We generalize the point information gain (PIG) and derived quantities, i.e., point information gain entropy (PIE) and point information gain entropy density (PIED), for the case of the Rényi entropy and simulate the behavior of PIG for typical distributions. We also use these methods for the analysis of multidimensional datasets. We demonstrate the main properties of PIE/PIED spectra for the real data on the example of several images, and discuss further possible utilizations in other fields of data processing.

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

The increasing availability of longitudinal data (repeated numerical observations of same units at different times) requires the development of flexible techniques to automatically detect common errors in such data. Besides obvious and easily identifiable cases, such as missing or out-of-range data, large longitudinal dataset often present problems not easily traceable by the techniques used for generic datasets. In particular, elusive and baffling problems are i) inversion of one or more values from one unit to another; ii) anomalous jumps in the series of values, iii) errors in the timing of the values due to a recalculation operated by the data providers to compensate previous errors. This work proposes a statistical-mathematical approach based on a system of indicators that is able to capture the complexity of the described problems by working at the formal level, regardless of the specific meaning of the data. The proposed approach identifies suspect erroneous data and is applicable in a variety of contexts. We implement this approach in a relevant database of European Higher Education institutions (ETER) by analyzing Total academic staff, that is one of the most important variables, used in empirical analysis as proxy of size and also considered by policy makers at European level.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

Historic expeditions are events that are flavored by exploratory, scientific, military or geographic characteristics. Such events are often documented in literature, journey notes or personal diaries. A typical historic expedition involves multiple site visits and their descriptions contain spatiotemporal and attributive contexts. Expeditions involve movements in space that can be represented by triplet features (location, time and description). However, such features are implicit and innate parts of textual documents. Extracting the geospatial information from these documents requires understanding the contextualized entities in the text. To this end, we developed a semi-automated framework that has multiple Information Retrieval and Natural Language Processing components to extract the spatiotemporal information from a two-volumes historic expedition gazetteer. Our framework has three basic components, namely, the Text Preprocessor, the Gazetteer Processing Machine and the JAPE (Java Annotation Pattern Engine) Transducer. We used the Brazilian Ornithological Gazetteer as an experimental dataset and extracted the spatial and temporal entities from entries that refer to three expeditioners’ site visits and mapped the trajectory of each expedition using the extracted information. Finally, one of the mapped trajectories was manually compared with a historical reference map of that expedition to assess the reliability of our framework. The reference map was manually prepared in previous research work by others.

"Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law". In accordance with the aforementioned declaration on Free Access to Law by Legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques, should be employed in the context of legal information retrieval, as to increase user satisfaction. We address diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard data set from the Common Law domain that we subjectively annotated with relevance judgments for this purpose. Our results i) reveal that users receive broader insights across the results they get from a legal information retrieval system, ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification and iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

Iterated games, in which the same economic interaction is repeatedly played between the same agents, are an important framework for understanding the effectiveness of strategic choices over time. To date very little work has applied information theory to the information sets used by agents in order to decide what action to take next in such strategic situations. This article looks at the mutual information between previous game states and an agent's next action by introducing two new classes of games: ’invertible games’ and ‘cyclical games'. By explicitly expanding out the mutual information between past states and the next action we show under what circumstances these expressions can be simplified. These information measures are then applied to the Traveler's Dilemma game and the Prisoner's Dilemma game, the Prisoner's Dilemma being invertible, to illustrate their use. In the Prisoner's Dilemma a novel connection is made between the computational principles of logic gates and both the structure of games and the agents' decision strategies. This approach is applied to the cyclical game Matching Pennies to analyse the foundations of a behavioural ambiguity between two well studied strategies: ‘Tit-for-Tat' and ’Win-Stay, Lose-Switch'.

In Taiwan, mountainous areas account for approximately two-thirds of the total area. The steep terrain and concentrated rainfall during typhoons cause landslides, which pose a considerable threat to mountain settlements. Therefore, models for analyzing rainfall-induced landslide hazards are urgently required to ensure adequate land use in mountainous areas. In this study, focusing on Pingtung County in southern Taiwan, we developed a landslide hazard index (IRL) to land use. Using FORMOSA-2 and SPOT-5 satellite images, data were collected before and after four typhoons (one in 2009 and three in 2013). The ArcGIS random tree classifier was used for interpreting satellite images to explore surface changes and disasters, which were used to analyze slope disturbances. The product of the maximum 3-h rolling rainfall intensity and effective accumulated rainfall was used as a rainfall trigger index (IRT). Considering environmental and slope disturbance factors, an index of slope environmental strength potential (ISESP) was developed through logistic regression (LR). Landslide hazard to land use was estimated using IRT and ISESP. The average coefficient of agreement (Kappa) was approximately 0.71 (medium to high accuracy); the overall accuracy of slope environmental strength potential analysis was approximately 80.4%. At a constant ISESP, IRT increased with the increasing hazard potential of rainfall-induced landslides. Furthermore, IRT and ISESP were positively correlated with landslide occurrence. When large ISESP values occur (e.g., fragile environment and high land development intensity), small IRT values may induce landslides.

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

This paper establishes methods that quantify the structure of statistical interactions within a given data set using the characterization of information theory in cohomology by finite methods, and provides their expression in terms of statistical physic and machine learning. Following [1–3], we show directly that k multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles are given by I_k = 0, which generalize statistical independence to arbitrary dimension k. The topological approach allows to investigate Shannon’s information in the multivariate case without the assumptions of independent identically distributed variables. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes. The I₁ component defines a self-internal energy functional U_k, and (−1)^k I_k,k≥2 components define the contribution to a free energy functional G_k of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a topological expression of the 2nd law and points toward a discrete Noether theorem (1st law). The local minima of free-energy, related to conditional information negativity and the non-Shannonian cone of Yeung [4], characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. Finite data size effects and estimation bias severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and for the k-dependences following [5]. We give an example of application of these methods to genetic expression and cell-type classification. The maximal positive I_k identifies the variables that co-vary the most in the population, whereas the minimal negative I_k identifies clusters and the variables that differentiate-segregate the most. The methods unravel biologically relevant I₁₀ with a sample size of 41. It establishes generic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism.

To better understand farmers’ refusal and corresponding negative emotions to pay agricultural water fee under current policy in rural China, this paper applies mental accounting, a behavioral economics framework, to explore how the governmental policies of reform of rural taxes and fees, direct agricultural subsidy programs and agricultural water fee individually influence farmers’ decisions in paying agricultural water fee. Using fieldwork data from 577 farmers and 20 water managers in Sichuan, we explore farmers’ information processing regarding paying agricultural water fee via three sequential mental accounting processes, with the associated underlying principles and measures behind each process. We find that the information processing in three mental accounting scenarios related to the agricultural water fee elucidates farmers’ observed behaviors in rural China. Generally, in the three mental accounting scenarios, two conditional intuitive expectations and nine conditional intuitive preferences are formed, however, the conditions of those expectations or preferences cannot be matched with the facts due to the reform of rural taxes and fees, the direct agricultural subsidy programs and the internal attributes of agricultural water fee, which interpret those negative behaviors in rural China. Additionally, this paper offers a view into how previous policies create negative psychological externalities (such as farmers’ psychological dependence on the government) through mental accounting to negatively influence agents’ subsequent decision making; it highlights the significance of underlying mental factors and information processing of negative behaviors in policymaking for managing or conserving common pool resources.

There has been rampant fold-ups, merger and acquisitions occurring in the Ghanaian banking industry. Then, the questions arise: Is the Ghanaian Financial System in Crisis? This study was conducted to find answers to these problems unsolved with prior literature. A sample of seventy customers of the Royal Bank, 8 employees of the Royal Bank and 2 managers of the Royal Bank were selected for a case-survey. The study also monitored the Trend of the Ghanaian Financial System through the reading and monitoring of daily news on the Financial System and reports of banks. The data from the field and the secondary data from news and reports were analysed symmetrically. The study drew on Minsky’s Financial Crisis Theory to explain the phenomenon in the Ghanaian economy and to draw predictions of what would happen in other developing economies. The study found out that: (1) The Ghanaian financial system is fragile and it holds true for most developing economies; (2) The financial system suffers greatly when the confidence level of customers falls significantly; (3) Management information systems raises the confidence level of customers (borrowers and lenders) such that there is a greater fall and impact in times of instability in the economy; (4) The higher the level of MIS adoption in an unstable economy, the more fragile the Financial System becomes and (5) A higher adoption of Management Information Systems in a Fragile Financial System indirectly contributes to Financial Crisis of the Financial System.

Recycling information can be complex and often confusing which may subsequently reduce the participations in any waste recycling schemes. As a result, this research explored the roles as well as the importance of a holistic approach in designing recycling information using 15 expert-based in-depth interviews. The rationale was to offer a better understanding of what constitutes waste, recycling, and how recycling information should be designed and presented to make recycling more attractive/convenient. Based on the research participants’ perceptions with supports from the existing studies, this research sub-categorised recycling information into three different themes, termed the “WWW” of recycling information components. As a result, these components (or attributes) were extensively described (using findings of semi-structured interviews) to elicit pragmatic guidance for practitioners, policy-makers, and other stakeholders in designing structured communication or information strategies that may simplify and subsequently increase waste-recycling practices. The policy implications of holistic information in enhancing recycling are further discussed.

In order to comprehensively evaluate the employee satisfaction of mine occupational health and safety management system, an analytic method based on fuzzy analytic hierarchy process and 2- tuple linguistic model was established. Firstly, based on the establishment of 5 first- grade indicators and 20 second- grade ones, the weight of each indicator is calculated and validated by method of FAHP and root mean square model. Secondly, a path based on the time-ordered Weighted Averaging Operator (T-OWA) model is constructed. Finally, the model is validated by empirical analysis. The results demonstrate that the employee satisfaction of the mine occupational health and safety management system is of the “general” rank. The method including the comprehensive evaluation of employee satisfaction and the quantitative analysis of language evaluation information ensures the authenticity of the language evaluation information.

With the fintech industry growing at an unprecedented rate, it is critical to protect cybersecurity and ensure data privacy. The research presented here offers a thorough examination of the challenges faced by financial technology companies, highlighting the growing risks posed by malware, phishing, and network vulnerabilities. As essential components of a proactive defense plan, the report promotes strong cybersecurity measures like frequent security assessments, encryption, and stringent access controls. The findings highlight how important it is for fintech companies to give cybersecurity equal importance with open and honest data privacy policies in order to win over customers. In order to protect the industry from cybersecurity risks regulatory compliance, intrusion detection systems, and collaborative information sharing are considered essential components. The article advises businesses to include cybersecurity and data privacy into their core business operations and customer relations, highlighting the critical role these factors play in sustaining success in the rapidly evolving fintech industry.

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.

This paper presents a new information technology platform specially tailored for infrastructure asset management of urban water systems operated by water utilities of lower digital maturity level, developed in the scope of DECIdE research project. This platform aims at the integration of different data from the water utilities with several information systems and the assessment of the system performance, in terms of water losses, energy efficiency and quality of service by using developed tools (i.e., water and energy balances and key performance indicators). This platform was tested with data from five small to medium size Portuguese water utilities with different maturity levels in terms of technological and human resources. Obtained results are very promising since the platform allows to assess the systems performance periodically which constitute an important part of the infrastructure asset management for small and medium-sized water utilities

Linear mixed models are frequently used for analysing heterogeneous data in a broad range of applications. The restricted maximum likelihood method is often preferred to estimate co-variance parameters in such models due to its unbiased estimation of the underlying variance parameters. The restricted log-likelihood function involves log determinants of a complicated co-variance matrix. An efficient statistical estimate of the underlying model parameters and quantifying the accuracy of the estimation requires the first derivatives and the second derivatives of the restricted log-likelihood function, i.e., the observed information. Standard approaches to compute the observed information and its expectation, the Fisher information, is computationally prohibitive for linear mixed models with thousands random and fixed effects. Customized algorithms are of highly demand to keep mixed models analysis scalable for increasing high-throughput heterogeneous data sets. In this paper, we explore how to leverage an averaged information splitting technique and dedicate matrix transform to significantly reduce computations and to accelerate computing. Together with a fill-in reducing multi-frontal sparse direct solver, the averaged information splitting approach improves the performance of the computation process.

The Indian Himalayan Region (IHR), due to its topography, geography, and active tectonics, a rough mountain zone, is among the most vulnerable zones to the landslip danger. The most cutting-edge and accurate ways for creating a landslip susceptibility model (LSM) are advanced statistical techniques. The goal of the current work was to use advanced statistical techniques to analyse and evaluate the updated landslip susceptibility for East District in the NE Himalayas of Sikkim, India. The spatiotemporal landslip inventory for the years are produced using literature surveys, historical satellite imageries and on-site observations. Slope, aspect, elevation, curvature, plane curvature, profile curvature, topographic wetness index (TWI), lithology, distance to faults, distance to streams, distance to roads, normalised difference vegetation index (NDVI), rainfall, drainage density and land use/ land cover (LULC) are some of the topographic, environmental, geologic, and anthropogenic factors that were included in the spatial database. These LCFs were chosen to study the area's periodic landslip vulnerability. An inventory of 151 landslides from historical published records, field visits and Imagery interpretations, respectively, were used in the experimental design. Information Value Model (IVM), was used to evaluate the vulnerability to landslides as determined by fifteen LCFs. The goal of the study is to reduce the number of fatalities and possible economic harm caused by the region's frequent slope instabilities. It is expected that the application of statistical algorithms would assist relevant authorities and organisations in properly planning for and managing the region's landslip threat.

The administration and management of urban transport has become an urgent need, in the context of the increasing congestion problems and the environmental and economic damage, resulting from rapid urban expansion witnessed by the Algerian cities in recent times, in order to achieve prosperity and quality of urban life. It became necessary to use the tools of modern technology, which administrative and technical departments are seeking to use primarily to support ways of decision-making and actions required. Boosted by high capabilities in the statistical data and other information is stored, and coordination and tabulation, with the possibility of installation and analysis by operations. Especially against the inability of traditional methods, which have become less able to cope with the changes that led to the urban dynamic currently experienced by urban communities in our country. This research aims to employ geographic information systems in the administration and management of the urban transport network, facing both the population and the local administration of the city of Batna. Which was chosen as a model for major Algerian cities, which is suffering more than ever of transportation bottlenecks, as well as the unintended damage for these networks and other networks. Hence the importance of the subject of our research, which aims to use GIS MapInfo systems as a means to snipe (Data Capture) and storage (Data Storage) and address (Data Processing) and directed by spatial information (Spatial Data) and link them to the descriptive information (Attribute Data). To develop a modern digital data and charts the base of each of these elements of the urban service lines equipment and mechanisms, ... etc. As a tool available to those creating and carrying out urban spaces, as well as decision-makers. It was concluded that the possibilities offered by this system would facilitate the processes of monitoring and management in order to ensure the quality of the service.

A probabilistic description is essential for understanding the dynamics of stochastic systems far from equilibrium. To compare different Probability Density Functions (PDFs), it is extremely useful to quantify the difference among different PDFs by assigning an appropriate metric to probability such that the distance increases with the difference between the two PDFs. This metric structure then provides a key link between stochastic processes and geometry. For a non-equilibrium process, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory of the system quantifies the total number of different states that the system undergoes in time and is called the information length. By using this concept, we investigate classical and quantum systems and demonstrate the utility of the information length as a unique Lagrangian diagnostic to quantify the information change as a system continuously evolves in time and to map out attractor structure. We further elucidate quantum effects (uncertainty relation) and the dual role of the width of PDF in quantum systems.

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.

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.

The study of the concentrations and effects of fine particulate matter in urban areas have been of great interest to researchers in recent times. This is due to the acknowledgment of the far-reaching impacts of fine particulate matter on public health. Remote sensing data have been used to monitor the trend of concentrations of particulate matter by deriving aerosol optical depth (AOD) from satellite images. The Center for International Earth Science Information Network (CIESIN) has released the second version of its global PM_2.5 data with improvement in spatial resolution. This paper revisits the study of spatial and temporal variations in particulate matter in Saudi Arabia by exploring the cluster analysis of the new data. Cluster analysis of the PM_2.5 values of Saudi cities is performed by using Anselin local Moran’s I statistic. Also, the analysis is carried out at the regional level by using self-organizing map (SOM). The results show an increasing trend in the concentrations of particulate matter in Saudi Arabia, especially in some selected urban areas. The eastern and south-western parts of the Kingdom have significantly clustering high values. Some of the PM2.5 values have passed the threshold indicated by the World Health Organization (WHO) standard and targets posing health risks to Saudi urban population.

We introduce the Symplectic Structure of Information Geometry based on Souriau’s Lie Group Thermodynamics model, with a covariant definition of Gibbs equilibrium via invariances through co-adjoint action of a group on its moment space, defining physical observables like energy, heat, and moment as pure geometrical objects. Using Geometric (Planck) Temperature of Souriau model and Symplectic cocycle notion, the Fisher metric is identified as a Souriau Geometric Heat Capacity. Souriau model is based on affine representation of Lie Group and Lie algebra that we compare with Koszul works on G/K homogeneous space and bijective correspondence between the set of G-invariant flat connections on G/K and the set of affine representations of the Lie algebra of G. In the framework of Lie Group Thermodynamics, an Euler-Poincaré equation is elaborated with respect to thermodynamic variables, and a new variational principal for thermodynamics is built through an invariant Poincaré-Cartan-Souriau integral. The Souriau-Fisher metric is linked to KKS (Kostant-Kirillov-Souriau) 2-form that associates a canonical homogeneous symplectic manifold to the co-adjoint orbits. We apply this model in the framework of Information Geometry for the action of an affine Group for exponentiel families, and provide some illustrations of use cases for multivariate Gaussian densities. Information Geometry is presented in the context of seminal work of Fréchet and his Clairaut-Legendre equation. Souriau model of Statistical Physics is validated as compatible with Balian gauge model of thermodynamics. We recall the precursor work of Casalis on affine group invariance for natural exponential families.

Previous work has shown that perturbation analysis in algorithmic information dynamics can uncover generative causal processes of finite objects and quantify each of its element's information contribution to computably constructing the objects. One of the challenges for defining emergence is that the dependency on the observer's previous knowledge may cause a phenomenon to present itself as emergent for one observer at the same time that reducible for another observer. Thus, in order to quantify emergence of algorithmic information in computable generative processes, perturbation analyses may inherit such a problem of the dependency on the observer's previous formal knowledge. In this sense, by formalizing the act of observing as mutual perturbations, the emergence of algorithmic information becomes invariant, minimal, and robust to information costs and distortions, while it indeed depends on the observer. Then, we demonstrate that the unbounded increase of emergent algorithmic information implies asymptotically observer-independent emergence, which eventually overcomes any formal theory that any observer might devise. In addition, we discuss weak and strong emergence and analyze the concepts of observer-dependent emergence and asymptotically observer-independent emergence found in previous definitions and models in the literature of deterministic dynamical and computable systems.

The article explores the subject of information and accounting solutions for accounts receivable management that include appropriate accounting solutions and analytical techniques. The study focuses on the statutory framework for accounting for accounts receivable under the Russian Accounting Standards (RAP), International Financial Reporting Standards (IFRS), and the generally accepted accounting principles (GAAP). The analytical techniques are described in the context of the authors' view on the essence of accounts receivable management that implies analysis, the establishment of a credit policy and of a discount policy. The article places emphasis on the use of available information technology for accounts payable management, such as blockchain-based smart contracts.

Projects are rarely executed exactly as planned. Often, the actual durations of a project’s activities differ from the planned ones, resulting in costs stemming from the inaccurate estimation of the activities’ completion dates. While monitoring the project at various inspection points is pricy, it can lead to better estimation of the project completion time, hence saving on costs. Nonetheless, identifying the optimal inspection points is a difficult task, as it requires evaluating a large number of the project’s path options, even for small-scale projects. This paper proposes an analytical method for identifying the optimal project inspection points by using Information Theory measures. We search for monitoring (inspection) points that can maximize the information about the estimated project’s duration or completion time. The proposed methodology is based on a simulation-optimization scheme using a Monte Carlo engine that simulates potential activities’ durations. An exhaustive search is performed of all possible control points to find those with the highest expected information gain on the project duration. The proposed algorithm’s complexity is not affected the number of activities, and can address large projects with hundreds or thousands of activities. Numerical experimentation and analysis of various parameters are presented.

The efficiency success status of organization built environment assets management needs robust and comprehensive maintenance processes which depends on the efficiency of the contract information documents for the maintenance management within the assets life cycle and achieve positive return of the investments. Therefore, this paper highlights on the appropriate interactive approach to structuring information flow scope of the asset facilities management contract for construction projects based on GIS and BIM integration processes, sustainability standards, and As-built contractual documents of the project to support the organization owner and stakeholder to improve the asset management processes. Using expert interviews with comprehensive understand for the information type in several facility management contracts conducted in common Organization types based on reviewing available international facility management standards manual and contract of procedures for different services types, and required information flow inside international facility management firms. The study classify and build significant integrated information and data flow model contract drafting with measures and indicators of performance efficiency as guideline for monitoring, technically evaluating, and financially procedures for performance efficiency and quality of services provided in the facilities management contract for King Faisal University campus projects as a case study.

Intrusion Detection Systems (IDS) plays a part in modern cyber security, as a result of the increasing need for cyber security systems in the “real” world due to the increasing number of cyber attacks, more sophisticated systems are required in order to prevent these attacks - an IDS can provide this protection. Due to the sophistication of these systems, they must be properly understood, developed and analyzed - research papers can be used as a tool to improve IDS systems. This paper is composed of two main sections: a survey and a taxonomy, providing information, reviews and interpretations from relevant papers, a timeline of important papers, a discussion on the future of IDS and a classification on IDS and how to apply this.

This paper proposes an approach for image data extraction and driving behavior analysis using geographic information and driving data. Information derived from geographic and global positioning systems was used for image data extraction. In addition, we used an onboard diagnostic II and a controller area network bus logger to record driving data for driving behavior analysis. Driving behavior was analyzed using sparse automatic encoders and data exploration to detect abnormal and aggressive behavior. A regression analysis was used to derive the relationship between aggressive driving behavior and road facilities. Several traffic improvements were proposed for specific intersections and roads. The results indicate that lane ratios without lane markings and with straight lane markings are important features that affect aggressive driving behaviors.

Building Information Modelling (BIM) is increasingly adopted in supporting Facility Management (FM). However, in the future perspective of efficiently encouraging the management of building estates, e.g. owned by public authorities or institutions, providing an effective integration between Building Information Models and Geographic Information Systems (GIS) becomes also essential. This contribution is therefore aimed at presenting a methodological framework integrating BIM and GIS environments by mainly taking advantage of open-source tools (e.g. QGIS and Dynamo) and non-proprietary data exchange formats like the COBie (Construction-Operations Building Information Exchange) international standard. Unlike what has been mainly proposed in the literature concerning BIM-GIS integration so far, the methodology presented in this paper does not provide for an integral transfer of BIM data to the GIS platform. The work led instead to the development of an effective bidirectional integration between the two informative systems, by properly relating them and allowing for an easy switch from one system database to the other. This integrated framework is intended to enable facility managers to easily visualize in the GIS platform, through queryable 2D and 3D maps, some relevant information from BIM to efficiently manage workspaces. At the same time, this hybrid informative system allows BIM practitioners to access and manage more detailed information stored in Building Information Models. The findings of experimental applications of the proposed methodological approach to the ex-Macciotta pediatric hospital, a healthcare building owned by the University of Cagliari, are finally presented and discussed.

The paper is based on the scientific outcome of a PhD Thesis. It introduces the generic, model-based, reusable, and extensible conceptual framework to incorporate Facility Management data based within the three-dimensional model-based design and construction of an asset to enable smart applications, which are introduced. The conceptual framework is composed of empirical data from expert interviews, questionnaires, and factual analysis from 13 projects of varied sizes of public and private clients. It shows which phases need which data, who needs them, and which added value can be generated if intelligent data structuring is used at the beginning of the construction project and bridges the gap between requirement and practice. The term “smart application” is introduced.

With the development of Internet, big data and artificial intelligence technology, personal information protection has become the focus of social attention. The legal boundary of data protection has become increasingly prominent, involving the collection, storage, use, transmission and disclosure of personal information. This paper aims to explore the legal boundary of data protection, analyze the legal basis, legal principles and legal provisions of personal information protection, and put forward corresponding legal suggestions, in order to provide reference for the improvement of China's data protection legal system.

Social customer relationship management (SCRM) is a new philosophy influencing the relationship between customer and organization, where the customer gets opportunity to control relationship through social media. The paper aims on (a) identification of current level of SCRM and also on (b) influence of basic organizational characteristics on SCRM level. The data were gathered through the questionnaire distributed in 362 organization headquartered in the Czech Republic. The questionnaire comprised 54 questions focusing on the significance of marketing and CRM practices, establishing a relationship with the customer, online communities, the use of social media in marketing, and acquiring and managing information. The majority of questions were scalable and used typical five-level Likert scale. Results showed that larger firms more often set up their own online communities and manage them strategically, moreover they are able to manage information better. Contrariwise, small sized organizations use social networks as a way to establish communication with the customer more than large sized entities. Use of social media for marketing purposes is significantly higher in organizations oriented on consumer markets than on business markets.

This paper will discuss Data, Information, Knowledge, and Wisdom, which is commonly referred to as DIKW. The DIKW Pyramid Model that is often referenced in both academics and practitioner circles will be discussed and shown to be faulty on several levels, including. a lack of definitional agreement. A new DIKW framework will be proposed that focuses on what the DIKW elements do in the way humans think, not what they are by definition. Information as a replacement for wasted physical resources in goal-oriented tasks will be a central organizing point. The paper will move the DIKW discussion to the computer-based concept of Digital Twins (DTs) and its augmentation of how we can use DIKW to be more effective and efficient. This will especially be the case as we move to Intelligent Digital Twins (IDT) with Artificial Intelligence (AI).

Snakebite envenomations (SBE) are a significant global public health threat due to their morbidi-ty and mortality. It is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported in Brazil only in 2020, posing a high burden for this population. We describe the data structure of the Snakebite data on Notifiable Disease Information System (SINAN) made available by the Brazilian Minis-try of Health from 2007 to 2020. In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work is related to clinical and demographic information of SBE cases. Also, data on out-comes, laboratory results, and treatment is available. The database is available and freely acces-sible; however, preprocessing, adjustments, and standardization are necessary due to incom-pleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for as-sessing different aspects and the national burden of envenoming.

The identification of an action principle for directing basic system energy exchanges can be used in a general framework for the study of dynamics in systems. The application of such action principles using Lagrangian procedures has become a very powerful methodology for the analysis of complicated problems in modern physics. This form of analytics has recently been considered as the mapping of the system state information in the context of system constraints. The process of translating perceived information into adaptive responses is considered to be the driving action dynamic for the living system. Reconciliation processes that provide for a minimization of the divergence of this information signature from a state of system stability are preferred. Such a state is naturally that of maximal entropy in the context of the operational constraints of the open living system. A mathematical expression incorporating inferential information entropy processing into biologic replicator dynamics describes the mechanics of the perception-action adaptive processes of living systems. From this framework, Kullback’s Principle of Minimum Information Discrimination is identified as the innate axiomatic action principle for guiding the dynamics of biological systems.

This theoretical article provides a brief description of the model of living systems' functioning by defining them as resource flows patterns. It reviews the living systems growth limitation between their development cycles by the Fibonacci sequence. Besides, there are presented systems resource base criteria, necessary for accumulating the resources and their investment. The article also considers the conditions for the formation of various systems strategies. Then we reviewed the principles of elemental analysis of information by a person as a living system according to the considered model. The study also shows the possibility of forming priorities in analyzing information for 16 combinations as maximum. At that, it remains crucial to divide a human's information analysis between the two hemispheres of the brain. The described combinations of priorities in a person's information analysis are compared with the existing differential personality models, such as the big five personality traits, the Myers-Briggs type indicator, temperaments model and Honey and Mumford Learning styles.

The exact dynamics of emergence remains one of the most prominent outstanding questions for the field of complexity science. I first discuss various perspectives on emergence in various contexts, then offer a different perspective on understanding emergence in a graph-theoretic representation. From the discussion, an observer’s choice in state space seems to have an effect for that observer to detect emergent behavior. To test these ideas, I analyze the dynamics of all possible spatial state spaces near the critical temperature in an Ising model. As a result, state space topologies that appear more deterministic flip more bits than topologies that appear more random, which is contrary to our intuitions about randomness. In addition, the size of different state spaces constrain a system’s ability to explore various states within the same time frame. These results are important to understanding emergent phenomena in biological systems, which are layered with various state spaces and observational perspectives.

Quantum Computation, in the gate array version, uses logical gates adopting convenient forms for computational algorithms based on those of classical computation. There, two-level quantum systems are the basic elements connecting the binary nature of classical computation with the settlement of quantum processing. Despite, their design depends on specific quantum systems and physical interactions involved, exacerbating the dynamics analysis. Predictable and controllable manipulation should be addressed to control the quantum states, but resources are restricted to limitations imposed by the physical settlement. This work presents a formalism to decompose the quantum information dynamics in SU(2^2d) for 2d-partite two-level systems into 2^2d-1 SU(2) quantum subsystems. Decomposition lets to set control procedures, to generate large entangled states and to design specialized dedicated quantum gates. There, easy and traditional operations proposed by quantum computation are recovered for more complex and large systems. Alternating the parameters of local and non-local interactions, the procedure states a universal exchange semantics on the generalized Bell states basis. It could be understood as a momentary splitting of the 2d information channels into 2^2d-1 pairs of 2 level quantum information subsystems and a settlement of the quantum information manipulation free of the imposed restrictions by the underlying physical system.

This paper presents a new interactive visualization system to represent and compare rate of spread of COVID 19 pandemic across different countries over time. We surveyed existing visualization techniques used in various websites and media outlets and introduced use of a knee detection algorithm that divides the exponential spread in multiple linear components. A set of use cases demonstrates the utility of the system in comparing the spread across different countries for both analysis and prediction.

This study introduces a method for quantitatively assessing the complexity and predictability of fish behavior in closed systems through the application of information entropy, offering a novel lens to understand how fish adapt to environmental changes. Utilizing simulations rooted in a random walk model for fish movement, we delve into entropy fluctuations under varying environmental conditions, including responses to feeding and external stimuli. Our findings underscore the utility of information entropy in capturing the intricacies of fish behavior, particularly highlighting the synchrony in collective actions and adaptations to environmental shifts. This research not only broadens our comprehension of fish behavior but also paves the way for its application in fields like aquaculture and resource management. Through our analysis, we discovered that smaller grid sizes in simulations capture detailed local fluctuations, while larger grids elucidate general trends, pinpointing a 2.5m grid as optimal for our study. Moreover, changes in swimming speeds and behavioral adaptations during feeding were quantitatively analyzed, with results illustrating significant behavior modifications. Additionally, employing a Gaussian Mixture Model helped to clarify the nuanced changes in fish behavior in response to altered light conditions, demonstrating the layered complexity of fish responses to environmental stimuli. This investigation confirms the efficacy of information entropy as a robust metric for evaluating fish shoal behavior, offering a fresh methodology for ecological and environmental studies, with promising implications for sustainable management practices.

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.

In this work we looked for a stable and accurate procedure to perform feature selection in datasets with a much higher number of predictors than individuals, as in Genome-Wide Association Studies. Due to the instability in feature selection when many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied to investigate Single Nucleotide Polymorphisms (SNPs) predictors associated to Alzheimer’s disease in the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset. Two data scenarios are investigated: one considering only the set of SNPs and another with the covariates age, sex, educational level and APOE4 genotype. The SNP rs2075650 and the APOE4 genotype are given as risk factors for AD, which is in line with the literature, and other four new SNPs are pointed, opening new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection.

The current paper provides a cutting-edge information data length- theoretic approach to human emotions. This is a revolutionary breakthrough that unifies information data length and the dynamics of emotions. The significant impact of both time and its mean value associate on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

This is a short introductory review on quantum-like modeling of cognition with applications to decision making and rationality. The aim of the review is twofold: a) to present briefly the apparatus of quantum information and probability theory useful for such modeling; b) to motivate applications of this apparatus in cognitive studies and artifical intelligence, psychology, decision making, social and political sciences. We define quantum rationality as decision making that is based on quantum information processing. Quantumly and classically rational agents behaves differently. A quantum-like agent can violate the Savage Sure Thing Principle, the Aumann theorem on impossibility of agreeing to disagree.Such an agent violates the basic laws of classical probability, e.g., the law of total probability and the Bayesian probability inference. In some contexts, ``irrational behavior'' (from the viewpoint of classical theory of rationality) can be profitable, especially for agents who are overloaded by a variety of information flows. Quantumly rational agents can save a lot of information processing resources. At the same time, this sort of rationality is the basis for quantum-like socio-political engineering, e.g., social laser. This rationality plays the important role in the process of decision making not only by biosystems, but even by AI-systems. The latter equipped with quantum(-like) information processors would behave irrationally, from the classical viewpoint. As for biosystems, quantum rational behavior of AI-systems has its advantages and disadvantages. Finally, we point out that quantum-like information processing in AI-systems can be based on classical physical devices, e.g., classical digital or analog computers.

Energy management, especially in communities, is attracting growing interest, but is often marred by confusion between the terms "Energy Information System" (EIS) and "Energy Man-agement System" (EMS). This ambiguity hinders understanding of their use and role in improv-ing energy performance. Our objective is to clarify these terms and their contribution to energy management. We used a mixed qualitative and quantitative approach to evaluate and compare the criteria of definition, objective, functionality, role, as well as correlated elements such as or-ganizational structure, responsibilities, planning and policy, according to the requirements of ISO 50001, in addition to criteria specific to the two systems. The study reveals differences in the roles of the EIS and the SME, with a complementarity of 40% between the two systems, depend-ing on the positioning of the EIS in relation to the elements of the SME process. The EIS plays a crucial role in facilitating the implementation of the EMS according to ISO 50001. This clarifica-tion contributes to a better understanding of the respective roles of these systems in energy management, thus helping communities to improve their energy performance in an effective manner.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) by the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization under-lying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow log normal probability distributions. This is attributed to the pre-ponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared to that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydropho-bicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

The current paper provides a cutting-edge information data length- theoretic approach to the transient M/M/Infinity queueing system. This is a first time ever investigation that unifies information data length and queueing theories. The significant impact of both time and the number of states for the transient M/M/ Infinity queue on both the upper and lower bounds of the obtained information data length is observed and noted. The paper concludes with some challenging open problems, combined with concluding remarks and future research pathways.

We present a first formal analysis of specific and complete local integration. Complete local integration was previously proposed as a criterion for detecting entities or wholes in distributed dynamical systems. Such entities in turn were conceived to form the basis of a theory of emergence of agents within dynamical systems. Here, we give a more thorough account of the underlying formal measures. The main contribution is the disintegration theorem which reveals a special role of completely locally integrated patterns (what we call ι-entities) within the trajectories they occur in. Apart from proving this theorem we introduce the disintegration hierarchy and its refinement-free version as a way to structure the patterns in a trajectory. Furthermore we construct the least upper bound and provide a candidate for the greatest lower bound of specific local integration. Finally, we calculate the i-entities in small example systems as a first sanity check and find that ι-entities largely fulfil simple expectations.

This research presents the design and characteristics of new wideband polarizer based on the square waveguide with irises. Matching and polarization characteristics of the polarizer have been simulated and optimized. Frequency dependences of the characteristics are presented. Developed polarizer can be applied in satellite information systems.

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex-post adaptation, typically associated with natural selection, and its proactive counterpart, ex-ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified an ecosystem stability, (ii) identified correlations between species richness and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information. Additionally, we observed that (v) changes in ecosystems are constrained to a low-dimensional state space, with three distinct alteration trajectories—fluctuations, rapid environmental responses, and long-term changes—converging into this state space in common. These findings suggest that daily fluctuations may predict broader ecosystem changes. Our experimental insights, coupled with an exploration of living systems' information dynamics from an ecosystem perspective, enhance our predictive capabilities for natural ecosystem behavior, providing a universal framework for understanding a broad spectrum of living systems.

Statistical management policy and system plays significant role in development of any nation as it guides the planner to analyze the gaps, design programs and allocate budget in efficient manner. Globally, design and usage of statistical management system varies relying on country specific ability and resources availability. Government of Nepal has been managing database in individual as well as thematic manner. Policy wise, thematic database is in operation based on Statistics Act 1958 and individual database is in operation based on Birth, Death and Other Personal Related Events Registration Act 1976. Though civil registration related policies have been either updated or newly formed, sectoral statistic management related policy has not been updated since its first promulgation period (1958). With no policy supporting the consolidated statistical management system, government as well as non-government agencies has been investing millions of dollars annually to collect, compile and analyze individual related data and fulfill their respective objectives. It has forced the government services and performance to be paper-based, time-consuming and tedious thus creating dis-trust among citizen each passing day. Though, formulation of legal instrument promoted digital movement and mandated shift towards e-government modality, the process has been low. Theoretically, Technology Acceptance Model (TAM) state the need of having advanced and efficient technology to be in place in current setting to improve governance, efficiency, accountability, transparency and trust. Also, the same theory state the requirement of strong willingness and motivation from the users (government, non-government and citizen) to adopt the technology based policy and system. As the integrated statistical management policy emphasizes on technology-based system or digital system, the perspectives of Technology Acceptance Model also termed as Information System Theory aid in understanding the context, conceptualizing and addressing the needs with an establishment and implementation of digital system or technology based system. This policy paper applied the Technology Acceptance Model (TAP) to examine the underlying opportunities and challenges associated with an integrated statistical management policy. With existing gaps in existing policy, system and practice alongside an overview of opportunities and challenges, this paper recommends the need of stand-alone integrated statistical management policy to consolidate sectoral as well as civil related information through single system based on recommended policy.

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.

Crowdsourced Data (CSD) generated by citizens is becoming more popular as its potential utilisation in many applications is increasing due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data collection procedures. The quality of CSD can be assessed according to a range of attributes including its relevance. Information relevance has been explored through using in Geographic Information Retrieval (GIR) techniques to identify relevant information. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. The thematic and geographic relevance were assessed using the Term Frequency-Inverse Document Frequency (TF-IDF), Vector Space Model (VSM) and Natural Language Processing (NLP) techniques. The thematic and geographic specificities of the queries were calculated as 0.44 and 0.67 respectively, which indicates the queries used were more geographically specific than thematically specific. The Spearman's rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment for other possible crowdsourced data analysis.

Unmanned aerial vehicles (UAV) have emerged as a solution to day to day survey tasks, allowing users to visualize phenomena in real-time. This paper explores the capabilities of UAV or drones in the collection of accurate, geo-tagged data quickly, including photogrammetry software processes to deliver standardized data output. In order to explore the capabilities of UAV, Gatu Township in Centenary, Muzabani District of Zimbabwe was chosen from the national mapping topographic series. This study demonstrates the efficiency of data collection using drones, generate 2D orthomosaics in real time, so that analysts can easily visualize land cover and identify any changes, map and model large areas to produce data for 2D and 3D models. The recent development of innovative optical image processing has further lowered the costs high resolution topographic surveys.

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.

The state of BIM adoption and implementation in facility management in Africa has been regarded as a developmental discourse gradually gaining grounds. While several studies have focused on manual review of the intellectual progression of BIM research, this confined review however understates the evolution of BIM in facility management. This approach therefore is not always representative of the picture of BIM in facility management at its formative stage. This paper aims to present a scientometric review of BIM in facility management exploring the intellectual progress made so far by authors. The state of the art of BIM in facility management research and practice and the challenges debilitating against BIM in facility management research. The review revealed a considerable amount of research endeavours limited by inadequate collaboration acorss organizations and institutions. The study also revealed the low degree of awareness amongst researchers in the AEC sector. While North America and China were leading countries due to the activity of few universities, Africa wasn’t as visible in research endeavours. The study recommended an increase in awareness drive across countries and increased government effort, joint funding for research to include gaps in practice issues.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

The problem of identifying functional regions in an urban setting has been approached in literature using two general methodologies: top-down, encoding expert knowledge on urban planning and design (e.g. into patterns) and using that knowledge for identification, and bottom-up, relying on crowdsourcing and Volunteered Geographic Information (VGI) to train learning models, using techniques such as Latent Dirichlet Allocation (LDA) topic modeling. Both approaches have their advantages but also face important limitations, with knowledge-based approaches being criticized for scalability and transferability issues and data-driven approaches for lacking interpretability and depending heavily on data quality. To mitigate these disadvantages, we propose a novel framework that fuses data and knowledge in three different ways: functional regions identified from individual approaches are evaluated against each other, knowledge from patterns is used to adjust learning model results and topic models are used to adjust pattern-based results. The proposed methodologies are demonstrated through the use case of identifying shopping-related functional regions in the Los Angeles metropolitan area. Results show that the combination of results from knowledge-based and data-driven techniques can help uncover discrepancies between the two different approaches and smoothen inaccuracies caused by the limitations of each approach.

The analysis of the land use dynamics of the Lac Télé Community Reserve (RCLT) using Landsat Thematic Mapper (TM) and (Enhance Thematic Mapper) ETM+ images highlight significant changes in the vegetation cover from 1980 to 2000 and 2018. Thus, the rate of forest area decreased by 21.41% for the entire LTCR in favor of savannahs which increased by 15.23%. The conversion of this forest area to savannah due to the practice of slash and burn agriculture facilitates the opening up of the forest area and contributes to greatly degrading the spawning grounds of fish species from the Likouala aux herbes river. For the mapping of fishing activity in general and the ecological characterization of the 151 identified spawning grounds in particular; the respective mean values of the physical and chemical water parameters; temperature (28.13°C), pH (4.23) and depth (3.34) did not vary significantly from one selected village to another between July and September 2019. The fish diversity unregistered during the study, in the 07 pilot villages would be due to the diversity of the microhabitats noted in the villages of the LTCR, especially from the villages; Botongo, Mossengue and Bouanela where the indices of ichthyological diversity were the highest.

Intelligent systems are increasingly present in various areas of society, gaining special relevance in healthcare environments. In this work, grounded on previous developments proposed by the authors, the evolution and improvement of a novel intelligent system focused on the diagnosis of obstructive sleep apnea (OSA) is addressed. To do this, starting from two packages of information of a heterogeneous nature, and deploying a set of Machine Learning classification algorithms, focused each of them on different levels of the apnea-hypopnea index (AHI), as well as a set of cascaded expert systems, it is possible to obtain a series of risk metric pairs (Statistical Risk, Symbolic Risk) for each AHI level, understood as a warning forecast associated with the early detection of the pathology. The risk indicators of each pair are subsequently aggregated by means of a symbolic inference system, whose knowledge base was determined using the automatic rule generation approach proposed by Wang and Mendel. At the output of each inference system, a risk indicator is obtained for each AHI level, the Apnea Risk, whose interpretation makes it possible to discriminate between patients who could suffer OSA and those who do not, as well as to generate the appropriate recommendations. For the initial tests of the system, the design and development of a specific software artifact was carried out using data from 4,400 patients from the Álvaro Cunqueiro Hospital in Vigo, obtaining when applied to the test set AUC values within the range 0.74–0.88, which invites optimism and points to the benefits expected from the architecture of the proposed intelligent system.

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.

Living systems must solve the problem of realizing their environment to act appropriately to maintain external relationships and internal order for survival and reproduction. This problem is a biological version of the philosophical enigma of how the self can escape solipsism. This study develops a comprehensive model to solve this adaptation problem. LSs must be composed of material entities that have the property of detecting their external states. The detection of the outside is conceptualized as “cognition,” a related state change that occurs at three levels of nested hierarchy: physical, chemical, and semiotic cognitions. Any entity that cognizes the surroundings is a “cognizer.” The hierarchical extension of cognition to all levels of the world provides a monistic view that only cognizers exist. Semiotic cognition is essential for the emergence and evolution of life. Living systems invented semiotic cognitions based on physical and chemical cognitions to manage the probability distribution of events that occur to them. This study proposes a theoretical model in which semiotic cognition is an adaptive process in which inverse causality operations produce particular internal states as symbols that signify hidden external states. This operation makes the living systems aware of the external world.

This study assessed the perception and use of digital applications for soil fertility management and conservation strategies among small-scale crop farmers in southwest Nigeria. A total of 376 farmers were randomly selected across the six southwest states. The data collected were analyzed using descriptive statistics and Logit regression. Majority of the farmers relied on perception and other non-conventional approaches such as the appearance of weeds and performance of crops in the previous season to assess soil fertility. Only 1.1% and 0.3% of the farmers assessed soil fertility through soil test and digital application respectively. Most farmers adopted bush fallowing and the use of inorganic fertilizer to improve soil fertility. Although, 4.8% of the farmers indicated that they had digital application on their mobile phones, only 2.9% reported to have used such. More than half (56.4%) of the famers stated that lack of awareness of the existence of digital application and internet enabled telephones were reasons they have not been able to use digital applications. Majority of the farmers (97.3%) indicated their willingness to embrace the use of new farm decision digital application which can provide more information especially on soil fertility if introduced. Logit model results revealed that literacy level, membership of farmers’ association and extension contact significantly increased the likelihood of willing to use soil digital application, but older farmers are less likely to use digital application.

Semantic interoperability is a foundational concept in modern healthcare, enabling seamless communication and meaningful data exchange across diverse EHR systems. At the heart of this concept lies archetype clinical information models, which serve as standardized templates to structure and encode clinical data, ensuring consistency and shared understanding among various healthcare stakeholders. This review paper delves into the intricacies of Electronic Health Records (EHRs) within the context of semantic interoperability and the pivotal role played by archetype clinical information models. Drawing from a wealth of recent research, this review paper examines the theoretical underpinnings of semantic interoperability. By surveying the literature, it synthesizes the diverse approaches and methods employed to achieve semantic interoperability within EHR systems. Furthermore, this review does not shy away from addressing the significant challenges that persist in this arena. These challenges encompass issues related to data quality, governance, resistance to change, and the scalability and integration hurdles that healthcare organizations encounter in their quest for interoperable EHR systems. Through a comprehensive review of relevant research papers from esteemed sources including PubMed, ScienceDirect, and Taylor & Francis, the paper offers a comprehensive understanding of the approaches and methods employed in this domain. This analysis encompasses a time frame ranging from 2015 to 2023, ensuring an up-to-date assessment of the field. By presenting a well-rounded picture of the field, this review paper offers valuable insights for healthcare stakeholders, researchers, and practitioners. It underscores the enduring importance of semantic interoperability in EHR systems, not only in advancing patient care, clinical decision support, and healthcare operations but also in driving data-driven innovations in the dynamic and ever-evolving medical healthcare sector. This comprehensive examination sets the stage for future advancements in the pursuit of truly interoperable and data-driven healthcare systems.

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.

Communication, compression of information, transmission of information through noisy channels, interconnecting different information systems, cryptography, gate construction –– these areas all depend on classical information theory. We show that, in classical terms, semantic aspects of communication are not at all irrelevant to the engineering problem, contrary to Shannon, and affect the message intended to be transmitted. This is revisited and captured by an analogy to trust, in that they are essential to the channel (for proper use), but cannot be transferred (under risk of flaws) through that same channel. Information is also described by, at least, a tri-state system — not by binary logic. The trust analogy semantics can be coded as the Curry-Howard relationship, connecting computer code with structural logic, by way of different categories. Two-state and Boolean logic (aka Shannon semantics) was used classically before, with Shannon theory, but without trust analogy semantics – found to be a sine qua non condition. This is now familiar in classical gate construction with physical systems with, e.g., Verilog and SystemVerilog. The applications to computation and quantum theory are further explored. The most fundamental entity in today`s theory of information is proposed to use at least three logical states, not bits, in all applications, including: cyber-physical systems, devices, in computation, and in quantum theory.

Competing and complementary models of resting-state brain dynamics contribute to our phenomenological and mechanistic understanding of whole-brain coordination and communication, and provide potential evidence for differential brain functioning associated with normal and pathological behavior. These neuroscientific theories stem from the perspectives of physics, engineering, mathematics, and psychology and create a complicated landscape of domain-specific terminology and meaning, which, when used outside of that domain, may lead to incorrect assumptions and conclusions within the neuroscience community. Here we review and clarify the key concepts of Connectivity, Computation, Criticality, and Coherence — the 4C’s — and outline a potential role for metastability as a common denominator across these propositions. We analyze and synthesize whole-brain neuroimaging research, examined through functional magnetic imaging (fMRI), to demonstrate that complexity science offers a principled and integrated approach to describe, and potentially understand, macroscale spontaneous brain functioning.

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.

Based on a measuring system to determine levels of spatial organization in 2D polygons (homogeneous or heterogeneous partition of defined areas) lying on principles of regularity, we propose the entropy term linked to the concept of “information”, from the “information theory field”, in order to obtain an information measurement regarding a quantity of or amount of information in the architecture of complex 2D biological organizations. The term “quantity” does not refer to the amount of data (size), but to the probability of a geometrical basic pattern within a set of possible statistical configurations regarding levels of homogeneity and heterogeneity. It is this notion of information that is important in information theory, and measures of information in units of bits, what we propose to use for measuring quantities of organization in the architecture of complex geometrical systems. Two complex systems are tested, biological and non biological in order to obtain experimental results, which are verified with the evaluation criteria “entropy”. Experimental results show that the lowest levels of information and entropy, in addition with low rates of heterogeneity and high rates of homogeneity are particular features of geometrical organizations in biological systems.

To proactively manage information security, enterprises often employ information security risk assessment techniques. Asset value - which is used to calculate the financial impact of possible threats - is one of the key parameters of information security risk. However, assets in a system are rarely independent, and their values are typically interdependent. Asset owners and IT teams may hold different views as regards these values, and there is thus the need to reduce subjectivity in a qualitative risk assessment. The research entails the development of a conceptual framework derived from the literature to minimize subjectivity, and the design of a system based on those concepts. The study uses the Unified Modeling Language as a design tool and puts forward an object-oriented model for defining asset values, in which the relationships between assets, vulnerabilities and related threats are identified. A “segregation of duties” approach is integrated into the risk management system to mitigate against subjectivity and better determine asset values. Survey responses from 16 practitioners working in the private and public sectors confirm the validity of the approach, but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate.

This paper explores Ghana's two-decade journey in developing a digital land information system (LIS), exemplified by Ghana Enterprise Land Information System (GELIS) and Enterprise Land Information System (ELIS). Despite advancements by Ghana’s Lands Commission, manual processes persist and coexist with digital ones. Our study uses a qualitative approach to assess the effectiveness of the LIS implemented in Accra. The study found that Accra’s LIS is highly aligned with clear institutional mandates, roles, and responsibilities as well as the availability of laws and policies to support analogue to digital conversion. Furthermore, a robust approach for protecting data, operating system and software underpins the system. Subsequently, the integration of a digital LIS has enhanced service delivery and accountability. Nonetheless, Accra’s LIS is still at the beginning of a comprehensive learning and development curve. To sustain the LIS requires furtherance in the implementation plan, funding, law implementation, ICT strategy, divisional integration, work processes, data quality, and communication strategy. These findings will inform the next steps of improvement for Accra's LIS and guide its nationwide scaling, contributing to discussions on technology acceptance dynamics in predominantly analogue environments.

The paper proposes a human explainable artificial intelligence approach for mapping the status of environmental phenomena from multisource geo data. It is both knowledge and data driven: it exploits remote sensing expert’s knowledge to define the contributing factors from which partial evidence of the environmental status can be computed. Furthermore, it aggregates the partial evidences to compute a map of the environmental status by adapting to a region of interest through a learning mechanism exploiting Volunteered Geographic Information (VGI), both from in situ observations and photointerpretation. The approach is capable to capture the specificities of local context as well as to cope with the subjectivity and incompleteness of expert’s knowledge. The proposal is exemplified to map the status of standing water areas (i.e. water bodies and river, human driven or natural hazard flooding) by considering satellite data and geotagged observations. Results of the validation experiments were performed in three areas of Northern Italy, characterized by distinct ecosystems. Results of the proposed methodological framework showed better performances than traditional approaches based on single spectral indexes thresholding. The use of expert’s knowledge, possibly imprecise/uncertain and incomplete, the need of few ground truth data for learning, and finally the explainability of learned rules are the distinguishing characteristics of the proposal with respect to traditional machine learning methods.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

Simulation tools capturing the interactions of communication and electrical system operation represent a powerful support for fully assessing the potential benefits and impacts of ICT in future smart power distribution network. A strong interest is upon the possibility of exploiting the last generation communication systems for supporting the transition of distribution network towards a smart grid scenario. Having in mind the above, the authors propose a numerical co-simulation tool useful to thoroughly understand the impact of the communication networks on the performance of whole power system dynamics. The co-simulation tool has been purposely developed to simulate the highly time-critical smart grid application of fault management and network reconfiguration and permits reproducing and evaluating the behavior of the public mobile telecommunication system 4G Long Term Evolution (LTE), as communication technology for smart grid applications. Results of the paper demonstrates that LTE provides good performances for supporting the data communication required to perform fault location, extinction and a subsequent network reconfiguration in smart power distribution networks.

Knowledge is not merely the characteristic of certain pieces of information being deemed true and justifiable, but rather a process. Within an axiomatic system, the set of information that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the process of justification (exchange of information), if it consistently justifies a particular piece of information, it elevates it to knowledge. In this paper, we provide a framework for knowledge within an axiomatic model where we define the fundamental elements of communication and grammar. As a result, the structure dictates that knowledge, to be considered as such, must be investigateable by anyone who utilizes it in its entirety, both in form and content. Ultimately, the development of knowledge processing, referred to as \emph{knowmatics}, represents a clear evolution of the computer science that processes information (subset of knowledge).

Effective management of the medicine supply chain is crucial for ensuring the availability of essential medicines and supplies in public health facilities. This study aimed to determine the utilization of the electronic logistic management information system (e-LMIS) in public health facilities and its implications for the medicine supply chain. A mixed-methods approach, combining both quantitative and qualitative data collection methods was used. The study was conducted in 106 healthcare providers from 35 public health facilities in Singida District. Six key informants were interviewed in a qualitative method. Of 106 participants, (62.3%) said they were somehow competent in e-LMIS utilization. In in-depth interviews, respondents underscored the system's utility for tracking stock levels, procurements, and managing orders. Staff shortage and lack of customized training were mentioned as major challenges, hindering efficiency in managing drug supplies. This study highlighted the positive impact of e-LMIS on various aspects of the medicine supply chain, including the timely submission of orders and enhanced inventory management. Sustained management support and regular utilization of the e-LMIS system are crucial for building and maintaining competence among healthcare providers, thereby optimizing the medicine supply chain and ultimately improving healthcare delivery.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

Information geometry (IG) seeks to characterize the structure of statistical geodesic models from a differential geometric point of view. By considering families of probability distributions as manifolds with coordinate charts determined by the parameters of each individual model, the tools of differential geometry, such as divergences and metric tensors, provide effective means of studying their characteristics. The research undertaken in this paper presents a novel approach to the modelling study of information geometrics of a queueing system. In this context, the manifold of a stable M/G/1queue is characterised from the viewpoint of IG. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of stable M/G/1 queue manifold are devised. In addition to that, new results that uncovered the significant impact of stability of M/G/1 queue manifold on the existence of (IFIM) are obtained. The Kullback’s divergence (KD), and J-divergence (JD).New result has been devised on the significant impact of both server utilization and squared coefficient of variation of the underlying M/G/1 queue manifold on both (KD) and (KD) are devised. Also, it is revealed that stable M/G/1 QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of M/G/1 queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability and the instability phases of the underlying M/G/1 queue manifold. Furthermore, a new discovery that presents the significant impact of stability of M/G/1 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. It is also shown that the obtained (IME) is unstable. Also, it is shown that stability of the devised (IME) enforces the instability of M/G/1 queue manifold. Unifying IG with Queueing Theory enables the study of dynamics of queueing system from a novel Riemannian Geometric (RG) point of view, leading to the analysis of the stable M/G/1 queue, based on the Theory of Relativity (TR).Extending the study over two new additional divergence measures, namely and together with a complete illustrative numerical results for all these measures including KD, JD. This links Queueing theory, IG with deep machine learning and metric learning. Furthermore, this reveals the revolutionary approach of queue learning. Full analytic study of Gaussian curvatures subject to both Angular and Monge techniques together with the overall stability dynamics impact on these curvatures. Full analytic study of Einestein Tensor and Stress Energy Tensor together with the overall stability dynamics impact on these curvatures. The inclusion of the definitions of Gaussian and Ricci, Ricci scalar curvatures and Einstein Tensor together with their physical interpretations; The proposed novel approach for the pioneer visualization of queueing systems via computational information geometry. The determination of new important links between classical queueing theory and other mathematical disciplines, such as IG, matrix theory Riemannian geometry and the THEORY OF RELATIVITY by providing for first time i) The full detailed derivations of the Gaussian curvature ii) The Ricci curvature tensor and iii) The full physical as well as the geometric interpretation of these new results. The provision of a novel link between Ricci Curvature (RCT) and the stability analysis of the stable M/G/1 QM. The full investigation of the newly introduced QT-IG unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them. The full investigation of the newly introduced (QIGU) unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them.

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.

Building information modeling (BIM) is the digital representation of physical and functional characteristics (such as geometry, spatial relationship, and geographic information) of a facility to support decisions during its life cycle. BIM has been extended beyond 3D geometrical representations in recent years, and now includes time as a fourth dimension and cost as a fifth dimension, as well as such other applications as virtual reality and augmented reality. The Internet of Things (IoT) has been increasingly applied in various products (smart homes, wearables) to enhance work productivity, living comfort, and entertainment. However, research addressing the integration of these two technologies (BIM and IoT) is still very limited, and has focused exclusively on the automatic transmission of sensor information to BIM models. This paper describes an attempt to represent and visualize sensor data in BIM with multiple perspectives in order to support complex decisions requiring interdisciplinary information. The study uses a university campus as an example and includes several scenarios, such as an auditorium with a dispersed audience and energy saving options for rooms with different functions (mechanical/electric equipment, classrooms, and laboratory). This paper also discusses the design of a common platform allowing communication among sensors with different protocols (Arduino, Raspberry Pi), the use of Dynamo to accept sensor data as input and automatically redraw visualized information in BIM, and how visualization may help in making energy-saving management decisions.

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.

Wireless Powered Communication Networks (WPCN), which has attracted much attention of researchers, also been recently recommended in 5^th generation (5G) wireless networks. With the help of the WPCN, the reliability and battery life of wireless low-power devices can be improved. In this paper, we investigate throughput and ergodic capacity in WPCN-assisted amplify-and-forward (AF) relaying system, considering two transmission modes including delay-tolerant and delay-limited. As important achievement, we propose symmetric energy harvesting protocol, namely time power switching relaying (TPSR) in order to find maximal throughput. In particular, both time switching and power switching coefficients in this schemes are considered. Unlike most of the previous works, we further focus on impact of outdated channel state information (CSI) in this WPCN. In order to evaluate information processing efficiency, the performance can be substantially improved by optimally harvesting time and power coefficients of the received signal at relay node for energy and information extraction, and by deploying several scenarios. By deploying Monte Carlo simulation, it is confirmed that the system performance is more sensitive to CSI estimation error, noise variance, signal-to-noise ratio (SNR) and resulting in other reasonable computations of TPSR need be deployed to obtain QoS requirement.

Others have proposed that living systems are characterized by the possession of a kind of information unique to them. This information, variously called semantic, symbolic, or coding information conveys meaning from senders to recipients, most commonly among genome and other cellular components, different cells, organisms, etc., enabling all biological functions. Unlike other kinds of information, it is only indirectly quantifiable and does not increase by copying. Here, the term system information is used to encompass the entire package of coding information enabling all organisms of a species to complete their life cycles through possession of its copy (Crkvenjakov and Heng 2021). System information grows in a nonreductive evolution only during speciation proper by the acquisition of novel functions by daughter species. Contemporary science agrees that species are units of evolution but has failed to recognize and parse the importance of phases of speciation and stasis for evolution. Confinement of system information change to speciation only explains for the first time why there are species and why their number increases nearly exponentially throughout evolution. More importantly, here we define that evolution as a process operating on a planetary scale, while chance based, is non-random mathematically since it obeys the Laws of Information which dictate the behavior of System information in organisms. Growth and persistence of system information on the non-degenerative portion of Tree of Life follow the law of conservation, which states that its growth is as parsimonious (minimal) as possible and the persistence of parental system information the maximal possible in each unit speciation step. This formulation is a restatement of Loewnstain's (1999) principle of information economy and our lock-in law (Crkvenjakov and Drmanac 2007). The principle holds that since, on chance assumption, the system information change cost is directly proportional to time, more costly pathways of achieving the same new functional goal will be, almost without exception, competed out by the less costly ones. As a corollary of the conservation law, the speciation law states that speciation follows the most economical way of growth of system information while conserving the existing one, which is horizontal information transfer by molecular sex (uptake of exogenous nucleic acid into the progeny genome) by exchange among two parents from maximally possible divergent lineages. The speciation law dictates that molecular sex mode be used since it is the most parsimonious one. The molecular sex cost in information bits is zero to the recipient, which is not the case with the generally accepted mode of vertical accumulation of information bits in a population gene pool within a lineage.

Construction Design Management (DM) involves the coordination of design process and activity to ensure quality project design, yet it involves many challenges. This work reports on a collaborative Knowledge Transfer Project (KTP) that tackled several challenges faced by construction design management practitioners. Employing a design science research methodology (DSRM), qualitative analysis of semi-structured interviews with purposively identified design managers first uncovered real world concerns around design coordination, and designer performance monitoring. Then to address these concerns, a web-based design management prototype dashboard was developed which uses typical project data to aid the management of design coordination activity and facilitate design management task prioritization and reporting functionalities. By digitalizing selected design management processes to improve efficiency, and productivity, the visual reporting capability of the dashboard enables design production to be monitored at project and portfolio levels; trends in designer performance to be assessed and focus to be retained on technical queries (TQs) and requests for information (RFIs). By reporting on the development of this web-based Design Management prototype dashboard and illustrating how it can enhance design management productivity in construction firms, this work also provides further evidence of how employing a design science research methodology in construction can produce practical solutions that address real world construction concerns.

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.

With the proliferation and development of social media platforms, social media data has become an important source for acquiring spatio-temporal information on various urban events. Providing accurate spatio-temporal information for events contributes to enhancing the capabilities of urban management and emergency response. However, existing research on mining spatio-temporal information of events often focuses solely on textual content, neglecting data from other modalities such as images and videos. Therefore, this study proposes an innovative spatio-temporal information extraction method for multi-modal social media data (MIST-SMMD), which extracts the spatio-temporal information of events from multi-modal data on Weibo at coarse and fine-grained hierarchical levels, serving as a beneficial supplement to existing urban event monitoring methods. This paper takes the "July 20th Zhengzhou Heavy Rainfall" incident as an example, to evaluate and analyze the effectiveness of the proposed method. The results indicate that in the coarse-grained spatial information extraction using only textual data, our method achieves a Spatial Precision of 87.54% within a 60m range, and reaches 100% Spatial Precision for ranges beyond 200m. For fine-grained spatial information extraction, the introduction of other modal data such as images and videos results in a significant improvement in Spatial Error. These results demonstrate the ability of the MIST-SMMD method to extract spatio-temporal information from urban events at both coarse and fine levels, and confirms the significant advantages of multi-modal data in enhancing the precision of spatial information extraction.

Data creation is often the only way for researchers to produce basic geospatial information for the pursuit of more complex tasks and procedures such as those that lead to the production of new data for studies concerning river basins, slope morphodynamics, applied geomorphology and geology, urban and territorial planning, detailed studies, for example, in architecture and civil engineering, among others. This exercise results from a reflection where specific data processing tasks executed in Google Sketchup (Pro version, 2018) can be used in a context of interoperability with Geographical Information Systems (GIS) software. The focus is based on the production of contour lines and Digital Elevation Models (DEM) using an innovative sequence of tasks and procedures in both environments (GS and GIS). It starts in Google Sketchup (GS) graphic interface, with the selection of a satellite image referring to the study area—which can be anywhere on Earth's surface; subsequent processing steps lead to the production of elevation data at the selected scale and equidistance. This new data must be exported to GIS software in vector formats such as Autodesk Design Web format—DWG or Autodesk Drawing Exchange format—DXF. In this essay the option for the use of GIS Open Source Software (gvSIG and QGIS) was made. Correcting the original SHP by removing “data noise” that resulted from DXF file conversion permits the author to create new clean vector data in SHP format and, at a later stage, generate DEM data. This means that new elevation data becomes available, using simple but intuitive and interoperable procedures and techniques which confgures a costless work flow.

Product introductions play a pivotal role in the success and competitiveness of manufacturing, particularly in the context of new product development. Low-volume manufacturing companies exhibit distinct characteristics that significantly influence the product introduction process. Notably, the quality of information exchange between product design and production interfaces becomes paramount in this high-mix and low-volume industry. Consequently, there exists a pressing need to explore various methods for adapting and customizing management mechanisms and strategies to align with the unique requirements and characteristics of low-volume manufacturing and its associated product introduction processes. While the need for this knowledge is critical in the context of low-volume manufacturing, a majority of existing studies predominantly focus on the management aspects within high-volume manufacturing. Therefore, the primary aim of this study is to investigate the customization of product introduction management strategies to enhance the quality of information content within the realm of low-volume manufacturing. Drawing upon a longitudinal analysis of new product introductions within a low-volume manufacturing company, this study narratively elucidates how the examined company implemented product introduction management strategies to facilitate the enhancement of information content quality during the product introduction phase. The study presents its findings on management strategies across three key stages: pre-implementation, post-implementation, and subsequent modification and refinement of strategies following the initial round of implementation. By shedding light on these strategies, this study offers a comprehensive understanding of management approaches in the context of low-volume product introduction. As a result, it makes a valuable contribution to the discourse on information management and interface management in the high-mix, low-volume manufacturing literature.

Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled - physically and virtually interconnected - data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms and Simulated Annealing. This algorithm can be used by orchestration applications to deploy services across large cluster of nodes by making better decision in the route design.

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.

There is a growing consensus that to effectively adapt to climate change, cities need user-friendly tools and reliable high-resolution biophysical and socio-economic data for analysis, mapping, modeling, and visualization. This study examines availability of various types of information used in climate adaptation plans of 40 municipalities with population less than 300,000 people in the United and in France, probing into the choice and usage of relevant information by small municipalities. We argue that non-climatic spatial data, such as population demographic and socio-economic patterns, urban infrastructure, and environmental data must be integrated with climate tools and datasets to inform effective vulnerability assessment and equitable climate adaptation planning goals. Climate adaptation plans frequently fail to address the existing structural inequalities and environmental injustices in urban infrastructure and land use. Adaptation methodological approaches should be reassessed in the context of much needed societal transformation. Lessons learned from our studies offer valuable insights for potential development of the national and state-level climate adaptation information services for cities.

The partial information decomposition (PID) framework is concerned with decomposing the information that a set of random variables has with respect to a target variable into three types of components: redundant, synergistic, and unique. Classical information theory alone does not provide a unique way to decompose information in this manner and additional assumptions have to be made. Inspired by Kolchinsky's recent proposal for measures of intersection information, we introduce three new measures based on well-known partial orders between communication channels and study some of their properties.

The quality of underground space information has become a major problem that endangers the safety of underground spaces. Currently, the main methods for the high-precision and long-distance transmission of detection information are radar and optical methods. However, in practical applications, we found that the radar method has the shortcomings of large energy loss and poor anti-jamming ability, which limit the accuracy of information data transmission and distance. The optical method has the shortcomings that the weather has a great impact on its accuracy and can only be applied to static objects above ground; therefore, it has the limitation of application objects and use environment. More importantly, the current high-precision information remote detection methods are limited to the detection of overground space objects and are not applicable to the detection of various information data in underground space. In this study, we analyze the spectral properties of the fractional differential operator and find that it is suitable for studying non-linear, non-causal, and non-stationary signals. The theory of fractional calculus is applied to the field of data processing, and a mathematical model of remote transmission and high-precision detection of information based on fractional difference is established, which realizes the functions of high-precision and remote detection of information. By fusing the information data to detect the mathematical model over a long distance and with high accuracy, a mathematical model for stratum data processing used to provide long-distance and high-accuracy data was established. Through application in engineering practice, the effectiveness of this method for underground space information data detection was verified.

The Release of government dataset for public use can potentially strengthen the relationship between the government and its constituents. However, research shows that there are several challenges for open data effectiveness. This paper reviews current determinants and issues associated with the open government data (OGD) procedures. The review concentrates on two ends of the spectrum: First, from the perspective of the preparation by the government, focusing on the organization of traditional governmental datasets and how the recording of the data is administered. Second, from the perspective of the users, focusing on the way in which the data is released to the general public and on human-computer interaction (HCI) issues between end-user and data-consumption interfaces. Following a thorough analysis of these two opposing challenges, the paper proposes approaches to mitigate them. This review and subsequent recommendations contribute and expand current understanding of open government data effectiveness and can lead to public policy changes, development of new procedures and strategies, and ultimately improvements at both ends of the federal open data endeavor.

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.

Air sampling using vortex air samplers combined with species specific amplification of pathogen DNA, was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic dynamics. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of the pathogen abundance data, but also indicated that practicality may limit the capacity for definitively classifying the dynamics of air borne plant pathogen inoculum. Over the two years of the study five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating the pathogen abundance data were increasing or not, revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves, and also (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence is positive or not.