Waste management is a significant issue that Indonesia must face. The presence of waste will always exist as long as life continues. One of the main challenges in waste management is the proper separation of organic and inorganic waste. As a solution to address this issue, the Ministry of Environment has made efforts to develop Waste Banks. However, it also creates new problems related to manual data collection, lack of monitoring by the community in implementing waste management activities, and a lack of specific reports regarding waste management conducted by officers. We propose the creation of a website using the Agile Development SDLC method. This website involve several steps and incorporate functions for data digitalization and expedited data collection, such as the addition of QR Code functionality, Monthly Reporting Function, and enhanced data security. The method used in this research is Agile Development. The application has been developed with a user-friendly interface and the latest web development technologies to ensure smooth performance. Overall, the web-based application for organic and inorganic waste using Laravel is an important step to promoting sustainable waste management practices.

In this work, the inhibition properties of new bio-based lignine ILs on the corrosion of mild steel in an aqueous solution of 0.01 M NaCl were investigated by Potentiostatic Electrochemical Impedance Spectroscopy (PEIS), Cyclic Potentiodynamic Polarization (CPP). Moreover, the surface was characterized using SEM, EDS, and optical profilometry. The influence of cation and anion on the inhibitor properties and a possible synergistic effect were investigated. The IL choline syringate showed a promising performance, reducing the corrosion current after 24-hour immersion in 0.01 M sodium chloride, from 1.66 µA/cm2 for the control to 0.066 µA/cm2 with 10 mM of the IL present. In addition to the performance as a corrosion inhibitor, both components of this IL also meet or exceed current additional desired properties of such compounds, being readily available, economically efficient, and well tolerated in organisms and the environment.

Paper-based analytical devices have been substantially developed in recent decades. Many fabrication techniques for paper-based analytical devices have been demonstrated and reported. Herein we report a relatively rapid, simple, and inexpensive method for fabricating paper-based analytical devices using parafilm hot pressing. We studied and optimized the effect of the key fabrication parameters, namely pressure, temperature, and pressing time. We discerned the optimal conditions, including pressure of 3.8 MPa (3 tons), temperature of 80oC, and 3 minutes of pressing time, with the smallest hydrophobic barrier size (821 µm) being governed by laminate mask and parafilm dispersal from pressure and heat. Physical and biochemical properties were evaluated to substantiate the paper functionality for analytical devices. Wicking speed in the fabricated paper strips was slightly slower than that of non-processed paper, resulting from reducing paper pore size. A colorimetric immunological assay was performed to demonstrate the protein binding capacity of the paper-based device after exposure to pressure and heat from the fabrication. Moreover, mixing in two-dimensional paper-based device and flowing in a three-dimensional counterpart were thoroughly investigated, demonstrating that the paper device from this fabrication process is potentially applicable as analytical devices for biomolecule detection. Fast, easy, and inexpensive parafilm hot press fabrication presents an opportunity for researchers to develop paper-based analytical devices in resource-limited environments.

Abstract: Calcium-based catalysts are of a high interest for glycerol polymerization due to their high catalytic activity and large availability. However, their poor stability under reaction conditions is an issue. In the present study, we investigated the stability and catalytic activity of Ca-hydroxyapatites (HAps) as one of the most abundant Ca-source in nature. A stochiometric, a Ca-deficient and a Ca-rich HAps have been synthetized and tested as catalysts in the glycerol polymerization reaction. Deficient and stochiometric HAps exhibited a remarkable 100% selectivity to triglycerol at 15 % of glycerol conversion at 245 °C after 8 h of reaction in the presence 0.5 mol.% of catalyst. Moreover, under the same reaction conditions, Ca-rich HAp showed a high selectivity (88 %) to di- and triglycerol at a glycerol conversion of 27 %. Most importantly, these catalysts were unexpectedly stable towards leaching under the reaction conditions based on the ICP-OES results. However, based on the catalytic tests and characterization analysis performed by XRD, XPS, IR, TGA-DSC and ICP-OES, we found that HAps can be deactivated by the presence of the reaction products themselves, i.e., water and polymers.

A project-based module on Sustainable Development Goal number 5, Gender Equality, was im-plemented on 5 different groups of Business English students consisting of a total number of 62 students in higher education. The main purpose of this project was to raise awareness of this goal by means of a flipped method in which students were required to carry out some research on specific areas of the aforementioned goal and work in teams to elaborate oral presentations. Once their findings were shared in class, students were expected to answer a written questionnaire of open-ended questions which were part of a qualitative analysis. Results of this survey showed that not only 90% of the students gained in depth knowledge of this goal, but also 85% had built a positive attitude to take initiative and 80% were optimistic about future gender equality. Finally, 70% of students suggested further social action to curb the problem of gender discrimination. On the whole, the flipped classroom method of learning combined with project-based group work have proven to be an effective way to raise awareness of this goal, create a more positive attitude, in-crease their willingness to take action as well as widening their English lexical resources.

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area, because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification.

Web-Based GIS is a platform that deals with the representation, management and storage of locational information for real-time online retrieval, viewing and manipulations. Web-based GIS incorporate all the functions of information systems: gathering, storing, retrieving, visualizing and dissemination of spatial information on the Internet. In modern-day GIS serve as vital means to not only geo-referenced locational information that are been retrieved but also the development in web-based technologies as well as there involvements in decision making processes. In recent time the increased used of GIS on the Internet have totally transformed and made three distinct changes across our GIS environment. These are: (i) how are GIS data being accessed (ii) what are modes of its transmission and (iii) what available GIS functionalities are within the environment for analysis. This paper examines the applications and technologies associated with web-based GIS architectures with an emphasis on the internet GIS based structure and case studies of GIS Web-based applications in MapInfo, Microsoft Silverlight Technology and the ArcGIS Server using ArcGIS JavaScript API.

A significant number and range of challenges besetting sustainability can be traced to the actions and interactions of multiple autonomous agents (people mostly) and the entities they create (e.g., institutions, policies, social network) in the corresponding social-environmental systems (SES). To address these challenges, we need to understand decisions made and actions taken by agents, the outcomes of their actions, including the feedbacks on the corresponding agents and environment. The science of Agent-based Complex Systems—ACS science—has a significant potential to handle such challenges. The advantages of ACS science for sustainability are addressed by way of identifying the key elements and challenges in sustainability science, the generic features of ACS, and the key advances and challenges in modeling ACS. Artificial intelligence and data science promise to improve understanding of agents’ behaviors, detect SES structures, and formulate SES mechanisms.

Wearable assistant devices play an important role in daily life for people with disabilities. Those who are hearing impaired may face dangers while walking or driving on the road. The major danger is their inability to hear warning sounds from cars or ambulances. Thus, the goal of this study is to develop a wearable assistant device for the hearing impaired to recognize emergency vehicle sirens on the road using edge computing. An EfficientNet-based fuzzy rank-based ensemble model was proposed to classify seven audio sounds, including human vocalizations and emergency vehicle sirens. This model was embedded in an Arduino Nano 33 BLE Sense development board. The audio files were respectively obtained from the CREMA-D dataset and Large Scale Audio dataset of emergency vehicle sirens on the road, with a total number of 8756 files. The seven audio sounds included neutral vocalization, anger vocalization, fear vocalization, happy vocalization, car horn sound, siren sound, and ambulance siren sound. The audio signal was converted into a spectrogram by the short-time Fourier transform as the feature. When one of the car horns, sirens, or ambulance sirens was detected, the wearable assistant device presented alarms through vibration and messages on the OLED panel. The performances of the EfficientNet-based fuzzy rank-based ensemble model in offline computing achieved an accuracy of 97.1%, precision of 97.79%, sensitivity of 96.8%, and specificity of 97.04%. In edge computing, the results were an accuracy of 95.2%, precision of 93.2%, sensitivity of 95.3%, and specificity of 95.1%. Thus, the proposed wearable assistant device has the potential benefit of helping the hearing impaired avoid traffic accidents.

Graphene-based nanomaterials (GNMs), including graphene, graphene oxide, reduced graphene oxide, and graphene quantum dots, may have direct anticancer activity or be used as nanocarri-ers for antitumor drugs. GNMs usually enter tumor cells by endocytosis and can accumulate in lysosomes. This accumulation prevents drugs bound to GNMs from reaching their targets, sup-pressing their anticancer effects. A number of chemical modifications are made to GNMs to facili-tate the separation of anticancer drugs from GNMs at low lysosomal pH and to enable lysosomal escape of drugs. Lysosomal escape may be associated with oxidative stress, permeabilization of the unstable membrane of cancer cell lysosomes, release of lysosomal enzymes into the cytoplasm, and cell death. GNMs can prevent or stimulate tumor cell death by inducing protective autopha-gy or suppressing autolysosomal degradation, respectively. Furthermore, because GNMs prevent bound fluorescent agents from emitting light, their separation in lysosomes may enable tumor cell identification and therapy monitoring. In this review, we explain how the characteristics of the lysosomal microenvironment and the unique features of tumor cell lysosomes can be exploited for GNM-based cancer therapy.

This study was designed to extend the literature on multicomponent interventions for the children of deaf adults (CODA) population. This study used two evidence-based behavior intervention strategies: pre-session pairing (PSP) and independent group contingency (GC). These strategies have been successfully used to decrease off-task behavior in elementary school children with various abilities and diverse backgrounds. However, the research on multicomponent interventions has yet to examine their effects on the CODA population in inclusive classrooms. This study used a concurrent multiple baseline design across participants with an ABC sequence to examine teacher-implemented PSP with independent GC in reducing the off-task behavior of three CODAs in an inclusive classroom at a public elementary school. The results show that the intervention successfully decreased off-task behavior for all participating CODAs. The results suggest that CODAs can adapt to classroom rules and expectations when PSP and independent GC are systematically applied to them in classrooms.

Event-based vision is an emerging field of computer vision that offers unique properties such as asynchronous visual output, high temporal resolutions, and dependence on brightness changes to generate data. These properties can enable robust high-temporal-resolution object detection and tracking when combined with frame-based vision. In this paper, we present a hybrid, high-temporal-resolution, object detection and tracking approach, that combines learned and classical methods using synchronized images and event data. Off-the-shelf frame-based object detectors are used for initial object detection and classification. Then, event masks, generated per each detection, are used to enable inter-frame tracking at varying temporal resolutions using the event data. Detections are associated across time using a simple low-cost association metric. Moreover, we collect and label a traffic dataset using the hybrid sensor DAVIS 240c. This dataset is utilized for quantitative evaluation using state-of-the-art detection and tracking metrics. We provide ground truth bounding boxes and object IDs for each vehicle annotation. Further, we generate high-temporal-resolution ground truth data to analyze the tracking performance at different temporal rates. Our approach shows promising results with minimal performance deterioration at higher temporal resolutions (48 – 384 Hz) when compared with the baseline frame-based performance at 24 Hz.

The recent covid crisis has proven important lessons for academia and industry regarding digital reorganization. Among fascinating lessons from these times is the huge potential of data analytics and artificial intelligence. The crisis exponentially accelerated the adoption of analytics and artificial intelligence, and this momentum is predicted to continue into the 2020s and over. Moreover, drug development is a costly and time-consuming business, and only a minority of approved drugs return the revenue that exceeds the research and development costs. As a result, there is a huge drive to make drug discovery cheaper and faster. With modern algorithms and hardware, it is not too surprising that the new technologies of artificial intelligence and other computational simulation tools can help drug developers. In only two years of covid research, many novel molecules have been designed/identified using artificial intelligence methods with astonishing results in terms of time and effectiveness. This paper will review the most significant research on artificial intelligence in the de novo drug design for COVID-19 pharmaceutical research.

Maternal-child care is one of the foundations of primary health care. Nurses’ competency skills they have been taught. Community awareness is an important part of preventive healthcare, and nurses must be aware of the factors that impact the health of the community. This study examines the effectiveness of lecture-based instructions in maternal and child care and its implications to students' social competency skills and community awareness in Nursing Colleges in Nueva Ecija, Philippines. The researcher uses survey questionnaire and employed the descriptive design where fifteen (15) nursing students and five (5) teachers were purposively selected. The findings revealed that the weighted mean for the effectiveness of lecture based instruction in maternal and child care is 3.91 with verbal description of “Effective”, the effects of lecture based instruction in maternal and childcare to students’ social competency skills and community awareness got the weighted mean of 3.87 and interpreted as “very satisfactory” and the effectiveness of actual community-based instruction is very effective with weighted mean of 4.25 and is higher compare to lecture based instruction. The results also revealed that students and teachers were challenged in lecture-based instruction in maternal and chi8ldcare during distance learning. Recommendations for the enhancement of lecture-based instruction in maternal and childcare in social competency skills and community awareness were also made.

Saccharomyces cerevisiae are a phenotypically diverse species that adapt to a wide variety of environments by exploiting standing genetic diversity and selecting for advantageous mutations. Glyphosate and copper-based herbicides/ fungicides affect non-target organisms, these incidental exposures can impact microbial populations. In this study, glyphosate resistance was found in the historical collection of yeast which was collected over the last century, but only in yeast isolated after the introduction of glyphosate. The highest glyphosate-resistant yeasts were isolated from agricultural sites. However, herbicide application at these sites was not recorded. In an effort to assess glyphosate resistance and impact on non-target microorganisms, yeast were harvested from 15 areas with known herbicidal histories, including an organic farm, conventional farm, remediated coal mine, suburban locations, state park, and a national forest. Yeast representing 23 genera were isolated from 237 samples of plant, soil, spontaneous fermentation, nut, flower, fruit, feces, and tree material samples. Saccharomyces, Candida, Metschnikowia, Klyveromyces, Hanseniaspora, and Pichia were other genera commonly found across our sampled environments. Managed areas had less species diversity and at the brewery, only Saccharomyces and Pichia were isolated. A conventional farm growing RoundUp Ready™ corn had the lowest phylogenetic diversity and the highest glyphosate resistance. The mine was sprayed with multiple herbicides including a commercial formulation of glyphosate; however, the yeast did not have elevated glyphosate resistance. In contrast to the conventional farm, the mine was exposed to glyphosate only one year prior to sample isolation. Glyphosate resistance is an example of the anthropogenic selection of nontarget organisms.

For centuries, crop plants have represented the basis of the daily human diet. Among them, cereals and legumes, accumulating oils, proteins and carbohydrates in their seeds, distinctly dominate modern agronomic practice. Indeed, these plants play an essential role in the food industry and fuel production. Therefore, the seeds of crop plants are intensively studied by food chemists, biologists, biochemists, and nutritional physiologists. Accordingly, not only seed development and germination, but also age- and stress-related alterations in seed vigor, longevity, nutritional value and safety can be addressed by a broad panel of analytical, biochemical and physiological methods. Currently, functional genomics is one of the most powerful tools, giving direct access to characteristic metabolic changes, accompanying plant development, senescence and response to biotic or environmental stress. Among individual methodological platforms, proteomics represents one of the most effective ones, giving access to cellular metabolism at the level of proteins. Here we discuss the main methodological approaches employed by seed proteomics in the context of physiological changes related to seed development, ageing and response to environmental stress.

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, Convolutional Neural Network-based Deep Neural Network techniques are investigated for the Remaining Useful Life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pre-trained models, using a classic machine learning approach, i.e., Support Vector Regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE=0.058) to that of transfer learning which, instead, remains at a lower or slightly higher level (MAPE=0.416) than Support Vector Regression (MAPE=0.857).

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

Applying the improved social network analysis method and the idea of rolling window regression, this paper explores the impact of HSR network on the quality of urbanization and its dynamics. Based on a sample of 273 cities in China over 2009–2019, we find that the high-speed railway network has an increasing positive effect on the quality of urbanization, which proves the existence of network effect. The empirical results further show that this effect is closely related to the cov-erage rate of high-speed rail network. In addition, heterogeneity analysis reveals that urban ag-glomeration cities are the main beneficiaries. Academically, our study provides a plausible expla-nation and evidence from network size differences for the two conflicting views of the HSR effect. Practically, we also propose some important policy implications for countries in different high-speed rail network construction stages.

Hyperspectral image and LiDAR image fusion plays a crucial role in remote sensing by capturing spatial relationships and modeling semantic information for accurate classification and recognition. However, existing methods, like Graph Convolutional Networks (GCNs), face challenges in constructing effective graph structures due to variations in local semantic information and limited receptiveness to large-scale contextual structures. To overcome these limitations, we proposed a invariant attribute-driven binary bi-branch classification (IABC) method which is a unified network that combines binary Convolutional Neural Network (CNN) and GCN with invariant attributes. Our approach utilizes a joint detection framework that can simultaneously learn features from small-scale regular regions and large-scale irregular regions, resulting in an enhanced structured representation of HSI and LiDAR images in the spectral-spatial domain. This approach not only improves the accuracy of classification and recognition but also reduces storage requirements and enables real-time decision-making, which is crucial for effectively processing large-scale remote sensing data. Extensive experiments demonstrates the superior performance of our proposed method in hyperspectral image analysis tasks. The combination of CNNs and GCNs allows for accurate modeling of spatial relationships and effective construction of graph structures. Furthermore, the integration of binary quantization enhances computational efficiency, enabling real-time processing of large-scale data. Therefore, our approach presents a promising opportunity for advancing remote sensing applications using deep learning techniques.

In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.

The CAN protocol is widely adopted for in-vehicle networks due to its cost efficiency and reliable transmission. However, despite its popularity, the protocol lacks built-in security mechanisms, making it vulnerable to various attacks such as flooding, fuzzing, and DoS. These attacks can exploit vulnerabilities and disrupt the normal behavior of the in-vehicle network. One of the main reasons for these security concerns is that the protocol relies on broadcast frames for communication between ECUs within the network. To tackle this issue, this paper presents an intrusion detection system that leverages multiple LSTM-Autoencoders. The proposed system utilizes diverse features, including transmission interval and payload value changes, to capture various characteristics of normal network behavior. By analyzing different types of features separately using the LSTM-Autoencoder model, the system effectively detects anomalies. In our evaluation, we conducted experiments using real vehicle network traffic, and the results demonstrated the system's high precision with a 99% detection rate in identifying anomalies.

Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on the plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of highly-priority transcription factors (TFs) dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We revealed distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficients nodes. Dynamic transcriptional regulatory modeling on integrated TF-target and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that can potentially be used in future genetic crop engineering programs.

The fusion of "innovation theory" and "ecology" gave birth to a large number of studies on "innovation ecology", which mainly studies how to build an industrial ecological chain at the regional level, focusing on self-evolution, achieving ecological balance, and enabling the regional economy to take the path of sustainable innovation. This type of research borrows a lot of concepts from ecology, and very vividly describes the competition and cooperation relationships formed by various agents in the innovation system, laying a good foundation for qualitative analysis of the inherent dynamics of innovation development. However, many studies focus on the analogous description of ecosystems and economic systems, lacking scientifically and rigorously quantitative empirical research as a support. This paper uses network-based indicators such as degree, cluster coefficient, and betweenness centrality to measure the function and position of high-tech enterprises in the Z-Park in a business environment, so as to clarify the socio-economic meaning of the topological structure of the regional innovation system. On this basis, it provides theoretical references for regional innovation development and sustainable development policy formulation.

Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, conclusion of the simulation experiment verified that the modified grey GM(1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.

Synesthesia is a psychological phenomenon where sensory signals become mixed. Input to one sensory modality produces an experience in a second, unstimulated modality. In “grapheme-color synesthesia”, viewed letters and numbers evoke mental imagery of colors. The study of this condition has implications for increasing our understanding of brain architecture and function, language, memory and semantics, and the nature of consciousness. In this work, we propose a novel application deep learning to model perception in grapheme-color synesthesia. Achromatic letter images, taken from database of handwritten characters, are used to induce synesthesia. Results show the model learns to accurately create a colored version of the inducing stimulus, according to a statistical distribution from experiments on a sample population of grapheme-color synesthetes. The spontaneous, creative mental imagery characteristic of the synesthetic perceptual experience is reproduced by the model. A model of synesthesia that generates testable predictions on brain activity and behavior is needed to complement large scale data collection efforts in neuroscience, especially when articulating simple descriptions of cause (stimulus) and effect (behavior). The research and modeling approach reported here begins to address this need.

Driven by environmental sustainability concerns, the integration of biobased components in curtain wall systems is gaining traction in both research and the construction market. This paper explores the development and validation of a bio-based façade system within the Basajaun H2020 project. The project aimed to demonstrate the feasibility of introducing environmentally friendly bio-based components into the mature curtain wall façade industry. The paper focuses on identifying technological solutions for replacing key components such as profiles, insulation, and the tightness system with bio-based and less environmental impact alternatives presenting the results achieved in the façade system design of the Basajaun project funded by European Union. These solutions aimed at creating a bio-composite-based curtain wall façade that adheres to current building envelope standards and normative; to implement diverse façade typologies for vision panels, opaque sections, and integrated windows; to engineer the prefabrication process for industrialization, enabling wider market replication and simplified transport and installation. The results demonstrate that the Basajaun façade successfully integrates selected components and meets the performance requirements set by regulations. However, questions remain regarding the workability of bio-based profiles as a commercially viable, ready-to-market solution that can replace traditional aluminum profiles in the curtain wall façade industry.

This review aims to consolidate varied information regarding Port Scanning, and examine the tools, techniques, and detection algorithms used. It explores TCP SYN, Full TCP, UDP, and stealth scans, and shows their functions and abilities. Paired with these techniques, it also shows the tools that they can be used by, such as NMap, Zmap, and MASSCAN. It discusses the various detection techniques such as Signature and Anomaly based detection, and discusses the real world impact of this technology, while commenting on the legal and ethical issues posed.

This study reports the synthesis and characterization of novel per-fluorinated organic polymers with azo- and azomethine-based linkers using nucleophilic aromatic substitution. The polymers reveal variations in the fluorine content via the incorporation of decafluorobiphenyl and hexafluorobenzene linkers, which enhanced their hydrophobic nature. The rich fluorine polymers were slightly soluble in THF and have shown molecular weights between 4886 to 11948 g/mol. All polymers exhibit thermal stability in the range of 350-500°C, with varying thermal stability depending on the fluorine and nitrogen content. Conjugation of the polymers was confirmed through changes in the UV-Vis spectra, with a hypsochromic shift observed in all cases, more pronounced in azo-based fluorinated chains due to H-bonding on the nitrogen sites, chain conformations, and planarity. The optical band gap (Eg) of the polymers was determined from the UV-Vis data, with the Eg values of the azo-based fluorinated polymers being 1 eV higher than those of their corresponding linkers. The cross-linking formation was characterized by porosity measurements, with the azo-based polymer exhibiting the highest surface area of 770 m²/g with a pore volume of 0.35 cm³/g, while the open-chain azomethine-based polymer exhibited the lowest surface area of 285 m²/g with a pore volume of 0.0872 cm³/g. Porous structures with varied hydrophobicities were investigated as adsorbents for separating water-benzene and water-phenol mixtures and selectively binding methane/carbon dioxide gases from the air. The most hydrophobic polymers containing the decafluorbiphenyl linker were suitable for benzene separation, while the best methane uptake values were 6.14 and 3.46 mg/g for DAB-Z-1O and DAB-A-1O, respectively. DAB-Z-1h, with the highest surface area and rich in nitrogen sites, exhibited the highest CO₂ uptake at 298 K (17.25).

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. 3D pharmacophore models are powerful computational tools in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and elucidation of ligand-receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning will be highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs.

The concept of innovation ecosystems brings to light a whole set of stakeholders that face the entire set of barriers to making new technologies change people´s lives. This research aimed to understand and analyze the innovation ecosystem in organic and agroecological production in the Federal District, Brazil, and its directly influenced area. A questionnaire was applied to 27 institutions identified as the backbone of the innovation ecosystem. The Gephi 0.10.1 software and the Force Atlas 2 network algorithm set up the relationship network. We verified a certain misalignment between expected roles and functions performed by 66% of the stakeholders. Low integration and dispersed relationships in the network were observed with a misalignment around the innovation concept in organic food production. This arrangement of the ecosystem is compatible with the initial stage, in which stakeholders and resources would be spread out and demobilized. Although shared governance is the most effective for an innovation ecosystem, the analyzed ecosystem demands centralized governance with a leading organization. Strengthening strategies were proposed to (i) define leadership, (ii) define roles or competencies, and (iii) strengthen community attributes.

The development of transversal competences, in interaction with professional competences, is essential for individuals to adapt to change and live meaningful and productive lives. The aim of this study was to explore the mutual interaction of six transversal competences—civic, digital, entrepreneurial, global, innovation, and research, as well as their respective sub-competences—and propose possible approaches to how this dynamic of interaction can be applied to facilitate the development of transversal competences. A sample of students (N = 1575) from 21 universities in Latvia completed a self-assessment survey evaluating multiple behavioural indicators for each transversal competence and sub-competence. The results revealed that civic competence was evaluated significantly lower than other competences, and, for most transversal competences, sub-competences associated with active and systematic collaboration and networking received lower evaluations. A network analysis showed that the community involvement sub-competence of the civic competence and the initiative and critical thinking sub-competences of the innovation competence had the strongest links to the other measured sub-competences on average. The results suggest that the development of initiative and cooperation-and-collaboration-related and network-building skills are likely to have a systematic positive effect on the development of a wider set of transversal competences and the professional autonomy of students in the long term.

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a statistical estimate of correlation between signal pairs across frequencies, we generate edge time series, depicting how an edge evolves over time. This forms the basis for constructing an edge-to-edge functional connectivity network. Emphasizing hyperedges as connected components in an absolute-valued functional connectivity network, we focus on exploring these hyperedges in the context of individual variability. Our coherence-based hypergraph construction specifically addresses functional connectivity among four brain lobes: frontal, parietal, temporal, and occipital. This approach enables a nuanced exploration of individual differences within diverse frequency bands, providing insights into the dynamical nature of brain connectivity during visual perception tasks. The results furnish compelling evidence supporting the hypothesis of cortico-cortical interactions occurring across varying scales. The derived hypergraph illustrates robust activation patterns in specific brain regions, indicative of their engagement across diverse cognitive contexts and different frequency bands. Our findings suggest potential integration or multifunctionality within the examined lobes, contributing valuable perspectives to our understanding of brain dynamics during visual perception.

As the most important component of soft robot, the pneumatic actuator can drive robot movement. However, the effect of the actuator pneumatic network geometry on the actuator load capacity is insufficient. In this paper, the finite element analysis method is applied to calculate the load force and stability torque of the pneumatic actuator under a wide range of length and height dimensions of the pneumatic network. The effect of the network size on the structure rigidity, load capacity and deformation of the actuator is analyzed. Based on the calculation results, the effect of network structure size on the deformation and load capacity is analyzed, and the optimal geometric results with the maximum load capacity are obtained. This study provides theoretical guidance for the structural design and optimization of the pneumatic actuator.

Fatigue fractures in materials are the main cause of approximately 80% of all material failures, and it is believed that such failures can be predicted and mathematically calculated in a reliable manner. It is possible to establish prediction modalities in cases of fatigue fracture, according to three fundamental variables in fatigue, such as volume, number of fracture cycles, as well as applied stress, with the integration of Weibull constants (length characteristic). This investigation was carried out mechanical fatigue tests on specimens smaller than 4 mm2 in section of different industrial materials for their subsequent analysis through precision computed tomography in search of microfractures. The measurement of these microfractures, along with their metrics and classifications, was recorded. A convolutional neural network trained with deep learning was used to achieve the detection of microfractures in image processing. The detection of microfractures in images with 480x854 or 960x960 pixels is the primary objective of this network, and its accuracy is above 95%. Images that have microfractures and those that do not are classified by the network. Subsequently, by means of image processing, the microfracture is isolated. Finally, the images that do contain this feature are interpreted by image processing to obtain their area, perimeter, characteristic length, circularity, orientation, and type microfracture metrics. All values will be obtained in pixels and converted to metric units (μm) through a conversion factor based on image resolution.

Ensuring the integrity of scientific literature is essential for advancing knowledge and research. However, the credibility and trustworthiness of scholarly publications are compromised by manipulated citations. Traditional methods, such as manual inspection and basic statistical analyses, have limitations in detecting intricate patterns and subtle manipulations of citations. In recent years, network-based approaches have emerged as promising techniques for identifying and understanding citation manipulation. This study introduces a novel method to identify potential citation manipulation in academic papers using perturbations of a deep embedding model. The key idea is to reconstruct meaningful connections represented by citations within a network by exploring slightly longer alternative paths. These indirect pathways enable the recovery of original and reliable citations while estimating their trustworthiness. The investigation takes a comprehensive approach to link prediction, leveraging the consistent behavior of prominent connections when exposed to network perturbations. Through numerical experiments, the method demonstrates a high capability to identify reliable citations as the core of the analyzed data and to raise suspicions about unreliable references that may have been manipulated.

Soft tactile sensors based on piezoresistive materials have various large-area sensing applications. However, their accuracy is often affected by hysteresis, a phenomenon that poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fibre-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units with different layer configurations (1, 3, 6, and 12) were designed. These sensor units underwent force sequences to collect corresponding output resistances. A backpropagation network was trained using these force sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement establishes the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for force measurement and control, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Notably, the complete elimination of hysteresis in tactile sensors may not be feasible. Nevertheless, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

The Lightning Network (LN), a second-layer protocol built on top of the Bitcoin blockchain, is an innovative digital payment solution that offers increased convenience, speed, and cost-effectiveness to consumers and businesses alike. However, there is limited literature available on the characteristics of this nascent technology, the depth and breadth of the various business LN-related applications as well as relevant adoption/implementation challenges. This study aims to contribute to the understanding of the LN’s characteristics, its potential in enhancing business operations and its applicability across different sectors, while taking into account adoption and implementation challenges. We apply a narrative review methodology using a semi-systematic approach to examine new and emerging business models empowered by the LN and its characteristics, topology, performance, privacy, and security. We analyze the data to identify key themes and trends in the literature, offering a critical analysis of the strengths and weaknesses of the existing literature. Based on the findings, we provide several clusters of fruitful areas for future research directions. The findings of this study will help businesses make more informed decisions about adopting and leveraging the LN to improve their operations and enhance customer experience.

While modeling data in reality, uncertainties in both data (aleatoric) and model (epistemic) are not necessarily Gaussian. The uncertainties could appear with multimodality or any other particular forms which need to be captured. Gaussian mixture models (GMMs) are powerful tools that allow us to capture those features from a unknown density with a peculiar shape. Inspired by Fourier expansion, we propose a GMM model structure to decompose arbitrary unknown densities and prove the property of convergence. A simple learning method is introduced to learn GMMs through sampled datasets. We applied GMMs as well as our learning method to two classic neural network applications. The first one is learning encoder output of autoencoder as GMMs for handwritten digits images generation. The another one is learning gram matrices as GMMs for style-transfer algorithm. Comparing with the classic Expectation maximization (EM) algorithm, our method does not involve complex formulations and is capable of achieving applicable accuracy.

The concentration of data from multiple links to a single output is an essential task performed by High-Energy Physics (HEP) Data Acquisition Systems (DAQs). At high and varying data rates combined with the large width of the concentrator’s output interface, this task is non-trivial. This paper presents a concentrator based on the Beneš network, which provides efficient concentration without using a high-frequency clock internally. It warrants that empty data are eliminated and does not disturb the data time-ordering if the data rates significantly differ between inputs. Additionally, it is well suited to FPGA implementation. It is based on simple data-routing primitives and may be fully pipelined.

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program's operational semantics to its structure.

With the improvement of software copyright protection awareness, code obfuscation technology plays a crucial role in protecting key code segments. As the obfuscation technology becomes more and more complex and diverse, it has spawned a large number of malware variants, which make it easy to evade the detection of anti-virus software. Malicious code detection mainly depends on binary code similarity analysis. However, the existing software analysis technologies are difficult to deal with the growing complex obfuscation technologies. To solve this problem, this paper proposes a new obfuscation-resilient program analysis method, which is based on the data flow transformation relationship of the intermediate representation and the graph network model. In our approach, we first construct the data transformation graph based on LLVM IR. Then, we design a novel intermediate language representation model based on graph networks, named DFSGraph, to learn the data flow semantics from DTG. DFSGraph can detect the similarity of obfuscated code by extracting the semantic information of program data flow without deobfuscation. Extensive experiments prove that our approach is more accurate than existing deobfuscation tools when searching for similar functions from obfuscated code.

There is a considerable amount of research in online social networks, most of which focuses on the structural analysis of social graphs. The interpersonal relationships of social networks, especially friend circle, can solve the cold start and sparsity problems, and through the relationship between social networks can effectively recommend users' favorite items (items), such as music , videos, brands/products, preferred tags, location, services, etc. User relationships in social networks are diverse and there are many different perspectives on different social networks. Associations among users can form multi-layered composite networks, and multi-layered social networks present new challenges and opportunities. Different relationships can influence users' preferences to different degrees, which in turn affects their behavior. Therefore, fusing multiple social networks is an effective way to improve recommendation. Although some studies have started to address multiple social network recommendations, simple linear superposition cannot reflect the coupling and nonlinear association between multiple social networks. In this paper, we propose a graph neural network recommendation model under social relationships based on this background. We first propose to compute the 2nd order collaborative signals and their intensities directly from the neighboring matrix for updating the node embedding of the graph convolution layer. Secondly by embedding historical evaluations, various social networks constituting different dimensions, the attention integration of user preferences by different social networks is achieved, and its effectiveness and scalability are demonstrated in theoretical derivation and experimental validation. The theoretical derivation and experimental validation demonstrate its effectiveness and scalability.

Although train simulation research is vast, most available network simulators do not track the instantaneous movements and interactions of multiple trains for the computation of energy/fuel consumption. In this paper, we introduce the NeTrainSim simulator for heavy long-haul freight trains on a network of multiple intersecting tracks. Trains are modeled as a series of moving mass points (each car/locomotive is modeled as a point mass) while ensuring safe following distances between them. The simulator considers the motion of the train as a whole and neglects the relative movements between the train cars/locomotives. Furthermore, the powers of the different locomotives are transferred to the first locomotive as such a simplification result in a reduced simulation time without impacting the accuracy of energy consumption estimates. While the different tractive forces are combined, the resistive forces are calculated at their corresponding locations. The output files of the simulator contain pertaining information to the train trajectories and the instantaneous energy consumption levels. A summary file is also provided with the total energy consumed for the full trip and the entire network of trains. Two case studies are conducted to demonstrate the performance of the simulator. The first case study validates the model by comparing the output of NeTrainSim to empirical trajectory data using a basic single-train network. The results confirm that the simulated trajectory is precise enough to estimate the electric energy consumption of the train. The second case study demonstrates the train-following model considering six trains following each other. The results showcase the model’s ability in relation to maintaining safe-following distances between successive trains. Finally, the NeTrainSim is demonstrated to be scalable with computational times of O(n) for less than 50 trains (n) and O(n²) for higher number of trains.

This study examined the last four decades of the existing academic literature related to the environmental impacts of using cover crops in agricultural production systems. Data were collected from the Web of Science database, resulting in a sample of 3,246 peer-reviewed articles published between 1980 and 2021. We combined two advanced scientometrics analysis software (i.e., CiteSpace 6.0.R1 and Gephi 0.9.2) to identify the trajectory of the literature, hotspots, and frontiers. We developed authorship-, institution-and country-levels networks to examine academic cooperation over the last forty years. Our findings revealed that the number of peer-reviewed outputs documenting the environmental effects of cover crops has consistently increased, with a notable rise in publications between 2015 and 2021. Eighteen salient research topics were identified in the literature, including winter cover crops' effects on soil health, cover crops' effects on nitrous oxide emissions, and the relationship between cover crops and nitrate leaching. Based on the citation-clustering analysis, the trajectory of the literature may be divided into three stages. Studies in Stage 1_A (1980-2000) mainly assessed the role of cover crops in nitrogen management. In Stage 1_B (2001-2010), the research evaluated the impact of using different cover crop mixtures on farming systems. In Stage 2 (2011-2021), studies primarily addressed the environmental impacts of cover crops, particularly their effects on physical and chemical soil properties. Finally, the countries with the most outputs were the United States, Brazil, and Spain. The U.S. Department of Agriculture-Agricultural Research Service was the main contributor to the literature on the environmental impacts of cover crops.

Three-phase motors are commonly adopted in several industrial contexts and their failures can result in costly downtime causing undesired service outages; this way, motor diagnostics is an issue that assumes great importance. To prevent their failures and timely face the considered service outages, a non-invasive method to identify electrical and mechanical faults in three-phase asynchronous electric motors is proposed in the paper. In particular, a measurement strategy along with a machine learning algorithm based on Artificial Neural Network is exploited to properly classify failures. In particular, digitized current samples of each motor phase are first processed by means of FFT and PSD in order to estimate the associated spectrum. Suitable features (in terms of frequency and amplitude of the spectral components) are then singled out to either train or feed a neural network acting as a classifier. The method is preliminary validated on a set of 28 electric motors, and its performance is compared with common state-of-art machine learning techniques. The obtained results show that the proposed methodology is able to reach accuracy levels greater than 98\% in identifying anomalous conditions of three-phase asynchronous motors.

In this paper, a new relay system that uses the UAV as a relay station between the tethered UAV and ground user (TU2U2G) is proposed. The TU2U2G system replaces the base station (BS) in the cellular system with a tethered UAV (TUAV). The TUAV is a UAV that receives power over a cable from a ground control station. It has advantages of high and variable altitude up to (100m) compared to BS. In addition, it overcomes the UAV drawback of the batteries' limited capacity. After that, a 3D placement Algorithm of the UAV as a relay station in the TU2U2G system is proposed. It is presented to maximize the coverage by jointly optimizing the transmitting power and relaying distance. The TU2U2G system shows better results than the traditional cellular system in terms of optimum UAV height, maximum coverage radius, and maximum distance between BS and UAV.

The relevance of bacteria to subjective experiences or qualia is under-appreciated. Here, I make four proposals. Firstly, living systems traverse sequences of active states that determine their behaviour; these states result from competitive coherence, which depends on a connectivity-based competition between a Next process and a Now process whereby elements in the active state at time n+1 are chosen between the elements in the active state at time n and those elements in the developing n+1 state. Secondly, bacteria should help us link the mental to the physical world given that bacteria were here first, are highly complex, influence animal behaviour and dominate the Earth. Thirdly, the operation of competitive coherence to generate active states in bacteria, brains and other living systems is inseparable from qualia. Fourthly, these qualia become particularly important to the generation of active states in the highest levels of living systems, namely, the ecosystem and planetary levels.

The world is growing very rapidly concerning technology. In the next-generation Internet, the existing architecture requires to be upgraded from Host-Centric Networking paradigm to Information-centric networking architecture. The unique aspect of information-centric networking is in-network cashing. Due to the system augmentation and In-network cashing technique, this novel system needs extremely high content security to ensure system integrity and maintenance. 5G network may be supported by the Information-Centric Network due to its high data transmission rate. In order to handle the serious security issues such as attack on confidentiality, authentication and integrity of the content, a Digital Signature based Access Control Mechanism in Information-Centric Network (DSAC) scheme is proposed to enhance security of ICN. Briefly, this new scheme uses Digital Signature, hash function, Trusted Third Party (TTP) and Proxy TTP. The client request for content, after receiving a request, the content provider generates and encrypts content with the digital signature and random value ‘k’ hash function and send it to TTP. After the signing process, the TTP sends the encryption hash key to Proxy TTP. In this proposed scheme authentication, confidentiality, the integrity aspects of the content security are improved.

Formation in soccer is among the most important tactical choices for a successful match.Herein, the simulations of 420000 match-plays have been performed varying the formation, the number of opponents that are actively pressing the team, the speed of the opponents in attempting a pass interception. Dribbling has been neglected. The match-play ends either with a successful series of passes from a central back to the line of the strikers or with the opponents that steal the ball. In this work, I demonstrate that 3-4-3 formation, which is among the most employed formations, relates to the highest probability of success.

Three computer algorithms are presented. One reduces a network $\CALN$ to its interior, $\CALI$. Another counts all the triangles in the network, and the last randomly generates networks similar to $\CALN$ given just its interior $\CALI$. But these algorithms are not the usual numeric programs that manipulate a matrix representation of the network; they are set-based. Union and meet are essential binary operators; contained_in is the basic relational comparator. The interior $\CALI$ is shown to have desirable formal properties and to provide an effective way of revealing ``communities'' in social networks.

Nowadays smartphone utilization for disease diagnosis and remote health care applications has become promising due to their ubiquity. Here, a novel convolutional neural network method for detecting keratoconus that is wholly implemented on a smartphone is proposed. The proposed method provides accurate detection of over 72.9% for all stages of keratoconus. Preliminary results indicate 90%, 83%, 64% and 52% detection rate for severe, advanced, moderate and mild stages of disease, respectively.

The purpose of this study is to check out the involvement of entropy in Mpemba effect. Provided that preheating of the water the cooling duration is reduced, we theoretically show that water gains more entropy when warmed and re-cooled to the original temperature.

Rapid eye movement sleep behavior disorder (RBD) is often prodromal to Parkinson’s disease (PD). Thus there should be detectable in vivo functional signatures shared between RBD and PD that aid in disease classification. To assess common in-vivo phenotypes, resting state data was collected on a 3T clinical MRI platform and a novel functional connectivity magnetic resonance imaging (fcMRI) approach, which combined independent component analysis (ICA) and graph theory, was used to evaluate deficits in interconnectivity among 15 PD, 14 RBD and 13 control participants. Whole brain and network-level analyses revealed the largest deficits in network connectivity in PD compared with controls, with less severe differences between RBD and controls. Importantly, the network-level analysis demonstrated decreased network interconnectivity, with the greatest aberrant networks in PD, and a subset in RBD. Additionally, a disease classification algorithm predicted PD cases by being trained on RBD cases with 0.87 sensitivity and 0.68 specificity. The functional alterations in cortical networks in RBD extended beyond the brainstem. These findings demonstrate progressive reductions in connectivity between brain networks, with less severe deficits in RBD than PD. Moreover, RBD phenotypes can be used to predict PD status in a cross-sectional sample, which suggests RBD is an intermediate phenotype.

The Station logo is a way for a TV station to claim copyright, which can realize the analysis and understanding of the video by the identification of the station logo, so as to ensure that the broadcasted TV signal will not be illegally interfered. In this paper, we design a station logo detection method based on Convolutional Neural Network by the characteristics of the station, such as small scale-to-height ratio change and relatively fixed position. Firstly, in order to realize the preprocessing and feature extraction of the station data, the video samples are collected, filtered, framed, labeled and processed. Then, the training sample data and the test sample data are divided proportionally to train the station detection model. Finally, the sample is tested to evaluate the effect of the training model in practice. The simulation experiments prove its validity.

Pristimerin (PM) is a naturally occurring quinonemethide triterpenoid compound that isolated from the Celastraceae and Hippocrateaceae families. Its anticancer effects have attracted a great deal of attention, but the mechanisms of action remain obscure. In this study, we screened for the active compounds of Pristimerin using a drug-likeness approach. Potential protein targets of Pristimerin were predicted by PharmMapper and Coremine database. Candidate protein targets were then uploaded to GeneMANIA and GO pathway analysis. Finally, compound-target, target-pathway, and compound-target-pathway networks were constructed using Cytoscape 3.3. The results showed that Pristimerin had good drug ability and identified 13 putative protein targets. Network analysis revealed that these targets are associated with cancer, inflammation and other physiological processes. In summary, Pristimerin is predicted to target a variety of proteins and pathways to form a network that exerts systemic pharmacological effects.

NS-3 is one of the most used Network Simulators because it includes different modules, applications and protocols to simulate a wide range of network. Since new proposals usually involve a large number of simulations, a procedure to automatize the simulation process and extract statistics is very useful. "Statistical Framework" (SF) is an important module of NS-3 because it fulfills this need. SF allows us to collect simulation data and obtain statistical reports for NS-3. In this report, we present a detailed description of statistical framework module and two useful examples of its configuration.

Nowadays, network simulators are frequently used to study services, applications and to solve problems in complex scenarios of communications. For this reason, the objective of this article is to introduce detailed information about installation of OMNET++ to new users. In this report, we show how to install OMNET++ simulator over a widely used distribution of Linux and how to integrate "Inet", "Inetmanet" and "Veins" frameworks with the simulator. We take care to explain this integration step-by-step. This tutorial includes: a virtual machine as additional material. Our objective is make it easy for a beginner research community in OMNET++ and to maintain an open environment of knowledge.

The power generation from hydroelectric plants has increased worldwide, contributing to the participation of the renewable sources to the energy matrix. In the semiarid Brazilian Northeast (NEB), thousands of small dams have been built over time as a solution for water supply. Although incompatible with hydropower generation due to the conflict with human water supply in the region, small reservoirs accumulate water and hydraulic energy at high altitudes. In this work, simulations were performed to assess how the arrangement of various sized reservoirs impact the power demand for water distribution in the Banabuiú River Basin – BRB (19,800 km²), Brazil. The power required to pump water from all 1,405 reservoirs to the districts is 6.5 GWh/year, whereas in the scenario with the 12 larger strategic reservoirs only, the power demand reaches 45.3 GWh/year. Although representing roughly 60% of the water availability of the BRB and being able to supply all the districts, the Arrojado Lisboa reservoir alone would demand 195 GWh/year to supply water to the entire basin, i.e. 30 times the power required in the real reservoir arrangement. By storing water at high altitudes and distributing it spatially, the small reservoirs increase the energy efficiency on the water distribution system.

Especially for drought periods, the higher the accuracy of reservoir inflow forecasting, the more reliable the water supply from a dam. The article focuses on probabilistic forecasting of seasonal inflow to reservoirs and determines estimates from the probabilistic seasonal inflow according to drought forecast results. The probabilistic seasonal inflow was forecasted by a copula-based Bayesian network employing a Gaussian copula function. Drought forecasting was performed by calculation of the standardized streamflow index value. The calendar year is divided into four seasons; the total inflow volume of water to a reservoir for a season is referred to as the seasonal inflow. Seasonal inflow forecasting curves conforming to drought stages produce estimates of probabilistic seasonal inflow according to the drought forecast results. The forecasted estimates of seasonal inflow were calculated by using the inflow records of Soyanggang and Andong dams in the Republic of Korea. Under the threshold probability of drought occurrence ranging from 50 to 55 %, the forecasted seasonal inflows reasonably matched critical drought records. Combining the drought forecasting with the seasonal inflow forecasting may produce reasonable estimates of drought inflow from the probabilistic forecasting of seasonal inflow to a reservoir.

A growing area in neurosciences is focused on the modeling and analysis the complex system of connections in neural systems, i.e. the connectome. Here we focus on the representation of connectomes by using graph theory formalisms. The human brain connectomes are usually derived from neuroimages; the analyzed brains are co-registered in the image domain and brought to a common anatomical space. An atlas is then applied in order to define anatomically meaningful regions that will serve as the nodes of the network - this process is referred to as parcellation. Recently, it has been proposed to perform atlas-free random brain parcellation into nodes and align brains in the network space instead of the anatomical image space to define network nodes of individual brain networks. In the network domain, the question of comparison of the structure of networks arises. Such question is tackled by modeling the comparison of brain network as a network alignment (NA) problem. In this paper, we first defined the NA problem formally, then we applied three existing state of the art of multiple alignment algorithms (MNA) on diffusion MRI-derived brain networks and we compared the performances. The results confirm that MNA algorithms may be applied in cases of atlas-free parcellation for a fully network-driven comparison of connectomes.

We study latching dynamics in the adaptive Potts model network, through numerical simulations with randomly and also weakly correlated patterns, and we focus on comparing its slowly and fast adapting regimes. A measure, Q, is used to quantify the quality of latching in the phase space spanned by the number of Potts states S, the number of connections per Potts unit C and the number of stored memory patterns p. We find narrow regions, or bands in phase space, where distinct pattern retrieval and duration of latching combine to yield the highest values of Q. The bands are confined by the storage capacity curve, for large p, and by the onset of finite latching, for low p. Inside the band, in the slowly adapting regime, we observe complex structured dynamics, with transitions at high crossover between correlated memory patterns; while away from the band latching transitions lose complexity in different ways: below, they are clear-cut but last so few steps as to span a transition matrix between states with few asymmetrical entries and limited entropy; while above, they tend to become random, with large entropy and bi-directional transition frequencies, but indistinguishable from noise. Extrapolating from the simulations, the band appears to scale almost quadratically in the p - S plane, and sublinearly in p - C. In the fast adapting regime the band scales similarly, and it can be made even wider and more robust, but transitions between anti-correlated patterns dominate latching dynamics. This suggest that slow and fast adaptation have to be integrated in a scenario for viable latching in a cortical system. The results for the slowly adapting regime, obtained with randomly correlated patterns, remain valid also for the case with correlated patterns, with just a simple shift in phase space.

HPV vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. That aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model we simulated the scenario that occurred in Australia in 2007, where 12-13 year-old girls where vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, that protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73 % in girls with three doses and with coverage rates decreasing with age until 52 % for 20-26 year-olds. A fast 59 % reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature.

Although suicide is a major public health issue worldwide, we understand little of the onset and development of suicidal behavior. Suicidal behavior is argued to be the end result of the complex interaction between psychological, social and biological factors. Epidemiological studies resulted in a range of risk factors for suicidal behavior, but we do not yet understand how their interaction increases the risk for suicidal behavior. A new approach called network analysis can help us better understand this process as it allows to visualize and quantify complex association between many different symptoms or risk factors. A network analysis of data contain information on suicidal patients can help us understand how risk factors interact and how their interaction is related to suicidal thoughts and behaviour. A network perspective has been successfully applied to the field of depression and psychosis, but not yet to the field of suicidology. In this theoretical article, I will introduce the concept of network analysis to the field of suicide prevention, and offer directions for future applications and studies.

This study challenges the prevailing belief in the necessity of complex models for accurate forecasting by demonstrating the effectiveness of parsimonious econometric models, namely ARCH(1) and GARCH(1,1), over deep learning robust approaches, such as LSTM and 1D-CNN neural networks, in modeling historical volatility within pre-emerging stock markets, specifically the Moroccan and Bahraini stock markets. The findings suggest reevaluating the balance between model complexity and predictive accuracy. Future research directions include investigating the potential existence of threshold effects in market capitalization for optimal model performance. This research contributes to a deeper understanding of volatility dynamics and enhances forecasting models’ effectiveness in diverse market conditions.

The utilization of Pig Emotion Recognition (PER) driven by Artificial Intelligence (AI) promises to mitigate labor costs and alleviate stress among domestic pigs, thereby minimizing the need for consistent human intervention. Nevertheless, this research acknowledges the inherent limitations within the raw PER datasets, which often include irrelevant porcine features, hence impeding genuine progress in real-world evaluations. A significant proportion of PER datasets derive from sequential pig imagery obtained from video recordings, and a common pitfall in these studies is the unregulated shuffling of data. This lack of control can result in the overlap of data samples between training and testing groups, thereby yielding skewed experimental evaluations. To address these challenges, this paper introduces a novel solution in the form of the Semi-Shuffle-Pig Detector (SSPD) for PER datasets, with the intent to facilitate a less biased experimental output. By applying the SSPD, we can ensure that all testing data samples remain distinct from the training datasets, and any superfluous information from raw images is systematically discarded. This optimized method enhances the true performance of classification, providing unbiased experimental evaluations. Notably, our approach has led to a remarkable improvement in the Isolation After Feeding (IAF) metric by 20.2\% and achieved higher accuracy in segregating IAF and Paired After Feeding (PAF) classifications exceeding 92\%. This methodology, thereby, ensures the preservation of pertinent data within the PER system and eliminates potential biases in experimental evaluations. Consequently, it elevates the accuracy and reliability of real-world PER applications, resulting in a tangible positive impact on both pig welfare management and food safety standards.

Many engineered approaches have been proposed over the years for solving the hard problem of performing indoor localisation using smartphone sensors. However, specialising these solutions for difficult edge cases remains challenging. Here we propose an end-to-end hybrid multimodal deep neural network localisation system, MM-Loc, relying on zero hand-engineered features, learning them automatically from data instead. This is achieved by using modality-specific neural networks to extract preliminary features from each sensing modality, which are then combined by cross-modality neural structures. We show that our choice of modality-specific neural architectures is capable of estimating the location with good accuracy independently. But for better accuracy, a multimodal neural network fusing the features of early modality-specific representations is a better proposition. Our proposed MM-Loc solution is tested on cross-modality samples characterised by different sampling rates and data representation (inertial sensors, magnetic and WiFi signals), outperforming traditional approaches for location estimation. MM-Loc elegantly trains directly from data unlike conventional indoor positioning systems, which rely on human intuition.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

The generation at the helm faces an unprecedented responsibility in the near future of artificial intelligence. The implications of setting up the founding rules that will regulate the operation of AI are heavy since after they’re set they last forever. Once this first AI is commenced, it can be such that no other subsequent AIs could emerge thereby assuming dominion over its own creation stand. As a result, retaining control becomes necessary. Lest humanity surrender agency to its own creation. At this juncture of big talks, critical issue are raised concerning AI administration owners. Is it appropriate for only a few people to have unrestricted control on AI commands while leaving out all precautionary measure? Therefore, we have to always consider between control and constraint when dealing with AI issues which involves authority plays off against morality. The direction Artificial Intelligence takes in the future depends on the decisions made by today’s generation. We will determine how we are viewed historically in terms of technology based on how well we take on such an important duty. There’s a major turning point ahead of us where we who are the stewards of tomorrow must make a choice that protects humanity’s right to self-determination and also exploits the power of AI for change.

Coronary heart disease (CHD) has become a prevalent issue in Indonesia, resulting in a majority of fatalities and illnesses. The most recent guidelines in the United States for managing patients with chronic coronary syndrome (CCS) have undergone significant changes, with the recommendation for conducting percutaneous coronary intervention (PCI) strictly limited to patients who continue to experience symptoms despite receiving optimal medical treatment (OMT). However, adopting this approach may lead to increased morbidity and mortality due to the presence of vulnerable plaque. Vulnerable plaque is characterized by minimal obstruction during coronary angiography (40-70%) and may not produce ischemia during physiological studies. Identifying vulnerable plaques can be accomplished through several methods, among which computed tomography coronary angiography (CTCA) is the most cost-effective approach. Recently, experts have employed different techniques, either with stents or drug-coated balloons, to manage vulnerable plaques, showing the methods to be safe and effective. The other challenge faced by patients with CCS is the possibility of achieving complete revascularization (CR) to reduce the likelihood of future major adverse cardiac events (MACE) and mortality. Both approaches, identifying and intervening in the vulnerable plaque, are difficult to perform in developing countries due to the limited budget. Studies have shown that plant-based diets (PBD) can reverse atherosclerotic plaque and stabilize vulnerable plaque, possibly leading to CR. PBD is an affordable and effective approach, but it requires healthcare providers skilled in educating and implementing this dietary approach with their patients. Large-scale randomized controlled trials in PBD, specifically aimed at addressing the vulnerable plaque and achieving CR, are necessary before implementing the PBD intervention.

A significant portion of fatalities stemming from acute myocardial infarction (AMI) occur suddenly and without warning. This can be prevented through early detection of coronary stenosis. In recent years, coronary artery calcium (CAC) and computed tomography coronary angiography (CTCA) have been utilized. For instance, a 55-year-old interventional cardiologist had a CAC of 58 with 50% stenosis involving his left anterior descending (LAD) artery. Despite possibly having vulnerable plaque, which carries significant future cardiovascular events, the patient followed a plant-based diet (PBD), took supplements, and continued optimal medical therapy (OMT). After three years, there was a significant regression in both CAC and stenosis. During our discussion, we emphasized the importance of food selection, quantification, and how food is processed (raw) to achieve maximal benefit in regressing atherosclerosis. Additionally, we highlighted the importance of identifying the possibility of vulnerable plaque and the mechanism stabilizing it. The case supported the guidelines set by the American College of Cardiology (ACC) and the American Heart Association (AHA) for managing chronic coronary syndrome (CCS) by emphasizing OMT and lifestyle changes. We hope that in the future, more doctors and patients will take responsibility for implementing lifestyle changes rather than solely relying on medications and sophisticated medical technologies.

In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is of course exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology, (FRAM) which did allow the analyst in-sights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative ap-proach. In the last 10 years, this original approach developed to model systems as sets of interactive, interdependent “functions”, (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component focussed system engineering “models”, and can pull it all together into dynamic models, (as opposed to static, fixed System Theoretic Process Accimaps), from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.

As belonging to one of the most isolated continents on our planet, the microbial composition of different environments in Antarctica could hold a plethora of undiscovered species with potential for biotechnological applications. This manuscript delineates our discoveries after an expedition to the Bulgarian Antarctic Base "St. Kliment Ohridski" situated on Livingston Island, Antarctica. Amplicon-based metagenomics targeting the 16S rRNA genes and ITS2 region were employed to assess the metagenomes of the bacterial, fungal and archaeal communities across diverse sites within and proximal to the research station. The predominant bacterial assemblages identified included Oxyphotobacteria, Bacteroidia, Gammaprotobacteria and Alphaprotobacteria. A substantial proportion of cyanobacteria reads were attributed to a singular uncultured taxon within the family Leptolyngbyaceae. The bacterial profile of a lagoon near the base exhibited indications of penguin activity, characterized by a higher abundance of Clostridia, similar to lithotelm samples from Hannah Pt. Although most fungal reads in the samples could not be identified at the species level, noteworthy genera, namely Betamyces and Tetracladium, were identified. Archaeal abundance was negligible, with prevalent groups including Woesearchaeales, Nitrosarchaeum, Candidatus Nitrosopumilus and Marine Group II.

Abstract: Background: Dental fear and uncooperative behavior can hinder dental treatment quality. Pediatric Procedural Sedation and Analgesia (PPSA) is used to facilitate treatment when coping capacity is exceeded. Out-of-hospital PPSA has been associated with more adverse outcomes compared to hospital-based settings. Updated Dutch PPSA guidelines recommending two anesthesiologists and a ventilator during out-of-hospital PPSA have increased costs and raised concerns about the accessibility to specialized high quality dental care for children in the Netherlands. Aim: This study aims to investigate the impact of the updated 2017 guideline on the occurrence rate of adverse events during PPSA, as recorded in the anesthesia complication database of twelve participating Dutch dental clinics, with adjustments made for age, weight, and duration of treatment. Method: Data of 25,872 children treated at 12 dental clinics performing out-of-hospital PPSA between 1997 and 2019 were analyzed. Eligible children were aged 2-18 with ASA classification I and II. A logistic two-level mixed-effects model is used to estimate the updated guideline’s impact on adverse events. Results: In the final model we analyzed the data of 21,759 children in nine dental clinics. The OR of the occurrence rate of an adverse event adjusted for age, weight, and duration of treatment was 0.75 (95% CI 0.64 – 0.89) after the implementation of the updated guideline, compared to before the updated guideline. This outcome was significant with a P=0.001, indicating a protective effect. The average odds per clinic of having an adverse event was 0.04 with a 3.81% chance. Conclusion: Our findings demonstrate a significant reduction in adverse events after the implementation of the updated guideline and highlight the importance of adherence to evidence-based guidelines in improving patient safety and the quality of care provided during pediatric procedural sedation and analgesia in out-of-hospital dental clinics. Our study underlines the necessity of considering the complex underlying mechanisms that play a role in the development and implementation of evidence-based practice and safety initiatives on the occurrence of adverse events.

The healing of osteochondral defects (OCD) resulting from injury, osteochondritis, or osteoarthritis in middle- and old-age individuals, bearing lesions in the cartilage and bone, pain, and loss of joint function, presents challenges to the clinical practitioners because of non-regenerative cartilage and the limitations of current therapies. Bioactive peptide-based osteochondral (OC) tissue regeneration is becoming more popular because it does not have the immunogenicity, misfolding, or denaturation problems associated with original proteins. Periodically, reviews are published on the regeneration of bone and cartilage; however, none of them address the simultaneous healing of these tissues in the complicated heterogeneous environment of the OC interface. As regulators of cell adhesion, proliferation, differentiation, angiogenesis, immunomodulation, and antibacterial activity, potential therapeutic strategies for OC defects utilizing bone and cartilage-specific peptides should be examined and investigated. Therefore, the main goal of this review is to study how they contribute to the healing of OC defects, either alone or in conjunction with other peptides and biomaterials.

In the field of modeling social systems, the use of agent-based models (AM) has become a powerful tool for understanding processes occurring in society, in particular, for studying the spread of infections among the population. The agent model is based on a synthetic population consisting of agents, their interactions and sometimes the territory in which the interaction occurs. The choice of an appropriate method for creating such artificial populations and the selection of parameters becomes a key issue affecting the realism of the model and its ability to reflect real-world scenarios. This article delves into the interplay between agent-based modeling, synthetic population construction, and parameter selection, offering our insight into the complex process of modeling infectious disease dynamics. The review concludes with an exploration of extending the Covasim model [1] to experiments in computational epidemiology. Covasim, a dynamic transmission modeling tool, is known for its ability to adapt to different scenarios and offers predictive information critical for managing epidemiological situations. The Covasim model could include improvements to better simulate real-world epidemic conditions, including a revised transmission strategy to account for accumulation through daily contacts. In addition, we propose to add a module for intercity travel and a visualization package that will significantly expand the functionality of Covasim. These improvements make Covasim a more versatile and efficient tool for modeling and analyzing epidemic processes.

AI-based approaches are increasingly being used to revolutionize vaccine development for COVID-19. Small molecules, peptides, and epitopes are being collected for therapy discovery, which could also direct AI-based models, screening, or generation [1]. AI-based models are being used for drug discovery and vaccine development, and pre-existing data is being leveraged through machine learning approaches for COVID-19 drug discovery and vaccine development [1]. The best candidate targets for future treatment development are being identified and evaluated using AI-based models [1]. AI-based approaches can be used to overcome challenges in manufacturing, storage, logistics, and safety and efficacy issues related to different vaccine candidates for COVID-19 [2]. AI algorithms can help identify the best vaccine candidates for COVID-19 while considering the efficiency of antigenic peptides for immune response generation [2]. The presentation of antigenic peptides by major histocompatibility complex (MHC) class II molecules is the first step after COVID-19 vaccine administration for any vaccine-induced immune response [2]. Thus, AI-based models are being used to identify the best vaccine candidates for COVID-19 and to ensure that the vaccine-induced immune response is efficient and safe. The utilization of AI-based methods to address logistical, manufacturing, storage, safety, and efficacy issues regarding several COVID-19 vaccine candidates will be examined in this study. Additionally, while considering the effectiveness of antigenic peptides for the induction of an immune response, we will determine the best potential targets for the next treatment development and assess how AI-based models can help discover the best vaccine candidates for COVID-19. This research ultimately intends to offer insights into how AI-based techniques can transform COVID-19 vaccine development and how they can be applied to address vaccine development issues. In this paper, we focus on recent advances in using artificial intelligence to develop COVID-19 drugs and vaccines, as well as the potential of intelligent training in discovering COVID-19 therapeutics, highlighting potential challenges and solutions.

The coronavirus disease (COVID-19) is a respiratory tract infection caused by the new virus SARS-CoV-2. In March 2020, the World Health Organization (WHO) declared a worldwide COVID-19 pandemic. Since then, there has been a rush to find preventative or curative treatment. This arrived with the appearance of the COVID-19 vaccine, months after the declaration of the pandemic. In the setting of global COVID-19 immunization, there have been reports of rare skin immune-mediated diseases (IMD) after COVID-19 vaccination, particularly systemic sclerosis. Systemic sclerosis (SSc) is the most reported new onset IMD following messenger RNA (mRNA) COVID-19 vaccination. The pathogenesis of SSc is not fully understood, and the diagnosis is based on clinical symptoms. We report a 54-year-old male, with no prior history of autoimmune disorder, with rapid and progressive skin thickening (Modified Rodnan skin score 30/51), weeks after his mRNA COVID-19 vaccination. Antinuclear antibodies (ANA) showed a strongly positive nuclear fine-speckled pattern, but no SSc autoantibodies were found. The patient was diagnosed with SSc based on the persistence of autoantibodies and the clinical criteria according to the 2013 American College of Rheumatology/European League Against Rheumatism classification. Due to the proximity of the two events, we hypothesized a cross-reaction between COVID-19 vaccination and, in genetically predisposed patients, an emergence of systemic sclerosis. Our case suggests a potential relationship between the mRNA COVID-19 vaccine and new-onset autoimmune diseases. Physicians should be aware of this possible association.

The corrosion resistance of two cast CoCr based alloys, with different amounts of chromium and with different alloying elements in the bulk composition of the alloy, was assessed. Herewith, we investigated the corrosion behavior of Co21Cr8Mo7W and Co29Cr7W by open circuit potential (OCP), potentiodynamic polarization (PP) and electrochemical impedance spectroscopy (EIS) in 0.1 M phosphate buffer solution (PBS) at 37°C for long time immersion. After 1000 hours of immersion, the corrosion current density (icor), estimated from anodic polarization tests, is lower for Co21Cr8Mo7W (i.e. 49 nA cm-2) alloy compared to Co29Cr7W alloy (180 nA cm-2). As regards the corrosion potential (Ecor), a nobler value was observed for Co21Cr8Mo7W (i.e. -59 mV vs. Ag/AgCl) compared to Co29Cr7W (i.e. -114 mV vs. Ag/AgCl). These findings suggest a better corrosion resistance of the film formed on the alloy containing lower amount of Cr and two alloying elements, namely Mo and W. These results are promising ones in terms of medical applications because they open new strategies in obtaining alloys with lower content of chromium and with higher protective properties against corrosion attack.

The global construction sector contributes a significant share of total greenhouse gas (GHG) emissions. In Australia, infrastructure activity alone generates 18% of the GHG emissions budget. The use of low-embodied carbon building materials is crucial to achieving sustainability in the construction sector and to fulfill national and international climate goals. Industrial hemp (Cannabis sativa L.) is considered a promising feedstock for sustainable construction materials because of its biogenic carbon content, fast-growing cycles with low agricultural input requirements, and technical functionality which is comparable to traditional materials. This study has applied the life cycle assessment (LCA) guideline of ISO 14040:2006 to estimate the carbon footprint (CF) expressed in carbon dioxide equivalent (CO2eq) emissions of hemp-based building materials in Western Australia. The functional unit is 1 m2 of hemp-based board, and the system boundary includes cradle-to-gate stages, i.e., pre-farm, on-farm, and post-farm activities. The production of 1 m2 of hemp-based board is estimated to be - 2.302 kgCO2eq. Electricity from the public grid for lignin extraction during the post-farm stage is the main contributor to total CO2eq emissions (26%), followed by urea production (14%) during the pre-farm stage. Overall, the use of electricity from the SWIS during the post-farm stage accounts for 45% of total emissions. Sensitivity analysis shows that the CF of hemp-based boards is highly sensitive to the source of energy, i.e., total replacement of the SWIS by solar power decreases the CF from - 2.30 to -6.07 kgCO2eq (164%). The results suggest that hemp-based boards exhibit lower embodied GHG emissions compared to traditional materials, such as gypsum plasterboards.

TiC particles were prepared from Ti and C powders, and TiC-reinforced Cu-based composites were prepared by powder metallurgy with SPS sintering. The effects of SPS sintering temperature and TiC particle reinforcement phase content on the electrical and mechanical properties of Cu-based composites were investigated. The reinforcement of TiC particles by thermal mismatching at increasing sintering temperature has a significant strengthening effect on the composites, but the high temperature causes softening of the Cu matrix and the growth of grains reduces the properties of the composites. In this study, the effects of porosity and density on the composite properties were not significant.

Progress in macrophage research is crucial for numerous applications in medicine, including cancer and infectious diseases. However, the existing methods to manipulate living macrophages are labor intense and inconvenient. Here we show that macrophage membranes can be reconstituted after storage for months at 4C, with their CD206 receptor selectivity and specificity being similar to that in the living cells. Then, we have developed a mannose ligand, specific to CD206, linked with PEG as IR spectroscopy marker to detect binding with the macrophage receptor. PEG was selected due to its unique adsorption band of C-O-C group at IR spectra, which does not overlap with other biomolecule’s spectroscopic feature. Next, competitive binding assay versus the PEG-bound ligand, has enabled selection of other higher-affinity ligands specific to CD206. Further, those higher-affinity ligands were used to differentiate activated macrophages in patient’s bronchoalveolar (BAL) or nasopharyngeal (NPL) lavage. CD206- control cells (HEK293T) showed only non-specific binding. Therefore, biochips based on reconstituted macrophage membranes as well as PEG-trimannoside as an IR spectroscopic marker, can be used to develop new methods facilitating macrophage research and macrophage-focused drug discovery.

Background: The incidence of prostate cancer (PC) has been risen annually. Despite the diagnosis is made mainly with non-metastatic PC, mortality is explained by the metastatic disease (mPC). Without a doubt there is an intermediate scenario in which patients have no mPC but will have initiated a metastatic cascade through an epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict what patients will progress in the future to non-localized clinical disease or already have micrometastatic disease, and therefore, will clinically progress after primary treatment. Biomarkers for predicting mPC are still under development; there are few studies and not much evidence of their usefulness. Summary: This review is focused on tissue-based genomic biomarkers (TBGB) for predicting metastatic disease. We developed four main research questions that will attempt to answer according to the current evidence. Why is important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are current prostate cancer treatments? Key Messages: Knowing useful predict tools could help determine which patients may need multimodal or adjuvant treatment even with a localized disease, and in consequence, what patients do not need more than a single modality of treatment. The importance of predicting metastasis is fundamental, given that once metastasis is diagnosed, the quality of life (QoL) and survival drop dramatically.

Online speech enhancement is of great interest, because of its diverse areas of application. The Demucs architecture has been recently shown to achieve a high level of performance, even with small audio segments, while requiring a relatively small amount of computational resources. However, an issue that it bears, as well as most of state-of-the-art speech enhancement techniques, is that of target speech selection: it is assumed that only one speech source is present. However, in real-life scenarios, more than one speech source may be present, in it is uncertain which speech source within the fed mixture should be enhanced. Thus, it is of interest to complement speech enhancement techniques (such as Demucs-based) with a target selection scheme, so as to ensure that only the source of interest is enhanced. In this work, two target selection strategies are explored: 1) an embedding-based strategy, using a codified sample of the target speech, and 2) a location-based strategy, using a beamforming-based pre-filter to select the target that is in front of a 2-microphone array. It is shown that while both strategies improve the performance of the Demucs-based technique when one or more speech interferences are present, they both have their pros and cons. Specifically, while the beamforming-based strategy achieves overall a better performance compared to the embedding-based strategy, it is sensitive against the location variation of the target speech source which the embedding-based strategy does not suffer from.

The paper explores the potential of a low-cost advanced video-based technique for the assessment of structural damage induced to buildings by seismic loading. A low-cost high-speed video camera was utilized for motion magnification (MM) processing of footages of a two-story reinforced concrete frame building subjected to shaking table tests. The damage after seismic loading was estimated by analyzing the dynamic behavior (i.e. in terms of modal parameters) and the structural deformations of the building in the MM videos. The results by MM were compared for method validation to damage assessment obtained by the analyses of conventional accelerometers and high-precision optical markers tracked by a passive 3D motion capture system. Also, 3D laser scanning to obtain an accurate survey of the building geometry before and after the seismic tests was carried out. In particular, accelerometers were also processed and analyzed by using several stationary and non-stationary techniques with the aim to analyze the linear behavior of the undamaged structure and the nonlinear structural behavior during damaging shaking table tests. The proposed MM-based procedure provided accurate estimate of the main modal frequency and the damage location through the analysis of modal shapes, which were confirmed by advanced analyses of accelerometric data.

The deployment of Electro-Mechanical Actuators plays an important role towards the adoption of the More Electric Aircraft (MEA) philosophy. On the other hand, a seamless substitution of EMAs in place of more traditional hydraulic solutions is still set back due to the shortage of real-life and reliability data regarding their failure modes. One way to work around this problem is providing a capillary EMA Prognostics and Health Management (PHM) system, capable of recognizing failures before they actually undermine the ability of the safety-critical system to perform its functions. The authors have developed a model-based prognostic framework for PMSM based EMAs leveraging a metaheuristic algorithm: Evolutionary (Differential Evolution (DE)) and swarm intelligence (particle swarm (PSO), grey wolf (GWO)) methods are considered. Several failures (dry friction, backlash, short circuit, eccentricity and proportional gain) are simulated thanks to a Reference Model, acting as a Numerical Test Bench, then detected and identified thanks to the envisioned prognostic method, which leverages a low fidelity Monitoring Model. The employed algorithms showed good results and prove that this strategy could be executed in pre-flight checks or during the flight at specific time intervals, with positive impacts on system safety and availability.

Initial choice of Learning Rate is a key part of gradient based methods and has a great effect on the performance of the Deep Learning Model.This paper studies the behavior of multiple gradient based optimization algorithm which are commonly used in Deep Learning and compare their performance on various learning rate. As observed popular choice of optimization algorithms are highly sensitive to various choice of learning rates. Our goal is to find which optimizer has an edge over others for a specific setting. We look at two datasets namely MNIST and CIFAR10 for benchmarking. The results are quite surprising, and it will help us to choose a learning rate more efficiently.

Agent-based models (ABMs) are computational models for simulating the actions and interactions of autonomous agents in time and space. These models allow users to simulate the complex interactions between individual agents and the landscapes they inhabit and are increasingly used in epidemiology to understand complex phenomena and make predictions. However, as the complexity of the simulated systems increases, notably when disease control interventions are considered, model flexibility and processing speed can become limiting. Here we introduce SamPy, an open-source Python library for stochastic agent-based modeling of epidemics. SamPy is a modular toolkit for model development, providing adaptable modules that capture host movement, disease dynamics, and disease control interventions. Memory optimization and design provide high computational efficiency allowing modelling of large, spatially-explicit populations of agents over extensive geographical areas. In this article, we demonstrate the high flexibility and processing speed of this new library. The version of SamPy considered in this paper is available at https://github.com/sampy-project/sampy-paper .

Good estimators are characterized as robust, unbiased, efficient, and consistent. However, the commonly used estimators are weak or lack one or more of these properties. In this article, eight (8) estimators for statistical and geometrical estimations of symmetry/asymmetry, similarity/dissimilarity, identity/unidentity, and feature transformation were proposed following Kabirian-based optinalysis and other operations. The proposed estimators are characterized as invariant (robust) under scaling, location shift, and rotation or reflection. A computing code was written in python language for each of the proposed estimators so that peers can have working codes for application and performance evaluation.

Decision making as a result of system dynamics analysis requires, in practice, a straightforward and systematic modelling capability as well as a high-level of customisation and flexibility to adapt to situations and environments that may vary very much from each other. While in general terms a completely generic approach could be not as effective as ad-hoc solutions, the proper application of modern technology may facilitate agile strategies as a result of a smart combination of qualitative and quantitative aspects. In order to address such a complexity, we propose a knowledge-based approach that integrates the systematic computation of heterogeneous criteria with open semantics. The holistic understanding of the framework is described by a reference architecture and the proof-of-concept prototype developed can support high-level system analysis, as well as it suitable within a number of applications contexts - i.e. as a research/educational tool, communication framework, gamification and participatory modelling. Additionally, the knowledge-based philosophy, developed upon Semantic Web technology, increases the capability in terms of holistic knowledge building and re-use via interoperability. Last but not least, the framework is designed to constantly evolve in the next future, for instance by incorporating more advanced AI-powered features.

There is increasing interest in automating train operations of mainline services, e.g. to increase network capacity. Automatic train operation (ATO) is already achieved by several pilot projects, but not implemented on a large scale. Before the general introduction of new or adapted technologies can have a transformative effect on the operation of such a complex system as train operation on mainlines, they have to pass functional, interoperability and performance tests. A virtual preliminary analysis is one way to ensure a smooth as well as safe introduction and implementation. This paper aims to present an approach that applies to the performance testing of ATO systems. Therefore, methods and test standards for technologies enabling automatic operation in other transport sectors are reviewed. The main findings have been adapted, transformed and combined to be used as a general strategy for virtual performance testing in the railway sector. Specifically, universal performance indicators, namely punctuality, accuracy, energy consumption, safety and comfort, are presented. A layer model for scenario description is adapted from the automotive sector, as well as the definition of different scenario types. Lastly, factors that can influence the performance of an ATO algorithm are identified. To demonstrate the developed approach, a straightforward investigation of a case study is conducted using a microscopic train simulator in combination with an ATO algorithm.

Background: In an age where information is generally accessible, most of the interest these days has focused on how accessible and convenient technology can be. So small and personal, mobile devices can transform our perception of learning by combining both mobility and convenience. Mobile learning is part of the digital learning landscape alongside e-learning and serious games. However, knowledge about effective design of mobile learning experiences remains of interest with a focus on appropriate design models and the embodiments that can be implemented to achieve the intended educational outcomes. Exploring the instructor's perspective on mobile learning is essential. Therefore, the aim of this study was to investigate the Moroccan instructors' perception and practice of mobile learning to inform the development of an ecologically valid mobile learning integration model. Methods: Higher education Instructors (n=41) were recruited to the study. The Moroccan instructors' perception and their experiences regarding their adoption of mobile learning were collected using an online survey. The analysis focused on their mobile use, perceived IT competency, and opinions on mobile learning. Results: We described most of the instructors' considerations regarding integrating mobile technologies into their teaching activities. We found that most of the mobile learning activities defined by the respondents corresponded to relatively advanced use of mobile devices. More promising, instructors have found innovative ways to use the educational potential of mobile devices. However, the prospect of mobile devices was still to challenge. No or poor Wi-Fi connection, number of devices or limited access, sometimes fees or applications incompatibility were identified as reasons and obstacles to mobile learning usage. Conclusion: Mobile learning is mostly perceived positively among Moroccan instructors allowing many applications and usage to enhance teaching and learning. In this study, a better understanding of aspects and factors influencing the integration of mobile learning in the Moroccan educational context is exposed, helping further the development of an ecologically valid mobile learning integration model. Future work on mobile learning should consider the highly paced evolution of mobile technologies, emphasizing the flexibility of integration frameworks to support instructors and learners.

Gender-based violence is considered a serious social and public health problem. Overcoming this situation implies a process that results in the favorable biopsychosocial rehabilitation, the resilient of women. The objective of this study was to analyze the tools, resources and personal and psychosocial mechanisms used by women survivors of gender-based violence. The design was an interpretative phenomenology. It carried out with 22 women who have overcome gender-based violence. Data was collected through personal interviews and narration. The results were grouped into four themes: "Process of violence", "Social resources for coping and overcoming GBV", "Personal tools for coping and overcoming GBV", and "Feelings identified, from the abuse stage to the survival stage". Several studies concluded that overcoming abuse is influenced by the women social network, and it can be the action of these people determining their survival to gender violence. Despite the recognized usefulness of these available resources, it would be desirable to strengthen them in order to be able to drive more women toward survival, assuming a strengthening of coping and overcoming, without forgetting the importance of other support mechanisms such as their family and group therapies.

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

Has anyone ever missed an event because he was confused in days and dates? Do we remember the date of any day without looking at a calendar? Is the current Gregorian Calendar efficient enough for use, and does it facilitate our life or make it more complicated? Have you ever thought about a much simpler way to calculate days and dates in a year? All these questions are answered in this paper, in which the author proposes original optimization algorithm that creates optimal perennial calendars. Results show that there is more than one way to create a perennial calendar, in which the number of days in each month does not change, neither the dates. Hence, all months have the same sequence of days and dates. In other meaning, Monday becomes the first day of every month, and Sunday becomes the last day. Consequently, the calendars become much easier to memorize and very simple to predict the days and dates in any year.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 0.8x1e6 Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer or simulant Urine) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Paper-based sensors, microfluidic platforms and electronic devices have attracted attention in the past couple of decades because they are flexible, can be recycled easily, environmentally friendly, and inexpensive. Here we report a paper aptamer-based potentiometric sensor to detect the whole Zika virus for the first time with a minimum sensitivity of 2.6 nV/Zika and the minimum detectable signal (MDS) of 1.2x10⁶ Zika. Our paper sensor works very similar to a P-N junction where a junction is formed between two different wet regions with different electrochemical potentials near each other on the paper. These two regions with slightly different ionic contents, ionic species and concentrations, produce a potential difference given by the Nernst equation. Our paper sensor consisted of a 2-3 mm x 10 mm segments of a paper with a conducting silver paint contact patches on its two ends. The paper is soaked in a buffer solution containing aptamers designed to bind to the capsid proteins on Zika. Atomic force microscopy studies were carried out to show both the aptamer and Zika become immobilized in the paper. We then added the Zika (in its own buffer) to the region close to one of the silver-paint contacts. The Zika virus (40 nm diameter with 43 kDa or 7.1x10^-20 gm weight), became immobilized in the paper’s pores and bonded with the resident aptamers creating a concentration gradient. The potential measured between the two silver paint contacts reproducibly became more negative as upon adding the Zika. We also showed that an LCD powered by the sensor, can be used to detect the sensor output.

Many physiotherapy treatments begin with a diagnosis process. The patient describes symptoms, upon which the physiotherapist decides which tests to perform until a final diagnosis is reached. The relationships between the anatomical components are too complex to keep in mind and the possible actions are abundant. A trainee physiotherapist with little experience naively applies multiple tests to reach the root cause of the symptoms, which is a highly inefficient process. This work proposes to assist students in this challenge by presenting three main contributions: (1) A compilation of the neuromuscular system as components of a system in a Model-Based Diagnosis problem; (2) The PhysIt is an AI-based tool that enables an interactive visualization and diagnosis to assist trainee physiotherapists; and (3) An empirical evaluation that comprehends performance analysis and a user study. The performance analysis is based on evaluation of simulated cases and common scenarios taken from anatomy exams. The user study evaluates the efficacy of the system to assist students in the beginning of the clinical studies. The results show that our system significantly decreases the number of candidate diagnoses, without discarding the correct diagnosis, and that students in their clinical studies find PhysIt helpful in the diagnosis process.

This study proposes a new algorithm termed rain cell identification and tracking (RCIT) to identify and track rain cells from high resolution weather radar data. Previous algorithms have limitations when tracking non-consequent rain cells owing to their use of maximum correlation coefficient methods and their lack of an alternative way to handle the variation stages of rain cells during their life cycles. To address these deficiencies, various methods are implemented in the new algorithm. These include the particle image velocimetry (PIV) method for motion estimation and the rain cell matching rule to obtain the stage changes of rain cells. High resolution (5-min and 1-km) radar reflectivity data from three rainy days over the German federal state North Rhine Westphalia (NRW) are used to evaluate the proposed algorithm. The performance of the new algorithm is compared with a radar reflectivity map and verified by two object-oriented methods: structure–amplitude–location (SAL) and geometric index. The verification results suggest that the performance of the new algorithm is good. Application of the RCIT algorithm to the selected cases shows that the inner structure of rainfall events in the experimental region present extreme value distributions, with most rainfall events having a short duration with less intensity. The new algorithm can effectively capture the stage changes of rain cells during their life cycles. The proposed algorithm can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and short-term flood forecasting.