From a differential geometric perspective, information geometry (IG) aims to characterise the structure of statistical geodesic models. The research done for this paper offers a novel method for modelling the information geometry of a queuing system. From the perspective of IG, the manifold of the transient M/M/infinity queue is described in this context. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of transient M/M/infinity QM are devised. In addition to that, new results that uncovered the significant impact of stability of QM on the existence of (IFIM) are obtained. Moreover, the Geodesic equations of motion of the coordinates of the underlying transient are obtained. Also, it is revealed that stable QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of transient M/M/iInfinity queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability(instability) phases of M/M/Infinity QM. Furthermore, a new discovery that presents the significant impact of stability of queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. Also, it is shown that stability of the devised (IME) enforces the instability of M/M/ Infinity QM. More interestingly, novel relativistic info-geometric queueing theoretic links are revealed .

In this paper, comprehensive datasets are presented to advance research on information security breaches. The datasets include data on disclosed information security breaches affecting S&P500 companies between 2020 and 2023, collected through manual search of the Internet. Overall, the datasets include 504 companies, with detailed information security breach and financial data available for 97 firms that experienced a disclosed information security breach. This document will describe the datasets in detail, explain the data collection procedure and shows the initial versions of the datasets.

Colonoscopy is used for colorectal cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. In this study, we developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to analyze relevant free-text colonoscopy reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports, including indications for the colonoscopy, findings during the colonoscopy, and pathology of the resected material. We then trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually annotated notes obtained from the colonoscopy reports of 3,867 patients. The clinical notes were from a group of patients aged over 40 years old enrolled in four Veterans Affairs Medical Centers. A total of 10% of the remaining annotated notes were used to train hyperparameter, while the remaining 10% were used to evaluate the accuracy of our model (Bio-Bi-LSTM-CRF) and to compare the results with the outcomes obtained using Bi-LSTM-CRF. The results of our experiments showed that the bidirectional encoder representations by integrating dictionary function vector from Bio-Bi-LSTM-CRF and strategies character sequence embedding approach is an effective way to identify colonoscopy features from EHR-extracted clinical notes. Therefore, the Bio-Bi-LSTM-CRF model is concluded to be capable of creating new opportunities to identify patients at risk for colon cancer and to study their health outcomes.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

In the context of video background-foreground separation, we propose a compressive online Robust Principal Component Analysis (RPCA) with optical flow that separates recursively a sequence of video frames into foreground (sparse) and background (low-rank) components. This separation method can process per video frame from a small set of measurements, in contrast to conventional batch-based RPCA, which processes the full data. The proposed method also leverages multiple prior information by incorporating previously separated background and foreground frames in an n-l₁ minimization problem. Moreover, optical flow is utilized to estimate motions between the previous foreground frames and then compensate the motions to achieve higher quality prior foregrounds for improving the separation. Our method is tested on several video sequences in different scenarios for online background-foreground separation given compressive measurements. The visual and quantitative results show that the proposed method outperforms other existing methods.

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management.

In this paper, we derive Jeffreys divergence, generalized Fisher divergence and corresponding De Bruijn identities on space-time random field. First, we determine the relation between Fisher information on the space-time random field in one of the space-time points and the ratio of Jeffreys divergence on a space-time random field at distinct space-time positions to the square of coordinate difference. In addition, we also find identities between the partial derivative of the Jeffreys divergence and the generalized Fisher divergence with respect to space-time variables, i.e. the De Bruijn identities, between two space-time random fields obtained by different parameters under the same Fokker-Planck equations. At the end of this paper, we present three examples of the Fokker-Planck equations on space-time random fields, identify their density functions, and derive the Jeffreys divergence, generalized Fisher information, generalized Fisher divergence, and accompanying De Bruijn identities.

Background: Zika virus (ZIKV) infection has significantly affected Latin America in 2015–2017. Most studies have been reported from Brazil and Colombia, and only a few from Central America. For these reasons we analyzed the incidence, incidence rates and evolution of cases in Honduras from 2016–2017. Methods: Using epidemiological weeks (EW) surveillance data on the ZIKV epidemics in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental and municipal levels. Results: From 1 January 2016 to 31 December 2017, a total of 32,607 cases of ZIKV were reported (98.5% in 2016 for an incidence rate of 36.85 cases/100,000 pop; 1% confirmed by RT-PCR). The highest peak was reached on the EW 6°, 2016 (2,559 cases; 29.34 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortés (13,128 cases, 791.08 cases/100,000 pop in 2016). Discussion: The pattern and evolution of ZIKV infection in Honduras has been similar to that which occurred for chikungunya in 2015. As previously reported, infection with chikungunya involved predominantly the central and capital area of the country, reaching incidences there >750 cases/100,000 pop. Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

This paper presents an Artificial Intelligence approach to mining context and emotions related to olfactory cultural heritage narratives, in particular to fairy tales. We provide an overview of the role of smell and emotions in literature, as well as highlight the importance of olfactory experience and emotions from psychology and linguistic perspectives. We introduce a methodology for extracting smells and emotions from text, as well as demonstrate the context-based visualizations related to smells and emotions implemented in a novel Smell Tracker tool. The evaluation is performed using a collection of fairy tales from Grimm and Andersen. We find out that fairy tales often connect smell with emotional charge of situations. The experimental results show that we can detect smells and emotions with F1 score of 92.7 and 79.2, respectively.

Total Nigeria Plc is a Marketing and Services subsidiary of Total; a multinational energy company operating in more than 130 countries and committed to providing sustainable products and services for its customers. For over 50 years, Total Nigeria Plc has remained the leader in the downstream sector of the Nigerian oil and gas industry. This study investigated the volatility of the stock price of Total Petroleum Nigeria plc using nine (9) GARCH models namely sGARCH, gjrGARCH, eGARCH, iGARCH, aPARCH, TGARCH, NGARCH, NAGARCH and AVGARCH. We also investigated the Value-at-Risk (VaR) and Backtesting of the Models. The aim actually of this study is to boost the confidence of the shareholders and investors of the Total Nigeria plc. To achieve this, daily stock price for Total petroleum Nigeria plc from secondary was collected from January 2^nd 2001 to May 8^th 2017. . The study used both normal and student t innovations, using Akaike Information Criterion (AIC) to select the best model, for normal innovations for log returns and cleansed log returns of Total plc, the eGARCH and sGARCH models performed best respectively, while NGARCH model performed best for student t innovation for both log returns and cleansed returns of Total plc. The persistence of the models are stable except in few cases where iGARCH, eGARCH where not stable. Also for student t innovation, the sGARCH and gjrGARCH fails to converge. The mean-reverting number of day for the returns of Total Nigeria plc differs from model to model. Evidence from the VaR Analysis revealed from the selected models revealed that the Risk of VaR losses is high at 99% confidence level, slightly high at 95% confidence level and better at 90% confidence level. Although The Duration-Based Tests of independence conducted revealed that the models are correctly specified since in all cases the null hypotheses were accepted. This means that the probability of an exception on any day did not depends on the outcome of the previous day. Finally, both the unconditional (Kupiec) and conditional (Christoffersen) coverage tests for the correct number of exceedances for both Total stock returns and cleansed returns. The tests revealed rejection of the models at 1% level of significance. This confirms that unconditional (Kupiec) and conditional (Christoffersen) coverage tests for the correct number of exceedances are reliable compared to the Duration-Based Tests of independence. Therefore we recommend that shareholders and investors in Total Nigeria plc are to remain and continue to investment in Total Nigeria plc because if there is any form of losses, the price of the stock has the potentials to improve in the future. Again, though the risk is high at 99% confidence level, this in line with the financial theory that states that an asset with high expected risk would pay higher return on the average.

The 2030 global agenda of the United Nations emphasizes peace, human rights, gender equality, partnership, and women's empowerment. It balances the social, economic, and environmental dimensions of sustainable development. The SDG framework was designed to be integrated and indivisible, making the interconnections of 17 Goals and 169 Sustainable Development Goals Targets (SDTs) more complex that need extensive and intensive investigative research. Recent studies have increased on the interrelationships of SDGs, but none of these have focused on integrating Climate Change (CC), Sustainable Forest Management (SFM), and Information and Communication Technology (ICT), also known as CSI Nexus. This study aims to investigate and identify the 169 SDTs linked to CC, SFM, and ICT and assesses the significance of the relationships between these variables. The alignment of SDTs to CSI Nexus was identified through clustering and mapping techniques. The result argued that 56 SDTs are directly connected within CC+SFM+ICT, 16 within CC+SFM, one within SFM+ICT, and 51 within ICT+CC. Our result suggests that CC is significantly associated with SFM; however, ICT has no significant association with CC and SFM. It further asserts that the ICT and SFM have minimal influence on the SDG 2030 framework. The proposed CSI Nexus and SDTs Integration Framework was a science-informed guide for priority-setting, policy coherence, and decision-making supporting the 2030 Sustainable Development Goals. This study does not include interactions, network analysis and visualization, simulations, and modeling between SDG targets.

The links between rural and urban have often been the subject of study in Portugal. Nevertheless, the asymmetries between rural and urban have not ceased to increase over the last decades, contributing to an imbalance of economic, social, and territorial dimensions between regions. In order to counter this trend, policies to attract the population and tourism have been endorsed but have not been sufficient to promote territorial cohesion. This makes it necessary to rethink the capacity and the potential of rural territories in terms of mobilising their resources and, through them, promoting new and/or renewed relations with urban territories. This article aims to explore and map the links between urban and rural areas by analysing the routes taken by agri-food products, from production to marketing, with the support of a spatial analysis, using Geographic Information Systems (GIS). For that, we analysed a set of specialised and/or gourmet shops in Oorto and Lisbon, and their relations with the territories from which the commercialized agri-food products come. Most of these products originate in the inland rural regions of the country, and there is a clear geographical connection between the shops analysed and specific territories. Thus, it was possible to verify the increase in interest and consumption of agri-food products of rural origin and the spontaneous increase of this type of shop in the cities analysed, as well as identify the target audience of the shops and the spatial influence they have in the respective cities. These shops, in addition to selling agro-food products, are also promoters of rural territories in an urban environment.

Background: After serious epidemics of chikungunya (CHIKV) and Zika (ZIKV) in the Americas, dengue (DENV) have reemerged in most countries. We analyzed the incidence, incidence rates, and evolution of DENV cases in Honduras from 2015-2018 and the ongoing 2019 epidemic. Methods: Using epidemiological weeks (EW) surveillance data on the DENV in Honduras, we estimated incidence rates (cases/100,000 population), and developed maps at national, departmental, and municipal levels. Results: From 1 January 2016 to 21 July 2019, a total of 109,557 cases of DENV were reported, 28,603 in 2019, for an incidence rate of 312.32 cases/100,000 pop this year; 0.13% laboratory-confirmed. The highest peak was reached on the EW 28°, 2019 (5,299 cases; 57.89 cases/100,000 pop). The department with the highest number of cases and incidence rate was Cortes (8,404 cases, 479.68 cases/100,000 pop in 2019). Discussion: The pattern and evolution of DENV epidemic in 2019 in Honduras has been similar to that which occurred for in 2015. As previously reported, this epidemic involved the north and central areas of the country predominantly, reaching municipality incidences there >1,000 cases/100,000 pop (1%). Studies using geographical information systems linked with clinical disease characteristics are necessary to attain accurate epidemiological data for public health systems. Such information is also useful for assessment of risk for travelers who visit specific areas in a destination country.

Ship detection using synthetic aperture radar (SAR) has been extensively utilized in both the military and civilian fields. On account of complex backgrounds, large scale variations, small-scale targets, and other challenges, it is difficult for current SAR ship detection methods to strike a balance between detection accuracy and computation efficiency. To overcome those challenges, ESarDet, an efficient SAR ship detection method based on context information and large effective receptive field (ERF) is proposed. We introduce the anchor-free object detection method YOLOX-tiny as a baseline model and make several improvements on it. First, the CAA-Net, which has a large ERF, is proposed to better merge the context and semantic information of ships in SAR images to improve ship detection, particularly for small-scale ships with complex backgrounds. Further, to prevent the loss of semantic information regarding ship targets in SAR images, we redesign a new spatial pyramid pooling network, namely A2SPPF. Finally, in consideration of the challenge posed by the large variation in ship scale in SAR images, we design a novel convolution block, called A2CSPlayer, to enhance the fusion of feature maps from different scales. Extensive experiments are conducted on three publicly available SAR ship datasets, DSSDD, SSDD, and HRSID, to validate the effectiveness of the proposed ESarDet. The experimental results demonstrate that ESarDet has distinct advantages over current state-of-the-art (SOTA) detectors in terms of detection accuracy, generalization capability, computational complexity, and detection speed.

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Image clustering and classification are fundamental tasks in computer vision, critical for applications overlap image retrieval, object recognition, and image classification. However, the robustness of clustering algorithms against adversarial attacks remains interesting topic. In this paper, we investigate how adversarial attacks on image classification algorithms impact Image Clustering, similarity obtained using the Dot Product, KNN, HNSW algorithms and model Gradient-Weighted Class Activation Mapping (Grad-CAM). In our work was proposed a targeted study of the impact of adversarial attacks on the clustering ability of ResNet50 under various adversarial scenarios. Was used ResNet50 as the basis for the experiments, a widely used architecture known for its effectiveness in image classification. This network was subjected to various adversarial attacks in order to understand how these perturbations affect its clustering capabilities. By thoroughly examining the resultant clustering outcomes under different attack scenarios, we aim to uncover vulnerabilities and nuances inherent in clustering algorithms and similarity metrics when confronted with adversarial input.

The digital world is increasingly invading our reality, which leads to the formation of a significant reflection of the processes and activities taking place in the smart city. Such activities include well-known urban events, celebrations, and those with a very local character. Due to the mass occurrence, events have a comparable influence on the formation of the spirit and the urban atmosphere. This work presents an enhanced semantic version of the ConvTree algorithm - SemConvTree. It allows considering the semantic component of the data obtained by using semi-supervised learning of topic modeling ensemble (consisting of improved models BERTopic, TSB-ARTM, SBert-Zero-Shot). We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results lead in the long term to talk about the potential perspective in creating a semantic platform for the analysis and monitoring of urban events in the future.

The Klaten Regency, Central Java Province, Indonesia is an leptospirosis endemic area. This study’s purpose is to spatially describe the abiotic and biotic environmental factors with the Leptospirosis incidence in the Klaten Regency in 2018. This was a descriptive observational with cross-sectional approach conducted in the Klaten Regency, Central Java, in 2019 with 59 respondents. The results revealed that the percentage of abiotic environment factors such as poor waste disposal facilities, poor gutter conditions, river <200 meters, and flooding history, namely 35.6%; 41.2%; 54.2%; and 6.8%, respectively. The highest leptospirosis cases occurred in April 2018 with 325 mm of rainfall, average temperature of 270C, average humidity of 82.3%, and altitude of 100-200 MASL (79.7%). Meanwhile, biotic factors included rat nest existence (100%), having pets at risk (32.2%), and ≥3 types of vegetation (79.7%). In conclusion, most respondents' dominant environmental factors were presence of rat nests, ≥3 vegetation types, and river availability <200 meters.

The dynamic and unpredictable nature of modern supply chains necessitates the development of resilient systems capable of withstanding various disruptions. This qualitative study examines the impact of collaborative practices on supply chain resilience, providing an in-depth analysis of their contributions to the robustness and adaptability of supply chains. Through extensive interviews with industry professionals, including supply chain managers, logistics coordinators, and strategic planners, the study identifies key collaborative practices—such as information sharing, joint decision-making, and coordinated response strategies—that significantly enhance resilience. These practices facilitate proactive risk management, improve response times during disruptions, and foster innovation through shared knowledge and resources. Thematic analysis of the collected data reveals that strong relationships among supply chain partners and integrated approaches that leverage each participant's strengths are crucial for effective risk mitigation and recovery. Additionally, the study underscores the role of technology in enabling real-time information sharing and decision-making, which are essential for effective collaboration. The findings highlight the importance of investing in robust collaborative networks and adopting supportive technologies to enhance communication and coordination. This research contributes to the existing body of knowledge by offering a nuanced understanding of how strategic implementation of collaborative practices can build more resilient supply chains. It provides practical recommendations for businesses seeking to improve their resilience, emphasizing the qualitative aspects of collaboration and its impact on supply chain management. Overall, this study presents a comprehensive exploration of how collaboration can bolster supply chain resilience, offering valuable insights for practitioners and scholars focused on creating stable and sustainable supply chain operations in the face of growing uncertainties.

In the era of rapid digitalisation, the widespread integration of digital technology into various aspects of daily life has sparked significant interest in understanding its impact on cognitive mental processes. While the emerging data suggests that its influence may be positive or negative, the depth of evidence regarding neurobiological mechanisms remains limited. This review aims to synthesise previously published studies and explore the implications of digital technology components, specifically those resembling mobile technology, internet search engines, and conversational artificial intelligence models, on cognitive functions embedded in the brain’s ability to reorganise itself, known as neuroplasticity. Despite acknowledged limitations, through an exhaustive approach, this paper intends to offer a dynamic perspective on the effects of digital media on the human brain, before the onset of addiction.

This research raises questions about the possibilities and options of using BIM methodology for wood design and construction associated with software for structure modeling along the asset's cycle life. Likewise, several academic and research initiatives are reviewed. In this sense, this paper aims to establish an appropriate link between two agendas that the architecture, engineering, and construction (AEC) industry, academia, and governments normally handle separately. By conducting several literature reviews (book, journals, and congresses) and extensive software tests (BIM software: Revit, Archicad, Tekla, and Wood plug-ins: AGACAD, Archiframe, Timber Framing 2015, WoodStud Frame, etc.), the state-of-the-art was assessed in both fields, several cases linking BIM and wood are shown in detail and discussed, various theoretical samples are modelled and shown, and the advantages and disadvantages of each technique and stage are explained. On the other hand, although wood construction has been the most common for hundreds of years, this is not the case of BIM Modeling software developments associated with this materiality. Furthermore, since the appearance of materials such as steel and reinforced concrete, all software developments have focused on these materials, leaving aside the possibility of developing applications for use in wood projects. According to previously discussed, it can be concluded that BIM for Wood has been used more frequently in academia and that both fields have several common processes and, in many cases, only a few BIM-wood tools have been used. Thus, disregarding the high potential and high level of benefits that results with the application of these methodologies for the complete building life cycle (design, construction, and operation).

The Yang-Mills conjecture addresses the fundamental nature of the strong force, one of the four fundamental forces in nature, responsible for binding quarks together to form protons, neutrons, and other hadrons. It was proposed in the early 1950s, the Yang-Mills suggests a mathematical framework for describing the behaviours of gauge fields, which are the carriers of fundamental forces. In particular, it focuses on non-abelian gauge theories, where the gauge fields themselves interact with each other. The non-abelian aspect refers to the fact that the order of operations matters when combining these gauge transformations, leading to a more intricate structure compared to abelian theories. This conjecture posits the existence and significance of certain mathematical structures, known as Yang-Mills fields, which play a crucial role in the formulation of the Standard Model of particle physics. The Standard Model describes the electromagnetic, weak, and strong nuclear forces, as well as the elementary particles that constitute matter. Despite its foundational importance, proving the Yang-Mills conjecture has proven to be a formidable challenge. The conjecture is intimately connected to the concept of mass gap, which refers to the idea that the particles involved in the strong force interactions exhibit mass, in contrast to the photon in electromagnetism, which is massless. The successful resolution of the Yang-Mills conjecture is expected to deepen our understanding of the nature of particle physics and may provide insights into the unification of fundamental forces. In this regard we hereby provide solutions to solve Yang-Mills conjecture using the method of Self Information Entanglement and Quantum Information Transport Equations to elucidate the origin of Universe from Unperceivable Information called “Avyaktham” through the emergence of perceivable information “Vyaktham”. For the first time in the world we provided an empirically verifiable solution to the origin of emergence and existence of Universe based on the methods to predict the emergence using working model of clinical Ayurveda. It is computed that the value of mass gap mentioned in Yang-Mills conjecture is around mYM = mbit ≈ 1.06×10⁻⁴¹kg.

The Topographic Ruggedness Index (TRI) is one of the essential measurement in topographic analysis, as well as in explaining biodiversity and geo-diversity. This study demonstrate how to utilize Geographic Information Systems (GIS) to generate TRI for Nyanga District using Digital Elevation Model (DEM). Though the TRI strongly depends on a local scale slope derived from an average adjacent neighbor slope algorithm, and selection of different lag distances in the computation of spatial variability, the measurement demonstrated that areas of higher altitude have higher ruggedness index. The TRI model was generated by computing focal statistics to determine focal sum of DEM and of DEM ². The final TRI was computed using Math Algebra (Raster Calculator) to generate layers of different ruggedness suitable for topographic and biodiversity analysis. The algorithm presented could also be used for smaller areas with high quality data and corrected DEMs. Despite widespread adoption, ruggedness metrics require thorough testing using both artificial landscapes and real world applications.

Artificial intelligence (AI) chatbots are the latest advance in information technology. They are next-word predictors built on large language models (LLM) that offer the possibility to process and generate information. In this theoretical article, we provide educational insights of the possibilities and challenges of educational usage of AI chatbots. The insights were produced in the context of chemical information-seeking activities designed for chemistry teacher education. The analysis was conducted via a SWOT approach using technological pedagogical content knowledge framework (TPACK) to improve the accuracy. The analysis revealed several internal and external possibilities and challenges. The key insight is that AI chatbots will change the way people interact with information. For example, they enable the building of personal learning environments with ubiquitous access to information and AI tutoring. Their ability to support chemistry learning is impressive. However, processing of chemical information reveals the limitations of current AI chatbots not being able to process multimodal chemical information. There are also ethical issues to address. Despite the benefits, wider educational adoption of AI chatbots will take time. The obstacles hindering the adoption of AI chatbots can be removed, for example, through integrating LLMs to curricula, focusing on open-source solutions, and training teachers with modern information literacy skills. This research presents theory-grounded examples of how to support the development of modern information literacy skills in chemistry teacher education. Based on the conducted analysis, we predict that AI chatbots will be a major technological change agent towards inclusive and equitable quality lifelong learning for all.

Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.

This research describes a detailed analysis in the enactment of a Lean Tool, the Last Planner System during pandemic period which helps in optimizing the resources for better coordination among all stakeholders in pandemic period. LPS as it is known, focusses on minimizing the factors such as uncertainties, barriers and variability to make construction projects more flexible for better project management. These include variations and deviations, supervision, delays in approvals, change resistance, subcontractor dedication which are all related to various stakeholders in the project. Following that, a Design Science Research technique is used to evaluate the effect of applying LPS in buildings. An action strategy is being used to attain this goal, and four case studies were being documented which were concerned in the implementation of LPS in the building of the Boy's Hostel, Research Scholar Quarters, Faculty Housing, and Girls' Hostel at Chennai, Tamilnadu, India. Data was gathered by observation of site activities, interviews, documentation analysis, and a questionnaire survey and grouped into various factors. While adopting the LPS tool in the research the factors affecting the implementation were found in the covid-19 pandemic period. Further these factors were anlayzed, measured, ranked and validated for adopting in projects.

In recent years, how to combine intelligent prospecting algorithms such as random forest with a large number of geological and mineral data for quantitative prediction of exploration geochemistry has become an important topic of concern to quantitatively improve the accuracy of target delineation. The ore-forming geological conditions in the central Kunlun area of Xinjiang are great and have good prospecting prospects. However, due to the exhaustion of shallow deposits and the lag of geological prospecting work in the past ten years, there has been no expected breakthrough in the search for large and super-large metal deposits for many years. There has been a serious shortage of reserve resources. The use of new theories, new methods and new technologies for mineral resources investigation and evaluation has become an urgent need in the current prospecting work. In view of this, based on the existing spatial database of geological and mineral resources in the central Kunlun of Xinjiang, combined with the geological characteristics, genesis and metallogenic regularity of the area, this paper carried out a series of studies on gold polymetallic minerals with the help of geographic information system and data science programming software platform. The researchers integrated geological and regional geochemical data, and constructed a random forest metallogenic discriminant model based on two different sampling methods (integrated random undersampling and selection of training samples) to predict the mineralization of gold polymetallic minerals in the central Kunlun area of Xinjiang and delineate the metallogenic target area. The quantitative prediction of gold polymetallic mineral resources in the central Kunlun area of Xinjiang by two random forest models is compared and discussed: the known ore spots, fault structures and geochemical information are extracted, and the known gold polymetallic ore spots and geochemical data are used to form a training set and a prediction set to construct a machine learning random forest model. The results of prediction evaluation and metallogenic prospect division show that for different sampling methods, the performance evaluation parameters of the training process show that the prediction accuracy of the selected training samples is higher, and the selected training samples are more reliable because they can fully learn the complex information of the original data. In the metallogenic prospect prediction and metallogenic potential division, the random forest model of selecting training samples has more reference value and further exploration research significance in the production problem considering the actual exploration cost because of its small area of high potential prediction area and high proportion of ore bearing per unit area. At the same time, this study innovatively improves the prediction accuracy, reduces the exploration risk, and expands the prospecting idea of machine learning algorithm in mathematical geology in the central Kunlun area of Xinjiang. The delineated metallogenic potential area has positive guiding significance for the actual gold polymetallic prospecting work in this area.

To solve the problem of low efficiency of source seeking in implicit information field, this paper presents an autonomous souring method based on balanced searching strategy is enlighten by biological homing behaviors. At the beginning of the research, the source seeking task come down to a multi-objective convergence issue. By taking the feasibility search behaviors as the individual sample of the evolutionary population drawing on the thought of evolutionary algorithms, then combine motion searching with population evolution, so the carrier can be guide to complete source seeking task by solving the multi-objective problem. Furthermore, the distribution entropy is also considerated d to measure the searching bias in the process of source seeking. Combined with the demand of the source seeking process, a new source seeking method of balanced searching strategy is designed. Ultimately, through theoretical analysis and simulation verification confirm the effectiveness and rationality of the proposed method.

Building Information Modelling (BIM) has been more popular in the construction industry as a consequence of the many advantages it offers, such as increased productivity, improved project outcomes, and reduced expenses. However, there are many obstacles that must be overcome before BIM can be widely used. This research uses a poll of construction industry experts to investigate the challenges associated with using BIM in projects. Cost of implementation, inadequate standards and procedures, and a lack of knowledge with BIM were some of the hurdles that were measured in the study. Statistical techniques were used to examine the data, and the findings indicate that the high cost of implementation and the absence of sufficient standards and procedures are the two main obstacles to the widespread adoption of BIM. Problems with supply chain management and a lack of experience with BIM are also mentioned as obstacles. The report also suggests solutions, such as a more focused teaching effort on BIM-related issues and the creation of a specialised BIM curriculum. Professionals in the construction industry may utilise the study’s results to their advantage by expanding their knowledge of the obstacles standing in the way of widespread adoption of BIM.

The information data accuracy affects the working reliability of production management system directly. Mobile equipment is an important part of integrated manufacturing system, in the course of its works, due to the complex working environment and the dynamic change of its position, it lead to the important problem of information detection distortion. Because the application of fractional differential operator has the dual functions of enhancing signal strength and reducing data error, we puts forward a data processing method of obtaining production information data of various accuracy by changing the parameters of fractional differential operator. Establish a mobile equipment detection data fusion processing model based on fractional order partial differential equations and apply it in the processing experiment of mobile equipment testing information detection data in an integrated manufacturing system, the feasibility and effectiveness of the method are verified. We conclude that the fractional order partial differential equations used in the processing of mobile equipment monitoring information data of an integrated manufacturing system has the function of obtaining production information data of various accuracy, which are of great significance to improve the working reliability in integrated manufacturing system. which is of great significance to improve the working reliability and scientific decision of integrated manufacturing system.

Human motor planning combines such interesting aspects as modular organization in motor primitives and characteristics of stochastic optimality. Motor primitives aided in the description of motor skill learning. In this study, for the first time, an interpretation is proposed to explain how motor primitives are shaped in a stochastic optimality process.To this end, sensory information is assumed to be random variables at each moment. Theoretically, the results confirmed that the variability of a movement increased with passing time. As a result, motor planning should be performed in shorter durations to achieve an accurate movement. It means that a task should be divided into several subtasks at various stages of time. Moreover, theoretically, the results confirmed that motor planning is statistically optimal if the movement is planned in minimum jerk space, which is in accordance with what is reported in the literature. Comparing the proposed mathematical model with the results of experimental data in two different types of motor action (i.e., arm reaching movement and sit-to-stand transfer from a chair) confirmed that the proposed model can be considered a framework to investigate the effect of bottom-up information on human motor planning level. Further experiments need to investigate it more deeply.

Big data analytics tools are the use of advanced analytic techniques targeting large and diverse volumes of data that include structured, semi-structured, and unstructured data from different sources and in different sizes from terabytes to zetabytes. The health sector is faced with the need to generate and manage large data sets from various health systems, such as electronic health records and clinical decision support systems. This data can be used by providers, clinicians, and policymakers to plan and implement interventions, detect disease more quickly, predict outcomes, and personalize care delivery. However, little attention is paid to the connection between big data analytics tools and the health sector. Thus, a systematic review of the bibliometric literature (LRSB) was developed to study how the adoption of big data analytics tools and infrastructures will revolutionize the healthcare industry. The review integrated 77 scientific and/or academic documents indexed in SCOPUS presenting up‐to‐date knowledge on current insights on how big data analytics technologies influence the healthcare sector and the different big data analytical tools used. The LRSB provides findings related to the impact of Big Data analytics on the health sector by introducing opportunities and technologies that provide practical solutions to various challenges.

Smart devices such as smartphones, smartwatches, etc. are promising platforms that are being used for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities due to inter-class pattern similarity, which occurs when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on the traditional sensors such as accelerometer and gyroscope, which are inbuilt in these devices. Therefore, apart from using information from the traditional sensors, these systems lack contextual information to support automatic activity recognition. In this article, we explore environment contexts such as illumination(light conditions) and noise level to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Networks and Long Short Time Memory(CNN_LSTM) learning models. The models performed sensor fusion by augmenting the low-level sensor signals with rich contextual data to improve recognition and generalisation ability of the proposed solution. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used inertial sensing data whilst the second set of extensive experiments combined inertial signals with contextual information from environment sensing data. Obtained results demonstrate that contextual information such as environment noise level and illumination using hybrid deep learning models achieved better recognition accuracy than the traditional activity recognition models without contextual information.

Due to the shortage of agricultural labor forces and rapid aging of farmers, the utilization of tractors is becoming popular and essential in Korea. Tractors can be classified into two types, a walking tractor called as a power tiller and a riding tractor. In this study, agricultural tractors including walking and riding types were categorized into 4 levels by rated output power. And diesel emission inventory of tractors was established and analyzed using 2011 and 2019 survey data in Korea. Emission inventory including CO, NOx, SOx, TSP(PM10), PM2.5, VOCs and NH3 were established using Tier 3 methodology. The total amount of emission using agricultural tractors was decreased about 13% from 2011 to 2019. The number of walking tractors were decreased by about 19% in 8 years, on the other hand that of riding tractors were increased by about 12%. However, the emission reduction is about 48% for walking tractors and the emission increment is about 5% for riding tractors. Thus, the total emission from agricultural tractors was decreased by about 16% in those periods. It is due to the decrease of 21% and 15% in the hours of use of walking and riding tractors, respectively, in 2019. Walking tractors mainly emit air pollutants from spraying and transporting. Riding tractors mainly 61% of total air pollutants emits from soil preparation and transporting operations. Geographic information system (GIS) was used to spatially assign air pollutants variables into 17 provinces and metropolitan cities in Korea. High emission generating regions and changes of emissions during 8 years were clearly seen in GIS analysis. High air pollutant emitting regions are mainly located in the western and southern regions of Korea, which have plenty of arable areas compared to other regions in Korea.

The adoption of health information technology (HIT) has increased considerably, contributing to better communication between physicians and patients and providing technological bases for learning and institutional improvement. This type of technology brings many challenges; therefore, understanding its adoption and assimilation is important to assess its potential for engendering desirable outcomes in health management. The assimilation of health information systems should be highlighted as their importance in health organisations is now recognised as a key facilitator assisting in providing better health outcomes. Thus, this study aimed to analyse HIT adoption based on models such as Technology, Organisation and Environment (TOE), which analyses at the organisational level, with other models, such as the Technology Acceptance Model (TAM), which analyses at the individual level, and the assimilation of the adopted technologies.

Neural decoding from spiking activity is an essential tool for understanding the information encoded in population neurons, especially in applications like brain-computer interface (BCI). Various quantitative methods have been proposed and have shown superiorities under different scenarios respectively. From the machine learning perspective, the decoding task is to map the high-dimensional spatial &amp; temporal neuronal activity to the low-dimensional physical quantities (e.g., velocity, position). Because of the complex interactions and the abundant dynamics among neural circuits, good decoding algorithms usually have the capability of capturing flexible spatiotemporal structures embedded in the input feature space. Recently, the Transformer-based models are widely used in processing natural languages and images due to its superior performances in handling long-range and global dependencies. Hence, in this work we examine the potential applications of Transformers in neural decoding and introduce two Transformer-based models. Besides adapting the Transformer to neuronal data, we also propose a data augmentation method for overcoming the data shortage issue. We test our models on three experimental datasets and their performances are comparable to the previous state-of-the-art (SOTA) RNN-based methods. In addition, Transformer-based models show increased decoding performances when the input sequences are longer, while LSTM-based models deteriorate quickly. Our research suggests that Transformer-based models are important additions to the existing neural decoding solutions, especially for large datasets with long temporal dependencies.

The ignorance of or lack of knowledge about cybersecurity aspects causes a critical problem regarding confidentiality and privacy. This security problem will continue to exist even if the user possesses less expertise in information security. The modern IT technologies are well developed, and almost everyone uses the features of IT technologies and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures. However, it is not possible to entirely avoid cybercrimes. Cybercrimes often lead to sufficient business losses and spread forbidden themes (hatred, terrorism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed web-based cybersecurity awareness program trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses the Blockchain technology to protect the model from the potential threats. The proposed model is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users.

The education system has become even more complex following the global pandemic, which saw face-to-face teaching transition to virtual teaching. To cope with this abrupt transition, it is essential that teachers had a sufficient level of digital teaching competence. This article aims to increase awareness of teachers' self-perception of their digital teaching competences in the educational field. Specifically, this study explores Spanish secondary school teachers’ knowledge and use of different ICT tools by evaluating their competence based on different areas proposed by The National Institute of Technology and Professional Development (Instituto Nacional de Tecnologías Educativas y de Formación del Profesorado, INTEF), Madrid, Spain. From the results of the questionnaires, we have determined that teachers consider themselves to have an upper intermediate level of digital teaching competence, although there are still shortcomings that need to be addressed in order to improve this level of competence, and its true integration in the teaching–learning process.

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

El objetivo del siguiente artículo es presentar algunos conceptos para entender un poco más sobre qué es la inteligencia empresarial, cuál es su significado y para qué sirve, cómo ha influido en la vida de las empresas para crear sus estrategias de negocio y por qué esta herramienta . nos ayuda a tomar decisiones en una organización.

In this paper, the definition of information entropy of Smoluchowski coagulation equation for Brownian motion is introduced based on coagulation probability. The expression of entropy is the function of geometric average particle volume and standard deviation with lognormal distribution assumption. The asymptotic solution with moment method shows that the entropy is a monotone increasing function of time, which is equivalence to the entropy based on particle size distribution. the result reveals that the present definition of entropy of Smoluchowski coagulation equation are inadequate because the particle average volume at equilibrium cannot be determined from the principle of maximum entropy. This provides a basis for further exploring the global properties of Smoluchowski coagulation equation.

As a complex system, our body acts as a whole system connected to the environmental incitements. It is ordered, coherent, and tries to maintain the least possible entropy, saving the greatest amount of energy. In order to explain the dynamics of the systemic regulative network, a theoretical and speculative model is proposed, with a comprehensive approach that allows seeing the entire regulative system as a continuous unicuum. This paper covers two themes: 1) the connections between the quantum level and the classical one, through some principles of the QFT and through the Coherence Domains. The system is modeled as a field described by the wave function, with synchronous and consistent events, driven in a global computing by the quantum potential Q. The quantum potential implies the non-locality, and it needs only ultra-weak waves to occur, so it may explain how the rapid and global activation of the organism in response to perturbation/punctiform information works. The initial hypothesis is that some consistent quantum phenomena are amplified through the systemic regulative network until they become macroscopic observable. This is possible because of Coherence Domains. 2) The reactions of the different systemic networks to perturbations/punctiform information, with the attempt to model and measure information in biology, going beyond the Shannon and Turing theories. Hopfield Networks and an informational point of view are used to address the crucial informational and organizational role of proteins and nucleic acids.

The National Breast and Cervical Cancer Early Detection Program (NBCCEDP) of Minnesota, “Sage”, provides breast cancer screening to uninsured women. We introduce a novel mapping technique, spatially adaptive filters (SAFs), to estimate utilization of Sage screening in Minnesota. Sage screenings (N = 74,712) were geocoded. The eligible population was modeled with the RTI synthetic population dataset. Between 2011 and 2015, 36,979 women a year were Sage eligible. Utilization was highly variable across Minnesota (M = 37.2%, range 0% - 131%, SD = 18.7%). This replicable approach modeled utilization rates to the neighborhood-level, allowing Sage to prioritize locations and engage communities.

In this paper, using the discrete time model, we consider the average age of all files for a cached-files-updating system where a server generates N files and transmits them to a local cache. In order that the cached files are fresh, in each time slot the server updates files with certain probabilities. The age of one file or its age of information (AoI) is defined as the time the file stays in cache since it was last time sent to cache. Assume that each file in cache has corresponding request popularity. In this paper, we obtain the distribution function of the popularity-weighted average age over all files, which gives a complete description of this average age. For the random age of single file, both the mean and its distribution have been derived before by establishing a simple Markov chain. Using the same idea, we show that an N dimensional stochastic process can be constituted to characterize the changes of N file ages simultaneously. By solving the steady-state of the resulting process, we obtain the explicit expression of stationary probability for an arbitrary state-vector. Then, the distribution function of the popularity-weighted average age can be derived by mergering a proper set of stationary probabilities. For the possible applications, the distribution function can be utilized to calculate the probability that the average age violates certain statistical guarantee.

Integrated information theory (IIT) was initially proposed to describe human consciousness in terms of intrinsic-causal brain network structures. This theory could potentially be used for conceptualising complex living systems. In a previous study, we analysed collective behaviour in {\it Plecoglossus altivelis}. We found that IIT 3.0 exhibits qualitative discontinuity between three and four schools of fish in terms of $\Phi$ values (i.e., group integrity). Other measures, such as mutual information, did not show such characteristics. In this study, we follow up on our previous findings and introduce two new factors. First, we define the global parameter settings to determine a different kind of group integrity. Second, we set several timescales (from $\Delta t =5/120$ s to $\Delta t =120/120$ s). The results showed that we succeeded in classifying fish school according to their group size in terms of the degree of group integrity, despite the small group size. The concrete classification includes the followership for a two-fish school, fission--fusion for a three-fish school, emergence of leadership for a four-fish school, and emergence of Boid-like behaviour for a five-fish school. These minute classifications have never been revealed before. Finally, we discuss one of the longstanding paradoxes in collective behaviour, known as the heap paradox, for which two tentative answers could be provided through our IIT analysis.

The importance of ICTs for dissemination of information to farmers has been verified by extension practitioners, information and communication scholars, policy makers and development agencies. Information related to new seed varieties, production technologies, livestock breeds, vaccines, including weed and pest control, as well as relevant market information is constantly required by smallholder farmers. The identification of specific attributes among smallholder farmers which contributes to their adoption of a proposed ICT-based information source provides an important tool for developing interventions which address the information needs of farmers. Using a literature survey methodology, pertinent studies related to adoption of ICTs, farmers’ information source usage and relevant frameworks were identified, including applicable theories and models in technology adoption and information behaviour. In the proposed framework, the socio-economic characteristics of smallholder farmers were posited as key variables influencing smallholder farmers, within a farming system, to adopt ICT-based information sources. The framework contributes to discern the prospects of adopting ICT-based information sources by individual farmers within a farming system, and may also envisage other related welfare outcomes and market participation pathways among smallholder farmers. The review also addresses the paucity of conceptual discourse, while contributing to a growing pool of research on ICT in African agriculture.

In this work, we present a model of the atom that is based on a nonclassical logic called paraconsistent logic (PL), which has the main property of accepting the contradiction in logical interpretations without the conclusions being annulled. The proposed model is constructed with an extension of PL called paraconsistent annotated logic with annotation of two values (PAL2v), which is associated with an interlaced bilattice of four vertices. We use the logarithmic function of the Shannon entropy H_(s) to construct the paraconsistent equations and thus adapt a probabilistic model for representations in quantum physics. Through analyses of the interlaced bilattice, comparative values are obtained for some of the phenomena and effects of quantum mechanics, such as superposition of states, quantum entanglement, wave functions, and equations that determine the energy levels of the layers of an atom. At the end of this article, we use the hydrogen atom as a basis of the representation of the PAL2v model, where the values of the energy levels in six orbital layers are obtained. As an example, we present a possible method of applying the PAL2v model to the use of Raman spectroscopy signals in the detection of lubricating mineral oil quality.

The Process Algebra model has been shown to provide an alternative mathematical framework for non-relativistic quantum mechanics (NRQM). It reproduces the wave functions of non-relativistic quantum mechanics to a high degree of accuracy. It posits a fundamental level of finite, discrete events upon which the usual entities of NRQM supervene. It has been suggested that the Process Algebra model provides a true completion of NRQM, free of divergences and paradoxes, with causally local information propagation, contextuality and realism. Arguments in support of these claims have been mathematical. Missing has been an ontology of this fundamental level from which the formalism naturally emerges. In this paper it is argued that information and information flow provides this ontology. Higher level constructs such as energy, momentum, mass, spacetime, are all emergent from this fundamental level.

This study contributes to the literature by exploring challenges to implementing ISO 20000-1 in an emerging economy context, and suggests ways to overcome these challenges. A survey-based methodology was adopted. The data were analyzed using principal component analysis. The results indicated that senior management support was the most significant challenge for the successful implementation of IT Service Management (ITSM) systems. Other significant challenges were the justification of significant investment, premium customer support, co-operation and co-ordination among IT support teams, proper documentation, and effective process design The findings help managers introduce IT service management system (ISO 20000-1:2011) as well as improving IT service delivery system in IT support organizations for managing big data in an emerging economy. In the future, cross-firm and cross-country studies on challenges to ISO 20000 can be conducted. Also, interpretive structural model (ISM) can be formulated to examine the interrelationships among the identified challenges to ISO 20000.

An article's tone and framing not only influence an audience's perception of a story but may also reveal attributes of author identity and bias. Building upon prior media, psychological, and machine learning research, this neural network-based system detects those writing characteristics in ten news agencies' reporting, discovering patterns that, intentional or not, may reveal an agency's topical perspectives or common contextualization patterns. Specifically, learning linguistic markers of different organizations through a newly released open database, this probabilistic classifier predicts an article's publishing agency with 74% hidden test set accuracy given only a short snippet of text. The resulting model demonstrates how unintentional 'filter bubbles' can emerge in machine learning systems and, by comparing agencies' patterns and highlighting outlets' prototypical articles through an open source exemplar search engine, this paper offers new insight into news media bias.

Considering the predictions from the standard model of particle physics coupled with experimental results from particle accelerators, we discuss a scenario in which from the infinite possibilities in the Lie groups we use to describe particle physics, nature needs only the lower dimensional representations - an important phenomenology that we argue indicates nature is code theoretic. We show that the quantum deformation of the SU(2) Lie algebra at the fifth root of unity can be used to address the quantum Lorentz group representation theory through its universal covering group and gives the right low dimensional physical realistic spin quantum numbers confirmed by experiments. In this manner we can describe the spacetime symmetry content of relativistic quantum fields in accordance with the well known Wigner classification. Further connections of the fifth root of unity quantization with the mass quantum number associated with the Poincaré Group and the SU(N) charge quantum numbers are discussed as well as their implication for quantum gravity.

Integrated information theory (IIT) was initially proposed to describe human consciousness in terms of intrinsic, causal brain-network structures. This theory has the potential to be useful for conceptualising complex living systems. In a previous study, we analysed collective behaviour in Plecoglossus altivelis and found that IIT 3.0 exhibits qualitative discontinuity between three and four fish. Other measures like mutual information did not have such characteristics. In this study, we followed up on our previous findings and examined timescale effects on integrated information of collective behaviour. We found that a long timescale (1 s) causes Boid-like local interactions to dominate over interactions with the whole, but only when the group size is five at a given time scale (i.e. around 0.2 s). Interestingly, the most suitable time scale is roughly equal to fish reaction time. We used these data to propose a new classification for fish schools, with each size group being a unique and autonomous system, despite small group sizes.

Many pilot-based initiatives have been developed to promote awareness and use of climate information services among vulnerable smallholder farmers in Africa through million-dollar investments. However, despite their experimental nature, these pilot projects have been successful in raising participating farmers’ awareness and use of climate information services and they can inform transferrable good practices. Through a systematic literature review approach, this review sought to understand ways in which these past pilot projects have contributed to climate risk management in the context of smallholder farming and the factors that led to their success. Results showed that climate information services main contribution to climate risk management has been through facilitating farm level decision making. Factors that led to success of the pilots include: use of downscaled information; building institutional partnerships to add value to climate information; involving farmers through the co-designing and co-developing process; face-to-face way of communication; embedding pre-seasonal workshops in the activities of local institutions for sustainability; using diversity of communication channels to enhance reach among others. These factors can be borrowed as good practices to inform future efforts focused on increasing adoption of climate information services among a wider population beyond pilot project reach.

A new approach is presented to defining the amount of information, in which information is understood as the data about a finite set as a whole, whereas the average length of an integrative code of elements serves as a measure of information. We obtain a formula for the syntropy of reflection, that is, the information that two intersecting finite sets reflect about each other. The specificities of reflection of discrete systems through the combination of their parts are considered, and it is shown that the additive syntropy and entropy of reflection are measures of structural order and chaos. Three information laws have been established: the law of conservation of the sum of chaos and order; information law of reflection; the law of conservation and transformation of information. An assessment of the structural organization and the level of development of discrete systems is presented. It is shown that various measures of information are structural characteristics of integrative codes of elements of discrete systems. The conclusion is made that, from the informational-genetic positions, the synergetic theory of information is primary in relation to the Hartley-Shannon information theory. In the appendix we consider the asymmetry of the mutual reflection of finite sets and the proportionality of arbitrary quantities.

This research aims to determine the influence of the independent commissioners, audit committee, institutional ownership, firm size and leverage against the integrity of the financial reporting information. This research is quantitative research with the causal approach. This study uses secondary data and panel data regression analysis method. The research results prove that audit committee, institutional ownership and leverage effect on the integrity of the financial reporting information. But it does not prove that the independent commissioner and firm size effect on the integrity of the financial reporting information.

The purpose of interdisciplinary communication during the early architectural design stage is to achieve the early integration of knowledge in different professional fields, which can help architects to choose correct design development strategies during the early architectural design stage. However, because there is too little information at the early design stage, and design solutions are still rapidly changing and developing, the uncertainties at this stage make it difficult for consultants in other disciplines to provide their views and analysis. In spite of this situation, the emergence of generative modeling is changing design procedures and methods of communication and cooperation for architectural teams, and has brought about a shift in the way architects transmit design information from "what" (declarative information) to "how" (procedural information). Generative modeling is like an aircraft's dashboard: It can provide a basis for interdisciplinary communication, provide interdisciplinary knowledge packages, and bring about a shift in interdisciplinary communication that will reduce the architectural team's communication needs and cost. This study uses a real design case to show the feasibility of generative modeling. Employing generative modeling as a basis, architects can enhance the efficiency of design change and multi-disciplinary communication during the early design stage, integrate specialized knowledge in relevant fields, use this knowledge to formulate design criteria for the next stage, and effectively transmit design decisions. As a consequence, the changes to the cost structure of design revisions and communication between different disciplines has initiated a paradigm shift toward multi-disciplinary communication in architectural design.

In the contemporary construction management, poor project performance, cost overrun, and low-quality construction have become a central point of attention for improving project performance including information, interaction and communication management. virtual design and construction and building information modelling play indispensable role in improving the project performance. The aim of this study is to propose a Virtual Information Modeling as a novel information and communication technology method with potential to overcome problems in the construction industry. Using one-sample t test with Kolmogorov-Smirnov approach, we analysed data collected from structured questionnaire survey involving project managers working at architecture, engineering and construction industries in Tehran, Iran. The results suggest that the most important benefits and challenges of using Virtual Information Modeling are related to the area of integration, and implementation of Virtual Information Modeling has the greatest impact on this area of project management knowledge. Thus, integration should be considered a top priority for construction companies implementing Virtual Information Modeling in their projects. Policymakers should understand that careful management of virtual information is essential for improving the performance of projects and enhancing the process efficiency.

Dictionary Learning (DL) and Sparse Representation (SR) based Classifier have impacted greatly on the classification performance and has had good recognition rate on image data. In Video Semantic Analysis (VSA), the local structure of video data contains more vital discriminative information needed for classification. However, this has not been fully exploited by the current DL based approaches. Besides, similar coding findings are not being realized from video features with the same video category. Based on the issues stated afore, a novel learning algorithm, called Sparsity based Locality-Sensitive Discriminative Dictionary Learning(SLSDDL) for VSA is proposed in this paper. In the proposed algorithm, a discriminant loss function for the category based on sparse coding of the sparse coefficients is introduced into structure of Locality-Sensitive Dictionary Learning (LSDL) algorithm. Finally, the sparse coefficients for the testing video feature sample are solved by the optimized method of SLSDDL and the classification result for video semantic is obtained by minimizing the error between the original and reconstructed samples. The experiment results show that, the proposed SLSDDL significantly improves the performance of video semantic detection compared with the comparative state-of-the-art approaches. Moreover, the robustness to various diverse environments in video is also demonstrated, which proves the universality of the novel approach.

At present, medical institutes generally use EMR to record patient's condition, including diagnostic information, procedures performed and treatment results. EMR has been recognized as a valuable resource for large scale analysis. However, EMR has the characteristics of diversity, incompleteness, redundancy and privacy, which make it difficult to carry out data mining and analysis directly. Therefore, it is necessary to preprocess the source data in order to improve data quality and improve the data mining results. Different types of data require different processing technologies. Most structured data commonly needs classic preprocessing technologies, including data cleansing, data integration, data transformation and data reduction. For semi-structured or unstructured data, such as medical text, containing more health information, it requires more complex and challenging processing methods. The task of information extraction for medical texts mainly includes NER (Named Entity Recognition) and RE (Relation Extraction). In this paper, we introduce the process of EMR processing, including data collection, data preprocessing, data mining, evaluation and knowledge application, analyze the current status of the key technologies, such as data preprocessing and data mining, and provide an overview of the application domains and prospects of EMR mining technologies. Finally, we summarize the existing problems in the research of EMR mining, and review the development trends.

This paper formulates the classic Monty Hall problem as a Bayesian game. Allowing Monty a small amount of freedom in his decisions facilitates a variety of solutions. The solution concept used is the Bayes Nash Equilibrium (BNE), and the set of BNE relies on Monty's motives and incentives. We endow Monty and the contestant with common prior probabilities (p) about the motives of Monty, and show that under certain conditions on p, the unique equilibrium is one where the contestant is indifferent between switching and not switching. This coincides and agrees with the typical responses and explanations by experimental subjects. Finally, we show that our formulation can explain the experimental results in Page (1998) [12]; that more people gradually choose switch as the number of doors in the problem increases.

This study examines the moderating effect of trained human capital on the associations of accounting information systems, generalized auditing software, and technology adoption with the performance of external auditors among Saudi listed companies. This study used a survey-based methodology. The final sample consisted of 136 responses with an 8% response rate. Using Smart-PL4 analysis, the results showed that there is a positive correlation between accounting information systems and external auditors. Conversely, a negative relationship between generalized auditing software and external auditors has been documented. In addition, this study reported a negative association between technology adoption and external auditors. However, the results highlight the positive relationship between trained human capital and external auditors. Overall, this study provides valuable insights for accounting and auditing policymakers on how external auditors’ performance is influenced by several factors in the Saudi audit market.

Scientists are gradually becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines ranging from cosmology, physics, across chemistry, biochemistry, and ending in biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively-parallel, self-organized, emergent, error-resilient, computational environments?” A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. This all together is opening the crucial question whether there is existing a deeper, beyond the Turing machine theoretical description of massively-parallel computing. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python software GoL-N24. The perspective, future directions including applications in robotics and biology of this research are discussed in the light of known information.

This proposal advocates using energy states as fundamental units for quantum computer programming. Each programming unit is conceptualized as an energy state, manifested as an essence-unit. The essence-unit, defined as the minimal form encapsulating complete uniqueness or specificity, serves as the cornerstone of this paradigm. This approach allows recording various forms of matter on quantum computers in essence-units representing their energy states. Recording energy states is achieved through the creation of four distinct coherent potentials facilitated by quantum dots or crystals. It is crucial to note that these energy states, embodied in essence-units, cannot be subdivided. The study examines the intricate relationship between similarity, fractals, and uniqueness in quantum dot operations, elucidating their profound implications for information transfer efficiency. Normalized entropy quantifies the charge localization in quantum dot impurities in systems exhibiting distortions. Utilizing N-level recording and entropy-fractal dimension equivalence enables this paper to elucidate the potential of quantum dots in reducing transmission time and modeling complex systems. The proposed methodology represents a significant departure from the current state of quantum computing, offering unprecedented potential for overcoming previously intractable challenges.

Telemedicine is the use of Information and Communication Technology (ICT) for clinical health care from a distance. The exchange of radiographic images and electronic patient health information/records (ePHI/R) for diagnostic purposes has the risk of confidentiality, ownership identity, and authenticity. In this paper, a data hiding technique for ePHI/R is proposed. The color information in the cover image is used for key generation, and stego-images are produced with ideal case. As a result, the whole stego-system is perfectly secure. This method includes the features of watermarking and steganography techniques. The method is applied to radiographic images. For the radiographic images, this method resembles watermarking, which is an ePHI/R data system. Experiments show promising results for the application of this method to radiographic images in ePHI/R for both transmission and storage purpose.

With the urgent need of precise positioning faced by internet of things (IoT) applications, the universality and cost of indoor positioning devices become key factors. Since the 5G network has been widely deployed, new opportunity is brought by tightly fusing the traditional low-cost sensors, i.e., the magnetometer. In this study, using 5G channel state information (CSI) and geomagnetic data, a multi-input convolutional neural network (CNN) localization system was proposed. First, to generate a data tensor that is easy for CNN processing, the raw data was reconstructed individually. Then, to comprehensively incorporate the features of 5G CSI and geomagnetic strength data, the ReLU function was chosen as the activation function of the convolutional layer. After that, a multi-input CNN was trained using the incorporated geomagnetic strength and CSI amplitude in the off-line side, and the trained CNN was recorded as a location fingerprint, which can be used for the user position prediction. Finally, in the online side and using a multi-input CNN, the 2-D coordinates are estimated and tested indoors in a typical conference room scenario. The results showed that longer sampling time of fingerprint data result in better uniqueness of the reference point, while the data collection time of locating points does not need to be long. Taking the positioning efficiency and accuracy into consideration, a sampling time of 3s at the reference point and 0.2s at the locating point are recommended. The positioning accuracy using the proposed method was 1.41 m, with an improvement of 22.9% compared with the 5G positioning, and an improvement of 18.0% compared with the 5G and geomagnetic fusing positioning using single CNN.

Objective To assess the quality and readability of online information on holmium laser enucleation of the prostate in management of benign prostate hyperplasia using the most used search engine worldwide, google. Methods The google search term “Holmium laser surgery” and “enlarged prostate” was used and the initial 150 webpages were reviewed. Webpages that were paywall, scientific literature, and/or promoted advertisements were excluded. Quality assessment tools - DISCERN, QUEST, and JAMA criteria were used, while readability tools like FKG, GFI, SMOG, and FRE score were used. Two authors independently reviewed the included sites for further analysis; for any discrepancy, a third author would be involved. Results 107 webpages were included in the data analysis. The median DISCERN score was 42 out of 80 (IQR 35-49). The median JAMA score was 0 out of 4 (IQR 0-1). The median QUEST score was 9 out of 28 (IQR 9-12). Using the non-parametric ANOVA and post-hoc test, significant differences were identified between rankings of webpages. Sponsorship had no influence on quality of webpages. Overall readability level required a minimum reading level of grade 11. Linear regression analysis showed a higher rank webpage is a positive predictor for all three quality assessment tools. Conclusion The overall quality of online information regarding HoLEP is poor. We identify that top ranked google searches have a higher DISCERN score and is a positive predictor for DISCERN/QUEST/JAMA. Quality online information can be used to benefit patients but should be used in conjunction with consultation from a medical professional.

Ninety percent of the existing building stock in Europe was built before 1990. These buildings are in urgent need for a significant improvement of energy-efficiency through renovation. Regrettably, so far only five percent of renovation projects have been able to yield energy-saving at deep renovation level. State-of-the-art renovation solutions are available, but costly and lengthy renovation processes and incomprehensible technical complexities hinder the achievement of a wide impact at a European scale. This paper presents research on Plug-and-Play (PnP) technologies supported by Building Information Modelling (BIM) to provide affordable, interchangeable and quick-installation solutions to overcome the main barriers of building deep renovation.

This article reviews all the major stages in the origins of life. From the emergence of matter in the initial Big Bang to the modern, civilized human being. On an immaterial level, it is proposed and explained how consciousness necessarily takes precedence over matter. Next, we explain how consciousness, with its ability to process information, selected the water molecule to breathe life into the periodic table of elements. We also explain why the notion of entropy allows us to evolve: "Die Entropie der Welt strebt einem Maximum zu" (second principle) and, therefore, takes precedence over the notion of energy, which, on the contrary, encourages us to preserve what we have: "Die Energie der Welt bleibt konstant" (first principle). This is followed by a discussion of the importance of quantum coherence and the need to rely on a second quantization formalism for a proper understanding of the physical-biochemical properties of water. Moreover, throughout the argument developed on the best and most fundamental things science has to offer, care is taken to link this knowledge to the great philosophies of the West (Greece), the East (China, India) or even to practices of a shamanic nature (Africa, America). Hence, finally, the proposal to reconsider all musical practice within the framework of the diapason of water at a frequency of 429.62 Hz, as well as all therapeutic practice on the basis of seven clearly identified and established frameworks of thought.

There have been many discussions on the globalisation of SMEs, but it is true that there is not enough academic achievement after such the study of Born global (BG) ventures. The internationalisation of SMEs (Small and Medium Enterprises) is not easy because they lack resources or capabilities compared to multinational corporations. This study investigated the role of government in assisting the internationalisation of SMEs. In particular, SMEs lacked the ability to acquire market-oriented information, so we’ve established the scheme of efficient information support system for the internationalisation of SMEs. In other words, we proposed an information analysis system through the establishment of a relational database constructed for market-oriented information support. KISTI (Korea Institute of Science and Technology Information), which is one of the government-funded research institutes in the Republic of Korea, provided information support to the SMEs dealing with hydrazine related products. This study suggests this case for the market-oriented information support of the government in the internationalisation of SMEs. The research on information support of the government is meaningful in that it suggests a way to support SMEs in practical level.

Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital and probably appeared during biogenesis four billion years ago. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and protein molecules, drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: tRNAs, aaRS, mRNAs, and ribosomes. When assembled in the right order, the translation machine created biosynthetic polypeptides, a process that transferred information from mRNAs to proteins. This was the beginning of the prebiotic information age. The molecular attraction between tRNA and amino acids led to different stages of the translation machines and the genetic code. tRNA is an ancient molecule that designed and built mRNA for storing the information of its cognate amino acid. Each mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional. The origin of the genetic code is enigmatic; herein we propose an evolutionary explanation: the demand for a wide range of specific enzymes in the peptide/RNA world was the main selective pressure for the origin of information-directed protein synthesis. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and the adaptive theory. These three theories are compatible with our coevolution model of the translation machines and the genetic code. We suggest biosynthetic pathways as the origin of the specific translation machines which provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machines: GNC code developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to understand the prebiotic information system better, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the translation might have worked in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics such as barcoding, DNA mining, or DNA fingerprinting. We constructed the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. Using AnyLogic software we were able to simulate and visualize the entire evolution of the translation machines and the genetic code. The results indicate that the emergence of the information age from the peptide/RNA world was a watershed event in the origin of life about four billion years ago.

A critical aspect of coronary heart disease (CHD) care and secondary prevention is ensuring patients have access to evidence-based information. The purpose of this review is to summarise the guiding principles, content, context and timing of information and education that is beneficial for supporting people with CHD and potential communication strategies including digital interventions. We conducted a scoping review involving searching four databases (Web of Science, PubMed, CINAHL, Medline) for articles published from January 2000 to August 2022. Literature was identified through title and abstract screening by expert reviewers. Evidence was synthesised according to the review aims. Results demonstrated that information-sharing, decision-making, goal-setting, positivity and practicality are important aspects of secondary prevention and should be patient-centred and evidenced based with consideration of patient need and preference. Initiation and duration of education is highly variable between and within people, but hence communication and support should be regular and ongoing. In conclusion, text messaging programs, smartphone applications and wearable devices are examples of digital health strategies that facilitate education and support for patients with heart disease. There is no one size fits all approach that suits all patients at all stages and hence flexibility and a suite of resources and strategies is optimal.

This paper studies a multihop amplify-and-forward (AF) simultaneous wireless information and power transfer (SWIPT) relaying system. Each relaying node harvests energy using power splitting (PS) scheme from a part of its received signal to amplify and forward the rest the received signal to the next relay. Based on this system model and signal flow, we derived and solved the convex energy minimization problem with optimal PS ratio. The influence of processing cost was then investigated for the AF-SWIPT system with the decode-and-forward SWIPT as benchmark, where AF-SWIPT was found to be superior.

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers the practical application of the theoretical principles of converged security presented in the publication to a functional sample, deployed and tested in practical conditions of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the context of resilience assessment is defined. In the beginning, the general principles of security/ safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment method is presented for better understanding. Subsequently, PSIM and SIEM systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

This article propose a didactic, through games, tool based on information and communication technologies, in order to eliminate possible communication barriers and to promote the inclusion of students with hearing impairment in Physical Education classes. To this end, a dossier of traditional games has been developed. These are structured in turn into objectives, materials, organisation, graphic description and a QR code for each game. These codes are linked to different videos hosted on the YouTube platform, in which the explanation of the games, mentioned above, can be visualised graphically using sign language. The whole creative process is described in the article, as well as possible pedagogical applications of the use of the tools created for this purpose in other educational contexts.

With 500+ papers and 20+ books spanning many scientific disciplines, Mark Burgin has left an indelible mark and legacy for future explorers of human thought and information technology professionals. In this paper, I discuss his contribution to the evolution of machine intelligence using his general theory of information (GTI) based on my discussions with him and various papers I co-authored during the past eight years. His construction of a new class of digital automata to overcome the barrier posed by the Church-Turing Thesis, and his contribution to Super-Symbolic Computing with Knowledge Structures, Cognizing Oracles, and Structural Machines are leading to practical applications changing the future landscape of information systems. GTI provides a model for the operational knowledge of biological systems to build, operate, and manage life processes using 30+ trillion cells capable of replication and metabolism. The schema, and associated operations derived from GTI are also used to model a digital genome specifying the operational knowledge of algorithms executing the software life processes with specific purposes using replication and metabolism. The result is a digital software system with a super-symbolic computing structure exhibiting autopoietic and cognitive behaviors that biological systems also exhibit. We discuss here one of these applications.

The artificial intelligence chatbot ChatGPT can answer questions and solve tasks posed by the user within seconds. Using an exemplary task, this study investigates the reliability and con-sistency of the answers given by ChatGPT. For this purpose, it was analyzed whether the an-swers differ when a question is asked repeatedly and whether selected content categories and associated word combinations coincide with those from scientific literature. ChatGPT was asked 400 times, how the connectedness between humans and nature can be increased. The responses were classified into a categorization system and the most frequently occurring word combina-tions for each category were extracted. These word combinations were used for a search of re-lated scientific literature. In addition, the Normalized Mutual Information (NMI) between the 400 individual responses was calculated. The results show that the phrases used by ChatGPT within the categories are also found in the scientific literature, however, the number of mentions between scientific literature and ChatGPT differs. In addition, the NMI suggested that there are large differences between the categories mentioned in the individual answers. These results in-dicate that ChatGPT currently still has difficulties in answering complex questions and cannot yet replace scientific literature research or academic writing.

Facing unprecedented global declines in the extent and integrity of ecosystems, the 15^th UN Biodiversity Conference (COP-15) in Kunming, China, presents an opportunity for transformative change. However, a lack of consensus on two key issues – resource mobilization and Access and Benefit Sharing (ABS) associated with Digital Sequence Information (DSI) – risks stalling negotiations for an ambitious ‘Post-2020 Global Biodiversity Framework’ as the next 10-year strategic plan under the Convention on Biological Diversity. We highlight systemic misconceptions concerning the financing of biodiversity and the burden this places on the ABS system. In the context of DSI, we caution that conflating ABS with resource mobilization risks disrupting modern science policy built on open access, with potentially severe ramifications for scientific research and innovation. To resolve these tensions, we call for a recalibration of discussions on ABS in order to maximize the value delivered by biodiversity for all of society, including indigenous peoples and local communities.

Physical roots, exemplifications and consequences of periodic and aperiodic ordering (represented by Fibonacci series) in biological systems are discussed. The role and physical and biological roots of symmetry and asymmetry appearing in biological patterns is addressed. Generalization of the Curie-Neumann Principle as applied to biological objects is presented, briefly summarized as: “asymmetry is what creates a biological phenomenon”. The “up-bottom approach” and “bottom up” approaches to the explanation of symmetry in organisms are presented in detail. The “up-bottom approach”, implies that the symmetry of the biological structure follows the symmetry of media in which this structure is functioning; the “bottom-up” approach, in turn, adopt that the symmetry of biological structures emerges from the symmetry of molecules constituting the structure. A diversity of mathematical measures applicable for quantification of ordering in biological patterns is introduced. The continuous, Shannon and Voronoi measures of symmetry/ordering and their application to biology objects are addressed. The fine structure of the notion of “ordering” is discussed. Informational/algorithmic roots of ordering inherent for the biological systems are considered. Ordered/symmetrical patterns provide economy of biological information, necessary for algorithmic description of a biological entity. Application of the Landauer principle bridging physics and theory of information to the biological systems is discussed.

This study analyzed the CO2 capture potential for Parque Natural Regional Serrania de las Quinchas buffer area in Colombia. For this purpose, multitemporal analysis for land covering for years 1989, 2000, 2006, 2011, 2017 and 2021 was made using the Normalized Difference Vegetation Index (NDVI), and Corine Land Cover (CLC) methodology. In the same way, Aboveground Biomass (AGB) was measured for representative parcels by measuring tree’s diameters and heights and applying the adequate allometric models; carbon content in soils was measured too. Results show that dense forest has grown since 1989 and the carbon stock in all the vegetation covers increased by 14,3% from 1989 until 2021, using literature data for tropical humid forests. If measured AGB values obtained in this study are used, the carbon stock average is 3 times this value. Measured carbon average content in AGB was 374,3 Ton C/Ha, well above the average for the content reported for humid tropical forests in Colombia; carbon content in soils was 897,19 Ton/Ha, 2,4 times that found in the AGB. In total, an average of 1271,49 Ton C/Ha (AGB+ soil) was found and a pattern of dense forest recovery was found, but in a more dispersed way than in the past. A very interesting potential for existing forests recovery was found for this area. Strategies for this include the development of sustainable practices, land use management, everything pursuing biodiversity preservation and the participation and leading of the local communities.

Structural design optimization (SDO) plays a pivotal role in enhancing various aspects of construction projects, including design quality, cost-efficiency, safety, and structural reliability. Recent endeavors in academia and industry have sought to harness the potential of Building Information Modeling (BIM) and optimization algorithms to optimize SDO and improve design outcomes. This review paper aims to synthesize these efforts, shedding light on how SDO contributes to project coordination. Furthermore, the integration of sustainability considerations and the application of innovative technologies and optimization algorithms in SDO necessitate more interactive early-stage collaboration among project stakeholders. This study offers a comprehensive exploration of contemporary research in integrated SDO employing BIM and optimization algorithms. It commences with an exploratory investigation, employing both qualitative and quantitative analysis techniques following the PRISMA systematic review methodology. Subsequently, an open-ended opinion survey was conducted among construction industry professionals in Europe. This survey yields valuable insights into the coordination challenges and potential solutions arising from technological shifts and interoperability concerns associated with widespread SDO implementation. These preliminary steps of systematic review and industry survey furnish a robust knowledge foundation, enabling the proposal of an intelligent framework for automating early-stage sustainable structural design optimization (ESSDO) within the construction sector. The framework ESSDO addresses the challenges of fragmented collaboration between architects and structural engineers. This proposed framework seamlessly integrates with the BIM platform, i.e., Autodesk Revit for architects. It extracts crucial architectural data and transfers it to the structural design and analysis platform, i.e., Autodesk Robot Structural Analysis (RSA), for structural engineers via the visual programming tool Dynamo. Once the optimization occurs, optimal outcomes are visualized within BIM environments. This visualization elevates interactive collaborations between architects and engineers, facilitating automation throughout the workflow and smoother information exchange.

Researchers need to delineate the shoreline for different applications with no access to costly resources such as topographic maps and high-resolution satellite images. With the increase of open source data, this study aims to present a methodology to use open source data in the best possible way to map the shoreline. Several methods have been tested using open source remote sensing data (Landsat and Aster), such as supervised classification, unsupervised classification, manual mapping, and by applying some spectral indices, among others. The accuracy of the extracted shoreline data was verified using high-resolution open database images (such as Google Earth basemap). The results showed that the manually mapped shoreline through applying spectral index (green- near infrared/green+ near infrared) is the most accurate, although it remains important to modify it using high-resolution images of open databases. Open-source data showed acceptable accuracy in mapping the shoreline.

Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

The Spiking Neural Network (SNN) has attracted more and more attention recently. It adopts binary spike signals to transmit information. Benefitting from the information passing paradigm of SNNs, the multiplications of activations and weights can be replaced by additions, which are more energy-efficient. However, its ``Hard Reset" mechanism for the firing activity would ignore the difference among membrane potentials when the membrane potential is above the firing threshold, causing information loss. Meanwhile, quantifying the membrane potential to 0/1 spikes at the firing instants will inevitably introduce the quantization error thus bringing about information loss too. To address these problems, we propose to use the ``Soft Reset" mechanism for the supervised training-based SNNs, which will drive the membrane potential to a dynamic reset potential according to its magnitude, and Membrane Potential Rectifier (MPR) to reduce the quantization error via redistributing the membrane potential to a range close to the spikes. Results show that the SNNs with the ``Soft Reset" mechanism and MPR outperform their vanilla counterparts on both static and dynamic datasets.

To accurately identify high-quality functional wood with superior mechanical properties and enhance its durability, it is imperative to understand the relationships between the physical and mechanical attributes of diverse wood types. Presently, there exist issues related to accuracy, efficiency, and differentiation in studying these performance correlations. The Liang Kleeman (L-K) information flow theory, a causative analytical approach rooted in fundamental physics principles, offers swift computational capabilities. Furthermore, the coefficient of variation in L-K information flow quantitatively gauges the intensity of causal effects. Therefore, this article adopts a causal artificial intelligence perspective and introduces a wood performance analysis method based on L-K information flow.This study focuses on four Chinese fir varieties, including fast-growing Chinese fir (YKS), red heart Chinese fir (CSH, XXH), and iron heart Chinese fir (XXT). By computing and comparing variation coefficients of L-K information flow for various physical properties and their impact on mechanical properties, causal relationships between these properties are elucidated. These findings are then applied to predict wood mechanical properties, achieving a remarkable maximum accuracy of 90%.In the future, these findings will prove invaluable for the selection of high-quality wood and for improving the manufacturing process to prolong the service life of functionalized wood.

Detecting rotated objects in remote sensing images poses a substantial challenge. These images usually cover a wide field of view, containing diverse and complex backgrounds with variously sized ground objects densely distributed. Therefore, identifying objects of interest in remote-sensing images is challenging. While integrating Convolutional Neural Networks (CNNs) and Transformer networks has exhibited progress in detecting rotated objects, there is still scope for enhancing feature extraction and fusion. To address this issue, we propose a feature extraction module, a feature context aggregation module, and a multi-scale feature fusion module. Initially, we substitute the Spatial Pyramid Pooling Bottleneck (SPPFBottleneck) with a new module aimed at extracting multi-scale features, thereby enhancing the detection of small objects in complex backgrounds. Next, we develop a novel module for the multi-scale fusion of contextual information within feature maps, extracting valuable information. Finally, we combine the original features with the fused ones to prevent the loss of specific features in the fusion process. We refer to our newly proposed model as the "Multi-scale Feature Context Aggregation Network" (MFCANet). We assess our approach on three challenging remote sensing datasets: MAR20, SRSDD, and HRSC. Comprehensive experimental results show that our method surpasses baseline models by 2.13%, 10.28%, and 1.46% mAP on the MAR20, SRSDD, and HRSC datasets, respectively.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emissions sources. In South Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m³ in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM_2.5 concentration was 161.7 µg/m³. The maximum NOx, NO₂, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Over the past decade, Knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purpose, understanding how effectively lev-erage entity-based representation still needs to be resolved. This paper proposes a Purely Enti-ty-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method, (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method, for queries whose all terms are annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.000). Thus, using our approach with any other document retrieval method would be an added value, unless that method achieves the maximum nDCG@5 score for those highly annotated queries.

Choosing a college major is a crucial issue for twelfth-grade students. Many students feel confused and influenced by their surroundings when making this decision. This can have negative consequences if it does not align with the students interests and talents. This research proposes a web application as a solution, providing introductory information for each major. We employed a design and developement approach to create an application that assists prospective students in identifying majors based on their interests. The primary objective is to alleviate students discomfort during their studies, which often leads to decreased performance. With this application, students can avoid selecting majors solely due to peer pressure or parental coercion.

Work done by us shows that rough topology is a good tool for dealing with things that happen in the real world. The concept of ”basis” has been used to show out the deciding elements of the recent outbreak of COVID-19 that was reported all over the world.

Natural hazards threaten millions of people all over the world. To address the risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowdsourcing based on the mobile App MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied at four regions. Our results show that the crowdsourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Nevertheless, this study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.

Multichannel EEGs were obtained from healthy participants in the eyes-closed no-task condition (where the alpha component is typically abolished). EEG dynamics in the two conditions were quantified with two related binary Lempel-Ziv measures of the first principal component and with three measures of integrated information including the more recently proposed integrated synergy. Both integrated information and integrated synergy with model order p=1 had greater values in the eyes closed condition. If the model order of integrated synergy was determined with the Bayesian Information Criterion, this pattern was reversed, and in common with other measures, integrated synergy was greater in the eyes open condition. Eyes open versus eyes closed separation was quantified by calculation of the between-condition effect size. Lempel-Ziv complexity of the first principal component showed greater separation than the measures of integrated information. The performance of the integrated information measures investigated here when distinguishing between indisputably different physiological states encourages caution when advocating for their use as measures of consciousness.

Medical records contain many terms which are difficult to process. Our aim in this study is to allow the visual exploration of the information in medical databases where the texts presents a large number of syntactic variations and abbreviations, through an interface which facilitates content identification, navigation and information retrieval. We propose the use of multi-term tag clouds as content representation tools and as assistants for the browsing and querying tasks. The tag cloud generation is achieved through a novelty mathematical method that allows related terms to remain grouped together within the tags To evaluate this proposal, we have used a database with 24,481 records. 23 expert users in the medical field were tasked to complete a survey to evaluate the generated tag clouds properties and we obtained a precision of 0.990, a recall of 0.870 and a F1score of 0.904 in the evaluation of the tag cloud as an information retrieval tool. The main contribution of this approach is that we automatically generate a visual interface over the text capable of capturing the semantics of the information and facilitating access to medical records.

Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range, and greatly reduce the time complexity. The experimental results on the two real data sets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm and pBmH algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large scale social network.

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate means of working with knowledge representations (as opposed to data) because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema. The goal of this paper is the development of theoretical and practical tools for processing schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machine as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic Machine is a technical system capable of regenerating, reproducing and maintaining itself by production, transformation and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

A large variety of molecular interactions occurs between biomolecular components in cells. When one or a cascade of molecular interactions results in a regulatory effect, by one component onto a downstream component, a so-called ‘causal interaction’ takes place. Causal interactions constitute the building blocks in our understanding of larger regulatory networks in cells. These causal interactions and the biological processes they enable (e.g., gene regulation) need to be described with a careful appreciation of molecular interactions that occur between entities. A proper description of this information enables archiving, sharing, and reuse by humans and for computational science. Various representations of causal relationships between biological components are currently used in a variety of resources. Here, we propose a checklist that accommodates current representations, and call it the Minimum Information about a Molecular Interaction CAusal STatement (MI2CAST). This checklist defines both the required core information, as well as a comprehensive set of other contextual details valuable to the end user and relevant for reusing and reproducing causal molecular interaction information. The MI2CAST checklist can be used as reporting guidelines when annotating and curating causal statements, while assuring uniformity and interoperability of the data across resources.

Purpose: There is limited research on the reasons behind startup failure, and none of the available studies use a competency approach. In this study we applied Spencer’s competency model to identify the competencies in startups which, according to their CEOs, contributed to failure. Methodology: Three coders analyzed the stories of 50 startup failures published online using modified Critical Incident Technique. Findings: Two salient competencies were identified playing a fundamental role in startup failures if missing: Information seeking and Customer service orientation. A network pattern of 9 more prevalent competency deficits was created: Technical/professional/managerial expertise, Analytical thinking, Flexibility, Self-control, Concern for order, quality and accuracy, Interpersonal understanding, Self-confidence, Team leadership and Teamwork and cooperation. Besides startup-specific behavior descriptions were added to Spencer’s competencies. Research implications: Competency approach and Critical Incident Technique method proved to be feasible to identify competency deficits in failed startups. Practical implications: The identified competency deficits offer relevant focus areas for the assessment and development of startup teams. Originality: The study provided research evidence to describe the competency deficits of startup teams that are connected to their failure.

In this paper, we propose a novel approach of video skimming by exploiting the fusion of video temporal information and keyword information representation extracted from multi-model video information including audio, text and visual indices. In addition, we introduce the brand-safe filtering and sentiment analysis in order to only reserve the user-friendly content in the video skim. In the experiment by using the videos from YouTube-8M dataset, we have proved that the semantic conservation in the video skim from the proposed approach highly outperforms the approaches by only partial information of the video in conserving the semantic content of the video.

The deepening of electricity reform results in increasingly frequent auctions and the surge of generators, it becomes difficult to analyze generators’ behaviors. Since it’s hard to find analytical market equilibriums, approximate equilibriums were obtained instead in previous studies by market simulations, which are strict to initial estimations and simulation results are chaotic in some cases. In this paper, a multi-unit power bidding model is proposed to reveal the bidding mechanism under clearing pricing rule by employing auction approach, for which initial estimations are non-essential. Normalized bidding price is introduced to construct generator's price-related bidding strategy. Nash equilibriums are derived depend on the marginal cost and the winning probability which are computed from bidding quantity, transmission cost and demand distribution. Furthermore, we propose a comparative analysis to explore the impact of uncertain elastic demand on the performance of the electricity market. The result indicates that, there exists market power among generators leading to social welfare decreases even under competitive conditions but elastic demand is an effective way to restrain generators’ market power. The feasibility of the models is verified by a case study. Our work provides decision support for generators and a direction for improving market efficiency.

Owing to the large ratio of consumption in the building sector, energy saving strategies are required. Energy feedback is an energy-saving strategy that consumers to change their energy-consumption behaviors. The strategy has been principally focused on providing energy-consumption information. However, realization of energy savings using only consumption information remains limited. In this paper, a building-energy three-dimensional (3D) visualization solution is thus proposed. This solution includes the process of diagnosing a building and providing prediction of energy requirements if a building improvement is undertaken. Accurate diagnostic information is provided by real-time measurement data from sensors and building models using a close-range photogrammetry (CRP) method without depending on blueprints. The information is provided by employing visualization effects to increase the energy-feedback efficiency. The proposed strategy is implemented on two testbeds, and building diagnostics are performed accordingly. For the first testbed, the predicted energy improvement amount resulting from the facility upgrade is provided. The second testbed is provided with a 3D visualization of the energy information. The aim is to determine if the building manager will replace the facility after our recommendation is given to improve the building energy efficiency driven from the energy information. Unlike existing systems, which provide only ambiguous data that lack quantitative information, this study is meaningful because it provides energy information with the aid of visualization effects before and after building improvements.