Past States Research Articles

APPROACHES TO FORECASTING THE LIKELY CONSEQUENCES OF INNOVATIVE TRANSFORMATIONS

The article considers the likely consequences of innovative re-engineering and approaches to their forecasting. Among the various variables, the variable states that characterize the current, past or future state of the production and technological system and which are very important for evaluating the effectiveness of innovative transformations are highlighted. It is justified that the normal law of error distribution is often used to predict the behavior of intensity variables. This determines the simplicity and convenience of calculations. The output variables are determined by the forecast model and the degree of its limitations. In order to build effective models for forecasting innovative transformations, initial data are needed, which are a sample of observation results or a posteriori data of other successful enterprises for some time periods (time series). Approaches to forecasting decisions regarding the financing of a general project of technological reengineering of production based on taking risks into account are considered. Critical and catastrophic financial risks are highlighted. It is noted that risks for investors and the company's management may arise in the event of changes in the organizational forms of interaction between partners, which occur with the transfer of intangible assets - temporary or permanent assignment of rights to intellectual property to investors in exchange for investments and return of rights to the owners of the enterprise after payment of the principal debt and profit to the investors. One of the main types of risk after carrying out technological reengineering of production based on innovative transformations is the risk of promoting innovative products created on a new technological base and the unpredictable reaction of potential consumers. The structure of the reserve for covering unforeseen costs is given.

A Multidimensional View of Intellectual Capital and Dynamic Innovative Performance

A review of the literature on intellectual capital development was conducted using systemic criteria for the inclusion of relevant studies. The concepts behind the ideas explored in the present study were discussed in respect to the subject matter. Examining the past state of the art in the intellectual capital sector for achieving high levels of innovation performance provided a multidimensional picture of intellectual capital, innovation performance, and dynamic capabilities. The present review was designed to illustrate the correlation between intellectual capital and innovation performance, as well as the role of dynamic capabilities in moderating the relationship between these constructs. Accordingly, we presented an extensive discussion on the relevant fundamental theoretical perspectives of contingency and resource-based views to provide an in-depth understanding of the abovementioned correlation. Finally, the conceptual framework was illustrated.

Automatic hermiticity for mixed states

Abstract We previously proposed a mechanism to effectively obtain, after a long time development, a Hamiltonian being Hermitian with regard to a modified inner product IQ that makes a given non-normal Hamiltonian normal by using an appropriately chosen Hermitian operator Q. We studied it for pure states. In this letter we show that a similar mechanism also works for mixed states by introducing density matrices to describe them and investigating their properties explicitly both in the future-not-included and future-included theories. In particular, in the latter, where not only a past state at the initial time TA but also a future state at the final time TB is given, we study a couple of candidates for it, and introduce a “skew density matrix” composed of both ensembles of the future and past states such that the trace of the product of it and an operator ${\cal O}$ matches a normalized matrix element of ${\cal O}$. We argue that the skew density matrix defined with IQ at the present time t for large TB − t and large t − TA approximately corresponds to another density matrix composed of only an ensemble of past states and defined with another inner product $I_{Q_J}$ for large t − TA.

Steller’s Sea Cow and Kelp Forest Regeneration in the North Pacific

Modern ecosystems are almost universally degraded relative to their past counterparts, from the Pleistocene to the present day. Thus, modern ecosystems may serve as poor guides to conservation actions. Conservation paleobiology is well-suited to address this challenge through enhanced understanding of systems dynamics during past periods of greater species and functional diversity, abundances, and resilience. However, past and present ecosystem dynamics must be integrated to model the future impacts of conservation actions. Here we propose a three-step, Past-Present-Future (PPF) methodology rooted in mathematical modeling. First, construct a model of primary species and interactions of the present-day ecosystem, including biotic and abiotic components. Second, integrate historical and/or paleontological data into the model to investigate past states and processes of the ecosystem, with an emphasis on critical elements (e.g., ecological engineer species) that are no longer present. Third, integrate analyses from the first two steps to predict putative future dynamics and states, and use these to make testable predictions regarding specific conservation interventions. We illustrate this approach with a study investigating impacts of the now-extinct Steller’s sea cow on north Pacific giant kelp forests. The model indicates that the historical system was distinct from the modern, with differing abundances of giant kelp and understory algae. Furthermore, the familiar kelp-dominated state is metastable, capable of rapid transitions to an urchin-dominated state if perturbed by extreme hydrodynamic events, disease-driven reductions of seastar predation, or disease coupled to extreme warming events. We explore the possibility of increasing the resilience of modern forests by artificially recreating at least some of the ecological impacts of sea cows, accounting for metabolic requirements, estimates of abundance based on recent analyses of ancient DNA, and trophic impact.

ASSESSING AND MODELLING URBAN HEAT ISLAND IN BAGUIO CITY USING LANDSAT IMAGERY AND MACHINE LEARNING

Abstract. The Urban Heat Island (UHI) is a phenomenon where an urban area experiences higher temperatures than its surroundings. A commonly observed phenomenon worldwide and is one of the serious environmental problems related to urbanization. This paper assessed the past and current state of UHI in Baguio City, the Summer Capital of the Philippines. Land Surface Temperature (LST) layers were generated from Landsat images (March 25, 2019, and March 09, 2022) using the Project GUHeat Toolbox and then used to calculate the Urban Thermal Field Variance Index (UTFVI). The study found out that the UHI has intensified in the past three years. In contrast LST in March 2022 was generally lower than that in March 2019, most likely due to differences in weather conditions. This implies that while it is important to examine the spatiotemporal variations of LST, it is critical that UHI indices are also examined. Random Forest regression was used to examine the UHI indices such as Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). The explanatory variables used in modelling are (1) NDBI (2) NDVI (3) combination of NDBI and NDVI. The performance of the models is evaluated with Mean Squared Error (MSE) and R-squared (R2). Using NDBI or NDVI alone yielded a less satisfactory model. The combination of NDBI and NDVI resulted in a good prediction of UHI with R2=0.89 and MSE=0.006.

Domain adaptation in segmenting historical maps: A weakly supervised approach through spatial co-occurrence

Historical maps depict past states of the Earth’s surface and make it possible to trace the natural or anthropogenic evolution of geographic objects back through time. However, the state of the depicted reality is not the only source of change: maps of varying age can differ in terms of graphical design, and also in terms of storage conditions, physical ageing of pigments, and the scanning process for digitization. Consequently, a computer vision system learned from a specific (source) map series will often not generalize well to older or newer (target) maps, calling for domain adaptation. In the present paper we examine – to our knowledge for the first time – domain adaptation for segmenting historical maps. We argue that for geo-spatial data like maps, which are geo-localized by definition, the spatial co-occurrence of geographical objects provides a supervision signal for domain adaptation. Since only a subset of all mapped objects co-occur, and even those are not perfectly aligned due to both real topographic changes and variations in map generalization/production, they only provide weak supervision — still they can bring a substantial benefit over completely unsupervised domain adaptation methods. The core of our proposed method is a novel self-supervised co-occurrence network that detects co-occurring objects across maps (specifically, domains) with a novel loss function that allows for object changes and spatial misalignment. Experiments show that, for the task of segmenting hydrological objects such as rivers, lakes and wetlands, our system significantly outperforms two state-of-art baselines, even with limited supervision (e.g., 5%). The source code is publicly available at https://github.com/sian-wusidi/spatialcooccurrence.

Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes.

Many species are restricted to a marginal or suboptimal fraction of their historical range due to anthropogenic impacts, making it hard to interpret their ecological preferences from modern-day data alone. However, inferring past ecological states is limited by the availability of robust data and biases in historical archives, posing a challenge for policy makers . To highlight how historical records can be used to understand the ecological requirements of threatened species and inform conservation, we investigated sperm whale (Physeter macrocephalus) distribution in the Western Indian Ocean. We assessed differences in information content and habitat suitability predictions based on whale occurrence data from Yankee whaling logs (1792-1912) and from modern cetacean surveys (1995-2020). We built maximum entropy habitat suitability models containing static (bathymetry-derived) variables to compare models comprising historical-only and modern-only data. Using both historical and modern habitat suitability predictions we assessed marine protected area (MPA) placement by contrasting suitability in- and outside MPAs. The historical model predicted high habitat suitability in shelf and coastal regions near continents and islands, whereas the modern model predicted a less coastal distribution with high habitat suitability more restricted to areas of steep topography. The proportion of high habitat suitability inside versus outside MPAs was higher when applying the historical predictions than the modern predictions, suggesting that different marine spatial planning optimums can be reached from either data sources. Moreover, differences in relative habitat suitability predictions between eras were consistent with the historical depletion of sperm whales from coastal regions, which were easily accessed and targeted by whalers, resulting in a modern distribution limited more to steep continental margins and remote oceanic ridges. The use of historical data can provide important new insights and, through cautious interpretation, inform conservation planning and policy, for example, by identifying refugee species and regions of anticipated population recovery.

The biopsychosocial model of pain in physiotherapy: past, present and future

Background The biopsychosocial model of pain may aid the understanding pain and its clinical presentations. Objectives This paper presents a discussion of the past, present and future state of the biopsychosocial model of pain within physiotherapy. Main Findings The biopsychosocial model of pain acknowledges the dynamic interdependent biological, psychological and social dimensions of peoples pain experiences and has been widely endorsed. However, the biopsychosocial model is not beyond criticism and its applicability to clinical practice has been questioned. Researchers have investigated how clinicians understand and apply the biopsychosocial model in clinical practice. Evidence suggests that physiotherapists demonstrate varying levels of confidence and proficiency in their psychosocially-oriented clinical knowledge and practice. Psychologically informed physiotherapy treatment approaches have been described and trialled and show inconsistent results and effect sizes with respect to patient-related outcomes. In addition, commentators suggest that the ‘-social’ dimension of the model has been relatively neglected. While there is some evidence that the biopsychosocial model is evolving, and efforts are underway to develop and validate clinically-applicable tools, physiotherapy clinicians, educators and researchers have been invited to consider existing barriers and enablers to the implementation of the biopsychosocial model in clinical practice in order to improve its understanding and application within healthcare. Conclusion The biopsychosocial model invites clinicians to understand and address the biological, psychological, and social dimensions of patients pain. Understanding and implementation of the biopsychosocial model of pain in physiotherapy is mixed. Improving education and training and developing and evaluating innovative biopsychosocial-oriented interventions appear to be important ways forward. The biopsychosocial model of pain is evolving in response to scientific and clinical developments.

Critical reflections on the Juukan Gorge parliamentary inquiry and prospects for industry change

In May 2020, when Rio Tinto destroyed ancient rockshelters in Western Australia to expand an iron ore mine, public outcry triggered a parliamentary inquiry. The value and effect of public sector inquiries have been debated for over a century. While the Juukan Gorge inquiry overlooked some important issues, it succeeded in illuminating critical flaws in company, regulatory and administrative systems that trade on injustice. These issues have not been altogether neglected by past state and federal governments, but previous inquiries failed to drive meaningful reform. We conclude that while systemic change seems improbable, the evolving political milieu in Australia may offer prospects for industry change.

Geometry of last glacial sorted nets from high-resolution airborne data

Relict periglacial sorted nets are valuable indicators of past permafrost and climate states, but their field studies are usually challenging due to high time requirements and poor visibility. This study therefore tests the suitability of high-resolution digital elevation model and aerial photographs with a horizontal resolution of 0.5 m and 0.2 m, respectively, to map and analyse the geometry of 2000 sorted nets from the Last Glacial Period at one site in the Krkonoše Mts., Czech Republic. Since the sorted nets occupy an area of ∼1.79 ha, the density of their network accounts for ∼1117 cells per hectare. The sorted nets have a mean diameter of 3.46 ± 0.83 m, a mean height of 0.28 ± 0.10 m, and a mean estimated sorting depth of 0.94 ± 0.22 m. The number of sides ranges between three and ten, but averages 5.99 ± 1.13, and 83.4 % of the sorted nets are pentagonal to heptagonal, and their sides mostly meet at three-way junctions at a mean angle of 119.9 ± 25.6°. However, isometric sorted nets are rather rare as a length-to-width ratio attains 1.47 ± 0.28. The estimated sorting depth indicates that permafrost superimposed by ∼1 m thick active layer occurred at the study site at the end of the Last Glacial Period. Generally, the remotely sensed parameters of the sorted nets are consistent with field data previously collected at the same study site and literature reports including those on past regional environmental conditions. This proves the utility of the high-resolution airborne data to map and analyse the geometry of large sets of sorted patterns, which would be difficult in conventional field surveys. Consequently, remote sensing could bring a wealth of new information on sorted patterned ground and its characteristics from past and present periglacial landscapes, and aid in past permafrost and climate modelling. The collected dataset of the parameters of the sorted nets could also have many other applications such as for choosing an effective sample size or the minimum network size to be included in statistical comparisons in future patterned-ground surveys, and for validating automated mapping and/or delineation tools and models of patterned-ground growth.

There is no capitalist conspiracy and the rich are not all‐powerful

There is no capitalist conspiracy and the rich are not all‐powerful

Can future observation of the living partner post-tag the past decayed state in entangled neutral K-mesons?

Novel quantum phenomena have been recently discussed [1] in association to a peculiar time correlation between entangled neutral kaons produced at a ϕ-factory: the past state of the first decayed kaon, when it was still entangled before its decay, is post-tagged by the result and the time of the future observation on the other kan decay. This surprising “from future to past” effect is fully observable and leads to the unique experimental tag of the KS state, an unsolved problem since the discovery of CP violation. The first preliminary results obtained on the analysis of data collected by the KLOE experiment at the DAΦNE collider, and showing experimental evidence of this new effect are presented.

Coagulant dosage determination using deep learning-based graph attention multivariate time series forecasting model

Determination of coagulant dosage in water treatment is a time-consuming process involving nonlinear data relationships and numerous factors. This study provides a deep learning approach to determine coagulant dosage and/or the settled water turbidity using long-term data between 2011 and 2021 to include the effect of various weather conditions. A graph attention multivariate time series forecasting (GAMTF) model was developed to determine coagulant dosage and was compared with conventional machine learning and deep learning models. The GAMTF model (R2 = 0.94, RMSE = 3.55) outperformed the other models (R2 = 0.63 - 0.89, RMSE = 4.80 - 38.98), and successfully predicted both coagulant dosage and settled water turbidity simultaneously. The GAMTF model improved the prediction accuracy by considering the hidden interrelationships between features and the past states of features. The results demonstrate the first successful application of multivariate time series deep learning model, especially, a state-of-the-art graph attention-based model, using long-term data for decision-support systems in water treatment processes.

Interaction between ecosystem service supply and urbanization in northern China

Rapid urbanization has profoundly changed ability of ecosystem to supply welfare and resulted a series of environmental issues, in turn, such results not only affected the economic growth, but also the quality of sustainable development. Therefore, deeply understanding the interaction between ecosystem service (ES) supply and urbanization is of great significance. As a case study of northern China, we attempt to build an effective framework to evaluate past and future states of the interaction between ES supply and urbanization from quantification spatiotemporal pattern of coupling coordination degree (CD), decoupling path exploration, driving factor analysis to grey prediction model construction. It tries to better improve the interaction between ES supply and urbanization, as well as provide some effective policies for local condition. Results showed that the synthesis coefficient of urbanization (URSC) and CD has been significantly improved from 1995 to 2020 at province scale, while the synthesis coefficient of ES supply (ESSC) and decoupling index score are fluctuating. All of indicators revealing differences between the west and east regions. The east region has the highest levels of ESSC, URSC, CD and decoupling index score during the last twenty-five years. The driving factors including annual precipitation (Precipitation), normalized difference vegetation index (NDVI), cropland area (Cropland), grassland area (Grassland), unused land area (Unused land) all have a spatiotemporal impact on the CD with divergent significances, among which the effect of NDVI is the most significant. Finally, the grey prediction model was applied to predict the ESSC, URSC, CD and decoupling index in the future. The findings reveal a growing state for ESSC and CD in province scale, while a decline state of URSC. Based on the aforementioned results, some scientific policies were proposed for different regions to improve sustainable development.

Reasoning over time into models with DataTime

Models at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of full-fledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present, and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (i.e., values of properties of interest). DataTime offers a unifying interface to query the past, present, and future (predicted) states of the system. This unifying interface provides (i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, (ii) the ability to get the last available value provided by the system’s sensors, and (iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This experimentation has demonstrated how DataTime can be used for managing data from the past, the present, and the future and facilitate the development of digital twins.

Hybrid blockchain-based spectrum sharing algorithm for dynamic channel selection in cognitive radio

Currently, the available CR spectrum is not being used to their full potential. Certainly, the CR system is capable of meeting this challenge. Whenever a licensed user has to start another transmission on that channel, spectrum handoff allows an unlicensed user to leave its current channel. The SU then switches to a different channel to finish the incomplete transmission. This technique uses the handshaking protocol to transmit and receive an acknowledgment before data transmission to obtain the user's wait time.This paper describes the Convolution Neural Network automatically extracts the features without any human supervision. For classification, a two-layer hidden neural-network is utilized once features are extracted. The CNN performs convolution operation to extract the complex features of the data that is significant for doing regression. First, the preprocessed data is delivered to the CNN's input layer. The CNN conducts a convolution operation on the data to extract the complex features that are important for regression. . The developed model consists of a sequence layer, Long Short Term Memory (LSTM) layer, fully connected layer, and a regression layer. LSTM memory cells were used in the LSTM layer to provide extended memory support. LSTM units save the essential past state information to increase performance by accounting for dependencies, and they erase the unnecessary information to save memory an [1]d energy. The fully linked layer collects the processed output from all of the LSTM hidden units. The regression layer receives a single output as a result of this. The regression layer computes an output for which the loss value is computed and propagated backwards during the training phase. This is done for all of the training iterations until the loss is zero. The anticipated values of waiting time are next tested using a fully trained regression model. The observed waiting time as a function of the number of rounds played. The loss and RMSE (Root mean square error) keep decreasing for increasing number of iterations of training.

RNA recording in single bacterial cells using reprogrammed tracrRNAs.

Capturing an individual cell's transcriptional history is a challenge exacerbated by the functional heterogeneity of cellular communities. Here, we leverage reprogrammed tracrRNAs (Rptrs) to record selected cellular transcripts as stored DNA edits in single living bacterial cells. Rptrs are designed to base pair with sensed transcripts, converting them into guide RNAs. The guide RNAs then direct a Cas9 base editor to target an introduced DNA target. The extent of base editing can then be read in the future by sequencing. We use this approach, called TIGER (transcribed RNAs inferred by genetically encoded records), to record heterologous and endogenous transcripts in individual bacterial cells. TIGER can quantify relative expression, distinguish single-nucleotide differences, record multiple transcripts simultaneously and read out single-cell phenomena. We further apply TIGER to record metabolic bet hedging and antibiotic resistance mobilization in Escherichia coli as well as host cell invasion by Salmonella. Through RNA recording, TIGER connects current cellular states with past transcriptional states to decipher complex cellular responses in single cells.

On the rate of convergence of Euler–Maruyama approximate solutions of stochastic differential equations with multiple delays and their confidence interval estimations

<abstract><p>In this paper, we investigate Euler–Maruyama approximate solutions of stochastic differential equations (SDEs) with multiple delay functions. Stochastic differential delay equations (SDDEs) are generalizations of SDEs. Solutions of SDDEs are influenced by both the present and past states. Because these solutions may include past information, they are not necessarily Markov processes. This makes representations of solutions complicated; therefore, approximate solutions are practical. We estimate the rate of convergence of approximate solutions of SDDEs to the exact solutions in the $ L^p $-mean for $ p \geq 2 $ and apply the result to obtain confidence interval estimations for the approximate solutions.</p></abstract>

A Comparative Survey of an Approximate Solution Method for Stochastic Delay Differential Equations

This study is focused on the approximate solution for the class of stochastic delay differential equations. The techniques applied involve the use of Caratheodory and Euler Maruyama procedures which approximated to stochastic delay differential equations. Based on the Caratheodory approximate procedure, it was proved that stochastic delay differential equations have unique solution and established that the Caratheodory approximate solution converges to the unique solution of stochastic delay differential equations under the Cauchy sequence and initial condition. This Caratheodory approximate procedure and Euler method both converge at the same rate. This is achieved by replacing the present state with past state. The existence and uniqueness of an approximate solution of the stochastic delay differential equation were shown and the approximate solution to the unique solution was also shown.

Applications of Biophysical Modeling to Pacific High-Latitude Ecosystems

The high-latitude Pacific is home to highly productive ecosystems, including vast populations of commercially and subsistence harvested fish. These regions can be challenging to sample directly. Over several decades at the NOAA Pacific Marine Environmental Laboratory, we have applied numerical models to infer past, present, and future states of these regional oceans and their biota. These estimates are provided to fisheries scientists to help identify the local biophysical dynamics that underlie marine resource fluctuations and to managers to help develop effective management strategies in the face of short- and long-term environmental changes.