex shows predominantly insertion into the intramolecular hydrogen bond, like the analogous lactate and glycolate complexes of methanol. The experimental findings are rationalized with computations and a uniform helicality induction in the alcohol by the lactate is predicted, independent on insertion into or association with the internal lactate hydrogen bond. p-Chlorination of benzyl alcohol has a stabilizing effect on association, because the insertion motif prevents a close contact between the chlorine and the hydroxy ester. After simple anharmonicity and substitution corrections, the B3LYP-D3 approach offers a fairly systematic description of the known spectroscopic data on alcohol complexes with α-hydroxy esters.

Alzheimer's disease (AD) remains a significant challenge in the field of neurodegenerative disor-ders, even nearly a century after its discovery, due to the elusive nature of its causes. The develop-ment of drugs that target multiple aspects of the disease has emerged as a promising strategy to address the complexities of AD and related conditions. The immune system's role, particularly in AD, has gained considerable interest, with nanobodies representing a new frontier in biomedical research. Advances in targeting antibodies against amyloid-β (Aβ) and using messenger RNA for genetic translation have revolutionized the production of antibodies and drug development, open-ing new possibilities for treatment. Despite these advancements, conventional treatments for AD, such as Cognex, Exelon, Razadyne, and Aricept, often have limited long-term effectiveness, under-scoring the need for innovative solutions. This necessity has led to the incorporating of advanced technologies like artificial intelligence and machine learning into the drug discovery process for neurodegenerative diseases. These technologies help identify therapeutic targets and optimize lead compounds, offering a more effective approach to addressing the challenges of AD and similar conditions.

This paper applies a new artificial intelligence architecture, the Temporal Fusion Transformer (TFT), for the joint GDP forecasting of 25 OECD countries at different time horizons. This new attention-based architecture offers significant advantages over other deep learning methods. First, results are interpretable since the impact of each explanatory variable on each forecast can be calculated. Second, it allows to visualize persistent temporal patterns and to identify significant events and different regimes. Third, it provides quantile regressions and permits to train the model on multiple time series from different distributions. Results suggest that TFTs outperform regression models, especially in periods of turbulence such as the COVID-19 shock. Interesting economic interpretations are obtained depending on whether the country is domestic demand-led or export-led growth. In essence, TFT is revealed as a new tool that artificial intelligence provides to economists and policy makers, with enormous prospects for the future.

To estimate the progressive collapse resistance capacity of a multi-column frame tube structure with the assembled truss beam composite floor (ATBCF), the pushdown analysis, and the nonlinear dynamic analysis as well, are conducted for such a structure by using the alternate load path (ALP) method. The bearing capacities of the remaining structures at three different work conditions, which are the side middle column failure, the edge middle column failure, and the corner column failure, are individually studied, and the collapse mechanism for the remaining structures is analyzed from the aspects of the internal force redistribution and the failure mode of the second defense line. Simultaneously, the influence of the column failure time on the dynamic response of the remaining structure and the dynamic amplification coefficient are discussed. The results show that the residual bearing capacity of the remaining structure with the bottom corner column failure is higher than that of the one with the side or edge middle column failure, while the latter has a stronger plastic deformation capacity. When the ALP method is adopted to operate the progressive collapse analysis, it is reasonable to take the column failure time as 0.1 times of the first-order vertical vibration period of the remaining structure, and it is suitable to set the dynamic amplification coefficient as of 2.0, which is the ratio of the maximum dynamic displacement to the static displacement of the remaining structure under the transient loading condition.

In a hyperconnected society, computer networks play a pivotal role in all activities that permeate our daily lives. In the context of smart cities, theses networks play a pivotal role in enhancing urban efficiency and sustainability. The continuous flow of packets in these networks poses security challenges, from personal data leakage to service unavailability. Anomaly-based methods are commonly used to detect network attacks. In this context, Software-Defined Networking (SDN) emerges as a solution for anomaly detection in network traffic, facilitating comprehensive, real-time analyses and dynamic adaptation to changes. This study aims to present a systematic review of feature selection techniques and evaluate the effectiveness of attribute selection with a multi-class classifier in network anomaly detection. The objective of this paper is to inspire future research and identify trade-offs among these techniques for detecting Denial of Service (DoS) attacks. Based on the findings, the analysis of the entire dataset yields superior results in terms of precision (99%), but using the output of OneR, although resulting in a slight loss of precision compared to the complete dataset, presents the highest precision among other studied techniques, indicating a trade-off between precision and processing time efficiency.

Bond Properties were performed on Ag-2.35Au-0.7Pd-0.2Pt-0.1Cu alloy wire with a diameter of 25 µm under different process parameters. Effects of electrical flaming off (EFO) current and EFO time on deformability of the free air ball (FAB) were investigated using scanning electron microscopy (SEM) as well as the effects of ultrasonic power and bonding force on the bond characteristic. The experimental results showed that, FAB grows from a preheated tip to a small ball, a regular ball and finally to a golf ball with increasing either the EFO current or the EFO time, and the FAB presented optimal shape at 25 mA and 650 μs. Moreover, a nonlinear relationship between FAB diameter and EFO time is obtained at the EFO current of 25 mA, which could be expressed by a cubic equation. Further, at a constant bonding force, as the ultrasonic power increased, the mashed ball diameter grew larger and larger, the capillary hole imprint became more and more obvious, and the tail width also increased larger and larger, and vice versa. The optimal ultrasonic power and bonding force are 70 mW and of 45 gf for ball bond and 90 mW and 75 gf for wedge bond, respectively. Finally, for all the bonded wire samples prepared under optimal process parameters, no ball and wedge bond lifts happened after destructive pull test, and full intermetallic compound coverage with perfect morphology occurred on the bond pad after ball shear test, which meant that the bonded wire samples have high bond strength, and hence improved the reliability of microelectronic products.

The existences of the N→C dative bonds in the complexes between N-containing molecules and fullerenes have been verified both theoretically and experimentally. However, finding stable N→C dative bonds is still a highly challenging task. In this work, we investigated computationally the N→C dative bonds in the complexes formed by fullerene C60 with 1,2,5-chalcogenadiazoles, 2,1,3-benzochalcogenadiazoles and 1,2,4,5-chalcogenatriazoles, respectively. It was found that the N→C dative bonds are formed along with the formation of the N–Ch···C (Ch = S, Se, Te) chalcogen bonds. In the gas phase, from S-containing complexes through Se-containing complexes to Te-containing complexes, the intrinsic interaction energies become more and more negative, which indicates that the N–Ch···C chalcogen bonds can facilitate the formation of the N→C dative bonds. The intrinsic interaction energies are compensated by the large deformation energy of fullerene C60. The total interaction energies of Te-containing complexes are negative, while both of the total interaction energies of S-containing complexes and Se-containing complexes are positive. This means that the N→C dative bonds in the Te-containing complexes are more easily observed in experiments in comparison with those in the S-containing complexes and Se-containing complexes. This study provides a new theoretical perspective on the experimental observation of the N→C dative bonds in the complexes involving fullerenes. Further, the formation of stable N→C dative bonds in the complexes involving fullerenes can significantly change the properties of fullerenes, which will greatly simulate and expand the application range of fullerenes.

In the recent past about 40 botulinum neurotoxin (BoNT) subtypes belonging to serotypes A, B, E, and F pathogenic to humans were identified among hundreds of independent isolates. BoNTs are the etiological factors of botulism and represent potential bioweapons, but are also recognized pharmaceuticals for efficient counteraction of hyperactive nerve terminals in a variety of human diseases. The detailed biochemical characterization of subtypes as the basis for development of suitable countermeasures and possible novel therapeutic applications is lagging behind the increase in new subtypes. Here we report the primary structure of a ninth subtype of BoNT/F. Its amino acid sequence diverges by at least 8.4% at the holotoxin and 13.4% on the enzymatic domain level from all other known BoNT/F subtypes. We found that BoNT/F9 shares the scissile Q⁵⁸/K⁵⁹ bond in its substrate vesicle associated membrane protein 2 with the prototype BoNT/F1. Comparative biochemical analyses of four BoNT/F enzymatic domains showed that the catalytic efficiencies decrease in the order F1 > F7 > F9 > F6 and vary by up to factor eight. K_M values increase in the order F1 > F9 > F6 ≈ F7, whereas k_cat decreases in the order F7 > F1 > F9 > F6. Comparative substrate scanning mutagenesis studies revealed a unique pattern of crucial substrate residues for each subtype. Based upon structural coordinates of F1 bound to an inhibitor polypeptide the mutational analyses suggest different substrate interactions in the substrate binding channel of each subtype.

One of the most crucial elements in deeply understanding humans on a psychological level is manifested through facial expressions. The analysis of a human behavior can be informed by their facial expressions, making it essential to employ indicators such as expression (Expr), valence-arousal (VA), and action units (AU). In this paper, we introduce the method proposed in the Challenge of the 6th Workshop and Competition on Affective Behavior Analysis in-the-wild (ABAW) at CVPR 2024. Our proposed method utilizes the multi-modal Aff-wild2 dataset, which is splitted into spatial and audio modalities. For the spatial data, we extract features using a SimMiM model that was pre-trained on a diverse set of facial expression data. For the audio data, we extract features using a WAV2VEC model. To fusion the extracted spatial and audio features, we employed the cascaded cross-attention mechanism of a transformer.

Background: Valid questionnaires to assess dogs’ health-related quality of life (HRQoL) in veterinary practice can improve canine health outcomes and communications between veterinarians and dog caretakers. The Canine HRQoL Questionnaire (HRQoL-Q) and the Human-Canine Bond Questionnaire (HCBQ) were developed and validated to fulfill this need. Methods: A literature review, interviews with veterinarians, and focus groups with caretakers were conducted to generate questionnaire items to develop draft questionnaires, piloted with caretakers to establish content validity. Measurement properties were evaluated using data from a prospective survey study (N = 327). Results: Draft Canine HRQoL-Q and HCBQ measures were developed, including a domain structure, items, recall period, and scale/response options. Refinements were made via iterative cognitive interviews with caretakers. When no additional revisions were indicated and content validity established, the questionnaires were psychometrically tested. Ceiling effects were observed for all items, and factor analyses indicated that pre-specified domains are appropriate. Internal consistency was demonstrated for the HCBQ (α = 0.79–0.86) and all but the canine HRQoL-Q social functioning domain (α = 0.60)). Test-retest reliability for the canine HRQoL-Q was generally moderate-to-good (intraclass correlation coefficients [ICCs] > 0.79). Test-retest reliability for the HCBQ was moderate (ICCs: 0.70–0.79), except the trust domain (ICC: 0.58). Known-groups validity was demonstrated by significant differences (p < 0.05) in scores by health/bonding groups. Convergent validity was supported (r > 0.40) between all domains and the total score for both questionnaires. Conclusions: The Canine HRQoL-Q and the HCBQ are valid, reliable measures of canine HRQoL for use in veterinary clinics and appear to measure related but distinct concepts that contribute to canine health and wellness.

Human influenza A viruses (IAVs) cause global pandemics and epidemics. These viruses evolve rapidly, making current treatment options ineffective. To identify novel modulators of IAV-host interactions, we re-analyzed our recent transcriptomics, metabolomics, proteomics, phosphoproteomics, and genomics/virtual ligand screening data. We identified 713 potential modulators targeting 200 cellular and two viral proteins. Anti-influenza activity for 48 of them has been reported previously, whereas the antiviral efficacy of the remaining 665 is unknown. Studying anti-influenza efficacy, immuno-modulating properties and potential resistance of these compounds or their combinations may lead to the discovery of novel modulators of IAV-host interactions, which might be more effective than the currently available anti-influenza therapeutics.

Ethiopian agriculture is not only affected by precipitation declines (meteorological droughts) but also soil dryness caused by temperature increases and associated long-term hydrological changes. Meteorological drought indicators (e.g., SPI), do not fully capture the water deficits in agricultural systems (i.e., agricultural droughts). An Ethiopia-wide assessment of meteorological and agricultural drought trends was carried out to characterize century-scale (1902 – 2016) changes in droughts. SPI and SPEI calculated using two-month accumulation and the Palmer Z-index were used for assessing intra-season drought trends. SPI and SPEI at six-month accumulations and PDSI were used to define full season droughts. Detrended variance corrected Mann-Kendall test was used for trend analysis during Bega (dry), Belg (short-rainy) and Meher (long-rainy) seasons. The SPEI-2 and PDSI were most aggressive in characterizing intra-season and seasonal-drought trends. There is on average 1% - 6% annual increase in dryness with the lower estimate based on precipitation declines and the upper end accounting for seasonal soil moisture dynamics. The area between 37.5° E – 42.5° E denotes a climate hot-spot. Precipitation declines in Belg along the Ethiopia-South-Sudan/Sudan border during Belg and along Eretria-Ethiopia border during Meher have the potential to exacerbate transboundary water conflicts and further threaten the food security of the region.

2-Pyridone ligand-facilitated palladium-catalyzed direct C–H bond functionalization via the transient directing group strategy has become an attractive topic. Here we report a Pd-catalyzed direct β-C(sp3) –H arylation reaction of tertiary aliphatic aldehydes by using an α-amino acid as a transient directing group in combination with a 2-pyridone ligand.

Flaviviruses replicate in membrane factories associated with the endoplasmic reticulum (ER). Significant levels of flavivirus viral protein accumulation contribute to ER stress. As a consequence, the host cell exhibits an Unfolded Protein Response (UPR), subsequently stimulating appropriate cellular responses such as adaptation, autophagy or apoptosis. The correct redox conditions of this compartment are essential to form native disulfide bonds in proteins. ZIKA virus (ZIKV) has ability to induce persistent ER stress leading to activation of UPR pathways. In this study, we wondered whether ZIKV affects the redox balance and consequently the oxidative protein folding in the ER. We found that ZIKV replication influences redox state leading to aggregation of viral envelope protein as amyloid-like structures in the infected cells.

Materials design for processing and application requires fundamental understanding of their properties based interatomic interaction. The use of the novel concept of total bond order density (TBOD) as a single quantum mechanical metric to characterize the internal cohesion of a crystal and correlate with the calculated physical properties is particularly appealing. This requires detailed first-principles calculation of the electronic structure, interatomic bonding and related properties. In this article, we use this new concept and apply it to chalcogenide crystals based on data obtained from 25 crystals: Ag₂S, Ag₂Se, Ag₂Te, As₂S₃, As₂Se₃, As₂Te₃, As₄Se₄, Cu₂S, Cu₂Se, Cu₂Te, Cu₄GeS₄, Cu₂SnS₃, Cu₂SnSe₃, GeS₂, GeSe₂, Ge₄Se₉, Sb₂S₃, Sb₂Se₃, Sb₂Te₃, SnS, SnSe, CdSe, CdTe, ZnSe, and ZnTe. Together with the calculated optical and mechanical properties, we demonstrate the efficacy of using this novel approach for materials design that could facilitate the exploration and development of new chalcogenide crystals and glasses. Moreover, the TBOD and its partial components (PBOD) could be the key descriptors in machine learning protocol for broader scale design when a large database is available.

Cloud detection in remote sensing images is a crucial preprocessing step that efficiently identifies and extracts cloud-covered areas within the images, ensuring the precision and reliability of subsequent analyses and applications. Given the diversity of clouds and the intricacies of the surface, distinguishing the boundaries between thin clouds and the underlying surface is a major challenge in cloud detection. To address these challenges, an advanced cloud detection method, CloudformerV3, is presented in this paper. The proposed method employs a multi-scale adapter to incorporate dark and bright channel prior information into the model's backbone, enhancing the model's ability to capture prior information and multi-scale details from remote sensing images. Additionally, multi-level large window attention is utilized, enabling high-resolution feature maps and low-resolution feature maps to mutually focus and subsequently merge during the resolution recovery phase. This facilitates the establishment of connections between different levels of feature maps and offers comprehensive contextual information for the model's decoder. Experimental results on the GF1_WHU dataset demonstrate that the method introduced in this paper exhibits superior detection accuracy when compared to state-of-the-art cloud detection models. Furthermore, enhanced detection performance is achieved along cloud edges and with respect to thin clouds, showcasing the efficacy of the proposed method.

The evolution of human societal dynamics over the course of decades and centuries has led to an ever-increasingly complex digital modern life, one of the hallmark of which is the need for multi-tasking, inclusive of that with media. Is there an empirical mapping between this macroscopic (digital) evolution of the human societal dynamics (in terms of multi-tasking) and the microscopic evolution of the functional molecular units within cells (i.e., proteins) and their manifold cross-talks? Multi-tasking requires a certain degree of intrinsic or adapted flexibility which is as true at the bio-molecular level. Evolutionary diversification of structure-function relationship in proteins emphasizes the functional importance of intrinsically disordered entities (IDPs/IDRs) which are highly dynamic biological soft matters. Multi-functionality is favorably supported by their fluid-like shapes and other new malleable members of the protein family. The new wealth of knowledge coming from recent research on protein intrinsic disorder, fold-switching, moonlighting, hub proteins etc. has given us new insights into protein structure – functional relationship. This has led to a paradigm shift in protein science, clearly diverging from the traditional 'one structure – one function' model applicable to enzyme classes to a more complex understanding of protein functionality. This paradigm shift has caused scientists to delve deeper into the subject, exploring various evolutionary toys and tools to fit the cumulative multi-functional demands in higher organisms. This commentary covers the different evolutionary arsenals to achieve the growing multi-functionality and argues in favor of protein intrinsic disorder as probably the sharpest weapon of all.

This study investigates the unique morphology and mechanical properties of multi-jet electrospun cashew gum (CG) when combined with high molecular weight polyethylene oxide (PEO) and glycerol. Cashew gum (CG) is an inexpensive, non-toxic polysaccharide derived from Anacardium occidentale trees. Initially, the electrospinnability of aqueous solutions of cashew gum alone or in combination with PEO was evaluated. It was found that cashew gum alone was not suitable for electrospinning; thus, adding a small quantity of PEO was needed to create the necessary molecular entanglements for fiber formation. By using a single emitter with a CG:PEO ratio of 85:15, straight and smooth fibers with some defects were obtained. However, additional purification of the cashew gum solution and the incorporation of glycerol as a plasticizer were necessary to produce defect-free straight and smooth fibers. Interestingly, when the optimized formulation was electrospun using multiple simultaneous emitters, thicker aligned fiber bundles were achieved. Moreover, the resulting oriented fiber mats exhibited unexpectedly high elongation at break at ambient conditions. These findings highlight the potential of this bio-polysaccharide-based formulation for non-direct water contact applications requiring elastic properties.

Although there are several access control systems in the literature for flexible policy management in multi-authority and multi-domain environments, achieving interoperability & scalability, without relying on strong trust assumptions, is still an open challenge. We present HMBAC, a distributed fine-grained access control model for shared and dynamic multi-authority and multi-domain environments, along with Janus, a practical system for HMBAC policy enforcement. The proposed HMBAC model supports: (a) dynamic trust management between different authorities; (b) flexible access control policy enforcement, defined at domain and cross-domain level; (c) a global source of truth for all entities, supported by an immutable, audit-friendly mechanism. Janus implements the HMBAC model and relies on the effective fusion of two core components. First, a Hierarchical Multi-Blockchain architecture that acts as a single access point that cannot be bypassed by users or authorities. Second, a Multi-Authority Attribute Based Encryption protocol that supports flexible shared multi-owner encryption, where attribute keys from different authorities are combined to decrypt data distributedly stored in different authorities. Our approach was implemented using Hyperledger Fabric as the underlying blockchain, with the system components placed in Kubernetes Docker container pods. We experimentally validated the effectiveness and efficiency of Janus, while fully reproducible artifacts of both our implementation and our measurements are provided.

Air quality is one of the most concerning problems in major industrialized cities in the world. Prediction of future air quality is highly relevant to public health. In some big cities, multiple air quality measurement stations are deployed at different locations to monitor air pollutants, such as NO2, CO, PM 2.5 and PM 10, over time. At every monitoring time stamp t, we observe one station×feature matrix xt of the pollutant data, which represents a spatio–temporal process. Traditional methods on prediction of air quality typically use data from one station or can only predict a single pollutant (such as PM 2.5) at a time, which ignores the spatial correlation among different stations. Moreover, the air pollution data are typically highly nonstationary, We propose a de-trending graph convolutional LSTM (long short term memory) to continuously predict the whole station×feature matrix in the next 1 to 48 hours, which not only captures the spatial dependency among multiple stations by replacing an inner product with convolution, but also incorporates the de-trending signals (transform a nonstationary process to a stationary one by differencing the data) into our model. Experiments on the air quality data of the city Chengdu and multiple major cities in China demonstrate the feasibility of our method and show promising results.

The paper describes problems with the current additive manufacturing chain before considering additive manufacturing as part of a modern manufacturing chain. Additive manufacturing can be used for near net-shape for finishing, for repair or for adding special features which cannot be made with traditional manufacturing. This paper describes how STEP-NC deals with these different scenarios in terms of accuracy, multi-material and variation of slice direction. The possibilities of multi-material objects also raises questions about the design of such objects and how these need to be handled by an advanced controller. The paper also describes non-planar slicing. Curved direction and cylindrical direction are shown to improve the accuracy of curved structure additive manufacturing. STEP-NC using boundary representation has better capability of depicting complex internal structures for additive processes. By using exact model of the final product represented by STEP-NC, the paper demonstrates improvements in data size reduction, slicing accuracy, and precise manipulation of internal structure.

We discuss slip bonds, catch bonds and the tug-of-war mechanism using mathematical arguments. We propose simple 2-dimensional surface models to demonstrate how the potential energy surface of a molecule under an external force behaves. Examples are selectines. Catch bonds in particular are explained in more detail. We demonstrate that Newton trajectories are a powerful tool to describe these phenomena. Newton trajectories form the theoretical background of Mechanochemistry.

Support effect is an important issue in heterogeneous catalysis while the explicit role of a catalytic support is often unclear for catalytic reactions. A systematic density functional theory computational study is reported in this paper to elucidate the effect of a model boron nitride (BN) support on the first N-H bond activation step of NH3 on Run (n =1, 2, 3) metal clusters. Geometry optimizations and energy calculations were carried out using density functional theory (DFT) calculation for intermediates and transition state from the starting materials undergoing the N-H activation process. The primary findings are summarized as follows. The involvement of the model BN support does not significantly alter the equilibrium structure of intermediates and transition state in the most favorable pathway (MFP). Moreover, the involvement of BN support decreases the free energy of activation, ΔG≠, thus improving the reaction rate constant. This improvement is more obvious at high temperatures like 673 K than low temperatures like 298 K. The BN support effect leading to the ΔG≠ decrease is most significant for the single Ru atom case among all three cases studied. Finally, the involvement of model BN may change the spin transition behavior of the reaction system during the N-H bond activation process. All these findings provide a deeper insight into the support effect on the N-H bond activation of NH3 for supported Ru catalyst in particular and for supported transition metal catalysts in general.

We report the results of the characterization of local Monte Carlo (MC) dynamics of an equilibrium bond fluctuation model polymer matrix (BFM), in time interval typical for MC simulations of non-linear optical phenomena in host-guest systems. The study contributes to the physical picture of the dynamical aspects of quasi-binary mosaic states characterized previously in the static regime. The polymer dynamics was studied at three temperatures (below, above and close to the glass transition), using time-dependent generalization of the static parameters which characterize local free volume and local mobility of the matrix. Those parameters play the central role in the kinetic MC model of host-guest systems. The analysis was done in terms of the probability distributions of instantaneous and time-averaged local parameters. The main result is the characterization of time scales characteristic of various local structural processes. Slowing down effects close to the glass transition are clearly marked. The approach yields an elegant geometric criterion for the glass transition temperature. A simplified quantitative physical picture of the dynamics of guest molecules dispersed in BFM matrix at low temperatures offers a starting point for stochastic modeling of host-guest systems.

The study of vegetation community and structural change has been central to ecology for over a century, yet how disturbances reshape the physical structure of forest canopies remains relatively unknown. Moderate severity disturbance including fire, ice storms, insect and pathogen outbreaks, affects different canopy strata and plant species, which may give rise to variable structural outcomes and ecological consequences. Terrestrial lidar (light detection and ranging) offers an unprecedented view of the interior arrangement and distribution of canopy elements, permitting the derivation of multidimensional measures of canopy structure that describe several canopy structural traits with known linkages to ecosystem functioning. We used lidar-derived canopy structural measured within a machine learning framework to detect and differentiate among various disturbance agents, including moderate severity fire, ice storm damage, age-related senescence, hemlock woolly adelgid, beech bark disease, and chronic acidification. We found that disturbance agents such as fire and ice storms primarily affected the amount and position of vegetation within canopies, while acidification, pathogen and insect infestation, and senescence altered canopy arrangement and complexity. Only two of the six disturbance agents significantly reduced leaf area, indicating that this commonly quantified canopy feature is insufficient to characterize many moderate severity disturbances. Rather, measures of canopy structure, including those that describe multidimensional change, are needed to characterize disturbance at moderate severities because structural changes from these events are spatially and quantitatively variable. Our findings suggest that standard disturbance detection methods, such as optical based remote sensing platforms, may currently be limited in their ability to detect, differentiate, and characterize disturbance. Further, we conclude that a more broadly inclusive definition of ecological disturbance that incorporates multiple aspects of canopy structure change will improve the modeling, detection, and prediction of functional implications of moderate severity disturbance.

In this study, we aim to evaluate how cyclodextrin molecules affect the hydrophilicity and selec-tivity of PVDF membrane. Herein, we prepared PVDF-PVP(1-x) β-CDx membranes, with x rang-ing from 0 to 1 wt%. The membranes were deeply characterized by means of X-ray diffraction (XRD), attenuated total reflectance infrared (ATR-IR), Raman spectroscopies, Thermal gravimet-ric analysis (TGA), Contact angle measurement (CA), scanning electron microscopy (SEM) and atomic force microscopy (AFM). The study unequivocally demonstrates the effectiveness of the addition of β-CD molecules on membranes' structural, morphological, and dielectric properties. Adding 0.25wt% β-CD enhanced hydrophilicity, with CA decreasing from 72.6° to 56° and roughness decreasing from 81 to 29 nm. The PVDF-PVP(1-x) β-CDx membranes were then fully characterized using electrochemical impedance spectroscopy (EIS) to determine their dielectric properties, which clearly showed that the presence of β-CD induced polarization of the mem-brane. The separation of various dyes with different sizes and charges was utilized. These dyes included methylene blue (MB, positive, 0.504 nm), methyl red (MR, neutral, 0.487 nm), rose Bengal (RB, negative, 0.588 nm), and brilliant blue (BB, negative, 0.798 nm). Based on our re-search, we found that the membrane's selectivity is determined by both exclusion factors (such as size and dielectric properties) and inclusion inside the β-CD cage. We found that using alpha-CD instead of beta-CD led to the disappearance of dye inclusion. These findings strongly sug-gest that β-CD molecules can be a viable solution for dye removal from wastewater.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease associated with the overproduction and accumulation of amyloid-β peptide and hyperphosphorylation of tau proteins in the brain. Despite extensive research on the amyloid-based mechanism of AD pathogenesis, the underlying cause of AD remains poorly understood. No disease-modifying therapies currently exist, and numerous clinical trials have failed to demonstrate any benefits. The recent discovery that the amyloid-β peptide has antimicrobial activities supports the possibility of an infectious aetiology of AD and suggests that amyloid-β plaque formation might be induced by infection. AD patients have a weakened blood-brain barrier and immune system and are thus at elevated risk of microbial infections. Such infections can cause chronic neuroinflammation, production of the antimicrobial amyloid-β peptide, and neurodegeneration. Various pathogens, including viruses, bacteria, fungi, and parasites have been associated with AD. Most research in this area has focused on individual pathogens, with herpesviruses and periodontal bacteria being most frequently implicated. The purpose of this review is to highlight the potential role of multi-pathogen infections in AD. Recognition of the potential coexistence of multiple pathogens and biofilms in AD's aetiology may stimulate the development of novel approaches to its diagnosis and treatment. Multiple diagnostic tests could be applied simultaneously to detect major pathogens, followed by anti-microbial treatment using antiviral, antibacterial, antifungal, and anti-biofilm agents.

The aim of the study was to examine the influence of different irrigation protocols on the shear bond strength (SBS) of self-adhesive resin composite cement (SARC) to primed radicular dentin. Radicular dentin slabs (N=58) were sectioned from extracted intact third molars, embedded in acrylate, polished, and randomly assigned to five experimental groups (N=8-12) and one control group, CG (N=8). Irrigation solutions used were sodium hypochlorite (H), ethylene-diamine-tetra acetic acid (EDTA), etidronic acid with hypochlorite (HEDP), saline solution (SS), and chlorhexi-dine (CHX). Each experimental group underwent different irrigation protocol for two minutes: 1) H/EDTA/H, 2) HEDP, 3) H/EDTA/CHX, 4)H/EDTA/SS, and 5) HEDP/SS. The CG samples were not treated with any irrigation solution. After drying, adhesive enhancing primer (AEP) and du-al-curing SARC were applied to the radicular dentin using molds corresponding to the Ultra Tester. Specimens were stored at 37°C in distilled water and fractured in shear mode after 10 days. The results were analyzed using the ANOVA statistical test and Post-hoc Games-Howell test, α=0.05. CG exhibited the highest SBS. Except for HEDP and HEDP/SS, all experimental groups showed significantly lower SBS compared to the control group (p< 0.05). HEDP seems to be ad-equate solution for root canal cleaning before dentin priming and fiber post cementation with self-adhesive resin cement. Cleaning protocols with H, EDTA and CHX cause a significant de-crease in bond strength.

This study identifies new Alkali Activated Composite Mud Mortar to alternate Cement Mortar used for laying masonry works. This study handles both experimental and numerical analysis of strength and durability for mortar, especially bond strength along mortar and brick joints have studied detail and compared with ANSYS. The bond strength has estimated through the triplet brick prism of shear behavior. The strength and deformation characteristics of masonry constituents obtained from these tests depend on the actual composite conduct of masonry. There is always a good reception for environmentally friendly, low energy, new construction materials, and used in preparing mortar/brick unit combinations all around the world. Also, this study fulfills those requirements. Especially, masonry laid with the mud mortar meets all. Fly Ash, Ground Granulated Blast furnace Slag, and Quarry Dust have utilized the new composites' preparation with 8Molority, 10Molority developed excellent strength. The composite specimens are having compression and bond strength, ranging from 4.84-5.14 and 0.12-0.18N/mm2. The mixed ratio of materials has taken (0.5 parts of Fly Ash, 0.5 parts of Steel Slag, 1.75 parts of Soil, and 0.25 parts of Quarry Dust). The samples tested resistance against acidic, sulfate, and alkalinity indicated PH values less than 7 and 9 simultaneously. Compression strength varied from 10-20% and gained more and fit composite used for mortar in masonry works.